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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 __defProp = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __require = /* @__PURE__ */ ((x) => typeof require !== "undefined" ? require : typeof Proxy !== "undefined" ? new Proxy(x, { + get: (a, b) => (typeof require !== "undefined" ? require : a)[b] +}) : x)(function(x) { + if (typeof require !== "undefined") + return require.apply(this, arguments); + throw Error('Dynamic require of "' + x + '" is not supported'); +}); +var __export = (target, all) => { + for (var name in all) + __defProp(target, name, { get: all[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __reExport = (target, mod, secondTarget) => (__copyProps(target, mod, "default"), secondTarget && __copyProps(secondTarget, mod, "default")); + +// dist/tfjs.esm.js +var tfjs_esm_exports = {}; +__export(tfjs_esm_exports, { + version: () => version6 +}); +__reExport(tfjs_esm_exports, dist_star); +__reExport(tfjs_esm_exports, dist_star2); +__reExport(tfjs_esm_exports, dist_star3); +import * as dist_star from "@tensorflow/tfjs/dist/index.js"; +import * as dist_star2 from "@tensorflow/tfjs-backend-webgl/dist/index.js"; +import * as dist_star3 from "@tensorflow/tfjs-backend-wasm/dist/index.js"; +var version = "4.16.0"; +var version2 = "4.16.0"; +var version3 = "4.16.0"; +var version4 = "4.16.0"; +var version5 = "4.16.0"; +var version6 = { + // tfjs: tfjsVersion, + tfjs: version, + "tfjs-core": version, + // 'tfjs-data': tfjsDataVersion, + // 'tfjs-layers': tfjsLayersVersion, + "tfjs-converter": version2, + "tfjs-backend-cpu": version3, + "tfjs-backend-webgl": version4, + "tfjs-backend-wasm": version5 +}; + +// src/draw/index.ts +var draw_exports = {}; +__export(draw_exports, { + AnchorPosition: () => AnchorPosition, + DrawBox: () => DrawBox, + DrawBoxOptions: () => DrawBoxOptions, + DrawFaceLandmarks: () => DrawFaceLandmarks, + DrawFaceLandmarksOptions: () => DrawFaceLandmarksOptions, + DrawTextField: () => DrawTextField, + DrawTextFieldOptions: () => DrawTextFieldOptions, + drawContour: () => drawContour, + drawDetections: () => drawDetections, + drawFaceExpressions: () => drawFaceExpressions, + drawFaceLandmarks: () => drawFaceLandmarks +}); + +// src/draw/drawContour.ts +function drawContour(ctx, points, isClosed = false) { + ctx.beginPath(); + points.slice(1).forEach(({ x, y }, prevIdx) => { + const from = points[prevIdx]; + ctx.moveTo(from.x, from.y); + ctx.lineTo(x, y); + }); + if (isClosed) { + const from = points[points.length - 1]; + const to = points[0]; + if (!from || !to) { + return; + } + ctx.moveTo(from.x, from.y); + ctx.lineTo(to.x, to.y); + } + ctx.stroke(); +} + +// src/utils/index.ts +var utils_exports = {}; +__export(utils_exports, { + computeReshapedDimensions: () => computeReshapedDimensions, + getCenterPoint: () => getCenterPoint, + isDimensions: () => isDimensions, + isEven: () => isEven, + isFloat: () => isFloat, + isTensor: () => isTensor, + isTensor1D: () => isTensor1D, + isTensor2D: () => isTensor2D, + isTensor3D: () => isTensor3D, + isTensor4D: () => isTensor4D, + isValidNumber: () => isValidNumber, + isValidProbablitiy: () => isValidProbablitiy, + range: () => range, + round: () => round +}); + +// src/classes/Dimensions.ts +var Dimensions = class _Dimensions { + constructor(width, height) { + if (!isValidNumber(width) || !isValidNumber(height)) { + throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`); + } + this._width = width; + this._height = height; + } + get width() { + return this._width; + } + get height() { + return this._height; + } + reverse() { + return new _Dimensions(1 / this.width, 1 / this.height); + } +}; + +// src/utils/index.ts +function isTensor(tensor2, dim) { + return tensor2 instanceof tfjs_esm_exports.Tensor && tensor2.shape.length === dim; +} +function isTensor1D(tensor2) { + return isTensor(tensor2, 1); +} +function isTensor2D(tensor2) { + return isTensor(tensor2, 2); +} +function isTensor3D(tensor2) { + return isTensor(tensor2, 3); +} +function isTensor4D(tensor2) { + return isTensor(tensor2, 4); +} +function isFloat(num) { + return num % 1 !== 0; +} +function isEven(num) { + return num % 2 === 0; +} +function round(num, prec = 2) { + const f = 10 ** prec; + return Math.floor(num * f) / f; +} +function isDimensions(obj) { + return obj && obj.width && obj.height; +} +function computeReshapedDimensions({ width, height }, inputSize) { + const scale2 = inputSize / Math.max(height, width); + return new Dimensions(Math.round(width * scale2), Math.round(height * scale2)); +} +function getCenterPoint(pts) { + return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length)); +} +function range(num, start, step) { + return Array(num).fill(0).map((_, i) => start + i * step); +} +function isValidNumber(num) { + return !!num && num !== Infinity && num !== -Infinity && !Number.isNaN(num) || num === 0; +} +function isValidProbablitiy(num) { + return isValidNumber(num) && num >= 0 && num <= 1; +} + +// src/classes/Point.ts +var Point = class _Point { + constructor(x, y) { + this._x = x; + this._y = y; + } + get x() { + return this._x; + } + get y() { + return this._y; + } + add(pt) { + return new _Point(this.x + pt.x, this.y + pt.y); + } + sub(pt) { + return new _Point(this.x - pt.x, this.y - pt.y); + } + mul(pt) { + return new _Point(this.x * pt.x, this.y * pt.y); + } + div(pt) { + return new _Point(this.x / pt.x, this.y / pt.y); + } + abs() { + return new _Point(Math.abs(this.x), Math.abs(this.y)); + } + magnitude() { + return Math.sqrt(this.x ** 2 + this.y ** 2); + } + floor() { + return new _Point(Math.floor(this.x), Math.floor(this.y)); + } +}; + +// src/classes/Box.ts +var Box = class _Box { + static isRect(rect) { + return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber); + } + static assertIsValidBox(box, callee, allowNegativeDimensions = false) { + if (!_Box.isRect(box)) { + throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`); + } + if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) { + throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`); + } + } + constructor(_box, allowNegativeDimensions = true) { + const box = _box || {}; + const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber); + const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber); + if (!isRect && !isBbox) { + throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`); + } + const [x, y, width, height] = isRect ? [box.x, box.y, box.width, box.height] : [box.left, box.top, box.right - box.left, box.bottom - box.top]; + _Box.assertIsValidBox({ + x, + y, + width, + height + }, "Box.constructor", allowNegativeDimensions); + this._x = x; + this._y = y; + this._width = width; + this._height = height; + } + 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 Point(this.left, this.top); + } + get topRight() { + return new Point(this.right, this.top); + } + get bottomLeft() { + return new Point(this.left, this.bottom); + } + get bottomRight() { + return new Point(this.right, this.bottom); + } + round() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.round(val)); + return new _Box({ + x, + y, + width, + height + }); + } + floor() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.floor(val)); + return new _Box({ + x, + y, + width, + height + }); + } + toSquare() { + let { + x, + y, + width, + height + } = this; + const diff = Math.abs(width - height); + if (width < height) { + x -= diff / 2; + width += diff; + } + if (height < width) { + y -= diff / 2; + height += diff; + } + return new _Box({ x, y, width, height }); + } + rescale(s) { + const scaleX = isDimensions(s) ? s.width : s; + const scaleY = isDimensions(s) ? s.height : s; + return new _Box({ + x: this.x * scaleX, + y: this.y * scaleY, + width: this.width * scaleX, + height: this.height * scaleY + }); + } + pad(padX, padY) { + const [x, y, width, height] = [ + this.x - padX / 2, + this.y - padY / 2, + this.width + padX, + this.height + padY + ]; + return new _Box({ x, y, width, height }); + } + clipAtImageBorders(imgWidth, imgHeight) { + const { x, y, right, bottom } = this; + const clippedX = Math.max(x, 0); + const clippedY = Math.max(y, 0); + const newWidth = right - clippedX; + const newHeight = bottom - clippedY; + const clippedWidth = Math.min(newWidth, imgWidth - clippedX); + const clippedHeight = Math.min(newHeight, imgHeight - clippedY); + return new _Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight }).floor(); + } + shift(sx, sy) { + const { width, height } = this; + const x = this.x + sx; + const y = this.y + sy; + return new _Box({ x, y, width, height }); + } + padAtBorders(imageHeight, imageWidth) { + const w = this.width + 1; + const h = this.height + 1; + const dx = 1; + const dy = 1; + let edx = w; + let edy = h; + let x = this.left; + let y = this.top; + let ex = this.right; + let ey = this.bottom; + if (ex > imageWidth) { + edx = -ex + imageWidth + w; + ex = imageWidth; + } + if (ey > imageHeight) { + edy = -ey + imageHeight + h; + ey = imageHeight; + } + if (x < 1) { + edy = 2 - x; + x = 1; + } + if (y < 1) { + edy = 2 - y; + y = 1; + } + return { dy, edy, dx, edx, y, ey, x, ex, w, h }; + } + calibrate(region) { + return new _Box({ + left: this.left + region.left * this.width, + top: this.top + region.top * this.height, + right: this.right + region.right * this.width, + bottom: this.bottom + region.bottom * this.height + }).toSquare().round(); + } +}; + +// src/classes/BoundingBox.ts +var BoundingBox = class extends Box { + constructor(left, top, right, bottom, allowNegativeDimensions = false) { + super({ left, top, right, bottom }, allowNegativeDimensions); + } +}; + +// src/classes/ObjectDetection.ts +var ObjectDetection = class _ObjectDetection { + constructor(score, classScore, className, relativeBox, imageDims) { + this._imageDims = new Dimensions(imageDims.width, imageDims.height); + this._score = score; + this._classScore = classScore; + this._className = className; + this._box = new Box(relativeBox).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 Box(this._box).rescale(this.imageDims.reverse()); + } + forSize(width, height) { + return new _ObjectDetection( + this.score, + this.classScore, + this.className, + this.relativeBox, + { width, height } + ); + } +}; + +// src/classes/FaceDetection.ts +var FaceDetection = class _FaceDetection extends ObjectDetection { + constructor(score, relativeBox, imageDims) { + super(score, score, "", relativeBox, imageDims); + } + forSize(width, height) { + const { score, relativeBox, imageDims } = super.forSize(width, height); + return new _FaceDetection(score, relativeBox, imageDims); + } +}; + +// src/ops/iou.ts +function iou(box1, box2, isIOU = true) { + const width = Math.max(0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left)); + const height = Math.max(0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top)); + const interSection = width * height; + return isIOU ? interSection / (box1.area + box2.area - interSection) : interSection / Math.min(box1.area, box2.area); +} + +// src/ops/minBbox.ts +function minBbox(pts) { + const xs = pts.map((pt) => pt.x); + const ys = pts.map((pt) => pt.y); + const minX = xs.reduce((min, x) => x < min ? x : min, Infinity); + const minY = ys.reduce((min, y) => y < min ? y : min, Infinity); + const maxX = xs.reduce((max, x) => max < x ? x : max, 0); + const maxY = ys.reduce((max, y) => max < y ? y : max, 0); + return new BoundingBox(minX, minY, maxX, maxY); +} + +// src/ops/nonMaxSuppression.ts +function nonMaxSuppression(boxes, scores, iouThreshold, isIOU = true) { + let indicesSortedByScore = scores.map((score, boxIndex) => ({ score, boxIndex })).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex); + const pick = []; + while (indicesSortedByScore.length > 0) { + const curr = indicesSortedByScore.pop(); + pick.push(curr); + const indices = indicesSortedByScore; + const outputs = []; + for (let i = 0; i < indices.length; i++) { + const idx = indices[i]; + const currBox = boxes[curr]; + const idxBox = boxes[idx]; + outputs.push(iou(currBox, idxBox, isIOU)); + } + indicesSortedByScore = indicesSortedByScore.filter( + (_, j) => outputs[j] <= iouThreshold + ); + } + return pick; +} + +// src/ops/normalize.ts +function normalize(x, meanRgb) { + return tfjs_esm_exports.tidy(() => { + const [r, g, b] = meanRgb; + const avg_r = tfjs_esm_exports.fill([...x.shape.slice(0, 3), 1], r, "float32"); + const avg_g = tfjs_esm_exports.fill([...x.shape.slice(0, 3), 1], g, "float32"); + const avg_b = tfjs_esm_exports.fill([...x.shape.slice(0, 3), 1], b, "float32"); + const avg_rgb = tfjs_esm_exports.concat([avg_r, avg_g, avg_b], 3); + return tfjs_esm_exports.sub(x, avg_rgb); + }); +} + +// src/ops/padToSquare.ts +function padToSquare(imgTensor, isCenterImage = false) { + return tfjs_esm_exports.tidy(() => { + const [height, width] = imgTensor.shape.slice(1); + if (height === width) + return imgTensor; + const dimDiff = Math.abs(height - width); + const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1)); + const paddingAxis = height > width ? 2 : 1; + const createPaddingTensor = (paddingAmountLocal) => { + const paddingTensorShape = imgTensor.shape.slice(); + paddingTensorShape[paddingAxis] = paddingAmountLocal; + return tfjs_esm_exports.fill(paddingTensorShape, 0, "float32"); + }; + const paddingTensorAppend = createPaddingTensor(paddingAmount); + const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis]; + const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null; + const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend].filter((t) => !!t).map((t) => tfjs_esm_exports.cast(t, "float32")); + return tfjs_esm_exports.concat(tensorsToStack, paddingAxis); + }); +} + +// src/ops/shuffleArray.ts +function shuffleArray(inputArray) { + const array = inputArray.slice(); + for (let i = array.length - 1; i > 0; i--) { + const j = Math.floor(Math.random() * (i + 1)); + const x = array[i]; + array[i] = array[j]; + array[j] = x; + } + return array; +} + +// src/ops/index.ts +function sigmoid(x) { + return 1 / (1 + Math.exp(-x)); +} +function inverseSigmoid(x) { + return Math.log(x / (1 - x)); +} + +// src/classes/Rect.ts +var Rect = class extends Box { + constructor(x, y, width, height, allowNegativeDimensions = false) { + super({ x, y, width, height }, allowNegativeDimensions); + } +}; + +// src/classes/FaceLandmarks.ts +var relX = 0.5; +var relY = 0.43; +var relScale = 0.45; +var FaceLandmarks = class { + constructor(relativeFaceLandmarkPositions, imgDims, shift = new Point(0, 0)) { + const { width, height } = imgDims; + this._imgDims = new Dimensions(width, height); + this._shift = shift; + this._positions = relativeFaceLandmarkPositions.map( + (pt) => pt.mul(new Point(width, height)).add(shift) + ); + } + get shift() { + return new Point(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( + (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)) + ); + } + forSize(width, height) { + return new this.constructor( + this.relativePositions, + { width, height } + ); + } + shiftBy(x, y) { + return new this.constructor( + this.relativePositions, + this._imgDims, + new Point(x, y) + ); + } + shiftByPoint(pt) { + return this.shiftBy(pt.x, pt.y); + } + /** + * Aligns the face landmarks after face detection from the relative positions of the faces + * bounding box, or it's current shift. This function should be used to align the face images + * after face detection has been performed, before they are passed to the face recognition net. + * This will make the computed face descriptor more accurate. + * + * @param detection (optional) The bounding box of the face or the face detection result. If + * no argument was passed the position of the face landmarks are assumed to be relative to + * it's current shift. + * @returns The bounding box of the aligned face. + */ + align(detection, options = {}) { + if (detection) { + const box = detection instanceof FaceDetection ? detection.box.floor() : new Box(detection); + return this.shiftBy(box.x, box.y).align(null, options); + } + const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options }; + if (useDlibAlignment) { + return this.alignDlib(); + } + return this.alignMinBbox(minBoxPadding); + } + alignDlib() { + const centers = this.getRefPointsForAlignment(); + const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers; + const distToMouth = (pt) => mouthCenter.sub(pt).magnitude(); + const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2; + const size = Math.floor(eyeToMouthDist / relScale); + const refPoint = getCenterPoint(centers); + const x = Math.floor(Math.max(0, refPoint.x - relX * size)); + const y = Math.floor(Math.max(0, refPoint.y - relY * size)); + return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y)); + } + alignMinBbox(padding) { + const box = minBbox(this.positions); + return box.pad(box.width * padding, box.height * padding); + } + getRefPointsForAlignment() { + throw new Error("getRefPointsForAlignment not implemented by base class"); + } +}; + +// src/classes/FaceLandmarks5.ts +var FaceLandmarks5 = class extends FaceLandmarks { + getRefPointsForAlignment() { + const pts = this.positions; + return [ + pts[0], + pts[1], + getCenterPoint([pts[3], pts[4]]) + ]; + } +}; + +// src/classes/FaceLandmarks68.ts +var FaceLandmarks68 = class extends FaceLandmarks { + 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(getCenterPoint); + } +}; + +// src/classes/FaceMatch.ts +var FaceMatch = class { + constructor(label, distance) { + this._label = label; + this._distance = distance; + } + get label() { + return this._label; + } + get distance() { + return this._distance; + } + toString(withDistance = true) { + return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`; + } +}; + +// src/classes/LabeledBox.ts +var LabeledBox = class extends Box { + static assertIsValidLabeledBox(box, callee) { + Box.assertIsValidBox(box, callee); + if (!isValidNumber(box.label)) { + throw new Error(`${callee} - expected property label (${box.label}) to be a number`); + } + } + constructor(box, label) { + super(box); + this._label = label; + } + get label() { + return this._label; + } +}; + +// src/classes/LabeledFaceDescriptors.ts +var LabeledFaceDescriptors = class _LabeledFaceDescriptors { + constructor(label, descriptors) { + if (!(typeof label === "string")) { + throw new Error("LabeledFaceDescriptors - constructor expected label to be a string"); + } + if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) { + throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array"); + } + this._label = label; + this._descriptors = descriptors; + } + get label() { + return this._label; + } + get descriptors() { + return this._descriptors; + } + toJSON() { + return { + label: this.label, + descriptors: this.descriptors.map((d) => Array.from(d)) + }; + } + static fromJSON(json) { + const descriptors = json.descriptors.map((d) => new Float32Array(d)); + return new _LabeledFaceDescriptors(json.label, descriptors); + } +}; + +// src/classes/PredictedBox.ts +var PredictedBox = class extends LabeledBox { + static assertIsValidPredictedBox(box, callee) { + LabeledBox.assertIsValidLabeledBox(box, callee); + if (!isValidProbablitiy(box.score) || !isValidProbablitiy(box.classScore)) { + throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`); + } + } + constructor(box, label, score, classScore) { + super(box, label); + this._score = score; + this._classScore = classScore; + } + get score() { + return this._score; + } + get classScore() { + return this._classScore; + } +}; + +// src/factories/WithFaceDetection.ts +function isWithFaceDetection(obj) { + return obj.detection instanceof FaceDetection; +} +function extendWithFaceDetection(sourceObj, detection) { + const extension = { detection }; + return { ...sourceObj, ...extension }; +} + +// src/env/createBrowserEnv.ts +function createBrowserEnv() { + const fetch = window.fetch; + if (!fetch) + throw new Error("fetch - missing fetch implementation for browser environment"); + const readFile = () => { + throw new Error("readFile - filesystem not available for browser environment"); + }; + return { + Canvas: HTMLCanvasElement, + CanvasRenderingContext2D, + Image: HTMLImageElement, + ImageData, + Video: HTMLVideoElement, + createCanvasElement: () => document.createElement("canvas"), + createImageElement: () => document.createElement("img"), + createVideoElement: () => document.createElement("video"), + fetch, + readFile + }; +} + +// src/env/isNodejs.ts +function isNodejs() { + return typeof global === "object" && typeof process !== "undefined" && process.versions != null && process.versions.node != null; +} + +// src/env/createFileSystem.ts +function createFileSystem(fs) { + let requireFsError = ""; + if (!fs && isNodejs()) { + try { + fs = __require("fs"); + } catch (err) { + requireFsError = err.toString(); + } + } + const readFile = fs ? (filePath) => new Promise((resolve, reject) => { + fs.readFile(filePath, (err, buffer) => err ? reject(err) : resolve(buffer)); + }) : () => { + throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); + }; + return { readFile }; +} + +// src/env/createNodejsEnv.ts +function createNodejsEnv() { + const Canvas = global["Canvas"] || global.HTMLCanvasElement; + const Image = global.Image || global.HTMLImageElement; + const Video = global["Video"] || global.HTMLVideoElement; + const createCanvasElement = () => { + if (Canvas) + return new Canvas(); + throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment"); + }; + const createImageElement = () => { + if (Image) + return new Image(); + throw new Error("createImageElement - missing Image implementation for nodejs environment"); + }; + const createVideoElement = () => { + if (Video) + return new Video(); + throw new Error("createVideoElement - missing Video implementation for nodejs environment"); + }; + const fetch = global.fetch; + const fileSystem = createFileSystem(); + return { + Canvas: Canvas || class { + }, + CanvasRenderingContext2D: global.CanvasRenderingContext2D || class { + }, + Image: Image || class { + }, + ImageData: global.ImageData || class { + }, + Video: global.HTMLVideoElement || class { + }, + createCanvasElement, + createImageElement, + createVideoElement, + fetch, + ...fileSystem + }; +} + +// src/env/isBrowser.ts +function isBrowser() { + return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined"; +} + +// src/env/index.ts +var environment; +function getEnv() { + if (!environment) { + throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()"); + } + return environment; +} +function setEnv(env2) { + environment = env2; +} +function initialize() { + if (isBrowser()) + return setEnv(createBrowserEnv()); + if (isNodejs()) + return setEnv(createNodejsEnv()); + return null; +} +function monkeyPatch(env2) { + if (!environment) { + initialize(); + } + if (!environment) { + throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()"); + } + const { Canvas = environment.Canvas, Image = environment.Image } = env2; + environment.Canvas = Canvas; + environment.Image = Image; + environment.createCanvasElement = env2.createCanvasElement || (() => new Canvas()); + environment.createImageElement = env2.createImageElement || (() => new Image()); + environment.ImageData = env2.ImageData || environment.ImageData; + environment.Video = env2.Video || environment.Video; + environment.fetch = env2.fetch || environment.fetch; + environment.readFile = env2.readFile || environment.readFile; +} +var env = { + getEnv, + setEnv, + initialize, + createBrowserEnv, + createFileSystem, + createNodejsEnv, + monkeyPatch, + isBrowser, + isNodejs +}; +initialize(); + +// src/dom/resolveInput.ts +function resolveInput(arg) { + if (!env.isNodejs() && typeof arg === "string") { + return document.getElementById(arg); + } + return arg; +} + +// src/dom/getContext2dOrThrow.ts +function getContext2dOrThrow(canvasArg) { + const { Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2 } = env.getEnv(); + if (canvasArg instanceof CanvasRenderingContext2D2) + return canvasArg; + const canvas = resolveInput(canvasArg); + if (!(canvas instanceof Canvas)) + throw new Error("resolveContext2d - expected canvas to be of instance of Canvas"); + const ctx = canvas.getContext("2d", { willReadFrequently: true }); + if (!ctx) + throw new Error("resolveContext2d - canvas 2d context is null"); + return ctx; +} + +// src/draw/DrawTextField.ts +var AnchorPosition = /* @__PURE__ */ ((AnchorPosition2) => { + AnchorPosition2["TOP_LEFT"] = "TOP_LEFT"; + AnchorPosition2["TOP_RIGHT"] = "TOP_RIGHT"; + AnchorPosition2["BOTTOM_LEFT"] = "BOTTOM_LEFT"; + AnchorPosition2["BOTTOM_RIGHT"] = "BOTTOM_RIGHT"; + return AnchorPosition2; +})(AnchorPosition || {}); +var DrawTextFieldOptions = class { + constructor(options = {}) { + const { + anchorPosition, + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = options; + this.anchorPosition = anchorPosition || "TOP_LEFT" /* TOP_LEFT */; + this.backgroundColor = backgroundColor || "rgba(0, 0, 0, 0.5)"; + this.fontColor = fontColor || "rgba(255, 255, 255, 1)"; + this.fontSize = fontSize || 14; + this.fontStyle = fontStyle || "Georgia"; + this.padding = padding || 4; + } +}; +var DrawTextField = class _DrawTextField { + constructor(text, anchor, options = {}) { + this.text = typeof text === "string" ? [text] : text instanceof _DrawTextField ? text.text : text; + this.anchor = anchor; + this.options = new DrawTextFieldOptions(options); + } + measureWidth(ctx) { + const { padding } = this.options; + return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + 2 * padding; + } + measureHeight() { + const { fontSize, padding } = this.options; + return this.text.length * fontSize + 2 * padding; + } + getUpperLeft(ctx, canvasDims) { + const { anchorPosition } = this.options; + const isShiftLeft = anchorPosition === "BOTTOM_RIGHT" /* BOTTOM_RIGHT */ || anchorPosition === "TOP_RIGHT" /* TOP_RIGHT */; + const isShiftTop = anchorPosition === "BOTTOM_LEFT" /* BOTTOM_LEFT */ || anchorPosition === "BOTTOM_RIGHT" /* BOTTOM_RIGHT */; + const textFieldWidth = this.measureWidth(ctx); + const textFieldHeight = this.measureHeight(); + const x = isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x; + const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y; + if (canvasDims) { + const { width, height } = canvasDims; + const newX = Math.max(Math.min(x, width - textFieldWidth), 0); + const newY = Math.max(Math.min(y, height - textFieldHeight), 0); + return { x: newX, y: newY }; + } + return { x, y }; + } + draw(canvasArg) { + const canvas = resolveInput(canvasArg); + const ctx = getContext2dOrThrow(canvas); + const { + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = this.options; + ctx.font = `${fontSize}px ${fontStyle}`; + const maxTextWidth = this.measureWidth(ctx); + const textHeight = this.measureHeight(); + ctx.fillStyle = backgroundColor; + const upperLeft = this.getUpperLeft(ctx, canvas); + ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight); + ctx.fillStyle = fontColor; + this.text.forEach((textLine, i) => { + const x = padding + upperLeft.x; + const y = padding + upperLeft.y + (i + 1) * fontSize; + ctx.fillText(textLine, x, y); + }); + } +}; + +// src/draw/DrawBox.ts +var DrawBoxOptions = class { + constructor(options = {}) { + const { + boxColor, + lineWidth, + label, + drawLabelOptions + } = options; + this.boxColor = boxColor || "rgba(0, 0, 255, 1)"; + this.lineWidth = lineWidth || 2; + this.label = label; + const defaultDrawLabelOptions = { + anchorPosition: "BOTTOM_LEFT" /* BOTTOM_LEFT */, + backgroundColor: this.boxColor + }; + this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions }); + } +}; +var DrawBox = class { + constructor(box, options = {}) { + this.box = new Box(box); + this.options = new DrawBoxOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { boxColor, lineWidth } = this.options; + const { + x, + y, + width, + height + } = this.box; + ctx.strokeStyle = boxColor; + ctx.lineWidth = lineWidth; + ctx.strokeRect(x, y, width, height); + const { label } = this.options; + if (label) { + new DrawTextField([label], { x: x - lineWidth / 2, y }, this.options.drawLabelOptions).draw(canvasArg); + } + } +}; + +// src/draw/drawDetections.ts +function drawDetections(canvasArg, detections) { + const detectionsArray = Array.isArray(detections) ? detections : [detections]; + detectionsArray.forEach((det) => { + const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0; + const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det); + const label = score ? `${round(score)}` : void 0; + new DrawBox(box, { label }).draw(canvasArg); + }); +} + +// src/dom/isMediaLoaded.ts +function isMediaLoaded(media) { + const { Image, Video } = env.getEnv(); + return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3; +} + +// src/dom/awaitMediaLoaded.ts +function awaitMediaLoaded(media) { + return new Promise((resolve, reject) => { + if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) + resolve(null); + function onError(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + reject(e); + } + function onLoad(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + resolve(e); + } + media.addEventListener("load", onLoad); + media.addEventListener("error", onError); + }); +} + +// src/dom/bufferToImage.ts +function bufferToImage(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToImage - expected buf to be of type: Blob")); + const reader = new FileReader(); + reader.onload = () => { + if (typeof reader.result !== "string") + reject(new Error("bufferToImage - expected reader.result to be a string, in onload")); + const img = env.getEnv().createImageElement(); + img.onload = () => resolve(img); + img.onerror = reject; + img.src = reader.result; + }; + reader.onerror = reject; + reader.readAsDataURL(buf); + }); +} + +// src/dom/getMediaDimensions.ts +function getMediaDimensions(input) { + const { Image, Video } = env.getEnv(); + if (input instanceof Image) { + return new Dimensions(input.naturalWidth, input.naturalHeight); + } + if (input instanceof Video) { + return new Dimensions(input.videoWidth, input.videoHeight); + } + return new Dimensions(input.width, input.height); +} + +// src/dom/createCanvas.ts +function createCanvas({ width, height }) { + const { createCanvasElement } = env.getEnv(); + const canvas = createCanvasElement(); + canvas.width = width; + canvas.height = height; + return canvas; +} +function createCanvasFromMedia(media, dims) { + const { ImageData: ImageData2 } = env.getEnv(); + if (!(media instanceof ImageData2) && !isMediaLoaded(media)) { + throw new Error("createCanvasFromMedia - media has not finished loading yet"); + } + const { width, height } = dims || getMediaDimensions(media); + const canvas = createCanvas({ width, height }); + if (media instanceof ImageData2) { + getContext2dOrThrow(canvas).putImageData(media, 0, 0); + } else { + getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height); + } + return canvas; +} + +// src/dom/imageTensorToCanvas.ts +async function imageTensorToCanvas(imgTensor, canvas) { + const targetCanvas = canvas || env.getEnv().createCanvasElement(); + const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0); + const imgTensor3D = tfjs_esm_exports.tidy(() => imgTensor.as3D(height, width, numChannels).toInt()); + await tfjs_esm_exports["browser"].toPixels(imgTensor3D, targetCanvas); + imgTensor3D.dispose(); + return targetCanvas; +} + +// src/dom/isMediaElement.ts +function isMediaElement(input) { + const { Image, Canvas, Video } = env.getEnv(); + return input instanceof Image || input instanceof Canvas || input instanceof Video; +} + +// src/dom/imageToSquare.ts +function imageToSquare(input, inputSize, centerImage = false) { + const { Image, Canvas } = env.getEnv(); + if (!(input instanceof Image || input instanceof Canvas)) { + throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement"); + } + if (inputSize <= 0) + return createCanvas({ width: 1, height: 1 }); + const dims = getMediaDimensions(input); + const scale2 = inputSize / Math.max(dims.height, dims.width); + const width = scale2 * dims.width; + const height = scale2 * dims.height; + const targetCanvas = createCanvas({ width: inputSize, height: inputSize }); + const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input); + const offset = Math.abs(width - height) / 2; + const dx = centerImage && width < height ? offset : 0; + const dy = centerImage && height < width ? offset : 0; + if (inputCanvas.width > 0 && inputCanvas.height > 0) + getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height); + return targetCanvas; +} + +// src/dom/NetInput.ts +var NetInput = class { + constructor(inputs, treatAsBatchInput = false) { + this._imageTensors = []; + this._canvases = []; + this._treatAsBatchInput = false; + this._inputDimensions = []; + this._inputSize = 0; + if (!Array.isArray(inputs)) { + throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`); + } + this._treatAsBatchInput = treatAsBatchInput; + this._batchSize = inputs.length; + inputs.forEach((input, idx) => { + if (isTensor3D(input)) { + this._imageTensors[idx] = input; + this._inputDimensions[idx] = input.shape; + return; + } + if (isTensor4D(input)) { + const batchSize = input.shape[0]; + if (batchSize !== 1) { + throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + this._imageTensors[idx] = input; + this._inputDimensions[idx] = input.shape.slice(1); + return; + } + const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input); + this._canvases[idx] = canvas; + this._inputDimensions[idx] = [canvas.height, canvas.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 range(this.batchSize, 0, 1).map( + (_, batchIdx) => this.getReshapedInputDimensions(batchIdx) + ); + } + getInput(batchIdx) { + return this.canvases[batchIdx] || this.imageTensors[batchIdx]; + } + getInputDimensions(batchIdx) { + return this._inputDimensions[batchIdx]; + } + getInputHeight(batchIdx) { + return this._inputDimensions[batchIdx][0]; + } + getInputWidth(batchIdx) { + return this._inputDimensions[batchIdx][1]; + } + getReshapedInputDimensions(batchIdx) { + if (typeof this.inputSize !== "number") { + throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet"); + } + const width = this.getInputWidth(batchIdx); + const height = this.getInputHeight(batchIdx); + return computeReshapedDimensions({ width, height }, this.inputSize); + } + /** + * Create a batch tensor from all input canvases and tensors + * with size [batchSize, inputSize, inputSize, 3]. + * + * @param inputSize Height and width of the tensor. + * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on + * both sides of the minor dimension oof the image. + * @returns The batch tensor. + */ + toBatchTensor(inputSize, isCenterInputs = true) { + this._inputSize = inputSize; + return tfjs_esm_exports.tidy(() => { + const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => { + const input = this.getInput(batchIdx); + if (input instanceof tfjs_esm_exports.Tensor) { + let imgTensor = isTensor4D(input) ? input : tfjs_esm_exports.expandDims(input); + imgTensor = padToSquare(imgTensor, isCenterInputs); + if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) { + imgTensor = tfjs_esm_exports["image"].resizeBilinear(imgTensor, [inputSize, inputSize], false, false); + } + return imgTensor.as3D(inputSize, inputSize, 3); + } + if (input instanceof env.getEnv().Canvas) { + return tfjs_esm_exports["browser"].fromPixels(imageToSquare(input, inputSize, isCenterInputs)); + } + throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`); + }); + const batchTensor = tfjs_esm_exports.stack(inputTensors.map((t) => tfjs_esm_exports.cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3); + return batchTensor; + }); + } +}; + +// src/dom/toNetInput.ts +async function toNetInput(inputs) { + if (inputs instanceof NetInput) + return inputs; + const inputArgArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArgArray.length) + throw new Error("toNetInput - empty array passed as input"); + const getIdxHint = (idx) => Array.isArray(inputs) ? ` at input index ${idx}:` : ""; + const inputArray = inputArgArray.map(resolveInput); + inputArray.forEach((input, i) => { + if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) { + if (typeof inputArgArray[i] === "string") + throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`); + throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`); + } + if (isTensor4D(input)) { + const batchSize = input.shape[0]; + if (batchSize !== 1) + throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + }); + await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input))); + return new NetInput(inputArray, Array.isArray(inputs)); +} + +// src/dom/extractFaces.ts +async function extractFaces(input, detections) { + const { Canvas } = env.getEnv(); + let canvas = input; + if (!(input instanceof Canvas)) { + const netInput = await toNetInput(input); + if (netInput.batchSize > 1) + throw new Error("extractFaces - batchSize > 1 not supported"); + const tensorOrCanvas = netInput.getInput(0); + canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas); + } + const ctx = getContext2dOrThrow(canvas); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det).map((box) => box.clipAtImageBorders(canvas.width, canvas.height)); + return boxes.map(({ x, y, width, height }) => { + const faceImg = createCanvas({ width, height }); + if (width > 0 && height > 0) + getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0); + return faceImg; + }); +} + +// src/dom/extractFaceTensors.ts +async function extractFaceTensors(imageTensor, detections) { + if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) { + throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D"); + } + if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) { + throw new Error("extractFaceTensors - batchSize > 1 not supported"); + } + return tfjs_esm_exports.tidy(() => { + const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight)); + const faceTensors = boxes.filter((box) => box.width > 0 && box.height > 0).map(({ x, y, width, height }) => tfjs_esm_exports.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])); + return faceTensors; + }); +} + +// src/dom/fetchOrThrow.ts +async function fetchOrThrow(url, init) { + const { fetch } = env.getEnv(); + const res = await fetch(url, init); + if (!(res.status < 400)) { + throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`); + } + return res; +} + +// src/dom/fetchImage.ts +async function fetchImage(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("image/")) { + throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToImage(blob); +} + +// src/dom/fetchJson.ts +async function fetchJson(uri) { + return (await fetchOrThrow(uri)).json(); +} + +// src/dom/fetchNetWeights.ts +async function fetchNetWeights(uri) { + return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer()); +} + +// src/dom/bufferToVideo.ts +function bufferToVideo(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToVideo - expected buf to be of type: Blob")); + const video = env.getEnv().createVideoElement(); + video.oncanplay = () => resolve(video); + video.onerror = reject; + video.playsInline = true; + video.muted = true; + video.src = URL.createObjectURL(buf); + video.play(); + }); +} + +// src/dom/fetchVideo.ts +async function fetchVideo(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("video/")) { + throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToVideo(blob); +} + +// src/common/getModelUris.ts +function getModelUris(uri, defaultModelName) { + const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`; + if (!uri) { + return { + modelBaseUri: "", + manifestUri: defaultManifestFilename + }; + } + if (uri === "/") { + return { + modelBaseUri: "/", + manifestUri: `/${defaultManifestFilename}` + }; + } + const protocol = uri.startsWith("http://") ? "http://" : uri.startsWith("https://") ? "https://" : ""; + uri = uri.replace(protocol, ""); + const parts = uri.split("/").filter((s) => s); + const manifestFile = uri.endsWith(".json") ? parts[parts.length - 1] : defaultManifestFilename; + let modelBaseUri = protocol + (uri.endsWith(".json") ? parts.slice(0, parts.length - 1) : parts).join("/"); + modelBaseUri = uri.startsWith("/") ? `/${modelBaseUri}` : modelBaseUri; + return { + modelBaseUri, + manifestUri: modelBaseUri === "/" ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}` + }; +} + +// src/dom/loadWeightMap.ts +async function loadWeightMap(uri, defaultModelName) { + const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName); + const manifest = await fetchJson(manifestUri); + return tfjs_esm_exports["io"].loadWeights(manifest, modelBaseUri); +} + +// src/dom/matchDimensions.ts +function matchDimensions(input, reference, useMediaDimensions = false) { + const { width, height } = useMediaDimensions ? getMediaDimensions(reference) : reference; + input.width = width; + input.height = height; + return { width, height }; +} + +// src/NeuralNetwork.ts +var NeuralNetwork = class { + constructor(name) { + this._params = void 0; + this._paramMappings = []; + this._name = name; + } + get params() { + return this._params; + } + get paramMappings() { + return this._paramMappings; + } + get isLoaded() { + return !!this.params; + } + getParamFromPath(paramPath) { + const { obj, objProp } = this.traversePropertyPath(paramPath); + return obj[objProp]; + } + reassignParamFromPath(paramPath, tensor2) { + const { obj, objProp } = this.traversePropertyPath(paramPath); + obj[objProp].dispose(); + obj[objProp] = tensor2; + } + getParamList() { + return this._paramMappings.map(({ paramPath }) => ({ + path: paramPath, + tensor: this.getParamFromPath(paramPath) + })); + } + getTrainableParams() { + return this.getParamList().filter((param) => param.tensor instanceof tfjs_esm_exports.Variable); + } + getFrozenParams() { + return this.getParamList().filter((param) => !(param.tensor instanceof tfjs_esm_exports.Variable)); + } + variable() { + this.getFrozenParams().forEach(({ path, tensor: tensor2 }) => { + this.reassignParamFromPath(path, tensor2.variable()); + }); + } + freeze() { + this.getTrainableParams().forEach(({ path, tensor: variable }) => { + const tensor2 = tfjs_esm_exports.tensor(variable.dataSync()); + variable.dispose(); + this.reassignParamFromPath(path, tensor2); + }); + } + dispose(throwOnRedispose = true) { + this.getParamList().forEach((param) => { + if (throwOnRedispose && param.tensor.isDisposed) { + throw new Error(`param tensor has already been disposed for path ${param.path}`); + } + param.tensor.dispose(); + }); + this._params = void 0; + } + serializeParams() { + return new Float32Array( + this.getParamList().map(({ tensor: tensor2 }) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)) + ); + } + async load(weightsOrUrl) { + if (weightsOrUrl instanceof Float32Array) { + this.extractWeights(weightsOrUrl); + return; + } + await this.loadFromUri(weightsOrUrl); + } + async loadFromUri(uri) { + if (uri && typeof uri !== "string") { + throw new Error(`${this._name}.loadFromUri - expected model uri`); + } + const weightMap = await loadWeightMap(uri, this.getDefaultModelName()); + this.loadFromWeightMap(weightMap); + } + async loadFromDisk(filePath) { + if (filePath && typeof filePath !== "string") { + throw new Error(`${this._name}.loadFromDisk - expected model file path`); + } + const { readFile } = env.getEnv(); + const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName()); + const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => typeof buf === "string" ? Buffer.from(buf) : buf.buffer))); + const loadWeights = tfjs_esm_exports["io"].weightsLoaderFactory(fetchWeightsFromDisk); + const manifest = JSON.parse((await readFile(manifestUri)).toString()); + const weightMap = await loadWeights(manifest, modelBaseUri); + this.loadFromWeightMap(weightMap); + } + loadFromWeightMap(weightMap) { + const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap); + this._paramMappings = paramMappings; + this._params = params; + } + extractWeights(weights) { + const { paramMappings, params } = this.extractParams(weights); + this._paramMappings = paramMappings; + this._params = params; + } + traversePropertyPath(paramPath) { + if (!this.params) { + throw new Error("traversePropertyPath - model has no loaded params"); + } + const result = paramPath.split("/").reduce((res, objProp2) => { + if (!res.nextObj.hasOwnProperty(objProp2)) { + throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`); + } + return { obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2] }; + }, { nextObj: this.params }); + const { obj, objProp } = result; + if (!obj || !objProp || !(obj[objProp] instanceof tfjs_esm_exports.Tensor)) { + throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`); + } + return { obj, objProp }; + } +}; + +// src/common/depthwiseSeparableConv.ts +function depthwiseSeparableConv(x, params, stride) { + return tfjs_esm_exports.tidy(() => { + let out = tfjs_esm_exports.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same"); + out = tfjs_esm_exports.add(out, params.bias); + return out; + }); +} + +// src/faceFeatureExtractor/denseBlock.ts +function denseBlock3(x, denseBlockParams, isFirstLayer = false) { + return tfjs_esm_exports.tidy(() => { + const out1 = tfjs_esm_exports.relu( + isFirstLayer ? tfjs_esm_exports.add( + tfjs_esm_exports.conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), + denseBlockParams.conv0.bias + ) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]) + ); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = tfjs_esm_exports.relu(tfjs_esm_exports.add(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + return tfjs_esm_exports.relu(tfjs_esm_exports.add(out1, tfjs_esm_exports.add(out2, out3))); + }); +} +function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) { + return tfjs_esm_exports.tidy(() => { + const out1 = tfjs_esm_exports.relu( + isFirstLayer ? tfjs_esm_exports.add( + tfjs_esm_exports.conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), + denseBlockParams.conv0.bias + ) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]) + ); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = tfjs_esm_exports.relu(tfjs_esm_exports.add(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + const in4 = tfjs_esm_exports.relu(tfjs_esm_exports.add(out1, tfjs_esm_exports.add(out2, out3))); + const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]); + return tfjs_esm_exports.relu(tfjs_esm_exports.add(out1, tfjs_esm_exports.add(out2, tfjs_esm_exports.add(out3, out4)))); + }); +} + +// src/common/convLayer.ts +function convLayer(x, params, padding = "same", withRelu = false) { + return tfjs_esm_exports.tidy(() => { + const out = tfjs_esm_exports.add( + tfjs_esm_exports.conv2d(x, params.filters, [1, 1], padding), + params.bias + ); + return withRelu ? tfjs_esm_exports.relu(out) : out; + }); +} + +// src/common/disposeUnusedWeightTensors.ts +function disposeUnusedWeightTensors(weightMap, paramMappings) { + Object.keys(weightMap).forEach((path) => { + if (!paramMappings.some((pm) => pm.originalPath === path)) { + weightMap[path].dispose(); + } + }); +} + +// src/common/extractConvParamsFactory.ts +function extractConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, filterSize, mappedPrefix) => { + const filters = tfjs_esm_exports.tensor4d( + extractWeights(channelsIn * channelsOut * filterSize * filterSize), + [filterSize, filterSize, channelsIn, channelsOut] + ); + const bias = tfjs_esm_exports.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { filters, bias }; + }; +} + +// src/common/extractFCParamsFactory.ts +function extractFCParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const fc_weights = tfjs_esm_exports.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]); + const fc_bias = tfjs_esm_exports.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/weights` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { + weights: fc_weights, + bias: fc_bias + }; + }; +} + +// src/common/types.ts +var SeparableConvParams = class { + // eslint-disable-next-line no-useless-constructor + constructor(depthwise_filter, pointwise_filter, bias) { + this.depthwise_filter = depthwise_filter; + this.pointwise_filter = pointwise_filter; + this.bias = bias; + } +}; + +// src/common/extractSeparableConvParamsFactory.ts +function extractSeparableConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const depthwise_filter = tfjs_esm_exports.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]); + const pointwise_filter = tfjs_esm_exports.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]); + const bias = tfjs_esm_exports.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/depthwise_filter` }, + { paramPath: `${mappedPrefix}/pointwise_filter` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return new SeparableConvParams( + depthwise_filter, + pointwise_filter, + bias + ); + }; +} +function loadSeparableConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4); + const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return new SeparableConvParams( + depthwise_filter, + pointwise_filter, + bias + ); + }; +} + +// src/common/extractWeightEntryFactory.ts +function extractWeightEntryFactory(weightMap, paramMappings) { + return (originalPath, paramRank, mappedPath) => { + const tensor2 = weightMap[originalPath]; + if (!isTensor(tensor2, paramRank)) { + throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`); + } + paramMappings.push( + { originalPath, paramPath: mappedPath || originalPath } + ); + return tensor2; + }; +} + +// src/common/extractWeightsFactory.ts +function extractWeightsFactory(weights) { + let remainingWeights = weights; + function extractWeights(numWeights) { + const ret = remainingWeights.slice(0, numWeights); + remainingWeights = remainingWeights.slice(numWeights); + return ret; + } + function getRemainingWeights() { + return remainingWeights; + } + return { + extractWeights, + getRemainingWeights + }; +} + +// src/faceFeatureExtractor/extractorsFactory.ts +function extractorsFactory(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`) : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`); + const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`); + const conv22 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`); + return { conv0, conv1, conv2: conv22 }; + } + function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const { conv0, conv1, conv2: conv22 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer); + const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParams.ts +function extractParams(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock4Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock4Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock4Params(32, 64, "dense1"); + const dense2 = extractDenseBlock4Params(64, 128, "dense2"); + const dense3 = extractDenseBlock4Params(128, 256, "dense3"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { + dense0, + dense1, + dense2, + dense3 + } + }; +} + +// src/common/loadConvParamsFactory.ts +function loadConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { filters, bias }; + }; +} + +// src/faceFeatureExtractor/loadParamsFactory.ts +function loadParamsFactory(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractDenseBlock3Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + return { conv0, conv1, conv2: conv22 }; + } + function extractDenseBlock4Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + const conv3 = extractSeparableConvParams(`${prefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap(weightMap) { + const paramMappings = []; + const { + extractDenseBlock4Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock4Params("dense0", true), + dense1: extractDenseBlock4Params("dense1"), + dense2: extractDenseBlock4Params("dense2"), + dense3: extractDenseBlock4Params("dense3") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceFeatureExtractor/FaceFeatureExtractor.ts +var FaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("FaceFeatureExtractor"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("FaceFeatureExtractor - load model before inference"); + } + return tfjs_esm_exports.tidy(() => { + const batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock4(normalized, params.dense0, true); + out = denseBlock4(out, params.dense1); + out = denseBlock4(out, params.dense2); + out = denseBlock4(out, params.dense3); + out = tfjs_esm_exports.avgPool(out, [7, 7], [2, 2], "valid"); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "face_feature_extractor_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap(weightMap); + } + extractParams(weights) { + return extractParams(weights); + } +}; + +// src/common/fullyConnectedLayer.ts +function fullyConnectedLayer(x, params) { + return tfjs_esm_exports.tidy(() => tfjs_esm_exports.add( + tfjs_esm_exports.matMul(x, params.weights), + params.bias + )); +} + +// src/faceProcessor/extractParams.ts +function extractParams2(weights, channelsIn, channelsOut) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const fc = extractFCParams(channelsIn, channelsOut, "fc"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { fc } + }; +} + +// src/faceProcessor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap2(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { weights, bias }; + } + const params = { + fc: extractFcParams("fc") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceProcessor/util.ts +function seperateWeightMaps(weightMap) { + const featureExtractorMap = {}; + const classifierMap = {}; + Object.keys(weightMap).forEach((key) => { + const map = key.startsWith("fc") ? classifierMap : featureExtractorMap; + map[key] = weightMap[key]; + }); + return { featureExtractorMap, classifierMap }; +} + +// src/faceProcessor/FaceProcessor.ts +var FaceProcessor = class extends NeuralNetwork { + constructor(_name, faceFeatureExtractor) { + super(_name); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input) { + const { params } = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tfjs_esm_exports.tidy(() => { + const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input; + return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc); + }); + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const { params, paramMappings } = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams2(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut()); + } + extractParamsFromWeightMap(weightMap) { + const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap2(classifierMap); + } + extractParams(weights) { + const cIn = this.getClassifierChannelsIn(); + const cOut = this.getClassifierChannelsOut(); + const classifierWeightSize = cOut * cIn + cOut; + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceExpressionNet/FaceExpressions.ts +var FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"]; +var FaceExpressions = class { + constructor(probabilities) { + this.neutral = 0; + this.happy = 0; + this.sad = 0; + this.angry = 0; + this.fearful = 0; + this.disgusted = 0; + this.surprised = 0; + if (probabilities.length !== 7) { + throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`); + } + FACE_EXPRESSION_LABELS.forEach((expression, idx) => { + this[expression] = probabilities[idx]; + }); + } + asSortedArray() { + return FACE_EXPRESSION_LABELS.map((expression) => ({ expression, probability: this[expression] })).sort((e0, e1) => e1.probability - e0.probability); + } +}; + +// src/faceExpressionNet/FaceExpressionNet.ts +var FaceExpressionNet = class extends FaceProcessor { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceExpressionNet", faceFeatureExtractor); + } + forwardInput(input) { + return tfjs_esm_exports.tidy(() => tfjs_esm_exports.softmax(this.runNet(input))); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async predictExpressions(input) { + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput); + const probabilitesByBatch = await Promise.all(tfjs_esm_exports.unstack(out).map(async (t) => { + const data = t.dataSync(); + t.dispose(); + return data; + })); + out.dispose(); + const predictionsByBatch = probabilitesByBatch.map((probabilites) => new FaceExpressions(probabilites)); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "face_expression_model"; + } + getClassifierChannelsIn() { + return 256; + } + getClassifierChannelsOut() { + return 7; + } +}; + +// src/factories/WithFaceExpressions.ts +function isWithFaceExpressions(obj) { + return obj.expressions instanceof FaceExpressions; +} +function extendWithFaceExpressions(sourceObj, expressions) { + const extension = { expressions }; + return { ...sourceObj, ...extension }; +} + +// src/draw/drawFaceExpressions.ts +function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, textFieldAnchor) { + const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]; + faceExpressionsArray.forEach((e) => { + const expr = e instanceof FaceExpressions ? e : isWithFaceExpressions(e) ? e.expressions : void 0; + if (!expr) { + throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof"); + } + const sorted = expr.asSortedArray(); + const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence); + const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0); + const drawTextField = new DrawTextField( + resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`), + anchor + ); + drawTextField.draw(canvasArg); + }); +} + +// src/factories/WithFaceLandmarks.ts +function isWithFaceLandmarks(obj) { + return isWithFaceDetection(obj) && obj["landmarks"] instanceof FaceLandmarks && obj["unshiftedLandmarks"] instanceof FaceLandmarks && obj["alignedRect"] instanceof FaceDetection; +} +function calculateFaceAngle(mesh) { + const degrees = (radians) => radians * 180 / Math.PI; + const calcLengthBetweenTwoPoints = (a, b) => Math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2); + const angle = { + roll: void 0, + pitch: void 0, + yaw: void 0 + }; + const calcYaw = (leftPoint, midPoint, rightPoint) => { + const leftToMidpoint = Math.floor(leftPoint.x - midPoint.x); + const rightToMidpoint = Math.floor(midPoint.x - rightPoint.x); + return leftToMidpoint - rightToMidpoint; + }; + const calcRoll = (lever, pivot) => { + const hypotenuse = Math.hypot(pivot.x - lever.x, pivot.y - lever.y); + const opposite = pivot.y - lever.y; + const angleInRadians = Math.asin(opposite / hypotenuse); + const angleInDegrees = degrees(angleInRadians); + const normalizeAngle = Math.floor(90 - angleInDegrees); + const tiltDirection = pivot.x - lever.x < 0 ? -1 : 1; + const result = normalizeAngle * tiltDirection; + return result; + }; + const calcPitch = (leftPoint, midPoint, rightPoint) => { + const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint); + const baseCoords = new Point((leftPoint.x + rightPoint.x) / 2, (leftPoint.y + rightPoint.y) / 2); + const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords); + const angleInRadians = Math.atan(midToBaseLength / base); + const angleInDegrees = Math.floor(degrees(angleInRadians)); + const direction = baseCoords.y - midPoint.y < 0 ? -1 : 1; + const result = angleInDegrees * direction; + return result; + }; + if (!mesh || !mesh.positions || mesh.positions.length !== 68) + return angle; + const pt = mesh.positions; + angle.roll = calcRoll(pt[27], pt[66]); + angle.pitch = calcPitch(pt[14], pt[30], pt[2]); + angle.yaw = calcYaw(pt[14], pt[33], pt[2]); + return angle; +} +function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) { + const { box: shift } = sourceObj.detection; + const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y); + const rect = landmarks.align(); + const { imageDims } = sourceObj.detection; + const alignedRect = new FaceDetection( + sourceObj.detection.score, + rect.rescale(imageDims.reverse()), + imageDims + ); + const angle = calculateFaceAngle(unshiftedLandmarks); + const extension = { landmarks, unshiftedLandmarks, alignedRect, angle }; + return { ...sourceObj, ...extension }; +} + +// src/draw/DrawFaceLandmarks.ts +var DrawFaceLandmarksOptions = class { + constructor(options = {}) { + const { + drawLines = true, + drawPoints = true, + lineWidth, + lineColor, + pointSize, + pointColor + } = options; + this.drawLines = drawLines; + this.drawPoints = drawPoints; + this.lineWidth = lineWidth || 1; + this.pointSize = pointSize || 2; + this.lineColor = lineColor || "rgba(0, 255, 255, 1)"; + this.pointColor = pointColor || "rgba(255, 0, 255, 1)"; + } +}; +var DrawFaceLandmarks = class { + constructor(faceLandmarks, options = {}) { + this.faceLandmarks = faceLandmarks; + this.options = new DrawFaceLandmarksOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { + drawLines, + drawPoints, + lineWidth, + lineColor, + pointSize, + pointColor + } = this.options; + if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) { + ctx.strokeStyle = lineColor; + ctx.lineWidth = lineWidth; + drawContour(ctx, this.faceLandmarks.getJawOutline()); + drawContour(ctx, this.faceLandmarks.getLeftEyeBrow()); + drawContour(ctx, this.faceLandmarks.getRightEyeBrow()); + drawContour(ctx, this.faceLandmarks.getNose()); + drawContour(ctx, this.faceLandmarks.getLeftEye(), true); + drawContour(ctx, this.faceLandmarks.getRightEye(), true); + drawContour(ctx, this.faceLandmarks.getMouth(), true); + } + if (drawPoints) { + ctx.strokeStyle = pointColor; + ctx.fillStyle = pointColor; + const drawPoint = (pt) => { + ctx.beginPath(); + ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI); + ctx.fill(); + }; + this.faceLandmarks.positions.forEach(drawPoint); + } + } +}; +function drawFaceLandmarks(canvasArg, faceLandmarks) { + const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]; + faceLandmarksArray.forEach((f) => { + const landmarks = f instanceof FaceLandmarks ? f : isWithFaceLandmarks(f) ? f.landmarks : void 0; + if (!landmarks) { + throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof"); + } + new DrawFaceLandmarks(landmarks).draw(canvasArg); + }); +} + +// package.json +var version7 = "1.7.12"; + +// src/xception/extractParams.ts +function extractorsFactory2(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`); + return { separable_conv0, separable_conv1, expansion_conv }; + } + function extractMainBlockParams(channels, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`); + return { separable_conv0, separable_conv1, separable_conv2 }; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParams3(weights, numMainBlocks) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = extractorsFactory2(extractWeights, paramMappings); + const entry_flow_conv_in = extractConvParams(3, 32, 3, "entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, "entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, "entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams(256, 512, "exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { entry_flow, middle_flow, exit_flow } + }; +} + +// src/xception/extractParamsFromWeightMap.ts +function loadParamsFactory2(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractReductionBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`); + return { separable_conv0, separable_conv1, expansion_conv }; + } + function extractMainBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`); + return { separable_conv0, separable_conv1, separable_conv2 }; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParamsFromWeightMap3(weightMap, numMainBlocks) { + const paramMappings = []; + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = loadParamsFactory2(weightMap, paramMappings); + const entry_flow_conv_in = extractConvParams("entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams("entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams("entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams("exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }; +} + +// src/xception/TinyXception.ts +function conv(x, params, stride) { + return tfjs_esm_exports.add(tfjs_esm_exports.conv2d(x, params.filters, stride, "same"), params.bias); +} +function reductionBlock(x, params, isActivateInput = true) { + let out = isActivateInput ? tfjs_esm_exports.relu(x) : x; + out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(tfjs_esm_exports.relu(out), params.separable_conv1, [1, 1]); + out = tfjs_esm_exports.maxPool(out, [3, 3], [2, 2], "same"); + out = tfjs_esm_exports.add(out, conv(x, params.expansion_conv, [2, 2])); + return out; +} +function mainBlock(x, params) { + let out = depthwiseSeparableConv(tfjs_esm_exports.relu(x), params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(tfjs_esm_exports.relu(out), params.separable_conv1, [1, 1]); + out = depthwiseSeparableConv(tfjs_esm_exports.relu(out), params.separable_conv2, [1, 1]); + out = tfjs_esm_exports.add(out, x); + return out; +} +var TinyXception = class extends NeuralNetwork { + constructor(numMainBlocks) { + super("TinyXception"); + this._numMainBlocks = numMainBlocks; + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("TinyXception - load model before inference"); + } + return tfjs_esm_exports.tidy(() => { + const batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = tfjs_esm_exports.relu(conv(normalized, params.entry_flow.conv_in, [2, 2])); + out = reductionBlock(out, params.entry_flow.reduction_block_0, false); + out = reductionBlock(out, params.entry_flow.reduction_block_1); + range(this._numMainBlocks, 0, 1).forEach((idx) => { + out = mainBlock(out, params.middle_flow[`main_block_${idx}`]); + }); + out = reductionBlock(out, params.exit_flow.reduction_block); + out = tfjs_esm_exports.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1])); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "tiny_xception_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap3(weightMap, this._numMainBlocks); + } + extractParams(weights) { + return extractParams3(weights, this._numMainBlocks); + } +}; + +// src/ageGenderNet/extractParams.ts +function extractParams4(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const age = extractFCParams(512, 1, "fc/age"); + const gender = extractFCParams(512, 2, "fc/gender"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { fc: { age, gender } } + }; +} + +// src/ageGenderNet/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap4(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { weights, bias }; + } + const params = { + fc: { + age: extractFcParams("fc/age"), + gender: extractFcParams("fc/gender") + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/ageGenderNet/types.ts +var Gender = /* @__PURE__ */ ((Gender2) => { + Gender2["FEMALE"] = "female"; + Gender2["MALE"] = "male"; + return Gender2; +})(Gender || {}); + +// src/ageGenderNet/AgeGenderNet.ts +var AgeGenderNet = class extends NeuralNetwork { + constructor(faceFeatureExtractor = new TinyXception(2)) { + super("AgeGenderNet"); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input) { + const { params } = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tfjs_esm_exports.tidy(() => { + const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input; + const pooled = tfjs_esm_exports.avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1); + const age = fullyConnectedLayer(pooled, params.fc.age).as1D(); + const gender = fullyConnectedLayer(pooled, params.fc.gender); + return { age, gender }; + }); + } + forwardInput(input) { + return tfjs_esm_exports.tidy(() => { + const { age, gender } = this.runNet(input); + return { age, gender: tfjs_esm_exports.softmax(gender) }; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async predictAgeAndGender(input) { + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput); + const ages = tfjs_esm_exports.unstack(out.age); + const genders = tfjs_esm_exports.unstack(out.gender); + const ageAndGenderTensors = ages.map((ageTensor, i) => ({ + ageTensor, + genderTensor: genders[i] + })); + const predictionsByBatch = await Promise.all( + ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => { + const age = ageTensor.dataSync()[0]; + const probMale = genderTensor.dataSync()[0]; + const isMale = probMale > 0.5; + const gender = isMale ? "male" /* MALE */ : "female" /* FEMALE */; + const genderProbability = isMale ? probMale : 1 - probMale; + ageTensor.dispose(); + genderTensor.dispose(); + return { age, gender, genderProbability }; + }) + ); + out.age.dispose(); + out.gender.dispose(); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "age_gender_model"; + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const { params, paramMappings } = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams4(weights); + } + extractParamsFromWeightMap(weightMap) { + const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap4(classifierMap); + } + extractParams(weights) { + const classifierWeightSize = 512 * 1 + 1 + (512 * 2 + 2); + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68NetBase.ts +var FaceLandmark68NetBase = class extends FaceProcessor { + postProcess(output, inputSize, originalDimensions) { + const inputDimensions = originalDimensions.map(({ width, height }) => { + const scale2 = inputSize / Math.max(height, width); + return { + width: width * scale2, + height: height * scale2 + }; + }); + const batchSize = inputDimensions.length; + return tfjs_esm_exports.tidy(() => { + const createInterleavedTensor = (fillX, fillY) => tfjs_esm_exports.stack([tfjs_esm_exports.fill([68], fillX, "float32"), tfjs_esm_exports.fill([68], fillY, "float32")], 1).as2D(1, 136).as1D(); + const getPadding = (batchIdx, cond) => { + const { width, height } = inputDimensions[batchIdx]; + return cond(width, height) ? Math.abs(width - height) / 2 : 0; + }; + const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h); + const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w); + const landmarkTensors = output.mul(tfjs_esm_exports.fill([batchSize, 136], inputSize, "float32")).sub(tfjs_esm_exports.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor( + getPaddingX(batchIdx), + getPaddingY(batchIdx) + )))).div(tfjs_esm_exports.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor( + inputDimensions[batchIdx].width, + inputDimensions[batchIdx].height + )))); + return landmarkTensors; + }); + } + forwardInput(input) { + return tfjs_esm_exports.tidy(() => { + const out = this.runNet(input); + return this.postProcess( + out, + input.inputSize, + input.inputDimensions.map(([height, width]) => ({ height, width })) + ); + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async detectLandmarks(input) { + const netInput = await toNetInput(input); + const landmarkTensors = tfjs_esm_exports.tidy( + () => tfjs_esm_exports.unstack(this.forwardInput(netInput)) + ); + const landmarksForBatch = await Promise.all(landmarkTensors.map( + async (landmarkTensor, batchIdx) => { + const landmarksArray = Array.from(landmarkTensor.dataSync()); + const xCoords = landmarksArray.filter((_, i) => isEven(i)); + const yCoords = landmarksArray.filter((_, i) => !isEven(i)); + return new FaceLandmarks68( + Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])), + { + height: netInput.getInputHeight(batchIdx), + width: netInput.getInputWidth(batchIdx) + } + ); + } + )); + landmarkTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? landmarksForBatch : landmarksForBatch[0]; + } + getClassifierChannelsOut() { + return 136; + } +}; + +// src/faceLandmarkNet/FaceLandmark68Net.ts +var FaceLandmark68Net = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceLandmark68Net", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_model"; + } + getClassifierChannelsIn() { + return 256; + } +}; + +// src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts +function extractParamsFromWeightMapTiny(weightMap) { + const paramMappings = []; + const { + extractDenseBlock3Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock3Params("dense0", true), + dense1: extractDenseBlock3Params("dense1"), + dense2: extractDenseBlock3Params("dense2") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceFeatureExtractor/extractParamsTiny.ts +function extractParamsTiny(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock3Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock3Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock3Params(32, 64, "dense1"); + const dense2 = extractDenseBlock3Params(64, 128, "dense2"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { dense0, dense1, dense2 } + }; +} + +// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts +var TinyFaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("TinyFaceFeatureExtractor"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("TinyFaceFeatureExtractor - load model before inference"); + } + return tfjs_esm_exports.tidy(() => { + const batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock3(normalized, params.dense0, true); + out = denseBlock3(out, params.dense1); + out = denseBlock3(out, params.dense2); + out = tfjs_esm_exports.avgPool(out, [14, 14], [2, 2], "valid"); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "face_feature_extractor_tiny_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMapTiny(weightMap); + } + extractParams(weights) { + return extractParamsTiny(weights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68TinyNet.ts +var FaceLandmark68TinyNet = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) { + super("FaceLandmark68TinyNet", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_tiny_model"; + } + getClassifierChannelsIn() { + return 128; + } +}; + +// src/faceLandmarkNet/index.ts +var FaceLandmarkNet = class extends FaceLandmark68Net { +}; + +// src/faceRecognitionNet/scaleLayer.ts +function scale(x, params) { + return tfjs_esm_exports.add(tfjs_esm_exports.mul(x, params.weights), params.biases); +} + +// src/faceRecognitionNet/convLayer.ts +function convLayer2(x, params, strides, withRelu, padding = "same") { + const { filters, bias } = params.conv; + let out = tfjs_esm_exports.conv2d(x, filters, strides, padding); + out = tfjs_esm_exports.add(out, bias); + out = scale(out, params.scale); + return withRelu ? tfjs_esm_exports.relu(out) : out; +} +function conv2(x, params) { + return convLayer2(x, params, [1, 1], true); +} +function convNoRelu(x, params) { + return convLayer2(x, params, [1, 1], false); +} +function convDown(x, params) { + return convLayer2(x, params, [2, 2], true, "valid"); +} + +// src/faceRecognitionNet/extractParams.ts +function extractorsFactory3(extractWeights, paramMappings) { + function extractFilterValues(numFilterValues, numFilters, filterSize) { + const weights = extractWeights(numFilterValues); + const depth = weights.length / (numFilters * filterSize * filterSize); + if (isFloat(depth)) { + throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`); + } + return tfjs_esm_exports.tidy( + () => tfjs_esm_exports.transpose( + tfjs_esm_exports.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), + [2, 3, 1, 0] + ) + ); + } + function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const filters = extractFilterValues(numFilterValues, numFilters, filterSize); + const bias = tfjs_esm_exports.tensor1d(extractWeights(numFilters)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { filters, bias }; + } + function extractScaleLayerParams(numWeights, mappedPrefix) { + const weights = tfjs_esm_exports.tensor1d(extractWeights(numWeights)); + const biases = tfjs_esm_exports.tensor1d(extractWeights(numWeights)); + paramMappings.push( + { paramPath: `${mappedPrefix}/weights` }, + { paramPath: `${mappedPrefix}/biases` } + ); + return { + weights, + biases + }; + } + function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const conv3 = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`); + const scale2 = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`); + return { conv: conv3, scale: scale2 }; + } + function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) { + const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`); + const conv22 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`); + return { conv1, conv2: conv22 }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParams5(weights) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory3(extractWeights, paramMappings); + const conv32_down = extractConvLayerParams(4704, 32, 7, "conv32_down"); + const conv32_1 = extractResidualLayerParams(9216, 32, 3, "conv32_1"); + const conv32_2 = extractResidualLayerParams(9216, 32, 3, "conv32_2"); + const conv32_3 = extractResidualLayerParams(9216, 32, 3, "conv32_3"); + const conv64_down = extractResidualLayerParams(36864, 64, 3, "conv64_down", true); + const conv64_1 = extractResidualLayerParams(36864, 64, 3, "conv64_1"); + const conv64_2 = extractResidualLayerParams(36864, 64, 3, "conv64_2"); + const conv64_3 = extractResidualLayerParams(36864, 64, 3, "conv64_3"); + const conv128_down = extractResidualLayerParams(147456, 128, 3, "conv128_down", true); + const conv128_1 = extractResidualLayerParams(147456, 128, 3, "conv128_1"); + const conv128_2 = extractResidualLayerParams(147456, 128, 3, "conv128_2"); + const conv256_down = extractResidualLayerParams(589824, 256, 3, "conv256_down", true); + const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1"); + const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2"); + const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out"); + const fc = tfjs_esm_exports.tidy( + () => tfjs_esm_exports.transpose(tfjs_esm_exports.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]) + ); + paramMappings.push({ paramPath: "fc" }); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + return { params, paramMappings }; +} + +// src/faceRecognitionNet/extractParamsFromWeightMap.ts +function extractorsFactory4(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractScaleLayerParams(prefix) { + const weights = extractWeightEntry(`${prefix}/scale/weights`, 1); + const biases = extractWeightEntry(`${prefix}/scale/biases`, 1); + return { weights, biases }; + } + function extractConvLayerParams(prefix) { + const filters = extractWeightEntry(`${prefix}/conv/filters`, 4); + const bias = extractWeightEntry(`${prefix}/conv/bias`, 1); + const scale2 = extractScaleLayerParams(prefix); + return { conv: { filters, bias }, scale: scale2 }; + } + function extractResidualLayerParams(prefix) { + return { + conv1: extractConvLayerParams(`${prefix}/conv1`), + conv2: extractConvLayerParams(`${prefix}/conv2`) + }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParamsFromWeightMap5(weightMap) { + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory4(weightMap, paramMappings); + const conv32_down = extractConvLayerParams("conv32_down"); + const conv32_1 = extractResidualLayerParams("conv32_1"); + const conv32_2 = extractResidualLayerParams("conv32_2"); + const conv32_3 = extractResidualLayerParams("conv32_3"); + const conv64_down = extractResidualLayerParams("conv64_down"); + const conv64_1 = extractResidualLayerParams("conv64_1"); + const conv64_2 = extractResidualLayerParams("conv64_2"); + const conv64_3 = extractResidualLayerParams("conv64_3"); + const conv128_down = extractResidualLayerParams("conv128_down"); + const conv128_1 = extractResidualLayerParams("conv128_1"); + const conv128_2 = extractResidualLayerParams("conv128_2"); + const conv256_down = extractResidualLayerParams("conv256_down"); + const conv256_1 = extractResidualLayerParams("conv256_1"); + const conv256_2 = extractResidualLayerParams("conv256_2"); + const conv256_down_out = extractResidualLayerParams("conv256_down_out"); + const { fc } = weightMap; + paramMappings.push({ originalPath: "fc", paramPath: "fc" }); + if (!isTensor2D(fc)) { + throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceRecognitionNet/residualLayer.ts +function residual(x, params) { + let out = conv2(x, params.conv1); + out = convNoRelu(out, params.conv2); + out = tfjs_esm_exports.add(out, x); + out = tfjs_esm_exports.relu(out); + return out; +} +function residualDown(x, params) { + let out = convDown(x, params.conv1); + out = convNoRelu(out, params.conv2); + let pooled = tfjs_esm_exports.avgPool(x, 2, 2, "valid"); + const zeros2 = tfjs_esm_exports.zeros(pooled.shape); + const isPad = pooled.shape[3] !== out.shape[3]; + const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]; + if (isAdjustShape) { + const padShapeX = [...out.shape]; + padShapeX[1] = 1; + const zerosW = tfjs_esm_exports.zeros(padShapeX); + out = tfjs_esm_exports.concat([out, zerosW], 1); + const padShapeY = [...out.shape]; + padShapeY[2] = 1; + const zerosH = tfjs_esm_exports.zeros(padShapeY); + out = tfjs_esm_exports.concat([out, zerosH], 2); + } + pooled = isPad ? tfjs_esm_exports.concat([pooled, zeros2], 3) : pooled; + out = tfjs_esm_exports.add(pooled, out); + out = tfjs_esm_exports.relu(out); + return out; +} + +// src/faceRecognitionNet/FaceRecognitionNet.ts +var FaceRecognitionNet = class extends NeuralNetwork { + constructor() { + super("FaceRecognitionNet"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("FaceRecognitionNet - load model before inference"); + } + return tfjs_esm_exports.tidy(() => { + const batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(150, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = convDown(normalized, params.conv32_down); + out = tfjs_esm_exports.maxPool(out, 3, 2, "valid"); + out = residual(out, params.conv32_1); + out = residual(out, params.conv32_2); + out = residual(out, params.conv32_3); + out = residualDown(out, params.conv64_down); + out = residual(out, params.conv64_1); + out = residual(out, params.conv64_2); + out = residual(out, params.conv64_3); + out = residualDown(out, params.conv128_down); + out = residual(out, params.conv128_1); + out = residual(out, params.conv128_2); + out = residualDown(out, params.conv256_down); + out = residual(out, params.conv256_1); + out = residual(out, params.conv256_2); + out = residualDown(out, params.conv256_down_out); + const globalAvg = out.mean([1, 2]); + const fullyConnected = tfjs_esm_exports.matMul(globalAvg, params.fc); + return fullyConnected; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async computeFaceDescriptor(input) { + var _a; + if ((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a.some((dim) => dim <= 0)) + return new Float32Array(128); + const netInput = await toNetInput(input); + const faceDescriptorTensors = tfjs_esm_exports.tidy(() => tfjs_esm_exports.unstack(this.forwardInput(netInput))); + const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())); + faceDescriptorTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0]; + } + getDefaultModelName() { + return "face_recognition_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap5(weightMap); + } + extractParams(weights) { + return extractParams5(weights); + } +}; + +// src/faceRecognitionNet/index.ts +function createFaceRecognitionNet(weights) { + const net = new FaceRecognitionNet(); + net.extractWeights(weights); + return net; +} + +// src/factories/WithFaceDescriptor.ts +function extendWithFaceDescriptor(sourceObj, descriptor) { + const extension = { descriptor }; + return { ...sourceObj, ...extension }; +} + +// src/factories/WithAge.ts +function isWithAge(obj) { + return typeof obj.age === "number"; +} +function extendWithAge(sourceObj, age) { + const extension = { age }; + return { ...sourceObj, ...extension }; +} + +// src/factories/WithGender.ts +function isWithGender(obj) { + return (obj.gender === "male" /* MALE */ || obj.gender === "female" /* FEMALE */) && isValidProbablitiy(obj.genderProbability); +} +function extendWithGender(sourceObj, gender, genderProbability) { + const extension = { gender, genderProbability }; + return { ...sourceObj, ...extension }; +} + +// src/ssdMobilenetv1/extractParams.ts +function extractorsFactory5(extractWeights, paramMappings) { + function extractDepthwiseConvParams(numChannels, mappedPrefix) { + const filters = tfjs_esm_exports.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]); + const batch_norm_scale = tfjs_esm_exports.tensor1d(extractWeights(numChannels)); + const batch_norm_offset = tfjs_esm_exports.tensor1d(extractWeights(numChannels)); + const batch_norm_mean = tfjs_esm_exports.tensor1d(extractWeights(numChannels)); + const batch_norm_variance = tfjs_esm_exports.tensor1d(extractWeights(numChannels)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/batch_norm_scale` }, + { paramPath: `${mappedPrefix}/batch_norm_offset` }, + { paramPath: `${mappedPrefix}/batch_norm_mean` }, + { paramPath: `${mappedPrefix}/batch_norm_variance` } + ); + return { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }; + } + function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) { + const filters = tfjs_esm_exports.tensor4d( + extractWeights(channelsIn * channelsOut * filterSize * filterSize), + [filterSize, filterSize, channelsIn, channelsOut] + ); + const bias = tfjs_esm_exports.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}` } + ); + return { filters, bias }; + } + function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) { + const { + filters, + bias + } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true); + return { + filters, + batch_norm_offset: bias + }; + } + function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) { + const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`); + const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`); + return { depthwise_conv, pointwise_conv }; + } + function extractMobilenetV1Params() { + const conv_0 = extractPointwiseConvParams(3, 32, 3, "mobilenetv1/conv_0"); + const conv_1 = extractConvPairParams(32, 64, "mobilenetv1/conv_1"); + const conv_2 = extractConvPairParams(64, 128, "mobilenetv1/conv_2"); + const conv_3 = extractConvPairParams(128, 128, "mobilenetv1/conv_3"); + const conv_4 = extractConvPairParams(128, 256, "mobilenetv1/conv_4"); + const conv_5 = extractConvPairParams(256, 256, "mobilenetv1/conv_5"); + const conv_6 = extractConvPairParams(256, 512, "mobilenetv1/conv_6"); + const conv_7 = extractConvPairParams(512, 512, "mobilenetv1/conv_7"); + const conv_8 = extractConvPairParams(512, 512, "mobilenetv1/conv_8"); + const conv_9 = extractConvPairParams(512, 512, "mobilenetv1/conv_9"); + const conv_10 = extractConvPairParams(512, 512, "mobilenetv1/conv_10"); + const conv_11 = extractConvPairParams(512, 512, "mobilenetv1/conv_11"); + const conv_12 = extractConvPairParams(512, 1024, "mobilenetv1/conv_12"); + const conv_13 = extractConvPairParams(1024, 1024, "mobilenetv1/conv_13"); + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + conv_8, + conv_9, + conv_10, + conv_11, + conv_12, + conv_13 + }; + } + function extractPredictionLayerParams() { + const conv_0 = extractPointwiseConvParams(1024, 256, 1, "prediction_layer/conv_0"); + const conv_1 = extractPointwiseConvParams(256, 512, 3, "prediction_layer/conv_1"); + const conv_2 = extractPointwiseConvParams(512, 128, 1, "prediction_layer/conv_2"); + const conv_3 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_3"); + const conv_4 = extractPointwiseConvParams(256, 128, 1, "prediction_layer/conv_4"); + const conv_5 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_5"); + const conv_6 = extractPointwiseConvParams(256, 64, 1, "prediction_layer/conv_6"); + const conv_7 = extractPointwiseConvParams(64, 128, 3, "prediction_layer/conv_7"); + const box_encoding_0_predictor = extractConvParams(512, 12, 1, "prediction_layer/box_predictor_0/box_encoding_predictor"); + const class_predictor_0 = extractConvParams(512, 9, 1, "prediction_layer/box_predictor_0/class_predictor"); + const box_encoding_1_predictor = extractConvParams(1024, 24, 1, "prediction_layer/box_predictor_1/box_encoding_predictor"); + const class_predictor_1 = extractConvParams(1024, 18, 1, "prediction_layer/box_predictor_1/class_predictor"); + const box_encoding_2_predictor = extractConvParams(512, 24, 1, "prediction_layer/box_predictor_2/box_encoding_predictor"); + const class_predictor_2 = extractConvParams(512, 18, 1, "prediction_layer/box_predictor_2/class_predictor"); + const box_encoding_3_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_3/box_encoding_predictor"); + const class_predictor_3 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_3/class_predictor"); + const box_encoding_4_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_4/box_encoding_predictor"); + const class_predictor_4 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_4/class_predictor"); + const box_encoding_5_predictor = extractConvParams(128, 24, 1, "prediction_layer/box_predictor_5/box_encoding_predictor"); + const class_predictor_5 = extractConvParams(128, 18, 1, "prediction_layer/box_predictor_5/class_predictor"); + const box_predictor_0 = { + box_encoding_predictor: box_encoding_0_predictor, + class_predictor: class_predictor_0 + }; + const box_predictor_1 = { + box_encoding_predictor: box_encoding_1_predictor, + class_predictor: class_predictor_1 + }; + const box_predictor_2 = { + box_encoding_predictor: box_encoding_2_predictor, + class_predictor: class_predictor_2 + }; + const box_predictor_3 = { + box_encoding_predictor: box_encoding_3_predictor, + class_predictor: class_predictor_3 + }; + const box_predictor_4 = { + box_encoding_predictor: box_encoding_4_predictor, + class_predictor: class_predictor_4 + }; + const box_predictor_5 = { + box_encoding_predictor: box_encoding_5_predictor, + class_predictor: class_predictor_5 + }; + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + box_predictor_0, + box_predictor_1, + box_predictor_2, + box_predictor_3, + box_predictor_4, + box_predictor_5 + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParams6(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory5(extractWeights, paramMappings); + const mobilenetv1 = extractMobilenetV1Params(); + const prediction_layer = extractPredictionLayerParams(); + const extra_dim = tfjs_esm_exports.tensor3d( + extractWeights(5118 * 4), + [1, 5118, 4] + ); + const output_layer = { + extra_dim + }; + paramMappings.push({ paramPath: "output_layer/extra_dim" }); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + params: { + mobilenetv1, + prediction_layer, + output_layer + }, + paramMappings + }; +} + +// src/ssdMobilenetv1/extractParamsFromWeightMap.ts +function extractorsFactory6(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractPointwiseConvParams(prefix, idx, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`); + const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`); + return { filters, batch_norm_offset }; + } + function extractConvPairParams(idx) { + const mappedPrefix = `mobilenetv1/conv_${idx}`; + const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`; + const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`; + const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`; + const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`); + const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`); + const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`); + const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`); + const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`); + return { + depthwise_conv: { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }, + pointwise_conv: extractPointwiseConvParams("MobilenetV1", idx, mappedPrefixPointwiseConv) + }; + } + function extractMobilenetV1Params() { + return { + conv_0: extractPointwiseConvParams("MobilenetV1", 0, "mobilenetv1/conv_0"), + conv_1: extractConvPairParams(1), + conv_2: extractConvPairParams(2), + conv_3: extractConvPairParams(3), + conv_4: extractConvPairParams(4), + conv_5: extractConvPairParams(5), + conv_6: extractConvPairParams(6), + conv_7: extractConvPairParams(7), + conv_8: extractConvPairParams(8), + conv_9: extractConvPairParams(9), + conv_10: extractConvPairParams(10), + conv_11: extractConvPairParams(11), + conv_12: extractConvPairParams(12), + conv_13: extractConvPairParams(13) + }; + } + function extractConvParams(prefix, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`); + const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`); + return { filters, bias }; + } + function extractBoxPredictorParams(idx) { + const box_encoding_predictor = extractConvParams( + `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, + `prediction_layer/box_predictor_${idx}/box_encoding_predictor` + ); + const class_predictor = extractConvParams( + `Prediction/BoxPredictor_${idx}/ClassPredictor`, + `prediction_layer/box_predictor_${idx}/class_predictor` + ); + return { box_encoding_predictor, class_predictor }; + } + function extractPredictionLayerParams() { + return { + conv_0: extractPointwiseConvParams("Prediction", 0, "prediction_layer/conv_0"), + conv_1: extractPointwiseConvParams("Prediction", 1, "prediction_layer/conv_1"), + conv_2: extractPointwiseConvParams("Prediction", 2, "prediction_layer/conv_2"), + conv_3: extractPointwiseConvParams("Prediction", 3, "prediction_layer/conv_3"), + conv_4: extractPointwiseConvParams("Prediction", 4, "prediction_layer/conv_4"), + conv_5: extractPointwiseConvParams("Prediction", 5, "prediction_layer/conv_5"), + conv_6: extractPointwiseConvParams("Prediction", 6, "prediction_layer/conv_6"), + conv_7: extractPointwiseConvParams("Prediction", 7, "prediction_layer/conv_7"), + box_predictor_0: extractBoxPredictorParams(0), + box_predictor_1: extractBoxPredictorParams(1), + box_predictor_2: extractBoxPredictorParams(2), + box_predictor_3: extractBoxPredictorParams(3), + box_predictor_4: extractBoxPredictorParams(4), + box_predictor_5: extractBoxPredictorParams(5) + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParamsFromWeightMap6(weightMap) { + const paramMappings = []; + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory6(weightMap, paramMappings); + const extra_dim = weightMap["Output/extra_dim"]; + paramMappings.push({ originalPath: "Output/extra_dim", paramPath: "output_layer/extra_dim" }); + if (!isTensor3D(extra_dim)) { + throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`); + } + const params = { + mobilenetv1: extractMobilenetV1Params(), + prediction_layer: extractPredictionLayerParams(), + output_layer: { + extra_dim + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/ssdMobilenetv1/pointwiseConvLayer.ts +function pointwiseConvLayer(x, params, strides) { + return tfjs_esm_exports.tidy(() => { + let out = tfjs_esm_exports.conv2d(x, params.filters, strides, "same"); + out = tfjs_esm_exports.add(out, params.batch_norm_offset); + return tfjs_esm_exports.clipByValue(out, 0, 6); + }); +} + +// src/ssdMobilenetv1/mobileNetV1.ts +var epsilon = 0.0010000000474974513; +function depthwiseConvLayer(x, params, strides) { + return tfjs_esm_exports.tidy(() => { + let out = tfjs_esm_exports.depthwiseConv2d(x, params.filters, strides, "same"); + out = tfjs_esm_exports.batchNorm( + out, + params.batch_norm_mean, + params.batch_norm_variance, + params.batch_norm_offset, + params.batch_norm_scale, + epsilon + ); + return tfjs_esm_exports.clipByValue(out, 0, 6); + }); +} +function getStridesForLayerIdx(layerIdx) { + return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1]; +} +function mobileNetV1(x, params) { + return tfjs_esm_exports.tidy(() => { + let conv11; + let out = pointwiseConvLayer(x, params.conv_0, [2, 2]); + const convPairParams = [ + params.conv_1, + params.conv_2, + params.conv_3, + params.conv_4, + params.conv_5, + params.conv_6, + params.conv_7, + params.conv_8, + params.conv_9, + params.conv_10, + params.conv_11, + params.conv_12, + params.conv_13 + ]; + convPairParams.forEach((param, i) => { + const layerIdx = i + 1; + const depthwiseConvStrides = getStridesForLayerIdx(layerIdx); + out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides); + out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]); + if (layerIdx === 11) + conv11 = out; + }); + if (conv11 === null) { + throw new Error("mobileNetV1 - output of conv layer 11 is null"); + } + return { + out, + conv11 + }; + }); +} + +// src/ssdMobilenetv1/nonMaxSuppression.ts +function IOU(boxes, i, j) { + const boxesData = boxes.arraySync(); + const yminI = Math.min(boxesData[i][0], boxesData[i][2]); + const xminI = Math.min(boxesData[i][1], boxesData[i][3]); + const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]); + const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]); + const yminJ = Math.min(boxesData[j][0], boxesData[j][2]); + const xminJ = Math.min(boxesData[j][1], boxesData[j][3]); + const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]); + const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]); + const areaI = (ymaxI - yminI) * (xmaxI - xminI); + const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); + if (areaI <= 0 || areaJ <= 0) + return 0; + const intersectionYmin = Math.max(yminI, yminJ); + const intersectionXmin = Math.max(xminI, xminJ); + const intersectionYmax = Math.min(ymaxI, ymaxJ); + const intersectionXmax = Math.min(xmaxI, xmaxJ); + const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0); + return intersectionArea / (areaI + areaJ - intersectionArea); +} +function nonMaxSuppression2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + const numBoxes = boxes.shape[0]; + const outputSize = Math.min(maxOutputSize, numBoxes); + const candidates = scores.map((score, boxIndex) => ({ score, boxIndex })).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score); + const suppressFunc = (x) => x <= iouThreshold ? 1 : 0; + const selected = []; + candidates.forEach((c) => { + if (selected.length >= outputSize) + return; + const originalScore = c.score; + for (let j = selected.length - 1; j >= 0; --j) { + const iou2 = IOU(boxes, c.boxIndex, selected[j]); + if (iou2 === 0) + continue; + c.score *= suppressFunc(iou2); + if (c.score <= scoreThreshold) + break; + } + if (originalScore === c.score) { + selected.push(c.boxIndex); + } + }); + return selected; +} + +// src/ssdMobilenetv1/outputLayer.ts +function getCenterCoordinatesAndSizesLayer(x) { + const vec = tfjs_esm_exports.unstack(tfjs_esm_exports.transpose(x, [1, 0])); + const sizes = [ + tfjs_esm_exports.sub(vec[2], vec[0]), + tfjs_esm_exports.sub(vec[3], vec[1]) + ]; + const centers = [ + tfjs_esm_exports.add(vec[0], tfjs_esm_exports.div(sizes[0], 2)), + tfjs_esm_exports.add(vec[1], tfjs_esm_exports.div(sizes[1], 2)) + ]; + return { sizes, centers }; +} +function decodeBoxesLayer(x0, x1) { + const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0); + const vec = tfjs_esm_exports.unstack(tfjs_esm_exports.transpose(x1, [1, 0])); + const div0_out = tfjs_esm_exports.div(tfjs_esm_exports.mul(tfjs_esm_exports.exp(tfjs_esm_exports.div(vec[2], 5)), sizes[0]), 2); + const add0_out = tfjs_esm_exports.add(tfjs_esm_exports.mul(tfjs_esm_exports.div(vec[0], 10), sizes[0]), centers[0]); + const div1_out = tfjs_esm_exports.div(tfjs_esm_exports.mul(tfjs_esm_exports.exp(tfjs_esm_exports.div(vec[3], 5)), sizes[1]), 2); + const add1_out = tfjs_esm_exports.add(tfjs_esm_exports.mul(tfjs_esm_exports.div(vec[1], 10), sizes[1]), centers[1]); + return tfjs_esm_exports.transpose( + tfjs_esm_exports.stack([ + tfjs_esm_exports.sub(add0_out, div0_out), + tfjs_esm_exports.sub(add1_out, div1_out), + tfjs_esm_exports.add(add0_out, div0_out), + tfjs_esm_exports.add(add1_out, div1_out) + ]), + [1, 0] + ); +} +function outputLayer(boxPredictions, classPredictions, params) { + return tfjs_esm_exports.tidy(() => { + const batchSize = boxPredictions.shape[0]; + let boxes = decodeBoxesLayer( + tfjs_esm_exports.reshape(tfjs_esm_exports.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), + tfjs_esm_exports.reshape(boxPredictions, [-1, 4]) + ); + boxes = tfjs_esm_exports.reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]); + const scoresAndClasses = tfjs_esm_exports.sigmoid(tfjs_esm_exports.slice(classPredictions, [0, 0, 1], [-1, -1, -1])); + let scores = tfjs_esm_exports.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]); + scores = tfjs_esm_exports.reshape(scores, [batchSize, scores.shape[1]]); + const boxesByBatch = tfjs_esm_exports.unstack(boxes); + const scoresByBatch = tfjs_esm_exports.unstack(scores); + return { boxes: boxesByBatch, scores: scoresByBatch }; + }); +} + +// src/ssdMobilenetv1/boxPredictionLayer.ts +function boxPredictionLayer(x, params) { + return tfjs_esm_exports.tidy(() => { + const batchSize = x.shape[0]; + const boxPredictionEncoding = tfjs_esm_exports.reshape( + convLayer(x, params.box_encoding_predictor), + [batchSize, -1, 1, 4] + ); + const classPrediction = tfjs_esm_exports.reshape( + convLayer(x, params.class_predictor), + [batchSize, -1, 3] + ); + return { boxPredictionEncoding, classPrediction }; + }); +} + +// src/ssdMobilenetv1/predictionLayer.ts +function predictionLayer(x, conv11, params) { + return tfjs_esm_exports.tidy(() => { + const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]); + const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]); + const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]); + const conv3 = pointwiseConvLayer(conv22, params.conv_3, [2, 2]); + const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]); + const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]); + const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]); + const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]); + const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0); + const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1); + const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2); + const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3); + const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4); + const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5); + const boxPredictions = tfjs_esm_exports.concat([ + boxPrediction0.boxPredictionEncoding, + boxPrediction1.boxPredictionEncoding, + boxPrediction2.boxPredictionEncoding, + boxPrediction3.boxPredictionEncoding, + boxPrediction4.boxPredictionEncoding, + boxPrediction5.boxPredictionEncoding + ], 1); + const classPredictions = tfjs_esm_exports.concat([ + boxPrediction0.classPrediction, + boxPrediction1.classPrediction, + boxPrediction2.classPrediction, + boxPrediction3.classPrediction, + boxPrediction4.classPrediction, + boxPrediction5.classPrediction + ], 1); + return { + boxPredictions, + classPredictions + }; + }); +} + +// src/ssdMobilenetv1/SsdMobilenetv1Options.ts +var SsdMobilenetv1Options = class { + constructor({ minConfidence, maxResults } = {}) { + this._name = "SsdMobilenetv1Options"; + this._minConfidence = minConfidence || 0.5; + this._maxResults = maxResults || 100; + if (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; + } +}; + +// src/ssdMobilenetv1/SsdMobilenetv1.ts +var SsdMobilenetv1 = class extends NeuralNetwork { + constructor() { + super("SsdMobilenetv1"); + } + forwardInput(input) { + const { params } = this; + if (!params) + throw new Error("SsdMobilenetv1 - load model before inference"); + return tfjs_esm_exports.tidy(() => { + const batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(512, false), "float32"); + const x = tfjs_esm_exports.sub(tfjs_esm_exports.div(batchTensor, 127.5), 1); + const features = mobileNetV1(x, params.mobilenetv1); + const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer); + return outputLayer(boxPredictions, classPredictions, params.output_layer); + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async locateFaces(input, options = {}) { + const { maxResults, minConfidence } = new SsdMobilenetv1Options(options); + const netInput = await toNetInput(input); + const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput); + const boxes = _boxes[0]; + const scores = _scores[0]; + for (let i = 1; i < _boxes.length; i++) { + _boxes[i].dispose(); + _scores[i].dispose(); + } + const scoresData = Array.from(scores.dataSync()); + const iouThreshold = 0.5; + const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence); + const reshapedDims = netInput.getReshapedInputDimensions(0); + const inputSize = netInput.inputSize; + const padX = inputSize / reshapedDims.width; + const padY = inputSize / reshapedDims.height; + const boxesData = boxes.arraySync(); + const results = indices.map((idx) => { + const [top, bottom] = [ + Math.max(0, boxesData[idx][0]), + Math.min(1, boxesData[idx][2]) + ].map((val) => val * padY); + const [left, right] = [ + Math.max(0, boxesData[idx][1]), + Math.min(1, boxesData[idx][3]) + ].map((val) => val * padX); + return new FaceDetection( + scoresData[idx], + new Rect(left, top, right - left, bottom - top), + { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) } + ); + }); + boxes.dispose(); + scores.dispose(); + return results; + } + getDefaultModelName() { + return "ssd_mobilenetv1_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap6(weightMap); + } + extractParams(weights) { + return extractParams6(weights); + } +}; + +// src/ssdMobilenetv1/index.ts +function createSsdMobilenetv1(weights) { + const net = new SsdMobilenetv1(); + net.extractWeights(weights); + return net; +} +function createFaceDetectionNet(weights) { + return createSsdMobilenetv1(weights); +} +var FaceDetectionNet = class extends SsdMobilenetv1 { +}; + +// src/tinyYolov2/const.ts +var IOU_THRESHOLD = 0.4; +var BOX_ANCHORS = [ + new Point(0.738768, 0.874946), + new Point(2.42204, 2.65704), + new Point(4.30971, 7.04493), + new Point(10.246, 4.59428), + new Point(12.6868, 11.8741) +]; +var BOX_ANCHORS_SEPARABLE = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404]; +var DEFAULT_MODEL_NAME = "tiny_yolov2_model"; +var DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model"; + +// src/tinyYolov2/config.ts +var isNumber = (arg) => typeof arg === "number"; +function validateConfig(config) { + if (!config) { + throw new Error(`invalid config: ${config}`); + } + if (typeof config.withSeparableConvs !== "boolean") { + throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`); + } + if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1) { + throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`); + } + if (!Array.isArray(config.classes) || !config.classes.length || !config.classes.every((c) => typeof c === "string")) { + throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`); + } + if (!Array.isArray(config.anchors) || !config.anchors.length || !config.anchors.map((a) => a || {}).every((a) => isNumber(a.x) && isNumber(a.y))) { + throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`); + } + if (config.meanRgb && (!Array.isArray(config.meanRgb) || config.meanRgb.length !== 3 || !config.meanRgb.every(isNumber))) { + throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`); + } +} + +// src/tinyYolov2/leaky.ts +function leaky(x) { + return tfjs_esm_exports.tidy(() => { + const min = tfjs_esm_exports.mul(x, tfjs_esm_exports.scalar(0.10000000149011612)); + return tfjs_esm_exports.add(tfjs_esm_exports.relu(tfjs_esm_exports.sub(x, min)), min); + }); +} + +// src/tinyYolov2/convWithBatchNorm.ts +function convWithBatchNorm(x, params) { + return tfjs_esm_exports.tidy(() => { + let out = tfjs_esm_exports.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = tfjs_esm_exports.conv2d(out, params.conv.filters, [1, 1], "valid"); + out = tfjs_esm_exports.sub(out, params.bn.sub); + out = tfjs_esm_exports.mul(out, params.bn.truediv); + out = tfjs_esm_exports.add(out, params.conv.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/depthwiseSeparableConv.ts +function depthwiseSeparableConv2(x, params) { + return tfjs_esm_exports.tidy(() => { + let out = tfjs_esm_exports.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = tfjs_esm_exports.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid"); + out = tfjs_esm_exports.add(out, params.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/extractParams.ts +function extractorsFactory7(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + function extractBatchNormParams(size, mappedPrefix) { + const sub6 = tfjs_esm_exports.tensor1d(extractWeights(size)); + const truediv = tfjs_esm_exports.tensor1d(extractWeights(size)); + paramMappings.push( + { paramPath: `${mappedPrefix}/sub` }, + { paramPath: `${mappedPrefix}/truediv` } + ); + return { sub: sub6, truediv }; + } + function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) { + const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`); + const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`); + return { conv: conv3, bn }; + } + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParams7(weights, config, boxEncodingSize, filterSizes) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory7(extractWeights, paramMappings); + let params; + if (config.withSeparableConvs) { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = config.isFirstLayerConv2d ? extractConvParams(s0, s1, 3, "conv0") : extractSeparableConvParams(s0, s1, "conv0"); + const conv1 = extractSeparableConvParams(s1, s2, "conv1"); + const conv22 = extractSeparableConvParams(s2, s3, "conv2"); + const conv3 = extractSeparableConvParams(s3, s4, "conv3"); + const conv4 = extractSeparableConvParams(s4, s5, "conv4"); + const conv5 = extractSeparableConvParams(s5, s6, "conv5"); + const conv6 = s7 ? extractSeparableConvParams(s6, s7, "conv6") : void 0; + const conv7 = s8 ? extractSeparableConvParams(s7, s8, "conv7") : void 0; + const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } else { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = extractConvWithBatchNormParams(s0, s1, "conv0"); + const conv1 = extractConvWithBatchNormParams(s1, s2, "conv1"); + const conv22 = extractConvWithBatchNormParams(s2, s3, "conv2"); + const conv3 = extractConvWithBatchNormParams(s3, s4, "conv3"); + const conv4 = extractConvWithBatchNormParams(s4, s5, "conv4"); + const conv5 = extractConvWithBatchNormParams(s5, s6, "conv5"); + const conv6 = extractConvWithBatchNormParams(s6, s7, "conv6"); + const conv7 = extractConvWithBatchNormParams(s7, s8, "conv7"); + const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { params, paramMappings }; +} + +// src/tinyYolov2/extractParamsFromWeightMap.ts +function extractorsFactory8(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractBatchNormParams(prefix) { + const sub6 = extractWeightEntry(`${prefix}/sub`, 1); + const truediv = extractWeightEntry(`${prefix}/truediv`, 1); + return { sub: sub6, truediv }; + } + function extractConvParams(prefix) { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { filters, bias }; + } + function extractConvWithBatchNormParams(prefix) { + const conv3 = extractConvParams(`${prefix}/conv`); + const bn = extractBatchNormParams(`${prefix}/bn`); + return { conv: conv3, bn }; + } + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParamsFromWeightMap7(weightMap, config) { + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory8(weightMap, paramMappings); + let params; + if (config.withSeparableConvs) { + const numFilters = config.filterSizes && config.filterSizes.length || 9; + params = { + conv0: config.isFirstLayerConv2d ? extractConvParams("conv0") : extractSeparableConvParams("conv0"), + conv1: extractSeparableConvParams("conv1"), + conv2: extractSeparableConvParams("conv2"), + conv3: extractSeparableConvParams("conv3"), + conv4: extractSeparableConvParams("conv4"), + conv5: extractSeparableConvParams("conv5"), + conv6: numFilters > 7 ? extractSeparableConvParams("conv6") : void 0, + conv7: numFilters > 8 ? extractSeparableConvParams("conv7") : void 0, + conv8: extractConvParams("conv8") + }; + } else { + params = { + conv0: extractConvWithBatchNormParams("conv0"), + conv1: extractConvWithBatchNormParams("conv1"), + conv2: extractConvWithBatchNormParams("conv2"), + conv3: extractConvWithBatchNormParams("conv3"), + conv4: extractConvWithBatchNormParams("conv4"), + conv5: extractConvWithBatchNormParams("conv5"), + conv6: extractConvWithBatchNormParams("conv6"), + conv7: extractConvWithBatchNormParams("conv7"), + conv8: extractConvParams("conv8") + }; + } + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/tinyYolov2/TinyYolov2Options.ts +var TinyYolov2Options = class { + constructor({ inputSize, scoreThreshold } = {}) { + this._name = "TinyYolov2Options"; + this._inputSize = inputSize || 416; + this._scoreThreshold = scoreThreshold || 0.5; + if (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; + } +}; + +// src/tinyYolov2/TinyYolov2Base.ts +var _TinyYolov2Base = class _TinyYolov2Base extends NeuralNetwork { + constructor(config) { + super("TinyYolov2"); + validateConfig(config); + this._config = config; + } + 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(x, params) { + let out = convWithBatchNorm(x, params.conv0); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv1); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv2); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv3); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv4); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv5); + out = tfjs_esm_exports.maxPool(out, [2, 2], [1, 1], "same"); + out = convWithBatchNorm(out, params.conv6); + out = convWithBatchNorm(out, params.conv7); + return convLayer(out, params.conv8, "valid", false); + } + runMobilenet(x, params) { + let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv2(x, params.conv0); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv1); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv2); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv3); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv4); + out = tfjs_esm_exports.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv5); + out = tfjs_esm_exports.maxPool(out, [2, 2], [1, 1], "same"); + out = params.conv6 ? depthwiseSeparableConv2(out, params.conv6) : out; + out = params.conv7 ? depthwiseSeparableConv2(out, params.conv7) : out; + return convLayer(out, params.conv8, "valid", false); + } + forwardInput(input, inputSize) { + const { params } = this; + if (!params) { + throw new Error("TinyYolov2 - load model before inference"); + } + return tfjs_esm_exports.tidy(() => { + let batchTensor = tfjs_esm_exports.cast(input.toBatchTensor(inputSize, false), "float32"); + batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor; + batchTensor = batchTensor.div(255); + return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params); + }); + } + async forward(input, inputSize) { + return this.forwardInput(await toNetInput(input), inputSize); + } + async detect(input, forwardParams = {}) { + const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams); + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput, inputSize); + const out0 = tfjs_esm_exports.tidy(() => tfjs_esm_exports.unstack(out)[0].expandDims()); + const inputDimensions = { + width: netInput.getInputWidth(0), + height: netInput.getInputHeight(0) + }; + const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold); + out.dispose(); + out0.dispose(); + const boxes = results.map((res) => res.box); + const scores = results.map((res) => res.score); + const classScores = results.map((res) => res.classScore); + const classNames = results.map((res) => this.config.classes[res.label]); + const indices = nonMaxSuppression( + boxes.map((box) => box.rescale(inputSize)), + scores, + this.config.iouThreshold, + true + ); + const detections = indices.map((idx) => new ObjectDetection( + scores[idx], + classScores[idx], + classNames[idx], + boxes[idx], + inputDimensions + )); + return detections; + } + getDefaultModelName() { + return ""; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap7(weightMap, this.config); + } + extractParams(weights) { + const filterSizes = this.config.filterSizes || _TinyYolov2Base.DEFAULT_FILTER_SIZES; + const numFilters = filterSizes ? filterSizes.length : void 0; + if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) { + throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`); + } + return extractParams7(weights, this.config, this.boxEncodingSize, filterSizes); + } + async extractBoxes(outputTensor, inputBlobDimensions, scoreThreshold) { + const { width, height } = inputBlobDimensions; + const inputSize = Math.max(width, height); + const correctionFactorX = inputSize / width; + const correctionFactorY = inputSize / height; + const numCells = outputTensor.shape[1]; + const numBoxes = this.config.anchors.length; + const [boxesTensor, scoresTensor, classScoresTensor] = tfjs_esm_exports.tidy(() => { + const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]); + const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]); + const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]); + const classScores = this.withClassScores ? tfjs_esm_exports.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : tfjs_esm_exports.scalar(0); + return [boxes, scores, classScores]; + }); + const results = []; + const scoresData = await scoresTensor.array(); + const boxesData = await boxesTensor.array(); + for (let row = 0; row < numCells; row++) { + for (let col = 0; col < numCells; col++) { + for (let anchor = 0; anchor < numBoxes; anchor++) { + const score = sigmoid(scoresData[row][col][anchor][0]); + if (!scoreThreshold || score > scoreThreshold) { + const ctX = (col + sigmoid(boxesData[row][col][anchor][0])) / numCells * correctionFactorX; + const ctY = (row + sigmoid(boxesData[row][col][anchor][1])) / numCells * correctionFactorY; + const widthLocal = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX; + const heightLocal = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY; + const x = ctX - widthLocal / 2; + const y = ctY - heightLocal / 2; + const pos = { row, col, anchor }; + const { classScore, label } = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : { classScore: 1, label: 0 }; + results.push({ + box: new BoundingBox(x, y, x + widthLocal, y + heightLocal), + score, + classScore: score * classScore, + label, + ...pos + }); + } + } + } + } + boxesTensor.dispose(); + scoresTensor.dispose(); + classScoresTensor.dispose(); + return results; + } + async extractPredictedClass(classesTensor, pos) { + const { row, col, anchor } = pos; + const classesData = await classesTensor.array(); + return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({ + classScore, + label + })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr); + } +}; +_TinyYolov2Base.DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024]; +var TinyYolov2Base = _TinyYolov2Base; + +// src/tinyYolov2/TinyYolov2.ts +var TinyYolov2 = class extends TinyYolov2Base { + constructor(withSeparableConvs = true) { + const config = { + withSeparableConvs, + iouThreshold: IOU_THRESHOLD, + classes: ["face"], + ...withSeparableConvs ? { + anchors: BOX_ANCHORS_SEPARABLE, + meanRgb: MEAN_RGB_SEPARABLE + } : { + anchors: BOX_ANCHORS, + withClassScores: true + } + }; + super(config); + } + get withSeparableConvs() { + return this.config.withSeparableConvs; + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input, forwardParams) { + const objectDetections = await this.detect(input, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight })); + } + getDefaultModelName() { + return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/tinyYolov2/index.ts +function createTinyYolov2(weights, withSeparableConvs = true) { + const net = new TinyYolov2(withSeparableConvs); + net.extractWeights(weights); + return net; +} + +// src/tinyFaceDetector/TinyFaceDetectorOptions.ts +var TinyFaceDetectorOptions = class extends TinyYolov2Options { + constructor() { + super(...arguments); + this._name = "TinyFaceDetectorOptions"; + } +}; + +// src/globalApi/ComposableTask.ts +var ComposableTask = class { + // eslint-disable-next-line no-unused-vars + async then(onfulfilled) { + return onfulfilled(await this.run()); + } + async run() { + throw new Error("ComposableTask - run is not implemented"); + } +}; + +// src/globalApi/extractFacesAndComputeResults.ts +async function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment = ({ alignedRect }) => alignedRect) { + const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection); + const faces = extractedFaces || (input instanceof tfjs_esm_exports.Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes)); + const results = await computeResults(faces); + faces.forEach((f) => f instanceof tfjs_esm_exports.Tensor && f.dispose()); + return results; +} +async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) { + return extractAllFacesAndComputeResults( + [parentResult], + input, + async (faces) => computeResult(faces[0]), + extractedFaces, + getRectForAlignment + ); +} + +// src/tinyFaceDetector/const.ts +var IOU_THRESHOLD2 = 0.4; +var BOX_ANCHORS2 = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB = [117.001, 114.697, 97.404]; + +// src/tinyFaceDetector/TinyFaceDetector.ts +var TinyFaceDetector = class extends TinyYolov2Base { + constructor() { + const config = { + withSeparableConvs: true, + iouThreshold: IOU_THRESHOLD2, + classes: ["face"], + anchors: BOX_ANCHORS2, + meanRgb: MEAN_RGB, + isFirstLayerConv2d: true, + filterSizes: [3, 16, 32, 64, 128, 256, 512] + }; + super(config); + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input, forwardParams) { + const objectDetections = await this.detect(input, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight })); + } + getDefaultModelName() { + return "tiny_face_detector_model"; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/globalApi/nets.ts +var nets = { + ssdMobilenetv1: new SsdMobilenetv1(), + tinyFaceDetector: new TinyFaceDetector(), + tinyYolov2: new TinyYolov2(), + faceLandmark68Net: new FaceLandmark68Net(), + faceLandmark68TinyNet: new FaceLandmark68TinyNet(), + faceRecognitionNet: new FaceRecognitionNet(), + faceExpressionNet: new FaceExpressionNet(), + ageGenderNet: new AgeGenderNet() +}; +var ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options); +var tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options); +var tinyYolov2 = (input, options) => nets.tinyYolov2.locateFaces(input, options); +var detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input); +var detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input); +var computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input); +var recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input); +var predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input); +var loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url); +var loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url); +var loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url); +var loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url); +var loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url); +var loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url); +var loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url); +var loadAgeGenderModel = (url) => nets.ageGenderNet.load(url); +var loadFaceDetectionModel = loadSsdMobilenetv1Model; +var locateFaces = ssdMobilenetv1; +var detectLandmarks = detectFaceLandmarks; + +// src/globalApi/PredictFaceExpressionsTask.ts +var PredictFaceExpressionsTaskBase = class extends ComposableTask { + constructor(parentTask, input, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResults = await this.parentTask; + const faceExpressionsByFace = await extractAllFacesAndComputeResults( + parentResults, + this.input, + async (faces) => Promise.all( + faces.map((face) => nets.faceExpressionNet.predictExpressions(face)) + ), + this.extractedFaces + ); + return parentResults.map( + (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]) + ); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const faceExpressions = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.faceExpressionNet.predictExpressions(face), + this.extractedFaces + ); + return extendWithFaceExpressions(parentResult, faceExpressions); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask(this, this.input); + } +}; +var PredictAllFaceExpressionsWithFaceAlignmentTask = class extends PredictAllFaceExpressionsTask { + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsWithFaceAlignmentTask = class extends PredictSingleFaceExpressionsTask { + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/PredictAgeAndGenderTask.ts +var PredictAgeAndGenderTaskBase = class extends ComposableTask { + constructor(parentTask, input, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResults = await this.parentTask; + const ageAndGenderByFace = await extractAllFacesAndComputeResults( + parentResults, + this.input, + async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face))), + this.extractedFaces + ); + return parentResults.map((parentResult, i) => { + const { age, gender, genderProbability } = ageAndGenderByFace[i]; + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + }); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.ageGenderNet.predictAgeAndGender(face), + this.extractedFaces + ); + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask(this, this.input); + } +}; +var PredictAllAgeAndGenderWithFaceAlignmentTask = class extends PredictAllAgeAndGenderTask { + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderWithFaceAlignmentTask = class extends PredictSingleAgeAndGenderTask { + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/ComputeFaceDescriptorsTasks.ts +var ComputeFaceDescriptorsTaskBase = class extends ComposableTask { + constructor(parentTask, input) { + super(); + this.parentTask = parentTask; + this.input = input; + } +}; +var ComputeAllFaceDescriptorsTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResults = await this.parentTask; + const descriptors = await extractAllFacesAndComputeResults( + parentResults, + this.input, + (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face))), + null, + (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }) + ); + return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor)); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; +var ComputeSingleFaceDescriptorTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const descriptor = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.faceRecognitionNet.computeFaceDescriptor(face), + null, + // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow + (parentResult2) => parentResult2.landmarks.align(null, { useDlibAlignment: true }) + ); + return extendWithFaceDescriptor(parentResult, descriptor); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; + +// src/globalApi/DetectFaceLandmarksTasks.ts +var DetectFaceLandmarksTaskBase = class extends ComposableTask { + constructor(parentTask, input, useTinyLandmarkNet) { + super(); + this.parentTask = parentTask; + this.input = input; + this.useTinyLandmarkNet = useTinyLandmarkNet; + } + get landmarkNet() { + return this.useTinyLandmarkNet ? nets.faceLandmark68TinyNet : nets.faceLandmark68Net; + } +}; +var DetectAllFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResults = await this.parentTask; + const detections = parentResults.map((res) => res.detection); + const faces = this.input instanceof tfjs_esm_exports.Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections); + const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))); + faces.forEach((f) => f instanceof tfjs_esm_exports.Tensor && f.dispose()); + const result = parentResults.filter((_parentResult, i) => faceLandmarksByFace[i]).map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i])); + return result; + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var DetectSingleFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const { detection } = parentResult; + const faces = this.input instanceof tfjs_esm_exports.Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]); + const landmarks = await this.landmarkNet.detectLandmarks(faces[0]); + faces.forEach((f) => f instanceof tfjs_esm_exports.Tensor && f.dispose()); + return extendWithFaceLandmarks(parentResult, landmarks); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/DetectFacesTasks.ts +var DetectFacesTaskBase = class extends ComposableTask { + // eslint-disable-next-line no-unused-vars + constructor(input, options = new SsdMobilenetv1Options()) { + super(); + this.input = input; + this.options = options; + } +}; +var DetectAllFacesTask = class extends DetectFacesTaskBase { + async run() { + const { input, options } = this; + let result; + if (options instanceof TinyFaceDetectorOptions) + result = nets.tinyFaceDetector.locateFaces(input, options); + else if (options instanceof SsdMobilenetv1Options) + result = nets.ssdMobilenetv1.locateFaces(input, options); + else if (options instanceof TinyYolov2Options) + result = nets.tinyYolov2.locateFaces(input, options); + else + throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options"); + return result; + } + runAndExtendWithFaceDetections() { + return new Promise((resolve, reject) => { + this.run().then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection)))).catch((err) => reject(err)); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectAllFaceLandmarksTask( + this.runAndExtendWithFaceDetections(), + this.input, + useTinyLandmarkNet + ); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask( + this.runAndExtendWithFaceDetections(), + this.input + ); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask( + this.runAndExtendWithFaceDetections(), + this.input + ); + } +}; +var DetectSingleFaceTask = class extends DetectFacesTaskBase { + async run() { + const faceDetections = await new DetectAllFacesTask(this.input, this.options); + let faceDetectionWithHighestScore = faceDetections[0]; + faceDetections.forEach((faceDetection) => { + if (faceDetection.score > faceDetectionWithHighestScore.score) + faceDetectionWithHighestScore = faceDetection; + }); + return faceDetectionWithHighestScore; + } + runAndExtendWithFaceDetection() { + return new Promise(async (resolve) => { + const detection = await this.run(); + resolve(detection ? extendWithFaceDetection({}, detection) : void 0); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectSingleFaceLandmarksTask( + this.runAndExtendWithFaceDetection(), + this.input, + useTinyLandmarkNet + ); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask( + this.runAndExtendWithFaceDetection(), + this.input + ); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask( + this.runAndExtendWithFaceDetection(), + this.input + ); + } +}; + +// src/globalApi/detectFaces.ts +function detectSingleFace(input, options = new SsdMobilenetv1Options()) { + return new DetectSingleFaceTask(input, options); +} +function detectAllFaces(input, options = new SsdMobilenetv1Options()) { + return new DetectAllFacesTask(input, options); +} + +// src/globalApi/allFaces.ts +async function allFacesSsdMobilenetv1(input, minConfidence) { + return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {})).withFaceLandmarks().withFaceDescriptors(); +} +async function allFacesTinyYolov2(input, forwardParams = {}) { + return detectAllFaces(input, new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors(); +} +var allFaces = allFacesSsdMobilenetv1; + +// src/euclideanDistance.ts +function euclideanDistance(arr1, arr2) { + if (arr1.length !== arr2.length) + throw new Error("euclideanDistance: arr1.length !== arr2.length"); + const desc1 = Array.from(arr1); + const desc2 = Array.from(arr2); + return Math.sqrt( + desc1.map((val, i) => val - desc2[i]).reduce((res, diff) => res + diff * diff, 0) + ); +} + +// src/globalApi/FaceMatcher.ts +var FaceMatcher = class _FaceMatcher { + constructor(inputs, distanceThreshold = 0.6) { + this._distanceThreshold = distanceThreshold; + const inputArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArray.length) + throw new Error("FaceRecognizer.constructor - expected atleast one input"); + let count = 1; + const createUniqueLabel = () => `person ${count++}`; + this._labeledDescriptors = inputArray.map((desc) => { + if (desc instanceof LabeledFaceDescriptors) + return desc; + if (desc instanceof Float32Array) + return new LabeledFaceDescriptors(createUniqueLabel(), [desc]); + if (desc.descriptor && desc.descriptor instanceof Float32Array) + return new LabeledFaceDescriptors(createUniqueLabel(), [desc.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(queryDescriptor, descriptors) { + return descriptors.map((d) => euclideanDistance(d, queryDescriptor)).reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1); + } + matchDescriptor(queryDescriptor) { + return this.labeledDescriptors.map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors))).reduce((best, curr) => best.distance < curr.distance ? best : curr); + } + findBestMatch(queryDescriptor) { + const bestMatch = this.matchDescriptor(queryDescriptor); + return bestMatch.distance < this._distanceThreshold ? bestMatch : new FaceMatch("unknown", bestMatch.distance); + } + toJSON() { + return { + distanceThreshold: this._distanceThreshold, + labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()) + }; + } + static fromJSON(json) { + const labeledDescriptors = json.labeledDescriptors.map((ld) => LabeledFaceDescriptors.fromJSON(ld)); + return new _FaceMatcher(labeledDescriptors, json.distanceThreshold); + } +}; + +// src/tinyFaceDetector/index.ts +function createTinyFaceDetector(weights) { + const net = new TinyFaceDetector(); + net.extractWeights(weights); + return net; +} + +// src/resizeResults.ts +function resizeResults(results, dimensions) { + const { width, height } = new Dimensions(dimensions.width, dimensions.height); + if (width <= 0 || height <= 0) { + throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`); + } + if (Array.isArray(results)) { + return results.map((obj) => resizeResults(obj, { width, height })); + } + if (isWithFaceLandmarks(results)) { + const resizedDetection = results.detection.forSize(width, height); + const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height); + return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks); + } + if (isWithFaceDetection(results)) { + return extendWithFaceDetection(results, results.detection.forSize(width, height)); + } + if (results instanceof FaceLandmarks || results instanceof FaceDetection) { + return results.forSize(width, height); + } + return results; +} + +// src/index.ts +var version8 = version7; +export { + 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_exports as 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, + tfjs_esm_exports as tf, + tinyFaceDetector, + tinyYolov2, + toNetInput, + utils_exports as utils, + validateConfig, + version8 as version +}; diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index dee42d5..c07dd03 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -4,69 +4,53136 @@ author: ' */ 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Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:p}=vH(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 vH(e,t){let n=jt.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 Ul=class extends Ar{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:gf("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 Ul||e instanceof Ar,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=u2({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 Ar({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new 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 w.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 Ul))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}}};Ul.className="Sequential";ne.registerClass(Ul);function wH(e){return new Ar(e)}function kH(e){return new Ul(e)}function A2(e){return u2(e)}function IH(e,t){Ca.registerCallbackConstructor(e,t)}var Gn=class extends ne.Serializable{getConfig(){return{}}},F2=class extends Gn{apply(e,t=1){return jU(e,t)}};F2.className="elu";ne.registerClass(F2);var $2=class extends Gn{apply(e){return Qm(e)}};$2.className="selu";ne.registerClass($2);var D2=class extends Gn{apply(e){return Ke(e)}};D2.className="relu";ne.registerClass(D2);var R2=class extends Gn{apply(e){return P(()=>ps(6,Ke(e)))}};R2.className="relu6";ne.registerClass(R2);var M2=class extends Gn{apply(e){return e}};M2.className="linear";ne.registerClass(M2);var P2=class extends Gn{apply(e){return ma(e)}};P2.className="sigmoid";ne.registerClass(P2);var O2=class extends Gn{apply(e){return XU(e)}};O2.className="hardSigmoid";ne.registerClass(O2);var L2=class extends Gn{apply(e){return Go(e)}};L2.className="softplus";ne.registerClass(L2);var z2=class extends Gn{apply(e){return KU(e)}};z2.className="softsign";ne.registerClass(z2);var W2=class extends Gn{apply(e){return hi(e)}};W2.className="tanh";ne.registerClass(W2);var m0=class extends Gn{apply(e,t=-1){return Xa(e,t)}};m0.className="softmax";ne.registerClass(m0);var B2=class extends Gn{apply(e,t=-1){return Hm(e,t)}};B2.className="logSoftmax";ne.registerClass(B2);var V2=class extends Gn{apply(e,t=1){return P(()=>z(ma(z(e,t)),e))}};V2.className="swish";ne.registerClass(V2);var U2=class extends Gn{apply(e){return P(()=>z(e,hi(Go(e))))}};U2.className="mish";ne.registerClass(U2);function ds(e){return e.getClassName()}function hx(e,t={}){return bd(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function hs(e){if(e==null){let t={};return t.className="linear",t.config={},hx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},hx(t)}else return e instanceof Gn?e:hx(e)}function f0(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 G2=class extends ne.Serializable{},kd=class extends G2{constructor(e){super(),f0(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,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 SH(e){return f0(e),new kd({l1:e!=null?e.l1:null,l2:0})}function NH(e){return f0(e),new kd({l2:e!=null?e.l2:null,l1:0})}var II={l1l2:"L1L2"};function mt(e){return jw(e)}function SI(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 II?II[e]:e,config:{}};return SI(t)}else return e instanceof G2?e:SI(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=Ce(e);let n=Ke(e);return this.maxValue!=null&&(n=sn(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=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}};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=Zt(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=Je(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 H2(e,t){return P(()=>(Pt(t),t==="channelsFirst"?De(e,[0,2,3,4,1]):e))}function TH(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=zm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ya(o,n)),o})}function NI(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 Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=zl.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 CH(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 ${e.rank}.`);let o=H2(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Yv(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ya(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var I0=class extends Be{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",I0.verifyArgs(t),this.rank=e,an(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=_l(t.kernelSize,e,"kernelSize"),this.strides=_l(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=hs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Tt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Zt(t.biasConstraint),this.biasRegularizer=Ct(t.biasRegularizer),this.activityRegularizer=Ct(t.activityRegularizer),this.dilationRate=_l(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"&&!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:ds(this.activation),useBias:this.useBias,biasInitializer:At(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Yt(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,an(this.filters,"filters"),this.kernelInitializer=Tt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Zt(t.kernelConstraint),this.kernelRegularizer=Ct(t.kernelRegularizer)}build(e){e=Je(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=QT(this.activation.getClassName());if(r!=null&&this.rank===2)n=NI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=TH(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=NI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=CH(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=Je(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 ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=Wm(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=Je(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}};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 ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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),v=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=Zv(n,this.kernel.read(),v,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=Je(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}};N0.className="Conv3DTranspose";ne.registerClass(N0);var q2=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=Zt(t.depthwiseConstraint),this.pointwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ct(t.pointwiseRegularizer),this.pointwiseConstraint=Zt(t.pointwiseConstraint)}build(e){if(e=Je(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=Cs(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=Yt(this.depthwiseConstraint),e.pointwiseConstraint=Yt(this.pointwiseConstraint),e}};q2.className="SeparableConv";var T0=class extends q2{constructor(e){super(2,e)}};T0.className="SeparableConv2D";ne.registerClass(T0);var _f=class extends Id{constructor(e){super(1,e),_f.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)}.`)}};_f.className="Conv1D";ne.registerClass(_f);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=Ce(e),this.dataFormat==="channelsLast"){let n=Ih(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ih(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ih(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ih(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,WU(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"?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}};_0.className="UpSampling2D";ne.registerClass(_0);function _H(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=Ss(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=Zt(e.depthwiseConstraint),this.depthwiseRegularizer=Ct(e.depthwiseRegularizer)}build(e){if(e=Je(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=_H(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=Je(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=Yt(this.depthwiseRegularizer),e}};E0.className="DepthwiseConv2D";ne.registerClass(E0);function j2(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 K2(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=se(se(r,"bool"),"float32"),r.rank===l-1&&(r=nn(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 v=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=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Dt(p,1)),[d,g,c]})}var fr=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 Ff({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 Sr("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;aqt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=j2(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=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=K2((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()===fr.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}))}};fr.className="RNN";ne.registerClass(fr);var Td=class extends Be{},Ef=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,an(this.units,"units"),this.activation=hs(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=Zt(e.kernelConstraint),this.recurrentConstraint=Zt(e.recurrentConstraint),this.biasConstraint=Zt(e.biasConstraint),this.dropout=Bl([1,cs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,cs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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:ds(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:Yt(this.kernelConstraint),recurrentConstraint:Yt(this.recurrentConstraint),biasConstraint:Yt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Ef.className="SimpleRNNCell";ne.registerClass(Ef);var A0=class extends fr{constructor(e){e.cell=new Ef(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 Af=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,an(this.units,"units"),this.activation=hs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=hs(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=Zt(e.kernelConstraint),this.recurrentConstraint=Zt(e.recurrentConstraint),this.biasConstraint=Zt(e.biasConstraint),this.dropout=Bl([1,cs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,cs([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=Je(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)}};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,an(this.units,"units"),this.activation=hs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=hs(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=Zt(e.kernelConstraint),this.recurrentConstraint=Zt(e.recurrentConstraint),this.biasConstraint=Zt(e.biasConstraint),this.dropout=Bl([1,cs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,cs([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=Je(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 yf().apply([s]),p=r.apply([s*2]);return uI(uI(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)}};$0.className="LSTM";ne.registerClass($0);var Ff=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 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a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):i2(t(),n),o=()=>vd(i,t,a);return!r||r<=1?qt(o().clone()):Array(r).fill(void 0).map(o).map(l=>qt(l.clone()))}var EH=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 Sr("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. 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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 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 ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0na(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0na(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,T]=zn(this.kernel.read(),i,y),[C,E,F,D]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,E,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,T,D,this.padding);let[$,S,M,B]=zn(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),j=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(j));return[K,K,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=EH(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=Rt(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ya(r,n,this.dataFormat):r}recurrentConv(e,t){return Rt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};$f.className="ConvLSTM2DCell";ne.registerClass($f);var D0=class extends X2{constructor(e){let t=new $f(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};D0.className="ConvLSTM2D";ne.registerClass(D0);var Df=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=Ce(e);if(0i2(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()}};Df.className="Dropout";ne.registerClass(Df);var R0=class extends Df{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,an(this.units,"units"),this.activation=hs(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=Zt(e.kernelConstraint),this.biasConstraint=Zt(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=Je(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=Je(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=QT(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:ds(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:Yt(this.kernelConstraint),biasConstraint:Yt(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=Je(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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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 Y2(e,t,n,a,r,s){return P(()=>{Pt(r),t2(s),va(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=H2(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 Z2=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(an(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 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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}},r1=class extends J2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),Rf(e,t,n,a,r,"max")}};r1.className="MaxPooling2D";ne.registerClass(r1);var s1=class extends J2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),Rf(e,t,n,a,r,"avg")}};s1.className="AveragePooling2D";ne.registerClass(s1);var Q2=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];an(this.poolSize,"poolSize"),an(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=Je(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}},i1=class extends Q2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),Y2(e,t,n,a,r,"max")}};i1.className="MaxPooling3D";ne.registerClass(i1);var o1=class extends Q2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),va(a),Y2(e,t,n,a,r,"avg")}};o1.className="AveragePooling3D";ne.registerClass(o1);var eC=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 Oe}},l1=class extends eC{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Ce(e);return Et(n,1)})}};l1.className="GlobalAveragePooling1D";ne.registerClass(l1);var u1=class extends eC{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Ce(e);return fa(n,1)})}};u1.className="GlobalMaxPooling1D";ne.registerClass(u1);var tC=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 Oe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},p1=class extends tC{call(e,t){return P(()=>{let n=Ce(e);return this.dataFormat==="channelsLast"?Et(n,[1,2]):Et(n,[2,3])})}};p1.className="GlobalAveragePooling2D";ne.registerClass(p1);var c1=class extends tC{call(e,t){return P(()=>{let n=Ce(e);return this.dataFormat==="channelsLast"?fa(n,[1,2]):fa(n,[2,3])})}};c1.className="GlobalMaxPooling2D";ne.registerClass(c1);var nC=class extends Be{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=Ga(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},d1=class extends nC{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=Je(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=Je(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=Ce(e),K2((n,a)=>[Ce(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};d1.className="TimeDistributed";ne.registerClass(d1);function MH(e){Ko(zU,"BidirectionalMergeMode",e)}var PH="concat",h1=class extends nC{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?PH:e.mergeMode,MH(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=j2(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 Bt({shape:p.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(a!=null)throw new Oe("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Va;for(let l of s)if(l instanceof Va!==o)throw new V("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=p,d}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=ba(r,1));let 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 Oe("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=Ce(e),e.dtype!=="float32"&&(e=ir(e,"float32")),X(z(e,this.scale),this.offset)))}};m1.className="Rescaling";ne.registerClass(m1);var{resizeBilinear:OH,cropAndResize:LH}=Qn,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=OH(e,[t,n]);return ir(r,a)})}call(e,t){return P(()=>{let n=Ce(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=Je(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 zH(e,t,n,a){let r=Ce(e);if(r.dtype!=="int32"&&(r=ir(r,"int32")),t==="int")return r;let s=r.shape;if(r.rank===0&&(r=nn(r,-1)),t==="oneHot"&&r.shape[r.shape.length-1]!==1&&(r=nn(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 ${r.rank}.`);let i=["multiHot","oneHot"].includes(t),o=r,l;if(typeof a!="undefined"&&t==="count"?l=Zh(o,a,n,i):l=Zh(o,[],n,i),t!=="tfIdf")return l;if(a)return z(l,a);throw new V("When outputMode is 'tfIdf', weights must be provided.")}var g1=class extends Be{constructor(e){super(e),this.numTokens=e.numTokens,e.outputMode?this.outputMode=e.outputMode:this.outputMode="multiHot"}getConfig(){let e={numTokens:this.numTokens,outputMode:this.outputMode},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){return e=Je(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return P(()=>{e=Ce(e),e.dtype!=="int32"&&(e=ir(e,"int32"));let n;if(typeof t.countWeights!="undefined"){if(this.outputMode!=="count")throw new V(`countWeights is not used when outputMode !== count. - Received countWeights=${t.countWeights}`);n=Ce(t.countWeights)}let a=fa(e),r=Dl(e),s=Cn(this.numTokens,a).bufferSync().get(0),i=Rr(r,0).bufferSync().get(0);if(!(s&&i))throw new V(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return zH(e,this.outputMode,this.numTokens,n)})}};g1.className="CategoryEncoding";ne.registerClass(g1);var WH=["bilinear","nearest"],TI=new Set(WH),b1=class extends Be{constructor(e){if(super(e),this.height=e.height,this.width=e.width,e.interpolation)if(TI.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=Je(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 ${[...TI]} are supported`)})}};b1.className="Resizing";ne.registerClass(b1);var aC=class{constructor(e){this.seed=e}next(){if(this.seed!==void 0)return this.seed++}};aC.className="RandomSeed";var rC=class extends Be{constructor(e){super(e),this.randomGenerator=new aC(e.seed)}getConfig(){let e={seed:this.randomGenerator.seed},t=super.getConfig();return Object.assign(e,t),e}};rC.className="BaseRandomLayer";var BH=["bilinear","nearest"],CI=new Set(BH),y1=class extends rC{constructor(e){super(e);let{factor:t,interpolation:n="bilinear"}=e;if(this.factor=t,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 V(`Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`);if(this.widthLower<-1||this.widthUpper<-1)throw new V(`factor must have values larger than -1. Got: ${this.factor}`);if(this.widthUpper typeof require !== "undefined" ? require : typeof Proxy !== "undefined" ? new Proxy(x, { + get: (a, b) => (typeof require !== "undefined" ? require : a)[b] +}) : x)(function(x) { + if (typeof require !== "undefined") + return require.apply(this, arguments); + throw Error('Dynamic require of "' + x + '" is not supported'); +}); +var __export = (target, all5) => { + for (var name in all5) + __defProp(target, name, { get: all5[name], enumerable: true }); +}; + +// dist/tfjs.esm.js +var tfjs_esm_exports = {}; +__export(tfjs_esm_exports, { + Abs: () => Abs, + Acos: () => Acos, + Acosh: () => Acosh, + AdadeltaOptimizer: () => AdadeltaOptimizer, + AdagradOptimizer: () => AdagradOptimizer, + AdamOptimizer: () => AdamOptimizer, + AdamaxOptimizer: () => AdamaxOptimizer, + Add: () => Add, + AddN: () => AddN, + All: () => All, + Any: () => Any, + ArgMax: () => ArgMax, + ArgMin: () => ArgMin, + Asin: () => Asin, + Asinh: () => Asinh, + Atan: () => Atan, + Atan2: () => Atan2, + Atanh: () => Atanh, + AvgPool: () => AvgPool, + AvgPool3D: () => AvgPool3D, + AvgPool3DGrad: () => AvgPool3DGrad, + AvgPoolGrad: () => AvgPoolGrad, + BackendWasm: () => BackendWasm, + BatchMatMul: () => BatchMatMul, + BatchToSpaceND: () => BatchToSpaceND, + Bincount: () => Bincount, + BitwiseAnd: () => BitwiseAnd, + BroadcastArgs: () => BroadcastArgs, + BroadcastTo: () => BroadcastTo, + Callback: () => Callback, + CallbackList: () => CallbackList, + Cast: () => Cast, + Ceil: () => Ceil, + ClipByValue: () => ClipByValue, + Complex: () => Complex, + ComplexAbs: () => ComplexAbs, + Concat: () => Concat, + Conv2D: () => Conv2D, + Conv2DBackpropFilter: () => Conv2DBackpropFilter, + Conv2DBackpropInput: () => Conv2DBackpropInput, + Conv3D: () => Conv3D, + Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2, + Conv3DBackpropInputV2: () => Conv3DBackpropInputV2, + Cos: () => Cos, + Cosh: () => Cosh, + CropAndResize: () => CropAndResize, + Cumprod: () => Cumprod, + Cumsum: () => Cumsum, + CustomCallback: () => CustomCallback, + DataStorage: () => DataStorage, + DenseBincount: () => DenseBincount, + DepthToSpace: () => DepthToSpace, + DepthwiseConv2dNative: () => DepthwiseConv2dNative, + DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter, + DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput, + Diag: () => Diag, + Dilation2D: () => Dilation2D, + Dilation2DBackpropFilter: () => Dilation2DBackpropFilter, + Dilation2DBackpropInput: () => Dilation2DBackpropInput, + Draw: () => Draw, + ENV: () => ENV, + EarlyStopping: () => EarlyStopping, + Einsum: () => Einsum, + Elu: () => Elu, + EluGrad: () => EluGrad, + Environment: () => Environment, + Equal: () => Equal, + Erf: () => Erf, + Exp: () => Exp, + ExpandDims: () => ExpandDims, + Expm1: () => Expm1, + FFT: () => FFT, + Fill: () => Fill, + FlipLeftRight: () => FlipLeftRight, + Floor: () => Floor, + FloorDiv: () => FloorDiv, + FromPixels: () => FromPixels, + FusedBatchNorm: () => FusedBatchNorm, + FusedConv2D: () => FusedConv2D, + FusedDepthwiseConv2D: () => FusedDepthwiseConv2D, + GPGPUContext: () => GPGPUContext, + GatherNd: () => GatherNd, + GatherV2: () => GatherV2, + GraphModel: () => GraphModel, + Greater: () => Greater, + GreaterEqual: () => GreaterEqual, + History: () => History, + IFFT: () => IFFT, + Identity: () => Identity, + Imag: () => Imag, + InputSpec: () => InputSpec, + IsFinite: () => IsFinite, + IsInf: () => IsInf, + IsNan: () => IsNan, + KernelBackend: () => KernelBackend, + LRN: () => LRN, + LRNGrad: () => LRNGrad, + LayerVariable: () => LayerVariable, + LayersModel: () => LayersModel, + LeakyRelu: () => LeakyRelu, + Less: () => Less, + LessEqual: () => LessEqual, + LinSpace: () => LinSpace, + Log: () => Log, + Log1p: () => Log1p, + LogSoftmax: () => LogSoftmax, + LogicalAnd: () => LogicalAnd, + LogicalNot: () => LogicalNot, + LogicalOr: () => LogicalOr, + LogicalXor: () => LogicalXor, + LowerBound: () => LowerBound, + MathBackendCPU: () => MathBackendCPU, + MathBackendWebGL: () => MathBackendWebGL, + MatrixBandPart: () => MatrixBandPart, + Max: () => Max, + MaxPool: () => MaxPool, + MaxPool3D: () => MaxPool3D, + MaxPool3DGrad: () => MaxPool3DGrad, + MaxPoolGrad: () => MaxPoolGrad, + MaxPoolWithArgmax: () => MaxPoolWithArgmax, + Maximum: () => Maximum, + Mean: () => Mean, + Min: () => Min, + Minimum: () => Minimum, + MirrorPad: () => MirrorPad, + Mod: () => Mod, + MomentumOptimizer: () => MomentumOptimizer, + Multinomial: () => Multinomial, + Multiply: () => Multiply, + Neg: () => Neg, + NonMaxSuppressionV3: () => NonMaxSuppressionV3, + NonMaxSuppressionV4: () => NonMaxSuppressionV4, + NonMaxSuppressionV5: () => NonMaxSuppressionV5, + NotEqual: () => NotEqual, + OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX, + OneHot: () => OneHot, + OnesLike: () => OnesLike, + Optimizer: () => Optimizer, + OptimizerConstructors: () => OptimizerConstructors, + Pack: () => Pack, + PadV2: () => PadV2, + Pool: () => Pool, + Pow: () => Pow, + Prelu: () => Prelu, + Prod: () => Prod, + RMSPropOptimizer: () => RMSPropOptimizer, + RNN: () => RNN, + RaggedGather: () => RaggedGather, + RaggedRange: () => RaggedRange, + RaggedTensorToTensor: () => RaggedTensorToTensor, + Range: () => Range, + Rank: () => Rank, + Real: () => Real, + RealDiv: () => RealDiv, + Reciprocal: () => Reciprocal, + Reduction: () => Reduction, + Relu: () => Relu, + Relu6: () => Relu6, + Reshape: () => Reshape, + ResizeBilinear: () => ResizeBilinear, + ResizeBilinearGrad: () => ResizeBilinearGrad, + ResizeNearestNeighbor: () => ResizeNearestNeighbor, + ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad, + Reverse: () => Reverse, + RotateWithOffset: () => RotateWithOffset, + Round: () => Round, + Rsqrt: () => Rsqrt, + SGDOptimizer: () => SGDOptimizer, + ScatterNd: () => ScatterNd, + SearchSorted: () => SearchSorted, + Select: () => Select, + Selu: () => Selu, + Sequential: () => Sequential, + Sigmoid: () => Sigmoid, + Sign: () => Sign, + Sin: () => Sin, + Sinh: () => Sinh, + Slice: () => Slice, + Softmax: () => Softmax, + Softplus: () => Softplus, + SpaceToBatchND: () => SpaceToBatchND, + SparseFillEmptyRows: () => SparseFillEmptyRows, + SparseReshape: () => SparseReshape, + SparseSegmentMean: () => SparseSegmentMean, + SparseSegmentSum: () => SparseSegmentSum, + SparseToDense: () => SparseToDense, + SplitV: () => SplitV, + Sqrt: () => Sqrt, + Square: () => Square, + SquaredDifference: () => SquaredDifference, + StaticRegexReplace: () => StaticRegexReplace, + Step: () => Step, + StridedSlice: () => StridedSlice, + StringNGrams: () => StringNGrams, + StringSplit: () => StringSplit, + StringToHashBucketFast: () => StringToHashBucketFast, + Sub: () => Sub, + Sum: () => Sum, + SymbolicTensor: () => SymbolicTensor, + Tan: () => Tan, + Tanh: () => Tanh, + Tensor: () => Tensor, + TensorBuffer: () => TensorBuffer, + TensorScatterUpdate: () => TensorScatterUpdate, + Tile: () => Tile, + TopK: () => TopK, + Transform: () => Transform, + Transpose: () => Transpose, + Unique: () => Unique, + Unpack: () => Unpack, + UnsortedSegmentSum: () => UnsortedSegmentSum, + UpperBound: () => UpperBound, + Variable: () => Variable, + ZerosLike: () => ZerosLike, + _FusedMatMul: () => _FusedMatMul, + abs: () => abs, + acos: () => acos, + acosh: () => acosh, + add: () => add2, + addN: () => addN, + all: () => all, + any: () => any, + argMax: () => argMax, + argMin: () => argMin, + asin: () => asin, + asinh: () => asinh, + atan: () => atan, + atan2: () => atan2, + atanh: () => atanh, + avgPool: () => avgPool, + avgPool3d: () => avgPool3d, + backend: () => backend, + backend_util: () => backend_util_exports, + basicLSTMCell: () => basicLSTMCell, + batchNorm: () => batchNorm, + batchNorm2d: () => batchNorm2d, + batchNorm3d: () => batchNorm3d, + batchNorm4d: () => batchNorm4d, + batchToSpaceND: () => batchToSpaceND, + bincount: () => bincount, + bitwiseAnd: () => bitwiseAnd, + booleanMaskAsync: () => booleanMaskAsync, + broadcastArgs: () => broadcastArgs, + broadcastTo: () => broadcastTo, + broadcast_util: () => broadcast_util_exports, + browser: () => browser_exports, + buffer: () => buffer, + callbacks: () => callbacks, + cast: () => cast, + ceil: () => ceil, + clipByValue: () => clipByValue, + clone: () => clone, + complex: () => complex, + concat: () => concat, + concat1d: () => concat1d, + concat2d: () => concat2d, + concat3d: () => concat3d, + concat4d: () => concat4d, + constraints: () => exports_constraints_exports, + conv1d: () => conv1d, + conv2d: () => conv2d, + conv2dTranspose: () => conv2dTranspose, + conv3d: () => conv3d, + conv3dTranspose: () => conv3dTranspose, + copyRegisteredKernels: () => copyRegisteredKernels, + cos: () => cos, + cosh: () => cosh, + cosineWindow: () => cosineWindow, + cumprod: () => cumprod, + cumsum: () => cumsum, + customGrad: () => customGrad, + data: () => dist_exports2, + denseBincount: () => denseBincount, + deprecationWarn: () => deprecationWarn, + depthToSpace: () => depthToSpace, + depthwiseConv2d: () => depthwiseConv2d, + deregisterOp: () => deregisterOp, + device_util: () => device_util_exports, + diag: () => diag, + dilation2d: () => dilation2d, + disableDeprecationWarnings: () => disableDeprecationWarnings, + dispose: () => dispose, + disposeVariables: () => disposeVariables, + div: () => div, + divNoNan: () => divNoNan, + dot: () => dot, + dropout: () => dropout, + einsum: () => einsum, + elu: () => elu, + enableDebugMode: () => enableDebugMode, + enableProdMode: () => enableProdMode, + enclosingPowerOfTwo: () => enclosingPowerOfTwo, + engine: () => engine, + ensureShape: () => ensureShape, + env: () => env, + equal: () => equal, + erf: () => erf, + euclideanNorm: () => euclideanNorm, + exp: () => exp, + expandDims: () => expandDims, + expm1: () => expm1, + eye: () => eye, + fft: () => fft, + fill: () => fill, + findBackend: () => findBackend, + findBackendFactory: () => findBackendFactory, + floor: () => floor, + floorDiv: () => floorDiv, + forceHalfFloat: () => forceHalfFloat, + fused: () => fused_ops_exports, + gather: () => gather, + gatherND: () => gatherND, + gather_util: () => gather_nd_util_exports, + getBackend: () => getBackend, + getGradient: () => getGradient, + getKernel: () => getKernel, + getKernelsForBackend: () => getKernelsForBackend, + getThreadsCount: () => getThreadsCount, + gpgpu_util: () => gpgpu_util_exports, + grad: () => grad, + grads: () => grads, + greater: () => greater, + greaterEqual: () => greaterEqual, + ifft: () => ifft, + imag: () => imag, + image: () => image, + inTopKAsync: () => inTopKAsync, + initializers: () => exports_initializers_exports, + input: () => input, + io: () => io_exports, + irfft: () => irfft, + isFinite: () => isFinite2, + isInf: () => isInf, + isNaN: () => isNaN2, + keep: () => keep, + kernel_impls: () => kernel_impls_exports, + layers: () => exports_layers_exports, + leakyRelu: () => leakyRelu, + less: () => less, + lessEqual: () => lessEqual, + linalg: () => linalg, + linspace: () => linspace, + loadGraphModel: () => loadGraphModel, + loadGraphModelSync: () => loadGraphModelSync, + loadLayersModel: () => loadLayersModel, + localResponseNormalization: () => localResponseNormalization, + log: () => log2, + log1p: () => log1p, + logSigmoid: () => logSigmoid, + logSoftmax: () => logSoftmax, + logSumExp: () => logSumExp, + logicalAnd: () => logicalAnd, + logicalNot: () => logicalNot, + logicalOr: () => logicalOr, + logicalXor: () => logicalXor, + losses: () => losses, + lowerBound: () => lowerBound, + matMul: () => matMul, + math: () => math_exports, + max: () => max, + maxPool: () => maxPool, + maxPool3d: () => maxPool3d, + maxPoolWithArgmax: () => maxPoolWithArgmax, + maximum: () => maximum, + mean: () => mean, + memory: () => memory, + meshgrid: () => meshgrid, + metrics: () => exports_metrics_exports, + min: () => min, + minimum: () => minimum, + mirrorPad: () => mirrorPad, + mod: () => mod, + model: () => model, + models: () => exports_models_exports, + moments: () => moments, + movingAverage: () => movingAverage, + mul: () => mul, + multiRNNCell: () => multiRNNCell, + multinomial: () => multinomial, + neg: () => neg, + nextFrame: () => nextFrame, + norm: () => norm, + notEqual: () => notEqual, + oneHot: () => oneHot, + ones: () => ones2, + onesLike: () => onesLike, + op: () => op, + outerProduct: () => outerProduct, + pad: () => pad, + pad1d: () => pad1d, + pad2d: () => pad2d, + pad3d: () => pad3d, + pad4d: () => pad4d, + pool: () => pool, + pow: () => pow, + prelu: () => prelu, + print: () => print, + prod: () => prod, + profile: () => profile, + raggedGather: () => raggedGather, + raggedRange: () => raggedRange, + raggedTensorToTensor: () => raggedTensorToTensor, + rand: () => rand, + randomGamma: () => randomGamma, + randomNormal: () => randomNormal, + randomStandardNormal: () => randomStandardNormal, + randomUniform: () => randomUniform, + randomUniformInt: () => randomUniformInt, + range: () => range, + ready: () => ready, + real: () => real, + reciprocal: () => reciprocal, + registerBackend: () => registerBackend, + registerCallbackConstructor: () => registerCallbackConstructor, + registerGradient: () => registerGradient, + registerKernel: () => registerKernel, + registerOp: () => registerOp, + regularizers: () => exports_regularizers_exports, + relu: () => relu, + relu6: () => relu6, + removeBackend: () => removeBackend, + reshape: () => reshape, + reverse: () => reverse, + reverse1d: () => reverse1d, + reverse2d: () => reverse2d, + reverse3d: () => reverse3d, + reverse4d: () => reverse4d, + rfft: () => rfft, + round: () => round2, + rsqrt: () => rsqrt, + scalar: () => scalar, + scatterND: () => scatterND, + scatter_util: () => scatter_nd_util_exports, + searchSorted: () => searchSorted, + selu: () => selu, + separableConv2d: () => separableConv2d, + sequential: () => sequential, + serialization: () => serialization_exports, + setBackend: () => setBackend, + setPlatform: () => setPlatform, + setThreadsCount: () => setThreadsCount, + setWasmPath: () => setWasmPath, + setWasmPaths: () => setWasmPaths, + setWebGLContext: () => setWebGLContext, + setdiff1dAsync: () => setdiff1dAsync, + shared: () => shared_exports, + sigmoid: () => sigmoid, + sign: () => sign, + signal: () => signal, + sin: () => sin, + sinh: () => sinh, + slice: () => slice, + slice1d: () => slice1d, + slice2d: () => slice2d, + slice3d: () => slice3d, + slice4d: () => slice4d, + slice_util: () => slice_util_exports, + softmax: () => softmax, + softplus: () => softplus, + spaceToBatchND: () => spaceToBatchND, + sparse: () => sparse, + sparseToDense: () => sparseToDense, + spectral: () => spectral, + split: () => split, + sqrt: () => sqrt, + square: () => square, + squaredDifference: () => squaredDifference, + squeeze: () => squeeze, + stack: () => stack, + step: () => step, + stridedSlice: () => stridedSlice, + string: () => string, + sub: () => sub, + sum: () => sum2, + sumOutType: () => sumOutType, + tan: () => tan, + tanh: () => tanh2, + tensor: () => tensor, + tensor1d: () => tensor1d, + tensor2d: () => tensor2d, + tensor3d: () => tensor3d, + tensor4d: () => tensor4d, + tensor5d: () => tensor5d, + tensor6d: () => tensor6d, + tensorScatterUpdate: () => tensorScatterUpdate, + tensor_util: () => tensor_util_exports, + test_util: () => test_util_exports, + tidy: () => tidy, + tile: () => tile, + time: () => time, + topk: () => topk, + train: () => train, + transpose: () => transpose, + truncatedNormal: () => truncatedNormal, + unique: () => unique, + unregisterGradient: () => unregisterGradient, + unregisterKernel: () => unregisterKernel, + unsortedSegmentSum: () => unsortedSegmentSum, + unstack: () => unstack, + upcastType: () => upcastType, + upperBound: () => upperBound, + util: () => util_exports, + valueAndGrad: () => valueAndGrad, + valueAndGrads: () => valueAndGrads, + variable: () => variable, + variableGrads: () => variableGrads, + version: () => version62, + version_converter: () => version3, + version_core: () => version, + version_cpu: () => version5, + version_layers: () => version2, + version_wasm: () => version8, + version_webgl: () => version6, + webgl: () => webgl, + webgl_util: () => webgl_util_exports, + where: () => where, + whereAsync: () => whereAsync, + zeros: () => zeros, + zerosLike: () => zerosLike +}); +var __create = Object.create; +var __defProp2 = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getProtoOf = Object.getPrototypeOf; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __commonJS = (cb, mod4) => function __require2() { + return mod4 || (0, cb[__getOwnPropNames(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports; +}; +var __export2 = (target, all5) => { + for (var name in all5) + __defProp2(target, name, { get: all5[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __toESM = (mod4, isNodeMode, target) => (target = mod4 != null ? __create(__getProtoOf(mod4)) : {}, __copyProps( + // If the importer is in node compatibility mode or this is not an ESM + // file that has been converted to a CommonJS file using a Babel- + // compatible transform (i.e. "__esModule" has not been set), then set + // "default" to the CommonJS "module.exports" for node compatibility. + isNodeMode || !mod4 || !mod4.__esModule ? __defProp2(target, "default", { value: mod4, enumerable: true }) : target, + mod4 +)); +var require_long = __commonJS({ + "node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js"(exports, module) { + "use strict"; + module.exports = Long2; + var wasm = null; + try { + wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([ + 0, + 97, + 115, + 109, + 1, + 0, + 0, + 0, + 1, + 13, + 2, + 96, + 0, + 1, + 127, + 96, + 4, + 127, + 127, + 127, + 127, + 1, + 127, + 3, + 7, + 6, + 0, + 1, + 1, + 1, + 1, + 1, + 6, + 6, + 1, + 127, + 1, + 65, + 0, + 11, + 7, + 50, + 6, + 3, + 109, + 117, + 108, + 0, + 1, + 5, + 100, + 105, + 118, + 95, + 115, + 0, + 2, + 5, + 100, + 105, + 118, + 95, + 117, + 0, + 3, + 5, + 114, + 101, + 109, + 95, + 115, + 0, + 4, + 5, + 114, + 101, + 109, + 95, + 117, + 0, + 5, + 8, + 103, + 101, + 116, + 95, + 104, + 105, + 103, + 104, + 0, + 0, + 10, + 191, + 1, + 6, + 4, + 0, + 35, + 0, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 126, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 127, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 128, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 129, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 130, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11 + ])), {}).exports; + } catch (e) { + } + function Long2(low, high, unsigned) { + this.low = low | 0; + this.high = high | 0; + this.unsigned = !!unsigned; + } + Long2.prototype.__isLong__; + Object.defineProperty(Long2.prototype, "__isLong__", { value: true }); + function isLong(obj) { + return (obj && obj["__isLong__"]) === true; + } + Long2.isLong = isLong; + var INT_CACHE = {}; + var UINT_CACHE = {}; + function fromInt(value, unsigned) { + var obj, cachedObj, cache; + if (unsigned) { + value >>>= 0; + if (cache = 0 <= value && value < 256) { + cachedObj = UINT_CACHE[value]; + if (cachedObj) + return cachedObj; + } + obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true); + if (cache) + UINT_CACHE[value] = obj; + return obj; + } else { + value |= 0; + if (cache = -128 <= value && value < 128) { + cachedObj = INT_CACHE[value]; + if (cachedObj) + return cachedObj; + } + obj = fromBits(value, value < 0 ? -1 : 0, false); + if (cache) + INT_CACHE[value] = obj; + return obj; + } + } + Long2.fromInt = fromInt; + function fromNumber(value, unsigned) { + if (isNaN(value)) + return unsigned ? UZERO : ZERO; + if (unsigned) { + if (value < 0) + return UZERO; + if (value >= TWO_PWR_64_DBL) + return MAX_UNSIGNED_VALUE; + } else { + if (value <= -TWO_PWR_63_DBL) + return MIN_VALUE; + if (value + 1 >= TWO_PWR_63_DBL) + return MAX_VALUE; + } + if (value < 0) + return fromNumber(-value, unsigned).neg(); + return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned); + } + Long2.fromNumber = fromNumber; + function fromBits(lowBits, highBits, unsigned) { + return new Long2(lowBits, highBits, unsigned); + } + Long2.fromBits = fromBits; + var pow_dbl = Math.pow; + function fromString(str, unsigned, radix) { + if (str.length === 0) + throw Error("empty string"); + if (str === "NaN" || str === "Infinity" || str === "+Infinity" || str === "-Infinity") + return ZERO; + if (typeof unsigned === "number") { + radix = unsigned, unsigned = false; + } else { + unsigned = !!unsigned; + } + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError("radix"); + var p2; + if ((p2 = str.indexOf("-")) > 0) + throw Error("interior hyphen"); + else if (p2 === 0) { + return fromString(str.substring(1), unsigned, radix).neg(); + } + var radixToPower = fromNumber(pow_dbl(radix, 8)); + var result = ZERO; + for (var i = 0; i < str.length; i += 8) { + var size = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size), radix); + if (size < 8) { + var power = fromNumber(pow_dbl(radix, size)); + result = result.mul(power).add(fromNumber(value)); + } else { + result = result.mul(radixToPower); + result = result.add(fromNumber(value)); + } + } + result.unsigned = unsigned; + return result; + } + Long2.fromString = fromString; + function fromValue(val, unsigned) { + if (typeof val === "number") + return fromNumber(val, unsigned); + if (typeof val === "string") + return fromString(val, unsigned); + return fromBits(val.low, val.high, typeof unsigned === "boolean" ? unsigned : val.unsigned); + } + Long2.fromValue = fromValue; + var TWO_PWR_16_DBL = 1 << 16; + var TWO_PWR_24_DBL = 1 << 24; + var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL; + var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL; + var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2; + var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL); + var ZERO = fromInt(0); + Long2.ZERO = ZERO; + var UZERO = fromInt(0, true); + Long2.UZERO = UZERO; + var ONE = fromInt(1); + Long2.ONE = ONE; + var UONE = fromInt(1, true); + Long2.UONE = UONE; + var NEG_ONE = fromInt(-1); + Long2.NEG_ONE = NEG_ONE; + var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false); + Long2.MAX_VALUE = MAX_VALUE; + var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true); + Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE; + var MIN_VALUE = fromBits(0, 2147483648 | 0, false); + Long2.MIN_VALUE = MIN_VALUE; + var LongPrototype = Long2.prototype; + LongPrototype.toInt = function toInt() { + return this.unsigned ? this.low >>> 0 : this.low; + }; + LongPrototype.toNumber = function toNumber() { + if (this.unsigned) + return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0); + return this.high * TWO_PWR_32_DBL + (this.low >>> 0); + }; + LongPrototype.toString = function toString(radix) { + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError("radix"); + if (this.isZero()) + return "0"; + if (this.isNegative()) { + if (this.eq(MIN_VALUE)) { + var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this); + return div3.toString(radix) + rem1.toInt().toString(radix); + } else + return "-" + this.neg().toString(radix); + } + var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this; + var result = ""; + while (true) { + var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix); + rem = remDiv; + if (rem.isZero()) + return digits + result; + else { + while (digits.length < 6) + digits = "0" + digits; + result = "" + digits + result; + } + } + }; + LongPrototype.getHighBits = function getHighBits() { + return this.high; + }; + LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() { + return this.high >>> 0; + }; + LongPrototype.getLowBits = function getLowBits() { + return this.low; + }; + LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() { + return this.low >>> 0; + }; + LongPrototype.getNumBitsAbs = function getNumBitsAbs() { + if (this.isNegative()) + return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs(); + var val = this.high != 0 ? this.high : this.low; + for (var bit = 31; bit > 0; bit--) + if ((val & 1 << bit) != 0) + break; + return this.high != 0 ? bit + 33 : bit + 1; + }; + LongPrototype.isZero = function isZero() { + return this.high === 0 && this.low === 0; + }; + LongPrototype.eqz = LongPrototype.isZero; + LongPrototype.isNegative = function isNegative() { + return !this.unsigned && this.high < 0; + }; + LongPrototype.isPositive = function isPositive() { + return this.unsigned || this.high >= 0; + }; + LongPrototype.isOdd = function isOdd() { + return (this.low & 1) === 1; + }; + LongPrototype.isEven = function isEven22() { + return (this.low & 1) === 0; + }; + LongPrototype.equals = function equals(other) { + if (!isLong(other)) + other = fromValue(other); + if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1) + return false; + return this.high === other.high && this.low === other.low; + }; + LongPrototype.eq = LongPrototype.equals; + LongPrototype.notEquals = function notEquals(other) { + return !this.eq( + /* validates */ + other + ); + }; + LongPrototype.neq = LongPrototype.notEquals; + LongPrototype.ne = LongPrototype.notEquals; + LongPrototype.lessThan = function lessThan(other) { + return this.comp( + /* validates */ + other + ) < 0; + }; + LongPrototype.lt = LongPrototype.lessThan; + LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) { + return this.comp( + /* validates */ + other + ) <= 0; + }; + LongPrototype.lte = LongPrototype.lessThanOrEqual; + LongPrototype.le = LongPrototype.lessThanOrEqual; + LongPrototype.greaterThan = function greaterThan(other) { + return this.comp( + /* validates */ + other + ) > 0; + }; + LongPrototype.gt = LongPrototype.greaterThan; + LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) { + return this.comp( + /* validates */ + other + ) >= 0; + }; + LongPrototype.gte = LongPrototype.greaterThanOrEqual; + LongPrototype.ge = LongPrototype.greaterThanOrEqual; + LongPrototype.compare = function compare(other) { + if (!isLong(other)) + other = fromValue(other); + if (this.eq(other)) + return 0; + var thisNeg = this.isNegative(), otherNeg = other.isNegative(); + if (thisNeg && !otherNeg) + return -1; + if (!thisNeg && otherNeg) + return 1; + if (!this.unsigned) + return this.sub(other).isNegative() ? -1 : 1; + return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1; + }; + LongPrototype.comp = LongPrototype.compare; + LongPrototype.negate = function negate() { + if (!this.unsigned && this.eq(MIN_VALUE)) + return MIN_VALUE; + return this.not().add(ONE); + }; + LongPrototype.neg = LongPrototype.negate; + LongPrototype.add = function add5(addend) { + if (!isLong(addend)) + addend = fromValue(addend); + var a48 = this.high >>> 16; + var a32 = this.high & 65535; + var a16 = this.low >>> 16; + var a00 = this.low & 65535; + var b48 = addend.high >>> 16; + var b32 = addend.high & 65535; + var b16 = addend.low >>> 16; + var b00 = addend.low & 65535; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 + b00; + c16 += c00 >>> 16; + c00 &= 65535; + c16 += a16 + b16; + c32 += c16 >>> 16; + c16 &= 65535; + c32 += a32 + b32; + c48 += c32 >>> 16; + c32 &= 65535; + c48 += a48 + b48; + c48 &= 65535; + return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned); + }; + LongPrototype.subtract = function subtract(subtrahend) { + if (!isLong(subtrahend)) + subtrahend = fromValue(subtrahend); + return this.add(subtrahend.neg()); + }; + LongPrototype.sub = LongPrototype.subtract; + LongPrototype.multiply = function multiply4(multiplier) { + if (this.isZero()) + return ZERO; + if (!isLong(multiplier)) + multiplier = fromValue(multiplier); + if (wasm) { + var low = wasm.mul( + this.low, + this.high, + multiplier.low, + multiplier.high + ); + return fromBits(low, wasm.get_high(), this.unsigned); + } + if (multiplier.isZero()) + return ZERO; + if (this.eq(MIN_VALUE)) + return multiplier.isOdd() ? MIN_VALUE : ZERO; + if (multiplier.eq(MIN_VALUE)) + return this.isOdd() ? MIN_VALUE : ZERO; + if (this.isNegative()) { + if (multiplier.isNegative()) + return this.neg().mul(multiplier.neg()); + else + return this.neg().mul(multiplier).neg(); + } else if (multiplier.isNegative()) + return this.mul(multiplier.neg()).neg(); + if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24)) + return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned); + var a48 = this.high >>> 16; + var a32 = this.high & 65535; + var a16 = this.low >>> 16; + var a00 = this.low & 65535; + var b48 = multiplier.high >>> 16; + var b32 = multiplier.high & 65535; + var b16 = multiplier.low >>> 16; + var b00 = multiplier.low & 65535; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 * b00; + c16 += c00 >>> 16; + c00 &= 65535; + c16 += a16 * b00; + c32 += c16 >>> 16; + c16 &= 65535; + c16 += a00 * b16; + c32 += c16 >>> 16; + c16 &= 65535; + c32 += a32 * b00; + c48 += c32 >>> 16; + c32 &= 65535; + c32 += a16 * b16; + c48 += c32 >>> 16; + c32 &= 65535; + c32 += a00 * b32; + c48 += c32 >>> 16; + c32 &= 65535; + c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48; + c48 &= 65535; + return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned); + }; + LongPrototype.mul = LongPrototype.multiply; + LongPrototype.divide = function divide(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + if (divisor.isZero()) + throw Error("division by zero"); + if (wasm) { + if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) { + return this; + } + var low = (this.unsigned ? wasm.div_u : wasm.div_s)( + this.low, + this.high, + divisor.low, + divisor.high + ); + return fromBits(low, wasm.get_high(), this.unsigned); + } + if (this.isZero()) + return this.unsigned ? UZERO : ZERO; + var approx, rem, res; + if (!this.unsigned) { + if (this.eq(MIN_VALUE)) { + if (divisor.eq(ONE) || divisor.eq(NEG_ONE)) + return MIN_VALUE; + else if (divisor.eq(MIN_VALUE)) + return ONE; + else { + var halfThis = this.shr(1); + approx = halfThis.div(divisor).shl(1); + if (approx.eq(ZERO)) { + return divisor.isNegative() ? ONE : NEG_ONE; + } else { + rem = this.sub(divisor.mul(approx)); + res = approx.add(rem.div(divisor)); + return res; + } + } + } else if (divisor.eq(MIN_VALUE)) + return this.unsigned ? UZERO : ZERO; + if (this.isNegative()) { + if (divisor.isNegative()) + return this.neg().div(divisor.neg()); + return this.neg().div(divisor).neg(); + } else if (divisor.isNegative()) + return this.div(divisor.neg()).neg(); + res = ZERO; + } else { + if (!divisor.unsigned) + divisor = divisor.toUnsigned(); + if (divisor.gt(this)) + return UZERO; + if (divisor.gt(this.shru(1))) + return UONE; + res = UZERO; + } + rem = this; + while (rem.gte(divisor)) { + approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber())); + var log22 = Math.ceil(Math.log(approx) / Math.LN2), delta = log22 <= 48 ? 1 : pow_dbl(2, log22 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor); + while (approxRem.isNegative() || approxRem.gt(rem)) { + approx -= delta; + approxRes = fromNumber(approx, this.unsigned); + approxRem = approxRes.mul(divisor); + } + if (approxRes.isZero()) + approxRes = ONE; + res = res.add(approxRes); + rem = rem.sub(approxRem); + } + return res; + }; + LongPrototype.div = LongPrototype.divide; + LongPrototype.modulo = function modulo(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + if (wasm) { + var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)( + this.low, + this.high, + divisor.low, + divisor.high + ); + return fromBits(low, wasm.get_high(), this.unsigned); + } + return this.sub(this.div(divisor).mul(divisor)); + }; + LongPrototype.mod = LongPrototype.modulo; + LongPrototype.rem = LongPrototype.modulo; + LongPrototype.not = function not() { + return fromBits(~this.low, ~this.high, this.unsigned); + }; + LongPrototype.and = function and(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low & other.low, this.high & other.high, this.unsigned); + }; + LongPrototype.or = function or(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low | other.low, this.high | other.high, this.unsigned); + }; + LongPrototype.xor = function xor(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned); + }; + LongPrototype.shiftLeft = function shiftLeft(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + if ((numBits &= 63) === 0) + return this; + else if (numBits < 32) + return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned); + else + return fromBits(0, this.low << numBits - 32, this.unsigned); + }; + LongPrototype.shl = LongPrototype.shiftLeft; + LongPrototype.shiftRight = function shiftRight(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + if ((numBits &= 63) === 0) + return this; + else if (numBits < 32) + return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned); + else + return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned); + }; + LongPrototype.shr = LongPrototype.shiftRight; + LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + numBits &= 63; + if (numBits === 0) + return this; + else { + var high = this.high; + if (numBits < 32) { + var low = this.low; + return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned); + } else if (numBits === 32) + return fromBits(high, 0, this.unsigned); + else + return fromBits(high >>> numBits - 32, 0, this.unsigned); + } + }; + LongPrototype.shru = LongPrototype.shiftRightUnsigned; + LongPrototype.shr_u = LongPrototype.shiftRightUnsigned; + LongPrototype.toSigned = function toSigned() { + if (!this.unsigned) + return this; + return fromBits(this.low, this.high, false); + }; + LongPrototype.toUnsigned = function toUnsigned() { + if (this.unsigned) + return this; + return fromBits(this.low, this.high, true); + }; + LongPrototype.toBytes = function toBytes(le) { + return le ? this.toBytesLE() : this.toBytesBE(); + }; + LongPrototype.toBytesLE = function toBytesLE() { + var hi = this.high, lo = this.low; + return [ + lo & 255, + lo >>> 8 & 255, + lo >>> 16 & 255, + lo >>> 24, + hi & 255, + hi >>> 8 & 255, + hi >>> 16 & 255, + hi >>> 24 + ]; + }; + LongPrototype.toBytesBE = function toBytesBE() { + var hi = this.high, lo = this.low; + return [ + hi >>> 24, + hi >>> 16 & 255, + hi >>> 8 & 255, + hi & 255, + lo >>> 24, + lo >>> 16 & 255, + lo >>> 8 & 255, + lo & 255 + ]; + }; + Long2.fromBytes = function fromBytes(bytes, unsigned, le) { + return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned); + }; + Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) { + return new Long2( + bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24, + bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24, + unsigned + ); + }; + Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) { + return new Long2( + bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7], + bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3], + unsigned + ); + }; + } +}); +var require_browser = __commonJS({ + "(disabled):node_modules/.pnpm/node-fetch@2.6.13/node_modules/node-fetch/browser.js"() { + "use strict"; + } +}); +var require_util = __commonJS({ + "(disabled):util"() { + "use strict"; + } +}); +var require_alea = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function Alea(seed) { + var me = this, mash = Mash(); + me.next = function() { + var t = 2091639 * me.s0 + me.c * 23283064365386963e-26; + me.s0 = me.s1; + me.s1 = me.s2; + return me.s2 = t - (me.c = t | 0); + }; + me.c = 1; + me.s0 = mash(" "); + me.s1 = mash(" "); + me.s2 = mash(" "); + me.s0 -= mash(seed); + if (me.s0 < 0) { + me.s0 += 1; + } + me.s1 -= mash(seed); + if (me.s1 < 0) { + me.s1 += 1; + } + me.s2 -= mash(seed); + if (me.s2 < 0) { + me.s2 += 1; + } + mash = null; + } + function copy(f, t) { + t.c = f.c; + t.s0 = f.s0; + t.s1 = f.s1; + t.s2 = f.s2; + return t; + } + function impl(seed, opts) { + var xg = new Alea(seed), state = opts && opts.state, prng = xg.next; + prng.int32 = function() { + return xg.next() * 4294967296 | 0; + }; + prng.double = function() { + return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32; + }; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + function Mash() { + var n = 4022871197; + var mash = function(data) { + data = String(data); + for (var i = 0; i < data.length; i++) { + n += data.charCodeAt(i); + var h = 0.02519603282416938 * n; + n = h >>> 0; + h -= n; + h *= n; + n = h >>> 0; + h -= n; + n += h * 4294967296; + } + return (n >>> 0) * 23283064365386963e-26; + }; + return mash; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.alea = impl; + } + })( + exports, + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); +var require_xor128 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.next = function() { + var t = me.x ^ me.x << 11; + me.x = me.y; + me.y = me.z; + me.z = me.w; + return me.w ^= me.w >>> 19 ^ t ^ t >>> 8; + }; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor128 = impl; + } + })( + exports, + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); +var require_xorwow = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var t = me.x ^ me.x >>> 2; + me.x = me.y; + me.y = me.z; + me.z = me.w; + me.w = me.v; + return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0; + }; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.v = 0; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + if (k == strseed.length) { + me.d = me.x << 10 ^ me.x >>> 4; + } + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + t.v = f.v; + t.d = f.d; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorwow = impl; + } + })( + exports, + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); +var require_xorshift7 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var X = me.x, i = me.i, t, v, w; + t = X[i]; + t ^= t >>> 7; + v = t ^ t << 24; + t = X[i + 1 & 7]; + v ^= t ^ t >>> 10; + t = X[i + 3 & 7]; + v ^= t ^ t >>> 3; + t = X[i + 4 & 7]; + v ^= t ^ t << 7; + t = X[i + 7 & 7]; + t = t ^ t << 13; + v ^= t ^ t << 9; + X[i] = v; + me.i = i + 1 & 7; + return v; + }; + function init2(me2, seed2) { + var j, w, X = []; + if (seed2 === (seed2 | 0)) { + w = X[0] = seed2; + } else { + seed2 = "" + seed2; + for (j = 0; j < seed2.length; ++j) { + X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13; + } + } + while (X.length < 8) + X.push(0); + for (j = 0; j < 8 && X[j] === 0; ++j) + ; + if (j == 8) + w = X[7] = -1; + else + w = X[j]; + me2.x = X; + me2.i = 0; + for (j = 256; j > 0; --j) { + me2.next(); + } + } + init2(me, seed); + } + function copy(f, t) { + t.x = f.x.slice(); + t.i = f.i; + return t; + } + function impl(seed, opts) { + if (seed == null) + seed = +/* @__PURE__ */ new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.x) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorshift7 = impl; + } + })( + exports, + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); +var require_xor4096 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var w = me.w, X = me.X, i = me.i, t, v; + me.w = w = w + 1640531527 | 0; + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + v = X[i] = v ^ t; + me.i = i; + return v + (w ^ w >>> 16) | 0; + }; + function init2(me2, seed2) { + var t, v, i, j, w, X = [], limit = 128; + if (seed2 === (seed2 | 0)) { + v = seed2; + seed2 = null; + } else { + seed2 = seed2 + "\0"; + v = 0; + limit = Math.max(limit, seed2.length); + } + for (i = 0, j = -32; j < limit; ++j) { + if (seed2) + v ^= seed2.charCodeAt((j + 32) % seed2.length); + if (j === 0) + w = v; + v ^= v << 10; + v ^= v >>> 15; + v ^= v << 4; + v ^= v >>> 13; + if (j >= 0) { + w = w + 1640531527 | 0; + t = X[j & 127] ^= v + w; + i = 0 == t ? i + 1 : 0; + } + } + if (i >= 128) { + X[(seed2 && seed2.length || 0) & 127] = -1; + } + i = 127; + for (j = 4 * 128; j > 0; --j) { + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + X[i] = v ^ t; + } + me2.w = w; + me2.X = X; + me2.i = i; + } + init2(me, seed); + } + function copy(f, t) { + t.i = f.i; + t.w = f.w; + t.X = f.X.slice(); + return t; + } + ; + function impl(seed, opts) { + if (seed == null) + seed = +/* @__PURE__ */ new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.X) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor4096 = impl; + } + })( + exports, + // window object or global + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); +var require_tychei = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var b = me.b, c = me.c, d = me.d, a = me.a; + b = b << 25 ^ b >>> 7 ^ c; + c = c - d | 0; + d = d << 24 ^ d >>> 8 ^ a; + a = a - b | 0; + me.b = b = b << 20 ^ b >>> 12 ^ c; + me.c = c = c - d | 0; + me.d = d << 16 ^ c >>> 16 ^ a; + return me.a = a - b | 0; + }; + me.a = 0; + me.b = 0; + me.c = 2654435769 | 0; + me.d = 1367130551; + if (seed === Math.floor(seed)) { + me.a = seed / 4294967296 | 0; + me.b = seed | 0; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 20; k++) { + me.b ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.a = f.a; + t.b = f.b; + t.c = f.c; + t.d = f.d; + return t; + } + ; + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.tychei = impl; + } + })( + exports, + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); +var require_crypto = __commonJS({ + "(disabled):crypto"() { + "use strict"; + } +}); +var require_seedrandom = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js"(exports, module) { + "use strict"; + (function(global2, pool3, math) { + var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; + function seedrandom5(seed, options, callback) { + var key = []; + options = options == true ? { entropy: true } : options || {}; + var shortseed = mixkey(flatten4( + options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, + 3 + ), key); + var arc4 = new ARC4(key); + var prng = function() { + var n = arc4.g(chunks), d = startdenom, x = 0; + while (n < significance) { + n = (n + x) * width; + d *= width; + x = arc4.g(1); + } + while (n >= overflow) { + n /= 2; + d /= 2; + x >>>= 1; + } + return (n + x) / d; + }; + prng.int32 = function() { + return arc4.g(4) | 0; + }; + prng.quick = function() { + return arc4.g(4) / 4294967296; + }; + prng.double = prng; + mixkey(tostring(arc4.S), pool3); + return (options.pass || callback || function(prng2, seed2, is_math_call, state) { + if (state) { + if (state.S) { + copy(state, arc4); + } + prng2.state = function() { + return copy(arc4, {}); + }; + } + if (is_math_call) { + math[rngname] = prng2; + return seed2; + } else + return prng2; + })( + prng, + shortseed, + "global" in options ? options.global : this == math, + options.state + ); + } + function ARC4(key) { + var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = []; + if (!keylen) { + key = [keylen++]; + } + while (i < width) { + s[i] = i++; + } + for (i = 0; i < width; i++) { + s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])]; + s[j] = t; + } + (me.g = function(count2) { + var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S; + while (count2--) { + t2 = s2[i2 = mask & i2 + 1]; + r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)]; + } + me.i = i2; + me.j = j2; + return r; + })(width); + } + function copy(f, t) { + t.i = f.i; + t.j = f.j; + t.S = f.S.slice(); + return t; + } + ; + function flatten4(obj, depth) { + var result = [], typ = typeof obj, prop; + if (depth && typ == "object") { + for (prop in obj) { + try { + result.push(flatten4(obj[prop], depth - 1)); + } catch (e) { + } + } + } + return result.length ? result : typ == "string" ? obj : obj + "\0"; + } + function mixkey(seed, key) { + var stringseed = seed + "", smear, j = 0; + while (j < stringseed.length) { + key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++); + } + return tostring(key); + } + function autoseed() { + try { + var out; + if (nodecrypto && (out = nodecrypto.randomBytes)) { + out = out(width); + } else { + out = new Uint8Array(width); + (global2.crypto || global2.msCrypto).getRandomValues(out); + } + return tostring(out); + } catch (e) { + var browser = global2.navigator, plugins = browser && browser.plugins; + return [+/* @__PURE__ */ new Date(), global2, plugins, global2.screen, tostring(pool3)]; + } + } + function tostring(a) { + return String.fromCharCode.apply(0, a); + } + mixkey(math.random(), pool3); + if (typeof module == "object" && module.exports) { + module.exports = seedrandom5; + try { + nodecrypto = require_crypto(); + } catch (ex) { + } + } else if (typeof define == "function" && define.amd) { + define(function() { + return seedrandom5; + }); + } else { + math["seed" + rngname] = seedrandom5; + } + })( + // global: `self` in browsers (including strict mode and web workers), + // otherwise `this` in Node and other environments + typeof self !== "undefined" ? self : exports, + [], + // pool: entropy pool starts empty + Math + // math: package containing random, pow, and seedrandom + ); + } +}); +var require_seedrandom2 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(exports, module) { + "use strict"; + var alea5 = require_alea(); + var xor128 = require_xor128(); + var xorwow = require_xorwow(); + var xorshift7 = require_xorshift7(); + var xor4096 = require_xor4096(); + var tychei = require_tychei(); + var sr = require_seedrandom(); + sr.alea = alea5; + sr.xor128 = xor128; + sr.xorwow = xorwow; + sr.xorshift7 = xorshift7; + sr.xor4096 = xor4096; + sr.tychei = tychei; + module.exports = sr; + } +}); +var require_string_decoder = __commonJS({ + "(disabled):node_modules/.pnpm/string_decoder@1.3.0/node_modules/string_decoder/lib/string_decoder.js"() { + "use strict"; + } +}); +var require_fs = __commonJS({ + "(disabled):fs"() { + "use strict"; + } +}); +var require_path = __commonJS({ + "(disabled):path"() { + "use strict"; + } +}); +var require_worker_threads = __commonJS({ + "(disabled):worker_threads"() { + "use strict"; + } +}); +var require_perf_hooks = __commonJS({ + "(disabled):perf_hooks"() { + "use strict"; + } +}); +var require_os = __commonJS({ + "(disabled):os"() { + "use strict"; + } +}); +var require_tfjs_backend_wasm_threaded_simd = __commonJS({ + "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(exports, module) { + "use strict"; + var WasmBackendModuleThreadedSimd2 = (() => { + var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; + if (typeof __filename !== "undefined") + _scriptDir = _scriptDir || __filename; + return function(WasmBackendModuleThreadedSimd3) { + WasmBackendModuleThreadedSimd3 = WasmBackendModuleThreadedSimd3 || {}; + function GROWABLE_HEAP_I8() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP8; + } + function GROWABLE_HEAP_U8() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPU8; + } + function GROWABLE_HEAP_I16() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP16; + } + function GROWABLE_HEAP_I32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP32; + } + function GROWABLE_HEAP_U32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPU32; + } + function GROWABLE_HEAP_F32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPF32; + } + function GROWABLE_HEAP_F64() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPF64; + } + var Module = typeof WasmBackendModuleThreadedSimd3 != "undefined" ? WasmBackendModuleThreadedSimd3 : {}; + var readyPromiseResolve, readyPromiseReject; + Module["ready"] = new Promise(function(resolve, reject) { + readyPromiseResolve = resolve; + readyPromiseReject = reject; + }); + var beforeListeners; + if (typeof process !== "undefined" && process.listeners) { + beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }; + } + var moduleOverrides = Object.assign({}, Module); + var arguments_ = []; + var thisProgram = "./this.program"; + var quit_ = (status, toThrow) => { + throw toThrow; + }; + var ENVIRONMENT_IS_WEB = typeof window == "object"; + var ENVIRONMENT_IS_WORKER = typeof importScripts == "function"; + var ENVIRONMENT_IS_NODE = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string"; + var ENVIRONMENT_IS_PTHREAD = Module["ENVIRONMENT_IS_PTHREAD"] || false; + var scriptDirectory = ""; + function locateFile(path) { + if (Module["locateFile"]) { + return Module["locateFile"](path, scriptDirectory); + } + return scriptDirectory + path; + } + var read_, readAsync, readBinary, setWindowTitle; + function logExceptionOnExit(e) { + if (e instanceof ExitStatus) + return; + let toLog = e; + err("exiting due to exception: " + toLog); + } + if (ENVIRONMENT_IS_NODE) { + var fs = require_fs(); + var nodePath = require_path(); + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = nodePath.dirname(scriptDirectory) + "/"; + } else { + scriptDirectory = __dirname + "/"; + } + read_ = (filename, binary) => { + filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename); + return fs.readFileSync(filename, binary ? void 0 : "utf8"); + }; + readBinary = (filename) => { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + return ret; + }; + readAsync = (filename, onload, onerror) => { + filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename); + fs.readFile(filename, function(err2, data) { + if (err2) + onerror(err2); + else + onload(data.buffer); + }); + }; + if (process["argv"].length > 1) { + thisProgram = process["argv"][1].replace(/\\/g, "/"); + } + arguments_ = process["argv"].slice(2); + process["on"]("uncaughtException", function(ex) { + if (!(ex instanceof ExitStatus)) { + throw ex; + } + }); + process["on"]("unhandledRejection", function(reason) { + throw reason; + }); + quit_ = (status, toThrow) => { + if (keepRuntimeAlive()) { + process["exitCode"] = status; + throw toThrow; + } + logExceptionOnExit(toThrow); + process["exit"](status); + }; + Module["inspect"] = function() { + return "[Emscripten Module object]"; + }; + let nodeWorkerThreads; + try { + nodeWorkerThreads = require_worker_threads(); + } catch (e) { + console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'); + throw e; + } + global.Worker = nodeWorkerThreads.Worker; + } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = self.location.href; + } else if (typeof document != "undefined" && document.currentScript) { + scriptDirectory = document.currentScript.src; + } + if (typeof _scriptDir !== "undefined" && _scriptDir) { + scriptDirectory = _scriptDir; + } + if (scriptDirectory.indexOf("blob:") !== 0) { + scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1); + } else { + scriptDirectory = ""; + } + if (!ENVIRONMENT_IS_NODE) { + read_ = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.send(null); + return xhr.responseText; + }; + if (ENVIRONMENT_IS_WORKER) { + readBinary = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.responseType = "arraybuffer"; + xhr.send(null); + return new Uint8Array(xhr.response); + }; + } + readAsync = (url, onload, onerror) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, true); + xhr.responseType = "arraybuffer"; + xhr.onload = () => { + if (xhr.status == 200 || xhr.status == 0 && xhr.response) { + onload(xhr.response); + return; + } + onerror(); + }; + xhr.onerror = onerror; + xhr.send(null); + }; + } + setWindowTitle = (title) => document.title = title; + } else { + } + if (ENVIRONMENT_IS_NODE) { + if (typeof performance == "undefined") { + global.performance = require_perf_hooks().performance; + } + } + var defaultPrint = console.log.bind(console); + var defaultPrintErr = console.warn.bind(console); + if (ENVIRONMENT_IS_NODE) { + defaultPrint = (str) => fs.writeSync(1, str + "\n"); + defaultPrintErr = (str) => fs.writeSync(2, str + "\n"); + } + var out = Module["print"] || defaultPrint; + var err = Module["printErr"] || defaultPrintErr; + Object.assign(Module, moduleOverrides); + moduleOverrides = null; + if (Module["arguments"]) + arguments_ = Module["arguments"]; + if (Module["thisProgram"]) + thisProgram = Module["thisProgram"]; + if (Module["quit"]) + quit_ = Module["quit"]; + var POINTER_SIZE = 4; + var Atomics_load = Atomics.load; + var Atomics_store = Atomics.store; + var Atomics_compareExchange = Atomics.compareExchange; + var wasmBinary; + if (Module["wasmBinary"]) + wasmBinary = Module["wasmBinary"]; + var noExitRuntime = Module["noExitRuntime"] || true; + if (typeof WebAssembly != "object") { + abort("no native wasm support detected"); + } + var wasmMemory; + var wasmModule; + var ABORT = false; + var EXITSTATUS; + function assert3(condition, text) { + if (!condition) { + abort(text); + } + } + var UTF8Decoder = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; + function UTF8ArrayToString(heapOrArray, idx, maxBytesToRead) { + idx >>>= 0; + var endIdx = idx + maxBytesToRead; + var endPtr = idx; + while (heapOrArray[endPtr] && !(endPtr >= endIdx)) + ++endPtr; + if (endPtr - idx > 16 && heapOrArray.buffer && UTF8Decoder) { + return UTF8Decoder.decode(heapOrArray.buffer instanceof SharedArrayBuffer ? heapOrArray.slice(idx, endPtr) : heapOrArray.subarray(idx, endPtr)); + } + var str = ""; + while (idx < endPtr) { + var u0 = heapOrArray[idx++]; + if (!(u0 & 128)) { + str += String.fromCharCode(u0); + continue; + } + var u1 = heapOrArray[idx++] & 63; + if ((u0 & 224) == 192) { + str += String.fromCharCode((u0 & 31) << 6 | u1); + continue; + } + var u2 = heapOrArray[idx++] & 63; + if ((u0 & 240) == 224) { + u0 = (u0 & 15) << 12 | u1 << 6 | u2; + } else { + u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heapOrArray[idx++] & 63; + } + if (u0 < 65536) { + str += String.fromCharCode(u0); + } else { + var ch = u0 - 65536; + str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023); + } + } + return str; + } + function UTF8ToString(ptr, maxBytesToRead) { + ptr >>>= 0; + return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : ""; + } + function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) { + outIdx >>>= 0; + if (!(maxBytesToWrite > 0)) + return 0; + var startIdx = outIdx; + var endIdx = outIdx + maxBytesToWrite - 1; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) { + var u1 = str.charCodeAt(++i); + u = 65536 + ((u & 1023) << 10) | u1 & 1023; + } + if (u <= 127) { + if (outIdx >= endIdx) + break; + heap[outIdx++ >>> 0] = u; + } else if (u <= 2047) { + if (outIdx + 1 >= endIdx) + break; + heap[outIdx++ >>> 0] = 192 | u >> 6; + heap[outIdx++ >>> 0] = 128 | u & 63; + } else if (u <= 65535) { + if (outIdx + 2 >= endIdx) + break; + heap[outIdx++ >>> 0] = 224 | u >> 12; + heap[outIdx++ >>> 0] = 128 | u >> 6 & 63; + heap[outIdx++ >>> 0] = 128 | u & 63; + } else { + if (outIdx + 3 >= endIdx) + break; + heap[outIdx++ >>> 0] = 240 | u >> 18; + heap[outIdx++ >>> 0] = 128 | u >> 12 & 63; + heap[outIdx++ >>> 0] = 128 | u >> 6 & 63; + heap[outIdx++ >>> 0] = 128 | u & 63; + } + } + heap[outIdx >>> 0] = 0; + return outIdx - startIdx; + } + function stringToUTF8(str, outPtr, maxBytesToWrite) { + return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite); + } + var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; + if (ENVIRONMENT_IS_PTHREAD) { + buffer2 = Module["buffer"]; + } + function updateGlobalBufferAndViews(buf) { + buffer2 = buf; + Module["HEAP8"] = HEAP8 = new Int8Array(buf); + Module["HEAP16"] = HEAP16 = new Int16Array(buf); + Module["HEAP32"] = HEAP32 = new Int32Array(buf); + Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf); + Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf); + Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf); + Module["HEAPF32"] = HEAPF32 = new Float32Array(buf); + Module["HEAPF64"] = HEAPF64 = new Float64Array(buf); + } + var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216; + if (ENVIRONMENT_IS_PTHREAD) { + wasmMemory = Module["wasmMemory"]; + buffer2 = Module["buffer"]; + } else { + if (Module["wasmMemory"]) { + wasmMemory = Module["wasmMemory"]; + } else { + wasmMemory = new WebAssembly.Memory({ "initial": INITIAL_MEMORY / 65536, "maximum": 4294967296 / 65536, "shared": true }); + if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) { + err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"); + if (ENVIRONMENT_IS_NODE) { + err("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and/or recent version)"); + } + throw Error("bad memory"); + } + } + } + if (wasmMemory) { + buffer2 = wasmMemory.buffer; + } + INITIAL_MEMORY = buffer2.byteLength; + updateGlobalBufferAndViews(buffer2); + var wasmTable; + var __ATPRERUN__ = []; + var __ATINIT__ = []; + var __ATPOSTRUN__ = []; + var runtimeInitialized = false; + function keepRuntimeAlive() { + return noExitRuntime; + } + function preRun() { + if (Module["preRun"]) { + if (typeof Module["preRun"] == "function") + Module["preRun"] = [Module["preRun"]]; + while (Module["preRun"].length) { + addOnPreRun(Module["preRun"].shift()); + } + } + callRuntimeCallbacks(__ATPRERUN__); + } + function initRuntime() { + runtimeInitialized = true; + if (ENVIRONMENT_IS_PTHREAD) + return; + callRuntimeCallbacks(__ATINIT__); + } + function postRun() { + if (ENVIRONMENT_IS_PTHREAD) + return; + if (Module["postRun"]) { + if (typeof Module["postRun"] == "function") + Module["postRun"] = [Module["postRun"]]; + while (Module["postRun"].length) { + addOnPostRun(Module["postRun"].shift()); + } + } + callRuntimeCallbacks(__ATPOSTRUN__); + } + function addOnPreRun(cb) { + __ATPRERUN__.unshift(cb); + } + function addOnInit(cb) { + __ATINIT__.unshift(cb); + } + function addOnPostRun(cb) { + __ATPOSTRUN__.unshift(cb); + } + var runDependencies = 0; + var runDependencyWatcher = null; + var dependenciesFulfilled = null; + function addRunDependency(id) { + runDependencies++; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + } + function removeRunDependency(id) { + runDependencies--; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + if (runDependencies == 0) { + if (runDependencyWatcher !== null) { + clearInterval(runDependencyWatcher); + runDependencyWatcher = null; + } + if (dependenciesFulfilled) { + var callback = dependenciesFulfilled; + dependenciesFulfilled = null; + callback(); + } + } + } + function abort(what) { + if (Module["onAbort"]) { + Module["onAbort"](what); + } + what = "Aborted(" + what + ")"; + err(what); + ABORT = true; + EXITSTATUS = 1; + what += ". Build with -sASSERTIONS for more info."; + var e = new WebAssembly.RuntimeError(what); + readyPromiseReject(e); + throw e; + } + var dataURIPrefix = "data:application/octet-stream;base64,"; + function isDataURI(filename) { + return filename.startsWith(dataURIPrefix); + } + function isFileURI(filename) { + return filename.startsWith("file://"); + } + var wasmBinaryFile; + wasmBinaryFile = "tfjs-backend-wasm-threaded-simd.wasm"; + if (!isDataURI(wasmBinaryFile)) { + wasmBinaryFile = locateFile(wasmBinaryFile); + } + function getBinary(file) { + try { + if (file == wasmBinaryFile && wasmBinary) { + return new Uint8Array(wasmBinary); + } + if (readBinary) { + return readBinary(file); + } + throw "both async and sync fetching of the wasm failed"; + } catch (err2) { + abort(err2); + } + } + function getBinaryPromise() { + if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) { + if (typeof fetch == "function" && !isFileURI(wasmBinaryFile)) { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + if (!response["ok"]) { + throw "failed to load wasm binary file at '" + wasmBinaryFile + "'"; + } + return response["arrayBuffer"](); + }).catch(function() { + return getBinary(wasmBinaryFile); + }); + } else { + if (readAsync) { + return new Promise(function(resolve, reject) { + readAsync(wasmBinaryFile, function(response) { + resolve(new Uint8Array(response)); + }, reject); + }); + } + } + } + return Promise.resolve().then(function() { + return getBinary(wasmBinaryFile); + }); + } + function createWasm() { + var info = { "env": asmLibraryArg, "wasi_snapshot_preview1": asmLibraryArg }; + function receiveInstance(instance, module2) { + var exports3 = instance.exports; + Module["asm"] = exports3; + registerTLSInit(Module["asm"]["_emscripten_tls_init"]); + wasmTable = Module["asm"]["__indirect_function_table"]; + addOnInit(Module["asm"]["__wasm_call_ctors"]); + wasmModule = module2; + if (!ENVIRONMENT_IS_PTHREAD) { + var numWorkersToLoad = PThread.unusedWorkers.length; + PThread.unusedWorkers.forEach(function(w) { + PThread.loadWasmModuleToWorker(w, function() { + if (!--numWorkersToLoad) + removeRunDependency("wasm-instantiate"); + }); + }); + } + } + if (!ENVIRONMENT_IS_PTHREAD) { + addRunDependency("wasm-instantiate"); + } + function receiveInstantiationResult(result) { + receiveInstance(result["instance"], result["module"]); + } + function instantiateArrayBuffer(receiver) { + return getBinaryPromise().then(function(binary) { + return WebAssembly.instantiate(binary, info); + }).then(function(instance) { + return instance; + }).then(receiver, function(reason) { + err("failed to asynchronously prepare wasm: " + reason); + abort(reason); + }); + } + function instantiateAsync() { + if (!wasmBinary && typeof WebAssembly.instantiateStreaming == "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && !ENVIRONMENT_IS_NODE && typeof fetch == "function") { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + var result = WebAssembly.instantiateStreaming(response, info); + return result.then(receiveInstantiationResult, function(reason) { + err("wasm streaming compile failed: " + reason); + err("falling back to ArrayBuffer instantiation"); + return instantiateArrayBuffer(receiveInstantiationResult); + }); + }); + } else { + return instantiateArrayBuffer(receiveInstantiationResult); + } + } + if (Module["instantiateWasm"]) { + try { + var exports2 = Module["instantiateWasm"](info, receiveInstance); + return exports2; + } catch (e) { + err("Module.instantiateWasm callback failed with error: " + e); + readyPromiseReject(e); + } + } + instantiateAsync().catch(readyPromiseReject); + return {}; + } + var tempDouble; + var tempI64; + var ASM_CONSTS = {}; + function ExitStatus(status) { + this.name = "ExitStatus"; + this.message = "Program terminated with exit(" + status + ")"; + this.status = status; + } + function killThread(pthread_ptr) { + var worker = PThread.pthreads[pthread_ptr]; + delete PThread.pthreads[pthread_ptr]; + worker.terminate(); + __emscripten_thread_free_data(pthread_ptr); + PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1); + worker.pthread_ptr = 0; + } + function cancelThread(pthread_ptr) { + var worker = PThread.pthreads[pthread_ptr]; + worker.postMessage({ "cmd": "cancel" }); + } + function cleanupThread(pthread_ptr) { + var worker = PThread.pthreads[pthread_ptr]; + assert3(worker); + PThread.returnWorkerToPool(worker); + } + function spawnThread(threadParams) { + var worker = PThread.getNewWorker(); + if (!worker) { + return 6; + } + PThread.runningWorkers.push(worker); + PThread.pthreads[threadParams.pthread_ptr] = worker; + worker.pthread_ptr = threadParams.pthread_ptr; + var msg = { "cmd": "run", "start_routine": threadParams.startRoutine, "arg": threadParams.arg, "pthread_ptr": threadParams.pthread_ptr }; + worker.runPthread = () => { + if (ENVIRONMENT_IS_NODE) { + worker.ref(); + } + worker.postMessage(msg, threadParams.transferList); + delete worker.runPthread; + }; + if (worker.loaded) { + worker.runPthread(); + } + return 0; + } + var SYSCALLS = { varargs: void 0, get: function() { + SYSCALLS.varargs += 4; + var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >>> 2]; + return ret; + }, getStr: function(ptr) { + var ret = UTF8ToString(ptr); + return ret; + } }; + function _proc_exit(code) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(1, 1, code); + EXITSTATUS = code; + if (!keepRuntimeAlive()) { + PThread.terminateAllThreads(); + if (Module["onExit"]) + Module["onExit"](code); + ABORT = true; + } + quit_(code, new ExitStatus(code)); + } + function exitJS(status, implicit) { + EXITSTATUS = status; + if (!implicit) { + if (ENVIRONMENT_IS_PTHREAD) { + exitOnMainThread(status); + throw "unwind"; + } else { + } + } + _proc_exit(status); + } + var _exit = exitJS; + function handleException(e) { + if (e instanceof ExitStatus || e == "unwind") { + return EXITSTATUS; + } + quit_(1, e); + } + var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], pthreads: {}, init: function() { + if (ENVIRONMENT_IS_PTHREAD) { + PThread.initWorker(); + } else { + PThread.initMainThread(); + } + }, initMainThread: function() { + var pthreadPoolSize = 8; + while (pthreadPoolSize--) { + PThread.allocateUnusedWorker(); + } + }, initWorker: function() { + noExitRuntime = false; + }, setExitStatus: function(status) { + EXITSTATUS = status; + }, terminateAllThreads: function() { + for (var worker of Object.values(PThread.pthreads)) { + PThread.returnWorkerToPool(worker); + } + for (var worker of PThread.unusedWorkers) { + worker.terminate(); + } + PThread.unusedWorkers = []; + }, returnWorkerToPool: function(worker) { + var pthread_ptr = worker.pthread_ptr; + delete PThread.pthreads[pthread_ptr]; + PThread.unusedWorkers.push(worker); + PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1); + worker.pthread_ptr = 0; + if (ENVIRONMENT_IS_NODE) { + worker.unref(); + } + __emscripten_thread_free_data(pthread_ptr); + }, receiveObjectTransfer: function(data) { + }, threadInitTLS: function() { + PThread.tlsInitFunctions.forEach((f) => f()); + }, loadWasmModuleToWorker: function(worker, onFinishedLoading) { + worker.onmessage = (e) => { + var d = e["data"]; + var cmd = d["cmd"]; + if (worker.pthread_ptr) + PThread.currentProxiedOperationCallerThread = worker.pthread_ptr; + if (d["targetThread"] && d["targetThread"] != _pthread_self()) { + var targetWorker = PThread.pthreads[d.targetThread]; + if (targetWorker) { + targetWorker.postMessage(d, d["transferList"]); + } else { + err('Internal error! Worker sent a message "' + cmd + '" to target pthread ' + d["targetThread"] + ", but that thread no longer exists!"); + } + PThread.currentProxiedOperationCallerThread = void 0; + return; + } + if (cmd === "processProxyingQueue") { + executeNotifiedProxyingQueue(d["queue"]); + } else if (cmd === "spawnThread") { + spawnThread(d); + } else if (cmd === "cleanupThread") { + cleanupThread(d["thread"]); + } else if (cmd === "killThread") { + killThread(d["thread"]); + } else if (cmd === "cancelThread") { + cancelThread(d["thread"]); + } else if (cmd === "loaded") { + worker.loaded = true; + if (ENVIRONMENT_IS_NODE) { + worker.unref(); + } + if (onFinishedLoading) + onFinishedLoading(worker); + if (worker.runPthread) { + worker.runPthread(); + } + } else if (cmd === "print") { + out("Thread " + d["threadId"] + ": " + d["text"]); + } else if (cmd === "printErr") { + err("Thread " + d["threadId"] + ": " + d["text"]); + } else if (cmd === "alert") { + alert("Thread " + d["threadId"] + ": " + d["text"]); + } else if (d.target === "setimmediate") { + worker.postMessage(d); + } else if (cmd === "callHandler") { + Module[d["handler"]](...d["args"]); + } else if (cmd) { + err("worker sent an unknown command " + cmd); + } + PThread.currentProxiedOperationCallerThread = void 0; + }; + worker.onerror = (e) => { + var message = "worker sent an error!"; + err(message + " " + e.filename + ":" + e.lineno + ": " + e.message); + throw e; + }; + if (ENVIRONMENT_IS_NODE) { + worker.on("message", function(data) { + worker.onmessage({ data }); + }); + worker.on("error", function(e) { + worker.onerror(e); + }); + worker.on("detachedExit", function() { + }); + } + var handlers = []; + var knownHandlers = ["onExit", "onAbort", "print", "printErr"]; + for (var handler of knownHandlers) { + if (Module.hasOwnProperty(handler)) { + handlers.push(handler); + } + } + worker.postMessage({ "cmd": "load", "handlers": handlers, "urlOrBlob": Module["mainScriptUrlOrBlob"] || _scriptDir, "wasmMemory": wasmMemory, "wasmModule": wasmModule }); + }, allocateUnusedWorker: function() { + var worker; + var pthreadMainJs = locateFile("tfjs-backend-wasm-threaded-simd.worker.js"); + worker = new Worker(pthreadMainJs); + PThread.unusedWorkers.push(worker); + }, getNewWorker: function() { + if (PThread.unusedWorkers.length == 0) { + PThread.allocateUnusedWorker(); + PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]); + } + return PThread.unusedWorkers.pop(); + } }; + Module["PThread"] = PThread; + function callRuntimeCallbacks(callbacks2) { + while (callbacks2.length > 0) { + callbacks2.shift()(Module); + } + } + function establishStackSpace() { + var pthread_ptr = _pthread_self(); + var stackTop = GROWABLE_HEAP_I32()[pthread_ptr + 52 >>> 2]; + var stackSize = GROWABLE_HEAP_I32()[pthread_ptr + 56 >>> 2]; + var stackMax = stackTop - stackSize; + _emscripten_stack_set_limits(stackTop, stackMax); + stackRestore(stackTop); + } + Module["establishStackSpace"] = establishStackSpace; + function exitOnMainThread(returnCode) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(2, 0, returnCode); + try { + _exit(returnCode); + } catch (e) { + handleException(e); + } + } + var wasmTableMirror = []; + function getWasmTableEntry(funcPtr) { + var func2 = wasmTableMirror[funcPtr]; + if (!func2) { + if (funcPtr >= wasmTableMirror.length) + wasmTableMirror.length = funcPtr + 1; + wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr); + } + return func2; + } + function invokeEntryPoint(ptr, arg) { + var result = getWasmTableEntry(ptr)(arg); + if (keepRuntimeAlive()) { + PThread.setExitStatus(result); + } else { + __emscripten_thread_exit(result); + } + } + Module["invokeEntryPoint"] = invokeEntryPoint; + function registerTLSInit(tlsInitFunc) { + PThread.tlsInitFunctions.push(tlsInitFunc); + } + function ___emscripten_init_main_thread_js(tb) { + __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1, !ENVIRONMENT_IS_WEB); + PThread.threadInitTLS(); + } + function ___emscripten_thread_cleanup(thread) { + if (!ENVIRONMENT_IS_PTHREAD) + cleanupThread(thread); + else + postMessage({ "cmd": "cleanupThread", "thread": thread }); + } + function pthreadCreateProxied(pthread_ptr, attr, startRoutine, arg) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(3, 1, pthread_ptr, attr, startRoutine, arg); + return ___pthread_create_js(pthread_ptr, attr, startRoutine, arg); + } + function ___pthread_create_js(pthread_ptr, attr, startRoutine, arg) { + if (typeof SharedArrayBuffer == "undefined") { + err("Current environment does not support SharedArrayBuffer, pthreads are not available!"); + return 6; + } + var transferList = []; + var error = 0; + if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) { + return pthreadCreateProxied(pthread_ptr, attr, startRoutine, arg); + } + if (error) + return error; + var threadParams = { startRoutine, pthread_ptr, arg, transferList }; + if (ENVIRONMENT_IS_PTHREAD) { + threadParams.cmd = "spawnThread"; + postMessage(threadParams, transferList); + return 0; + } + return spawnThread(threadParams); + } + function __emscripten_default_pthread_stack_size() { + return 65536; + } + var nowIsMonotonic = true; + function __emscripten_get_now_is_monotonic() { + return nowIsMonotonic; + } + function executeNotifiedProxyingQueue(queue) { + Atomics.store(GROWABLE_HEAP_I32(), queue >> 2, 1); + if (_pthread_self()) { + __emscripten_proxy_execute_task_queue(queue); + } + Atomics.compareExchange(GROWABLE_HEAP_I32(), queue >> 2, 1, 0); + } + Module["executeNotifiedProxyingQueue"] = executeNotifiedProxyingQueue; + function __emscripten_notify_task_queue(targetThreadId, currThreadId, mainThreadId, queue) { + if (targetThreadId == currThreadId) { + setTimeout(() => executeNotifiedProxyingQueue(queue)); + } else if (ENVIRONMENT_IS_PTHREAD) { + postMessage({ "targetThread": targetThreadId, "cmd": "processProxyingQueue", "queue": queue }); + } else { + var worker = PThread.pthreads[targetThreadId]; + if (!worker) { + return; + } + worker.postMessage({ "cmd": "processProxyingQueue", "queue": queue }); + } + return 1; + } + function __emscripten_set_offscreencanvas_size(target, width, height) { + return -1; + } + function _abort() { + abort(""); + } + function warnOnce(text) { + if (!warnOnce.shown) + warnOnce.shown = {}; + if (!warnOnce.shown[text]) { + warnOnce.shown[text] = 1; + if (ENVIRONMENT_IS_NODE) + text = "warning: " + text; + err(text); + } + } + function _emscripten_check_blocking_allowed() { + if (ENVIRONMENT_IS_NODE) + return; + if (ENVIRONMENT_IS_WORKER) + return; + warnOnce("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread"); + } + function _emscripten_date_now() { + return Date.now(); + } + function getHeapMax() { + return 4294901760; + } + function _emscripten_get_heap_max() { + return getHeapMax(); + } + var _emscripten_get_now; + if (ENVIRONMENT_IS_NODE) { + _emscripten_get_now = () => { + var t = process["hrtime"](); + return t[0] * 1e3 + t[1] / 1e6; + }; + } else + _emscripten_get_now = () => performance.timeOrigin + performance.now(); + function _emscripten_memcpy_big(dest, src, num) { + GROWABLE_HEAP_U8().copyWithin(dest >>> 0, src >>> 0, src + num >>> 0); + } + function _emscripten_num_logical_cores() { + if (ENVIRONMENT_IS_NODE) + return require_os().cpus().length; + return navigator["hardwareConcurrency"]; + } + function withStackSave(f) { + var stack2 = stackSave(); + var ret = f(); + stackRestore(stack2); + return ret; + } + function _emscripten_proxy_to_main_thread_js(index, sync) { + var numCallArgs = arguments.length - 2; + var outerArgs = arguments; + return withStackSave(() => { + var serializedNumCallArgs = numCallArgs; + var args = stackAlloc(serializedNumCallArgs * 8); + var b = args >> 3; + for (var i = 0; i < numCallArgs; i++) { + var arg = outerArgs[2 + i]; + GROWABLE_HEAP_F64()[b + i >>> 0] = arg; + } + return _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync); + }); + } + var _emscripten_receive_on_main_thread_js_callArgs = []; + function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) { + _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs; + var b = args >> 3; + for (var i = 0; i < numCallArgs; i++) { + _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i >>> 0]; + } + var isEmAsmConst = index < 0; + var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1]; + return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs); + } + function emscripten_realloc_buffer(size) { + try { + wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16); + updateGlobalBufferAndViews(wasmMemory.buffer); + return 1; + } catch (e) { + } + } + function _emscripten_resize_heap(requestedSize) { + var oldSize = GROWABLE_HEAP_U8().length; + requestedSize = requestedSize >>> 0; + if (requestedSize <= oldSize) { + return false; + } + var maxHeapSize = getHeapMax(); + if (requestedSize > maxHeapSize) { + return false; + } + let alignUp = (x, multiple) => x + (multiple - x % multiple) % multiple; + for (var cutDown = 1; cutDown <= 4; cutDown *= 2) { + var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown); + overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296); + var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536)); + var replacement = emscripten_realloc_buffer(newSize); + if (replacement) { + return true; + } + } + return false; + } + function _emscripten_unwind_to_js_event_loop() { + throw "unwind"; + } + function _fd_close(fd) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(4, 1, fd); + return 52; + } + function _fd_seek(fd, offset_low, offset_high, whence, newOffset) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(5, 1, fd, offset_low, offset_high, whence, newOffset); + return 70; + } + var printCharBuffers = [null, [], []]; + function printChar(stream, curr) { + var buffer3 = printCharBuffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0)); + buffer3.length = 0; + } else { + buffer3.push(curr); + } + } + function _fd_write(fd, iov, iovcnt, pnum) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(6, 1, fd, iov, iovcnt, pnum); + var num = 0; + for (var i = 0; i < iovcnt; i++) { + var ptr = GROWABLE_HEAP_U32()[iov >>> 2]; + var len = GROWABLE_HEAP_U32()[iov + 4 >>> 2]; + iov += 8; + for (var j = 0; j < len; j++) { + printChar(fd, GROWABLE_HEAP_U8()[ptr + j >>> 0]); + } + num += len; + } + GROWABLE_HEAP_U32()[pnum >>> 2] = num; + return 0; + } + function getCFunc(ident) { + var func2 = Module["_" + ident]; + return func2; + } + function writeArrayToMemory(array2, buffer3) { + GROWABLE_HEAP_I8().set(array2, buffer3 >>> 0); + } + function ccall(ident, returnType, argTypes, args, opts) { + var toC = { "string": (str) => { + var ret2 = 0; + if (str !== null && str !== void 0 && str !== 0) { + var len = (str.length << 2) + 1; + ret2 = stackAlloc(len); + stringToUTF8(str, ret2, len); + } + return ret2; + }, "array": (arr) => { + var ret2 = stackAlloc(arr.length); + writeArrayToMemory(arr, ret2); + return ret2; + } }; + function convertReturnValue(ret2) { + if (returnType === "string") { + return UTF8ToString(ret2); + } + if (returnType === "boolean") + return Boolean(ret2); + return ret2; + } + var func2 = getCFunc(ident); + var cArgs = []; + var stack2 = 0; + if (args) { + for (var i = 0; i < args.length; i++) { + var converter = toC[argTypes[i]]; + if (converter) { + if (stack2 === 0) + stack2 = stackSave(); + cArgs[i] = converter(args[i]); + } else { + cArgs[i] = args[i]; + } + } + } + var ret = func2.apply(null, cArgs); + function onDone(ret2) { + if (stack2 !== 0) + stackRestore(stack2); + return convertReturnValue(ret2); + } + ret = onDone(ret); + return ret; + } + function cwrap(ident, returnType, argTypes, opts) { + argTypes = argTypes || []; + var numericArgs = argTypes.every((type) => type === "number" || type === "boolean"); + var numericRet = returnType !== "string"; + if (numericRet && numericArgs && !opts) { + return getCFunc(ident); + } + return function() { + return ccall(ident, returnType, argTypes, arguments, opts); + }; + } + PThread.init(); + var proxiedFunctionTable = [null, _proc_exit, exitOnMainThread, pthreadCreateProxied, _fd_close, _fd_seek, _fd_write]; + var asmLibraryArg = { "__emscripten_init_main_thread_js": ___emscripten_init_main_thread_js, "__emscripten_thread_cleanup": ___emscripten_thread_cleanup, "__pthread_create_js": ___pthread_create_js, "_emscripten_default_pthread_stack_size": __emscripten_default_pthread_stack_size, "_emscripten_get_now_is_monotonic": __emscripten_get_now_is_monotonic, "_emscripten_notify_task_queue": __emscripten_notify_task_queue, "_emscripten_set_offscreencanvas_size": __emscripten_set_offscreencanvas_size, "abort": _abort, "emscripten_check_blocking_allowed": _emscripten_check_blocking_allowed, "emscripten_date_now": _emscripten_date_now, "emscripten_get_heap_max": _emscripten_get_heap_max, "emscripten_get_now": _emscripten_get_now, "emscripten_memcpy_big": _emscripten_memcpy_big, "emscripten_num_logical_cores": _emscripten_num_logical_cores, "emscripten_receive_on_main_thread_js": _emscripten_receive_on_main_thread_js, "emscripten_resize_heap": _emscripten_resize_heap, "emscripten_unwind_to_js_event_loop": _emscripten_unwind_to_js_event_loop, "exit": _exit, "fd_close": _fd_close, "fd_seek": _fd_seek, "fd_write": _fd_write, "memory": wasmMemory || Module["wasmMemory"] }; + var asm = createWasm(); + var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() { + return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["__wasm_call_ctors"]).apply(null, arguments); + }; + var _init = Module["_init"] = function() { + return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments); + }; + var _init_with_threads_count = Module["_init_with_threads_count"] = function() { + return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["init_with_threads_count"]).apply(null, arguments); + }; + var _get_threads_count = Module["_get_threads_count"] = function() { + return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["get_threads_count"]).apply(null, arguments); + }; + var _register_tensor = Module["_register_tensor"] = function() { + return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments); + }; + var _dispose_data = Module["_dispose_data"] = function() { + return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments); + }; + var _dispose = Module["_dispose"] = function() { + return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments); + }; + var _Abs = Module["_Abs"] = function() { + return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments); + }; + var _Acos = Module["_Acos"] = function() { + return (_Acos = Module["_Acos"] = Module["asm"]["Acos"]).apply(null, arguments); + }; + var _Acosh = Module["_Acosh"] = function() { + return (_Acosh = Module["_Acosh"] = Module["asm"]["Acosh"]).apply(null, arguments); + }; + var _Add = Module["_Add"] = function() { + return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments); + }; + var _AddN = Module["_AddN"] = function() { + return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments); + }; + var _All = Module["_All"] = function() { + return (_All = Module["_All"] = Module["asm"]["All"]).apply(null, arguments); + }; + var _Any = Module["_Any"] = function() { + return (_Any = Module["_Any"] = Module["asm"]["Any"]).apply(null, arguments); + }; + var _ArgMax = Module["_ArgMax"] = function() { + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments); + }; + var _ArgMin = Module["_ArgMin"] = function() { + return (_ArgMin = Module["_ArgMin"] = Module["asm"]["ArgMin"]).apply(null, arguments); + }; + var _Asin = Module["_Asin"] = function() { + return (_Asin = Module["_Asin"] = Module["asm"]["Asin"]).apply(null, arguments); + }; + var _Asinh = Module["_Asinh"] = function() { + return (_Asinh = Module["_Asinh"] = Module["asm"]["Asinh"]).apply(null, arguments); + }; + var _Atan = Module["_Atan"] = function() { + return (_Atan = Module["_Atan"] = Module["asm"]["Atan"]).apply(null, arguments); + }; + var _Atan2 = Module["_Atan2"] = function() { + return (_Atan2 = Module["_Atan2"] = Module["asm"]["Atan2"]).apply(null, arguments); + }; + var _Atanh = Module["_Atanh"] = function() { + return (_Atanh = Module["_Atanh"] = Module["asm"]["Atanh"]).apply(null, arguments); + }; + var _AvgPool = Module["_AvgPool"] = function() { + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments); + }; + var _AvgPool3D = Module["_AvgPool3D"] = function() { + return (_AvgPool3D = Module["_AvgPool3D"] = Module["asm"]["AvgPool3D"]).apply(null, arguments); + }; + var _AvgPool3DGrad = Module["_AvgPool3DGrad"] = function() { + return (_AvgPool3DGrad = Module["_AvgPool3DGrad"] = Module["asm"]["AvgPool3DGrad"]).apply(null, arguments); + }; + var _AvgPoolGrad = Module["_AvgPoolGrad"] = function() { + return (_AvgPoolGrad = Module["_AvgPoolGrad"] = Module["asm"]["AvgPoolGrad"]).apply(null, arguments); + }; + var _BatchMatMul = Module["_BatchMatMul"] = function() { + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments); + }; + var _Bincount = Module["_Bincount"] = function() { + return (_Bincount = Module["_Bincount"] = Module["asm"]["Bincount"]).apply(null, arguments); + }; + var _BitwiseAnd = Module["_BitwiseAnd"] = function() { + return (_BitwiseAnd = Module["_BitwiseAnd"] = Module["asm"]["BitwiseAnd"]).apply(null, arguments); + }; + var _Ceil = Module["_Ceil"] = function() { + return (_Ceil = Module["_Ceil"] = Module["asm"]["Ceil"]).apply(null, arguments); + }; + var _ClipByValue = Module["_ClipByValue"] = function() { + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments); + }; + var _Conv2D2 = Module["_Conv2D"] = function() { + return (_Conv2D2 = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments); + }; + var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments); + }; + var _Conv3D2 = Module["_Conv3D"] = function() { + return (_Conv3D2 = Module["_Conv3D"] = Module["asm"]["Conv3D"]).apply(null, arguments); + }; + var _Conv3DBackpropFilterV2 = Module["_Conv3DBackpropFilterV2"] = function() { + return (_Conv3DBackpropFilterV2 = Module["_Conv3DBackpropFilterV2"] = Module["asm"]["Conv3DBackpropFilterV2"]).apply(null, arguments); + }; + var _Conv3DBackpropInputV2 = Module["_Conv3DBackpropInputV2"] = function() { + return (_Conv3DBackpropInputV2 = Module["_Conv3DBackpropInputV2"] = Module["asm"]["Conv3DBackpropInputV2"]).apply(null, arguments); + }; + var _Cos = Module["_Cos"] = function() { + return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments); + }; + var _Cosh = Module["_Cosh"] = function() { + return (_Cosh = Module["_Cosh"] = Module["asm"]["Cosh"]).apply(null, arguments); + }; + var _CropAndResize = Module["_CropAndResize"] = function() { + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments); + }; + var _Cumprod = Module["_Cumprod"] = function() { + return (_Cumprod = Module["_Cumprod"] = Module["asm"]["Cumprod"]).apply(null, arguments); + }; + var _Cumsum = Module["_Cumsum"] = function() { + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments); + }; + var _DenseBincount = Module["_DenseBincount"] = function() { + return (_DenseBincount = Module["_DenseBincount"] = Module["asm"]["DenseBincount"]).apply(null, arguments); + }; + var _DepthToSpace = Module["_DepthToSpace"] = function() { + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments); + }; + var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments); + }; + var _Diag = Module["_Diag"] = function() { + return (_Diag = Module["_Diag"] = Module["asm"]["Diag"]).apply(null, arguments); + }; + var _Dilation2D = Module["_Dilation2D"] = function() { + return (_Dilation2D = Module["_Dilation2D"] = Module["asm"]["Dilation2D"]).apply(null, arguments); + }; + var _Dilation2DBackpropFilter = Module["_Dilation2DBackpropFilter"] = function() { + return (_Dilation2DBackpropFilter = Module["_Dilation2DBackpropFilter"] = Module["asm"]["Dilation2DBackpropFilter"]).apply(null, arguments); + }; + var _Dilation2DBackpropInput = Module["_Dilation2DBackpropInput"] = function() { + return (_Dilation2DBackpropInput = Module["_Dilation2DBackpropInput"] = Module["asm"]["Dilation2DBackpropInput"]).apply(null, arguments); + }; + var _Elu = Module["_Elu"] = function() { + return (_Elu = Module["_Elu"] = Module["asm"]["Elu"]).apply(null, arguments); + }; + var _EluGrad = Module["_EluGrad"] = function() { + return (_EluGrad = Module["_EluGrad"] = Module["asm"]["EluGrad"]).apply(null, arguments); + }; + var _Equal = Module["_Equal"] = function() { + return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments); + }; + var _Erf = Module["_Erf"] = function() { + return (_Erf = Module["_Erf"] = Module["asm"]["Erf"]).apply(null, arguments); + }; + var _Exp = Module["_Exp"] = function() { + return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments); + }; + var _Expm1 = Module["_Expm1"] = function() { + return (_Expm1 = Module["_Expm1"] = Module["asm"]["Expm1"]).apply(null, arguments); + }; + var _FlipLeftRight = Module["_FlipLeftRight"] = function() { + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments); + }; + var _Floor = Module["_Floor"] = function() { + return (_Floor = Module["_Floor"] = Module["asm"]["Floor"]).apply(null, arguments); + }; + var _FloorDiv = Module["_FloorDiv"] = function() { + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments); + }; + var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments); + }; + var _FusedConv2D = Module["_FusedConv2D"] = function() { + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments); + }; + var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments); + }; + var _Gather = Module["_Gather"] = function() { + return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments); + }; + var _GatherNd = Module["_GatherNd"] = function() { + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments); + }; + var _Greater = Module["_Greater"] = function() { + return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments); + }; + var _GreaterEqual = Module["_GreaterEqual"] = function() { + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments); + }; + var _IsFinite = Module["_IsFinite"] = function() { + return (_IsFinite = Module["_IsFinite"] = Module["asm"]["IsFinite"]).apply(null, arguments); + }; + var _IsInf = Module["_IsInf"] = function() { + return (_IsInf = Module["_IsInf"] = Module["asm"]["IsInf"]).apply(null, arguments); + }; + var _IsNan = Module["_IsNan"] = function() { + return (_IsNan = Module["_IsNan"] = Module["asm"]["IsNan"]).apply(null, arguments); + }; + var _LRN = Module["_LRN"] = function() { + return (_LRN = Module["_LRN"] = Module["asm"]["LRN"]).apply(null, arguments); + }; + var _LRNGrad = Module["_LRNGrad"] = function() { + return (_LRNGrad = Module["_LRNGrad"] = Module["asm"]["LRNGrad"]).apply(null, arguments); + }; + var _LeakyRelu = Module["_LeakyRelu"] = function() { + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["LeakyRelu"]).apply(null, arguments); + }; + var _Less = Module["_Less"] = function() { + return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments); + }; + var _LessEqual = Module["_LessEqual"] = function() { + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments); + }; + var _LinSpace = Module["_LinSpace"] = function() { + return (_LinSpace = Module["_LinSpace"] = Module["asm"]["LinSpace"]).apply(null, arguments); + }; + var _Log = Module["_Log"] = function() { + return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments); + }; + var _Log1p = Module["_Log1p"] = function() { + return (_Log1p = Module["_Log1p"] = Module["asm"]["Log1p"]).apply(null, arguments); + }; + var _LogicalAnd = Module["_LogicalAnd"] = function() { + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments); + }; + var _LogicalNot = Module["_LogicalNot"] = function() { + return (_LogicalNot = Module["_LogicalNot"] = Module["asm"]["LogicalNot"]).apply(null, arguments); + }; + var _LogicalOr = Module["_LogicalOr"] = function() { + return (_LogicalOr = Module["_LogicalOr"] = Module["asm"]["LogicalOr"]).apply(null, arguments); + }; + var _LogicalXor = Module["_LogicalXor"] = function() { + return (_LogicalXor = Module["_LogicalXor"] = Module["asm"]["LogicalXor"]).apply(null, arguments); + }; + var _Max = Module["_Max"] = function() { + return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments); + }; + var _MaxPool = Module["_MaxPool"] = function() { + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments); + }; + var _MaxPool3D = Module["_MaxPool3D"] = function() { + return (_MaxPool3D = Module["_MaxPool3D"] = Module["asm"]["MaxPool3D"]).apply(null, arguments); + }; + var _MaxPool3DGrad = Module["_MaxPool3DGrad"] = function() { + return (_MaxPool3DGrad = Module["_MaxPool3DGrad"] = Module["asm"]["MaxPool3DGrad"]).apply(null, arguments); + }; + var _MaxPoolGrad = Module["_MaxPoolGrad"] = function() { + return (_MaxPoolGrad = Module["_MaxPoolGrad"] = Module["asm"]["MaxPoolGrad"]).apply(null, arguments); + }; + var _MaxPoolWithArgmax = Module["_MaxPoolWithArgmax"] = function() { + return (_MaxPoolWithArgmax = Module["_MaxPoolWithArgmax"] = Module["asm"]["MaxPoolWithArgmax"]).apply(null, arguments); + }; + var _Maximum = Module["_Maximum"] = function() { + return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments); + }; + var _Mean = Module["_Mean"] = function() { + return (_Mean = Module["_Mean"] = Module["asm"]["Mean"]).apply(null, arguments); + }; + var _Min = Module["_Min"] = function() { + return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments); + }; + var _Minimum = Module["_Minimum"] = function() { + return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments); + }; + var _MirrorPad = Module["_MirrorPad"] = function() { + return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["MirrorPad"]).apply(null, arguments); + }; + var _Mod = Module["_Mod"] = function() { + return (_Mod = Module["_Mod"] = Module["asm"]["Mod"]).apply(null, arguments); + }; + var _Multinomial = Module["_Multinomial"] = function() { + return (_Multinomial = Module["_Multinomial"] = Module["asm"]["Multinomial"]).apply(null, arguments); + }; + var _Multiply = Module["_Multiply"] = function() { + return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments); + }; + var _Neg = Module["_Neg"] = function() { + return (_Neg = Module["_Neg"] = Module["asm"]["Neg"]).apply(null, arguments); + }; + var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments); + }; + var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments); + }; + var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments); + }; + var _NotEqual = Module["_NotEqual"] = function() { + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments); + }; + var _OneHot = Module["_OneHot"] = function() { + return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments); + }; + var _PadV2 = Module["_PadV2"] = function() { + return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments); + }; + var _Pow = Module["_Pow"] = function() { + return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments); + }; + var _Prelu = Module["_Prelu"] = function() { + return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments); + }; + var _Prod = Module["_Prod"] = function() { + return (_Prod = Module["_Prod"] = Module["asm"]["Prod"]).apply(null, arguments); + }; + var _RealDiv = Module["_RealDiv"] = function() { + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["RealDiv"]).apply(null, arguments); + }; + var _Reciprocal = Module["_Reciprocal"] = function() { + return (_Reciprocal = Module["_Reciprocal"] = Module["asm"]["Reciprocal"]).apply(null, arguments); + }; + var _Relu = Module["_Relu"] = function() { + return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments); + }; + var _Relu6 = Module["_Relu6"] = function() { + return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments); + }; + var _ResizeBilinear = Module["_ResizeBilinear"] = function() { + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments); + }; + var _ResizeBilinearGrad = Module["_ResizeBilinearGrad"] = function() { + return (_ResizeBilinearGrad = Module["_ResizeBilinearGrad"] = Module["asm"]["ResizeBilinearGrad"]).apply(null, arguments); + }; + var _ResizeNearestNeighbor = Module["_ResizeNearestNeighbor"] = function() { + return (_ResizeNearestNeighbor = Module["_ResizeNearestNeighbor"] = Module["asm"]["ResizeNearestNeighbor"]).apply(null, arguments); + }; + var _ResizeNearestNeighborGrad = Module["_ResizeNearestNeighborGrad"] = function() { + return (_ResizeNearestNeighborGrad = Module["_ResizeNearestNeighborGrad"] = Module["asm"]["ResizeNearestNeighborGrad"]).apply(null, arguments); + }; + var _Reverse = Module["_Reverse"] = function() { + return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments); + }; + var _RotateWithOffset = Module["_RotateWithOffset"] = function() { + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments); + }; + var _Round = Module["_Round"] = function() { + return (_Round = Module["_Round"] = Module["asm"]["Round"]).apply(null, arguments); + }; + var _Rsqrt = Module["_Rsqrt"] = function() { + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments); + }; + var _ScatterNd = Module["_ScatterNd"] = function() { + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments); + }; + var _SearchSorted = Module["_SearchSorted"] = function() { + return (_SearchSorted = Module["_SearchSorted"] = Module["asm"]["SearchSorted"]).apply(null, arguments); + }; + var _SelectV2 = Module["_SelectV2"] = function() { + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments); + }; + var _Selu = Module["_Selu"] = function() { + return (_Selu = Module["_Selu"] = Module["asm"]["Selu"]).apply(null, arguments); + }; + var _Sigmoid = Module["_Sigmoid"] = function() { + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments); + }; + var _Sign = Module["_Sign"] = function() { + return (_Sign = Module["_Sign"] = Module["asm"]["Sign"]).apply(null, arguments); + }; + var _Sin = Module["_Sin"] = function() { + return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments); + }; + var _Sinh = Module["_Sinh"] = function() { + return (_Sinh = Module["_Sinh"] = Module["asm"]["Sinh"]).apply(null, arguments); + }; + var _Softmax = Module["_Softmax"] = function() { + return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments); + }; + var _Softplus = Module["_Softplus"] = function() { + return (_Softplus = Module["_Softplus"] = Module["asm"]["Softplus"]).apply(null, arguments); + }; + var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() { + return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["SparseFillEmptyRows"]).apply(null, arguments); + }; + var _SparseReshape = Module["_SparseReshape"] = function() { + return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["SparseReshape"]).apply(null, arguments); + }; + var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() { + return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["SparseSegmentReduction"]).apply(null, arguments); + }; + var _SparseToDense = Module["_SparseToDense"] = function() { + return (_SparseToDense = Module["_SparseToDense"] = Module["asm"]["SparseToDense"]).apply(null, arguments); + }; + var _Sqrt = Module["_Sqrt"] = function() { + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments); + }; + var _Square = Module["_Square"] = function() { + return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments); + }; + var _SquaredDifference = Module["_SquaredDifference"] = function() { + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments); + }; + var _Step = Module["_Step"] = function() { + return (_Step = Module["_Step"] = Module["asm"]["Step"]).apply(null, arguments); + }; + var _StridedSlice = Module["_StridedSlice"] = function() { + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments); + }; + var _Sub = Module["_Sub"] = function() { + return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments); + }; + var _Sum = Module["_Sum"] = function() { + return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments); + }; + var _Tan = Module["_Tan"] = function() { + return (_Tan = Module["_Tan"] = Module["asm"]["Tan"]).apply(null, arguments); + }; + var _Tanh = Module["_Tanh"] = function() { + return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments); + }; + var _TensorScatterUpdate = Module["_TensorScatterUpdate"] = function() { + return (_TensorScatterUpdate = Module["_TensorScatterUpdate"] = Module["asm"]["TensorScatterUpdate"]).apply(null, arguments); + }; + var _Tile = Module["_Tile"] = function() { + return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments); + }; + var _TopK = Module["_TopK"] = function() { + return (_TopK = Module["_TopK"] = Module["asm"]["TopK"]).apply(null, arguments); + }; + var _Transform = Module["_Transform"] = function() { + return (_Transform = Module["_Transform"] = Module["asm"]["Transform"]).apply(null, arguments); + }; + var _Transpose = Module["_Transpose"] = function() { + return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments); + }; + var __FusedMatMul = Module["__FusedMatMul"] = function() { + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments); + }; + var _malloc = Module["_malloc"] = function() { + return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments); + }; + var _free = Module["_free"] = function() { + return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments); + }; + var __emscripten_tls_init = Module["__emscripten_tls_init"] = function() { + return (__emscripten_tls_init = Module["__emscripten_tls_init"] = Module["asm"]["_emscripten_tls_init"]).apply(null, arguments); + }; + var _pthread_self = Module["_pthread_self"] = function() { + return (_pthread_self = Module["_pthread_self"] = Module["asm"]["pthread_self"]).apply(null, arguments); + }; + var ___errno_location = Module["___errno_location"] = function() { + return (___errno_location = Module["___errno_location"] = Module["asm"]["__errno_location"]).apply(null, arguments); + }; + var __emscripten_thread_init = Module["__emscripten_thread_init"] = function() { + return (__emscripten_thread_init = Module["__emscripten_thread_init"] = Module["asm"]["_emscripten_thread_init"]).apply(null, arguments); + }; + var __emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = function() { + return (__emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = Module["asm"]["_emscripten_thread_crashed"]).apply(null, arguments); + }; + var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() { + return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["emscripten_main_thread_process_queued_calls"]).apply(null, arguments); + }; + var _emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = function() { + return (_emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = Module["asm"]["emscripten_main_browser_thread_id"]).apply(null, arguments); + }; + var _emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = function() { + return (_emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = Module["asm"]["emscripten_run_in_main_runtime_thread_js"]).apply(null, arguments); + }; + var _emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = function() { + return (_emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = Module["asm"]["emscripten_dispatch_to_thread_"]).apply(null, arguments); + }; + var __emscripten_proxy_execute_task_queue = Module["__emscripten_proxy_execute_task_queue"] = function() { + return (__emscripten_proxy_execute_task_queue = Module["__emscripten_proxy_execute_task_queue"] = Module["asm"]["_emscripten_proxy_execute_task_queue"]).apply(null, arguments); + }; + var __emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = function() { + return (__emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = Module["asm"]["_emscripten_thread_free_data"]).apply(null, arguments); + }; + var __emscripten_thread_exit = Module["__emscripten_thread_exit"] = function() { + return (__emscripten_thread_exit = Module["__emscripten_thread_exit"] = Module["asm"]["_emscripten_thread_exit"]).apply(null, arguments); + }; + var _emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = function() { + return (_emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = Module["asm"]["emscripten_stack_set_limits"]).apply(null, arguments); + }; + var stackSave = Module["stackSave"] = function() { + return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments); + }; + var stackRestore = Module["stackRestore"] = function() { + return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments); + }; + var stackAlloc = Module["stackAlloc"] = function() { + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments); + }; + var dynCall_iijjiiii = Module["dynCall_iijjiiii"] = function() { + return (dynCall_iijjiiii = Module["dynCall_iijjiiii"] = Module["asm"]["dynCall_iijjiiii"]).apply(null, arguments); + }; + var dynCall_jiji = Module["dynCall_jiji"] = function() { + return (dynCall_jiji = Module["dynCall_jiji"] = Module["asm"]["dynCall_jiji"]).apply(null, arguments); + }; + Module["keepRuntimeAlive"] = keepRuntimeAlive; + Module["wasmMemory"] = wasmMemory; + Module["cwrap"] = cwrap; + Module["ExitStatus"] = ExitStatus; + Module["PThread"] = PThread; + var calledRun; + dependenciesFulfilled = function runCaller() { + if (!calledRun) + run(); + if (!calledRun) + dependenciesFulfilled = runCaller; + }; + function run(args) { + args = args || arguments_; + if (runDependencies > 0) { + return; + } + if (ENVIRONMENT_IS_PTHREAD) { + readyPromiseResolve(Module); + initRuntime(); + startWorker(Module); + return; + } + preRun(); + if (runDependencies > 0) { + return; + } + function doRun() { + if (calledRun) + return; + calledRun = true; + Module["calledRun"] = true; + if (ABORT) + return; + initRuntime(); + readyPromiseResolve(Module); + if (Module["onRuntimeInitialized"]) + Module["onRuntimeInitialized"](); + postRun(); + } + if (Module["setStatus"]) { + Module["setStatus"]("Running..."); + setTimeout(function() { + setTimeout(function() { + Module["setStatus"](""); + }, 1); + doRun(); + }, 1); + } else { + doRun(); + } + } + if (Module["preInit"]) { + if (typeof Module["preInit"] == "function") + Module["preInit"] = [Module["preInit"]]; + while (Module["preInit"].length > 0) { + Module["preInit"].pop()(); + } + } + run(); + var listenersAdded; + if (beforeListeners) { + listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) { + return !beforeListeners.uncaughtException.indexOf(listener) > -1; + }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) { + return !beforeListeners.unhandledRejection.indexOf(listener) > -1; + }) }; + } + var actualModule; + if (typeof WasmBackendModule !== "undefined") { + actualModule = WasmBackendModule; + } else if (typeof WasmBackendModuleThreadedSimd3 !== "undefined") { + actualModule = WasmBackendModuleThreadedSimd3; + } else { + throw new Error("Could not find wasm module in post.js"); + } + if (listenersAdded) { + var tmpDispose = actualModule["_dispose"]; + actualModule["_dispose"] = function() { + tmpDispose(); + listenersAdded.uncaughtException.forEach(function(listener) { + process.removeListener("uncaughtException", listener); + }); + listenersAdded.unhandledRejection.forEach(function(listener) { + process.removeListener("unhandledRejection", listener); + }); + }; + } + return WasmBackendModuleThreadedSimd3.ready; + }; + })(); + if (typeof exports === "object" && typeof module === "object") + module.exports = WasmBackendModuleThreadedSimd2; + else if (typeof define === "function" && define["amd"]) + define([], function() { + return WasmBackendModuleThreadedSimd2; + }); + else if (typeof exports === "object") + exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd2; + } +}); +var require_tfjs_backend_wasm_threaded_simd_worker = __commonJS({ + "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js"(exports, module) { + "use strict"; + module.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+" +");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.startWorker=instance=>{Module=instance;postMessage({"cmd":"loaded"})};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:"callHandler",handler:handler,args:[...arguments]})}}Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module)}else if(e.data.cmd==="run"){Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};`; + } +}); +var require_tfjs_backend_wasm = __commonJS({ + "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(exports, module) { + "use strict"; + var WasmBackendModule2 = (() => { + var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; + if (typeof __filename !== "undefined") + _scriptDir = _scriptDir || __filename; + return function(WasmBackendModule3) { + WasmBackendModule3 = WasmBackendModule3 || {}; + var Module = typeof WasmBackendModule3 != "undefined" ? WasmBackendModule3 : {}; + var readyPromiseResolve, readyPromiseReject; + Module["ready"] = new Promise(function(resolve, reject) { + readyPromiseResolve = resolve; + readyPromiseReject = reject; + }); + var beforeListeners; + if (typeof process !== "undefined" && process.listeners) { + beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }; + } + var moduleOverrides = Object.assign({}, Module); + var arguments_ = []; + var thisProgram = "./this.program"; + var quit_ = (status, toThrow) => { + throw toThrow; + }; + var ENVIRONMENT_IS_WEB = typeof window == "object"; + var ENVIRONMENT_IS_WORKER = typeof importScripts == "function"; + var ENVIRONMENT_IS_NODE = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string"; + var scriptDirectory = ""; + function locateFile(path) { + if (Module["locateFile"]) { + return Module["locateFile"](path, scriptDirectory); + } + return scriptDirectory + path; + } + var read_, readAsync, readBinary, setWindowTitle; + function logExceptionOnExit(e) { + if (e instanceof ExitStatus) + return; + let toLog = e; + err("exiting due to exception: " + toLog); + } + if (ENVIRONMENT_IS_NODE) { + var fs = require_fs(); + var nodePath = require_path(); + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = nodePath.dirname(scriptDirectory) + "/"; + } else { + scriptDirectory = __dirname + "/"; + } + read_ = (filename, binary) => { + filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename); + return fs.readFileSync(filename, binary ? void 0 : "utf8"); + }; + readBinary = (filename) => { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + return ret; + }; + readAsync = (filename, onload, onerror) => { + filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename); + fs.readFile(filename, function(err2, data) { + if (err2) + onerror(err2); + else + onload(data.buffer); + }); + }; + if (process["argv"].length > 1) { + thisProgram = process["argv"][1].replace(/\\/g, "/"); + } + arguments_ = process["argv"].slice(2); + process["on"]("uncaughtException", function(ex) { + if (!(ex instanceof ExitStatus)) { + throw ex; + } + }); + process["on"]("unhandledRejection", function(reason) { + throw reason; + }); + quit_ = (status, toThrow) => { + if (keepRuntimeAlive()) { + process["exitCode"] = status; + throw toThrow; + } + logExceptionOnExit(toThrow); + process["exit"](status); + }; + Module["inspect"] = function() { + return "[Emscripten Module object]"; + }; + } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = self.location.href; + } else if (typeof document != "undefined" && document.currentScript) { + scriptDirectory = document.currentScript.src; + } + if (_scriptDir) { + scriptDirectory = _scriptDir; + } + if (scriptDirectory.indexOf("blob:") !== 0) { + scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1); + } else { + scriptDirectory = ""; + } + { + read_ = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.send(null); + return xhr.responseText; + }; + if (ENVIRONMENT_IS_WORKER) { + readBinary = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.responseType = "arraybuffer"; + xhr.send(null); + return new Uint8Array(xhr.response); + }; + } + readAsync = (url, onload, onerror) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, true); + xhr.responseType = "arraybuffer"; + xhr.onload = () => { + if (xhr.status == 200 || xhr.status == 0 && xhr.response) { + onload(xhr.response); + return; + } + onerror(); + }; + xhr.onerror = onerror; + xhr.send(null); + }; + } + setWindowTitle = (title) => document.title = title; + } else { + } + var out = Module["print"] || console.log.bind(console); + var err = Module["printErr"] || console.warn.bind(console); + Object.assign(Module, moduleOverrides); + moduleOverrides = null; + if (Module["arguments"]) + arguments_ = Module["arguments"]; + if (Module["thisProgram"]) + thisProgram = Module["thisProgram"]; + if (Module["quit"]) + quit_ = Module["quit"]; + var POINTER_SIZE = 4; + var wasmBinary; + if (Module["wasmBinary"]) + wasmBinary = Module["wasmBinary"]; + var noExitRuntime = Module["noExitRuntime"] || true; + if (typeof WebAssembly != "object") { + abort("no native wasm support detected"); + } + var wasmMemory; + var ABORT = false; + var EXITSTATUS; + function assert3(condition, text) { + if (!condition) { + abort(text); + } + } + var UTF8Decoder = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; + function UTF8ArrayToString(heapOrArray, idx, maxBytesToRead) { + idx >>>= 0; + var endIdx = idx + maxBytesToRead; + var endPtr = idx; + while (heapOrArray[endPtr] && !(endPtr >= endIdx)) + ++endPtr; + if (endPtr - idx > 16 && heapOrArray.buffer && UTF8Decoder) { + return UTF8Decoder.decode(heapOrArray.subarray(idx, endPtr)); + } + var str = ""; + while (idx < endPtr) { + var u0 = heapOrArray[idx++]; + if (!(u0 & 128)) { + str += String.fromCharCode(u0); + continue; + } + var u1 = heapOrArray[idx++] & 63; + if ((u0 & 224) == 192) { + str += String.fromCharCode((u0 & 31) << 6 | u1); + continue; + } + var u2 = heapOrArray[idx++] & 63; + if ((u0 & 240) == 224) { + u0 = (u0 & 15) << 12 | u1 << 6 | u2; + } else { + u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heapOrArray[idx++] & 63; + } + if (u0 < 65536) { + str += String.fromCharCode(u0); + } else { + var ch = u0 - 65536; + str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023); + } + } + return str; + } + function UTF8ToString(ptr, maxBytesToRead) { + ptr >>>= 0; + return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : ""; + } + function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) { + outIdx >>>= 0; + if (!(maxBytesToWrite > 0)) + return 0; + var startIdx = outIdx; + var endIdx = outIdx + maxBytesToWrite - 1; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) { + var u1 = str.charCodeAt(++i); + u = 65536 + ((u & 1023) << 10) | u1 & 1023; + } + if (u <= 127) { + if (outIdx >= endIdx) + break; + heap[outIdx++ >>> 0] = u; + } else if (u <= 2047) { + if (outIdx + 1 >= endIdx) + break; + heap[outIdx++ >>> 0] = 192 | u >> 6; + heap[outIdx++ >>> 0] = 128 | u & 63; + } else if (u <= 65535) { + if (outIdx + 2 >= endIdx) + break; + heap[outIdx++ >>> 0] = 224 | u >> 12; + heap[outIdx++ >>> 0] = 128 | u >> 6 & 63; + heap[outIdx++ >>> 0] = 128 | u & 63; + } else { + if (outIdx + 3 >= endIdx) + break; + heap[outIdx++ >>> 0] = 240 | u >> 18; + heap[outIdx++ >>> 0] = 128 | u >> 12 & 63; + heap[outIdx++ >>> 0] = 128 | u >> 6 & 63; + heap[outIdx++ >>> 0] = 128 | u & 63; + } + } + heap[outIdx >>> 0] = 0; + return outIdx - startIdx; + } + function stringToUTF8(str, outPtr, maxBytesToWrite) { + return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite); + } + var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; + function updateGlobalBufferAndViews(buf) { + buffer2 = buf; + Module["HEAP8"] = HEAP8 = new Int8Array(buf); + Module["HEAP16"] = HEAP16 = new Int16Array(buf); + Module["HEAP32"] = HEAP32 = new Int32Array(buf); + Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf); + Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf); + Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf); + Module["HEAPF32"] = HEAPF32 = new Float32Array(buf); + Module["HEAPF64"] = HEAPF64 = new Float64Array(buf); + } + var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216; + var wasmTable; + var __ATPRERUN__ = []; + var __ATINIT__ = []; + var __ATPOSTRUN__ = []; + var runtimeInitialized = false; + function keepRuntimeAlive() { + return noExitRuntime; + } + function preRun() { + if (Module["preRun"]) { + if (typeof Module["preRun"] == "function") + Module["preRun"] = [Module["preRun"]]; + while (Module["preRun"].length) { + addOnPreRun(Module["preRun"].shift()); + } + } + callRuntimeCallbacks(__ATPRERUN__); + } + function initRuntime() { + runtimeInitialized = true; + callRuntimeCallbacks(__ATINIT__); + } + function postRun() { + if (Module["postRun"]) { + if (typeof Module["postRun"] == "function") + Module["postRun"] = [Module["postRun"]]; + while (Module["postRun"].length) { + addOnPostRun(Module["postRun"].shift()); + } + } + callRuntimeCallbacks(__ATPOSTRUN__); + } + function addOnPreRun(cb) { + __ATPRERUN__.unshift(cb); + } + function addOnInit(cb) { + __ATINIT__.unshift(cb); + } + function addOnPostRun(cb) { + __ATPOSTRUN__.unshift(cb); + } + var runDependencies = 0; + var runDependencyWatcher = null; + var dependenciesFulfilled = null; + function addRunDependency(id) { + runDependencies++; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + } + function removeRunDependency(id) { + runDependencies--; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + if (runDependencies == 0) { + if (runDependencyWatcher !== null) { + clearInterval(runDependencyWatcher); + runDependencyWatcher = null; + } + if (dependenciesFulfilled) { + var callback = dependenciesFulfilled; + dependenciesFulfilled = null; + callback(); + } + } + } + function abort(what) { + if (Module["onAbort"]) { + Module["onAbort"](what); + } + what = "Aborted(" + what + ")"; + err(what); + ABORT = true; + EXITSTATUS = 1; + what += ". Build with -sASSERTIONS for more info."; + var e = new WebAssembly.RuntimeError(what); + readyPromiseReject(e); + throw e; + } + var dataURIPrefix = "data:application/octet-stream;base64,"; + function isDataURI(filename) { + return filename.startsWith(dataURIPrefix); + } + function isFileURI(filename) { + return filename.startsWith("file://"); + } + var wasmBinaryFile; + wasmBinaryFile = "tfjs-backend-wasm.wasm"; + if (!isDataURI(wasmBinaryFile)) { + wasmBinaryFile = locateFile(wasmBinaryFile); + } + function getBinary(file) { + try { + if (file == wasmBinaryFile && wasmBinary) { + return new Uint8Array(wasmBinary); + } + if (readBinary) { + return readBinary(file); + } + throw "both async and sync fetching of the wasm failed"; + } catch (err2) { + abort(err2); + } + } + function getBinaryPromise() { + if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) { + if (typeof fetch == "function" && !isFileURI(wasmBinaryFile)) { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + if (!response["ok"]) { + throw "failed to load wasm binary file at '" + wasmBinaryFile + "'"; + } + return response["arrayBuffer"](); + }).catch(function() { + return getBinary(wasmBinaryFile); + }); + } else { + if (readAsync) { + return new Promise(function(resolve, reject) { + readAsync(wasmBinaryFile, function(response) { + resolve(new Uint8Array(response)); + }, reject); + }); + } + } + } + return Promise.resolve().then(function() { + return getBinary(wasmBinaryFile); + }); + } + function createWasm() { + var info = { "env": asmLibraryArg, "wasi_snapshot_preview1": asmLibraryArg }; + function receiveInstance(instance, module2) { + var exports3 = instance.exports; + Module["asm"] = exports3; + wasmMemory = Module["asm"]["memory"]; + updateGlobalBufferAndViews(wasmMemory.buffer); + wasmTable = Module["asm"]["__indirect_function_table"]; + addOnInit(Module["asm"]["__wasm_call_ctors"]); + removeRunDependency("wasm-instantiate"); + } + addRunDependency("wasm-instantiate"); + function receiveInstantiationResult(result) { + receiveInstance(result["instance"]); + } + function instantiateArrayBuffer(receiver) { + return getBinaryPromise().then(function(binary) { + return WebAssembly.instantiate(binary, info); + }).then(function(instance) { + return instance; + }).then(receiver, function(reason) { + err("failed to asynchronously prepare wasm: " + reason); + abort(reason); + }); + } + function instantiateAsync() { + if (!wasmBinary && typeof WebAssembly.instantiateStreaming == "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && !ENVIRONMENT_IS_NODE && typeof fetch == "function") { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + var result = WebAssembly.instantiateStreaming(response, info); + return result.then(receiveInstantiationResult, function(reason) { + err("wasm streaming compile failed: " + reason); + err("falling back to ArrayBuffer instantiation"); + return instantiateArrayBuffer(receiveInstantiationResult); + }); + }); + } else { + return instantiateArrayBuffer(receiveInstantiationResult); + } + } + if (Module["instantiateWasm"]) { + try { + var exports2 = Module["instantiateWasm"](info, receiveInstance); + return exports2; + } catch (e) { + err("Module.instantiateWasm callback failed with error: " + e); + readyPromiseReject(e); + } + } + instantiateAsync().catch(readyPromiseReject); + return {}; + } + var tempDouble; + var tempI64; + function ExitStatus(status) { + this.name = "ExitStatus"; + this.message = "Program terminated with exit(" + status + ")"; + this.status = status; + } + function callRuntimeCallbacks(callbacks2) { + while (callbacks2.length > 0) { + callbacks2.shift()(Module); + } + } + function _abort() { + abort(""); + } + function getHeapMax() { + return 4294901760; + } + function _emscripten_get_heap_max() { + return getHeapMax(); + } + function _emscripten_memcpy_big(dest, src, num) { + HEAPU8.copyWithin(dest >>> 0, src >>> 0, src + num >>> 0); + } + function emscripten_realloc_buffer(size) { + try { + wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16); + updateGlobalBufferAndViews(wasmMemory.buffer); + return 1; + } catch (e) { + } + } + function _emscripten_resize_heap(requestedSize) { + var oldSize = HEAPU8.length; + requestedSize = requestedSize >>> 0; + var maxHeapSize = getHeapMax(); + if (requestedSize > maxHeapSize) { + return false; + } + let alignUp = (x, multiple) => x + (multiple - x % multiple) % multiple; + for (var cutDown = 1; cutDown <= 4; cutDown *= 2) { + var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown); + overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296); + var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536)); + var replacement = emscripten_realloc_buffer(newSize); + if (replacement) { + return true; + } + } + return false; + } + var SYSCALLS = { varargs: void 0, get: function() { + SYSCALLS.varargs += 4; + var ret = HEAP32[SYSCALLS.varargs - 4 >>> 2]; + return ret; + }, getStr: function(ptr) { + var ret = UTF8ToString(ptr); + return ret; + } }; + function _fd_close(fd) { + return 52; + } + function _fd_seek(fd, offset_low, offset_high, whence, newOffset) { + return 70; + } + var printCharBuffers = [null, [], []]; + function printChar(stream, curr) { + var buffer3 = printCharBuffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0)); + buffer3.length = 0; + } else { + buffer3.push(curr); + } + } + function _fd_write(fd, iov, iovcnt, pnum) { + var num = 0; + for (var i = 0; i < iovcnt; i++) { + var ptr = HEAPU32[iov >>> 2]; + var len = HEAPU32[iov + 4 >>> 2]; + iov += 8; + for (var j = 0; j < len; j++) { + printChar(fd, HEAPU8[ptr + j >>> 0]); + } + num += len; + } + HEAPU32[pnum >>> 2] = num; + return 0; + } + function getCFunc(ident) { + var func2 = Module["_" + ident]; + return func2; + } + function writeArrayToMemory(array2, buffer3) { + HEAP8.set(array2, buffer3 >>> 0); + } + function ccall(ident, returnType, argTypes, args, opts) { + var toC = { "string": (str) => { + var ret2 = 0; + if (str !== null && str !== void 0 && str !== 0) { + var len = (str.length << 2) + 1; + ret2 = stackAlloc(len); + stringToUTF8(str, ret2, len); + } + return ret2; + }, "array": (arr) => { + var ret2 = stackAlloc(arr.length); + writeArrayToMemory(arr, ret2); + return ret2; + } }; + function convertReturnValue(ret2) { + if (returnType === "string") { + return UTF8ToString(ret2); + } + if (returnType === "boolean") + return Boolean(ret2); + return ret2; + } + var func2 = getCFunc(ident); + var cArgs = []; + var stack2 = 0; + if (args) { + for (var i = 0; i < args.length; i++) { + var converter = toC[argTypes[i]]; + if (converter) { + if (stack2 === 0) + stack2 = stackSave(); + cArgs[i] = converter(args[i]); + } else { + cArgs[i] = args[i]; + } + } + } + var ret = func2.apply(null, cArgs); + function onDone(ret2) { + if (stack2 !== 0) + stackRestore(stack2); + return convertReturnValue(ret2); + } + ret = onDone(ret); + return ret; + } + function cwrap(ident, returnType, argTypes, opts) { + argTypes = argTypes || []; + var numericArgs = argTypes.every((type) => type === "number" || type === "boolean"); + var numericRet = returnType !== "string"; + if (numericRet && numericArgs && !opts) { + return getCFunc(ident); + } + return function() { + return ccall(ident, returnType, argTypes, arguments, opts); + }; + } + var asmLibraryArg = { "abort": _abort, "emscripten_get_heap_max": _emscripten_get_heap_max, "emscripten_memcpy_big": _emscripten_memcpy_big, "emscripten_resize_heap": _emscripten_resize_heap, "fd_close": _fd_close, "fd_seek": _fd_seek, "fd_write": _fd_write }; + var asm = createWasm(); + var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() { + return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["__wasm_call_ctors"]).apply(null, arguments); + }; + var _init = Module["_init"] = function() { + return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments); + }; + var _init_with_threads_count = Module["_init_with_threads_count"] = function() { + return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["init_with_threads_count"]).apply(null, arguments); + }; + var _get_threads_count = Module["_get_threads_count"] = function() { + return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["get_threads_count"]).apply(null, arguments); + }; + var _register_tensor = Module["_register_tensor"] = function() { + return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments); + }; + var _dispose_data = Module["_dispose_data"] = function() { + return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments); + }; + var _dispose = Module["_dispose"] = function() { + return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments); + }; + var _Abs = Module["_Abs"] = function() { + return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments); + }; + var _Acos = Module["_Acos"] = function() { + return (_Acos = Module["_Acos"] = Module["asm"]["Acos"]).apply(null, arguments); + }; + var _Acosh = Module["_Acosh"] = function() { + return (_Acosh = Module["_Acosh"] = Module["asm"]["Acosh"]).apply(null, arguments); + }; + var _Add = Module["_Add"] = function() { + return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments); + }; + var _AddN = Module["_AddN"] = function() { + return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments); + }; + var _All = Module["_All"] = function() { + return (_All = Module["_All"] = Module["asm"]["All"]).apply(null, arguments); + }; + var _Any = Module["_Any"] = function() { + return (_Any = Module["_Any"] = Module["asm"]["Any"]).apply(null, arguments); + }; + var _ArgMax = Module["_ArgMax"] = function() { + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments); + }; + var _ArgMin = Module["_ArgMin"] = function() { + return (_ArgMin = Module["_ArgMin"] = Module["asm"]["ArgMin"]).apply(null, arguments); + }; + var _Asin = Module["_Asin"] = function() { + return (_Asin = Module["_Asin"] = Module["asm"]["Asin"]).apply(null, arguments); + }; + var _Asinh = Module["_Asinh"] = function() { + return (_Asinh = Module["_Asinh"] = Module["asm"]["Asinh"]).apply(null, arguments); + }; + var _Atan = Module["_Atan"] = function() { + return (_Atan = Module["_Atan"] = Module["asm"]["Atan"]).apply(null, arguments); + }; + var _Atan2 = Module["_Atan2"] = function() { + return (_Atan2 = Module["_Atan2"] = Module["asm"]["Atan2"]).apply(null, arguments); + }; + var _Atanh = Module["_Atanh"] = function() { + return (_Atanh = Module["_Atanh"] = Module["asm"]["Atanh"]).apply(null, arguments); + }; + var _AvgPool = Module["_AvgPool"] = function() { + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments); + }; + var _AvgPool3D = Module["_AvgPool3D"] = function() { + return (_AvgPool3D = Module["_AvgPool3D"] = Module["asm"]["AvgPool3D"]).apply(null, arguments); + }; + var _AvgPool3DGrad = Module["_AvgPool3DGrad"] = function() { + return (_AvgPool3DGrad = Module["_AvgPool3DGrad"] = Module["asm"]["AvgPool3DGrad"]).apply(null, arguments); + }; + var _AvgPoolGrad = Module["_AvgPoolGrad"] = function() { + return (_AvgPoolGrad = Module["_AvgPoolGrad"] = Module["asm"]["AvgPoolGrad"]).apply(null, arguments); + }; + var _BatchMatMul = Module["_BatchMatMul"] = function() { + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments); + }; + var _Bincount = Module["_Bincount"] = function() { + return (_Bincount = Module["_Bincount"] = Module["asm"]["Bincount"]).apply(null, arguments); + }; + var _BitwiseAnd = Module["_BitwiseAnd"] = function() { + return (_BitwiseAnd = Module["_BitwiseAnd"] = Module["asm"]["BitwiseAnd"]).apply(null, arguments); + }; + var _Ceil = Module["_Ceil"] = function() { + return (_Ceil = Module["_Ceil"] = Module["asm"]["Ceil"]).apply(null, arguments); + }; + var _ClipByValue = Module["_ClipByValue"] = function() { + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments); + }; + var _Conv2D2 = Module["_Conv2D"] = function() { + return (_Conv2D2 = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments); + }; + var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments); + }; + var _Conv3D2 = Module["_Conv3D"] = function() { + return (_Conv3D2 = Module["_Conv3D"] = Module["asm"]["Conv3D"]).apply(null, arguments); + }; + var _Conv3DBackpropFilterV2 = Module["_Conv3DBackpropFilterV2"] = function() { + return (_Conv3DBackpropFilterV2 = Module["_Conv3DBackpropFilterV2"] = Module["asm"]["Conv3DBackpropFilterV2"]).apply(null, arguments); + }; + var _Conv3DBackpropInputV2 = Module["_Conv3DBackpropInputV2"] = function() { + return (_Conv3DBackpropInputV2 = Module["_Conv3DBackpropInputV2"] = Module["asm"]["Conv3DBackpropInputV2"]).apply(null, arguments); + }; + var _Cos = Module["_Cos"] = function() { + return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments); + }; + var _Cosh = Module["_Cosh"] = function() { + return (_Cosh = Module["_Cosh"] = Module["asm"]["Cosh"]).apply(null, arguments); + }; + var _CropAndResize = Module["_CropAndResize"] = function() { + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments); + }; + var _Cumprod = Module["_Cumprod"] = function() { + return (_Cumprod = Module["_Cumprod"] = Module["asm"]["Cumprod"]).apply(null, arguments); + }; + var _Cumsum = Module["_Cumsum"] = function() { + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments); + }; + var _DenseBincount = Module["_DenseBincount"] = function() { + return (_DenseBincount = Module["_DenseBincount"] = Module["asm"]["DenseBincount"]).apply(null, arguments); + }; + var _DepthToSpace = Module["_DepthToSpace"] = function() { + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments); + }; + var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments); + }; + var _Diag = Module["_Diag"] = function() { + return (_Diag = Module["_Diag"] = Module["asm"]["Diag"]).apply(null, arguments); + }; + var _Dilation2D = Module["_Dilation2D"] = function() { + return (_Dilation2D = Module["_Dilation2D"] = Module["asm"]["Dilation2D"]).apply(null, arguments); + }; + var _Dilation2DBackpropFilter = Module["_Dilation2DBackpropFilter"] = function() { + return (_Dilation2DBackpropFilter = Module["_Dilation2DBackpropFilter"] = Module["asm"]["Dilation2DBackpropFilter"]).apply(null, arguments); + }; + var _Dilation2DBackpropInput = Module["_Dilation2DBackpropInput"] = function() { + return (_Dilation2DBackpropInput = Module["_Dilation2DBackpropInput"] = Module["asm"]["Dilation2DBackpropInput"]).apply(null, arguments); + }; + var _Elu = Module["_Elu"] = function() { + return (_Elu = Module["_Elu"] = Module["asm"]["Elu"]).apply(null, arguments); + }; + var _EluGrad = Module["_EluGrad"] = function() { + return (_EluGrad = Module["_EluGrad"] = Module["asm"]["EluGrad"]).apply(null, arguments); + }; + var _Equal = Module["_Equal"] = function() { + return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments); + }; + var _Erf = Module["_Erf"] = function() { + return (_Erf = Module["_Erf"] = Module["asm"]["Erf"]).apply(null, arguments); + }; + var _Exp = Module["_Exp"] = function() { + return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments); + }; + var _Expm1 = Module["_Expm1"] = function() { + return (_Expm1 = Module["_Expm1"] = Module["asm"]["Expm1"]).apply(null, arguments); + }; + var _FlipLeftRight = Module["_FlipLeftRight"] = function() { + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments); + }; + var _Floor = Module["_Floor"] = function() { + return (_Floor = Module["_Floor"] = Module["asm"]["Floor"]).apply(null, arguments); + }; + var _FloorDiv = Module["_FloorDiv"] = function() { + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments); + }; + var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments); + }; + var _FusedConv2D = Module["_FusedConv2D"] = function() { + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments); + }; + var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments); + }; + var _Gather = Module["_Gather"] = function() { + return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments); + }; + var _GatherNd = Module["_GatherNd"] = function() { + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments); + }; + var _Greater = Module["_Greater"] = function() { + return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments); + }; + var _GreaterEqual = Module["_GreaterEqual"] = function() { + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments); + }; + var _IsFinite = Module["_IsFinite"] = function() { + return (_IsFinite = Module["_IsFinite"] = Module["asm"]["IsFinite"]).apply(null, arguments); + }; + var _IsInf = Module["_IsInf"] = function() { + return (_IsInf = Module["_IsInf"] = Module["asm"]["IsInf"]).apply(null, arguments); + }; + var _IsNan = Module["_IsNan"] = function() { + return (_IsNan = Module["_IsNan"] = Module["asm"]["IsNan"]).apply(null, arguments); + }; + var _LRN = Module["_LRN"] = function() { + return (_LRN = Module["_LRN"] = Module["asm"]["LRN"]).apply(null, arguments); + }; + var _LRNGrad = Module["_LRNGrad"] = function() { + return (_LRNGrad = Module["_LRNGrad"] = Module["asm"]["LRNGrad"]).apply(null, arguments); + }; + var _LeakyRelu = Module["_LeakyRelu"] = function() { + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["LeakyRelu"]).apply(null, arguments); + }; + var _Less = Module["_Less"] = function() { + return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments); + }; + var _LessEqual = Module["_LessEqual"] = function() { + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments); + }; + var _LinSpace = Module["_LinSpace"] = function() { + return (_LinSpace = Module["_LinSpace"] = Module["asm"]["LinSpace"]).apply(null, arguments); + }; + var _Log = Module["_Log"] = function() { + return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments); + }; + var _Log1p = Module["_Log1p"] = function() { + return (_Log1p = Module["_Log1p"] = Module["asm"]["Log1p"]).apply(null, arguments); + }; + var _LogicalAnd = Module["_LogicalAnd"] = function() { + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments); + }; + var _LogicalNot = Module["_LogicalNot"] = function() { + return (_LogicalNot = Module["_LogicalNot"] = Module["asm"]["LogicalNot"]).apply(null, arguments); + }; + var _LogicalOr = Module["_LogicalOr"] = function() { + return (_LogicalOr = Module["_LogicalOr"] = Module["asm"]["LogicalOr"]).apply(null, arguments); + }; + var _LogicalXor = Module["_LogicalXor"] = function() { + return (_LogicalXor = Module["_LogicalXor"] = Module["asm"]["LogicalXor"]).apply(null, arguments); + }; + var _Max = Module["_Max"] = function() { + return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments); + }; + var _MaxPool = Module["_MaxPool"] = function() { + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments); + }; + var _MaxPool3D = Module["_MaxPool3D"] = function() { + return (_MaxPool3D = Module["_MaxPool3D"] = Module["asm"]["MaxPool3D"]).apply(null, arguments); + }; + var _MaxPool3DGrad = Module["_MaxPool3DGrad"] = function() { + return (_MaxPool3DGrad = Module["_MaxPool3DGrad"] = Module["asm"]["MaxPool3DGrad"]).apply(null, arguments); + }; + var _MaxPoolGrad = Module["_MaxPoolGrad"] = function() { + return (_MaxPoolGrad = Module["_MaxPoolGrad"] = Module["asm"]["MaxPoolGrad"]).apply(null, arguments); + }; + var _MaxPoolWithArgmax = Module["_MaxPoolWithArgmax"] = function() { + return (_MaxPoolWithArgmax = Module["_MaxPoolWithArgmax"] = Module["asm"]["MaxPoolWithArgmax"]).apply(null, arguments); + }; + var _Maximum = Module["_Maximum"] = function() { + return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments); + }; + var _Mean = Module["_Mean"] = function() { + return (_Mean = Module["_Mean"] = Module["asm"]["Mean"]).apply(null, arguments); + }; + var _Min = Module["_Min"] = function() { + return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments); + }; + var _Minimum = Module["_Minimum"] = function() { + return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments); + }; + var _MirrorPad = Module["_MirrorPad"] = function() { + return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["MirrorPad"]).apply(null, arguments); + }; + var _Mod = Module["_Mod"] = function() { + return (_Mod = Module["_Mod"] = Module["asm"]["Mod"]).apply(null, arguments); + }; + var _Multinomial = Module["_Multinomial"] = function() { + return (_Multinomial = Module["_Multinomial"] = Module["asm"]["Multinomial"]).apply(null, arguments); + }; + var _Multiply = Module["_Multiply"] = function() { + return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments); + }; + var _Neg = Module["_Neg"] = function() { + return (_Neg = Module["_Neg"] = Module["asm"]["Neg"]).apply(null, arguments); + }; + var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments); + }; + var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments); + }; + var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments); + }; + var _NotEqual = Module["_NotEqual"] = function() { + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments); + }; + var _OneHot = Module["_OneHot"] = function() { + return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments); + }; + var _PadV2 = Module["_PadV2"] = function() { + return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments); + }; + var _Pow = Module["_Pow"] = function() { + return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments); + }; + var _Prelu = Module["_Prelu"] = function() { + return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments); + }; + var _Prod = Module["_Prod"] = function() { + return (_Prod = Module["_Prod"] = Module["asm"]["Prod"]).apply(null, arguments); + }; + var _RealDiv = Module["_RealDiv"] = function() { + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["RealDiv"]).apply(null, arguments); + }; + var _Reciprocal = Module["_Reciprocal"] = function() { + return (_Reciprocal = Module["_Reciprocal"] = Module["asm"]["Reciprocal"]).apply(null, arguments); + }; + var _Relu = Module["_Relu"] = function() { + return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments); + }; + var _Relu6 = Module["_Relu6"] = function() { + return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments); + }; + var _ResizeBilinear = Module["_ResizeBilinear"] = function() { + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments); + }; + var _ResizeBilinearGrad = Module["_ResizeBilinearGrad"] = function() { + return (_ResizeBilinearGrad = Module["_ResizeBilinearGrad"] = Module["asm"]["ResizeBilinearGrad"]).apply(null, arguments); + }; + var _ResizeNearestNeighbor = Module["_ResizeNearestNeighbor"] = function() { + return (_ResizeNearestNeighbor = Module["_ResizeNearestNeighbor"] = Module["asm"]["ResizeNearestNeighbor"]).apply(null, arguments); + }; + var _ResizeNearestNeighborGrad = Module["_ResizeNearestNeighborGrad"] = function() { + return (_ResizeNearestNeighborGrad = Module["_ResizeNearestNeighborGrad"] = Module["asm"]["ResizeNearestNeighborGrad"]).apply(null, arguments); + }; + var _Reverse = Module["_Reverse"] = function() { + return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments); + }; + var _RotateWithOffset = Module["_RotateWithOffset"] = function() { + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments); + }; + var _Round = Module["_Round"] = function() { + return (_Round = Module["_Round"] = Module["asm"]["Round"]).apply(null, arguments); + }; + var _Rsqrt = Module["_Rsqrt"] = function() { + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments); + }; + var _ScatterNd = Module["_ScatterNd"] = function() { + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments); + }; + var _SearchSorted = Module["_SearchSorted"] = function() { + return (_SearchSorted = Module["_SearchSorted"] = Module["asm"]["SearchSorted"]).apply(null, arguments); + }; + var _SelectV2 = Module["_SelectV2"] = function() { + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments); + }; + var _Selu = Module["_Selu"] = function() { + return (_Selu = Module["_Selu"] = Module["asm"]["Selu"]).apply(null, arguments); + }; + var _Sigmoid = Module["_Sigmoid"] = function() { + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments); + }; + var _Sign = Module["_Sign"] = function() { + return (_Sign = Module["_Sign"] = Module["asm"]["Sign"]).apply(null, arguments); + }; + var _Sin = Module["_Sin"] = function() { + return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments); + }; + var _Sinh = Module["_Sinh"] = function() { + return (_Sinh = Module["_Sinh"] = Module["asm"]["Sinh"]).apply(null, arguments); + }; + var _Softmax = Module["_Softmax"] = function() { + return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments); + }; + var _Softplus = Module["_Softplus"] = function() { + return (_Softplus = Module["_Softplus"] = Module["asm"]["Softplus"]).apply(null, arguments); + }; + var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() { + return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["SparseFillEmptyRows"]).apply(null, arguments); + }; + var _SparseReshape = Module["_SparseReshape"] = function() { + return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["SparseReshape"]).apply(null, arguments); + }; + var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() { + return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["SparseSegmentReduction"]).apply(null, arguments); + }; + var _SparseToDense = Module["_SparseToDense"] = function() { + return (_SparseToDense = Module["_SparseToDense"] = Module["asm"]["SparseToDense"]).apply(null, arguments); + }; + var _Sqrt = Module["_Sqrt"] = function() { + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments); + }; + var _Square = Module["_Square"] = function() { + return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments); + }; + var _SquaredDifference = Module["_SquaredDifference"] = function() { + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments); + }; + var _Step = Module["_Step"] = function() { + return (_Step = Module["_Step"] = Module["asm"]["Step"]).apply(null, arguments); + }; + var _StridedSlice = Module["_StridedSlice"] = function() { + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments); + }; + var _Sub = Module["_Sub"] = function() { + return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments); + }; + var _Sum = Module["_Sum"] = function() { + return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments); + }; + var _Tan = Module["_Tan"] = function() { + return (_Tan = Module["_Tan"] = Module["asm"]["Tan"]).apply(null, arguments); + }; + var _Tanh = Module["_Tanh"] = function() { + return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments); + }; + var _TensorScatterUpdate = Module["_TensorScatterUpdate"] = function() { + return (_TensorScatterUpdate = Module["_TensorScatterUpdate"] = Module["asm"]["TensorScatterUpdate"]).apply(null, arguments); + }; + var _Tile = Module["_Tile"] = function() { + return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments); + }; + var _TopK = Module["_TopK"] = function() { + return (_TopK = Module["_TopK"] = Module["asm"]["TopK"]).apply(null, arguments); + }; + var _Transform = Module["_Transform"] = function() { + return (_Transform = Module["_Transform"] = Module["asm"]["Transform"]).apply(null, arguments); + }; + var _Transpose = Module["_Transpose"] = function() { + return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments); + }; + var __FusedMatMul = Module["__FusedMatMul"] = function() { + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments); + }; + var _malloc = Module["_malloc"] = function() { + return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments); + }; + var _free = Module["_free"] = function() { + return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments); + }; + var ___errno_location = Module["___errno_location"] = function() { + return (___errno_location = Module["___errno_location"] = Module["asm"]["__errno_location"]).apply(null, arguments); + }; + var stackSave = Module["stackSave"] = function() { + return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments); + }; + var stackRestore = Module["stackRestore"] = function() { + return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments); + }; + var stackAlloc = Module["stackAlloc"] = function() { + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments); + }; + var dynCall_iijjiiii = Module["dynCall_iijjiiii"] = function() { + return (dynCall_iijjiiii = Module["dynCall_iijjiiii"] = Module["asm"]["dynCall_iijjiiii"]).apply(null, arguments); + }; + var dynCall_jiji = Module["dynCall_jiji"] = function() { + return (dynCall_jiji = Module["dynCall_jiji"] = Module["asm"]["dynCall_jiji"]).apply(null, arguments); + }; + Module["cwrap"] = cwrap; + var calledRun; + dependenciesFulfilled = function runCaller() { + if (!calledRun) + run(); + if (!calledRun) + dependenciesFulfilled = runCaller; + }; + function run(args) { + args = args || arguments_; + if (runDependencies > 0) { + return; + } + preRun(); + if (runDependencies > 0) { + return; + } + function doRun() { + if (calledRun) + return; + calledRun = true; + Module["calledRun"] = true; + if (ABORT) + return; + initRuntime(); + readyPromiseResolve(Module); + if (Module["onRuntimeInitialized"]) + Module["onRuntimeInitialized"](); + postRun(); + } + if (Module["setStatus"]) { + Module["setStatus"]("Running..."); + setTimeout(function() { + setTimeout(function() { + Module["setStatus"](""); + }, 1); + doRun(); + }, 1); + } else { + doRun(); + } + } + if (Module["preInit"]) { + if (typeof Module["preInit"] == "function") + Module["preInit"] = [Module["preInit"]]; + while (Module["preInit"].length > 0) { + Module["preInit"].pop()(); + } + } + run(); + var listenersAdded; + if (beforeListeners) { + listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) { + return !beforeListeners.uncaughtException.indexOf(listener) > -1; + }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) { + return !beforeListeners.unhandledRejection.indexOf(listener) > -1; + }) }; + } + var actualModule; + if (typeof WasmBackendModule3 !== "undefined") { + actualModule = WasmBackendModule3; + } else if (typeof WasmBackendModuleThreadedSimd !== "undefined") { + actualModule = WasmBackendModuleThreadedSimd; + } else { + throw new Error("Could not find wasm module in post.js"); + } + if (listenersAdded) { + var tmpDispose = actualModule["_dispose"]; + actualModule["_dispose"] = function() { + tmpDispose(); + listenersAdded.uncaughtException.forEach(function(listener) { + process.removeListener("uncaughtException", listener); + }); + listenersAdded.unhandledRejection.forEach(function(listener) { + process.removeListener("unhandledRejection", listener); + }); + }; + } + return WasmBackendModule3.ready; + }; + })(); + if (typeof exports === "object" && typeof module === "object") + module.exports = WasmBackendModule2; + else if (typeof define === "function" && define["amd"]) + define([], function() { + return WasmBackendModule2; + }); + else if (typeof exports === "object") + exports["WasmBackendModule"] = WasmBackendModule2; + } +}); +var EPSILON_FLOAT32 = 1e-7; +var EPSILON_FLOAT16 = 1e-4; +var DataStorage = class { + constructor(backend2, dataMover) { + this.backend = backend2; + this.dataMover = dataMover; + this.data = /* @__PURE__ */ new WeakMap(); + this.dataIdsCount = 0; + } + get(dataId) { + if (!this.data.has(dataId)) { + this.dataMover.moveData(this.backend, dataId); + } + return this.data.get(dataId); + } + set(dataId, value) { + this.dataIdsCount++; + this.data.set(dataId, value); + } + has(dataId) { + return this.data.has(dataId); + } + delete(dataId) { + this.dataIdsCount--; + return this.data.delete(dataId); + } + numDataIds() { + return this.dataIdsCount; + } +}; +var KernelBackend = class { + refCount(dataId) { + return notYetImplemented("refCount"); + } + incRef(dataId) { + return notYetImplemented("incRef"); + } + timerAvailable() { + return true; + } + time(f) { + return notYetImplemented("time"); + } + read(dataId) { + return notYetImplemented("read"); + } + readSync(dataId) { + return notYetImplemented("readSync"); + } + readToGPU(dataId, options) { + return notYetImplemented("readToGPU"); + } + numDataIds() { + return notYetImplemented("numDataIds"); + } + disposeData(dataId, force) { + return notYetImplemented("disposeData"); + } + write(values, shape, dtype) { + return notYetImplemented("write"); + } + move(dataId, values, shape, dtype, refCount) { + return notYetImplemented("move"); + } + createTensorFromGPUData(values, shape, dtype) { + return notYetImplemented("createTensorFromGPUData"); + } + memory() { + return notYetImplemented("memory"); + } + /** Returns the highest precision for floats in bits (e.g. 16 or 32) */ + floatPrecision() { + return notYetImplemented("floatPrecision"); + } + /** Returns the smallest representable number. */ + epsilon() { + return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16; + } + dispose() { + return notYetImplemented("dispose"); + } +}; +function notYetImplemented(kernelName) { + throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`); +} +function shuffle(array2) { + let counter = array2.length; + let index = 0; + while (counter > 0) { + index = Math.random() * counter | 0; + counter--; + swap(array2, counter, index); + } +} +function shuffleCombo(array2, array22) { + if (array2.length !== array22.length) { + throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`); + } + let counter = array2.length; + let index = 0; + while (counter > 0) { + index = Math.random() * counter | 0; + counter--; + swap(array2, counter, index); + swap(array22, counter, index); + } +} +function clamp(min6, x, max6) { + return Math.max(min6, Math.min(x, max6)); +} +function nearestLargerEven(val) { + return val % 2 === 0 ? val : val + 1; +} +function swap(object, left, right) { + const temp = object[left]; + object[left] = object[right]; + object[right] = temp; +} +function sum(arr) { + let sum6 = 0; + for (let i = 0; i < arr.length; i++) { + sum6 += arr[i]; + } + return sum6; +} +function randUniform(a, b) { + const r = Math.random(); + return b * r + (1 - r) * a; +} +function distSquared(a, b) { + let result = 0; + for (let i = 0; i < a.length; i++) { + const diff = Number(a[i]) - Number(b[i]); + result += diff * diff; + } + return result; +} +function assert(expr, msg) { + if (!expr) { + throw new Error(typeof msg === "string" ? msg : msg()); + } +} +function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") { + assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); +} +function assertNonNull(a) { + assert(a != null, () => `The input to the tensor constructor must be a non-null value.`); +} +function sizeFromShape(shape) { + if (shape.length === 0) { + return 1; + } + let size = shape[0]; + for (let i = 1; i < shape.length; i++) { + size *= shape[i]; + } + return size; +} +function isScalarShape(shape) { + return shape.length === 0; +} +function arraysEqualWithNull(n1, n2) { + if (n1 === n2) { + return true; + } + if (n1 == null || n2 == null) { + return false; + } + if (n1.length !== n2.length) { + return false; + } + for (let i = 0; i < n1.length; i++) { + if (n1[i] !== null && n2[i] !== null && n1[i] !== n2[i]) { + return false; + } + } + return true; +} +function arraysEqual(n1, n2) { + if (n1 === n2) { + return true; + } + if (n1 == null || n2 == null) { + return false; + } + if (n1.length !== n2.length) { + return false; + } + for (let i = 0; i < n1.length; i++) { + if (n1[i] !== n2[i]) { + return false; + } + } + return true; +} +function isInt(a) { + return a % 1 === 0; +} +function tanh(x) { + if (Math.tanh != null) { + return Math.tanh(x); + } + if (x === Infinity) { + return 1; + } else if (x === -Infinity) { + return -1; + } else { + const e2x = Math.exp(2 * x); + return (e2x - 1) / (e2x + 1); + } +} +function sizeToSquarishShape(size) { + const width = Math.ceil(Math.sqrt(size)); + return [width, Math.ceil(size / width)]; +} +function createShuffledIndices(n) { + const shuffledIndices = new Uint32Array(n); + for (let i = 0; i < n; ++i) { + shuffledIndices[i] = i; + } + shuffle(shuffledIndices); + return shuffledIndices; +} +function rightPad(a, size) { + if (size <= a.length) { + return a; + } + return a + " ".repeat(size - a.length); +} +function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter, scheduleFn) { + return new Promise((resolve, reject) => { + let tryCount = 0; + const tryFn = () => { + if (checkFn()) { + resolve(); + return; + } + tryCount++; + const nextBackoff = delayFn(tryCount); + if (maxCounter != null && tryCount >= maxCounter) { + reject(); + return; + } + if (scheduleFn != null) { + scheduleFn(tryFn, nextBackoff); + } else { + setTimeout(tryFn, nextBackoff); + } + }; + tryFn(); + }); +} +function inferFromImplicitShape(shape, size) { + let shapeProd = 1; + let implicitIdx = -1; + for (let i = 0; i < shape.length; ++i) { + if (shape[i] >= 0) { + shapeProd *= shape[i]; + } else if (shape[i] === -1) { + if (implicitIdx !== -1) { + throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`); + } + implicitIdx = i; + } else if (shape[i] < 0) { + throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`); + } + } + if (implicitIdx === -1) { + if (size > 0 && size !== shapeProd) { + throw Error(`Size(${size}) must match the product of shape ${shape}`); + } + return shape; + } + if (shapeProd === 0) { + throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`); + } + if (size % shapeProd !== 0) { + throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`); + } + const newShape = shape.slice(); + newShape[implicitIdx] = size / shapeProd; + return newShape; +} +function parseAxisParam(axis, shape) { + const rank = shape.length; + axis = axis == null ? shape.map((s, i) => i) : [].concat(axis); + assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`); + assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`); + return axis.map((a) => a < 0 ? rank + a : a); +} +function squeezeShape(shape, axis) { + const newShape = []; + const keptDims = []; + const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0; + const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort(); + let j = 0; + for (let i = 0; i < shape.length; ++i) { + if (axes != null) { + if (axes[j] === i && shape[i] !== 1) { + throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`); + } + if ((axes[j] == null || axes[j] > i) && shape[i] === 1) { + newShape.push(shape[i]); + keptDims.push(i); + } + if (axes[j] <= i) { + j++; + } + } + if (shape[i] !== 1) { + newShape.push(shape[i]); + keptDims.push(i); + } + } + return { newShape, keptDims }; +} +function getTypedArrayFromDType(dtype, size) { + return getArrayFromDType(dtype, size); +} +function getArrayFromDType(dtype, size) { + let values = null; + if (dtype == null || dtype === "float32") { + values = new Float32Array(size); + } else if (dtype === "int32") { + values = new Int32Array(size); + } else if (dtype === "bool") { + values = new Uint8Array(size); + } else if (dtype === "string") { + values = new Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } + return values; +} +function checkConversionForErrors(vals, dtype) { + for (let i = 0; i < vals.length; i++) { + const num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`); + } + } +} +function isValidDtype(dtype) { + return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string"; +} +function hasEncodingLoss(oldType, newType) { + if (newType === "complex64") { + return false; + } + if (newType === "float32" && oldType !== "complex64") { + return false; + } + if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") { + return false; + } + if (newType === "bool" && oldType === "bool") { + return false; + } + return true; +} +function bytesPerElement(dtype) { + if (dtype === "float32" || dtype === "int32") { + return 4; + } else if (dtype === "complex64") { + return 8; + } else if (dtype === "bool") { + return 1; + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} +function bytesFromStringArray(arr) { + if (arr == null) { + return 0; + } + let bytes = 0; + arr.forEach((x) => bytes += x.length); + return bytes; +} +function isString(value) { + return typeof value === "string" || value instanceof String; +} +function isBoolean(value) { + return typeof value === "boolean"; +} +function isNumber(value) { + return typeof value === "number"; +} +function inferDtype(values) { + if (Array.isArray(values)) { + return inferDtype(values[0]); + } + if (values instanceof Float32Array) { + return "float32"; + } else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) { + return "int32"; + } else if (isNumber(values)) { + return "float32"; + } else if (isString(values)) { + return "string"; + } else if (isBoolean(values)) { + return "bool"; + } + return "float32"; +} +function isFunction(f) { + return !!(f && f.constructor && f.call && f.apply); +} +function nearestDivisor(size, start) { + for (let i = start; i < size; ++i) { + if (size % i === 0) { + return i; + } + } + return size; +} +function computeStrides(shape) { + const rank = shape.length; + if (rank < 2) { + return []; + } + const strides = new Array(rank - 1); + strides[rank - 2] = shape[rank - 1]; + for (let i = rank - 3; i >= 0; --i) { + strides[i] = strides[i + 1] * shape[i + 1]; + } + return strides; +} +function createNestedArray(offset, shape, a, isComplex = false) { + const ret = new Array(); + if (shape.length === 1) { + const d = shape[0] * (isComplex ? 2 : 1); + for (let i = 0; i < d; i++) { + ret[i] = a[offset + i]; + } + } else { + const d = shape[0]; + const rest = shape.slice(1); + const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1); + for (let i = 0; i < d; i++) { + ret[i] = createNestedArray(offset + i * len, rest, a, isComplex); + } + } + return ret; +} +function toNestedArray(shape, a, isComplex = false) { + if (shape.length === 0) { + return a[0]; + } + const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1); + if (size === 0) { + return []; + } + if (size !== a.length) { + throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? " for a complex tensor" : ""}.`); + } + return createNestedArray(0, shape, a, isComplex); +} +function convertBackendValuesAndArrayBuffer(data, dtype) { + if (Array.isArray(data)) { + return data; + } + if (dtype === "float32") { + return data instanceof Float32Array ? data : new Float32Array(data); + } else if (dtype === "int32") { + return data instanceof Int32Array ? data : new Int32Array(data); + } else if (dtype === "bool" || dtype === "string") { + return Uint8Array.from(new Int32Array(data)); + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} +function makeOnesTypedArray(size, dtype) { + const array2 = makeZerosTypedArray(size, dtype); + for (let i = 0; i < array2.length; i++) { + array2[i] = 1; + } + return array2; +} +function makeZerosTypedArray(size, dtype) { + if (dtype == null || dtype === "float32" || dtype === "complex64") { + return new Float32Array(size); + } else if (dtype === "int32") { + return new Int32Array(size); + } else if (dtype === "bool") { + return new Uint8Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function makeZerosNestedTypedArray(shape, dtype) { + const size = shape.reduce((prev, curr) => prev * curr, 1); + if (dtype == null || dtype === "float32") { + return toNestedArray(shape, new Float32Array(size)); + } else if (dtype === "int32") { + return toNestedArray(shape, new Int32Array(size)); + } else if (dtype === "bool") { + return toNestedArray(shape, new Uint8Array(size)); + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function assertNonNegativeIntegerDimensions(shape) { + shape.forEach((dimSize) => { + assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`); + }); +} +function locToIndex(locs, rank, strides) { + if (rank === 0) { + return 0; + } else if (rank === 1) { + return locs[0]; + } + let index = locs[locs.length - 1]; + for (let i = 0; i < locs.length - 1; ++i) { + index += strides[i] * locs[i]; + } + return index; +} +function indexToLoc(index, rank, strides) { + if (rank === 0) { + return []; + } else if (rank === 1) { + return [index]; + } + const locs = new Array(rank); + for (let i = 0; i < locs.length - 1; ++i) { + locs[i] = Math.floor(index / strides[i]); + index -= locs[i] * strides[i]; + } + locs[locs.length - 1] = index; + return locs; +} +function isPromise(object) { + return object && object.then && typeof object.then === "function"; +} +var TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags"; +var Environment = class { + // tslint:disable-next-line: no-any + constructor(global2) { + this.global = global2; + this.flags = {}; + this.flagRegistry = {}; + this.urlFlags = {}; + this.getQueryParams = getQueryParams; + this.populateURLFlags(); + } + setPlatform(platformName, platform) { + if (this.platform != null) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platformName}.`); + } + } + this.platformName = platformName; + this.platform = platform; + } + registerFlag(flagName, evaluationFn, setHook) { + this.flagRegistry[flagName] = { evaluationFn, setHook }; + if (this.urlFlags[flagName] != null) { + const flagValue = this.urlFlags[flagName]; + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`); + } + this.set(flagName, flagValue); + } + } + async getAsync(flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + this.flags[flagName] = await this.evaluateFlag(flagName); + return this.flags[flagName]; + } + get(flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + const flagValue = this.evaluateFlag(flagName); + if (isPromise(flagValue)) { + throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`); + } + this.flags[flagName] = flagValue; + return this.flags[flagName]; + } + getNumber(flagName) { + return this.get(flagName); + } + getBool(flagName) { + return this.get(flagName); + } + getString(flagName) { + return this.get(flagName); + } + getFlags() { + return this.flags; + } + // For backwards compatibility. + get features() { + return this.flags; + } + set(flagName, value) { + if (this.flagRegistry[flagName] == null) { + throw new Error(`Cannot set flag ${flagName} as it has not been registered.`); + } + this.flags[flagName] = value; + if (this.flagRegistry[flagName].setHook != null) { + this.flagRegistry[flagName].setHook(value); + } + } + evaluateFlag(flagName) { + if (this.flagRegistry[flagName] == null) { + throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`); + } + return this.flagRegistry[flagName].evaluationFn(); + } + setFlags(flags) { + this.flags = Object.assign({}, flags); + } + reset() { + this.flags = {}; + this.urlFlags = {}; + this.populateURLFlags(); + } + populateURLFlags() { + if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") { + return; + } + const urlParams = this.getQueryParams(this.global.location.search); + if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) { + const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(","); + keyValues.forEach((keyValue) => { + const [key, value] = keyValue.split(":"); + this.urlFlags[key] = parseValue(key, value); + }); + } + } +}; +function getQueryParams(queryString) { + const params = {}; + queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => { + decodeParam(params, t[0], t[1]); + return t.join("="); + }); + return params; +} +function decodeParam(params, name, value) { + params[decodeURIComponent(name)] = decodeURIComponent(value || ""); +} +function parseValue(flagName, value) { + const lowerCaseValue = value.toLowerCase(); + if (lowerCaseValue === "true" || lowerCaseValue === "false") { + return lowerCaseValue === "true"; + } else if (`${+lowerCaseValue}` === lowerCaseValue) { + return +lowerCaseValue; + } else { + return value; + } +} +function env() { + return ENV; +} +var ENV = null; +function setEnvironmentGlobal(environment2) { + ENV = environment2; +} +var globalNameSpace; +function getGlobalNamespace() { + if (globalNameSpace == null) { + let ns; + if (typeof window !== "undefined") { + ns = window; + } else if (typeof global !== "undefined") { + ns = global; + } else if (typeof process !== "undefined") { + ns = process; + } else if (typeof self !== "undefined") { + ns = self; + } else { + throw new Error("Could not find a global object"); + } + globalNameSpace = ns; + } + return globalNameSpace; +} +function getGlobalMap() { + const ns = getGlobalNamespace(); + if (ns._tfGlobals == null) { + ns._tfGlobals = /* @__PURE__ */ new Map(); + } + return ns._tfGlobals; +} +function getGlobal(key, init2) { + const globalMap = getGlobalMap(); + if (globalMap.has(key)) { + return globalMap.get(key); + } else { + const singleton = init2(); + globalMap.set(key, singleton); + return globalMap.get(key); + } +} +var Abs = "Abs"; +var Acos = "Acos"; +var Acosh = "Acosh"; +var Add = "Add"; +var AddN = "AddN"; +var All = "All"; +var Any = "Any"; +var ArgMax = "ArgMax"; +var ArgMin = "ArgMin"; +var Asin = "Asin"; +var Asinh = "Asinh"; +var Atan = "Atan"; +var Atanh = "Atanh"; +var Atan2 = "Atan2"; +var AvgPool = "AvgPool"; +var AvgPoolGrad = "AvgPoolGrad"; +var AvgPool3D = "AvgPool3D"; +var AvgPool3DGrad = "AvgPool3DGrad"; +var BatchMatMul = "BatchMatMul"; +var BatchToSpaceND = "BatchToSpaceND"; +var Bincount = "Bincount"; +var BitwiseAnd = "BitwiseAnd"; +var BroadcastTo = "BroadcastTo"; +var BroadcastArgs = "BroadcastArgs"; +var Cast = "Cast"; +var Ceil = "Ceil"; +var ClipByValue = "ClipByValue"; +var Complex = "Complex"; +var ComplexAbs = "ComplexAbs"; +var Concat = "Concat"; +var Conv2D = "Conv2D"; +var Conv2DBackpropFilter = "Conv2DBackpropFilter"; +var Conv2DBackpropInput = "Conv2DBackpropInput"; +var Conv3D = "Conv3D"; +var Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2"; +var Conv3DBackpropInputV2 = "Conv3DBackpropInputV2"; +var Cos = "Cos"; +var Cosh = "Cosh"; +var Cumprod = "Cumprod"; +var Cumsum = "Cumsum"; +var CropAndResize = "CropAndResize"; +var DenseBincount = "DenseBincount"; +var DepthToSpace = "DepthToSpace"; +var DepthwiseConv2dNative = "DepthwiseConv2dNative"; +var DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter"; +var DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput"; +var Diag = "Diag"; +var Dilation2D = "Dilation2D"; +var Dilation2DBackpropInput = "Dilation2DBackpropInput"; +var Dilation2DBackpropFilter = "Dilation2DBackpropFilter"; +var Draw = "Draw"; +var RealDiv = "RealDiv"; +var Einsum = "Einsum"; +var Elu = "Elu"; +var EluGrad = "EluGrad"; +var Erf = "Erf"; +var Equal = "Equal"; +var Exp = "Exp"; +var ExpandDims = "ExpandDims"; +var Expm1 = "Expm1"; +var FFT = "FFT"; +var Fill = "Fill"; +var FlipLeftRight = "FlipLeftRight"; +var Floor = "Floor"; +var FloorDiv = "FloorDiv"; +var FusedBatchNorm = "FusedBatchNorm"; +var GatherV2 = "GatherV2"; +var GatherNd = "GatherNd"; +var Greater = "Greater"; +var GreaterEqual = "GreaterEqual"; +var Identity = "Identity"; +var IFFT = "IFFT"; +var Imag = "Imag"; +var IsFinite = "IsFinite"; +var IsInf = "IsInf"; +var IsNan = "IsNan"; +var LeakyRelu = "LeakyRelu"; +var Less = "Less"; +var LessEqual = "LessEqual"; +var LinSpace = "LinSpace"; +var Log = "Log"; +var Log1p = "Log1p"; +var LogicalAnd = "LogicalAnd"; +var LogicalNot = "LogicalNot"; +var LogicalOr = "LogicalOr"; +var LogicalXor = "LogicalXor"; +var LogSoftmax = "LogSoftmax"; +var LowerBound = "LowerBound"; +var LRN = "LRN"; +var LRNGrad = "LRNGrad"; +var MatrixBandPart = "MatrixBandPart"; +var Max = "Max"; +var Maximum = "Maximum"; +var MaxPool = "MaxPool"; +var MaxPoolGrad = "MaxPoolGrad"; +var MaxPool3D = "MaxPool3D"; +var MaxPool3DGrad = "MaxPool3DGrad"; +var MaxPoolWithArgmax = "MaxPoolWithArgmax"; +var Mean = "Mean"; +var Min = "Min"; +var Minimum = "Minimum"; +var MirrorPad = "MirrorPad"; +var Mod = "Mod"; +var Multinomial = "Multinomial"; +var Multiply = "Multiply"; +var Neg = "Neg"; +var NotEqual = "NotEqual"; +var NonMaxSuppressionV3 = "NonMaxSuppressionV3"; +var NonMaxSuppressionV4 = "NonMaxSuppressionV4"; +var NonMaxSuppressionV5 = "NonMaxSuppressionV5"; +var OnesLike = "OnesLike"; +var OneHot = "OneHot"; +var Pack = "Pack"; +var PadV2 = "PadV2"; +var Pool = "Pool"; +var Pow = "Pow"; +var Prelu = "Prelu"; +var Prod = "Prod"; +var RaggedGather = "RaggedGather"; +var RaggedRange = "RaggedRange"; +var RaggedTensorToTensor = "RaggedTensorToTensor"; +var Range = "Range"; +var Real = "Real"; +var Reciprocal = "Reciprocal"; +var Relu = "Relu"; +var Reshape = "Reshape"; +var ResizeNearestNeighbor = "ResizeNearestNeighbor"; +var ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad"; +var ResizeBilinear = "ResizeBilinear"; +var ResizeBilinearGrad = "ResizeBilinearGrad"; +var Relu6 = "Relu6"; +var Reverse = "Reverse"; +var Round = "Round"; +var Rsqrt = "Rsqrt"; +var ScatterNd = "ScatterNd"; +var TensorScatterUpdate = "TensorScatterUpdate"; +var SearchSorted = "SearchSorted"; +var Select = "Select"; +var Selu = "Selu"; +var Slice = "Slice"; +var Sin = "Sin"; +var Sinh = "Sinh"; +var Sign = "Sign"; +var Sigmoid = "Sigmoid"; +var Softplus = "Softplus"; +var Sqrt = "Sqrt"; +var Sum = "Sum"; +var SpaceToBatchND = "SpaceToBatchND"; +var SplitV = "SplitV"; +var Softmax = "Softmax"; +var SparseFillEmptyRows = "SparseFillEmptyRows"; +var SparseReshape = "SparseReshape"; +var SparseSegmentMean = "SparseSegmentMean"; +var SparseSegmentSum = "SparseSegmentSum"; +var SparseToDense = "SparseToDense"; +var SquaredDifference = "SquaredDifference"; +var Square = "Square"; +var StaticRegexReplace = "StaticRegexReplace"; +var StridedSlice = "StridedSlice"; +var StringNGrams = "StringNGrams"; +var StringSplit = "StringSplit"; +var StringToHashBucketFast = "StringToHashBucketFast"; +var Sub = "Sub"; +var Tan = "Tan"; +var Tanh = "Tanh"; +var Tile = "Tile"; +var TopK = "TopK"; +var Transform = "Transform"; +var Transpose = "Transpose"; +var Unique = "Unique"; +var Unpack = "Unpack"; +var UnsortedSegmentSum = "UnsortedSegmentSum"; +var UpperBound = "UpperBound"; +var ZerosLike = "ZerosLike"; +var Step = "Step"; +var FromPixels = "FromPixels"; +var RotateWithOffset = "RotateWithOffset"; +var _FusedMatMul = "_FusedMatMul"; +var FusedConv2D = "FusedConv2D"; +var FusedDepthwiseConv2D = "FusedDepthwiseConv2D"; +function warn(...msg) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(...msg); + } +} +function log(...msg) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.log(...msg); + } +} +var kernelRegistry = getGlobal("kernelRegistry", () => /* @__PURE__ */ new Map()); +var gradRegistry = getGlobal("gradRegistry", () => /* @__PURE__ */ new Map()); +function getKernel(kernelName, backendName) { + const key = makeKey(kernelName, backendName); + return kernelRegistry.get(key); +} +function getGradient(kernelName) { + return gradRegistry.get(kernelName); +} +function getKernelsForBackend(backendName) { + const it = kernelRegistry.entries(); + const result = []; + while (true) { + const { done, value } = it.next(); + if (done) { + break; + } + const [key, config] = value; + const [backend2] = key.split("_"); + if (backend2 === backendName) { + result.push(config); + } + } + return result; +} +function registerKernel(config) { + const { kernelName, backendName } = config; + const key = makeKey(kernelName, backendName); + if (kernelRegistry.has(key)) { + warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`); + } + kernelRegistry.set(key, config); +} +function registerGradient(config) { + const { kernelName } = config; + if (gradRegistry.has(kernelName)) { + if (env().getBool("DEBUG")) { + warn(`Overriding the gradient for '${kernelName}'`); + } + } + gradRegistry.set(kernelName, config); +} +function unregisterKernel(kernelName, backendName) { + const key = makeKey(kernelName, backendName); + if (!kernelRegistry.has(key)) { + throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`); + } + kernelRegistry.delete(key); +} +function unregisterGradient(kernelName) { + if (!gradRegistry.has(kernelName)) { + throw new Error(`The gradient '${kernelName}' for backend is not registered`); + } + gradRegistry.delete(kernelName); +} +function copyRegisteredKernels(registeredBackendName, newBackendName) { + const kernels = getKernelsForBackend(registeredBackendName); + kernels.forEach((kernelConfig) => { + const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName }); + registerKernel(newKernelConfig); + }); +} +function makeKey(kernelName, backendName) { + return `${backendName}_${kernelName}`; +} +var util_exports = {}; +__export2(util_exports, { + arraysEqual: () => arraysEqual, + arraysEqualWithNull: () => arraysEqualWithNull, + assert: () => assert, + assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions, + assertNonNull: () => assertNonNull, + assertShapesMatch: () => assertShapesMatch, + bytesFromStringArray: () => bytesFromStringArray, + bytesPerElement: () => bytesPerElement, + checkConversionForErrors: () => checkConversionForErrors, + clamp: () => clamp, + computeStrides: () => computeStrides, + convertBackendValuesAndArrayBuffer: () => convertBackendValuesAndArrayBuffer, + createScalarValue: () => createScalarValue, + createShuffledIndices: () => createShuffledIndices, + decodeString: () => decodeString, + distSquared: () => distSquared, + encodeString: () => encodeString, + fetch: () => fetch3, + fingerPrint64: () => fingerPrint64, + flatten: () => flatten, + getArrayFromDType: () => getArrayFromDType, + getTypedArrayFromDType: () => getTypedArrayFromDType, + hasEncodingLoss: () => hasEncodingLoss, + hexToLong: () => hexToLong, + indexToLoc: () => indexToLoc, + inferDtype: () => inferDtype, + inferFromImplicitShape: () => inferFromImplicitShape, + isBoolean: () => isBoolean, + isFunction: () => isFunction, + isInt: () => isInt, + isNumber: () => isNumber, + isPromise: () => isPromise, + isScalarShape: () => isScalarShape, + isString: () => isString, + isTypedArray: () => isTypedArray, + isValidDtype: () => isValidDtype, + locToIndex: () => locToIndex, + makeOnesTypedArray: () => makeOnesTypedArray, + makeZerosNestedTypedArray: () => makeZerosNestedTypedArray, + makeZerosTypedArray: () => makeZerosTypedArray, + nearestDivisor: () => nearestDivisor, + nearestLargerEven: () => nearestLargerEven, + now: () => now, + parseAxisParam: () => parseAxisParam, + randUniform: () => randUniform, + repeatedTry: () => repeatedTry, + rightPad: () => rightPad, + shuffle: () => shuffle, + shuffleCombo: () => shuffleCombo, + sizeFromShape: () => sizeFromShape, + sizeToSquarishShape: () => sizeToSquarishShape, + squeezeShape: () => squeezeShape, + sum: () => sum, + swap: () => swap, + tanh: () => tanh, + toNestedArray: () => toNestedArray, + toTypedArray: () => toTypedArray +}); +function isTypedArrayBrowser(a) { + return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array || a instanceof Uint8ClampedArray; +} +var LongExports = __toESM(require_long()); +var Long = ( + // tslint:disable-next-line + LongExports.default || LongExports +); +function hexToLong(hex) { + return Long.fromString(hex, true, 16); +} +var k0 = hexToLong("c3a5c85c97cb3127"); +var k1 = hexToLong("b492b66fbe98f273"); +var k2 = hexToLong("9ae16a3b2f90404f"); +function shiftMix(val) { + return val.xor(val.shru(47)); +} +function fetch2(s, offset, numBytes) { + const bytes = s.slice(offset, offset + numBytes); + return Long.fromBytes(Array.from(bytes), true, true); +} +function fetch64(s, offset) { + return fetch2(s, offset, 8); +} +function fetch32(s, offset) { + return fetch2(s, offset, 4); +} +function rotate64(val, shift) { + return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift)); +} +function hashLen16(u, v, mul2 = hexToLong("9ddfea08eb382d69")) { + let a = u.xor(v).mul(mul2); + a = a.xor(a.shru(47)); + let b = v.xor(a).mul(mul2); + b = b.xor(b.shru(47)); + b = b.mul(mul2); + return b; +} +function weakHashLen32WithSeeds(w, x, y, z, a, b) { + a = a.add(w); + b = rotate64(b.add(a).add(z), 21); + const c = a; + a = a.add(x); + a = a.add(y); + b = b.add(rotate64(a, 44)); + return [a.add(z), b.add(c)]; +} +function weakHashLen32WithSeedsStr(s, offset, a, b) { + return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b); +} +function hashLen0to16(s, len = s.length) { + if (len >= 8) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).add(k2); + const b = fetch64(s, len - 8); + const c = rotate64(b, 37).mul(mul2).add(a); + const d = rotate64(a, 25).add(b).mul(mul2); + return hashLen16(c, d, mul2); + } + if (len >= 4) { + const mul2 = k2.add(len * 2); + const a = fetch32(s, 0); + return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul2); + } + if (len > 0) { + const a = s[0]; + const b = s[len >> 1]; + const c = s[len - 1]; + const y = a + (b << 8); + const z = len + (c << 2); + return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2); + } + return k2; +} +function hashLen17to32(s, len = s.length) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).mul(k1); + const b = fetch64(s, 8); + const c = fetch64(s, len - 8).mul(mul2); + const d = fetch64(s, len - 16).mul(k2); + return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul2); +} +function hashLen33to64(s, len = s.length) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).mul(k2); + const b = fetch64(s, 8); + const c = fetch64(s, len - 8).mul(mul2); + const d = fetch64(s, len - 16).mul(k2); + const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d); + const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul2); + const e = fetch64(s, 16).mul(mul2); + const f = fetch64(s, 24); + const g = y.add(fetch64(s, len - 32)).mul(mul2); + const h = z.add(fetch64(s, len - 24)).mul(mul2); + return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul2); +} +function fingerPrint64(s, len = s.length) { + const seed = Long.fromNumber(81, true); + if (len <= 32) { + if (len <= 16) { + return hashLen0to16(s, len); + } else { + return hashLen17to32(s, len); + } + } else if (len <= 64) { + return hashLen33to64(s, len); + } + let x = seed; + let y = seed.mul(k1).add(113); + let z = shiftMix(y.mul(k2).add(113)).mul(k2); + let v = [Long.UZERO, Long.UZERO]; + let w = [Long.UZERO, Long.UZERO]; + x = x.mul(k2).add(fetch64(s, 0)); + let offset = 0; + const end = (len - 1 >> 6) * 64; + const last64 = end + (len - 1 & 63) - 63; + do { + x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1); + y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1); + x = x.xor(w[1]); + y = y.add(v[0]).add(fetch64(s, offset + 40)); + z = rotate64(z.add(w[0]), 33).mul(k1); + v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0])); + w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16))); + [z, x] = [x, z]; + offset += 64; + } while (offset !== end); + const mul2 = k1.add(z.and(255).shl(1)); + offset = last64; + w[0] = w[0].add(len - 1 & 63); + v[0] = v[0].add(w[0]); + w[0] = w[0].add(v[0]); + x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul2); + y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul2); + x = x.xor(w[1].mul(9)); + y = y.add(v[0].mul(9).add(fetch64(s, offset + 40))); + z = rotate64(z.add(w[0]), 33).mul(mul2); + v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul2), x.add(w[0])); + w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16))); + [z, x] = [x, z]; + return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2); +} +function createScalarValue(value, dtype) { + if (dtype === "string") { + return encodeString(value); + } + return toTypedArray([value], dtype); +} +function noConversionNeeded(a, dtype) { + return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool"; +} +function toTypedArray(a, dtype) { + if (dtype === "string") { + throw new Error("Cannot convert a string[] to a TypedArray"); + } + if (Array.isArray(a)) { + a = flatten(a); + } + if (env().getBool("DEBUG")) { + checkConversionForErrors(a, dtype); + } + if (noConversionNeeded(a, dtype)) { + return a; + } + if (dtype == null || dtype === "float32" || dtype === "complex64") { + return new Float32Array(a); + } else if (dtype === "int32") { + return new Int32Array(a); + } else if (dtype === "bool") { + const bool = new Uint8Array(a.length); + for (let i = 0; i < bool.length; ++i) { + if (Math.round(a[i]) !== 0) { + bool[i] = 1; + } + } + return bool; + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function now() { + return env().platform.now(); +} +function fetch3(path, requestInits) { + return env().platform.fetch(path, requestInits); +} +function encodeString(s, encoding = "utf-8") { + encoding = encoding || "utf-8"; + return env().platform.encode(s, encoding); +} +function decodeString(bytes, encoding = "utf-8") { + encoding = encoding || "utf-8"; + return env().platform.decode(bytes, encoding); +} +function isTypedArray(a) { + if (env().platform.isTypedArray != null) { + return env().platform.isTypedArray(a); + } else { + return isTypedArrayBrowser(a); + } +} +function flatten(arr, result = [], skipTypedArray = false) { + if (result == null) { + result = []; + } + if (typeof arr === "boolean" || typeof arr === "number" || typeof arr === "string" || isPromise(arr) || arr == null || isTypedArray(arr) && skipTypedArray) { + result.push(arr); + } else if (Array.isArray(arr) || isTypedArray(arr)) { + for (let i = 0; i < arr.length; ++i) { + flatten(arr[i], result, skipTypedArray); + } + } else { + let maxIndex = -1; + for (const key of Object.keys(arr)) { + if (/^([1-9]+[0-9]*|0)$/.test(key)) { + maxIndex = Math.max(maxIndex, Number(key)); + } + } + for (let i = 0; i <= maxIndex; i++) { + flatten(arr[i], result, skipTypedArray); + } + } + return result; +} +var Profiler = class { + constructor(backendTimer, logger) { + this.backendTimer = backendTimer; + this.logger = logger; + if (logger == null) { + this.logger = new Logger(); + } + } + profileKernel(kernelName, inputs, f) { + let outputs; + const holdResultWrapperFn = () => { + outputs = f(); + }; + let timer; + const start = now(); + if (this.backendTimer.timerAvailable()) { + timer = this.backendTimer.time(holdResultWrapperFn); + } else { + holdResultWrapperFn(); + for (const output of outputs) { + output.dataSync(); + } + timer = Promise.resolve({ kernelMs: now() - start }); + } + if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) { + for (let i = 0; i < outputs.length; i++) { + const output = outputs[i]; + output.data().then((tensorVals) => { + checkComputationForErrors(tensorVals, output.dtype, kernelName); + }); + } + } + const kernelProfile = { + kernelName, + outputs, + inputs, + timeMs: timer.then((timing) => timing.kernelMs), + extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "") + }; + return kernelProfile; + } + logKernelProfile(kernelProfile) { + const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile; + outputs.forEach((result) => { + Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => { + this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]); + }); + }); + } +}; +function checkComputationForErrors(vals, dtype, kernelName) { + if (dtype !== "float32") { + return false; + } + for (let i = 0; i < vals.length; i++) { + const num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + console.warn(`Found ${num} in the result of '${kernelName}'`); + return true; + } + } + return false; +} +var Logger = class { + logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) { + const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"]; + const paddedName = rightPad(name, 25); + const rank = result.rank; + const size = result.size; + const shape = rightPad(result.shape.toString(), 14); + let inputShapesDescription = ""; + for (const name2 in inputs) { + const input2 = inputs[name2]; + if (input2 != null) { + const inputShape = input2.shape || result.shape; + const inputRank = inputShape.length; + inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `; + } + } + console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue"); + } +}; +function getFilteredNodesXToY(tape, xs, y) { + const tensorsFromX = {}; + const nodesFromX = {}; + for (let i = 0; i < xs.length; i++) { + tensorsFromX[xs[i].id] = true; + } + for (let i = 0; i < tape.length; i++) { + const node = tape[i]; + const nodeInputs = node.inputs; + for (const inputName in nodeInputs) { + const input2 = nodeInputs[inputName]; + let anyInputFromX = false; + for (let j = 0; j < xs.length; j++) { + if (tensorsFromX[input2.id]) { + node.outputs.forEach((output) => tensorsFromX[output.id] = true); + anyInputFromX = true; + nodesFromX[node.id] = true; + break; + } + } + if (anyInputFromX) { + break; + } + } + } + const tensorsLeadToY = {}; + tensorsLeadToY[y.id] = true; + const nodesToY = {}; + for (let i = tape.length - 1; i >= 0; i--) { + const node = tape[i]; + const nodeInputs = node.inputs; + for (let j = 0; j < node.outputs.length; j++) { + if (tensorsLeadToY[node.outputs[j].id]) { + for (const inputName in nodeInputs) { + tensorsLeadToY[nodeInputs[inputName].id] = true; + nodesToY[node.id] = true; + } + break; + } + } + } + const filteredTape = []; + for (let i = 0; i < tape.length; i++) { + const node = tape[i]; + if (nodesFromX[node.id] && nodesToY[node.id]) { + const prunedInputs = {}; + for (const inputName in node.inputs) { + const nodeInput = node.inputs[inputName]; + if (tensorsFromX[nodeInput.id]) { + prunedInputs[inputName] = nodeInput; + } + } + const prunedNode = Object.assign({}, node); + prunedNode.inputs = prunedInputs; + prunedNode.outputs = node.outputs; + filteredTape.push(prunedNode); + } + } + return filteredTape; +} +function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) { + for (let i = filteredTape.length - 1; i >= 0; i--) { + const node = filteredTape[i]; + const dys = []; + node.outputs.forEach((o) => { + const gradTensor = tensorAccumulatedGradientMap[o.id]; + if (gradTensor != null) { + dys.push(gradTensor); + } else { + dys.push(null); + } + }); + if (node.gradient == null) { + throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`); + } + const inputGradients = node.gradient(dys); + for (const inputName in node.inputs) { + if (!(inputName in inputGradients)) { + throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`); + } + const dx = tidy2(() => inputGradients[inputName]()); + if (dx.dtype !== "float32") { + throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`); + } + const x = node.inputs[inputName]; + if (!arraysEqual(dx.shape, x.shape)) { + throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`); + } + if (tensorAccumulatedGradientMap[x.id] == null) { + tensorAccumulatedGradientMap[x.id] = dx; + } else { + const curGradient = tensorAccumulatedGradientMap[x.id]; + tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx); + curGradient.dispose(); + } + } + } +} +var FORMAT_LIMIT_NUM_VALS = 20; +var FORMAT_NUM_FIRST_LAST_VALS = 3; +var FORMAT_NUM_SIG_DIGITS = 7; +function tensorToString(vals, shape, dtype, verbose) { + const strides = computeStrides(shape); + const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides); + const rank = shape.length; + const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol); + const lines = ["Tensor"]; + if (verbose) { + lines.push(` dtype: ${dtype}`); + lines.push(` rank: ${rank}`); + lines.push(` shape: [${shape}]`); + lines.push(` values:`); + } + lines.push(valsLines.map((l) => " " + l).join("\n")); + return lines.join("\n"); +} +function computeMaxSizePerColumn(vals, shape, dtype, strides) { + const n = sizeFromShape(shape); + const numCols = strides[strides.length - 1]; + const padPerCol = new Array(numCols).fill(0); + const rank = shape.length; + const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals; + if (rank > 1) { + for (let row = 0; row < n / numCols; row++) { + const offset = row * numCols; + for (let j = 0; j < numCols; j++) { + padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length); + } + } + } + return padPerCol; +} +function valToString(val, pad3, dtype) { + let valStr; + if (Array.isArray(val)) { + valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`; + } else if (isString(val)) { + valStr = `'${val}'`; + } else if (dtype === "bool") { + valStr = boolNumToString(val); + } else { + valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(); + } + return rightPad(valStr, pad3); +} +function boolNumToString(v) { + return v === 0 ? "false" : "true"; +} +function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) { + const storagePerElement = dtype === "complex64" ? 2 : 1; + const size = shape[0]; + const rank = shape.length; + if (rank === 0) { + if (dtype === "complex64") { + const complexTuple = createComplexTuples(vals); + return [valToString(complexTuple[0], 0, dtype)]; + } + if (dtype === "bool") { + return [boolNumToString(vals[0])]; + } + return [vals[0].toString()]; + } + if (rank === 1) { + if (size > FORMAT_LIMIT_NUM_VALS) { + const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement; + let firstVals = Array.from(vals.slice(0, firstValsSize)); + let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement)); + if (dtype === "complex64") { + firstVals = createComplexTuples(firstVals); + lastVals = createComplexTuples(lastVals); + } + return [ + "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]" + ]; + } + const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals); + return [ + "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]" + ]; + } + const subshape = shape.slice(1); + const substrides = strides.slice(1); + const stride = strides[0] * storagePerElement; + const lines = []; + if (size > FORMAT_LIMIT_NUM_VALS) { + for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) { + const start = i * stride; + const end = start + stride; + lines.push(...subTensorToString( + vals.slice(start, end), + subshape, + dtype, + substrides, + padPerCol, + false + /* isLast */ + )); + } + lines.push("..."); + for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) { + const start = i * stride; + const end = start + stride; + lines.push(...subTensorToString( + vals.slice(start, end), + subshape, + dtype, + substrides, + padPerCol, + i === size - 1 + /* isLast */ + )); + } + } else { + for (let i = 0; i < size; i++) { + const start = i * stride; + const end = start + stride; + lines.push(...subTensorToString( + vals.slice(start, end), + subshape, + dtype, + substrides, + padPerCol, + i === size - 1 + /* isLast */ + )); + } + } + const sep = rank === 2 ? "," : ""; + lines[0] = "[" + (size > 0 ? lines[0] + sep : ""); + for (let i = 1; i < lines.length - 1; i++) { + lines[i] = " " + lines[i] + sep; + } + let newLineSep = ",\n"; + for (let i = 2; i < rank; i++) { + newLineSep += "\n"; + } + lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep); + return lines; +} +function createComplexTuples(vals) { + const complexTuples = []; + for (let i = 0; i < vals.length; i += 2) { + complexTuples.push([vals[i], vals[i + 1]]); + } + return complexTuples; +} +var TensorBuffer = class { + constructor(shape, dtype, values) { + this.dtype = dtype; + this.shape = shape.slice(); + this.size = sizeFromShape(shape); + if (values != null) { + const n = values.length; + assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`); + } + if (dtype === "complex64") { + throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`); + } + this.values = values || getArrayFromDType(dtype, this.size); + this.strides = computeStrides(shape); + } + /** + * Sets a value in the buffer at a given location. + * + * @param value The value to set. + * @param locs The location indices. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + set(value, ...locs) { + if (locs.length === 0) { + locs = [0]; + } + assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`); + const index = this.locToIndex(locs); + this.values[index] = value; + } + /** + * Returns the value in the buffer at the provided location. + * + * @param locs The location indices. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + get(...locs) { + if (locs.length === 0) { + locs = [0]; + } + let i = 0; + for (const loc of locs) { + if (loc < 0 || loc >= this.shape[i]) { + const msg = `Requested out of range element at ${locs}. Buffer shape=${this.shape}`; + throw new Error(msg); + } + i++; + } + let index = locs[locs.length - 1]; + for (let i2 = 0; i2 < locs.length - 1; ++i2) { + index += this.strides[i2] * locs[i2]; + } + return this.values[index]; + } + locToIndex(locs) { + if (this.rank === 0) { + return 0; + } else if (this.rank === 1) { + return locs[0]; + } + let index = locs[locs.length - 1]; + for (let i = 0; i < locs.length - 1; ++i) { + index += this.strides[i] * locs[i]; + } + return index; + } + indexToLoc(index) { + if (this.rank === 0) { + return []; + } else if (this.rank === 1) { + return [index]; + } + const locs = new Array(this.shape.length); + for (let i = 0; i < locs.length - 1; ++i) { + locs[i] = Math.floor(index / this.strides[i]); + index -= locs[i] * this.strides[i]; + } + locs[locs.length - 1] = index; + return locs; + } + get rank() { + return this.shape.length; + } + /** + * Creates an immutable `tf.Tensor` object from the buffer. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + toTensor() { + return trackerFn().makeTensor(this.values, this.shape, this.dtype); + } +}; +var trackerFn = null; +var opHandler = null; +var deprecationWarningFn = null; +function setTensorTracker(fn) { + trackerFn = fn; +} +function setOpHandler(handler) { + opHandler = handler; +} +function setDeprecationWarningFn(fn) { + deprecationWarningFn = fn; +} +var Tensor = class { + constructor(shape, dtype, dataId, id) { + this.kept = false; + this.isDisposedInternal = false; + this.shape = shape.slice(); + this.dtype = dtype || "float32"; + this.size = sizeFromShape(shape); + this.strides = computeStrides(shape); + this.dataId = dataId; + this.id = id; + this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; + } + get rank() { + return this.shape.length; + } + /** + * Returns a promise of `tf.TensorBuffer` that holds the underlying data. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + async buffer() { + const vals = await this.data(); + return opHandler.buffer(this.shape, this.dtype, vals); + } + /** + * Returns a `tf.TensorBuffer` that holds the underlying data. + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + bufferSync() { + return opHandler.buffer(this.shape, this.dtype, this.dataSync()); + } + /** + * Returns the tensor data as a nested array. The transfer of data is done + * asynchronously. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + async array() { + const vals = await this.data(); + return toNestedArray(this.shape, vals, this.dtype === "complex64"); + } + /** + * Returns the tensor data as a nested array. The transfer of data is done + * synchronously. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + arraySync() { + return toNestedArray(this.shape, this.dataSync(), this.dtype === "complex64"); + } + /** + * Asynchronously downloads the values from the `tf.Tensor`. Returns a + * promise of `TypedArray` that resolves when the computation has finished. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + async data() { + this.throwIfDisposed(); + const data = trackerFn().read(this.dataId); + if (this.dtype === "string") { + const bytes = await data; + try { + return bytes.map((b) => decodeString(b)); + } catch (_a) { + throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); + } + } + return data; + } + /** + * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()` + * and `data()`, this method prevents data from being downloaded to CPU. + * + * For WebGL backend, the data will be stored on a densely packed texture. + * This means that the texture will use the RGBA channels to store value. + * + * For WebGPU backend, the data will be stored on a buffer. There is no + * parameter, so can not use a user-defined size to create the buffer. + * + * @param options: + * For WebGL, + * - customTexShape: Optional. If set, will use the user defined + * texture shape to create the texture. + * + * @returns For WebGL backend, a GPUData contains the new texture and + * its information. + * { + * tensorRef: The tensor that is associated with this texture, + * texture: WebGLTexture, + * texShape: [number, number] // [height, width] + * } + * + * For WebGPU backend, a GPUData contains the new buffer. + * { + * tensorRef: The tensor that is associated with this buffer, + * buffer: GPUBuffer, + * } + * + * Remember to dispose the GPUData after it is used by + * `res.tensorRef.dispose()`. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + dataToGPU(options) { + this.throwIfDisposed(); + return trackerFn().readToGPU(this.dataId, options); + } + /** + * Synchronously downloads the values from the `tf.Tensor`. This blocks the + * UI thread until the values are ready, which can cause performance issues. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + dataSync() { + this.throwIfDisposed(); + const data = trackerFn().readSync(this.dataId); + if (this.dtype === "string") { + try { + return data.map((b) => decodeString(b)); + } catch (_a) { + throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); + } + } + return data; + } + /** Returns the underlying bytes of the tensor's data. */ + async bytes() { + this.throwIfDisposed(); + const data = await trackerFn().read(this.dataId); + if (this.dtype === "string") { + return data; + } else { + return new Uint8Array(data.buffer); + } + } + /** + * Disposes `tf.Tensor` from memory. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + dispose() { + if (this.isDisposed) { + return; + } + if (this.kerasMask) { + this.kerasMask.dispose(); + } + trackerFn().disposeTensor(this); + this.isDisposedInternal = true; + } + get isDisposed() { + return this.isDisposedInternal; + } + throwIfDisposed() { + if (this.isDisposed) { + throw new Error(`Tensor is disposed.`); + } + } + /** + * Prints the `tf.Tensor`. See `tf.print` for details. + * + * @param verbose Whether to print verbose information about the tensor, + * including dtype and size. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + print(verbose = false) { + return opHandler.print(this, verbose); + } + /** + * Returns a copy of the tensor. See `tf.clone` for details. + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + clone() { + this.throwIfDisposed(); + return opHandler.clone(this); + } + /** + * Returns a human-readable description of the tensor. Useful for logging. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + toString(verbose = false) { + const vals = this.dataSync(); + return tensorToString(vals, this.shape, this.dtype, verbose); + } + cast(dtype) { + this.throwIfDisposed(); + return opHandler.cast(this, dtype); + } + variable(trainable = true, name, dtype) { + this.throwIfDisposed(); + return trackerFn().makeVariable(this, trainable, name, dtype); + } +}; +Object.defineProperty(Tensor, Symbol.hasInstance, { + value: (instance) => { + return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null; + } +}); +function getGlobalTensorClass() { + return getGlobal("Tensor", () => { + return Tensor; + }); +} +getGlobalTensorClass(); +var Variable = class extends Tensor { + constructor(initialValue, trainable, name, tensorId) { + super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId); + this.trainable = trainable; + this.name = name; + } + /** + * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have + * the same shape and dtype as the old `tf.Tensor`. + * + * @param newValue New tensor to be assigned to this variable. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + assign(newValue) { + if (newValue.dtype !== this.dtype) { + throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`); + } + if (!arraysEqual(newValue.shape, this.shape)) { + throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`); + } + trackerFn().disposeTensor(this); + this.dataId = newValue.dataId; + trackerFn().incRef( + this, + null + /* backend */ + ); + } + dispose() { + trackerFn().disposeVariable(this); + this.isDisposedInternal = true; + } +}; +Object.defineProperty(Variable, Symbol.hasInstance, { + value: (instance) => { + return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function; + } +}); +var tensor_util_exports = {}; +__export2(tensor_util_exports, { + assertTypesMatch: () => assertTypesMatch, + getTensorsInContainer: () => getTensorsInContainer, + isTensorInList: () => isTensorInList, + makeTypesMatch: () => makeTypesMatch +}); +var Rank; +(function(Rank2) { + Rank2["R0"] = "R0"; + Rank2["R1"] = "R1"; + Rank2["R2"] = "R2"; + Rank2["R3"] = "R3"; + Rank2["R4"] = "R4"; + Rank2["R5"] = "R5"; + Rank2["R6"] = "R6"; +})(Rank || (Rank = {})); +var UpcastInt32AndMap; +(function(UpcastInt32AndMap2) { + UpcastInt32AndMap2["float32"] = "float32"; + UpcastInt32AndMap2["int32"] = "int32"; + UpcastInt32AndMap2["bool"] = "int32"; + UpcastInt32AndMap2["complex64"] = "complex64"; +})(UpcastInt32AndMap || (UpcastInt32AndMap = {})); +var UpcastBoolAndMap; +(function(UpcastBoolAndMap2) { + UpcastBoolAndMap2["float32"] = "float32"; + UpcastBoolAndMap2["int32"] = "int32"; + UpcastBoolAndMap2["bool"] = "bool"; + UpcastBoolAndMap2["complex64"] = "complex64"; +})(UpcastBoolAndMap || (UpcastBoolAndMap = {})); +var UpcastFloat32AndMap; +(function(UpcastFloat32AndMap2) { + UpcastFloat32AndMap2["float32"] = "float32"; + UpcastFloat32AndMap2["int32"] = "float32"; + UpcastFloat32AndMap2["bool"] = "float32"; + UpcastFloat32AndMap2["complex64"] = "complex64"; +})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {})); +var UpcastComplex64AndMap; +(function(UpcastComplex64AndMap2) { + UpcastComplex64AndMap2["float32"] = "complex64"; + UpcastComplex64AndMap2["int32"] = "complex64"; + UpcastComplex64AndMap2["bool"] = "complex64"; + UpcastComplex64AndMap2["complex64"] = "complex64"; +})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {})); +var upcastTypeMap = { + "float32": UpcastFloat32AndMap, + "int32": UpcastInt32AndMap, + "bool": UpcastBoolAndMap, + "complex64": UpcastComplex64AndMap +}; +function upcastType(typeA, typeB) { + if (typeA === "string" || typeB === "string") { + if (typeA === "string" && typeB === "string") { + return "string"; + } + throw new Error(`Can not upcast ${typeA} with ${typeB}`); + } + return upcastTypeMap[typeA][typeB]; +} +function sumOutType(type) { + return upcastType(type, "int32"); +} +function isWebGLData(values) { + return values != null && typeof values === "object" && "texture" in values && values.texture instanceof WebGLTexture; +} +function isWebGPUData(values) { + return typeof GPUBuffer !== "undefined" && values != null && typeof values === "object" && "buffer" in values && values.buffer instanceof GPUBuffer; +} +function makeTypesMatch(a, b) { + if (a.dtype === b.dtype) { + return [a, b]; + } + const dtype = upcastType(a.dtype, b.dtype); + return [a.cast(dtype), b.cast(dtype)]; +} +function assertTypesMatch(a, b) { + assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`); +} +function isTensorInList(tensor2, tensorList) { + return tensorList.some((x) => x.id === tensor2.id); +} +function getTensorsInContainer(result) { + const list = []; + const seen = /* @__PURE__ */ new Set(); + walkTensorContainer(result, list, seen); + return list; +} +function walkTensorContainer(container, list, seen) { + if (container == null) { + return; + } + if (container instanceof Tensor) { + list.push(container); + return; + } + if (!isIterable(container)) { + return; + } + const iterable = container; + for (const k in iterable) { + const val = iterable[k]; + if (!seen.has(val)) { + seen.add(val); + walkTensorContainer(val, list, seen); + } + } +} +function isIterable(obj) { + return Array.isArray(obj) || typeof obj === "object"; +} +function isRegisteredKernelInvocation(kernelInvocation) { + return kernelInvocation.kernelName != null; +} +var EngineState = 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 = /* @__PURE__ */ new WeakMap(); + this.profiling = false; + this.activeProfile = { + newBytes: 0, + newTensors: 0, + peakBytes: 0, + kernels: [], + result: null, + get kernelNames() { + return Array.from(new Set(this.kernels.map((k) => k.name))); + } + }; + } + dispose() { + for (const variableName in this.registeredVariables) { + this.registeredVariables[variableName].dispose(); + } + } +}; +var Engine = class _Engine { + constructor(ENV7) { + this.ENV = ENV7; + this.registry = {}; + this.registryFactory = {}; + this.pendingBackendInitId = 0; + this.state = new EngineState(); + } + async ready() { + if (this.pendingBackendInit != null) { + return this.pendingBackendInit.then(() => { + }); + } + if (this.backendInstance != null) { + return; + } + const sortedBackends = this.getSortedBackends(); + for (let i = 0; i < sortedBackends.length; i++) { + const backendName = sortedBackends[i]; + const success = await this.initializeBackend(backendName).success; + if (success) { + await this.setBackend(backendName); + return; + } + } + throw new Error(`Could not initialize any backends, all backend initializations failed.`); + } + get backend() { + if (this.pendingBackendInit != null) { + throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); + } + if (this.backendInstance == null) { + const { name, asyncInit } = this.initializeBackendsAndReturnBest(); + if (asyncInit) { + throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); + } + this.setBackend(name); + } + return this.backendInstance; + } + backendNames() { + return Object.keys(this.registryFactory); + } + findBackend(backendName) { + if (!(backendName in this.registry)) { + if (backendName in this.registryFactory) { + const { asyncInit } = this.initializeBackend(backendName); + if (asyncInit) { + return null; + } + } else { + return null; + } + } + return this.registry[backendName]; + } + findBackendFactory(backendName) { + if (!(backendName in this.registryFactory)) { + return null; + } + return this.registryFactory[backendName].factory; + } + registerBackend(backendName, factory, priority = 1) { + if (backendName in this.registryFactory) { + warn(`${backendName} backend was already registered. Reusing existing backend factory.`); + return false; + } + this.registryFactory[backendName] = { factory, priority }; + return true; + } + async setBackend(backendName) { + if (this.registryFactory[backendName] == null) { + throw new Error(`Backend name '${backendName}' not found in registry`); + } + this.backendName = backendName; + if (this.registry[backendName] == null) { + this.backendInstance = null; + const { success, asyncInit } = this.initializeBackend(backendName); + const result = asyncInit ? await success : success; + if (!result) { + return false; + } + } + this.backendInstance = this.registry[backendName]; + this.setupRegisteredKernels(); + this.profiler = new Profiler(this.backendInstance); + return true; + } + setupRegisteredKernels() { + const kernels = getKernelsForBackend(this.backendName); + kernels.forEach((kernel) => { + if (kernel.setupFunc != null) { + kernel.setupFunc(this.backendInstance); + } + }); + } + disposeRegisteredKernels(backendName) { + const kernels = getKernelsForBackend(backendName); + kernels.forEach((kernel) => { + if (kernel.disposeFunc != null) { + kernel.disposeFunc(this.registry[backendName]); + } + }); + } + /** + * Initializes a backend by looking up the backend name in the factory + * registry and calling the factory method. Returns a boolean representing + * whether the initialization of the backend suceeded. Throws an error if + * there is no backend in the factory registry. + */ + initializeBackend(backendName) { + const registryFactoryEntry = this.registryFactory[backendName]; + if (registryFactoryEntry == null) { + throw new Error(`Cannot initialize backend ${backendName}, no registration found.`); + } + try { + const backend2 = registryFactoryEntry.factory(); + if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === "function") { + const promiseId = ++this.pendingBackendInitId; + const success = backend2.then((backendInstance) => { + if (promiseId < this.pendingBackendInitId) { + return false; + } + this.registry[backendName] = backendInstance; + this.pendingBackendInit = null; + return true; + }).catch((err) => { + if (promiseId < this.pendingBackendInitId) { + return false; + } + this.pendingBackendInit = null; + warn(`Initialization of backend ${backendName} failed`); + warn(err.stack || err.message); + return false; + }); + this.pendingBackendInit = success; + return { success, asyncInit: true }; + } else { + this.registry[backendName] = backend2; + return { success: true, asyncInit: false }; + } + } catch (err) { + warn(`Initialization of backend ${backendName} failed`); + warn(err.stack || err.message); + return { success: false, asyncInit: false }; + } + } + removeBackend(backendName) { + if (!(backendName in this.registryFactory)) { + throw new Error(`${backendName} backend not found in registry`); + } + if (this.backendName === backendName && this.pendingBackendInit != null) { + this.pendingBackendInitId++; + } + if (backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + delete this.registryFactory[backendName]; + if (this.backendName === backendName) { + this.pendingBackendInit = null; + this.backendName = null; + this.backendInstance = null; + } + } + getSortedBackends() { + if (Object.keys(this.registryFactory).length === 0) { + throw new Error("No backend found in registry."); + } + return Object.keys(this.registryFactory).sort((a, b) => { + return this.registryFactory[b].priority - this.registryFactory[a].priority; + }); + } + initializeBackendsAndReturnBest() { + const sortedBackends = this.getSortedBackends(); + for (let i = 0; i < sortedBackends.length; i++) { + const backendName = sortedBackends[i]; + const { success, asyncInit } = this.initializeBackend(backendName); + if (asyncInit || success) { + return { name: backendName, asyncInit }; + } + } + throw new Error(`Could not initialize any backends, all backend initializations failed.`); + } + moveData(backend2, dataId) { + const info = this.state.tensorInfo.get(dataId); + const srcBackend = info.backend; + const values = this.readSync(dataId); + const refCount = srcBackend.refCount(dataId); + srcBackend.disposeData(dataId, true); + info.backend = backend2; + backend2.move(dataId, values, info.shape, info.dtype, refCount); + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; + } + } + tidy(nameOrFn, fn) { + let name = null; + if (fn == null) { + if (typeof nameOrFn !== "function") { + throw new Error("Please provide a function to tidy()"); + } + fn = nameOrFn; + } else { + if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) { + throw new Error("When calling with two arguments, the first argument to tidy() must be a string"); + } + if (typeof fn !== "function") { + throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function"); + } + name = nameOrFn; + } + let result; + return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => { + result = fn(); + if (result instanceof Promise) { + console.error("Cannot return a Promise inside of tidy."); + } + return result; + }); + } + scopedRun(start, end, f) { + start(); + try { + const res = f(); + end(); + return res; + } catch (ex) { + end(); + throw ex; + } + } + nextTensorId() { + return _Engine.nextTensorId++; + } + nextVariableId() { + return _Engine.nextVariableId++; + } + /** + * This method is called instead of the public-facing tensor.clone() when + * saving a tensor for backwards pass. It makes sure to add the clone + * operation to the tape regardless of being called inside a kernel + * execution. + */ + clone(x) { + const y = ENGINE.runKernel(Identity, { x }); + const inputs = { x }; + const grad2 = (dy) => ({ + x: () => { + const dtype = "float32"; + const gradInputs = { x: dy }; + const attrs = { dtype }; + return ENGINE.runKernel( + Cast, + gradInputs, + // tslint:disable-next-line: no-unnecessary-type-assertion + attrs + ); + } + }); + const saved = []; + this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {}); + return y; + } + /** + * Execute a kernel with the given name and return the output tensor. + * + * @param kernelName The name of the kernel to execute. + * @param inputs A map of input names to tensors. + * @param attrs A map of attribute names to their values. An attribute is a + * primitive (non-tensor) input to the kernel. + * @param inputsToSave A list of tensors, inputs to save for the backprop + * computation. + * @param outputsToSave A list of booleans, specifying which output to save + * for the backprop computation. These are booleans since the output + * tensors are not visible to the user. + */ + runKernel(kernelName, inputs, attrs) { + if (this.backendName == null) { + this.backend; + } + const hasKernel = getKernel(kernelName, this.backendName) != null; + if (!hasKernel) { + throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`); + } + return this.runKernelFunc({ kernelName, inputs, attrs }); + } + shouldCheckForMemLeaks() { + return this.ENV.getBool("IS_TEST"); + } + checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) { + const numDataIdsAfter = this.backend.numDataIds(); + let numOutputDataIds = 0; + outInfos.forEach((info) => { + numOutputDataIds += info.dtype === "complex64" ? 3 : 1; + }); + const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]; + const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves; + if (dataIdsLeaked > 0) { + throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`); + } + } + /** + * Internal helper method to execute a kernel Func + * + * Use `runKernel` to execute kernels from outside of engine. + */ + runKernelFunc(kernelParams) { + let outputs; + let saved = []; + const isTapeOn = this.isTapeOn(); + const startingBytecount = this.state.numBytes; + const startingNumTensors = this.state.numTensors; + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack.push(0); + } + let kernelFunc3; + if (this.backendName == null) { + this.backend; + } + let out; + const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ""; + if (isRegisteredKernelInvocation(kernelParams)) { + const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams; + if (this.backendName == null) { + this.backend; + } + const kernel = getKernel(kernelName, this.backendName); + assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`); + kernelFunc3 = () => { + const numDataIdsBefore = this.backend.numDataIds(); + out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend }); + const outInfos = Array.isArray(out) ? out : [out]; + if (this.shouldCheckForMemLeaks()) { + this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos); + } + const outTensors = outInfos.map((outInfo) => { + if (outInfo.rank != null) { + return outInfo; + } + return this.makeTensorFromTensorInfo(outInfo); + }); + if (isTapeOn) { + const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors); + saved = this.saveTensorsForBackwardMode(tensorsToSave); + } + return outTensors; + }; + } else { + const { forwardFunc } = kernelParams; + const saveFunc = (tensors) => { + if (!isTapeOn) { + return; + } + saved = tensors.map((tensor2) => this.keep(this.clone(tensor2))); + }; + kernelFunc3 = () => { + const numDataIdsBefore = this.backend.numDataIds(); + out = this.tidy(() => forwardFunc(this.backend, saveFunc)); + const outs = Array.isArray(out) ? out : [out]; + if (this.shouldCheckForMemLeaks()) { + this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs); + } + return outs; + }; + } + const { inputs, attrs } = kernelParams; + const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc; + let kernelProfile; + this.scopedRun( + // Stop recording to a tape when running a kernel. + () => this.state.kernelDepth++, + () => this.state.kernelDepth--, + () => { + if (!this.ENV.getBool("DEBUG") && !this.state.profiling) { + outputs = kernelFunc3(); + } else { + kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3()); + if (this.ENV.getBool("DEBUG")) { + this.profiler.logKernelProfile(kernelProfile); + } + outputs = kernelProfile.outputs; + } + } + ); + if (isTapeOn) { + this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs); + } + if (this.state.profiling) { + this.state.activeProfile.kernels.push({ + name: kernelOrScopeName, + bytesAdded: this.state.numBytes - startingBytecount, + totalBytesSnapshot: this.state.numBytes, + tensorsAdded: this.state.numTensors - startingNumTensors, + totalTensorsSnapshot: this.state.numTensors, + inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null), + outputShapes: outputs.map((item) => item.shape), + kernelTimeMs: kernelProfile.timeMs, + extraInfo: kernelProfile.extraInfo + }); + } + return Array.isArray(out) ? outputs : outputs[0]; + } + /** + * Saves tensors used in forward mode for use in backward mode. + * + * @param tensors the list of tensors to save. + */ + saveTensorsForBackwardMode(tensors) { + const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2))); + return saved; + } + /** + * Returns a list of tensors to save for a given gradient calculation. + * + * @param kernelName name of kernel to look up gradient for. + * @param inputs a map of input tensors. + * @param outputs an array of output tensors from forward mode of kernel. + */ + getTensorsForGradient(kernelName, inputs, outputs) { + const gradConfig = getGradient(kernelName); + if (gradConfig != null) { + const inputsToSave = gradConfig.inputsToSave || []; + const outputsToSave = gradConfig.outputsToSave || []; + let inputTensorsToSave; + if (gradConfig.saveAllInputs) { + assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array."); + inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]); + } else { + inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]); + } + const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]); + return inputTensorsToSave.concat(outputTensorsToSave); + } + return []; + } + /** + * Internal method used by public APIs for tensor creation. Makes a new + * tensor with the provided shape, dtype and values. It always + * creates a new data id and writes the values to the underlying backend. + */ + makeTensor(values, shape, dtype, backend2) { + if (values == null) { + throw new Error("Values passed to engine.makeTensor() are null"); + } + dtype = dtype || "float32"; + backend2 = backend2 || this.backend; + let backendVals = values; + if (dtype === "string" && isString(values[0])) { + backendVals = values.map((d) => encodeString(d)); + } + const dataId = backend2.write(backendVals, shape, dtype); + const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend2); + if (dtype === "string") { + const info = this.state.tensorInfo.get(dataId); + const newBytes = bytesFromStringArray(backendVals); + this.state.numBytes += newBytes - info.bytes; + info.bytes = newBytes; + } + return t; + } + /** + * Internal method used by backends. Makes a new tensor + * that is a wrapper around an existing data id. It doesn't create + * a new data id, only increments the ref count used in memory tracking. + * @deprecated + */ + makeTensorFromDataId(dataId, shape, dtype, backend2) { + dtype = dtype || "float32"; + const tensorInfo = { dataId, shape, dtype }; + return this.makeTensorFromTensorInfo(tensorInfo, backend2); + } + /** + * Internal method used by backends. Makes a new tensor that is a wrapper + * around an existing data id in TensorInfo. It doesn't create a new data id, + * only increments the ref count used in memory tracking. + */ + makeTensorFromTensorInfo(tensorInfo, backend2) { + const { dataId, shape, dtype } = tensorInfo; + const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend2); + return t; + } + makeVariable(initialValue, trainable = true, name, dtype) { + name = name || this.nextVariableId().toString(); + if (dtype != null && dtype !== initialValue.dtype) { + initialValue = initialValue.cast(dtype); + } + const v = new Variable(initialValue, trainable, name, this.nextTensorId()); + if (this.state.registeredVariables[v.name] != null) { + throw new Error(`Variable with name ${v.name} was already registered`); + } + this.state.registeredVariables[v.name] = v; + this.incRef(v, this.backend); + return v; + } + trackTensor(a, backend2) { + this.state.numTensors++; + if (a.dtype === "string") { + this.state.numStringTensors++; + } + let bytes = 0; + if (a.dtype !== "complex64" && a.dtype !== "string") { + bytes = a.size * bytesPerElement(a.dtype); + } + this.state.numBytes += bytes; + if (!this.state.tensorInfo.has(a.dataId)) { + this.state.numDataBuffers++; + this.state.tensorInfo.set(a.dataId, { + backend: backend2 || this.backend, + dtype: a.dtype, + shape: a.shape, + bytes + }); + } + if (!(a instanceof Variable)) { + this.track(a); + } + } + // Track the tensor by dataId and increase the refCount for the dataId in the + // backend. + // TODO(pyu10055): This is currently used by makeVariable method, to increase + // refCount on the backend for the dataId. It can potentially be replaced with + // Identity op indead of calling backend directly. + incRef(a, backend2) { + this.trackTensor(a, backend2); + this.backend.incRef(a.dataId); + } + removeDataId(dataId, backend2) { + if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) { + this.state.tensorInfo.delete(dataId); + this.state.numDataBuffers--; + } + } + disposeTensor(a) { + if (!this.state.tensorInfo.has(a.dataId)) { + return; + } + const info = this.state.tensorInfo.get(a.dataId); + this.state.numTensors--; + if (a.dtype === "string") { + this.state.numStringTensors--; + this.state.numBytes -= info.bytes; + } + if (a.dtype !== "complex64" && a.dtype !== "string") { + const bytes = a.size * bytesPerElement(a.dtype); + this.state.numBytes -= bytes; + } + if (info.backend.disposeData(a.dataId)) { + this.removeDataId(a.dataId, info.backend); + } + } + disposeVariables() { + for (const varName in this.state.registeredVariables) { + const v = this.state.registeredVariables[varName]; + this.disposeVariable(v); + } + } + disposeVariable(v) { + this.disposeTensor(v); + if (this.state.registeredVariables[v.name] != null) { + delete this.state.registeredVariables[v.name]; + } + } + memory() { + const info = this.backend.memory(); + info.numTensors = this.state.numTensors; + info.numDataBuffers = this.state.numDataBuffers; + info.numBytes = this.state.numBytes; + if (this.state.numStringTensors > 0) { + info.unreliable = true; + if (info.reasons == null) { + info.reasons = []; + } + info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)"); + } + return info; + } + async profile(query) { + this.state.profiling = true; + const startBytes = this.state.numBytes; + const startNumTensors = this.state.numTensors; + this.state.activeProfile.kernels = []; + this.state.activeProfile.result = await query(); + this.state.profiling = false; + this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot)); + this.state.activeProfile.newBytes = this.state.numBytes - startBytes; + this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors; + for (const kernel of this.state.activeProfile.kernels) { + kernel.kernelTimeMs = await kernel.kernelTimeMs; + kernel.extraInfo = await kernel.extraInfo; + } + return this.state.activeProfile; + } + isTapeOn() { + return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; + } + addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) { + const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved }; + const gradConfig = getGradient(kernelName); + if (gradConfig != null) { + gradientsFunc = gradConfig.gradFunc; + } + if (gradientsFunc != null) { + tapeNode.gradient = (dys) => { + dys = dys.map((dy, i) => { + if (dy == null) { + const output = outputs[i]; + const vals = makeZerosTypedArray(output.size, output.dtype); + return this.makeTensor(vals, output.shape, output.dtype); + } + return dy; + }); + return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs); + }; + } + this.state.activeTape.push(tapeNode); + } + keep(result) { + result.kept = true; + return result; + } + startTape() { + if (this.state.gradientDepth === 0) { + this.state.activeTape = []; + } + this.state.gradientDepth++; + } + endTape() { + this.state.gradientDepth--; + } + /** + * Start a scope. Use this with endScope() to achieve the same functionality + * as scope() without the need for a function closure. + */ + startScope(name) { + const scopeInfo = { + track: [], + name: "unnamed scope", + id: this.state.nextScopeId++ + }; + if (name) { + scopeInfo.name = name; + } + this.state.scopeStack.push(scopeInfo); + this.state.activeScope = scopeInfo; + } + /** + * End a scope. Use this with startScope() to achieve the same functionality + * as scope() without the need for a function closure. + */ + endScope(result) { + const tensorsToTrackInParent = getTensorsInContainer(result); + const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id)); + for (let i = 0; i < this.state.activeScope.track.length; i++) { + const tensor2 = this.state.activeScope.track[i]; + if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) { + tensor2.dispose(); + } + } + const oldScope = this.state.scopeStack.pop(); + this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1]; + tensorsToTrackInParent.forEach((tensor2) => { + if (!tensor2.kept && tensor2.scopeId === oldScope.id) { + this.track(tensor2); + } + }); + } + /** + * Returns gradients of `f` with respect to each of the `xs`. The gradients + * returned are of the same length as `xs`, but some might be null if `f` + * was not a function of that `x`. It also takes optional dy to multiply the + * gradient, which defaults to `1`. + */ + gradients(f, xs, dy, allowNoGradients = false) { + assert(xs.length > 0, () => "gradients() received an empty list of xs."); + if (dy != null && dy.dtype !== "float32") { + throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`); + } + const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f)); + assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor."); + const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y); + if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) { + throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y."); + } + return this.tidy("backward", () => { + const accumulatedGradientMap = {}; + accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy; + backpropagateGradients( + accumulatedGradientMap, + filteredTape, + // Pass the tidy function to avoid circular dep with `tape.ts`. + (f2) => this.tidy(f2), + // Pass an add function to avoide a circular dep with `tape.ts`. + add + ); + const grads2 = xs.map((x) => accumulatedGradientMap[x.id]); + if (this.state.gradientDepth === 0) { + this.state.activeTape.forEach((node) => { + for (const tensor2 of node.saved) { + tensor2.dispose(); + } + }); + this.state.activeTape = null; + } + return { value: y, grads: grads2 }; + }); + } + customGrad(f) { + assert(isFunction(f), () => "The f passed in customGrad(f) must be a function."); + return (...inputs) => { + assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); + let res; + const inputMap = {}; + inputs.forEach((input2, i) => { + inputMap[i] = input2; + }); + const forwardFunc = (_, save) => { + res = f(...[...inputs, save]); + assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"); + assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."); + return res.value; + }; + const backwardsFunc = (dy, saved) => { + const gradRes = res.gradFunc(dy, saved); + const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes]; + assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."); + assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); + const gradMap = {}; + grads2.forEach((grad2, i) => { + gradMap[i] = () => grad2; + }); + return gradMap; + }; + return this.runKernelFunc({ + forwardFunc, + backwardsFunc, + inputs: inputMap + }); + }; + } + readSync(dataId) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.readSync(dataId); + } + read(dataId) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.read(dataId); + } + readToGPU(dataId, options) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.readToGPU(dataId, options); + } + async time(query) { + const start = now(); + const timingInfo = await this.backend.time(query); + timingInfo.wallMs = now() - start; + return timingInfo; + } + /** + * Tracks a Tensor in the current scope to be automatically cleaned up + * when the current scope ends, and returns the value. + * + * @param result The Tensor to track in the current scope. + */ + track(result) { + if (this.state.activeScope != null) { + result.scopeId = this.state.activeScope.id; + this.state.activeScope.track.push(result); + } + return result; + } + get registeredVariables() { + return this.state.registeredVariables; + } + /** + * Resets the engine state. Removes all backends but does not remove + * registered backend factories. + */ + reset() { + this.pendingBackendInitId++; + this.state.dispose(); + this.ENV.reset(); + this.state = new EngineState(); + for (const backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + this.backendName = null; + this.backendInstance = null; + this.pendingBackendInit = null; + } +}; +Engine.nextTensorId = 0; +Engine.nextVariableId = 0; +function ones(shape) { + const values = makeOnesTypedArray(sizeFromShape(shape), "float32"); + return ENGINE.makeTensor(values, shape, "float32"); +} +function getOrMakeEngine() { + const ns = getGlobalNamespace(); + if (ns._tfengine == null) { + const environment2 = new Environment(ns); + ns._tfengine = new Engine(environment2); + } + setEnvironmentGlobal(ns._tfengine.ENV); + setTensorTracker(() => ns._tfengine); + return ns._tfengine; +} +var ENGINE = getOrMakeEngine(); +function add(a, b) { + const inputs = { a, b }; + return ENGINE.runKernel(Add, inputs); +} +var device_util_exports = {}; +__export2(device_util_exports, { + isBrowser: () => isBrowser, + isMobile: () => isMobile, + mockIsMobile: () => mockIsMobile +}); +function _isNavigatorDefined() { + return typeof navigator !== "undefined" && navigator != null; +} +var isMobileMockValue; +function mockIsMobile(value) { + isMobileMockValue = value; +} +function isMobile(nav) { + if (isMobileMockValue !== void 0) { + return isMobileMockValue; + } + if (nav || _isNavigatorDefined()) { + if (!nav) { + nav = navigator; + } + if (nav.product === "ReactNative") { + return true; + } + const a = nav.userAgent || nav.vendor || // tslint:disable-next-line:no-any + (typeof window !== "undefined" ? window.opera : ""); + if (!a) { + const navAny = nav; + return navAny.userAgentData && navAny.userAgentData.mobile; + } + return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || // tslint:disable-next-line:max-line-length + /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4)); + } + return false; +} +function isBrowser() { + return typeof window !== "undefined" && window.document != null || //@ts-ignore + typeof WorkerGlobalScope !== "undefined"; +} +var ENV2 = env(); +ENV2.registerFlag("DEBUG", () => false, (debugValue) => { + if (debugValue) { + console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance."); + } +}); +ENV2.registerFlag("IS_BROWSER", () => isBrowser()); +ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"); +ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); +ENV2.registerFlag("IS_SAFARI", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Safari/.test(navigator.userAgent) && /Apple/.test(navigator.vendor)); +ENV2.registerFlag("PROD", () => false); +ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG")); +ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true); +ENV2.registerFlag("IS_TEST", () => false); +ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => ENV2.getBool("DEBUG")); +ENV2.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); +ENV2.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", () => false); +ENV2.registerFlag("USE_SETTIMEOUTCUSTOM", () => false); +function inferShape(val, dtype) { + let firstElem = val; + if (isTypedArray(val)) { + return dtype === "string" ? [] : [val.length]; + } + if (isWebGLData(val)) { + const usedChannels = val.channels || "RGBA"; + return [val.height, val.width * usedChannels.length]; + } else if (isWebGPUData(val)) { + return [val.buffer.size / (dtype == null ? 4 : bytesPerElement(dtype))]; + } + if (!Array.isArray(val)) { + return []; + } + const shape = []; + while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") { + shape.push(firstElem.length); + firstElem = firstElem[0]; + } + if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) { + deepAssertShapeConsistency(val, shape, []); + } + return shape; +} +function deepAssertShapeConsistency(val, shape, indices) { + indices = indices || []; + if (!Array.isArray(val) && !isTypedArray(val)) { + assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`); + return; + } + assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`); + assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`); + const subShape = shape.slice(1); + for (let i = 0; i < val.length; ++i) { + deepAssertShapeConsistency(val[i], subShape, indices.concat(i)); + } +} +function assertDtype(expectedDtype, actualDType, argName, functionName) { + if (expectedDtype === "string_or_numeric") { + return; + } + if (expectedDtype == null) { + throw new Error(`Expected dtype cannot be null.`); + } + if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") { + throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`); + } +} +function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") { + if (x instanceof getGlobalTensorClass()) { + assertDtype(parseAsDtype, x.dtype, argName, functionName); + return x; + } + let inferredDtype = inferDtype(x); + if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) { + inferredDtype = parseAsDtype; + } + assertDtype(parseAsDtype, inferredDtype, argName, functionName); + if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") { + const type = x == null ? "null" : x.constructor.name; + throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`); + } + const inferredShape = inferShape(x, inferredDtype); + if (!isTypedArray(x) && !Array.isArray(x)) { + x = [x]; + } + const skipTypedArray = true; + const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray); + return ENGINE.makeTensor(values, inferredShape, inferredDtype); +} +function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") { + if (!Array.isArray(arg)) { + throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``); + } + const tensors = arg; + return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype)); +} +var OP_SCOPE_SUFFIX = "__op"; +function op(f) { + const keys = Object.keys(f); + if (keys.length !== 1) { + throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`); + } + let opName = keys[0]; + const fn = f[opName]; + if (opName.endsWith("_")) { + opName = opName.substring(0, opName.length - 1); + } + opName = opName + OP_SCOPE_SUFFIX; + const f2 = (...args) => { + ENGINE.startScope(opName); + try { + const result = fn(...args); + if (isPromise(result)) { + console.error("Cannot return a Promise inside of tidy."); + } + ENGINE.endScope(result); + return result; + } catch (ex) { + ENGINE.endScope(null); + throw ex; + } + }; + Object.defineProperty(f2, "name", { value: opName, configurable: true }); + return f2; +} +function complex_(real4, imag4) { + const $real = convertToTensor(real4, "real", "complex"); + const $imag = convertToTensor(imag4, "imag", "complex"); + assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`); + const inputs = { real: $real, imag: $imag }; + return ENGINE.runKernel(Complex, inputs); +} +var complex = op({ complex_ }); +function makeTensor(values, shape, inferredShape, dtype) { + if (dtype == null) { + dtype = inferDtype(values); + } else if (dtype === "complex64") { + throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`); + } + if (isWebGPUData(values) || isWebGLData(values)) { + if (dtype !== "float32" && dtype !== "int32") { + throw new Error(`Creating tensor from GPU data only supports 'float32'|'int32' dtype, while the dtype is ${dtype}.`); + } + return ENGINE.backend.createTensorFromGPUData(values, shape || inferredShape, dtype); + } + if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") { + throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray"); + } + if (shape != null) { + assertNonNegativeIntegerDimensions(shape); + const providedSize = sizeFromShape(shape); + const inferredSize = sizeFromShape(inferredShape); + assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`); + for (let i = 0; i < inferredShape.length; ++i) { + const inferred = inferredShape[i]; + const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true; + assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `); + } + } + if (!isTypedArray(values) && !Array.isArray(values)) { + values = [values]; + } + shape = shape || inferredShape; + values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true); + return ENGINE.makeTensor(values, shape, dtype); +} +function tensor(values, shape, dtype) { + const inferredShape = inferShape(values, dtype); + return makeTensor(values, shape, inferredShape, dtype); +} +var DTYPE_VALUE_SIZE_MAP = { + "float32": 4, + "float16": 2, + "int32": 4, + "uint16": 2, + "uint8": 1, + "bool": 1, + "complex64": 8 +}; +var CompositeArrayBuffer = class _CompositeArrayBuffer { + /** + * Concatenate a number of ArrayBuffers into one. + * + * @param buffers An array of ArrayBuffers to concatenate, or a single + * ArrayBuffer. + * @returns Result of concatenating `buffers` in order. + */ + static join(buffers) { + return new _CompositeArrayBuffer(buffers).slice(); + } + constructor(buffers) { + this.shards = []; + this.previousShardIndex = 0; + if (buffers == null) { + return; + } + if (!(buffers instanceof Array)) { + buffers = [buffers]; + } + buffers = buffers.map((bufferOrTypedArray) => { + if (isTypedArray(bufferOrTypedArray)) { + return bufferOrTypedArray.buffer; + } + return bufferOrTypedArray; + }); + if (buffers.length === 0) { + return; + } + this.bufferUniformSize = buffers[0].byteLength; + let start = 0; + for (let i = 0; i < buffers.length; i++) { + const buffer2 = buffers[i]; + if (i !== buffers.length - 1 && buffer2.byteLength !== this.bufferUniformSize) { + this.bufferUniformSize = void 0; + } + const end = start + buffer2.byteLength; + this.shards.push({ buffer: buffer2, start, end }); + start = end; + } + if (this.shards.length === 0) { + this.byteLength = 0; + } + this.byteLength = this.shards[this.shards.length - 1].end; + } + slice(start = 0, end = this.byteLength) { + if (this.shards.length === 0) { + return new ArrayBuffer(0); + } + start = isNaN(Number(start)) ? 0 : start; + end = isNaN(Number(end)) ? 0 : end; + start = Math.max(0, start); + end = Math.min(this.byteLength, end); + if (end <= start) { + return new ArrayBuffer(0); + } + const startShardIndex = this.findShardForByte(start); + if (startShardIndex === -1) { + throw new Error(`Could not find start shard for byte ${start}`); + } + const size = end - start; + const outputBuffer = new ArrayBuffer(size); + const outputArray = new Uint8Array(outputBuffer); + let sliced = 0; + for (let i = startShardIndex; i < this.shards.length; i++) { + const shard = this.shards[i]; + const globalStart = start + sliced; + const localStart = globalStart - shard.start; + const outputStart = sliced; + const globalEnd = Math.min(end, shard.end); + const localEnd = globalEnd - shard.start; + const outputSlice = new Uint8Array(shard.buffer, localStart, localEnd - localStart); + outputArray.set(outputSlice, outputStart); + sliced += outputSlice.length; + if (end < shard.end) { + break; + } + } + return outputBuffer; + } + /** + * Get the index of the shard that contains the byte at `byteIndex`. + */ + findShardForByte(byteIndex) { + if (this.shards.length === 0 || byteIndex < 0 || byteIndex >= this.byteLength) { + return -1; + } + if (this.bufferUniformSize != null) { + this.previousShardIndex = Math.floor(byteIndex / this.bufferUniformSize); + return this.previousShardIndex; + } + function check(shard) { + if (byteIndex < shard.start) { + return -1; + } + if (byteIndex >= shard.end) { + return 1; + } + return 0; + } + if (check(this.shards[this.previousShardIndex]) === 0) { + return this.previousShardIndex; + } + const index = search(this.shards, check); + if (index === -1) { + return -1; + } + this.previousShardIndex = index; + return this.previousShardIndex; + } +}; +function search(sortedArray, compare) { + let min6 = 0; + let max6 = sortedArray.length; + while (min6 <= max6) { + const middle = Math.floor((max6 - min6) / 2) + min6; + const side = compare(sortedArray[middle]); + if (side === 0) { + return middle; + } else if (side < 0) { + max6 = middle; + } else { + min6 = middle + 1; + } + } + return -1; +} +function enableProdMode() { + env().set("PROD", true); +} +function enableDebugMode() { + env().set("DEBUG", true); +} +function disableDeprecationWarnings() { + env().set("DEPRECATION_WARNINGS_ENABLED", false); + console.warn(`TensorFlow.js deprecation warnings have been disabled.`); +} +function deprecationWarn(msg) { + if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) { + console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings()."); + } +} +setDeprecationWarningFn(deprecationWarn); +function disposeVariables() { + ENGINE.disposeVariables(); +} +function engine() { + return ENGINE; +} +function memory() { + return ENGINE.memory(); +} +function profile(f) { + return ENGINE.profile(f); +} +function tidy(nameOrFn, fn) { + return ENGINE.tidy(nameOrFn, fn); +} +function dispose(container) { + const tensors = getTensorsInContainer(container); + tensors.forEach((tensor2) => tensor2.dispose()); +} +function keep(result) { + return ENGINE.keep(result); +} +function time(f) { + return ENGINE.time(f); +} +function setBackend(backendName) { + return ENGINE.setBackend(backendName); +} +function ready() { + return ENGINE.ready(); +} +function getBackend() { + return ENGINE.backendName; +} +function removeBackend(name) { + ENGINE.removeBackend(name); +} +function findBackend(name) { + return ENGINE.findBackend(name); +} +function findBackendFactory(name) { + return ENGINE.findBackendFactory(name); +} +function registerBackend(name, factory, priority = 1) { + return ENGINE.registerBackend(name, factory, priority); +} +function backend() { + return ENGINE.backend; +} +function setPlatform(platformName, platform) { + env().setPlatform(platformName, platform); +} +var NUM_BYTES_STRING_LENGTH = 4; +async function encodeWeights(tensors, group) { + const specs = []; + const dataPromises = []; + const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors); + for (let i = 0; i < names.length; ++i) { + const name = names[i]; + const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name]; + if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") { + throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`); + } + const spec = { name, shape: t.shape, dtype: t.dtype }; + if (t.dtype === "string") { + const utf8bytes = new Promise(async (resolve) => { + const vals = await t.bytes(); + const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length; + const bytes = new Uint8Array(totalNumBytes); + let offset = 0; + for (let i2 = 0; i2 < vals.length; i2++) { + const val = vals[i2]; + const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer); + bytes.set(bytesOfLength, offset); + offset += NUM_BYTES_STRING_LENGTH; + bytes.set(val, offset); + offset += val.length; + } + resolve(bytes); + }); + dataPromises.push(utf8bytes); + } else { + dataPromises.push(t.data()); + } + if (group != null) { + spec.group = group; + } + specs.push(spec); + } + const tensorValues = await Promise.all(dataPromises); + return { data: concatenateTypedArrays(tensorValues), specs }; +} +function decodeWeights(weightData, specs) { + const compositeBuffer = new CompositeArrayBuffer(weightData); + const out = {}; + let offset = 0; + for (const spec of specs) { + const byteLength = getWeightBytelength(spec, (start, end) => { + return compositeBuffer.slice(offset + start, offset + end); + }); + out[spec.name] = decodeWeight(spec, compositeBuffer.slice(offset, offset + byteLength)); + offset += byteLength; + } + return out; +} +function getWeightBytelength(spec, slice5) { + const size = sizeFromShape(spec.shape); + let bytesPerValue; + if ("quantization" in spec) { + const quantization = spec.quantization; + bytesPerValue = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; + } else if (spec.dtype === "string") { + let byteLength = 0; + for (let i = 0; i < size; i++) { + byteLength += NUM_BYTES_STRING_LENGTH + new Uint32Array(slice5(byteLength, byteLength + NUM_BYTES_STRING_LENGTH))[0]; + } + return byteLength; + } else { + bytesPerValue = DTYPE_VALUE_SIZE_MAP[spec.dtype]; + } + return size * bytesPerValue; +} +async function getWeightBytelengthAsync(spec, slice5) { + const size = sizeFromShape(spec.shape); + let bytesPerValue; + if ("quantization" in spec) { + const quantization = spec.quantization; + bytesPerValue = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; + } else if (spec.dtype === "string") { + let byteLength = 0; + for (let i = 0; i < size; i++) { + byteLength += NUM_BYTES_STRING_LENGTH + new Uint32Array(await slice5(byteLength, byteLength + NUM_BYTES_STRING_LENGTH))[0]; + } + return byteLength; + } else { + bytesPerValue = DTYPE_VALUE_SIZE_MAP[spec.dtype]; + } + return size * bytesPerValue; +} +function decodeWeight(spec, byteBuffer) { + const name = spec.name; + const dtype = spec.dtype; + const shape = spec.shape; + const size = sizeFromShape(shape); + let values; + let offset = 0; + if ("quantization" in spec) { + const quantization = spec.quantization; + if (quantization.dtype === "uint8" || quantization.dtype === "uint16") { + if (!("min" in quantization && "scale" in quantization)) { + throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`); + } + } else if (quantization.dtype === "float16") { + if (dtype !== "float32") { + throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`); + } + } else { + throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`); + } + const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; + const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer); + if (dtype === "float32") { + if (quantization.dtype === "uint8" || quantization.dtype === "uint16") { + values = new Float32Array(quantizedArray.length); + for (let i = 0; i < quantizedArray.length; i++) { + const v = quantizedArray[i]; + values[i] = v * quantization.scale + quantization.min; + } + } else if (quantization.dtype === "float16") { + const float16Decode = getFloat16Decoder(); + values = float16Decode(quantizedArray); + } else { + throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`); + } + } else if (dtype === "int32") { + if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") { + throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`); + } + values = new Int32Array(quantizedArray.length); + for (let i = 0; i < quantizedArray.length; i++) { + const v = quantizedArray[i]; + values[i] = Math.round(v * quantization.scale + quantization.min); + } + } else { + throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); + } + offset += size * quantizationSizeFactor; + } else if (dtype === "string") { + const size2 = sizeFromShape(spec.shape); + values = []; + for (let i = 0; i < size2; i++) { + const byteLength = new Uint32Array(byteBuffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0]; + offset += NUM_BYTES_STRING_LENGTH; + const bytes = new Uint8Array(byteBuffer.slice(offset, offset + byteLength)); + values.push(bytes); + offset += byteLength; + } + } else { + const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype]; + if (dtype === "float32") { + values = new Float32Array(byteBuffer); + } else if (dtype === "int32") { + values = new Int32Array(byteBuffer); + } else if (dtype === "bool") { + values = new Uint8Array(byteBuffer); + } else if (dtype === "complex64") { + values = new Float32Array(byteBuffer); + const real4 = new Float32Array(values.length / 2); + const image2 = new Float32Array(values.length / 2); + for (let i = 0; i < real4.length; i++) { + real4[i] = values[i * 2]; + image2[i] = values[i * 2 + 1]; + } + const realTensor = tensor(real4, shape, "float32"); + const imageTensor = tensor(image2, shape, "float32"); + const complexTensor = complex(realTensor, imageTensor); + realTensor.dispose(); + imageTensor.dispose(); + return complexTensor; + } else { + throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); + } + offset += size * dtypeFactor; + } + return tensor(values, shape, dtype); +} +async function readToLength(reader, initialData, length) { + let data = new Uint8Array(initialData); + while (data.byteLength < length) { + const { done, value } = await reader.read(); + if (done && value == null) { + const missing = length - data.byteLength; + throw new Error(`Reader is done but ${missing} bytes are still expected`); + } + const newData = new Uint8Array(data.length + value.byteLength); + newData.set(data, 0); + newData.set(new Uint8Array(value), data.length); + data = newData; + } + return data.buffer; +} +async function decodeWeightsStream(weightStream, specs) { + const tensors = {}; + const reader = weightStream.getReader(); + let data = new ArrayBuffer(0); + for (const spec of specs) { + const byteLength = await getWeightBytelengthAsync(spec, async (start, end) => { + data = await readToLength(reader, data, end); + return data.slice(start, end); + }); + data = await readToLength(reader, data, byteLength); + const tensorData = data.slice(0, byteLength); + data = data.slice(byteLength); + const weightTensor = decodeWeight(spec, tensorData); + tensors[spec.name] = weightTensor; + if (getBackend() === "webgpu") { + const b = backend(); + if ("uploadToGPU" in b && sizeFromShape(weightTensor.shape) >= env().get("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD")) { + b.uploadToGPU(weightTensor.dataId); + } + } + } + return tensors; +} +function concatenateTypedArrays(xs) { + if (xs === null) { + throw new Error(`Invalid input value: ${JSON.stringify(xs)}`); + } + let totalByteLength = 0; + const normalizedXs = []; + xs.forEach((x) => { + totalByteLength += x.byteLength; + normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x)); + if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) { + throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`); + } + }); + const y = new Uint8Array(totalByteLength); + let offset = 0; + normalizedXs.forEach((x) => { + y.set(new Uint8Array(x.buffer), offset); + offset += x.byteLength; + }); + return y.buffer; +} +var useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined"); +function stringByteLength(str) { + if (useNodeBuffer) { + return Buffer.byteLength(str, "utf8"); + } + return new Blob([str]).size; +} +function arrayBufferToBase64String(buffer2) { + if (useNodeBuffer) { + return Buffer.from(buffer2).toString("base64"); + } + const buf = new Uint8Array(buffer2); + let s = ""; + for (let i = 0, l = buf.length; i < l; i++) { + s += String.fromCharCode(buf[i]); + } + return btoa(s); +} +function base64StringToArrayBuffer(str) { + if (useNodeBuffer) { + const buf = Buffer.from(str, "base64"); + return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength); + } + const s = atob(str); + const buffer2 = new Uint8Array(s.length); + for (let i = 0; i < s.length; ++i) { + buffer2.set([s.charCodeAt(i)], i); + } + return buffer2.buffer; +} +function concatenateArrayBuffers(buffers) { + return CompositeArrayBuffer.join(buffers); +} +function basename(path) { + const SEPARATOR = "/"; + path = path.trim(); + while (path.endsWith(SEPARATOR)) { + path = path.slice(0, path.length - 1); + } + const items = path.split(SEPARATOR); + return items[items.length - 1]; +} +function getModelJSONForModelArtifacts(artifacts, manifest) { + const result = { + modelTopology: artifacts.modelTopology, + format: artifacts.format, + generatedBy: artifacts.generatedBy, + convertedBy: artifacts.convertedBy, + weightsManifest: manifest + }; + if (artifacts.signature != null) { + result.signature = artifacts.signature; + } + if (artifacts.userDefinedMetadata != null) { + result.userDefinedMetadata = artifacts.userDefinedMetadata; + } + if (artifacts.modelInitializer != null) { + result.modelInitializer = artifacts.modelInitializer; + } + if (artifacts.initializerSignature != null) { + result.initializerSignature = artifacts.initializerSignature; + } + if (artifacts.trainingConfig != null) { + result.trainingConfig = artifacts.trainingConfig; + } + return result; +} +function getModelArtifactsForJSONSync(modelJSON, weightSpecs, weightData) { + const modelArtifacts = { + modelTopology: modelJSON.modelTopology, + format: modelJSON.format, + generatedBy: modelJSON.generatedBy, + convertedBy: modelJSON.convertedBy + }; + if (modelJSON.trainingConfig != null) { + modelArtifacts.trainingConfig = modelJSON.trainingConfig; + } + if (modelJSON.weightsManifest != null) { + if (!weightSpecs) { + throw new Error("modelJSON has weightsManifest but weightSpecs is null"); + } + if (!weightData) { + throw new Error("modelJSON has weightsManifest but weightData is null"); + } + modelArtifacts.weightSpecs = weightSpecs; + modelArtifacts.weightData = weightData; + } + if (modelJSON.signature != null) { + modelArtifacts.signature = modelJSON.signature; + } + if (modelJSON.userDefinedMetadata != null) { + modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata; + } + if (modelJSON.modelInitializer != null) { + modelArtifacts.modelInitializer = modelJSON.modelInitializer; + } + if (modelJSON.initializerSignature != null) { + modelArtifacts.initializerSignature = modelJSON.initializerSignature; + } + return modelArtifacts; +} +async function getModelArtifactsForJSON(modelJSON, loadWeights2) { + let weightSpecs; + let weightData; + if (modelJSON.weightsManifest != null) { + [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest); + } + return getModelArtifactsForJSONSync(modelJSON, weightSpecs, weightData); +} +function getModelArtifactsInfoForJSON(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("Expected JSON model topology, received ArrayBuffer."); + } + return { + dateSaved: /* @__PURE__ */ new Date(), + modelTopologyType: "JSON", + modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)), + weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)), + weightDataBytes: modelArtifacts.weightData == null ? 0 : new CompositeArrayBuffer(modelArtifacts.weightData).byteLength + }; +} +function getWeightSpecs(weightsManifest) { + const weightSpecs = []; + for (const entry of weightsManifest) { + weightSpecs.push(...entry.weights); + } + return weightSpecs; +} +function computeFloat16MantisaTable() { + const convertMantissa = (i) => { + let m = i << 13; + let e = 0; + while ((m & 8388608) === 0) { + e -= 8388608; + m <<= 1; + } + m &= ~8388608; + e += 947912704; + return m | e; + }; + const mantisaTable = new Uint32Array(2048); + mantisaTable[0] = 0; + for (let i = 1; i < 1024; i++) { + mantisaTable[i] = convertMantissa(i); + } + for (let i = 1024; i < 2048; i++) { + mantisaTable[i] = 939524096 + (i - 1024 << 13); + } + return mantisaTable; +} +function computeFloat16ExponentTable() { + const exponentTable = new Uint32Array(64); + exponentTable[0] = 0; + exponentTable[31] = 1199570944; + exponentTable[32] = 2147483648; + exponentTable[63] = 3347054592; + for (let i = 1; i < 31; i++) { + exponentTable[i] = i << 23; + } + for (let i = 33; i < 63; i++) { + exponentTable[i] = 2147483648 + (i - 32 << 23); + } + return exponentTable; +} +function computeFloat16OffsetTable() { + const offsetTable = new Uint32Array(64); + for (let i = 0; i < 64; i++) { + offsetTable[i] = 1024; + } + offsetTable[0] = offsetTable[32] = 0; + return offsetTable; +} +function getFloat16Decoder() { + const mantisaTable = computeFloat16MantisaTable(); + const exponentTable = computeFloat16ExponentTable(); + const offsetTable = computeFloat16OffsetTable(); + return (quantizedArray) => { + const buffer2 = new ArrayBuffer(4 * quantizedArray.length); + const bufferUint32View = new Uint32Array(buffer2); + for (let index = 0; index < quantizedArray.length; index++) { + const float16Bits = quantizedArray[index]; + const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10]; + bufferUint32View[index] = float32Bits; + } + return new Float32Array(buffer2); + }; +} +var IORouterRegistry = class _IORouterRegistry { + constructor() { + this.saveRouters = []; + this.loadRouters = []; + } + static getInstance() { + if (_IORouterRegistry.instance == null) { + _IORouterRegistry.instance = new _IORouterRegistry(); + } + return _IORouterRegistry.instance; + } + /** + * Register a save-handler router. + * + * @param saveRouter A function that maps a URL-like string onto an instance + * of `IOHandler` with the `save` method defined or `null`. + */ + static registerSaveRouter(saveRouter) { + _IORouterRegistry.getInstance().saveRouters.push(saveRouter); + } + /** + * Register a load-handler router. + * + * @param loadRouter A function that maps a URL-like string onto an instance + * of `IOHandler` with the `load` method defined or `null`. + */ + static registerLoadRouter(loadRouter) { + _IORouterRegistry.getInstance().loadRouters.push(loadRouter); + } + /** + * Look up IOHandler for saving, given a URL-like string. + * + * @param url + * @returns If only one match is found, an instance of IOHandler with the + * `save` method defined. If no match is found, `null`. + * @throws Error, if more than one match is found. + */ + static getSaveHandlers(url) { + return _IORouterRegistry.getHandlers(url, "save"); + } + /** + * Look up IOHandler for loading, given a URL-like string. + * + * @param url + * @param loadOptions Optional, custom load options. + * @returns All valid handlers for `url`, given the currently registered + * handler routers. + */ + static getLoadHandlers(url, loadOptions) { + return _IORouterRegistry.getHandlers(url, "load", loadOptions); + } + static getHandlers(url, handlerType, loadOptions) { + const validHandlers = []; + const routers = handlerType === "load" ? _IORouterRegistry.getInstance().loadRouters : _IORouterRegistry.getInstance().saveRouters; + routers.forEach((router) => { + const handler = router(url, loadOptions); + if (handler !== null) { + validHandlers.push(handler); + } + }); + return validHandlers; + } +}; +var registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter); +var registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter); +var getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url); +var getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions); +var DATABASE_NAME = "tensorflowjs"; +var DATABASE_VERSION = 1; +var MODEL_STORE_NAME = "models_store"; +var INFO_STORE_NAME = "model_info_store"; +function getIndexedDBFactory() { + if (!env().getBool("IS_BROWSER")) { + throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser."); + } + const theWindow = typeof window === "undefined" ? self : window; + const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB; + if (factory == null) { + throw new Error("The current browser does not appear to support IndexedDB."); + } + return factory; +} +function setUpDatabase(openRequest) { + const db = openRequest.result; + db.createObjectStore(MODEL_STORE_NAME, { keyPath: "modelPath" }); + db.createObjectStore(INFO_STORE_NAME, { keyPath: "modelPath" }); +} +var BrowserIndexedDB = class { + constructor(modelPath) { + this.indexedDB = getIndexedDBFactory(); + if (modelPath == null || !modelPath) { + throw new Error("For IndexedDB, modelPath must not be null, undefined or empty."); + } + this.modelPath = modelPath; + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); + } + return this.databaseAction(this.modelPath, modelArtifacts); + } + async load() { + return this.databaseAction(this.modelPath); + } + /** + * Perform database action to put model artifacts into or read model artifacts + * from IndexedDB object store. + * + * Whether the action is put or get depends on whether `modelArtifacts` is + * specified. If it is specified, the action will be put; otherwise the action + * will be get. + * + * @param modelPath A unique string path for the model. + * @param modelArtifacts If specified, it will be the model artifacts to be + * stored in IndexedDB. + * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise` + * of `ModelArtifacts`, if the action is get. + */ + databaseAction(modelPath, modelArtifacts) { + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + if (modelArtifacts == null) { + const modelTx = db.transaction(MODEL_STORE_NAME, "readonly"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const getRequest = modelStore.get(this.modelPath); + getRequest.onsuccess = () => { + if (getRequest.result == null) { + db.close(); + return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`)); + } else { + resolve(getRequest.result.modelArtifacts); + } + }; + getRequest.onerror = (error) => { + db.close(); + return reject(getRequest.error); + }; + modelTx.oncomplete = () => db.close(); + } else { + modelArtifacts.weightData = CompositeArrayBuffer.join(modelArtifacts.weightData); + const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts); + const infoTx = db.transaction(INFO_STORE_NAME, "readwrite"); + let infoStore = infoTx.objectStore(INFO_STORE_NAME); + let putInfoRequest; + try { + putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo }); + } catch (error) { + return reject(error); + } + let modelTx; + putInfoRequest.onsuccess = () => { + modelTx = db.transaction(MODEL_STORE_NAME, "readwrite"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + let putModelRequest; + try { + putModelRequest = modelStore.put({ + modelPath: this.modelPath, + modelArtifacts, + modelArtifactsInfo + }); + } catch (error) { + return reject(error); + } + putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo }); + putModelRequest.onerror = (error) => { + infoStore = infoTx.objectStore(INFO_STORE_NAME); + const deleteInfoRequest = infoStore.delete(this.modelPath); + deleteInfoRequest.onsuccess = () => { + db.close(); + return reject(putModelRequest.error); + }; + deleteInfoRequest.onerror = (error2) => { + db.close(); + return reject(putModelRequest.error); + }; + }; + }; + putInfoRequest.onerror = (error) => { + db.close(); + return reject(putInfoRequest.error); + }; + infoTx.oncomplete = () => { + if (modelTx == null) { + db.close(); + } else { + modelTx.oncomplete = () => db.close(); + } + }; + } + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } +}; +BrowserIndexedDB.URL_SCHEME = "indexeddb://"; +var indexedDBRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) { + return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(indexedDBRouter); +IORouterRegistry.registerLoadRouter(indexedDBRouter); +function browserIndexedDB(modelPath) { + return new BrowserIndexedDB(modelPath); +} +function maybeStripScheme(key) { + return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key; +} +var BrowserIndexedDBManager = class { + constructor() { + this.indexedDB = getIndexedDBFactory(); + } + async listModels() { + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + const tx = db.transaction(INFO_STORE_NAME, "readonly"); + const store = tx.objectStore(INFO_STORE_NAME); + const getAllInfoRequest = store.getAll(); + getAllInfoRequest.onsuccess = () => { + const out = {}; + for (const item of getAllInfoRequest.result) { + out[item.modelPath] = item.modelArtifactsInfo; + } + resolve(out); + }; + getAllInfoRequest.onerror = (error) => { + db.close(); + return reject(getAllInfoRequest.error); + }; + tx.oncomplete = () => db.close(); + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } + async removeModel(path) { + path = maybeStripScheme(path); + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + const infoTx = db.transaction(INFO_STORE_NAME, "readwrite"); + const infoStore = infoTx.objectStore(INFO_STORE_NAME); + const getInfoRequest = infoStore.get(path); + let modelTx; + getInfoRequest.onsuccess = () => { + if (getInfoRequest.result == null) { + db.close(); + return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`)); + } else { + const deleteInfoRequest = infoStore.delete(path); + const deleteModelData = () => { + modelTx = db.transaction(MODEL_STORE_NAME, "readwrite"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const deleteModelRequest = modelStore.delete(path); + deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo); + deleteModelRequest.onerror = (error) => reject(getInfoRequest.error); + }; + deleteInfoRequest.onsuccess = deleteModelData; + deleteInfoRequest.onerror = (error) => { + deleteModelData(); + db.close(); + return reject(getInfoRequest.error); + }; + } + }; + getInfoRequest.onerror = (error) => { + db.close(); + return reject(getInfoRequest.error); + }; + infoTx.oncomplete = () => { + if (modelTx == null) { + db.close(); + } else { + modelTx.oncomplete = () => db.close(); + } + }; + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } +}; +var PATH_SEPARATOR = "/"; +var PATH_PREFIX = "tensorflowjs_models"; +var INFO_SUFFIX = "info"; +var MODEL_TOPOLOGY_SUFFIX = "model_topology"; +var WEIGHT_SPECS_SUFFIX = "weight_specs"; +var WEIGHT_DATA_SUFFIX = "weight_data"; +var MODEL_METADATA_SUFFIX = "model_metadata"; +function getModelKeys(path) { + return { + info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR), + topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR), + weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR), + weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR), + modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR) + }; +} +function removeItems(keys) { + for (const key of Object.values(keys)) { + window.localStorage.removeItem(key); + } +} +function getModelPathFromKey(key) { + const items = key.split(PATH_SEPARATOR); + if (items.length < 3) { + throw new Error(`Invalid key format: ${key}`); + } + return items.slice(1, items.length - 1).join(PATH_SEPARATOR); +} +function maybeStripScheme2(key) { + return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key; +} +var BrowserLocalStorage = class { + constructor(modelPath) { + if (!env().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") { + throw new Error("The current environment does not support local storage."); + } + this.LS = window.localStorage; + if (modelPath == null || !modelPath) { + throw new Error("For local storage, modelPath must not be null, undefined or empty."); + } + this.modelPath = modelPath; + this.keys = getModelKeys(this.modelPath); + } + /** + * Save model artifacts to browser local storage. + * + * See the documentation to `browserLocalStorage` for details on the saved + * artifacts. + * + * @param modelArtifacts The model artifacts to be stored. + * @returns An instance of SaveResult. + */ + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); + } else { + const topology = JSON.stringify(modelArtifacts.modelTopology); + const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs); + const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts); + const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData); + try { + this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo)); + this.LS.setItem(this.keys.topology, topology); + this.LS.setItem(this.keys.weightSpecs, weightSpecs); + this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(weightBuffer)); + const metadata = { + format: modelArtifacts.format, + generatedBy: modelArtifacts.generatedBy, + convertedBy: modelArtifacts.convertedBy, + signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0, + userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0, + modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0, + initializerSignature: modelArtifacts.initializerSignature != null ? modelArtifacts.initializerSignature : void 0, + trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0 + }; + this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata)); + return { modelArtifactsInfo }; + } catch (err) { + removeItems(this.keys); + throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`); + } + } + } + /** + * Load a model from local storage. + * + * See the documentation to `browserLocalStorage` for details on the saved + * artifacts. + * + * @returns The loaded model (if loading succeeds). + */ + async load() { + const info = JSON.parse(this.LS.getItem(this.keys.info)); + if (info == null) { + throw new Error(`In local storage, there is no model with name '${this.modelPath}'`); + } + if (info.modelTopologyType !== "JSON") { + throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet."); + } + const out = {}; + const topology = JSON.parse(this.LS.getItem(this.keys.topology)); + if (topology == null) { + throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`); + } + out.modelTopology = topology; + const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs)); + if (weightSpecs == null) { + throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`); + } + out.weightSpecs = weightSpecs; + const metadataString = this.LS.getItem(this.keys.modelMetadata); + if (metadataString != null) { + const metadata = JSON.parse(metadataString); + out.format = metadata.format; + out.generatedBy = metadata.generatedBy; + out.convertedBy = metadata.convertedBy; + if (metadata.signature != null) { + out.signature = metadata.signature; + } + if (metadata.userDefinedMetadata != null) { + out.userDefinedMetadata = metadata.userDefinedMetadata; + } + if (metadata.modelInitializer != null) { + out.modelInitializer = metadata.modelInitializer; + } + if (metadata.initializerSignature != null) { + out.initializerSignature = metadata.initializerSignature; + } + if (metadata.trainingConfig != null) { + out.trainingConfig = metadata.trainingConfig; + } + } + const weightDataBase64 = this.LS.getItem(this.keys.weightData); + if (weightDataBase64 == null) { + throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`); + } + out.weightData = base64StringToArrayBuffer(weightDataBase64); + return out; + } +}; +BrowserLocalStorage.URL_SCHEME = "localstorage://"; +var localStorageRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) { + return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(localStorageRouter); +IORouterRegistry.registerLoadRouter(localStorageRouter); +function browserLocalStorage(modelPath) { + return new BrowserLocalStorage(modelPath); +} +var BrowserLocalStorageManager = class { + constructor() { + assert(env().getBool("IS_BROWSER"), () => "Current environment is not a web browser"); + assert(typeof window === "undefined" || typeof window.localStorage !== "undefined", () => "Current browser does not appear to support localStorage"); + this.LS = window.localStorage; + } + async listModels() { + const out = {}; + const prefix = PATH_PREFIX + PATH_SEPARATOR; + const suffix = PATH_SEPARATOR + INFO_SUFFIX; + for (let i = 0; i < this.LS.length; ++i) { + const key = this.LS.key(i); + if (key.startsWith(prefix) && key.endsWith(suffix)) { + const modelPath = getModelPathFromKey(key); + out[modelPath] = JSON.parse(this.LS.getItem(key)); + } + } + return out; + } + async removeModel(path) { + path = maybeStripScheme2(path); + const keys = getModelKeys(path); + if (this.LS.getItem(keys.info) == null) { + throw new Error(`Cannot find model at path '${path}'`); + } + const info = JSON.parse(this.LS.getItem(keys.info)); + removeItems(keys); + return info; + } +}; +var URL_SCHEME_SUFFIX = "://"; +var ModelStoreManagerRegistry = class _ModelStoreManagerRegistry { + constructor() { + this.managers = {}; + } + static getInstance() { + if (_ModelStoreManagerRegistry.instance == null) { + _ModelStoreManagerRegistry.instance = new _ModelStoreManagerRegistry(); + } + return _ModelStoreManagerRegistry.instance; + } + /** + * Register a save-handler router. + * + * @param saveRouter A function that maps a URL-like string onto an instance + * of `IOHandler` with the `save` method defined or `null`. + */ + static registerManager(scheme, manager) { + assert(scheme != null, () => "scheme must not be undefined or null."); + if (scheme.endsWith(URL_SCHEME_SUFFIX)) { + scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX)); + } + assert(scheme.length > 0, () => "scheme must not be an empty string."); + const registry = _ModelStoreManagerRegistry.getInstance(); + assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`); + registry.managers[scheme] = manager; + } + static getManager(scheme) { + const manager = _ModelStoreManagerRegistry.getInstance().managers[scheme]; + if (manager == null) { + throw new Error(`Cannot find model manager for scheme '${scheme}'`); + } + return manager; + } + static getSchemes() { + return Object.keys(_ModelStoreManagerRegistry.getInstance().managers); + } +}; +function parseURL(url) { + if (url.indexOf(URL_SCHEME_SUFFIX) === -1) { + throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`); + } + return { + scheme: url.split(URL_SCHEME_SUFFIX)[0], + path: url.split(URL_SCHEME_SUFFIX)[1] + }; +} +async function cloneModelInternal(sourceURL, destURL, deleteSource = false) { + assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`); + const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL); + assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`); + assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`); + const loadHandler = loadHandlers[0]; + const saveHandlers = IORouterRegistry.getSaveHandlers(destURL); + assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`); + assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`); + const saveHandler = saveHandlers[0]; + const sourceScheme = parseURL(sourceURL).scheme; + const sourcePath = parseURL(sourceURL).path; + const sameMedium = sourceScheme === parseURL(sourceURL).scheme; + const modelArtifacts = await loadHandler.load(); + if (deleteSource && sameMedium) { + await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath); + } + const saveResult = await saveHandler.save(modelArtifacts); + if (deleteSource && !sameMedium) { + await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath); + } + return saveResult.modelArtifactsInfo; +} +async function listModels() { + const schemes = ModelStoreManagerRegistry.getSchemes(); + const out = {}; + for (const scheme of schemes) { + const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels(); + for (const path in schemeOut) { + const url = scheme + URL_SCHEME_SUFFIX + path; + out[url] = schemeOut[path]; + } + } + return out; +} +async function removeModel(url) { + const schemeAndPath = parseURL(url); + const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme); + return manager.removeModel(schemeAndPath.path); +} +async function copyModel(sourceURL, destURL) { + const deleteSource = false; + return cloneModelInternal(sourceURL, destURL, deleteSource); +} +async function moveModel(sourceURL, destURL) { + const deleteSource = true; + return cloneModelInternal(sourceURL, destURL, deleteSource); +} +var PlatformBrowser = class { + constructor() { + this.messageName = "setTimeoutCustom"; + this.functionRefs = []; + this.handledMessageCount = 0; + this.hasEventListener = false; + } + fetch(path, init2) { + return fetch(path, init2); + } + now() { + return performance.now(); + } + encode(text, encoding) { + if (encoding !== "utf-8" && encoding !== "utf8") { + throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`); + } + if (this.textEncoder == null) { + this.textEncoder = new TextEncoder(); + } + return this.textEncoder.encode(text); + } + decode(bytes, encoding) { + return new TextDecoder(encoding).decode(bytes); + } + // If the setTimeout nesting level is greater than 5 and timeout is less + // than 4ms, timeout will be clamped to 4ms, which hurts the perf. + // Interleaving window.postMessage and setTimeout will trick the browser and + // avoid the clamp. + setTimeoutCustom(functionRef, delay) { + if (typeof window === "undefined" || !env().getBool("USE_SETTIMEOUTCUSTOM")) { + setTimeout(functionRef, delay); + return; + } + this.functionRefs.push(functionRef); + setTimeout(() => { + window.postMessage({ name: this.messageName, index: this.functionRefs.length - 1 }, "*"); + }, delay); + if (!this.hasEventListener) { + this.hasEventListener = true; + window.addEventListener("message", (event) => { + if (event.source === window && event.data.name === this.messageName) { + event.stopPropagation(); + const functionRef2 = this.functionRefs[event.data.index]; + functionRef2(); + this.handledMessageCount++; + if (this.handledMessageCount === this.functionRefs.length) { + this.functionRefs = []; + this.handledMessageCount = 0; + } + } + }, true); + } + } + isTypedArray(a) { + return isTypedArrayBrowser(a); + } +}; +if (env().get("IS_BROWSER")) { + env().setPlatform("browser", new PlatformBrowser()); + try { + ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager()); + } catch (err) { + } + try { + ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager()); + } catch (err) { + } +} +var getNodeFetch = { + // tslint:disable-next-line:no-require-imports + importFetch: () => require_browser() +}; +var systemFetch; +var PlatformNode = class { + constructor() { + this.util = require_util(); + this.textEncoder = new this.util.TextEncoder(); + } + fetch(path, requestInits) { + if (env().global.fetch != null) { + return env().global.fetch(path, requestInits); + } + if (systemFetch == null) { + systemFetch = getNodeFetch.importFetch(); + } + return systemFetch(path, requestInits); + } + now() { + const time2 = process.hrtime(); + return time2[0] * 1e3 + time2[1] / 1e6; + } + encode(text, encoding) { + if (encoding !== "utf-8" && encoding !== "utf8") { + throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`); + } + return this.textEncoder.encode(text); + } + decode(bytes, encoding) { + if (bytes.length === 0) { + return ""; + } + return new this.util.TextDecoder(encoding).decode(bytes); + } + isTypedArray(a) { + return this.util.types.isFloat32Array(a) || this.util.types.isInt32Array(a) || this.util.types.isUint8Array(a) || this.util.types.isUint8ClampedArray(a); + } +}; +if (env().get("IS_NODE") && !env().get("IS_BROWSER")) { + env().setPlatform("node", new PlatformNode()); +} +function buffer(shape, dtype = "float32", values) { + dtype = dtype || "float32"; + assertNonNegativeIntegerDimensions(shape); + return new TensorBuffer(shape, dtype, values); +} +function cast_(x, dtype) { + const $x = convertToTensor(x, "x", "cast"); + if (!isValidDtype(dtype)) { + throw new Error(`Failed to cast to unknown dtype ${dtype}`); + } + if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") { + throw new Error("Only strings can be casted to strings"); + } + const inputs = { x: $x }; + const attrs = { dtype }; + return ENGINE.runKernel(Cast, inputs, attrs); +} +var cast = op({ cast_ }); +function clone_(x) { + const $x = convertToTensor(x, "x", "clone", "string_or_numeric"); + const inputs = { x: $x }; + return ENGINE.runKernel(Identity, inputs); +} +var clone = op({ clone_ }); +function print(x, verbose = false) { + console.log(x.toString(verbose)); +} +getOrMakeEngine(); +var opHandler2 = { + buffer, + cast, + clone, + print +}; +setOpHandler(opHandler2); +function add_(a, b) { + let $a = convertToTensor(a, "a", "add"); + let $b = convertToTensor(b, "b", "add"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Add, inputs); +} +var add2 = op({ add_ }); +function floorDiv_(a, b) { + let $a = convertToTensor(a, "a", "floorDiv"); + let $b = convertToTensor(b, "b", "floorDiv"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(FloorDiv, inputs); +} +var floorDiv = op({ floorDiv_ }); +function div_(a, b) { + let $a = convertToTensor(a, "a", "div"); + let $b = convertToTensor(b, "b", "div"); + [$a, $b] = makeTypesMatch($a, $b); + if ($a.dtype === "int32" && $b.dtype === "int32") { + return floorDiv($a, $b); + } + const inputs = { a: $a, b: $b }; + const attrs = {}; + return ENGINE.runKernel(RealDiv, inputs, attrs); +} +var div = op({ div_ }); +function mul_(a, b) { + let $a = convertToTensor(a, "a", "mul"); + let $b = convertToTensor(b, "b", "mul"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Multiply, inputs); +} +var mul = op({ mul_ }); +function abs_(x) { + const $x = convertToTensor(x, "x", "abs"); + if ($x.dtype === "complex64") { + const inputs = { x: $x }; + return ENGINE.runKernel(ComplexAbs, inputs); + } else { + const inputs = { x: $x }; + return ENGINE.runKernel(Abs, inputs); + } +} +var abs = op({ abs_ }); +function acos_(x) { + const $x = convertToTensor(x, "x", "acos"); + const inputs = { x: $x }; + return ENGINE.runKernel(Acos, inputs); +} +var acos = op({ acos_ }); +function acosh_(x) { + const $x = convertToTensor(x, "x", "acosh"); + const inputs = { x: $x }; + return ENGINE.runKernel(Acosh, inputs); +} +var acosh = op({ acosh_ }); +function addN_(tensors) { + assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors"); + assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`); + const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "addN")); + const firstTensor = $tensors[0]; + $tensors.forEach((t) => { + if (t.dtype !== firstTensor.dtype) { + throw new Error("All tensors passed to tf.addN() must have the same dtype"); + } + }); + $tensors.forEach((t) => { + if (!arraysEqual(t.shape, firstTensor.shape)) { + throw new Error("All tensors passed to tf.addN() must have the same shape"); + } + }); + const inputs = $tensors; + return ENGINE.runKernel(AddN, inputs); +} +var addN = op({ addN_ }); +function all_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "all", "bool"); + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(All, inputs, attrs); +} +var all = op({ all_ }); +function any_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "any", "bool"); + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(Any, inputs, attrs); +} +var any = op({ any_ }); +function argMax_(x, axis = 0) { + const $x = convertToTensor(x, "x", "argMax"); + const inputs = { x: $x }; + const attrs = { axis }; + return ENGINE.runKernel(ArgMax, inputs, attrs); +} +var argMax = op({ argMax_ }); +function argMin_(x, axis = 0) { + const $x = convertToTensor(x, "x", "argMin"); + const inputs = { x: $x }; + const attrs = { axis }; + return ENGINE.runKernel(ArgMin, inputs, attrs); +} +var argMin = op({ argMin_ }); +function asin_(x) { + const $x = convertToTensor(x, "x", "asin"); + const inputs = { x: $x }; + return ENGINE.runKernel(Asin, inputs); +} +var asin = op({ asin_ }); +function asinh_(x) { + const $x = convertToTensor(x, "x", "asinh"); + const inputs = { x: $x }; + return ENGINE.runKernel(Asinh, inputs); +} +var asinh = op({ asinh_ }); +function atan_(x) { + const $x = convertToTensor(x, "x", "atan"); + const inputs = { x: $x }; + return ENGINE.runKernel(Atan, inputs); +} +var atan = op({ atan_ }); +function atan2_(a, b) { + let $a = convertToTensor(a, "a", "atan2"); + let $b = convertToTensor(b, "b", "atan2"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Atan2, inputs); +} +var atan2 = op({ atan2_ }); +function atanh_(x) { + const $x = convertToTensor(x, "x", "atanh"); + const inputs = { x: $x }; + return ENGINE.runKernel(Atanh, inputs); +} +var atanh = op({ atanh_ }); +function computeDilation2DInfo(inputShape, filterShape, strides, pad3, dataFormat = "NHWC", dilations) { + const inputChannels = inputShape[3]; + const $filterShape = [...filterShape, inputChannels]; + const $dataFormat = convertConv2DDataFormat(dataFormat); + return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad3, null, null, $dataFormat); +} +function computePool2DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = "channelsLast") { + const [filterHeight, filterWidth] = parseTupleParam(filterSize); + let filterShape; + if (dataFormat === "channelsLast") { + filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]]; + } else if (dataFormat === "channelsFirst") { + filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]]; + } else { + throw new Error(`Unknown dataFormat ${dataFormat}`); + } + return computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, false, dataFormat); +} +function computePool3DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = "NDHWC") { + const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize); + let filterShape; + let $dataFormat; + if (dataFormat === "NDHWC") { + $dataFormat = "channelsLast"; + filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]]; + } else if (dataFormat === "NCDHW") { + $dataFormat = "channelsFirst"; + filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]]; + } else { + throw new Error(`Unknown dataFormat ${dataFormat}`); + } + return computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, false, $dataFormat, roundingMode); +} +function computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, depthwise = false, dataFormat = "channelsLast") { + let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1]; + if (dataFormat === "channelsLast") { + [batchSize, inHeight, inWidth, inChannels] = inShape; + } else if (dataFormat === "channelsFirst") { + [batchSize, inChannels, inHeight, inWidth] = inShape; + } else { + throw new Error(`Unknown dataFormat ${dataFormat}`); + } + const [filterHeight, filterWidth, , filterChannels] = filterShape; + const [strideHeight, strideWidth] = parseTupleParam(strides); + const [dilationHeight, dilationWidth] = parseTupleParam(dilations); + const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight); + const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth); + const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat); + const outChannels = depthwise ? filterChannels * inChannels : filterChannels; + let outShape; + if (dataFormat === "channelsFirst") { + outShape = [batchSize, outChannels, outHeight, outWidth]; + } else if (dataFormat === "channelsLast") { + outShape = [batchSize, outHeight, outWidth, outChannels]; + } + return { + batchSize, + dataFormat, + inHeight, + inWidth, + inChannels, + outHeight, + outWidth, + outChannels, + padInfo, + strideHeight, + strideWidth, + filterHeight, + filterWidth, + effectiveFilterHeight, + effectiveFilterWidth, + dilationHeight, + dilationWidth, + inShape, + outShape, + filterShape + }; +} +function computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, depthwise = false, dataFormat = "channelsLast", roundingMode) { + let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1]; + if (dataFormat === "channelsLast") { + [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape; + } else if (dataFormat === "channelsFirst") { + [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape; + } else { + throw new Error(`Unknown dataFormat ${dataFormat}`); + } + const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape; + const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides); + const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations); + const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth); + const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight); + const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth); + const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode); + const outChannels = depthwise ? filterChannels * inChannels : filterChannels; + let outShape; + if (dataFormat === "channelsFirst") { + outShape = [batchSize, outChannels, outDepth, outHeight, outWidth]; + } else if (dataFormat === "channelsLast") { + outShape = [batchSize, outDepth, outHeight, outWidth, outChannels]; + } + return { + batchSize, + dataFormat, + inDepth, + inHeight, + inWidth, + inChannels, + outDepth, + outHeight, + outWidth, + outChannels, + padInfo, + strideDepth, + strideHeight, + strideWidth, + filterDepth, + filterHeight, + filterWidth, + effectiveFilterDepth, + effectiveFilterHeight, + effectiveFilterWidth, + dilationDepth, + dilationHeight, + dilationWidth, + inShape, + outShape, + filterShape + }; +} +function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) { + if (zeroPad == null) { + zeroPad = computeDefaultPad(inShape, fieldSize, stride); + } + const inputRows = inShape[0]; + const inputCols = inShape[1]; + const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode); + const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode); + return [outputRows, outputCols]; +} +function computeOutputShape4D(inShape, filterShape, outChannels, strides, zeroPad, roundingMode) { + if (zeroPad == null) { + zeroPad = computeDefaultPad(inShape, filterShape[0], strides[0]); + } + const outShape = [0, 0, 0, outChannels]; + for (let index = 0; index < 3; index++) { + if (inShape[index] + 2 * zeroPad >= filterShape[index]) { + outShape[index] = round((inShape[index] - filterShape[index] + 2 * zeroPad) / strides[index] + 1, roundingMode); + } + } + return outShape; +} +function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) { + const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation); + return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2); +} +function parseTupleParam(param) { + if (typeof param === "number") { + return [param, param, param]; + } + if (param.length === 2) { + return [param[0], param[1], 1]; + } + return param; +} +function parse3TupleParam(param) { + return typeof param === "number" ? [param, param, param] : param; +} +function getEffectiveFilterSize(filterSize, dilation) { + if (dilation <= 1) { + return filterSize; + } + return filterSize + (filterSize - 1) * (dilation - 1); +} +function getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) { + let padInfo; + let outHeight; + let outWidth; + if (typeof pad3 === "number") { + const padType = pad3 === 0 ? "VALID" : "NUMBER"; + padInfo = { top: pad3, bottom: pad3, left: pad3, right: pad3, type: padType }; + const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad3, roundingMode); + outHeight = outShape[0]; + outWidth = outShape[1]; + } else if (pad3 === "same") { + outHeight = Math.ceil(inHeight / strideHeight); + outWidth = Math.ceil(inWidth / strideWidth); + const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight); + const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth); + const top = Math.floor(padAlongHeight / 2); + const bottom = padAlongHeight - top; + const left = Math.floor(padAlongWidth / 2); + const right = padAlongWidth - left; + padInfo = { top, bottom, left, right, type: "SAME" }; + } else if (pad3 === "valid") { + padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }; + outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight); + outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth); + } else if (typeof pad3 === "object") { + const top = dataFormat === "channelsLast" ? pad3[1][0] : pad3[2][0]; + const bottom = dataFormat === "channelsLast" ? pad3[1][1] : pad3[2][1]; + const left = dataFormat === "channelsLast" ? pad3[2][0] : pad3[3][0]; + const right = dataFormat === "channelsLast" ? pad3[2][1] : pad3[3][1]; + const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT"; + padInfo = { top, bottom, left, right, type: padType }; + outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode); + outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode); + } else { + throw Error(`Unknown padding parameter: ${pad3}`); + } + return { padInfo, outHeight, outWidth }; +} +function get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) { + let padInfo; + let outDepth; + let outHeight; + let outWidth; + if (pad3 === "valid") { + pad3 = 0; + } + if (typeof pad3 === "number") { + const padType = pad3 === 0 ? "VALID" : "NUMBER"; + padInfo = { + top: pad3, + bottom: pad3, + left: pad3, + right: pad3, + front: pad3, + back: pad3, + type: padType + }; + const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], [filterDepth, filterHeight, filterWidth], 1, [strideDepth, strideHeight, strideWidth], pad3, roundingMode); + outDepth = outShape[0]; + outHeight = outShape[1]; + outWidth = outShape[2]; + } else if (pad3 === "same") { + outDepth = Math.ceil(inDepth / strideDepth); + outHeight = Math.ceil(inHeight / strideHeight); + outWidth = Math.ceil(inWidth / strideWidth); + const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth; + const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight; + const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth; + const front = Math.floor(padAlongDepth / 2); + const back = padAlongDepth - front; + const top = Math.floor(padAlongHeight / 2); + const bottom = padAlongHeight - top; + const left = Math.floor(padAlongWidth / 2); + const right = padAlongWidth - left; + padInfo = { top, bottom, left, right, front, back, type: "SAME" }; + } else { + throw Error(`Unknown padding parameter: ${pad3}`); + } + return { padInfo, outDepth, outHeight, outWidth }; +} +function round(value, roundingMode) { + if (!roundingMode) { + return Math.trunc(value); + } + switch (roundingMode) { + case "round": + return Math.round(value); + case "ceil": + return Math.ceil(value); + case "floor": + return Math.floor(value); + default: + throw new Error(`Unknown roundingMode ${roundingMode}`); + } +} +function tupleValuesAreOne(param) { + const [dimA, dimB, dimC] = parseTupleParam(param); + return dimA === 1 && dimB === 1 && dimC === 1; +} +function eitherStridesOrDilationsAreOne(strides, dilations) { + return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations); +} +function stridesOrDilationsArePositive(values) { + return parseTupleParam(values).every((value) => value > 0); +} +function convertConv2DDataFormat(dataFormat) { + if (dataFormat === "NHWC") { + return "channelsLast"; + } else if (dataFormat === "NCHW") { + return "channelsFirst"; + } else { + throw new Error(`Unknown dataFormat ${dataFormat}`); + } +} +function checkPadOnDimRoundingMode(opDesc, pad3, dimRoundingMode) { + if (dimRoundingMode != null) { + if (typeof pad3 === "string") { + throw Error(`Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); + } else if (typeof pad3 === "number") { + assert(isInt(pad3), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); + } else if (typeof pad3 === "object") { + pad3.forEach((p2) => { + p2.forEach((v) => { + assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${v}.`); + }); + }); + } else { + throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad3}`); + } + } +} +function reshape_(x, shape) { + const $x = convertToTensor(x, "x", "reshape", "string_or_numeric"); + const inputs = { x: $x }; + const attrs = { shape }; + return ENGINE.runKernel(Reshape, inputs, attrs); +} +var reshape = op({ reshape_ }); +function avgPool_(x, filterSize, strides, pad3, dimRoundingMode) { + const $x = convertToTensor(x, "x", "avgPool", "float32"); + const dilations = 1; + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`); + checkPadOnDimRoundingMode("avgPool", pad3, dimRoundingMode); + const inputs = { x: x4D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; + let res = ENGINE.runKernel(AvgPool, inputs, attrs); + res = cast(res, $x.dtype); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var avgPool = op({ avgPool_ }); +function avgPool3d_(x, filterSize, strides, pad3, dimRoundingMode, dataFormat = "NDHWC") { + const $x = convertToTensor(x, "x", "avgPool3d", "float32"); + let x5D = $x; + let reshapedTo5D = false; + if ($x.rank === 4) { + reshapedTo5D = true; + x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]); + } + assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`); + assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`); + assert(typeof strides === "number" && strides > 0 || Array.isArray(strides) && strides[0] > 0 && strides[1] > 0 && strides[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${strides}'`); + checkPadOnDimRoundingMode("avgPool3d", pad3, dimRoundingMode); + const inputs = { x: x5D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat }; + let res = ENGINE.runKernel(AvgPool3D, inputs, attrs); + res = cast(res, x5D.dtype); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var avgPool3d = op({ avgPool3d_ }); +function concat_(tensors, axis = 0) { + assert(tensors.length >= 1, () => "Pass at least one tensor to concat"); + const $tensors = convertToTensorArray(tensors, "tensors", "concat", "string_or_numeric"); + if ($tensors[0].dtype === "complex64") { + $tensors.forEach((tensor2) => { + if (tensor2.dtype !== "complex64") { + throw new Error(`Cannot concatenate complex64 tensors with a tensor + with dtype ${tensor2.dtype}. `); + } + }); + } + if ($tensors.length === 1) { + return clone($tensors[0]); + } + const inputs = $tensors; + const attr = { axis }; + return ENGINE.runKernel(Concat, inputs, attr); +} +var concat = op({ concat_ }); +function matMul_(a, b, transposeA = false, transposeB = false) { + let $a = convertToTensor(a, "a", "matMul"); + let $b = convertToTensor(b, "b", "matMul"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + const attrs = { transposeA, transposeB }; + return ENGINE.runKernel(BatchMatMul, inputs, attrs); +} +var matMul = op({ matMul_ }); +function sigmoid_(x) { + const $x = convertToTensor(x, "x", "sigmoid", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Sigmoid, inputs); +} +var sigmoid = op({ sigmoid_ }); +function slice_(x, begin, size) { + const $x = convertToTensor(x, "x", "slice", "string_or_numeric"); + if ($x.rank === 0) { + throw new Error("Slicing scalar is not possible"); + } + const inputs = { x: $x }; + const attrs = { begin, size }; + return ENGINE.runKernel(Slice, inputs, attrs); +} +var slice = op({ slice_ }); +function tanh_(x) { + const $x = convertToTensor(x, "x", "tanh", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Tanh, inputs); +} +var tanh2 = op({ tanh_ }); +function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) { + const $forgetBias = convertToTensor(forgetBias, "forgetBias", "basicLSTMCell"); + const $lstmKernel = convertToTensor(lstmKernel, "lstmKernel", "basicLSTMCell"); + const $lstmBias = convertToTensor(lstmBias, "lstmBias", "basicLSTMCell"); + const $data = convertToTensor(data, "data", "basicLSTMCell"); + const $c = convertToTensor(c, "c", "basicLSTMCell"); + const $h = convertToTensor(h, "h", "basicLSTMCell"); + const combined = concat([$data, $h], 1); + const weighted = matMul(combined, $lstmKernel); + const res = add2(weighted, $lstmBias); + const batchSize = res.shape[0]; + const sliceCols = res.shape[1] / 4; + const sliceSize = [batchSize, sliceCols]; + const i = slice(res, [0, 0], sliceSize); + const j = slice(res, [0, sliceCols], sliceSize); + const f = slice(res, [0, sliceCols * 2], sliceSize); + const o = slice(res, [0, sliceCols * 3], sliceSize); + const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f)))); + const newH = mul(tanh2(newC), sigmoid(o)); + return [newC, newH]; +} +var basicLSTMCell = op({ basicLSTMCell_ }); +function batchToSpaceND_(x, blockShape, crops) { + const $x = convertToTensor(x, "x", "batchToSpaceND"); + const prod5 = blockShape.reduce((a, b) => a * b); + assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`); + assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length ${blockShape.length}`); + assert($x.shape[0] % prod5 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`); + const inputs = { x: $x }; + const attrs = { blockShape, crops }; + return ENGINE.runKernel(BatchToSpaceND, inputs, attrs); +} +var batchToSpaceND = op({ batchToSpaceND_ }); +function xAs4D(x) { + let x4D; + if (x.rank === 0 || x.rank === 1) { + x4D = reshape(x, [1, 1, 1, x.size]); + } else if (x.rank === 2) { + x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]); + } else if (x.rank === 3) { + x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); + } else { + x4D = x; + } + return x4D; +} +function batchNorm_(x, mean4, variance, offset, scale22, varianceEpsilon) { + if (varianceEpsilon == null) { + varianceEpsilon = 1e-3; + } + const $x = convertToTensor(x, "x", "batchNorm"); + const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $variance = convertToTensor(variance, "variance", "batchNorm"); + let $scale; + if (scale22 != null) { + $scale = convertToTensor(scale22, "scale", "batchNorm"); + } + let $offset; + if (offset != null) { + $offset = convertToTensor(offset, "offset", "batchNorm"); + } + assert($mean.rank === $variance.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."); + assert($offset == null || $mean.rank === $offset.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."); + assert($scale == null || $mean.rank === $scale.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks."); + const x4D = xAs4D($x); + const inputs = { + x: x4D, + scale: $scale, + offset: $offset, + mean: $mean, + variance: $variance + }; + const attrs = { varianceEpsilon }; + const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs); + return reshape(res, $x.shape); +} +var batchNorm = op({ batchNorm_ }); +function batchNorm2d_(x, mean4, variance, offset, scale22, varianceEpsilon) { + const $x = convertToTensor(x, "x", "batchNorm"); + const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $variance = convertToTensor(variance, "variance", "batchNorm"); + let $scale; + if (scale22 != null) { + $scale = convertToTensor(scale22, "scale", "batchNorm"); + } + let $offset; + if (offset != null) { + $offset = convertToTensor(offset, "offset", "batchNorm"); + } + assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`); + assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`); + assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`); + if ($scale != null) { + assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`); + } + if ($offset != null) { + assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`); + } + return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); +} +var batchNorm2d = op({ batchNorm2d_ }); +function batchNorm3d_(x, mean4, variance, offset, scale22, varianceEpsilon) { + const $x = convertToTensor(x, "x", "batchNorm"); + const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $variance = convertToTensor(variance, "variance", "batchNorm"); + let $scale; + if (scale22 != null) { + $scale = convertToTensor(scale22, "scale", "batchNorm"); + } + let $offset; + if (offset != null) { + $offset = convertToTensor(offset, "offset", "batchNorm"); + } + assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`); + assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`); + assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`); + if ($scale != null) { + assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`); + } + if ($offset != null) { + assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`); + } + return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); +} +var batchNorm3d = op({ batchNorm3d_ }); +function batchNorm4d_(x, mean4, variance, offset, scale22, varianceEpsilon) { + const $x = convertToTensor(x, "x", "batchNorm"); + const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $variance = convertToTensor(variance, "variance", "batchNorm"); + let $scale; + if (scale22 != null) { + $scale = convertToTensor(scale22, "scale", "batchNorm"); + } + let $offset; + if (offset != null) { + $offset = convertToTensor(offset, "offset", "batchNorm"); + } + assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`); + assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`); + assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`); + if ($scale != null) { + assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`); + } + if ($offset != null) { + assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`); + } + return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); +} +var batchNorm4d = op({ batchNorm4d_ }); +function bincount_(x, weights, size) { + const $x = convertToTensor(x, "x", "bincount"); + const $weights = convertToTensor(weights, "weights", "bincount"); + assert($x.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${$x.dtype}`); + assert(size >= 0, () => `size must be non-negative, but got ${size}.`); + assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`); + const inputs = { x: $x, weights: $weights }; + const attrs = { size }; + return ENGINE.runKernel(Bincount, inputs, attrs); +} +var bincount = op({ bincount_ }); +function bitwiseAnd_(x, y) { + const $x = convertToTensor(x, "x", "bitwiseAnd"); + const $y = convertToTensor(y, "y", "bitwiseAnd"); + if (!arraysEqual($x.shape, $y.shape)) { + throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${$x.shape}, y: ${$y.shape}`); + } + if ($x.dtype !== "int32" || $y.dtype !== "int32") { + throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${$x.dtype} and type of y: ${$y.dtype}`); + } + const inputs = { a: $x, b: $y }; + return ENGINE.runKernel(BitwiseAnd, inputs); +} +var bitwiseAnd = op({ bitwiseAnd_ }); +function broadcastArgs_(s0, s1) { + const shape1Input = convertToTensor(s0, "s0", "broadcastArgs", "int32"); + const shape2Input = convertToTensor(s1, "s1", "broadcastArgs", "int32"); + if (shape1Input.rank !== 1) { + throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${shape1Input.rank}`); + } + if (shape2Input.rank !== 1) { + throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${shape2Input.rank}`); + } + const inputs = { s0: shape1Input, s1: shape2Input }; + return ENGINE.runKernel(BroadcastArgs, inputs); +} +var broadcastArgs = op({ broadcastArgs_ }); +function broadcastTo_(x, shape) { + let input2 = convertToTensor(x, "broadcastTo", "x"); + const xShape = input2.shape; + assertNonNegativeIntegerDimensions(shape); + if (shape.length < input2.rank) { + throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`); + } + if (shape.length > input2.rank) { + const newShape = input2.shape.slice(); + while (newShape.length < shape.length) { + newShape.unshift(1); + } + input2 = reshape(input2, newShape); + } + const inputShape = input2.shape; + const reps = Array.from(shape); + for (let i = shape.length - 1; i >= 0; i--) { + if (inputShape[i] === shape[i]) { + reps[i] = 1; + } else if (input2.shape[i] !== 1) { + throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`); + } + } + const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0); + if (axes.length === 0) { + return clone(input2); + } + const inputs = { x: input2 }; + const attrs = { reps }; + return ENGINE.runKernel(Tile, inputs, attrs); +} +var broadcastTo = op({ broadcastTo_ }); +function ceil_(x) { + const $x = convertToTensor(x, "x", "ceil", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Ceil, inputs); +} +var ceil = op({ ceil_ }); +function fill(shape, value, dtype) { + assertNonNegativeIntegerDimensions(shape); + dtype = dtype || inferDtype(value); + const attrs = { shape, value, dtype }; + return ENGINE.runKernel(Fill, {}, attrs); +} +function clipByValue_(x, clipValueMin, clipValueMax) { + const $x = convertToTensor(x, "x", "clipByValue"); + assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`); + if (clipValueMin === clipValueMax) { + return fill($x.shape, clipValueMin, $x.dtype); + } + const inputs = { x: $x }; + const attrs = { clipValueMin, clipValueMax }; + return ENGINE.runKernel(ClipByValue, inputs, attrs); +} +var clipByValue = op({ clipByValue_ }); +function concat1d_(tensors) { + return concat( + tensors, + 0 + /* axis */ + ); +} +var concat1d = op({ concat1d_ }); +function concat2d_(tensors, axis) { + return concat(tensors, axis); +} +var concat2d = op({ concat2d_ }); +function concat3d_(tensors, axis) { + return concat(tensors, axis); +} +var concat3d = op({ concat3d_ }); +function concat4d_(tensors, axis) { + return concat(tensors, axis); +} +var concat4d = op({ concat4d_ }); +function conv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) { + const $x = convertToTensor(x, "x", "conv2d", "float32"); + const $filter = convertToTensor(filter, "filter", "conv2d", "float32"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`); + assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`); + checkPadOnDimRoundingMode("conv2d", pad3, dimRoundingMode); + const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1]; + assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`); + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + assert(stridesOrDilationsArePositive(dilations), () => "Error in conv2D: Dilated rates should be larger than 0."); + assert(stridesOrDilationsArePositive(strides), () => "Error in conv2D: Strides should be larger than 0."); + const inputs = { x: x4D, filter: $filter }; + const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }; + const res = ENGINE.runKernel(Conv2D, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var conv2d = op({ conv2d_ }); +function conv1d_(x, filter, stride, pad3, dataFormat = "NWC", dilation = 1, dimRoundingMode) { + const $x = convertToTensor(x, "x", "conv1d"); + const $filter = convertToTensor(filter, "filter", "conv1d"); + let x3D = $x; + let reshapedTo3D = false; + if ($x.rank === 2) { + reshapedTo3D = true; + x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]); + } + assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`); + assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`); + checkPadOnDimRoundingMode("conv1d", pad3, dimRoundingMode); + assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`); + assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`); + assert(stridesOrDilationsArePositive(dilation), () => "Error in conv1D: Dilated rates should be larger than 0."); + assert(stridesOrDilationsArePositive(stride), () => "Error in conv1D: Stride should be larger than 0."); + assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`); + const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]); + const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]); + const strides = [1, stride]; + const dilations = [1, dilation]; + const conv2dDataFormat = "NHWC"; + const res = conv2d(input4D, filter4D, strides, pad3, conv2dDataFormat, dilations, dimRoundingMode); + if (reshapedTo3D) { + return reshape(res, [res.shape[2], res.shape[3]]); + } + return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]); +} +var conv1d = op({ conv1d_ }); +function conv2DBackpropInput_(xShape, dy, filter, strides, pad3, dataFormat = "NHWC", dimRoundingMode) { + assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`); + let xShape4D = xShape; + let dy4D = dy; + let reshapedTo4D = false; + if (dy.rank === 3) { + reshapedTo4D = true; + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + xShape4D = [1, xShape[0], xShape[1], xShape[2]]; + } + assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`); + assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`); + assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`); + const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1]; + const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1]; + assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`); + assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`); + checkPadOnDimRoundingMode("conv2dDerInput", pad3, dimRoundingMode); + const inputs = { dy: dy4D, filter }; + const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape: xShape4D }; + const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var conv2DBackpropInput = op({ conv2DBackpropInput_ }); +function conv2dTranspose_(x, filter, outputShape, strides, pad3, dimRoundingMode) { + const $x = convertToTensor(x, "x", "conv2dTranspose"); + const $filter = convertToTensor(filter, "filter", "conv2dTranspose"); + return conv2DBackpropInput(outputShape, $x, $filter, strides, pad3, "NHWC", dimRoundingMode); +} +var conv2dTranspose = op({ conv2dTranspose_ }); +function conv3d_(x, filter, strides, pad3, dataFormat = "NDHWC", dilations = [1, 1, 1]) { + const $x = convertToTensor(x, "x", "conv3d"); + const $filter = convertToTensor(filter, "filter", "conv3d"); + let x5D = $x; + let reshapedTo5D = false; + if ($x.rank === 4) { + reshapedTo5D = true; + x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]); + } + assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`); + assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`); + assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`); + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + assert(dataFormat === "NDHWC", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`); + assert(stridesOrDilationsArePositive(dilations), () => "Error in conv3D: Dilated rates should be larger than 0."); + assert(stridesOrDilationsArePositive(strides), () => "Error in conv3D: Strides should be larger than 0."); + const inputs = { x: x5D, filter: $filter }; + const attrs = { strides, pad: pad3, dataFormat, dilations }; + const res = ENGINE.runKernel(Conv3D, inputs, attrs); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var conv3d = op({ conv3d_ }); +function conv3DBackpropInput_(xShape, dy, filter, strides, pad3) { + assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`); + let xShape5D = xShape; + let dy5D = dy; + let reshapedTo5D = false; + if (dy.rank === 4) { + reshapedTo5D = true; + dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]); + xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]]; + } + const inDepth = xShape5D[4]; + const outDepth = dy5D.shape[4]; + assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`); + assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`); + assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`); + assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`); + assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`); + const inputs = { dy: dy5D, filter }; + const attrs = { pad: pad3, strides, inputShape: xShape5D }; + const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var conv3DBackpropInput = op({ conv3DBackpropInput_ }); +function conv3dTranspose_(x, filter, outputShape, strides, pad3) { + const $x = convertToTensor(x, "x", "conv3dTranspose"); + const $filter = convertToTensor(filter, "filter", "conv3dTranspose"); + return conv3DBackpropInput(outputShape, $x, $filter, strides, pad3); +} +var conv3dTranspose = op({ conv3dTranspose_ }); +function cos_(x) { + const $x = convertToTensor(x, "x", "cos", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Cos, inputs); +} +var cos = op({ cos_ }); +function cosh_(x) { + const $x = convertToTensor(x, "x", "cosh", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Cosh, inputs); +} +var cosh = op({ cosh_ }); +function cumprod_(x, axis = 0, exclusive = false, reverse5 = false) { + const $x = convertToTensor(x, "x", "cumprod"); + const inputs = { x: $x }; + const attrs = { axis, exclusive, reverse: reverse5 }; + return ENGINE.runKernel(Cumprod, inputs, attrs); +} +var cumprod = op({ cumprod_ }); +function cumsum_(x, axis = 0, exclusive = false, reverse5 = false) { + const $x = convertToTensor(x, "x", "cumsum"); + const inputs = { x: $x }; + const attrs = { axis, exclusive, reverse: reverse5 }; + return ENGINE.runKernel(Cumsum, inputs, attrs); +} +var cumsum = op({ cumsum_ }); +function denseBincount_(x, weights, size, binaryOutput = false) { + const $x = convertToTensor(x, "x", "denseBincount"); + const $weights = convertToTensor(weights, "weights", "denseBincount"); + assert($x.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`); + assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`); + assert(size >= 0, () => `size must be non-negative, but got ${size}.`); + assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`); + const inputs = { x: $x, weights: $weights }; + const attrs = { size, binaryOutput }; + return ENGINE.runKernel(DenseBincount, inputs, attrs); +} +var denseBincount = op({ denseBincount_ }); +function depthToSpace_(x, blockSize, dataFormat = "NHWC") { + const $x = convertToTensor(x, "x", "depthToSpace", "float32"); + const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2]; + const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3]; + const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1]; + assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`); + assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying + ${inputHeight} and ${blockSize} for depthToSpace with input shape + ${$x.shape}`); + assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying + ${inputWidth} and ${blockSize} for depthToSpace with input shape + ${$x.shape}`); + assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`); + const inputs = { x: $x }; + const attrs = { blockSize, dataFormat }; + return ENGINE.runKernel(DepthToSpace, inputs, attrs); +} +var depthToSpace = op({ depthToSpace_ }); +function depthwiseConv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) { + const $x = convertToTensor(x, "x", "depthwiseConv2d", "float32"); + const $filter = convertToTensor(filter, "filter", "depthwiseConv2d", "float32"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`); + assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`); + const inChannels = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1]; + assert(inChannels === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${inChannels}) must match the inChannels dimension in filter ${$filter.shape[2]}.`); + checkPadOnDimRoundingMode("depthwiseConv2d", pad3, dimRoundingMode); + const inputs = { x: x4D, filter: $filter }; + const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }; + const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var depthwiseConv2d = op({ depthwiseConv2d_ }); +function diag_(x) { + const $x = convertToTensor(x, "x", "diag"); + const inputs = { x: $x }; + return ENGINE.runKernel(Diag, inputs); +} +var diag = op({ diag_ }); +function dilation2d_(x, filter, strides, pad3, dilations = [1, 1], dataFormat = "NHWC") { + const $x = convertToTensor(x, "x", "dilation2d"); + const $filter = convertToTensor(filter, "filter", "dilation2d"); + assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`); + assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`); + assert(dataFormat === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + reshapedTo4D = true; + } + assert(x4D.shape[3] === $filter.shape[2], () => `Error in dilation2d: input and filter must have the same depth: ${x4D.shape[3]} vs ${$filter.shape[2]}`); + const inputs = { x: x4D, filter: $filter }; + const attrs = { strides, pad: pad3, dilations }; + const res = ENGINE.runKernel(Dilation2D, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var dilation2d = op({ dilation2d_ }); +var broadcast_util_exports = {}; +__export2(broadcast_util_exports, { + assertAndGetBroadcastShape: () => assertAndGetBroadcastShape, + getBroadcastDims: () => getBroadcastDims, + getReductionAxes: () => getReductionAxes +}); +function getBroadcastDims(inShape, outShape) { + const inRank = inShape.length; + const dims = []; + for (let i = 0; i < inRank; i++) { + const dim = inRank - 1 - i; + const a = inShape[dim] || 1; + const b = outShape[outShape.length - 1 - i] || 1; + if (b > 1 && a === 1) { + dims.unshift(dim); + } + } + return dims; +} +function getReductionAxes(inShape, outShape) { + const result = []; + for (let i = 0; i < outShape.length; i++) { + const inDim = inShape[inShape.length - i - 1]; + const outAxis = outShape.length - i - 1; + const outDim = outShape[outAxis]; + if (inDim == null || inDim === 1 && outDim > 1) { + result.unshift(outAxis); + } + } + return result; +} +function assertAndGetBroadcastShape(shapeA, shapeB) { + const l = Math.max(shapeA.length, shapeB.length); + const result = new Array(l); + for (let i = 0; i < l; i++) { + let a = shapeA[shapeA.length - i - 1]; + if (a == null) { + a = 1; + } + let b = shapeB[shapeB.length - i - 1]; + if (b == null) { + b = 1; + } + if (a === 1) { + result[l - i - 1] = b; + } else if (b === 1) { + result[l - i - 1] = a; + } else if (a !== b) { + const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`; + throw Error(errMsg); + } else { + result[l - i - 1] = a; + } + } + return result; +} +function equal_(a, b) { + let $a = convertToTensor(a, "a", "equal", "string_or_numeric"); + let $b = convertToTensor(b, "b", "equal", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Equal, inputs); +} +var equal = op({ equal_ }); +function where_(condition, a, b) { + const $a = convertToTensor(a, "a", "where"); + const $b = convertToTensor(b, "b", "where"); + const $condition = convertToTensor(condition, "condition", "where", "bool"); + const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape); + const $broadcastedCondition = broadcastTo($condition, broadcastShape); + const $broadcastedA = broadcastTo($a, broadcastShape); + const $broadcastedB = broadcastTo($b, broadcastShape); + const inputs = { + condition: $broadcastedCondition, + t: $broadcastedA, + e: $broadcastedB + }; + return ENGINE.runKernel(Select, inputs); +} +var where = op({ where_ }); +function zerosLike_(x) { + const $x = convertToTensor(x, "x", "zerosLike"); + const inputs = { x: $x }; + return ENGINE.runKernel(ZerosLike, inputs); +} +var zerosLike = op({ zerosLike_ }); +function divNoNan_(a, b) { + let $a = convertToTensor(a, "a", "div"); + let $b = convertToTensor(b, "b", "div"); + [$a, $b] = makeTypesMatch($a, $b); + const divResult = div($a, $b); + const zeros4 = zerosLike(divResult); + const bEqualsZero = equal($b, zeros4); + return where(bEqualsZero, zeros4, divResult); +} +var divNoNan = op({ divNoNan_ }); +function dot_(t1, t2) { + const $t1 = convertToTensor(t1, "t1", "dot"); + const $t2 = convertToTensor(t2, "t2", "dot"); + assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`); + const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1]; + const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0]; + assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`); + if ($t1.rank === 1 && $t2.rank === 1) { + const t12D = reshape($t1, [1, -1]); + const t22D = reshape($t2, [-1, 1]); + const t1t2 = matMul(t12D, t22D); + return reshape(t1t2, []); + } else if ($t1.rank === 1 && $t2.rank === 2) { + const t12D = reshape($t1, [1, -1]); + const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]); + const t1t2 = matMul(t12D, t22D); + return reshape(t1t2, [t1t2.size]); + } else if ($t1.rank === 2 && $t2.rank === 1) { + const t22D = reshape($t2, [-1, 1]); + const t1t2 = matMul($t1, t22D); + return reshape(t1t2, [t1t2.size]); + } else { + const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]); + const t1t2 = matMul($t1, t22D); + return t1t2; + } +} +var dot = op({ dot_ }); +function einsum_(equation, ...tensors) { + const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "einsum")); + const attrs = { equation }; + return ENGINE.runKernel(Einsum, $tensors, attrs); +} +var einsum = op({ einsum_ }); +function elu_(x) { + const $x = convertToTensor(x, "x", "elu", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Elu, inputs); +} +var elu = op({ elu_ }); +function ensureShape_(x, shape) { + const $x = convertToTensor(x, "x", "ensureShape", "string_or_numeric"); + if (!arraysEqualWithNull($x.shape, shape)) { + throw new Error(`EnsureShape: Shape of tensor ${$x.shape} is not compatible with expected shape ${shape}`); + } + return x; +} +var ensureShape = op({ ensureShape_ }); +function erf_(x) { + let $x = convertToTensor(x, "x", "erf"); + assert($x.dtype === "int32" || $x.dtype === "float32", () => "Input dtype must be `int32` or `float32`."); + if ($x.dtype === "int32") { + $x = cast($x, "float32"); + } + const inputs = { x: $x }; + return ENGINE.runKernel(Erf, inputs); +} +var erf = op({ erf_ }); +function axesAreInnerMostDims(axes, rank) { + for (let i = 0; i < axes.length; ++i) { + if (axes[axes.length - i - 1] !== rank - 1 - i) { + return false; + } + } + return true; +} +function combineLocations(outputLoc, reduceLoc, axes) { + const rank = outputLoc.length + reduceLoc.length; + const loc = []; + let outIdx = 0; + let reduceIdx = 0; + for (let dim = 0; dim < rank; dim++) { + if (axes.indexOf(dim) === -1) { + loc.push(outputLoc[outIdx++]); + } else { + loc.push(reduceLoc[reduceIdx++]); + } + } + return loc; +} +function computeOutAndReduceShapes(aShape, axes) { + const outShape = []; + const rank = aShape.length; + for (let dim = 0; dim < rank; dim++) { + if (axes.indexOf(dim) === -1) { + outShape.push(aShape[dim]); + } + } + const reduceShape = axes.map((dim) => aShape[dim]); + return [outShape, reduceShape]; +} +function expandShapeToKeepDim(shape, axes) { + const reduceSubShape = axes.map((x) => 1); + return combineLocations(shape, reduceSubShape, axes); +} +function assertAxesAreInnerMostDims(msg, axes, rank) { + assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`); +} +function getAxesPermutation(axes, rank) { + if (axesAreInnerMostDims(axes, rank)) { + return null; + } + const result = []; + for (let i = 0; i < rank; ++i) { + if (axes.indexOf(i) === -1) { + result.push(i); + } + } + axes.forEach((axis) => result.push(axis)); + return result; +} +function getUndoAxesPermutation(axes) { + return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]); +} +function getInnerMostAxes(numAxes, rank) { + const res = []; + for (let i = rank - numAxes; i < rank; ++i) { + res.push(i); + } + return res; +} +function max_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "max"); + const inputs = { x: $x }; + const attrs = { reductionIndices: axis, keepDims }; + return ENGINE.runKernel(Max, inputs, attrs); +} +var max = op({ max_ }); +function min_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "min"); + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(Min, inputs, attrs); +} +var min = op({ min_ }); +function pow_(base, exp4) { + let $base = convertToTensor(base, "base", "pow"); + let $exp = convertToTensor(exp4, "exp", "pow"); + [$base, $exp] = makeTypesMatch($base, $exp); + const inputs = { a: $base, b: $exp }; + return ENGINE.runKernel(Pow, inputs); +} +var pow = op({ pow_ }); +function scalar(value, dtype) { + if ((isTypedArray(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") { + throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)"); + } + if (dtype === "string" && isTypedArray(value) && !(value instanceof Uint8Array)) { + throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`."); + } + const shape = []; + const inferredShape = []; + return makeTensor(value, shape, inferredShape, dtype); +} +function sqrt_(x) { + const $x = convertToTensor(x, "x", "sqrt", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Sqrt, inputs); +} +var sqrt = op({ sqrt_ }); +function square_(x) { + const $x = convertToTensor(x, "x", "square"); + const attrs = {}; + return ENGINE.runKernel("Square", { x: $x }, attrs); +} +var square = op({ square_ }); +function sum_(x, axis = null, keepDims = false) { + let $x = convertToTensor(x, "x", "sum"); + if ($x.dtype === "bool") { + $x = cast($x, "int32"); + } + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(Sum, inputs, attrs); +} +var sum2 = op({ sum_ }); +function norm_(x, ord = "euclidean", axis = null, keepDims = false) { + x = convertToTensor(x, "x", "norm"); + const norm2 = normImpl(x, ord, axis); + let keepDimsShape = norm2.shape; + if (keepDims) { + const axes = parseAxisParam(axis, x.shape); + keepDimsShape = expandShapeToKeepDim(norm2.shape, axes); + } + return reshape(norm2, keepDimsShape); +} +function normImpl(x, p2, axis = null) { + if (x.rank === 0) { + return abs(x); + } + if (x.rank !== 1 && axis === null) { + return normImpl(reshape(x, [-1]), p2, axis); + } + if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) { + if (p2 === 1) { + return sum2(abs(x), axis); + } + if (p2 === Infinity) { + return max(abs(x), axis); + } + if (p2 === -Infinity) { + return min(abs(x), axis); + } + if (p2 === "euclidean" || p2 === 2) { + return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis)); + } + throw new Error(`Error in norm: invalid ord value: ${p2}`); + } + if (Array.isArray(axis) && axis.length === 2) { + if (p2 === 1) { + return max(sum2(abs(x), axis[0]), axis[1] - 1); + } + if (p2 === Infinity) { + return max(sum2(abs(x), axis[1]), axis[0]); + } + if (p2 === -Infinity) { + return min(sum2(abs(x), axis[1]), axis[0]); + } + if (p2 === "fro" || p2 === "euclidean") { + return sqrt(sum2(square(x), axis)); + } + throw new Error(`Error in norm: invalid ord value: ${p2}`); + } + throw new Error(`Error in norm: invalid axis: ${axis}`); +} +var norm = op({ norm_ }); +function euclideanNorm_(x, axis = null, keepDims = false) { + return norm(x, "euclidean", axis, keepDims); +} +var euclideanNorm = op({ euclideanNorm_ }); +function exp_(x) { + const $x = convertToTensor(x, "x", "exp"); + const inputs = { x: $x }; + return ENGINE.runKernel(Exp, inputs); +} +var exp = op({ exp_ }); +function expandDims_(x, axis = 0) { + const $x = convertToTensor(x, "x", "expandDims", "string_or_numeric"); + assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor"); + const inputs = { input: $x }; + const attrs = { dim: axis }; + return ENGINE.runKernel(ExpandDims, inputs, attrs); +} +var expandDims = op({ expandDims_ }); +function expm1_(x) { + const $x = convertToTensor(x, "x", "expm1"); + const inputs = { x: $x }; + return ENGINE.runKernel(Expm1, inputs); +} +var expm1 = op({ expm1_ }); +function tile_(x, reps) { + const $x = convertToTensor(x, "x", "tile", "string_or_numeric"); + assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`); + const inputs = { x: $x }; + const attrs = { reps }; + return ENGINE.runKernel(Tile, inputs, attrs); +} +var tile = op({ tile_ }); +function eye_(numRows, numColumns, batchShape, dtype = "float32") { + if (numColumns == null) { + numColumns = numRows; + } + const buff = buffer([numRows, numColumns], dtype); + const n = numRows <= numColumns ? numRows : numColumns; + for (let i = 0; i < n; ++i) { + buff.set(1, i, i); + } + const out = reshape(buff.toTensor(), [numRows, numColumns]); + if (batchShape == null) { + return out; + } else { + if (batchShape.length === 1) { + return tile(expandDims(out, 0), [batchShape[0], 1, 1]); + } else if (batchShape.length === 2) { + return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]); + } else if (batchShape.length === 3) { + return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [ + batchShape[0], + batchShape[1], + batchShape[2], + 1, + 1 + ]); + } else { + throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`); + } + } +} +var eye = op({ eye_ }); +function floor_(x) { + const $x = convertToTensor(x, "x", "floor", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Floor, inputs); +} +var floor = op({ floor_ }); +function gather_(x, indices, axis = 0, batchDims = 0) { + const $x = convertToTensor(x, "x", "gather"); + const $indices = convertToTensor(indices, "indices", "gather", "int32"); + const inputs = { x: $x, indices: $indices }; + const attrs = { axis, batchDims }; + return ENGINE.runKernel(GatherV2, inputs, attrs); +} +var gather = op({ gather_ }); +function greater_(a, b) { + let $a = convertToTensor(a, "a", "greater", "string_or_numeric"); + let $b = convertToTensor(b, "b", "greater", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Greater, inputs); +} +var greater = op({ greater_ }); +function greaterEqual_(a, b) { + let $a = convertToTensor(a, "a", "greaterEqual", "string_or_numeric"); + let $b = convertToTensor(b, "b", "greaterEqual", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(GreaterEqual, inputs); +} +var greaterEqual = op({ greaterEqual_ }); +function imag_(input2) { + const $input = convertToTensor(input2, "input", "imag"); + const inputs = { input: $input }; + return ENGINE.runKernel(Imag, inputs); +} +var imag = op({ imag_ }); +function isFinite_(x) { + const $x = convertToTensor(x, "x", "isFinite"); + const inputs = { x: $x }; + return ENGINE.runKernel(IsFinite, inputs); +} +var isFinite2 = op({ isFinite_ }); +function isInf_(x) { + const $x = convertToTensor(x, "x", "isInf"); + const inputs = { x: $x }; + return ENGINE.runKernel(IsInf, inputs); +} +var isInf = op({ isInf_ }); +function isNaN_(x) { + const $x = convertToTensor(x, "x", "isNaN"); + const inputs = { x: $x }; + return ENGINE.runKernel(IsNan, inputs); +} +var isNaN2 = op({ isNaN_ }); +function leakyRelu_(x, alpha = 0.2) { + const $x = convertToTensor(x, "x", "leakyRelu"); + const inputs = { x: $x }; + const attrs = { alpha }; + return ENGINE.runKernel(LeakyRelu, inputs, attrs); +} +var leakyRelu = op({ leakyRelu_ }); +function less_(a, b) { + let $a = convertToTensor(a, "a", "less", "string_or_numeric"); + let $b = convertToTensor(b, "b", "less", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Less, inputs); +} +var less = op({ less_ }); +function lessEqual_(a, b) { + let $a = convertToTensor(a, "a", "lessEqual", "string_or_numeric"); + let $b = convertToTensor(b, "b", "lessEqual", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(LessEqual, inputs); +} +var lessEqual = op({ lessEqual_ }); +function linspace(start, stop, num) { + if (num <= 0) { + throw new Error("The number of values should be positive."); + } + const attrs = { start, stop, num }; + return ENGINE.runKernel(LinSpace, {}, attrs); +} +function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) { + const $x = convertToTensor(x, "x", "localResponseNormalization"); + assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got + rank ${$x.rank}.`); + assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + const inputs = { x: x4D }; + const attrs = { depthRadius, bias, alpha, beta }; + const res = ENGINE.runKernel(LRN, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } else { + return res; + } +} +var localResponseNormalization = op({ localResponseNormalization_ }); +function log_(x) { + const $x = convertToTensor(x, "x", "log", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Log, inputs); +} +var log2 = op({ log_ }); +function log1p_(x) { + const $x = convertToTensor(x, "x", "log1p"); + const inputs = { x: $x }; + return ENGINE.runKernel(Log1p, inputs); +} +var log1p = op({ log1p_ }); +function grad(f) { + assert(isFunction(f), () => "The f passed in grad(f) must be a function"); + return (x, dy) => { + const $x = convertToTensor(x, "x", "tf.grad", "string_or_numeric"); + const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grad") : null; + return ENGINE.tidy(() => { + const { value, grads: grads2 } = ENGINE.gradients(() => f($x), [$x], $dy); + if ($dy != null) { + assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"); + } + checkGrads(grads2); + return grads2[0]; + }); + }; +} +function grads(f) { + assert(isFunction(f), () => "The f passed in grads(f) must be a function"); + return (args, dy) => { + assert(Array.isArray(args), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s"); + const $args = convertToTensorArray(args, "args", "tf.grads", "string_or_numeric"); + const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grads") : null; + return ENGINE.tidy(() => { + const { value, grads: grads2 } = ENGINE.gradients(() => f(...$args), $args, $dy); + if ($dy != null) { + assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"); + } + checkGrads(grads2); + return grads2; + }); + }; +} +function valueAndGrad(f) { + assert(isFunction(f), () => "The f passed in valueAndGrad(f) must be a function"); + return (x, dy) => { + assert(x instanceof Tensor, () => "The x passed in valueAndGrad(f)(x) must be a tensor"); + assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor"); + const { grads: grads2, value } = ENGINE.gradients(() => f(x), [x], dy); + checkGrads(grads2); + return { grad: grads2[0], value }; + }; +} +function valueAndGrads(f) { + assert(isFunction(f), () => "The f passed in valueAndGrads(f) must be a function"); + return (args, dy) => { + assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"); + assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor"); + const res = ENGINE.gradients(() => f(...args), args, dy); + if (dy != null) { + assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"); + } + checkGrads(res.grads); + return res; + }; +} +function variableGrads(f, varList) { + assert(isFunction(f), () => "The f passed in variableGrads(f) must be a function"); + assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => "The varList passed in variableGrads(f, varList) must be an array of variables"); + const specifiedVarList = varList != null; + if (!specifiedVarList) { + varList = []; + for (const varName in ENGINE.registeredVariables) { + varList.push(ENGINE.registeredVariables[varName]); + } + } + const specifiedNonTrainable = specifiedVarList ? varList.filter((variable2) => !variable2.trainable) : null; + const originalVarCount = varList.length; + varList = varList.filter((variable2) => variable2.trainable); + assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`); + const allowNoGradients = true; + const { value, grads: grads2 } = ENGINE.gradients(f, varList, null, allowNoGradients); + assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."); + assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`); + const namedGrads = {}; + varList.forEach((v, i) => { + if (grads2[i] != null) { + namedGrads[v.name] = grads2[i]; + } + }); + if (specifiedNonTrainable != null) { + specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null); + } + return { value, grads: namedGrads }; +} +function customGrad(f) { + return ENGINE.customGrad(f); +} +function checkGrads(grads2) { + const numNullGradients = grads2.filter((g) => g == null).length; + if (numNullGradients > 0) { + throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that + the f you passed encloses all operations that lead from x to y.`); + } +} +function neg_(x) { + const $x = convertToTensor(x, "x", "neg"); + const inputs = { x: $x }; + return ENGINE.runKernel(Neg, inputs); +} +var neg = op({ neg_ }); +function softplus_(x) { + const $x = convertToTensor(x, "x", "softplus"); + const inputs = { x: $x }; + return ENGINE.runKernel(Softplus, inputs); +} +var softplus = op({ softplus_ }); +function logSigmoid_(x) { + const $x = convertToTensor(x, "x", "logSigmoid"); + const customOp = customGrad((x2) => { + const value = neg(softplus(neg(x2))); + const gradFunc = (dy) => { + const derX = mul(dy, sigmoid(neg(x2))); + return derX; + }; + return { value, gradFunc }; + }); + return customOp($x); +} +var logSigmoid = op({ logSigmoid_ }); +function sub_(a, b) { + let $a = convertToTensor(a, "a", "sub"); + let $b = convertToTensor(b, "b", "sub"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Sub, inputs); +} +var sub = op({ sub_ }); +function logSoftmax_(logits, axis = -1) { + const $logits = convertToTensor(logits, "logits", "logSoftmax"); + if (axis === -1) { + axis = $logits.rank - 1; + } + if (axis !== $logits.rank - 1) { + throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`); + } + const customOp = customGrad((logits2, save) => { + const keepDims = true; + const xMax = max(logits2, axis, true); + const shifted = sub(logits2, xMax); + const value = sub(cast(shifted, "float32"), log2(sum2(exp(shifted), axis, keepDims))); + save([value]); + const gradFunc = (dy, saved) => { + const [value2] = saved; + const keepDims2 = true; + const softmax6 = exp(value2); + return sub(dy, mul(sum2(dy, axis, keepDims2), softmax6)); + }; + return { value, gradFunc }; + }); + return customOp($logits); +} +var logSoftmax = op({ logSoftmax_ }); +function logSumExp_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "logSumExp"); + const axes = parseAxisParam(axis, $x.shape); + const xMax = max( + $x, + axes, + true + /* keepDims */ + ); + const a = sub($x, xMax); + const b = exp(a); + const c = sum2(b, axes); + const d = log2(c); + const res = add2(reshape(xMax, d.shape), d); + if (keepDims) { + const newShape = expandShapeToKeepDim(res.shape, axes); + return reshape(res, newShape); + } + return res; +} +var logSumExp = op({ logSumExp_ }); +function logicalAnd_(a, b) { + const $a = convertToTensor(a, "a", "logicalAnd", "bool"); + const $b = convertToTensor(b, "b", "logicalAnd", "bool"); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(LogicalAnd, inputs); +} +var logicalAnd = op({ logicalAnd_ }); +function logicalNot_(x) { + const $x = convertToTensor(x, "x", "logicalNot", "bool"); + const inputs = { x: $x }; + return ENGINE.runKernel(LogicalNot, inputs); +} +var logicalNot = op({ logicalNot_ }); +function logicalOr_(a, b) { + const $a = convertToTensor(a, "a", "logicalOr", "bool"); + const $b = convertToTensor(b, "b", "logicalOr", "bool"); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(LogicalOr, inputs); +} +var logicalOr = op({ logicalOr_ }); +function logicalXor_(a, b) { + const $a = convertToTensor(a, "a", "logicalXor", "bool"); + const $b = convertToTensor(b, "b", "logicalXor", "bool"); + assertAndGetBroadcastShape($a.shape, $b.shape); + return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b))); +} +var logicalXor = op({ logicalXor_ }); +var INT32_MAX = 2147483648; +function searchSorted_(sortedSequence, values, side = "left") { + const $sortedSequence = convertToTensor(sortedSequence, "sortedSequence", "searchSorted"); + const $values = convertToTensor(values, "values", "searchSorted"); + const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1]; + const valuesSize = $values.shape[$values.shape.length - 1]; + const $sortedSequence2D = reshape($sortedSequence, [-1, sequenceSize]); + const $values2D = reshape($values, [-1, valuesSize]); + if ($sortedSequence2D.rank < 2) { + throw new Error(`Sorted input argument must be at least 2-dimensional`); + } + if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) { + throw new Error(`Leading dimension of 'sortedSequence' and 'values' must match.`); + } + if (sizeFromShape($values2D.shape) >= INT32_MAX) { + throw new Error(`values tensor size must less than ${INT32_MAX}`); + } + if ($sortedSequence2D.shape[1] >= INT32_MAX) { + throw new Error(`trailing dim_size must less than ${INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`); + } + const inputs = { + sortedSequence: $sortedSequence2D, + values: $values2D + }; + const attrs = { side }; + return ENGINE.runKernel(SearchSorted, inputs, attrs); +} +var searchSorted = op({ searchSorted_ }); +function lowerBound(sortedSequence, values) { + return searchSorted(sortedSequence, values, "left"); +} +function maxPool_(x, filterSize, strides, pad3, dimRoundingMode) { + const $x = convertToTensor(x, "x", "maxPool"); + const dilations = 1; + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`); + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + checkPadOnDimRoundingMode("maxPool", pad3, dimRoundingMode); + const inputs = { x: x4D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; + const res = ENGINE.runKernel(MaxPool, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var maxPool = op({ maxPool_ }); +function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad3, dimRoundingMode, dataFormat = "NDHWC") { + const $x = convertToTensor(x, "x", "maxPool3d"); + let x5D = $x; + let reshapedTo5D = false; + if ($x.rank === 4) { + reshapedTo5D = true; + x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]); + } + assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`); + assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`); + checkPadOnDimRoundingMode("maxPool3d", pad3, dimRoundingMode); + const inputs = { x: x5D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat }; + const res = ENGINE.runKernel(MaxPool3D, inputs, attrs); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var maxPool3d = op({ maxPool3d_ }); +function maxPoolWithArgmax_(x, filterSize, strides, pad3, includeBatchInIndex = false) { + const $x = convertToTensor(x, "x", "maxPoolWithArgmax"); + const inputs = { x: $x }; + const attrs = { filterSize, strides, pad: pad3, includeBatchInIndex }; + const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs); + return { result: result[0], indexes: result[1] }; +} +var maxPoolWithArgmax = op({ maxPoolWithArgmax_ }); +function maximum_(a, b) { + let $a = convertToTensor(a, "a", "maximum"); + let $b = convertToTensor(b, "b", "maximum"); + [$a, $b] = makeTypesMatch($a, $b); + if ($a.dtype === "bool") { + $a = cast($a, "int32"); + $b = cast($b, "int32"); + } + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Maximum, inputs); +} +var maximum = op({ maximum_ }); +function mean_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "mean"); + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(Mean, inputs, attrs); +} +var mean = op({ mean_ }); +function zeros(shape, dtype = "float32") { + assertNonNegativeIntegerDimensions(shape); + if (dtype === "complex64") { + const real4 = zeros(shape, "float32"); + const imag4 = zeros(shape, "float32"); + return complex(real4, imag4); + } + const values = makeZerosTypedArray(sizeFromShape(shape), dtype); + return ENGINE.makeTensor(values, shape, dtype); +} +function ones2(shape, dtype = "float32") { + assertNonNegativeIntegerDimensions(shape); + if (dtype === "complex64") { + const real4 = ones2(shape, "float32"); + const imag4 = zeros(shape, "float32"); + return complex(real4, imag4); + } + const values = makeOnesTypedArray(sizeFromShape(shape), dtype); + return ENGINE.makeTensor(values, shape, dtype); +} +function meshgrid(x, y, { indexing = "xy" } = {}) { + if (indexing !== "xy" && indexing !== "ij") { + throw new TypeError(`${indexing} is not a valid third argument to meshgrid`); + } + if (x === void 0) { + return []; + } + let $x = convertToTensor(x, "x", "meshgrid", x instanceof Tensor ? x.dtype : "float32"); + if (y === void 0) { + return [$x]; + } + let $y = convertToTensor(y, "y", "meshgrid", y instanceof Tensor ? y.dtype : "float32"); + const w = sizeFromShape($x.shape); + const h = sizeFromShape($y.shape); + if (indexing === "xy") { + $x = reshape($x, [1, -1]); + $y = reshape($y, [-1, 1]); + return [ + matMul(ones2([h, 1], $x.dtype), $x), + matMul($y, ones2([1, w], $y.dtype)) + ]; + } + $x = reshape($x, [-1, 1]); + $y = reshape($y, [1, -1]); + return [ + matMul($x, ones2([1, h], $x.dtype)), + matMul(ones2([w, 1], $y.dtype), $y) + ]; +} +function minimum_(a, b) { + let $a = convertToTensor(a, "a", "minimum"); + let $b = convertToTensor(b, "b", "minimum"); + [$a, $b] = makeTypesMatch($a, $b); + if ($a.dtype === "bool") { + $a = cast($a, "int32"); + $b = cast($b, "int32"); + } + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Minimum, inputs); +} +var minimum = op({ minimum_ }); +function mirrorPad_(x, paddings, mode) { + assert(mode === "reflect" || mode === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`); + const $x = convertToTensor(x, "x", "mirrorPad"); + if ($x.rank === 0) { + throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad"); + } + assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`); + const shapeOffset = mode === "reflect" ? 1 : 0; + for (let i = 0; i < $x.rank; i++) { + assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`); + assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset && paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i] - shapeOffset} or less than 0 for input of shape ${$x.shape}`); + } + const attrs = { paddings, mode }; + const inputs = { x: $x }; + return ENGINE.runKernel(MirrorPad, inputs, attrs); +} +var mirrorPad = op({ mirrorPad_ }); +function mod_(a, b) { + let $a = convertToTensor(a, "a", "mod"); + let $b = convertToTensor(b, "b", "mod"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Mod, inputs); +} +var mod = op({ mod_ }); +function moments_(x, axis = null, keepDims = false) { + x = convertToTensor(x, "x", "moments"); + const axes = parseAxisParam(axis, x.shape); + const xMean = mean(x, axes, keepDims); + let keepDimsShape = xMean.shape; + if (!keepDims) { + keepDimsShape = expandShapeToKeepDim(xMean.shape, axes); + } + const devSquared = square(sub(cast(x, "float32"), reshape(xMean, keepDimsShape))); + const variance = mean(devSquared, axes, keepDims); + return { mean: xMean, variance }; +} +var moments = op({ moments_ }); +function multiRNNCell_(lstmCells, data, c, h) { + const $data = convertToTensor(data, "data", "multiRNNCell"); + const $c = convertToTensorArray(c, "c", "multiRNNCell"); + const $h = convertToTensorArray(h, "h", "multiRNNCell"); + let input2 = $data; + const newStates = []; + for (let i = 0; i < lstmCells.length; i++) { + const output = lstmCells[i](input2, $c[i], $h[i]); + newStates.push(output[0]); + newStates.push(output[1]); + input2 = output[1]; + } + const newC = []; + const newH = []; + for (let i = 0; i < newStates.length; i += 2) { + newC.push(newStates[i]); + newH.push(newStates[i + 1]); + } + return [newC, newH]; +} +var multiRNNCell = op({ multiRNNCell_ }); +function multinomial_(logits, numSamples, seed, normalized = false) { + const $logits = convertToTensor(logits, "logits", "multinomial"); + const numOutcomes = $logits.size; + const origRank = $logits.rank; + if (numOutcomes < 2) { + throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`); + } + if (origRank > 2) { + throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`); + } + seed = seed || Math.random(); + const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits; + const inputs = { logits: logits2D }; + const attrs = { numSamples, seed, normalized }; + const res = ENGINE.runKernel(Multinomial, inputs, attrs); + return origRank === 1 ? reshape(res, [res.size]) : res; +} +var multinomial = op({ multinomial_ }); +function notEqual_(a, b) { + let $a = convertToTensor(a, "a", "notEqual", "string_or_numeric"); + let $b = convertToTensor(b, "b", "notEqual", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(NotEqual, inputs); +} +var notEqual = op({ notEqual_ }); +function oneHot_(indices, depth, onValue = 1, offValue = 0, dtype = "int32") { + if (depth < 2) { + throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`); + } + const $indices = convertToTensor(indices, "indices", "oneHot", "int32"); + const inputs = { indices: $indices }; + const attrs = { dtype, depth, onValue, offValue }; + return ENGINE.runKernel(OneHot, inputs, attrs); +} +var oneHot = op({ oneHot_ }); +function onesLike_(x) { + const $x = convertToTensor(x, "x", "onesLike"); + const inputs = { x: $x }; + return ENGINE.runKernel(OnesLike, inputs); +} +var onesLike = op({ onesLike_ }); +function outerProduct_(v1, v2) { + const $v1 = convertToTensor(v1, "v1", "outerProduct"); + const $v2 = convertToTensor(v2, "v2", "outerProduct"); + assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`); + const v12D = reshape($v1, [-1, 1]); + const v22D = reshape($v2, [1, -1]); + return matMul(v12D, v22D); +} +var outerProduct = op({ outerProduct_ }); +function pad_(x, paddings, constantValue = 0) { + const $x = convertToTensor(x, "x", "pad"); + if ($x.rank === 0) { + throw new Error("pad(scalar) is not defined. Pass non-scalar to pad"); + } + const attrs = { paddings, constantValue }; + const inputs = { x: $x }; + return ENGINE.runKernel(PadV2, inputs, attrs); +} +var pad = op({ pad_ }); +function pad1d_(x, paddings, constantValue = 0) { + assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2."); + return pad(x, [paddings], constantValue); +} +var pad1d = op({ pad1d_ }); +function pad2d_(x, paddings, constantValue = 0) { + assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."); + return pad(x, paddings, constantValue); +} +var pad2d = op({ pad2d_ }); +function pad3d_(x, paddings, constantValue = 0) { + assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."); + return pad(x, paddings, constantValue); +} +var pad3d = op({ pad3d_ }); +function pad4d_(x, paddings, constantValue = 0) { + assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."); + return pad(x, paddings, constantValue); +} +var pad4d = op({ pad4d_ }); +function spaceToBatchND_(x, blockShape, paddings) { + const $x = convertToTensor(x, "x", "spaceToBatchND"); + assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`); + assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`); + assert($x.shape.reduce((a, b, i) => { + if (i > 0 && i <= blockShape.length) { + return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0; + } + return a; + }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`); + const inputs = { x: $x }; + const attrs = { blockShape, paddings }; + return ENGINE.runKernel(SpaceToBatchND, inputs, attrs); +} +var spaceToBatchND = op({ spaceToBatchND_ }); +function pool_(input2, windowShape, poolingType, pad3, dilations, strides, dimRoundingMode) { + if (dilations == null) { + dilations = [1, 1]; + } + if (strides == null) { + strides = 1; + } + if (pad3 === 0) { + pad3 = "valid"; + } + const $x = convertToTensor(input2, "x", "maxPool"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad3); + const dilation = [convInfo.dilationHeight, convInfo.dilationWidth]; + let basePadding; + if (pad3 === "same") { + basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation); + } else { + basePadding = [[0, 0], [0, 0]]; + } + const isDilationOne = dilation[0] === 1 && dilation[1] === 1; + const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding); + const convertedPad = isDilationOne ? pad3 : "valid"; + const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding); + const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode); + const y = forwardOp(); + const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) { + const padStart = basePadding.map((b) => b[0]); + const origPadEnd = basePadding.map((b) => b[1]); + const fullInputShape = inputShape.concat(padStart, origPadEnd); + const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b); + const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]); + const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]); + const crops = blockShape.map((_, i) => [0, padEndExtra[i]]); + return [paddings, crops]; +} +function withSpaceToBatchBasePaddings(filterShape, dilation) { + const dilatedFilterShape = filterShape.map((s, i) => { + return s + (s - 1) * (dilation[i] - 1); + }); + const padExtraShape = dilatedFilterShape.map((s) => s - 1); + const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2)); + const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]); + return padExtraShape.map((_, i) => { + return [padExtraStart[i], padExtraEnd[i]]; + }); +} +var pool = op({ pool_ }); +function prelu_(x, alpha) { + const $x = convertToTensor(x, "x", "prelu"); + const $alpha = convertToTensor(alpha, "alpha", "prelu"); + const inputs = { x: $x, alpha: $alpha }; + return ENGINE.runKernel(Prelu, inputs); +} +var prelu = op({ prelu_ }); +function prod_(x, axis = null, keepDims = false) { + let $x = convertToTensor(x, "x", "prod"); + if ($x.dtype === "bool") { + $x = cast($x, "int32"); + } + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(Prod, inputs, attrs); +} +var prod = op({ prod_ }); +function raggedGather_(paramsNestedSplits, paramsDenseValues, indices, outputRaggedRank) { + const $paramsNestedSplits = paramsNestedSplits.map((t, i) => convertToTensor(t, `tensors${i}`, "raggedGather", "int32")); + const $paramsDenseValues = convertToTensor(paramsDenseValues, "paramsDenseValues", "raggedGather"); + const $indices = convertToTensor(indices, "indices", "raggedGather", "int32"); + const inputs = { + paramsNestedSplits: $paramsNestedSplits, + paramsDenseValues: $paramsDenseValues, + indices: $indices + }; + const attrs = { outputRaggedRank }; + const result = ENGINE.runKernel(RaggedGather, inputs, attrs); + return { + outputNestedSplits: result.slice(0, result.length - 1), + outputDenseValues: result[result.length - 1] + }; +} +var raggedGather = op({ raggedGather_ }); +function raggedRange_(starts, limits, deltas) { + const $starts = convertToTensor(starts, "starts", "raggedRange"); + const $limits = convertToTensor(limits, "limits", "raggedRange", $starts.dtype); + const $deltas = convertToTensor(deltas, "deltas", "raggedRange", $starts.dtype); + const inputs = { + starts: $starts, + limits: $limits, + deltas: $deltas + }; + const result = ENGINE.runKernel(RaggedRange, inputs); + return { + rtNestedSplits: result[0], + rtDenseValues: result[1] + }; +} +var raggedRange = op({ raggedRange_ }); +function raggedTensorToTensor_(shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes) { + const $shape = convertToTensor(shape, "shape", "raggedTensorToTensor", "int32"); + const $values = convertToTensor(values, "values", "raggedTensorToTensor"); + const $defaultValue = convertToTensor(defaultValue, "defaultValue", "raggedTensorToTensor", $values.dtype); + const $rowPartitionTensors = rowPartitionTensors.map((t, i) => convertToTensor(t, `tensors${i}`, "raggedTensorToTensor", "int32")); + const inputs = { + shape: $shape, + values: $values, + defaultValue: $defaultValue, + rowPartitionTensors: $rowPartitionTensors + }; + const attrs = { rowPartitionTypes }; + return ENGINE.runKernel(RaggedTensorToTensor, inputs, attrs); +} +var raggedTensorToTensor = op({ raggedTensorToTensor_ }); +function rand_(shape, randFunction, dtype) { + assertNonNegativeIntegerDimensions(shape); + const size = sizeFromShape(shape); + let values = null; + if (dtype == null || dtype === "float32") { + values = new Float32Array(size); + } else if (dtype === "int32") { + values = new Int32Array(size); + } else if (dtype === "bool") { + values = new Uint8Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } + for (let i = 0; i < size; i++) { + values[i] = randFunction(); + } + return ENGINE.makeTensor(values, shape, dtype); +} +var rand = op({ rand_ }); +var seedrandom = __toESM(require_seedrandom2()); +var test_util_exports = {}; +__export2(test_util_exports, { + TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16, + createVideoElement: () => createVideoElement, + encodeStrings: () => encodeStrings, + expectArrayBuffersEqual: () => expectArrayBuffersEqual, + expectArraysClose: () => expectArraysClose, + expectArraysEqual: () => expectArraysEqual, + expectNumbersClose: () => expectNumbersClose, + expectPromiseToFail: () => expectPromiseToFail, + expectValuesInRange: () => expectValuesInRange, + play: () => play, + testEpsilon: () => testEpsilon +}); +var TEST_EPSILON_FLOAT32 = 1e-3; +var TEST_EPSILON_FLOAT16 = 0.1; +function expectArraysClose(actual, expected, epsilon32) { + if (epsilon32 == null) { + epsilon32 = testEpsilon(); + } + return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon32)); +} +function testEpsilon() { + return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16; +} +function expectArraysPredicate(actual, expected, predicate) { + let checkClassType = true; + if (isTypedArray(actual) || isTypedArray(expected)) { + checkClassType = false; + } + if (isTypedArray(actual) && isTypedArray(expected)) { + checkClassType = true; + } + if (checkClassType) { + const aType = actual.constructor.name; + const bType = expected.constructor.name; + if (aType !== bType) { + throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`); + } + } + if (Array.isArray(actual) && Array.isArray(expected)) { + const actualShape = inferShape(actual); + const expectedShape = inferShape(expected); + if (!arraysEqual(actualShape, expectedShape)) { + throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`); + } + } + const actualFlat = isTypedArray(actual) ? actual : flatten(actual); + const expectedFlat = isTypedArray(expected) ? expected : flatten(expected); + if (actualFlat.length !== expectedFlat.length) { + throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}. +Actual: ${actualFlat}. +Expected: ${expectedFlat}.`); + } + for (let i = 0; i < expectedFlat.length; ++i) { + const a = actualFlat[i]; + const e = expectedFlat[i]; + if (!predicate(a, e)) { + throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}. +Actual: ${actualFlat}. +Expected: ${expectedFlat}.`); + } + } + if (typeof expect !== "undefined") { + expect().nothing(); + } +} +function expectPromiseToFail(fn, done) { + fn().then(() => done.fail(), () => done()); + if (typeof expect !== "undefined") { + expect().nothing(); + } +} +function expectArraysEqual(actual, expected) { + const exp4 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected; + if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) { + return expectArraysPredicate(actual, exp4, (a, b) => a == b); + } + return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0)); +} +function expectNumbersClose(a, e, epsilon32) { + if (epsilon32 == null) { + epsilon32 = testEpsilon(); + } + if (!areClose(a, e, epsilon32)) { + throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`); + } + if (typeof expect !== "undefined") { + expect().nothing(); + } +} +function areClose(a, e, epsilon32) { + if (!isFinite(a) && !isFinite(e)) { + return true; + } + if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon32) { + return false; + } + return true; +} +function expectValuesInRange(actual, low, high) { + for (let i = 0; i < actual.length; i++) { + if (actual[i] < low || actual[i] > high) { + throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`); + } + } +} +function expectArrayBuffersEqual(actual, expected) { + const actualArray = new Float32Array(actual); + const expectedArray = new Float32Array(expected); + if (actualArray.length !== expectedArray.length) { + throw new Error(`Expected ArrayBuffer to be of length ${expectedArray.length}, but it was ${actualArray.length}`); + } + for (let i = 0; i < expectedArray.length; i++) { + if (actualArray[i] !== expectedArray[i]) { + throw new Error(`Expected ArrayBuffer value at ${i} to be ${expectedArray[i]} but got ${actualArray[i]} instead`); + } + } +} +function encodeStrings(a) { + for (let i = 0; i < a.length; i++) { + const val = a[i]; + if (Array.isArray(val)) { + encodeStrings(val); + } else { + a[i] = encodeString(val); + } + } + return a; +} +function createVideoElement(source) { + const video = document.createElement("video"); + if ("playsInline" in video) { + video.playsInline = true; + } + video.muted = true; + video.loop = true; + video.style.position = "fixed"; + video.style.left = "0px"; + video.style.top = "0px"; + video.preload = "auto"; + video.appendChild(source); + return new Promise((resolve) => { + video.addEventListener("loadeddata", (_) => resolve(video)); + video.load(); + }); +} +async function play(video) { + await video.play(); + if ("requestVideoFrameCallback" in video) { + await new Promise((resolve) => { + video.requestVideoFrameCallback(resolve); + }); + } +} +var MPRandGauss = class { + constructor(mean4, stdDeviation, dtype, truncated, seed) { + this.mean = mean4; + this.stdDev = stdDeviation; + this.dtype = dtype; + this.nextVal = NaN; + this.truncated = truncated; + if (this.truncated) { + this.upper = this.mean + this.stdDev * 2; + this.lower = this.mean - this.stdDev * 2; + } + const seedValue = seed ? seed : Math.random(); + this.random = seedrandom.alea(seedValue.toString()); + } + /** Returns next sample from a Gaussian distribution. */ + nextValue() { + if (!isNaN(this.nextVal)) { + const value = this.nextVal; + this.nextVal = NaN; + return value; + } + let resultX, resultY; + let isValid = false; + while (!isValid) { + let v1, v2, s; + do { + v1 = 2 * this.random() - 1; + v2 = 2 * this.random() - 1; + s = v1 * v1 + v2 * v2; + } while (s >= 1 || s === 0); + const mul2 = Math.sqrt(-2 * Math.log(s) / s); + resultX = this.mean + this.stdDev * v1 * mul2; + resultY = this.mean + this.stdDev * v2 * mul2; + if (!this.truncated || this.isValidTruncated(resultX)) { + isValid = true; + } + } + if (!this.truncated || this.isValidTruncated(resultY)) { + this.nextVal = this.convertValue(resultY); + } + return this.convertValue(resultX); + } + /** Handles proper rounding for non-floating-point numbers. */ + convertValue(value) { + if (this.dtype == null || this.dtype === "float32") { + return value; + } + return Math.round(value); + } + /** Returns true if less than 2-standard-deviations from the mean. */ + isValidTruncated(value) { + return value <= this.upper && value >= this.lower; + } +}; +var RandGamma = class { + constructor(alpha, beta, dtype, seed) { + this.alpha = alpha; + this.beta = 1 / beta; + this.dtype = dtype; + const seedValue = seed ? seed : Math.random(); + this.randu = seedrandom.alea(seedValue.toString()); + this.randn = new MPRandGauss(0, 1, dtype, false, this.randu()); + if (alpha < 1) { + this.d = alpha + 2 / 3; + } else { + this.d = alpha - 1 / 3; + } + this.c = 1 / Math.sqrt(9 * this.d); + } + /** Returns next sample from a gamma distribution. */ + nextValue() { + let x2, v0, v1, x, u, v; + while (true) { + do { + x = this.randn.nextValue(); + v = 1 + this.c * x; + } while (v <= 0); + v *= v * v; + x2 = x * x; + v0 = 1 - 0.331 * x2 * x2; + v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v)); + u = this.randu(); + if (u < v0 || Math.log(u) < v1) { + break; + } + } + v = 1 / this.beta * this.d * v; + if (this.alpha < 1) { + v *= Math.pow(this.randu(), 1 / this.alpha); + } + return this.convertValue(v); + } + /** Handles proper rounding for non-floating-point numbers. */ + convertValue(value) { + if (this.dtype === "float32") { + return value; + } + return Math.round(value); + } +}; +var UniformRandom = class { + constructor(min6 = 0, max6 = 1, dtype, seed) { + this.canReturnFloat = () => this.dtype == null || this.dtype === "float32"; + this.min = min6; + this.range = max6 - min6; + this.dtype = dtype; + if (seed == null) { + seed = Math.random(); + } + if (typeof seed === "number") { + seed = seed.toString(); + } + if (!this.canReturnFloat() && this.range <= 1) { + throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`); + } + this.random = seedrandom.alea(seed); + } + convertValue(value) { + if (this.canReturnFloat()) { + return value; + } + return Math.round(value); + } + nextValue() { + return this.convertValue(this.min + this.range * this.random()); + } +}; +function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) { + assertNonNegativeIntegerDimensions(shape); + if (beta == null) { + beta = 1; + } + if (dtype == null) { + dtype = "float32"; + } + if (dtype !== "float32" && dtype !== "int32") { + throw new Error(`Unsupported data type ${dtype}`); + } + const rgamma = new RandGamma(alpha, beta, dtype, seed); + const res = buffer(shape, dtype); + for (let i = 0; i < res.values.length; i++) { + res.values[i] = rgamma.nextValue(); + } + return res.toTensor(); +} +var randomGamma = op({ randomGamma_ }); +function randomNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) { + assertNonNegativeIntegerDimensions(shape); + if (dtype != null && dtype === "bool") { + throw new Error(`Unsupported data type ${dtype}`); + } + const randGauss = new MPRandGauss(mean4, stdDev, dtype, false, seed); + const res = buffer(shape, dtype); + for (let i = 0; i < res.values.length; i++) { + res.values[i] = randGauss.nextValue(); + } + return res.toTensor(); +} +var randomNormal = op({ randomNormal_ }); +function randomStandardNormal_(shape, dtype, seed) { + if (dtype != null && dtype === "bool") { + throw new Error(`Unsupported data type ${dtype}`); + } + return randomNormal(shape, 0, 1, dtype, seed); +} +var randomStandardNormal = op({ randomStandardNormal_ }); +function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) { + assertNonNegativeIntegerDimensions(shape); + const res = buffer(shape, dtype); + const random = new UniformRandom(minval, maxval, null, seed); + for (let i = 0; i < res.values.length; i++) { + res.values[i] = random.nextValue(); + } + return res.toTensor(); +} +var randomUniform = op({ randomUniform_ }); +function randomUniformInt_(shape, minval, maxval, seed) { + return randomUniform(shape, minval, maxval, "int32", seed); +} +var randomUniformInt = op({ randomUniformInt_ }); +function range(start, stop, step5 = 1, dtype = "float32") { + if (step5 === 0) { + throw new Error("Cannot have a step of zero"); + } + const attrs = { start, stop, step: step5, dtype }; + return ENGINE.runKernel(Range, {}, attrs); +} +function real_(input2) { + const $input = convertToTensor(input2, "input", "real"); + const inputs = { input: $input }; + return ENGINE.runKernel(Real, inputs); +} +var real = op({ real_ }); +function reciprocal_(x) { + const $x = convertToTensor(x, "x", "reciprocal"); + const inputs = { x: $x }; + return ENGINE.runKernel(Reciprocal, inputs); +} +var reciprocal = op({ reciprocal_ }); +function relu_(x) { + const $x = convertToTensor(x, "x", "relu"); + const inputs = { x: $x }; + return ENGINE.runKernel(Relu, inputs); +} +var relu = op({ relu_ }); +function relu6_(x) { + const $x = convertToTensor(x, "x", "relu6"); + const inputs = { x: $x }; + return ENGINE.runKernel(Relu6, inputs); +} +var relu6 = op({ relu6_ }); +function reverse_(x, axis) { + const $x = convertToTensor(x, "x", "reverse"); + const inputs = { x: $x }; + const attrs = { dims: axis }; + return ENGINE.runKernel(Reverse, inputs, attrs); +} +var reverse = op({ reverse_ }); +function reverse1d_(x) { + const $x = convertToTensor(x, "x", "reverse"); + assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`); + return reverse($x, 0); +} +var reverse1d = op({ reverse1d_ }); +function reverse2d_(x, axis) { + const $x = convertToTensor(x, "x", "reverse"); + assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`); + return reverse($x, axis); +} +var reverse2d = op({ reverse2d_ }); +function reverse3d_(x, axis) { + const $x = convertToTensor(x, "x", "reverse"); + assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`); + return reverse($x, axis); +} +var reverse3d = op({ reverse3d_ }); +function reverse4d_(x, axis) { + const $x = convertToTensor(x, "x", "reverse"); + assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`); + return reverse($x, axis); +} +var reverse4d = op({ reverse4d_ }); +function round_(x) { + const $x = convertToTensor(x, "x", "round"); + const inputs = { x: $x }; + return ENGINE.runKernel(Round, inputs); +} +var round2 = op({ round_ }); +function rsqrt_(x) { + const $x = convertToTensor(x, "x", "rsqrt", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Rsqrt, inputs); +} +var rsqrt = op({ rsqrt_ }); +function selu_(x) { + const $x = convertToTensor(x, "x", "selu"); + const inputs = { x: $x }; + return ENGINE.runKernel(Selu, inputs); +} +var selu = op({ selu_ }); +function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad3, dilation = [1, 1], dataFormat = "NHWC") { + const $x = convertToTensor(x, "x", "separableConv2d"); + const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d"); + const $pointwiseFilter = convertToTensor(pointwiseFilter, "pointwiseFilter", "separableConv2d"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + if (dataFormat === "NCHW") { + throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported"); + } + assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`); + assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`); + assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`); + assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[0]}.`); + assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`); + const inChannels = $depthwiseFilter.shape[2]; + const channelMultiplier = $depthwiseFilter.shape[3]; + assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`); + const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad3, dataFormat, dilation); + const pointwiseStride = 1; + const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var separableConv2d = op({ separableConv2d_ }); +async function setdiff1dAsync_(x, y) { + const $x = convertToTensor(x, "x", "setdiff1d"); + const $y = convertToTensor(y, "y", "setdiff1d"); + assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`); + assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`); + assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`); + const xVals = await $x.data(); + const yVals = await $y.data(); + const ySet = new Set(yVals); + let outputSize = 0; + for (let i = 0; i < xVals.length; i++) { + if (!ySet.has(xVals[i])) { + outputSize++; + } + } + const buffer2 = new TensorBuffer([outputSize], $x.dtype); + const indices = new TensorBuffer([outputSize], "int32"); + for (let i = 0, p2 = 0; i < xVals.length; i++) { + if (!ySet.has(xVals[i])) { + buffer2.values[p2] = xVals[i]; + indices.values[p2] = i; + p2++; + } + } + return [buffer2.toTensor(), indices.toTensor()]; +} +var setdiff1dAsync = setdiff1dAsync_; +function sign_(x) { + const $x = convertToTensor(x, "x", "sign"); + const inputs = { x: $x }; + return ENGINE.runKernel(Sign, inputs); +} +var sign = op({ sign_ }); +function sin_(x) { + const $x = convertToTensor(x, "x", "sin", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Sin, inputs); +} +var sin = op({ sin_ }); +function sinh_(x) { + const $x = convertToTensor(x, "x", "sinh"); + const inputs = { x: $x }; + return ENGINE.runKernel(Sinh, inputs); +} +var sinh = op({ sinh_ }); +function slice1d_(x, begin, size) { + const $x = convertToTensor(x, "x", "slice1d"); + assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`); + return slice($x, [begin], [size]); +} +var slice1d = op({ slice1d_ }); +function slice2d_(x, begin, size) { + const $x = convertToTensor(x, "x", "slice2d"); + assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`); + return slice($x, begin, size); +} +var slice2d = op({ slice2d_ }); +function slice3d_(x, begin, size) { + const $x = convertToTensor(x, "x", "slice3d"); + assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`); + return slice($x, begin, size); +} +var slice3d = op({ slice3d_ }); +function slice4d_(x, begin, size) { + const $x = convertToTensor(x, "x", "slice4d"); + assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`); + return slice($x, begin, size); +} +var slice4d = op({ slice4d_ }); +function softmax_(logits, dim = -1) { + const $logits = convertToTensor(logits, "logits", "softmax", "float32"); + if (dim === -1) { + dim = $logits.rank - 1; + } + if (dim !== $logits.rank - 1) { + throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`); + } + const inputs = { logits: $logits }; + const attrs = { dim }; + return ENGINE.runKernel(Softmax, inputs, attrs); +} +var softmax = op({ softmax_ }); +function fft_(input2) { + assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`); + const inputs = { input: input2 }; + return ENGINE.runKernel(FFT, inputs); +} +var fft = op({ fft_ }); +function ifft_(input2) { + assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`); + const inputs = { input: input2 }; + return ENGINE.runKernel(IFFT, inputs); +} +var ifft = op({ ifft_ }); +function irfft_(input2) { + const innerDimensionSize = input2.shape[input2.shape.length - 1]; + const batch = input2.size / innerDimensionSize; + let ret; + if (innerDimensionSize <= 2) { + const complexInput = reshape(input2, [batch, innerDimensionSize]); + ret = ifft(complexInput); + } else { + const outputShape = [batch, 2 * (innerDimensionSize - 1)]; + const realInput = reshape(real(input2), [batch, innerDimensionSize]); + const imagInput = reshape(imag(input2), [batch, innerDimensionSize]); + const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1); + const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1)); + const r = concat([realInput, realConjugate], 1); + const i = concat([imagInput, imagConjugate], 1); + const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]); + ret = ifft(complexInput); + } + ret = real(ret); + if (input2.rank === 3 && input2.shape[0] !== 0) { + const temp = ret; + const batch2 = input2.shape[0]; + ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]); + temp.dispose(); + } + return ret; +} +var irfft = op({ irfft_ }); +function split_(x, numOrSizeSplits, axis = 0) { + const $x = convertToTensor(x, "x", "split"); + const inputs = { x: $x }; + const attr = { numOrSizeSplits, axis }; + return ENGINE.runKernel(SplitV, inputs, attr); +} +var split = op({ split_ }); +function rfft_(input2, fftLength) { + assert(input2.dtype === "float32", () => `The dtype for rfft() must be real value but got ${input2.dtype}`); + let innerDimensionSize = input2.shape[input2.shape.length - 1]; + const batch = input2.size / innerDimensionSize; + let adjustedInput; + if (fftLength != null && fftLength < innerDimensionSize) { + const begin = input2.shape.map((v) => 0); + const size = input2.shape.map((v) => v); + size[input2.shape.length - 1] = fftLength; + adjustedInput = slice(input2, begin, size); + innerDimensionSize = fftLength; + } else if (fftLength != null && fftLength > innerDimensionSize) { + const zerosShape = input2.shape.map((v) => v); + zerosShape[input2.shape.length - 1] = fftLength - innerDimensionSize; + adjustedInput = concat([input2, zeros(zerosShape)], input2.shape.length - 1); + innerDimensionSize = fftLength; + } else { + adjustedInput = input2; + } + const zerosInput = zerosLike(adjustedInput); + const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]); + const ret = fft(complexInput); + const half = Math.floor(innerDimensionSize / 2) + 1; + const realValues = real(ret); + const imagValues = imag(ret); + const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1); + const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1); + const outputShape = adjustedInput.shape.slice(); + outputShape[adjustedInput.shape.length - 1] = half; + return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape); +} +var rfft = op({ rfft_ }); +function squaredDifference_(a, b) { + let $a = convertToTensor(a, "a", "squaredDifference"); + let $b = convertToTensor(b, "b", "squaredDifference"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + const attrs = {}; + return ENGINE.runKernel(SquaredDifference, inputs, attrs); +} +var squaredDifference = op({ squaredDifference_ }); +function squeeze_(x, axis) { + const $x = convertToTensor(x, "x", "squeeze", "string_or_numeric"); + return reshape($x, squeezeShape($x.shape, axis).newShape); +} +var squeeze = op({ squeeze_ }); +function stack_(tensors, axis = 0) { + const $tensors = convertToTensorArray(tensors, "tensors", "stack", "string_or_numeric"); + assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack"); + if ($tensors.length > 0) { + assert(axis <= $tensors[0].rank, () => "Axis must be <= rank of the tensor"); + } + const inputs = $tensors; + const attrs = { axis }; + return ENGINE.runKernel(Pack, inputs, attrs); +} +var stack = op({ stack_ }); +function step_(x, alpha = 0) { + const $x = convertToTensor(x, "x", "step"); + const inputs = { x: $x }; + const attrs = { alpha }; + return ENGINE.runKernel(Step, inputs, attrs); +} +var step = op({ step_ }); +function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) { + const $x = convertToTensor(x, "x", "stridedSlice", "string_or_numeric"); + const inputs = { x: $x }; + const attrs = { + begin, + end, + strides, + beginMask, + endMask, + ellipsisMask, + newAxisMask, + shrinkAxisMask + }; + return ENGINE.runKernel(StridedSlice, inputs, attrs); +} +var stridedSlice = op({ stridedSlice_ }); +function tan_(x) { + const $x = convertToTensor(x, "x", "tan", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Tan, inputs); +} +var tan = op({ tan_ }); +function tensor1d(values, dtype) { + assertNonNull(values); + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 1) { + throw new Error("tensor1d() requires values to be a flat/TypedArray"); + } + const shape = null; + return makeTensor(values, shape, inferredShape, dtype); +} +function tensor2d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 2) { + throw new Error("tensor2d() requires shape to have two numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 2 && inferredShape.length !== 1) { + throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray"); + } + return makeTensor(values, shape, inferredShape, dtype); +} +function tensor3d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 3) { + throw new Error("tensor3d() requires shape to have three numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 3 && inferredShape.length !== 1) { + throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor3d() requires shape to be provided when `values` are a flat array"); + } + return makeTensor(values, shape, inferredShape, dtype); +} +function tensor4d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 4) { + throw new Error("tensor4d() requires shape to have four numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 4 && inferredShape.length !== 1) { + throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor4d() requires shape to be provided when `values` are a flat array"); + } + return makeTensor(values, shape, inferredShape, dtype); +} +function tensor5d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 5) { + throw new Error("tensor5d() requires shape to have five numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 5 && inferredShape.length !== 1) { + throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor5d() requires shape to be provided when `values` are a flat array"); + } + return makeTensor(values, shape, inferredShape, dtype); +} +function tensor6d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 6) { + throw new Error("tensor6d() requires shape to have six numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 6 && inferredShape.length !== 1) { + throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor6d() requires shape to be provided when `values` are a flat array"); + } + shape = shape || inferredShape; + return makeTensor(values, shape, inferredShape, dtype); +} +var scatter_nd_util_exports = {}; +__export2(scatter_nd_util_exports, { + calculateShapes: () => calculateShapes, + validateInput: () => validateInput, + validateUpdateShape: () => validateUpdateShape +}); +function validateUpdateShape(shape, indices, updates) { + const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1; + const batchDim = indices.rank > 1 ? indices.rank - 1 : 1; + const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`; + if (updates.rank < batchDim) { + throw new Error(shapeError + ` update.rank < ${batchDim}. `); + } + if (shape.length < sliceDim + (updates.rank - batchDim)) { + throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`); + } + if (updates.rank !== batchDim + shape.length - sliceDim) { + throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`); + } + for (let d = 0; d < batchDim; ++d) { + if (updates.shape[d] !== indices.shape[d]) { + throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`); + } + } + for (let d = 0; d < updates.rank - batchDim; ++d) { + if (updates.shape[d + batchDim] !== shape[d + sliceDim]) { + throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`); + } + } +} +function validateInput(updates, indices, shape) { + if (indices.rank < 1) { + throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`); + } + if (updates.rank < 1) { + throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`); + } + if (indices.dtype !== "int32") { + throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`); + } + if (shape.length < 1) { + throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`); + } + if (shape.length === 0) { + if (indices.size === 0) { + throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`); + } + if (updates.size === 0) { + throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`); + } + } + validateUpdateShape(shape, indices, updates); +} +function calculateShapes(updates, indices, shape) { + const indicesRank = indices.shape.length; + const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1; + const totalNd = shape.length; + let sliceSize = 1; + for (let i = sliceRank; i < totalNd; ++i) { + sliceSize *= shape[i]; + } + const safeSliceDim = sliceRank < 1 ? 1 : sliceRank; + const numUpdates = sizeFromShape(indices.shape) / safeSliceDim; + const strides = [...computeStrides(shape.slice(0, sliceRank)), 1]; + const outputSize = sizeFromShape(shape); + return { sliceRank, numUpdates, sliceSize, strides, outputSize }; +} +function tensorScatterUpdate_(tensor2, indices, updates) { + const $tensor = convertToTensor(tensor2, "tensor", "tensorScatterupdate"); + const $indices = convertToTensor(indices, "indices", "tensorScatterupdate", "int32"); + const $updates = convertToTensor(updates, "updates", "tensorScatterupdate"); + validateInput($updates, $indices, $tensor.shape); + if ($tensor.dtype !== $updates.dtype) { + throw new Error(`tensor and updates must have the same dtype, instead they are ${$tensor.dtype} and ${$updates.dtype}.`); + } + const inputs = { + tensor: $tensor, + indices: $indices, + updates: $updates + }; + const attrs = {}; + return ENGINE.runKernel(TensorScatterUpdate, inputs, attrs); +} +var tensorScatterUpdate = op({ tensorScatterUpdate_ }); +function topk_(x, k = 1, sorted = true) { + const $x = convertToTensor(x, "x", "topk"); + if ($x.rank === 0) { + throw new Error("topk() expects the input to be of rank 1 or higher"); + } + const lastDim = $x.shape[$x.shape.length - 1]; + if (k < 0) { + throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`); + } + if (k > lastDim) { + throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`); + } + const inputs = { x: $x }; + const attrs = { k, sorted }; + const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs); + return { values, indices }; +} +var topk = op({ topk_ }); +function truncatedNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) { + assertNonNegativeIntegerDimensions(shape); + if (dtype != null && dtype === "bool") { + throw new Error(`Unsupported data type $ { dtype }`); + } + const randGauss = new MPRandGauss(mean4, stdDev, dtype, true, seed); + const res = buffer(shape, dtype); + for (let i = 0; i < res.values.length; i++) { + res.values[i] = randGauss.nextValue(); + } + return res.toTensor(); +} +var truncatedNormal = op({ truncatedNormal_ }); +function unique_(x, axis = 0) { + const $x = convertToTensor(x, "x", "unique", "string_or_numeric"); + assert($x.rank > 0, () => "The input tensor must be at least 1D"); + const inputs = { x: $x }; + const attrs = { axis }; + const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs); + return { values, indices }; +} +var unique = op({ unique_ }); +function unsortedSegmentSum_(x, segmentIds, numSegments) { + const $x = convertToTensor(x, "x", "unsortedSegmentSum"); + const $segmentIds = convertToTensor(segmentIds, "segmentIds", "unsortedSegmentSum", "int32"); + assert(isInt(numSegments), () => "numSegments must be of dtype int"); + const inputs = { x: $x, segmentIds: $segmentIds }; + const attrs = { numSegments }; + return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs); +} +var unsortedSegmentSum = op({ unsortedSegmentSum_ }); +function unstack_(x, axis = 0) { + const $x = convertToTensor(x, "x", "unstack", "string_or_numeric"); + assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`); + const inputs = { value: $x }; + const attrs = { axis }; + return ENGINE.runKernel(Unpack, inputs, attrs); +} +var unstack = op({ unstack_ }); +function upperBound(sortedSequence, values) { + return searchSorted(sortedSequence, values, "right"); +} +function variable(initialValue, trainable = true, name, dtype) { + return ENGINE.makeVariable(initialValue, trainable, name, dtype); +} +function whereImpl(condShape, condVals) { + const indices = []; + for (let i = 0; i < condVals.length; i++) { + if (condVals[i]) { + indices.push(i); + } + } + const inBuffer = buffer(condShape, "int32"); + const out = buffer([indices.length, condShape.length], "int32"); + for (let i = 0; i < indices.length; i++) { + const loc = inBuffer.indexToLoc(indices[i]); + const offset = i * condShape.length; + out.values.set(loc, offset); + } + return out.toTensor(); +} +async function whereAsync_(condition) { + const $condition = convertToTensor(condition, "condition", "whereAsync", "bool"); + const vals = await $condition.data(); + const res = whereImpl($condition.shape, vals); + if (condition !== $condition) { + $condition.dispose(); + } + return res; +} +var whereAsync = whereAsync_; +async function booleanMaskAsync_(tensor2, mask, axis) { + const $tensor = convertToTensor(tensor2, "tensor", "boolMask"); + const $mask = convertToTensor(mask, "mask", "boolMask", "bool"); + const axisFrom = axis == null ? 0 : axis; + const maskDim = $mask.rank; + const tensorShape = $tensor.shape; + assert(maskDim > 0, () => "mask cannot be scalar"); + assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`); + let leadingSize = 1; + for (let i = axisFrom; i < axisFrom + maskDim; i++) { + leadingSize *= tensorShape[i]; + } + const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim)); + const reshapedTensor = reshape($tensor, targetTensorShape); + const reshapedMask = reshape($mask, [-1]); + const positivePositions = await whereAsync(reshapedMask); + const indices = squeeze(positivePositions, [1]); + const res = gather(reshapedTensor, indices, axisFrom); + if (tensor2 !== $tensor) { + $tensor.dispose(); + } + if (mask !== $mask) { + $mask.dispose(); + } + indices.dispose(); + reshapedTensor.dispose(); + reshapedMask.dispose(); + positivePositions.dispose(); + return res; +} +var booleanMaskAsync = booleanMaskAsync_; +function transpose_(x, perm, conjugate) { + const $x = convertToTensor(x, "x", "transpose"); + if (perm == null) { + perm = $x.shape.map((s, i) => i).reverse(); + } + assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`); + perm.forEach((axis) => { + assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`); + }); + if ($x.rank <= 1) { + return $x.clone(); + } + const inputs = { x: $x }; + const attrs = { perm }; + if ($x.dtype === "complex64") { + return tidy(() => { + let $real = real($x); + let $imag = imag($x); + $real = ENGINE.runKernel(Transpose, { x: $real }, attrs); + $imag = ENGINE.runKernel(Transpose, { x: $imag }, attrs); + if (conjugate) { + $imag = neg($imag); + } + return complex($real, $imag); + }); + } + return ENGINE.runKernel(Transpose, inputs, attrs); +} +var transpose = op({ transpose_ }); +function movingAverage_(v, x, decay, step5, zeroDebias = true) { + const $v = convertToTensor(v, "v", "movingAverage"); + const $x = convertToTensor(x, "x", "movingAverage"); + const $decay = convertToTensor(decay, "decay", "movingAverage"); + assertTypesMatch($v, $x); + assert(arraysEqual($v.shape, $x.shape), () => "Shape mismatch in v and x"); + const one = scalar(1); + const oneMinusDecay = sub(one, $decay); + let update = mul(sub($x, $v), oneMinusDecay); + if (zeroDebias) { + assert(step5 != null, () => "When using zeroDebias: true, step is required."); + const $step = convertToTensor(step5, "step", "movingAverage"); + update = div(update, sub(one, pow($decay, $step))); + } + return add2($v, update); +} +var movingAverage = op({ movingAverage_ }); +function scatterND_(indices, updates, shape) { + assertNonNegativeIntegerDimensions(shape); + const $indices = convertToTensor(indices, "indices", "scatterND", "int32"); + const $updates = convertToTensor(updates, "updates", "scatterND"); + validateInput($updates, $indices, shape); + const inputs = { indices: $indices, updates: $updates }; + const attrs = { shape }; + return ENGINE.runKernel(ScatterNd, inputs, attrs); +} +var scatterND = op({ scatterND_ }); +function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) { + if (sparseIndices.dtype !== "int32") { + throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`); + } + if (sparseIndices.rank > 2) { + throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`); + } + const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1; + const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1; + if (outputShape.length !== numDims) { + throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`); + } + const numValues = sparseValues.size; + if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) { + throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`); + } + if (sparseValues.dtype !== defaultValues.dtype) { + throw new Error("sparseValues.dtype must match defaultValues.dtype"); + } +} +function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) { + assertNonNegativeIntegerDimensions(outputShape); + const $sparseIndices = convertToTensor(sparseIndices, "sparseIndices", "sparseToDense", "int32"); + const $sparseValues = convertToTensor(sparseValues, "sparseValues", "sparseToDense", "string_or_numeric"); + const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype); + validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue); + const inputs = { + sparseIndices: $sparseIndices, + sparseValues: $sparseValues, + defaultValue: $defaultValue + }; + const attrs = { outputShape }; + return ENGINE.runKernel(SparseToDense, inputs, attrs); +} +var sparseToDense = op({ sparseToDense_ }); +function gatherND_(x, indices) { + const $indices = convertToTensor(indices, "indices", "gatherND", "int32"); + const $x = convertToTensor(x, "x", "gatherND", "string_or_numeric"); + const inputs = { params: $x, indices: $indices }; + return ENGINE.runKernel(GatherNd, inputs); +} +var gatherND = op({ gatherND_ }); +function getNoiseShape(x, noiseShape) { + if (noiseShape == null) { + return x.shape.slice(); + } + if (arraysEqual(x.shape, noiseShape)) { + return noiseShape; + } + if (x.shape.length === noiseShape.length) { + const newDimension = []; + for (let i = 0; i < x.shape.length; i++) { + if (noiseShape[i] == null && x.shape[i] != null) { + newDimension.push(x.shape[i]); + } else { + newDimension.push(noiseShape[i]); + } + } + return newDimension; + } + return noiseShape; +} +function dropout_(x, rate, noiseShape, seed) { + const $x = convertToTensor(x, "x", "dropout"); + assert($x.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`); + assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`); + if (rate === 0) { + return x instanceof Tensor ? $x.clone() : $x; + } + const $noiseShape = getNoiseShape($x, noiseShape); + const keepProb = 1 - rate; + const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb); + return mul($x, multiplier); +} +var dropout = op({ dropout_ }); +function enclosingPowerOfTwo(value) { + return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2)))); +} +function cosineWindow(windowLength, a, b) { + const even = 1 - windowLength % 2; + const newValues = new Float32Array(windowLength); + for (let i = 0; i < windowLength; ++i) { + const cosArg = 2 * Math.PI * i / (windowLength + even - 1); + newValues[i] = a - b * Math.cos(cosArg); + } + return tensor1d(newValues, "float32"); +} +async function inTopKAsync_(predictions, targets, k = 1) { + const $predictions = convertToTensor(predictions, "predictions", "inTopK"); + const $targets = convertToTensor(targets, "targets", "inTopK"); + assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`); + assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`); + assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`); + const lastDim = $predictions.shape[$predictions.shape.length - 1]; + assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`); + const predictionsVals = await $predictions.data(); + const targetsVals = await $targets.data(); + const [batch, size] = [predictionsVals.length / lastDim, lastDim]; + const precision3 = getTypedArrayFromDType("bool", batch); + for (let b = 0; b < batch; b++) { + const offset = b * size; + const vals = predictionsVals.subarray(offset, offset + size); + const valAndInd = []; + for (let i = 0; i < vals.length; i++) { + valAndInd.push({ value: vals[i], index: i }); + } + valAndInd.sort((a, b2) => b2.value - a.value); + precision3[b] = 0; + for (let i = 0; i < k; i++) { + if (valAndInd[i].index === targetsVals[b]) { + precision3[b] = 1; + break; + } + } + } + if (predictions !== $predictions) { + $predictions.dispose(); + } + if (targets !== $targets) { + $targets.dispose(); + } + return tensor(precision3, $targets.shape, "bool"); +} +var inTopKAsync = inTopKAsync_; +var fused_ops_exports = {}; +__export2(fused_ops_exports, { + conv2d: () => conv2d2, + depthwiseConv2d: () => depthwiseConv2d2, + matMul: () => matMul2 +}); +function conv2DBackpropFilter_(x, dy, filterShape, strides, pad3, dataFormat = "NHWC", dimRoundingMode) { + let x4D = x; + if (x.rank === 3) { + x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); + } + let dy4D = dy; + if (dy4D.rank === 3) { + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`); + assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`); + assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`); + const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1]; + const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1]; + assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`); + assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`); + checkPadOnDimRoundingMode("conv2dDerFilter", pad3, dimRoundingMode); + const inputs = { x: x4D, dy: dy4D }; + const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape }; + return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs); +} +var conv2DBackpropFilter = op({ conv2DBackpropFilter_ }); +function getFusedDyActivation(dy, y, activation2) { + if (activation2 == null || activation2 === "linear") { + return dy; + } + if (activation2 === "relu") { + return mul(dy, step(y)); + } + throw new Error(`Cannot compute gradient for fused activation ${activation2}.`); +} +function getFusedBiasGradient(bias, dyActivation) { + let res = dyActivation; + const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, bias.shape); +} +function applyActivation(x, activation2, preluActivationWeights, leakyreluAlpha) { + if (activation2 === "linear") { + return x; + } else if (activation2 === "relu") { + return relu(x); + } else if (activation2 === "elu") { + return elu(x); + } else if (activation2 === "relu6") { + return relu6(x); + } else if (activation2 === "prelu") { + return prelu(x, preluActivationWeights); + } else if (activation2 === "leakyrelu") { + return leakyRelu(x, leakyreluAlpha); + } else if (activation2 === "sigmoid") { + return sigmoid(x); + } + throw new Error(`Unknown fused activation ${activation2}.`); +} +var shouldFuse = (gradientDepth, activation2) => { + const gradientMode = gradientDepth > 0; + return !gradientMode || activation2 === "linear"; +}; +function fusedConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) { + activation2 = activation2 || "linear"; + if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) { + assert(dataFormat === "NHWC", () => `Error in fused conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`); + let result = conv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); + if (bias != null) { + result = add2(result, bias); + } + return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha); + } + const $x = convertToTensor(x, "x", "conv2d", "float32"); + const $filter = convertToTensor(filter, "filter", "conv2d", "float32"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`); + assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`); + checkPadOnDimRoundingMode("fused conv2d", pad3, dimRoundingMode); + const inputChannels = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1]; + assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) must match input depth for filter ${$filter.shape[2]}.`); + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode); + let $bias; + if (bias != null) { + $bias = convertToTensor(bias, "bias", "fused conv2d"); + [$bias] = makeTypesMatch($bias, $x); + if (dataFormat === "NHWC") { + assertAndGetBroadcastShape(convInfo.outShape, $bias.shape); + } else { + assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${$bias.shape.length}.`); + assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not compatible with the number of output channels (${convInfo.outChannels})`); + } + } + let $preluActivationWeights; + if (preluActivationWeights != null) { + const alphaShape = preluActivationWeights.shape; + assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${alphaShape.length}.`); + if (alphaShape.length === 1) { + assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the number of output channels (${convInfo.outChannels}).`); + } else if (alphaShape.length === 3) { + try { + assertAndGetBroadcastShape(alphaShape, convInfo.outShape); + } catch (e) { + const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`; + throw Error(errMsg); + } + } + $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d"); + } + const grad2 = (dy, saved) => { + assert(dataFormat === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`); + const [$filter2, x4D2, y, $bias2] = saved; + const dyActivation = getFusedDyActivation(dy, y, activation2); + assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`); + const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3); + const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3); + const der = [xDer, filterDer]; + if ($bias2 != null) { + const biasDer = getFusedBiasGradient($bias2, dyActivation); + der.push(biasDer); + } + return der; + }; + const inputs = { + x: x4D, + filter: $filter, + bias: $bias, + preluActivationWeights: $preluActivationWeights + }; + const attrs = { + strides, + pad: pad3, + dataFormat, + dilations, + dimRoundingMode, + activation: activation2, + leakyreluAlpha + }; + if (bias == null) { + const customOp = customGrad((x4D2, filter2, save) => { + let res = ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(FusedConv2D, inputs, attrs) + ); + save([filter2, x4D2, res]); + if (reshapedTo4D) { + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad2 }; + }); + return customOp(x4D, $filter); + } else { + const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => { + let res = ENGINE.runKernel(FusedConv2D, inputs, attrs); + save([filter2, x4D2, res, bias2]); + if (reshapedTo4D) { + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad2 }; + }); + return customOpWithBias(x4D, $filter, $bias); + } +} +var conv2d2 = op({ fusedConv2d_ }); +function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad3, dilations = [1, 1], dimRoundingMode) { + let x4D = x; + if (x.rank === 3) { + x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); + } + let dy4D = dy; + if (dy4D.rank === 3) { + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + } + const inputs = { x: x4D, dy: dy4D }; + const attrs = { strides, pad: pad3, dimRoundingMode, dilations, filterShape }; + return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs); +} +var depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ }); +function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad3, dilations = [1, 1], dimRoundingMode) { + let dy4D = dy; + let reshapedTo4D = false; + if (dy.rank === 3) { + reshapedTo4D = true; + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + } + const inputs = { dy: dy4D, filter }; + const attrs = { strides, pad: pad3, dimRoundingMode, dilations, inputShape: xShape }; + const res = ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs) + ); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ }); +function fusedDepthwiseConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) { + if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) { + let result = depthwiseConv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); + if (bias != null) { + result = add2(result, bias); + } + return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha); + } + const $x = convertToTensor(x, "x", "depthwiseConv2d", "float32"); + const $filter = convertToTensor(filter, "filter", "depthwiseConv2d", "float32"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`); + assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`); + assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`); + if (dilations == null) { + dilations = [1, 1]; + } + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + checkPadOnDimRoundingMode("fused depthwiseConv2d", pad3, dimRoundingMode); + const convInfo = computeConv2DInfo( + x4D.shape, + $filter.shape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + let $bias; + if (bias != null) { + $bias = convertToTensor(bias, "bias", "fused conv2d"); + [$bias] = makeTypesMatch($bias, $x); + assertAndGetBroadcastShape(convInfo.outShape, $bias.shape); + } + let $preluActivationWeights; + if (preluActivationWeights != null) { + $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d"); + } + const grad2 = (dy, saved) => { + assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`); + const [$filter2, x4D2, y, bias2] = saved; + const dyActivation = getFusedDyActivation(dy, y, activation2); + const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3, dilations, dimRoundingMode); + const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3, dilations, dimRoundingMode); + if (bias2 != null) { + const biasDer = getFusedBiasGradient($bias, dyActivation); + return [xDer, filterDer, biasDer]; + } + return [xDer, filterDer]; + }; + const inputs = { + x: x4D, + filter: $filter, + bias: $bias, + preluActivationWeights: $preluActivationWeights + }; + const attrs = { + strides, + pad: pad3, + dataFormat, + dilations, + dimRoundingMode, + activation: activation2, + leakyreluAlpha + }; + if (bias == null) { + const customOp = customGrad((x4D2, filter2, save) => { + let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs); + save([filter2, x4D2, res]); + if (reshapedTo4D) { + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad2 }; + }); + return customOp(x4D, $filter); + } else { + const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => { + let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs); + save([filter2, x4D2, res, bias2]); + if (reshapedTo4D) { + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad2 }; + }); + return customOpWithBias(x4D, $filter, $bias); + } +} +var depthwiseConv2d2 = op({ fusedDepthwiseConv2d_ }); +function fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha = 0.2 }) { + if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) { + let result = matMul(a, b, transposeA, transposeB); + if (bias != null) { + result = add2(result, bias); + } + return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha); + } + let $a = convertToTensor(a, "a", "fused matMul"); + let $b = convertToTensor(b, "b", "fused matMul"); + [$a, $b] = makeTypesMatch($a, $b); + const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1]; + const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2]; + const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2]; + const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1]; + const outerDimsA = $a.shape.slice(0, -2); + const outerDimsB = $b.shape.slice(0, -2); + const batchDimA = sizeFromShape(outerDimsA); + const batchDimB = sizeFromShape(outerDimsB); + assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); + const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2)); + const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]); + const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]); + let $bias; + if (bias != null) { + $bias = convertToTensor(bias, "bias", "fused matMul"); + [$bias] = makeTypesMatch($bias, $a); + assertAndGetBroadcastShape(outShape, $bias.shape); + } + let $preluActivationWeights; + if (preluActivationWeights != null) { + $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused matMul"); + } + const grad2 = (dy, saved) => { + const [a3D2, b3D2, y, $bias2] = saved; + const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation2); + let aDer; + let bDer; + if (!transposeA && !transposeB) { + aDer = matMul(dyActivation, b3D2, false, true); + bDer = matMul(a3D2, dyActivation, true, false); + } else if (!transposeA && transposeB) { + aDer = matMul(dyActivation, b3D2, false, false); + bDer = matMul(dyActivation, a3D2, true, false); + } else if (transposeA && !transposeB) { + aDer = matMul(b3D2, dyActivation, false, true); + bDer = matMul(a3D2, dyActivation, false, false); + } else { + aDer = matMul(b3D2, dyActivation, true, true); + bDer = matMul(dyActivation, a3D2, true, true); + } + if (bias != null) { + const biasDer = getFusedBiasGradient($bias2, dyActivation); + return [aDer, bDer, biasDer]; + } else { + return [aDer, bDer]; + } + }; + const inputs = { + a: a3D, + b: b3D, + bias: $bias, + preluActivationWeights: $preluActivationWeights + }; + const attrs = { transposeA, transposeB, activation: activation2, leakyreluAlpha }; + if (bias == null) { + const customOp = customGrad((a3D2, b3D2, save) => { + const res = ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(_FusedMatMul, inputs, attrs) + ); + save([a3D2, b3D2, res]); + return { value: reshape(res, outShape), gradFunc: grad2 }; + }); + return customOp(a3D, b3D); + } else { + const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => { + const res = ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(_FusedMatMul, inputs, attrs) + ); + save([a3D2, b3D2, res, $bias2]); + return { value: reshape(res, outShape), gradFunc: grad2 }; + }); + return customOpWithBias(a3D, b3D, $bias); + } +} +var matMul2 = op({ fusedMatMul_ }); +function hammingWindow_(windowLength) { + return cosineWindow(windowLength, 0.54, 0.46); +} +var hammingWindow = op({ hammingWindow_ }); +function hannWindow_(windowLength) { + return cosineWindow(windowLength, 0.5, 0.5); +} +var hannWindow = op({ hannWindow_ }); +function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) { + let start = 0; + const output = []; + while (start + frameLength <= signal2.size) { + output.push(slice(signal2, start, frameLength)); + start += frameStep; + } + if (padEnd) { + while (start < signal2.size) { + const padLen = start + frameLength - signal2.size; + const pad3 = concat([ + slice(signal2, start, frameLength - padLen), + fill([padLen], padValue) + ]); + output.push(pad3); + start += frameStep; + } + } + if (output.length === 0) { + return tensor2d([], [0, frameLength]); + } + return reshape(concat(output), [output.length, frameLength]); +} +var frame = op({ frame_ }); +function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) { + if (fftLength == null) { + fftLength = enclosingPowerOfTwo(frameLength); + } + const framedSignal = frame(signal2, frameLength, frameStep); + const windowedSignal = mul(framedSignal, windowFn(frameLength)); + return rfft(windowedSignal, fftLength); +} +var stft = op({ stft_ }); +function cropAndResize_(image2, boxes, boxInd, cropSize, method = "bilinear", extrapolationValue = 0) { + const $image = convertToTensor(image2, "image", "cropAndResize"); + const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32"); + const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32"); + const numBoxes = $boxes.shape[0]; + assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`); + assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`); + assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`); + assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`); + assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`); + assert(method === "bilinear" || method === "nearest", () => `method must be bilinear or nearest, but was ${method}`); + const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd }; + const attrs = { method, extrapolationValue, cropSize }; + const res = ENGINE.runKernel(CropAndResize, inputs, attrs); + return res; +} +var cropAndResize = op({ cropAndResize_ }); +function flipLeftRight_(image2) { + const $image = convertToTensor(image2, "image", "flipLeftRight", "float32"); + assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`); + const inputs = { image: $image }; + const res = ENGINE.runKernel(FlipLeftRight, inputs, {}); + return res; +} +var flipLeftRight = op({ flipLeftRight_ }); +function grayscaleToRGB_(image2) { + const $image = convertToTensor(image2, "image", "grayscaleToRGB"); + const lastDimsIdx = $image.rank - 1; + const lastDims = $image.shape[lastDimsIdx]; + assert($image.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${$image.rank}.`); + assert(lastDims === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${lastDims}.`); + const reps = new Array($image.rank); + reps.fill(1, 0, lastDimsIdx); + reps[lastDimsIdx] = 3; + return tile($image, reps); +} +var grayscaleToRGB = op({ grayscaleToRGB_ }); +function rgbToGrayscale_(image2) { + const $image = convertToTensor(image2, "image", "RGBToGrayscale"); + const lastDimsIdx = $image.rank - 1; + const lastDims = $image.shape[lastDimsIdx]; + assert($image.rank >= 2, () => `Error in RGBToGrayscale: images must be at least rank 2, but got rank ${$image.rank}.`); + assert(lastDims === 3, () => `Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${lastDims}.`); + const origDtype = $image.dtype; + const fltImage = cast($image, "float32"); + const rgbWeights = tensor1d([0.2989, 0.587, 0.114]); + let grayFloat; + switch ($image.rank) { + case 2: + grayFloat = einsum("ij,j->i", fltImage, rgbWeights); + break; + case 3: + grayFloat = einsum("ijk,k->ij", fltImage, rgbWeights); + break; + case 4: + grayFloat = einsum("ijkl,l->ijk", fltImage, rgbWeights); + break; + case 5: + grayFloat = einsum("ijklm,m->ijkl", fltImage, rgbWeights); + break; + case 6: + grayFloat = einsum("ijklmn,n->ijklm", fltImage, rgbWeights); + break; + default: + throw new Error("Not a valid tensor rank."); + } + grayFloat = expandDims(grayFloat, -1); + return cast(grayFloat, origDtype); +} +var rgbToGrayscale = op({ rgbToGrayscale_ }); +function rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) { + const $image = convertToTensor(image2, "image", "rotateWithOffset", "float32"); + assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`); + const inputs = { image: $image }; + const attrs = { radians, fillValue, center }; + const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs); + return res; +} +var rotateWithOffset = op({ rotateWithOffset_ }); +function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) { + if (iouThreshold == null) { + iouThreshold = 0.5; + } + if (scoreThreshold == null) { + scoreThreshold = Number.NEGATIVE_INFINITY; + } + if (softNmsSigma == null) { + softNmsSigma = 0; + } + const numBoxes = boxes.shape[0]; + maxOutputSize = Math.min(maxOutputSize, numBoxes); + assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`); + assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`); + assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`); + assert(scores.rank === 1, () => "scores must be a 1D tensor"); + assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`); + assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`); + return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma }; +} +function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression", "float32"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppression", "float32"); + const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); + maxOutputSize = inputs.maxOutputSize; + iouThreshold = inputs.iouThreshold; + scoreThreshold = inputs.scoreThreshold; + const attrs = { maxOutputSize, iouThreshold, scoreThreshold }; + return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs); +} +var nonMaxSuppression = op({ nonMaxSuppression_ }); +function binaryInsert(arr, element, comparator) { + const index = binarySearch(arr, element, comparator); + const insertionPoint = index < 0 ? -(index + 1) : index; + arr.splice(insertionPoint, 0, element); +} +function binarySearch(arr, target, comparator) { + return binarySearch_(arr, target, comparator || defaultComparator); +} +function defaultComparator(a, b) { + return a > b ? 1 : a < b ? -1 : 0; +} +function binarySearch_(arr, target, comparator) { + let left = 0; + let right = arr.length; + let middle = 0; + let found = false; + while (left < right) { + middle = left + (right - left >>> 1); + const compareResult = comparator(target, arr[middle]); + if (compareResult > 0) { + left = middle + 1; + } else { + right = middle; + found = !compareResult; + } + } + return found ? left : -left - 1; +} +function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + return nonMaxSuppressionImpl_( + boxes, + scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + 0 + /* softNmsSigma */ + ); +} +function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) { + return nonMaxSuppressionImpl_( + boxes, + scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + 0, + false, + padToMaxOutputSize, + true + /* returnValidOutputs */ + ); +} +function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) { + return nonMaxSuppressionImpl_( + boxes, + scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + softNmsSigma, + true + /* returnScoresTensor */ + ); +} +function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) { + const candidates = []; + for (let i = 0; i < scores.length; i++) { + if (scores[i] > scoreThreshold) { + candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 }); + } + } + candidates.sort(ascendingComparator); + const scale22 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0; + const selectedIndices = []; + const selectedScores = []; + while (selectedIndices.length < maxOutputSize && candidates.length > 0) { + const candidate = candidates.pop(); + const { score: originalScore, boxIndex, suppressBeginIndex } = candidate; + if (originalScore < scoreThreshold) { + break; + } + let ignoreCandidate = false; + for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) { + const iou2 = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]); + if (iou2 >= iouThreshold) { + ignoreCandidate = true; + break; + } + candidate.score = candidate.score * suppressWeight(iouThreshold, scale22, iou2); + if (candidate.score <= scoreThreshold) { + break; + } + } + candidate.suppressBeginIndex = selectedIndices.length; + if (!ignoreCandidate) { + if (candidate.score === originalScore) { + selectedIndices.push(boxIndex); + selectedScores.push(candidate.score); + } else if (candidate.score > scoreThreshold) { + binaryInsert(candidates, candidate, ascendingComparator); + } + } + } + const validOutputs = selectedIndices.length; + const elemsToPad = maxOutputSize - validOutputs; + if (padToMaxOutputSize && elemsToPad > 0) { + selectedIndices.push(...new Array(elemsToPad).fill(0)); + selectedScores.push(...new Array(elemsToPad).fill(0)); + } + const result = { selectedIndices }; + if (returnScoresTensor) { + result["selectedScores"] = selectedScores; + } + if (returnValidOutputs) { + result["validOutputs"] = validOutputs; + } + return result; +} +function intersectionOverUnion(boxes, i, j) { + const iCoord = boxes.subarray(i * 4, i * 4 + 4); + const jCoord = boxes.subarray(j * 4, j * 4 + 4); + const yminI = Math.min(iCoord[0], iCoord[2]); + const xminI = Math.min(iCoord[1], iCoord[3]); + const ymaxI = Math.max(iCoord[0], iCoord[2]); + const xmaxI = Math.max(iCoord[1], iCoord[3]); + const yminJ = Math.min(jCoord[0], jCoord[2]); + const xminJ = Math.min(jCoord[1], jCoord[3]); + const ymaxJ = Math.max(jCoord[0], jCoord[2]); + const xmaxJ = Math.max(jCoord[1], jCoord[3]); + const areaI = (ymaxI - yminI) * (xmaxI - xminI); + const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); + if (areaI <= 0 || areaJ <= 0) { + return 0; + } + const intersectionYmin = Math.max(yminI, yminJ); + const intersectionXmin = Math.max(xminI, xminJ); + const intersectionYmax = Math.min(ymaxI, ymaxJ); + const intersectionXmax = Math.min(xmaxI, xmaxJ); + const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0); + return intersectionArea / (areaI + areaJ - intersectionArea); +} +function suppressWeight(iouThreshold, scale22, iou2) { + const weight = Math.exp(scale22 * iou2 * iou2); + return iou2 <= iouThreshold ? weight : 0; +} +function ascendingComparator(c1, c2) { + return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex; +} +async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync"); + const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); + maxOutputSize = inputs.maxOutputSize; + iouThreshold = inputs.iouThreshold; + scoreThreshold = inputs.scoreThreshold; + const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]); + const boxesVals = boxesAndScores[0]; + const scoresVals = boxesAndScores[1]; + const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold); + if ($boxes !== boxes) { + $boxes.dispose(); + } + if ($scores !== scores) { + $scores.dispose(); + } + return tensor1d(selectedIndices, "int32"); +} +var nonMaxSuppressionAsync = nonMaxSuppressionAsync_; +function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppression"); + const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + maxOutputSize = params.maxOutputSize; + iouThreshold = params.iouThreshold; + scoreThreshold = params.scoreThreshold; + softNmsSigma = params.softNmsSigma; + const inputs = { boxes: $boxes, scores: $scores }; + const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma }; + const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs); + return { selectedIndices: result[0], selectedScores: result[1] }; +} +var nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ }); +async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync"); + const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + maxOutputSize = params.maxOutputSize; + iouThreshold = params.iouThreshold; + scoreThreshold = params.scoreThreshold; + softNmsSigma = params.softNmsSigma; + const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]); + const boxesVals = boxesAndScores[0]; + const scoresVals = boxesAndScores[1]; + const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + if ($boxes !== boxes) { + $boxes.dispose(); + } + if ($scores !== scores) { + $scores.dispose(); + } + return { + selectedIndices: tensor1d(selectedIndices, "int32"), + selectedScores: tensor1d(selectedScores) + }; +} +var nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_; +function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppression"); + const params = nonMaxSuppSanityCheck( + $boxes, + $scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + null + /* softNmsSigma */ + ); + const $maxOutputSize = params.maxOutputSize; + const $iouThreshold = params.iouThreshold; + const $scoreThreshold = params.scoreThreshold; + const inputs = { boxes: $boxes, scores: $scores }; + const attrs = { + maxOutputSize: $maxOutputSize, + iouThreshold: $iouThreshold, + scoreThreshold: $scoreThreshold, + padToMaxOutputSize + }; + const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs); + return { selectedIndices: result[0], validOutputs: result[1] }; +} +var nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ }); +async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync"); + const params = nonMaxSuppSanityCheck( + $boxes, + $scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + null + /* softNmsSigma */ + ); + const $maxOutputSize = params.maxOutputSize; + const $iouThreshold = params.iouThreshold; + const $scoreThreshold = params.scoreThreshold; + const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]); + const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize); + if ($boxes !== boxes) { + $boxes.dispose(); + } + if ($scores !== scores) { + $scores.dispose(); + } + return { + selectedIndices: tensor1d(selectedIndices, "int32"), + validOutputs: scalar(validOutputs, "int32") + }; +} +var nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_; +function resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) { + const $images = convertToTensor(images, "images", "resizeBilinear"); + assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`); + assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`); + assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.`); + let batchImages = $images; + let reshapedTo4D = false; + if ($images.rank === 3) { + reshapedTo4D = true; + batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]); + } + const [] = size; + const inputs = { images: batchImages }; + const attrs = { alignCorners, halfPixelCenters, size }; + const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var resizeBilinear = op({ resizeBilinear_ }); +function resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) { + const $images = convertToTensor(images, "images", "resizeNearestNeighbor"); + assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`); + assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`); + assert($images.dtype === "float32" || $images.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"); + assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.`); + let batchImages = $images; + let reshapedTo4D = false; + if ($images.rank === 3) { + reshapedTo4D = true; + batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]); + } + const [] = size; + const inputs = { images: batchImages }; + const attrs = { alignCorners, halfPixelCenters, size }; + const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var resizeNearestNeighbor = op({ resizeNearestNeighbor_ }); +function threshold_(image2, method = "binary", inverted = false, threshValue = 0.5) { + const $image = convertToTensor(image2, "image", "threshold"); + const RED_INTENCITY_COEF = 0.2989; + const GREEN_INTENCITY_COEF = 0.587; + const BLUE_INTENCITY_COEF = 0.114; + const totalPixelsInImage = $image.shape[0] * $image.shape[1]; + let $threshold = mul(tensor1d([threshValue]), 255); + let r, g, b, grayscale; + assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`); + assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`); + assert($image.dtype === "int32" || $image.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`); + assert(method === "otsu" || method === "binary", () => `Method must be binary or otsu, but was ${method}`); + if ($image.shape[2] === 3) { + [r, g, b] = split($image, [1, 1, 1], -1); + const $r = mul(r, RED_INTENCITY_COEF); + const $g = mul(g, GREEN_INTENCITY_COEF); + const $b = mul(b, BLUE_INTENCITY_COEF); + grayscale = add2(add2($r, $g), $b); + } else { + grayscale = image2; + } + if (method === "otsu") { + const $histogram = bincount(cast(round2(grayscale), "int32"), tensor([]), 256); + $threshold = otsu($histogram, totalPixelsInImage); + } + const invCondition = inverted ? lessEqual(grayscale, $threshold) : greater(grayscale, $threshold); + const result = cast(mul(invCondition, 255), "int32"); + return result; +} +function otsu(histogram, total) { + let bestThresh = tensor1d([-1]); + let bestInBetVar = tensor1d([0]); + let cInBetVar = tensor1d([0]); + let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack; + for (let index = 0; index < histogram.size - 1; index++) { + classFirst = slice(histogram, 0, index + 1); + classSecond = slice(histogram, index + 1); + weightForeground = div(sum2(classFirst), total); + weightBack = div(sum2(classSecond), total); + const meanFirstDivA = sum2(mul(classFirst, range(0, classFirst.size))); + meanFirst = div(meanFirstDivA, sum2(classFirst)); + const meanSecFill = fill(classSecond.shape, classFirst.size); + const meanSecAdd = add2(range(0, classSecond.size), meanSecFill); + const meanSecMul = mul(classSecond, meanSecAdd); + meanSec = div(sum2(meanSecMul), sum2(classSecond)); + const cInBetVarSubA = sub(meanFirst, meanSec); + const cInBetVarSubB = sub(meanFirst, meanSec); + const cInBetVarMul = mul(weightForeground, weightBack); + cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB); + const condition = greater(cInBetVar, bestInBetVar); + bestInBetVar = where(condition, cInBetVar, bestInBetVar); + bestThresh = where(condition, tensor1d([index]), bestThresh); + } + return bestThresh; +} +var threshold = op({ threshold_ }); +function transform_(image2, transforms, interpolation = "nearest", fillMode = "constant", fillValue = 0, outputShape) { + const $image = convertToTensor(image2, "image", "transform", "float32"); + const $transforms = convertToTensor(transforms, "transforms", "transform", "float32"); + assert($image.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${$image.rank}.`); + assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`); + assert(outputShape == null || outputShape.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${outputShape}.`); + const inputs = { image: $image, transforms: $transforms }; + const attrs = { interpolation, fillMode, fillValue, outputShape }; + return ENGINE.runKernel(Transform, inputs, attrs); +} +var transform = op({ transform_ }); +function bandPart_(a, numLower, numUpper) { + const $a = convertToTensor(a, "a", "bandPart"); + assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`); + const shape = $a.shape; + const [M, N] = $a.shape.slice(-2); + let $numLower; + let $numUpper; + if (typeof numLower === "number") { + assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`); + assert(numLower <= M, () => `bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`); + $numLower = convertToTensor(numLower < 0 ? M : numLower, "numLower", "bandPart"); + } else { + assert(numLower.dtype === "int32", () => `bandPart(): numLower's dtype must be an int32.`); + $numLower = where(less(numLower, 0), M, minimum(numLower, M)); + } + if (typeof numUpper === "number") { + assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`); + assert(numUpper <= N, () => `bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`); + $numUpper = convertToTensor(numUpper < 0 ? N : numUpper, "numUpper", "bandPart"); + } else { + assert(numUpper.dtype === "int32", () => `bandPart(): numUpper's dtype must be an int32.`); + $numUpper = where(less(numUpper, 0), N, minimum(numUpper, N)); + } + const i = reshape(range(0, M, 1, "int32"), [-1, 1]); + const j = range(0, N, 1, "int32"); + const ij = sub(i, j); + const inBand = logicalAnd(lessEqual(ij, $numLower), greaterEqual(ij, neg($numUpper))); + const zero = zeros([M, N], $a.dtype); + return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape); +} +var bandPart = op({ bandPart_ }); +function gramSchmidt_(xs) { + let inputIsTensor2D; + if (Array.isArray(xs)) { + inputIsTensor2D = false; + assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty"); + const dim = xs[0].shape[0]; + for (let i = 1; i < xs.length; ++i) { + assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`); + } + } else { + inputIsTensor2D = true; + xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0])); + } + assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`); + const ys = []; + const xs1d = xs; + for (let i = 0; i < xs.length; ++i) { + ys.push(ENGINE.tidy(() => { + let x = xs1d[i]; + if (i > 0) { + for (let j = 0; j < i; ++j) { + const proj = mul(sum2(mul(ys[j], x)), ys[j]); + x = sub(x, proj); + } + } + return div(x, norm(x, "euclidean")); + })); + } + if (inputIsTensor2D) { + return stack(ys, 0); + } else { + return ys; + } +} +var gramSchmidt = op({ gramSchmidt_ }); +function qr_(x, fullMatrices = false) { + assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`); + if (x.rank === 2) { + return qr2d(x, fullMatrices); + } else { + const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev); + const x2ds = unstack(reshape(x, [ + outerDimsProd, + x.shape[x.shape.length - 2], + x.shape[x.shape.length - 1] + ]), 0); + const q2ds = []; + const r2ds = []; + x2ds.forEach((x2d) => { + const [q2d, r2d] = qr2d(x2d, fullMatrices); + q2ds.push(q2d); + r2ds.push(r2d); + }); + const q = reshape(stack(q2ds, 0), x.shape); + const r = reshape(stack(r2ds, 0), x.shape); + return [q, r]; + } +} +function qr2d(x, fullMatrices = false) { + return ENGINE.tidy(() => { + assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`); + const m = x.shape[0]; + const n = x.shape[1]; + let q = eye(m); + let r = clone(x); + const one2D = tensor2d([[1]], [1, 1]); + let w = clone(one2D); + const iters = m >= n ? n : m; + for (let j = 0; j < iters; ++j) { + const rTemp = r; + const wTemp = w; + const qTemp = q; + [w, r, q] = ENGINE.tidy(() => { + const rjEnd1 = slice(r, [j, j], [m - j, 1]); + const normX = norm(rjEnd1); + const rjj = slice(r, [j, j], [1, 1]); + const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]])); + const u1 = sub(rjj, mul(s, normX)); + const wPre = div(rjEnd1, u1); + if (wPre.shape[0] === 1) { + w = clone(one2D); + } else { + w = concat([ + one2D, + slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) + ], 0); + } + const tau = neg(div(matMul(s, u1), normX)); + const rjEndAll = slice(r, [j, 0], [m - j, n]); + const tauTimesW = mul(tau, w); + const wT = transpose(w); + if (j === 0) { + r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll))); + } else { + const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll))); + r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0); + } + const tawTimesWT = transpose(tauTimesW); + const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]); + if (j === 0) { + q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT)); + } else { + const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT)); + q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1); + } + return [w, r, q]; + }); + dispose([rTemp, wTemp, qTemp]); + } + if (!fullMatrices && m > n) { + q = slice(q, [0, 0], [m, n]); + r = slice(r, [0, 0], [n, n]); + } + return [q, r]; + }); +} +var qr = op({ qr_ }); +var Reduction; +(function(Reduction2) { + Reduction2[Reduction2["NONE"] = 0] = "NONE"; + Reduction2[Reduction2["MEAN"] = 1] = "MEAN"; + Reduction2[Reduction2["SUM"] = 2] = "SUM"; + Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS"; +})(Reduction || (Reduction = {})); +function computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $losses = convertToTensor(losses2, "losses", "computeWeightedLoss"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "computeWeightedLoss"); + } + const weightedLoss = $weights == null ? $losses : mul($losses, $weights); + if (reduction === Reduction.NONE) { + return weightedLoss; + } + if (reduction === Reduction.SUM) { + return sum2(weightedLoss); + } + if (reduction === Reduction.MEAN) { + if ($weights == null) { + return mean(weightedLoss); + } else { + const broadcastFactor = $losses.size / $weights.size; + const result = div(sum2(weightedLoss), sum2($weights)); + return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result; + } + } + if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) { + if ($weights == null) { + return div(sum2(weightedLoss), scalar($losses.size)); + } else { + const broadcastedWeights = mul($weights, ones2($losses.shape)); + const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), "float32"); + return div(sum2(weightedLoss), numNonZeros); + } + } + throw Error(`Unknown reduction: ${reduction}`); +} +var computeWeightedLoss = op({ computeWeightedLoss_ }); +function absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $labels = convertToTensor(labels, "labels", "absoluteDifference"); + const $predictions = convertToTensor(predictions, "predictions", "absoluteDifference"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "absoluteDifference"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in absoluteDifference: "); + const losses2 = abs(sub($labels, $predictions)); + return computeWeightedLoss(losses2, $weights, reduction); +} +var absoluteDifference = op({ absoluteDifference_ }); +function cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $labels = convertToTensor(labels, "labels", "cosineDistance"); + const $predictions = convertToTensor(predictions, "predictions", "cosineDistance"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "cosineDistance"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in cosineDistance: "); + const one = scalar(1); + const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true)); + return computeWeightedLoss(losses2, $weights, reduction); +} +var cosineDistance = op({ cosineDistance_ }); +function hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + let $labels = convertToTensor(labels, "labels", "hingeLoss"); + const $predictions = convertToTensor(predictions, "predictions", "hingeLoss"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "hingeLoss"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in hingeLoss: "); + const one = scalar(1); + $labels = sub(mul(scalar(2), $labels), one); + const losses2 = relu(sub(one, mul($labels, $predictions))); + return computeWeightedLoss(losses2, $weights, reduction); +} +var hingeLoss = op({ hingeLoss_ }); +function huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $labels = convertToTensor(labels, "labels", "huberLoss"); + const $predictions = convertToTensor(predictions, "predictions", "huberLoss"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "huberLoss"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in huberLoss: "); + const deltaScalar = scalar(delta); + const error = abs(sub($predictions, $labels)); + const quadratic = minimum(error, deltaScalar); + const linear = sub(error, quadratic); + const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear)); + return computeWeightedLoss(losses2, $weights, reduction); +} +var huberLoss = op({ huberLoss_ }); +function logLoss_(labels, predictions, weights, epsilon32 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $labels = convertToTensor(labels, "labels", "logLoss"); + const $predictions = convertToTensor(predictions, "predictions", "logLoss"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "logLoss"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: "); + const one = scalar(1); + const epsilonScalar = scalar(epsilon32); + const l13 = neg(mul($labels, log2(add2($predictions, epsilonScalar)))); + const l23 = mul(sub(one, $labels), log2(add2(sub(one, $predictions), epsilonScalar))); + const losses2 = sub(l13, l23); + return computeWeightedLoss(losses2, $weights, reduction); +} +var logLoss = op({ logLoss_ }); +function meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $labels = convertToTensor(labels, "labels", "meanSquaredError"); + const $predictions = convertToTensor(predictions, "predictions", "meanSquaredError"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "meanSquaredError"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in meanSquaredError: "); + const losses2 = squaredDifference($labels, $predictions); + return computeWeightedLoss(losses2, $weights, reduction); +} +var meanSquaredError = op({ meanSquaredError_ }); +function sigmoidCrossEntropyWithLogits_(labels, logits) { + const $labels = convertToTensor(labels, "labels", "sigmoidCrossEntropyWithLogits"); + const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropyWithLogits"); + assertShapesMatch($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: "); + const maxOutput = relu($logits); + const outputXTarget = mul($logits, $labels); + const sigmoidOutput = log1p(exp(neg(abs($logits)))); + return add2(sub(maxOutput, outputXTarget), sigmoidOutput); +} +function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + let $multiClassLabels = convertToTensor(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy"); + const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropy"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "sigmoidCrossEntropy"); + } + assertShapesMatch($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: "); + if (labelSmoothing > 0) { + const labelSmoothingScalar = scalar(labelSmoothing); + const one = scalar(1); + const half = scalar(0.5); + $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar)); + } + const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits); + return computeWeightedLoss(losses2, $weights, reduction); +} +var sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ }); +function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) { + if (dim === -1) { + dim = logits.rank - 1; + } + if (dim !== logits.rank - 1) { + throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`); + } + const customOp = customGrad((labels2, logits2, save) => { + const keepDims = true; + const lse = logSumExp(logits2, [dim], keepDims); + const logResult = sub(cast(logits2, "float32"), lse); + save([labels2, logResult]); + const costVector = neg(mul(logResult, labels2)); + const value = sum2(costVector, [dim]); + const gradFunc = (dy, saved) => { + const [labels3, logResult2] = saved; + const dyShape = expandShapeToKeepDim(dy.shape, [dim]); + return [ + mul(reshape(dy, dyShape), sub(cast(labels3, "float32"), exp(logResult2))), + mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, "float32"))) + ]; + }; + return { value, gradFunc }; + }); + return customOp(labels, logits); +} +function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + let $onehotLabels = convertToTensor(onehotLabels, "onehotLabels", "softmaxCrossEntropy"); + const $logits = convertToTensor(logits, "logits", "softmaxCrossEntropy"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "softmaxCrossEntropy"); + } + assertShapesMatch($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: "); + if (labelSmoothing > 0) { + const labelSmoothingScalar = scalar(labelSmoothing); + const one = scalar(1); + const numClasses = scalar($onehotLabels.shape[1]); + $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses)); + } + const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits); + return computeWeightedLoss(losses2, $weights, reduction); +} +var softmaxCrossEntropy = op({ softmaxCrossEntropy_ }); +function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) { + const $indices = convertToTensor(indices, "indices", "sparseFillEmptyRows", "int32"); + const $values = convertToTensor(values, "values", "sparseFillEmptyRows"); + const $denseShape = convertToTensor(denseShape, "denseShape", "sparseFillEmptyRows", "int32"); + const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseFillEmptyRows", $values.dtype); + if ($indices.rank !== 2) { + throw new Error(`Indices should be Tensor2D but received shape + ${$indices.shape}`); + } + if ($values.rank !== 1) { + throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`); + } + if ($denseShape.rank !== 1) { + throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`); + } + if ($defaultValue.rank !== 0) { + throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`); + } + const inputs = { + indices: $indices, + values: $values, + denseShape: $denseShape, + defaultValue: $defaultValue + }; + const result = ENGINE.runKernel(SparseFillEmptyRows, inputs); + return { + outputIndices: result[0], + outputValues: result[1], + emptyRowIndicator: result[2], + reverseIndexMap: result[3] + }; +} +var sparseFillEmptyRows = op({ sparseFillEmptyRows_ }); +function sparseReshape_(inputIndices, inputShape, newShape) { + const $inputIndices = convertToTensor(inputIndices, "inputIndices", "sparseReshape", "int32"); + const $inputShape = convertToTensor(inputShape, "inputShape", "sparseReshape", "int32"); + const $newShape = convertToTensor(newShape, "newShape", "sparseReshape", "int32"); + if ($inputIndices.rank !== 2) { + throw new Error(`Input indices should be Tensor2D but received shape + ${$inputIndices.shape}`); + } + if ($inputShape.rank !== 1) { + throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`); + } + if ($newShape.rank !== 1) { + throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`); + } + const inputs = { + inputIndices: $inputIndices, + inputShape: $inputShape, + newShape: $newShape + }; + const result = ENGINE.runKernel(SparseReshape, inputs); + return { outputIndices: result[0], outputShape: result[1] }; +} +var sparseReshape = op({ sparseReshape_ }); +function sparseSegmentMean_(data, indices, segmentIds) { + const $data = convertToTensor(data, "data", "sparseSegmentMean"); + const $indices = convertToTensor(indices, "indices", "sparseSegmentMean", "int32"); + const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentMean", "int32"); + if ($data.rank < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if ($indices.rank !== 1) { + throw new Error(`Indices should be Tensor1D but received shape + ${$indices.shape}`); + } + if ($segmentIds.rank !== 1) { + throw new Error(`Segment ids should be Tensor1D but received shape + ${$segmentIds.shape}`); + } + const inputs = { + data: $data, + indices: $indices, + segmentIds: $segmentIds + }; + return ENGINE.runKernel(SparseSegmentMean, inputs); +} +var sparseSegmentMean = op({ sparseSegmentMean_ }); +function sparseSegmentSum_(data, indices, segmentIds) { + const $data = convertToTensor(data, "data", "sparseSegmentSum"); + const $indices = convertToTensor(indices, "indices", "sparseSegmentSum", "int32"); + const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentSum", "int32"); + if ($data.rank < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if ($indices.rank !== 1) { + throw new Error(`Indices should be Tensor1D but received shape + ${$indices.shape}`); + } + if ($segmentIds.rank !== 1) { + throw new Error(`Segment ids should be Tensor1D but received shape + ${$segmentIds.shape}`); + } + const inputs = { + data: $data, + indices: $indices, + segmentIds: $segmentIds + }; + return ENGINE.runKernel(SparseSegmentSum, inputs); +} +var sparseSegmentSum = op({ sparseSegmentSum_ }); +function stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) { + const $data = convertToTensor(data, "data", "stringNGrams", "string"); + if ($data.dtype !== "string") { + throw new Error("Data must be of datatype string"); + } + if ($data.shape.length !== 1) { + throw new Error(`Data must be a vector, saw: ${$data.shape}`); + } + const $dataSplits = convertToTensor(dataSplits, "dataSplits", "stringNGrams"); + if ($dataSplits.dtype !== "int32") { + throw new Error("Data splits must be of datatype int32"); + } + const attrs = { + separator, + nGramWidths, + leftPad, + rightPad: rightPad2, + padWidth, + preserveShortSequences + }; + const inputs = { data: $data, dataSplits: $dataSplits }; + const result = ENGINE.runKernel(StringNGrams, inputs, attrs); + return { nGrams: result[0], nGramsSplits: result[1] }; +} +var stringNGrams = op({ stringNGrams_ }); +function stringSplit_(input2, delimiter, skipEmpty = true) { + const $input = convertToTensor(input2, "input", "stringSplit", "string"); + const $delimiter = convertToTensor(delimiter, "delimiter", "stringSplit", "string"); + if ($input.rank !== 1) { + throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`); + } + if ($delimiter.rank !== 0) { + throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`); + } + const attrs = { skipEmpty }; + const inputs = { input: $input, delimiter: $delimiter }; + const result = ENGINE.runKernel(StringSplit, inputs, attrs); + return { indices: result[0], values: result[1], shape: result[2] }; +} +var stringSplit = op({ stringSplit_ }); +function stringToHashBucketFast_(input2, numBuckets) { + const $input = convertToTensor(input2, "input", "stringToHashBucketFast", "string"); + const attrs = { numBuckets }; + if (numBuckets <= 0) { + throw new Error(`Number of buckets must be at least 1`); + } + const inputs = { input: $input }; + return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs); +} +var stringToHashBucketFast = op({ stringToHashBucketFast_ }); +function staticRegexReplace_(input2, pattern, rewrite, replaceGlobal = true) { + const $input = convertToTensor(input2, "input", "staticRegexReplace", "string"); + const attrs = { pattern, rewrite, replaceGlobal }; + return ENGINE.runKernel(StaticRegexReplace, { x: $input }, attrs); +} +var staticRegexReplace = op({ staticRegexReplace_ }); +var spectral = { + fft, + ifft, + rfft, + irfft +}; +var signal = { + hammingWindow, + hannWindow, + frame, + stft +}; +var image = { + flipLeftRight, + grayscaleToRGB, + resizeNearestNeighbor, + resizeBilinear, + rgbToGrayscale, + rotateWithOffset, + cropAndResize, + nonMaxSuppression, + nonMaxSuppressionAsync, + nonMaxSuppressionWithScore, + nonMaxSuppressionWithScoreAsync, + nonMaxSuppressionPadded, + nonMaxSuppressionPaddedAsync, + threshold, + transform +}; +var linalg = { + bandPart, + gramSchmidt, + qr +}; +var losses = { + absoluteDifference, + computeWeightedLoss, + cosineDistance, + hingeLoss, + huberLoss, + logLoss, + meanSquaredError, + sigmoidCrossEntropy, + softmaxCrossEntropy +}; +var sparse = { + sparseFillEmptyRows, + sparseReshape, + sparseSegmentMean, + sparseSegmentSum +}; +var string = { + stringNGrams, + stringSplit, + stringToHashBucketFast, + staticRegexReplace +}; +var serialization_exports = {}; +__export2(serialization_exports, { + Serializable: () => Serializable, + SerializationMap: () => SerializationMap, + getRegisteredName: () => getRegisteredName, + registerClass: () => registerClass +}); +var GLOBAL_CUSTOM_OBJECT = /* @__PURE__ */ new Map(); +var GLOBAL_CUSTOM_NAMES = /* @__PURE__ */ new Map(); +var Serializable = class { + /** + * Return the class name for this class to use in serialization contexts. + * + * Generally speaking this will be the same thing that constructor.name + * would have returned. However, the class name needs to be robust + * against minification for serialization/deserialization to work properly. + * + * There's also places such as initializers.VarianceScaling, where + * implementation details between different languages led to different + * class hierarchies and a non-leaf node is used for serialization purposes. + */ + getClassName() { + return this.constructor.className; + } + /** + * Creates an instance of T from a ConfigDict. + * + * This works for most descendants of serializable. A few need to + * provide special handling. + * @param cls A Constructor for the class to instantiate. + * @param config The Configuration for the object. + */ + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config); + } +}; +var SerializationMap = class _SerializationMap { + constructor() { + this.classNameMap = {}; + } + /** + * Returns the singleton instance of the map. + */ + static getMap() { + if (_SerializationMap.instance == null) { + _SerializationMap.instance = new _SerializationMap(); + } + return _SerializationMap.instance; + } + /** + * Registers the class as serializable. + */ + static register(cls) { + _SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig]; + } +}; +function registerClass(cls, pkg, name) { + assert(cls.className != null, () => `Class being registered does not have the static className property defined.`); + assert(typeof cls.className === "string", () => `className is required to be a string, but got type ` + typeof cls.className); + assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`); + if (typeof pkg === "undefined") { + pkg = "Custom"; + } + if (typeof name === "undefined") { + name = cls.className; + } + const className = name; + const registerName = pkg + ">" + className; + SerializationMap.register(cls); + GLOBAL_CUSTOM_OBJECT.set(registerName, cls); + GLOBAL_CUSTOM_NAMES.set(cls, registerName); + return cls; +} +function getRegisteredName(cls) { + if (GLOBAL_CUSTOM_NAMES.has(cls)) { + return GLOBAL_CUSTOM_NAMES.get(cls); + } else { + return cls.className; + } +} +var Optimizer = class extends Serializable { + /** + * Executes `f()` and minimizes the scalar output of `f()` by computing + * gradients of y with respect to the list of trainable variables provided by + * `varList`. If no list is provided, it defaults to all trainable variables. + * + * @param f The function to execute and whose output to minimize. + * @param returnCost Whether to return the scalar cost value produced by + * executing `f()`. + * @param varList An optional list of variables to update. If specified, only + * the trainable variables in varList will be updated by minimize. Defaults to + * all trainable variables. + * + * @doc {heading: 'Training', subheading: 'Optimizers'} + */ + minimize(f, returnCost = false, varList) { + const { value, grads: grads2 } = this.computeGradients(f, varList); + if (varList != null) { + const gradArray = varList.map((v) => ({ name: v.name, tensor: grads2[v.name] })); + this.applyGradients(gradArray); + } else { + this.applyGradients(grads2); + } + dispose(grads2); + if (returnCost) { + return value; + } else { + value.dispose(); + return null; + } + } + /** + * The number of iterations that this optimizer instance has been invoked for. + */ + get iterations() { + if (this.iterations_ == null) { + this.iterations_ = 0; + } + return this.iterations_; + } + incrementIterations() { + this.iterations_ = this.iterations + 1; + } + /** + * Executes f() and computes the gradient of the scalar output of f() with + * respect to the list of trainable variables provided by `varList`. If no + * list is provided, it defaults to all trainable variables. + * + * @param f The function to execute and whose output to use for computing + * gradients with respect to variables. + * @param varList An optional list of variables to compute gradients with + * respect to. If specified, only the trainable variables in varList will have + * gradients computed with respect to. Defaults to all trainable variables. + * + * @doc {heading: 'Training', subheading: 'Optimizers'} + */ + computeGradients(f, varList) { + return variableGrads(f, varList); + } + /** + * Dispose the variables (if any) owned by this optimizer instance. + */ + dispose() { + if (this.iterations_ != null) { + dispose(this.iterations_); + } + } + async saveIterations() { + if (this.iterations_ == null) { + this.iterations_ = 0; + } + return { + name: "iter", + // TODO(cais): Use 'int64' type when available. + tensor: scalar(this.iterations_, "int32") + }; + } + async getWeights() { + throw new Error("getWeights() is not implemented for this optimizer yet."); + } + async setWeights(weightValues) { + throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`); + } + /** + * Extract the first element of the weight values and set it + * as the iterations counter variable of this instance of optimizer. + * + * @param weightValues + * @returns Weight values with the first element consumed and excluded. + */ + async extractIterations(weightValues) { + this.iterations_ = (await weightValues[0].tensor.data())[0]; + return weightValues.slice(1); + } +}; +Object.defineProperty(Optimizer, Symbol.hasInstance, { + value: (instance) => { + return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null; + } +}); +var AdadeltaOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "Adadelta"; + } + constructor(learningRate, rho, epsilon32 = null) { + super(); + this.learningRate = learningRate; + this.rho = rho; + this.epsilon = epsilon32; + this.accumulatedGrads = []; + this.accumulatedUpdates = []; + if (epsilon32 == null) { + this.epsilon = ENGINE.backend.epsilon(); + } + } + applyGradients(variableGradients) { + const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients); + variableNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + const trainable = false; + if (this.accumulatedGrads[i] == null) { + this.accumulatedGrads[i] = { + originalName: `${name}/accum_grad`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + if (this.accumulatedUpdates[i] == null) { + this.accumulatedUpdates[i] = { + originalName: `${name}/accum_var`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const accumulatedGrad = this.accumulatedGrads[i].variable; + const accumulatedUpdate = this.accumulatedUpdates[i].variable; + tidy(() => { + const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho)); + const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient); + const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho)); + accumulatedGrad.assign(newAccumulatedGrad); + accumulatedUpdate.assign(newAccumulatedUpdate); + const newValue = add2(mul(updates, -this.learningRate), value); + value.assign(newValue); + }); + }); + this.incrementIterations(); + } + dispose() { + if (this.accumulatedUpdates != null) { + dispose(this.accumulatedGrads.map((v) => v.variable)); + dispose(this.accumulatedUpdates.map((v) => v.variable)); + } + } + async getWeights() { + const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates]; + return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable }))); + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + const variableCount = weightValues.length / 2; + const trainable = false; + this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + } + getConfig() { + return { + "learningRate": this.learningRate, + "rho": this.rho, + "epsilon": this.epsilon + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["rho"], config["epsilon"]); + } +}; +var AdagradOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "Adagrad"; + } + constructor(learningRate, initialAccumulatorValue = 0.1) { + super(); + this.learningRate = learningRate; + this.initialAccumulatorValue = initialAccumulatorValue; + this.accumulatedGrads = []; + } + applyGradients(variableGradients) { + const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients); + variableNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + if (this.accumulatedGrads[i] == null) { + const trainable = false; + this.accumulatedGrads[i] = { + originalName: `${name}/accumulator`, + variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable)) + }; + } + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const accumulatedGrad = this.accumulatedGrads[i].variable; + tidy(() => { + const newAccumulatedGrad = add2(accumulatedGrad, square(gradient)); + accumulatedGrad.assign(newAccumulatedGrad); + const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value); + value.assign(newValue); + }); + }); + this.incrementIterations(); + } + dispose() { + if (this.accumulatedGrads != null) { + dispose(this.accumulatedGrads.map((v) => v.variable)); + } + } + async getWeights() { + return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({ name: v.originalName, tensor: v.variable }))); + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + const trainable = false; + this.accumulatedGrads = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) })); + } + getConfig() { + return { + "learningRate": this.learningRate, + "initialAccumulatorValue": this.initialAccumulatorValue + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["initialAccumulatorValue"]); + } +}; +var AdamOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "Adam"; + } + constructor(learningRate, beta1, beta2, epsilon32 = null) { + super(); + this.learningRate = learningRate; + this.beta1 = beta1; + this.beta2 = beta2; + this.epsilon = epsilon32; + this.accumulatedFirstMoment = []; + this.accumulatedSecondMoment = []; + tidy(() => { + this.accBeta1 = scalar(beta1).variable(); + this.accBeta2 = scalar(beta2).variable(); + }); + if (epsilon32 == null) { + this.epsilon = ENGINE.backend.epsilon(); + } + } + applyGradients(variableGradients) { + const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients); + tidy(() => { + const oneMinusAccBeta1 = sub(1, this.accBeta1); + const oneMinusAccBeta2 = sub(1, this.accBeta2); + varNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + const trainable = false; + if (this.accumulatedFirstMoment[i] == null) { + this.accumulatedFirstMoment[i] = { + originalName: `${name}/m`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + if (this.accumulatedSecondMoment[i] == null) { + this.accumulatedSecondMoment[i] = { + originalName: `${name}/v`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const firstMoment = this.accumulatedFirstMoment[i].variable; + const secondMoment = this.accumulatedSecondMoment[i].variable; + const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1)); + const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2)); + const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1); + const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2); + firstMoment.assign(newFirstMoment); + secondMoment.assign(newSecondMoment); + const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value); + value.assign(newValue); + }); + this.accBeta1.assign(mul(this.accBeta1, this.beta1)); + this.accBeta2.assign(mul(this.accBeta2, this.beta2)); + }); + this.incrementIterations(); + } + dispose() { + this.accBeta1.dispose(); + this.accBeta2.dispose(); + if (this.accumulatedFirstMoment != null) { + dispose(this.accumulatedFirstMoment.map((v) => v.variable)); + } + if (this.accumulatedSecondMoment != null) { + dispose(this.accumulatedSecondMoment.map((v) => v.variable)); + } + } + async getWeights() { + const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment]; + return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable }))); + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + tidy(() => { + this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1)); + this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1)); + }); + const variableCount = weightValues.length / 2; + const trainable = false; + this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + } + getConfig() { + return { + "learningRate": this.learningRate, + "beta1": this.beta1, + "beta2": this.beta2, + "epsilon": this.epsilon + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"]); + } +}; +var AdamaxOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "Adamax"; + } + constructor(learningRate, beta1, beta2, epsilon32 = null, decay = 0) { + super(); + this.learningRate = learningRate; + this.beta1 = beta1; + this.beta2 = beta2; + this.epsilon = epsilon32; + this.decay = decay; + this.accumulatedFirstMoment = []; + this.accumulatedWeightedInfNorm = []; + tidy(() => { + this.iteration = scalar(0).variable(); + this.accBeta1 = scalar(beta1).variable(); + }); + if (epsilon32 == null) { + this.epsilon = ENGINE.backend.epsilon(); + } + } + applyGradients(variableGradients) { + const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients); + tidy(() => { + const oneMinusAccBeta1 = sub(1, this.accBeta1); + const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1)); + variableNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + const trainable = false; + if (this.accumulatedFirstMoment[i] == null) { + this.accumulatedFirstMoment[i] = { + originalName: `${name}/m`, + variable: zerosLike(value).variable(trainable) + }; + } + if (this.accumulatedWeightedInfNorm[i] == null) { + this.accumulatedWeightedInfNorm[i] = { + originalName: `${name}/v`, + variable: zerosLike(value).variable(trainable) + }; + } + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const firstMoment = this.accumulatedFirstMoment[i].variable; + const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable; + const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1)); + const ut0 = mul(weightedInfNorm, this.beta2); + const ut1 = abs(gradient); + const newWeightedInfNorm = maximum(ut0, ut1); + firstMoment.assign(newFirstMoment); + weightedInfNorm.assign(newWeightedInfNorm); + const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value); + value.assign(newValue); + }); + this.iteration.assign(add2(this.iteration, 1)); + this.accBeta1.assign(mul(this.accBeta1, this.beta1)); + }); + this.incrementIterations(); + } + dispose() { + this.accBeta1.dispose(); + this.iteration.dispose(); + if (this.accumulatedFirstMoment != null) { + dispose(this.accumulatedFirstMoment.map((v) => v.variable)); + } + if (this.accumulatedWeightedInfNorm != null) { + dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable)); + } + } + async getWeights() { + throw new Error("getWeights() is not implemented for Adamax yet."); + } + async setWeights(weightValues) { + 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 + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"], config["decay"]); + } +}; +var SGDOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "SGD"; + } + constructor(learningRate) { + super(); + this.learningRate = learningRate; + this.setLearningRate(learningRate); + } + applyGradients(variableGradients) { + const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients); + varNames.forEach((name, i) => { + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const value = ENGINE.registeredVariables[name]; + tidy(() => { + const newValue = add2(mul(this.c, gradient), value); + value.assign(newValue); + }); + }); + this.incrementIterations(); + } + /** + * Sets the learning rate of the optimizer. + */ + setLearningRate(learningRate) { + this.learningRate = learningRate; + if (this.c != null) { + this.c.dispose(); + } + this.c = keep(scalar(-learningRate)); + } + dispose() { + this.c.dispose(); + } + async getWeights() { + return [await this.saveIterations()]; + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + if (weightValues.length !== 0) { + throw new Error("SGD optimizer does not have settable weights."); + } + } + getConfig() { + return { "learningRate": this.learningRate }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"]); + } +}; +var MomentumOptimizer = class extends SGDOptimizer { + /** @nocollapse */ + // Name matters for Python compatibility. + static get className() { + return "Momentum"; + } + constructor(learningRate, momentum, useNesterov = false) { + super(learningRate); + this.learningRate = learningRate; + this.momentum = momentum; + this.useNesterov = useNesterov; + this.accumulations = []; + this.m = scalar(this.momentum); + } + applyGradients(variableGradients) { + const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients); + variableNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + if (this.accumulations[i] == null) { + const trainable = false; + this.accumulations[i] = { + originalName: `${name}/momentum`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + const accumulation = this.accumulations[i].variable; + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + tidy(() => { + let newValue; + const newAccumulation = add2(mul(this.m, accumulation), gradient); + if (this.useNesterov) { + newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value); + } else { + newValue = add2(mul(this.c, newAccumulation), value); + } + accumulation.assign(newAccumulation); + value.assign(newValue); + }); + }); + this.incrementIterations(); + } + dispose() { + this.m.dispose(); + if (this.accumulations != null) { + dispose(this.accumulations.map((v) => v.variable)); + } + } + /** + * Sets the momentum of the optimizer. + * + * @param momentum + */ + setMomentum(momentum) { + this.momentum = momentum; + } + async getWeights() { + return [await this.saveIterations()].concat(this.accumulations.map((v) => ({ name: v.originalName, tensor: v.variable }))); + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + const trainable = false; + this.accumulations = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) })); + } + getConfig() { + return { + "learningRate": this.learningRate, + "momentum": this.momentum, + "useNesterov": this.useNesterov + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["momentum"], config["useNesterov"]); + } +}; +var RMSPropOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "RMSProp"; + } + constructor(learningRate, decay = 0.9, momentum = 0, epsilon32 = null, centered = false) { + super(); + this.learningRate = learningRate; + this.decay = decay; + this.momentum = momentum; + this.epsilon = epsilon32; + this.accumulatedMeanSquares = []; + this.accumulatedMoments = []; + this.accumulatedMeanGrads = []; + this.centered = centered; + if (epsilon32 == null) { + this.epsilon = ENGINE.backend.epsilon(); + } + if (learningRate == null) { + throw new Error(`learningRate for RMSPropOptimizer must be defined.`); + } + } + applyGradients(variableGradients) { + const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients); + variableNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + const trainable = false; + if (this.accumulatedMeanSquares[i] == null) { + this.accumulatedMeanSquares[i] = { + originalName: `${name}/rms`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + if (this.accumulatedMoments[i] == null) { + this.accumulatedMoments[i] = { + originalName: `${name}/momentum`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + if (this.accumulatedMeanGrads[i] == null && this.centered) { + this.accumulatedMeanGrads[i] = { + originalName: `${name}/mg`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable; + const accumulatedMoments = this.accumulatedMoments[i].variable; + tidy(() => { + const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay)); + if (this.centered) { + const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable; + const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay)); + const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon)))); + const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution); + accumulatedMeanSquare.assign(newAccumulatedMeanSquare); + accumulatedMeanGrad.assign(newAccumulatedMeanGrad); + accumulatedMoments.assign(newAccumulatedMoments); + const newValue = sub(value, newAccumulatedMoments); + value.assign(newValue); + } else { + const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay)); + const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon)))); + accumulatedMeanSquare.assign(newAccumulatedMeanSquare2); + accumulatedMoments.assign(newAccumulatedMoments); + const newValue = sub(value, newAccumulatedMoments); + value.assign(newValue); + } + }); + }); + this.incrementIterations(); + } + dispose() { + if (this.accumulatedMeanSquares != null) { + dispose(this.accumulatedMeanSquares.map((v) => v.variable)); + } + if (this.accumulatedMeanGrads != null && this.centered) { + dispose(this.accumulatedMeanGrads.map((v) => v.variable)); + } + if (this.accumulatedMoments != null) { + dispose(this.accumulatedMoments.map((v) => v.variable)); + } + } + async getWeights() { + const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments]; + if (this.centered) { + variables.push(...this.accumulatedMeanGrads); + } + return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable }))); + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2; + const trainable = false; + this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + if (this.centered) { + this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + } + } + getConfig() { + return { + "learningRate": this.learningRate, + "decay": this.decay, + "momentum": this.momentum, + "epsilon": this.epsilon, + "centered": this.centered + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["decay"], config["momentum"], config["epsilon"], config["centered"]); + } +}; +var OPTIMIZERS = [ + AdadeltaOptimizer, + AdagradOptimizer, + AdamOptimizer, + AdamaxOptimizer, + MomentumOptimizer, + RMSPropOptimizer, + SGDOptimizer +]; +function registerOptimizers() { + for (const optimizer of OPTIMIZERS) { + registerClass(optimizer); + } +} +var io_exports = {}; +__export2(io_exports, { + CompositeArrayBuffer: () => CompositeArrayBuffer, + browserFiles: () => browserFiles, + browserHTTPRequest: () => browserHTTPRequest, + concatenateArrayBuffers: () => concatenateArrayBuffers, + copyModel: () => copyModel, + decodeWeights: () => decodeWeights, + decodeWeightsStream: () => decodeWeightsStream, + encodeWeights: () => encodeWeights, + fromMemory: () => fromMemory, + fromMemorySync: () => fromMemorySync, + getLoadHandlers: () => getLoadHandlers, + getModelArtifactsForJSON: () => getModelArtifactsForJSON, + getModelArtifactsForJSONSync: () => getModelArtifactsForJSONSync, + getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON, + getSaveHandlers: () => getSaveHandlers, + getWeightSpecs: () => getWeightSpecs, + http: () => http, + isHTTPScheme: () => isHTTPScheme, + listModels: () => listModels, + loadWeights: () => loadWeights, + moveModel: () => moveModel, + registerLoadRouter: () => registerLoadRouter, + registerSaveRouter: () => registerSaveRouter, + removeModel: () => removeModel, + weightsLoaderFactory: () => weightsLoaderFactory, + withSaveHandler: () => withSaveHandler, + withSaveHandlerSync: () => withSaveHandlerSync +}); +var DEFAULT_FILE_NAME_PREFIX = "model"; +var DEFAULT_JSON_EXTENSION_NAME = ".json"; +var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin"; +function defer(f) { + return new Promise((resolve) => setTimeout(resolve)).then(f); +} +var BrowserDownloads = class _BrowserDownloads { + constructor(fileNamePrefix) { + if (!env().getBool("IS_BROWSER")) { + throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); + } + if (fileNamePrefix.startsWith(_BrowserDownloads.URL_SCHEME)) { + fileNamePrefix = fileNamePrefix.slice(_BrowserDownloads.URL_SCHEME.length); + } + if (fileNamePrefix == null || fileNamePrefix.length === 0) { + fileNamePrefix = DEFAULT_FILE_NAME_PREFIX; + } + this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME; + this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME; + } + async save(modelArtifacts) { + if (typeof document === "undefined") { + throw new Error("Browser downloads are not supported in this environment since `document` is not present"); + } + const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData); + const weightsURL = window.URL.createObjectURL(new Blob([weightBuffer], { type: "application/octet-stream" })); + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet."); + } else { + const weightsManifest = [{ + paths: ["./" + this.weightDataFileName], + weights: modelArtifacts.weightSpecs + }]; + const modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest); + const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: "application/json" })); + const jsonAnchor = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; + jsonAnchor.download = this.modelJsonFileName; + jsonAnchor.href = modelJsonURL; + await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click"))); + if (modelArtifacts.weightData != null) { + const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; + weightDataAnchor.download = this.weightDataFileName; + weightDataAnchor.href = weightsURL; + await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click"))); + } + return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) }; + } + } +}; +BrowserDownloads.URL_SCHEME = "downloads://"; +var BrowserFiles = class { + constructor(files) { + if (files == null || files.length < 1) { + throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`); + } + this.jsonFile = files[0]; + this.weightsFiles = files.slice(1); + } + async load() { + return new Promise((resolve, reject) => { + const jsonReader = new FileReader(); + jsonReader.onload = (event) => { + const modelJSON = JSON.parse(event.target.result); + const modelTopology = modelJSON.modelTopology; + if (modelTopology == null) { + reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`)); + return; + } + const weightsManifest = modelJSON.weightsManifest; + if (weightsManifest == null) { + reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`)); + return; + } + if (this.weightsFiles.length === 0) { + resolve({ modelTopology }); + return; + } + const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2)); + resolve(modelArtifactsPromise); + }; + jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`); + jsonReader.readAsText(this.jsonFile); + }); + } + loadWeights(weightsManifest) { + const weightSpecs = []; + const paths = []; + for (const entry of weightsManifest) { + weightSpecs.push(...entry.weights); + paths.push(...entry.paths); + } + const pathToFile = this.checkManifestAndWeightFiles(weightsManifest); + const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path])); + return Promise.all(promises).then((buffers) => [weightSpecs, buffers]); + } + loadWeightsFile(path, file) { + return new Promise((resolve, reject) => { + const weightFileReader = new FileReader(); + weightFileReader.onload = (event) => { + const weightData = event.target.result; + resolve(weightData); + }; + weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`); + weightFileReader.readAsArrayBuffer(file); + }); + } + /** + * Check the compatibility between weights manifest and weight files. + */ + checkManifestAndWeightFiles(manifest) { + const basenames = []; + const fileNames = this.weightsFiles.map((file) => basename(file.name)); + const pathToFile = {}; + for (const group of manifest) { + group.paths.forEach((path) => { + const pathBasename = basename(path); + if (basenames.indexOf(pathBasename) !== -1) { + throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`); + } + basenames.push(pathBasename); + if (fileNames.indexOf(pathBasename) === -1) { + throw new Error(`Weight file with basename '${pathBasename}' is not provided.`); + } else { + pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)]; + } + }); + } + if (basenames.length !== this.weightsFiles.length) { + throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`); + } + return pathToFile; + } +}; +var browserDownloadsRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) { + return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(browserDownloadsRouter); +function browserDownloads(fileNamePrefix = "model") { + return new BrowserDownloads(fileNamePrefix); +} +function browserFiles(files) { + return new BrowserFiles(files); +} +function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) { + checkPromises(promises); + startFraction = startFraction == null ? 0 : startFraction; + endFraction = endFraction == null ? 1 : endFraction; + checkFraction(startFraction, endFraction); + let resolvedPromise = 0; + const registerMonitor = (promise) => { + promise.then((value) => { + const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction); + onProgress(fraction); + return value; + }); + return promise; + }; + function checkPromises(promises2) { + assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array"); + } + function checkFraction(startFraction2, endFraction2) { + assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`); + assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`); + assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`); + } + return Promise.all(promises.map(registerMonitor)); +} +async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) { + if (loadOptions == null) { + loadOptions = {}; + } + const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc; + const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true })); + const fetchStartFraction = 0; + const fetchEndFraction = 0.5; + const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction); + const bufferPromises = responses.map((response) => response.arrayBuffer()); + const bufferStartFraction = 0.5; + const bufferEndFraction = 1; + const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction); + return buffers; +} +function streamWeights(fetchURLs, loadOptions) { + var _a; + const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc; + let fetchIndex = 0; + let chunkReader; + (_a = loadOptions.onProgress) === null || _a === void 0 ? void 0 : _a.call(loadOptions, 0); + return new ReadableStream({ + pull: async (controller) => { + var _a2; + while (fetchIndex < fetchURLs.length) { + if (!chunkReader) { + const body = (await fetchFunc(fetchURLs[fetchIndex], loadOptions.requestInit, { isBinary: true })).body; + chunkReader = body.getReader(); + } + const { done, value } = await chunkReader.read(); + if (done) { + fetchIndex++; + chunkReader = void 0; + (_a2 = loadOptions.onProgress) === null || _a2 === void 0 ? void 0 : _a2.call(loadOptions, fetchIndex / fetchURLs.length); + continue; + } + controller.enqueue(value); + return; + } + controller.close(); + } + }); +} +async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) { + const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit }); + const loadWeights2 = weightsLoaderFactory(fetchWeights); + return loadWeights2(manifest, filePathPrefix, weightNames); +} +function weightsLoaderFactory(fetchWeightsFunction) { + return async (manifest, filePathPrefix = "", weightNames) => { + const groupIndicesToFetchMap = manifest.map(() => false); + const groupWeightsToFetch = {}; + const weightsFound = weightNames != null ? weightNames.map(() => false) : []; + const allManifestWeightNames = []; + manifest.forEach((manifestGroupConfig, groupIndex) => { + let groupOffset = 0; + manifestGroupConfig.weights.forEach((weightsEntry) => { + const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype; + const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape); + const enqueueWeightsForFetchingFn = () => { + groupIndicesToFetchMap[groupIndex] = true; + if (groupWeightsToFetch[groupIndex] == null) { + groupWeightsToFetch[groupIndex] = []; + } + groupWeightsToFetch[groupIndex].push({ + manifestEntry: weightsEntry, + groupOffset, + sizeBytes: weightsBytes + }); + }; + if (weightNames != null) { + weightNames.forEach((weightName, weightIndex) => { + if (weightName === weightsEntry.name) { + enqueueWeightsForFetchingFn(); + weightsFound[weightIndex] = true; + } + }); + } else { + enqueueWeightsForFetchingFn(); + } + allManifestWeightNames.push(weightsEntry.name); + groupOffset += weightsBytes; + }); + }); + if (!weightsFound.every((found) => found)) { + const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]); + throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. +Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`); + } + const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => { + if (shouldFetch) { + accumulator.push(i); + } + return accumulator; + }, []); + const fetchUrls = []; + groupIndicesToFetch.forEach((i) => { + manifest[i].paths.forEach((filepath) => { + const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath; + fetchUrls.push(fetchUrl); + }); + }); + const buffers = await fetchWeightsFunction(fetchUrls); + const weightsTensorMap = {}; + let bufferIndexOffset = 0; + groupIndicesToFetch.forEach((i) => { + const numBuffers = manifest[i].paths.length; + const weightsBuffer = new CompositeArrayBuffer(buffers.slice(bufferIndexOffset, bufferIndexOffset + numBuffers)); + const weightsEntries = groupWeightsToFetch[i]; + weightsEntries.forEach((weightsEntry) => { + const byteBuffer = weightsBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes); + const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]); + for (const name in nameToTensorMap) { + weightsTensorMap[name] = nameToTensorMap[name]; + } + }); + bufferIndexOffset += numBuffers; + }); + return weightsTensorMap; + }; +} +var OCTET_STREAM_MIME_TYPE = "application/octet-stream"; +var JSON_TYPE = "application/json"; +var HTTPRequest = class { + constructor(path, loadOptions) { + this.DEFAULT_METHOD = "POST"; + if (loadOptions == null) { + loadOptions = {}; + } + this.weightPathPrefix = loadOptions.weightPathPrefix; + this.weightUrlConverter = loadOptions.weightUrlConverter; + if (loadOptions.fetchFunc != null) { + assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"); + this.fetch = loadOptions.fetchFunc; + } else { + this.fetch = env().platform.fetch; + } + assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty."); + if (Array.isArray(path)) { + assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`); + } + this.path = path; + if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) { + throw new Error("requestInit is expected to have no pre-existing body, but has one."); + } + this.requestInit = loadOptions.requestInit || {}; + this.loadOptions = loadOptions; + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet."); + } + const init2 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); + init2.body = new FormData(); + const weightsManifest = [{ + paths: ["./model.weights.bin"], + weights: modelArtifacts.weightSpecs + }]; + const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest); + init2.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), "model.json"); + if (modelArtifacts.weightData != null) { + const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData); + init2.body.append("model.weights.bin", new Blob([weightBuffer], { type: OCTET_STREAM_MIME_TYPE }), "model.weights.bin"); + } + const response = await this.fetch(this.path, init2); + if (response.ok) { + return { + modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts), + responses: [response] + }; + } else { + throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`); + } + } + async loadModelJSON() { + const modelConfigRequest = await this.fetch(this.path, this.requestInit); + if (!modelConfigRequest.ok) { + throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`); + } + let modelJSON; + try { + modelJSON = await modelConfigRequest.json(); + } catch (e) { + let message = `Failed to parse model JSON of response from ${this.path}.`; + if (this.path.endsWith(".pb")) { + message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository."; + } else { + message += " Please make sure the server is serving valid JSON for this request."; + } + throw new Error(message); + } + const modelTopology = modelJSON.modelTopology; + const weightsManifest = modelJSON.weightsManifest; + if (modelTopology == null && weightsManifest == null) { + throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`); + } + return modelJSON; + } + /** + * Load model artifacts via HTTP request(s). + * + * See the documentation to `tf.io.http` for details on the saved + * artifacts. + * + * @returns The loaded model artifacts (if loading succeeds). + */ + async load() { + if (this.loadOptions.streamWeights) { + return this.loadStream(); + } + const modelJSON = await this.loadModelJSON(); + return getModelArtifactsForJSON(modelJSON, (weightsManifest) => this.loadWeights(weightsManifest)); + } + async loadStream() { + const modelJSON = await this.loadModelJSON(); + const fetchURLs = await this.getWeightUrls(modelJSON.weightsManifest); + const weightSpecs = getWeightSpecs(modelJSON.weightsManifest); + const stream = () => streamWeights(fetchURLs, this.loadOptions); + return Object.assign(Object.assign({}, modelJSON), { weightSpecs, getWeightStream: stream }); + } + async getWeightUrls(weightsManifest) { + const weightPath = Array.isArray(this.path) ? this.path[1] : this.path; + const [prefix, suffix] = parseUrl(weightPath); + const pathPrefix = this.weightPathPrefix || prefix; + const fetchURLs = []; + const urlPromises = []; + for (const weightsGroup of weightsManifest) { + for (const path of weightsGroup.paths) { + if (this.weightUrlConverter != null) { + urlPromises.push(this.weightUrlConverter(path)); + } else { + fetchURLs.push(pathPrefix + path + suffix); + } + } + } + if (this.weightUrlConverter) { + fetchURLs.push(...await Promise.all(urlPromises)); + } + return fetchURLs; + } + async loadWeights(weightsManifest) { + const fetchURLs = await this.getWeightUrls(weightsManifest); + const weightSpecs = getWeightSpecs(weightsManifest); + const buffers = await loadWeightsAsArrayBuffer(fetchURLs, this.loadOptions); + return [weightSpecs, buffers]; + } +}; +HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//; +function parseUrl(url) { + const lastSlash = url.lastIndexOf("/"); + const lastSearchParam = url.lastIndexOf("?"); + const prefix = url.substring(0, lastSlash); + const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : ""; + return [prefix + "/", suffix]; +} +function isHTTPScheme(url) { + return url.match(HTTPRequest.URL_SCHEME_REGEX) != null; +} +var httpRouter = (url, loadOptions) => { + if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) { + return null; + } else { + let isHTTP = true; + if (Array.isArray(url)) { + isHTTP = url.every((urlItem) => isHTTPScheme(urlItem)); + } else { + isHTTP = isHTTPScheme(url); + } + if (isHTTP) { + return http(url, loadOptions); + } + } + return null; +}; +IORouterRegistry.registerSaveRouter(httpRouter); +IORouterRegistry.registerLoadRouter(httpRouter); +function http(path, loadOptions) { + return new HTTPRequest(path, loadOptions); +} +function browserHTTPRequest(path, loadOptions) { + return http(path, loadOptions); +} +var PassthroughLoader = class { + constructor(modelArtifacts) { + this.modelArtifacts = modelArtifacts; + } + load() { + return this.modelArtifacts; + } +}; +var PassthroughSaver = class { + constructor(saveHandler) { + this.saveHandler = saveHandler; + } + save(modelArtifacts) { + return this.saveHandler(modelArtifacts); + } +}; +var PassthroughAsync = class { + constructor(handler) { + if (handler.load) { + this.load = () => Promise.resolve(handler.load()); + } + if (handler.save) { + this.save = (modelArtifacts) => Promise.resolve(handler.save(modelArtifacts)); + } + } +}; +function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) { + const args = arguments; + return new PassthroughAsync(fromMemorySync(...args)); +} +function fromMemorySync(modelArtifacts, weightSpecs, weightData, trainingConfig) { + if (arguments.length === 1) { + const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null; + if (isModelArtifacts) { + return new PassthroughLoader(modelArtifacts); + } else { + console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."); + return new PassthroughLoader({ modelTopology: modelArtifacts }); + } + } else { + console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."); + return new PassthroughLoader({ + modelTopology: modelArtifacts, + weightSpecs, + weightData, + trainingConfig + }); + } +} +function withSaveHandler(saveHandler) { + return new PassthroughSaver(saveHandler); +} +function withSaveHandlerSync(saveHandler) { + return new PassthroughSaver(saveHandler); +} +var math_exports = {}; +__export2(math_exports, { + confusionMatrix: () => confusionMatrix +}); +function confusionMatrix_(labels, predictions, numClasses) { + const $labels = convertToTensor(labels, "labels", "confusionMatrix"); + const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix"); + assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`); + assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`); + assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`); + assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`); + assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`); + const oneHotLabels = oneHot(cast($labels, "int32"), numClasses); + const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses); + const oneHotLabelsT = transpose(oneHotLabels); + const product = matMul(oneHotLabelsT, oneHotPredictions); + return cast(product, "int32"); +} +var confusionMatrix = op({ confusionMatrix_ }); +var browser_exports = {}; +__export2(browser_exports, { + draw: () => draw, + fromPixels: () => fromPixels, + fromPixelsAsync: () => fromPixelsAsync, + toPixels: () => toPixels +}); +var fromPixels2DContext; +var hasToPixelsWarned = false; +function fromPixels_(pixels, numChannels = 3) { + if (numChannels > 4) { + throw new Error("Cannot construct Tensor with more than 4 channels from pixels."); + } + if (pixels == null) { + throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); + } + let isPixelData2 = false; + let isImageData = false; + let isVideo = false; + let isImage = false; + let isCanvasLike = false; + let isImageBitmap = false; + if (pixels.data instanceof Uint8Array) { + isPixelData2 = true; + } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) { + isImageData = true; + } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) { + isVideo = true; + } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) { + isImage = true; + } else if (pixels.getContext != null) { + isCanvasLike = true; + } else if (typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap) { + isImageBitmap = true; + } else { + throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`); + } + const kernel = getKernel(FromPixels, ENGINE.backendName); + if (kernel != null) { + const inputs = { pixels }; + const attrs = { numChannels }; + return ENGINE.runKernel(FromPixels, inputs, attrs); + } + const [width, height] = isVideo ? [ + pixels.videoWidth, + pixels.videoHeight + ] : [pixels.width, pixels.height]; + let vals; + if (isCanvasLike) { + vals = // tslint:disable-next-line:no-any + pixels.getContext("2d").getImageData(0, 0, width, height).data; + } else if (isImageData || isPixelData2) { + vals = pixels.data; + } else if (isImage || isVideo || isImageBitmap) { + if (fromPixels2DContext == null) { + if (typeof document === "undefined") { + if (typeof OffscreenCanvas !== "undefined" && typeof OffscreenCanvasRenderingContext2D !== "undefined") { + fromPixels2DContext = new OffscreenCanvas(1, 1).getContext("2d"); + } else { + throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported."); + } + } else { + fromPixels2DContext = document.createElement("canvas").getContext("2d", { willReadFrequently: true }); + } + } + fromPixels2DContext.canvas.width = width; + fromPixels2DContext.canvas.height = height; + fromPixels2DContext.drawImage(pixels, 0, 0, width, height); + vals = fromPixels2DContext.getImageData(0, 0, width, height).data; + } + let values; + if (numChannels === 4) { + values = new Int32Array(vals); + } else { + const numPixels = width * height; + values = new Int32Array(numPixels * numChannels); + for (let i = 0; i < numPixels; i++) { + for (let channel = 0; channel < numChannels; ++channel) { + values[i * numChannels + channel] = vals[i * 4 + channel]; + } + } + } + const outShape = [height, width, numChannels]; + return tensor3d(values, outShape, "int32"); +} +function isPixelData(pixels) { + return pixels != null && pixels.data instanceof Uint8Array; +} +function isImageBitmapFullySupported() { + return typeof window !== "undefined" && typeof ImageBitmap !== "undefined" && window.hasOwnProperty("createImageBitmap"); +} +function isNonEmptyPixels(pixels) { + return pixels != null && pixels.width !== 0 && pixels.height !== 0; +} +function canWrapPixelsToImageBitmap(pixels) { + return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels); +} +async function fromPixelsAsync(pixels, numChannels = 3) { + let inputs = null; + if (env().getBool("WRAP_TO_IMAGEBITMAP") && canWrapPixelsToImageBitmap(pixels)) { + let imageBitmap; + try { + imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: "none" }); + } catch (e) { + imageBitmap = null; + } + if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) { + inputs = imageBitmap; + } else { + inputs = pixels; + } + } else { + inputs = pixels; + } + return fromPixels_(inputs, numChannels); +} +function validateImgTensor(img) { + if (img.rank !== 2 && img.rank !== 3) { + throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${img.rank}.`); + } + const depth = img.rank === 2 ? 1 : img.shape[2]; + if (depth > 4 || depth === 2) { + throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`); + } + if (img.dtype !== "float32" && img.dtype !== "int32") { + throw new Error(`Unsupported type for toPixels: ${img.dtype}. Please use float32 or int32 tensors.`); + } +} +function validateImageOptions(imageOptions) { + const alpha = (imageOptions === null || imageOptions === void 0 ? void 0 : imageOptions.alpha) || 1; + if (alpha > 1 || alpha < 0) { + throw new Error(`Alpha value ${alpha} is suppoed to be in range [0 - 1].`); + } +} +async function toPixels(img, canvas) { + let $img = convertToTensor(img, "img", "toPixels"); + if (!(img instanceof Tensor)) { + const originalImgTensor = $img; + $img = cast(originalImgTensor, "int32"); + originalImgTensor.dispose(); + } + validateImgTensor($img); + const [height, width] = $img.shape.slice(0, 2); + const depth = $img.rank === 2 ? 1 : $img.shape[2]; + const data = await $img.data(); + const multiplier = $img.dtype === "float32" ? 255 : 1; + const bytes = new Uint8ClampedArray(width * height * 4); + for (let i = 0; i < height * width; ++i) { + const rgba = [0, 0, 0, 255]; + for (let d = 0; d < depth; d++) { + const value = data[i * depth + d]; + if ($img.dtype === "float32") { + if (value < 0 || value > 1) { + throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`); + } + } else if ($img.dtype === "int32") { + if (value < 0 || value > 255) { + throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`); + } + } + if (depth === 1) { + rgba[0] = value * multiplier; + rgba[1] = value * multiplier; + rgba[2] = value * multiplier; + } else { + rgba[d] = value * multiplier; + } + } + const j = i * 4; + bytes[j + 0] = Math.round(rgba[0]); + bytes[j + 1] = Math.round(rgba[1]); + bytes[j + 2] = Math.round(rgba[2]); + bytes[j + 3] = Math.round(rgba[3]); + } + if (canvas != null) { + if (!hasToPixelsWarned) { + const kernel = getKernel(Draw, ENGINE.backendName); + if (kernel != null) { + console.warn("tf.browser.toPixels is not efficient to draw tensor on canvas. Please try tf.browser.draw instead."); + hasToPixelsWarned = true; + } + } + canvas.width = width; + canvas.height = height; + const ctx = canvas.getContext("2d"); + const imageData = new ImageData(bytes, width, height); + ctx.putImageData(imageData, 0, 0); + } + if ($img !== img) { + $img.dispose(); + } + return bytes; +} +function draw(image2, canvas, options) { + let $img = convertToTensor(image2, "img", "draw"); + if (!(image2 instanceof Tensor)) { + const originalImgTensor = $img; + $img = cast(originalImgTensor, "int32"); + originalImgTensor.dispose(); + } + validateImgTensor($img); + validateImageOptions(options === null || options === void 0 ? void 0 : options.imageOptions); + const inputs = { image: $img }; + const attrs = { canvas, options }; + ENGINE.runKernel(Draw, inputs, attrs); +} +var fromPixels = op({ fromPixels_ }); +var gather_nd_util_exports = {}; +__export2(gather_nd_util_exports, { + prepareAndValidate: () => prepareAndValidate +}); +function prepareAndValidate(tensor2, indices) { + const tensorRank = tensor2.shape.length; + const indicesRank = indices.shape.length; + if (tensorRank < 1) { + throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`); + } + if (indicesRank < 1) { + throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`); + } + if (indices.dtype !== "int32") { + throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`); + } + if (indices.shape[indicesRank - 1] > tensorRank) { + throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank - 1]} vs. ${tensorRank}`); + } + if (sizeFromShape(tensor2.shape) === 0) { + throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`); + } + const indicesShape = indices.shape; + const sliceRank = indicesShape[indicesShape.length - 1]; + let nResult = 1; + for (let i = 0; i < indicesShape.length - 1; ++i) { + nResult *= indicesShape[i]; + } + const inputShape = tensor2.shape; + const resultShape = indicesShape.slice(); + resultShape.pop(); + let sliceSize = 1; + for (let i = sliceRank; i < tensorRank; ++i) { + sliceSize *= inputShape[i]; + resultShape.push(inputShape[i]); + } + const strides = [ + ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize), + 1 + ].slice(0, sliceRank); + return [resultShape, nResult, sliceSize, strides]; +} +var slice_util_exports = {}; +__export2(slice_util_exports, { + assertParamsValid: () => assertParamsValid, + computeFlatOffset: () => computeFlatOffset, + computeOutShape: () => computeOutShape, + getNormalizedAxes: () => getNormalizedAxes, + isSliceContinous: () => isSliceContinous, + maskToAxes: () => maskToAxes, + parseSliceParams: () => parseSliceParams, + sliceInfo: () => sliceInfo, + startForAxis: () => startForAxis, + startIndicesWithElidedDims: () => startIndicesWithElidedDims, + stopForAxis: () => stopForAxis, + stopIndicesWithElidedDims: () => stopIndicesWithElidedDims, + stridesForAxis: () => stridesForAxis, + stridesWithElidedDims: () => stridesWithElidedDims +}); +var NEW_AXIS = -2; +var SHRINK_AXIS = -1; +function assertParamsValid(input2, begin, size) { + const inputRank = input2.shape.length; + assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`); + assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`); + for (let i = 0; i < inputRank; ++i) { + assert(begin[i] + size[i] <= input2.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`); + } +} +function maskToAxes(mask) { + const axes = []; + let axis = 0; + while (mask > 0) { + if (mask & 1) { + axes.push(axis); + } + mask /= 2; + axis++; + } + return axes; +} +function computeOutShape(begin, end, strides) { + const size = []; + for (let axis = 0; axis < begin.length; axis++) { + size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]); + } + return size; +} +function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) { + const newStrides = [...strides]; + for (let i = newStrides.length; i < inputShape.length; i++) { + newStrides.push(1); + } + for (let i = 0; i < numElidedAxes; i++) { + if (i === 0) { + newStrides[ellipsisInsertionIndex] = 1; + } else { + newStrides.splice( + ellipsisInsertionIndex, + 0, + 1 + /* element to add */ + ); + newStrides.pop(); + } + } + return newStrides; +} +function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) { + if (normalizedAxis <= ellipsisInsertionIndex) { + return normalizedAxis; + } + return normalizedAxis - (numElidedAxes - 1); +} +function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) { + const elidedAxes = []; + for (let i = 0; i < numElidedAxes; i++) { + elidedAxes.push(ellipsisInsertionIndex + i); + } + return elidedAxes; +} +function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) { + const inputRank = inputShape.length; + let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank); + if (ellipsisAxes.length && numInterpolatedAxes > 0) { + const fullIndex = ellipsisAxes[0]; + const numElidedAxes = numInterpolatedAxes + 1; + normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape); + normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape); + normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape); + } else { + for (let axis = 0; axis < inputRank; axis++) { + normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask); + normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask); + normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask); + } + } + return { + begin: normalizedBegin, + end: normalizedEnd, + strides: normalizedStrides + }; +} +function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) { + const newIndices = [...inputShape]; + const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex); + for (let axis = 0; axis < newIndices.length; axis++) { + if (elidedAxes.indexOf(axis) > -1) { + newIndices[axis] = 0; + } else { + const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis); + let originalValue = originalBegin[originalAxis]; + if (beginMask & 1 << originalAxis) { + originalValue = 0; + } + newIndices[axis] = originalValue; + } + } + return newIndices; +} +function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) { + const newIndices = [...inputShape]; + const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex); + for (let axis = 0; axis < newIndices.length; axis++) { + if (elidedAxes.indexOf(axis) > -1) { + newIndices[axis] = Number.MAX_SAFE_INTEGER; + } else { + const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis); + let originalValue = originalEnd[originalAxis]; + if (endMask & 1 << originalAxis) { + originalValue = Number.MAX_SAFE_INTEGER; + } + newIndices[axis] = originalValue; + } + } + for (let i = 0; i < newIndices.length; i++) { + const axisSize = inputShape[i]; + if (newIndices[i] < 0) { + newIndices[i] += axisSize; + } + newIndices[i] = clamp(0, newIndices[i], inputShape[i]); + } + return newIndices; +} +function stridesForAxis(strides, axis, ellipsisMask) { + let stride = strides[axis]; + if (ellipsisMask & 1 << axis || stride == null) { + stride = 1; + } + return stride; +} +function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) { + let start = startIndices[axis]; + const stride = strides[axis] || 1; + if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) { + if (stride > 0) { + start = Number.MIN_SAFE_INTEGER; + } else { + start = Number.MAX_SAFE_INTEGER; + } + } + const axisSize = inputShape[axis]; + if (start < 0) { + start += axisSize; + } + start = clamp(0, start, axisSize - 1); + return start; +} +function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) { + let stop = stopIndices[axis]; + const stride = strides[axis] || 1; + if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) { + if (stride > 0) { + stop = Number.MAX_SAFE_INTEGER; + } else { + stop = Number.MIN_SAFE_INTEGER; + } + } + const axisSize = inputShape[axis]; + if (stop < 0) { + stop += axisSize; + } + if (stride > 0) { + stop = clamp(0, stop, axisSize); + } else { + stop = clamp(-1, stop, axisSize - 1); + } + return stop; +} +function isSliceContinous(shape, begin, size) { + let firstNonOneAxis = size.length; + for (let i = 0; i < size.length; i++) { + if (size[i] > 1) { + firstNonOneAxis = i; + break; + } + } + for (let i = firstNonOneAxis + 1; i < size.length; i++) { + if (begin[i] > 0 || size[i] !== shape[i]) { + return false; + } + } + return true; +} +function computeFlatOffset(begin, strides) { + let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1; + for (let i = 0; i < begin.length - 1; i++) { + flatOffset += begin[i] * strides[i]; + } + return flatOffset; +} +function parseSliceParams(x, begin, size) { + let begin_; + const xRank = x.shape.length; + if (typeof begin === "number") { + begin_ = [begin, ...new Array(xRank - 1).fill(0)]; + } else if (begin.length < xRank) { + begin_ = begin.concat(new Array(xRank - begin.length).fill(0)); + } else { + begin_ = begin.slice(); + } + begin_.forEach((d) => { + assert(d !== -1, () => "slice() does not support negative begin indexing."); + }); + let size_; + if (size == null) { + size_ = new Array(xRank).fill(-1); + } else if (typeof size === "number") { + size_ = [size, ...new Array(xRank - 1).fill(-1)]; + } else if (size.length < xRank) { + size_ = size.concat(new Array(xRank - size.length).fill(-1)); + } else { + size_ = size; + } + size_ = size_.map((d, i) => { + if (d >= 0) { + return d; + } else { + assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`); + return x.shape[i] - begin_[i]; + } + }); + return [begin_, size_]; +} +function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) { + let stridesNonNull; + if (strides == null) { + stridesNonNull = new Array(begin.length); + stridesNonNull.fill(1); + } else { + stridesNonNull = strides; + } + if (ellipsisMask != null && (ellipsisMask & ellipsisMask - 1) !== 0) { + throw new Error("Multiple ellipses in slice is not allowed."); + } + let ellipsisSeen = false; + const sparseSpec = { + dims: stridesNonNull.length, + numAddAxisAfterEllipsis: 0, + begin: begin.slice(), + end: end.slice(), + strides: stridesNonNull.slice(), + beginMask, + endMask, + ellipsisMask, + newAxisMask, + shrinkAxisMask + }; + for (let i = 0; i < sparseSpec.dims; i++) { + if (ellipsisSeen && (1 << i & newAxisMask) !== 0) { + sparseSpec.numAddAxisAfterEllipsis++; + } + if (1 << i & ellipsisMask) { + ellipsisSeen = true; + } + } + if (!ellipsisSeen) { + sparseSpec.ellipsisMask |= 1 << sparseSpec.dims; + sparseSpec.dims++; + } + const denseSpec = { + dims: xShape.length, + beginMask: 0, + endMask: 0, + beginValid: false, + endValid: false + }; + buildDenseSpec(sparseSpec, denseSpec); + let isIdentity = true; + let sliceDim0 = true; + let isSimpleSlice = true; + const processingShape = []; + const finalShape = []; + for (let i = 0; i < xShape.length; ++i) { + if (denseSpec.strides[i] === 0) { + throw Error(`strides[${i}] must be non-zero`); + } + const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i); + const dimI = xShape[i]; + if (dimI === -1) { + processingShape.push(shrinkI ? 1 : -1); + continue; + } + const masks = [denseSpec.beginMask & 1 << i, denseSpec.endMask & 1 << i]; + const validRange = [ + denseSpec.strides[i] > 0 ? 0 : -1, + denseSpec.strides[i] > 0 ? dimI : dimI - 1 + ]; + if (shrinkI && denseSpec.strides[i] <= 0) { + throw Error("only stride 1 allowed on non-range indexing."); + } + isSimpleSlice = isSimpleSlice && denseSpec.strides[i] === 1; + const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i && denseSpec.endMask & 1 << i); + if (denseSpec.beginValid && denseSpec.endValid) { + if (shrinkI) { + const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] : denseSpec.begin[i]; + denseSpec.begin[i] = xFwd; + denseSpec.end[i] = denseSpec.begin[i] + 1; + if (xFwd < 0 || xFwd >= dimI) { + throw Error(`slice index ${denseSpec.begin[i]} of dimension ${i} out of bounds.`); + } + } else { + denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange); + denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange); + } + const takeAllInDimension = denseSpec.strides[i] === 1 && denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI; + isIdentity = isIdentity && takeAllInDimension; + sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || takeAllInDimension); + } else { + isIdentity = isIdentity && (denseSpec.strides[i] === 1 && beginAndEndMasked); + sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || beginAndEndMasked); + } + let intervalLength; + let knownInterval = false; + if (denseSpec.beginValid && denseSpec.endValid) { + intervalLength = denseSpec.end[i] - denseSpec.begin[i]; + knownInterval = true; + } else if (shrinkI) { + intervalLength = 1; + knownInterval = true; + } else if (beginAndEndMasked) { + if (dimI >= 0) { + if (denseSpec.strides[i] < 0) { + intervalLength = -dimI; + } else { + intervalLength = dimI; + } + knownInterval = true; + } + } + if (knownInterval) { + let sizeI; + if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i] < 0) { + sizeI = 0; + } else { + sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) + (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0); + } + processingShape.push(sizeI); + } else { + processingShape.push(-1); + } + } + for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) { + const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim]; + if (gatherIndex >= 0) { + finalShape.push(processingShape[gatherIndex]); + } else if (gatherIndex === NEW_AXIS) { + finalShape.push(1); + } + } + const finalShapeSparse = finalShape.filter((dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS); + return { + finalShapeSparse, + finalShape, + isIdentity, + sliceDim0, + isSimpleSlice, + begin: denseSpec.begin, + end: denseSpec.end, + strides: denseSpec.strides + }; +} +function buildDenseSpec(sparse2, dense2) { + dense2.beginMask = 0; + dense2.endMask = 0; + dense2.shrinkAxisMask = 0; + let fullIndex = 0; + dense2.beginValid = sparse2.begin != null; + dense2.endValid = sparse2.end != null; + dense2.begin = new Array(dense2.dims); + dense2.end = new Array(dense2.dims); + dense2.strides = new Array(dense2.dims); + dense2.finalShapeGatherIndices = []; + dense2.finalShapeGatherIndicesSparse = []; + dense2.inputShapeGatherIndicesSparse = new Array(dense2.dims); + for (let i = 0; i < sparse2.dims; i++) { + if (1 << i & sparse2.ellipsisMask) { + const nextIndex = Math.min(dense2.dims - (sparse2.dims - i) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims); + for (; fullIndex < nextIndex; fullIndex++) { + dense2.begin[fullIndex] = 0; + dense2.end[fullIndex] = 0; + dense2.strides[fullIndex] = 1; + dense2.beginMask |= 1 << fullIndex; + dense2.endMask |= 1 << fullIndex; + dense2.finalShapeGatherIndices.push(fullIndex); + dense2.finalShapeGatherIndicesSparse.push(-1); + dense2.inputShapeGatherIndicesSparse[fullIndex] = i; + } + } else if (1 << i & sparse2.newAxisMask) { + dense2.finalShapeGatherIndices.push(NEW_AXIS); + dense2.finalShapeGatherIndicesSparse.push(-1); + } else { + if (fullIndex === dense2.begin.length) { + throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense2.dims} dims, ${dense2.begin.length}.`); + } + if (sparse2.begin != null) { + dense2.begin[fullIndex] = sparse2.begin[i]; + } + if (sparse2.end != null) { + dense2.end[fullIndex] = sparse2.end[i]; + } + dense2.strides[fullIndex] = sparse2.strides[i]; + if (sparse2.beginMask & 1 << i) { + dense2.beginMask |= 1 << fullIndex; + } + if (sparse2.endMask & 1 << i) { + dense2.endMask |= 1 << fullIndex; + } + if (sparse2.shrinkAxisMask & 1 << i) { + dense2.finalShapeGatherIndices.push(SHRINK_AXIS); + dense2.finalShapeGatherIndicesSparse.push(-1); + dense2.shrinkAxisMask |= 1 << fullIndex; + } else { + dense2.finalShapeGatherIndices.push(fullIndex); + dense2.finalShapeGatherIndicesSparse.push(i); + } + dense2.inputShapeGatherIndicesSparse[fullIndex] = i; + fullIndex++; + } + } +} +function canonical(x, c, strideI, dimI, masks, validRange) { + if (masks[c]) { + return strideI > 0 ? validRange[c] : validRange[c + 1 & 1]; + } else { + const xFwd = x < 0 ? dimI + x : x; + return xFwd < validRange[0] ? validRange[0] : xFwd > validRange[1] ? validRange[1] : xFwd; + } +} +var version = "4.16.0"; +var OptimizerConstructors = class { + /** + * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent. + * + * ```js + * // Fit a quadratic function by learning the coefficients a, b, c. + * const xs = tf.tensor1d([0, 1, 2, 3]); + * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]); + * + * const a = tf.scalar(Math.random()).variable(); + * const b = tf.scalar(Math.random()).variable(); + * const c = tf.scalar(Math.random()).variable(); + * + * // y = a * x^2 + b * x + c. + * const f = x => a.mul(x.square()).add(b.mul(x)).add(c); + * const loss = (pred, label) => pred.sub(label).square().mean(); + * + * const learningRate = 0.01; + * const optimizer = tf.train.sgd(learningRate); + * + * // Train the model. + * for (let i = 0; i < 10; i++) { + * optimizer.minimize(() => loss(f(xs), ys)); + * } + * + * // Make predictions. + * console.log( + * `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`); + * const preds = f(xs).dataSync(); + * preds.forEach((pred, i) => { + * console.log(`x: ${i}, pred: ${pred}`); + * }); + * ``` + * + * @param learningRate The learning rate to use for the SGD algorithm. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static sgd(learningRate) { + return new SGDOptimizer(learningRate); + } + /** + * Constructs a `tf.MomentumOptimizer` that uses momentum gradient + * descent. + * + * See + * [http://proceedings.mlr.press/v28/sutskever13.pdf]( + * http://proceedings.mlr.press/v28/sutskever13.pdf) + * + * @param learningRate The learning rate to use for the Momentum gradient + * descent algorithm. + * @param momentum The momentum to use for the momentum gradient descent + * algorithm. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static momentum(learningRate, momentum, useNesterov = false) { + return new MomentumOptimizer(learningRate, momentum, useNesterov); + } + /** + * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient + * descent. This implementation uses plain momentum and is not centered + * version of RMSProp. + * + * See + * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf]( + * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf) + * + * @param learningRate The learning rate to use for the RMSProp gradient + * descent algorithm. + * @param decay The discounting factor for the history/coming gradient. + * @param momentum The momentum to use for the RMSProp gradient descent + * algorithm. + * @param epsilon Small value to avoid zero denominator. + * @param centered If true, gradients are normalized by the estimated + * variance of the gradient. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon32 = null, centered = false) { + return new RMSPropOptimizer(learningRate, decay, momentum, epsilon32, centered); + } + /** + * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm. + * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980) + * + * @param learningRate The learning rate to use for the Adam gradient + * descent algorithm. + * @param beta1 The exponential decay rate for the 1st moment estimates. + * @param beta2 The exponential decay rate for the 2nd moment estimates. + * @param epsilon A small constant for numerical stability. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon32 = null) { + return new AdamOptimizer(learningRate, beta1, beta2, epsilon32); + } + /** + * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm. + * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701) + * + * @param learningRate The learning rate to use for the Adadelta gradient + * descent algorithm. + * @param rho The learning rate decay over each update. + * @param epsilon A constant epsilon used to better condition the grad + * update. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static adadelta(learningRate = 1e-3, rho = 0.95, epsilon32 = null) { + return new AdadeltaOptimizer(learningRate, rho, epsilon32); + } + /** + * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm. + * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980) + * + * @param learningRate The learning rate to use for the Adamax gradient + * descent algorithm. + * @param beta1 The exponential decay rate for the 1st moment estimates. + * @param beta2 The exponential decay rate for the 2nd moment estimates. + * @param epsilon A small constant for numerical stability. + * @param decay The learning rate decay over each update. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon32 = null, decay = 0) { + return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon32, decay); + } + /** + * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm. + * See + * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf]( + * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) + * or + * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad]( + * http://ruder.io/optimizing-gradient-descent/index.html#adagrad) + * + * @param learningRate The learning rate to use for the Adagrad gradient + * descent algorithm. + * @param initialAccumulatorValue Starting value for the accumulators, must be + * positive. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static adagrad(learningRate, initialAccumulatorValue = 0.1) { + return new AdagradOptimizer(learningRate, initialAccumulatorValue); + } +}; +var train = OptimizerConstructors; +var delayCallback = (() => { + if (typeof requestAnimationFrame !== "undefined") { + return requestAnimationFrame; + } else if (typeof setImmediate !== "undefined") { + return setImmediate; + } + return (f) => f(); +})(); +function nextFrame() { + return new Promise((resolve) => delayCallback(() => resolve())); +} +var backend_util_exports = {}; +__export2(backend_util_exports, { + ERF_A1: () => ERF_A1, + ERF_A2: () => ERF_A2, + ERF_A3: () => ERF_A3, + ERF_A4: () => ERF_A4, + ERF_A5: () => ERF_A5, + ERF_P: () => ERF_P, + PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD, + RowPartitionType: () => RowPartitionType, + SELU_SCALE: () => SELU_SCALE, + SELU_SCALEALPHA: () => SELU_SCALEALPHA, + applyActivation: () => applyActivation, + assertAndGetBroadcastShape: () => assertAndGetBroadcastShape, + assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims, + assertParamsConsistent: () => assertParamsConsistent, + assignToTypedArray: () => assignToTypedArray, + axesAreInnerMostDims: () => axesAreInnerMostDims, + calculateShapes: () => calculateShapes, + checkEinsumDimSizes: () => checkEinsumDimSizes, + checkPadOnDimRoundingMode: () => checkPadOnDimRoundingMode, + combineLocations: () => combineLocations, + combineRaggedTensorToTensorShapes: () => combineRaggedTensorToTensorShapes, + complexWithEvenIndex: () => complexWithEvenIndex, + complexWithOddIndex: () => complexWithOddIndex, + computeConv2DInfo: () => computeConv2DInfo, + computeConv3DInfo: () => computeConv3DInfo, + computeDefaultPad: () => computeDefaultPad, + computeDilation2DInfo: () => computeDilation2DInfo, + computeOptimalWindowSize: () => computeOptimalWindowSize, + computeOutAndReduceShapes: () => computeOutAndReduceShapes, + computeOutShape: () => computeOutShape2, + computePool2DInfo: () => computePool2DInfo, + computePool3DInfo: () => computePool3DInfo, + convertConv2DDataFormat: () => convertConv2DDataFormat, + decodeEinsumEquation: () => decodeEinsumEquation, + eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne, + expandShapeToKeepDim: () => expandShapeToKeepDim, + exponent: () => exponent, + exponents: () => exponents, + fromStringArrayToUint8: () => fromStringArrayToUint8, + fromUint8ToStringArray: () => fromUint8ToStringArray, + getAxesPermutation: () => getAxesPermutation, + getBroadcastDims: () => getBroadcastDims, + getComplexWithIndex: () => getComplexWithIndex, + getEinsumComputePath: () => getEinsumComputePath, + getEinsumPermutation: () => getEinsumPermutation, + getFusedBiasGradient: () => getFusedBiasGradient, + getFusedDyActivation: () => getFusedDyActivation, + getImageCenter: () => getImageCenter, + getInnerMostAxes: () => getInnerMostAxes, + getPermuted: () => getPermuted, + getRaggedRank: () => getRaggedRank, + getReductionAxes: () => getReductionAxes, + getReshaped: () => getReshaped, + getReshapedPermuted: () => getReshapedPermuted, + getRowPartitionTypesHelper: () => getRowPartitionTypesHelper, + getSliceBeginCoords: () => getSliceBeginCoords, + getSliceSize: () => getSliceSize, + getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => getSparseFillEmptyRowsIndicesDenseShapeMismatch, + getSparseFillEmptyRowsNegativeIndexErrorMessage: () => getSparseFillEmptyRowsNegativeIndexErrorMessage, + getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => getSparseFillEmptyRowsOutOfRangeIndexErrorMessage, + getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => getSparseReshapeEmptyTensorZeroOutputDimErrorMessage, + getSparseReshapeInputOutputMismatchErrorMessage: () => getSparseReshapeInputOutputMismatchErrorMessage, + getSparseReshapeInputOutputMultipleErrorMessage: () => getSparseReshapeInputOutputMultipleErrorMessage, + getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => getSparseReshapeMultipleNegativeOneOutputDimErrorMessage, + getSparseReshapeNegativeOutputDimErrorMessage: () => getSparseReshapeNegativeOutputDimErrorMessage, + getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => getSparseSegmentReductionIndicesOutOfRangeErrorMessage, + getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => getSparseSegmentReductionNegativeSegmentIdsErrorMessage, + getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage, + getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage, + getUndoAxesPermutation: () => getUndoAxesPermutation, + isIdentityPermutation: () => isIdentityPermutation, + log: () => log, + mergeRealAndImagArrays: () => mergeRealAndImagArrays, + prepareAndValidate: () => prepareAndValidate, + prepareSplitSize: () => prepareSplitSize, + segment_util: () => segment_util_exports, + shouldFuse: () => shouldFuse, + slice_util: () => slice_util_exports, + splitRealAndImagArrays: () => splitRealAndImagArrays, + stridesOrDilationsArePositive: () => stridesOrDilationsArePositive, + tupleValuesAreOne: () => tupleValuesAreOne, + upcastType: () => upcastType, + validateDefaultValueShape: () => validateDefaultValueShape, + validateInput: () => validateInput, + validateUpdateShape: () => validateUpdateShape, + warn: () => warn +}); +function assertParamsConsistent(shapes, axis) { + const rank = shapes[0].length; + shapes.forEach((shape, i) => { + assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`); + }); + assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`); + const firstShape = shapes[0]; + shapes.forEach((shape, i) => { + for (let r = 0; r < rank; r++) { + assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`); + } + }); +} +function computeOutShape2(shapes, axis) { + const outputShape = shapes[0].slice(); + for (let i = 1; i < shapes.length; i++) { + outputShape[axis] += shapes[i][axis]; + } + return outputShape; +} +var RowPartitionType; +(function(RowPartitionType3) { + RowPartitionType3[RowPartitionType3["FIRST_DIM_SIZE"] = 0] = "FIRST_DIM_SIZE"; + RowPartitionType3[RowPartitionType3["VALUE_ROWIDS"] = 1] = "VALUE_ROWIDS"; + RowPartitionType3[RowPartitionType3["ROW_LENGTHS"] = 2] = "ROW_LENGTHS"; + RowPartitionType3[RowPartitionType3["ROW_SPLITS"] = 3] = "ROW_SPLITS"; + RowPartitionType3[RowPartitionType3["ROW_LIMITS"] = 4] = "ROW_LIMITS"; + RowPartitionType3[RowPartitionType3["ROW_STARTS"] = 5] = "ROW_STARTS"; +})(RowPartitionType || (RowPartitionType = {})); +function combineRaggedTensorToTensorShapes(raggedRank, shape, valueShape) { + let outputShape = new Array(); + if (valueShape == null && shape == null) { + return outputShape; + } + if (shape == null) { + while (outputShape.length < raggedRank + valueShape.length) { + outputShape.push(-1); + } + } else { + outputShape = shape.slice(); + } + if (valueShape == null) { + return outputShape; + } + if (raggedRank + valueShape.length !== outputShape.length) { + throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.rank = ${raggedRank + valueShape.length}, but shape.rank = ${outputShape.length}`); + } + for (let i = 1; i < valueShape.length; ++i) { + const valueDim = valueShape[i]; + const outputShapeDimIndex = outputShape[outputShape.length - valueShape.length + i]; + const outputShapeDim = outputShape[outputShapeDimIndex]; + if (valueDim >= 0) { + if (outputShapeDim >= 0) { + if (outputShapeDim !== valueDim) { + throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.shape[${i + raggedRank}] = ${valueDim} but shape[${i + raggedRank}] = ${outputShapeDim}`); + } + } else { + outputShape[outputShapeDimIndex] = valueDim; + } + } + } + return outputShape; +} +function getRowPartitionTypesHelper(rowPartitionTypeStrings) { + const stringToType = { + "FIRST_DIM_SIZE": RowPartitionType.FIRST_DIM_SIZE, + "VALUE_ROWIDS": RowPartitionType.VALUE_ROWIDS, + "ROW_LENGTHS": RowPartitionType.ROW_LENGTHS, + "ROW_SPLITS": RowPartitionType.ROW_SPLITS, + "ROW_LIMITS": RowPartitionType.ROW_LIMITS, + "ROW_STARTS": RowPartitionType.ROW_STARTS + }; + const result = []; + for (const typeStr of rowPartitionTypeStrings) { + if (typeStr in stringToType) { + result.push(stringToType[typeStr]); + } else { + break; + } + } + return result; +} +function getRaggedRank(rowPartitionTypes) { + if (rowPartitionTypes.length === 0) { + return 0; + } + if (rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) { + return rowPartitionTypes.length - 1; + } + return rowPartitionTypes.length; +} +function validateDefaultValueShape(defaultValueShape, valueShape) { + if (defaultValueShape == null || valueShape == null) { + return; + } + const defaultNDims = defaultValueShape.length; + const valuesNDims = valueShape.length; + if (defaultNDims >= valuesNDims) { + throw new Error(`defaultValue.shape=${defaultValueShape} and ragged tensor flatValues.shape=${valueShape}, are incompatible: defaultValue.rank = ${defaultNDims} must be less than ragged tensor input flatValues.rank = ${valuesNDims})`); + } + for (let i = 0; i < Math.min(defaultNDims, valuesNDims - 1); ++i) { + const defaultDim = defaultValueShape[i]; + const valueDim = valueShape[i + 1]; + if (defaultDim >= 0 && valueDim >= 0 && defaultDim !== 1 && defaultDim !== valueDim) { + throw new Error(`defaultValue.shape=${defaultValueShape}, and ragged tensor input flatValues.shape=${valueShape} are incompatible: defaultValue.shape[${i - defaultValueShape.length}] = ${defaultDim} but ragged tensor input.flatValues.shape[${i - defaultValueShape.length}] = ${valueDim}`); + } + } +} +var PARALLELIZE_THRESHOLD = 30; +function computeOptimalWindowSize(inSize) { + if (inSize <= PARALLELIZE_THRESHOLD) { + return inSize; + } + return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize))); +} +function getImageCenter(center, imageHeight, imageWidth) { + const centerX = imageWidth * (typeof center === "number" ? center : center[0]); + const centerY = imageHeight * (typeof center === "number" ? center : center[1]); + return [centerX, centerY]; +} +function getReshaped(inputShape, blockShape, prod5, batchToSpace = true) { + let reshaped = []; + if (batchToSpace) { + reshaped = reshaped.concat(blockShape.slice(0)); + reshaped.push(inputShape[0] / prod5); + reshaped = reshaped.concat(inputShape.slice(1)); + } else { + reshaped = reshaped.concat(inputShape[0]); + const spatialLength = blockShape.length; + for (let i = 0; i < spatialLength; ++i) { + reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]); + } + reshaped = reshaped.concat(inputShape.slice(spatialLength + 1)); + } + return reshaped; +} +function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) { + const permuted = []; + if (batchToSpace) { + permuted.push(blockShapeRank); + for (let i = blockShapeRank + 1; i < reshapedRank; ++i) { + if (i <= 2 * blockShapeRank) { + permuted.push(i); + permuted.push(i - (blockShapeRank + 1)); + } else { + permuted.push(i); + } + } + } else { + const permutedBeforeBatch = []; + const permutedAfterBatch = []; + for (let i = 1; i < reshapedRank; ++i) { + if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) { + permutedAfterBatch.push(i); + } else { + permutedBeforeBatch.push(i); + } + } + permuted.push(...permutedBeforeBatch); + permuted.push(0); + permuted.push(...permutedAfterBatch); + } + return permuted; +} +function getReshapedPermuted(inputShape, blockShape, prod5, batchToSpace = true) { + const reshapedPermuted = []; + if (batchToSpace) { + reshapedPermuted.push(inputShape[0] / prod5); + } else { + reshapedPermuted.push(inputShape[0] * prod5); + } + for (let i = 1; i < inputShape.length; ++i) { + if (i <= blockShape.length) { + if (batchToSpace) { + reshapedPermuted.push(blockShape[i - 1] * inputShape[i]); + } else { + reshapedPermuted.push(inputShape[i] / blockShape[i - 1]); + } + } else { + reshapedPermuted.push(inputShape[i]); + } + } + return reshapedPermuted; +} +function getSliceBeginCoords(crops, blockShape) { + const sliceBeginCoords = [0]; + for (let i = 0; i < blockShape; ++i) { + sliceBeginCoords.push(crops[i][0]); + } + return sliceBeginCoords; +} +function getSliceSize(uncroppedShape, crops, blockShape) { + const sliceSize = uncroppedShape.slice(0, 1); + for (let i = 0; i < blockShape; ++i) { + sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]); + } + return sliceSize; +} +var SELU_SCALEALPHA = 1.7580993408473768; +var SELU_SCALE = 1.0507009873554805; +var ERF_P = 0.3275911; +var ERF_A1 = 0.254829592; +var ERF_A2 = -0.284496736; +var ERF_A3 = 1.421413741; +var ERF_A4 = -1.453152027; +var ERF_A5 = 1.061405429; +function mergeRealAndImagArrays(real4, imag4) { + if (real4.length !== imag4.length) { + throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`); + } + const result = new Float32Array(real4.length * 2); + for (let i = 0; i < result.length; i += 2) { + result[i] = real4[i / 2]; + result[i + 1] = imag4[i / 2]; + } + return result; +} +function splitRealAndImagArrays(complex4) { + const real4 = new Float32Array(complex4.length / 2); + const imag4 = new Float32Array(complex4.length / 2); + for (let i = 0; i < complex4.length; i += 2) { + real4[i / 2] = complex4[i]; + imag4[i / 2] = complex4[i + 1]; + } + return { real: real4, imag: imag4 }; +} +function complexWithEvenIndex(complex4) { + const len = Math.ceil(complex4.length / 4); + const real4 = new Float32Array(len); + const imag4 = new Float32Array(len); + for (let i = 0; i < complex4.length; i += 4) { + real4[Math.floor(i / 4)] = complex4[i]; + imag4[Math.floor(i / 4)] = complex4[i + 1]; + } + return { real: real4, imag: imag4 }; +} +function complexWithOddIndex(complex4) { + const len = Math.floor(complex4.length / 4); + const real4 = new Float32Array(len); + const imag4 = new Float32Array(len); + for (let i = 2; i < complex4.length; i += 4) { + real4[Math.floor(i / 4)] = complex4[i]; + imag4[Math.floor(i / 4)] = complex4[i + 1]; + } + return { real: real4, imag: imag4 }; +} +function getComplexWithIndex(complex4, index) { + const real4 = complex4[index * 2]; + const imag4 = complex4[index * 2 + 1]; + return { real: real4, imag: imag4 }; +} +function assignToTypedArray(data, real4, imag4, index) { + data[index * 2] = real4; + data[index * 2 + 1] = imag4; +} +function exponents(n, inverse) { + const real4 = new Float32Array(n / 2); + const imag4 = new Float32Array(n / 2); + for (let i = 0; i < Math.ceil(n / 2); i++) { + const x = (inverse ? 2 : -2) * Math.PI * (i / n); + real4[i] = Math.cos(x); + imag4[i] = Math.sin(x); + } + return { real: real4, imag: imag4 }; +} +function exponent(k, n, inverse) { + const x = (inverse ? 2 : -2) * Math.PI * (k / n); + const real4 = Math.cos(x); + const imag4 = Math.sin(x); + return { real: real4, imag: imag4 }; +} +var ARROW = "->"; +var ARROW_REGEX = /->/g; +var COMMA = ","; +var ELLIPSIS = "..."; +function decodeEinsumEquation(equation, numTensors) { + equation = equation.replace(/\s/g, ""); + const numArrows = (equation.length - equation.replace(ARROW_REGEX, "").length) / ARROW.length; + if (numArrows < 1) { + throw new Error("Equations without an arrow are not supported."); + } else if (numArrows > 1) { + throw new Error(`Equation must contain exactly one arrow ("${ARROW}").`); + } + const [inputString, outputString] = equation.split(ARROW); + assert(inputString.indexOf(ELLIPSIS) === -1, () => `The ellipsis notation ("${ELLIPSIS}") is not supported yet.`); + const inputTerms = inputString.split(COMMA); + const numInputs = inputTerms.length; + if (numTensors !== numInputs) { + throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`); + } + if (numInputs > 2) { + throw new Error("Support for more than 2 input tensors is not implemented yet."); + } + const allDims = []; + for (let i = 0; i < outputString.length; ++i) { + const dimName = outputString[i]; + if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) { + throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`); + } + if (allDims.indexOf(dimName) === -1) { + allDims.push(dimName); + } + } + for (let i = 0; i < inputString.length; ++i) { + const dimName = inputString[i]; + if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) { + allDims.push(dimName); + } + } + const idDims = new Array(inputTerms.length); + for (let i = 0; i < numInputs; ++i) { + if (new Set(inputTerms[i].split("")).size !== inputTerms[i].length) { + throw new Error(`Found duplicate axes in input component ${inputTerms[i]}. Support for duplicate axes in input is not implemented yet.`); + } + idDims[i] = []; + for (let j = 0; j < inputTerms[i].length; ++j) { + idDims[i].push(allDims.indexOf(inputTerms[i][j])); + } + } + const numDims = allDims.length; + const numOutDims = outputString.length; + const summedDims = []; + for (let i = numOutDims; i < numDims; ++i) { + summedDims.push(i); + } + return { allDims, summedDims, idDims }; +} +function getEinsumPermutation(nDims, idDims) { + let permutationIndices = new Array(nDims); + permutationIndices.fill(-1); + for (let i = 0; i < idDims.length; ++i) { + permutationIndices[idDims[i]] = i; + } + const expandDims6 = []; + for (let i = 0; i < nDims; ++i) { + if (permutationIndices[i] === -1) { + expandDims6.push(i); + } + } + permutationIndices = permutationIndices.filter((d) => d !== -1); + return { permutationIndices, expandDims: expandDims6 }; +} +function checkEinsumDimSizes(nDims, idDims, tensors) { + const dimSizes = new Array(nDims); + for (let i = 0; i < tensors.length; ++i) { + const shape = tensors[i].shape; + for (let j = 0; j < idDims[i].length; ++j) { + if (dimSizes[idDims[i][j]] === void 0) { + dimSizes[idDims[i][j]] = shape[j]; + } else { + assert(dimSizes[idDims[i][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`); + } + } + } +} +function getEinsumComputePath(summedDims, idDims) { + const path = summedDims; + const steps = []; + let nSteps = 0; + if (summedDims.length === 0) { + path.push(-1); + } + nSteps = summedDims.length + 1; + for (let i = 0; i < nSteps; ++i) { + steps.push([]); + } + const computedTermIndices = []; + for (let i = 0; i < path.length; ++i) { + const summedDim = path[i]; + const termIndices = findTermsWithDim(idDims, summedDim); + for (const termIndex of termIndices) { + if (computedTermIndices.indexOf(termIndex) === -1) { + steps[i].push(termIndex); + computedTermIndices.push(termIndex); + } + } + } + return { path, steps }; +} +function isIdentityPermutation(perm) { + return perm.every((dim, index) => dim === index); +} +function findTermsWithDim(idDims, dim) { + const termIndices = []; + for (let i = 0; i < idDims.length; ++i) { + if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) { + termIndices.push(i); + } + } + return termIndices; +} +function prepareSplitSize(x, numOrSizeSplits, axis = 0) { + let splitSizes = []; + if (typeof numOrSizeSplits === "number") { + assert(x.shape[axis] % numOrSizeSplits === 0, () => "Number of splits must evenly divide the axis."); + splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits); + } else { + const numOfNegs = numOrSizeSplits.reduce((count2, value) => { + if (value === -1) { + count2 += 1; + } + return count2; + }, 0); + assert(numOfNegs <= 1, () => "There should be only one negative value in split array."); + const negIndex = numOrSizeSplits.indexOf(-1); + if (negIndex !== -1) { + const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a); + numOrSizeSplits[negIndex] = x.shape[axis] - total; + } + assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => "The sum of sizes must match the size of the axis dimension."); + splitSizes = numOrSizeSplits; + } + return splitSizes; +} +function getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) { + return `Received SparseTensor with denseShape[0] = 0 but + indices.shape[0] = ${indicesLength}`; +} +function getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) { + return `indices(${index}, 0) is invalid: ${value} < 0`; +} +function getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) { + return `indices(${index}, 0) is invalid: ${value} >= ${limit}`; +} +function getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) { + return `only one output dimension may be -1, not both ${dim1} and ${dim2}`; +} +function getSparseReshapeNegativeOutputDimErrorMessage(dim, value) { + return `size ${dim} must be non-negative, not ${value}`; +} +function getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() { + return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"; +} +function getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) { + const inputSize = sizeFromShape(inputShape); + const outputSize = sizeFromShape(outputShape); + return `Input to reshape is a SparseTensor with ${inputSize} + dense values, but the requested shape requires a multiple of ${outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`; +} +function getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) { + const inputSize = sizeFromShape(inputShape); + const outputSize = sizeFromShape(outputShape); + return `Input to reshape is a tensor with ${inputSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`; +} +function getSparseSegmentReductionNegativeSegmentIdsErrorMessage() { + return `segment ids must be >= 0`; +} +function getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() { + return `segment ids are not increasing`; +} +function getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) { + return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`; +} +function getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) { + return `Bad: indices[${index}] == ${indexValue} out of range [0, ${inputRows})`; +} +var segment_util_exports = {}; +__export2(segment_util_exports, { + collectGatherOpShapeInfo: () => collectGatherOpShapeInfo, + computeOutShape: () => computeOutShape3, + segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize +}); +function segOpComputeOptimalWindowSize(inSize, numSegments) { + let done = false; + let res; + if (inSize <= PARALLELIZE_THRESHOLD) { + res = inSize; + done = true; + } else { + res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize))); + } + while (!done) { + if (res > numSegments || res === inSize) { + done = true; + } else { + res = nearestDivisor(inSize, res + 1); + } + } + return res; +} +function computeOutShape3(aShape, axis, numSegments) { + const outShape = []; + const rank = aShape.length; + for (let dim = 0; dim < rank; dim++) { + if (dim !== axis) { + outShape.push(aShape[dim]); + } else { + outShape.push(numSegments); + } + } + return outShape; +} +function collectGatherOpShapeInfo(x, indices, axis, batchDims) { + const indicesRank = indices.shape.length; + const xRank = x.shape.length; + if (batchDims !== 0) { + if (batchDims < -indicesRank || batchDims > indicesRank) { + throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`); + } + } + if (batchDims < 0) { + batchDims += indicesRank; + } + if (batchDims > xRank) { + throw new Error(`batchDims (${batchDims}) must be less than rank(x) ( + ${xRank}).`); + } + if (axis < batchDims) { + throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`); + } + for (let i = 0; i < batchDims; ++i) { + if (x.shape[i] !== indices.shape[i]) { + throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`); + } + } + const dimSize = x.shape[axis]; + const outputShape = []; + let batchSize = 1; + let outerSize = 1; + let sliceSize = 1; + for (let i = 0; i < batchDims; ++i) { + outputShape.push(x.shape[i]); + batchSize *= x.shape[i]; + } + for (let i = batchDims; i < axis; i++) { + outputShape.push(x.shape[i]); + outerSize *= x.shape[i]; + } + for (let i = batchDims; i < indicesRank; i++) { + outputShape.push(indices.shape[i]); + } + for (let i = axis + 1; i < xRank; i++) { + outputShape.push(x.shape[i]); + sliceSize *= x.shape[i]; + } + return { batchSize, sliceSize, outerSize, dimSize, outputShape }; +} +function fromUint8ToStringArray(vals) { + try { + return vals.map((val) => decodeString(val)); + } catch (err) { + throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`); + } +} +function fromStringArrayToUint8(strings) { + return strings.map((s) => encodeString(s)); +} +var kernel_impls_exports = {}; +__export2(kernel_impls_exports, { + nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl, + nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl, + nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl, + whereImpl: () => whereImpl +}); +registerOptimizers(); +var absGradConfig = { + kernelName: Abs, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(dy, step(cast(x, "float32"), -1)) }; + } +}; +var acosGradConfig = { + kernelName: Acos, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { + x: () => { + const a = square(cast(x, "float32")); + const b = sqrt(sub(scalar(1), a)); + return neg(div(dy, b)); + } + }; + } +}; +var acoshGradConfig = { + kernelName: Acosh, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { + x: () => { + const a = sqrt(sub(square(cast(x, "float32")), 1)); + return div(dy, a); + } + }; + } +}; +var addGradConfig = { + kernelName: Add, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + let res = dy; + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, a.shape); + }; + const derB = () => { + let res = dy; + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, b.shape); + }; + return { a: derA, b: derB }; + } +}; +var addNGradConfig = { + kernelName: AddN, + saveAllInputs: true, + gradFunc: (dy, saved) => { + const ders = {}; + saved.forEach((_, i) => { + ders[i] = () => dy.clone(); + }); + return ders; + } +}; +var argMaxGradConfig = { + kernelName: ArgMax, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => zerosLike(x) }; + } +}; +var argMinGradConfig = { + kernelName: ArgMin, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => zerosLike(x) }; + } +}; +var asinGradConfig = { + kernelName: Asin, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, "float32"))))) }; + } +}; +var asinhGradConfig = { + kernelName: Asinh, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { + x: () => { + const a = sqrt(add2(scalar(1), square(cast(x, "float32")))); + return div(dy, a); + } + }; + } +}; +var atan2GradConfig = { + kernelName: Atan2, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + const d = add2(square(a), square(b)); + let res = mul(dy, div(b, d)); + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, a.shape); + }; + const derB = () => { + const d = add2(square(a), square(b)); + let res = neg(mul(dy, div(a, d))); + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, b.shape); + }; + return { a: derA, b: derB }; + } +}; +var atanGradConfig = { + kernelName: Atan, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, add2(square(cast(x, "float32")), 1)) }; + } +}; +var atanhGradConfig = { + kernelName: Atanh, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, sub(scalar(1), square(cast(x, "float32")))) }; + } +}; +function avgPool3dGrad_(dy, input2, filterSize, strides, pad3, dimRoundingMode) { + const $dy = convertToTensor(dy, "dy", "avgPool3dGrad"); + const $input = convertToTensor(input2, "input", "avgPool3dGrad"); + let dy5D = $dy; + let input5D = $input; + let reshapedTo5D = false; + if ($input.rank === 4) { + reshapedTo5D = true; + dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]); + input5D = reshape($input, [ + 1, + $input.shape[0], + $input.shape[1], + $input.shape[2], + $input.shape[3] + ]); + } + assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`); + assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`); + checkPadOnDimRoundingMode("avgPool3dGrad", pad3, dimRoundingMode); + const inputs = { dy: dy5D, input: input5D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; + const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var avgPool3dGrad = op({ avgPool3dGrad_ }); +var avgPool3DGradConfig = { + kernelName: AvgPool3D, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + return { + x: () => avgPool3dGrad(dy, x, filterSize, strides, pad3, dimRoundingMode) + }; + } +}; +function avgPoolGrad_(dy, input2, filterSize, strides, pad3) { + const $dy = convertToTensor(dy, "dy", "avgPoolGrad"); + const $input = convertToTensor(input2, "input", "avgPoolGrad"); + assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`); + let input4D = $input; + let dy4D = $dy; + let reshapedTo4D = false; + if ($input.rank === 3) { + reshapedTo4D = true; + input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]); + dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]); + } + assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`); + assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`); + const inputs = { dy: dy4D, input: input4D }; + const attrs = { filterSize, strides, pad: pad3 }; + const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var avgPoolGrad = op({ avgPoolGrad_ }); +var avgPoolGradConfig = { + kernelName: AvgPool, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { filterSize, strides, pad: pad3 } = attrs; + return { x: () => avgPoolGrad(dy, x, filterSize, strides, pad3) }; + } +}; +var batchMatMulGradConfig = { + kernelName: BatchMatMul, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved, attrs) => { + const [a, b] = saved; + const { transposeA, transposeB } = attrs; + if (!transposeA && !transposeB) { + return { + a: () => matMul(dy, b, false, true), + b: () => matMul(a, dy, true, false) + }; + } else if (!transposeA && transposeB) { + return { + a: () => matMul(dy, b, false, false), + b: () => matMul(dy, a, true, false) + }; + } else if (transposeA && !transposeB) { + return { + a: () => matMul(b, dy, false, true), + b: () => matMul(a, dy, false, false) + }; + } else { + return { + a: () => matMul(b, dy, true, true), + b: () => matMul(dy, a, true, true) + }; + } + } +}; +var batchToSpaceNDGradConfig = { + kernelName: BatchToSpaceND, + gradFunc: (dy, saved, attrs) => { + const { blockShape, crops } = attrs; + return { x: () => spaceToBatchND(dy, blockShape, crops) }; + } +}; +var broadcastToGradConfig = { + kernelName: BroadcastTo, + gradFunc: (dy, saved, attrs) => { + const broadCastToAttrs = attrs; + const inputShape = broadCastToAttrs.inputShape; + const outputShape = broadCastToAttrs.shape; + const reps = Array.from(outputShape); + for (let i = inputShape.length - 1; i >= 0; i--) { + if (inputShape[i] === outputShape[i]) { + reps[i] = 1; + } else if (inputShape[i] !== 1) { + throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`); + } + } + const axes = []; + for (let i = 0; i < reps.length; i++) { + if (reps[i] > 1) { + axes.push(i); + } + } + return { x: () => sum2( + dy, + axes, + true + /* keepDims */ + ) }; + } +}; +var castGradConfig = { + kernelName: Cast, + gradFunc: (dy) => { + return { x: () => dy.clone() }; + } +}; +var ceilGradConfig = { + kernelName: Ceil, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; +var clipByValueGradConfig = { + kernelName: ClipByValue, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { clipValueMin, clipValueMax } = attrs; + return { + x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)) + }; + } +}; +var complexAbsGradConfig = { + kernelName: ComplexAbs, + inputsToSave: ["x"], + gradFunc: absGradConfig.gradFunc +}; +var concatGradConfig = { + kernelName: Concat, + saveAllInputs: true, + gradFunc: (dy, saved, attrs) => { + const shapes = saved.map((t) => t.shape); + const { axis } = attrs; + const $axis = parseAxisParam(axis, saved[0].shape)[0]; + const sizeSplits = shapes.map((s) => s[$axis]); + const derTensors = split(dy, sizeSplits, $axis); + return derTensors.map((t) => () => t); + } +}; +var conv2DGradConfig = { + kernelName: Conv2D, + inputsToSave: ["x", "filter"], + gradFunc: (dy, saved, attrs) => { + const [x4D, $filter] = saved; + const { dilations, strides, pad: pad3, dataFormat } = attrs; + assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`); + return { + x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad3, dataFormat), + filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad3, dataFormat) + }; + } +}; +var conv2DBackpropInputGradConfig = { + kernelName: Conv2DBackpropInput, + inputsToSave: ["dy", "filter"], + gradFunc: (ddx, saved, attrs) => { + const [dy, filter] = saved; + const { strides, pad: pad3, dataFormat, dimRoundingMode } = attrs; + return { + dy: () => conv2d(ddx, filter, strides, pad3, dataFormat, 1, dimRoundingMode), + filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad3, dataFormat, dimRoundingMode) + }; + } +}; +function conv3DBackpropFilter_(x, dy, filterShape, strides, pad3) { + let x5D = x; + if (x.rank === 4) { + x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]); + } + let dy5D = dy; + if (dy5D.rank === 4) { + dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]); + } + assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`); + assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`); + assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`); + assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`); + assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`); + const inputs = { x: x5D, dy: dy5D }; + const attrs = { strides, pad: pad3, filterShape }; + return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs); +} +var conv3DBackpropFilter = op({ conv3DBackpropFilter_ }); +var conv3DGradConfig = { + kernelName: Conv3D, + inputsToSave: ["x", "filter"], + gradFunc: (dy, saved, attrs) => { + const { dilations, strides, pad: pad3 } = attrs; + assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`); + const [x5D, $filter] = saved; + return { + x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad3), + filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad3) + }; + } +}; +var cosGradConfig = { + kernelName: Cos, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(neg(sin(cast(x, "float32"))), dy) }; + } +}; +var coshGradConfig = { + kernelName: Cosh, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(sinh(cast(x, "float32")), dy) }; + } +}; +var cumsumGradConfig = { + kernelName: Cumsum, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { axis, exclusive, reverse: reverse5 } = attrs; + return { + x: () => { + const permutation = getAxesPermutation([axis], x.rank); + let out = cumsum(dy, axis, exclusive, !reverse5); + if (permutation != null) { + out = transpose(out, permutation); + } + return out; + } + }; + } +}; +var depthwiseConv2dNativeGradConfig = { + kernelName: DepthwiseConv2dNative, + inputsToSave: ["x", "filter"], + gradFunc: (dy, saved, attrs) => { + const { dilations, strides, pad: pad3, dimRoundingMode } = attrs; + const $dilations = dilations == null ? [1, 1] : dilations; + assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`); + const [x, filter] = saved; + assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`); + assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`); + assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`); + assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'.`); + checkPadOnDimRoundingMode("depthwiseConv2d", pad3, dimRoundingMode); + return { + x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad3, $dilations, dimRoundingMode), + filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad3, $dilations, dimRoundingMode) + }; + } +}; +var dilation2dGradConfig = { + kernelName: Dilation2D, + inputsToSave: ["x", "filter"], + gradFunc: (dy, saved, attrs) => { + const [x, filter] = saved; + const inputInputs = { x, filter, dy }; + const filterInputs = { x, filter, dy }; + return { + x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs), + filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs) + }; + } +}; +var eluGradConfig = { + kernelName: Elu, + outputsToSave: [true], + gradFunc: (dy, saved) => { + const [y] = saved; + const inputs = { dy, y }; + return { x: () => ENGINE.runKernel(EluGrad, inputs) }; + } +}; +var erfGradConfig = { + kernelName: Erf, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI)); + return { x: () => mul(dy, a) }; + } +}; +var expGradConfig = { + kernelName: Exp, + outputsToSave: [true], + gradFunc: (dy, saved) => { + const [y] = saved; + return { x: () => mul(dy, y) }; + } +}; +var expandDimsGradConfig = { + kernelName: ExpandDims, + inputsToSave: ["input"], + gradFunc: (dy, saved) => { + const [input2] = saved; + return { input: () => reshape(dy, input2.shape) }; + } +}; +var expm1GradConfig = { + kernelName: Expm1, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(dy, exp(x)) }; + } +}; +var floorGradConfig = { + kernelName: Floor, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; +var floorDivGradConfig = { + kernelName: FloorDiv, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + const res = div(dy, cast(b, "float32")); + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(res, reduceAxes), a.shape); + } + return res; + }; + const derB = () => { + let res = mul(dy, cast(a, "float32")); + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + res = reshape(sum2(res, reduceAxes), b.shape); + } + const tmp = square(b); + return neg(div(res, cast(tmp, "float32"))); + }; + return { a: derA, b: derB }; + } +}; +var fusedBatchNormGradConfig = { + kernelName: FusedBatchNorm, + inputsToSave: ["x", "mean", "variance", "scale"], + gradFunc: (dy, saved, attrs) => { + const { varianceEpsilon } = attrs; + const [x, mean4, variance, scale22] = saved; + const scaleValue = scale22 == null ? scalar(1) : scale22; + const reductionAxes = getReductionAxes(mean4.shape, x.shape); + const tileShape = []; + if (mean4.rank === 1) { + for (let i = 0; i < x.shape.length - 1; ++i) { + tileShape.push(x.shape[i]); + } + tileShape.push(1); + } + const xMinusMean = sub(x, mean4); + const dyTimesScaleValue = mul(dy, scaleValue); + const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon))); + const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5)); + const derX = () => { + if (mean4.rank === 1) { + return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean4.shape[0]]), tileShape)), scaleValue), x.shape); + } else { + return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape); + } + }; + const derMean = () => { + let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue); + if (mean4.rank === 1) { + meanDer = sum2(meanDer, reductionAxes); + } + return reshape(meanDer, mean4.shape); + }; + const derVariance = () => { + let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue); + if (mean4.rank === 1) { + varianceDer = sum2(varianceDer, reductionAxes); + } + return reshape(varianceDer, mean4.shape); + }; + const derScale = () => { + const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance); + let scaleDer = mul(dy, xMinusMean2TimesRsqrt); + if (mean4.rank === 1) { + scaleDer = sum2(scaleDer, reductionAxes); + } + return reshape(scaleDer, mean4.shape); + }; + const derOffset = () => { + let offsetDer = dy; + if (mean4.rank === 1) { + offsetDer = sum2(offsetDer, reductionAxes); + } + return reshape(offsetDer, mean4.shape); + }; + return { + x: derX, + mean: derMean, + variance: derVariance, + scale: derScale, + offset: derOffset + }; + } +}; +var gatherGradConfig = { + kernelName: GatherV2, + inputsToSave: ["x", "indices"], + gradFunc: (dy, saved, attrs) => { + const [x, indices] = saved; + const { axis, batchDims } = attrs; + const parsedAxis = parseAxisParam(axis, x.shape)[0]; + const derXBatch = (x2, indices2, dy2) => { + return () => { + const paramsShape = x2.shape; + const indicesSize = indices2.size; + const outerShape = paramsShape.slice(0, parsedAxis); + const outerDims = outerShape.length; + const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1); + const innerDims = innerShape.length; + const outerAxesIndices = arrayRange(0, outerDims); + const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims); + const valuesShape = arrayConcat([ + outerShape, + [indicesSize], + innerShape + ]); + const values = reshape(dy2, valuesShape); + const reshapedIndices = reshape(indices2, [indicesSize]); + const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]); + const valuesTranspose = transpose(values, transposeDims); + let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x2.shape[parsedAxis]); + const invertTransposeDims = getUndoAxesPermutation(transposeDims); + paramsGrad = transpose(paramsGrad, invertTransposeDims); + return paramsGrad; + }; + }; + if (batchDims === 1) { + const batchSize = x.shape[0]; + const xBatch = x.split(batchSize, 0); + const derXBatched = () => { + const stacked = stack(xBatch.map((x2, i) => { + return derXBatch(x2, indices.slice(i, 1), dy.slice(i, 1))(); + })); + return stacked.reshape(x.shape); + }; + return { x: derXBatched, indices: () => indices }; + } else { + return { x: derXBatch(x, indices, dy), indices: () => indices }; + } + } +}; +function arrayRange(start, stop) { + const result = []; + for (let i = start; i < stop; ++i) { + result.push(i); + } + return result; +} +function arrayConcat(arrays) { + const result = []; + for (let i = 0; i < arrays.length; ++i) { + for (let j = 0; j < arrays[i].length; ++j) { + result.push(arrays[i][j]); + } + } + return result; +} +var greaterEqualGradConfig = { + kernelName: GreaterEqual, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + return { a: () => zerosLike(a), b: () => zerosLike(b) }; + } +}; +var identityGradConfig = { + kernelName: Identity, + gradFunc: (dy) => { + return { x: () => cast(dy, "float32") }; + } +}; +var isFiniteGradConfig = { + kernelName: IsFinite, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; +var isInfGradConfig = { + kernelName: IsInf, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; +var isNanGradConfig = { + kernelName: IsNan, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; +var leakyReluGradConfig = { + kernelName: LeakyRelu, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { alpha } = attrs; + const mask = greater(x, 0); + return { x: () => where(mask, dy, mul(dy, alpha)) }; + } +}; +var log1pGradConfig = { + kernelName: Log1p, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, add2(x, 1)) }; + } +}; +var logGradConfig = { + kernelName: Log, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, cast(x, "float32")) }; + } +}; +var logSoftmaxGradConfig = { + kernelName: LogSoftmax, + inputsToSave: [], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const [value] = saved; + const { axis } = attrs; + return { + logits: () => { + const keepDims = true; + const softmax6 = exp(value); + return sub(dy, mul(sum2(dy, axis, keepDims), softmax6)); + } + }; + } +}; +function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) { + const inputs = { x, y, dy }; + const attrs = { depthRadius, bias, alpha, beta }; + return ENGINE.runKernel(LRNGrad, inputs, attrs); +} +var localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ }); +var lrnGradConfig = { + kernelName: LRN, + inputsToSave: ["x"], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const [x, y] = saved; + const { depthRadius, bias, alpha, beta } = attrs; + return { + x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta) + }; + } +}; +function gradForMinAndMax(dy, y, xOrig, origAxes) { + if (y.rank < xOrig.rank) { + y = reshape(y, expandShapeToKeepDim(y.shape, origAxes)); + } + if (dy.rank < xOrig.rank) { + dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes)); + } + return { + x: () => { + const dx = mul(dy, cast(equal(xOrig, y), dy.dtype)); + return dx; + } + }; +} +var maxGradConfig = { + kernelName: Max, + inputsToSave: ["x"], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const maxAttrs = attrs; + const { reductionIndices } = maxAttrs; + const x = saved[0]; + const y = saved[1]; + const origAxes = parseAxisParam(reductionIndices, x.shape); + const maxGrad = gradForMinAndMax(dy, y, x, origAxes); + return { + x: () => { + return maxGrad["x"](); + } + }; + } +}; +var maximumGradConfig = { + kernelName: Maximum, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const derA = () => mul(dy, cast(greaterEqual(a, b), "float32")); + const derB = () => mul(dy, cast(less(a, b), "float32")); + return { a: derA, b: derB }; + } +}; +function maxPool3dGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) { + const $dy = convertToTensor(dy, "dy", "maxPool3dGrad"); + const $input = convertToTensor(input2, "input", "maxPool3dGrad"); + const $output = convertToTensor(output, "output", "maxPool3dGrad"); + let dy5D = $dy; + let input5D = $input; + let output5D = $output; + let reshapedTo5D = false; + if ($input.rank === 4) { + reshapedTo5D = true; + dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]); + input5D = reshape($input, [ + 1, + $input.shape[0], + $input.shape[1], + $input.shape[2], + $input.shape[3] + ]); + output5D = reshape($output, [ + 1, + $output.shape[0], + $output.shape[1], + $output.shape[2], + $output.shape[3] + ]); + } + assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`); + assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`); + assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`); + checkPadOnDimRoundingMode("maxPool3dGrad", pad3, dimRoundingMode); + const inputs = { dy: dy5D, input: input5D, output: output5D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; + const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var maxPool3dGrad = op({ maxPool3dGrad_ }); +var maxPool3DGradConfig = { + kernelName: MaxPool3D, + inputsToSave: ["x"], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const [x, y] = saved; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + return { + x: () => maxPool3dGrad(dy, x, y, filterSize, strides, pad3, dimRoundingMode) + }; + } +}; +function maxPoolGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) { + const $dy = convertToTensor(dy, "dy", "maxPoolGrad"); + const $input = convertToTensor(input2, "input", "maxPoolGrad"); + const $output = convertToTensor(output, "output", "maxPoolGrad"); + assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`); + assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`); + assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`); + checkPadOnDimRoundingMode("maxPoolGrad", pad3, dimRoundingMode); + const inputs = { dy: $dy, input: $input, output: $output }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; + return ENGINE.runKernel(MaxPoolGrad, inputs, attrs); +} +var maxPoolGrad = op({ maxPoolGrad_ }); +var maxPoolGradConfig = { + kernelName: MaxPool, + inputsToSave: ["x"], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const [x, y] = saved; + const { filterSize, strides, pad: pad3 } = attrs; + return { + x: () => maxPoolGrad(dy, x, y, filterSize, strides, pad3) + }; + } +}; +var meanGradConfig = { + kernelName: Mean, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { axis } = attrs; + const axes = parseAxisParam(axis, x.shape); + const shapes = computeOutAndReduceShapes(x.shape, axes); + const reduceShape = shapes[1]; + const reduceSize = sizeFromShape(reduceShape); + const derX = () => { + const expandedDyShape = x.shape.slice(); + axes.forEach((axis2) => { + expandedDyShape[axis2] = 1; + }); + const expandedDy = reshape(dy, expandedDyShape); + const res = div(mul(expandedDy, ones2(x.shape, "float32")), reduceSize); + return res; + }; + return { x: derX }; + } +}; +var minGradConfig = { + kernelName: Min, + inputsToSave: ["x"], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const minAttrs = attrs; + const { axis } = minAttrs; + const [x, y] = saved; + const origAxes = parseAxisParam(axis, x.shape); + const minGrad = gradForMinAndMax(dy, y, x, origAxes); + return { + x: () => { + return minGrad["x"](); + } + }; + } +}; +var minimumGradConfig = { + kernelName: Minimum, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const derA = () => mul(dy, cast(lessEqual(a, b), "float32")); + const derB = () => mul(dy, cast(greater(a, b), "float32")); + return { a: derA, b: derB }; + } +}; +var mirrorPadGradConfig = { + kernelName: MirrorPad, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const x = saved[0]; + const { paddings } = attrs; + const begin = paddings.map((p2) => p2[0]); + return { x: () => slice(dy, begin, x.shape) }; + } +}; +var modGradConfig = { + kernelName: Mod, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(dy, reduceAxes), a.shape); + } + return dy; + }; + const derB = () => { + const res = mul(dy, neg(floor(div(a, b)))); + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(res, reduceAxes), b.shape); + } + return res; + }; + return { a: derA, b: derB }; + } +}; +var multiplyGradConfig = { + kernelName: Multiply, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + const res = mul(dy, cast(b, "float32")); + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(res, reduceAxes), a.shape); + } + return res; + }; + const derB = () => { + const res = mul(dy, cast(a, "float32")); + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(res, reduceAxes), b.shape); + } + return res; + }; + return { a: derA, b: derB }; + } +}; +var negGradConfig = { + kernelName: Neg, + gradFunc: (dy) => { + return { x: () => neg(dy) }; + } +}; +var oneHotGradConfig = { + kernelName: OneHot, + inputsToSave: ["indices"], + gradFunc: (dy, saved) => { + const indices = saved[0]; + return { indices: () => zeros(indices.shape, "float32") }; + } +}; +var onesLikeGradConfig = { + kernelName: OnesLike, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; +var packGradConfig = { + kernelName: Pack, + saveAllInputs: true, + gradFunc: (dy, saved, attrs) => { + const { axis } = attrs; + const derTensors = unstack(dy, axis); + return derTensors.map((t) => () => t); + } +}; +var padV2GradConfig = { + kernelName: PadV2, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const x = saved[0]; + const { paddings } = attrs; + const begin = paddings.map((p2) => p2[0]); + return { x: () => slice(dy, begin, x.shape) }; + } +}; +var powGradConfig = { + kernelName: Pow, + inputsToSave: ["a", "b"], + outputsToSave: [true], + gradFunc: (dy, saved) => { + const [a, b, y] = saved; + const base = a; + const exp4 = b; + const outShape = assertAndGetBroadcastShape(base.shape, exp4.shape); + const derBase = () => { + const expFloat = cast(exp4, "float32"); + let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1))))); + const reduceAxes = getReductionAxes(base.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, base.shape); + }; + const derExp = () => { + const condition = greater(base, 0); + const logBase = where(condition, log2(base), zerosLike(base)); + let res = mul(dy, mul(y, logBase)); + const reduceAxes = getReductionAxes(exp4.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, exp4.shape); + }; + return { a: derBase, b: derExp }; + } +}; +var preluGradConfig = { + kernelName: Prelu, + inputsToSave: ["x", "alpha"], + gradFunc: (dy, saved) => { + const [x, alpha] = saved; + const mask = greater(x, 0); + return { + x: () => where(mask, dy, mul(dy, alpha)), + alpha: () => { + let res = where(mask, zerosLike(dy), mul(dy, x)); + const reduceAxes = getReductionAxes(alpha.shape, dy.shape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, alpha.shape); + } + }; + } +}; +function prodGradFn_(x, dy, axis) { + const expandedYShape = x.shape.slice(); + expandedYShape[axis] = 1; + const expandedDy = reshape(dy, expandedYShape); + const xCumProd = cumprod(x, axis, true, false); + const xCumRevProd = cumprod(x, axis, true, true); + const dx = mul(xCumProd, xCumRevProd); + return mul(expandedDy, dx); +} +function prodsGradFn_(x, dy, axis) { + const xRank = x.shape.length; + const finalProdAxis = xRank - axis.length; + const xPermutation = backend_util_exports.getAxesPermutation(axis, xRank); + let permutedX = x; + if (xPermutation != null) { + permutedX = transpose(x, xPermutation); + } + const newShape = permutedX.shape.slice(); + const removedShape = newShape.splice(xRank - axis.length, axis.length); + const endPartShape = removedShape.reduce((p2, c) => p2 * c, 1); + newShape.push(endPartShape); + const reshapedPermutedX = permutedX.reshape(newShape); + let prodGrad = prodGradFn_(reshapedPermutedX, dy, finalProdAxis); + prodGrad = prodGrad.reshape(permutedX.shape); + if (xPermutation != null) { + const undoPermutation = backend_util_exports.getUndoAxesPermutation(xPermutation); + prodGrad = transpose(prodGrad, undoPermutation); + } + return prodGrad; +} +var prodGradConfig = { + kernelName: Prod, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { axis } = attrs; + let axisArr = []; + if (axis === void 0 || axis === null) { + axisArr = x.shape.map((_, i) => i); + } else if (typeof axis === "number") { + axisArr = [axis]; + } else { + axisArr = axis; + } + return { x: () => prodsGradFn_(x, dy, axisArr) }; + } +}; +var divGradConfig = { + kernelName: RealDiv, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + const res = div(dy, cast(b, "float32")); + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(res, reduceAxes), a.shape); + } + return res; + }; + const derB = () => { + let res = mul(dy, cast(a, "float32")); + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + res = reshape(sum2(res, reduceAxes), b.shape); + } + const tmp = square(b); + return neg(div(res, cast(tmp, "float32"))); + }; + return { a: derA, b: derB }; + } +}; +var reciprocalGradConfig = { + kernelName: Reciprocal, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, neg(square(x))) }; + } +}; +var relu6GradConfig = { + kernelName: Relu6, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + const mask = mul(lessEqual(x, 6), step(x)); + return { x: () => mul(dy, cast(mask, "float32")) }; + } +}; +var reluGradConfig = { + kernelName: Relu, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(dy, cast(step(x), "float32")) }; + } +}; +var reshapeGradConfig = { + kernelName: Reshape, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => reshape(dy, x.shape) }; + } +}; +var resizeBilinearGradConfig = { + kernelName: ResizeBilinear, + inputsToSave: ["images"], + gradFunc: (dy, saved, attrs) => { + const [images] = saved; + const inputs = { dy, images }; + const imagesDer = () => ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs) + ); + return { images: imagesDer }; + } +}; +var resizeNearestNeighborGradConfig = { + kernelName: ResizeNearestNeighbor, + inputsToSave: ["images"], + gradFunc: (dy, saved, attrs) => { + const [images] = saved; + const inputs = { dy, images }; + const imagesDer = () => ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs) + ); + return { images: imagesDer }; + } +}; +var reverseGradConfig = { + kernelName: Reverse, + gradFunc: (dy, saved, attrs) => { + const { dims } = attrs; + const axes = parseAxisParam(dims, dy.shape); + return { x: () => reverse(dy, axes) }; + } +}; +var roundGradConfig = { + kernelName: Round, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; +var rsqrtGradConfig = { + kernelName: Rsqrt, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) }; + } +}; +var selectGradConfig = { + kernelName: Select, + inputsToSave: ["condition"], + gradFunc: (dy, saved) => { + const [condition] = saved; + return { + // TODO(julianoks): Return null for condition gradient + // when backprop supports it. + condition: () => cast(zerosLike(condition), "float32"), + t: () => mul(dy, cast(condition, dy.dtype)), + e: () => mul(dy, cast(logicalNot(condition), dy.dtype)) + }; + } +}; +var seluGradConfig = { + kernelName: Selu, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { + x: () => { + const mask = greater(x, scalar(0)); + const scaleAlpha2 = scalar(SELU_SCALEALPHA); + const scale22 = scalar(SELU_SCALE); + const greaterThanZeroDer = mul(dy, scale22); + const lessEqualZeroDer = mul(mul(dy, scaleAlpha2), exp(cast(x, "float32"))); + return where(mask, greaterThanZeroDer, lessEqualZeroDer); + } + }; + } +}; +var sigmoidGradConfig = { + kernelName: Sigmoid, + outputsToSave: [true], + gradFunc: (dy, saved) => { + const [y] = saved; + return { x: () => mul(dy, mul(y, sub(scalar(1), y))) }; + } +}; +var signGradConfig = { + kernelName: Sign, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; +var sinGradConfig = { + kernelName: Sin, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(cos(cast(x, "float32")), dy) }; + } +}; +var sinhGradConfig = { + kernelName: Sinh, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(cosh(cast(x, "float32")), dy) }; + } +}; +var sliceGradConfig = { + kernelName: Slice, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { begin, size } = attrs; + const inputShape = x.shape; + const [begin_, size_] = parseSliceParams(x, begin, size); + const paddings = []; + for (let i = 0; i < dy.rank; i++) { + paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]); + } + return { x: () => pad(dy, paddings) }; + } +}; +var softmaxGradConfig = { + kernelName: Softmax, + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const [y] = saved; + const { dim } = attrs; + const keepDims = true; + const dyTimesY = mul(dy, y); + return { + logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y)) + }; + } +}; +var softplusGradConfig = { + kernelName: Softplus, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(dy, sigmoid(x)) }; + } +}; +var spaceToBatchNDGradConfig = { + kernelName: SpaceToBatchND, + gradFunc: (dy, saved, attrs) => { + const { blockShape, paddings } = attrs; + return { x: () => batchToSpaceND(dy, blockShape, paddings) }; + } +}; +var splitVGradConfig = { + kernelName: SplitV, + gradFunc: (dy, saved, attrs) => { + const { axis } = attrs; + return { x: () => concat(dy, axis) }; + } +}; +var sqrtGradConfig = { + kernelName: Sqrt, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, mul(sqrt(cast(x, "float32")), 2)) }; + } +}; +var squareGradConfig = { + kernelName: Square, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(dy, mul(cast(x, "float32"), 2)) }; + } +}; +var squaredDifferenceGradConfig = { + kernelName: SquaredDifference, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const two = scalar(2); + const derA = () => mul(dy, mul(two, sub(a, b))); + const derB = () => mul(dy, mul(two, sub(b, a))); + return { a: derA, b: derB }; + } +}; +var stepGradConfig = { + kernelName: Step, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; +var subGradConfig = { + kernelName: Sub, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + let res = dy; + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, a.shape); + }; + const derB = () => { + let res = dy; + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(neg(res), b.shape); + }; + return { a: derA, b: derB }; + } +}; +var sumGradConfig = { + kernelName: Sum, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const expandedDyShape = x.shape.slice(); + const { axis } = attrs; + const axes = parseAxisParam(axis, x.shape); + axes.forEach((axis2) => { + expandedDyShape[axis2] = 1; + }); + const expandedDy = reshape(dy, expandedDyShape); + const derX = mul(expandedDy, ones2(x.shape, "float32")); + return { x: () => derX }; + } +}; +var tanGradConfig = { + kernelName: Tan, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, square(cos(x))) }; + } +}; +var tanhGradConfig = { + kernelName: Tanh, + outputsToSave: [true], + gradFunc: (dy, saved) => { + const [y] = saved; + return { x: () => mul(sub(scalar(1), square(y)), dy) }; + } +}; +var tileGradConfig = { + kernelName: Tile, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { reps } = attrs; + const derX = () => { + let xGrad = zerosLike(x); + if (x.rank === 1) { + for (let i = 0; i < reps[0]; ++i) { + xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]])); + } + } else if (x.rank === 2) { + for (let i = 0; i < reps[0]; ++i) { + for (let j = 0; j < reps[1]; ++j) { + xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [ + x.shape[0], + x.shape[1] + ])); + } + } + } else if (x.rank === 3) { + for (let i = 0; i < reps[0]; ++i) { + for (let j = 0; j < reps[1]; ++j) { + for (let k = 0; k < reps[2]; ++k) { + xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]])); + } + } + } + } else if (x.rank === 4) { + for (let i = 0; i < reps[0]; ++i) { + for (let j = 0; j < reps[1]; ++j) { + for (let k = 0; k < reps[2]; ++k) { + for (let l = 0; l < reps[3]; ++l) { + xGrad = add2(xGrad, slice(dy, [ + i * x.shape[0], + j * x.shape[1], + k * x.shape[2], + l * x.shape[3] + ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]])); + } + } + } + } + } else { + throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`); + } + return xGrad; + }; + return { x: derX }; + } +}; +var transposeGradConfig = { + kernelName: Transpose, + gradFunc: (dy, saved, attrs) => { + const transposeAttrs = attrs; + const { perm } = transposeAttrs; + const undoPerm = getUndoAxesPermutation(perm); + return { x: () => transpose(dy, undoPerm) }; + } +}; +var unpackGradConfig = { + kernelName: Unpack, + gradFunc: (dy, saved, attrs) => { + const unpackAttrs = attrs; + const { axis } = unpackAttrs; + return { value: () => stack(dy, axis) }; + } +}; +var unsortedSegmentSumGradConfig = { + kernelName: UnsortedSegmentSum, + inputsToSave: ["segmentIds"], + gradFunc: (dy, saved) => { + const [segmentIds] = saved; + const derX = () => { + return gatherDropNegatives(dy, segmentIds); + }; + return { x: derX }; + } +}; +function gatherDropNegatives(x, indices) { + const zeroClippedIndices = maximum(indices, zerosLike(indices)); + const gathered = gather(x, zeroClippedIndices); + let isPositive = greaterEqual(indices, scalar(0, "int32")); + const numIters = gathered.rank - isPositive.rank; + for (let i = 0; i < numIters; ++i) { + isPositive = expandDims(isPositive, i + 1); + } + isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool")); + const zeroSlice = zerosLike(gathered); + return where(isPositive, gathered, zeroSlice); +} +var zerosLikeGradConfig = { + kernelName: ZerosLike, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; +var gradConfigs = [ + absGradConfig, + acosGradConfig, + acoshGradConfig, + addGradConfig, + addNGradConfig, + argMaxGradConfig, + argMinGradConfig, + asinGradConfig, + asinhGradConfig, + atan2GradConfig, + atanGradConfig, + atanhGradConfig, + avgPool3DGradConfig, + avgPoolGradConfig, + batchMatMulGradConfig, + batchToSpaceNDGradConfig, + broadcastToGradConfig, + castGradConfig, + ceilGradConfig, + clipByValueGradConfig, + complexAbsGradConfig, + concatGradConfig, + conv2DBackpropInputGradConfig, + conv2DGradConfig, + conv3DGradConfig, + cosGradConfig, + coshGradConfig, + cumsumGradConfig, + depthwiseConv2dNativeGradConfig, + dilation2dGradConfig, + divGradConfig, + eluGradConfig, + erfGradConfig, + expGradConfig, + expandDimsGradConfig, + expm1GradConfig, + floorDivGradConfig, + floorGradConfig, + fusedBatchNormGradConfig, + gatherGradConfig, + greaterEqualGradConfig, + identityGradConfig, + isFiniteGradConfig, + isInfGradConfig, + isNanGradConfig, + leakyReluGradConfig, + log1pGradConfig, + logGradConfig, + logSoftmaxGradConfig, + lrnGradConfig, + maxGradConfig, + maxGradConfig, + maximumGradConfig, + maxPool3DGradConfig, + maxPoolGradConfig, + meanGradConfig, + minGradConfig, + minimumGradConfig, + mirrorPadGradConfig, + modGradConfig, + multiplyGradConfig, + negGradConfig, + oneHotGradConfig, + onesLikeGradConfig, + packGradConfig, + padV2GradConfig, + padV2GradConfig, + powGradConfig, + preluGradConfig, + prodGradConfig, + reciprocalGradConfig, + relu6GradConfig, + reluGradConfig, + reshapeGradConfig, + resizeBilinearGradConfig, + resizeNearestNeighborGradConfig, + reverseGradConfig, + roundGradConfig, + rsqrtGradConfig, + selectGradConfig, + seluGradConfig, + sigmoidGradConfig, + signGradConfig, + sinGradConfig, + sinhGradConfig, + sliceGradConfig, + softmaxGradConfig, + softplusGradConfig, + spaceToBatchNDGradConfig, + spaceToBatchNDGradConfig, + splitVGradConfig, + splitVGradConfig, + sqrtGradConfig, + squaredDifferenceGradConfig, + squareGradConfig, + stepGradConfig, + subGradConfig, + sumGradConfig, + tanGradConfig, + tanhGradConfig, + tileGradConfig, + transposeGradConfig, + unpackGradConfig, + unsortedSegmentSumGradConfig, + zerosLikeGradConfig +]; +for (const gradientConfig of gradConfigs) { + registerGradient(gradientConfig); +} +getGlobalTensorClass().prototype.abs = function() { + this.throwIfDisposed(); + return abs(this); +}; +getGlobalTensorClass().prototype.acos = function() { + this.throwIfDisposed(); + return acos(this); +}; +getGlobalTensorClass().prototype.acosh = function() { + this.throwIfDisposed(); + return acosh(this); +}; +getGlobalTensorClass().prototype.add = function(b) { + this.throwIfDisposed(); + return add2(this, b); +}; +getGlobalTensorClass().prototype.all = function(axis, keepDims) { + this.throwIfDisposed(); + return all(this, axis, keepDims); +}; +getGlobalTensorClass().prototype.any = function(axis, keepDims) { + this.throwIfDisposed(); + return any(this, axis, keepDims); +}; +getGlobalTensorClass().prototype.argMax = function(axis) { + this.throwIfDisposed(); + return argMax(this, axis); +}; +getGlobalTensorClass().prototype.argMin = function(axis) { + this.throwIfDisposed(); + return argMin(this, axis); +}; +getGlobalTensorClass().prototype.asScalar = function() { + this.throwIfDisposed(); + assert(this.size === 1, () => "The array must have only 1 element."); + return reshape(this, []); +}; +getGlobalTensorClass().prototype.asType = function(dtype) { + this.throwIfDisposed(); + return cast(this, dtype); +}; +getGlobalTensorClass().prototype.as1D = function() { + this.throwIfDisposed(); + return reshape(this, [this.size]); +}; +getGlobalTensorClass().prototype.as2D = function(rows, columns) { + this.throwIfDisposed(); + return reshape(this, [rows, columns]); +}; +getGlobalTensorClass().prototype.as3D = function(rows, columns, depth) { + this.throwIfDisposed(); + return reshape(this, [rows, columns, depth]); +}; +getGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) { + this.throwIfDisposed(); + return reshape(this, [rows, columns, depth, depth2]); +}; +getGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) { + this.throwIfDisposed(); + return reshape(this, [rows, columns, depth, depth2, depth3]); +}; +getGlobalTensorClass().prototype.asin = function() { + this.throwIfDisposed(); + return asin(this); +}; +getGlobalTensorClass().prototype.asinh = function() { + this.throwIfDisposed(); + return asinh(this); +}; +getGlobalTensorClass().prototype.atan = function() { + this.throwIfDisposed(); + return atan(this); +}; +getGlobalTensorClass().prototype.atan2 = function(b) { + this.throwIfDisposed(); + return atan2(this, b); +}; +getGlobalTensorClass().prototype.atanh = function() { + this.throwIfDisposed(); + return atanh(this); +}; +getGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad3, dimRoundingMode) { + this.throwIfDisposed(); + return avgPool(this, filterSize, strides, pad3, dimRoundingMode); +}; +getGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) { + this.throwIfDisposed(); + return batchToSpaceND(this, blockShape, crops); +}; +getGlobalTensorClass().prototype.batchNorm = function(mean4, variance, offset, scale22, varianceEpsilon) { + this.throwIfDisposed(); + return batchNorm(this, mean4, variance, offset, scale22, varianceEpsilon); +}; +getGlobalTensorClass().prototype.broadcastTo = function(shape) { + this.throwIfDisposed(); + return broadcastTo(this, shape); +}; +getGlobalTensorClass().prototype.cast = function(dtype) { + this.throwIfDisposed(); + return cast(this, dtype); +}; +getGlobalTensorClass().prototype.ceil = function() { + this.throwIfDisposed(); + return ceil(this); +}; +getGlobalTensorClass().prototype.clipByValue = function(min6, max6) { + this.throwIfDisposed(); + return clipByValue(this, min6, max6); +}; +getGlobalTensorClass().prototype.concat = function(x, axis) { + this.throwIfDisposed(); + if (x instanceof Tensor) { + x = [x]; + } + return concat([this, ...x], axis); +}; +getGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFormat, dilation, dimRoundingMode) { + this.throwIfDisposed(); + return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode); +}; +getGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides, pad3, dimRoundingMode) { + this.throwIfDisposed(); + return conv2dTranspose(this, filter, outputShape, strides, pad3, dimRoundingMode); +}; +getGlobalTensorClass().prototype.conv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) { + this.throwIfDisposed(); + return conv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); +}; +getGlobalTensorClass().prototype.cos = function() { + this.throwIfDisposed(); + return cos(this); +}; +getGlobalTensorClass().prototype.cosh = function() { + this.throwIfDisposed(); + return cosh(this); +}; +getGlobalTensorClass().prototype.cumprod = function(axis, exclusive, reverse5) { + this.throwIfDisposed(); + return cumprod(this, axis, exclusive, reverse5); +}; +getGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) { + this.throwIfDisposed(); + return cumsum(this, axis, exclusive, reverse5); +}; +getGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) { + this.throwIfDisposed(); + return depthToSpace(this, blockSize, dataFormat); +}; +getGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) { + this.throwIfDisposed(); + return depthwiseConv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); +}; +getGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad3, dilations, dataFormat) { + this.throwIfDisposed(); + return dilation2d(this, filter, strides, pad3, dilations, dataFormat); +}; +getGlobalTensorClass().prototype.divNoNan = function(b) { + this.throwIfDisposed(); + return divNoNan(this, b); +}; +getGlobalTensorClass().prototype.div = function(b) { + this.throwIfDisposed(); + return div(this, b); +}; +getGlobalTensorClass().prototype.dot = function(b) { + this.throwIfDisposed(); + return dot(this, b); +}; +getGlobalTensorClass().prototype.elu = function() { + this.throwIfDisposed(); + return elu(this); +}; +getGlobalTensorClass().prototype.equal = function(b) { + this.throwIfDisposed(); + return equal(this, b); +}; +getGlobalTensorClass().prototype.erf = function() { + this.throwIfDisposed(); + return erf(this); +}; +getGlobalTensorClass().prototype.euclideanNorm = function(axis, keepDims) { + this.throwIfDisposed(); + return euclideanNorm(this, axis, keepDims); +}; +getGlobalTensorClass().prototype.exp = function() { + this.throwIfDisposed(); + return exp(this); +}; +getGlobalTensorClass().prototype.expandDims = function(axis) { + this.throwIfDisposed(); + return expandDims(this, axis); +}; +getGlobalTensorClass().prototype.expm1 = function() { + this.throwIfDisposed(); + return expm1(this); +}; +getGlobalTensorClass().prototype.fft = function() { + this.throwIfDisposed(); + return fft(this); +}; +getGlobalTensorClass().prototype.flatten = function() { + this.throwIfDisposed(); + return reshape(this, [this.size]); +}; +getGlobalTensorClass().prototype.floor = function() { + this.throwIfDisposed(); + return floor(this); +}; +getGlobalTensorClass().prototype.floorDiv = function(b) { + this.throwIfDisposed(); + return floorDiv(this, b); +}; +getGlobalTensorClass().prototype.gather = function(indices, axis, batchDims) { + this.throwIfDisposed(); + return gather(this, indices, axis, batchDims); +}; +getGlobalTensorClass().prototype.greaterEqual = function(b) { + this.throwIfDisposed(); + return greaterEqual(this, b); +}; +getGlobalTensorClass().prototype.greater = function(b) { + this.throwIfDisposed(); + return greater(this, b); +}; +getGlobalTensorClass().prototype.ifft = function() { + this.throwIfDisposed(); + return ifft(this); +}; +getGlobalTensorClass().prototype.irfft = function() { + this.throwIfDisposed(); + return irfft(this); +}; +getGlobalTensorClass().prototype.isFinite = function() { + this.throwIfDisposed(); + return isFinite2(this); +}; +getGlobalTensorClass().prototype.isInf = function() { + this.throwIfDisposed(); + return isInf(this); +}; +getGlobalTensorClass().prototype.isNaN = function() { + this.throwIfDisposed(); + return isNaN2(this); +}; +getGlobalTensorClass().prototype.leakyRelu = function(alpha) { + this.throwIfDisposed(); + return leakyRelu(this, alpha); +}; +getGlobalTensorClass().prototype.lessEqual = function(b) { + this.throwIfDisposed(); + return lessEqual(this, b); +}; +getGlobalTensorClass().prototype.less = function(b) { + this.throwIfDisposed(); + return less(this, b); +}; +getGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha, beta) { + this.throwIfDisposed(); + return localResponseNormalization(this, depthRadius, bias, alpha, beta); +}; +getGlobalTensorClass().prototype.logSigmoid = function() { + this.throwIfDisposed(); + return logSigmoid(this); +}; +getGlobalTensorClass().prototype.logSoftmax = function(axis) { + this.throwIfDisposed(); + return logSoftmax(this, axis); +}; +getGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) { + this.throwIfDisposed(); + return logSumExp(this, axis, keepDims); +}; +getGlobalTensorClass().prototype.log = function() { + this.throwIfDisposed(); + return log2(this); +}; +getGlobalTensorClass().prototype.log1p = function() { + this.throwIfDisposed(); + return log1p(this); +}; +getGlobalTensorClass().prototype.logicalAnd = function(b) { + this.throwIfDisposed(); + return logicalAnd(this, b); +}; +getGlobalTensorClass().prototype.logicalNot = function() { + this.throwIfDisposed(); + return logicalNot(this); +}; +getGlobalTensorClass().prototype.logicalOr = function(b) { + this.throwIfDisposed(); + return logicalOr(this, b); +}; +getGlobalTensorClass().prototype.logicalXor = function(b) { + this.throwIfDisposed(); + return logicalXor(this, b); +}; +getGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) { + this.throwIfDisposed(); + return matMul(this, b, transposeA, transposeB); +}; +getGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad3, dimRoundingMode) { + this.throwIfDisposed(); + return maxPool(this, filterSize, strides, pad3, dimRoundingMode); +}; +getGlobalTensorClass().prototype.max = function(axis, keepDims) { + this.throwIfDisposed(); + return max(this, axis, keepDims); +}; +getGlobalTensorClass().prototype.maximum = function(b) { + this.throwIfDisposed(); + return maximum(this, b); +}; +getGlobalTensorClass().prototype.mean = function(axis, keepDims) { + this.throwIfDisposed(); + return mean(this, axis, keepDims); +}; +getGlobalTensorClass().prototype.min = function(axis, keepDims) { + this.throwIfDisposed(); + return min(this, axis, keepDims); +}; +getGlobalTensorClass().prototype.minimum = function(b) { + this.throwIfDisposed(); + return minimum(this, b); +}; +getGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) { + this.throwIfDisposed(); + return mirrorPad(this, paddings, mode); +}; +getGlobalTensorClass().prototype.mod = function(b) { + this.throwIfDisposed(); + return mod(this, b); +}; +getGlobalTensorClass().prototype.mul = function(b) { + this.throwIfDisposed(); + return mul(this, b); +}; +getGlobalTensorClass().prototype.neg = function() { + this.throwIfDisposed(); + return neg(this); +}; +getGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) { + this.throwIfDisposed(); + return norm(this, ord, axis, keepDims); +}; +getGlobalTensorClass().prototype.notEqual = function(b) { + this.throwIfDisposed(); + return notEqual(this, b); +}; +getGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) { + this.throwIfDisposed(); + return oneHot(this, depth, onValue, offValue); +}; +getGlobalTensorClass().prototype.onesLike = function() { + this.throwIfDisposed(); + return onesLike(this); +}; +getGlobalTensorClass().prototype.pad = function(paddings, constantValue) { + this.throwIfDisposed(); + return pad(this, paddings, constantValue); +}; +getGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode) { + this.throwIfDisposed(); + return pool(this, windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode); +}; +getGlobalTensorClass().prototype.pow = function(exp4) { + this.throwIfDisposed(); + return pow(this, exp4); +}; +getGlobalTensorClass().prototype.prelu = function(alpha) { + this.throwIfDisposed(); + return prelu(this, alpha); +}; +getGlobalTensorClass().prototype.prod = function(axis, keepDims) { + this.throwIfDisposed(); + return prod(this, axis, keepDims); +}; +getGlobalTensorClass().prototype.reciprocal = function() { + this.throwIfDisposed(); + return reciprocal(this); +}; +getGlobalTensorClass().prototype.relu = function() { + this.throwIfDisposed(); + return relu(this); +}; +getGlobalTensorClass().prototype.relu6 = function() { + this.throwIfDisposed(); + return relu6(this); +}; +getGlobalTensorClass().prototype.reshapeAs = function(x) { + this.throwIfDisposed(); + return reshape(this, x.shape); +}; +getGlobalTensorClass().prototype.reshape = function(shape) { + this.throwIfDisposed(); + return reshape(this, shape); +}; +getGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) { + this.throwIfDisposed(); + return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters); +}; +getGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) { + this.throwIfDisposed(); + return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters); +}; +getGlobalTensorClass().prototype.reverse = function(axis) { + this.throwIfDisposed(); + return reverse(this, axis); +}; +getGlobalTensorClass().prototype.rfft = function() { + this.throwIfDisposed(); + return rfft(this); +}; +getGlobalTensorClass().prototype.round = function() { + this.throwIfDisposed(); + return round2(this); +}; +getGlobalTensorClass().prototype.rsqrt = function() { + this.throwIfDisposed(); + return rsqrt(this); +}; +getGlobalTensorClass().prototype.selu = function() { + this.throwIfDisposed(); + return selu(this); +}; +getGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat) { + this.throwIfDisposed(); + return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat); +}; +getGlobalTensorClass().prototype.sigmoid = function() { + this.throwIfDisposed(); + return sigmoid(this); +}; +getGlobalTensorClass().prototype.sign = function() { + this.throwIfDisposed(); + return sign(this); +}; +getGlobalTensorClass().prototype.sin = function() { + this.throwIfDisposed(); + return sin(this); +}; +getGlobalTensorClass().prototype.sinh = function() { + this.throwIfDisposed(); + return sinh(this); +}; +getGlobalTensorClass().prototype.slice = function(begin, size) { + this.throwIfDisposed(); + return slice(this, begin, size); +}; +getGlobalTensorClass().prototype.softmax = function(dim) { + this.throwIfDisposed(); + return softmax(this, dim); +}; +getGlobalTensorClass().prototype.softplus = function() { + this.throwIfDisposed(); + return softplus(this); +}; +getGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) { + this.throwIfDisposed(); + return spaceToBatchND(this, blockShape, paddings); +}; +getGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) { + this.throwIfDisposed(); + return split(this, numOrSizeSplits, axis); +}; +getGlobalTensorClass().prototype.sqrt = function() { + this.throwIfDisposed(); + return sqrt(this); +}; +getGlobalTensorClass().prototype.square = function() { + this.throwIfDisposed(); + return square(this); +}; +getGlobalTensorClass().prototype.squaredDifference = function(b) { + this.throwIfDisposed(); + return squaredDifference(this, b); +}; +getGlobalTensorClass().prototype.squeeze = function(axis) { + this.throwIfDisposed(); + return squeeze(this, axis); +}; +getGlobalTensorClass().prototype.stack = function(x, axis) { + this.throwIfDisposed(); + const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x]; + return stack(tensorsToBeStacked, axis); +}; +getGlobalTensorClass().prototype.step = function(alpha) { + this.throwIfDisposed(); + return step(this, alpha); +}; +getGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) { + this.throwIfDisposed(); + return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); +}; +getGlobalTensorClass().prototype.sub = function(b) { + this.throwIfDisposed(); + return sub(this, b); +}; +getGlobalTensorClass().prototype.sum = function(axis, keepDims) { + this.throwIfDisposed(); + return sum2(this, axis, keepDims); +}; +getGlobalTensorClass().prototype.tan = function() { + this.throwIfDisposed(); + return tan(this); +}; +getGlobalTensorClass().prototype.tanh = function() { + this.throwIfDisposed(); + return tanh2(this); +}; +getGlobalTensorClass().prototype.tile = function(reps) { + this.throwIfDisposed(); + return tile(this, reps); +}; +getGlobalTensorClass().prototype.toBool = function() { + this.throwIfDisposed(); + return cast(this, "bool"); +}; +getGlobalTensorClass().prototype.toFloat = function() { + this.throwIfDisposed(); + return cast(this, "float32"); +}; +getGlobalTensorClass().prototype.toInt = function() { + this.throwIfDisposed(); + return cast(this, "int32"); +}; +getGlobalTensorClass().prototype.topk = function(k, sorted) { + this.throwIfDisposed(); + return topk(this, k, sorted); +}; +getGlobalTensorClass().prototype.transpose = function(perm) { + this.throwIfDisposed(); + return transpose(this, perm); +}; +getGlobalTensorClass().prototype.unique = function(axis) { + this.throwIfDisposed(); + return unique(this, axis); +}; +getGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) { + this.throwIfDisposed(); + return unsortedSegmentSum(this, segmentIds, numSegments); +}; +getGlobalTensorClass().prototype.unstack = function(axis) { + this.throwIfDisposed(); + return unstack(this, axis); +}; +getGlobalTensorClass().prototype.where = function(condition, x) { + this.throwIfDisposed(); + return where(condition, this, x); +}; +getGlobalTensorClass().prototype.zerosLike = function() { + this.throwIfDisposed(); + return zerosLike(this); +}; +var AttributeError = class _AttributeError extends Error { + constructor(message) { + super(message); + Object.setPrototypeOf(this, _AttributeError.prototype); + } +}; +var RuntimeError = class _RuntimeError extends Error { + constructor(message) { + super(message); + Object.setPrototypeOf(this, _RuntimeError.prototype); + } +}; +var ValueError = class _ValueError extends Error { + constructor(message) { + super(message); + Object.setPrototypeOf(this, _ValueError.prototype); + } +}; +var NotImplementedError = class _NotImplementedError extends Error { + constructor(message) { + super(message); + Object.setPrototypeOf(this, _NotImplementedError.prototype); + } +}; +var AssertionError = class _AssertionError extends Error { + constructor(message) { + super(message); + Object.setPrototypeOf(this, _AssertionError.prototype); + } +}; +var LruCache = class { + constructor(maxEntries) { + this.maxEntries = maxEntries || 100; + this.cache = /* @__PURE__ */ new Map(); + } + /** + * Get the entry for the key and mark it as used recently. + */ + get(key) { + let entry; + if (this.cache.has(key)) { + entry = this.cache.get(key); + this.cache.delete(key); + this.cache.set(key, entry); + } + return entry; + } + /** + * Put the entry into the cache. If the key already existed, mark the key as + * used recently. + */ + put(key, value) { + if (this.cache.has(key)) { + this.cache.delete(key); + } else if (this.cache.size >= this.maxEntries) { + const keyToDelete = this.cache.keys().next().value; + this.cache.delete(keyToDelete); + } + this.cache.set(key, value); + } + /** + * Get the MaxEntries of the cache. + */ + getMaxEntries() { + return this.maxEntries; + } + /** + * Set the MaxEntries of the cache. If the maxEntries is decreased, reduce + * entries in the cache. + */ + setMaxEntries(maxEntries) { + if (maxEntries < 0) { + throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`); + } + if (this.maxEntries > maxEntries) { + for (let i = 0; i < this.maxEntries - maxEntries; i++) { + const keyToDelete = this.cache.keys().next().value; + this.cache.delete(keyToDelete); + } + } + this.maxEntries = maxEntries; + } +}; +function pyListRepeat(value, numValues) { + if (Array.isArray(value)) { + let newArray = []; + for (let i = 0; i < numValues; i++) { + newArray = newArray.concat(value); + } + return newArray; + } else { + const newArray = new Array(numValues); + newArray.fill(value); + return newArray; + } +} +function assert2(val, message) { + if (!val) { + throw new AssertionError(message); + } +} +function count(array2, refernce) { + let counter = 0; + for (const item of array2) { + if (item === refernce) { + counter++; + } + } + return counter; +} +function singletonOrArray(xs) { + if (xs.length === 1) { + return xs[0]; + } + return xs; +} +function toList(x) { + if (Array.isArray(x)) { + return x; + } + return [x]; +} +function toSnakeCase(name) { + const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, "$1_$2"); + const insecure = intermediate.replace(/([a-z])([A-Z])/g, "$1_$2").toLowerCase(); + if (insecure[0] !== "_") { + return insecure; + } + return "private" + insecure; +} +function toCamelCase(identifier) { + if (identifier.length <= 1) { + return identifier; + } + if (identifier.indexOf("_") === -1) { + return identifier; + } + return identifier.replace(/[_]+(\w|$)/g, (m, p1) => p1.toUpperCase()); +} +var _GLOBAL_CUSTOM_OBJECTS = {}; +function serializeKerasObject(instance) { + if (instance === null || instance === void 0) { + return null; + } + const dict = {}; + dict["className"] = instance.getClassName(); + dict["config"] = instance.getConfig(); + return dict; +} +function convertNDArrayScalarsInConfig(config) { + if (config == null || typeof config !== "object") { + return; + } else if (Array.isArray(config)) { + config.forEach((configItem) => convertNDArrayScalarsInConfig(configItem)); + } else { + const fields = Object.keys(config); + for (const field of fields) { + const value = config[field]; + if (value != null && typeof value === "object") { + if (!Array.isArray(value) && value["type"] === "ndarray" && typeof value["value"] === "number") { + config[field] = value["value"]; + } else { + convertNDArrayScalarsInConfig(value); + } + } + } + } +} +function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = "object", fastWeightInit = false) { + if (typeof identifier === "string") { + const functionName = identifier; + let fn; + if (functionName in customObjects) { + fn = customObjects[functionName]; + } else if (functionName in _GLOBAL_CUSTOM_OBJECTS) { + fn = _GLOBAL_CUSTOM_OBJECTS[functionName]; + } else { + fn = moduleObjects[functionName]; + if (fn == null) { + throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons: +1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. +2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`); + } + } + return fn; + } else { + const config = identifier; + if (config["className"] == null || config["config"] == null) { + throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}. +'className' and 'config' must set.`); + } + const className = config["className"]; + let cls, fromConfig; + if (className in customObjects) { + [cls, fromConfig] = customObjects[className]; + } else if (className in _GLOBAL_CUSTOM_OBJECTS) { + [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS["className"]; + } else if (className in moduleObjects) { + [cls, fromConfig] = moduleObjects[className]; + } + if (cls == null) { + throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons: +1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. +2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`); + } + if (fromConfig != null) { + const customObjectsCombined = {}; + for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) { + customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key]; + } + for (const key of Object.keys(customObjects)) { + customObjectsCombined[key] = customObjects[key]; + } + const nestedConfig = config["config"]; + nestedConfig["customObjects"] = customObjectsCombined; + const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS); + for (const key of Object.keys(customObjects)) { + _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key]; + } + convertNDArrayScalarsInConfig(config["config"]); + const returnObj = fromConfig(cls, config["config"], customObjects, fastWeightInit); + _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects); + return returnObj; + } else { + const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS); + for (const key of Object.keys(customObjects)) { + _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key]; + } + const returnObj = new cls(config["config"]); + _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects); + return returnObj; + } + } +} +function numberCompare(a, b) { + return a < b ? -1 : a > b ? 1 : 0; +} +function reverseNumberCompare(a, b) { + return -1 * numberCompare(a, b); +} +function unique2(xs) { + if (xs == null) { + return xs; + } + const out = []; + for (const x of xs) { + if (out.indexOf(x) === -1) { + out.push(x); + } + } + return out; +} +function isObjectEmpty(obj) { + if (obj == null) { + throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`); + } + for (const key in obj) { + if (obj.hasOwnProperty(key)) { + return false; + } + } + return true; +} +function checkStringTypeUnionValue(values, label, value) { + if (value == null) { + return; + } + if (values.indexOf(value) < 0) { + throw new ValueError(`${value} is not a valid ${label}. Valid values are ${values} or null/undefined.`); + } +} +function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) { + assert2(minLength >= 0); + assert2(maxLength >= minLength); + return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e) => typeof e === expectedType); +} +function assertPositiveInteger(value, name) { + if (Array.isArray(value)) { + util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`); + value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`)); + } else { + util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`); + } +} +function formatAsFriendlyString(value) { + if (value === null) { + return "null"; + } else if (Array.isArray(value)) { + return "[" + value.map((v) => formatAsFriendlyString(v)).join(",") + "]"; + } else if (typeof value === "string") { + return `"${value}"`; + } else { + return `${value}`; + } +} +function debounce(f, waitMs, nowFunc) { + let lastTime = nowFunc != null ? nowFunc() : util_exports.now(); + let lastResult; + const f2 = (...args) => { + const now2 = nowFunc != null ? nowFunc() : util_exports.now(); + if (now2 - lastTime < waitMs) { + return lastResult; + } + lastTime = now2; + lastResult = f(...args); + return lastResult; + }; + return f2; +} +function mapActivationToFusedKernel(activationName) { + if (activationName === "relu") { + return "relu"; + } + if (activationName === "linear") { + return "linear"; + } + if (activationName === "elu") { + return "elu"; + } + return null; +} +var _nextUniqueTensorId = 0; +function getNextUniqueTensorId() { + return _nextUniqueTensorId++; +} +var _uidPrefixes = {}; +function getUid(prefix = "") { + if (!(prefix in _uidPrefixes)) { + _uidPrefixes[prefix] = 0; + } + _uidPrefixes[prefix] += 1; + return prefix + _uidPrefixes[prefix].toString(); +} +var VALID_DATA_FORMAT_VALUES = ["channelsFirst", "channelsLast"]; +var VALID_INTERPOLATION_FORMAT_VALUES = ["nearest", "bilinear"]; +var VALID_PADDING_MODE_VALUES = ["valid", "same", "causal"]; +var VALID_POOL_MODE_VALUES = ["max", "avg"]; +var VALID_BIDIRECTIONAL_MERGE_MODES = ["sum", "mul", "concat", "ave"]; +var nameMap = /* @__PURE__ */ new Map(); +function checkDataFormat(value) { + checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, "DataFormat", value); +} +function checkInterpolationFormat(value) { + checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES, "InterpolationFormat", value); +} +function checkPaddingMode(value) { + checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, "PaddingMode", value); +} +function checkPoolMode(value) { + checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, "PoolMode", value); +} +var _nameScopeStack = []; +var _nameScopeDivider = "/"; +function nameScope(name, fn) { + _nameScopeStack.push(name); + try { + const val = fn(); + _nameScopeStack.pop(); + return val; + } catch (e) { + _nameScopeStack.pop(); + throw e; + } +} +function currentNameScopePrefix() { + if (_nameScopeStack.length === 0) { + return ""; + } else { + return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider; + } +} +function getScopedTensorName(tensorName) { + if (!isValidTensorName(tensorName)) { + throw new Error("Not a valid tensor name: '" + tensorName + "'"); + } + return currentNameScopePrefix() + tensorName; +} +function getUniqueTensorName(scopedName) { + if (!isValidTensorName(scopedName)) { + throw new Error("Not a valid tensor name: '" + scopedName + "'"); + } + if (!nameMap.has(scopedName)) { + nameMap.set(scopedName, 0); + } + const index = nameMap.get(scopedName); + nameMap.set(scopedName, nameMap.get(scopedName) + 1); + if (index > 0) { + const result = `${scopedName}_${index}`; + nameMap.set(result, 1); + return result; + } else { + return scopedName; + } +} +var tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/); +function isValidTensorName(name) { + return !!name.match(tensorNameRegex); +} +function isInteger(x) { + return x === parseInt(x.toString(), 10); +} +function arrayProd(array2, begin, end) { + if (begin == null) { + begin = 0; + } + if (end == null) { + end = array2.length; + } + let prod5 = 1; + for (let i = begin; i < end; ++i) { + prod5 *= array2[i]; + } + return prod5; +} +function min2(array2) { + if (array2.length === 0) { + return Number.NaN; + } + let min6 = Number.POSITIVE_INFINITY; + for (let i = 0; i < array2.length; i++) { + const value = array2[i]; + if (value < min6) { + min6 = value; + } + } + return min6; +} +function max2(array2) { + if (array2.length === 0) { + return Number.NaN; + } + let max6 = Number.NEGATIVE_INFINITY; + for (let i = 0; i < array2.length; i++) { + const value = array2[i]; + if (value > max6) { + max6 = value; + } + } + return max6; +} +function range2(begin, end) { + if (end < begin) { + throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`); + } + const out = []; + for (let i = begin; i < end; ++i) { + out.push(i); + } + return out; +} +var _epsilon; +function epsilon() { + if (_epsilon == null) { + _epsilon = backend().epsilon(); + } + return _epsilon; +} +function imageDataFormat() { + return "channelsLast"; +} +function cast2(x, dtype) { + return cast(x, dtype); +} +function expandDims2(x, axis = -1) { + const outShape = x.shape.slice(); + if (axis < 0) { + axis = outShape.length + axis + 1; + } + outShape.splice(axis, 0, 1); + return reshape(x, outShape); +} +function repeat(x, n) { + return tidy(() => { + if (x.shape.length !== 2) { + throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`); + } + const y = expandDims2(x, 1); + return tile2(y, [1, n, 1]); + }); +} +function flatten2(x) { + const newShape = [arrayProd(x.shape)]; + return reshape(x, newShape); +} +function batchFlatten(x) { + if (x.rank <= 1) { + throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`); + } + const newShape = [x.shape[0], arrayProd(x.shape, 1)]; + return reshape(x, newShape); +} +function sliceAlongFirstAxis(array2, start, size) { + return tidy(() => { + switch (array2.rank) { + case 1: + return slice1d(array2, start, size); + case 2: + return slice2d(array2, [start, 0], [size, array2.shape[1]]); + case 3: + return slice3d(array2, [start, 0, 0], [size, array2.shape[1], array2.shape[2]]); + case 4: + return slice4d(array2, [start, 0, 0, 0], [size, array2.shape[1], array2.shape[2], array2.shape[3]]); + case 5: + return slice(array2, [start, 0, 0, 0, 0], [ + size, + array2.shape[1], + array2.shape[2], + array2.shape[3], + array2.shape[4] + ]); + case 6: + return slice(array2, [start, 0, 0, 0, 0, 0], [ + size, + array2.shape[1], + array2.shape[2], + array2.shape[3], + array2.shape[4], + array2.shape[5] + ]); + default: + throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`); + } + }); +} +function sliceAlongLastAxis(array2, start, size) { + return tidy(() => { + switch (array2.rank) { + case 1: + return slice1d(array2, start, size); + case 2: + return slice2d(array2, [0, start], [array2.shape[0], size]); + case 3: + return slice3d(array2, [0, 0, start], [array2.shape[0], array2.shape[1], size]); + case 4: + return slice4d(array2, [0, 0, 0, start], [array2.shape[0], array2.shape[1], array2.shape[2], size]); + default: + throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`); + } + }); +} +function sliceAlongAxis(array2, start, size, axis) { + return tidy(() => { + switch (array2.rank) { + case 1: + return slice1d(array2, start, size); + case 2: + switch (axis) { + case 1: + return sliceAlongFirstAxis(array2, start, size); + case 2: + return sliceAlongLastAxis(array2, start, size); + default: + throw new ValueError(`The axis is not within the rank of the tensor ${axis}`); + } + case 3: + switch (axis) { + case 1: + return sliceAlongFirstAxis(array2, start, size); + case 2: + return slice3d(array2, [0, start, 0], [array2.shape[0], size, array2.shape[2]]); + case 3: + return sliceAlongLastAxis(array2, start, size); + default: + throw new ValueError(`The axis is not within the rank of the tensor ${axis}`); + } + case 4: + switch (axis) { + case 1: + return sliceAlongFirstAxis(array2, start, size); + case 2: + return slice4d(array2, [0, start, 0, 0], [array2.shape[0], size, array2.shape[2], array2.shape[3]]); + case 3: + return slice4d(array2, [0, 0, start, 0], [array2.shape[0], array2.shape[1], size, array2.shape[3]]); + case 4: + return sliceAlongLastAxis(array2, start, size); + default: + throw new ValueError(`The axis is not within the rank of the tensor ${axis}`); + } + default: + throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`); + } + }); +} +function concatenate(tensors, axis = -1) { + let rank; + if (axis < 0) { + rank = tensors[0].rank; + if (rank !== 0) { + axis = rank; + } else { + axis = 0; + } + } + if (axis === tensors[0].rank) { + axis = -1; + } + return concat(tensors, axis); +} +function concatAlongFirstAxis(a, b) { + switch (a.rank) { + case 1: + return concat1d([a, b]); + case 2: + return concat2d([a, b], 0); + case 3: + return concat3d([a, b], 0); + case 4: + return concat4d([a, b], 0); + default: + throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`); + } +} +function tile2(x, n) { + if (!Array.isArray(n)) { + n = [n]; + } + if (x.rank !== n.length) { + throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`); + } + return tile(x, n); +} +function randomNormal2(shape, mean4 = 0, stddev = 1, dtype, seed) { + return randomNormal(shape, mean4, stddev, dtype, seed); +} +function dot2(a, b, activation2, bias) { + if (a.rank < 2 || b.rank < 2) { + throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`); + } + if (b.rank >= 3) { + const xLastDim = a.shape.slice(-1)[0]; + const ySecondLastDim = b.shape.slice(-2)[0]; + if (xLastDim !== ySecondLastDim) { + throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and y shape = ${b.shape}`); + } + } + if (a.rank === 2 && b.rank === 2) { + const transposeA = false; + const transposeB = false; + return fused_ops_exports.matMul({ + a, + b, + transposeA, + transposeB, + bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null, + activation: activation2 + }); + } else { + const aFirstDims = a.shape.slice(); + const aLastDim = aFirstDims.pop(); + a = reshape(a, [-1, aLastDim]); + const bShape = b.shape.slice(); + const bLastDim = bShape.pop(); + const ySecondLastDim = bShape.pop(); + const yOtherDims = [...bShape, bLastDim]; + const perm = Array.from({ length: b.rank }, (_, i) => { + if (i === 0) { + return b.rank - 2; + } else if (i <= b.rank - 2) { + return i - 1; + } + return i; + }); + b = reshape(transpose(b, perm), [ySecondLastDim, -1]); + const outputShape = [...aFirstDims, ...yOtherDims]; + const transposeA = false; + const transposeB = false; + return reshape(fused_ops_exports.matMul({ + a, + b, + transposeA, + transposeB, + bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null, + activation: activation2 + }), outputShape); + } +} +function gather2(reference, indices, axis) { + return tidy(() => { + if (Array.isArray(indices)) { + indices = tensor1d(indices, "int32"); + } else { + indices = cast(indices, "int32"); + } + return gather(reference, indices, axis); + }); +} +function square2(x) { + return mul(x, x); +} +function reshapeBias(xRank, bias, dataFormat) { + const biasShape = bias.shape; + if (bias.rank !== 1 && bias.rank !== xRank) { + throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`); + } + if (xRank === 5) { + if (dataFormat === "channelsFirst") { + if (biasShape.length === 1) { + return reshape(bias, [1, biasShape[0], 1, 1, 1]); + } else { + return reshape(bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]); + } + } else if (dataFormat === "channelsLast") { + if (biasShape.length === 1) { + return reshape(bias, [1, 1, 1, 1, biasShape[0]]); + } else { + return reshape(bias, [1].concat(biasShape)); + } + } + } else if (xRank === 4) { + if (dataFormat === "channelsFirst") { + if (biasShape.length === 1) { + return reshape(bias, [1, biasShape[0], 1, 1]); + } else { + return reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]); + } + } else if (dataFormat === "channelsLast") { + if (biasShape.length === 1) { + return reshape(bias, [1, 1, 1, biasShape[0]]); + } else { + return reshape(bias, [1].concat(biasShape)); + } + } + } else if (xRank === 3) { + if (dataFormat === "channelsFirst") { + if (biasShape.length === 1) { + return reshape(bias, [1, biasShape[0], 1]); + } else { + return reshape(bias, [1, biasShape[1], biasShape[0]]); + } + } else if (dataFormat === "channelsLast") { + if (biasShape.length === 1) { + return reshape(bias, [1, 1, biasShape[0]]); + } else { + return reshape(bias, [1].concat(biasShape)); + } + } + } else if (xRank < 3) { + return bias; + } + throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`); +} +function biasAdd(x, bias, dataFormat) { + return tidy(() => { + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + checkDataFormat(dataFormat); + return add2(x, reshapeBias(x.rank, bias, dataFormat)); + }); +} +function elu2(x, alpha = 1) { + if (alpha !== 1) { + throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`); + } + return elu(x); +} +function softsign(x) { + return tidy(() => div(x, add2(abs(x), 1))); +} +function dropout2(x, level, noiseShape, seed) { + return tidy(() => dropout(x, level, noiseShape, seed)); +} +function hardSigmoid(x) { + return tidy(() => { + const y = add2(0.5, mul(0.2, x)); + return clipByValue(y, 0, 1); + }); +} +function inTrainPhase(x, alt, training = false) { + return training ? x() : alt(); +} +var VALID_FAN_MODE_VALUES = ["fanIn", "fanOut", "fanAvg"]; +var VALID_DISTRIBUTION_VALUES = ["normal", "uniform", "truncatedNormal"]; +function checkFanMode(value) { + checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, "FanMode", value); +} +function checkDistribution(value) { + checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, "Distribution", value); +} +var Initializer = class extends serialization_exports.Serializable { + fromConfigUsesCustomObjects() { + return false; + } + getConfig() { + return {}; + } +}; +var Zeros = class extends Initializer { + apply(shape, dtype) { + return zeros(shape, dtype); + } +}; +Zeros.className = "Zeros"; +serialization_exports.registerClass(Zeros); +var Ones = class extends Initializer { + apply(shape, dtype) { + return ones2(shape, dtype); + } +}; +Ones.className = "Ones"; +serialization_exports.registerClass(Ones); +var Constant = class extends Initializer { + constructor(args) { + super(); + if (typeof args !== "object") { + throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`); + } + if (args.value === void 0) { + throw new ValueError(`config must have value set but got ${args}`); + } + this.value = args.value; + } + apply(shape, dtype) { + return tidy(() => mul(scalar(this.value), ones2(shape, dtype))); + } + getConfig() { + return { + value: this.value + }; + } +}; +Constant.className = "Constant"; +serialization_exports.registerClass(Constant); +var RandomUniform = class extends Initializer { + constructor(args) { + super(); + this.DEFAULT_MINVAL = -0.05; + this.DEFAULT_MAXVAL = 0.05; + this.minval = args.minval || this.DEFAULT_MINVAL; + this.maxval = args.maxval || this.DEFAULT_MAXVAL; + this.seed = args.seed; + } + apply(shape, dtype) { + return randomUniform(shape, this.minval, this.maxval, dtype, this.seed); + } + getConfig() { + return { minval: this.minval, maxval: this.maxval, seed: this.seed }; + } +}; +RandomUniform.className = "RandomUniform"; +serialization_exports.registerClass(RandomUniform); +var RandomNormal = class extends Initializer { + constructor(args) { + super(); + this.DEFAULT_MEAN = 0; + this.DEFAULT_STDDEV = 0.05; + this.mean = args.mean || this.DEFAULT_MEAN; + this.stddev = args.stddev || this.DEFAULT_STDDEV; + this.seed = args.seed; + } + apply(shape, dtype) { + dtype = dtype || "float32"; + if (dtype !== "float32" && dtype !== "int32") { + throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`); + } + return randomNormal2(shape, this.mean, this.stddev, dtype, this.seed); + } + getConfig() { + return { mean: this.mean, stddev: this.stddev, seed: this.seed }; + } +}; +RandomNormal.className = "RandomNormal"; +serialization_exports.registerClass(RandomNormal); +var TruncatedNormal = class extends Initializer { + constructor(args) { + super(); + this.DEFAULT_MEAN = 0; + this.DEFAULT_STDDEV = 0.05; + this.mean = args.mean || this.DEFAULT_MEAN; + this.stddev = args.stddev || this.DEFAULT_STDDEV; + this.seed = args.seed; + } + apply(shape, dtype) { + dtype = dtype || "float32"; + if (dtype !== "float32" && dtype !== "int32") { + throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`); + } + return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed); + } + getConfig() { + return { mean: this.mean, stddev: this.stddev, seed: this.seed }; + } +}; +TruncatedNormal.className = "TruncatedNormal"; +serialization_exports.registerClass(TruncatedNormal); +var Identity2 = class extends Initializer { + constructor(args) { + super(); + this.gain = args.gain != null ? args.gain : 1; + } + apply(shape, dtype) { + return tidy(() => { + if (shape.length !== 2 || shape[0] !== shape[1]) { + throw new ValueError("Identity matrix initializer can only be used for 2D square matrices."); + } else { + return mul(this.gain, eye(shape[0])); + } + }); + } + getConfig() { + return { gain: this.gain }; + } +}; +Identity2.className = "Identity"; +serialization_exports.registerClass(Identity2); +function computeFans(shape, dataFormat = "channelsLast") { + let fanIn; + let fanOut; + checkDataFormat(dataFormat); + if (shape.length === 2) { + fanIn = shape[0]; + fanOut = shape[1]; + } else if ([3, 4, 5].indexOf(shape.length) !== -1) { + if (dataFormat === "channelsFirst") { + const receptiveFieldSize = arrayProd(shape, 2); + fanIn = shape[1] * receptiveFieldSize; + fanOut = shape[0] * receptiveFieldSize; + } else if (dataFormat === "channelsLast") { + const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2); + fanIn = shape[shape.length - 2] * receptiveFieldSize; + fanOut = shape[shape.length - 1] * receptiveFieldSize; + } + } else { + const shapeProd = arrayProd(shape); + fanIn = Math.sqrt(shapeProd); + fanOut = Math.sqrt(shapeProd); + } + return [fanIn, fanOut]; +} +var VarianceScaling = class extends Initializer { + /** + * Constructor of VarianceScaling. + * @throws ValueError for invalid value in scale. + */ + constructor(args) { + super(); + if (args.scale < 0) { + throw new ValueError(`scale must be a positive float. Got: ${args.scale}`); + } + this.scale = args.scale == null ? 1 : args.scale; + this.mode = args.mode == null ? "fanIn" : args.mode; + checkFanMode(this.mode); + this.distribution = args.distribution == null ? "normal" : args.distribution; + checkDistribution(this.distribution); + this.seed = args.seed; + } + apply(shape, dtype) { + const fans = computeFans(shape); + const fanIn = fans[0]; + const fanOut = fans[1]; + let scale22 = this.scale; + if (this.mode === "fanIn") { + scale22 /= Math.max(1, fanIn); + } else if (this.mode === "fanOut") { + scale22 /= Math.max(1, fanOut); + } else { + scale22 /= Math.max(1, (fanIn + fanOut) / 2); + } + if (this.distribution === "normal") { + const stddev = Math.sqrt(scale22); + dtype = dtype || "float32"; + if (dtype !== "float32" && dtype !== "int32") { + throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`); + } + return truncatedNormal(shape, 0, stddev, dtype, this.seed); + } else { + const limit = Math.sqrt(3 * scale22); + return randomUniform(shape, -limit, limit, dtype, this.seed); + } + } + getConfig() { + return { + scale: this.scale, + mode: this.mode, + distribution: this.distribution, + seed: this.seed + }; + } +}; +VarianceScaling.className = "VarianceScaling"; +serialization_exports.registerClass(VarianceScaling); +var GlorotUniform = class extends VarianceScaling { + /** + * Constructor of GlorotUniform + * @param scale + * @param mode + * @param distribution + * @param seed + */ + constructor(args) { + super({ + scale: 1, + mode: "fanAvg", + distribution: "uniform", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +GlorotUniform.className = "GlorotUniform"; +serialization_exports.registerClass(GlorotUniform); +var GlorotNormal = class extends VarianceScaling { + /** + * Constructor of GlorotNormal. + * @param scale + * @param mode + * @param distribution + * @param seed + */ + constructor(args) { + super({ + scale: 1, + mode: "fanAvg", + distribution: "normal", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +GlorotNormal.className = "GlorotNormal"; +serialization_exports.registerClass(GlorotNormal); +var HeNormal = class extends VarianceScaling { + constructor(args) { + super({ + scale: 2, + mode: "fanIn", + distribution: "normal", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +HeNormal.className = "HeNormal"; +serialization_exports.registerClass(HeNormal); +var HeUniform = class extends VarianceScaling { + constructor(args) { + super({ + scale: 2, + mode: "fanIn", + distribution: "uniform", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +HeUniform.className = "HeUniform"; +serialization_exports.registerClass(HeUniform); +var LeCunNormal = class extends VarianceScaling { + constructor(args) { + super({ + scale: 1, + mode: "fanIn", + distribution: "normal", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +LeCunNormal.className = "LeCunNormal"; +serialization_exports.registerClass(LeCunNormal); +var LeCunUniform = class extends VarianceScaling { + constructor(args) { + super({ + scale: 1, + mode: "fanIn", + distribution: "uniform", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +LeCunUniform.className = "LeCunUniform"; +serialization_exports.registerClass(LeCunUniform); +var Orthogonal = class extends Initializer { + constructor(args) { + super(); + this.DEFAULT_GAIN = 1; + this.ELEMENTS_WARN_SLOW = 2e3; + this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain; + this.seed = args.seed; + } + apply(shape, dtype) { + return tidy(() => { + if (shape.length < 2) { + throw new NotImplementedError("Shape must be at least 2D."); + } + if (dtype !== "int32" && dtype !== "float32" && dtype !== void 0) { + throw new TypeError(`Unsupported data type ${dtype}.`); + } + dtype = dtype; + const numRows = util_exports.sizeFromShape(shape.slice(0, -1)); + const numCols = shape[shape.length - 1]; + const numElements = numRows * numCols; + if (numElements > this.ELEMENTS_WARN_SLOW) { + console.warn(`Orthogonal initializer is being called on a matrix with more than ${this.ELEMENTS_WARN_SLOW} (${numElements}) elements: Slowness may result.`); + } + const flatShape = [Math.max(numCols, numRows), Math.min(numCols, numRows)]; + const randNormalMat = randomNormal2(flatShape, 0, 1, dtype, this.seed); + const qr2 = linalg.qr(randNormalMat, false); + let qMat = qr2[0]; + const rMat = qr2[1]; + const diag5 = rMat.flatten().stridedSlice([0], [Math.min(numCols, numRows) * Math.min(numCols, numRows)], [Math.min(numCols, numRows) + 1]); + qMat = mul(qMat, diag5.sign()); + if (numRows < numCols) { + qMat = qMat.transpose(); + } + return mul(scalar(this.gain), qMat.reshape(shape)); + }); + } + getConfig() { + return { + gain: this.gain, + seed: this.seed + }; + } +}; +Orthogonal.className = "Orthogonal"; +serialization_exports.registerClass(Orthogonal); +var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = { + "constant": "Constant", + "glorotNormal": "GlorotNormal", + "glorotUniform": "GlorotUniform", + "heNormal": "HeNormal", + "heUniform": "HeUniform", + "identity": "Identity", + "leCunNormal": "LeCunNormal", + "leCunUniform": "LeCunUniform", + "ones": "Ones", + "orthogonal": "Orthogonal", + "randomNormal": "RandomNormal", + "randomUniform": "RandomUniform", + "truncatedNormal": "TruncatedNormal", + "varianceScaling": "VarianceScaling", + "zeros": "Zeros" +}; +function deserializeInitializer(config, customObjects = {}) { + return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "initializer"); +} +function serializeInitializer(initializer) { + return serializeKerasObject(initializer); +} +function getInitializer(identifier) { + if (typeof identifier === "string") { + const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier; + if (className === "GlorotNormal") { + return new GlorotNormal(); + } else if (className === "GlorotUniform") { + return new GlorotUniform(); + } else if (className === "HeNormal") { + return new HeNormal(); + } else if (className === "HeUniform") { + return new HeUniform(); + } else if (className === "LeCunNormal") { + return new LeCunNormal(); + } else if (className === "LeCunUniform") { + return new LeCunUniform(); + } else { + const config = {}; + config["className"] = className; + config["config"] = {}; + return deserializeInitializer(config); + } + } else if (identifier instanceof Initializer) { + return identifier; + } else { + return deserializeInitializer(identifier); + } +} +function isArrayOfShapes(x) { + return Array.isArray(x) && Array.isArray(x[0]); +} +function normalizeShapeList(x) { + if (x.length === 0) { + return []; + } + if (!Array.isArray(x[0])) { + return [x]; + } + return x; +} +function getExactlyOneTensor(xs) { + let x; + if (Array.isArray(xs)) { + if (xs.length !== 1) { + throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`); + } + x = xs[0]; + } else { + x = xs; + } + return x; +} +function getExactlyOneShape(shapes) { + if (Array.isArray(shapes) && Array.isArray(shapes[0])) { + if (shapes.length === 1) { + shapes = shapes; + return shapes[0]; + } else { + throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`); + } + } else { + return shapes; + } +} +function countParamsInWeights(weights) { + let count2 = 0; + for (const weight of weights) { + if (weight.shape.length === 0) { + count2 += 1; + } else { + count2 += weight.shape.reduce((a, b) => a * b); + } + } + return count2; +} +var DEFAULT_VARIABLE_NAME_PREFIX = "Variable"; +var LayerVariable = class { + /** + * Construct Variable from a `tf.Tensor`. + * + * If not explicitly named, the Variable will be given a name with the + * prefix 'Variable'. Variable names are unique. In the case of name + * collision, suffixies '_' will be added to the name. + * + * @param val Initial value of the Variable. + * @param name Name of the variable. If `null` or `undefined` is provided, it + * will default a name with the prefix 'Variable'. + * @param constraint Optional, projection function to be applied to the + * variable after optimize updates + * @throws ValueError if `name` is `null` or `undefined`. + */ + constructor(val, dtype = "float32", name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) { + this.dtype = dtype == null ? "float32" : dtype; + this.shape = val.shape; + this.id = getNextUniqueTensorId(); + name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name; + this.originalName = getScopedTensorName(name); + this.name = getUniqueTensorName(this.originalName); + this.trainable_ = trainable; + this.constraint = constraint; + this.val = variable(val, this.trainable_, this.name, this.dtype); + } + /** + * Get a snapshot of the Variable's value. + * + * The returned value is a snapshot of the Variable's value at the time of + * the invocation. Future mutations in the value of the tensor will only + * be reflected by future calls to this method. + */ + read() { + this.assertNotDisposed(); + return this.val; + } + /** + * Update the value of the Variable. + * + * @param newVal: The new value to update to. Must be consistent with the + * dtype and shape of the Variable. + * @return This Variable. + */ + write(newVal) { + this.assertNotDisposed(); + checkShapesMatch(this.val, newVal); + if (this.val.id !== newVal.id) { + this.val.assign(newVal); + if (this.constraint != null) { + this.val.assign(this.constraint.apply(this.val)); + } + } + return this; + } + /** + * Dispose this LayersVariable instance from memory. + */ + dispose() { + this.assertNotDisposed(); + this.val.dispose(); + } + assertNotDisposed() { + if (this.val.isDisposed) { + throw new Error(`LayersVariable ${this.name} is already disposed.`); + } + } + get trainable() { + return this.trainable_; + } + set trainable(trainable) { + this.trainable_ = trainable; + this.val.trainable = trainable; + } +}; +function checkShapesMatch(x, y) { + if (x.shape.toString() !== y.shape.toString()) { + throw new Error("Shape mismatch: " + JSON.stringify(x.shape) + " vs. " + JSON.stringify(y.shape)); + } +} +function batchGetValue(xs) { + return xs.map((x) => x.read()); +} +function batchSetValue(variablesAndValues) { + variablesAndValues.forEach((variableAndValue) => { + const variable2 = variableAndValue[0]; + variable2.write(variableAndValue[1]); + }); +} +var InputSpec = class { + constructor(args) { + this.dtype = args.dtype; + this.shape = args.shape; + if (args.shape != null) { + this.ndim = args.shape.length; + } else { + this.ndim = args.ndim; + } + this.maxNDim = args.maxNDim; + this.minNDim = args.minNDim; + this.axes = args.axes || {}; + } +}; +var SymbolicTensor = class { + /** + * + * @param dtype + * @param shape + * @param sourceLayer The Layer that produced this symbolic tensor. + * @param inputs The inputs passed to sourceLayer's __call__() method. + * @param nodeIndex + * @param tensorIndex + * @param callArgs The keyword arguments passed to the __call__() method. + * @param name + * @param outputTensorIndex The index of this tensor in the list of outputs + * returned by apply(). + */ + constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) { + this.dtype = dtype; + this.shape = shape; + this.sourceLayer = sourceLayer; + this.inputs = inputs; + this.callArgs = callArgs; + this.outputTensorIndex = outputTensorIndex; + this.id = getNextUniqueTensorId(); + if (name != null) { + this.originalName = getScopedTensorName(name); + this.name = getUniqueTensorName(this.originalName); + } + this.rank = shape.length; + } +}; +var _nextNodeID = 0; +var Node = class { + constructor(args, callArgs) { + this.callArgs = callArgs; + this.id = _nextNodeID++; + this.outboundLayer = args.outboundLayer; + this.inboundLayers = args.inboundLayers; + this.nodeIndices = args.nodeIndices; + this.tensorIndices = args.tensorIndices; + this.inputTensors = args.inputTensors; + this.outputTensors = args.outputTensors; + this.inputMasks = args.inputMasks; + this.outputMasks = args.outputMasks; + this.inputShapes = args.inputShapes; + this.outputShapes = args.outputShapes; + for (const layer of args.inboundLayers) { + if (layer != null) { + layer.outboundNodes.push(this); + } + } + args.outboundLayer.inboundNodes.push(this); + } + getConfig() { + const inboundNames = []; + for (const layer of this.inboundLayers) { + if (layer != null) { + inboundNames.push(layer.name); + } else { + inboundNames.push(null); + } + } + return { + outboundLayer: this.outboundLayer ? this.outboundLayer.name : null, + inboundLayers: inboundNames, + nodeIndices: this.nodeIndices, + tensorIndices: this.tensorIndices + }; + } +}; +var _nextLayerID = 0; +var Layer = class extends serialization_exports.Serializable { + constructor(args = {}) { + super(); + this._callHook = null; + this._addedWeightNames = []; + this._stateful = false; + this.id = _nextLayerID++; + this.activityRegularizer = null; + this.inputSpec = null; + this.supportsMasking = false; + this._trainableWeights = []; + this._nonTrainableWeights = []; + this._losses = []; + this._updates = []; + this._built = false; + this.inboundNodes = []; + this.outboundNodes = []; + let name = args.name; + if (!name) { + const prefix = this.getClassName(); + name = toSnakeCase(prefix) + "_" + getUid(prefix); + } + this.name = name; + this.trainable_ = args.trainable == null ? true : args.trainable; + if (args.inputShape != null || args.batchInputShape != null) { + let batchInputShape; + if (args.batchInputShape != null) { + batchInputShape = args.batchInputShape; + } else if (args.inputShape != null) { + let batchSize = null; + if (args.batchSize != null) { + batchSize = args.batchSize; + } + batchInputShape = [batchSize].concat(args.inputShape); + } + this.batchInputShape = batchInputShape; + let dtype = args.dtype; + if (dtype == null) { + dtype = args.inputDType; + } + if (dtype == null) { + dtype = "float32"; + } + this.dtype = dtype; + } + if (args.weights != null) { + this.initialWeights = args.weights; + } else { + this.initialWeights = null; + } + this._refCount = null; + this.fastWeightInitDuringBuild = false; + } + /** + * Converts a layer and its index to a unique (immutable type) name. + * This function is used internally with `this.containerNodes`. + * @param layer The layer. + * @param nodeIndex The layer's position (e.g. via enumerate) in a list of + * nodes. + * + * @returns The unique name. + */ + static nodeKey(layer, nodeIndex) { + return layer.name + "_ib-" + nodeIndex.toString(); + } + /** + * Returns this.inboundNode at index nodeIndex. + * + * Porting note: This is a replacement for _get_node_attribute_at_index() + * @param nodeIndex + * @param attrName The name of the attribute related to request for this node. + */ + getNodeAtIndex(nodeIndex, attrName) { + if (this.inboundNodes.length === 0) { + throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`); + } + if (this.inboundNodes.length <= nodeIndex) { + throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`); + } + return this.inboundNodes[nodeIndex]; + } + /** + * Retrieves the input tensor(s) of a layer at a given node. + * + * @param nodeIndex Integer, index of the node from which to retrieve the + * attribute. E.g. `nodeIndex=0` will correspond to the first time the layer + * was called. + * + * @return A tensor (or list of tensors if the layer has multiple inputs). + */ + getInputAt(nodeIndex) { + return singletonOrArray(this.getNodeAtIndex(nodeIndex, "input").inputTensors); + } + /** + * Retrieves the output tensor(s) of a layer at a given node. + * + * @param nodeIndex Integer, index of the node from which to retrieve the + * attribute. E.g. `nodeIndex=0` will correspond to the first time the layer + * was called. + * + * @return A tensor (or list of tensors if the layer has multiple outputs). + */ + getOutputAt(nodeIndex) { + return singletonOrArray(this.getNodeAtIndex(nodeIndex, "output").outputTensors); + } + // Properties + /** + * Retrieves the input tensor(s) of a layer. + * + * Only applicable if the layer has exactly one inbound node, + * i.e. if it is connected to one incoming layer. + * + * @return Input tensor or list of input tensors. + * + * @exception AttributeError if the layer is connected to more than one + * incoming layers. + */ + get input() { + if (this.inboundNodes.length > 1) { + throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`); + } else if (this.inboundNodes.length === 0) { + throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`); + } + return singletonOrArray(this.getNodeAtIndex(0, "input").inputTensors); + } + /** + * Retrieves the output tensor(s) of a layer. + * + * Only applicable if the layer has exactly one inbound node, + * i.e. if it is connected to one incoming layer. + * + * @return Output tensor or list of output tensors. + * + * @exception AttributeError if the layer is connected to more than one + * incoming layers. + */ + get output() { + if (this.inboundNodes.length === 0) { + throw new AttributeError(`Layer ${this.name} has no inbound nodes.`); + } + if (this.inboundNodes.length > 1) { + throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`); + } + return singletonOrArray(this.getNodeAtIndex(0, "output").outputTensors); + } + get losses() { + return this._losses; + } + /** + * Retrieves the Layer's current loss values. + * + * Used for regularizers during training. + */ + calculateLosses() { + return this.losses.map((lossFn) => lossFn()); + } + get updates() { + return this._updates; + } + get built() { + return this._built; + } + set built(built) { + this._built = built; + } + get trainable() { + return this.trainable_; + } + set trainable(trainable) { + this._trainableWeights.forEach((w) => w.trainable = trainable); + this.trainable_ = trainable; + } + get trainableWeights() { + if (this.trainable_) { + return this._trainableWeights.filter((w) => w.trainable); + } else { + return []; + } + } + set trainableWeights(weights) { + this._trainableWeights = weights; + } + get nonTrainableWeights() { + if (this.trainable) { + return this._trainableWeights.filter((w) => !w.trainable).concat(this._nonTrainableWeights); + } else { + return this._trainableWeights.concat(this._nonTrainableWeights); + } + } + set nonTrainableWeights(weights) { + this._nonTrainableWeights = weights; + } + /** + * The concatenation of the lists trainableWeights and nonTrainableWeights + * (in this order). + */ + get weights() { + return this.trainableWeights.concat(this.nonTrainableWeights); + } + get stateful() { + return this._stateful; + } + /** + * Reset the states of the layer. + * + * This method of the base Layer class is essentially a no-op. + * Subclasses that are stateful (e.g., stateful RNNs) should override this + * method. + */ + resetStates() { + if (!this.stateful) { + throw new Error("Cannot call the resetStates() method of a non-stateful Layer object."); + } + } + /** + * Checks compatibility between the layer and provided inputs. + * + * This checks that the tensor(s) `input` + * verify the input assumptions of the layer + * (if any). If not, exceptions are raised. + * + * @param inputs Input tensor or list of input tensors. + * + * @exception ValueError in case of mismatch between + * the provided inputs and the expectations of the layer. + */ + assertInputCompatibility(inputs) { + const inputsList = toList(inputs); + if (this.inputSpec == null || this.inputSpec.length === 0) { + return; + } + const inputSpec = toList(this.inputSpec); + if (inputsList.length !== inputSpec.length) { + throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputsList.length} input tensors. Input received: ${inputs}`); + } + for (let inputIndex = 0; inputIndex < inputsList.length; inputIndex++) { + const x = inputsList[inputIndex]; + const spec = inputSpec[inputIndex]; + if (spec == null) { + continue; + } + const ndim = x.rank; + if (spec.ndim != null) { + if (ndim !== spec.ndim) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`); + } + } + if (spec.maxNDim != null) { + if (ndim > spec.maxNDim) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`); + } + } + if (spec.minNDim != null) { + if (ndim < spec.minNDim) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`); + } + } + if (spec.dtype != null) { + if (x.dtype !== spec.dtype) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`); + } + } + if (spec.axes) { + const xShape = x.shape; + for (const key in spec.axes) { + const axis = Number(key); + const value = spec.axes[key]; + const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis]; + if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`); + } + } + } + if (spec.shape != null) { + for (let i = 0; i < spec.shape.length; ++i) { + const specDim = spec.shape[i]; + const dim = x.shape[i]; + if (specDim != null && dim != null) { + if (specDim !== dim) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`); + } + } + } + } + } + } + /** + * This is where the layer's logic lives. + * + * @param inputs Input tensor, or list/tuple of input tensors. + * @param kwargs Additional keyword arguments. + * + * @return A tensor or list/tuple of tensors. + */ + call(inputs, kwargs) { + return inputs; + } + invokeCallHook(inputs, kwargs) { + if (this._callHook != null) { + this._callHook(inputs, kwargs); + } + } + /** + * Set call hook. + * This is currently used for testing only. + * @param callHook + */ + setCallHook(callHook) { + this._callHook = callHook; + } + /** + * Clear call hook. + * This is currently used for testing only. + */ + clearCallHook() { + this._callHook = null; + } + /** + * Builds or executes a `Layer`'s logic. + * + * When called with `tf.Tensor`(s), execute the `Layer`'s computation and + * return Tensor(s). For example: + * + * ```js + * const denseLayer = tf.layers.dense({ + * units: 1, + * kernelInitializer: 'zeros', + * useBias: false + * }); + * + * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete + * // numeric values). + * const input = tf.ones([2, 2]); + * const output = denseLayer.apply(input); + * + * // The output's value is expected to be [[0], [0]], due to the fact that + * // the dense layer has a kernel initialized to all-zeros and does not have + * // a bias. + * output.print(); + * ``` + * + * When called with `tf.SymbolicTensor`(s), this will prepare the layer for + * future execution. This entails internal book-keeping on shapes of + * expected Tensors, wiring layers together, and initializing weights. + * + * Calling `apply` with `tf.SymbolicTensor`s are typically used during the + * building of non-`tf.Sequential` models. For example: + * + * ```js + * const flattenLayer = tf.layers.flatten(); + * const denseLayer = tf.layers.dense({units: 1}); + * + * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply(). + * const input = tf.input({shape: [2, 2]}); + * const output1 = flattenLayer.apply(input); + * + * // output1.shape is [null, 4]. The first dimension is the undetermined + * // batch size. The second dimension comes from flattening the [2, 2] + * // shape. + * console.log(JSON.stringify(output1.shape)); + * + * // The output SymbolicTensor of the flatten layer can be used to call + * // the apply() of the dense layer: + * const output2 = denseLayer.apply(output1); + * + * // output2.shape is [null, 1]. The first dimension is the undetermined + * // batch size. The second dimension matches the number of units of the + * // dense layer. + * console.log(JSON.stringify(output2.shape)); + * + * // The input and output can be used to construct a model that consists + * // of the flatten and dense layers. + * const model = tf.model({inputs: input, outputs: output2}); + * ``` + * + * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them. + * @param kwargs Additional keyword arguments to be passed to `call()`. + * + * @return Output of the layer's `call` method. + * + * @exception ValueError error in case the layer is missing shape information + * for its `build` call. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + // Porting Note: This is a replacement for __call__() in Python. + apply(inputs, kwargs) { + kwargs = kwargs || {}; + this.assertNotDisposed(); + const inputsList = toList(inputs); + const allAreSymbolic = checkAllSymbolic(inputs); + const noneAreSymbolic = checkNoneSymbolic(inputs); + if (allAreSymbolic === noneAreSymbolic) { + throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors"); + } + return nameScope(this.name, () => { + if (!this.built) { + this.assertInputCompatibility(inputs); + const inputShapes = []; + for (const xElem of toList(inputs)) { + inputShapes.push(xElem.shape); + } + this.build(singletonOrArray(inputShapes)); + this.built = true; + if (this.initialWeights) { + this.setWeights(this.initialWeights); + } + if (this._refCount === null && noneAreSymbolic) { + this._refCount = 1; + } + } + this.assertInputCompatibility(inputs); + if (noneAreSymbolic) { + let output = this.call(inputs, kwargs); + if (this.supportsMasking) { + this.setMaskMetadata(inputs, output); + } + const outputList = toList(output); + const outputListCopy = []; + for (let x of outputList) { + if (inputsList.indexOf(x) !== -1) { + x = x.clone(); + } + outputListCopy.push(x); + } + output = singletonOrArray(outputListCopy); + if (this.activityRegularizer != null) { + throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet."); + } + return output; + } else { + const inputShape = collectInputShape(inputs); + const outputShape = this.computeOutputShape(inputShape); + let output; + const outputDType = guessOutputDType(inputs); + this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] : inputShape); + if (outputShape != null && outputShape.length > 0 && Array.isArray(outputShape[0])) { + output = outputShape.map((shape, index) => new SymbolicTensor(outputDType, shape, this, toList(inputs), kwargs, this.name, index)); + } else { + output = new SymbolicTensor(outputDType, outputShape, this, toList(inputs), kwargs, this.name); + } + this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs); + this._refCount++; + if (this.activityRegularizer != null) { + throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet."); + } + return output; + } + }); + } + /** + * Check compatibility between input shape and this layer's batchInputShape. + * + * Print warning if any incompatibility is found. + * + * @param inputShape Input shape to be checked. + */ + warnOnIncompatibleInputShape(inputShape) { + if (this.batchInputShape == null) { + return; + } else if (inputShape.length !== this.batchInputShape.length) { + console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`); + } else { + let dimMismatch = false; + this.batchInputShape.forEach((dimension, i) => { + if (dimension != null && inputShape[i] != null && inputShape[i] !== dimension) { + dimMismatch = true; + } + }); + if (dimMismatch) { + console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`); + } + } + } + /** + * Retrieves the output shape(s) of a layer. + * + * Only applicable if the layer has only one inbound node, or if all inbound + * nodes have the same output shape. + * + * @returns Output shape or shapes. + * @throws AttributeError: if the layer is connected to more than one incoming + * nodes. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + get outputShape() { + if (this.inboundNodes == null || this.inboundNodes.length === 0) { + throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`); + } + const allOutputShapes = []; + for (const node of this.inboundNodes) { + const shapeString = JSON.stringify(node.outputShapes); + if (allOutputShapes.indexOf(shapeString) === -1) { + allOutputShapes.push(shapeString); + } + } + if (allOutputShapes.length === 1) { + const outputShapes = this.inboundNodes[0].outputShapes; + if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) && outputShapes.length === 1) { + return outputShapes[0]; + } else { + return outputShapes; + } + } else { + throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`); + } + } + /** + * Counts the total number of numbers (e.g., float32, int32) in the + * weights. + * + * @returns An integer count. + * @throws RuntimeError: If the layer is not built yet (in which case its + * weights are not defined yet.) + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + countParams() { + if (!this.built) { + throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`); + } + return countParamsInWeights(this.weights); + } + /** + * Creates the layer weights. + * + * Must be implemented on all layers that have weights. + * + * Called when apply() is called to construct the weights. + * + * @param inputShape A `Shape` or array of `Shape` (unused). + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + build(inputShape) { + this.built = true; + } + /** + * Returns the current values of the weights of the layer. + * + * @param trainableOnly Whether to get the values of only trainable weights. + * @returns Weight values as an `Array` of `tf.Tensor`s. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + getWeights(trainableOnly = false) { + return batchGetValue(trainableOnly ? this.trainableWeights : this.weights); + } + /** + * Sets the weights of the layer, from Tensors. + * + * @param weights a list of Tensors. The number of arrays and their shape + * must match number of the dimensions of the weights of the layer (i.e. + * it should match the output of `getWeights`). + * + * @exception ValueError If the provided weights list does not match the + * layer's specifications. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + setWeights(weights) { + tidy(() => { + const params = this.weights; + if (params.length !== weights.length) { + throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`); + } + if (params.length === 0) { + return; + } + const weightValueTuples = []; + const paramValues = batchGetValue(params); + for (let i = 0; i < paramValues.length; ++i) { + const pv = paramValues[i]; + const p2 = params[i]; + const w = weights[i]; + if (!util_exports.arraysEqual(pv.shape, w.shape)) { + throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`); + } + weightValueTuples.push([p2, w]); + } + batchSetValue(weightValueTuples); + }); + } + /** + * Adds a weight variable to the layer. + * + * @param name Name of the new weight variable. + * @param shape The shape of the weight. + * @param dtype The dtype of the weight. + * @param initializer An initializer instance. + * @param regularizer A regularizer instance. + * @param trainable Whether the weight should be trained via backprop or not + * (assuming that the layer itself is also trainable). + * @param constraint An optional trainable. + * @return The created weight variable. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint, getInitializerFunc) { + if (this._addedWeightNames.indexOf(name) !== -1) { + throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`); + } + this._addedWeightNames.push(name); + if (dtype == null) { + dtype = "float32"; + } + if (this.fastWeightInitDuringBuild) { + initializer = getInitializerFunc != null ? getInitializerFunc() : getInitializer("zeros"); + } + const initValue = initializer.apply(shape, dtype); + const weight = new LayerVariable(initValue, dtype, name, trainable, constraint); + initValue.dispose(); + if (regularizer != null) { + this.addLoss(() => regularizer.apply(weight.read())); + } + if (trainable == null) { + trainable = true; + } + if (trainable) { + this._trainableWeights.push(weight); + } else { + this._nonTrainableWeights.push(weight); + } + return weight; + } + /** + * Set the fast-weight-initialization flag. + * + * In cases where the initialized weight values will be immediately + * overwritten by loaded weight values during model loading, setting + * the flag to `true` saves unnecessary calls to potentially expensive + * initializers and speeds up the loading process. + * + * @param value Target value of the flag. + */ + setFastWeightInitDuringBuild(value) { + this.fastWeightInitDuringBuild = value; + } + /** + * Add losses to the layer. + * + * The loss may potentially be conditional on some inputs tensors, + * for instance activity losses are conditional on the layer's inputs. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + addLoss(losses2) { + if (losses2 == null || Array.isArray(losses2) && losses2.length === 0) { + return; + } + losses2 = toList(losses2); + if (this._losses !== void 0 && this._losses !== null) { + this.losses.push(...losses2); + } + } + /** + * Computes the output shape of the layer. + * + * Assumes that the layer will be built to match that input shape provided. + * + * @param inputShape A shape (tuple of integers) or a list of shape tuples + * (one per output tensor of the layer). Shape tuples can include null for + * free dimensions, instead of an integer. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + computeOutputShape(inputShape) { + return inputShape; + } + /** + * Computes an output mask tensor. + * + * @param inputs Tensor or list of tensors. + * @param mask Tensor or list of tensors. + * + * @return null or a tensor (or list of tensors, one per output tensor of the + * layer). + */ + computeMask(inputs, mask) { + if (!this.supportsMasking) { + if (mask != null) { + if (Array.isArray(mask)) { + mask.forEach((maskElement) => { + if (maskElement != null) { + throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`); + } + }); + } else { + throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`); + } + } + return null; + } + return mask; + } + setMaskMetadata(inputs, outputs, previousMask) { + if (!this.supportsMasking) { + return; + } + const outputMasks = this.computeMask(inputs, previousMask); + const outputsList = toList(outputs); + const outputMasksList = toList(outputMasks); + if (outputsList.length !== outputMasksList.length) { + throw new Error(`${this.name} outputs ${outputsList.length} tensors but ${outputsList.length} masks for those tensors`); + } + for (let i = 0; i < outputsList.length; i++) { + outputsList[i].kerasMask = outputMasksList[i]; + } + } + /** + * Internal method to create an inbound node for the layer. + * + * @param inputTensors List of input tensors. + * @param outputTensors List of output tensors. + * @param inputMasks List of input masks (a mask can be a tensor, or null). + * @param outputMasks List of output masks (a mask can be a tensor, or null). + * @param inputShapes List of input shape tuples. + * @param outputShapes List of output shape tuples. + * @param kwargs Dictionary of keyword arguments that were passed to the + * `call` method of the layer at the call that created the node. + */ + addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) { + const inputTensorList = toList(inputTensors); + outputTensors = toList(outputTensors); + inputMasks = toList(inputMasks); + outputMasks = toList(outputMasks); + inputShapes = normalizeShapeList(inputShapes); + outputShapes = normalizeShapeList(outputShapes); + const inboundLayers = []; + const nodeIndices = []; + const tensorIndices = []; + for (const x of inputTensorList) { + inboundLayers.push(x.sourceLayer); + nodeIndices.push(x.nodeIndex); + tensorIndices.push(x.tensorIndex); + } + new Node({ + outboundLayer: this, + inboundLayers, + nodeIndices, + tensorIndices, + inputTensors: inputTensorList, + outputTensors, + inputMasks, + outputMasks, + inputShapes, + outputShapes + }, kwargs); + for (let i = 0; i < outputTensors.length; i++) { + outputTensors[i].sourceLayer = this; + outputTensors[i].nodeIndex = this.inboundNodes.length - 1; + outputTensors[i].tensorIndex = i; + } + } + /** + * Returns the config of the layer. + * + * A layer config is a TS dictionary (serializable) + * containing the configuration of a layer. + * The same layer can be reinstantiated later + * (without its trained weights) from this configuration. + * + * The config of a layer does not include connectivity + * information, nor the layer class name. These are handled + * by 'Container' (one layer of abstraction above). + * + * Porting Note: The TS dictionary follows TS naming standards for + * keys, and uses tfjs-layers type-safe Enums. Serialization methods + * should use a helper function to convert to the pythonic storage + * standard. (see serialization_utils.convertTsToPythonic) + * + * @returns TS dictionary of configuration. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + getConfig() { + const config = { name: this.name, trainable: this.trainable }; + if (this.batchInputShape != null) { + config["batchInputShape"] = this.batchInputShape; + } + if (this.dtype != null) { + config["dtype"] = this.dtype; + } + return config; + } + /** + * Dispose the weight variables that this Layer instance holds. + * + * @returns {number} Number of disposed variables. + */ + disposeWeights() { + this.weights.forEach((weight) => weight.dispose()); + return this.weights.length; + } + assertNotDisposed() { + if (this._refCount === 0) { + throw new Error(`Layer '${this.name}' is already disposed.`); + } + } + /** + * Attempt to dispose layer's weights. + * + * This method decreases the reference count of the Layer object by 1. + * + * A Layer is reference-counted. Its reference count is incremented by 1 + * the first item its `apply()` method is called and when it becomes a part + * of a new `Node` (through calling the `apply()` method on a + * `tf.SymbolicTensor`). + * + * If the reference count of a Layer becomes 0, all the weights will be + * disposed and the underlying memory (e.g., the textures allocated in WebGL) + * will be freed. + * + * Note: If the reference count is greater than 0 after the decrement, the + * weights of the Layer will *not* be disposed. + * + * After a Layer is disposed, it cannot be used in calls such as `apply()`, + * `getWeights()` or `setWeights()` anymore. + * + * @returns A DisposeResult Object with the following fields: + * - refCountAfterDispose: The reference count of the Container after this + * `dispose()` call. + * - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed + * during this `dispose()` call. + * @throws {Error} If the layer is not built yet, or if the layer has already + * been disposed. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + dispose() { + if (!this.built) { + throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`); + } + if (this._refCount === null) { + throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`); + } + this.assertNotDisposed(); + let numDisposedVariables = 0; + if (--this._refCount === 0) { + numDisposedVariables = this.disposeWeights(); + } + return { refCountAfterDispose: this._refCount, numDisposedVariables }; + } +}; +function collectInputShape(inputTensors) { + inputTensors = toList(inputTensors); + const shapes = []; + for (const x of inputTensors) { + shapes.push(x.shape); + } + return singletonOrArray(shapes); +} +function guessOutputDType(inputTensors) { + return "float32"; +} +function getSourceInputs(tensor2, layer, nodeIndex) { + if (layer == null || nodeIndex != null && nodeIndex > 0) { + layer = tensor2.sourceLayer; + nodeIndex = tensor2.nodeIndex; + } + if (layer.inboundNodes.length === 0) { + return [tensor2]; + } else { + const node = layer.inboundNodes[nodeIndex]; + if (node.inboundLayers.length === 0) { + return node.inputTensors; + } else { + const sourceTensors = []; + for (let i = 0; i < node.inboundLayers.length; i++) { + const x = node.inputTensors[i]; + const layer2 = node.inboundLayers[i]; + const nodeIndex2 = node.nodeIndices[i]; + const previousSources = getSourceInputs(x, layer2, nodeIndex2); + for (const x2 of previousSources) { + if (sourceTensors.indexOf(x2) === -1) { + sourceTensors.push(x2); + } + } + } + return sourceTensors; + } + } +} +function checkAllSymbolic(tensors) { + let allAreSymbolic = true; + for (const tensor2 of toList(tensors)) { + if (!(tensor2 instanceof SymbolicTensor)) { + allAreSymbolic = false; + break; + } + } + return allAreSymbolic; +} +function checkNoneSymbolic(tensors) { + let noneAreSymbolic = true; + for (const tensor2 of toList(tensors)) { + if (tensor2 instanceof SymbolicTensor) { + noneAreSymbolic = false; + break; + } + } + return noneAreSymbolic; +} +var InputLayer = class extends Layer { + constructor(args) { + super({ + dtype: args.dtype, + name: args.name != null ? args.name : getUid("input").toString() + }); + if (args.batchSize == null) { + args.batchSize = null; + } + if (args.sparse == null) { + args.sparse = false; + } + this.trainable = false; + this.built = true; + this.sparse = args.sparse; + if (args.inputShape != null && args.batchInputShape != null) { + throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time."); + } + let batchInputShape = args.batchInputShape; + if (batchInputShape == null) { + if (args.inputShape == null) { + throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`."); + } else { + batchInputShape = [args.batchSize].concat(args.inputShape); + } + } else { + if (args.batchSize != null) { + throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer."); + } + } + const dtype = args.dtype || "float32"; + this.batchInputShape = batchInputShape; + this.dtype = dtype; + this.inputSpec = [{ shape: batchInputShape }]; + const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name); + inputTensor.nodeIndex = 0; + inputTensor.tensorIndex = 0; + new Node({ + outboundLayer: this, + inboundLayers: [], + nodeIndices: [], + tensorIndices: [], + inputTensors: [inputTensor], + outputTensors: [inputTensor], + inputMasks: [null], + outputMasks: [null], + inputShapes: [batchInputShape], + outputShapes: [batchInputShape] + }); + } + apply(inputs, kwargs) { + throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`); + } + dispose() { + return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 }; + } + getConfig() { + return { + batchInputShape: this.batchInputShape, + dtype: this.dtype, + sparse: this.sparse, + name: this.name + }; + } +}; +InputLayer.className = "InputLayer"; +serialization_exports.registerClass(InputLayer); +function Input(config) { + if (config.batchShape == null && config.shape == null) { + throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension."); + } + if (config.batchShape != null && config.shape != null) { + throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both."); + } + let batchShape = config.batchShape; + if (config.shape != null && batchShape == null) { + batchShape = [null].concat(config.shape); + } + let dtype = config.dtype; + if (dtype == null) { + dtype = "float32"; + } + const inputLayer2 = new InputLayer({ + batchInputShape: batchShape, + name: config.name, + dtype, + sparse: config.sparse + }); + const outputs = inputLayer2.inboundNodes[0].outputTensors; + return outputs[0]; +} +function assertFeedCompatibility(key, val) { + if (key.dtype == null || key.dtype === val.dtype) { + return val; + } + try { + return cast(val, key.dtype); + } catch (err) { + throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`); + } +} +var FeedDict = class _FeedDict { + /** + * Constructor, optionally does copy-construction. + * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case + * copy-construction will be performed. + */ + constructor(feeds) { + this.id2Value = {}; + this.id2Mask = {}; + this.name2Id = {}; + if (feeds instanceof _FeedDict) { + for (const id in feeds.id2Value) { + this.id2Value[id] = feeds.id2Value[id]; + if (id in feeds.id2Mask) { + this.id2Mask[id] = feeds.id2Mask[id]; + } + } + } else { + if (feeds == null) { + return; + } + for (const feed of feeds) { + this.add(feed.key, feed.value); + } + } + } + /** + * Add a key-value pair to the FeedDict. + * + * @param key The key of the feed. + * @param value The value of the tensor feed. + * @param mask The value of the mask feed (optional). + * @returns This `FeedDict`. + * @throws ValueError: If the key `SymbolicTensor` already exists in the + * `FeedDict`. + */ + add(key, value, mask) { + if (this.id2Value[key.id] == null) { + this.id2Value[key.id] = assertFeedCompatibility(key, value); + this.name2Id[key.name] = key.id; + if (mask != null) { + this.id2Mask[key.id] = mask; + } + } else { + throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`); + } + return this; + } + /** + * Add a Feed to the FeedDict. + * @param feed The new `Feed` to add. + * @returns This `FeedDict`. + */ + addFeed(feed) { + this.add(feed.key, feed.value); + } + /** + * Probe whether a key already exists in the FeedDict. + * @param key + */ + hasKey(key) { + return this.id2Value[key.id] != null; + } + /** + * Get all the SymbolicTensor available in this FeedDict. + */ + names() { + return Object.keys(this.name2Id); + } + /** + * Get the feed value for given key. + * @param key The SymbolicTensor, or its name (as a string), of which the + * value is sought. + * @returns If `key` exists, the corresponding feed value. + * @throws ValueError: If `key` does not exist in this `FeedDict`. + */ + getValue(key) { + if (key instanceof SymbolicTensor) { + if (this.id2Value[key.id] == null) { + throw new ValueError(`Nonexistent key: ${key.name}`); + } else { + return this.id2Value[key.id]; + } + } else { + const id = this.name2Id[key]; + if (id == null) { + throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`); + } + return this.id2Value[id]; + } + } + /** + * Get the feed mask for given key. + * @param key The SymbolicTensor, or its name (as a string), of which the + * value is sought. + * @returns If `key` exists, the corresponding feed mask. + * @throws ValueError: If `key` does not exist in this `FeedDict`. + */ + getMask(key) { + if (key instanceof SymbolicTensor) { + if (this.id2Value[key.id] == null) { + throw new ValueError(`Nonexistent key: ${key.name}`); + } else { + return this.id2Mask[key.id]; + } + } else { + const id = this.name2Id[key]; + if (id == null) { + throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`); + } + return this.id2Mask[id]; + } + } + /** Dispose all mask Tensors held by this object. */ + disposeMasks() { + if (this.id2Mask != null) { + dispose(this.id2Mask); + } + } +}; +var cachedSorted = new LruCache(); +var cachedRecipientCounts = new LruCache(); +function updateCacheMaxEntries(maxEntries) { + if (cachedSorted != null) { + cachedSorted.setMaxEntries(maxEntries); + } + if (cachedRecipientCounts != null) { + cachedRecipientCounts.setMaxEntries(maxEntries); + } +} +function execute(fetches, feedDict, kwargs, probe) { + const training = kwargs == null ? false : kwargs["training"]; + const arrayFetches = Array.isArray(fetches); + const fetchArray = arrayFetches ? fetches : [fetches]; + const outputNames = fetchArray.map((t) => t.name); + const finalOutputs = []; + const feedNames = feedDict.names(); + for (const outputName of outputNames) { + if (feedNames.indexOf(outputName) !== -1) { + finalOutputs.push(feedDict.getValue(outputName)); + } else { + finalOutputs.push(null); + } + } + if (probe != null) { + probe.maxNumTensors = -Infinity; + probe.minNumTensors = Infinity; + } + const fetchAndFeedKey = outputNames.join(",") + "|" + feedDict.names().sort().join(","); + let sorted = cachedSorted.get(fetchAndFeedKey); + let recipientCounts; + if (sorted == null) { + const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict); + sorted = out.sorted; + recipientCounts = out.recipientCounts; + cachedSorted.put(fetchAndFeedKey, sorted); + cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts); + } + recipientCounts = {}; + if (!training) { + Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey)); + } + const internalFeedDict = new FeedDict(feedDict); + for (let i = 0; i < sorted.length; ++i) { + if (probe != null) { + const numTensors = memory().numTensors; + if (numTensors > probe.maxNumTensors) { + probe.maxNumTensors = numTensors; + } + if (numTensors < probe.minNumTensors) { + probe.minNumTensors = numTensors; + } + } + const symbolic = sorted[i]; + const srcLayer = symbolic.sourceLayer; + if (srcLayer instanceof InputLayer) { + continue; + } + const inputValues = []; + const inputMasks = []; + const tensorsToDispose = []; + let maskExists = false; + for (const input2 of symbolic.inputs) { + const value = internalFeedDict.getValue(input2); + const mask = internalFeedDict.getMask(input2); + inputValues.push(value); + inputMasks.push(mask); + if (mask != null) { + maskExists = true; + } + if (!training) { + recipientCounts[input2.name]--; + if (recipientCounts[input2.name] === 0 && !feedDict.hasKey(input2) && outputNames.indexOf(input2.name) === -1 && !value.isDisposed && input2.sourceLayer.stateful !== true) { + tensorsToDispose.push(value); + } + } + } + if (maskExists) { + kwargs = kwargs || {}; + kwargs["mask"] = inputMasks[0]; + } + const outputTensors = toList(srcLayer.apply(inputValues, kwargs)); + let outputMask = null; + if (srcLayer.supportsMasking) { + outputMask = srcLayer.computeMask(inputValues, inputMasks); + } + const layerOutputs = getNodeOutputs(symbolic); + const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs]; + for (let i2 = 0; i2 < outputSymbolicTensors.length; ++i2) { + if (!internalFeedDict.hasKey(outputSymbolicTensors[i2])) { + internalFeedDict.add(outputSymbolicTensors[i2], outputTensors[i2], Array.isArray(outputMask) ? outputMask[0] : outputMask); + } + const index = outputNames.indexOf(outputSymbolicTensors[i2].name); + if (index !== -1) { + finalOutputs[index] = outputTensors[i2]; + } + } + if (!training) { + dispose(tensorsToDispose); + } + } + internalFeedDict.disposeMasks(); + return arrayFetches ? finalOutputs : finalOutputs[0]; +} +function getTopologicalSortAndRecipientCounts(fetches, feedDict) { + util_exports.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`); + let finalSorted = []; + let finalRecipientMap = {}; + if (fetches.length === 1) { + const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict); + finalSorted = out.sorted; + finalRecipientMap = out.recipientMap; + } else { + const visited = /* @__PURE__ */ new Set(); + for (const fetch4 of fetches) { + const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict); + for (const symbolicTensor of sorted) { + if (!visited.has(symbolicTensor.name)) { + finalSorted.push(symbolicTensor); + visited.add(symbolicTensor.name); + } + } + for (const name in recipientMap) { + if (finalRecipientMap[name] == null) { + finalRecipientMap[name] = /* @__PURE__ */ new Set(); + } + recipientMap[name].forEach((recipient) => finalRecipientMap[name].add(recipient)); + } + } + } + return { + sorted: finalSorted, + recipientCounts: recipientMap2Counts(finalRecipientMap) + }; +} +function recipientMap2Counts(recipientMap) { + const recipientCounts = {}; + for (const name in recipientMap) { + recipientCounts[name] = recipientMap[name].size; + } + return recipientCounts; +} +function getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict) { + const visited = /* @__PURE__ */ new Set(); + const sorted = []; + const recipientMap = {}; + for (const key of feedDict.names()) { + visited.add(key); + } + const stack2 = []; + const marks = []; + stack2.push(fetch4); + while (stack2.length > 0) { + const top = stack2[stack2.length - 1]; + if (visited.has(top.name)) { + stack2.pop(); + continue; + } + const topIsMarked = marks[marks.length - 1] === stack2.length - 1; + if (top.inputs.length === 0 || topIsMarked) { + stack2.pop(); + sorted.push(top); + visited.add(top.name); + if (topIsMarked) { + marks.pop(); + } + } else { + marks.push(stack2.length - 1); + for (const input2 of top.inputs) { + if (recipientMap[input2.name] == null) { + recipientMap[input2.name] = /* @__PURE__ */ new Set(); + } + recipientMap[input2.name].add(top.name); + if (visited.has(input2.name)) { + continue; + } + stack2.push(input2); + } + } + } + return { sorted, recipientMap }; +} +function getNodeOutputs(fetch4) { + let layerOutputs; + if (fetch4.sourceLayer.inboundNodes.length === 1) { + layerOutputs = fetch4.sourceLayer.output; + } else { + let nodeIndex = null; + for (let i = 0; i < fetch4.sourceLayer.inboundNodes.length; ++i) { + for (const outputTensor of fetch4.sourceLayer.inboundNodes[i].outputTensors) { + if (outputTensor.id === fetch4.id) { + nodeIndex = i; + break; + } + } + } + layerOutputs = fetch4.sourceLayer.getOutputAt(nodeIndex); + } + return layerOutputs; +} +var ENV3 = env(); +ENV3.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES", () => 100, updateCacheMaxEntries); +var exports_constraints_exports = {}; +__export2(exports_constraints_exports, { + maxNorm: () => maxNorm, + minMaxNorm: () => minMaxNorm, + nonNeg: () => nonNeg, + unitNorm: () => unitNorm +}); +function calcL2Norms(w, axis) { + return tidy(() => sqrt(sum2(mul(w, w), axis, true))); +} +var Constraint = class extends serialization_exports.Serializable { + getConfig() { + return {}; + } +}; +var MaxNorm = class extends Constraint { + constructor(args) { + super(); + this.defaultMaxValue = 2; + this.defaultAxis = 0; + this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue; + this.axis = args.axis != null ? args.axis : this.defaultAxis; + } + apply(w) { + return tidy(() => { + const norms = calcL2Norms(w, this.axis); + const desired = clipByValue(norms, 0, this.maxValue); + return mul(w, div(desired, add2(epsilon(), norms))); + }); + } + getConfig() { + return { maxValue: this.maxValue, axis: this.axis }; + } +}; +MaxNorm.className = "MaxNorm"; +serialization_exports.registerClass(MaxNorm); +var UnitNorm = class extends Constraint { + constructor(args) { + super(); + this.defaultAxis = 0; + this.axis = args.axis != null ? args.axis : this.defaultAxis; + } + apply(w) { + return tidy(() => div(w, add2(epsilon(), calcL2Norms(w, this.axis)))); + } + getConfig() { + return { axis: this.axis }; + } +}; +UnitNorm.className = "UnitNorm"; +serialization_exports.registerClass(UnitNorm); +var NonNeg = class extends Constraint { + apply(w) { + return relu(w); + } +}; +NonNeg.className = "NonNeg"; +serialization_exports.registerClass(NonNeg); +var MinMaxNorm = class extends Constraint { + constructor(args) { + super(); + this.defaultMinValue = 0; + this.defaultMaxValue = 1; + this.defaultRate = 1; + this.defaultAxis = 0; + this.minValue = args.minValue != null ? args.minValue : this.defaultMinValue; + this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue; + this.rate = args.rate != null ? args.rate : this.defaultRate; + this.axis = args.axis != null ? args.axis : this.defaultAxis; + } + apply(w) { + return tidy(() => { + const norms = calcL2Norms(w, this.axis); + const desired = add2(mul(this.rate, clipByValue(norms, this.minValue, this.maxValue)), mul(1 - this.rate, norms)); + return mul(w, div(desired, add2(epsilon(), norms))); + }); + } + getConfig() { + return { + minValue: this.minValue, + maxValue: this.maxValue, + rate: this.rate, + axis: this.axis + }; + } +}; +MinMaxNorm.className = "MinMaxNorm"; +serialization_exports.registerClass(MinMaxNorm); +var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = { + "maxNorm": "MaxNorm", + "minMaxNorm": "MinMaxNorm", + "nonNeg": "NonNeg", + "unitNorm": "UnitNorm" +}; +function serializeConstraint(constraint) { + return serializeKerasObject(constraint); +} +function deserializeConstraint(config, customObjects = {}) { + return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "constraint"); +} +function getConstraint(identifier) { + if (identifier == null) { + return null; + } + if (typeof identifier === "string") { + const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ? CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier; + const config = { className, config: {} }; + return deserializeConstraint(config); + } else if (identifier instanceof Constraint) { + return identifier; + } else { + return deserializeConstraint(identifier); + } +} +function maxNorm(args) { + return new MaxNorm(args); +} +function unitNorm(args) { + return new UnitNorm(args); +} +function nonNeg() { + return new NonNeg(); +} +function minMaxNorm(config) { + return new MinMaxNorm(config); +} +var exports_initializers_exports = {}; +__export2(exports_initializers_exports, { + constant: () => constant, + glorotNormal: () => glorotNormal, + glorotUniform: () => glorotUniform, + heNormal: () => heNormal, + heUniform: () => heUniform, + identity: () => identity, + leCunNormal: () => leCunNormal, + leCunUniform: () => leCunUniform, + ones: () => ones3, + orthogonal: () => orthogonal, + randomNormal: () => randomNormal3, + randomUniform: () => randomUniform2, + truncatedNormal: () => truncatedNormal2, + varianceScaling: () => varianceScaling, + zeros: () => zeros2 +}); +function zeros2() { + return new Zeros(); +} +function ones3() { + return new Ones(); +} +function constant(args) { + return new Constant(args); +} +function randomUniform2(args) { + return new RandomUniform(args); +} +function randomNormal3(args) { + return new RandomNormal(args); +} +function truncatedNormal2(args) { + return new TruncatedNormal(args); +} +function identity(args) { + return new Identity2(args); +} +function varianceScaling(config) { + return new VarianceScaling(config); +} +function glorotUniform(args) { + return new GlorotUniform(args); +} +function glorotNormal(args) { + return new GlorotNormal(args); +} +function heNormal(args) { + return new HeNormal(args); +} +function heUniform(args) { + return new HeUniform(args); +} +function leCunNormal(args) { + return new LeCunNormal(args); +} +function leCunUniform(args) { + return new LeCunUniform(args); +} +function orthogonal(args) { + return new Orthogonal(args); +} +var exports_layers_exports = {}; +__export2(exports_layers_exports, { + Layer: () => Layer, + RNN: () => RNN, + RNNCell: () => RNNCell, + activation: () => activation, + add: () => add3, + alphaDropout: () => alphaDropout, + average: () => average, + averagePooling1d: () => averagePooling1d, + averagePooling2d: () => averagePooling2d, + averagePooling3d: () => averagePooling3d, + avgPool1d: () => avgPool1d, + avgPool2d: () => avgPool2d, + avgPool3d: () => avgPool3d2, + avgPooling1d: () => avgPooling1d, + avgPooling2d: () => avgPooling2d, + avgPooling3d: () => avgPooling3d, + batchNormalization: () => batchNormalization2, + bidirectional: () => bidirectional, + categoryEncoding: () => categoryEncoding, + centerCrop: () => centerCrop, + concatenate: () => concatenate2, + conv1d: () => conv1d2, + conv2d: () => conv2d3, + conv2dTranspose: () => conv2dTranspose2, + conv3d: () => conv3d2, + conv3dTranspose: () => conv3dTranspose2, + convLstm2d: () => convLstm2d, + convLstm2dCell: () => convLstm2dCell, + cropping2D: () => cropping2D, + dense: () => dense, + depthwiseConv2d: () => depthwiseConv2d4, + dot: () => dot3, + dropout: () => dropout3, + elu: () => elu3, + embedding: () => embedding, + flatten: () => flatten3, + gaussianDropout: () => gaussianDropout, + gaussianNoise: () => gaussianNoise, + globalAveragePooling1d: () => globalAveragePooling1d, + globalAveragePooling2d: () => globalAveragePooling2d, + globalMaxPool1d: () => globalMaxPool1d, + globalMaxPool2d: () => globalMaxPool2d, + globalMaxPooling1d: () => globalMaxPooling1d, + globalMaxPooling2d: () => globalMaxPooling2d, + gru: () => gru, + gruCell: () => gruCell, + input: () => input, + inputLayer: () => inputLayer, + layerNormalization: () => layerNormalization, + leakyReLU: () => leakyReLU, + lstm: () => lstm, + lstmCell: () => lstmCell, + masking: () => masking, + maxPool1d: () => maxPool1d, + maxPool2d: () => maxPool2d, + maxPooling1d: () => maxPooling1d, + maxPooling2d: () => maxPooling2d, + maxPooling3d: () => maxPooling3d, + maximum: () => maximum2, + minimum: () => minimum2, + multiply: () => multiply, + permute: () => permute, + prelu: () => prelu2, + randomWidth: () => randomWidth, + reLU: () => reLU, + repeatVector: () => repeatVector, + rescaling: () => rescaling, + reshape: () => reshape2, + resizing: () => resizing, + rnn: () => rnn2, + separableConv2d: () => separableConv2d2, + simpleRNN: () => simpleRNN, + simpleRNNCell: () => simpleRNNCell, + softmax: () => softmax2, + spatialDropout1d: () => spatialDropout1d, + stackedRNNCells: () => stackedRNNCells, + thresholdedReLU: () => thresholdedReLU, + timeDistributed: () => timeDistributed, + upSampling2d: () => upSampling2d, + zeroPadding2d: () => zeroPadding2d +}); +async function resolveScalarsInLogs(logs) { + if (logs == null) { + return; + } + const promises = []; + const keys = []; + const scalarsToDispose = []; + for (const key in logs) { + const value = logs[key]; + if (typeof value !== "number") { + const valueScalar = value; + promises.push(valueScalar.data()); + keys.push(key); + scalarsToDispose.push(valueScalar); + } + } + if (promises.length > 0) { + const values = await Promise.all(promises); + for (let i = 0; i < values.length; ++i) { + logs[keys[i]] = values[i][0]; + } + dispose(scalarsToDispose); + } +} +function disposeTensorsInLogs(logs) { + if (logs == null) { + return; + } + for (const key in logs) { + const value = logs[key]; + if (typeof value !== "number") { + value.dispose(); + } + } +} +var ModelLoggingVerbosity; +(function(ModelLoggingVerbosity2) { + ModelLoggingVerbosity2[ModelLoggingVerbosity2["SILENT"] = 0] = "SILENT"; + ModelLoggingVerbosity2[ModelLoggingVerbosity2["VERBOSE"] = 1] = "VERBOSE"; +})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {})); +var DEFAULT_YIELD_EVERY_MS = 125; +var BaseCallback = class { + constructor() { + this.validationData = null; + } + setParams(params) { + this.params = params; + } + async onEpochBegin(epoch, logs) { + } + async onEpochEnd(epoch, logs) { + } + async onBatchBegin(batch, logs) { + } + async onBatchEnd(batch, logs) { + } + async onTrainBegin(logs) { + } + async onTrainEnd(logs) { + } + // LayersModel needs to call Callback.setModel(), but cannot actually depend + // on Callback because that creates a cyclic dependency. Providing this no-op + // method on BaseCallback breaks the cycle: this way LayersModel can depend on + // BaseCallback but not on Callback. The argument is typed as `Container` + // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback + // overrides this method and enforces that the argument is really a + // LayersModel. + setModel(model2) { + } +}; +var CallbackList = class { + // TODO(cais): When the need arises, uncomment the following lines and + // implement the queue for time values. + // private deltaTBatch: number; + // private deltaTsBatchBegin: Array; + // private deltaTsBatchEnd: Array; + /** + * Constructor of CallbackList. + * @param callbacks Array of `Callback` instances. + * @param queueLength Queue length for keeping running statistics over + * callback execution time. + */ + constructor(callbacks2, queueLength = 10) { + if (callbacks2 == null) { + callbacks2 = []; + } + this.callbacks = callbacks2; + this.queueLength = queueLength; + } + append(callback) { + this.callbacks.push(callback); + } + setParams(params) { + for (const callback of this.callbacks) { + callback.setParams(params); + } + } + setModel(model2) { + for (const callback of this.callbacks) { + callback.setModel(model2); + } + } + /** + * Called at the start of an epoch. + * @param epoch Index of epoch. + * @param logs Dictionary of logs. + */ + async onEpochBegin(epoch, logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onEpochBegin(epoch, logs); + } + } + /** + * Called at the end of an epoch. + * @param epoch Index of epoch. + * @param logs Dictionary of logs. + */ + async onEpochEnd(epoch, logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onEpochEnd(epoch, logs); + } + } + /** + * Called right before processing a batch. + * @param batch Index of batch within the current epoch. + * @param logs Dictionary of logs. + */ + async onBatchBegin(batch, logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onBatchBegin(batch, logs); + } + } + /** + * Called at the end of a batch. + * @param batch Index of batch within the current epoch. + * @param logs Dictionary of logs. + */ + async onBatchEnd(batch, logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onBatchEnd(batch, logs); + } + } + /** + * Called at the beginning of training. + * @param logs Dictionary of logs. + */ + async onTrainBegin(logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onTrainBegin(logs); + } + } + /** + * Called at the end of training. + * @param logs Dictionary of logs. + */ + async onTrainEnd(logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onTrainEnd(logs); + } + } +}; +var BaseLogger = class extends BaseCallback { + constructor() { + super(); + } + async onEpochBegin(epoch) { + this.seen = 0; + this.totals = {}; + } + async onBatchEnd(batch, logs) { + if (logs == null) { + logs = {}; + } + const batchSize = logs["size"] == null ? 0 : logs["size"]; + this.seen += batchSize; + for (const key in logs) { + const value = logs[key]; + if (typeof value === "number") { + if (!this.totals.hasOwnProperty(key)) { + this.totals[key] = 0; + } + this.totals[key] = this.totals[key] + value * batchSize; + } else { + let oldTotalsToDispose; + if (key in this.totals) { + oldTotalsToDispose = this.totals[key]; + } else { + this.totals[key] = 0; + } + const total = tidy(() => add2(this.totals[key], mul(value, batchSize))); + this.totals[key] = total; + if (oldTotalsToDispose != null) { + oldTotalsToDispose.dispose(); + } + } + } + } + async onEpochEnd(epoch, logs) { + if (logs != null) { + for (const key of this.params["metrics"]) { + if (this.totals[key] == null) { + continue; + } + if (typeof this.totals[key] === "number") { + logs[key] = this.totals[key] / this.seen; + } else { + tidy(() => { + const log5 = mul(div(1, this.seen), this.totals[key]); + logs[key] = log5; + this.totals[key].dispose(); + keep(logs[key]); + }); + } + } + } + } +}; +var History = class extends BaseCallback { + async onTrainBegin(logs) { + this.epoch = []; + this.history = {}; + } + async onEpochEnd(epoch, logs) { + if (logs == null) { + logs = {}; + } + this.epoch.push(epoch); + for (const key in logs) { + if (this.history[key] == null) { + this.history[key] = []; + } + this.history[key].push(logs[key]); + } + } + /** + * Await the values of all losses and metrics. + */ + async syncData() { + const promises = []; + const keys = []; + const indices = []; + for (const key in this.history) { + const valueArray = this.history[key]; + for (let i = 0; i < valueArray.length; ++i) { + if (typeof valueArray[i] !== "number") { + const valueScalar = valueArray[i]; + promises.push(valueScalar.data()); + keys.push(key); + indices.push(i); + } + } + } + const values = await Promise.all(promises); + for (let n = 0; n < values.length; ++n) { + const tensorToDispose = this.history[keys[n]][indices[n]]; + tensorToDispose.dispose(); + this.history[keys[n]][indices[n]] = values[n][0]; + } + } +}; +var CustomCallback = class extends BaseCallback { + constructor(args, yieldEvery) { + super(); + this.currentEpoch = 0; + this.nowFunc = args.nowFunc; + this.nextFrameFunc = args.nextFrameFunc || nextFrame; + this.yieldEvery = yieldEvery || "auto"; + if (this.yieldEvery === "auto") { + this.yieldEvery = DEFAULT_YIELD_EVERY_MS; + } + if (this.yieldEvery === "never" && args.onYield != null) { + throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback"); + } + if (util_exports.isNumber(this.yieldEvery)) { + this.maybeWait = debounce(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc); + } + this.trainBegin = args.onTrainBegin; + this.trainEnd = args.onTrainEnd; + this.epochBegin = args.onEpochBegin; + this.epochEnd = args.onEpochEnd; + this.batchBegin = args.onBatchBegin; + this.batchEnd = args.onBatchEnd; + this.yield = args.onYield; + } + async maybeWait(epoch, batch, logs) { + const ps = []; + if (this.yield != null) { + await resolveScalarsInLogs(logs); + ps.push(this.yield(epoch, batch, logs)); + } + ps.push(this.nextFrameFunc()); + await Promise.all(ps); + } + async onEpochBegin(epoch, logs) { + this.currentEpoch = epoch; + if (this.epochBegin != null) { + await resolveScalarsInLogs(logs); + await this.epochBegin(epoch, logs); + } + } + async onEpochEnd(epoch, logs) { + const ps = []; + if (this.epochEnd != null) { + await resolveScalarsInLogs(logs); + ps.push(this.epochEnd(epoch, logs)); + } + if (this.yieldEvery === "epoch") { + ps.push(this.nextFrameFunc()); + } + await Promise.all(ps); + } + async onBatchBegin(batch, logs) { + if (this.batchBegin != null) { + await resolveScalarsInLogs(logs); + await this.batchBegin(batch, logs); + } + } + async onBatchEnd(batch, logs) { + const ps = []; + if (this.batchEnd != null) { + await resolveScalarsInLogs(logs); + ps.push(this.batchEnd(batch, logs)); + } + if (this.yieldEvery === "batch") { + ps.push(this.nextFrameFunc()); + } else if (util_exports.isNumber(this.yieldEvery)) { + ps.push(this.maybeWait(this.currentEpoch, batch, logs)); + } + await Promise.all(ps); + } + async onTrainBegin(logs) { + if (this.trainBegin != null) { + await resolveScalarsInLogs(logs); + await this.trainBegin(logs); + } + } + async onTrainEnd(logs) { + if (this.trainEnd != null) { + await resolveScalarsInLogs(logs); + await this.trainEnd(logs); + } + } +}; +function standardizeCallbacks(callbacks2, yieldEvery) { + if (callbacks2 == null) { + callbacks2 = {}; + } + if (callbacks2 instanceof BaseCallback) { + return [callbacks2]; + } + if (Array.isArray(callbacks2) && callbacks2[0] instanceof BaseCallback) { + return callbacks2; + } + const callbackConfigs = toList(callbacks2); + return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery)); +} +var CallbackConstructorRegistry = class _CallbackConstructorRegistry { + /** + * Blocks public access to constructor. + */ + constructor() { + } + /** + * Register a tf.LayersModel.fit() callback constructor. + * + * The registered callback constructor will be used to instantiate + * callbacks for every tf.LayersModel.fit() call afterwards. + * + * @param verbosityLevel Level of verbosity at which the `callbackConstructor` + * is to be reigstered. + * @param callbackConstructor A no-arg constructor for `tf.Callback`. + * @throws Error, if the same callbackConstructor has been registered before, + * either at the same or a different `verbosityLevel`. + */ + static registerCallbackConstructor(verbosityLevel, callbackConstructor) { + util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`); + _CallbackConstructorRegistry.checkForDuplicate(callbackConstructor); + if (_CallbackConstructorRegistry.constructors[verbosityLevel] == null) { + _CallbackConstructorRegistry.constructors[verbosityLevel] = []; + } + _CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor); + } + static checkForDuplicate(callbackConstructor) { + for (const levelName in _CallbackConstructorRegistry.constructors) { + const constructors = _CallbackConstructorRegistry.constructors[+levelName]; + constructors.forEach((ctor) => { + if (ctor === callbackConstructor) { + throw new ValueError("Duplicate callback constructor."); + } + }); + } + } + /** + * Clear all registered callback constructors. + */ + static clear() { + _CallbackConstructorRegistry.constructors = {}; + } + /** + * Create callbacks using the registered callback constructors. + * + * Given `verbosityLevel`, all constructors registered at that level or above + * will be called and the instantiated callbacks will be used. + * + * @param verbosityLevel: Level of verbosity. + */ + static createCallbacks(verbosityLevel) { + const constructors = []; + for (const levelName in _CallbackConstructorRegistry.constructors) { + const level = +levelName; + if (verbosityLevel >= level) { + constructors.push(..._CallbackConstructorRegistry.constructors[level]); + } + } + return constructors.map((ctor) => new ctor()); + } +}; +CallbackConstructorRegistry.constructors = {}; +function configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) { + const history = new History(); + const actualCallbacks = [ + new BaseLogger(), + ...CallbackConstructorRegistry.createCallbacks(verbose) + ]; + if (callbacks2 != null) { + actualCallbacks.push(...callbacks2); + } + actualCallbacks.push(history); + const callbackList = new CallbackList(actualCallbacks); + callbackList.setParams({ + epochs, + initialEpoch, + samples: numTrainSamples, + steps: stepsPerEpoch, + batchSize, + verbose, + doValidation, + metrics: callbackMetrics + }); + return { callbackList, history }; +} +function deserialize(config, customObjects = {}, fastWeightInit = false) { + return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "layer", fastWeightInit); +} +function l2Normalize(x, axis) { + return tidy(() => { + if (x.dtype !== "float32") { + x = cast(x, "float32"); + } + const squareSum = sum2(square2(x), axis, true); + const epsilonTensor = fill(squareSum.shape, epsilon()); + const norm2 = sqrt(maximum(squareSum, epsilonTensor)); + return div(x, norm2); + }); +} +function meanSquaredError2(yTrue, yPred) { + return tidy(() => mean(square2(sub(yPred, yTrue)), -1)); +} +function meanAbsoluteError(yTrue, yPred) { + return tidy(() => mean(abs(sub(yPred, yTrue)), -1)); +} +function meanAbsolutePercentageError(yTrue, yPred) { + return tidy(() => { + const diff = sub(yTrue, yPred); + const clippedTrue = clipByValue(abs(yTrue), epsilon(), Number.MAX_VALUE); + const absResult = abs(div(diff, clippedTrue)); + return mul(100, mean(absResult, -1)); + }); +} +function meanSquaredLogarithmicError(yTrue, yPred) { + return tidy(() => { + const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE); + const firstLog = log2(add2(1, clippedPred)); + const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE); + const secondLog = log2(add2(1, clippedTrue)); + return mean(square2(sub(firstLog, secondLog)), -1); + }); +} +function squaredHinge(yTrue, yPred) { + return tidy(() => { + const maxResult = maximum(0, sub(1, mul(yTrue, yPred))); + return mean(square2(maxResult), -1); + }); +} +function hinge(yTrue, yPred) { + return tidy(() => { + const maxResult = maximum(0, sub(1, mul(yTrue, yPred))); + return mean(maxResult, -1); + }); +} +function categoricalHinge(yTrue, yPred) { + return tidy(() => { + const pos = sum2(mul(yTrue, yPred), -1); + const neg4 = max(mul(sub(1, yTrue), yPred), -1); + return maximum(0, add2(1, sub(neg4, pos))); + }); +} +function logcosh(yTrue, yPred) { + return tidy(() => { + const log22 = Math.log(2); + const predictionDiff = sub(yPred, yTrue); + const logcoshResult = sub(add2(predictionDiff, softplus(mul(-2, predictionDiff))), log22); + return mean(logcoshResult, -1); + }); +} +function categoricalCrossentropy(target, output, fromLogits = false) { + return tidy(() => { + if (fromLogits) { + output = softmax(output); + } else { + const outputSum = sum2(output, output.shape.length - 1, true); + output = div(output, outputSum); + } + output = clipByValue(output, epsilon(), 1 - epsilon()); + return neg(sum2(mul(cast(target, "float32"), log2(output)), output.shape.length - 1)); + }); +} +function sparseCategoricalCrossentropy(target, output, fromLogits = false) { + return tidy(() => { + const flatTarget = cast(floor(flatten2(target)), "int32"); + output = clipByValue(output, epsilon(), 1 - epsilon()); + const outputShape = output.shape; + const oneHotTarget = reshape(oneHot(flatTarget, outputShape[outputShape.length - 1]), outputShape); + return categoricalCrossentropy(oneHotTarget, output, fromLogits); + }); +} +function sigmoidCrossEntropyWithLogits(labels, logits) { + if (!util_exports.arraysEqual(labels.shape, logits.shape)) { + throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`); + } + return tidy(() => { + const reluLogits = relu(logits); + const negAbsLogits = neg(abs(logits)); + return add2(sub(reluLogits, mul(logits, labels)), log1p(exp(negAbsLogits))); + }); +} +function binaryCrossentropy(yTrue, yPred) { + return tidy(() => { + let y; + y = clipByValue(yPred, epsilon(), 1 - epsilon()); + y = log2(div(y, sub(1, y))); + return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1); + }); +} +function kullbackLeiblerDivergence(yTrue, yPred) { + return tidy(() => { + const clippedTrue = clipByValue(yTrue, epsilon(), 1); + const clippedPred = clipByValue(yPred, epsilon(), 1); + return sum2(mul(yTrue, log2(div(clippedTrue, clippedPred))), -1); + }); +} +function poisson(yTrue, yPred) { + return tidy(() => { + const logPred = log2(add2(epsilon(), yPred)); + return mean(sub(yPred, mul(yTrue, logPred)), -1); + }); +} +function cosineProximity(yTrue, yPred) { + return tidy(() => { + const trueNormalized = l2Normalize(yTrue, -1); + const predNormalized = l2Normalize(yPred, -1); + const trueXPred = mul(trueNormalized, predNormalized); + return neg(sum2(trueXPred, -1)); + }); +} +var lossesMap = { + meanSquaredError: meanSquaredError2, + meanAbsoluteError, + meanAbsolutePercentageError, + meanSquaredLogarithmicError, + squaredHinge, + hinge, + categoricalHinge, + logcosh, + categoricalCrossentropy, + sparseCategoricalCrossentropy, + binaryCrossentropy, + kullbackLeiblerDivergence, + poisson, + cosineProximity +}; +function get(identifierOrFn) { + if (typeof identifierOrFn === "string") { + if (identifierOrFn in lossesMap) { + return lossesMap[identifierOrFn]; + } + let errMsg = `Unknown loss ${identifierOrFn}`; + if (identifierOrFn.toLowerCase().includes("softmaxcrossentropy")) { + errMsg = `Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`; + } + throw new ValueError(errMsg); + } else { + return identifierOrFn; + } +} +function binaryAccuracy(yTrue, yPred) { + return tidy(() => { + const threshold3 = mul(0.5, onesLike(yPred)); + const yPredThresholded = cast2(greater(yPred, threshold3), yTrue.dtype); + return mean(equal(yTrue, yPredThresholded), -1); + }); +} +function categoricalAccuracy(yTrue, yPred) { + return tidy(() => cast2(equal(argMax(yTrue, -1), argMax(yPred, -1)), "float32")); +} +function truePositives(yTrue, yPred) { + return tidy(() => { + return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 1))), "float32"); + }); +} +function falseNegatives(yTrue, yPred) { + return tidy(() => { + return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 0))), "float32"); + }); +} +function falsePositives(yTrue, yPred) { + return tidy(() => { + return cast(sum2(logicalAnd(equal(yTrue, 0), equal(yPred, 1))), "float32"); + }); +} +function precision(yTrue, yPred) { + return tidy(() => { + const tp = truePositives(yTrue, yPred); + const fp = falsePositives(yTrue, yPred); + const denominator = add2(tp, fp); + return cast(where(greater(denominator, 0), div(tp, denominator), 0), "float32"); + }); +} +function recall(yTrue, yPred) { + return tidy(() => { + const tp = truePositives(yTrue, yPred); + const fn = falseNegatives(yTrue, yPred); + const denominator = add2(tp, fn); + return cast(where(greater(denominator, 0), div(tp, denominator), 0), "float32"); + }); +} +function binaryCrossentropy2(yTrue, yPred) { + return binaryCrossentropy(yTrue, yPred); +} +function sparseCategoricalAccuracy(yTrue, yPred) { + if (yTrue.rank === yPred.rank) { + yTrue = squeeze(yTrue, [yTrue.rank - 1]); + } + yPred = argMax(yPred, -1); + if (yPred.dtype !== yTrue.dtype) { + yPred = cast(yPred, yTrue.dtype); + } + return cast(equal(yTrue, yPred), "float32"); +} +var mse = meanSquaredError2; +var MSE = meanSquaredError2; +var mae = meanAbsoluteError; +var MAE = meanAbsoluteError; +var mape = meanAbsolutePercentageError; +var MAPE = meanAbsolutePercentageError; +var categoricalCrossentropy2 = categoricalCrossentropy; +var cosine = cosineProximity; +var sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy; +var metricsMap = { + binaryAccuracy, + categoricalAccuracy, + precision, + categoricalCrossentropy: categoricalCrossentropy2, + sparseCategoricalCrossentropy: sparseCategoricalCrossentropy2, + mse, + MSE, + mae, + MAE, + mape, + MAPE, + cosine +}; +function get2(identifier) { + if (typeof identifier === "string" && identifier in metricsMap) { + return metricsMap[identifier]; + } else if (typeof identifier !== "string" && identifier != null) { + return identifier; + } else { + throw new ValueError(`Unknown metric ${identifier}`); + } +} +function getLossOrMetricName(fn) { + assert2(fn !== null, `Unknown LossOrMetricFn ${fn}`); + if (typeof fn === "string") { + return fn; + } else { + let fnName; + for (const key of Object.keys(lossesMap)) { + if (lossesMap[key] === fn) { + fnName = key; + break; + } + } + if (fnName !== void 0) { + return fnName; + } + for (const key of Object.keys(metricsMap)) { + if (metricsMap[key] === fn) { + fnName = key; + break; + } + } + if (fnName !== void 0) { + return fnName; + } + return fn.name; + } +} +function getOptimizer(identifier) { + const optimizerMap = { + "Adagrad": () => train.adagrad(0.01), + "Adadelta": () => train.adadelta(1, 0.95, epsilon()), + "Adam": () => train.adam(1e-3, 0.9, 0.999, epsilon()), + "Adamax": () => train.adamax(2e-3, 0.9, 0.999, epsilon(), 0), + "RMSProp": () => train.rmsprop(1e-3, 0.9, 0, epsilon()), + "SGD": () => train.sgd(0.01) + }; + optimizerMap["adagrad"] = optimizerMap["Adagrad"]; + optimizerMap["adadelta"] = optimizerMap["Adadelta"]; + optimizerMap["adam"] = optimizerMap["Adam"]; + optimizerMap["adamax"] = optimizerMap["Adamax"]; + optimizerMap["rmsprop"] = optimizerMap["RMSProp"]; + optimizerMap["sgd"] = optimizerMap["SGD"]; + if (identifier in optimizerMap) { + return optimizerMap[identifier](); + } + throw new ValueError(`Unknown Optimizer ${identifier}`); +} +var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024; +function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) { + if (userDefinedMetadata == null || typeof userDefinedMetadata !== "object" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) { + throw new Error("User-defined metadata is expected to be a JSON object, but is not."); + } + if (checkSize) { + const out = JSON.stringify(userDefinedMetadata); + if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) { + console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`); + } + } +} +function plainObjectCheck(x) { + if (x === null) { + return true; + } else if (typeof x === "object") { + if (Object.getPrototypeOf(x) === Object.prototype) { + const keys = Object.keys(x); + for (const key of keys) { + if (typeof key !== "string") { + return false; + } + if (!plainObjectCheck(x[key])) { + return false; + } + } + return true; + } else { + if (Array.isArray(x)) { + for (const item of x) { + if (!plainObjectCheck(item)) { + return false; + } + } + return true; + } else { + return false; + } + } + } else { + const xType = typeof x; + return xType === "string" || xType === "number" || xType === "boolean"; + } +} +function printSummary(model2, lineLength, positions, printFn = console.log) { + const sequentialLike = isModelSequentialLike(model2); + const toDisplay = ["Layer (type)", "Input Shape", "Output shape", "Param #"]; + if (sequentialLike) { + lineLength = lineLength || 90; + positions = positions || [0.32, 0.61, 0.89, 1]; + } else { + lineLength = lineLength || 115; + positions = positions || [0.24, 0.48, 0.7, 0.8, 1]; + } + if (positions[positions.length - 1] <= 1) { + positions = positions.map((p2) => Math.floor(lineLength * p2)); + } + let relevantNodes; + if (!sequentialLike) { + toDisplay.push("Receives inputs"); + relevantNodes = []; + for (const depth in model2.nodesByDepth) { + relevantNodes.push(...model2.nodesByDepth[depth]); + } + } + printFn("_".repeat(lineLength)); + printRow(toDisplay, positions, printFn); + printFn("=".repeat(lineLength)); + const layers = model2.layers; + for (let i = 0; i < layers.length; ++i) { + if (sequentialLike) { + printLayerSummary(layers[i], positions, printFn); + } else { + printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn); + } + printFn((i === layers.length - 1 ? "=" : "_").repeat(lineLength)); + } + model2.checkTrainableWeightsConsistency(); + const trainableCount = countTrainableParams(model2); + const nonTrainableCount = countParamsInWeights(model2.nonTrainableWeights); + printFn(`Total params: ${trainableCount + nonTrainableCount}`); + printFn(`Trainable params: ${trainableCount}`); + printFn(`Non-trainable params: ${nonTrainableCount}`); + printFn("_".repeat(lineLength)); +} +function countTrainableParams(model2) { + let trainableCount; + if (model2.collectedTrainableWeights != null) { + trainableCount = countParamsInWeights(model2.collectedTrainableWeights); + } else { + trainableCount = countParamsInWeights(model2.trainableWeights); + } + return trainableCount; +} +function isModelSequentialLike(model2) { + let sequentialLike = true; + const nodesByDepth = []; + const nodes = []; + for (const depth in model2.nodesByDepth) { + nodesByDepth.push(model2.nodesByDepth[depth]); + } + for (const depthNodes of nodesByDepth) { + if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) { + sequentialLike = false; + break; + } + nodes.push(...depthNodes); + } + if (sequentialLike) { + for (const layer of model2.layers) { + let flag = false; + for (const node of layer.inboundNodes) { + if (nodes.indexOf(node) !== -1) { + if (flag) { + sequentialLike = false; + break; + } else { + flag = true; + } + } + } + if (!sequentialLike) { + break; + } + } + } + return sequentialLike; +} +function printRow(fields, positions, printFn = console.log) { + let line = ""; + for (let i = 0; i < fields.length; ++i) { + if (i > 0) { + line = line.slice(0, line.length - 1) + " "; + } + line += fields[i]; + line = line.slice(0, positions[i]); + line += " ".repeat(positions[i] - line.length); + } + printFn(line); +} +function printLayerSummary(layer, positions, printFn) { + let outputShape; + let inputShape; + try { + inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(","); + } catch (err) { + inputShape = "multiple"; + } + try { + outputShape = JSON.stringify(layer.outputShape); + } catch (err) { + outputShape = "multiple"; + } + const name = layer.name; + const className = layer.getClassName(); + const fields = [ + `${name} (${className})`, + inputShape, + outputShape, + layer.countParams().toString() + ]; + printRow(fields, positions, printFn); +} +function printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) { + let outputShape; + let inputShape; + try { + inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(","); + } catch (err) { + inputShape = "multiple"; + } + try { + outputShape = JSON.stringify(layer.outputShape); + } catch (err) { + outputShape = "multiple"; + } + const connections = []; + for (const node of layer.inboundNodes) { + if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node) === -1) { + continue; + } + for (let i = 0; i < node.inboundLayers.length; ++i) { + const inboundLayer = node.inboundLayers[i].name; + const inboundLayerIndex = node.nodeIndices[i]; + const inboundTensorIndex = node.tensorIndices[i]; + connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`); + } + } + const name = layer.name; + const className = layer.getClassName(); + const firstConnection = connections.length === 0 ? "" : connections[0]; + const fields = [ + `${name} (${className})`, + inputShape, + outputShape, + layer.countParams().toString(), + firstConnection + ]; + printRow(fields, positions, printFn); + for (let i = 1; i < connections.length; ++i) { + printRow(["", "", "", "", connections[i]], positions, printFn); + } +} +function isArrayItemInputOrOutputName(key, index, value) { + return (key === "inboundNodes" || key === "outputLayers" || key === "inputLayers") && index === 0 && typeof value === "string"; +} +function convertPythonicToTs(pythonicConfig, key) { + if (pythonicConfig === null) { + return null; + } else if (typeof pythonicConfig === "string") { + return toCamelCase(pythonicConfig); + } else if (typeof pythonicConfig === "number" || typeof pythonicConfig === "boolean") { + return pythonicConfig; + } else if (pythonicConfig instanceof Array) { + const tsArray = []; + const arrayLength = pythonicConfig.length; + for (let i = 0; i < arrayLength; ++i) { + const item = pythonicConfig[i]; + if (isArrayItemInputOrOutputName(key, i, item)) { + tsArray.push(item); + } else { + tsArray.push(convertPythonicToTs(item, key)); + } + } + return tsArray; + } else { + const tsDict = {}; + for (const pythonicKey of Object.keys(pythonicConfig)) { + const pythonicValue = pythonicConfig[pythonicKey]; + if (pythonicKey === "name" && typeof pythonicValue === "string") { + tsDict[pythonicKey] = pythonicValue; + } else { + const tsKey = toCamelCase(pythonicKey); + tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey); + } + } + return tsDict; + } +} +function convertTsToPythonic(tsConfig, key) { + if (tsConfig === null || tsConfig === void 0) { + return null; + } else if (typeof tsConfig === "string") { + return toSnakeCase(tsConfig); + } else if (typeof tsConfig === "number" || typeof tsConfig === "boolean") { + return tsConfig; + } else if (tsConfig instanceof Array) { + const pyArray = []; + const arrayLength = tsConfig.length; + for (let i = 0; i < arrayLength; ++i) { + const item = tsConfig[i]; + if (isArrayItemInputOrOutputName(key, i, item)) { + pyArray.push(item); + } else { + pyArray.push(convertTsToPythonic(item, key)); + } + } + return pyArray; + } else { + const pyDict = {}; + for (const tsKey of Object.keys(tsConfig)) { + const tsValue = tsConfig[tsKey]; + const pyKey = toSnakeCase(tsKey); + if ((tsKey === "name" || tsKey === "className") && typeof tsValue === "string") { + pyDict[pyKey] = tsValue; + } else { + pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey); + } + } + return pyDict; + } +} +var version2 = "4.16.0"; +var isKerasSavedModelFormat = (weights) => { + const keys = Object.keys(weights); + if (keys.length === 0) { + return false; + } + const key = keys[0].split("/"); + return !isNaN(parseInt(key[key.length - 1], 10)); +}; +var Container = class _Container extends Layer { + constructor(args) { + super({}); + this.containerNodes = /* @__PURE__ */ new Set(); + this.name = args.name; + if (this.name == null) { + const prefix = this.getClassName().toLowerCase(); + this.name = getUid(prefix); + } + this.supportsMasking = false; + this.trainable_ = true; + if (Array.isArray(args.inputs)) { + this.inputs = args.inputs.slice(); + } else { + this.inputs = [args.inputs]; + } + if (Array.isArray(args.outputs)) { + this.outputs = args.outputs.slice(); + } else { + this.outputs = [args.outputs]; + } + if (unique2(this.inputs).length !== this.inputs.length) { + throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((x) => x.name)}`); + } + if (unique2(this.outputs).length !== this.outputs.length) { + console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((x) => x.name)}`); + } + this.inputLayers = []; + this.inputLayersNodeIndices = []; + this.inputLayersTensorIndices = []; + this.outputLayers = []; + this.outputLayersNodeIndices = []; + this.outputLayersTensorIndices = []; + this.layers = []; + this.internalContainerRefs = []; + for (const x of this.outputs) { + const layer = x.sourceLayer; + const nodeIndex = x.nodeIndex; + const tensorIndex = x.tensorIndex; + this.outputLayers.push(layer); + this.outputLayersNodeIndices.push(nodeIndex); + this.outputLayersTensorIndices.push(tensorIndex); + } + for (const x of this.inputs) { + const layer = x.sourceLayer; + const nodeIndex = x.nodeIndex; + const tensorIndex = x.tensorIndex; + assert2(nodeIndex === 0, "input layer has >1 nodes"); + assert2(tensorIndex === 0, "input layer has >1 tensors"); + this.inputLayers.push(layer); + this.inputLayersNodeIndices.push(nodeIndex); + this.inputLayersTensorIndices.push(tensorIndex); + } + this.inputNames = []; + this.outputNames = []; + this.feedInputShapes = []; + this.feedInputNames = []; + this.feedOutputNames = []; + for (let i = 0; i < this.inputLayers.length; i++) { + const layer = this.inputLayers[i]; + if (!(layer instanceof InputLayer)) { + throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`); + } + this.inputNames.push(layer.name); + this.feedInputShapes.push(layer.batchInputShape); + this.feedInputNames.push(layer.name); + } + for (const layer of this.outputLayers) { + this.outputNames.push(layer.name); + } + this.internalInputShapes = this.inputs.map((x) => x.shape); + this.internalOutputShapes = this.outputs.map((x) => x.shape); + const nodesDepths = {}; + const nodeIDToNode = {}; + const layersDepths = {}; + const layerIDToLayer = {}; + const layerIndices = {}; + const nodesInDecreasingDepth = []; + const buildMapOfGraph = (tensor2, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => { + if (layer == null || nodeIndex == null || tensorIndex == null) { + layer = tensor2.sourceLayer; + nodeIndex = tensor2.nodeIndex; + tensorIndex = tensor2.tensorIndex; + } + const node = layer.inboundNodes[nodeIndex]; + if (nodesInProgress2.indexOf(node) !== -1) { + throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`); + } + if (finishedNodes2.indexOf(node) !== -1) { + return; + } + this.containerNodes.add(_Container.nodeKey(layer, nodeIndex)); + if (!(layer.id in layerIndices)) { + layerIndices[layer.id] = Object.keys(layerIndices).length; + } + if (nodesInProgress2.indexOf(node) === -1) { + nodesInProgress2.push(node); + } + const numInboundLayers = node.inboundLayers.length; + for (let i = 0; i < numInboundLayers; i++) { + const x = node.inputTensors[i]; + const layer2 = node.inboundLayers[i]; + const nodeIndex2 = node.nodeIndices[i]; + const tensorIndex2 = node.tensorIndices[i]; + buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2); + } + finishedNodes2.push(node); + while (nodesInProgress2.indexOf(node) >= 0) { + nodesInProgress2.splice(nodesInProgress2.indexOf(node), 1); + } + nodesInDecreasingDepth.push(node); + }; + const finishedNodes = []; + const nodesInProgress = []; + for (const x of this.outputs) { + buildMapOfGraph(x, finishedNodes, nodesInProgress); + } + const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse(); + for (const node of reversedNodesInDecreasingDepth) { + nodeIDToNode[node.id] = node; + if (!(node.id in nodesDepths)) { + nodesDepths[node.id] = 0; + } + let depth = nodesDepths[node.id]; + const previousDepth = layersDepths[node.outboundLayer.id] == null ? 0 : layersDepths[node.outboundLayer.id]; + depth = Math.max(depth, previousDepth); + layersDepths[node.outboundLayer.id] = depth; + layerIDToLayer[node.outboundLayer.id] = node.outboundLayer; + nodesDepths[node.id] = depth; + for (let i = 0; i < node.inboundLayers.length; i++) { + const inboundLayer = node.inboundLayers[i]; + const nodeIndex = node.nodeIndices[i]; + const inboundNode = inboundLayer.inboundNodes[nodeIndex]; + const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id]; + nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2); + nodeIDToNode[inboundNode.id] = inboundNode; + } + } + const nodesByDepth = {}; + for (const nodeID in nodesDepths) { + const depth = nodesDepths[nodeID]; + if (!(depth in nodesByDepth)) { + nodesByDepth[depth] = []; + } + nodesByDepth[depth].push(nodeIDToNode[nodeID]); + } + const layersByDepth = {}; + for (const layerID in layersDepths) { + const depth = layersDepths[layerID]; + if (!(depth in layersByDepth)) { + layersByDepth[depth] = []; + } + layersByDepth[depth].push(layerIDToLayer[layerID]); + } + let depthKeys = Object.keys(layersByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare); + this.layers = []; + for (const depth of depthKeys) { + const layersForDepth = layersByDepth[depth]; + layersForDepth.sort((a, b) => { + const aIndex = layerIndices[a.id]; + const bIndex = layerIndices[b.id]; + if (aIndex < bIndex) { + return -1; + } + if (aIndex > bIndex) { + return 1; + } + return 0; + }); + for (const layer of layersForDepth) { + if (layer instanceof _Container) { + this.internalContainerRefs.push(layer); + } + this.layers.push(layer); + } + } + this.layersByDepth = layersByDepth; + depthKeys = Object.keys(nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare); + const computableTensors = this.inputs.slice(); + const layersWithCompleteInput = []; + for (const depth of depthKeys) { + for (const node of nodesByDepth[depth]) { + const layer = node.outboundLayer; + if (layer != null) { + for (const x of node.inputTensors) { + if (computableTensors.indexOf(x) === -1) { + throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`); + } + } + for (const x of node.outputTensors) { + computableTensors.push(x); + } + layersWithCompleteInput.push(layer.name); + } + } + } + this.nodesByDepth = nodesByDepth; + const allNames = this.layers.map((x) => x.name); + for (const name of allNames) { + const numOccurrences = allNames.filter((x) => x === name).length; + if (numOccurrences !== 1) { + throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(allNames)); + } + } + this.outboundNodes = []; + this.inboundNodes = []; + new Node({ + outboundLayer: this, + inboundLayers: [], + nodeIndices: [], + tensorIndices: [], + inputTensors: this.inputs, + outputTensors: this.outputs, + inputMasks: this.inputs.map((x) => null), + outputMasks: this.outputs.map((x) => null), + inputShapes: this.inputs.map((x) => x.shape), + outputShapes: this.outputs.map((x) => x.shape) + }); + this.built = true; + this._refCount = 1; + } + assertNotDisposed() { + if (this._refCount === 0) { + throw new Error(`Container '${this.name}' is already disposed.`); + } + } + /** + * Attempt to dispose a LayersModel's weights. + * + * This method decrease the reference count of the LayersModel object by 1. + * + * A LayersModel is reference-counted. Its reference count is incremented by 1 + * when it is first constructed and when it is used as a Layer of another + * LayersModel. + * + * If the reference count of a LayersModel becomes 0, the `dispose` method of + * all its constituent `Layer`s will be called. + * + * Note: If the reference count is greater than 0 after the decrement, the + * `dispose` method of its constituent `Layer`s will *not* be called. + * + * After a LayersModel is disposed, it cannot be used in calls such as + * 'predict`, `evaluate` or `fit` anymore. + * + * @returns A DisposeResult Object with the following fields: + * - refCountAfterDispose: The reference count of the LayersModel after this + * `dispose()` call. + * - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed + * during this `dispose()` call. + * @throws {Error} If the layer is not built yet, or if the LayersModel has + * already been disposed. + */ + dispose() { + this.assertNotDisposed(); + const result = { refCountAfterDispose: null, numDisposedVariables: 0 }; + if (--this._refCount === 0) { + for (const layer of this.layers) { + result.numDisposedVariables += layer.dispose().numDisposedVariables; + } + for (const container of this.internalContainerRefs) { + result.numDisposedVariables += container.dispose().numDisposedVariables; + } + } + result.refCountAfterDispose = this._refCount; + return result; + } + get trainable() { + return this.trainable_; + } + set trainable(trainable) { + this.layers.forEach((layer) => { + layer._trainableWeights.forEach((w) => w.trainable = trainable); + }); + this.trainable_ = trainable; + } + get trainableWeights() { + if (this._trainableWeights.length > 0) { + throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array."); + } + if (!this.trainable) { + return []; + } + let weights = []; + for (const layer of this.layers) { + weights = weights.concat(layer.trainableWeights); + } + return weights; + } + get nonTrainableWeights() { + const weights = []; + for (const layer of this.layers) { + weights.push(...layer.nonTrainableWeights); + } + if (!this.trainable) { + const trainableWeights = []; + for (const layer of this.layers) { + trainableWeights.push(...layer.trainableWeights); + } + return trainableWeights.concat(weights); + } + return weights; + } + get weights() { + return this.trainableWeights.concat(this.nonTrainableWeights); + } + /** + * Loads all layer weights from a JSON object. + * + * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript / + * TypeScript. The utility script at `scripts/pykeras.py` offers means + * to convert them into JSON strings compatible with this method. + * Porting Note: TensorFlow.js Layers supports only loading by name currently. + * + * @param weights A JSON mapping weight names to weight values as nested + * arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight + * names to `tf.Tensor` objects. + * @param strict Require that the provided weights exactly match those + * required by the container. Default: `true`. Passing `false` means that + * extra weights and missing weights will be silently ignored. + */ + loadWeights(weights, strict = true) { + const nameToWeight = {}; + let totalWeightsCount = 0; + const modelIsKerasSavedModelFormat = isKerasSavedModelFormat(weights); + if (modelIsKerasSavedModelFormat) { + this.parseWeights(weights); + } + for (const layer of this.layers) { + for (const [index, weight] of layer.weights.entries()) { + const parsedName = modelIsKerasSavedModelFormat ? `${weight.name.split("/").slice(0, -1).join("/") + "/"}${index}` : weight.originalName; + if (nameToWeight[parsedName] != null) { + throw new ValueError(`Duplicate weight name: ${parsedName}`); + } + nameToWeight[parsedName] = weight; + totalWeightsCount++; + } + } + const weightValueTuples = []; + for (const name in weights) { + let validatedName = name; + if (nameToWeight[name] == null) { + const tokens = name.split("/"); + const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]); + validatedName = shortenNameArray.join("/"); + } + if (nameToWeight[validatedName] != null) { + weightValueTuples.push([nameToWeight[validatedName], weights[name]]); + } else if (strict) { + throw new ValueError(`Provided weight data has no target variable: ${name}`); + } + delete nameToWeight[validatedName]; + } + if (strict) { + const unsetNames = []; + for (const name in nameToWeight) { + unsetNames.push(name); + } + if (unsetNames.length > 0) { + throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`); + } + } + batchSetValue(weightValueTuples); + } + parseWeights(weights) { + for (const key in Object.keys(weights)) { + const listParts = key.split("/"); + const list = ["vars", "layer_checkpoint_dependencies"]; + const newKey = listParts.map((str) => { + if (str.startsWith("_")) { + return str.slice(1); + } + return str; + }).filter((str) => !list.includes(str)).join("/"); + if (newKey !== key) { + weights[newKey] = weights[key]; + delete weights[key]; + } + } + } + /** + * Util shared between different serialization methods. + * @returns LayersModel config with Keras version information added. + */ + updatedConfig() { + const theConfig = this.getConfig(); + const modelConfig = {}; + modelConfig["className"] = this.getClassName(); + modelConfig["config"] = theConfig; + modelConfig["kerasVersion"] = `tfjs-layers ${version2}`; + modelConfig["backend"] = "TensorFlow.js"; + return modelConfig; + } + /** + * Returns a JSON string containing the network configuration. + * + * To load a network from a JSON save file, use + * models.modelFromJSON(jsonString); + * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras + * @param returnString Whether the return value should be stringified + * (default: `true`). + * @returns a JSON string if `returnString` (default), or a JSON object if + * `!returnString`. + */ + // tslint:disable-next-line:no-any + toJSON(unused, returnString = true) { + const modelConfig = convertTsToPythonic(this.updatedConfig()); + return returnString ? JSON.stringify(modelConfig) : modelConfig; + } + /** + * Call the model on new inputs. + * + * In this case `call` just reapplies all ops in the graph to the new inputs + * (e.g. build a new computational graph from the provided inputs). + * + * @param inputs A tensor or list of tensors. + * @param mask A mask or list of masks. A mask can be either a tensor or null + * (no mask). + * + * @return A tensor if there is a single output, or a list of tensors if there + * are more than one outputs. + */ + call(inputs, kwargs) { + return tidy(() => { + inputs = toList(inputs); + const feedDict = new FeedDict(); + for (let i = 0; i < this.inputs.length; ++i) { + feedDict.add(this.inputs[i], inputs[i]); + } + return execute(this.outputs, feedDict, kwargs); + }); + } + /** + * Computes an output mask tensor. + * + * @param inputs Tensor or list of tensors. + * @param mask Tensor or list of tensors. + * + * @return null or a tensor (or list of tensors, one per output tensor of the + * layer). + */ + computeMask(inputs, mask) { + return tidy(() => { + inputs = toList(inputs); + let masks; + if (mask == null) { + masks = pyListRepeat(null, inputs.length); + } else { + masks = toList(mask); + } + return this.runInternalGraph(inputs, masks)[1]; + }); + } + /** + * Computes the output shape of the layer. + * + * Assumes that the layer will be built to match that input shape provided. + * + * @param inputShape A shape (tuple of integers) or a list of shape tuples + * (one per output tensor of the layer). Shape tuples can include null for + * free dimensions, instead of an integer. + */ + computeOutputShape(inputShape) { + const inputShapes = normalizeShapeList(inputShape); + if (inputShapes.length !== this.inputLayers.length) { + throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`); + } + const layersToOutputShapes = {}; + for (let i = 0; i < inputShapes.length; i++) { + const layer = this.inputLayers[i]; + const inputShape2 = inputShapes[i]; + const shapeKey = layer.name + "_0_0"; + layersToOutputShapes[shapeKey] = inputShape2; + } + const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare); + if (depthKeys.length > 1) { + for (const depth of depthKeys) { + const nodes = this.nodesByDepth[depth]; + for (const node of nodes) { + const layer = node.outboundLayer; + if (this.inputLayers.map((x) => x.id).indexOf(layer.id) !== -1) { + continue; + } + const inputShapes2 = []; + for (let j = 0; j < node.inboundLayers.length; j++) { + const inboundLayer = node.inboundLayers[j]; + const nodeIndex2 = node.nodeIndices[j]; + const tensorIndex = node.tensorIndices[j]; + const shapeKey = `${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`; + const inputShape2 = layersToOutputShapes[shapeKey]; + inputShapes2.push(inputShape2); + } + const outputShape = layer.computeOutputShape(singletonOrArray(inputShapes2)); + const outputShapes2 = normalizeShapeList(outputShape); + const nodeIndex = layer.inboundNodes.indexOf(node); + for (let j = 0; j < outputShapes2.length; j++) { + const shapeKey = `${layer.name}_${nodeIndex}_${j}`; + layersToOutputShapes[shapeKey] = outputShapes2[j]; + } + } + } + } + const outputShapes = []; + const outputShapeKeys = []; + for (let i = 0; i < this.outputLayers.length; i++) { + const layer = this.outputLayers[i]; + const nodeIndex = this.outputLayersNodeIndices[i]; + const tensorIndex = this.outputLayersTensorIndices[i]; + const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`; + outputShapeKeys.push(shapeKey); + } + for (let i = 0; i < outputShapeKeys.length; i++) { + const key = outputShapeKeys[i]; + assert2(key in layersToOutputShapes); + outputShapes.push(layersToOutputShapes[key]); + } + return singletonOrArray(outputShapes); + } + /** + * Computes output tensors for new inputs. + * + * Note: + * - Expects `inputs` to be a list (potentially with 1 element). + * + * @param inputs List of tensors + * @param masks List of masks (tensors or null). + * @return Three lists: outputTensors, outputMasks, outputShapes + */ + runInternalGraph(inputs, masks) { + if (masks == null) { + masks = pyListRepeat(null, inputs.length); + } + const tensorMap = {}; + for (let i = 0; i < this.inputs.length; ++i) { + const x = this.inputs[i]; + const y = inputs[i]; + const mask = masks[i]; + tensorMap[x.id] = [y, mask]; + } + const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare); + for (const depth of depthKeys) { + const nodes = this.nodesByDepth[depth]; + for (const node of nodes) { + const layer = node.outboundLayer; + const referenceInputTensors = node.inputTensors; + const referenceOutputTensors = node.outputTensors; + const computedData = new Array(); + for (const x of referenceInputTensors) { + if (x.id in tensorMap) { + computedData.push(tensorMap[x.id]); + } + } + if (computedData.length === referenceInputTensors.length) { + let kwargs = {}; + let computedTensors; + let computedMasks; + let outputTensors2; + let outputMasks2; + if (node.callArgs != null) { + kwargs = node.callArgs; + } + if (computedData.length === 1) { + const [computedTensor, computedMask] = computedData[0]; + if (kwargs["mask"] == null) { + kwargs["mask"] = computedMask; + } + outputTensors2 = toList(layer.call(computedTensor, kwargs)); + outputMasks2 = toList(layer.computeMask(computedTensor, computedMask)); + computedTensors = [computedTensor]; + computedMasks = [computedMask]; + } else { + computedTensors = computedData.map((x) => x[0]); + computedMasks = computedData.map((x) => x[1]); + if (kwargs["mask"] == null) { + kwargs["mask"] = computedMasks; + } + outputTensors2 = toList(layer.call(computedTensors, kwargs)); + outputMasks2 = toList(layer.computeMask(computedTensors, computedMasks)); + } + if (layer.activityRegularizer) { + throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet."); + } + for (let i = 0; i < referenceOutputTensors.length; ++i) { + const x = referenceOutputTensors[i]; + const y = outputTensors2[i]; + const mask = outputMasks2[i]; + tensorMap[x.id] = [y, mask]; + } + } + } + } + const outputTensors = []; + const outputMasks = []; + const outputShapes = []; + for (const x of this.outputs) { + assert2(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`); + const [tensor2, mask] = tensorMap[x.id]; + outputShapes.push(tensor2.shape); + outputTensors.push(tensor2); + outputMasks.push(mask); + } + return [outputTensors, outputMasks, outputShapes]; + } + /** + * Builds a map of internal node keys to node ordering. + * Used in serializaion a node orderings may change as unused nodes are + * dropped. Porting Note: This helper method was pulled out of getConfig to + * improve readability. + * @param layers An array of Layers in the model. + * @returns Map of Node Keys to index order within the layer. + */ + buildNodeConversionMap(layers) { + const nodeConversionMap = {}; + let keptNodes; + for (const layer of this.layers) { + keptNodes = layer instanceof _Container ? 1 : 0; + for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) { + const nodeKey = _Container.nodeKey(layer, originalNodeIndex); + if (this.containerNodes.has(nodeKey)) { + nodeConversionMap[nodeKey] = keptNodes; + keptNodes += 1; + } + } + } + return nodeConversionMap; + } + getLayer(nameOrIndex, index) { + if (index != null) { + return this.findLayer(index); + } else { + if (nameOrIndex == null) { + throw new ValueError("Provide either a layer name or layer index"); + } + if (typeof nameOrIndex === "number") { + return this.findLayer(nameOrIndex); + } + } + for (const layer of this.layers) { + if (layer.name === nameOrIndex) { + return layer; + } + } + throw new ValueError(`No such layer: ${nameOrIndex}`); + } + findLayer(index) { + if (this.layers.length <= index) { + throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`); + } else { + return this.layers[index]; + } + } + /** + * Retrieves the Container's current loss values. + * + * Used for regularizers during training. + */ + calculateLosses() { + return tidy(() => { + const losses2 = []; + for (const layer of this.layers) { + for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) { + const nodeKey = _Container.nodeKey(layer, nodeIndex); + if (this.containerNodes.has(nodeKey)) { + losses2.push(...layer.calculateLosses()); + } + } + } + return losses2; + }); + } + getConfig() { + const config = { name: this.name }; + const nodeConversionMap = this.buildNodeConversionMap(this.layers); + const layerConfigs = []; + for (const layer of this.layers) { + const layerClassName = layer.getClassName(); + const layerConfig = layer.getConfig(); + const filteredInboundNodes = []; + for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) { + const node = layer.inboundNodes[originalNodeIndex]; + const nodeKey = _Container.nodeKey(layer, originalNodeIndex); + let kwargs = {}; + if (this.containerNodes.has(nodeKey)) { + if (node.callArgs) { + try { + JSON.stringify(node.callArgs); + kwargs = node.callArgs; + } catch (err) { + console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`); + kwargs = {}; + } + } + if (node.inboundLayers.length > 0) { + const nodeData = []; + for (let i = 0; i < node.inboundLayers.length; i++) { + const inboundLayer = node.inboundLayers[i]; + const nodeIndex = node.nodeIndices[i]; + const tensorIndex = node.tensorIndices[i]; + const nodeKey2 = _Container.nodeKey(inboundLayer, nodeIndex); + let newNodeIndex = nodeConversionMap[nodeKey2]; + if (newNodeIndex == null) { + newNodeIndex = 0; + } + nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]); + } + filteredInboundNodes.push(nodeData); + } + } + } + const dict = {}; + dict["name"] = layer.name; + dict["className"] = layerClassName; + dict["config"] = layerConfig; + dict["inboundNodes"] = filteredInboundNodes; + layerConfigs.push(dict); + } + config["layers"] = layerConfigs; + const modelInputs = []; + for (let i = 0; i < this.inputLayers.length; i++) { + const layer = this.inputLayers[i]; + const nodeIndex = this.inputLayersNodeIndices[i]; + const nodeKey = _Container.nodeKey(layer, nodeIndex); + if (!this.containerNodes.has(nodeKey)) { + continue; + } + let newNodeIndex = nodeConversionMap[nodeKey]; + if (newNodeIndex === null || newNodeIndex === void 0) { + newNodeIndex = 0; + } + const tensorIndex = this.inputLayersTensorIndices[i]; + modelInputs.push([layer.name, newNodeIndex, tensorIndex]); + } + config["inputLayers"] = modelInputs; + const modelOutputs = []; + for (let i = 0; i < this.outputLayers.length; i++) { + const layer = this.outputLayers[i]; + const nodeIndex = this.outputLayersNodeIndices[i]; + const nodeKey = _Container.nodeKey(layer, nodeIndex); + if (!this.containerNodes.has(nodeKey)) { + continue; + } + let newNodeIndex = nodeConversionMap[nodeKey]; + if (newNodeIndex === null || newNodeIndex === void 0) { + newNodeIndex = 0; + } + const tensorIndex = this.outputLayersTensorIndices[i]; + modelOutputs.push([layer.name, newNodeIndex, tensorIndex]); + } + config["outputLayers"] = modelOutputs; + return config; + } + /** + * Instantiates a LayersModel from its config (output of `get_config()`). + * @param cls the class to create + * @param config LayersModel config dictionary. + * @param customObjects An optional dictionary of custom objects. + * @param fastWeightInit Optional flag to use fast weight initialization + * during deserialization. This is applicable to cases in which + * the initialization will be immediately overwritten by loaded weight + * values. Default: `false`. + * @returns A LayersModel instance. + * @throws ValueError: In case of improperly formatted config dict. + */ + /** @nocollapse */ + static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) { + const createdLayers = {}; + const unprocessedNodes = {}; + function addUnprocessedNode(layer, nodeData) { + if (!(layer.name in unprocessedNodes)) { + unprocessedNodes[layer.name] = [nodeData]; + } else { + unprocessedNodes[layer.name].push(nodeData); + } + } + function processNode(layer, nodeData) { + const inputTensors2 = []; + let kwargs; + for (const inputData of nodeData) { + const inboundLayerName = inputData[0]; + const inboundNodeIndex = inputData[1]; + const inboundTensorIndex = inputData[2]; + kwargs = inputData[3] == null ? {} : inputData[3]; + if (!(inboundLayerName in createdLayers)) { + addUnprocessedNode(layer, nodeData); + return; + } + const inboundLayer = createdLayers[inboundLayerName]; + if (inboundLayer.inboundNodes.length <= inboundNodeIndex) { + addUnprocessedNode(layer, nodeData); + return; + } + const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex]; + inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex]); + } + if (inputTensors2.length > 0) { + layer.apply(singletonOrArray(inputTensors2), kwargs); + } + } + function processLayer(layerData) { + const layerName = layerData["name"]; + const layer = deserialize(layerData, config["customObjects"] != null ? config["customObjects"] : {}); + layer.setFastWeightInitDuringBuild(fastWeightInit); + createdLayers[layerName] = layer; + const inboundNodesData = layerData["inboundNodes"]; + inboundNodesData.forEach((nodeData) => { + if (!(nodeData instanceof Array)) { + throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`); + } + addUnprocessedNode(layer, nodeData); + }); + } + const name = config["name"]; + const layersFromConfig = config["layers"]; + for (const layerData of layersFromConfig) { + processLayer(layerData); + } + while (!isObjectEmpty(unprocessedNodes)) { + for (const layerData of layersFromConfig) { + const layer = createdLayers[layerData["name"]]; + if (layer.name in unprocessedNodes) { + const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name]; + delete unprocessedNodes[layer.name]; + for (const nodeData of currentUnprocessedNodesForLayer) { + processNode(layer, nodeData); + } + } + } + } + const inputTensors = []; + const outputTensors = []; + const inputLayersFromConfig = config["inputLayers"]; + for (const layerData of inputLayersFromConfig) { + const layerName = layerData[0]; + const nodeIndex = layerData[1]; + const tensorIndex = layerData[2]; + assert2(layerName in createdLayers); + const layer = createdLayers[layerName]; + const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors; + inputTensors.push(layerOutputTensors[tensorIndex]); + } + const outputLayersFromConfig = config["outputLayers"]; + for (const layerData of outputLayersFromConfig) { + const layerName = layerData[0]; + const nodeIndex = layerData[1]; + const tensorIndex = layerData[2]; + assert2(layerName in createdLayers); + const layer = createdLayers[layerName]; + const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors; + outputTensors.push(layerOutputTensors[tensorIndex]); + } + return new cls({ inputs: inputTensors, outputs: outputTensors, name }); + } + /** + * Determine whether the container is stateful. + * + * Porting Note: this is the equivalent of the stateful @property of + * the Container class in PyKeras. + */ + get stateful() { + if (this._stateful) { + throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false."); + } + for (const layer of this.layers) { + if (layer.stateful) { + return true; + } + } + return false; + } + /** + * Reset the state of all stateful constituent layers (if any). + * + * Examples of stateful layers include RNN layers whose `stateful` property + * is set as `true`. + */ + resetStates() { + tidy(() => { + this.layers.forEach((layer) => { + if (layer.stateful) { + layer.resetStates(); + } + }); + }); + } +}; +function standardizeSampleOrClassWeights(xWeight, outputNames, weightType) { + const numOutputs = outputNames.length; + if (xWeight == null || Array.isArray(xWeight) && xWeight.length === 0) { + return outputNames.map((name) => null); + } + if (numOutputs === 1) { + if (Array.isArray(xWeight) && xWeight.length === 1) { + return xWeight; + } else if (typeof xWeight === "object" && outputNames[0] in xWeight) { + return [xWeight[outputNames[0]]]; + } else { + return [xWeight]; + } + } + if (Array.isArray(xWeight)) { + if (xWeight.length !== numOutputs) { + throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`); + } + return xWeight; + } else if (typeof xWeight === "object" && Object.keys(xWeight).length > 0 && typeof xWeight[Object.keys(xWeight)[0]] === "object") { + const output = []; + outputNames.forEach((outputName) => { + if (outputName in xWeight) { + output.push(xWeight[outputName]); + } else { + output.push(null); + } + }); + return output; + } else { + throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`); + } +} +function standardizeClassWeights(classWeight, outputNames) { + return standardizeSampleOrClassWeights(classWeight, outputNames, "classWeight"); +} +async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) { + if (sampleWeight != null || sampleWeightMode != null) { + throw new Error("Support sampleWeight is not implemented yet"); + } + if (classWeight != null) { + const yClasses = tidy(() => { + if (y.shape.length === 1) { + return clone(y); + } else if (y.shape.length === 2) { + if (y.shape[1] > 1) { + const axis = 1; + return argMax(y, axis); + } else if (y.shape[1] === 1) { + return reshape(y, [y.shape[0]]); + } else { + throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`); + } + } else { + throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`); + } + }); + const yClassIndices = Array.from(await yClasses.data()); + dispose(yClasses); + const classSampleWeight = []; + yClassIndices.forEach((classIndex) => { + if (classWeight[classIndex] == null) { + throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`); + } else { + classSampleWeight.push(classWeight[classIndex]); + } + }); + return tensor1d(classSampleWeight, "float32"); + } else { + return null; + } +} +function computeWeightedLoss2(losses2, sampleWeights) { + return mul(losses2, sampleWeights); +} +var DEFAULT_VALIDATION_BATCH_SIZE = 32; +function standardizeDataIteratorOutput(model2, iteratorOut) { + let xs; + let ys; + const iteratorOutObj = iteratorOut; + xs = iteratorOutObj["xs"]; + ys = iteratorOutObj["ys"]; + util_exports.assert(xs != null && ys != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${iteratorOut}`); + const flattenedXs = flattenTensorOrArrayOrMap("input", model2.inputNames, xs); + const flattenedYs = flattenTensorOrArrayOrMap("output", model2.outputNames, ys); + const batchSize = flattenedXs[0].shape[0]; + util_exports.assert(flattenedXs.length === model2.inputs.length, () => `LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model2.inputNames)})`); + util_exports.assert(flattenedYs.length === model2.outputs.length, () => `LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model2.outputNames)})`); + for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) { + util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`); + } + for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) { + util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`); + } + return { xs: flattenedXs, ys: flattenedYs }; +} +function flattenTensorOrArrayOrMap(inputOrOutput, names, values) { + if (values instanceof Tensor) { + return [values]; + } else if (Array.isArray(values)) { + util_exports.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`); + return values; + } else { + const result = []; + for (const name of names) { + if (values[name] == null) { + throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`); + } + result.push(values[name]); + } + return result; + } +} +function standardizeTensorValidationData(data) { + if (data.length === 3) { + throw new NotImplementedError("Validation with sample weights is not implemented yet."); + } + return { xs: data[0], ys: data[1] }; +} +async function fitDataset(model2, dataset, args) { + const hasBatchesPerEpoch = args.batchesPerEpoch != null; + util_exports.assert(model2.optimizer != null, () => "You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."); + util_exports.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.`); + util_exports.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`); + util_exports.assert(!hasBatchesPerEpoch || args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`); + util_exports.assert( + // tslint:disable-next-line:no-any + args["validationSplit"] == null, + () => "`validationSplit` is not supported by `fitDataset()`. Use validationData instead." + ); + if (model2.isTraining) { + throw new Error("Cannot start training because another fit() call is ongoing."); + } + model2.isTraining = true; + try { + const doValidation = args.validationData != null; + let valXs; + let valYs; + if (doValidation) { + if (isDatasetObject(args.validationData)) { + util_exports.assert(args.validationBatches == null || args.validationBatches > 0 && Number.isInteger(args.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`); + } else { + const validationData = standardizeTensorValidationData(args.validationData); + valXs = validationData.xs; + valYs = validationData.ys; + } + } + const trainFunction = model2.makeTrainFunction(); + const outLabels = model2.getDedupedMetricsNames(); + let callbackMetrics; + if (doValidation) { + callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n)); + } else { + callbackMetrics = outLabels.slice(); + } + const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery); + const verbose = args.verbose == null ? 1 : args.verbose; + const { callbackList, history } = configureCallbacks( + callbacks2, + verbose, + args.epochs, + null, + null, + getStepsPerEpoch(dataset, args), + null, + // Batch size determined by the dataset itself. + doValidation, + callbackMetrics + ); + callbackList.setModel(model2); + model2.history = history; + await callbackList.onTrainBegin(); + model2.stopTraining_ = false; + let epoch = args.initialEpoch == null ? 0 : args.initialEpoch; + let dataIterator = await dataset.iterator(); + while (epoch < args.epochs) { + const epochLogs = {}; + await callbackList.onEpochBegin(epoch); + let stepsDone = 0; + let batchIndex = 0; + if (!hasBatchesPerEpoch) { + dataIterator = await dataset.iterator(); + } + while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) { + const iteratorOut = await dataIterator.next(); + if (hasBatchesPerEpoch && iteratorOut.done) { + console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`); + break; + } + if (iteratorOut.value != null) { + const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value); + const batchLogs = {}; + batchLogs["batch"] = batchIndex; + batchLogs["size"] = xs[0].shape[0]; + await callbackList.onBatchBegin(batchIndex, batchLogs); + const sampleWeights = []; + if (args.classWeight != null) { + const standardClassWeights = standardizeClassWeights(args.classWeight, model2.outputNames); + for (let i = 0; i < standardClassWeights.length; ++i) { + sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i])); + } + } + const ins = xs.concat(ys).concat(sampleWeights); + const outs = trainFunction(ins); + dispose(ins); + for (let i = 0; i < outLabels.length; ++i) { + const label = outLabels[i]; + const out = outs[i]; + batchLogs[label] = out; + keep(out); + } + await callbackList.onBatchEnd(batchIndex, batchLogs); + disposeTensorsInLogs(batchLogs); + batchIndex++; + stepsDone++; + } + if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) { + if (doValidation) { + let valOuts; + if (isDatasetObject(args.validationData)) { + valOuts = toList(await model2.evaluateDataset(args.validationData, { batches: args.validationBatches })); + } else { + valOuts = toList(model2.evaluate(valXs, valYs, { + batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize, + verbose: 0 + })); + } + for (let i = 0; i < model2.metricsNames.length; ++i) { + epochLogs[`val_${model2.metricsNames[i]}`] = valOuts[i]; + } + } + break; + } + if (model2.stopTraining_) { + break; + } + } + await callbackList.onEpochEnd(epoch, epochLogs); + epoch++; + if (model2.stopTraining_) { + break; + } + } + await callbackList.onTrainEnd(); + await model2.history.syncData(); + return model2.history; + } finally { + model2.isTraining = false; + } +} +function getStepsPerEpoch(dataset, args) { + let stepsPerEpoch = null; + if (args.batchesPerEpoch != null) { + stepsPerEpoch = args.batchesPerEpoch; + } else if (Number.isFinite(dataset.size)) { + stepsPerEpoch = dataset.size; + } + return stepsPerEpoch; +} +function isDatasetObject(dataset) { + return typeof dataset.iterator === "function"; +} +function isLazyIteratorObject(iterator) { + return typeof iterator.next === "function"; +} +async function evaluateDataset(model2, dataset, args) { + args = args || {}; + const hasBatches = args.batches != null; + const f = model2.testFunction; + let outs = []; + if (args.verbose > 0) { + throw new NotImplementedError("Verbose mode is not implemented yet."); + } + util_exports.assert(!hasBatches || args.batches > 0 && Number.isInteger(args.batches), () => `Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`); + const dataIterator = isLazyIteratorObject(dataset) ? dataset : await dataset.iterator(); + let numExamples = 0; + let batch = 0; + while (hasBatches ? batch < args.batches : true) { + const iteratorOut = await dataIterator.next(); + outs = tidy(() => { + if (iteratorOut.value) { + const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value); + const xsAndYs = xs.concat(ys); + const batchOuts = tidy(() => f(xsAndYs)); + dispose(xsAndYs); + if (batch === 0) { + for (let i = 0; i < batchOuts.length; ++i) { + outs.push(scalar(0)); + } + } + const batchSize = xsAndYs[0].shape[0]; + for (let i = 0; i < batchOuts.length; ++i) { + const batchOut = batchOuts[i]; + const oldScalar = outs[i]; + outs[i] = tidy(() => add2(outs[i], mul(batchSize, batchOut))); + if (batch > 0) { + dispose(oldScalar); + } + } + dispose(batchOuts); + numExamples += batchSize; + ++batch; + } + return outs; + }); + if (iteratorOut.done) { + if (hasBatches) { + console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`); + } + break; + } + } + for (let i = 0; i < outs.length; ++i) { + const oldScalar = outs[i]; + outs[i] = div(outs[i], numExamples); + dispose(oldScalar); + } + return singletonOrArray(outs); +} +function checkBatchSize(batchSize) { + util_exports.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`); +} +function sliceArrays(arrays, start, stop) { + if (arrays == null) { + return [null]; + } else if (Array.isArray(arrays)) { + return arrays.map((array2) => sliceAlongFirstAxis(array2, start, stop - start)); + } else { + return sliceAlongFirstAxis(arrays, start, stop - start); + } +} +function sliceArraysByIndices(arrays, indices) { + return tidy(() => { + if (arrays == null) { + return null; + } else if (Array.isArray(arrays)) { + return arrays.map((array2) => sliceArraysByIndices(array2, indices)); + } else { + return gather2(arrays, indices.dtype === "int32" ? indices : cast(indices, "int32")); + } + }); +} +function makeBatches(size, batchSize) { + const output = []; + let batchStart = 0; + let batchEnd = null; + while (batchStart < size) { + batchEnd = batchStart + batchSize; + if (batchEnd >= size) { + batchEnd = size; + } + output.push([batchStart, batchEnd]); + batchStart = batchEnd; + } + return output; +} +function ensureTensorsRank2OrHigher(tensors) { + const outs = []; + if (tensors instanceof Tensor) { + tensors = [tensors]; + } + for (let i = 0; i < tensors.length; ++i) { + const tensor2 = tensors[i]; + if (tensor2.rank === 1) { + outs.push(expandDims2(tensor2, 1)); + } else if (tensor2.rank === 0) { + throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar)."); + } else { + outs.push(tensor2); + } + } + return outs; +} +function disposeNewTensors(tensors, refTensors) { + if (tensors == null) { + return; + } + const oldTensorIds = []; + if (refTensors instanceof Tensor) { + oldTensorIds.push(refTensors.id); + } else if (Array.isArray(refTensors)) { + refTensors.forEach((t) => oldTensorIds.push(t.id)); + } else if (refTensors != null) { + for (const name in refTensors) { + const oldTensor = refTensors[name]; + oldTensorIds.push(oldTensor.id); + } + } + const tensorsToDispose = []; + if (tensors instanceof Tensor) { + if (oldTensorIds.indexOf(tensors.id) === -1) { + tensorsToDispose.push(tensors); + } + } else if (Array.isArray(tensors)) { + tensors.forEach((t) => { + if (oldTensorIds.indexOf(t.id) === -1) { + tensorsToDispose.push(t); + } + }); + } else if (tensors != null) { + for (const name in tensors) { + const tensor2 = tensors[name]; + if (oldTensorIds.indexOf(tensor2.id) === -1) { + tensorsToDispose.push(tensor2); + } + } + } + tensorsToDispose.forEach((t) => { + if (!t.isDisposed) { + t.dispose(); + } + }); +} +function isDataTensor(x) { + return x instanceof Tensor; +} +function isDataArray(x) { + return Array.isArray(x); +} +function isDataDict(x) { + return !isDataTensor(x) && !isDataArray(x); +} +function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") { + if (names == null || names.length === 0) { + if (data != null) { + let gotUnexpectedData = false; + if (isDataArray(data) && data.length > 0) { + gotUnexpectedData = true; + } else if (isDataDict(data)) { + for (const key in data) { + if (data.hasOwnProperty(key)) { + gotUnexpectedData = true; + break; + } + } + } else { + gotUnexpectedData = true; + } + if (gotUnexpectedData) { + throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`); + } + } + return []; + } + if (data == null) { + return names.map((name) => null); + } + let arrays; + if (isDataDict(data)) { + data = data; + arrays = []; + for (const name of names) { + if (data[name] == null) { + throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`); + } + arrays.push(data[name]); + } + } else if (isDataArray(data)) { + data = data; + if (data.length !== names.length) { + throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`); + } + arrays = data; + } else { + data = data; + if (names.length > 1) { + throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`); + } + arrays = [data]; + } + arrays = ensureTensorsRank2OrHigher(arrays); + if (shapes != null) { + for (let i = 0; i < names.length; ++i) { + if (shapes[i] == null) { + continue; + } + const array2 = arrays[i]; + if (array2.shape.length !== shapes[i].length) { + throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`); + } + for (let j = 0; j < shapes[i].length; ++j) { + if (j === 0 && !checkBatchAxis) { + continue; + } + const dim = array2.shape[j]; + const refDim = shapes[i][j]; + if (refDim != null && refDim >= 0 && dim !== refDim) { + throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i].slice(1, shapes[i].length)}] (i.e.,tensor shape [*,${shapes[i].slice(1, shapes[i].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`); + } + } + } + } + return arrays; +} +function checkArrayLengths(inputs, targets, weights) { + const setX = unique2(inputs.map((input2) => input2.shape[0])); + setX.sort(); + const setY = unique2(targets.map((target) => target.shape[0])); + setY.sort(); + if (setX.length > 1) { + throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map((input2) => input2.shape))}`); + } + if (setY.length > 1) { + throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map((target) => target.shape))}`); + } + if (setX.length > 0 && setY.length > 0 && !util_exports.arraysEqual(setX, setY)) { + throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`); + } +} +function checkLossAndTargetCompatibility(targets, lossFns, outputShapes) { + const keyLosses = [ + meanSquaredError2, + binaryCrossentropy, + categoricalCrossentropy + ]; + for (let i = 0; i < targets.length; ++i) { + const y = targets[i]; + const loss = lossFns[i]; + const shape = outputShapes[i]; + if (loss == null) { + continue; + } + if (loss === categoricalCrossentropy) { + if (y.shape[y.shape.length - 1] === 1) { + throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`); + } + } + if (keyLosses.indexOf(loss) !== -1) { + const slicedYShape = y.shape.slice(1); + const slicedShape = shape.slice(1); + for (let j = 0; j < slicedYShape.length; ++j) { + const targetDim = slicedYShape[j]; + const outDim = slicedShape[j]; + if (outDim != null && targetDim !== outDim) { + throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`); + } + } + } + } +} +function checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") { + let arrays; + if (Array.isArray(data)) { + if (data.length !== names.length) { + throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`); + } + arrays = data; + } else { + if (names.length > 1) { + throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`); + } + arrays = [data]; + } + if (shapes != null) { + for (let i = 0; i < names.length; ++i) { + if (shapes[i] == null) { + continue; + } + const array2 = arrays[i]; + if (array2.shape.length !== shapes[i].length) { + throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`); + } + for (let j = 0; j < shapes[i].length; ++j) { + if (j === 0 && !checkBatchAxis) { + continue; + } + const dim = array2.shape[j]; + const refDim = shapes[i][j]; + if (refDim != null) { + if (refDim !== dim) { + throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`); + } + } + } + } + } +} +function collectMetrics(metrics, outputNames) { + if (metrics == null || Array.isArray(metrics) && metrics.length === 0) { + return outputNames.map((name) => []); + } + let wrappedMetrics; + if (typeof metrics === "string" || typeof metrics === "function") { + wrappedMetrics = [metrics]; + } else if (Array.isArray(metrics) || typeof metrics === "object") { + wrappedMetrics = metrics; + } else { + throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics}`); + } + if (Array.isArray(wrappedMetrics)) { + return outputNames.map((name) => wrappedMetrics); + } else { + const nestedMetrics = []; + for (const name of outputNames) { + let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : []; + if (!Array.isArray(outputMetrics)) { + outputMetrics = [outputMetrics]; + } + nestedMetrics.push(outputMetrics); + } + return nestedMetrics; + } +} +var LAYERS_MODEL_FORMAT_NAME = "layers-model"; +var LayersModel = class extends Container { + constructor(args) { + super(args); + this.isTraining = false; + } + /** + * Print a text summary of the model's layers. + * + * The summary includes + * - Name and type of all layers that comprise the model. + * - Output shape(s) of the layers + * - Number of weight parameters of each layer + * - If the model has non-sequential-like topology, the inputs each layer + * receives + * - The total number of trainable and non-trainable parameters of the model. + * + * ```js + * const input1 = tf.input({shape: [10]}); + * const input2 = tf.input({shape: [20]}); + * const dense1 = tf.layers.dense({units: 4}).apply(input1); + * const dense2 = tf.layers.dense({units: 8}).apply(input2); + * const concat = tf.layers.concatenate().apply([dense1, dense2]); + * const output = + * tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat); + * + * const model = tf.model({inputs: [input1, input2], outputs: output}); + * model.summary(); + * ``` + * + * @param lineLength Custom line length, in number of characters. + * @param positions Custom widths of each of the columns, as either + * fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number + * of characters (e.g., `[30, 50, 65]`). Each number corresponds to + * right-most (i.e., ending) position of a column. + * @param printFn Custom print function. Can be used to replace the default + * `console.log`. For example, you can use `x => {}` to mute the printed + * messages in the console. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + summary(lineLength, positions, printFn = console.log) { + if (!this.built) { + throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`); + } + printSummary(this, lineLength, positions, printFn); + } + /** + * Configures and prepares the model for training and evaluation. Compiling + * outfits the model with an optimizer, loss, and/or metrics. Calling `fit` + * or `evaluate` on an un-compiled model will throw an error. + * + * @param args a `ModelCompileArgs` specifying the loss, optimizer, and + * metrics to be used for fitting and evaluating this model. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + compile(args) { + if (args.loss == null) { + args.loss = []; + } + this.loss = args.loss; + if (typeof args.optimizer === "string") { + this.optimizer_ = getOptimizer(args.optimizer); + this.isOptimizerOwned = true; + } else { + if (!(args.optimizer instanceof Optimizer)) { + throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`); + } + this.optimizer_ = args.optimizer; + this.isOptimizerOwned = false; + } + let lossFunctions = []; + if (!Array.isArray(args.loss) && typeof args.loss !== "string" && typeof args.loss !== "function") { + args.loss = args.loss; + for (const name in args.loss) { + if (this.outputNames.indexOf(name) === -1) { + throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`); + } + } + for (const name of this.outputNames) { + if (args.loss[name] == null) { + console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`); + } + lossFunctions.push(get(args.loss[name])); + } + } else if (Array.isArray(args.loss)) { + if (args.loss.length !== this.outputs.length) { + throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`); + } + const theLosses = args.loss; + lossFunctions = theLosses.map((l) => get(l)); + } else { + const lossFunction = get(args.loss); + this.outputs.forEach((_) => { + lossFunctions.push(lossFunction); + }); + } + this.lossFunctions = lossFunctions; + this.feedOutputNames = []; + this.feedOutputShapes = []; + this.feedLossFns = []; + for (let i = 0; i < this.outputs.length; ++i) { + const shape = this.internalOutputShapes[i]; + const name = this.outputNames[i]; + this.feedOutputNames.push(name); + this.feedOutputShapes.push(shape); + this.feedLossFns.push(this.lossFunctions[i]); + } + const skipTargetIndices = []; + this.metrics = args.metrics; + this.metricsNames = ["loss"]; + this.metricsTensors = []; + nameScope("loss", () => { + for (let i = 0; i < this.outputs.length; ++i) { + if (skipTargetIndices.indexOf(i) !== -1) { + continue; + } + const weightedLoss = this.lossFunctions[i]; + if (this.outputs.length > 1) { + this.metricsTensors.push([weightedLoss, i]); + this.metricsNames.push(this.outputNames[i] + "_loss"); + } + } + }); + const nestedMetrics = collectMetrics(args.metrics, this.outputNames); + const appendMetric = (outputIndex, metricName, metricTensor) => { + if (this.outputNames.length > 1) { + metricName = this.outputNames[outputIndex] + "_" + metricName; + } + this.metricsNames.push(metricName); + this.metricsTensors.push([metricTensor, outputIndex]); + }; + nameScope("metric", () => { + for (let i = 0; i < this.outputs.length; ++i) { + if (skipTargetIndices.indexOf(i) !== -1) { + continue; + } + const outputMetrics = nestedMetrics[i]; + const handleMetrics = (metrics) => { + const metricNamePrefix = ""; + let metricName; + let accFn; + let weightedMetricFn; + for (const metric of metrics) { + if (typeof metric === "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(metric) !== -1) { + const outputShape = this.internalOutputShapes[i]; + if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i] === binaryCrossentropy) { + if (["accuracy", "acc"].indexOf(metric) !== -1) { + accFn = binaryAccuracy; + } else if (["crossentropy", "ce"].indexOf(metric) !== -1) { + accFn = binaryCrossentropy2; + } + } else if (this.lossFunctions[i] === sparseCategoricalCrossentropy) { + if (["accuracy", "acc"].indexOf(metric) !== -1) { + accFn = sparseCategoricalAccuracy; + } else if (["crossentropy", "ce"].indexOf(metric) !== -1) { + accFn = sparseCategoricalCrossentropy2; + } + } else { + if (["accuracy", "acc"].indexOf(metric) !== -1) { + accFn = categoricalAccuracy; + } else if (["crossentropy", "ce"].indexOf(metric) !== -1) { + accFn = categoricalCrossentropy2; + } + } + let suffix; + if (["accuracy", "acc"].indexOf(metric) !== -1) { + suffix = "acc"; + } else if (["crossentropy", "ce"].indexOf(metric) !== -1) { + suffix = "ce"; + } + weightedMetricFn = accFn; + metricName = metricNamePrefix + suffix; + } else { + const metricFn = get2(metric); + weightedMetricFn = metricFn; + metricName = metricNamePrefix + getLossOrMetricName(metric); + } + let metricResult; + nameScope(metricName, () => { + metricResult = weightedMetricFn; + }); + appendMetric(i, metricName, metricResult); + } + }; + handleMetrics(outputMetrics); + } + }); + this.collectedTrainableWeights = this.trainableWeights; + } + /** + * Check trainable weights count consistency. + * + * This will raise a warning if `this.trainableWeights` and + * `this.collectedTrainableWeights` are inconsistent (i.e., have different + * numbers of parameters). + * Inconsistency will typically arise when one modifies `model.trainable` + * without calling `model.compile()` again. + */ + checkTrainableWeightsConsistency() { + if (this.collectedTrainableWeights == null) { + return; + } + if (this.trainableWeights.length !== this.collectedTrainableWeights.length) { + console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?"); + } + } + /** + * Returns the loss value & metrics values for the model in test mode. + * + * Loss and metrics are specified during `compile()`, which needs to happen + * before calls to `evaluate()`. + * + * Computation is done in batches. + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'}); + * const result = model.evaluate( + * tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4}); + * result.print(); + * ``` + * + * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the + * model has multiple inputs. + * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the + * model has multiple outputs. + * @param args A `ModelEvaluateArgs`, containing optional fields. + * + * @return `Scalar` test loss (if the model has a single output and no + * metrics) or `Array` of `Scalar`s (if the model has multiple outputs + * and/or metrics). The attribute `model.metricsNames` + * will give you the display labels for the scalar outputs. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + evaluate(x, y, args = {}) { + const batchSize = args.batchSize == null ? 32 : args.batchSize; + checkBatchSize(batchSize); + const checkBatchAxis = true; + const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize); + try { + const ins = standardizedOuts[0].concat(standardizedOuts[1]); + this.makeTestFunction(); + const f = this.testFunction; + const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps); + return singletonOrArray(testOuts); + } finally { + disposeNewTensors(standardizedOuts[0], x); + disposeNewTensors(standardizedOuts[1], y); + } + } + // TODO(cais): Add code snippet below once real dataset objects are + // available. + /** + * Evaluate model using a dataset object. + * + * Note: Unlike `evaluate()`, this method is asynchronous (`async`). + * + * @param dataset A dataset object. Its `iterator()` method is expected + * to generate a dataset iterator object, the `next()` method of which + * is expected to produce data batches for evaluation. The return value + * of the `next()` call ought to contain a boolean `done` field and a + * `value` field. The `value` field is expected to be an array of two + * `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former + * case is for models with exactly one input and one output (e.g. + * a sequential model). The latter case is for models with multiple + * inputs and/or multiple outputs. Of the two items in the array, the + * first is the input feature(s) and the second is the output target(s). + * @param args A configuration object for the dataset-based evaluation. + * @returns Loss and metric values as an Array of `Scalar` objects. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async evaluateDataset(dataset, args) { + this.makeTestFunction(); + return evaluateDataset(this, dataset, args); + } + /** + * Get number of samples provided for training, evaluation or prediction. + * + * @param ins Input `tf.Tensor`. + * @param batchSize Integer batch size, optional. + * @param steps Total number of steps (batches of samples) before + * declaring loop finished. Optional. + * @param stepsName The public API's parameter name for `steps`. + * @returns Number of samples provided. + */ + checkNumSamples(ins, batchSize, steps, stepsName = "steps") { + let numSamples; + if (steps != null) { + numSamples = null; + if (batchSize != null) { + throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`); + } + } else if (ins != null) { + if (Array.isArray(ins)) { + numSamples = ins[0].shape[0]; + } else { + numSamples = ins.shape[0]; + } + } else { + throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`); + } + return numSamples; + } + /** + * Execute internal tensors of the model with input data feed. + * @param inputs Input data feed. Must match the inputs of the model. + * @param outputs Names of the output tensors to be fetched. Must match + * names of the SymbolicTensors that belong to the graph. + * @returns Fetched values for `outputs`. + */ + execute(inputs, outputs) { + if (Array.isArray(outputs) && outputs.length === 0) { + throw new ValueError("`outputs` is an empty Array, which is not allowed."); + } + const outputsIsArray = Array.isArray(outputs); + const outputNames = outputsIsArray ? outputs : [outputs]; + const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames); + const feedDict = new FeedDict(); + if (inputs instanceof Tensor) { + inputs = [inputs]; + } + if (Array.isArray(inputs)) { + if (inputs.length !== this.inputs.length) { + throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`); + } + for (let i = 0; i < this.inputs.length; ++i) { + feedDict.add(this.inputs[i], inputs[i]); + } + } else { + for (const input2 of this.inputs) { + const tensorValue = inputs[input2.name]; + if (tensorValue == null) { + throw new ValueError(`No value is provided for the model's input ${input2.name}`); + } + feedDict.add(input2, tensorValue); + } + } + const executeOutputs = execute(outputSymbolicTensors, feedDict); + return outputsIsArray ? executeOutputs : executeOutputs[0]; + } + /** + * Retrieve the model's internal symbolic tensors from symbolic-tensor names. + */ + retrieveSymbolicTensors(symbolicTensorNames) { + const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length); + let outputsRemaining = symbolicTensorNames.length; + for (const layer of this.layers) { + const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output]; + const layerOutputNames = layerOutputs.map((output) => output.name); + for (let i = 0; i < symbolicTensorNames.length; ++i) { + const index = layerOutputNames.indexOf(symbolicTensorNames[i]); + if (index !== -1) { + outputSymbolicTensors[i] = layerOutputs[index]; + outputsRemaining--; + } + if (outputsRemaining === 0) { + break; + } + } + if (outputsRemaining === 0) { + break; + } + } + if (outputsRemaining > 0) { + const remainingNames = []; + outputSymbolicTensors.forEach((tensor2, i) => { + if (tensor2 == null) { + remainingNames.push(symbolicTensorNames[i]); + } + }); + throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`); + } + return outputSymbolicTensors; + } + /** + * Helper method to loop over some data in batches. + * + * Porting Note: Not using the functional approach in the Python equivalent + * due to the imperative backend. + * Porting Note: Does not support step mode currently. + * + * @param ins: input data + * @param batchSize: integer batch size. + * @param verbose: verbosity model + * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of + * `tf.Tensor` (if multipe outputs). + */ + predictLoop(ins, batchSize = 32, verbose = false) { + return tidy(() => { + const numSamples = this.checkNumSamples(ins); + if (verbose) { + throw new NotImplementedError("Verbose predictLoop() is not implemented yet."); + } + const batches = makeBatches(numSamples, batchSize); + const outsBatches = this.outputs.map((output) => []); + for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) { + const batchOuts = tidy(() => { + const batchStart = batches[batchIndex][0]; + const batchEnd = batches[batchIndex][1]; + const insBatch = sliceArrays(ins, batchStart, batchEnd); + const feeds = []; + if (Array.isArray(insBatch)) { + for (let i = 0; i < insBatch.length; ++i) { + feeds.push({ key: this.inputs[i], value: insBatch[i] }); + } + } else { + feeds.push({ key: this.inputs[0], value: insBatch }); + } + const feedDict = new FeedDict(feeds); + return execute(this.outputs, feedDict); + }); + batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut)); + } + return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0))); + }); + } + /** + * Generates output predictions for the input samples. + * + * Computation is done in batches. + * + * Note: the "step" mode of predict() is currently not supported. + * This is because the TensorFlow.js core backend is imperative only. + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.predict(tf.ones([8, 10]), {batchSize: 4}).print(); + * ``` + * + * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if + * the model has multiple inputs. + * @param args A `ModelPredictArgs` object containing optional fields. + * + * @return Prediction results as a `tf.Tensor`(s). + * + * @exception ValueError In case of mismatch between the provided input data + * and the model's expectations, or in case a stateful model receives a + * number of samples that is not a multiple of the batch size. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + predict(x, args = {}) { + const xsRank2OrHigher = ensureTensorsRank2OrHigher(x); + checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false); + try { + const batchSize = args.batchSize == null ? 32 : args.batchSize; + checkBatchSize(batchSize); + return this.predictLoop(xsRank2OrHigher, batchSize); + } finally { + disposeNewTensors(xsRank2OrHigher, x); + } + } + /** + * Returns predictions for a single batch of samples. + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.predictOnBatch(tf.ones([8, 10])).print(); + * ``` + * @param x: Input samples, as a Tensor (for models with exactly one + * input) or an array of Tensors (for models with more than one input). + * @return Tensor(s) of predictions + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + predictOnBatch(x) { + checkInputData(x, this.inputNames, this.feedInputShapes, true); + const batchSize = (Array.isArray(x) ? x[0] : x).shape[0]; + return this.predictLoop(x, batchSize); + } + standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) { + if (this.optimizer_ == null) { + throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs)."); + } + const outputShapes = []; + for (let i = 0; i < this.feedOutputShapes.length; ++i) { + const outputShape = this.feedOutputShapes[i]; + const lossFn = this.feedLossFns[i]; + if (lossFn === sparseCategoricalCrossentropy) { + outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1])); + } else { + outputShapes.push(outputShape); + } + } + x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, "input"); + y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, "target"); + checkArrayLengths(x, y, null); + checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes); + if (this.stateful && batchSize != null && batchSize > 0) { + if (x[0].shape[0] % batchSize !== 0) { + throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`); + } + } + return [x, y]; + } + async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) { + const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize); + if (sampleWeight != null) { + throw new Error("sample weight is not supported yet."); + } + let standardSampleWeights = null; + if (classWeight != null) { + const classWeights = standardizeClassWeights(classWeight, this.outputNames); + standardSampleWeights = []; + for (let i = 0; i < classWeights.length; ++i) { + standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i])); + } + } + return [standardXs, standardYs, standardSampleWeights]; + } + /** + * Loop over some test data in batches. + * @param f A Function returning a list of tensors. + * @param ins Array of tensors to be fed to `f`. + * @param batchSize Integer batch size or `null` / `undefined`. + * @param verbose verbosity mode. + * @param steps Total number of steps (batches of samples) before + * declaring test finished. Ignored with the default value of `null` / + * `undefined`. + * @returns Array of Scalars. + */ + testLoop(f, ins, batchSize, verbose = 0, steps) { + return tidy(() => { + const numSamples = this.checkNumSamples(ins, batchSize, steps, "steps"); + const outs = []; + if (verbose > 0) { + throw new NotImplementedError("Verbose mode is not implemented yet."); + } + if (steps != null) { + throw new NotImplementedError("steps mode in testLoop() is not implemented yet"); + } else { + const batches = makeBatches(numSamples, batchSize); + const indexArray = tensor1d(range2(0, numSamples)); + for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) { + const batchStart = batches[batchIndex][0]; + const batchEnd = batches[batchIndex][1]; + const batchIds = sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart); + const insBatch = sliceArraysByIndices(ins, batchIds); + const batchOuts = f(insBatch); + if (batchIndex === 0) { + for (let i = 0; i < batchOuts.length; ++i) { + outs.push(scalar(0)); + } + } + for (let i = 0; i < batchOuts.length; ++i) { + const batchOut = batchOuts[i]; + outs[i] = add2(outs[i], mul(batchEnd - batchStart, batchOut)); + } + } + for (let i = 0; i < outs.length; ++i) { + outs[i] = div(outs[i], numSamples); + } + } + return outs; + }); + } + getDedupedMetricsNames() { + const outLabels = this.metricsNames; + const dedupedOutLabels = []; + for (let i = 0; i < outLabels.length; ++i) { + const label = outLabels[i]; + let newLabel = label; + if (count(outLabels, label) > 1) { + const dupIndex = count(outLabels.slice(0, i), label); + newLabel += `_${dupIndex}`; + } + dedupedOutLabels.push(newLabel); + } + return dedupedOutLabels; + } + /** + * Creates a function that performs the following actions: + * + * 1. computes the losses + * 2. sums them to get the total loss + * 3. call the optimizer computes the gradients of the LayersModel's + * trainable weights w.r.t. the total loss and update the variables + * 4. calculates the metrics + * 5. returns the values of the losses and metrics. + */ + makeTrainFunction() { + return (data) => { + const lossValues = []; + const inputs = data.slice(0, this.inputs.length); + const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length); + const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2); + const metricsValues = []; + const totalLossFunction = () => { + const feeds = []; + for (let i = 0; i < this.inputs.length; ++i) { + feeds.push({ key: this.inputs[i], value: inputs[i] }); + } + const feedDict = new FeedDict(feeds); + const outputs = execute(this.outputs, feedDict, { "training": true }); + let totalLoss; + for (let i = 0; i < this.lossFunctions.length; ++i) { + const lossFunction = this.lossFunctions[i]; + let loss = lossFunction(targets[i], outputs[i]); + if (sampleWeights[i] != null) { + loss = computeWeightedLoss2(loss, sampleWeights[i]); + } + const meanLoss = mean(loss); + lossValues.push(meanLoss); + if (i === 0) { + totalLoss = loss; + } else { + totalLoss = add2(totalLoss, loss); + } + } + for (let i = 0; i < this.metricsTensors.length; ++i) { + let weightedMetric; + if (this.outputs.length > 1 && i < this.outputs.length) { + weightedMetric = lossValues[i]; + } else { + const metric = this.metricsTensors[i][0]; + const outputIndex = this.metricsTensors[i][1]; + weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex])); + } + keep(weightedMetric); + metricsValues.push(weightedMetric); + } + totalLoss = mean(totalLoss); + this.calculateLosses().forEach((regularizerLoss) => { + totalLoss = add2(totalLoss, regularizerLoss); + }); + return totalLoss; + }; + const variables = this.collectedTrainableWeights.map((param) => param.read()); + const returnCost = true; + const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables); + return [totalLossValue].concat(metricsValues); + }; + } + /** + * Create a function which, when invoked with an array of `tf.Tensor`s as a + * batch of inputs, returns the prespecified loss and metrics of the model + * under the batch of input data. + */ + makeTestFunction() { + this.testFunction = (data) => { + return tidy(() => { + const valOutputs = []; + let totalLoss; + const inputs = data.slice(0, this.inputs.length); + const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length); + const feeds = []; + for (let i = 0; i < this.inputs.length; ++i) { + feeds.push({ key: this.inputs[i], value: inputs[i] }); + } + const feedDict = new FeedDict(feeds); + const outputs = execute(this.outputs, feedDict); + for (let i = 0; i < this.lossFunctions.length; ++i) { + const lossFunction = this.lossFunctions[i]; + const loss = mean(lossFunction(targets[i], outputs[i])); + if (i === 0) { + totalLoss = loss; + } else { + totalLoss = add2(totalLoss, loss); + } + valOutputs.push(totalLoss); + } + for (let i = 0; i < this.metricsTensors.length; ++i) { + const metric = this.metricsTensors[i][0]; + const outputIndex = this.metricsTensors[i][1]; + const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex])); + valOutputs.push(meanMetric); + } + return valOutputs; + }); + }; + } + /** + * Trains the model for a fixed number of epochs (iterations on a + * dataset). + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'}); + * for (let i = 1; i < 5 ; ++i) { + * const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), { + * batchSize: 4, + * epochs: 3 + * }); + * console.log("Loss after Epoch " + i + " : " + h.history.loss[0]); + * } + * ``` + * + * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the + * model has multiple inputs. If all inputs in the model are named, you + * can also pass a dictionary mapping input names to `tf.Tensor`s. + * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if + * the model has multiple outputs. If all outputs in the model are named, + * you can also pass a dictionary mapping output names to `tf.Tensor`s. + * @param args A `ModelFitArgs`, containing optional fields. + * + * @return A `History` instance. Its `history` attribute contains all + * information collected during training. + * + * @exception ValueError In case of mismatch between the provided input + * data and what the model expects. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async fit(x, y, args = {}) { + if (this.isTraining) { + throw new Error("Cannot start training because another fit() call is ongoing."); + } + this.isTraining = true; + let inputs; + let targets; + let originalInputs; + let originalTargets; + let inputValX; + let inputValY; + let valX; + let valY; + let sampleWeights; + try { + const batchSize = args.batchSize == null ? 32 : args.batchSize; + checkBatchSize(batchSize); + const checkBatchAxis = false; + const standardizedOuts = await this.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize); + inputs = standardizedOuts[0]; + targets = standardizedOuts[1]; + sampleWeights = standardizedOuts[2]; + let doValidation = false; + let valIns; + if (args.validationData != null && args.validationData.length > 0) { + doValidation = true; + if (args.validationData.length === 2) { + inputValX = args.validationData[0]; + inputValY = args.validationData[1]; + } else if (args.validationData.length === 3) { + throw new NotImplementedError("validationData including sample weights is not supported yet."); + } else { + throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`); + } + const checkBatchAxis2 = true; + const valStandardized = await this.standardizeUserData( + inputValX, + inputValY, + null, + /** Unused sample weights. */ + null, + /** Unused class weights. */ + checkBatchAxis2, + batchSize + ); + valX = valStandardized[0]; + valY = valStandardized[1]; + valIns = valX.concat(valY); + } else if (args.validationSplit != null && args.validationSplit > 0 && args.validationSplit < 1) { + doValidation = true; + const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit)); + const originalBatchSize = inputs[0].shape[0]; + valX = sliceArrays(inputs, splitAt, originalBatchSize); + originalInputs = inputs; + inputs = sliceArrays(inputs, 0, splitAt); + valY = sliceArrays(targets, splitAt, originalBatchSize); + originalTargets = targets; + targets = sliceArrays(targets, 0, splitAt); + valIns = valX.concat(valY); + } else if (args.validationSteps != null) { + doValidation = true; + } + const ins = inputs.concat(targets).concat(sampleWeights); + this.checkTrainableWeightsConsistency(); + const trainFunction = this.makeTrainFunction(); + const outLabels = this.getDedupedMetricsNames(); + let valFunction; + let callbackMetrics; + if (doValidation) { + this.makeTestFunction(); + valFunction = this.testFunction; + callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n)); + } else { + valFunction = null; + valIns = []; + callbackMetrics = outLabels.slice(); + } + const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery); + const out = await this.fitLoop(trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks2, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null); + return out; + } finally { + this.isTraining = false; + disposeNewTensors(inputs, x); + disposeNewTensors(targets, y); + disposeNewTensors(originalInputs, x); + disposeNewTensors(originalTargets, y); + disposeNewTensors(valX, inputValX); + disposeNewTensors(valY, inputValY); + if (sampleWeights != null) { + dispose(sampleWeights); + } + } + } + /** + * Abstract fit function for `f(ins)`. + * @param f A Function returning a list of tensors. For training, this + * function is expected to perform the updates to the variables. + * @param ins List of tensors to be fed to `f`. + * @param outLabels List of strings, display names of the outputs of `f`. + * @param batchSize Integer batch size or `== null` if unknown. Default : 32. + * @param epochs Number of times to iterate over the data. Default : 1. + * @param verbose Verbosity mode: 0, 1, or 2. Default: 1. + * @param callbacks List of callbacks to be called during training. + * @param valF Function to call for validation. + * @param valIns List of tensors to be fed to `valF`. + * @param shuffle Whether to shuffle the data at the beginning of every + * epoch. Default : true. + * @param callbackMetrics List of strings, the display names of the metrics + * passed to the callbacks. They should be the concatenation of the + * display names of the outputs of `f` and the list of display names + * of the outputs of `valF`. + * @param initialEpoch Epoch at which to start training (useful for + * resuming a previous training run). Default : 0. + * @param stepsPerEpoch Total number of steps (batches on samples) before + * declaring one epoch finished and starting the next epoch. Ignored with + * the default value of `undefined` or `null`. + * @param validationSteps Number of steps to run validation for (only if + * doing validation from data tensors). Not applicable for tfjs-layers. + * @returns A `History` object. + */ + async fitLoop(f, ins, outLabels, batchSize, epochs, verbose, callbacks2, valF, valIns, shuffle2, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) { + if (batchSize == null) { + batchSize = 32; + } + if (epochs == null) { + epochs = 1; + } + if (shuffle2 == null) { + shuffle2 = true; + } + if (initialEpoch == null) { + initialEpoch = 0; + } + let doValidation = false; + if (valF != null && valIns != null) { + doValidation = true; + } + if (validationSteps != null) { + doValidation = true; + if (stepsPerEpoch == null) { + throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set."); + } + } + const numTrainSamples = this.checkNumSamples(ins, batchSize, stepsPerEpoch, "steps_per_epoch"); + let indexArray; + if (numTrainSamples != null) { + indexArray = range2(0, numTrainSamples); + } + if (verbose == null) { + verbose = 1; + } + const { callbackList, history } = configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics); + callbackList.setModel(this); + this.history = history; + await callbackList.onTrainBegin(); + this.stopTraining_ = false; + for (let epoch = initialEpoch; epoch < epochs; ++epoch) { + await callbackList.onEpochBegin(epoch); + const epochLogs = {}; + if (stepsPerEpoch != null) { + throw new NotImplementedError("stepsPerEpoch mode is not implemented yet."); + } else { + if (shuffle2 === "batch") { + throw new NotImplementedError("batch shuffling is not implemneted yet"); + } else if (shuffle2) { + util_exports.shuffle(indexArray); + } + const epochIndexArray1D = tensor1d(indexArray); + const batches = makeBatches(numTrainSamples, batchSize); + for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) { + const batchLogs = {}; + await callbackList.onBatchBegin(batchIndex, batchLogs); + tidy(() => { + const batchStart = batches[batchIndex][0]; + const batchEnd = batches[batchIndex][1]; + const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart); + batchLogs["batch"] = batchIndex; + batchLogs["size"] = batchEnd - batchStart; + const insBatch = sliceArraysByIndices(ins, batchIds); + const outs = f(insBatch); + for (let i = 0; i < outLabels.length; ++i) { + const label = outLabels[i]; + const out = outs[i]; + batchLogs[label] = out; + keep(out); + } + if (batchIndex === batches.length - 1) { + if (doValidation) { + const valOuts = this.testLoop(valF, valIns, batchSize); + for (let i = 0; i < outLabels.length; ++i) { + const label = outLabels[i]; + const out = valOuts[i]; + keep(out); + epochLogs["val_" + label] = out; + } + } + } + }); + await callbackList.onBatchEnd(batchIndex, batchLogs); + disposeTensorsInLogs(batchLogs); + if (this.stopTraining_) { + break; + } + } + epochIndexArray1D.dispose(); + } + await callbackList.onEpochEnd(epoch, epochLogs); + if (this.stopTraining_) { + break; + } + } + await callbackList.onTrainEnd(); + await this.history.syncData(); + return this.history; + } + // TODO(cais): Add code snippet below when it's possible to instantiate + // actual dataset objects. + /** + * Trains the model using a dataset object. + * + * @param dataset A dataset object. Its `iterator()` method is expected + * to generate a dataset iterator object, the `next()` method of which + * is expected to produce data batches for training. The return value + * of the `next()` call ought to contain a boolean `done` field and a + * `value` field. The `value` field is expected to be an array of two + * `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former + * case is for models with exactly one input and one output (e.g. + * a sequential model). The latter case is for models with multiple + * inputs and/or multiple outputs. + * Of the two items in the array, the first is the input feature(s) and + * the second is the output target(s). + * @param args A `ModelFitDatasetArgs`, containing optional fields. + * + * @return A `History` instance. Its `history` attribute contains all + * information collected during training. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async fitDataset(dataset, args) { + return fitDataset(this, dataset, args); + } + /** + * Runs a single gradient update on a single batch of data. + * + * This method differs from `fit()` and `fitDataset()` in the following + * regards: + * - It operates on exactly one batch of data. + * - It returns only the loss and metric values, instead of + * returning the batch-by-batch loss and metric values. + * - It doesn't support fine-grained options such as verbosity and + * callbacks. + * + * @param x Input data. It could be one of the following: + * - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has + * multiple inputs). + * - An Object mapping input names to corresponding `tf.Tensor` (if the + * model has named inputs). + * @param y Target data. It could be either a `tf.Tensor` or multiple + * `tf.Tensor`s. It should be consistent with `x`. + * @returns Training loss or losses (in case the model has + * multiple outputs), along with metrics (if any), as numbers. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async trainOnBatch(x, y) { + const standardizeOut = await this.standardizeUserData(x, y); + const inputs = standardizeOut[0]; + const targets = standardizeOut[1]; + const trainFunction = this.makeTrainFunction(); + const losses2 = trainFunction(inputs.concat(targets)); + const lossValues = []; + for (const loss of losses2) { + const v = await loss.data(); + lossValues.push(v[0]); + } + dispose(losses2); + disposeNewTensors(standardizeOut[0], x); + disposeNewTensors(standardizeOut[1], y); + return singletonOrArray(lossValues); + } + /** + * Extract weight values of the model. + * + * @param config: An instance of `io.SaveConfig`, which specifies + * model-saving options such as whether only trainable weights are to be + * saved. + * @returns A `NamedTensorMap` mapping original weight names (i.e., + * non-uniqueified weight names) to their values. + */ + getNamedWeights(config) { + const namedWeights = []; + const trainableOnly = config != null && config.trainableOnly; + const weights = trainableOnly ? this.trainableWeights : this.weights; + const weightValues = this.getWeights(trainableOnly); + for (let i = 0; i < weights.length; ++i) { + if (trainableOnly && !weights[i].trainable) { + continue; + } + namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] }); + } + return namedWeights; + } + /** + * Setter used for force stopping of LayersModel.fit() (i.e., training). + * + * Example: + * + * ```js + * const input = tf.input({shape: [10]}); + * const output = tf.layers.dense({units: 1}).apply(input); + * const model = tf.model({inputs: [input], outputs: [output]}); + * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'}); + * const xs = tf.ones([8, 10]); + * const ys = tf.zeros([8, 1]); + * + * const history = await model.fit(xs, ys, { + * epochs: 10, + * callbacks: { + * onEpochEnd: async (epoch, logs) => { + * if (epoch === 2) { + * model.stopTraining = true; + * } + * } + * } + * }); + * + * // There should be only 3 values in the loss array, instead of 10 + * values, + * // due to the stopping after 3 epochs. + * console.log(history.history.loss); + * ``` + */ + set stopTraining(stop) { + this.stopTraining_ = stop; + } + get stopTraining() { + return this.stopTraining_; + } + get optimizer() { + return this.optimizer_; + } + set optimizer(optimizer) { + if (this.optimizer_ !== optimizer) { + this.optimizer_ = optimizer; + this.isOptimizerOwned = false; + } + } + dispose() { + const result = super.dispose(); + if (result.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) { + const numTensorsBeforeOptmizerDisposal = memory().numTensors; + this.optimizer_.dispose(); + result.numDisposedVariables += numTensorsBeforeOptmizerDisposal - memory().numTensors; + } + return result; + } + getLossIdentifiers() { + let lossNames; + if (typeof this.loss === "string") { + lossNames = toSnakeCase(this.loss); + } else if (Array.isArray(this.loss)) { + for (const loss of this.loss) { + if (typeof loss !== "string") { + throw new Error("Serialization of non-string loss is not supported."); + } + } + lossNames = this.loss.map((name) => toSnakeCase(name)); + } else { + const outputNames = Object.keys(this.loss); + lossNames = {}; + const losses2 = this.loss; + for (const outputName of outputNames) { + if (typeof losses2[outputName] === "string") { + lossNames[outputName] = toSnakeCase(losses2[outputName]); + } else { + throw new Error("Serialization of non-string loss is not supported."); + } + } + } + return lossNames; + } + getMetricIdentifiers() { + if (typeof this.metrics === "string" || typeof this.metrics === "function") { + return [toSnakeCase(getLossOrMetricName(this.metrics))]; + } else if (Array.isArray(this.metrics)) { + return this.metrics.map((metric) => toSnakeCase(getLossOrMetricName(metric))); + } else { + const metricsIdentifiers = {}; + for (const key in this.metrics) { + metricsIdentifiers[key] = toSnakeCase(getLossOrMetricName(this.metrics[key])); + } + return metricsIdentifiers; + } + } + getTrainingConfig() { + return { + loss: this.getLossIdentifiers(), + metrics: this.getMetricIdentifiers(), + optimizer_config: { + class_name: this.optimizer.getClassName(), + config: this.optimizer.getConfig() + } + }; + } + loadTrainingConfig(trainingConfig) { + if (trainingConfig.weighted_metrics != null) { + throw new Error("Loading weight_metrics is not supported yet."); + } + if (trainingConfig.loss_weights != null) { + throw new Error("Loading loss_weights is not supported yet."); + } + if (trainingConfig.sample_weight_mode != null) { + throw new Error("Loading sample_weight_mode is not supported yet."); + } + const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config); + const optimizer = deserialize(tsConfig); + let loss; + if (typeof trainingConfig.loss === "string") { + loss = toCamelCase(trainingConfig.loss); + } else if (Array.isArray(trainingConfig.loss)) { + loss = trainingConfig.loss.map((lossEntry) => toCamelCase(lossEntry)); + } else if (trainingConfig.loss != null) { + loss = {}; + for (const key in trainingConfig.loss) { + loss[key] = toCamelCase(trainingConfig.loss[key]); + } + } + let metrics; + if (Array.isArray(trainingConfig.metrics)) { + metrics = trainingConfig.metrics.map((metric) => toCamelCase(metric)); + } else if (trainingConfig.metrics != null) { + metrics = {}; + for (const key in trainingConfig.metrics) { + metrics[key] = toCamelCase(trainingConfig.metrics[key]); + } + } + this.compile({ loss, metrics, optimizer }); + } + /** + * Save the configuration and/or weights of the LayersModel. + * + * An `IOHandler` is an object that has a `save` method of the proper + * signature defined. The `save` method manages the storing or + * transmission of serialized data ("artifacts") that represent the + * model's topology and weights onto or via a specific medium, such as + * file downloads, local storage, IndexedDB in the web browser and HTTP + * requests to a server. TensorFlow.js provides `IOHandler` + * implementations for a number of frequently used saving mediums, such as + * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io` + * for more details. + * + * This method also allows you to refer to certain types of `IOHandler`s + * as URL-like string shortcuts, such as 'localstorage://' and + * 'indexeddb://'. + * + * Example 1: Save `model`'s topology and weights to browser [local + * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage); + * then load it back. + * + * ```js + * const model = tf.sequential( + * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]}); + * console.log('Prediction from original model:'); + * model.predict(tf.ones([1, 3])).print(); + * + * const saveResults = await model.save('localstorage://my-model-1'); + * + * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1'); + * console.log('Prediction from loaded model:'); + * loadedModel.predict(tf.ones([1, 3])).print(); + * ``` + * + * Example 2. Saving `model`'s topology and weights to browser + * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API); + * then load it back. + * + * ```js + * const model = tf.sequential( + * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]}); + * console.log('Prediction from original model:'); + * model.predict(tf.ones([1, 3])).print(); + * + * const saveResults = await model.save('indexeddb://my-model-1'); + * + * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1'); + * console.log('Prediction from loaded model:'); + * loadedModel.predict(tf.ones([1, 3])).print(); + * ``` + * + * Example 3. Saving `model`'s topology and weights as two files + * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from + * browser. + * + * ```js + * const model = tf.sequential( + * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]}); + * const saveResults = await model.save('downloads://my-model-1'); + * ``` + * + * Example 4. Send `model`'s topology and weights to an HTTP server. + * See the documentation of `tf.io.http` for more details + * including specifying request parameters and implementation of the + * server. + * + * ```js + * const model = tf.sequential( + * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]}); + * const saveResults = await model.save('http://my-server/model/upload'); + * ``` + * + * @param handlerOrURL An instance of `IOHandler` or a URL-like, + * scheme-based string shortcut for `IOHandler`. + * @param config Options for saving the model. + * @returns A `Promise` of `SaveResult`, which summarizes the result of + * the saving, such as byte sizes of the saved artifacts for the model's + * topology and weight values. + * + * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} + */ + async save(handlerOrURL, config) { + if (typeof handlerOrURL === "string") { + const handlers = io_exports.getSaveHandlers(handlerOrURL); + if (handlers.length === 0) { + throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`); + } else if (handlers.length > 1) { + throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`); + } + handlerOrURL = handlers[0]; + } + if (handlerOrURL.save == null) { + throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined."); + } + const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config)); + const returnString = false; + const unusedArg = null; + const modelConfig = this.toJSON(unusedArg, returnString); + const modelArtifacts = { + modelTopology: modelConfig, + format: LAYERS_MODEL_FORMAT_NAME, + generatedBy: `TensorFlow.js tfjs-layers v${version2}`, + convertedBy: null + }; + const includeOptimizer = config == null ? false : config.includeOptimizer; + if (includeOptimizer && this.optimizer != null) { + modelArtifacts.trainingConfig = this.getTrainingConfig(); + const weightType = "optimizer"; + const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io_exports.encodeWeights(await this.optimizer.getWeights(), weightType); + weightDataAndSpecs.specs.push(...optimizerWeightSpecs); + weightDataAndSpecs.data = io_exports.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]); + } + if (this.userDefinedMetadata != null) { + const checkSize = true; + checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize); + modelArtifacts.userDefinedMetadata = this.userDefinedMetadata; + } + modelArtifacts.weightData = weightDataAndSpecs.data; + modelArtifacts.weightSpecs = weightDataAndSpecs.specs; + return handlerOrURL.save(modelArtifacts); + } + /** + * Set user-defined metadata. + * + * The set metadata will be serialized together with the topology + * and weights of the model during `save()` calls. + * + * @param setUserDefinedMetadata + */ + setUserDefinedMetadata(userDefinedMetadata) { + checkUserDefinedMetadata(userDefinedMetadata, this.name); + this.userDefinedMetadata = userDefinedMetadata; + } + /** + * Get user-defined metadata. + * + * The metadata is supplied via one of the two routes: + * 1. By calling `setUserDefinedMetadata()`. + * 2. Loaded during model loading (if the model is constructed + * via `tf.loadLayersModel()`.) + * + * If no user-defined metadata is available from either of the + * two routes, this function will return `undefined`. + */ + getUserDefinedMetadata() { + return this.userDefinedMetadata; + } +}; +LayersModel.className = "Model"; +serialization_exports.registerClass(LayersModel); +var Functional = class extends LayersModel { +}; +Functional.className = "Functional"; +serialization_exports.registerClass(Functional); +async function modelFromJSON(modelAndWeightsConfig, customObjects) { + if (!("modelTopology" in modelAndWeightsConfig)) { + modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig }; + } + modelAndWeightsConfig = modelAndWeightsConfig; + let modelTopology = modelAndWeightsConfig.modelTopology; + if (modelTopology["model_config"] != null) { + modelTopology = modelTopology["model_config"]; + } + const tsConfig = convertPythonicToTs(modelTopology); + const model2 = deserialize(tsConfig, customObjects); + if (modelAndWeightsConfig.weightsManifest != null) { + const weightValues = await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model2.weights.map((weight) => weight.originalName)); + const uniqueWeightValues = {}; + for (const weight of model2.weights) { + uniqueWeightValues[weight.originalName] = weightValues[weight.originalName]; + } + model2.loadWeights(uniqueWeightValues); + dispose(weightValues); + } + return model2; +} +async function loadLayersModel(pathOrIOHandler, options) { + if (options == null) { + options = {}; + } + if (typeof pathOrIOHandler === "string") { + const handlers = io_exports.getLoadHandlers(pathOrIOHandler, options); + if (handlers.length === 0) { + handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler, options)); + } else if (handlers.length > 1) { + throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`); + } + pathOrIOHandler = handlers[0]; + } + return loadLayersModelFromIOHandler(pathOrIOHandler, void 0, options); +} +async function loadLayersModelFromIOHandler(handler, customObjects, options) { + if (options == null) { + options = {}; + } + if (handler.load == null) { + throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented."); + } + const artifacts = await handler.load(); + let modelTopology = artifacts.modelTopology; + if (modelTopology["model_config"] != null) { + modelTopology = modelTopology["model_config"]; + } + const strict = options.strict == null ? true : options.strict; + const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict; + const model2 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit); + const trainingConfig = artifacts.trainingConfig; + if (trainingConfig != null) { + model2.loadTrainingConfig(trainingConfig); + } + if (artifacts.userDefinedMetadata != null) { + model2.setUserDefinedMetadata(artifacts.userDefinedMetadata); + } + if (artifacts.weightData != null) { + if (artifacts.weightSpecs == null) { + throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed."); + } + const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs); + model2.loadWeights(modelWeights, strict); + if (model2.optimizer != null && optimizerWeights.length > 0) { + await model2.optimizer.setWeights(optimizerWeights); + } + dispose(modelWeights); + dispose(optimizerWeights.map((w) => w.tensor)); + } + return model2; +} +function decodeModelAndOptimizerWeights(weightData, specs) { + const name2Tensor = io_exports.decodeWeights(weightData, specs); + const modelWeights = {}; + const optimizerWeights = []; + specs.forEach((spec) => { + if (spec.group === "optimizer") { + optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] }); + } else { + modelWeights[spec.name] = name2Tensor[spec.name]; + } + }); + return { modelWeights, optimizerWeights }; +} +var Sequential = class _Sequential extends LayersModel { + constructor(args) { + super({ inputs: [], outputs: [] }); + args = args || {}; + this.trainable = true; + this.built = false; + this.name = args.name != null ? args.name : getUid("sequential_"); + if (args.layers != null) { + for (const layer of args.layers) { + this.add(layer); + } + } + } + // Helper function to Sequential.add Throws if the new output shape will be + // invalid. + checkShape(layer) { + const shape = layer.inboundNodes[0].outputTensors[0].shape; + if (shape.some((x) => x < 0)) { + throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`); + } + } + /** + * Adds a layer instance on top of the layer stack. + * + * ```js + * const model = tf.sequential(); + * model.add(tf.layers.dense({units: 8, inputShape: [1]})); + * model.add(tf.layers.dense({units: 4, activation: 'relu6'})); + * model.add(tf.layers.dense({units: 1, activation: 'relu6'})); + * // Note that the untrained model is random at this point. + * model.predict(tf.randomNormal([10, 1])).print(); + * ``` + * @param layer Layer instance. + * + * @exception ValueError In case the `layer` argument does not know its + * input shape. + * @exception ValueError In case the `layer` argument has multiple output + * tensors, or is already connected somewhere else (forbidden in + * `Sequential` models). + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + add(layer) { + const isLayerModelInstance = layer instanceof _Sequential || layer instanceof LayersModel; + let modelLayer; + if (isLayerModelInstance) { + modelLayer = layer; + if (modelLayer.outputs.length !== 1) { + throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API."); + } + if (modelLayer.inputs.length !== 1) { + throw new ValueError("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 (layer.inboundNodes.length === 0) { + if (layer.batchInputShape == null) { + throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument."); + } + const x = Input({ + batchShape: layer.batchInputShape, + dtype: layer.dtype, + name: layer.name + "_input" + }); + layer.apply(x); + } + if (isLayerModelInstance) { + this.outputs = modelLayer.outputs; + this.inputs = modelLayer.inputs; + } else { + if (layer.inboundNodes.length !== 1) { + throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`); + } + if (layer.inboundNodes[0].outputTensors.length !== 1) { + throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API."); + } + this.checkShape(layer); + this.outputs = [layer.inboundNodes[0].outputTensors[0]]; + this.inputs = getSourceInputs(this.outputs[0]); + } + this.inboundNodes = []; + new Node({ + outboundLayer: this, + inboundLayers: [], + nodeIndices: [], + tensorIndices: [], + inputTensors: this.inputs, + outputTensors: this.outputs, + // no model-level masking for now + inputMasks: pyListRepeat(null, this.inputs.length), + outputMasks: [null], + inputShapes: this.inputs.map((x) => x.shape), + outputShapes: this.outputs[0].shape + }); + } else { + const outputTensor = layer.apply(this.outputs[0]); + if (Array.isArray(outputTensor)) { + 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(layer); + this.outputs = [outputTensor]; + this.inboundNodes[0].outputTensors = this.outputs; + this.inboundNodes[0].outputShapes = [this.outputs[0].shape]; + } + this.layers.push(layer); + this.built = false; + } + /** + * Removes the last layer in the model. + * + * @exception TypeError if there are no layers in the model. + */ + pop() { + if (this.layers.length === 0) { + throw new TypeError("There are no layers in the model."); + } + this.layers.pop(); + if (this.layers.length === 0) { + this.outputs = []; + this.inboundNodes = []; + this.outboundNodes = []; + } else { + const lastLayerIndex = this.layers.length - 1; + this.layers[lastLayerIndex].outboundNodes = []; + this.outputs = [this.layers[lastLayerIndex].output]; + this.inboundNodes[0].outputTensors = this.outputs; + this.inboundNodes[0].outputShapes = [this.outputs[0].shape]; + } + } + call(inputs, kwargs) { + if (this.model == null) { + this.build(); + } + return this.model.call(inputs, kwargs); + } + build(inputShape) { + getExactlyOneShape(inputShape); + if (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 LayersModel({ + 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 = true; + } + countParams() { + if (!this.built) { + this.build(); + } + return super.countParams(); + } + /** + * Print a text summary of the Sequential model's layers. + * + * The summary includes + * - Name and type of all layers that comprise the model. + * - Output shape(s) of the layers + * - Number of weight parameters of each layer + * - The total number of trainable and non-trainable parameters of the + * model. + * + * ```js + * const model = tf.sequential(); + * model.add( + * tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'})); + * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'})); + * + * model.summary(); + * ``` + * + * @param lineLength Custom line length, in number of characters. + * @param positions Custom widths of each of the columns, as either + * fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number + * of characters (e.g., `[30, 50, 65]`). Each number corresponds to + * right-most (i.e., ending) position of a column. + * @param printFn Custom print function. Can be used to replace the default + * `console.log`. For example, you can use `x => {}` to mute the printed + * messages in the console. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + summary(lineLength, positions, printFn = console.log) { + if (!this.built) { + this.build(); + } + super.summary(lineLength, positions, printFn); + } + /** + * Sets the weights of the model. + * + * @param weights Should be a list of Tensors with shapes and types matching + * the output of `model.getWeights()`. + */ + setWeights(weights) { + if (this.model == null) { + this.build(); + } + this.model.setWeights(weights); + } + /** + * Returns the loss value & metrics values for the model in test mode. + * + * Loss and metrics are specified during `compile()`, which needs to happen + * before calls to `evaluate()`. + * + * Computation is done in batches. + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'}); + * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), { + * batchSize: 4, + * }); + * result.print(); + * ``` + * + * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the + * model has multiple inputs. + * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the + * model has multiple outputs. + * @param args A `ModelEvaluateConfig`, containing optional fields. + * + * @return `Scalar` test loss (if the model has a single output and no + * metrics) or `Array` of `Scalar`s (if the model has multiple outputs + * and/or metrics). The attribute `model.metricsNames` + * will give you the display labels for the scalar outputs. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + evaluate(x, y, args = {}) { + if (!this.built) { + throw new RuntimeError("The model needs to be compiled before being used."); + } + return this.model.evaluate(x, y, args); + } + // TODO(cais): Add code snippet below once real dataset objects are + // available. + /** + * Evaluate model using a dataset object. + * + * Note: Unlike `evaluate()`, this method is asynchronous (`async`). + * + * @param dataset A dataset object. Its `iterator()` method is expected + * to generate a dataset iterator object, the `next()` method of which + * is expected to produce data batches for evaluation. The return value + * of the `next()` call ought to contain a boolean `done` field and a + * `value` field. The `value` field is expected to be an array of two + * `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former + * case is for models with exactly one input and one output (e.g. + * a sequential model). The latter case is for models with multiple + * inputs and/or multiple outputs. Of the two items in the array, the + * first is the input feature(s) and the second is the output target(s). + * @param args A configuration object for the dataset-based evaluation. + * @returns Loss and metric values as an Array of `Scalar` objects. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async evaluateDataset(dataset, args) { + if (!this.built) { + throw new RuntimeError("The model needs to be compiled before being used."); + } + return this.model.evaluateDataset(dataset, args); + } + /** + * Generates output predictions for the input samples. + * + * Computation is done in batches. + * + * Note: the "step" mode of predict() is currently not supported. + * This is because the TensorFlow.js core backend is imperative only. + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.predict(tf.ones([2, 10])).print(); + * ``` + * + * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if + * the model has multiple inputs. + * @param conifg A `ModelPredictConfig` object containing optional fields. + * + * @return `tf.Tensor`(s) of predictions. + * + * @exception ValueError In case of mismatch between the provided input data + * and the model's expectations, or in case a stateful model receives a + * number of samples that is not a multiple of the batch size. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + predict(x, args = {}) { + if (this.model == null) { + this.build(); + } + return this.model.predict(x, args); + } + /** + * Returns predictions for a single batch of samples. + * + * @param x: Input samples, as a Tensor, or list of Tensors (if the model + * has multiple inputs). + * @return Tensor(s) of predictions + */ + predictOnBatch(x) { + if (this.model == null) { + this.build(); + } + return this.model.predictOnBatch(x); + } + /** + * See `LayersModel.compile`. + * + * @param args + */ + compile(args) { + this.build(); + this.model.compile(args); + 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(optimizer) { + this.model.optimizer = optimizer; + } + /** + * Trains the model for a fixed number of epochs (iterations on a dataset). + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'}); + * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), { + * batchSize: 4, + * epochs: 3 + * }); + * console.log(history.history.loss[0]); + * ``` + * + * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the + * model has multiple inputs. If all inputs in the model are named, you can + * also pass a dictionary mapping input names to `tf.Tensor`s. + * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if + * the model has multiple outputs. If all outputs in the model are named, you + * can also pass a dictionary mapping output names to `tf.Tensor`s. + * @param args A `ModelFitConfig`, containing optional fields. + * + * @return A `History` instance. Its `history` attribute contains all + * information collected during training. + * + * @exception ValueError In case of mismatch between the provided input data + * and what the model expects. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async fit(x, y, args = {}) { + if (!this.built) { + throw new RuntimeError("The model needs to be compiled before being used."); + } + return this.model.fit(x, y, args); + } + /** + * Trains the model using a dataset object. + * + * ```js + * const xArray = [ + * [1, 1, 1, 1, 1, 1, 1, 1, 1], + * [1, 1, 1, 1, 1, 1, 1, 1, 1], + * [1, 1, 1, 1, 1, 1, 1, 1, 1], + * [1, 1, 1, 1, 1, 1, 1, 1, 1], + * ]; + * const yArray = [1, 1, 1, 1]; + * // Create a dataset from the JavaScript array. + * const xDataset = tf.data.array(xArray); + * const yDataset = tf.data.array(yArray); + * // Zip combines the `x` and `y` Datasets into a single Dataset, the + * // iterator of which will return an object containing of two tensors, + * // corresponding to `x` and `y`. The call to `batch(4)` will bundle + * // four such samples into a single object, with the same keys now pointing + * // to tensors that hold 4 examples, organized along the batch dimension. + * // The call to `shuffle(4)` causes each iteration through the dataset to + * // happen in a different order. The size of the shuffle window is 4. + * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset}) + * .batch(4) + * .shuffle(4); + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [9]})] + * }); + * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'}); + * const history = await model.fitDataset(xyDataset, { + * epochs: 4, + * callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)} + * }); + * ``` + * + * @param dataset A dataset object. Its `iterator()` method is expected to + * generate a dataset iterator object, the `next()` method of which is + * expected to produce data batches for evaluation. The return value of the + * `next()` call ought to contain a boolean `done` field and a `value` + * field. + * + * The `value` field is expected to be an object of with fields + * `xs` and `ys`, which point to the feature tensor and the target tensor, + * respectively. This case is for models with exactly one input and one + * output (e.g. a sequential model). For example: + * ```js + * {value: {xs: xsTensor, ys: ysTensor}, done: false} + * ``` + * + * If the model has multiple inputs, the `xs` field of `value` should + * be an object mapping input names to their respective feature tensors. + * For example: + * ```js + * { + * value: { + * xs: { + * input_1: xsTensor1, + * input_2: xsTensor2 + * }, + * ys: ysTensor + * }, + * done: false + * } + * ``` + * If the model has multiple outputs, the `ys` field of `value` should + * be an object mapping output names to their respective target tensors. + * For example: + * ```js + * { + * value: { + * xs: xsTensor, + * ys: { + * output_1: ysTensor1, + * output_2: ysTensor2 + * }, + * }, + * done: false + * } + * ``` + * @param args A `ModelFitDatasetArgs`, containing optional fields. + * + * @return A `History` instance. Its `history` attribute contains all + * information collected during training. + * + * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} + */ + async fitDataset(dataset, args) { + if (!this.built) { + throw new RuntimeError("The model needs to be compiled before being used."); + } + return this.model.fitDataset(dataset, args); + } + /** + * Runs a single gradient update on a single batch of data. + * + * This method differs from `fit()` and `fitDataset()` in the following + * regards: + * - It operates on exactly one batch of data. + * - It returns only the loss and metric values, instead of + * returning the batch-by-batch loss and metric values. + * - It doesn't support fine-grained options such as verbosity and + * callbacks. + * + * @param x Input data. It could be one of the following: + * - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has + * multiple inputs). + * - An Object mapping input names to corresponding `tf.Tensor` (if the + * model has named inputs). + * @param y Target data. It could be either a `tf.Tensor` or multiple + * `tf.Tensor`s. It should be consistent with `x`. + * @returns Training loss or losses (in case the model has + * multiple outputs), along with metrics (if any), as numbers. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async trainOnBatch(x, y) { + return this.model.trainOnBatch(x, y); + } + /* See parent class for JsDoc */ + /** @nocollapse */ + static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) { + let configArray; + let extraModelConfig = {}; + if (config instanceof Array) { + if (!(config[0].className != null) || config[0]["className"] === "Merge") { + throw new ValueError("Legacy serialization format not supported yet."); + } + configArray = config; + } else { + util_exports.assert(config["layers"] != null, () => `When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.`); + configArray = config["layers"]; + delete config["layers"]; + extraModelConfig = config; + } + const model2 = new cls(extraModelConfig); + if (!(model2 instanceof _Sequential)) { + throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`); + } + for (const conf of configArray) { + const customObjects2 = void 0; + const layer = deserialize(conf, customObjects2, fastWeightInit); + if (fastWeightInit) { + layer.setFastWeightInitDuringBuild(true); + } + model2.add(layer); + } + return model2; + } + /** + * Setter used for force stopping of LayersModel.fit() (i.e., training). + * + * Example: + * + * ```js + * const model = tf.sequential(); + * model.add(tf.layers.dense({units: 1, inputShape: [10]})); + * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'}); + * const xs = tf.ones([8, 10]); + * const ys = tf.zeros([8, 1]); + * + * const history = await model.fit(xs, ys, { + * epochs: 10, + * callbacks: { + * onEpochEnd: async (epoch, logs) => { + * if (epoch === 2) { + * model.stopTraining = true; + * } + * } + * } + * }); + * + * // There should be only 3 values in the loss array, instead of 10 values, + * // due to the stopping after 3 epochs. + * console.log(history.history.loss); + * ``` + */ + set stopTraining(stop) { + if (this.model == null) { + throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled."); + } + this.model.stopTraining = stop; + } + get stopTraining() { + if (this.model == null) { + throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled."); + } + return this.model.stopTraining; + } + // TODO(cais): Override get trainableWeights() here + // tslint:disable-next-line:no-any + getConfig() { + const layers = []; + for (const layer of this.layers) { + const dict = {}; + dict["className"] = layer.getClassName(); + dict["config"] = layer.getConfig(); + layers.push(dict); + } + return { name: this.name, layers }; + } +}; +Sequential.className = "Sequential"; +serialization_exports.registerClass(Sequential); +function model(args) { + return new LayersModel(args); +} +function sequential(config) { + return new Sequential(config); +} +function input(config) { + return Input(config); +} +function registerCallbackConstructor(verbosityLevel, callbackConstructor) { + CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor); +} +var Activation = class extends serialization_exports.Serializable { + getConfig() { + return {}; + } +}; +var Elu2 = class extends Activation { + /** + * Calculate the activation function. + * + * @param x: Input. + * @param alpha: Scaling factor the negative section. + * @return Output of the ELU activation. + */ + apply(x, alpha = 1) { + return elu2(x, alpha); + } +}; +Elu2.className = "elu"; +serialization_exports.registerClass(Elu2); +var Selu2 = class extends Activation { + apply(x) { + return selu(x); + } +}; +Selu2.className = "selu"; +serialization_exports.registerClass(Selu2); +var Relu2 = class extends Activation { + apply(x) { + return relu(x); + } +}; +Relu2.className = "relu"; +serialization_exports.registerClass(Relu2); +var Relu62 = class extends Activation { + apply(x) { + return tidy(() => minimum(6, relu(x))); + } +}; +Relu62.className = "relu6"; +serialization_exports.registerClass(Relu62); +var Linear = class extends Activation { + apply(x) { + return x; + } +}; +Linear.className = "linear"; +serialization_exports.registerClass(Linear); +var Sigmoid2 = class extends Activation { + apply(x) { + return sigmoid(x); + } +}; +Sigmoid2.className = "sigmoid"; +serialization_exports.registerClass(Sigmoid2); +var HardSigmoid = class extends Activation { + apply(x) { + return hardSigmoid(x); + } +}; +HardSigmoid.className = "hardSigmoid"; +serialization_exports.registerClass(HardSigmoid); +var Softplus2 = class extends Activation { + apply(x) { + return softplus(x); + } +}; +Softplus2.className = "softplus"; +serialization_exports.registerClass(Softplus2); +var Softsign = class extends Activation { + apply(x) { + return softsign(x); + } +}; +Softsign.className = "softsign"; +serialization_exports.registerClass(Softsign); +var Tanh2 = class extends Activation { + apply(x) { + return tanh2(x); + } +}; +Tanh2.className = "tanh"; +serialization_exports.registerClass(Tanh2); +var Softmax2 = class extends Activation { + /** + * Calculate the activation function. + * + * @param x Tensor. + * @param axis Integer, axis along which the softmax normalization is applied. + * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be + * an error. + * + * @returns a Tensor of the same shape as x + * + * @throws ValueError: In case `dim(x) < 2`. + */ + apply(x, axis = -1) { + return softmax(x, axis); + } +}; +Softmax2.className = "softmax"; +serialization_exports.registerClass(Softmax2); +var LogSoftmax2 = class extends Activation { + /** + * Calculate the activation function of log softmax: + * log( exp(x_i) / sum(exp(x)) ) + * + * @param x Tensor. + * @param axis Integer, axis along which the softmax normalization is applied. + * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be + * an error. + * + * @returns a Tensor of the same shape as x + * + * @throws ValueError: In case `dim(x) < 2`. + */ + apply(x, axis = -1) { + return logSoftmax(x, axis); + } +}; +LogSoftmax2.className = "logSoftmax"; +serialization_exports.registerClass(LogSoftmax2); +var Swish = class extends Activation { + /** + * Calculate the activation function. + * + * @param x Tensor. + * @param alpha Scaling factor for the sigmoid function. + * @returns a Tensor of the same shape as x + */ + apply(x, alpha = 1) { + return tidy(() => mul(sigmoid(mul(x, alpha)), x)); + } +}; +Swish.className = "swish"; +serialization_exports.registerClass(Swish); +var Mish = class extends Activation { + /** + * Calculate the activation function. + * + * @param x Tensor. + * @returns a Tensor of the same shape as x + */ + apply(x) { + return tidy(() => mul(x, tanh2(softplus(x)))); + } +}; +Mish.className = "mish"; +serialization_exports.registerClass(Mish); +function serializeActivation(activation2) { + return activation2.getClassName(); +} +function deserializeActivation(config, customObjects = {}) { + return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "activation"); +} +function getActivation(identifier) { + if (identifier == null) { + const config = {}; + config["className"] = "linear"; + config["config"] = {}; + return deserializeActivation(config); + } + if (typeof identifier === "string") { + const config = {}; + config["className"] = identifier; + config["config"] = {}; + return deserializeActivation(config); + } else if (identifier instanceof Activation) { + return identifier; + } else { + return deserializeActivation(identifier); + } +} +function assertObjectArgs(args) { + if (args != null && typeof args !== "object") { + throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`); + } +} +var Regularizer = class extends serialization_exports.Serializable { +}; +var L1L2 = class extends Regularizer { + constructor(args) { + super(); + assertObjectArgs(args); + this.l1 = args == null || args.l1 == null ? 0.01 : args.l1; + this.l2 = args == null || args.l2 == null ? 0.01 : args.l2; + this.hasL1 = this.l1 !== 0; + this.hasL2 = this.l2 !== 0; + } + /** + * Porting note: Renamed from __call__. + * @param x Variable of which to calculate the regularization score. + */ + apply(x) { + return tidy(() => { + let regularization = zeros([1]); + if (this.hasL1) { + regularization = add2(regularization, sum2(mul(this.l1, abs(x)))); + } + if (this.hasL2) { + regularization = add2(regularization, sum2(mul(this.l2, square2(x)))); + } + return reshape(regularization, []); + }); + } + getConfig() { + return { "l1": this.l1, "l2": this.l2 }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls({ l1: config["l1"], l2: config["l2"] }); + } +}; +L1L2.className = "L1L2"; +serialization_exports.registerClass(L1L2); +function l1(args) { + assertObjectArgs(args); + return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 }); +} +function l2(args) { + assertObjectArgs(args); + return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 }); +} +var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = { + "l1l2": "L1L2" +}; +function serializeRegularizer(constraint) { + return serializeKerasObject(constraint); +} +function deserializeRegularizer(config, customObjects = {}) { + return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "regularizer"); +} +function getRegularizer(identifier) { + if (identifier == null) { + return null; + } + if (typeof identifier === "string") { + const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier; + const config = { className, config: {} }; + return deserializeRegularizer(config); + } else if (identifier instanceof Regularizer) { + return identifier; + } else { + return deserializeRegularizer(identifier); + } +} +var ReLU = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.supportsMasking = true; + if (args != null) { + this.maxValue = args.maxValue; + } + } + call(inputs, kwargs) { + inputs = getExactlyOneTensor(inputs); + let output = relu(inputs); + if (this.maxValue != null) { + output = clipByValue(output, 0, this.maxValue); + } + return output; + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const config = { maxValue: this.maxValue }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +ReLU.className = "ReLU"; +serialization_exports.registerClass(ReLU); +var LeakyReLU = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.DEFAULT_ALPHA = 0.3; + if (args == null) { + args = {}; + } + this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha; + } + call(inputs, kwargs) { + const x = getExactlyOneTensor(inputs); + return leakyRelu(x, this.alpha); + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const config = { alpha: this.alpha }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +LeakyReLU.className = "LeakyReLU"; +serialization_exports.registerClass(LeakyReLU); +var PReLU = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.DEFAULT_ALPHA_INITIALIZER = "zeros"; + if (args == null) { + args = {}; + } + this.supportsMasking = true; + this.alphaInitializer = getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER); + this.alphaRegularizer = getRegularizer(args.alphaRegularizer); + this.alphaConstraint = getConstraint(args.alphaConstraint); + if (args.sharedAxes == null) { + this.sharedAxes = null; + } else if (Array.isArray(args.sharedAxes)) { + this.sharedAxes = args.sharedAxes; + } else if (typeof args.sharedAxes === "number") { + this.sharedAxes = [args.sharedAxes]; + } else { + throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`); + } + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const paramShape = inputShape.slice(1); + if (this.sharedAxes != null) { + for (const i of this.sharedAxes) { + paramShape[i - 1] = 1; + } + } + this.alpha = this.addWeight("alpha", paramShape, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint); + const axes = {}; + if (this.sharedAxes != null) { + for (let i = 1; i < inputShape.length; ++i) { + axes[i] = inputShape[i]; + } + } + this.inputSpec = [new InputSpec({ + ndim: inputShape.length, + axes + })]; + this.built = true; + } + call(inputs, kwargs) { + inputs = getExactlyOneTensor(inputs); + return prelu(inputs, this.alpha.read()); + } + getConfig() { + const config = { + alphaInitializer: serializeInitializer(this.alphaInitializer), + alphaRegularizer: serializeRegularizer(this.alphaRegularizer), + alphaConstraint: serializeConstraint(this.alphaConstraint), + sharedAxes: this.sharedAxes + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +PReLU.className = "PReLU"; +serialization_exports.registerClass(PReLU); +var ELU = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.DEFAULT_ALPHA = 1; + if (args == null) { + args = {}; + } + if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) { + throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`); + } + this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha; + } + call(inputs, kwargs) { + const x = getExactlyOneTensor(inputs); + return elu(x); + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const config = { alpha: this.alpha }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +ELU.className = "ELU"; +serialization_exports.registerClass(ELU); +var ThresholdedReLU = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.DEFAULT_THETA = 1; + if (args == null) { + args = {}; + } + this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta; + } + call(inputs, kwargs) { + const x = getExactlyOneTensor(inputs); + return mul(x, cast(greater(x, this.theta), "float32")); + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const config = { theta: this.theta }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +ThresholdedReLU.className = "ThresholdedReLU"; +serialization_exports.registerClass(ThresholdedReLU); +var Softmax3 = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.DEFAULT_AXIS = 1; + if (args == null) { + args = {}; + } + this.softmax = new Softmax2().apply; + this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis; + } + call(inputs, kwargs) { + return tidy(() => { + let x = getExactlyOneTensor(inputs); + const mask = kwargs["mask"]; + if (mask != null) { + const adder = mul(sub(ones2(x.shape), cast(mask, x.dtype)), scalar(-1e9)); + x = add2(x, adder); + } + if (this.axis instanceof Array) { + if (this.axis.length > 1) { + return exp(sub(x, logSumExp(x, this.axis, true))); + } else { + return this.softmax(x, this.axis[0]); + } + } + return this.softmax(x, this.axis); + }); + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const config = { axis: this.axis }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Softmax3.className = "Softmax"; +serialization_exports.registerClass(Softmax3); +function normalizeArray(value, n, name) { + if (typeof value === "number") { + return pyListRepeat(value, n); + } else { + if (value.length !== n) { + throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`); + } + for (let i = 0; i < n; ++i) { + const singleValue = value[i]; + if (!isInteger(singleValue)) { + throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`); + } + } + return value; + } +} +function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) { + if (inputLength == null) { + return inputLength; + } + const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1); + let outputLength; + if (padding === "same") { + outputLength = inputLength; + } else { + outputLength = inputLength - dilatedFilterSize + 1; + } + return Math.floor((outputLength + stride - 1) / stride); +} +function deconvLength(dimSize, strideSize, kernelSize, padding) { + if (dimSize == null) { + return null; + } + if (padding === "valid") { + dimSize = dimSize * strideSize + max2([kernelSize - strideSize, 0]); + } else if (padding === "same") { + dimSize = dimSize * strideSize; + } else { + throw new ValueError(`Unsupport padding mode: ${padding}.`); + } + return dimSize; +} +function preprocessConv2DInput(x, dataFormat) { + return tidy(() => { + checkDataFormat(dataFormat); + if (dataFormat === "channelsFirst") { + return transpose(x, [0, 2, 3, 1]); + } else { + return x; + } + }); +} +function preprocessConv3DInput(x, dataFormat) { + return tidy(() => { + checkDataFormat(dataFormat); + if (dataFormat === "channelsFirst") { + return transpose(x, [0, 2, 3, 4, 1]); + } else { + return x; + } + }); +} +function conv1dWithBias(x, kernel, bias, strides = 1, padding = "valid", dataFormat, dilationRate = 1) { + return tidy(() => { + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + checkDataFormat(dataFormat); + if (x.shape.length !== 3) { + throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`); + } + if (kernel.shape.length !== 3) { + throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`); + } + if (bias != null && bias.shape.length !== 1) { + throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`); + } + if (dataFormat === "channelsFirst") { + x = transpose(x, [0, 2, 1]); + } + if (padding === "causal") { + throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet."); + } + let y = conv1d(x, kernel, strides, padding === "same" ? "same" : "valid", "NWC", dilationRate); + if (bias != null) { + y = biasAdd(y, bias); + } + return y; + }); +} +function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = "valid", dataFormat, dilationRate, activation2 = null) { + return tidy(() => { + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + checkDataFormat(dataFormat); + if (x.rank !== 3 && x.rank !== 4) { + throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`); + } + if (kernel.rank !== 3 && kernel.rank !== 4) { + throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`); + } + let y = preprocessConv2DInput(x, dataFormat); + if (padding === "causal") { + throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet."); + } + y = fused_ops_exports.conv2d({ + x: y, + filter: kernel, + strides, + pad: padding === "same" ? "same" : "valid", + dilations: dilationRate, + dataFormat: "NHWC", + bias, + activation: activation2 + }); + if (dataFormat === "channelsFirst") { + y = transpose(y, [0, 3, 1, 2]); + } + return y; + }); +} +function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = "valid", dataFormat, dilationRate) { + return tidy(() => { + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + checkDataFormat(dataFormat); + if (x.rank !== 4 && x.rank !== 5) { + throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`); + } + if (kernel.rank !== 4 && kernel.rank !== 5) { + throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`); + } + let y = preprocessConv3DInput(x, dataFormat); + if (padding === "causal") { + throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet."); + } + y = conv3d(y, kernel, strides, padding === "same" ? "same" : "valid", "NDHWC", dilationRate); + if (bias != null) { + y = biasAdd(y, bias); + } + if (dataFormat === "channelsFirst") { + y = transpose(y, [0, 4, 1, 2, 3]); + } + return y; + }); +} +var BaseConv = class _BaseConv extends Layer { + constructor(rank, args) { + super(args); + this.bias = null; + this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal"; + this.DEFAULT_BIAS_INITIALIZER = "zeros"; + _BaseConv.verifyArgs(args); + this.rank = rank; + assertPositiveInteger(this.rank, "rank"); + if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) { + throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`); + } + this.kernelSize = normalizeArray(args.kernelSize, rank, "kernelSize"); + this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, "strides"); + this.padding = args.padding == null ? "valid" : args.padding; + checkPaddingMode(this.padding); + this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat; + checkDataFormat(this.dataFormat); + this.activation = getActivation(args.activation); + this.useBias = args.useBias == null ? true : args.useBias; + this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER); + this.biasConstraint = getConstraint(args.biasConstraint); + this.biasRegularizer = getRegularizer(args.biasRegularizer); + this.activityRegularizer = getRegularizer(args.activityRegularizer); + this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, "dilationRate"); + if (this.rank === 1 && (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) { + throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`); + } else if (this.rank === 2) { + if (typeof this.dilationRate === "number") { + this.dilationRate = [this.dilationRate, this.dilationRate]; + } else if (this.dilationRate.length !== 2) { + throw new ValueError(`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 ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`); + } + } + } + static verifyArgs(args) { + assert2("kernelSize" in args, `required key 'kernelSize' not in config`); + if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 3)) { + throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`); + } + } + getConfig() { + const config = { + kernelSize: this.kernelSize, + strides: this.strides, + padding: this.padding, + dataFormat: this.dataFormat, + dilationRate: this.dilationRate, + activation: serializeActivation(this.activation), + useBias: this.useBias, + biasInitializer: serializeInitializer(this.biasInitializer), + biasRegularizer: serializeRegularizer(this.biasRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + biasConstraint: serializeConstraint(this.biasConstraint) + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +var Conv = class _Conv extends BaseConv { + constructor(rank, args) { + super(rank, args); + this.kernel = null; + _Conv.verifyArgs(args); + this.filters = args.filters; + assertPositiveInteger(this.filters, "filters"); + this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.kernelConstraint = getConstraint(args.kernelConstraint); + this.kernelRegularizer = getRegularizer(args.kernelRegularizer); + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1; + if (inputShape[channelAxis] == null) { + throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`); + } + const inputDim = inputShape[channelAxis]; + const kernelShape = this.kernelSize.concat([inputDim, this.filters]); + this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } + this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }]; + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + let outputs; + const biasValue = this.bias == null ? null : this.bias.read(); + const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName()); + if (fusedActivationName != null && this.rank === 2) { + outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName); + } else { + if (this.rank === 1) { + outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]); + } else if (this.rank === 2) { + outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate); + } else if (this.rank === 3) { + outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate); + } else { + throw new NotImplementedError("convolutions greater than 3D are not implemented yet."); + } + if (this.activation != null) { + outputs = this.activation.apply(outputs); + } + } + return outputs; + }); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const newSpace = []; + const space = this.dataFormat === "channelsLast" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2); + for (let i = 0; i < space.length; ++i) { + const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === "number" ? this.dilationRate : this.dilationRate[i]); + newSpace.push(newDim); + } + let outputShape = [inputShape[0]]; + if (this.dataFormat === "channelsLast") { + outputShape = outputShape.concat(newSpace); + outputShape.push(this.filters); + } else { + outputShape.push(this.filters); + outputShape = outputShape.concat(newSpace); + } + return outputShape; + } + getConfig() { + const config = { + filters: this.filters, + kernelInitializer: serializeInitializer(this.kernelInitializer), + kernelRegularizer: serializeRegularizer(this.kernelRegularizer), + kernelConstraint: serializeConstraint(this.kernelConstraint) + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + static verifyArgs(args) { + if (!("filters" in args) || typeof args.filters !== "number" || args.filters < 1) { + throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`); + } + } +}; +var Conv2D2 = class _Conv2D extends Conv { + constructor(args) { + super(2, args); + _Conv2D.verifyArgs(args); + } + getConfig() { + const config = super.getConfig(); + delete config["rank"]; + return config; + } + static verifyArgs(args) { + if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 2)) { + throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`); + } + } +}; +Conv2D2.className = "Conv2D"; +serialization_exports.registerClass(Conv2D2); +var Conv3D2 = class _Conv3D extends Conv { + constructor(args) { + super(3, args); + _Conv3D.verifyArgs(args); + } + getConfig() { + const config = super.getConfig(); + delete config["rank"]; + return config; + } + static verifyArgs(args) { + if (typeof args.kernelSize !== "number") { + if (!(Array.isArray(args.kernelSize) && (args.kernelSize.length === 1 || args.kernelSize.length === 3))) { + throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`); + } + } + } +}; +Conv3D2.className = "Conv3D"; +serialization_exports.registerClass(Conv3D2); +var Conv2DTranspose = class extends Conv2D2 { + constructor(args) { + super(args); + this.inputSpec = [new InputSpec({ ndim: 4 })]; + if (this.padding !== "same" && this.padding !== "valid") { + throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`); + } + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape.length !== 4) { + throw new ValueError("Input should have rank 4; Received input shape: " + JSON.stringify(inputShape)); + } + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1; + if (inputShape[channelAxis] == null) { + throw new ValueError("The channel dimension of the inputs should be defined. Found `None`."); + } + const inputDim = inputShape[channelAxis]; + const kernelShape = this.kernelSize.concat([this.filters, inputDim]); + this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } + this.inputSpec = [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })]; + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + let input2 = getExactlyOneTensor(inputs); + if (input2.shape.length !== 4) { + throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`); + } + const inputShape = input2.shape; + const batchSize = inputShape[0]; + let hAxis; + let wAxis; + if (this.dataFormat === "channelsFirst") { + hAxis = 2; + wAxis = 3; + } else { + hAxis = 1; + wAxis = 2; + } + const height = inputShape[hAxis]; + const width = inputShape[wAxis]; + const kernelH = this.kernelSize[0]; + const kernelW = this.kernelSize[1]; + const strideH = this.strides[0]; + const strideW = this.strides[1]; + const outHeight = deconvLength(height, strideH, kernelH, this.padding); + const outWidth = deconvLength(width, strideW, kernelW, this.padding); + const outputShape = [batchSize, outHeight, outWidth, this.filters]; + if (this.dataFormat !== "channelsLast") { + input2 = transpose(input2, [0, 2, 3, 1]); + } + let outputs = conv2dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding); + if (this.dataFormat !== "channelsLast") { + outputs = transpose(outputs, [0, 3, 1, 2]); + } + if (this.bias != null) { + outputs = biasAdd(outputs, this.bias.read(), this.dataFormat); + } + if (this.activation != null) { + outputs = this.activation.apply(outputs); + } + return outputs; + }); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const outputShape = inputShape.slice(); + let channelAxis; + let heightAxis; + let widthAxis; + if (this.dataFormat === "channelsFirst") { + channelAxis = 1; + heightAxis = 2; + widthAxis = 3; + } else { + channelAxis = 3; + heightAxis = 1; + widthAxis = 2; + } + const kernelH = this.kernelSize[0]; + const kernelW = this.kernelSize[1]; + const strideH = this.strides[0]; + const strideW = this.strides[1]; + outputShape[channelAxis] = this.filters; + outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding); + outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding); + return outputShape; + } + getConfig() { + const config = super.getConfig(); + delete config["dilationRate"]; + return config; + } +}; +Conv2DTranspose.className = "Conv2DTranspose"; +serialization_exports.registerClass(Conv2DTranspose); +var Conv3DTranspose = class extends Conv3D2 { + constructor(args) { + super(args); + this.inputSpec = [new InputSpec({ ndim: 5 })]; + if (this.padding !== "same" && this.padding !== "valid") { + throw new ValueError(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`); + } + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape.length !== 5) { + throw new ValueError("Input should have rank 5; Received input shape: " + JSON.stringify(inputShape)); + } + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1; + if (inputShape[channelAxis] == null) { + throw new ValueError("The channel dimension of the inputs should be defined. Found `None`."); + } + const inputDim = inputShape[channelAxis]; + const kernelShape = this.kernelSize.concat([this.filters, inputDim]); + this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } + this.inputSpec = [new InputSpec({ ndim: 5, axes: { [channelAxis]: inputDim } })]; + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + let input2 = getExactlyOneTensor(inputs); + if (input2.shape.length !== 5) { + throw new ValueError(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`); + } + const inputShape = input2.shape; + const batchSize = inputShape[0]; + let hAxis; + let wAxis; + let dAxis; + if (this.dataFormat === "channelsFirst") { + dAxis = 2; + hAxis = 3; + wAxis = 4; + } else { + dAxis = 1; + hAxis = 2; + wAxis = 3; + } + const depth = inputShape[dAxis]; + const height = inputShape[hAxis]; + const width = inputShape[wAxis]; + const kernelD = this.kernelSize[0]; + const kernelH = this.kernelSize[1]; + const kernelW = this.kernelSize[2]; + const strideD = this.strides[0]; + const strideH = this.strides[1]; + const strideW = this.strides[2]; + const outDepth = deconvLength(depth, strideD, kernelD, this.padding); + const outHeight = deconvLength(height, strideH, kernelH, this.padding); + const outWidth = deconvLength(width, strideW, kernelW, this.padding); + const outputShape = [batchSize, outDepth, outHeight, outWidth, this.filters]; + if (this.dataFormat !== "channelsLast") { + input2 = transpose(input2, [0, 2, 3, 4, 1]); + } + let outputs = conv3dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding); + if (this.dataFormat !== "channelsLast") { + outputs = transpose(outputs, [0, 4, 1, 2, 3]); + } + if (this.bias !== null) { + outputs = biasAdd(outputs, this.bias.read(), this.dataFormat); + } + if (this.activation !== null) { + outputs = this.activation.apply(outputs); + } + return outputs; + }); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const outputShape = inputShape.slice(); + let channelAxis; + let depthAxis; + let heightAxis; + let widthAxis; + if (this.dataFormat === "channelsFirst") { + channelAxis = 1; + depthAxis = 2; + heightAxis = 3; + widthAxis = 4; + } else { + channelAxis = 4; + depthAxis = 1; + heightAxis = 2; + widthAxis = 3; + } + const kernelD = this.kernelSize[0]; + const kernelH = this.kernelSize[1]; + const kernelW = this.kernelSize[2]; + const strideD = this.strides[0]; + const strideH = this.strides[1]; + const strideW = this.strides[2]; + outputShape[channelAxis] = this.filters; + outputShape[depthAxis] = deconvLength(outputShape[depthAxis], strideD, kernelD, this.padding); + outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding); + outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding); + return outputShape; + } + getConfig() { + const config = super.getConfig(); + delete config["dilationRate"]; + return config; + } +}; +Conv3DTranspose.className = "Conv3DTranspose"; +serialization_exports.registerClass(Conv3DTranspose); +var SeparableConv = class extends Conv { + constructor(rank, config) { + super(rank, config); + this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform"; + this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform"; + this.depthwiseKernel = null; + this.pointwiseKernel = null; + if (config.filters == null) { + throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified."); + } + if (config.kernelInitializer != null || config.kernelRegularizer != null || config.kernelConstraint != null) { + throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead."); + } + if (config.padding != null && config.padding !== "same" && config.padding !== "valid") { + throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`); + } + this.depthMultiplier = config.depthMultiplier == null ? 1 : config.depthMultiplier; + this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER); + this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer); + this.depthwiseConstraint = getConstraint(config.depthwiseConstraint); + this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER); + this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer); + this.pointwiseConstraint = getConstraint(config.pointwiseConstraint); + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape.length < this.rank + 2) { + throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(inputShape)}`); + } + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1; + if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) { + throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`); + } + const inputDim = inputShape[channelAxis]; + const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]); + const pointwiseKernelShape = []; + for (let i = 0; i < this.rank; ++i) { + pointwiseKernelShape.push(1); + } + pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters); + const trainable = true; + this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, "float32", this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint); + this.pointwiseKernel = this.addWeight("pointwise_kernel", pointwiseKernelShape, "float32", this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint); + } else { + this.bias = null; + } + this.inputSpec = [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })]; + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + let output; + if (this.rank === 1) { + throw new NotImplementedError("1D separable convolution is not implemented yet."); + } else if (this.rank === 2) { + if (this.dataFormat === "channelsFirst") { + inputs = transpose(inputs, [0, 2, 3, 1]); + } + output = separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC"); + } + if (this.useBias) { + output = biasAdd(output, this.bias.read(), this.dataFormat); + } + if (this.activation != null) { + output = this.activation.apply(output); + } + if (this.dataFormat === "channelsFirst") { + output = transpose(output, [0, 3, 1, 2]); + } + return output; + }); + } + getConfig() { + const config = super.getConfig(); + delete config["rank"]; + delete config["kernelInitializer"]; + delete config["kernelRegularizer"]; + delete config["kernelConstraint"]; + config["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer); + config["pointwiseInitializer"] = serializeInitializer(this.pointwiseInitializer); + config["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer); + config["pointwiseRegularizer"] = serializeRegularizer(this.pointwiseRegularizer); + config["depthwiseConstraint"] = serializeConstraint(this.depthwiseConstraint); + config["pointwiseConstraint"] = serializeConstraint(this.pointwiseConstraint); + return config; + } +}; +SeparableConv.className = "SeparableConv"; +var SeparableConv2D = class extends SeparableConv { + constructor(args) { + super(2, args); + } +}; +SeparableConv2D.className = "SeparableConv2D"; +serialization_exports.registerClass(SeparableConv2D); +var Conv1D = class _Conv1D extends Conv { + constructor(args) { + super(1, args); + _Conv1D.verifyArgs(args); + this.inputSpec = [{ ndim: 3 }]; + } + getConfig() { + const config = super.getConfig(); + delete config["rank"]; + delete config["dataFormat"]; + return config; + } + static verifyArgs(args) { + if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 1)) { + throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`); + } + } +}; +Conv1D.className = "Conv1D"; +serialization_exports.registerClass(Conv1D); +var Cropping2D = class extends Layer { + constructor(args) { + super(args); + if (typeof args.cropping === "number") { + this.cropping = [[args.cropping, args.cropping], [args.cropping, args.cropping]]; + } else if (typeof args.cropping[0] === "number") { + this.cropping = [ + [args.cropping[0], args.cropping[0]], + [args.cropping[1], args.cropping[1]] + ]; + } else { + this.cropping = args.cropping; + } + this.dataFormat = args.dataFormat === void 0 ? "channelsLast" : args.dataFormat; + this.inputSpec = [{ ndim: 4 }]; + } + computeOutputShape(inputShape) { + if (this.dataFormat === "channelsFirst") { + return [ + inputShape[0], + inputShape[1], + inputShape[2] - this.cropping[0][0] - this.cropping[0][1], + inputShape[3] - this.cropping[1][0] - this.cropping[1][1] + ]; + } else { + return [ + inputShape[0], + inputShape[1] - this.cropping[0][0] - this.cropping[0][1], + inputShape[2] - this.cropping[1][0] - this.cropping[1][1], + inputShape[3] + ]; + } + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + if (this.dataFormat === "channelsLast") { + const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2); + return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3); + } else { + const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3); + return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4); + } + }); + } + getConfig() { + const config = { cropping: this.cropping, dataFormat: this.dataFormat }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Cropping2D.className = "Cropping2D"; +serialization_exports.registerClass(Cropping2D); +var UpSampling2D = class extends Layer { + constructor(args) { + super(args); + this.DEFAULT_SIZE = [2, 2]; + this.inputSpec = [{ ndim: 4 }]; + this.size = args.size == null ? this.DEFAULT_SIZE : args.size; + this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat; + checkDataFormat(this.dataFormat); + this.interpolation = args.interpolation == null ? "nearest" : args.interpolation; + checkInterpolationFormat(this.interpolation); + } + computeOutputShape(inputShape) { + if (this.dataFormat === "channelsFirst") { + const height = inputShape[2] == null ? null : this.size[0] * inputShape[2]; + const width = inputShape[3] == null ? null : this.size[1] * inputShape[3]; + return [inputShape[0], inputShape[1], height, width]; + } else { + const height = inputShape[1] == null ? null : this.size[0] * inputShape[1]; + const width = inputShape[2] == null ? null : this.size[1] * inputShape[2]; + return [inputShape[0], height, width, inputShape[3]]; + } + } + call(inputs, kwargs) { + return tidy(() => { + let input2 = getExactlyOneTensor(inputs); + const inputShape = input2.shape; + if (this.dataFormat === "channelsFirst") { + input2 = transpose(input2, [0, 2, 3, 1]); + const height = this.size[0] * inputShape[2]; + const width = this.size[1] * inputShape[3]; + const resized = this.interpolation === "nearest" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]); + return transpose(resized, [0, 3, 1, 2]); + } else { + const height = this.size[0] * inputShape[1]; + const width = this.size[1] * inputShape[2]; + return this.interpolation === "nearest" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]); + } + }); + } + getConfig() { + const config = { + size: this.size, + dataFormat: this.dataFormat, + interpolation: this.interpolation + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +UpSampling2D.className = "UpSampling2D"; +serialization_exports.registerClass(UpSampling2D); +function depthwiseConv2d3(x, depthwiseKernel, strides = [1, 1], padding = "valid", dataFormat, dilationRate) { + return tidy(() => { + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + checkDataFormat(dataFormat); + let y = preprocessConv2DInput(x, dataFormat); + if (x.rank !== 4) { + throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`); + } + if (depthwiseKernel.rank !== 4) { + throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`); + } + y = depthwiseConv2d(y, depthwiseKernel, strides, padding === "same" ? "same" : "valid", "NHWC", dilationRate); + if (dataFormat === "channelsFirst") { + y = transpose(y, [0, 3, 1, 2]); + } + return y; + }); +} +var DepthwiseConv2D = class extends BaseConv { + constructor(args) { + super(2, args); + this.depthwiseKernel = null; + this.depthMultiplier = args.depthMultiplier == null ? 1 : args.depthMultiplier; + this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.depthwiseConstraint = getConstraint(args.depthwiseConstraint); + this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer); + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape.length < 4) { + throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`); + } + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : 3; + if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) { + throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`); + } + const inputDim = inputShape[channelAxis]; + const depthwiseKernelShape = [ + this.kernelSize[0], + this.kernelSize[1], + inputDim, + this.depthMultiplier + ]; + this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } else { + this.bias = null; + } + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + let outputs = depthwiseConv2d3(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null); + if (this.useBias) { + outputs = biasAdd(outputs, this.bias.read(), this.dataFormat); + } + if (this.activation != null) { + outputs = this.activation.apply(outputs); + } + return outputs; + }); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1]; + const cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2]; + const outFilters = this.dataFormat === "channelsFirst" ? inputShape[1] * this.depthMultiplier : inputShape[3] * this.depthMultiplier; + const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]); + const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]); + if (this.dataFormat === "channelsFirst") { + return [inputShape[0], outFilters, outRows, outCols]; + } else { + return [inputShape[0], outRows, outCols, outFilters]; + } + } + getConfig() { + const config = super.getConfig(); + config["depthMultiplier"] = this.depthMultiplier; + config["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer); + config["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer); + config["depthwiseConstraint"] = serializeConstraint(this.depthwiseRegularizer); + return config; + } +}; +DepthwiseConv2D.className = "DepthwiseConv2D"; +serialization_exports.registerClass(DepthwiseConv2D); +function standardizeArgs(inputs, initialState, constants, numConstants) { + if (Array.isArray(inputs)) { + if (initialState != null || constants != null) { + throw new ValueError("When inputs is an array, neither initialState or constants should be provided"); + } + if (numConstants != null) { + constants = inputs.slice(inputs.length - numConstants, inputs.length); + inputs = inputs.slice(0, inputs.length - numConstants); + } + if (inputs.length > 1) { + initialState = inputs.slice(1, inputs.length); + } + inputs = inputs[0]; + } + function toListOrNull(x) { + if (x == null || Array.isArray(x)) { + return x; + } else { + return [x]; + } + } + initialState = toListOrNull(initialState); + constants = toListOrNull(constants); + return { inputs, initialState, constants }; +} +function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) { + return tidy(() => { + const ndim = inputs.shape.length; + if (ndim < 3) { + throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`); + } + const axes = [1, 0].concat(range2(2, ndim)); + inputs = transpose(inputs, axes); + if (constants != null) { + throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet."); + } + if (unroll) { + console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."); + } + if (mask != null) { + mask = cast(cast(mask, "bool"), "float32"); + if (mask.rank === ndim - 1) { + mask = expandDims(mask, -1); + } + mask = transpose(mask, axes); + } + if (goBackwards) { + inputs = reverse(inputs, 0); + if (mask != null) { + mask = reverse(mask, 0); + } + } + const perStepOutputs = []; + let lastOutput; + let states = initialStates; + const timeSteps = inputs.shape[0]; + const perStepInputs = unstack(inputs); + let perStepMasks; + if (mask != null) { + perStepMasks = unstack(mask); + } + for (let t = 0; t < timeSteps; ++t) { + const currentInput = perStepInputs[t]; + const stepOutputs = tidy(() => stepFunction(currentInput, states)); + if (mask == null) { + lastOutput = stepOutputs[0]; + states = stepOutputs[1]; + } else { + const maskedOutputs = tidy(() => { + const stepMask = perStepMasks[t]; + const negStepMask = sub(onesLike(stepMask), stepMask); + const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask)); + const newStates = states.map((state, i) => { + return add2(mul(stepOutputs[1][i], stepMask), mul(state, negStepMask)); + }); + return { output, newStates }; + }); + lastOutput = maskedOutputs.output; + states = maskedOutputs.newStates; + } + if (needPerStepOutputs) { + perStepOutputs.push(lastOutput); + } + } + let outputs; + if (needPerStepOutputs) { + const axis = 1; + outputs = stack(perStepOutputs, axis); + } + return [lastOutput, outputs, states]; + }); +} +var RNN = class _RNN extends Layer { + constructor(args) { + super(args); + let cell; + if (args.cell == null) { + throw new ValueError("cell property is missing for the constructor of RNN."); + } else if (Array.isArray(args.cell)) { + cell = new StackedRNNCells({ cells: args.cell }); + } else { + cell = args.cell; + } + if (cell.stateSize == null) { + throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state)."); + } + this.cell = cell; + this.returnSequences = args.returnSequences == null ? false : args.returnSequences; + this.returnState = args.returnState == null ? false : args.returnState; + this.goBackwards = args.goBackwards == null ? false : args.goBackwards; + this._stateful = args.stateful == null ? false : args.stateful; + this.unroll = args.unroll == null ? false : args.unroll; + this.supportsMasking = true; + this.inputSpec = [new InputSpec({ ndim: 3 })]; + this.stateSpec = null; + this.states_ = null; + this.numConstants = null; + this.keptStates = []; + } + // Porting Note: This is the equivalent of `RNN.states` property getter in + // PyKeras. + getStates() { + if (this.states_ == null) { + const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1; + return range2(0, numStates).map((x) => null); + } else { + return this.states_; + } + } + // Porting Note: This is the equivalent of the `RNN.states` property setter in + // PyKeras. + setStates(states) { + this.states_ = states; + } + computeOutputShape(inputShape) { + if (isArrayOfShapes(inputShape)) { + inputShape = inputShape[0]; + } + inputShape = inputShape; + let stateSize = this.cell.stateSize; + if (!Array.isArray(stateSize)) { + stateSize = [stateSize]; + } + const outputDim = stateSize[0]; + let outputShape; + if (this.returnSequences) { + outputShape = [inputShape[0], inputShape[1], outputDim]; + } else { + outputShape = [inputShape[0], outputDim]; + } + if (this.returnState) { + const stateShape = []; + for (const dim of stateSize) { + stateShape.push([inputShape[0], dim]); + } + return [outputShape].concat(stateShape); + } else { + return outputShape; + } + } + computeMask(inputs, mask) { + return tidy(() => { + if (Array.isArray(mask)) { + mask = mask[0]; + } + const outputMask = this.returnSequences ? mask : null; + if (this.returnState) { + const stateMask = this.states.map((s) => null); + return [outputMask].concat(stateMask); + } else { + return outputMask; + } + }); + } + /** + * Get the current state tensors of the RNN. + * + * If the state hasn't been set, return an array of `null`s of the correct + * length. + */ + get states() { + if (this.states_ == null) { + const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1; + const output = []; + for (let i = 0; i < numStates; ++i) { + output.push(null); + } + return output; + } else { + return this.states_; + } + } + set states(s) { + this.states_ = s; + } + build(inputShape) { + const constantShape = null; + if (this.numConstants != null) { + throw new NotImplementedError("Constants support is not implemented in RNN yet."); + } + if (isArrayOfShapes(inputShape)) { + inputShape = inputShape[0]; + } + inputShape = inputShape; + const batchSize = this.stateful ? inputShape[0] : null; + const inputDim = inputShape.slice(2); + this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] }); + const stepInputShape = [inputShape[0]].concat(inputShape.slice(2)); + if (constantShape != null) { + throw new NotImplementedError("Constants support is not implemented in RNN yet."); + } else { + this.cell.build(stepInputShape); + } + let stateSize; + if (Array.isArray(this.cell.stateSize)) { + stateSize = this.cell.stateSize; + } else { + stateSize = [this.cell.stateSize]; + } + if (this.stateSpec != null) { + if (!util_exports.arraysEqual(this.stateSpec.map((spec) => spec.shape[spec.shape.length - 1]), stateSize)) { + throw new ValueError(`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 = stateSize.map((dim) => new InputSpec({ shape: [null, dim] })); + } + if (this.stateful) { + this.resetStates(); + } + } + /** + * Reset the state tensors of the RNN. + * + * If the `states` argument is `undefined` or `null`, will set the + * state tensor(s) of the RNN to all-zero tensors of the appropriate + * shape(s). + * + * If `states` is provided, will set the state tensors of the RNN to its + * value. + * + * @param states Optional externally-provided initial states. + * @param training Whether this call is done during training. For stateful + * RNNs, this affects whether the old states are kept or discarded. In + * particular, if `training` is `true`, the old states will be kept so + * that subsequent backpropgataion through time (BPTT) may work properly. + * Else, the old states will be discarded. + */ + resetStates(states, training = false) { + tidy(() => { + if (!this.stateful) { + throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful."); + } + const batchSize = this.inputSpec[0].shape[0]; + if (batchSize == null) { + throw new ValueError("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) { + if (Array.isArray(this.cell.stateSize)) { + this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim])); + } else { + this.states_ = [zeros([batchSize, this.cell.stateSize])]; + } + } else if (states == null) { + dispose(this.states_); + if (this.keptStates != null) { + dispose(this.keptStates); + this.keptStates = []; + } + if (Array.isArray(this.cell.stateSize)) { + this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim])); + } else { + this.states_[0] = zeros([batchSize, this.cell.stateSize]); + } + } else { + if (!Array.isArray(states)) { + states = [states]; + } + if (states.length !== this.states_.length) { + throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`); + } + if (training === true) { + this.keptStates.push(this.states_.slice()); + } else { + dispose(this.states_); + } + for (let index = 0; index < this.states_.length; ++index) { + const value = states[index]; + const dim = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[index] : this.cell.stateSize; + const expectedShape = [batchSize, dim]; + if (!util_exports.arraysEqual(value.shape, expectedShape)) { + throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`); + } + this.states_[index] = value; + } + } + this.states_ = this.states_.map((state) => keep(state.clone())); + }); + } + apply(inputs, kwargs) { + let initialState = kwargs == null ? null : kwargs["initialState"]; + let constants = kwargs == null ? null : kwargs["constants"]; + if (kwargs == null) { + kwargs = {}; + } + const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants); + inputs = standardized.inputs; + initialState = standardized.initialState; + constants = standardized.constants; + let additionalInputs = []; + let additionalSpecs = []; + if (initialState != null) { + kwargs["initialState"] = initialState; + additionalInputs = additionalInputs.concat(initialState); + this.stateSpec = []; + for (const state of initialState) { + this.stateSpec.push(new InputSpec({ shape: state.shape })); + } + additionalSpecs = additionalSpecs.concat(this.stateSpec); + } + if (constants != null) { + kwargs["constants"] = constants; + additionalInputs = additionalInputs.concat(constants); + this.numConstants = constants.length; + } + const isTensor2 = additionalInputs[0] instanceof SymbolicTensor; + if (isTensor2) { + const fullInput = [inputs].concat(additionalInputs); + const fullInputSpec = this.inputSpec.concat(additionalSpecs); + const originalInputSpec = this.inputSpec; + this.inputSpec = fullInputSpec; + const output = super.apply(fullInput, kwargs); + this.inputSpec = originalInputSpec; + return output; + } else { + return super.apply(inputs, kwargs); + } + } + // tslint:disable-next-line:no-any + call(inputs, kwargs) { + return tidy(() => { + const mask = kwargs == null ? null : kwargs["mask"]; + const training = kwargs == null ? null : kwargs["training"]; + let initialState = kwargs == null ? null : kwargs["initialState"]; + inputs = getExactlyOneTensor(inputs); + if (initialState == null) { + if (this.stateful) { + initialState = this.states_; + } else { + initialState = this.getInitialState(inputs); + } + } + const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1; + if (initialState.length !== numStates) { + throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`); + } + if (this.unroll) { + console.warn("Ignoring unroll = true for RNN layer, due to imperative backend."); + } + const cellCallKwargs = { training }; + const step5 = (inputs2, states2) => { + const outputs2 = this.cell.call([inputs2].concat(states2), cellCallKwargs); + return [outputs2[0], outputs2.slice(1)]; + }; + const rnnOutputs = rnn(step5, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences); + const lastOutput = rnnOutputs[0]; + const outputs = rnnOutputs[1]; + const states = rnnOutputs[2]; + if (this.stateful) { + this.resetStates(states, training); + } + const output = this.returnSequences ? outputs : lastOutput; + if (this.returnState) { + return [output].concat(states); + } else { + return output; + } + }); + } + getInitialState(inputs) { + return tidy(() => { + let initialState = zeros(inputs.shape); + initialState = sum2(initialState, [1, 2]); + initialState = expandDims2(initialState); + if (Array.isArray(this.cell.stateSize)) { + return this.cell.stateSize.map((dim) => dim > 1 ? tile2(initialState, [1, dim]) : initialState); + } else { + return this.cell.stateSize > 1 ? [tile2(initialState, [1, this.cell.stateSize])] : [initialState]; + } + }); + } + get trainableWeights() { + if (!this.trainable) { + return []; + } + return this.cell.trainableWeights; + } + get nonTrainableWeights() { + if (!this.trainable) { + return this.cell.weights; + } + return this.cell.nonTrainableWeights; + } + setFastWeightInitDuringBuild(value) { + super.setFastWeightInitDuringBuild(value); + if (this.cell != null) { + this.cell.setFastWeightInitDuringBuild(value); + } + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { + returnSequences: this.returnSequences, + returnState: this.returnState, + goBackwards: this.goBackwards, + stateful: this.stateful, + unroll: this.unroll + }; + if (this.numConstants != null) { + config["numConstants"] = this.numConstants; + } + const cellConfig = this.cell.getConfig(); + if (this.getClassName() === _RNN.className) { + config["cell"] = { + "className": this.cell.getClassName(), + "config": cellConfig + }; + } + return Object.assign(Object.assign(Object.assign({}, cellConfig), baseConfig), config); + } + /** @nocollapse */ + static fromConfig(cls, config, customObjects = {}) { + const cellConfig = config["cell"]; + const cell = deserialize(cellConfig, customObjects); + return new cls(Object.assign(config, { cell })); + } +}; +RNN.className = "RNN"; +serialization_exports.registerClass(RNN); +var RNNCell = class extends Layer { +}; +var SimpleRNNCell = class extends RNNCell { + constructor(args) { + super(args); + this.DEFAULT_ACTIVATION = "tanh"; + this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal"; + this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal"; + this.DEFAULT_BIAS_INITIALIZER = "zeros"; + this.units = args.units; + assertPositiveInteger(this.units, `units`); + this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation); + this.useBias = args.useBias == null ? true : args.useBias; + this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER); + this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER); + this.kernelRegularizer = getRegularizer(args.kernelRegularizer); + this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer); + this.biasRegularizer = getRegularizer(args.biasRegularizer); + this.kernelConstraint = getConstraint(args.kernelConstraint); + this.recurrentConstraint = getConstraint(args.recurrentConstraint); + this.biasConstraint = getConstraint(args.biasConstraint); + this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]); + this.recurrentDropout = min2([ + 1, + max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout]) + ]); + this.dropoutFunc = args.dropoutFunc; + this.stateSize = this.units; + this.dropoutMask = null; + this.recurrentDropoutMask = null; + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + this.kernel = this.addWeight("kernel", [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } else { + this.bias = null; + } + this.built = true; + } + // Porting Note: PyKeras' equivalent of this method takes two tensor inputs: + // `inputs` and `states`. Here, the two tensors are combined into an + // `Tensor[]` Array as the first input argument. + // Similarly, PyKeras' equivalent of this method returns two values: + // `output` and `[output]`. Here the two are combined into one length-2 + // `Tensor[]`, consisting of `output` repeated. + call(inputs, kwargs) { + return tidy(() => { + inputs = inputs; + if (inputs.length !== 2) { + throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`); + } + let prevOutput = inputs[1]; + inputs = inputs[0]; + const training = kwargs["training"] == null ? false : kwargs["training"]; + if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) { + this.dropoutMask = generateDropoutMask({ + ones: () => onesLike(inputs), + rate: this.dropout, + training, + dropoutFunc: this.dropoutFunc + }); + } + if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) { + this.recurrentDropoutMask = generateDropoutMask({ + ones: () => onesLike(prevOutput), + rate: this.recurrentDropout, + training, + dropoutFunc: this.dropoutFunc + }); + } + let h; + const dpMask = this.dropoutMask; + const recDpMask = this.recurrentDropoutMask; + if (dpMask != null) { + h = dot2(mul(inputs, dpMask), this.kernel.read()); + } else { + h = dot2(inputs, this.kernel.read()); + } + if (this.bias != null) { + h = biasAdd(h, this.bias.read()); + } + if (recDpMask != null) { + prevOutput = mul(prevOutput, recDpMask); + } + let output = add2(h, dot2(prevOutput, this.recurrentKernel.read())); + if (this.activation != null) { + output = this.activation.apply(output); + } + return [output, output]; + }); + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { + units: this.units, + activation: serializeActivation(this.activation), + useBias: this.useBias, + kernelInitializer: serializeInitializer(this.kernelInitializer), + recurrentInitializer: serializeInitializer(this.recurrentInitializer), + biasInitializer: serializeInitializer(this.biasInitializer), + kernelRegularizer: serializeRegularizer(this.kernelRegularizer), + recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer), + biasRegularizer: serializeRegularizer(this.biasRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + kernelConstraint: serializeConstraint(this.kernelConstraint), + recurrentConstraint: serializeConstraint(this.recurrentConstraint), + biasConstraint: serializeConstraint(this.biasConstraint), + dropout: this.dropout, + recurrentDropout: this.recurrentDropout + }; + return Object.assign(Object.assign({}, baseConfig), config); + } +}; +SimpleRNNCell.className = "SimpleRNNCell"; +serialization_exports.registerClass(SimpleRNNCell); +var SimpleRNN = class extends RNN { + constructor(args) { + args.cell = new SimpleRNNCell(args); + super(args); + } + call(inputs, kwargs) { + return tidy(() => { + if (this.cell.dropoutMask != null) { + dispose(this.cell.dropoutMask); + this.cell.dropoutMask = null; + } + if (this.cell.recurrentDropoutMask != null) { + dispose(this.cell.recurrentDropoutMask); + this.cell.recurrentDropoutMask = null; + } + const mask = kwargs == null ? null : kwargs["mask"]; + const training = kwargs == null ? null : kwargs["training"]; + const initialState = kwargs == null ? null : kwargs["initialState"]; + return super.call(inputs, { mask, training, initialState }); + }); + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config); + } +}; +SimpleRNN.className = "SimpleRNN"; +serialization_exports.registerClass(SimpleRNN); +var GRUCell = class extends RNNCell { + constructor(args) { + super(args); + this.DEFAULT_ACTIVATION = "tanh"; + this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid"; + this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal"; + this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal"; + this.DEFAULT_BIAS_INITIALIZER = "zeros"; + if (args.resetAfter) { + throw new ValueError(`GRUCell does not support reset_after parameter set to true.`); + } + this.units = args.units; + assertPositiveInteger(this.units, "units"); + this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation); + this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation); + this.useBias = args.useBias == null ? true : args.useBias; + this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER); + this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER); + this.kernelRegularizer = getRegularizer(args.kernelRegularizer); + this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer); + this.biasRegularizer = getRegularizer(args.biasRegularizer); + this.kernelConstraint = getConstraint(args.kernelConstraint); + this.recurrentConstraint = getConstraint(args.recurrentConstraint); + this.biasConstraint = getConstraint(args.biasConstraint); + this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]); + this.recurrentDropout = min2([ + 1, + max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout]) + ]); + this.dropoutFunc = args.dropoutFunc; + this.implementation = args.implementation; + this.stateSize = this.units; + this.dropoutMask = null; + this.recurrentDropoutMask = null; + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const inputDim = inputShape[inputShape.length - 1]; + this.kernel = this.addWeight("kernel", [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } else { + this.bias = null; + } + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = inputs; + if (inputs.length !== 2) { + throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`); + } + const training = kwargs["training"] == null ? false : kwargs["training"]; + let hTMinus1 = inputs[1]; + inputs = inputs[0]; + if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) { + this.dropoutMask = generateDropoutMask({ + ones: () => onesLike(inputs), + rate: this.dropout, + training, + count: 3, + dropoutFunc: this.dropoutFunc + }); + } + if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) { + this.recurrentDropoutMask = generateDropoutMask({ + ones: () => onesLike(hTMinus1), + rate: this.recurrentDropout, + training, + count: 3, + dropoutFunc: this.dropoutFunc + }); + } + const dpMask = this.dropoutMask; + const recDpMask = this.recurrentDropoutMask; + let z; + let r; + let hh; + if (0 < this.dropout && this.dropout < 1) { + inputs = mul(inputs, dpMask[0]); + } + let matrixX = dot2(inputs, this.kernel.read()); + if (this.useBias) { + matrixX = biasAdd(matrixX, this.bias.read()); + } + if (0 < this.recurrentDropout && this.recurrentDropout < 1) { + hTMinus1 = mul(hTMinus1, recDpMask[0]); + } + const recurrentKernelValue = this.recurrentKernel.read(); + const [rk1, rk2] = split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1); + const matrixInner = dot2(hTMinus1, rk1); + const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1); + const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1); + z = this.recurrentActivation.apply(add2(xZ, recurrentZ)); + r = this.recurrentActivation.apply(add2(xR, recurrentR)); + const recurrentH = dot2(mul(r, hTMinus1), rk2); + hh = this.activation.apply(add2(xH, recurrentH)); + const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh)); + return [h, h]; + }); + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { + units: this.units, + activation: serializeActivation(this.activation), + recurrentActivation: serializeActivation(this.recurrentActivation), + useBias: this.useBias, + kernelInitializer: serializeInitializer(this.kernelInitializer), + recurrentInitializer: serializeInitializer(this.recurrentInitializer), + biasInitializer: serializeInitializer(this.biasInitializer), + kernelRegularizer: serializeRegularizer(this.kernelRegularizer), + recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer), + biasRegularizer: serializeRegularizer(this.biasRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + kernelConstraint: serializeConstraint(this.kernelConstraint), + recurrentConstraint: serializeConstraint(this.recurrentConstraint), + biasConstraint: serializeConstraint(this.biasConstraint), + dropout: this.dropout, + recurrentDropout: this.recurrentDropout, + implementation: this.implementation, + resetAfter: false + }; + return Object.assign(Object.assign({}, baseConfig), config); + } +}; +GRUCell.className = "GRUCell"; +serialization_exports.registerClass(GRUCell); +var GRU = class extends RNN { + constructor(args) { + if (args.implementation === 0) { + console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."); + } + args.cell = new GRUCell(args); + super(args); + } + call(inputs, kwargs) { + return tidy(() => { + if (this.cell.dropoutMask != null) { + dispose(this.cell.dropoutMask); + this.cell.dropoutMask = null; + } + if (this.cell.recurrentDropoutMask != null) { + dispose(this.cell.recurrentDropoutMask); + this.cell.recurrentDropoutMask = null; + } + const mask = kwargs == null ? null : kwargs["mask"]; + const training = kwargs == null ? null : kwargs["training"]; + const initialState = kwargs == null ? null : kwargs["initialState"]; + return super.call(inputs, { mask, training, initialState }); + }); + } + /** @nocollapse */ + static fromConfig(cls, config) { + if (config["implmentation"] === 0) { + config["implementation"] = 1; + } + return new cls(config); + } +}; +GRU.className = "GRU"; +serialization_exports.registerClass(GRU); +var LSTMCell = class extends RNNCell { + constructor(args) { + super(args); + 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 = args.units; + assertPositiveInteger(this.units, "units"); + this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation); + this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation); + this.useBias = args.useBias == null ? true : args.useBias; + this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER); + this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER); + this.unitForgetBias = args.unitForgetBias; + this.kernelRegularizer = getRegularizer(args.kernelRegularizer); + this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer); + this.biasRegularizer = getRegularizer(args.biasRegularizer); + this.kernelConstraint = getConstraint(args.kernelConstraint); + this.recurrentConstraint = getConstraint(args.recurrentConstraint); + this.biasConstraint = getConstraint(args.biasConstraint); + this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]); + this.recurrentDropout = min2([ + 1, + max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout]) + ]); + this.dropoutFunc = args.dropoutFunc; + this.implementation = args.implementation; + this.stateSize = [this.units, this.units]; + this.dropoutMask = null; + this.recurrentDropoutMask = null; + } + build(inputShape) { + var _a; + inputShape = getExactlyOneShape(inputShape); + const inputDim = inputShape[inputShape.length - 1]; + this.kernel = this.addWeight("kernel", [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint); + let biasInitializer; + if (this.useBias) { + if (this.unitForgetBias) { + const capturedBiasInit = this.biasInitializer; + const capturedUnits = this.units; + biasInitializer = new (_a = class CustomInit extends Initializer { + apply(shape, dtype) { + const bI = capturedBiasInit.apply([capturedUnits]); + const bF = new Ones().apply([capturedUnits]); + const bCAndH = capturedBiasInit.apply([capturedUnits * 2]); + return concatAlongFirstAxis(concatAlongFirstAxis(bI, bF), bCAndH); + } + }, /** @nocollapse */ + _a.className = "CustomInit", _a)(); + } else { + biasInitializer = this.biasInitializer; + } + this.bias = this.addWeight("bias", [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } else { + this.bias = null; + } + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + const training = kwargs["training"] == null ? false : kwargs["training"]; + inputs = inputs; + if (inputs.length !== 3) { + throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`); + } + let hTMinus1 = inputs[1]; + const cTMinus1 = inputs[2]; + inputs = inputs[0]; + if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) { + this.dropoutMask = generateDropoutMask({ + ones: () => onesLike(inputs), + rate: this.dropout, + training, + count: 4, + dropoutFunc: this.dropoutFunc + }); + } + if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) { + this.recurrentDropoutMask = generateDropoutMask({ + ones: () => onesLike(hTMinus1), + rate: this.recurrentDropout, + training, + count: 4, + dropoutFunc: this.dropoutFunc + }); + } + const dpMask = this.dropoutMask; + const recDpMask = this.recurrentDropoutMask; + let i; + let f; + let c; + let o; + if (0 < this.dropout && this.dropout < 1) { + inputs = mul(inputs, dpMask[0]); + } + let z = dot2(inputs, this.kernel.read()); + if (0 < this.recurrentDropout && this.recurrentDropout < 1) { + hTMinus1 = mul(hTMinus1, recDpMask[0]); + } + z = add2(z, dot2(hTMinus1, this.recurrentKernel.read())); + if (this.useBias) { + z = biasAdd(z, this.bias.read()); + } + const [z0, z1, z2, z3] = split(z, 4, z.rank - 1); + i = this.recurrentActivation.apply(z0); + f = this.recurrentActivation.apply(z1); + c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(z2))); + o = this.recurrentActivation.apply(z3); + const h = mul(o, this.activation.apply(c)); + return [h, h, c]; + }); + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { + units: this.units, + activation: serializeActivation(this.activation), + recurrentActivation: serializeActivation(this.recurrentActivation), + useBias: this.useBias, + kernelInitializer: serializeInitializer(this.kernelInitializer), + recurrentInitializer: serializeInitializer(this.recurrentInitializer), + biasInitializer: serializeInitializer(this.biasInitializer), + unitForgetBias: this.unitForgetBias, + kernelRegularizer: serializeRegularizer(this.kernelRegularizer), + recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer), + biasRegularizer: serializeRegularizer(this.biasRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + kernelConstraint: serializeConstraint(this.kernelConstraint), + recurrentConstraint: serializeConstraint(this.recurrentConstraint), + biasConstraint: serializeConstraint(this.biasConstraint), + dropout: this.dropout, + recurrentDropout: this.recurrentDropout, + implementation: this.implementation + }; + return Object.assign(Object.assign({}, baseConfig), config); + } +}; +LSTMCell.className = "LSTMCell"; +serialization_exports.registerClass(LSTMCell); +var LSTM = class extends RNN { + constructor(args) { + if (args.implementation === 0) { + console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."); + } + args.cell = new LSTMCell(args); + super(args); + } + call(inputs, kwargs) { + return tidy(() => { + if (this.cell.dropoutMask != null) { + dispose(this.cell.dropoutMask); + this.cell.dropoutMask = null; + } + if (this.cell.recurrentDropoutMask != null) { + dispose(this.cell.recurrentDropoutMask); + this.cell.recurrentDropoutMask = null; + } + const mask = kwargs == null ? null : kwargs["mask"]; + const training = kwargs == null ? null : kwargs["training"]; + const initialState = kwargs == null ? null : kwargs["initialState"]; + return super.call(inputs, { mask, training, initialState }); + }); + } + /** @nocollapse */ + static fromConfig(cls, config) { + if (config["implmentation"] === 0) { + config["implementation"] = 1; + } + return new cls(config); + } +}; +LSTM.className = "LSTM"; +serialization_exports.registerClass(LSTM); +var StackedRNNCells = class extends RNNCell { + constructor(args) { + super(args); + this.cells = args.cells; + } + get stateSize() { + const stateSize = []; + for (const cell of this.cells.slice().reverse()) { + if (Array.isArray(cell.stateSize)) { + stateSize.push(...cell.stateSize); + } else { + stateSize.push(cell.stateSize); + } + } + return stateSize; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = inputs; + let states = inputs.slice(1); + const nestedStates = []; + for (const cell of this.cells.slice().reverse()) { + if (Array.isArray(cell.stateSize)) { + nestedStates.push(states.splice(0, cell.stateSize.length)); + } else { + nestedStates.push(states.splice(0, 1)); + } + } + nestedStates.reverse(); + const newNestedStates = []; + let callInputs; + for (let i = 0; i < this.cells.length; ++i) { + const cell = this.cells[i]; + states = nestedStates[i]; + if (i === 0) { + callInputs = [inputs[0]].concat(states); + } else { + callInputs = [callInputs[0]].concat(states); + } + callInputs = cell.call(callInputs, kwargs); + newNestedStates.push(callInputs.slice(1)); + } + states = []; + for (const cellStates of newNestedStates.slice().reverse()) { + states.push(...cellStates); + } + return [callInputs[0]].concat(states); + }); + } + build(inputShape) { + if (isArrayOfShapes(inputShape)) { + inputShape = inputShape[0]; + } + inputShape = inputShape; + let outputDim; + this.cells.forEach((cell, i) => { + nameScope(`RNNCell_${i}`, () => { + cell.build(inputShape); + if (Array.isArray(cell.stateSize)) { + outputDim = cell.stateSize[0]; + } else { + outputDim = cell.stateSize; + } + inputShape = [inputShape[0], outputDim]; + }); + }); + this.built = true; + } + getConfig() { + const baseConfig = super.getConfig(); + const getCellConfig = (cell) => { + return { + "className": cell.getClassName(), + "config": cell.getConfig() + }; + }; + const cellConfigs = this.cells.map(getCellConfig); + const config = { "cells": cellConfigs }; + return Object.assign(Object.assign({}, baseConfig), config); + } + /** @nocollapse */ + static fromConfig(cls, config, customObjects = {}) { + const cells = []; + for (const cellConfig of config["cells"]) { + cells.push(deserialize(cellConfig, customObjects)); + } + return new cls({ cells }); + } + get trainableWeights() { + if (!this.trainable) { + return []; + } + const weights = []; + for (const cell of this.cells) { + weights.push(...cell.trainableWeights); + } + return weights; + } + get nonTrainableWeights() { + const weights = []; + for (const cell of this.cells) { + weights.push(...cell.nonTrainableWeights); + } + if (!this.trainable) { + const trainableWeights = []; + for (const cell of this.cells) { + trainableWeights.push(...cell.trainableWeights); + } + return trainableWeights.concat(weights); + } + return weights; + } + /** + * Retrieve the weights of a the model. + * + * @returns A flat `Array` of `tf.Tensor`s. + */ + getWeights() { + const weights = []; + for (const cell of this.cells) { + weights.push(...cell.weights); + } + return batchGetValue(weights); + } + /** + * Set the weights of the model. + * + * @param weights An `Array` of `tf.Tensor`s with shapes and types matching + * the output of `getWeights()`. + */ + setWeights(weights) { + const tuples = []; + for (const cell of this.cells) { + const numParams = cell.weights.length; + const inputWeights = weights.splice(numParams); + for (let i = 0; i < cell.weights.length; ++i) { + tuples.push([cell.weights[i], inputWeights[i]]); + } + } + batchSetValue(tuples); + } +}; +StackedRNNCells.className = "StackedRNNCells"; +serialization_exports.registerClass(StackedRNNCells); +function generateDropoutMask(args) { + const { ones: ones4, rate, training = false, count: count2 = 1, dropoutFunc } = args; + const droppedInputs = () => dropoutFunc != null ? dropoutFunc(ones4(), rate) : dropout2(ones4(), rate); + const createMask = () => inTrainPhase(droppedInputs, ones4, training); + if (!count2 || count2 <= 1) { + return keep(createMask().clone()); + } + const masks = Array(count2).fill(void 0).map(createMask); + return masks.map((m) => keep(m.clone())); +} +var __rest = function(s, e) { + var t = {}; + for (var p2 in s) + if (Object.prototype.hasOwnProperty.call(s, p2) && e.indexOf(p2) < 0) + t[p2] = s[p2]; + if (s != null && typeof Object.getOwnPropertySymbols === "function") + for (var i = 0, p2 = Object.getOwnPropertySymbols(s); i < p2.length; i++) { + if (e.indexOf(p2[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p2[i])) + t[p2[i]] = s[p2[i]]; + } + return t; +}; +var ConvRNN2D = class extends RNN { + constructor(args) { + if (args.unroll) { + throw new NotImplementedError("Unrolling is not possible with convolutional RNNs."); + } + if (Array.isArray(args.cell)) { + throw new NotImplementedError("It is not possible at the moment to stack convolutional cells."); + } + super(args); + this.inputSpec = [new InputSpec({ ndim: 5 })]; + } + call(inputs, kwargs) { + return tidy(() => { + if (this.cell.dropoutMask != null) { + dispose(this.cell.dropoutMask); + this.cell.dropoutMask = null; + } + if (this.cell.recurrentDropoutMask != null) { + dispose(this.cell.recurrentDropoutMask); + this.cell.recurrentDropoutMask = null; + } + if (kwargs && kwargs["constants"]) { + throw new ValueError("ConvRNN2D cell does not support constants"); + } + const mask = kwargs == null ? null : kwargs["mask"]; + const training = kwargs == null ? null : kwargs["training"]; + const initialState = kwargs == null ? null : kwargs["initialState"]; + return super.call(inputs, { mask, training, initialState }); + }); + } + computeOutputShape(inputShape) { + let outShape = this.computeSingleOutputShape(inputShape); + if (!this.returnSequences) { + outShape = [outShape[0], ...outShape.slice(2)]; + } + if (this.returnState) { + outShape = [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])]; + } + return outShape; + } + getInitialState(inputs) { + return tidy(() => { + const { stateSize } = this.cell; + const inputShape = inputs.shape; + const outputShape = this.computeSingleOutputShape(inputShape); + const stateShape = [outputShape[0], ...outputShape.slice(2)]; + const initialState = zeros(stateShape); + if (Array.isArray(stateSize)) { + return Array(stateSize.length).fill(initialState); + } + return [initialState]; + }); + } + resetStates(states, training = false) { + tidy(() => { + if (!this.stateful) { + throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful."); + } + const inputShape = this.inputSpec[0].shape; + const outputShape = this.computeSingleOutputShape(inputShape); + const stateShape = [outputShape[0], ...outputShape.slice(2)]; + const batchSize = inputShape[0]; + if (batchSize == null) { + throw new ValueError("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) { + if (Array.isArray(this.cell.stateSize)) { + this.states_ = this.cell.stateSize.map(() => zeros(stateShape)); + } else { + this.states_ = [zeros(stateShape)]; + } + } else if (states == null) { + dispose(this.states_); + if (this.keptStates != null) { + dispose(this.keptStates); + this.keptStates = []; + } + if (Array.isArray(this.cell.stateSize)) { + this.states_ = this.cell.stateSize.map(() => zeros(stateShape)); + } else { + this.states_[0] = zeros(stateShape); + } + } else { + if (!Array.isArray(states)) { + states = [states]; + } + if (states.length !== this.states_.length) { + throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`); + } + if (training) { + this.keptStates.push(this.states_.slice()); + } else { + dispose(this.states_); + } + for (let index = 0; index < this.states_.length; ++index) { + const value = states[index]; + const expectedShape = stateShape; + if (!util_exports.arraysEqual(value.shape, expectedShape)) { + throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`); + } + this.states_[index] = value; + } + } + this.states_ = this.states_.map((state) => keep(state.clone())); + }); + } + computeSingleOutputShape(inputShape) { + const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell; + const isChannelsFirst = dataFormat === "channelsFirst"; + const h = inputShape[isChannelsFirst ? 3 : 2]; + const w = inputShape[isChannelsFirst ? 4 : 3]; + const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]); + const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]); + const outShape = [ + ...inputShape.slice(0, 2), + ...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters] + ]; + return outShape; + } +}; +ConvRNN2D.className = "ConvRNN2D"; +var ConvLSTM2DCell = class extends LSTMCell { + constructor(args) { + const { filters, kernelSize, strides, padding, dataFormat, dilationRate } = args; + super(Object.assign(Object.assign({}, args), { units: filters })); + this.filters = filters; + assertPositiveInteger(this.filters, "filters"); + this.kernelSize = normalizeArray(kernelSize, 2, "kernelSize"); + this.kernelSize.forEach((size) => assertPositiveInteger(size, "kernelSize")); + this.strides = normalizeArray(strides || 1, 2, "strides"); + this.strides.forEach((stride) => assertPositiveInteger(stride, "strides")); + this.padding = padding || "valid"; + checkPaddingMode(this.padding); + this.dataFormat = dataFormat || "channelsLast"; + checkDataFormat(this.dataFormat); + this.dilationRate = normalizeArray(dilationRate || 1, 2, "dilationRate"); + this.dilationRate.forEach((rate) => assertPositiveInteger(rate, "dilationRate")); + } + build(inputShape) { + var _a; + inputShape = getExactlyOneShape(inputShape); + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1; + if (inputShape[channelAxis] == null) { + throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`); + } + const inputDim = inputShape[channelAxis]; + const numOfKernels = 4; + const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]); + this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]); + this.recurrentKernel = this.addWeight("recurrent_kernel", recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint); + if (this.useBias) { + let biasInitializer; + if (this.unitForgetBias) { + const init2 = this.biasInitializer; + const filters = this.filters; + biasInitializer = new (_a = class CustomInit extends Initializer { + apply(shape, dtype) { + const biasI = init2.apply([filters]); + const biasF = ones2([filters]); + const biasCAndO = init2.apply([filters * 2]); + return concatenate([biasI, biasF, biasCAndO]); + } + }, /** @nocollapse */ + _a.className = "CustomInit", _a)(); + } else { + biasInitializer = this.biasInitializer; + } + this.bias = this.addWeight("bias", [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + if (inputs.length !== 3) { + throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`); + } + const training = kwargs["training"] || false; + const x = inputs[0]; + const hTMinus1 = inputs[1]; + const cTMinus1 = inputs[2]; + const numOfKernels = 4; + if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) { + this.dropoutMask = generateDropoutMask({ + ones: () => onesLike(x), + rate: this.dropout, + training, + count: numOfKernels, + dropoutFunc: this.dropoutFunc + }); + } + const dropoutMask = this.dropoutMask; + const applyDropout = (x2, mask, index) => { + if (!mask || !mask[index]) { + return x2; + } + return mul(mask[index], x2); + }; + let xI = applyDropout(x, dropoutMask, 0); + let xF = applyDropout(x, dropoutMask, 1); + let xC = applyDropout(x, dropoutMask, 2); + let xO = applyDropout(x, dropoutMask, 3); + if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) { + this.recurrentDropoutMask = generateDropoutMask({ + ones: () => onesLike(hTMinus1), + rate: this.recurrentDropout, + training, + count: numOfKernels, + dropoutFunc: this.dropoutFunc + }); + } + const recDropoutMask = this.recurrentDropoutMask; + let hI = applyDropout(hTMinus1, recDropoutMask, 0); + let hF = applyDropout(hTMinus1, recDropoutMask, 1); + let hC = applyDropout(hTMinus1, recDropoutMask, 2); + let hO = applyDropout(hTMinus1, recDropoutMask, 3); + const kernelChannelAxis = 3; + const [kernelI, kernelF, kernelC, kernelO] = split(this.kernel.read(), numOfKernels, kernelChannelAxis); + const [biasI, biasF, biasC, biasO] = this.useBias ? split(this.bias.read(), numOfKernels) : [null, null, null, null]; + xI = this.inputConv(xI, kernelI, biasI, this.padding); + xF = this.inputConv(xF, kernelF, biasF, this.padding); + xC = this.inputConv(xC, kernelC, biasC, this.padding); + xO = this.inputConv(xO, kernelO, biasO, this.padding); + const [recKernelI, recKernelF, recKernelC, recKernelO] = split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis); + hI = this.recurrentConv(hI, recKernelI); + hF = this.recurrentConv(hF, recKernelF); + hC = this.recurrentConv(hC, recKernelC); + hO = this.recurrentConv(hO, recKernelO); + const i = this.recurrentActivation.apply(add2(xI, hI)); + const f = this.recurrentActivation.apply(add2(xF, hF)); + const c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(add2(xC, hC)))); + const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c)); + return [h, h, c]; + }); + } + getConfig() { + const _a = super.getConfig(), { "units": _ } = _a, baseConfig = __rest(_a, ["units"]); + const config = { + filters: this.filters, + kernelSize: this.kernelSize, + padding: this.padding, + dataFormat: this.dataFormat, + dilationRate: this.dilationRate, + strides: this.strides + }; + return Object.assign(Object.assign({}, baseConfig), config); + } + inputConv(x, w, b, padding) { + const out = conv2d(x, w, this.strides, padding || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate); + if (b) { + return biasAdd(out, b, this.dataFormat); + } + return out; + } + recurrentConv(x, w) { + const strides = 1; + return conv2d(x, w, strides, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC"); + } +}; +ConvLSTM2DCell.className = "ConvLSTM2DCell"; +serialization_exports.registerClass(ConvLSTM2DCell); +var ConvLSTM2D = class extends ConvRNN2D { + constructor(args) { + const cell = new ConvLSTM2DCell(args); + super(Object.assign(Object.assign({}, args), { cell })); + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config); + } +}; +ConvLSTM2D.className = "ConvLSTM2D"; +serialization_exports.registerClass(ConvLSTM2D); +var Dropout = class extends Layer { + constructor(args) { + super(args); + this.rate = Math.max(Math.min(args.rate, 1), 0); + this.noiseShape = args.noiseShape; + this.seed = args.seed; + this.supportsMasking = true; + } + getNoiseShape(input2) { + if (this.noiseShape == null) { + return this.noiseShape; + } + const inputShape = input2.shape; + const noiseShape = []; + for (let i = 0; i < this.noiseShape.length; ++i) { + noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]); + } + return noiseShape; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + if (0 < this.rate && this.rate < 1) { + const training = kwargs["training"] == null ? false : kwargs["training"]; + const noiseShape = this.getNoiseShape(input2); + const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training); + return output; + } + return inputs; + }); + } + getConfig() { + const config = { + rate: this.rate, + noiseShape: this.noiseShape, + seed: this.seed + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + dispose() { + return super.dispose(); + } +}; +Dropout.className = "Dropout"; +serialization_exports.registerClass(Dropout); +var SpatialDropout1D = class extends Dropout { + constructor(args) { + super(args); + this.inputSpec = [{ ndim: 3 }]; + } + getNoiseShape(input2) { + const inputShape = input2.shape; + return [inputShape[0], 1, inputShape[2]]; + } +}; +SpatialDropout1D.className = "SpatialDropout1D"; +serialization_exports.registerClass(SpatialDropout1D); +var Dense = class extends Layer { + constructor(args) { + super(args); + this.activation = null; + this.useBias = true; + this.kernel = null; + this.bias = null; + this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal"; + this.DEFAULT_BIAS_INITIALIZER = "zeros"; + if (args.batchInputShape == null && args.inputShape == null && args.inputDim != null) { + let batchSize = null; + if (args.batchSize != null) { + batchSize = args.batchSize; + } + this.batchInputShape = [batchSize, args.inputDim]; + } + this.units = args.units; + assertPositiveInteger(this.units, "units"); + this.activation = getActivation(args.activation); + if (args.useBias != null) { + this.useBias = args.useBias; + } + this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER); + this.kernelConstraint = getConstraint(args.kernelConstraint); + this.biasConstraint = getConstraint(args.biasConstraint); + this.kernelRegularizer = getRegularizer(args.kernelRegularizer); + this.biasRegularizer = getRegularizer(args.biasRegularizer); + this.activityRegularizer = getRegularizer(args.activityRegularizer); + this.supportsMasking = true; + this.inputSpec = [{ minNDim: 2 }]; + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const inputLastDim = inputShape[inputShape.length - 1]; + if (this.kernel == null) { + this.kernel = this.addWeight("kernel", [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } + } + this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }]; + this.built = true; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const outputShape = inputShape.slice(); + outputShape[outputShape.length - 1] = this.units; + return outputShape; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName()); + let output; + if (fusedActivationName != null) { + output = dot2(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null); + } else { + output = dot2(input2, this.kernel.read()); + if (this.bias != null) { + output = biasAdd(output, this.bias.read()); + } + if (this.activation != null) { + output = this.activation.apply(output); + } + } + return output; + }); + } + getConfig() { + const config = { + units: this.units, + activation: serializeActivation(this.activation), + useBias: this.useBias, + kernelInitializer: serializeInitializer(this.kernelInitializer), + biasInitializer: serializeInitializer(this.biasInitializer), + kernelRegularizer: serializeRegularizer(this.kernelRegularizer), + biasRegularizer: serializeRegularizer(this.biasRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + kernelConstraint: serializeConstraint(this.kernelConstraint), + biasConstraint: serializeConstraint(this.biasConstraint) + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Dense.className = "Dense"; +serialization_exports.registerClass(Dense); +var Flatten = class extends Layer { + constructor(args) { + args = args || {}; + super(args); + this.inputSpec = [{ minNDim: 3 }]; + this.dataFormat = args.dataFormat; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + for (const dim of inputShape.slice(1)) { + if (dim == null) { + throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`); + } + } + return [inputShape[0], arrayProd(inputShape, 1)]; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + let input2 = getExactlyOneTensor(inputs); + if (this.dataFormat === "channelsFirst" && input2.rank > 1) { + const permutation = [0]; + for (let i = 2; i < input2.rank; ++i) { + permutation.push(i); + } + permutation.push(1); + input2 = transpose(input2, permutation); + } + return batchFlatten(input2); + }); + } + getConfig() { + const config = {}; + if (this.dataFormat != null) { + config["dataFormat"] = this.dataFormat; + } + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Flatten.className = "Flatten"; +serialization_exports.registerClass(Flatten); +var Activation2 = class extends Layer { + constructor(args) { + super(args); + this.supportsMasking = true; + this.activation = getActivation(args.activation); + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + return this.activation.apply(input2); + }); + } + getConfig() { + const config = { activation: serializeActivation(this.activation) }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Activation2.className = "Activation"; +serialization_exports.registerClass(Activation2); +var RepeatVector = class extends Layer { + constructor(args) { + super(args); + this.n = args.n; + this.inputSpec = [{ ndim: 2 }]; + } + computeOutputShape(inputShape) { + return [inputShape[0], this.n, inputShape[1]]; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + return repeat(inputs, this.n); + }); + } + getConfig() { + const config = { + n: this.n + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +RepeatVector.className = "RepeatVector"; +serialization_exports.registerClass(RepeatVector); +var Reshape2 = class extends Layer { + constructor(args) { + super(args); + this.targetShape = args.targetShape; + for (let i = 0; i < this.targetShape.length; ++i) { + if (this.isUnknown(this.targetShape[i])) { + this.targetShape[i] = null; + } + } + } + isUnknown(dim) { + return dim < 0 || dim == null; + } + /** + * Finds and replaces a missing dimension in output shape. + * + * This is a near direct port of the internal Numpy function + * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`. + * + * @param inputShape: Original shape of array begin reshape. + * @param outputShape: Target shape of the array, with at most a single + * `null` or negative number, which indicates an underdetermined dimension + * that should be derived from `inputShape` and the known dimensions of + * `outputShape`. + * @returns: The output shape with `null` replaced with its computed value. + * @throws: ValueError: If `inputShape` and `outputShape` do not match. + */ + fixUnknownDimension(inputShape, outputShape) { + const errorMsg = "Total size of new array must be unchanged."; + const finalShape = outputShape.slice(); + let known = 1; + let unknown = null; + for (let i = 0; i < finalShape.length; ++i) { + const dim = finalShape[i]; + if (this.isUnknown(dim)) { + if (unknown === null) { + unknown = i; + } else { + throw new ValueError("Can only specifiy one unknown dimension."); + } + } else { + known *= dim; + } + } + const originalSize = arrayProd(inputShape); + if (unknown !== null) { + if (known === 0 || originalSize % known !== 0) { + throw new ValueError(errorMsg); + } + finalShape[unknown] = originalSize / known; + } else if (originalSize !== known) { + throw new ValueError(errorMsg); + } + return finalShape; + } + computeOutputShape(inputShape) { + let anyUnknownDims = false; + for (let i = 0; i < inputShape.length; ++i) { + if (this.isUnknown(inputShape[i])) { + anyUnknownDims = true; + break; + } + } + if (anyUnknownDims) { + return inputShape.slice(0, 1).concat(this.targetShape); + } else { + return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape)); + } + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + const inputShape = input2.shape; + const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape)); + return reshape(input2, outputShape); + }); + } + getConfig() { + const config = { + targetShape: this.targetShape + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Reshape2.className = "Reshape"; +serialization_exports.registerClass(Reshape2); +var Permute = class extends Layer { + constructor(args) { + super(args); + if (args.dims == null) { + throw new Error("Required configuration field `dims` is missing during Permute constructor call."); + } + if (!Array.isArray(args.dims)) { + throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`); + } + const expectedSortedIndices = range2(1, args.dims.length + 1); + if (!util_exports.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) { + throw new Error("Invalid permutation `dims`: " + JSON.stringify(args.dims) + " `dims` must contain consecutive integers starting from 1."); + } + this.dims = args.dims; + this.dimsIncludingBatch = [0].concat(this.dims); + this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })]; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const outputShape = inputShape.slice(); + this.dims.forEach((dim, i) => { + outputShape[i + 1] = inputShape[dim]; + }); + return outputShape; + } + call(inputs, kwargs) { + return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch); + } + getConfig() { + const config = { + dims: this.dims + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Permute.className = "Permute"; +serialization_exports.registerClass(Permute); +var Masking = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.supportsMasking = true; + if (args != null) { + this.maskValue = args.maskValue == null ? 0 : args.maskValue; + } else { + this.maskValue = 0; + } + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { maskValue: this.maskValue }; + Object.assign(config, baseConfig); + return config; + } + computeMask(inputs, mask) { + const input2 = getExactlyOneTensor(inputs); + const axis = -1; + return any(notEqual(input2, this.maskValue), axis); + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + const axis = -1; + const keepDims = true; + const booleanMask = any(notEqual(input2, this.maskValue), axis, keepDims); + const output = mul(input2, cast(booleanMask, input2.dtype)); + return output; + }); + } +}; +Masking.className = "Masking"; +serialization_exports.registerClass(Masking); +var Embedding = class extends Layer { + constructor(args) { + super(args); + this.embeddings = null; + this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform"; + if (args.batchInputShape == null && args.inputShape == null) { + let batchSize = null; + if (args.batchSize != null) { + batchSize = args.batchSize; + } + if (args.inputLength == null) { + this.batchInputShape = [batchSize, null]; + } else { + this.batchInputShape = [batchSize].concat(toList(args.inputLength)); + } + } + this.inputDim = args.inputDim; + assertPositiveInteger(this.inputDim, "inputDim"); + this.outputDim = args.outputDim; + assertPositiveInteger(this.outputDim, "outputDim"); + this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER); + this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer); + this.activityRegularizer = getRegularizer(args.activityRegularizer); + this.embeddingsConstraint = getConstraint(args.embeddingsConstraint); + this.maskZero = args.maskZero; + this.supportsMasking = args.maskZero; + this.inputLength = args.inputLength; + } + build(inputShape) { + this.embeddings = this.addWeight("embeddings", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint); + this.built = true; + } + // Override warnOnIncompatibleInputShape because an embedding layer allows + // the input to have varying ranks. + warnOnIncompatibleInputShape(inputShape) { + } + computeMask(inputs, mask) { + return tidy(() => { + if (!this.maskZero) { + return null; + } else { + inputs = getExactlyOneTensor(inputs); + return notEqual(inputs, zerosLike(inputs)); + } + }); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (this.inputLength == null) { + return [...inputShape, this.outputDim]; + } + const inLens = toList(this.inputLength); + if (inLens.length !== inputShape.length - 1) { + throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`); + } else { + let i = 0; + for (let k = 0; k < inLens.length; ++k) { + const s1 = inLens[k]; + const s2 = inputShape[k + 1]; + if (s1 != null && s2 != null && s1 !== s2) { + throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`); + } else if (s1 == null) { + inLens[i] = s2; + } + i++; + } + } + return [inputShape[0], ...inLens, this.outputDim]; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + let input2 = getExactlyOneTensor(inputs); + if (input2.dtype !== "int32") { + input2 = cast2(input2, "int32"); + } + const output = gather2(this.embeddings.read(), reshape(input2, [input2.size])); + return reshape(output, getExactlyOneShape(this.computeOutputShape(input2.shape))); + }); + } + getConfig() { + const config = { + inputDim: this.inputDim, + outputDim: this.outputDim, + embeddingsInitializer: serializeInitializer(this.embeddingsInitializer), + embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + embeddingsConstraint: serializeConstraint(this.embeddingsConstraint), + maskZero: this.maskZero, + inputLength: this.inputLength + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Embedding.className = "Embedding"; +serialization_exports.registerClass(Embedding); +var Merge = class extends Layer { + constructor(args) { + super(args || {}); + this.supportsMasking = true; + } + /** + * Logic for merging multiple tensors, to be overridden by subclasses. + * @param inputs + */ + mergeFunction(inputs) { + throw new NotImplementedError(); + } + /** + * Computes the shape of the result of an elementwise operation. + * + * @param shape1: Shape of the first tensor. + * @param shape2: Shape of the second tensor. + * @returns Expected output shape when an elementwise operation is carried + * out on 2 tensors with shapes `shape1` and `shape2`. + * @throws ValueError: If `shape1` and `shape2` are not compatible for + * element-wise operations. + */ + computeElementwiseOpOutputShape(shape1, shape2) { + if (shape1 == null || shape2 == null) { + return null; + } else if (shape1.length < shape2.length) { + return this.computeElementwiseOpOutputShape(shape2, shape1); + } else if (shape2.length === 0) { + return shape1; + } + const outputShape = shape1.slice(0, shape1.length - shape2.length); + for (let k = 0; k < shape2.length; ++k) { + const i = shape1[shape1.length - shape2.length + k]; + const j = shape2[k]; + if (i == null || j == null || i < 0 || j < 0) { + outputShape.push(null); + } else if (i === 1) { + outputShape.push(j); + } else if (j === 1) { + outputShape.push(i); + } else { + if (i !== j) { + throw new ValueError("Operands could not be broadcast together with shapes " + JSON.stringify(shape1) + " " + JSON.stringify(shape2)); + } + outputShape.push(i); + } + } + return outputShape; + } + build(inputShape) { + if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) { + inputShape = [getExactlyOneShape(inputShape)]; + } + inputShape = inputShape; + if (inputShape.length < 2) { + throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`); + } + let batchSizes = []; + for (const shape of inputShape) { + if (shape != null && shape[0] !== null) { + batchSizes.push(shape[0]); + } + } + batchSizes = unique2(batchSizes); + if (batchSizes.length > 1) { + throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`); + } + let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1); + for (let i = 1; i < inputShape.length; ++i) { + const shape = inputShape[i] == null ? null : inputShape[i].slice(1); + outputShape = this.computeElementwiseOpOutputShape(outputShape, shape); + } + const allRanks = inputShape.map((shape) => shape.length); + if (inputShape.indexOf(null) === -1 && unique2(allRanks).length === 1) { + this.reshapeRequired = false; + } else { + this.reshapeRequired = true; + } + } + call(inputs, kwargs) { + return tidy(() => { + inputs = inputs; + if (this.reshapeRequired) { + const reshapedInputs = []; + const inputDims = inputs.map((input2) => input2.rank); + if (inputDims.indexOf(null) === -1) { + const maxNDim = max2(inputDims); + for (let x of inputs) { + const xNDim = x.rank; + for (let k = 0; k < maxNDim - xNDim; ++k) { + x = expandDims2(x, 1); + } + reshapedInputs.push(x); + } + return this.mergeFunction(reshapedInputs); + } else { + let transposed = false; + for (const x of inputs) { + const xNDim = x.rank; + if (xNDim == null) { + const xShape = x.shape; + const batchSize = xShape[0]; + const newShape = xShape.slice(1).concat([batchSize]); + let xTransposed = reshape(x, [batchSize].concat(arrayProd(xShape.slice(1)))); + xTransposed = transpose(xTransposed, [1, 0]); + xTransposed = reshape(xTransposed, newShape); + reshapedInputs.push(xTransposed); + transposed = true; + } else if (xNDim > 1) { + const dims = range2(1, xNDim).concat([0]); + reshapedInputs.push(transpose(x, dims)); + transposed = true; + } else { + reshapedInputs.push(x); + } + } + let y = this.mergeFunction(reshapedInputs); + const yNDim = y.rank; + if (transposed) { + if (yNDim == null) { + const yShape = y.shape; + const yNDim2 = yShape.length; + const batchSize = yShape[yNDim2 - 1]; + const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1)); + y = reshape(transpose(reshape(y, [-1, batchSize]), [1, 0]), newShape); + } else if (yNDim > 1) { + const dims = [yNDim - 1].concat(range2(0, yNDim - 1)); + y = transpose(y, dims); + } + } + return y; + } + } else { + return this.mergeFunction(inputs); + } + }); + } + computeOutputShape(inputShape) { + inputShape = inputShape; + let outputShape; + if (inputShape[0] == null) { + outputShape = null; + } else { + outputShape = inputShape[0].slice(1); + } + for (let i = 1; i < inputShape.length; ++i) { + const shape = inputShape[i] == null ? null : inputShape[i].slice(1); + outputShape = this.computeElementwiseOpOutputShape(outputShape, shape); + } + let batchSizes = []; + for (const shape of inputShape) { + if (shape != null && shape[0] !== null) { + batchSizes.push(shape[0]); + } + } + batchSizes = unique2(batchSizes); + if (batchSizes.length === 1) { + outputShape = batchSizes.concat(outputShape); + } else { + outputShape = [null].concat(outputShape); + } + return outputShape; + } + computeMask(inputs, mask) { + return tidy(() => { + if (mask == null) { + return null; + } + if (!Array.isArray(mask)) { + throw new ValueError("`mask` should be an Array"); + } + if (!Array.isArray(inputs)) { + throw new ValueError("`inputs` should be an Array"); + } + if (mask.length !== inputs.length) { + throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`); + } + if (mask.every((m) => m == null)) { + return null; + } + mask = mask.map((m) => m == null ? m : expandDims(m, 0)); + let output = mask[0]; + for (let i = 1; i < mask.length - 1; ++i) { + output = logicalAnd(output, mask[i]); + } + return output; + }); + } +}; +var Add2 = class extends Merge { + constructor(args) { + super(args); + } + mergeFunction(inputs) { + return tidy(() => { + let output = inputs[0].clone(); + for (let i = 1; i < inputs.length; ++i) { + output = add2(output, inputs[i]); + } + return output; + }); + } +}; +Add2.className = "Add"; +serialization_exports.registerClass(Add2); +var Multiply2 = class extends Merge { + constructor(args) { + super(args); + } + mergeFunction(inputs) { + return tidy(() => { + let output = inputs[0].clone(); + for (let i = 1; i < inputs.length; ++i) { + output = mul(output, inputs[i]); + } + return output; + }); + } +}; +Multiply2.className = "Multiply"; +serialization_exports.registerClass(Multiply2); +var Average = class extends Merge { + constructor(args) { + super(args); + } + mergeFunction(inputs) { + return tidy(() => { + let output = inputs[0].clone(); + for (let i = 1; i < inputs.length; ++i) { + output = add2(output, inputs[i]); + } + return mul(1 / inputs.length, output); + }); + } +}; +Average.className = "Average"; +serialization_exports.registerClass(Average); +var Maximum2 = class extends Merge { + constructor(args) { + super(args); + } + mergeFunction(inputs) { + return tidy(() => { + let output = inputs[0]; + for (let i = 1; i < inputs.length; ++i) { + output = maximum(output, inputs[i]); + } + return output; + }); + } +}; +Maximum2.className = "Maximum"; +serialization_exports.registerClass(Maximum2); +var Minimum2 = class extends Merge { + constructor(args) { + super(args); + } + mergeFunction(inputs) { + return tidy(() => { + let output = inputs[0]; + for (let i = 1; i < inputs.length; ++i) { + output = minimum(output, inputs[i]); + } + return output; + }); + } +}; +Minimum2.className = "Minimum"; +serialization_exports.registerClass(Minimum2); +var Concatenate = class extends Merge { + constructor(args) { + super(args); + this.DEFAULT_AXIS = -1; + if (args == null) { + args = {}; + } + this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis; + this.supportsMasking = true; + this.reshapeRequired = false; + } + build(inputShape) { + if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) || inputShape.length === 1) { + throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs"); + } + inputShape = inputShape; + let allNoneShape = true; + for (const shape of inputShape) { + if (shape != null) { + allNoneShape = false; + break; + } + } + if (allNoneShape) { + return; + } + const shapeSet = []; + for (let i = 0; i < inputShape.length; ++i) { + const shapeWithoutConcatAxis = inputShape[i].slice(); + shapeWithoutConcatAxis.splice(this.axis, 1); + let exists = false; + for (const shape of shapeSet) { + if (util_exports.arraysEqual(shape, shapeWithoutConcatAxis)) { + exists = true; + break; + } + } + if (!exists) { + shapeSet.push(shapeWithoutConcatAxis); + } + } + if (shapeSet.length > 1) { + throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(inputShape)); + } + } + mergeFunction(inputs) { + return tidy(() => { + return concatenate(inputs, this.axis); + }); + } + computeOutputShape(inputShape) { + if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) { + throw new ValueError("A `Concatenate` layer should be called on a list of inputs."); + } + const inputShapes = inputShape; + const outputShape = inputShapes[0].slice(); + const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis; + for (const shape of inputShapes.slice(1)) { + if (outputShape[axis] == null || shape[axis] == null) { + outputShape[axis] = null; + break; + } + outputShape[axis] += shape[axis]; + } + return outputShape; + } + computeMask(inputs, mask) { + if (mask == null) { + return null; + } + if (!Array.isArray(mask)) { + throw new ValueError("`mask` should be an array for Concatenate"); + } + if (!Array.isArray(inputs)) { + throw new ValueError("`inputs` should be an array for Concatenate"); + } + if (mask.length !== inputs.length) { + throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`); + } + return tidy(() => { + let allNullMasks = true; + mask.forEach((m) => { + if (m != null) { + allNullMasks = false; + return; + } + }); + if (allNullMasks) { + return null; + } + const outputMasks = []; + for (let i = 0; i < inputs.length; ++i) { + if (mask[i] == null) { + outputMasks.push(cast(onesLike(inputs[i]), "bool")); + } else if (mask[i].rank < inputs[i].rank) { + outputMasks.push(expandDims(mask[i], -1)); + } else { + outputMasks.push(mask[i]); + } + } + const concatenatedMasks = concat(outputMasks, this.axis); + return all(concatenatedMasks, -1, false); + }); + } + getConfig() { + const config = { + "axis": this.axis + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Concatenate.className = "Concatenate"; +serialization_exports.registerClass(Concatenate); +function interpretAxis(axis, dim) { + while (axis < 0) { + axis += dim; + } + return axis; +} +function batchDot(x, y, axes) { + if (x.shape.length > 3 || y.shape.length > 3) { + throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet"); + } + util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`); + util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`); + if (typeof axes === "number") { + axes = [axes, axes]; + } + if (x.dtype === "complex64" || y.dtype === "complex64") { + throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet."); + } + const xNDim = x.shape.length; + const yNDim = y.shape.length; + if (axes == null) { + axes = [xNDim - 1, yNDim - 2]; + } + const axesArray = axes; + return tidy(() => { + let diff; + if (xNDim > yNDim) { + diff = xNDim - yNDim; + const diffShape = []; + for (let i = 0; i < diff; ++i) { + diffShape.push(1); + } + y = reshape(y, y.shape.concat(diffShape)); + } else if (yNDim > xNDim) { + diff = yNDim - xNDim; + const diffShape = []; + for (let i = 0; i < diff; ++i) { + diffShape.push(1); + } + x = reshape(x, x.shape.concat(diffShape)); + } else { + diff = 0; + } + let out; + if (x.shape.length === 2 && y.shape.length === 2) { + if (axesArray[0] === axesArray[1]) { + out = sum2(mul(x, y), axesArray[0]); + } else { + out = sum2(mul(transpose(x, [1, 0]), y), axesArray[1]); + } + } else { + const adjX = axesArray[0] !== x.shape.length - 1; + const adjY = axesArray[1] === y.shape.length - 1; + out = matMul(x, y, adjX, adjY); + } + if (diff > 0) { + let idx; + if (xNDim > yNDim) { + idx = xNDim + yNDim - 3; + } else { + idx = xNDim - 1; + } + const squeezeAxes = []; + for (let i = idx; i < idx + diff; ++i) { + squeezeAxes.push(i); + } + out = squeeze(out, squeezeAxes); + } + if (out.shape.length === 1) { + out = expandDims(out, 1); + } + return out; + }); +} +var Dot = class extends Merge { + constructor(args) { + super(args); + this.axes = args.axes; + this.normalize = args.normalize == null ? false : args.normalize; + this.supportsMasking = true; + this.reshapeRequired = false; + } + build(inputShape) { + util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs."); + const shape1 = inputShape[0]; + const shape2 = inputShape[1]; + if (shape1.length > 3 || shape2.length > 3) { + throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet."); + } + const axes = this.interpretAxes(shape1, shape2); + if (shape1[axes[0]] !== shape2[axes[1]]) { + throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`); + } + } + mergeFunction(inputs) { + if (inputs.length !== 2) { + throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`); + } + let x1 = inputs[0]; + let x2 = inputs[1]; + let axes; + if (!Array.isArray(this.axes)) { + axes = [ + interpretAxis(this.axes, x1.shape.length), + interpretAxis(this.axes, x2.shape.length) + ]; + } else { + axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length)); + } + if (this.normalize) { + x1 = l2Normalize(x1, axes[0]); + x2 = l2Normalize(x2, axes[1]); + } + return batchDot(x1, x2, axes); + } + interpretAxes(shape1, shape2) { + let axes; + if (!Array.isArray(this.axes)) { + axes = [ + interpretAxis(this.axes, shape1.length), + interpretAxis(this.axes, shape2.length) + ]; + } else { + axes = this.axes; + } + return axes; + } + computeOutputShape(inputShape) { + util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs."); + const shape1 = inputShape[0].slice(); + const shape2 = inputShape[1].slice(); + if (shape1.length > 3 || shape2.length > 3) { + throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet."); + } + const axes = this.interpretAxes(shape1, shape2); + shape1.splice(axes[0], 1); + shape2.splice(axes[1], 1); + shape2.splice(0, 1); + const outputShape = shape1.concat(shape2); + if (outputShape.length === 1) { + outputShape.push(1); + } + return outputShape; + } + computeMask(inputs, mask) { + return null; + } + getConfig() { + const config = { + "axes": this.axes, + "normalize": this.normalize + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Dot.className = "Dot"; +serialization_exports.registerClass(Dot); +var GaussianNoise = class extends Layer { + constructor(args) { + super(args); + this.supportsMasking = true; + this.stddev = args.stddev; + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { stddev: this.stddev }; + Object.assign(config, baseConfig); + return config; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + const noised = () => add2(randomNormal2(input2.shape, 0, this.stddev), input2); + const output = inTrainPhase(noised, () => input2, kwargs["training"] || false); + return output; + }); + } +}; +GaussianNoise.className = "GaussianNoise"; +serialization_exports.registerClass(GaussianNoise); +var GaussianDropout = class extends Layer { + constructor(args) { + super(args); + this.supportsMasking = true; + this.rate = args.rate; + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { rate: this.rate }; + Object.assign(config, baseConfig); + return config; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + if (this.rate > 0 && this.rate < 1) { + const noised = () => { + const stddev = Math.sqrt(this.rate / (1 - this.rate)); + return mul(input2, randomNormal2(input2.shape, 1, stddev)); + }; + return inTrainPhase(noised, () => input2, kwargs["training"] || false); + } + return input2; + }); + } +}; +GaussianDropout.className = "GaussianDropout"; +serialization_exports.registerClass(GaussianDropout); +var AlphaDropout = class extends Layer { + constructor(args) { + super(args); + this.supportsMasking = true; + this.rate = args.rate; + this.noiseShape = args.noiseShape; + } + _getNoiseShape(inputs) { + return this.noiseShape || getExactlyOneTensor(inputs).shape; + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { rate: this.rate }; + Object.assign(config, baseConfig); + return config; + } + call(inputs, kwargs) { + return tidy(() => { + if (this.rate < 1 && this.rate > 0) { + const noiseShape = this._getNoiseShape(inputs); + const droppedInputs = () => { + const input2 = getExactlyOneTensor(inputs); + const alpha = 1.6732632423543772; + const scale22 = 1.0507009873554805; + const alphaP = -alpha * scale22; + let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate); + keptIdx = cast2(keptIdx, "float32"); + const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5; + const b = -a * alphaP * this.rate; + const x = add2(mul(input2, keptIdx), mul(add2(keptIdx, -1), alphaP)); + return add2(mul(x, a), b); + }; + return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs["training"] || false); + } + return inputs; + }); + } +}; +AlphaDropout.className = "AlphaDropout"; +serialization_exports.registerClass(AlphaDropout); +function batchNormalization(x, mean4, variance, beta, gamma, epsilon32 = 1e-3) { + let out; + if (x.rank === 2) { + out = batchNorm2d(x, mean4, variance, beta, gamma, epsilon32); + } else if (x.rank === 3) { + out = batchNorm3d(x, mean4, variance, beta, gamma, epsilon32); + } else if (x.rank === 4) { + out = batchNorm4d(x, mean4, variance, beta, gamma, epsilon32); + } else { + throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`); + } + return out; +} +function regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32 = 1e-3) { + return tidy(() => { + const meanAndVariance = moments(x, reductionAxes); + const mean4 = meanAndVariance.mean; + const variance = meanAndVariance.variance; + const normed = batchNormalization(x, mean4, variance, beta, gamma, epsilon32); + return [normed, mean4, variance]; + }); +} +function broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32 = 1e-3) { + return tidy(() => { + const meanAndVariance = moments(x, reductionAxes); + const mean4 = meanAndVariance.mean; + const variance = meanAndVariance.variance; + const targetShape = []; + for (const axis of range2(0, x.rank)) { + if (reductionAxes.indexOf(axis) !== -1) { + targetShape.push(1); + } else { + targetShape.push(x.shape[axis]); + } + } + const broadcastMean = reshape(mean4, targetShape); + const broadcastVariance = reshape(variance, targetShape); + const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape); + const broadcastBeta = beta == null ? null : reshape(beta, targetShape); + const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon32); + return [normed, mean4, variance]; + }); +} +function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32 = 1e-3) { + if (util_exports.arraysEqual(reductionAxes.slice().sort(), range2(0, x.rank - 1))) { + return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32); + } else { + return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32); + } +} +var BatchNormalization = class extends Layer { + constructor(args) { + if (args == null) { + args = {}; + } + super(args); + this.supportsMasking = true; + this.axis = args.axis == null ? -1 : args.axis; + this.momentum = args.momentum == null ? 0.99 : args.momentum; + this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon; + this.center = args.center == null ? true : args.center; + this.scale = args.scale == null ? true : args.scale; + this.betaInitializer = getInitializer(args.betaInitializer || "zeros"); + this.gammaInitializer = getInitializer(args.gammaInitializer || "ones"); + this.movingMeanInitializer = getInitializer(args.movingMeanInitializer || "zeros"); + this.movingVarianceInitializer = getInitializer(args.movingVarianceInitializer || "ones"); + this.betaConstraint = getConstraint(args.betaConstraint); + this.gammaConstraint = getConstraint(args.gammaConstraint); + this.betaRegularizer = getRegularizer(args.betaRegularizer); + this.gammaRegularizer = getRegularizer(args.gammaRegularizer); + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const axis = this.axis >= 0 ? this.axis : this.axis + inputShape.length; + const dim = inputShape[axis]; + if (dim == null) { + throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`); + } + this.inputSpec = [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })]; + const shape = [dim]; + if (this.scale) { + this.gamma = this.addWeight("gamma", shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint); + } + if (this.center) { + this.beta = this.addWeight("beta", shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint); + } + this.movingMean = this.addWeight("moving_mean", shape, null, this.movingMeanInitializer, null, false); + this.movingVariance = this.addWeight("moving_variance", shape, null, this.movingVarianceInitializer, null, false); + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + const training = kwargs["training"] == null ? false : kwargs["training"]; + const input2 = getExactlyOneTensor(inputs); + const inputShape = input2.shape; + const ndim = inputShape.length; + const reductionAxes = range2(0, ndim); + const axis = this.axis >= 0 ? this.axis : this.axis + ndim; + reductionAxes.splice(axis, 1); + const broadcastShape = pyListRepeat(1, ndim); + broadcastShape[axis] = inputShape[axis]; + const sortedReductionAxes = reductionAxes.slice(); + sortedReductionAxes.sort(); + const needsBroadcasting = !util_exports.arraysEqual(sortedReductionAxes, range2(0, ndim).slice(0, ndim - 1)); + const normalizeInference = () => { + if (needsBroadcasting) { + const broadcastMovingMean = reshape(this.movingMean.read(), broadcastShape); + const broadcastMovingVariance = reshape(this.movingVariance.read(), broadcastShape); + const broadcastBeta = this.center ? reshape(this.beta.read(), broadcastShape) : null; + const broadcastGamma = this.scale ? reshape(this.gamma.read(), broadcastShape) : null; + return batchNormalization(input2, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon); + } else { + return batchNormalization(input2, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon); + } + }; + if (!training) { + return normalizeInference(); + } + const [normedTraining, mean4, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon); + const doMovingAverage = (variable2, value, momentum) => { + tidy(() => { + const decay = 1 - momentum; + const origValue = variable2.read(); + const updateDelta = mul(sub(origValue, value), decay); + variable2.write(sub(origValue, updateDelta)); + }); + }; + const updateMovingMeanAndVariance = () => { + doMovingAverage(this.movingMean, mean4, this.momentum); + doMovingAverage(this.movingVariance, variance, this.momentum); + }; + updateMovingMeanAndVariance(); + return normedTraining; + }); + } + getConfig() { + const config = { + axis: this.axis, + momentum: this.momentum, + epsilon: this.epsilon, + center: this.center, + scale: this.scale, + betaInitializer: serializeInitializer(this.betaInitializer), + gammaInitializer: serializeInitializer(this.gammaInitializer), + movingMeanInitializer: serializeInitializer(this.movingMeanInitializer), + movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer), + betaRegularizer: serializeRegularizer(this.betaRegularizer), + gammaRegularizer: serializeRegularizer(this.gammaRegularizer), + betaConstraint: serializeConstraint(this.betaConstraint), + gammaConstraint: serializeConstraint(this.gammaConstraint) + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +BatchNormalization.className = "BatchNormalization"; +serialization_exports.registerClass(BatchNormalization); +var LayerNormalization = class extends Layer { + constructor(args) { + if (args == null) { + args = {}; + } + super(args); + this.axis = args.axis == null ? -1 : args.axis; + if (typeof this.axis === "number") { + if (!Number.isInteger(this.axis)) { + throw new Error(`Expected axis to be an integer, but received ${this.axis}`); + } + } else if (Array.isArray(this.axis)) { + for (const axis of this.axis) { + if (!Number.isInteger(axis)) { + 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 = args.epsilon == null ? 1e-3 : args.epsilon; + this.center = args.center == null ? true : args.center; + this.scale = args.scale == null ? true : args.scale; + this.betaInitializer = getInitializer(args.betaInitializer || "zeros"); + this.gammaInitializer = getInitializer(args.gammaInitializer || "ones"); + this.betaRegularizer = getRegularizer(args.betaRegularizer); + this.gammaRegularizer = getRegularizer(args.gammaRegularizer); + this.supportsMasking = true; + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const nDims = inputShape.length; + if (typeof this.axis === "number") { + this.axis = [this.axis]; + } + for (let i = 0; i < this.axis.length; ++i) { + if (this.axis[i] < 0) { + this.axis[i] += nDims; + } + } + for (const axis of this.axis) { + if (axis < 0 || axis >= nDims) { + throw new Error(`Invalid axis: ${axis}`); + } + } + if (this.axis.length !== unique2(this.axis).length) { + throw new Error(`Found duplicate axes in: ${this.axis}`); + } + const paramShape = this.axis.map((axis) => inputShape[axis]); + const trainable = true; + if (this.scale) { + this.gamma = this.addWeight("gamma", paramShape, "float32", this.gammaInitializer, this.gammaRegularizer, trainable); + } else { + this.gamma = null; + } + if (this.center) { + this.beta = this.addWeight("beta", paramShape, "float32", this.betaInitializer, this.betaRegularizer, trainable); + } else { + this.beta = null; + } + this.built = true; + } + call(inputs, kwargs) { + const input2 = getExactlyOneTensor(inputs); + const inputShape = input2.shape; + const nDims = inputShape.length; + return tidy(() => { + const keepDims = true; + let { mean: mean4, variance } = moments(input2, this.axis, keepDims); + const broadcastShape = pyListRepeat(1, nDims); + for (const dim of this.axis) { + broadcastShape[dim] = inputShape[dim]; + } + const broadcast = (v) => { + if (v != null && v.shape.length !== nDims) { + return reshape(v, broadcastShape); + } else { + return v; + } + }; + let scale22 = this.scale ? broadcast(this.gamma.read()) : null; + let offset = this.center ? broadcast(this.beta.read()) : null; + const momentsTiling = []; + const scaleOffsetTiling = []; + for (let i = 0; i < nDims; ++i) { + if (this.axis.indexOf(i) !== -1) { + momentsTiling.push(inputShape[i]); + scaleOffsetTiling.push(1); + } else { + momentsTiling.push(1); + scaleOffsetTiling.push(inputShape[i]); + } + } + mean4 = tile(mean4, momentsTiling); + variance = tile(variance, momentsTiling); + if (scale22 != null) { + scale22 = tile(scale22, scaleOffsetTiling); + } + if (offset != null) { + offset = tile(offset, scaleOffsetTiling); + } + return batchNormalization(input2, mean4, variance, offset, scale22, this.epsilon); + }); + } + getConfig() { + const config = { + axis: this.axis, + epsilon: this.epsilon, + center: this.center, + scale: this.scale, + betaInitializer: serializeInitializer(this.betaInitializer), + gammaInitializer: serializeInitializer(this.gammaInitializer), + betaRegularizer: serializeRegularizer(this.betaRegularizer), + gammaRegularizer: serializeRegularizer(this.gammaRegularizer) + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +LayerNormalization.className = "LayerNormalization"; +serialization_exports.registerClass(LayerNormalization); +function spatial2dPadding(x, padding, dataFormat) { + return tidy(() => { + if (x.rank !== 4) { + throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`); + } + if (padding == null) { + padding = [[1, 1], [1, 1]]; + } + if (padding.length !== 2 || padding[0].length !== 2 || padding[1].length !== 2) { + throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers."); + } + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + if (dataFormat !== "channelsLast" && dataFormat !== "channelsFirst") { + throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`); + } + let pattern; + if (dataFormat === "channelsFirst") { + pattern = [[0, 0], [0, 0], padding[0], padding[1]]; + } else { + pattern = [[0, 0], padding[0], padding[1], [0, 0]]; + } + return pad(x, pattern); + }); +} +var ZeroPadding2D = class extends Layer { + constructor(args) { + if (args == null) { + args = {}; + } + super(args); + this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat; + if (args.padding == null) { + this.padding = [[1, 1], [1, 1]]; + } else if (typeof args.padding === "number") { + this.padding = [[args.padding, args.padding], [args.padding, args.padding]]; + } else { + args.padding = args.padding; + if (args.padding.length !== 2) { + throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`); + } + let heightPadding; + let widthPadding; + if (typeof args.padding[0] === "number") { + heightPadding = [args.padding[0], args.padding[0]]; + widthPadding = [args.padding[1], args.padding[1]]; + } else { + args.padding = args.padding; + if (args.padding[0].length !== 2) { + throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`); + } + heightPadding = args.padding[0]; + if (args.padding[1].length !== 2) { + throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`); + } + widthPadding = args.padding[1]; + } + this.padding = [heightPadding, widthPadding]; + } + this.inputSpec = [new InputSpec({ ndim: 4 })]; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + let rows; + let cols; + if (this.dataFormat === "channelsFirst") { + if (inputShape[2] != null && inputShape[2] >= 0) { + rows = inputShape[2] + this.padding[0][0] + this.padding[0][1]; + } else { + rows = null; + } + if (inputShape[3] != null && inputShape[3] >= 0) { + cols = inputShape[3] + this.padding[1][0] + this.padding[1][1]; + } else { + cols = null; + } + return [inputShape[0], inputShape[1], rows, cols]; + } else { + if (inputShape[1] != null && inputShape[1] >= 0) { + rows = inputShape[1] + this.padding[0][0] + this.padding[0][1]; + } else { + rows = null; + } + if (inputShape[2] != null && inputShape[2] >= 0) { + cols = inputShape[2] + this.padding[1][0] + this.padding[1][1]; + } else { + cols = null; + } + return [inputShape[0], rows, cols, inputShape[3]]; + } + } + call(inputs, kwargs) { + return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat)); + } + getConfig() { + const config = { + padding: this.padding, + dataFormat: this.dataFormat + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +ZeroPadding2D.className = "ZeroPadding2D"; +serialization_exports.registerClass(ZeroPadding2D); +function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) { + return tidy(() => { + checkDataFormat(dataFormat); + checkPoolMode(poolMode); + checkPaddingMode(padding); + if (strides == null) { + strides = [1, 1]; + } + if (padding == null) { + padding = "valid"; + } + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + if (poolMode == null) { + poolMode = "max"; + } + x = preprocessConv2DInput(x, dataFormat); + let y; + const paddingString = padding === "same" ? "same" : "valid"; + if (poolMode === "max") { + y = maxPool(x, poolSize, strides, paddingString); + } else { + y = avgPool( + // TODO(cais): Rank check? + x, + poolSize, + strides, + paddingString + ); + } + if (dataFormat === "channelsFirst") { + y = transpose(y, [0, 3, 1, 2]); + } + return y; + }); +} +function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) { + return tidy(() => { + checkDataFormat(dataFormat); + checkPoolMode(poolMode); + checkPaddingMode(padding); + if (strides == null) { + strides = [1, 1, 1]; + } + if (padding == null) { + padding = "valid"; + } + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + if (poolMode == null) { + poolMode = "max"; + } + x = preprocessConv3DInput(x, dataFormat); + let y; + const paddingString = padding === "same" ? "same" : "valid"; + if (poolMode === "max") { + y = maxPool3d(x, poolSize, strides, paddingString); + } else { + y = avgPool3d(x, poolSize, strides, paddingString); + } + if (dataFormat === "channelsFirst") { + y = transpose(y, [0, 4, 1, 2, 3]); + } + return y; + }); +} +var Pooling1D = class extends Layer { + /** + * + * @param args Parameters for the Pooling layer. + * + * config.poolSize defaults to 2. + */ + constructor(args) { + if (args.poolSize == null) { + args.poolSize = 2; + } + super(args); + if (typeof args.poolSize === "number") { + this.poolSize = [args.poolSize]; + } else if (Array.isArray(args.poolSize) && args.poolSize.length === 1 && typeof args.poolSize[0] === "number") { + this.poolSize = args.poolSize; + } else { + throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`); + } + assertPositiveInteger(this.poolSize, "poolSize"); + if (args.strides == null) { + this.strides = this.poolSize; + } else { + if (typeof args.strides === "number") { + this.strides = [args.strides]; + } else if (Array.isArray(args.strides) && args.strides.length === 1 && typeof args.strides[0] === "number") { + this.strides = args.strides; + } else { + throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`); + } + } + assertPositiveInteger(this.strides, "strides"); + this.padding = args.padding == null ? "valid" : args.padding; + checkPaddingMode(this.padding); + this.inputSpec = [new InputSpec({ ndim: 3 })]; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]); + return [inputShape[0], length, inputShape[2]]; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + inputs = expandDims2(getExactlyOneTensor(inputs), 2); + const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast"); + return squeeze(output, [2]); + }); + } + getConfig() { + const config = { + poolSize: this.poolSize, + padding: this.padding, + strides: this.strides + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +var MaxPooling1D = class extends Pooling1D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool2d(inputs, poolSize, strides, padding, dataFormat, "max"); + } +}; +MaxPooling1D.className = "MaxPooling1D"; +serialization_exports.registerClass(MaxPooling1D); +var AveragePooling1D = class extends Pooling1D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool2d(inputs, poolSize, strides, padding, dataFormat, "avg"); + } +}; +AveragePooling1D.className = "AveragePooling1D"; +serialization_exports.registerClass(AveragePooling1D); +var Pooling2D = class extends Layer { + constructor(args) { + if (args.poolSize == null) { + args.poolSize = [2, 2]; + } + super(args); + this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize]; + if (args.strides == null) { + this.strides = this.poolSize; + } else if (Array.isArray(args.strides)) { + if (args.strides.length !== 2) { + throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`); + } + this.strides = args.strides; + } else { + this.strides = [args.strides, args.strides]; + } + assertPositiveInteger(this.poolSize, "poolSize"); + assertPositiveInteger(this.strides, "strides"); + this.padding = args.padding == null ? "valid" : args.padding; + this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat; + checkDataFormat(this.dataFormat); + checkPaddingMode(this.padding); + this.inputSpec = [new InputSpec({ ndim: 4 })]; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + let rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1]; + let cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2]; + rows = convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]); + cols = convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]); + if (this.dataFormat === "channelsFirst") { + return [inputShape[0], inputShape[1], rows, cols]; + } else { + return [inputShape[0], rows, cols, inputShape[3]]; + } + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat); + }); + } + getConfig() { + const config = { + poolSize: this.poolSize, + padding: this.padding, + strides: this.strides, + dataFormat: this.dataFormat + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +var MaxPooling2D = class extends Pooling2D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool2d(inputs, poolSize, strides, padding, dataFormat, "max"); + } +}; +MaxPooling2D.className = "MaxPooling2D"; +serialization_exports.registerClass(MaxPooling2D); +var AveragePooling2D = class extends Pooling2D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool2d(inputs, poolSize, strides, padding, dataFormat, "avg"); + } +}; +AveragePooling2D.className = "AveragePooling2D"; +serialization_exports.registerClass(AveragePooling2D); +var Pooling3D = class extends Layer { + constructor(args) { + if (args.poolSize == null) { + args.poolSize = [2, 2, 2]; + } + super(args); + this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize, args.poolSize]; + if (args.strides == null) { + this.strides = this.poolSize; + } else if (Array.isArray(args.strides)) { + if (args.strides.length !== 3) { + throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`); + } + this.strides = args.strides; + } else { + this.strides = [args.strides, args.strides, args.strides]; + } + assertPositiveInteger(this.poolSize, "poolSize"); + assertPositiveInteger(this.strides, "strides"); + this.padding = args.padding == null ? "valid" : args.padding; + this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat; + checkDataFormat(this.dataFormat); + checkPaddingMode(this.padding); + this.inputSpec = [new InputSpec({ ndim: 5 })]; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + let depths = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1]; + let rows = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2]; + let cols = this.dataFormat === "channelsFirst" ? inputShape[4] : inputShape[3]; + depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]); + rows = convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]); + cols = convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]); + if (this.dataFormat === "channelsFirst") { + return [inputShape[0], inputShape[1], depths, rows, cols]; + } else { + return [inputShape[0], depths, rows, cols, inputShape[4]]; + } + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat); + }); + } + getConfig() { + const config = { + poolSize: this.poolSize, + padding: this.padding, + strides: this.strides, + dataFormat: this.dataFormat + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +var MaxPooling3D = class extends Pooling3D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool3d(inputs, poolSize, strides, padding, dataFormat, "max"); + } +}; +MaxPooling3D.className = "MaxPooling3D"; +serialization_exports.registerClass(MaxPooling3D); +var AveragePooling3D = class extends Pooling3D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool3d(inputs, poolSize, strides, padding, dataFormat, "avg"); + } +}; +AveragePooling3D.className = "AveragePooling3D"; +serialization_exports.registerClass(AveragePooling3D); +var GlobalPooling1D = class extends Layer { + constructor(args) { + super(args); + this.inputSpec = [new InputSpec({ ndim: 3 })]; + } + computeOutputShape(inputShape) { + return [inputShape[0], inputShape[2]]; + } + call(inputs, kwargs) { + throw new NotImplementedError(); + } +}; +var GlobalAveragePooling1D = class extends GlobalPooling1D { + constructor(args) { + super(args || {}); + } + call(inputs, kwargs) { + return tidy(() => { + const input2 = getExactlyOneTensor(inputs); + return mean(input2, 1); + }); + } +}; +GlobalAveragePooling1D.className = "GlobalAveragePooling1D"; +serialization_exports.registerClass(GlobalAveragePooling1D); +var GlobalMaxPooling1D = class extends GlobalPooling1D { + constructor(args) { + super(args || {}); + } + call(inputs, kwargs) { + return tidy(() => { + const input2 = getExactlyOneTensor(inputs); + return max(input2, 1); + }); + } +}; +GlobalMaxPooling1D.className = "GlobalMaxPooling1D"; +serialization_exports.registerClass(GlobalMaxPooling1D); +var GlobalPooling2D = class extends Layer { + constructor(args) { + super(args); + this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat; + checkDataFormat(this.dataFormat); + this.inputSpec = [new InputSpec({ ndim: 4 })]; + } + computeOutputShape(inputShape) { + inputShape = inputShape; + if (this.dataFormat === "channelsLast") { + return [inputShape[0], inputShape[3]]; + } else { + return [inputShape[0], inputShape[1]]; + } + } + call(inputs, kwargs) { + throw new NotImplementedError(); + } + getConfig() { + const config = { dataFormat: this.dataFormat }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +var GlobalAveragePooling2D = class extends GlobalPooling2D { + call(inputs, kwargs) { + return tidy(() => { + const input2 = getExactlyOneTensor(inputs); + if (this.dataFormat === "channelsLast") { + return mean(input2, [1, 2]); + } else { + return mean(input2, [2, 3]); + } + }); + } +}; +GlobalAveragePooling2D.className = "GlobalAveragePooling2D"; +serialization_exports.registerClass(GlobalAveragePooling2D); +var GlobalMaxPooling2D = class extends GlobalPooling2D { + call(inputs, kwargs) { + return tidy(() => { + const input2 = getExactlyOneTensor(inputs); + if (this.dataFormat === "channelsLast") { + return max(input2, [1, 2]); + } else { + return max(input2, [2, 3]); + } + }); + } +}; +GlobalMaxPooling2D.className = "GlobalMaxPooling2D"; +serialization_exports.registerClass(GlobalMaxPooling2D); +var Wrapper = class extends Layer { + constructor(args) { + super(args); + this.layer = args.layer; + } + build(inputShape) { + this.built = true; + } + // TODO(cais): Implement activityRegularizer getter. + get trainable() { + if (this.layer != null) { + return this.layer.trainable; + } else { + return false; + } + } + set trainable(value) { + if (this.layer != null) { + this.layer.trainable = value; + } + } + get trainableWeights() { + return this.layer.trainableWeights; + } + // TODO(cais): Implement setter for trainableWeights. + get nonTrainableWeights() { + return this.layer.nonTrainableWeights; + } + // TODO(cais): Implement setter for nonTrainableWeights. + get updates() { + return this.layer._updates; + } + // TODO(cais): Implement getUpdatesFor(). + get losses() { + return this.layer.losses; + } + // TODO(cais): Implement getLossesFor(). + getWeights() { + return this.layer.getWeights(); + } + setWeights(weights) { + this.layer.setWeights(weights); + } + getConfig() { + const config = { + "layer": { + "className": this.layer.getClassName(), + "config": this.layer.getConfig() + } + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + setFastWeightInitDuringBuild(value) { + super.setFastWeightInitDuringBuild(value); + if (this.layer != null) { + this.layer.setFastWeightInitDuringBuild(value); + } + } + /** @nocollapse */ + static fromConfig(cls, config, customObjects = {}) { + const layerConfig = config["layer"]; + const layer = deserialize(layerConfig, customObjects); + delete config["layer"]; + const newConfig = { layer }; + Object.assign(newConfig, config); + return new cls(newConfig); + } +}; +var TimeDistributed = class extends Wrapper { + constructor(args) { + super(args); + this.supportsMasking = true; + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape.length < 3) { + throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`); + } + this.inputSpec = [{ shape: inputShape }]; + const childInputShape = [inputShape[0]].concat(inputShape.slice(2)); + if (!this.layer.built) { + this.layer.build(childInputShape); + this.layer.built = true; + } + super.build(inputShape); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const childInputShape = [inputShape[0]].concat(inputShape.slice(2)); + const childOutputShape = this.layer.computeOutputShape(childInputShape); + const timesteps = inputShape[1]; + return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1)); + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + const step5 = (inputs2, states) => { + const output = getExactlyOneTensor(this.layer.call(inputs2, kwargs)); + return [output, []]; + }; + const rnnOutputs = rnn( + step5, + inputs, + [], + false, + null, + null, + false, + true + /* needPerStepOutputs */ + ); + const y = rnnOutputs[1]; + return y; + }); + } +}; +TimeDistributed.className = "TimeDistributed"; +serialization_exports.registerClass(TimeDistributed); +function checkBidirectionalMergeMode(value) { + checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, "BidirectionalMergeMode", value); +} +var DEFAULT_BIDIRECTIONAL_MERGE_MODE = "concat"; +var Bidirectional = class extends Wrapper { + constructor(args) { + super(args); + const layerConfig = args.layer.getConfig(); + const forwDict = {}; + forwDict["className"] = args.layer.getClassName(); + forwDict["config"] = layerConfig; + this.forwardLayer = deserialize(forwDict); + layerConfig["goBackwards"] = layerConfig["goBackwards"] === true ? false : true; + const backDict = {}; + backDict["className"] = args.layer.getClassName(); + backDict["config"] = layerConfig; + this.backwardLayer = deserialize(backDict); + this.forwardLayer.name = "forward_" + this.forwardLayer.name; + this.backwardLayer.name = "backward_" + this.backwardLayer.name; + this.mergeMode = args.mergeMode === void 0 ? DEFAULT_BIDIRECTIONAL_MERGE_MODE : args.mergeMode; + checkBidirectionalMergeMode(this.mergeMode); + if (args.weights) { + throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet."); + } + this._stateful = args.layer.stateful; + this.returnSequences = args.layer.returnSequences; + this.returnState = args.layer.returnState; + this.supportsMasking = true; + this._trainable = true; + this.inputSpec = args.layer.inputSpec; + this.numConstants = null; + } + get trainable() { + return this._trainable; + } + set trainable(value) { + this._trainable = value; + if (this.forwardLayer != null) { + this.forwardLayer.trainable = value; + } + if (this.backwardLayer != null) { + this.backwardLayer.trainable = value; + } + } + getWeights() { + return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights()); + } + setWeights(weights) { + const numWeights = weights.length; + const numeightsOver2 = Math.floor(numWeights / 2); + this.forwardLayer.setWeights(weights.slice(0, numeightsOver2)); + this.backwardLayer.setWeights(weights.slice(numeightsOver2)); + } + computeOutputShape(inputShape) { + let layerShapes = this.forwardLayer.computeOutputShape(inputShape); + if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) { + layerShapes = [layerShapes]; + } + layerShapes = layerShapes; + let outputShape; + let outputShapes; + let stateShape; + if (this.returnState) { + stateShape = layerShapes.slice(1); + outputShape = layerShapes[0]; + } else { + outputShape = layerShapes[0]; + } + outputShape = outputShape; + if (this.mergeMode === "concat") { + outputShape[outputShape.length - 1] *= 2; + outputShapes = [outputShape]; + } else if (this.mergeMode == null) { + outputShapes = [outputShape, outputShape.slice()]; + } else { + outputShapes = [outputShape]; + } + if (this.returnState) { + if (this.mergeMode == null) { + return outputShapes.concat(stateShape).concat(stateShape.slice()); + } + return [outputShape].concat(stateShape).concat(stateShape.slice()); + } + return singletonOrArray(outputShapes); + } + apply(inputs, kwargs) { + let initialState = kwargs == null ? null : kwargs["initialState"]; + let constants = kwargs == null ? null : kwargs["constants"]; + if (kwargs == null) { + kwargs = {}; + } + const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants); + inputs = standardized.inputs; + initialState = standardized.initialState; + constants = standardized.constants; + if (Array.isArray(inputs)) { + initialState = inputs.slice(1); + inputs = inputs[0]; + } + if ((initialState == null || initialState.length === 0) && constants == null) { + return super.apply(inputs, kwargs); + } + const additionalInputs = []; + const additionalSpecs = []; + if (initialState != null) { + const numStates = initialState.length; + if (numStates % 2 > 0) { + throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs."); + } + kwargs["initialState"] = initialState; + additionalInputs.push(...initialState); + const stateSpecs = initialState.map((state) => new InputSpec({ shape: state.shape })); + this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2); + this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2); + additionalSpecs.push(...stateSpecs); + } + if (constants != null) { + throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet."); + } + const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor; + for (const tensor2 of additionalInputs) { + if (tensor2 instanceof SymbolicTensor !== isSymbolicTensor) { + throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors"); + } + } + if (isSymbolicTensor) { + const fullInput = [inputs].concat(additionalInputs); + const fullInputSpec = this.inputSpec.concat(additionalSpecs); + const originalInputSpec = this.inputSpec; + this.inputSpec = fullInputSpec; + const output = super.apply(fullInput, kwargs); + this.inputSpec = originalInputSpec; + return output; + } else { + return super.apply(inputs, kwargs); + } + } + call(inputs, kwargs) { + return tidy(() => { + const initialState = kwargs["initialState"]; + let y; + let yRev; + if (initialState == null) { + y = this.forwardLayer.call(inputs, kwargs); + yRev = this.backwardLayer.call(inputs, kwargs); + } else { + const forwardState = initialState.slice(0, initialState.length / 2); + const backwardState = initialState.slice(initialState.length / 2); + y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState })); + yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState })); + } + let states; + if (this.returnState) { + if (Array.isArray(y)) { + states = y.slice(1).concat(yRev.slice(1)); + } else { + } + y = y[0]; + yRev = yRev[0]; + } + if (this.returnSequences) { + yRev = reverse(yRev, 1); + } + let output; + if (this.mergeMode === "concat") { + output = concatenate([y, yRev]); + } else if (this.mergeMode === "sum") { + output = add2(y, yRev); + } else if (this.mergeMode === "ave") { + output = mul(0.5, add2(y, yRev)); + } else if (this.mergeMode === "mul") { + output = mul(y, yRev); + } else if (this.mergeMode == null) { + output = [y, yRev]; + } + if (this.returnState) { + if (this.mergeMode == null) { + return output.concat(states); + } + return [output].concat(states); + } + return output; + }); + } + resetStates(states) { + this.forwardLayer.resetStates(); + this.backwardLayer.resetStates(); + } + build(inputShape) { + nameScope(this.forwardLayer.name, () => { + this.forwardLayer.build(inputShape); + }); + nameScope(this.backwardLayer.name, () => { + this.backwardLayer.build(inputShape); + }); + this.built = true; + } + computeMask(inputs, mask) { + if (Array.isArray(mask)) { + mask = mask[0]; + } + let outputMask; + if (this.returnSequences) { + if (this.mergeMode == null) { + outputMask = [mask, mask]; + } else { + outputMask = mask; + } + } else { + if (this.mergeMode == null) { + outputMask = [null, null]; + } else { + outputMask = null; + } + } + if (this.returnState) { + const states = this.forwardLayer.states; + const stateMask = states.map((state) => null); + if (Array.isArray(outputMask)) { + return outputMask.concat(stateMask).concat(stateMask); + } else { + return [outputMask].concat(stateMask).concat(stateMask); + } + } else { + return outputMask; + } + } + get trainableWeights() { + return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights); + } + get nonTrainableWeights() { + return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights); + } + // TODO(cais): Implement constraints(). + setFastWeightInitDuringBuild(value) { + super.setFastWeightInitDuringBuild(value); + if (this.forwardLayer != null) { + this.forwardLayer.setFastWeightInitDuringBuild(value); + } + if (this.backwardLayer != null) { + this.backwardLayer.setFastWeightInitDuringBuild(value); + } + } + getConfig() { + const config = { + "mergeMode": this.mergeMode + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + /** @nocollapse */ + static fromConfig(cls, config) { + const rnnLayer = deserialize(config["layer"]); + delete config["layer"]; + if (config["numConstants"] != null) { + throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`); + } + const newConfig = config; + newConfig["layer"] = rnnLayer; + return new cls(newConfig); + } +}; +Bidirectional.className = "Bidirectional"; +serialization_exports.registerClass(Bidirectional); +var Rescaling = class extends Layer { + constructor(args) { + super(args); + this.scale = args.scale; + if (args.offset) { + this.offset = args.offset; + } else { + this.offset = 0; + } + } + getConfig() { + const config = { + "scale": this.scale, + "offset": this.offset + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + if (inputs.dtype !== "float32") { + inputs = cast2(inputs, "float32"); + } + return add2(mul(inputs, this.scale), this.offset); + }); + } +}; +Rescaling.className = "Rescaling"; +serialization_exports.registerClass(Rescaling); +var { resizeBilinear: resizeBilinear2, cropAndResize: cropAndResize2 } = image; +var CenterCrop = class extends Layer { + constructor(args) { + super(args); + this.height = args.height; + this.width = args.width; + } + centerCrop(inputs, hBuffer, wBuffer, height, width, inputHeight, inputWidth, dtype) { + return tidy(() => { + let input2; + let isRank3 = false; + const top = hBuffer / inputHeight; + const left = wBuffer / inputWidth; + const bottom = (height + hBuffer) / inputHeight; + const right = (width + wBuffer) / inputWidth; + const bound = [top, left, bottom, right]; + const boxesArr = []; + if (inputs.rank === 3) { + isRank3 = true; + input2 = stack([inputs]); + } else { + input2 = inputs; + } + for (let i = 0; i < input2.shape[0]; i++) { + boxesArr.push(bound); + } + const boxes = tensor(boxesArr, [boxesArr.length, 4]); + const boxInd = range(0, boxesArr.length, 1, "int32"); + const cropSize = [height, width]; + const cropped = cropAndResize2(input2, boxes, boxInd, cropSize, "nearest"); + if (isRank3) { + return cast2(getExactlyOneTensor(unstack(cropped)), dtype); + } + return cast2(cropped, dtype); + }); + } + upsize(inputs, height, width, dtype) { + return tidy(() => { + const outputs = resizeBilinear2(inputs, [height, width]); + return cast2(outputs, dtype); + }); + } + call(inputs, kwargs) { + return tidy(() => { + const rankedInputs = getExactlyOneTensor(inputs); + const dtype = rankedInputs.dtype; + const inputShape = rankedInputs.shape; + const inputHeight = inputShape[inputShape.length - 3]; + const inputWidth = inputShape[inputShape.length - 2]; + let hBuffer = 0; + if (inputHeight !== this.height) { + hBuffer = Math.floor((inputHeight - this.height) / 2); + } + let wBuffer = 0; + if (inputWidth !== this.width) { + wBuffer = Math.floor((inputWidth - this.width) / 2); + if (wBuffer === 0) { + wBuffer = 1; + } + } + if (hBuffer >= 0 && wBuffer >= 0) { + return this.centerCrop(rankedInputs, hBuffer, wBuffer, this.height, this.width, inputHeight, inputWidth, dtype); + } else { + return this.upsize(inputs, this.height, this.width, dtype); + } + }); + } + getConfig() { + const config = { + "height": this.height, + "width": this.width + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const hAxis = inputShape.length - 3; + const wAxis = inputShape.length - 2; + inputShape[hAxis] = this.height; + inputShape[wAxis] = this.width; + return inputShape; + } +}; +CenterCrop.className = "CenterCrop"; +serialization_exports.registerClass(CenterCrop); +function encodeCategoricalInputs(inputs, outputMode, depth, weights) { + let input2 = getExactlyOneTensor(inputs); + if (input2.dtype !== "int32") { + input2 = cast2(input2, "int32"); + } + if (outputMode === "int") { + return input2; + } + const originalShape = input2.shape; + if (input2.rank === 0) { + input2 = expandDims(input2, -1); + } + if (outputMode === "oneHot") { + if (input2.shape[input2.shape.length - 1] !== 1) { + input2 = expandDims(input2, -1); + } + } + if (input2.rank > 2) { + throw new ValueError(`When outputMode is not int, maximum output rank is 2 Received outputMode ${outputMode} and input shape ${originalShape} which would result in output rank ${input2.rank}.`); + } + const binaryOutput = ["multiHot", "oneHot"].includes(outputMode); + const denseBincountInput = input2; + let binCounts; + if (typeof weights !== "undefined" && outputMode === "count") { + binCounts = denseBincount(denseBincountInput, weights, depth, binaryOutput); + } else { + binCounts = denseBincount(denseBincountInput, [], depth, binaryOutput); + } + if (outputMode !== "tfIdf") { + return binCounts; + } + if (weights) { + return mul(binCounts, weights); + } else { + throw new ValueError(`When outputMode is 'tfIdf', weights must be provided.`); + } +} +var CategoryEncoding = class extends Layer { + constructor(args) { + super(args); + this.numTokens = args.numTokens; + if (args.outputMode) { + this.outputMode = args.outputMode; + } else { + this.outputMode = "multiHot"; + } + } + getConfig() { + const config = { + "numTokens": this.numTokens, + "outputMode": this.outputMode + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape == null) { + return [this.numTokens]; + } + if (this.outputMode === "oneHot" && inputShape[inputShape.length - 1] !== 1) { + inputShape.push(this.numTokens); + return inputShape; + } + inputShape[inputShape.length - 1] = this.numTokens; + return inputShape; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + if (inputs.dtype !== "int32") { + inputs = cast2(inputs, "int32"); + } + let countWeights; + if (typeof kwargs["countWeights"] !== "undefined") { + if (this.outputMode !== "count") { + throw new ValueError(`countWeights is not used when outputMode !== count. + Received countWeights=${kwargs["countWeights"]}`); + } + countWeights = getExactlyOneTensor(kwargs["countWeights"]); + } + const maxValue = max(inputs); + const minValue = min(inputs); + const greaterEqualMax = greater(this.numTokens, maxValue).bufferSync().get(0); + const greaterMin = greaterEqual(minValue, 0).bufferSync().get(0); + if (!(greaterEqualMax && greaterMin)) { + throw new ValueError(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`); + } + return encodeCategoricalInputs(inputs, this.outputMode, this.numTokens, countWeights); + }); + } +}; +CategoryEncoding.className = "CategoryEncoding"; +serialization_exports.registerClass(CategoryEncoding); +var INTERPOLATION_KEYS = ["bilinear", "nearest"]; +var INTERPOLATION_METHODS = new Set(INTERPOLATION_KEYS); +var Resizing = class extends Layer { + constructor(args) { + super(args); + this.height = args.height; + this.width = args.width; + if (args.interpolation) { + if (INTERPOLATION_METHODS.has(args.interpolation)) { + this.interpolation = args.interpolation; + } else { + throw new ValueError(`Invalid interpolation parameter: ${args.interpolation} is not implemented`); + } + } else { + this.interpolation = "bilinear"; + } + this.cropToAspectRatio = Boolean(args.cropToAspectRatio); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const numChannels = inputShape[2]; + return [this.height, this.width, numChannels]; + } + getConfig() { + const config = { + "height": this.height, + "width": this.width, + "interpolation": this.interpolation, + "cropToAspectRatio": this.cropToAspectRatio + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + call(inputs, kwargs) { + return tidy(() => { + const size = [this.height, this.width]; + if (this.interpolation === "bilinear") { + return image.resizeBilinear(inputs, size, !this.cropToAspectRatio); + } else if (this.interpolation === "nearest") { + return image.resizeNearestNeighbor(inputs, size, !this.cropToAspectRatio); + } else { + throw new Error(`Interpolation is ${this.interpolation} but only ${[...INTERPOLATION_METHODS]} are supported`); + } + }); + } +}; +Resizing.className = "Resizing"; +serialization_exports.registerClass(Resizing); +var RandomSeed = class { + constructor(seed) { + this.seed = seed; + } + next() { + if (this.seed === void 0) { + return void 0; + } + return this.seed++; + } +}; +RandomSeed.className = "RandomSeed"; +var BaseRandomLayer = class extends Layer { + constructor(args) { + super(args); + this.randomGenerator = new RandomSeed(args.seed); + } + getConfig() { + const config = { + "seed": this.randomGenerator.seed + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +BaseRandomLayer.className = "BaseRandomLayer"; +var INTERPOLATION_KEYS2 = ["bilinear", "nearest"]; +var INTERPOLATION_METHODS2 = new Set(INTERPOLATION_KEYS2); +var RandomWidth = class extends BaseRandomLayer { + constructor(args) { + super(args); + const { factor, interpolation = "bilinear" } = args; + this.factor = factor; + if (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 ValueError(`Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`); + } + if (this.widthLower < -1 || this.widthUpper < -1) { + throw new ValueError(`factor must have values larger than -1. Got: ${this.factor}`); + } + if (this.widthUpper < this.widthLower) { + throw new ValueError(`factor cannot have upper bound less than lower bound. Got upper bound: ${this.widthUpper}. 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Xq=class{constructor(e,t,n,a,r,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=a,this.identicalElementShapes=r,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ve(0),qt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, - because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),Ea(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,qt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,a)=>this.write(n,t[a]))}gather(e,t){if(t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let a=0;a=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,pt(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,a=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to + `); + } + if (interpolation) { + if (INTERPOLATION_METHODS2.has(interpolation)) { + this.interpolation = interpolation; + } else { + throw new ValueError(`Invalid interpolation parameter: ${interpolation} is not implemented`); + } + } + } + getConfig() { + const config = { + "factor": this.factor, + "interpolation": this.interpolation + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const numChannels = inputShape[2]; + return [this.imgHeight, -1, numChannels]; + } + call(inputs, kwargs) { + return tidy(() => { + const input2 = getExactlyOneTensor(inputs); + this.imgHeight = input2.shape[input2.shape.length - 3]; + const imgWidth = input2.shape[input2.shape.length - 2]; + this.widthFactor = randomUniform([1], 1 + this.widthLower, 1 + this.widthUpper, "float32", this.randomGenerator.next()); + let adjustedWidth = this.widthFactor.dataSync()[0] * imgWidth; + adjustedWidth = Math.round(adjustedWidth); + const size = [this.imgHeight, adjustedWidth]; + switch (this.interpolation) { + case "bilinear": + return image.resizeBilinear(inputs, size); + case "nearest": + return image.resizeNearestNeighbor(inputs, size); + default: + throw new Error(`Interpolation is ${this.interpolation} + but only ${[...INTERPOLATION_METHODS2]} are supported`); + } + }); + } +}; +RandomWidth.className = "RandomWidth"; +serialization_exports.registerClass(RandomWidth); +function inputLayer(args) { + return new InputLayer(args); +} +function elu3(args) { + return new ELU(args); +} +function reLU(args) { + return new ReLU(args); +} +function leakyReLU(args) { + return new LeakyReLU(args); +} +function prelu2(args) { + return new PReLU(args); +} +function softmax2(args) { + return new Softmax3(args); +} +function thresholdedReLU(args) { + return new ThresholdedReLU(args); +} +function conv1d2(args) { + return new Conv1D(args); +} +function conv2d3(args) { + return new Conv2D2(args); +} +function conv2dTranspose2(args) { + return new Conv2DTranspose(args); +} +function conv3d2(args) { + return new Conv3D2(args); +} +function conv3dTranspose2(args) { + return new Conv3DTranspose(args); +} +function separableConv2d2(args) { + return new SeparableConv2D(args); +} +function cropping2D(args) { + return new Cropping2D(args); +} +function upSampling2d(args) { + return new UpSampling2D(args); +} +function depthwiseConv2d4(args) { + return new DepthwiseConv2D(args); +} +function activation(args) { + return new Activation2(args); +} +function dense(args) { + return new Dense(args); +} +function dropout3(args) { + return new Dropout(args); +} +function spatialDropout1d(args) { + return new SpatialDropout1D(args); +} +function flatten3(args) { + return new Flatten(args); +} +function repeatVector(args) { + return new RepeatVector(args); +} +function reshape2(args) { + return new Reshape2(args); +} +function permute(args) { + return new Permute(args); +} +function embedding(args) { + return new Embedding(args); +} +function add3(args) { + return new Add2(args); +} +function average(args) { + return new Average(args); +} +function concatenate2(args) { + return new Concatenate(args); +} +function maximum2(args) { + return new Maximum2(args); +} +function minimum2(args) { + return new Minimum2(args); +} +function multiply(args) { + return new Multiply2(args); +} +function dot3(args) { + return new Dot(args); +} +function batchNormalization2(args) { + return new BatchNormalization(args); +} +function layerNormalization(args) { + return new LayerNormalization(args); +} +function zeroPadding2d(args) { + return new ZeroPadding2D(args); +} +function averagePooling1d(args) { + return new AveragePooling1D(args); +} +function avgPool1d(args) { + return averagePooling1d(args); +} +function avgPooling1d(args) { + return averagePooling1d(args); +} +function averagePooling2d(args) { + return new AveragePooling2D(args); +} +function avgPool2d(args) { + return averagePooling2d(args); +} +function avgPooling2d(args) { + return averagePooling2d(args); +} +function averagePooling3d(args) { + return new AveragePooling3D(args); +} +function avgPool3d2(args) { + return averagePooling3d(args); +} +function avgPooling3d(args) { + return averagePooling3d(args); +} +function globalAveragePooling1d(args) { + return new GlobalAveragePooling1D(args); +} +function globalAveragePooling2d(args) { + return new GlobalAveragePooling2D(args); +} +function globalMaxPooling1d(args) { + return new GlobalMaxPooling1D(args); +} +function globalMaxPooling2d(args) { + return new GlobalMaxPooling2D(args); +} +function maxPooling1d(args) { + return new MaxPooling1D(args); +} +function maxPooling2d(args) { + return new MaxPooling2D(args); +} +function maxPooling3d(args) { + return new MaxPooling3D(args); +} +function gru(args) { + return new GRU(args); +} +function gruCell(args) { + return new GRUCell(args); +} +function lstm(args) { + return new LSTM(args); +} +function lstmCell(args) { + return new LSTMCell(args); +} +function simpleRNN(args) { + return new SimpleRNN(args); +} +function simpleRNNCell(args) { + return new SimpleRNNCell(args); +} +function convLstm2d(args) { + return new ConvLSTM2D(args); +} +function convLstm2dCell(args) { + return new ConvLSTM2DCell(args); +} +function rnn2(args) { + return new RNN(args); +} +function stackedRNNCells(args) { + return new StackedRNNCells(args); +} +function bidirectional(args) { + return new Bidirectional(args); +} +function timeDistributed(args) { + return new TimeDistributed(args); +} +var globalMaxPool1d = globalMaxPooling1d; +var globalMaxPool2d = globalMaxPooling2d; +var maxPool1d = maxPooling1d; +var maxPool2d = maxPooling2d; +function gaussianNoise(args) { + return new GaussianNoise(args); +} +function gaussianDropout(args) { + return new GaussianDropout(args); +} +function alphaDropout(args) { + return new AlphaDropout(args); +} +function masking(args) { + return new Masking(args); +} +function rescaling(args) { + return new Rescaling(args); +} +function centerCrop(args) { + return new CenterCrop(args); +} +function resizing(args) { + return new Resizing(args); +} +function categoryEncoding(args) { + return new CategoryEncoding(args); +} +function randomWidth(args) { + return new RandomWidth(args); +} +var exports_metrics_exports = {}; +__export2(exports_metrics_exports, { + MAPE: () => MAPE2, + MSE: () => MSE2, + binaryAccuracy: () => binaryAccuracy2, + binaryCrossentropy: () => binaryCrossentropy3, + categoricalAccuracy: () => categoricalAccuracy2, + categoricalCrossentropy: () => categoricalCrossentropy3, + cosineProximity: () => cosineProximity2, + mape: () => mape2, + meanAbsoluteError: () => meanAbsoluteError2, + meanAbsolutePercentageError: () => meanAbsolutePercentageError2, + meanSquaredError: () => meanSquaredError3, + mse: () => mse2, + precision: () => precision2, + recall: () => recall2, + sparseCategoricalAccuracy: () => sparseCategoricalAccuracy2 +}); +function binaryAccuracy2(yTrue, yPred) { + return binaryAccuracy(yTrue, yPred); +} +function binaryCrossentropy3(yTrue, yPred) { + return binaryCrossentropy2(yTrue, yPred); +} +function sparseCategoricalAccuracy2(yTrue, yPred) { + return sparseCategoricalAccuracy(yTrue, yPred); +} +function categoricalAccuracy2(yTrue, yPred) { + return categoricalAccuracy(yTrue, yPred); +} +function categoricalCrossentropy3(yTrue, yPred) { + return categoricalCrossentropy2(yTrue, yPred); +} +function precision2(yTrue, yPred) { + return precision(yTrue, yPred); +} +function recall2(yTrue, yPred) { + return recall(yTrue, yPred); +} +function cosineProximity2(yTrue, yPred) { + return cosineProximity(yTrue, yPred); +} +function meanAbsoluteError2(yTrue, yPred) { + return meanAbsoluteError(yTrue, yPred); +} +function meanAbsolutePercentageError2(yTrue, yPred) { + return meanAbsolutePercentageError(yTrue, yPred); +} +function MAPE2(yTrue, yPred) { + return meanAbsolutePercentageError(yTrue, yPred); +} +function mape2(yTrue, yPred) { + return meanAbsolutePercentageError(yTrue, yPred); +} +function meanSquaredError3(yTrue, yPred) { + return meanSquaredError2(yTrue, yPred); +} +function MSE2(yTrue, yPred) { + return meanSquaredError2(yTrue, yPred); +} +function mse2(yTrue, yPred) { + return meanSquaredError2(yTrue, yPred); +} +var exports_models_exports = {}; +__export2(exports_models_exports, { + modelFromJSON: () => modelFromJSON +}); +var exports_regularizers_exports = {}; +__export2(exports_regularizers_exports, { + l1: () => l12, + l1l2: () => l1l2, + l2: () => l22 +}); +function l1l2(config) { + return new L1L2(config); +} +function l12(config) { + return l1(config); +} +function l22(config) { + return l2(config); +} +var Callback = class extends BaseCallback { + constructor() { + super(...arguments); + this.model = null; + } + setModel(model2) { + if (!(model2 instanceof LayersModel)) { + throw new Error("model must be a LayersModel, not some other Container"); + } + this.model = model2; + } +}; +function less2(currVal, prevVal) { + return currVal < prevVal; +} +function greater2(currVal, prevVal) { + return currVal > prevVal; +} +var EarlyStopping = class extends Callback { + constructor(args) { + super(); + if (args == null) { + args = {}; + } + if (args.restoreBestWeights) { + throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet."); + } + this.monitor = args.monitor || "val_loss"; + this.minDelta = Math.abs(args.minDelta || 0); + this.patience = args.patience || 0; + this.verbose = args.verbose || 0; + this.mode = args.mode || "auto"; + this.baseline = args.baseline; + if (["auto", "min", "max"].indexOf(this.mode) === -1) { + console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`); + this.mode = "auto"; + } + if (this.mode === "min") { + this.monitorFunc = less2; + } else if (this.mode === "max") { + this.monitorFunc = greater2; + } else { + if (this.monitor.indexOf("acc") !== -1) { + this.monitorFunc = greater2; + } else { + this.monitorFunc = less2; + } + } + if (this.monitorFunc === less2) { + this.minDelta *= -1; + } + } + async onTrainBegin(logs) { + this.wait = 0; + this.stoppedEpoch = 0; + if (this.baseline != null) { + this.best = this.baseline; + } else { + this.best = this.monitorFunc === less2 ? Infinity : -Infinity; + } + } + async onEpochEnd(epoch, logs) { + await resolveScalarsInLogs(logs); + const current = this.getMonitorValue(logs); + if (current == null) { + return; + } + if (this.monitorFunc(current - this.minDelta, this.best)) { + this.best = current; + this.wait = 0; + } else { + this.wait++; + if (this.wait >= this.patience) { + this.stoppedEpoch = epoch; + this.model.stopTraining = true; + } + } + } + async onTrainEnd(logs) { + if (this.stoppedEpoch > 0 && this.verbose) { + console.log(`Epoch ${this.stoppedEpoch}: early stopping.`); + } + } + getMonitorValue(logs) { + if (logs == null) { + logs = {}; + } + const monitorValue = logs[this.monitor]; + if (monitorValue == null) { + console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`); + } + return monitorValue; + } +}; +function earlyStopping(args) { + return new EarlyStopping(args); +} +var callbacks = { earlyStopping }; +var ENV4 = env(); +ENV4.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (debugValue) => { + if (debugValue) { + console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance."); + } +}); +var DataType; +(function(DataType2) { + DataType2[DataType2["DT_INVALID"] = 0] = "DT_INVALID"; + DataType2[DataType2["DT_FLOAT"] = 1] = "DT_FLOAT"; + DataType2[DataType2["DT_DOUBLE"] = 2] = "DT_DOUBLE"; + DataType2[DataType2["DT_INT32"] = 3] = "DT_INT32"; + DataType2[DataType2["DT_UINT8"] = 4] = "DT_UINT8"; + DataType2[DataType2["DT_INT16"] = 5] = "DT_INT16"; + DataType2[DataType2["DT_INT8"] = 6] = "DT_INT8"; + DataType2[DataType2["DT_STRING"] = 7] = "DT_STRING"; + DataType2[DataType2["DT_COMPLEX64"] = 8] = "DT_COMPLEX64"; + DataType2[DataType2["DT_INT64"] = 9] = "DT_INT64"; + DataType2[DataType2["DT_BOOL"] = 10] = "DT_BOOL"; + DataType2[DataType2["DT_QINT8"] = 11] = "DT_QINT8"; + DataType2[DataType2["DT_QUINT8"] = 12] = "DT_QUINT8"; + DataType2[DataType2["DT_QINT32"] = 13] = "DT_QINT32"; + DataType2[DataType2["DT_BFLOAT16"] = 14] = "DT_BFLOAT16"; + DataType2[DataType2["DT_QINT16"] = 15] = "DT_QINT16"; + DataType2[DataType2["DT_QUINT16"] = 16] = "DT_QUINT16"; + DataType2[DataType2["DT_UINT16"] = 17] = "DT_UINT16"; + DataType2[DataType2["DT_COMPLEX128"] = 18] = "DT_COMPLEX128"; + DataType2[DataType2["DT_HALF"] = 19] = "DT_HALF"; + DataType2[DataType2["DT_RESOURCE"] = 20] = "DT_RESOURCE"; + DataType2[DataType2["DT_VARIANT"] = 21] = "DT_VARIANT"; + DataType2[DataType2["DT_UINT32"] = 22] = "DT_UINT32"; + DataType2[DataType2["DT_UINT64"] = 23] = "DT_UINT64"; + DataType2[DataType2["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF"; + DataType2[DataType2["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF"; + DataType2[DataType2["DT_INT32_REF"] = 103] = "DT_INT32_REF"; + DataType2[DataType2["DT_UINT8_REF"] = 104] = "DT_UINT8_REF"; + DataType2[DataType2["DT_INT16_REF"] = 105] = "DT_INT16_REF"; + DataType2[DataType2["DT_INT8_REF"] = 106] = "DT_INT8_REF"; + DataType2[DataType2["DT_STRING_REF"] = 107] = "DT_STRING_REF"; + DataType2[DataType2["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF"; + DataType2[DataType2["DT_INT64_REF"] = 109] = "DT_INT64_REF"; + DataType2[DataType2["DT_BOOL_REF"] = 110] = "DT_BOOL_REF"; + DataType2[DataType2["DT_QINT8_REF"] = 111] = "DT_QINT8_REF"; + DataType2[DataType2["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF"; + DataType2[DataType2["DT_QINT32_REF"] = 113] = "DT_QINT32_REF"; + DataType2[DataType2["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF"; + DataType2[DataType2["DT_QINT16_REF"] = 115] = "DT_QINT16_REF"; + DataType2[DataType2["DT_QUINT16_REF"] = 116] = "DT_QUINT16_REF"; + DataType2[DataType2["DT_UINT16_REF"] = 117] = "DT_UINT16_REF"; + DataType2[DataType2["DT_COMPLEX128_REF"] = 118] = "DT_COMPLEX128_REF"; + DataType2[DataType2["DT_HALF_REF"] = 119] = "DT_HALF_REF"; + DataType2[DataType2["DT_RESOURCE_REF"] = 120] = "DT_RESOURCE_REF"; + DataType2[DataType2["DT_VARIANT_REF"] = 121] = "DT_VARIANT_REF"; + DataType2[DataType2["DT_UINT32_REF"] = 122] = "DT_UINT32_REF"; + DataType2[DataType2["DT_UINT64_REF"] = 123] = "DT_UINT64_REF"; +})(DataType || (DataType = {})); +var SaverDef; +(function(SaverDef2) { + let CheckpointFormatVersion; + (function(CheckpointFormatVersion2) { + CheckpointFormatVersion2[CheckpointFormatVersion2["LEGACY"] = 0] = "LEGACY"; + CheckpointFormatVersion2[CheckpointFormatVersion2["V1"] = 1] = "V1"; + CheckpointFormatVersion2[CheckpointFormatVersion2["V2"] = 2] = "V2"; + })(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {})); +})(SaverDef || (SaverDef = {})); +var CUSTOM_OPS = {}; +function registerOp(name, opFunc) { + const opMapper = { + tfOpName: name, + category: "custom", + inputs: [], + attrs: [], + customExecutor: opFunc + }; + CUSTOM_OPS[name] = opMapper; +} +function getRegisteredOp(name) { + return CUSTOM_OPS[name]; +} +function deregisterOp(name) { + delete CUSTOM_OPS[name]; +} +function getParamValue(paramName, node, tensorMap, context, resourceManager) { + const inputParam = node.inputParams[paramName]; + if (inputParam && inputParam.inputIndexStart !== void 0) { + const start = inputParam.inputIndexStart; + const end = inputParam.inputIndexEnd === 0 ? void 0 : inputParam.inputIndexEnd === void 0 ? start + 1 : inputParam.inputIndexEnd; + const shiftedStart = start < 0 ? node.inputNames.length + start : start; + if (inputParam.type === "tensor") { + return getTensor(node.inputNames[shiftedStart], tensorMap, context, resourceManager); + } + if (inputParam.type === "tensors") { + const inputs = node.inputs.slice(start, end); + const inputNames = node.inputNames.slice(start, end).filter((_name, index) => { + var _a; + return ((_a = inputs[index]) === null || _a === void 0 ? void 0 : _a.op) !== "NoOp"; + }); + return inputNames.map((name) => getTensor(name, tensorMap, context, resourceManager)); + } + const tensor2 = getTensor(node.inputNames[shiftedStart], tensorMap, context, resourceManager); + const data = tensor2.dataSync(); + return inputParam.type === "number" ? data[0] : util_exports.toNestedArray(tensor2.shape, data); + } + const attrParam = node.attrParams[paramName]; + return attrParam && attrParam.value; +} +function getTensor(name, tensorsMap, context, resourceManager) { + const [nodeName, index] = parseNodeName(name, context); + if (resourceManager != null) { + const tensor2 = resourceManager.getHashTableHandleByName(nodeName); + if (tensor2 != null) { + return tensor2; + } + } + const contextId = context.currentContextIds.find((contextId2) => { + return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId2)]; + }); + return contextId !== void 0 ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : void 0; +} +function getTensorsForCurrentContext(name, tensorsMap, context) { + return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)]; +} +function getNodeNameAndIndex(inputName, context) { + const [nodeName, index, outputName] = parseNodeName(inputName, context); + return [ + getNodeNameWithContextId(nodeName, context && context.currentContextId), + index, + outputName + ]; +} +function getNodeNameWithContextId(name, contextId) { + return !!contextId ? `${name}-${contextId}` : name; +} +function parseNodeName(name, context) { + if (name === "") { + return ["", 0, void 0]; + } + const isCacheEnabled = context != null && context.parseNodeNameCache != null; + if (isCacheEnabled) { + const cachedResult = context.parseNodeNameCache.get(name); + if (cachedResult != null) { + return cachedResult; + } + } + const parts = name.split(":"); + let result; + if (parts.length === 1) { + result = [name, 0, void 0]; + } else { + const nodeName = parts[0]; + const outputName = parts.length === 3 ? parts[1] : void 0; + const index = Number(parts[parts.length - 1]); + result = [nodeName, index, outputName]; + } + if (isCacheEnabled) { + context.parseNodeNameCache.set(name, result); + } + return result; +} +function getPadding(node, tensorMap, context) { + let pad3 = getParamValue("pad", node, tensorMap, context); + if (pad3 === "explicit") { + pad3 = getParamValue("explicitPaddings", node, tensorMap, context); + const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]]; + for (let i = 0; i < 4; i++) { + explicitPadding[i][0] = pad3[i * 2]; + explicitPadding[i][1] = pad3[i * 2 + 1]; + } + return explicitPadding; + } + return pad3; +} +function cloneTensor(tensor2) { + return tensor2.kept ? tensor2 : clone(tensor2); +} +var arithmetic_exports = {}; +__export2(arithmetic_exports, { + json: () => json +}); +var json = [ + { + "tfOpName": "Add", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "AddV2", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "AddN", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "end": 0, + "name": "tensors", + "type": "tensors" + } + ] + }, + { + "tfOpName": "BiasAdd", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "notSupported": true + } + ] + }, + { + "tfOpName": "Sub", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "RealDiv", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Div", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "DivNoNan", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "FloorDiv", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Mul", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Maximum", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Minimum", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Pow", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "SquaredDifference", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Mod", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "FloorMod", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + } +]; +var basic_math_exports = {}; +__export2(basic_math_exports, { + json: () => json2 +}); +var json2 = [ + { + "tfOpName": "Abs", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Acos", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Asin", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": 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}, + { + "tfOpName": "Pack", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "end": 0, + "name": "tensors", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "axis", + "name": "axis", + "type": "number", + "defaultValue": 0 + } + ] + }, + { + "tfOpName": "Unpack", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "axis", + "name": "axis", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "num", + "name": "num", + "type": "number", + "defaultValue": 0, + "notSupported": true + } + ] + }, + { + "tfOpName": "Tile", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "reps", + "type": "number[]" + } + ] + }, + { + "tfOpName": "Split", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "axis", + "type": "number", + "defaultValue": 0 + }, + { + "start": 1, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "num_split", + "name": "numOrSizeSplits", + "type": "number", + "defaultValue": 1 + } + ] + }, + { + "tfOpName": "SplitV", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "numOrSizeSplits", + "type": "number[]" + }, + { + "start": 2, + "name": "axis", + "type": "number", + "defaultValue": 0 + } + ] + }, + { + "tfOpName": "ScatterNd", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "indices", + "type": "tensor" + }, + { + "start": 1, + "name": "values", + "type": "tensor" + }, + { + "start": 2, + "name": "shape", + "type": "number[]" + } + ] + }, + { + "tfOpName": "GatherNd", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "tensor" + } + ] + }, + { + "tfOpName": "SparseToDense", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "sparseIndices", + "type": "tensor" + }, + { + "start": 1, + "name": "outputShape", + "type": "number[]" + }, + { + "start": 2, + "name": "sparseValues", + "type": "tensor" + }, + { + "start": 3, + "name": "defaultValue", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "validate_indices", + "name": "validateIndices", + "type": "bool", + "defaultValue": false, + "notSupported": true + } + ] + }, + { + "tfOpName": "TensorScatterUpdate", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "tensor" + }, + { + "start": 2, + "name": "values", + "type": "tensor" + } + ] + } +]; +var sparse_exports = {}; +__export2(sparse_exports, { + json: () => json16 +}); +var json16 = [ + { + "tfOpName": "SparseFillEmptyRows", + "category": "sparse", + "inputs": [ + { + "start": 0, + "name": "indices", + "type": "tensor" + }, + { + "start": 1, + "name": "values", + "type": "tensor" + }, + { + "start": 2, + "name": "denseShape", + "type": "tensor" + }, + { + "start": 3, + "name": "defaultValue", + "type": "tensor" + } + ] + }, + { + "tfOpName": "SparseReshape", + "category": "sparse", + "inputs": [ + { + "start": 0, + "name": "inputIndices", + "type": "tensor" + }, + { + "start": 1, + "name": "inputShape", + "type": "tensor" + }, + { + "start": 2, + "name": "newShape", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "SparseSegmentMean", + "category": "sparse", + "inputs": [ + { + "start": 0, + "name": "data", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "tensor" + }, + { + "start": 2, + "name": "segmentIds", + "type": "tensor" + } + ] + }, + { + "tfOpName": "SparseSegmentSum", + "category": "sparse", + "inputs": [ + { + "start": 0, + "name": "data", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "tensor" + }, + { + "start": 2, + "name": "segmentIds", + "type": "tensor" + } + ] + } +]; +var spectral_exports = {}; +__export2(spectral_exports, { + json: () => json17 +}); +var json17 = [ + { + "tfOpName": "FFT", + "category": "spectral", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "IFFT", + "category": "spectral", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "RFFT", + "category": "spectral", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "fft_length", + "type": "number", + "notSupported": true + } + ] + }, + { + "tfOpName": "IRFFT", + "category": "spectral", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "fft_length", + "type": "number", + "notSupported": true + } + ] + } +]; +var string_exports = {}; +__export2(string_exports, { + json: () => json18 +}); +var json18 = [ + { + "tfOpName": "StaticRegexReplace", + "category": "string", + "inputs": [ + { + "start": 0, + "name": "input", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "pattern", + "name": "pattern", + "type": "string" + }, + { + "tfName": "rewrite", + "name": "rewrite", + "type": "string" + }, + { + "tfName": "replace_global", + "name": "replaceGlobal", + "type": "bool" + } + ] + }, + { + "tfOpName": "StringNGrams", + "category": "string", + "inputs": [ + { + "start": 0, + "name": "data", + "type": "tensor" + }, + { + "start": 1, + "name": "dataSplits", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "separator", + "name": "separator", + "type": "string" + }, + { + "tfName": "ngram_widths", + "name": "nGramWidths", + "type": "number[]" + }, + { + "tfName": "left_pad", + "name": "leftPad", + "type": "string" + }, + { + "tfName": "right_pad", + "name": "rightPad", + "type": "string" + }, + { + "tfName": "pad_width", + "name": "padWidth", + "type": "number" + }, + { + "tfName": "preserve_short_sequences", + "name": "preserveShortSequences", + "type": "bool" + } + ], + "outputs": [ + "ngrams", + "ngrams_splits" + ] + }, + { + "tfOpName": "StringSplit", + "category": "string", + "inputs": [ + { + "start": 0, + "name": "input", + "type": "tensor" + }, + { + "start": 1, + "name": "delimiter", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "skip_empty", + "name": "skipEmpty", + "type": "bool" + } + ], + "outputs": [ + "indices", + "values", + "shape" + ] + }, + { + "tfOpName": "StringToHashBucketFast", + "category": "string", + "inputs": [ + { + "start": 0, + "name": "input", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "num_buckets", + "name": "numBuckets", + "type": "number" + } + ] + } +]; +var transformation_exports = {}; +__export2(transformation_exports, { + json: () => json19 +}); +var json19 = [ + { + "tfOpName": "Cast", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "SrcT", + "name": "sdtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "DstT", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "ExpandDims", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number" + } + ] + }, + { + "tfOpName": "MirrorPad", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "padding", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "mode", + "name": "mode", + "type": "string" + } + ] + }, + { + "tfOpName": "Pad", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "padding", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "constant_value", + "name": "constantValue", + "type": "number", + "defaultValue": 0 + } + ] + }, + { + "tfOpName": "PadV2", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "padding", + "type": "number[]" + }, + { + "start": 2, + "name": "constantValue", + "type": "number", + "defaultValue": 0 + } + ] + }, + { + "tfOpName": "Reshape", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "shape", + "type": "number[]" + } + ] + }, + { + "tfOpName": "EnsureShape", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "shape", + "type": "number[]" + } + ] + }, + { + "tfOpName": "Squeeze", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "axis", + "tfDeprecatedName": "squeeze_dims", + "name": "axis", + "type": "number[]" + } + ] + }, + { + "tfOpName": "SpaceToBatchND", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "blockShape", + "type": "number[]" + }, + { + "start": 2, + "name": "paddings", + "type": "number[]" + } + ] + }, + { + "tfOpName": "BatchToSpaceND", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "blockShape", + "type": "number[]" + }, + { + "start": 2, + "name": "crops", + "type": "number[]" + } + ] + }, + { + "tfOpName": "DepthToSpace", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "block_size", + "name": "blockSize", + "type": "number" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string" + } + ] + }, + { + "tfOpName": "BroadcastTo", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "shape", + "type": "number[]" + } + ], + "attrs": [] + }, + { + "tfOpName": "BroadcastArgs", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "s0", + "type": "tensor" + }, + { + "start": 1, + "name": "s1", + "type": "tensor" + } + ], + "attrs": [] + } +]; +var OperationMapper = class { + // Singleton instance for the mapper + static get Instance() { + return this._instance || (this._instance = new this()); + } + // Loads the op mapping from the JSON file. + constructor() { + const ops = [ + arithmetic_exports, + basic_math_exports, + control_exports, + convolution_exports, + creation_exports, + dynamic_exports, + evaluation_exports, + graph_exports, + hash_table_exports, + image_exports, + logical_exports, + matrices_exports, + normalization_exports, + reduction_exports, + slice_join_exports, + sparse_exports, + spectral_exports, + string_exports, + transformation_exports + ]; + const mappersJson = [].concat(...ops.map((op2) => op2.json)); + this.opMappers = mappersJson.reduce((map, mapper) => { + map[mapper.tfOpName] = mapper; + return map; + }, {}); + } + // Converts the model inference graph from Tensorflow GraphDef to local + // representation for TensorFlow.js API + transformGraph(graph, signature = {}) { + const tfNodes = graph.node; + const placeholders = []; + const weights = []; + const initNodes = []; + const nodes = tfNodes.reduce((map, node) => { + map[node.name] = this.mapNode(node); + if (node.op.startsWith("Placeholder")) { + placeholders.push(map[node.name]); + } else if (node.op === "Const") { + weights.push(map[node.name]); + } else if (node.input == null || node.input.length === 0) { + initNodes.push(map[node.name]); + } + return map; + }, {}); + let inputs = []; + const outputs = []; + let inputNodeNameToKey = {}; + let outputNodeNameToKey = {}; + if (signature != null) { + inputNodeNameToKey = this.mapSignatureEntries(signature.inputs); + outputNodeNameToKey = this.mapSignatureEntries(signature.outputs); + } + const allNodes = Object.keys(nodes); + allNodes.forEach((key) => { + const node = nodes[key]; + node.inputNames.forEach((name, index) => { + const [nodeName, , outputName] = getNodeNameAndIndex(name); + const inputNode = nodes[nodeName]; + if (inputNode.outputs != null) { + const outputIndex = inputNode.outputs.indexOf(outputName); + if (outputIndex !== -1) { + const inputName = `${nodeName}:${outputIndex}`; + node.inputNames[index] = inputName; + } + } + node.inputs.push(inputNode); + inputNode.children.push(node); + }); + }); + if (Object.keys(outputNodeNameToKey).length === 0) { + allNodes.forEach((key) => { + const node = nodes[key]; + if (node.children.length === 0) { + outputs.push(node); + } + }); + } else { + Object.keys(outputNodeNameToKey).forEach((name) => { + const [nodeName] = getNodeNameAndIndex(name); + const node = nodes[nodeName]; + if (node != null) { + node.signatureKey = outputNodeNameToKey[name]; + outputs.push(node); + } + }); + } + if (Object.keys(inputNodeNameToKey).length > 0) { + Object.keys(inputNodeNameToKey).forEach((name) => { + const [nodeName] = getNodeNameAndIndex(name); + const node = nodes[nodeName]; + if (node) { + node.signatureKey = inputNodeNameToKey[name]; + inputs.push(node); + } + }); + } else { + inputs = placeholders; + } + let functions = {}; + if (graph.library != null && graph.library.function != null) { + functions = graph.library.function.reduce((functions2, func2) => { + functions2[func2.signature.name] = this.mapFunction(func2); + return functions2; + }, {}); + } + const result = { nodes, inputs, outputs, weights, placeholders, signature, functions }; + if (initNodes.length > 0) { + result.initNodes = initNodes; + } + return result; + } + mapSignatureEntries(entries) { + return Object.keys(entries || {}).reduce((prev, curr) => { + prev[entries[curr].name] = curr; + return prev; + }, {}); + } + mapNode(node) { + const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {}; + if (node.attr == null) { + node.attr = {}; + } + const newNode = { + name: node.name, + op: node.op, + category: mapper.category, + inputNames: (node.input || []).map((input2) => input2.startsWith("^") ? input2.slice(1) : input2), + inputs: [], + children: [], + inputParams: {}, + attrParams: {}, + rawAttrs: node.attr, + outputs: mapper.outputs + }; + if (mapper.inputs != null) { + newNode.inputParams = mapper.inputs.reduce((map, param) => { + map[param.name] = { + type: param.type, + inputIndexStart: param.start, + inputIndexEnd: param.end + }; + return map; + }, {}); + } + if (mapper.attrs != null) { + newNode.attrParams = mapper.attrs.reduce((map, param) => { + const type = param.type; + let value = void 0; + switch (param.type) { + case "string": + value = getStringParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "string[]": + value = getStringArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "number": + value = getNumberParam(node.attr, param.tfName, param.defaultValue || 0); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "number[]": + value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "bool": + value = getBoolParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "bool[]": + value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "shape": + value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "shape[]": + value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "dtype": + value = getDtypeParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "dtype[]": + value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "func": + value = getFuncParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "tensor": + case "tensors": + break; + default: + throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`); + } + map[param.name] = { value, type }; + return map; + }, {}); + } + return newNode; + } + // map the TFunctionDef to TFJS graph object + mapFunction(functionDef) { + const tfNodes = functionDef.nodeDef; + const placeholders = []; + const weights = []; + let nodes = {}; + if (tfNodes != null) { + nodes = tfNodes.reduce((map, node) => { + map[node.name] = this.mapNode(node); + if (node.op === "Const") { + weights.push(map[node.name]); + } + return map; + }, {}); + } + const inputs = []; + const outputs = []; + functionDef.signature.inputArg.forEach((arg) => { + const [nodeName] = getNodeNameAndIndex(arg.name); + const node = { + name: nodeName, + op: "Placeholder", + inputs: [], + inputNames: [], + category: "graph", + inputParams: {}, + attrParams: { dtype: { value: parseDtypeParam(arg.type), type: "dtype" } }, + children: [] + }; + node.signatureKey = arg.name; + inputs.push(node); + nodes[nodeName] = node; + }); + const allNodes = Object.keys(nodes); + allNodes.forEach((key) => { + const node = nodes[key]; + node.inputNames.forEach((name, index) => { + const [nodeName, , outputName] = getNodeNameAndIndex(name); + const inputNode = nodes[nodeName]; + if (inputNode.outputs != null) { + const outputIndex = inputNode.outputs.indexOf(outputName); + if (outputIndex !== -1) { + const inputName = `${nodeName}:${outputIndex}`; + node.inputNames[index] = inputName; + } + } + node.inputs.push(inputNode); + inputNode.children.push(node); + }); + }); + const returnNodeMap = functionDef.ret; + functionDef.signature.outputArg.forEach((output) => { + const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]); + const node = nodes[nodeName]; + if (node != null) { + node.defaultOutput = index; + outputs.push(node); + } + }); + const signature = this.mapArgsToSignature(functionDef); + return { nodes, inputs, outputs, weights, placeholders, signature }; + } + mapArgsToSignature(functionDef) { + return { + methodName: functionDef.signature.name, + inputs: functionDef.signature.inputArg.reduce((map, arg) => { + map[arg.name] = this.mapArgToTensorInfo(arg); + return map; + }, {}), + outputs: functionDef.signature.outputArg.reduce((map, arg) => { + map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret); + return map; + }, {}) + }; + } + mapArgToTensorInfo(arg, nameMap2) { + let name = arg.name; + if (nameMap2 != null) { + name = nameMap2[name]; + } + return { name, dtype: arg.type }; + } +}; +function decodeBase64(text) { + const global2 = env().global; + if (typeof global2.atob !== "undefined") { + return global2.atob(text); + } else if (typeof Buffer !== "undefined") { + return new Buffer(text, "base64").toString(); + } else { + throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()"); + } +} +function parseStringParam(s, keepCase) { + const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s); + return keepCase ? value : value.toLowerCase(); +} +function getStringParam(attrs, name, def, keepCase = false) { + const param = attrs[name]; + if (param != null) { + return parseStringParam(param.s, keepCase); + } + return def; +} +function getBoolParam(attrs, name, def) { + const param = attrs[name]; + return param ? param.b : def; +} +function getNumberParam(attrs, name, def) { + const param = attrs[name] || {}; + const value = param["i"] != null ? param["i"] : param["f"] != null ? param["f"] : def; + return typeof value === "number" ? value : parseInt(value, 10); +} +function parseDtypeParam(value) { + if (typeof value === "string") { + value = DataType[value]; + } + switch (value) { + case DataType.DT_FLOAT: + case DataType.DT_HALF: + return "float32"; + case DataType.DT_INT32: + case DataType.DT_INT64: + case DataType.DT_INT8: + case DataType.DT_UINT8: + return "int32"; + case DataType.DT_BOOL: + return "bool"; + case DataType.DT_DOUBLE: + return "float32"; + case DataType.DT_STRING: + return "string"; + case DataType.DT_COMPLEX64: + case DataType.DT_COMPLEX128: + return "complex64"; + default: + return null; + } +} +function getFuncParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.func) { + return param.func.name; + } + return def; +} +function getDtypeParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.type) { + return parseDtypeParam(param.type); + } + return def; +} +function getDtypeArrayParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.list && param.list.type) { + return param.list.type.map((v) => parseDtypeParam(v)); + } + return def; +} +function parseTensorShapeParam(shape) { + if (shape.unknownRank) { + return void 0; + } + if (shape.dim != null) { + return shape.dim.map((dim) => typeof dim.size === "number" ? dim.size : parseInt(dim.size, 10)); + } + return []; +} +function getTensorShapeParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.shape) { + return parseTensorShapeParam(param.shape); + } + return def; +} +function getNumericArrayParam(attrs, name, def) { + const param = attrs[name]; + if (param) { + return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v) => typeof v === "number" ? v : parseInt(v, 10)); + } + return def; +} +function getStringArrayParam(attrs, name, def, keepCase = false) { + const param = attrs[name]; + if (param && param.list && param.list.s) { + return param.list.s.map((v) => { + return parseStringParam(v, keepCase); + }); + } + return def; +} +function getTensorShapeArrayParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.list && param.list.shape) { + return param.list.shape.map((v) => { + return parseTensorShapeParam(v); + }); + } + return def; +} +function getBoolArrayParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.list && param.list.b) { + return param.list.b; + } + return def; +} +var NodeValueImpl = class { + constructor(node, tensorMap, context) { + this.node = node; + this.tensorMap = tensorMap; + this.context = context; + this.inputs = []; + this.attrs = {}; + this.inputs = node.inputNames.map((name) => this.getInput(name)); + if (node.rawAttrs != null) { + this.attrs = Object.keys(node.rawAttrs).reduce((attrs, key) => { + attrs[key] = this.getAttr(key); + return attrs; + }, {}); + } + } + /** + * Return the value of the attribute or input param. + * @param name String: name of attribute or input param. + */ + getInput(name) { + return getTensor(name, this.tensorMap, this.context); + } + /** + * Return the value of the attribute or input param. + * @param name String: name of attribute or input param. + */ + getAttr(name, defaultValue) { + const value = this.node.rawAttrs[name]; + if (value.tensor != null) { + return getTensor(name, this.tensorMap, this.context); + } + if (value.i != null || value.f != null) { + return getNumberParam(this.node.rawAttrs, name, defaultValue); + } + if (value.s != null) { + return getStringParam(this.node.rawAttrs, name, defaultValue); + } + if (value.b != null) { + return getBoolParam(this.node.rawAttrs, name, defaultValue); + } + if (value.shape != null) { + return getTensorShapeParam(this.node.rawAttrs, name, defaultValue); + } + if (value.type != null) { + return getDtypeParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list != null) { + if (value.list.i != null || value.list.f != null) { + return getNumericArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.s != null) { + return getStringArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.shape != null) { + return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.b != null) { + return getBoolArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.type != null) { + return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue); + } + } + return defaultValue; + } +}; +var ops_for_converter_exports = {}; +__export2(ops_for_converter_exports, { + OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX, + abs: () => abs, + acos: () => acos, + acosh: () => acosh, + add: () => add2, + addN: () => addN, + all: () => all, + any: () => any, + argMax: () => argMax, + argMin: () => argMin, + asin: () => asin, + asinh: () => asinh, + atan: () => atan, + atan2: () => atan2, + atanh: () => atanh, + avgPool: () => avgPool, + avgPool3d: () => avgPool3d, + basicLSTMCell: () => basicLSTMCell, + batchNorm: () => batchNorm, + batchNorm2d: () => batchNorm2d, + batchNorm3d: () => batchNorm3d, + batchNorm4d: () => batchNorm4d, + batchToSpaceND: () => batchToSpaceND, + bincount: () => bincount, + bitwiseAnd: () => bitwiseAnd, + booleanMaskAsync: () => booleanMaskAsync, + broadcastArgs: () => broadcastArgs, + broadcastTo: () => broadcastTo, + buffer: () => buffer, + cast: () => cast, + ceil: () => ceil, + clipByValue: () => clipByValue, + clone: () => clone, + complex: () => complex, + concat: () => concat, + concat1d: () => concat1d, + concat2d: () => concat2d, + concat3d: () => concat3d, + concat4d: () => concat4d, + conv1d: () => conv1d, + conv2d: () => conv2d, + conv2dTranspose: () => conv2dTranspose, + conv3d: () => conv3d, + conv3dTranspose: () => conv3dTranspose, + cos: () => cos, + cosh: () => cosh, + cosineWindow: () => cosineWindow, + cumprod: () => cumprod, + cumsum: () => cumsum, + denseBincount: () => denseBincount, + depthToSpace: () => depthToSpace, + depthwiseConv2d: () => depthwiseConv2d, + diag: () => diag, + dilation2d: () => dilation2d, + div: () => div, + divNoNan: () => divNoNan, + dot: () => dot, + dropout: () => dropout, + einsum: () => einsum, + elu: () => elu, + enclosingPowerOfTwo: () => enclosingPowerOfTwo, + ensureShape: () => ensureShape, + equal: () => equal, + erf: () => erf, + euclideanNorm: () => euclideanNorm, + exp: () => exp, + expandDims: () => expandDims, + expm1: () => expm1, + eye: () => eye, + fft: () => fft, + fill: () => fill, + floor: () => floor, + floorDiv: () => floorDiv, + fused: () => fused_ops_exports, + gather: () => gather, + gatherND: () => gatherND, + greater: () => greater, + greaterEqual: () => greaterEqual, + ifft: () => ifft, + imag: () => imag, + image: () => image, + inTopKAsync: () => inTopKAsync, + irfft: () => irfft, + isFinite: () => isFinite2, + isInf: () => isInf, + isNaN: () => isNaN2, + leakyRelu: () => leakyRelu, + less: () => less, + lessEqual: () => lessEqual, + linalg: () => linalg, + linspace: () => linspace, + localResponseNormalization: () => localResponseNormalization, + log: () => log2, + log1p: () => log1p, + logSigmoid: () => logSigmoid, + logSoftmax: () => logSoftmax, + logSumExp: () => logSumExp, + logicalAnd: () => logicalAnd, + logicalNot: () => logicalNot, + logicalOr: () => logicalOr, + logicalXor: () => logicalXor, + losses: () => losses, + lowerBound: () => lowerBound, + matMul: () => matMul, + max: () => max, + maxPool: () => maxPool, + maxPool3d: () => maxPool3d, + maxPoolWithArgmax: () => maxPoolWithArgmax, + maximum: () => maximum, + mean: () => mean, + meshgrid: () => meshgrid, + min: () => min, + minimum: () => minimum, + mirrorPad: () => mirrorPad, + mod: () => mod, + moments: () => moments, + movingAverage: () => movingAverage, + mul: () => mul, + multiRNNCell: () => multiRNNCell, + multinomial: () => multinomial, + neg: () => neg, + norm: () => norm, + notEqual: () => notEqual, + oneHot: () => oneHot, + ones: () => ones2, + onesLike: () => onesLike, + op: () => op, + outerProduct: () => outerProduct, + pad: () => pad, + pad1d: () => pad1d, + pad2d: () => pad2d, + pad3d: () => pad3d, + pad4d: () => pad4d, + pool: () => pool, + pow: () => pow, + prelu: () => prelu, + print: () => print, + prod: () => prod, + raggedGather: () => raggedGather, + raggedRange: () => raggedRange, + raggedTensorToTensor: () => raggedTensorToTensor, + rand: () => rand, + randomGamma: () => randomGamma, + randomNormal: () => randomNormal, + randomStandardNormal: () => randomStandardNormal, + randomUniform: () => randomUniform, + randomUniformInt: () => randomUniformInt, + range: () => range, + real: () => real, + reciprocal: () => reciprocal, + relu: () => relu, + relu6: () => relu6, + reshape: () => reshape, + reverse: () => reverse, + reverse1d: () => reverse1d, + reverse2d: () => reverse2d, + reverse3d: () => reverse3d, + reverse4d: () => reverse4d, + rfft: () => rfft, + round: () => round2, + rsqrt: () => rsqrt, + scalar: () => scalar, + scatterND: () => scatterND, + searchSorted: () => searchSorted, + selu: () => selu, + separableConv2d: () => separableConv2d, + setdiff1dAsync: () => setdiff1dAsync, + sigmoid: () => sigmoid, + sign: () => sign, + signal: () => signal, + sin: () => sin, + sinh: () => sinh, + slice: () => slice, + slice1d: () => slice1d, + slice2d: () => slice2d, + slice3d: () => slice3d, + slice4d: () => slice4d, + softmax: () => softmax, + softplus: () => softplus, + spaceToBatchND: () => spaceToBatchND, + sparse: () => sparse, + sparseToDense: () => sparseToDense, + spectral: () => spectral, + split: () => split, + sqrt: () => sqrt, + square: () => square, + squaredDifference: () => squaredDifference, + squeeze: () => squeeze, + stack: () => stack, + step: () => step, + stridedSlice: () => stridedSlice, + string: () => string, + sub: () => sub, + sum: () => sum2, + tan: () => tan, + tanh: () => tanh2, + tensor: () => tensor, + tensor1d: () => tensor1d, + tensor2d: () => tensor2d, + tensor3d: () => tensor3d, + tensor4d: () => tensor4d, + tensor5d: () => tensor5d, + tensor6d: () => tensor6d, + tensorScatterUpdate: () => tensorScatterUpdate, + tile: () => tile, + topk: () => topk, + transpose: () => transpose, + truncatedNormal: () => truncatedNormal, + unique: () => unique, + unsortedSegmentSum: () => unsortedSegmentSum, + unstack: () => unstack, + upperBound: () => upperBound, + variable: () => variable, + where: () => where, + whereAsync: () => whereAsync, + zeros: () => zeros, + zerosLike: () => zerosLike +}); +var executeOp = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "BiasAdd": + case "AddV2": + case "Add": { + return [ops.add(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "AddN": { + return [ops.addN(getParamValue("tensors", node, tensorMap, context))]; + } + case "FloorMod": + case "Mod": + return [ops.mod(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + case "Mul": + return [ops.mul(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + case "RealDiv": + case "Div": { + return [ops.div(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "DivNoNan": { + return [ops.divNoNan(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "FloorDiv": { + return [ops.floorDiv(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Sub": { + return [ops.sub(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Minimum": { + return [ops.minimum(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Maximum": { + return [ops.maximum(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Pow": { + return [ops.pow(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "SquaredDifference": { + return [ops.squaredDifference(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp2 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Abs": + case "ComplexAbs": + return [ops.abs(getParamValue("x", node, tensorMap, context))]; + case "Acos": + return [ops.acos(getParamValue("x", node, tensorMap, context))]; + case "Acosh": + return [ops.acosh(getParamValue("x", node, tensorMap, context))]; + case "Asin": + return [ops.asin(getParamValue("x", node, tensorMap, context))]; + case "Asinh": + return [ops.asinh(getParamValue("x", node, tensorMap, context))]; + case "Atan": + return [ops.atan(getParamValue("x", node, tensorMap, context))]; + case "Atan2": + return [ops.atan2(getParamValue("x", node, tensorMap, context), getParamValue("y", node, tensorMap, context))]; + case "Atanh": + return [ops.atanh(getParamValue("x", node, tensorMap, context))]; + case "Ceil": + return [ops.ceil(getParamValue("x", node, tensorMap, context))]; + case "Complex": + return [ops.complex(getParamValue("real", node, tensorMap, context), getParamValue("imag", node, tensorMap, context))]; + case "Cos": + return [ops.cos(getParamValue("x", node, tensorMap, context))]; + case "Cosh": + return [ops.cosh(getParamValue("x", node, tensorMap, context))]; + case "Elu": + return [ops.elu(getParamValue("x", node, tensorMap, context))]; + case "Erf": + return [ops.erf(getParamValue("x", node, tensorMap, context))]; + case "Exp": + return [ops.exp(getParamValue("x", node, tensorMap, context))]; + case "Expm1": { + return [ops.expm1(getParamValue("x", node, tensorMap, context))]; + } + case "Floor": + return [ops.floor(getParamValue("x", node, tensorMap, context))]; + case "Log": + return [ops.log(getParamValue("x", node, tensorMap, context))]; + case "Log1p": { + return [ops.log1p(getParamValue("x", node, tensorMap, context))]; + } + case "Imag": + return [ops.imag(getParamValue("x", node, tensorMap, context))]; + case "Neg": + return [ops.neg(getParamValue("x", node, tensorMap, context))]; + case "Reciprocal": { + return [ops.reciprocal(getParamValue("x", node, tensorMap, context))]; + } + case "Real": + return [ops.real(getParamValue("x", node, tensorMap, context))]; + case "Relu": + return [ops.relu(getParamValue("x", node, tensorMap, context))]; + case "Round": { + return [ops.round(getParamValue("x", node, tensorMap, context))]; + } + case "Selu": + return [ops.selu(getParamValue("x", node, tensorMap, context))]; + case "Sigmoid": + return [ops.sigmoid(getParamValue("x", node, tensorMap, context))]; + case "Sin": + return [ops.sin(getParamValue("x", node, tensorMap, context))]; + case "Sign": { + return [ops.sign(getParamValue("x", node, tensorMap, context))]; + } + case "Sinh": { + return [ops.sinh(getParamValue("x", node, tensorMap, context))]; + } + case "Softplus": { + return [ops.softplus(getParamValue("x", node, tensorMap, context))]; + } + case "Sqrt": { + return [ops.sqrt(getParamValue("x", node, tensorMap, context))]; + } + case "Square": { + return [ops.square(getParamValue("x", node, tensorMap, context))]; + } + case "Tanh": { + return [ops.tanh(getParamValue("x", node, tensorMap, context))]; + } + case "Tan": + return [ops.tan(getParamValue("x", node, tensorMap, context))]; + case "ClipByValue": + return [ops.clipByValue(getParamValue("x", node, tensorMap, context), getParamValue("clipValueMin", node, tensorMap, context), getParamValue("clipValueMax", node, tensorMap, context))]; + case "Relu6": + return [ops.relu6(getParamValue("x", node, tensorMap, context))]; + case "Rsqrt": + return [ops.rsqrt(getTensor(node.inputNames[0], tensorMap, context))]; + case "LeakyRelu": + return [ops.leakyRelu(getParamValue("x", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context))]; + case "Prelu": + return [ops.prelu(getParamValue("x", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context))]; + case "IsNan": + return [ops.isNaN(getTensor(node.inputNames[0], tensorMap, context))]; + case "IsInf": + return [ops.isInf(getTensor(node.inputNames[0], tensorMap, context))]; + case "IsFinite": + return [ops.isFinite(getTensor(node.inputNames[0], tensorMap, context))]; + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = "") { + if (typeof shapeA === "number" || typeof shapeB === "number") { + return; + } + util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); + for (let i = 0; i < shapeA.length; i++) { + const dim0 = shapeA[i]; + const dim1 = shapeB[i]; + util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); + } +} +function fullDefinedShape(elementShape) { + if (typeof elementShape === "number" || elementShape.some((dim) => dim < 0)) { + return false; + } + return true; +} +function inferElementShape(listElementShape, tensors, elementShape) { + let partialShape = mergeElementShape(listElementShape, elementShape); + const notfullDefinedShape = !fullDefinedShape(partialShape); + if (notfullDefinedShape && tensors.length === 0) { + throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${partialShape}`); + } + if (notfullDefinedShape) { + tensors.forEach((tensor2) => { + partialShape = mergeElementShape(tensor2.shape, partialShape); + }); + } + if (!fullDefinedShape(partialShape)) { + throw new Error(`Non-fully-defined elementShape: ${partialShape}`); + } + return partialShape; +} +function mergeElementShape(elementShapeA, elementShapeB) { + if (typeof elementShapeA === "number") { + return elementShapeB; + } + if (typeof elementShapeB === "number") { + return elementShapeA; + } + if (elementShapeA.length !== elementShapeB.length) { + throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`); + } + const result = []; + for (let i = 0; i < elementShapeA.length; ++i) { + const dim0 = elementShapeA[i]; + const dim1 = elementShapeB[i]; + if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) { + throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`); + } + result[i] = dim0 >= 0 ? dim0 : dim1; + } + return result; +} +var TensorArray = class { + constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) { + this.name = name; + this.dtype = dtype; + this.maxSize = maxSize; + this.elementShape = elementShape; + this.identicalElementShapes = identicalElementShapes; + this.dynamicSize = dynamicSize; + this.clearAfterRead = clearAfterRead; + this.tensors = []; + this.closed_ = false; + this.idTensor = scalar(0); + keep(this.idTensor); + } + get id() { + return this.idTensor.id; + } + get closed() { + return this.closed_; + } + /** + * Dispose the tensors and idTensor and mark the TensoryArray as closed. + */ + clearAndClose(keepIds) { + this.tensors.forEach((tensor2) => { + if (keepIds == null || !keepIds.has(tensor2.tensor.id)) { + tensor2.tensor.dispose(); + } + }); + this.tensors = []; + this.closed_ = true; + this.idTensor.dispose(); + } + size() { + return this.tensors.length; + } + /** + * Read the value at location index in the TensorArray. + * @param index Number the index to read from. + */ + read(index) { + if (this.closed_) { + throw new Error(`TensorArray ${this.name} has already been closed.`); + } + if (index < 0 || index >= this.size()) { + throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`); + } + const tensorWithState = this.tensors[index]; + if (tensorWithState.cleared) { + throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`); + } + if (this.clearAfterRead) { + tensorWithState.cleared = true; + } + tensorWithState.read = true; + return tensorWithState.tensor; + } + /** + * Helper method to read multiple tensors from the specified indices. + */ + readMany(indices) { + return indices.map((index) => this.read(index)); + } + /** + * Write value into the index of the TensorArray. + * @param index number the index to write to. + * @param tensor + */ + write(index, tensor2) { + if (this.closed_) { + throw new Error(`TensorArray ${this.name} has already been closed.`); + } + if (index < 0 || !this.dynamicSize && index >= this.maxSize) { + throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`); + } + const t = this.tensors[index] || {}; + if (tensor2.dtype !== this.dtype) { + throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, + because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`); + } + if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) { + this.elementShape = tensor2.shape; + } + assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`); + if (t.read) { + throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`); + } + if (t.written) { + throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`); + } + t.tensor = tensor2; + keep(tensor2); + t.written = true; + this.tensors[index] = t; + } + /** + * Helper method to write multiple tensors to the specified indices. + */ + writeMany(indices, tensors) { + if (indices.length !== tensors.length) { + throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`); + } + indices.forEach((i, index) => this.write(i, tensors[index])); + } + /** + * Return selected values in the TensorArray as a packed Tensor. All of + * selected values must have been written and their shapes must all match. + * @param [indices] number[] Optional. Taking values in [0, max_value). If the + * TensorArray is not dynamic, max_value=size(). If not specified returns + * all tensors in the original order. + * @param [dtype] + */ + gather(indices, dtype) { + if (!!dtype && dtype !== this.dtype) { + throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`); + } + if (!indices) { + indices = []; + for (let i = 0; i < this.size(); i++) { + indices.push(i); + } + } else { + indices = indices.slice(0, this.size()); + } + if (indices.length === 0) { + return tensor([], [0].concat(this.elementShape)); + } + const tensors = this.readMany(indices); + assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, "TensorArray shape mismatch: "); + return stack(tensors, 0); + } + /** + * Return the values in the TensorArray as a concatenated Tensor. + */ + concat(dtype) { + if (!!dtype && dtype !== this.dtype) { + throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`); + } + if (this.size() === 0) { + return tensor([], [0].concat(this.elementShape)); + } + const indices = []; + for (let i = 0; i < this.size(); i++) { + indices.push(i); + } + const tensors = this.readMany(indices); + assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`); + return concat(tensors, 0); + } + /** + * Scatter the values of a Tensor in specific indices of a TensorArray. + * @param indices nummber[] values in [0, max_value). If the + * TensorArray is not dynamic, max_value=size(). + * @param tensor Tensor input tensor. + */ + scatter(indices, tensor2) { + if (tensor2.dtype !== this.dtype) { + throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`); + } + if (indices.length !== tensor2.shape[0]) { + throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`); + } + const maxIndex = Math.max(...indices); + if (!this.dynamicSize && maxIndex >= this.maxSize) { + throw new Error(`Max index must be < array size (${maxIndex} vs. ${this.maxSize})`); + } + this.writeMany(indices, unstack(tensor2, 0)); + } + /** + * Split the values of a Tensor into the TensorArray. + * @param length number[] with the lengths to use when splitting value along + * its first dimension. + * @param tensor Tensor, the tensor to split. + */ + split(length, tensor2) { + if (tensor2.dtype !== this.dtype) { + throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`); + } + let totalLength = 0; + const cumulativeLengths = length.map((len) => { + totalLength += len; + return totalLength; + }); + if (totalLength !== tensor2.shape[0]) { + throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,s=[];P(()=>{t=W(t,[1,n,r]);for(let o=0;o{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);Ea(t,r.shape,"TensorList shape mismatch: "),qt(r)}),this.idTensor=ve(0),this.maxNumElements=a,qt(this.idTensor)}copy(){return new Gl([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);Ea(e,this.elementShape,"TensorList shape mismatch: ");let a=Xp(this.elementShape,this.tensors,e);return P(()=>{let r=this.tensors.map(s=>W(s,a));return Dt(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Xp(this.elementShape,this.tensors,e),a=this.tensors.pop();return a.kept=!1,Ea(a.shape,e,"TensorList shape mismatch: "),W(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Ea(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");qt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. 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Defaults to -1 + * meaning that the size of `tensors` is unbounded. + */ + constructor(tensors, elementShape, elementDtype, maxNumElements = -1) { + this.tensors = tensors; + this.elementShape = elementShape; + this.elementDtype = elementDtype; + if (tensors != null) { + tensors.forEach((tensor2) => { + if (elementDtype !== tensor2.dtype) { + throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`); + } + assertShapesMatchAllowUndefinedSize(elementShape, tensor2.shape, "TensorList shape mismatch: "); + keep(tensor2); + }); + } + this.idTensor = scalar(0); + this.maxNumElements = maxNumElements; + keep(this.idTensor); + } + /** + * Get a new TensorList containing a copy of the underlying tensor container. + */ + copy() { + return new _TensorList([...this.tensors], this.elementShape, this.elementDtype); + } + /** + * Dispose the tensors and idTensor and clear the tensor list. + */ + clearAndClose(keepIds) { + this.tensors.forEach((tensor2) => { + if (keepIds == null || !keepIds.has(tensor2.id)) { + tensor2.dispose(); + } + }); + this.tensors.length = 0; + this.idTensor.dispose(); + } + /** + * The size of the tensors in the tensor list. + */ + size() { + return this.tensors.length; + } + /** + * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1) + * tf.Tensor. + * @param elementShape shape of each tensor + * @param elementDtype data type of each tensor + * @param numElements the number of elements to stack + */ + stack(elementShape, elementDtype, numElements = -1) { + if (elementDtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); + } + if (numElements !== -1 && this.tensors.length !== numElements) { + throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`); + } + assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, "TensorList shape mismatch: "); + const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + return tidy(() => { + const reshapedTensors = this.tensors.map((tensor2) => reshape(tensor2, outputElementShape)); + return stack(reshapedTensors, 0); + }); + } + /** + * Pop a tensor from the end of the list. + * @param elementShape shape of the tensor + * @param elementDtype data type of the tensor + */ + popBack(elementShape, elementDtype) { + if (elementDtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); + } + if (this.size() === 0) { + throw new Error("Trying to pop from an empty list."); + } + const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + const tensor2 = this.tensors.pop(); + tensor2.kept = false; + assertShapesMatchAllowUndefinedSize(tensor2.shape, elementShape, "TensorList shape mismatch: "); + return reshape(tensor2, outputElementShape); + } + /** + * Push a tensor to the end of the list. + * @param tensor Tensor to be pushed. + */ + pushBack(tensor2) { + if (tensor2.dtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`); + } + assertShapesMatchAllowUndefinedSize(tensor2.shape, this.elementShape, "TensorList shape mismatch: "); + if (this.maxNumElements === this.size()) { + throw new Error(`Trying to push element into a full list.`); + } + keep(tensor2); + this.tensors.push(tensor2); + } + /** + * Update the size of the list. + * @param size the new size of the list. + */ + resize(size) { + if (size < 0) { + throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`); + } + if (this.maxNumElements !== -1 && size > this.maxNumElements) { + throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`); + } + const destTensorList = new _TensorList([], this.elementShape, this.elementDtype, this.maxNumElements); + destTensorList.tensors.length = size; + for (let i = 0; i < Math.min(this.tensors.length, size); ++i) { + destTensorList.tensors[i] = this.tensors[i]; + } + return destTensorList; + } + /** + * Retrieve the element at the provided index + * @param elementShape shape of the tensor + * @param elementDtype dtype of the tensor + * @param elementIndex index of the tensor + */ + getItem(elementIndex, elementShape, elementDtype) { + if (elementDtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); + } + if (elementIndex < 0 || elementIndex > this.tensors.length) { + throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`); + } + if (this.tensors[elementIndex] == null) { + throw new Error(`element at index ${elementIndex} is null.`); + } + assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, "TensorList shape mismatch: "); + const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + return reshape(this.tensors[elementIndex], outputElementShape); + } + /** + * Set the tensor at the index + * @param elementIndex index of the tensor + * @param tensor the tensor to be inserted into the list + */ + setItem(elementIndex, tensor2) { + if (tensor2.dtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`); + } + if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) { + throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`); + } + assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, "TensorList shape mismatch: "); + keep(tensor2); + if (this.tensors[elementIndex] != null) { + this.tensors[elementIndex].kept = false; + } + this.tensors[elementIndex] = tensor2; + } + /** + * Return selected values in the TensorList as a stacked Tensor. All of + * selected values must have been written and their shapes must all match. + * @param indices indices of tensors to gather + * @param elementDtype output tensor dtype + * @param elementShape output tensor element shape + */ + gather(indices, elementDtype, elementShape) { + if (elementDtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); + } + assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: "); + indices = indices.slice(0, this.size()); + const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + if (indices.length === 0) { + return tensor([], [0].concat(outputElementShape)); + } + return tidy(() => { + const tensors = indices.map((i) => reshape(this.tensors[i], outputElementShape)); + return stack(tensors, 0); + }); + } + /** + * Return the values in the TensorList as a concatenated Tensor. + * @param elementDtype output tensor dtype + * @param elementShape output tensor element shape + */ + concat(elementDtype, elementShape) { + if (!!elementDtype && elementDtype !== this.elementDtype) { + throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`); + } + assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: "); + const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + if (this.size() === 0) { + return tensor([], [0].concat(outputElementShape)); + } + return tidy(() => { + const tensors = this.tensors.map((t) => reshape(t, outputElementShape)); + return concat(tensors, 0); + }); + } +}; +function fromTensor(tensor2, elementShape, elementDtype) { + const dtype = tensor2.dtype; + if (tensor2.shape.length < 1) { + throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`); + } + if (tensor2.dtype !== elementDtype) { + throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`); + } + const tensorElementShape = tensor2.shape.slice(1); + assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, "TensorList shape mismatch: "); + const tensorList = unstack(tensor2); + return new TensorList(tensorList, elementShape, dtype); +} +function reserve(elementShape, elementDtype, numElements, maxNumElements) { + return new TensorList([], elementShape, elementDtype, maxNumElements); +} +function scatter(tensor2, indices, elementShape, numElements) { + if (indices.length !== tensor2.shape[0]) { + throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`); + } + const maxIndex = Math.max(...indices); + if (numElements != null && numElements !== -1 && maxIndex >= numElements) { + throw new Error(`Max index must be < array size (${maxIndex} vs. ${numElements})`); + } + const list = new TensorList([], elementShape, tensor2.dtype, numElements); + const tensors = unstack(tensor2, 0); + indices.forEach((value, index) => { + list.setItem(value, tensors[index]); + }); + return list; +} +function split2(tensor2, length, elementShape) { + let totalLength = 0; + const cumulativeLengths = length.map((len) => { + totalLength += len; + return totalLength; + }); + if (totalLength !== tensor2.shape[0]) { + throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${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 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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=pt(t),r=n.length,s=a.length;w.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 ij(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 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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`)}},uj=(e,t,n,a=ln)=>{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`)}},pj=(e,t,n,a=ln)=>{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`)}},cj=(e,t,n,a=ln)=>{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`)}},dj=(e,t,n,a=ln)=>{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`)}},hj=(e,t,n,a=ln)=>{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`)}},mj=(e,t,n,a=ln)=>{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=w.arraysEqual(u.shape,i);if(!p&&!w.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`)}},fj=(e,t,n,a=ln)=>{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`)}},gj=(e,t,n,a=ln)=>{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`)}},bj=(e,t,n,a=ln)=>{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`)}},yj=(e,t,n,a=ln)=>{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 MI(e,t,n,a,r=P){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>jq(i,o,l));case"basic_math":return r(()=>Kq(i,o,l));case"control":return ej(i,o,l);case"convolution":return r(()=>tj(i,o,l));case"creation":return r(()=>nj(i,o,l));case"dynamic":return aj(i,o,l);case"evaluation":return r(()=>rj(i,o,l));case"image":return r(()=>lj(i,o,l));case"graph":return r(()=>sj(i,o,l));case"logical":return r(()=>uj(i,o,l));case"matrices":return r(()=>pj(i,o,l));case"normalization":return r(()=>cj(i,o,l));case"ragged":return r(()=>dj(i,o,l));case"reduction":return r(()=>hj(i,o,l));case"slice_join":return r(()=>mj(i,o,l));case"sparse":return r(()=>fj(i,o,l));case"spectral":return r(()=>gj(i,o,l));case"string":return r(()=>bj(i,o,l));case"transformation":return r(()=>yj(i,o,l));case"hash_table":return oj(i,o,l,a);case"custom":let u=mC(i.op);if(u&&u.customExecutor)return u.customExecutor(new qq(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 w.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var PI=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 OI(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((Js(c)||Tj(c)||Cj(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 xj(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=vj(m,l);return wj(f,l),f}function vj(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 Th=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function wj(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 Th(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Th(`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 Th(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Th(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function kj(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>Js(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;oe[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new ev(e.functions[n],this)})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPARATOR)+"--"+a.join(this.SEPARATOR)}compile(e,t){let n=OI(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${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: [${a}]`)}let i=xj(this.graph,n),o=kj(i);return{orderedNodes:i,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return qt(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,n])=>[t,this.cloneTensorList(n)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[Yn(d)[0]]),r=t.map(d=>Yn(d)[0]),s=new Set(r),i=r.map(d=>this.graph.nodes[d]);i.length===0&&(i=this._outputs);let o=this.getCompilationKey(a,i),l=this.compiledMap.get(o);l==null&&(l=this.compile(e,i),this.compiledMap.set(o,l));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let u={},p={};return P(()=>{let d=new PI(this.weightMap,u,p,this.functionExecutorMap,this.parseNodeNameCache),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(g=>{let[b,y]=Yn(g,d),x=[];x[y]=e[g],c[b]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(x))});let h=this.getFrozenTensorIds(c),{orderedNodes:m,nodeLiveUntilMap:f}=l;for(let g of m){if(c[g.name])continue;let b=MI(g,c,d,this._resourceManager);if(w.isPromise(b))throw new Error(`The execution of the op '${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(!(Js(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(Js(o))continue;let l=AI(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 Js(o)||r.has(o.name)}if(!(Js(e)||s==null))for(let o of s){if(i(o))continue;let l=AI(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 PI(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}=OI(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[v,I]=Yn(x),T=[];T[I]=e[x],m[v]=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=>!Js(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 [${r}]. 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u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Tr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!cn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!cn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Yn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.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 '${t}' is not found in the graph`)})}},_j=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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a=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ev(FI.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=FI.Instance.transformGraph(e.modelInitializer);this.initializer=new ev(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 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 ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let 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on{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}},Zj=class extends on{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},Jj=class extends on{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${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}}},Qj=class extends on{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},LI=class extends on{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${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 on{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}}},e5=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}},VC=class extends on{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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zC(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ts.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ts.SHORTEST:return{value:null,done:!0};case ts.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},UC=class extends on{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new WC(t)}summary(){return`${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()}},n5=class extends UC{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Pj.alea(n||w.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}}},ip=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is - ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Xn(async()=>(await n.iterator()).columnMajorBatch(e,t,s5),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Xn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Xn(async()=>(await t.iterator()).filter(a=>P(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Xn(async()=>(await t.iterator()).map(n=>P(()=>e(n))),this.size)}mapAsync(e){let t=this;return Xn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Xn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Xn(async()=>{let a=T1(async()=>({value:await t.iterator(),done:!1}));return Uj(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=Mj.alea(t||w.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()}};ip.MAX_BUFFER_SIZE=1e4;function Xn(e,t=null){return new class extends ip{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function a5(e){return Xn(async()=>BC(e),e.length)}function r5(e){if(!Hl(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 zC(e,a=>{if(a instanceof ip)return{value:a.iterator(),recurse:!1};if(Hl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Gj(n,ts.SHORTEST)},t)}function s5(e){if(e===null)return null;let t=e[0];return zj(t)?{value:i5(e),recurse:!1}:{value:null,recurse:!0}}function i5(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 GC=class extends ip{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))}},Ch='"',Yp=Symbol("out"),zI=Symbol("field"),_h=Symbol("quote"),fx=Symbol("quoteafterquote"),WI=Symbol("quoteinquote"),HC=class extends ip{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&&w.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(w.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 GC(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.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 qC(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(w.sizeFromShape(t));return n.set(e,n.length-e.length),bn(n,t)}},jC=class extends on{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 jC(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.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=jo.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=nn(se(e,"float32"),0),n;n=Qn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},KC=class{},XC=class extends on{split(e){return new o5(this,e)}},o5=class extends 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t("utf8")}}summary(){return`${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}},YC=class extends u5{constructor(e,t={}){super(),this.file=e,this.options=t,w.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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof 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p=N.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,T=p.effectiveFilterWidth,C=v-1-p.padInfo.front,E=T-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Le(s.shape,"float32"),$=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(ie)!==ie))for(let ye=0;ye=p.outWidth||Math.floor(ue)!==ue)continue;let be=S.get(M,te,ie,ue,B);ee+=be}}}D.set(ee*$,M,U,H,j,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var v8={kernelName:$c,backendName:"cpu",kernelFunc:x8};function w8(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,v=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 k8={kernelName:Fc,backendName:"cpu",kernelFunc:w8};function I8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.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 T8={kernelName:nu,backendName:"cpu",kernelFunc:N8};function C8(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 _8={kernelName:au,backendName:"cpu",kernelFunc:C8};function E8(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 A8={kernelName:Dc,backendName:"cpu",kernelFunc:E8},F8=it(xs,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.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(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>w.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(v=>yi({inputs:{input:v},backend:n})),g=l.map(v=>jl({inputs:{input:v},backend:n})),b=Kl({inputs:f,backend:n,attrs:{axis:s}}),y=Kl({inputs:g,backend:n,attrs:{axis:s}}),x=Zn({inputs:{real:b,imag:y},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,w.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 P8={kernelName:su,backendName:"cpu",kernelFunc:Kl};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",v=new Vt(c.outShape,r.dtype),I=w.computeStrides(r.shape),T=w.computeStrides(s.shape),C=I[0],E=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,j=v.values;for(let K=0;K=c.inHeight)continue;let ye=re*T[0],ue=Z+ie*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=$+j*E[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],ye=Z+re*E[2];for(let ue=0;ue=u.inWidth)continue;let Ge=ie+Se*F[2],ht=ye+We*u.inChannels,st=Ge;for(let tt=0;ttMath.cos(e)),X8={kernelName:Wi,backendName:"cpu",kernelFunc:K8},Y8=it(Bi,e=>Math.cosh(e)),Z8={kernelName:Bi,backendName:"cpu",kernelFunc:Y8};function J8(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Le([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),T=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-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,v=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=$+j*p[1];for(let J=0;J=h.inWidth)continue;let ie=K+te*d[1],ye=Z+re*h.inChannels,ue=ee,be=ie;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:T,filterWidth:C,dilationHeight:E,dilationWidth:F,outShape:D}=N.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(D),S=D.length,M=w.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&ieJ&&(J=be)}}}let ee=w.locToIndex([B,U,j,Z],S,w.computeStrides(D));M[ee]=J}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},gX={kernelName:Fl,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=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.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:v,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Fl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=ie,Z=ee,J=te)}}}$[Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},bX={kernelName:Al,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=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.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:v,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Al}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=ie,Z=ae,J=re)}}}$[S][Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function yX(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{canvas:s,options:i}=a,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let d=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(d==null)throw new Error(`Could not get the context with ${p} type.`);let[c,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=n.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*c*4);for(let x=0;x1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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l=o?r:Q_({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],d=n.data.get(l.dataId).values,c=[u,s],h=w.makeZerosTypedArray(w.sizeFromShape(c),"int32");for(let m=0;m=0&&d[c]{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=dm({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=Kl({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var r7={kernelName:Du,backendName:"cpu",kernelFunc:tE};function s7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;ge(r,"pad");let o=s.map((b,y)=>b[0]+r.shape[y]+b[1]),l=s.map(b=>b[0]),u=n.data.get(r.dataId).values,p=w.sizeFromShape(r.shape),d=r.shape.length,c=w.computeStrides(r.shape),h=w.sizeFromShape(o),m=o.length,f=w.computeStrides(o),g=w.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let 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+ const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape); + const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength; + const tensors = tidy(() => { + const tensors2 = []; + tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]); + for (let i = 0; i < length.length; ++i) { + const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1]; + const indices = [0, previousLength, 0]; + const sizes = [1, length[i], elementPerRow]; + tensors2[i] = reshape(slice(tensor2, indices, sizes), outputElementShape); + } + tensor2.dispose(); + return tensors2; + }); + const list = new TensorList([], elementShape, tensor2.dtype, length.length); + for (let i = 0; i < tensors.length; i++) { + list.setItem(i, tensors[i]); + } + return list; +} +var executeOp3 = async (node, tensorMap, context) => { + switch (node.op) { + case "If": + case "StatelessIf": { + const thenFunc = getParamValue("thenBranch", node, tensorMap, context); + const elseFunc = getParamValue("elseBranch", node, tensorMap, context); + const cond = getParamValue("cond", node, tensorMap, context); + const args = getParamValue("args", node, tensorMap, context); + const condValue = await cond.data(); + if (condValue[0]) { + return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap); + } else { + return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap); + } + } + case "While": + case "StatelessWhile": { + const bodyFunc = getParamValue("body", node, tensorMap, context); + const condFunc = getParamValue("cond", node, tensorMap, context); + const args = getParamValue("args", node, tensorMap, context); + const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap); + const argIds = args.map((tensor2) => tensor2.id); + let condValue = await condResult[0].data(); + condResult.forEach((tensor2) => { + if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1) { + tensor2.dispose(); + } + }); + let result = args; + while (condValue[0]) { + const origResult = result; + result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap); + const resultIds = result.map((tensor2) => tensor2.id); + origResult.forEach((tensor2) => { + if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) { + tensor2.dispose(); + } + }); + const condResult2 = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap); + condValue = await condResult2[0].data(); + condResult2.forEach((tensor2) => { + if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) { + tensor2.dispose(); + } + }); + } + return result; + } + case "LoopCond": { + const pred = getParamValue("pred", node, tensorMap, context); + return [cloneTensor(pred)]; + } + case "Switch": { + const pred = getParamValue("pred", node, tensorMap, context); + let data = getParamValue("data", node, tensorMap, context); + if (!data.kept) { + data = cloneTensor(data); + } + return (await pred.data())[0] ? [void 0, data] : [data, void 0]; + } + case "Merge": { + const inputName = node.inputNames.find((name) => getTensor(name, tensorMap, context) !== void 0); + if (inputName) { + const data = getTensor(inputName, tensorMap, context); + return [cloneTensor(data)]; + } + return void 0; + } + case "Enter": { + const frameId = getParamValue("frameName", node, tensorMap, context); + const data = getParamValue("tensor", node, tensorMap, context); + context.enterFrame(frameId); + return [cloneTensor(data)]; + } + case "Exit": { + const data = getParamValue("tensor", node, tensorMap, context); + context.exitFrame(); + return [cloneTensor(data)]; + } + case "NextIteration": { + const data = getParamValue("tensor", node, tensorMap, context); + context.nextIteration(); + return [cloneTensor(data)]; + } + case "TensorArrayV3": { + const size = getParamValue("size", node, tensorMap, context); + const dtype = getParamValue("dtype", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const dynamicSize = getParamValue("dynamicSize", node, tensorMap, context); + const clearAfterRead = getParamValue("clearAfterRead", node, tensorMap, context); + const identicalElementShapes = getParamValue("identicalElementShapes", node, tensorMap, context); + const name = getParamValue("name", node, tensorMap, context); + const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead); + context.addTensorArray(tensorArray); + return [tensorArray.idTensor, scalar(1)]; + } + case "TensorArrayWriteV3": { + const id = getParamValue("tensorArrayId", node, tensorMap, context); + const index = getParamValue("index", node, tensorMap, context); + const writeTensor = getParamValue("tensor", node, tensorMap, context); + const writeTensorArray = context.getTensorArray(id.id); + writeTensorArray.write(index, writeTensor); + return [writeTensorArray.idTensor]; + } + case "TensorArrayReadV3": { + const readId = getParamValue("tensorArrayId", node, tensorMap, context); + const readIndex = getParamValue("index", node, tensorMap, context); + const readTensorArray = context.getTensorArray(readId.id); + return [readTensorArray.read(readIndex)]; + } + case "TensorArrayGatherV3": { + const gatherId = getParamValue("tensorArrayId", node, tensorMap, context); + const gatherIndices = getParamValue("indices", node, tensorMap, context); + const gatherDtype = getParamValue("dtype", node, tensorMap, context); + const gatherTensorArray = context.getTensorArray(gatherId.id); + return [gatherTensorArray.gather(gatherIndices, gatherDtype)]; + } + case "TensorArrayScatterV3": { + const scatterId = getParamValue("tensorArrayId", node, tensorMap, context); + const scatterIndices = getParamValue("indices", node, tensorMap, context); + const scatterTensor = getParamValue("tensor", node, tensorMap, context); + const scatterTensorArray = context.getTensorArray(scatterId.id); + scatterTensorArray.scatter(scatterIndices, scatterTensor); + return [scatterTensorArray.idTensor]; + } + case "TensorArrayConcatV3": { + const concatId = getParamValue("tensorArrayId", node, tensorMap, context); + const concatTensorArray = context.getTensorArray(concatId.id); + const concatDtype = getParamValue("dtype", node, tensorMap, context); + return [concatTensorArray.concat(concatDtype)]; + } + case "TensorArraySplitV3": { + const splitId = getParamValue("tensorArrayId", node, tensorMap, context); + const splitTensor = getParamValue("tensor", node, tensorMap, context); + const lengths = getParamValue("lengths", node, tensorMap, context); + const splitTensorArray = context.getTensorArray(splitId.id); + splitTensorArray.split(lengths, splitTensor); + return [splitTensorArray.idTensor]; + } + case "TensorArraySizeV3": { + const sizeId = getParamValue("tensorArrayId", node, tensorMap, context); + const sizeTensorArray = context.getTensorArray(sizeId.id); + return [scalar(sizeTensorArray.size(), "int32")]; + } + case "TensorArrayCloseV3": { + const closeId = getParamValue("tensorArrayId", node, tensorMap, context); + const closeTensorArray = context.getTensorArray(closeId.id); + closeTensorArray.clearAndClose(); + return [closeTensorArray.idTensor]; + } + case "TensorListSetItem": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const index = getParamValue("index", node, tensorMap, context); + const writeTensor = getParamValue("tensor", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + tensorList.setItem(index, writeTensor); + return [tensorList.idTensor]; + } + case "TensorListGetItem": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const readIndex = getParamValue("index", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDType = getParamValue("elementDType", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + return [tensorList.getItem(readIndex, elementShape, elementDType)]; + } + case "TensorListScatterV2": + case "TensorListScatter": { + const scatterIndices = getParamValue("indices", node, tensorMap, context); + const scatterTensor = getParamValue("tensor", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const numElements = getParamValue("numElements", node, tensorMap, context); + const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements); + context.addTensorList(tensorList); + return [tensorList.idTensor]; + } + case "TensorListReserve": + case "EmptyTensorList": { + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDtype = getParamValue("elementDType", node, tensorMap, context); + let numElementsParam; + if (node.op === "TensorListReserve") { + numElementsParam = "numElements"; + } else { + numElementsParam = "maxNumElements"; + } + const numElements = getParamValue(numElementsParam, node, tensorMap, context); + const maxNumElements = node.op === "TensorListReserve" ? -1 : numElements; + const tensorList = reserve(elementShape, elementDtype, numElements, maxNumElements); + context.addTensorList(tensorList); + return [tensorList.idTensor]; + } + case "TensorListGather": { + const gatherId = getParamValue("tensorListId", node, tensorMap, context); + const gatherIndices = getParamValue("indices", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDtype = getParamValue("elementDType", node, tensorMap, context); + const tensorList = context.getTensorList(gatherId.id); + return [tensorList.gather(gatherIndices, elementDtype, elementShape)]; + } + case "TensorListStack": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDtype = getParamValue("elementDType", node, tensorMap, context); + const numElements = getParamValue("numElements", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + return [tensorList.stack(elementShape, elementDtype, numElements)]; + } + case "TensorListFromTensor": { + const tensor2 = getParamValue("tensor", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDtype = getParamValue("elementDType", node, tensorMap, context); + const tensorList = fromTensor(tensor2, elementShape, elementDtype); + context.addTensorList(tensorList); + return [tensorList.idTensor]; + } + case "TensorListConcat": + case "TensorListConcatV2": { + const concatId = getParamValue("tensorListId", node, tensorMap, context); + const tensorList = context.getTensorList(concatId.id); + const concatDtype = getParamValue("dtype", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + return [tensorList.concat(concatDtype, elementShape)]; + } + case "TensorListPushBack": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const writeTensor = getParamValue("tensor", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + tensorList.pushBack(writeTensor); + return [tensorList.idTensor]; + } + case "TensorListPopBack": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDType = getParamValue("elementDType", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + return [tensorList.popBack(elementShape, elementDType)]; + } + case "TensorListSplit": { + const splitTensor = getParamValue("tensor", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const lengths = getParamValue("lengths", node, tensorMap, context); + const tensorList = split2(splitTensor, lengths, elementShape); + context.addTensorList(tensorList); + return [tensorList.idTensor]; + } + case "TensorListLength": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + return [scalar(tensorList.size(), "int32")]; + } + case "TensorListResize": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const size = getParamValue("size", node, tensorMap, context); + const srcTensorList = context.getTensorList(idTensor.id); + const destTensorList = srcTensorList.resize(size); + context.addTensorList(destTensorList); + return [destTensorList.idTensor]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +function fusedConvAndDepthWiseParams(node, tensorMap, context) { + const [extraOp, activationFunc] = getParamValue("fusedOps", node, tensorMap, context); + const isBiasAdd = extraOp === "biasadd"; + const noBiasAdd = !isBiasAdd; + const isPrelu = activationFunc === "prelu"; + const isBatchNorm = extraOp === "fusedbatchnorm"; + const numArgs = getParamValue("numArgs", node, tensorMap, context); + if (isBiasAdd) { + if (isPrelu && numArgs !== 2) { + throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha."); + } + if (!isPrelu && isBiasAdd && numArgs !== 1) { + throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias."); + } + } + if (isBatchNorm) { + throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported"); + } + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getPadding(node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + const dilations = getParamValue("dilations", node, tensorMap, context); + let [biasArg, preluArg] = getParamValue("args", node, tensorMap, context); + if (noBiasAdd) { + preluArg = biasArg; + biasArg = void 0; + } + const leakyreluAlpha = getParamValue("leakyreluAlpha", node, tensorMap, context); + return { + stride, + pad: pad3, + dataFormat, + dilations, + biasArg, + preluArg, + activationFunc, + leakyreluAlpha + }; +} +var executeOp4 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Conv1D": { + const stride = getParamValue("stride", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + const dilation = getParamValue("dilation", node, tensorMap, context); + return [ops.conv1d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), stride, pad3, dataFormat, dilation)]; + } + case "Conv2D": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getPadding(node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + const dilations = getParamValue("dilations", node, tensorMap, context); + return [ops.conv2d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])]; + } + case "_FusedConv2D": { + const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context); + return [ops.fused.conv2d({ + x: getParamValue("x", node, tensorMap, context), + filter: getParamValue("filter", node, tensorMap, context), + strides: [stride[1], stride[2]], + pad: pad3, + dataFormat, + dilations: [dilations[1], dilations[2]], + bias: biasArg, + activation: activationFunc, + preluActivationWeights: preluArg, + leakyreluAlpha + })]; + } + case "FusedDepthwiseConv2dNative": { + const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context); + return [ops.fused.depthwiseConv2d({ + x: getParamValue("x", node, tensorMap, context), + filter: getParamValue("filter", node, tensorMap, context), + strides: [stride[1], stride[2]], + pad: pad3, + dataFormat, + dilations: [dilations[1], dilations[2]], + bias: biasArg, + activation: activationFunc, + preluActivationWeights: preluArg, + leakyreluAlpha + })]; + } + case "Conv2DBackpropInput": + case "Conv2dTranspose": { + const shape = getParamValue("outputShape", node, tensorMap, context); + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getPadding(node, tensorMap, context); + return [ops.conv2dTranspose(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), shape, [stride[1], stride[2]], pad3)]; + } + case "DepthwiseConv2dNative": + case "DepthwiseConv2d": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getPadding(node, tensorMap, context); + const dilations = getParamValue("dilations", node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + return [ops.depthwiseConv2d(getParamValue("input", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])]; + } + case "Conv3D": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + const dilations = getParamValue("dilations", node, tensorMap, context); + return [ops.conv3d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2], stride[3]], pad3, dataFormat, [dilations[1], dilations[2], dilations[3]])]; + } + case "AvgPool": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const kernelSize = getParamValue("kernelSize", node, tensorMap, context); + return [ops.avgPool(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)]; + } + case "MaxPool": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const kernelSize = getParamValue("kernelSize", node, tensorMap, context); + return [ops.maxPool(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)]; + } + case "MaxPoolWithArgmax": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const kernelSize = getParamValue("kernelSize", node, tensorMap, context); + const includeBatchInIndex = getParamValue("includeBatchInIndex", node, tensorMap, context); + const { result, indexes } = ops.maxPoolWithArgmax(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3, includeBatchInIndex); + return [result, indexes]; + } + case "AvgPool3D": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const kernelSize = getParamValue("kernelSize", node, tensorMap, context); + return [ops.avgPool3d(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)]; + } + case "MaxPool3D": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const kernelSize = getParamValue("kernelSize", node, tensorMap, context); + return [ops.maxPool3d(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)]; + } + case "Dilation2D": { + const strides = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const dilations = getParamValue("dilations", node, tensorMap, context); + const strideHeight = strides[1]; + const strideWidth = strides[2]; + const dilationHeight = dilations[1]; + const dilationWidth = dilations[2]; + return [ops.dilation2d( + getParamValue("x", node, tensorMap, context), + getParamValue("filter", node, tensorMap, context), + [strideHeight, strideWidth], + pad3, + [dilationHeight, dilationWidth], + "NHWC" + /* dataFormat */ + )]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp5 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Fill": { + const shape = getParamValue("shape", node, tensorMap, context); + const dtype = getParamValue("dtype", node, tensorMap, context); + const value = getParamValue("value", node, tensorMap, context); + return [ops.fill(shape, value, dtype)]; + } + case "LinSpace": { + const start = getParamValue("start", node, tensorMap, context); + const stop = getParamValue("stop", node, tensorMap, context); + const num = getParamValue("num", node, tensorMap, context); + return [ops.linspace(start, stop, num)]; + } + case "Multinomial": { + const logits = getParamValue("logits", node, tensorMap, context); + const numSamples = getParamValue("numSamples", node, tensorMap, context); + const seed = getParamValue("seed", node, tensorMap, context); + return [ops.multinomial(logits, numSamples, seed)]; + } + case "OneHot": { + const indices = getParamValue("indices", node, tensorMap, context); + const depth = getParamValue("depth", node, tensorMap, context); + const onValue = getParamValue("onValue", node, tensorMap, context); + const offValue = getParamValue("offValue", node, tensorMap, context); + const dtype = getParamValue("dtype", node, tensorMap, context); + return [ops.oneHot(indices, depth, onValue, offValue, dtype)]; + } + case "Ones": { + return [ops.ones(getParamValue("shape", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))]; + } + case "OnesLike": { + return [ops.onesLike(getParamValue("x", node, tensorMap, context))]; + } + case "RandomStandardNormal": { + return [ops.randomStandardNormal(getParamValue("shape", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context), getParamValue("seed", node, tensorMap, context))]; + } + case "RandomUniform": { + return [ops.randomUniform( + // tslint:disable-next-line:no-any + getParamValue("shape", node, tensorMap, context), + getParamValue("minval", node, tensorMap, context), + getParamValue("maxval", node, tensorMap, context), + getParamValue("dtype", node, tensorMap, context) + )]; + } + case "RandomUniformInt": { + return [ops.randomUniformInt(getParamValue("shape", node, tensorMap, context), getParamValue("minval", node, tensorMap, context), getParamValue("maxval", node, tensorMap, context), getParamValue("seed", node, tensorMap, context))]; + } + case "Range": { + const start = getParamValue("start", node, tensorMap, context); + const stop = getParamValue("stop", node, tensorMap, context); + const step5 = getParamValue("step", node, tensorMap, context); + return [ops.range(start, stop, step5, getParamValue("dtype", node, tensorMap, context))]; + } + case "TruncatedNormal": { + const shape = getParamValue("shape", node, tensorMap, context); + const mean4 = getParamValue("mean", node, tensorMap, context); + const stdDev = getParamValue("stdDev", node, tensorMap, context); + const seed = getParamValue("seed", node, tensorMap, context); + return [ops.truncatedNormal(shape, mean4, stdDev, getParamValue("dtype", node, tensorMap, context), seed)]; + } + case "Zeros": { + return [ops.zeros(getParamValue("shape", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))]; + } + case "ZerosLike": { + return [ops.zerosLike(getParamValue("x", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +function nmsParams(node, tensorMap, context) { + const boxes = getParamValue("boxes", node, tensorMap, context); + const scores = getParamValue("scores", node, tensorMap, context); + const maxOutputSize = getParamValue("maxOutputSize", node, tensorMap, context); + const iouThreshold = getParamValue("iouThreshold", node, tensorMap, context); + const scoreThreshold = getParamValue("scoreThreshold", node, tensorMap, context); + const softNmsSigma = getParamValue("softNmsSigma", node, tensorMap, context); + return { + boxes, + scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + softNmsSigma + }; +} +var executeOp6 = async (node, tensorMap, context, resourceManager, ops = ops_for_converter_exports) => { + switch (node.op) { + case "NonMaxSuppressionV5": { + const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context); + const result = await ops.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + return [result.selectedIndices, result.selectedScores]; + } + case "NonMaxSuppressionV4": { + const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context); + const padToMaxOutputSize = getParamValue("padToMaxOutputSize", node, tensorMap, context); + const result = await ops.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize); + return [result.selectedIndices, result.validOutputs]; + } + case "NonMaxSuppressionV3": + case "NonMaxSuppressionV2": { + const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context); + return [await ops.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)]; + } + case "Where": { + const condition = ops.cast(getParamValue("condition", node, tensorMap, context), "bool"); + const result = [await ops.whereAsync(condition)]; + condition.dispose(); + return result; + } + case "ListDiff": { + return ops.setdiff1dAsync(getParamValue("x", node, tensorMap, context), getParamValue("y", node, tensorMap, context)); + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp7 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "LowerBound": { + const sortedSequence = getParamValue("sortedSequence", node, tensorMap, context); + const values = getParamValue("values", node, tensorMap, context); + return [ops.lowerBound(sortedSequence, values)]; + } + case "TopKV2": { + const x = getParamValue("x", node, tensorMap, context); + const k = getParamValue("k", node, tensorMap, context); + const sorted = getParamValue("sorted", node, tensorMap, context); + const result = ops.topk(x, k, sorted); + return [result.values, result.indices]; + } + case "UpperBound": { + const sortedSequence = getParamValue("sortedSequence", node, tensorMap, context); + const values = getParamValue("values", node, tensorMap, context); + return [ops.upperBound(sortedSequence, values)]; + } + case "Unique": { + const x = getParamValue("x", node, tensorMap, context); + const result = ops.unique(x); + return [result.values, result.indices]; + } + case "UniqueV2": { + const x = getParamValue("x", node, tensorMap, context); + const axis = getParamValue("axis", node, tensorMap, context); + const result = ops.unique(x, axis); + return [result.values, result.indices]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp8 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Const": { + return tensorMap[node.name]; + } + case "PlaceholderWithDefault": + const def = getParamValue("default", node, tensorMap, context); + return [getTensor(node.name, tensorMap, context) || def]; + case "Placeholder": + return [getTensor(node.name, tensorMap, context)]; + case "Identity": + case "StopGradient": + case "FakeQuantWithMinMaxVars": { + const data2 = getParamValue("x", node, tensorMap, context); + return [cloneTensor(data2)]; + } + case "IdentityN": + return getParamValue("x", node, tensorMap, context).map((t) => cloneTensor(t)); + case "Snapshot": + const snapshot = getParamValue("x", node, tensorMap, context); + return [cloneTensor(snapshot)]; + case "Shape": + return [ops.tensor1d(getParamValue("x", node, tensorMap, context).shape, "int32")]; + case "ShapeN": + return getParamValue("x", node, tensorMap, context).map((t) => ops.tensor1d(t.shape)); + case "Size": + return [ops.scalar(getParamValue("x", node, tensorMap, context).size, "int32")]; + case "Rank": + return [ops.scalar(getParamValue("x", node, tensorMap, context).rank, "int32")]; + case "NoOp": + return [ops.scalar(1)]; + case "Print": + const input2 = getParamValue("x", node, tensorMap, context); + const data = getParamValue("data", node, tensorMap, context); + const message = getParamValue("message", node, tensorMap, context); + const summarize = getParamValue("summarize", node, tensorMap, context); + console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."); + console.log(message); + for (let i = 0; i < data.length; i++) { + console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0, summarize)); + } + return [input2]; + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var HashTable = class { + get id() { + return this.handle.id; + } + /** + * Constructor of HashTable. Creates a hash table. + * + * @param keyDType `dtype` of the table keys. + * @param valueDType `dtype` of the table values. + */ + constructor(keyDType, valueDType) { + this.keyDType = keyDType; + this.valueDType = valueDType; + this.handle = scalar(0); + this.tensorMap = /* @__PURE__ */ new Map(); + keep(this.handle); + } + /** + * Dispose the tensors and handle and clear the hashtable. + */ + clearAndClose() { + this.tensorMap.forEach((value) => value.dispose()); + this.tensorMap.clear(); + this.handle.dispose(); + } + /** + * The number of items in the hash table. + */ + size() { + return this.tensorMap.size; + } + /** + * The number of items in the hash table as a rank-0 tensor. + */ + tensorSize() { + return scalar(this.size(), "int32"); + } + /** + * Replaces the contents of the table with the specified keys and values. + * @param keys Keys to store in the hashtable. + * @param values Values to store in the hashtable. + */ + async import(keys, values) { + this.checkKeyAndValueTensor(keys, values); + const $keys = await keys.data(); + this.tensorMap.forEach((value) => value.dispose()); + this.tensorMap.clear(); + return tidy(() => { + const $values = unstack(values); + const keysLength = $keys.length; + const valuesLength = $values.length; + util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`); + for (let i = 0; i < keysLength; i++) { + const key = $keys[i]; + const value = $values[i]; + keep(value); + this.tensorMap.set(key, value); + } + return this.handle; + }); + } + /** + * Looks up keys in a hash table, outputs the corresponding values. + * + * Performs batch lookups, for every element in the key tensor, `find` + * stacks the corresponding value into the return tensor. + * + * If an element is not present in the table, the given `defaultValue` is + * used. + * + * @param keys Keys to look up. Must have the same type as the keys of the + * table. + * @param defaultValue The scalar `defaultValue` is the value output for keys + * not present in the table. It must also be of the same type as the + * table values. + */ + async find(keys, defaultValue) { + this.checkKeyAndValueTensor(keys, defaultValue); + const $keys = await keys.data(); + return tidy(() => { + const result = []; + for (let i = 0; i < $keys.length; i++) { + const key = $keys[i]; + const value = this.findWithDefault(key, defaultValue); + result.push(value); + } + return stack(result); + }); + } + // tslint:disable-next-line: no-any + findWithDefault(key, defaultValue) { + const result = this.tensorMap.get(key); + return result != null ? result : defaultValue; + } + checkKeyAndValueTensor(key, value) { + if (key.dtype !== this.keyDType) { + throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`); + } + if (value.dtype !== this.valueDType) { + throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`); + } + } +}; +var executeOp9 = async (node, tensorMap, context, resourceManager) => { + switch (node.op) { + case "HashTable": + case "HashTableV2": { + const existingTableHandle = resourceManager.getHashTableHandleByName(node.name); + if (existingTableHandle != null) { + return [existingTableHandle]; + } else { + const keyDType = getParamValue("keyDType", node, tensorMap, context); + const valueDType = getParamValue("valueDType", node, tensorMap, context); + const hashTable = new HashTable(keyDType, valueDType); + resourceManager.addHashTable(node.name, hashTable); + return [hashTable.handle]; + } + } + case "InitializeTable": + case "InitializeTableV2": + case "LookupTableImport": + case "LookupTableImportV2": { + const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager); + const keys = getParamValue("keys", node, tensorMap, context); + const values = getParamValue("values", node, tensorMap, context); + const hashTable = resourceManager.getHashTableById(handle.id); + return [await hashTable.import(keys, values)]; + } + case "LookupTableFind": + case "LookupTableFindV2": { + const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager); + const keys = getParamValue("keys", node, tensorMap, context); + const defaultValue = getParamValue("defaultValue", node, tensorMap, context); + const hashTable = resourceManager.getHashTableById(handle.id); + return [await hashTable.find(keys, defaultValue)]; + } + case "LookupTableSize": + case "LookupTableSizeV2": { + const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager); + const hashTable = resourceManager.getHashTableById(handle.id); + return [hashTable.tensorSize()]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp10 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "ResizeBilinear": { + const images = getParamValue("images", node, tensorMap, context); + const size = getParamValue("size", node, tensorMap, context); + const alignCorners = getParamValue("alignCorners", node, tensorMap, context); + const halfPixelCenters = getParamValue("halfPixelCenters", node, tensorMap, context); + return [ops.image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)]; + } + case "ResizeNearestNeighbor": { + const images = getParamValue("images", node, tensorMap, context); + const size = getParamValue("size", node, tensorMap, context); + const alignCorners = getParamValue("alignCorners", node, tensorMap, context); + const halfPixelCenters = getParamValue("halfPixelCenters", node, tensorMap, context); + return [ops.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)]; + } + case "CropAndResize": { + const image2 = getParamValue("image", node, tensorMap, context); + const boxes = getParamValue("boxes", node, tensorMap, context); + const boxInd = getParamValue("boxInd", node, tensorMap, context); + const cropSize = getParamValue("cropSize", node, tensorMap, context); + const method = getParamValue("method", node, tensorMap, context); + const extrapolationValue = getParamValue("extrapolationValue", node, tensorMap, context); + return [ops.image.cropAndResize(image2, boxes, boxInd, cropSize, method, extrapolationValue)]; + } + case "ImageProjectiveTransformV3": { + const images = getParamValue("images", node, tensorMap, context); + const transforms = getParamValue("transforms", node, tensorMap, context); + const outputShape = getParamValue("outputShape", node, tensorMap, context); + const fillValue = getParamValue("fillValue", node, tensorMap, context); + const interpolation = getParamValue("interpolation", node, tensorMap, context); + const fillMode = getParamValue("fillMode", node, tensorMap, context); + return [ops.image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp11 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Equal": { + return [ops.equal(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "NotEqual": { + return [ops.notEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Greater": { + return [ops.greater(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "GreaterEqual": { + return [ops.greaterEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Less": { + return [ops.less(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "LessEqual": { + return [ops.lessEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "LogicalAnd": { + return [ops.logicalAnd(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "LogicalNot": { + return [ops.logicalNot(getParamValue("a", node, tensorMap, context))]; + } + case "LogicalOr": { + return [ops.logicalOr(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Select": + case "SelectV2": { + return [ops.where(getParamValue("condition", node, tensorMap, context), getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "BitwiseAnd": { + return [ops.bitwiseAnd(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp12 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "BatchMatMul": + case "BatchMatMulV2": + case "MatMul": + return [ops.matMul(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context), getParamValue("transposeA", node, tensorMap, context), getParamValue("transposeB", node, tensorMap, context))]; + case "Einsum": + return [ops.einsum(getParamValue("equation", node, tensorMap, context), ...getParamValue("tensors", node, tensorMap, context))]; + case "Transpose": + return [ops.transpose(getParamValue("x", node, tensorMap, context), getParamValue("perm", node, tensorMap, context))]; + case "_FusedMatMul": + const [extraOp, activationFunc] = getParamValue("fusedOps", node, tensorMap, context); + const isBiasAdd = extraOp === "biasadd"; + const isPrelu = activationFunc === "prelu"; + const numArgs = getParamValue("numArgs", node, tensorMap, context); + const leakyreluAlpha = getParamValue("leakyreluAlpha", node, tensorMap, context); + if (isBiasAdd) { + if (isPrelu && numArgs !== 2) { + throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha."); + } + if (!isPrelu && numArgs !== 1) { + throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias."); + } + } + const [biasArg, preluArg] = getParamValue("args", node, tensorMap, context); + return [ops.fused.matMul({ + a: getParamValue("a", node, tensorMap, context), + b: getParamValue("b", node, tensorMap, context), + transposeA: getParamValue("transposeA", node, tensorMap, context), + transposeB: getParamValue("transposeB", node, tensorMap, context), + bias: biasArg, + activation: activationFunc, + preluActivationWeights: preluArg, + leakyreluAlpha + })]; + case "MatrixBandPart": + return [ops.linalg.bandPart(getParamValue("a", node, tensorMap, context), getParamValue("numLower", node, tensorMap, context), getParamValue("numUpper", node, tensorMap, context))]; + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp13 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "EuclideanNorm": + return [ops.euclideanNorm(getParamValue("x", node, tensorMap, context), getParamValue("axis", node, tensorMap, context), getParamValue("keepDims", node, tensorMap, context))]; + case "FusedBatchNorm": + case "FusedBatchNormV2": { + return [ops.batchNorm(getParamValue("x", node, tensorMap, context), getParamValue("mean", node, tensorMap, context), getParamValue("variance", node, tensorMap, context), getParamValue("offset", node, tensorMap, context), getParamValue("scale", node, tensorMap, context), getParamValue("epsilon", node, tensorMap, context))]; + } + case "FusedBatchNormV3": { + return [ops.batchNorm(getParamValue("x", node, tensorMap, context), getParamValue("mean", node, tensorMap, context), getParamValue("variance", node, tensorMap, context), getParamValue("offset", node, tensorMap, context), getParamValue("scale", node, tensorMap, context), getParamValue("epsilon", node, tensorMap, context))]; + } + case "LRN": { + return [ops.localResponseNormalization(getParamValue("x", node, tensorMap, context), getParamValue("radius", node, tensorMap, context), getParamValue("bias", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context), getParamValue("beta", node, tensorMap, context))]; + } + case "Softmax": { + return [ops.softmax(getParamValue("x", node, tensorMap, context))]; + } + case "LogSoftmax": { + return [ops.logSoftmax(getParamValue("x", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp14 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "RaggedGather": { + const { outputNestedSplits, outputDenseValues } = ops.raggedGather(getParamValue("paramsNestedSplits", node, tensorMap, context), getParamValue("paramsDenseValues", node, tensorMap, context), getParamValue("indices", node, tensorMap, context), getParamValue("outputRaggedRank", node, tensorMap, context)); + return outputNestedSplits.concat(outputDenseValues); + } + case "RaggedRange": { + const { rtNestedSplits, rtDenseValues } = ops.raggedRange(getParamValue("starts", node, tensorMap, context), getParamValue("limits", node, tensorMap, context), getParamValue("splits", node, tensorMap, context)); + return [rtNestedSplits, rtDenseValues]; + } + case "RaggedTensorToTensor": { + return [ops.raggedTensorToTensor(getParamValue("shape", node, tensorMap, context), getParamValue("values", node, tensorMap, context), getParamValue("defaultValue", node, tensorMap, context), getParamValue("rowPartitionTensors", node, tensorMap, context), getParamValue("rowPartitionTypes", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp15 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Max": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.max(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "Mean": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.mean(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "Min": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.min(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "Sum": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.sum(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "All": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.all(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "Any": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.any(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "ArgMax": { + const axis = getParamValue("axis", node, tensorMap, context); + return [ops.argMax(getParamValue("x", node, tensorMap, context), axis)]; + } + case "ArgMin": { + const axis = getParamValue("axis", node, tensorMap, context); + return [ops.argMin(getParamValue("x", node, tensorMap, context), axis)]; + } + case "Prod": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.prod(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "Cumprod": { + const axis = getParamValue("axis", node, tensorMap, context); + const exclusive = getParamValue("exclusive", node, tensorMap, context); + const reverse5 = getParamValue("reverse", node, tensorMap, context); + return [ops.cumprod(getParamValue("x", node, tensorMap, context), axis, exclusive, reverse5)]; + } + case "Cumsum": { + const axis = getParamValue("axis", node, tensorMap, context); + const exclusive = getParamValue("exclusive", node, tensorMap, context); + const reverse5 = getParamValue("reverse", node, tensorMap, context); + return [ops.cumsum(getParamValue("x", node, tensorMap, context), axis, exclusive, reverse5)]; + } + case "Bincount": + const x = getParamValue("x", node, tensorMap, context); + const weights = getParamValue("weights", node, tensorMap, context); + const size = getParamValue("size", node, tensorMap, context); + return [ops.bincount(x, weights, size)]; + case "DenseBincount": { + const x2 = getParamValue("x", node, tensorMap, context); + const weights2 = getParamValue("weights", node, tensorMap, context); + const size2 = getParamValue("size", node, tensorMap, context); + const binaryOutput = getParamValue("binaryOutput", node, tensorMap, context); + return [ops.denseBincount(x2, weights2, size2, binaryOutput)]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp16 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "ConcatV2": + case "Concat": { + const n = getParamValue("n", node, tensorMap, context); + const axis = getParamValue("axis", node, tensorMap, context); + let inputs = getParamValue("tensors", node, tensorMap, context); + inputs = inputs.slice(0, n); + return [ops.concat(inputs, axis)]; + } + case "Gather": { + const input2 = getParamValue("x", node, tensorMap, context); + const indices = getParamValue("indices", node, tensorMap, context); + return [ops.gather(input2, ops.cast(indices, "int32"), 0)]; + } + case "GatherV2": { + const axis = getParamValue("axis", node, tensorMap, context); + const batchDims = getParamValue("batchDims", node, tensorMap, context); + const input2 = getParamValue("x", node, tensorMap, context); + const indices = getParamValue("indices", node, tensorMap, context); + return [ops.gather(input2, ops.cast(indices, "int32"), axis, batchDims)]; + } + case "Reverse": { + const dims = getParamValue("dims", node, tensorMap, context); + const axis = []; + for (let i = 0; i < dims.length; i++) { + if (dims[i]) { + axis.push(i); + } + } + const input2 = getParamValue("x", node, tensorMap, context); + return [ops.reverse(input2, axis)]; + } + case "ReverseV2": { + const axis = getParamValue("axis", node, tensorMap, context); + const input2 = getParamValue("x", node, tensorMap, context); + return [ops.reverse(input2, axis)]; + } + case "Slice": { + const begin = getParamValue("begin", node, tensorMap, context); + const size = getParamValue("size", node, tensorMap, context); + return [ops.slice(getParamValue("x", node, tensorMap, context), begin, size)]; + } + case "StridedSlice": { + const begin = getParamValue("begin", node, tensorMap, context); + const end = getParamValue("end", node, tensorMap, context); + const strides = getParamValue("strides", node, tensorMap, context); + const beginMask = getParamValue("beginMask", node, tensorMap, context); + const endMask = getParamValue("endMask", node, tensorMap, context); + const ellipsisMask = getParamValue("ellipsisMask", node, tensorMap, context); + const newAxisMask = getParamValue("newAxisMask", node, tensorMap, context); + const shrinkAxisMask = getParamValue("shrinkAxisMask", node, tensorMap, context); + const tensor2 = getParamValue("x", node, tensorMap, context); + return [ops.stridedSlice(tensor2, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)]; + } + case "Pack": { + return tidy(() => { + const axis = getParamValue("axis", node, tensorMap, context); + const tensors = getParamValue("tensors", node, tensorMap, context); + const shape = tensors[0].shape; + const squeezedShape = ops.squeeze(tensors[0]).shape; + const mapped = tensors.map((tensor2) => { + const sameShape = util_exports.arraysEqual(tensor2.shape, shape); + if (!sameShape && !util_exports.arraysEqual(ops.squeeze(tensor2).shape, squeezedShape)) { + throw new Error("the input tensors shape does not match"); + } + return sameShape ? tensor2 : ops.reshape(tensor2, shape); + }); + return [ops.stack(mapped, axis)]; + }); + } + case "Unpack": { + const axis = getParamValue("axis", node, tensorMap, context); + const tensor2 = getParamValue("tensor", node, tensorMap, context); + return ops.unstack(tensor2, axis); + } + case "Tile": { + const reps = getParamValue("reps", node, tensorMap, context); + return [ops.tile(getParamValue("x", node, tensorMap, context), reps)]; + } + case "Split": + case "SplitV": { + const axis = getParamValue("axis", node, tensorMap, context); + const numOrSizeSplits = getParamValue("numOrSizeSplits", node, tensorMap, context); + const tensor2 = getParamValue("x", node, tensorMap, context); + return ops.split(tensor2, numOrSizeSplits, axis); + } + case "ScatterNd": { + const indices = getParamValue("indices", node, tensorMap, context); + const values = getParamValue("values", node, tensorMap, context); + const shape = getParamValue("shape", node, tensorMap, context); + return [ops.scatterND(indices, values, shape)]; + } + case "GatherNd": { + const x = getParamValue("x", node, tensorMap, context); + const indices = getParamValue("indices", node, tensorMap, context); + return [ops.gatherND(x, indices)]; + } + case "SparseToDense": { + const indices = getParamValue("sparseIndices", node, tensorMap, context); + const shape = getParamValue("outputShape", node, tensorMap, context); + const sparseValues = getParamValue("sparseValues", node, tensorMap, context); + const defaultValue = getParamValue("defaultValue", node, tensorMap, context); + return [ops.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : ops.cast(defaultValue, sparseValues.dtype))]; + } + case "TensorScatterUpdate": { + const indices = getParamValue("indices", node, tensorMap, context); + const values = getParamValue("values", node, tensorMap, context); + const tensor2 = getParamValue("tensor", node, tensorMap, context); + return [ops.tensorScatterUpdate(tensor2, indices, values)]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp17 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "SparseFillEmptyRows": { + const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = ops.sparse.sparseFillEmptyRows(getParamValue("indices", node, tensorMap, context), getParamValue("values", node, tensorMap, context), getParamValue("denseShape", node, tensorMap, context), getParamValue("defaultValue", node, tensorMap, context)); + return [ + outputIndices, + outputValues, + emptyRowIndicator, + reverseIndexMap + ]; + } + case "SparseReshape": { + const { outputIndices, outputShape } = ops.sparse.sparseReshape(getParamValue("inputIndices", node, tensorMap, context), getParamValue("inputShape", node, tensorMap, context), getParamValue("newShape", node, tensorMap, context)); + return [outputIndices, outputShape]; + } + case "SparseSegmentMean": { + const outputData = ops.sparse.sparseSegmentMean(getParamValue("data", node, tensorMap, context), getParamValue("indices", node, tensorMap, context), getParamValue("segmentIds", node, tensorMap, context)); + return [outputData]; + } + case "SparseSegmentSum": { + const outputData = ops.sparse.sparseSegmentSum(getParamValue("data", node, tensorMap, context), getParamValue("indices", node, tensorMap, context), getParamValue("segmentIds", node, tensorMap, context)); + return [outputData]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp18 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "FFT": { + return [ops.fft(getParamValue("x", node, tensorMap, context))]; + } + case "IFFT": { + return [ops.ifft(getParamValue("x", node, tensorMap, context))]; + } + case "RFFT": { + return [ops.rfft(getParamValue("x", node, tensorMap, context))]; + } + case "IRFFT": { + return [ops.irfft(getParamValue("x", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp19 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "StaticRegexReplace": { + return [ops.string.staticRegexReplace(getParamValue("input", node, tensorMap, context), getParamValue("pattern", node, tensorMap, context), getParamValue("rewrite", node, tensorMap, context), getParamValue("replaceGlobal", node, tensorMap, context))]; + } + case "StringNGrams": { + const { nGrams, nGramsSplits } = ops.string.stringNGrams(getParamValue("data", node, tensorMap, context), getParamValue("dataSplits", node, tensorMap, context), getParamValue("separator", node, tensorMap, context), getParamValue("nGramWidths", node, tensorMap, context), getParamValue("leftPad", node, tensorMap, context), getParamValue("rightPad", node, tensorMap, context), getParamValue("padWidth", node, tensorMap, context), getParamValue("preserveShortSequences", node, tensorMap, context)); + return [nGrams, nGramsSplits]; + } + case "StringSplit": { + const { indices, values, shape } = ops.string.stringSplit(getParamValue("input", node, tensorMap, context), getParamValue("delimiter", node, tensorMap, context), getParamValue("skipEmpty", node, tensorMap, context)); + return [indices, values, shape]; + } + case "StringToHashBucketFast": { + const output = ops.string.stringToHashBucketFast(getParamValue("input", node, tensorMap, context), getParamValue("numBuckets", node, tensorMap, context)); + return [output]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +var executeOp20 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Cast": { + return [ops.cast(getParamValue("x", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))]; + } + case "ExpandDims": { + const axis = getParamValue("axis", node, tensorMap, context); + return [ops.expandDims(getParamValue("x", node, tensorMap, context), axis)]; + } + case "Squeeze": { + const axis = getParamValue("axis", node, tensorMap, context); + return [ops.squeeze(getParamValue("x", node, tensorMap, context), axis)]; + } + case "Reshape": { + return [ops.reshape(getParamValue("x", node, tensorMap, context), getParamValue("shape", node, tensorMap, context))]; + } + case "EnsureShape": { + return [ops.ensureShape(getParamValue("x", node, tensorMap, context), getParamValue("shape", node, tensorMap, context))]; + } + case "MirrorPad": { + return [ops.mirrorPad(getParamValue("x", node, tensorMap, context), getParamValue("padding", node, tensorMap, context), getParamValue("mode", node, tensorMap, context))]; + } + case "PadV2": + case "Pad": { + return [ops.pad(getParamValue("x", node, tensorMap, context), getParamValue("padding", node, tensorMap, context), getParamValue("constantValue", node, tensorMap, context))]; + } + case "SpaceToBatchND": { + const blockShape = getParamValue("blockShape", node, tensorMap, context); + const paddings = getParamValue("paddings", node, tensorMap, context); + return [ops.spaceToBatchND(getParamValue("x", node, tensorMap, context), blockShape, paddings)]; + } + case "BatchToSpaceND": { + const blockShape = getParamValue("blockShape", node, tensorMap, context); + const crops = getParamValue("crops", node, tensorMap, context); + return [ops.batchToSpaceND(getParamValue("x", node, tensorMap, context), blockShape, crops)]; + } + case "DepthToSpace": { + const blockSize = getParamValue("blockSize", node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + return [ops.depthToSpace(getParamValue("x", node, tensorMap, context), blockSize, dataFormat)]; + } + case "BroadcastTo": { + return [ops.broadcastTo(getParamValue("x", node, tensorMap, context), getParamValue("shape", node, tensorMap, context))]; + } + case "BroadcastArgs": { + return [ops.broadcastArgs(getParamValue("s0", node, tensorMap, context), getParamValue("s1", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; +function executeOp21(node, tensorMap, context, resourceManager, tidy2 = tidy) { + const value = ((node2, tensorMap2, context2) => { + switch (node2.category) { + case "arithmetic": + return tidy2(() => executeOp(node2, tensorMap2, context2)); + case "basic_math": + return tidy2(() => executeOp2(node2, tensorMap2, context2)); + case "control": + return executeOp3(node2, tensorMap2, context2); + case "convolution": + return tidy2(() => executeOp4(node2, tensorMap2, context2)); + case "creation": + return tidy2(() => executeOp5(node2, tensorMap2, context2)); + case "dynamic": + return executeOp6(node2, tensorMap2, context2); + case "evaluation": + return tidy2(() => executeOp7(node2, tensorMap2, context2)); + case "image": + return tidy2(() => executeOp10(node2, tensorMap2, context2)); + case "graph": + return tidy2(() => executeOp8(node2, tensorMap2, context2)); + case "logical": + return tidy2(() => executeOp11(node2, tensorMap2, context2)); + case "matrices": + return tidy2(() => executeOp12(node2, tensorMap2, context2)); + case "normalization": + return tidy2(() => executeOp13(node2, tensorMap2, context2)); + case "ragged": + return tidy2(() => executeOp14(node2, tensorMap2, context2)); + case "reduction": + return tidy2(() => executeOp15(node2, tensorMap2, context2)); + case "slice_join": + return tidy2(() => executeOp16(node2, tensorMap2, context2)); + case "sparse": + return tidy2(() => executeOp17(node2, tensorMap2, context2)); + case "spectral": + return tidy2(() => executeOp18(node2, tensorMap2, context2)); + case "string": + return tidy2(() => executeOp19(node2, tensorMap2, context2)); + case "transformation": + return tidy2(() => executeOp20(node2, tensorMap2, context2)); + case "hash_table": + return executeOp9(node2, tensorMap2, context2, resourceManager); + case "custom": + const opMapper = getRegisteredOp(node2.op); + if (opMapper && opMapper.customExecutor) { + return opMapper.customExecutor(new NodeValueImpl(node2, tensorMap2, context2)); + } else { + throw TypeError(`Custom op ${node2.op} is not registered.`); + } + default: + throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`); + } + })(node, tensorMap, context); + if (util_exports.isPromise(value)) { + return value.then((data) => [].concat(data)); + } + return [].concat(value); +} +var ExecutionContext = class { + constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}, parseNodeNameCache) { + this.weightMap = weightMap; + this.tensorArrayMap = tensorArrayMap; + this.tensorListMap = tensorListMap; + this.functionMap = functionMap; + this.parseNodeNameCache = parseNodeNameCache; + this.rootContext = { id: 0, frameName: "", iterationId: 0 }; + this.contexts = [this.rootContext]; + this.lastId = 0; + this.generateCurrentContextIds(); + } + newFrame(id, frameName) { + return { id, frameName, iterationId: 0 }; + } + /** + * Set the current context + * @param contexts: ExecutionContextInfo[] the current path of execution + * frames + */ + set currentContext(contexts2) { + if (this.contexts !== contexts2) { + this.contexts = contexts2; + this.generateCurrentContextIds(); + } + } + get currentContext() { + return this.contexts; + } + /** + * Returns the current context in string format. + */ + get currentContextId() { + return this._currentContextIds[0]; + } + /** + * Returns the current context and all parent contexts in string format. + * This allow access to the nodes in the current and parent frames. + */ + get currentContextIds() { + return this._currentContextIds; + } + generateCurrentContextIds() { + const names = []; + for (let i = 0; i < this.contexts.length - 1; i++) { + const contexts2 = this.contexts.slice(0, this.contexts.length - i); + names.push(this.contextIdforContexts(contexts2)); + } + names.push(""); + this._currentContextIds = names; + } + contextIdforContexts(contexts2) { + return contexts2 ? contexts2.map((context) => context.id === 0 && context.iterationId === 0 ? "" : `${context.frameName}-${context.iterationId}`).join("/") : ""; + } + /** + * Enter a new frame, a new context is pushed on the current context list. + * @param frameId new frame id + */ + enterFrame(frameId) { + if (this.contexts) { + this.lastId++; + this.contexts = this.contexts.slice(); + this.contexts.push(this.newFrame(this.lastId, frameId)); + this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)); + } + } + /** + * Exit the current frame, the last context is removed from the current + * context list. + */ + 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"); + } + } + /** + * Enter the next iteration of a loop, the iteration id of last context is + * increased. + */ + nextIteration() { + if (this.contexts && this.contexts.length > 0) { + this.contexts = this.contexts.slice(); + this.lastId++; + const context = Object.assign({}, this.contexts[this.contexts.length - 1]); + context.iterationId += 1; + context.id = this.lastId; + this.contexts.splice(-1, 1, context); + this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts)); + } else { + throw new Error("Cannot increase frame iteration, the context is empty"); + } + } + getWeight(name) { + return this.weightMap[name]; + } + addTensorArray(tensorArray) { + this.tensorArrayMap[tensorArray.id] = tensorArray; + } + getTensorArray(id) { + return this.tensorArrayMap[id]; + } + addTensorList(tensorList) { + this.tensorListMap[tensorList.id] = tensorList; + } + getTensorList(id) { + return this.tensorListMap[id]; + } + dispose(keepIds) { + for (const key in this.tensorArrayMap) { + this.tensorArrayMap[key].clearAndClose(keepIds); + } + for (const key in this.tensorListMap) { + this.tensorListMap[key].clearAndClose(keepIds); + } + } +}; +function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) { + const usedNodes = /* @__PURE__ */ new Set(); + const missingInputs = []; + let dynamicNode = null; + let syncInputs = null; + const seen = /* @__PURE__ */ new Set(); + const inputNodeNames = new Set(Object.keys(inputs).map((name) => parseNodeName(name)[0])); + initNodes = initNodes || []; + const initNodeNames = new Set(initNodes.map((node) => parseNodeName(node.name)[0])); + const frontier = [...outputs]; + while (frontier.length > 0) { + const node = frontier.pop(); + if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) { + if (dynamicNode == null) { + dynamicNode = node; + syncInputs = dynamicNode.children.map((child) => child.name).filter((name) => usedNodes.has(name)); + } + } + usedNodes.add(node.name); + if (weightMap[node.name] != null) { + continue; + } + if (inputNodeNames.has(node.name)) { + continue; + } + if (initNodeNames.has(node.name)) { + continue; + } + if (node.inputs.length === 0) { + missingInputs.push(node.name); + continue; + } + node.inputs.forEach((input2) => { + if (seen.has(input2.name)) { + return; + } + seen.add(input2.name); + frontier.push(input2); + }); + } + return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs }; +} +function getNodesInTopologicalOrder(graph, executionInfo) { + const { usedNodes, inputs } = executionInfo; + const inputNodes = Object.keys(inputs).map((name) => parseNodeName(name)[0]).map((name) => graph.nodes[name]); + const initNodes = graph.initNodes || []; + const isUsed = (node) => usedNodes.has(typeof node === "string" ? node : node.name); + function unique6(nodes) { + return [...new Map(nodes.map((node) => [node.name, node])).values()]; + } + const predefinedNodes = unique6([ + ...inputNodes, + ...graph.weights, + ...initNodes + ]).filter(isUsed); + const allNodes = unique6([ + ...predefinedNodes, + ...Object.values(graph.nodes) + ]).filter(isUsed); + const nameToNode = new Map(allNodes.map((node) => [node.name, node])); + const inCounts = {}; + for (const node of allNodes) { + inCounts[node.name] = inCounts[node.name] || 0; + for (const child of node.children) { + if (!isUsed(child)) { + inCounts[child.name] = Number.POSITIVE_INFINITY; + } + inCounts[child.name] = (inCounts[child.name] || 0) + 1; + } + } + const frontier = Object.entries(inCounts).filter(([, inCount]) => inCount === 0).map(([name]) => name); + const orderedNodeNames = [...frontier]; + while (frontier.length > 0) { + const nodeName = frontier.pop(); + const node = nameToNode.get(nodeName); + for (const child of node.children.filter(isUsed)) { + if (--inCounts[child.name] === 0) { + orderedNodeNames.push(child.name); + frontier.push(child.name); + } + } + } + const orderedNodes = orderedNodeNames.map((name) => nameToNode.get(name)); + const filteredOrderedNodes = filterPredefinedReachableNodes(orderedNodes, predefinedNodes); + validateNodesExecutionOrder(filteredOrderedNodes, predefinedNodes); + return filteredOrderedNodes; +} +function filterPredefinedReachableNodes(orderedNodes, predefinedNodes) { + const nameToNode = new Map(orderedNodes.map((node) => [node.name, node])); + const stack2 = predefinedNodes.map((node) => node.name); + const predefinedReachableNodeNames = new Set(stack2); + while (stack2.length > 0) { + const nodeName = stack2.pop(); + const node = nameToNode.get(nodeName); + for (const child of node.children) { + if (!nameToNode.has(child.name) || predefinedReachableNodeNames.has(child.name)) { + continue; + } + predefinedReachableNodeNames.add(child.name); + stack2.push(child.name); + } + } + const filteredOrderedNodes = orderedNodes.filter((node) => predefinedReachableNodeNames.has(node.name)); + return filteredOrderedNodes; +} +var NodesExecutionOrderError = class extends Error { + constructor(message) { + super(`NodesExecutionOrderError: ${message}`); + } +}; +function validateNodesExecutionOrder(orderedNodes, predefinedNodes) { + const nodeNameToOrder = new Map(orderedNodes.map((node, order) => [node.name, order])); + const predefinedNodeNames = new Set(predefinedNodes.map((node) => node.name)); + const isPredefined = (node) => predefinedNodeNames.has(typeof node === "string" ? node : node.name); + const willBeExecutedNodeNames = new Set(orderedNodes.map((node) => node.name)); + const willBeExecuted = (node) => willBeExecutedNodeNames.has(typeof node === "string" ? node : node.name); + for (const node of orderedNodes) { + for (const child of node.children.filter(willBeExecuted)) { + if (!nodeNameToOrder.has(child.name)) { + throw new NodesExecutionOrderError(`Child ${child.name} of node ${node.name} is unreachable.`); + } + if (nodeNameToOrder.get(node.name) > nodeNameToOrder.get(child.name)) { + throw new NodesExecutionOrderError(`Node ${node.name} is scheduled to run after its child ${child.name}.`); + } + } + if (!isPredefined(node)) { + for (const input2 of node.inputs) { + if (!nodeNameToOrder.has(input2.name)) { + throw new NodesExecutionOrderError(`Input ${input2.name} of node ${node.name} is unreachable.`); + } + if (nodeNameToOrder.get(input2.name) > nodeNameToOrder.get(node.name)) { + throw new NodesExecutionOrderError(`Node ${node.name} is scheduled to run before its input ${input2.name}.`); + } + } + } + } +} +function getNodeLiveUntilMap(orderedNodes) { + const nodeNameToOrder = new Map(orderedNodes.map((node, order) => [node.name, order])); + const INF_LIFE = Number.MAX_SAFE_INTEGER; + const selfLifespans = orderedNodes.map((node, nodeOrder) => isControlFlow(node) ? INF_LIFE : nodeOrder); + const getSelfLifeSpan = (node) => { + const selfLife = selfLifespans[nodeNameToOrder.get(node.name)]; + if (selfLife == null) { + return -1; + } + return selfLife; + }; + const liveUntilOrders = orderedNodes.map((node, nodeOrder) => { + return node.children.map(getSelfLifeSpan).reduce((a, b) => Math.max(a, b), selfLifespans[nodeOrder]); + }); + const liveUntilMap = /* @__PURE__ */ new Map(); + for (let nodeOrder = 0; nodeOrder < orderedNodes.length; ++nodeOrder) { + const liveUntilOrder = liveUntilOrders[nodeOrder]; + if (liveUntilOrder === INF_LIFE) { + continue; + } + const node = orderedNodes[nodeOrder]; + const liveUntilNode = orderedNodes[liveUntilOrder]; + if (!liveUntilMap.has(liveUntilNode.name)) { + liveUntilMap.set(liveUntilNode.name, []); + } + liveUntilMap.get(liveUntilNode.name).push(node); + } + return liveUntilMap; +} +var CONTROL_FLOW_OPS = /* @__PURE__ */ new Set([ + "Switch", + "Merge", + "Enter", + "Exit", + "NextIteration", + "StatelessIf", + "StatelessWhile", + "if", + "While" +]); +var DYNAMIC_SHAPE_OPS = /* @__PURE__ */ new Set([ + "NonMaxSuppressionV2", + "NonMaxSuppressionV3", + "NonMaxSuppressionV5", + "Where" +]); +var HASH_TABLE_OPS = /* @__PURE__ */ new Set([ + "HashTable", + "HashTableV2", + "LookupTableImport", + "LookupTableImportV2", + "LookupTableFind", + "LookupTableFindV2", + "LookupTableSize", + "LookupTableSizeV2" +]); +function isControlFlow(node) { + return CONTROL_FLOW_OPS.has(node.op); +} +function isDynamicShape(node) { + return DYNAMIC_SHAPE_OPS.has(node.op); +} +function isHashTable(node) { + return HASH_TABLE_OPS.has(node.op); +} +var GraphExecutor = class _GraphExecutor { + get weightIds() { + return this.parent ? this.parent.weightIds : this._weightIds; + } + get functionExecutorMap() { + return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap; + } + get weightMap() { + return this.parent ? this.parent.weightMap : this._weightMap; + } + set weightMap(weightMap) { + const weightIds = Object.keys(weightMap).map((key) => weightMap[key].map((tensor2) => tensor2.id)); + this._weightIds = [].concat(...weightIds); + this._weightMap = weightMap; + } + /** + * Set `ResourceManager` shared by executors of a model. + * @param resourceManager: `ResourceManager` of the `GraphModel`. + */ + set resourceManager(resourceManager) { + this._resourceManager = resourceManager; + } + get inputs() { + return this._inputs.map((node) => { + return { + name: node.name, + shape: node.attrParams["shape"] ? node.attrParams["shape"].value : void 0, + dtype: node.attrParams["dtype"] ? node.attrParams["dtype"].value : void 0 + }; + }); + } + get outputs() { + return this._outputs.map((node) => { + return { + name: node.name, + shape: node.attrParams["shape"] ? node.attrParams["shape"].value : void 0, + dtype: node.attrParams["dtype"] ? node.attrParams["dtype"].value : void 0 + }; + }); + } + get inputNodes() { + return this._inputs.map((node) => node.signatureKey || node.name); + } + get outputNodes() { + return this._outputs.map((node) => { + const name = node.signatureKey || node.name; + return node.defaultOutput ? `${name}:${node.defaultOutput}` : name; + }); + } + get functions() { + return Object.keys(this._functions).reduce((map, key) => { + map[key] = this._functions[key].signature; + return map; + }, {}); + } + /** + * + * @param graph Graph the model or function graph to be executed. + * @param parent When building function exector you need to set the parent + * executor. Since the weights and function executor maps are set at parant + * level, that function executor can access the function maps and weight maps + * through the parent. + */ + constructor(graph, parent) { + this.graph = graph; + this.parent = parent; + this.compiledMap = /* @__PURE__ */ new Map(); + this.parseNodeNameCache = /* @__PURE__ */ new Map(); + this._weightMap = {}; + this.SEPARATOR = ","; + this._functions = {}; + this._functionExecutorMap = {}; + this.keepIntermediateTensors = false; + this._outputs = graph.outputs; + this._inputs = graph.inputs; + this._initNodes = graph.initNodes; + this._signature = graph.signature; + this._functions = graph.functions; + if (graph.functions != null) { + Object.keys(graph.functions).forEach((name) => { + this._functionExecutorMap[name] = new _GraphExecutor(graph.functions[name], this); + }); + } + } + getCompilationKey(inputs, outputs) { + const sortedInputs = inputs.map((node) => node.name).sort(); + const sortedOutputs = outputs.map((node) => node.name).sort(); + return sortedInputs.join(this.SEPARATOR) + "--" + sortedOutputs.join(this.SEPARATOR); + } + /** + * Compiles the inference graph and returns the minimal set of nodes that are + * required for execution, in the correct execution order. + * @returns {Object} compilation The compile result. + * @returns {Node[]} compilation.orderedNodes Nodes in the correct execution + * order. + * @returns {Map} compilation.nodeLiveUntilMap A map from node + * to disposable nodes after its execution. That is, for a node `x`, + * `nodeLiveUntilMap[x]` indicates all nodes whose intermediate + * tensors should be disposed after `x` is executed. + */ + compile(inputs, outputs) { + const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes); + const { missingInputs, dynamicNode, syncInputs } = executionInfo; + if (dynamicNode != null) { + throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`); + } + if (missingInputs.length > 0) { + const outNames = outputs.map((n) => n.name); + const inNames = Object.keys(inputs); + throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`); + } + const orderedNodes = getNodesInTopologicalOrder(this.graph, executionInfo); + const nodeLiveUntilMap = getNodeLiveUntilMap(orderedNodes); + return { orderedNodes, nodeLiveUntilMap }; + } + cloneAndKeepTensor(tensor2) { + if (tensor2 == null) { + return null; + } + const clone2 = tensor2.clone(); + keep(clone2); + return clone2; + } + cloneTensorList(tensors) { + if (!tensors) { + return null; + } + const clonedTensor = tensors.map((tensor2) => { + return this.cloneAndKeepTensor(tensor2); + }); + return clonedTensor; + } + cloneTensorMap(tensorsMap) { + return Object.fromEntries(Object.entries(tensorsMap).map(([name, tensorsList]) => { + return [name, this.cloneTensorList(tensorsList)]; + })); + } + /** + * Executes the inference for given input tensors. + * @param inputs Tensor map for the model inputs, keyed by the input node + * names. + * @param outputs Optional. output node name from the Tensorflow model, if + * no outputs are specified, the default outputs of the model would be used. + * You can inspect intermediate nodes of the model by adding them to the + * outputs array. + */ + execute(inputs, outputs) { + this.disposeIntermediateTensors(); + inputs = this.mapInputs(inputs); + const names = Object.keys(inputs).sort(); + this.checkInputs(inputs); + this.checkInputShapeAndType(inputs); + outputs = this.mapOutputs(outputs); + this.checkOutputs(outputs); + const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]); + const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]); + const outputNodeNameSet = new Set(outputNodeNames); + let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]); + if (outputNodes.length === 0) { + outputNodes = this._outputs; + } + const compilationKey = this.getCompilationKey(inputNodes, outputNodes); + let compilation = this.compiledMap.get(compilationKey); + if (compilation == null) { + compilation = this.compile(inputs, outputNodes); + this.compiledMap.set(compilationKey, compilation); + } + try { + this.keepIntermediateTensors = env().getBool("KEEP_INTERMEDIATE_TENSORS"); + } catch (e) { + this.keepIntermediateTensors = false; + console.warn(e.message); + } + const tensorArrayMap = {}; + const tensorListMap = {}; + return tidy(() => { + const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache); + const tensorsMap = Object.assign({}, this.weightMap); + if (this.keepIntermediateTensors) { + this.clonedTensorsMap = this.cloneTensorMap(this.weightMap); + } + Object.keys(inputs).forEach((name) => { + const [nodeName, index] = parseNodeName(name, context); + const tensors = []; + tensors[index] = inputs[name]; + tensorsMap[nodeName] = tensors; + if (this.keepIntermediateTensors) { + this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors); + } + }); + const tensorsToKeep = this.getFrozenTensorIds(tensorsMap); + const { orderedNodes, nodeLiveUntilMap } = compilation; + for (const node of orderedNodes) { + if (tensorsMap[node.name]) { + continue; + } + const tensors = executeOp21(node, tensorsMap, context, this._resourceManager); + if (util_exports.isPromise(tensors)) { + throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`); + } + tensorsMap[node.name] = tensors; + if (this.keepIntermediateTensors) { + this.clonedTensorsMap[node.name] = this.cloneTensorList(tensors); + } + this.checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorsMap, context, tensorsToKeep, outputNodeNameSet, nodeLiveUntilMap.get(node.name)); + } + if (this.parent == null) { + context.dispose(tensorsToKeep); + } + return outputs.map((name) => getTensor(name, tensorsMap, context)); + }); + } + getFrozenTensorIds(tensorMap) { + const ids = [].concat.apply([], Object.keys(tensorMap).map((key) => tensorMap[key]).map((tensors) => tensors.map((tensor2) => tensor2.id))); + return new Set(ids); + } + checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount) { + if (isControlFlow(node) || outputNodeNameSet.has(nodeName)) { + return; + } + for (const tensor2 of tensorMap[nodeName]) { + if (tensor2 == null) { + continue; + } + intermediateTensorConsumerCount[tensor2.id] = (intermediateTensorConsumerCount[tensor2.id] || 0) + node.children.length; + } + for (const input2 of node.inputs) { + if (isControlFlow(input2)) { + continue; + } + const tensors = getTensorsForCurrentContext(input2.name, tensorMap, context); + if (tensors == null) { + continue; + } + for (const tensor2 of tensors) { + if (!tensor2 || tensor2.kept || tensorsToKeep.has(tensor2.id)) { + continue; + } + const count2 = intermediateTensorConsumerCount[tensor2.id]; + if (count2 === 1) { + tensor2.dispose(); + delete intermediateTensorConsumerCount[tensor2.id]; + } else if (count2 != null) { + intermediateTensorConsumerCount[tensor2.id]--; + } + } + } + } + checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorMap, context, tensorsToKeep, outputNodeNameSet, liveUntilNodes) { + function isNonDisposableNode(node2) { + return isControlFlow(node2) || outputNodeNameSet.has(node2.name); + } + if (isControlFlow(node) || liveUntilNodes == null) { + return; + } + for (const nodeToDispose of liveUntilNodes) { + if (isNonDisposableNode(nodeToDispose)) { + continue; + } + const tensors = getTensorsForCurrentContext(nodeToDispose.name, tensorMap, context); + for (const tensor2 of tensors) { + if (!tensor2 || tensor2.kept || tensorsToKeep.has(tensor2.id)) { + continue; + } + tensor2.dispose(); + } + } + } + /** + * Executes the inference for given input tensors in Async fashion. + * @param inputs Tensor map for the model inputs, keyed by the input node + * names. + * @param outputs output node name from the Tensorflow model, if no outputs + * are specified, the default outputs of the model would be used. You can + * inspect intermediate nodes of the model by adding them to the outputs + * array. + */ + async executeAsync(inputs, outputs) { + return this._executeAsync(inputs, outputs); + } + disposeIntermediateTensors() { + if (!this.clonedTensorsMap) { + return; + } + Object.values(this.clonedTensorsMap).forEach((tensorsList) => { + for (const tensor2 of tensorsList) { + if (tensor2 && !tensor2.isDisposed) { + tensor2.dispose(); + } + } + }); + this.clonedTensorsMap = null; + } + getIntermediateTensors() { + return this.clonedTensorsMap; + } + /** + * Executes the inference for given input tensors in Async fashion. + * @param inputs Tensor map for the model inputs, keyed by the input node + * names. + * @param outputs Optional. output node name from the Tensorflow model, + * if no outputs are specified, the default outputs of the model would be + * used. You can inspect intermediate nodes of the model by adding them to + * the outputs array. + * @param isFunctionExecution Optional. Flag for executing a function. + * @param tensorArrayMap Optional, global TensorArray map by id. Used for + * function execution. + * @param tensorArrayMap Optinal global TensorList map by id. Used for + * function execution. + */ + async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) { + this.disposeIntermediateTensors(); + if (!isFunctionExecution) { + inputs = this.mapInputs(inputs); + this.checkInputs(inputs); + this.checkInputShapeAndType(inputs); + outputs = this.mapOutputs(outputs); + this.checkOutputs(outputs); + } + try { + this.keepIntermediateTensors = env().getBool("KEEP_INTERMEDIATE_TENSORS"); + } catch (e) { + this.keepIntermediateTensors = false; + console.warn(e.message); + } + const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache); + if (this.keepIntermediateTensors) { + this.clonedTensorsMap = this.cloneTensorMap(this.weightMap); + } + const tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution); + const results = outputs.map((name) => getTensor(name, tensorsMap, context)); + const outputIds = results.map((t) => t.id); + const inputIds = Object.keys(inputs).map((name) => inputs[name].id); + const keepIds = /* @__PURE__ */ new Set([...outputIds, ...inputIds, ...this.weightIds]); + Object.values(tensorsMap).forEach((tensorsList) => { + tensorsList.forEach((tensor2) => { + if (tensor2 && !tensor2.isDisposed && !keepIds.has(tensor2.id)) { + tensor2.dispose(); + } + }); + }); + if (this.parent == null) { + context.dispose(keepIds); + } + return results; + } + async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) { + const mappedInputs = inputs.reduce((map, tensor2, index) => { + map[this.inputs[index].name] = tensor2; + return map; + }, {}); + return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap); + } + /** + * When there are control flow nodes in the graph, the graph execution use + * ExecutionContext to keep track of the frames and loop iterators. + * @param inputs placeholder tensors for the graph. + * @param context the execution context object for current execution. + * @param outputNames Optional. output node name from the Tensorflow model, + * if no outputs are specified, the default outputs of the model would be + * used. You can inspect intermediate nodes of the model by adding them to + * the outputs array. + * @param isFunctionExecution Flag for executing a function. + */ + async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) { + const names = Object.keys(inputs); + const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]); + const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]); + const outputNodeNameSet = new Set(outputNodeNames); + let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]); + if (outputNodes.length === 0) { + outputNodes = this._outputs; + } + const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes); + const stack2 = [ + ...inputNodes, + ...this.graph.weights, + ...this._initNodes || [] + ].map((node) => { + return { node, contexts: context.currentContext }; + }); + const tensorsMap = Object.assign({}, this.weightMap); + Object.keys(inputs).forEach((name) => { + const [nodeName, index] = parseNodeName(name); + const tensors = []; + tensors[index] = inputs[name]; + tensorsMap[nodeName] = tensors; + }); + const intermediateTensorConsumerCount = {}; + const tensorsToKeep = this.getFrozenTensorIds(tensorsMap); + const added = {}; + while (stack2.length > 0) { + const promises = this.processStack(inputNodes, stack2, context, tensorsMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes); + await Promise.all(promises); + } + if (dynamicNode == null && !isFunctionExecution) { + console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`); + } + const missingOutputs = outputNodes.filter((node) => !isControlFlow(node) && !getTensor(node.name, tensorsMap, context)).map((node) => node.name); + if (missingOutputs.length > 0) { + let alternativeMsg = ""; + if (dynamicNode != null) { + alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`; + } + throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`); + } + return tensorsMap; + } + processStack(inputNodes, stack2, context, tensorMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes) { + const promises = []; + while (stack2.length > 0) { + const item = stack2.pop(); + context.currentContext = item.contexts; + let nodeName = ""; + if (item.node.op === "Enter" && getParamValue("isConstant", item.node, tensorMap, context)) { + [nodeName] = getNodeNameAndIndex(item.node.name, context); + } + if (tensorMap[item.node.name] == null) { + const tensors = executeOp21(item.node, tensorMap, context, this._resourceManager); + if (!nodeName) { + [nodeName] = getNodeNameAndIndex(item.node.name, context); + } + const currentContext = context.currentContext; + if (util_exports.isPromise(tensors)) { + promises.push(tensors.then((t) => { + tensorMap[nodeName] = t; + if (this.keepIntermediateTensors) { + this.clonedTensorsMap[nodeName] = this.cloneTensorList(t); + } + context.currentContext = currentContext; + this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount); + this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes); + return t; + })); + } else { + tensorMap[nodeName] = tensors; + if (this.keepIntermediateTensors) { + this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors); + } + this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount); + this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes); + } + } else { + this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes); + } + } + return promises; + } + processChildNodes(node, stack2, context, tensorMap, added, usedNodes) { + node.children.forEach((childNode) => { + const [nodeName] = getNodeNameAndIndex(childNode.name, context); + if (added[nodeName] || !usedNodes.has(childNode.name)) { + return; + } + if (childNode.op === "Merge") { + if (childNode.inputNames.some((name) => { + return !!getTensor(name, tensorMap, context); + })) { + added[nodeName] = true; + stack2.push({ contexts: context.currentContext, node: childNode }); + } + } else if (childNode.inputNames.every((name) => { + return !!getTensor(name, tensorMap, context); + })) { + added[nodeName] = true; + stack2.push({ contexts: context.currentContext, node: childNode }); + } + }); + } + /** + * Releases the memory used by the weight tensors. + */ + dispose() { + Object.keys(this.weightMap).forEach((key) => this.weightMap[key].forEach((tensor2) => tensor2.dispose())); + } + checkInputShapeAndType(inputs) { + Object.keys(inputs).forEach((name) => { + const input2 = inputs[name]; + const [nodeName] = parseNodeName(name); + const node = this.graph.nodes[nodeName]; + if (node.attrParams["shape"] && node.attrParams["shape"].value) { + const shape = node.attrParams["shape"].value; + const match = shape.length === input2.shape.length && input2.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim); + util_exports.assert(match, () => `The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`); + } + if (node.attrParams["dtype"] && node.attrParams["dtype"].value) { + util_exports.assert(input2.dtype === node.attrParams["dtype"].value, () => `The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams["dtype"].value}, but was ${input2.dtype}`); + } + }); + } + mapInputs(inputs) { + var _a, _b; + const result = {}; + for (const inputName in inputs) { + const tensor2 = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.inputs) === null || _b === void 0 ? void 0 : _b[inputName]; + if (tensor2 != null) { + result[tensor2.name] = inputs[inputName]; + } else { + result[inputName] = inputs[inputName]; + } + } + return result; + } + checkInputs(inputs) { + const notInGraph = Object.keys(inputs).filter((name) => { + const [nodeName] = parseNodeName(name); + return this.graph.nodes[nodeName] == null; + }); + if (notInGraph.length > 0) { + throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`); + } + } + mapOutputs(outputs) { + return outputs.map((name) => { + var _a, _b; + const tensor2 = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.outputs) === null || _b === void 0 ? void 0 : _b[name]; + if (tensor2 != null) { + return tensor2.name; + } + return name; + }, {}); + } + checkOutputs(outputs) { + outputs.forEach((name) => { + const [normalizedName] = parseNodeName(name); + if (!this.graph.nodes[normalizedName]) { + throw new Error(`The output '${name}' is not found in the graph`); + } + }); + } +}; +var ResourceManager = class { + constructor(hashTableNameToHandle = {}, hashTableMap = {}) { + this.hashTableNameToHandle = hashTableNameToHandle; + this.hashTableMap = hashTableMap; + } + /** + * Register a `HashTable` in the resource manager. + * + * The `HashTable` can be retrieved by `resourceManager.getHashTableById`, + * where id is the table handle tensor's id. + * + * @param name Op node name that creates the `HashTable`. + * @param hashTable The `HashTable` to be added to resource manager. + */ + addHashTable(name, hashTable) { + this.hashTableNameToHandle[name] = hashTable.handle; + this.hashTableMap[hashTable.id] = hashTable; + } + /** + * Get the table handle by node name. + * @param name Op node name that creates the `HashTable`. This name is also + * used in the inputs list of lookup and import `HashTable` ops. + */ + getHashTableHandleByName(name) { + return this.hashTableNameToHandle[name]; + } + /** + * Get the actual `HashTable` by its handle tensor's id. + * @param id The id of the handle tensor. + */ + getHashTableById(id) { + return this.hashTableMap[id]; + } + /** + * Dispose `ResourceManager`, including its hashTables and tensors in them. + */ + dispose() { + for (const key in this.hashTableMap) { + this.hashTableMap[key].clearAndClose(); + delete this.hashTableMap[key]; + } + for (const name in this.hashTableNameToHandle) { + this.hashTableNameToHandle[name].dispose(); + delete this.hashTableNameToHandle[name]; + } + } +}; +var TFHUB_SEARCH_PARAM = "?tfjs-format=file"; +var DEFAULT_MODEL_NAME = "model.json"; +var GraphModel = class { + // Returns the version information for the tensorflow model GraphDef. + 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; + } + /** + * @param modelUrl url for the model, or an `io.IOHandler`. + * @param weightManifestUrl url for the weight file generated by + * scripts/convert.py script. + * @param requestOption options for Request, which allows to send credentials + * and custom headers. + * @param onProgress Optional, progress callback function, fired periodically + * before the load is completed. + */ + constructor(modelUrl, loadOptions = {}, tfio = io_exports) { + this.modelUrl = modelUrl; + this.loadOptions = loadOptions; + this.version = "n/a"; + this.io = tfio; + if (loadOptions == null) { + this.loadOptions = {}; + } + this.resourceManager = new ResourceManager(); + } + findIOHandler() { + const path = this.modelUrl; + if (path.load != null) { + this.handler = path; + } else if (this.loadOptions.requestInit != null) { + this.handler = this.io.browserHTTPRequest(path, this.loadOptions); + } else { + const handlers = this.io.getLoadHandlers(path, this.loadOptions); + if (handlers.length === 0) { + handlers.push(this.io.browserHTTPRequest(path, this.loadOptions)); + } else if (handlers.length > 1) { + throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`); + } + this.handler = handlers[0]; + } + } + /** + * Loads the model and weight files, construct the in memory weight map and + * compile the inference graph. + */ + load() { + this.findIOHandler(); + if (this.handler.load == null) { + throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented."); + } + const loadResult = this.handler.load(); + if (util_exports.isPromise(loadResult)) { + return loadResult.then((artifacts) => { + if (artifacts.getWeightStream == null) { + return this.loadSync(artifacts); + } + return this.loadStreaming(artifacts); + }); + } + return this.loadSync(loadResult); + } + /** + * Synchronously construct the in memory weight map and + * compile the inference graph. + * + * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} + */ + loadSync(artifacts) { + const weightMap = this.io.decodeWeights(artifacts.weightData, artifacts.weightSpecs); + return this.loadWithWeightMap(artifacts, weightMap); + } + async loadStreaming(artifacts) { + if (artifacts.getWeightStream == null) { + throw new Error("Model artifacts missing streamWeights function"); + } + const weightMap = await decodeWeightsStream(artifacts.getWeightStream(), artifacts.weightSpecs); + return this.loadWithWeightMap(artifacts, weightMap); + } + loadWithWeightMap(artifacts, weightMap) { + this.artifacts = artifacts; + const graph = this.artifacts.modelTopology; + let signature = this.artifacts.signature; + if (this.artifacts.userDefinedMetadata != null) { + const metadata = this.artifacts.userDefinedMetadata; + if (metadata.signature != null) { + signature = metadata.signature; + } + if (metadata.structuredOutputKeys != null) { + this.structuredOutputKeys = metadata.structuredOutputKeys; + } + } + this.signature = signature; + this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`; + this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature)); + this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap); + this.executor.resourceManager = this.resourceManager; + if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) { + const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer); + this.initializer = new GraphExecutor(initializer); + this.initializer.weightMap = this.executor.weightMap; + this.initializer.resourceManager = this.resourceManager; + this.initializerSignature = artifacts.initializerSignature; + } + return true; + } + /** + * Save the configuration and/or weights of the GraphModel. + * + * An `IOHandler` is an object that has a `save` method of the proper + * signature defined. The `save` method manages the storing or + * transmission of serialized data ("artifacts") that represent the + * model's topology and weights onto or via a specific medium, such as + * file downloads, local storage, IndexedDB in the web browser and HTTP + * requests to a server. TensorFlow.js provides `IOHandler` + * implementations for a number of frequently used saving mediums, such as + * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io` + * for more details. + * + * This method also allows you to refer to certain types of `IOHandler`s + * as URL-like string shortcuts, such as 'localstorage://' and + * 'indexeddb://'. + * + * Example 1: Save `model`'s topology and weights to browser [local + * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage); + * then load it back. + * + * ```js + * const modelUrl = + * 'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json'; + * const model = await tf.loadGraphModel(modelUrl); + * const zeros = tf.zeros([1, 224, 224, 3]); + * model.predict(zeros).print(); + * + * const saveResults = await model.save('localstorage://my-model-1'); + * + * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1'); + * console.log('Prediction from loaded model:'); + * model.predict(zeros).print(); + * ``` + * + * @param handlerOrURL An instance of `IOHandler` or a URL-like, + * scheme-based string shortcut for `IOHandler`. + * @param config Options for saving the model. + * @returns A `Promise` of `SaveResult`, which summarizes the result of + * the saving, such as byte sizes of the saved artifacts for the model's + * topology and weight values. + * + * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} + */ + async save(handlerOrURL, config) { + if (typeof handlerOrURL === "string") { + const handlers = this.io.getSaveHandlers(handlerOrURL); + if (handlers.length === 0) { + throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`); + } else if (handlers.length > 1) { + throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`); + } + handlerOrURL = handlers[0]; + } + if (handlerOrURL.save == null) { + throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined."); + } + return handlerOrURL.save(this.artifacts); + } + addStructuredOutputNames(outputTensors) { + if (this.structuredOutputKeys) { + const outputTensorsArray = outputTensors instanceof Tensor ? [outputTensors] : outputTensors; + const outputTensorMap = {}; + outputTensorsArray.forEach((outputTensor, i) => outputTensorMap[this.structuredOutputKeys[i]] = outputTensor); + return outputTensorMap; + } + return outputTensors; + } + /** + * Execute the inference for the input tensors. + * + * @param input The input tensors, when there is single input for the model, + * inputs param should be a `tf.Tensor`. For models with mutliple inputs, + * inputs params should be in either `tf.Tensor`[] if the input order is + * fixed, or otherwise NamedTensorMap format. + * + * For model with multiple inputs, we recommend you use NamedTensorMap as the + * input type, if you use `tf.Tensor`[], the order of the array needs to + * follow the + * order of inputNodes array. @see {@link GraphModel.inputNodes} + * + * You can also feed any intermediate nodes using the NamedTensorMap as the + * input type. For example, given the graph + * InputNode => Intermediate => OutputNode, + * you can execute the subgraph Intermediate => OutputNode by calling + * model.execute('IntermediateNode' : tf.tensor(...)); + * + * This is useful for models that uses tf.dynamic_rnn, where the intermediate + * state needs to be fed manually. + * + * For batch inference execution, the tensors for each input need to be + * concatenated together. For example with mobilenet, the required input shape + * is [1, 244, 244, 3], which represents the [batch, height, width, channel]. + * If we are provide a batched data of 100 images, the input tensor should be + * in the shape of [100, 244, 244, 3]. + * + * @param config Prediction configuration for specifying the batch size. + * Currently the batch size option is ignored for graph model. + * + * @returns Inference result tensors. If the model is converted and it + * originally had structured_outputs in tensorflow, then a NamedTensorMap + * will be returned matching the structured_outputs. If no structured_outputs + * are present, the output will be single `tf.Tensor` if the model has single + * output node, otherwise Tensor[]. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + predict(inputs, config) { + const outputTensors = this.execute(inputs, this.outputNodes); + return this.addStructuredOutputNames(outputTensors); + } + /** + * Execute the inference for the input tensors in async fashion, use this + * method when your model contains control flow ops. + * + * @param input The input tensors, when there is single input for the model, + * inputs param should be a `tf.Tensor`. For models with mutliple inputs, + * inputs params should be in either `tf.Tensor`[] if the input order is + * fixed, or otherwise NamedTensorMap format. + * + * For model with multiple inputs, we recommend you use NamedTensorMap as the + * input type, if you use `tf.Tensor`[], the order of the array needs to + * follow the + * order of inputNodes array. @see {@link GraphModel.inputNodes} + * + * You can also feed any intermediate nodes using the NamedTensorMap as the + * input type. For example, given the graph + * InputNode => Intermediate => OutputNode, + * you can execute the subgraph Intermediate => OutputNode by calling + * model.execute('IntermediateNode' : tf.tensor(...)); + * + * This is useful for models that uses tf.dynamic_rnn, where the intermediate + * state needs to be fed manually. + * + * For batch inference execution, the tensors for each input need to be + * concatenated together. For example with mobilenet, the required input shape + * is [1, 244, 244, 3], which represents the [batch, height, width, channel]. + * If we are provide a batched data of 100 images, the input tensor should be + * in the shape of [100, 244, 244, 3]. + * + * @param config Prediction configuration for specifying the batch size. + * Currently the batch size option is ignored for graph model. + * + * @returns A Promise of inference result tensors. If the model is converted + * and it originally had structured_outputs in tensorflow, then a + * NamedTensorMap will be returned matching the structured_outputs. If no + * structured_outputs are present, the output will be single `tf.Tensor` if + * the model has single output node, otherwise Tensor[]. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async predictAsync(inputs, config) { + const outputTensors = await this.executeAsync(inputs, this.outputNodes); + return this.addStructuredOutputNames(outputTensors); + } + normalizeInputs(inputs) { + var _a; + if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) { + const signatureInputs = (_a = this.signature) === null || _a === void 0 ? void 0 : _a.inputs; + if (signatureInputs != null) { + for (const input2 in signatureInputs) { + const tensor2 = signatureInputs[input2]; + if (tensor2.resourceId != null) { + inputs[input2] = this.resourceIdToCapturedInput[tensor2.resourceId]; + } + } + } + return inputs; + } + inputs = Array.isArray(inputs) ? inputs : [inputs]; + const numCapturedInputs = Object.keys(this.resourceIdToCapturedInput).length; + if (inputs.length + numCapturedInputs !== this.inputNodes.length) { + throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length - numCapturedInputs} non-resource placeholders, while there are ${inputs.length} input tensors provided.`); + } + let inputIndex = 0; + return this.inputNodes.reduce((map, inputName) => { + var _a2, _b, _c; + const resourceId = (_c = (_b = (_a2 = this.signature) === null || _a2 === void 0 ? void 0 : _a2.inputs) === null || _b === void 0 ? void 0 : _b[inputName]) === null || _c === void 0 ? void 0 : _c.resourceId; + if (resourceId != null) { + map[inputName] = this.resourceIdToCapturedInput[resourceId]; + } else { + map[inputName] = inputs[inputIndex++]; + } + return map; + }, {}); + } + normalizeOutputs(outputs) { + outputs = outputs || this.outputNodes; + return !Array.isArray(outputs) ? [outputs] : outputs; + } + executeInitializerGraph() { + if (this.initializer == null) { + return []; + } + if (this.initializerSignature == null) { + return this.initializer.execute({}, []); + } else { + return this.initializer.execute({}, Object.keys(this.initializerSignature.outputs)); + } + } + async executeInitializerGraphAsync() { + if (this.initializer == null) { + return []; + } + if (this.initializerSignature == null) { + return this.initializer.executeAsync({}, []); + } else { + return this.initializer.executeAsync({}, Object.keys(this.initializerSignature.outputs)); + } + } + setResourceIdToCapturedInput(outputs) { + this.resourceIdToCapturedInput = {}; + if (this.initializerSignature) { + const signatureOutputs = this.initializerSignature.outputs; + const outputNames = Object.keys(signatureOutputs); + for (let i = 0; i < outputNames.length; i++) { + const outputName = outputNames[i]; + const tensorInfo = signatureOutputs[outputName]; + this.resourceIdToCapturedInput[tensorInfo.resourceId] = outputs[i]; + } + } + } + /** + * Executes inference for the model for given input tensors. + * @param inputs tensor, tensor array or tensor map of the inputs for the + * model, keyed by the input node names. + * @param outputs output node name from the TensorFlow model, if no + * outputs are specified, the default outputs of the model would be used. + * You can inspect intermediate nodes of the model by adding them to the + * outputs array. + * + * @returns A single tensor if provided with a single output or no outputs + * are provided and there is only one default output, otherwise return a + * tensor array. The order of the tensor array is the same as the outputs + * if provided, otherwise the order of outputNodes attribute of the model. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + execute(inputs, outputs) { + if (this.resourceIdToCapturedInput == null) { + this.setResourceIdToCapturedInput(this.executeInitializerGraph()); + } + inputs = this.normalizeInputs(inputs); + outputs = this.normalizeOutputs(outputs); + const result = this.executor.execute(inputs, outputs); + return result.length > 1 ? result : result[0]; + } + /** + * Executes inference for the model for given input tensors in async + * fashion, use this method when your model contains control flow ops. + * @param inputs tensor, tensor array or tensor map of the inputs for the + * model, keyed by the input node names. + * @param outputs output node name from the TensorFlow model, if no outputs + * are specified, the default outputs of the model would be used. You can + * inspect intermediate nodes of the model by adding them to the outputs + * array. + * + * @returns A Promise of single tensor if provided with a single output or + * no outputs are provided and there is only one default output, otherwise + * return a tensor map. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async executeAsync(inputs, outputs) { + if (this.resourceIdToCapturedInput == null) { + this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()); + } + inputs = this.normalizeInputs(inputs); + outputs = this.normalizeOutputs(outputs); + const result = await this.executor.executeAsync(inputs, outputs); + return result.length > 1 ? result : result[0]; + } + /** + * Get intermediate tensors for model debugging mode (flag + * KEEP_INTERMEDIATE_TENSORS is true). + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + getIntermediateTensors() { + return this.executor.getIntermediateTensors(); + } + /** + * Dispose intermediate tensors for model debugging mode (flag + * KEEP_INTERMEDIATE_TENSORS is true). + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + disposeIntermediateTensors() { + this.executor.disposeIntermediateTensors(); + } + convertTensorMapToTensorsMap(map) { + return Object.keys(map).reduce((newMap, key) => { + newMap[key] = [map[key]]; + return newMap; + }, {}); + } + /** + * Releases the memory used by the weight tensors and resourceManager. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + dispose() { + this.executor.dispose(); + if (this.initializer) { + this.initializer.dispose(); + if (this.resourceIdToCapturedInput) { + dispose(this.resourceIdToCapturedInput); + } + } + this.resourceManager.dispose(); + } +}; +async function loadGraphModel(modelUrl, options = {}, tfio = io_exports) { + if (modelUrl == null) { + throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model"); + } + if (options == null) { + options = {}; + } + if (options.fromTFHub && typeof modelUrl === "string") { + modelUrl = getTFHubUrl(modelUrl); + } + const model2 = new GraphModel(modelUrl, options, tfio); + await model2.load(); + return model2; +} +function loadGraphModelSync(modelSource) { + if (modelSource == null) { + throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model"); + } + let ioHandler; + if (modelSource instanceof Array) { + const [modelJSON, weights] = modelSource; + if (!modelJSON) { + throw new Error("modelJSON must be the first element of the array"); + } + if (!weights || !(weights instanceof ArrayBuffer)) { + throw new Error("An ArrayBuffer of weights must be the second element of the array"); + } + if (!("modelTopology" in modelJSON)) { + throw new Error("Model JSON is missing 'modelTopology'"); + } + if (!("weightsManifest" in modelJSON)) { + throw new Error("Model JSON is missing 'weightsManifest'"); + } + const weightSpecs = io_exports.getWeightSpecs(modelJSON.weightsManifest); + const modelArtifacts = io_exports.getModelArtifactsForJSONSync(modelJSON, weightSpecs, weights); + ioHandler = io_exports.fromMemorySync(modelArtifacts); + } else if ("load" in modelSource) { + ioHandler = modelSource; + } else if ("modelTopology" in modelSource && "weightSpecs" in modelSource && "weightData" in modelSource) { + ioHandler = io_exports.fromMemorySync(modelSource); + } else { + throw new Error("Unknown model format"); + } + const model2 = new GraphModel(ioHandler); + model2.load(); + return model2; +} +function getTFHubUrl(modelUrl) { + if (!modelUrl.endsWith("/")) { + modelUrl = modelUrl + "/"; + } + return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`; +} +var version3 = "4.16.0"; +var dist_exports2 = {}; +__export2(dist_exports2, { + CSVDataset: () => CSVDataset, + Dataset: () => Dataset, + FileDataSource: () => FileDataSource, + TextLineDataset: () => TextLineDataset, + URLDataSource: () => URLDataSource, + array: () => array, + csv: () => csv, + func: () => func, + generator: () => generator, + microphone: () => microphone, + version_data: () => version4, + webcam: () => webcam, + zip: () => zip +}); +var seedrandom3 = __toESM(require_seedrandom2()); +var seedrandom2 = __toESM(require_seedrandom2()); +function deepMap(input2, mapFn) { + return deepMapInternal(input2, mapFn); +} +function deepMapInternal(input2, mapFn, seen = /* @__PURE__ */ new Map(), containedIn = /* @__PURE__ */ new Set()) { + if (input2 == null) { + return null; + } + if (typeof Blob === "function" && input2 instanceof Blob) { + return input2.slice(); + } + if (containedIn.has(input2)) { + throw new Error("Circular references are not supported."); + } + if (seen.has(input2)) { + return seen.get(input2); + } + const result = mapFn(input2); + if (result.recurse && result.value !== null) { + throw new Error("A deep map function may not return both a value and recurse=true."); + } + if (!result.recurse) { + seen.set(input2, result.value); + return result.value; + } else if (isIterable2(input2)) { + const mappedIterable = Array.isArray(input2) ? [] : {}; + containedIn.add(input2); + for (const k in input2) { + const child = input2[k]; + const childResult = deepMapInternal(child, mapFn, seen, containedIn); + mappedIterable[k] = childResult; + } + containedIn.delete(input2); + if (input2.__proto__) { + mappedIterable.__proto__ = input2.__proto__; + } + return mappedIterable; + } else { + throw new Error(`Can't recurse into non-iterable type: ${input2}`); + } +} +function deepZip(inputs, zipFn = zipToList) { + return deepZipInternal(inputs, zipFn); +} +function deepZipInternal(inputs, zipFn, containedIn = /* @__PURE__ */ new Set()) { + const input2 = inputs[0]; + if (containedIn.has(input2)) { + throw new Error("Circular references are not supported."); + } + const result = zipFn(inputs); + if (result.recurse && result.value !== null) { + throw new Error("A deep zip function may not return both a value and recurse=true."); + } + if (!result.recurse) { + return result.value; + } else if (isIterable2(input2)) { + const mappedIterable = Array.isArray(input2) ? [] : {}; + containedIn.add(input2); + for (const k in input2) { + const children = inputs.map((x) => x[k]); + const childResult = deepZipInternal(children, zipFn, containedIn); + mappedIterable[k] = childResult; + } + containedIn.delete(input2); + return mappedIterable; + } else { + throw new Error(`Can't recurse into non-iterable type: ${input2}`); + } +} +function zipToList(x) { + if (x === null) { + return null; + } + if (isIterable2(x[0])) { + return { value: null, recurse: true }; + } else { + return { value: x, recurse: false }; + } +} +async function deepMapAndAwaitAll(input2, mapFn) { + const seen = /* @__PURE__ */ new Map(); + deepMapInternal(input2, mapFn, seen); + for (const key of Array.from(seen.keys())) { + const value = seen.get(key); + if (util_exports.isPromise(value)) { + const mappedValue = await value; + seen.set(key, mappedValue); + } + } + const result = deepMapInternal(input2, mapFn, seen); + return result; +} +function isIterable2(obj) { + let isTextDecoder = false; + if (env().get("IS_BROWSER")) { + isTextDecoder = obj instanceof TextDecoder; + } else { + const { StringDecoder } = require_string_decoder(); + isTextDecoder = obj instanceof StringDecoder; + } + return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === "object" && !(obj instanceof Tensor) && !(obj instanceof Promise) && !isTextDecoder); +} +function canTensorify(obj) { + return obj == null || isPrimitive(obj) || Array.isArray(obj) || typeof obj === "object" && obj instanceof Tensor || util_exports.isTypedArray(obj); +} +function isPrimitive(value) { + return value === null || typeof value !== "object" && typeof value !== "function"; +} +function deepClone(container) { + return deepMap(container, cloneIfTensor); +} +function cloneIfTensor(item) { + if (item instanceof Tensor) { + return { value: item.clone(), recurse: false }; + } else if (isIterable2(item)) { + return { value: null, recurse: true }; + } else { + return { value: item, recurse: false }; + } +} +var RingBuffer = class { + /** + * Constructs a `RingBuffer`. + * @param capacity The number of items that the buffer can accomodate. + */ + constructor(capacity) { + this.capacity = capacity; + this.begin = 0; + this.end = 0; + if (capacity == null) { + throw new RangeError("Can't create a ring buffer of unknown capacity."); + } + if (capacity < 1) { + throw new RangeError("Can't create ring buffer of capacity < 1."); + } + this.data = new Array(capacity); + this.doubledCapacity = 2 * capacity; + } + /** + * Map any index into the range 0 <= index < 2*capacity. + */ + wrap(index) { + while (index < 0) { + index += this.doubledCapacity; + } + return index % this.doubledCapacity; + } + get(index) { + if (index < 0) { + throw new RangeError("Can't get item at a negative index."); + } + return this.data[index % this.capacity]; + } + set(index, value) { + if (index < 0) { + throw new RangeError("Can't set item at a negative index."); + } + this.data[index % this.capacity] = value; + } + /** + * Returns the current number of items in the buffer. + */ + length() { + let length = this.end - this.begin; + if (length < 0) { + length = this.doubledCapacity + length; + } + return length; + } + /** + * Reports whether the buffer is full. + * @returns true if the number of items in the buffer equals its capacity, and + * false otherwise. + */ + isFull() { + return this.length() === this.capacity; + } + /** + * Reports whether the buffer is empty. + * @returns true if the number of items in the buffer equals zero, and + * false otherwise. + */ + isEmpty() { + return this.length() === 0; + } + /** + * Adds an item to the end of the buffer. + */ + push(value) { + if (this.isFull()) { + throw new RangeError("Ring buffer is full."); + } + this.set(this.end, value); + this.end = this.wrap(this.end + 1); + } + /** + * Adds many items to the end of the buffer, in order. + */ + pushAll(values) { + for (const value of values) { + this.push(value); + } + } + /** + * Removes and returns the last item in the buffer. + */ + pop() { + if (this.isEmpty()) { + throw new RangeError("Ring buffer is empty."); + } + this.end = this.wrap(this.end - 1); + const result = this.get(this.end); + this.set(this.end, void 0); + return result; + } + /** + * Adds an item to the beginning of the buffer. + */ + unshift(value) { + if (this.isFull()) { + throw new RangeError("Ring buffer is full."); + } + this.begin = this.wrap(this.begin - 1); + this.set(this.begin, value); + } + /** + * Removes and returns the first item in the buffer. + */ + shift() { + if (this.isEmpty()) { + throw new RangeError("Ring buffer is empty."); + } + const result = this.get(this.begin); + this.set(this.begin, void 0); + this.begin = this.wrap(this.begin + 1); + return result; + } + /** + * Removes and returns a specific item in the buffer, and moves the last item + * to the vacated slot. This is useful for implementing a shuffling stream. + * Note that this operation necessarily scrambles the original order. + * + * @param relativeIndex: the index of the item to remove, relative to the + * first item in the buffer (e.g., hiding the ring nature of the underlying + * storage). + */ + shuffleExcise(relativeIndex) { + if (this.isEmpty()) { + throw new RangeError("Ring buffer is empty."); + } + const index = this.wrap(this.begin + relativeIndex); + const result = this.get(index); + this.set(index, this.pop()); + return result; + } +}; +var GrowingRingBuffer = class _GrowingRingBuffer extends RingBuffer { + /** + * Constructs a `GrowingRingBuffer`. + */ + constructor() { + super(_GrowingRingBuffer.INITIAL_CAPACITY); + } + isFull() { + return false; + } + push(value) { + if (super.isFull()) { + this.expand(); + } + super.push(value); + } + unshift(value) { + if (super.isFull()) { + this.expand(); + } + super.unshift(value); + } + /** + * Doubles the capacity of the buffer. + */ + expand() { + const newCapacity = this.capacity * 2; + const newData = new Array(newCapacity); + const len = this.length(); + for (let i = 0; i < len; i++) { + newData[i] = this.get(this.wrap(this.begin + i)); + } + this.data = newData; + this.capacity = newCapacity; + this.doubledCapacity = 2 * this.capacity; + this.begin = 0; + this.end = len; + } +}; +GrowingRingBuffer.INITIAL_CAPACITY = 32; +function iteratorFromItems(items) { + return new ArrayIterator(items); +} +function iteratorFromFunction(func2) { + return new FunctionCallIterator(func2); +} +function iteratorFromConcatenated(baseIterators, baseErrorHandler) { + return new ChainedIterator(baseIterators, baseErrorHandler); +} +function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) { + return new ZipIterator(iterators, mismatchMode); +} +var LazyIterator = class { + /** + * Collect all remaining elements of a bounded stream into an array. + * Obviously this will succeed only for small streams that fit in memory. + * Useful for testing. + * + * @returns A Promise for an array of stream elements, which will resolve + * when the stream is exhausted. + */ + async toArray() { + const result = []; + let x = await this.next(); + while (!x.done) { + result.push(x.value); + x = await this.next(); + } + return result; + } + /** + * Collect all elements of this dataset into an array with prefetching 100 + * elements. This is useful for testing, because the prefetch changes the + * order in which the Promises are resolved along the processing pipeline. + * This may help expose bugs where results are dependent on the order of + * Promise resolution rather than on the logical order of the stream (i.e., + * due to hidden mutable state). + * + * @returns A Promise for an array of stream elements, which will resolve + * when the stream is exhausted. + */ + async toArrayForTest() { + const stream = this.prefetch(100); + const result = []; + let x = await stream.next(); + while (!x.done) { + result.push(x.value); + x = await stream.next(); + } + return result; + } + /** + * Draw items from the stream until it is exhausted. + * + * This can be useful when the stream has side effects but no output. In + * that case, calling this function guarantees that the stream will be + * fully processed. + */ + async resolveFully() { + let x = await this.next(); + while (!x.done) { + x = await this.next(); + } + } + /** + * Draw items from the stream until it is exhausted, or a predicate fails. + * + * This can be useful when the stream has side effects but no output. In + * that case, calling this function guarantees that the stream will be + * fully processed. + */ + async resolveWhile(predicate) { + let x = await this.next(); + let shouldContinue = predicate(x.value); + while (!x.done && shouldContinue) { + x = await this.next(); + shouldContinue = predicate(x.value); + } + } + /** + * Handles errors thrown on this stream using a provided handler function. + * + * @param handler A function that handles any `Error` thrown during a `next()` + * call and returns true if the stream should continue (dropping the failed + * call) or false if the stream should quietly terminate. If the handler + * itself throws (or rethrows) an `Error`, that will be propagated. + * + * @returns A `LazyIterator` of elements passed through from upstream, + * possibly filtering or terminating on upstream `next()` calls that + * throw an `Error`. + */ + handleErrors(handler) { + return new ErrorHandlingLazyIterator(this, handler); + } + // TODO(soergel): Implement reduce() etc. + /** + * Filters this stream according to `predicate`. + * + * @param predicate A function mapping a stream element to a boolean or a + * `Promise` for one. + * + * @returns A `LazyIterator` of elements for which the predicate was true. + */ + filter(predicate) { + return new FilterIterator(this, predicate); + } + /** + * Maps this stream through a 1-to-1 transform. + * + * @param transform A function mapping a stream element to a transformed + * element. + * + * @returns A `LazyIterator` of transformed elements. + */ + map(transform5) { + return new MapIterator(this, transform5); + } + /** + * Maps this stream through an async 1-to-1 transform. + * + * @param transform A function mapping a stream element to a `Promise` for a + * transformed stream element. + * + * @returns A `LazyIterator` of transformed elements. + */ + mapAsync(transform5) { + return new AsyncMapIterator(this, transform5); + } + /** + * Maps this stream through a 1-to-1 transform, forcing serial execution. + * + * @param transform A function mapping a stream element to a transformed + * element. + * + * @returns A `LazyIterator` of transformed elements. + */ + serialMapAsync(transform5) { + return new AsyncMapIterator(this, transform5).serial(); + } + /** + * Maps this stream through a 1-to-many transform. + * + * @param transform A function mapping a stream element to an array of + * transformed elements. + * + * @returns A `DataStream` of transformed elements. + */ + flatmap(transform5) { + return new FlatmapIterator(this, transform5); + } + /** + * Apply a function to every element of the stream. + * + * @param f A function to apply to each stream element. + */ + async forEachAsync(f) { + return this.map(f).resolveFully(); + } + /** + * Apply a function to every element of the stream, forcing serial execution. + * + * @param f A function to apply to each stream element. Should return 'true' + * to indicate that the stream should continue, or 'false' to cause it to + * terminate. + */ + async serialForEach(f) { + return this.serialMapAsync(f).resolveWhile((x) => x === true); + } + /** + * Groups elements into batches, represented as arrays of elements. + * + * We can think of the elements of this iterator as 'rows' (even if they are + * nested structures). By the same token, consecutive values for a given + * key within the elements form a 'column'. This matches the usual sense of + * 'row' and 'column' when processing tabular data (e.g., parsing a CSV). + * + * Thus, "Row-major" means that the resulting batch is simply a collection of + * rows: `[row1, row2, row3, ...]`. This is contrast to the column-major + * form, which is needed for vectorized computation. + * + * @param batchSize The number of elements desired per batch. + * @param smallLastBatch Whether to emit the final batch when it has fewer + * than batchSize elements. Default true. + * @returns A `LazyIterator` of batches of elements, represented as arrays + * of the original element type. + */ + rowMajorBatch(batchSize, smallLastBatch = true) { + return new RowMajorBatchIterator(this, batchSize, smallLastBatch); + } + /** + * Groups elements into batches, represented in column-major form. + * + * We can think of the elements of this iterator as 'rows' (even if they are + * nested structures). By the same token, consecutive values for a given + * key within the elements form a 'column'. This matches the usual sense of + * 'row' and 'column' when processing tabular data (e.g., parsing a CSV). + * + * Thus, "column-major" means that the resulting batch is a (potentially + * nested) structure representing the columns. Each column entry, then, + * contains a collection of the values found in that column for a range of + * input elements. This representation allows for vectorized computation, in + * contrast to the row-major form. + * + * The inputs should all have the same nested structure (i.e., of arrays and + * dicts). The result is a single object with the same nested structure, + * where the leaves are arrays collecting the values of the inputs at that + * location (or, optionally, the result of a custom function applied to those + * arrays). + * + * @param batchSize The number of elements desired per batch. + * @param smallLastBatch Whether to emit the final batch when it has fewer + * than batchSize elements. Default true. + * @param zipFn: (optional) A function that expects an array of elements at a + * single node of the object tree, and returns a `DeepMapResult`. The + * `DeepMapResult` either provides a result value for that node (i.e., + * representing the subtree), or indicates that the node should be processed + * recursively. The default zipFn recurses as far as possible and places + * arrays at the leaves. + * @returns A `LazyIterator` of batches of elements, represented as an object + * with collections at the leaves. + */ + columnMajorBatch(batchSize, smallLastBatch = true, zipFn = zipToList) { + const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch); + return rowBatches.map((x) => deepZip(x, zipFn)); + } + /** + * Concatenate this `LazyIterator` with another. + * + * @param iterator A `LazyIterator` to be concatenated onto this one. + * @param baseErrorHandler An optional function that can intercept `Error`s + * raised during a `next()` call on the base stream. This function can + * decide whether the error should be propagated, whether the error should + * be ignored, or whether the base stream should be terminated. + * @returns A `LazyIterator`. + */ + concatenate(iterator, baseErrorHandler) { + return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler); + } + /** + * Limits this stream to return at most `count` items. + * + * @param count The maximum number of items to provide from the stream. If + * a negative or undefined value is given, the entire stream is returned + * unaltered. + */ + take(count2) { + if (count2 < 0 || count2 == null) { + return this; + } + return new TakeIterator(this, count2); + } + /** + * Skips the first `count` items in this stream. + * + * @param count The number of items to skip. If a negative or undefined + * value is given, the entire stream is returned unaltered. + */ + skip(count2) { + if (count2 < 0 || count2 == null) { + return this; + } + return new SkipIterator(this, count2); + } + /** + * Prefetch the first `bufferSize` items in this stream. + * + * Note this prefetches Promises, but makes no guarantees about when those + * Promises resolve. + * + * @param bufferSize: An integer specifying the number of elements to be + * prefetched. + */ + prefetch(bufferSize) { + return new PrefetchIterator(this, bufferSize); + } + // TODO(soergel): deep sharded shuffle, where supported + /** + * Randomly shuffles the elements of this stream. + * + * @param bufferSize: An integer specifying the number of elements from + * this stream from which the new stream will sample. + * @param seed: (Optional.) An integer specifying the random seed that + * will be used to create the distribution. + */ + shuffle(windowSize, seed) { + return new ShuffleIterator(this, windowSize, seed); + } + /** + * Force an iterator to execute serially: each next() call will await the + * prior one, so that they cannot execute concurrently. + */ + serial() { + return new SerialIterator(this); + } +}; +var ArrayIterator = class extends LazyIterator { + constructor(items) { + super(); + this.items = items; + this.trav = 0; + } + summary() { + return `Array of ${this.items.length} items`; + } + async next() { + if (this.trav >= this.items.length) { + return { value: null, done: true }; + } + const item = this.items[this.trav]; + this.trav++; + return { value: deepClone(item), done: false }; + } +}; +var FunctionCallIterator = class extends LazyIterator { + constructor(nextFn) { + super(); + this.nextFn = nextFn; + } + summary() { + return `Function call`; + } + async next() { + try { + return this.nextFn(); + } catch (e) { + e.message = `Error thrown while iterating through a dataset: ${e.message}`; + throw e; + } + } +}; +var SerialIterator = class extends LazyIterator { + constructor(upstream) { + super(); + this.upstream = upstream; + this.lastRead = Promise.resolve({ value: null, done: false }); + } + summary() { + return `${this.upstream.summary()} -> Serial`; + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + return this.upstream.next(); + } +}; +var SkipIterator = class extends LazyIterator { + constructor(upstream, maxCount) { + super(); + this.upstream = upstream; + this.maxCount = maxCount; + this.count = 0; + this.lastRead = Promise.resolve({ value: null, done: false }); + } + summary() { + return `${this.upstream.summary()} -> Skip`; + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + while (this.count++ < this.maxCount) { + const skipped = await this.upstream.next(); + if (skipped.done) { + return skipped; + } + dispose(skipped.value); + } + return this.upstream.next(); + } +}; +var TakeIterator = class extends LazyIterator { + constructor(upstream, maxCount) { + super(); + this.upstream = upstream; + this.maxCount = maxCount; + this.count = 0; + } + summary() { + return `${this.upstream.summary()} -> Take`; + } + async next() { + if (this.count++ >= this.maxCount) { + return { value: null, done: true }; + } + return this.upstream.next(); + } +}; +var RowMajorBatchIterator = class extends LazyIterator { + constructor(upstream, batchSize, enableSmallLastBatch = true) { + super(); + this.upstream = upstream; + this.batchSize = batchSize; + this.enableSmallLastBatch = enableSmallLastBatch; + this.lastRead = Promise.resolve({ value: null, done: false }); + } + summary() { + return `${this.upstream.summary()} -> RowMajorBatch`; + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + const batch = []; + while (batch.length < this.batchSize) { + const item = await this.upstream.next(); + if (item.done) { + if (this.enableSmallLastBatch && batch.length > 0) { + return { value: batch, done: false }; + } + return { value: null, done: true }; + } + batch.push(item.value); + } + return { value: batch, done: false }; + } +}; +var FilterIterator = class extends LazyIterator { + constructor(upstream, predicate) { + super(); + this.upstream = upstream; + this.predicate = predicate; + this.lastRead = Promise.resolve({ value: null, done: false }); + } + summary() { + return `${this.upstream.summary()} -> Filter`; + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + while (true) { + const item = await this.upstream.next(); + if (item.done || this.predicate(item.value)) { + return item; + } + dispose(item.value); + } + } +}; +var MapIterator = class extends LazyIterator { + constructor(upstream, transform5) { + super(); + this.upstream = upstream; + this.transform = transform5; + } + summary() { + return `${this.upstream.summary()} -> Map`; + } + async next() { + const item = await this.upstream.next(); + if (item.done) { + return { value: null, done: true }; + } + const inputTensors = tensor_util_exports.getTensorsInContainer(item.value); + const mapped = this.transform(item.value); + const outputTensors = tensor_util_exports.getTensorsInContainer(mapped); + for (const t of inputTensors) { + if (!tensor_util_exports.isTensorInList(t, outputTensors)) { + t.dispose(); + } + } + return { value: mapped, done: false }; + } +}; +var ErrorHandlingLazyIterator = class extends LazyIterator { + constructor(upstream, handler) { + super(); + this.upstream = upstream; + this.handler = handler; + this.count = 0; + this.lastRead = Promise.resolve({ value: null, done: false }); + } + summary() { + return `${this.upstream.summary()} -> handleErrors`; + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + while (true) { + try { + return await this.upstream.next(); + } catch (e) { + if (!this.handler(e)) { + return { value: null, done: true }; + } + } + } + } +}; +var AsyncMapIterator = class extends LazyIterator { + constructor(upstream, transform5) { + super(); + this.upstream = upstream; + this.transform = transform5; + } + summary() { + return `${this.upstream.summary()} -> AsyncMap`; + } + async next() { + const item = await this.upstream.next(); + if (item.done) { + return { value: null, done: true }; + } + const inputTensors = tensor_util_exports.getTensorsInContainer(item.value); + const mapped = await this.transform(item.value); + const outputTensors = tensor_util_exports.getTensorsInContainer(mapped); + for (const t of inputTensors) { + if (!tensor_util_exports.isTensorInList(t, outputTensors)) { + t.dispose(); + } + } + return { value: mapped, done: false }; + } +}; +var OneToManyIterator = class extends LazyIterator { + constructor() { + super(); + this.outputQueue = new GrowingRingBuffer(); + this.lastRead = Promise.resolve({ value: null, done: false }); + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + while (this.outputQueue.length() === 0) { + if (!await this.pump()) { + return { value: null, done: true }; + } + } + return { value: this.outputQueue.shift(), done: false }; + } +}; +var FlatmapIterator = class extends OneToManyIterator { + constructor(upstream, transform5) { + super(); + this.upstream = upstream; + this.transform = transform5; + } + summary() { + return `${this.upstream.summary()} -> Flatmap`; + } + async pump() { + const item = await this.upstream.next(); + if (item.done) { + return false; + } + const inputTensors = tensor_util_exports.getTensorsInContainer(item.value); + const mappedArray = this.transform(item.value); + const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray); + this.outputQueue.pushAll(mappedArray); + for (const t of inputTensors) { + if (!tensor_util_exports.isTensorInList(t, outputTensors)) { + t.dispose(); + } + } + return true; + } +}; +var ChainedIterator = class extends LazyIterator { + constructor(iterators, baseErrorHandler) { + super(); + this.baseErrorHandler = baseErrorHandler; + this.lastRead = null; + this.iterator = null; + this.moreIterators = iterators; + } + summary() { + const upstreamSummaries = "TODO: fill in upstream of chained summaries"; + return `${upstreamSummaries} -> Chained`; + } + async next() { + this.lastRead = this.readFromChain(this.lastRead); + return this.lastRead; + } + async readFromChain(lastRead) { + await lastRead; + if (this.iterator == null) { + const iteratorResult = await this.moreIterators.next(); + if (iteratorResult.done) { + return { value: null, done: true }; + } + this.iterator = iteratorResult.value; + if (this.baseErrorHandler != null) { + this.iterator = this.iterator.handleErrors(this.baseErrorHandler); + } + } + const itemResult = await this.iterator.next(); + if (itemResult.done) { + this.iterator = null; + return this.readFromChain(lastRead); + } + return itemResult; + } +}; +var ZipMismatchMode; +(function(ZipMismatchMode2) { + ZipMismatchMode2[ZipMismatchMode2["FAIL"] = 0] = "FAIL"; + ZipMismatchMode2[ZipMismatchMode2["SHORTEST"] = 1] = "SHORTEST"; + ZipMismatchMode2[ZipMismatchMode2["LONGEST"] = 2] = "LONGEST"; +})(ZipMismatchMode || (ZipMismatchMode = {})); +var ZipIterator = class extends LazyIterator { + constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) { + super(); + this.iterators = iterators; + this.mismatchMode = mismatchMode; + this.count = 0; + this.currentPromise = null; + } + summary() { + const upstreamSummaries = "TODO: fill in upstream of zip summaries"; + return `{${upstreamSummaries}} -> Zip`; + } + async nextState(afterState) { + await afterState; + let numIterators = 0; + let iteratorsDone = 0; + function getNext(container) { + if (container instanceof LazyIterator) { + const result = container.next(); + return { + value: result.then((x) => { + numIterators++; + if (x.done) { + iteratorsDone++; + } + return x.value; + }), + recurse: false + }; + } else { + return { value: null, recurse: true }; + } + } + const mapped = await deepMapAndAwaitAll(this.iterators, getNext); + if (numIterators === iteratorsDone) { + return { value: null, done: true }; + } + if (iteratorsDone > 0) { + switch (this.mismatchMode) { + case ZipMismatchMode.FAIL: + throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`); + case ZipMismatchMode.SHORTEST: + return { value: null, done: true }; + case ZipMismatchMode.LONGEST: + default: + } + } + this.count++; + return { value: mapped, done: false }; + } + async next() { + this.currentPromise = this.nextState(this.currentPromise); + return this.currentPromise; + } +}; +var PrefetchIterator = class extends LazyIterator { + constructor(upstream, bufferSize) { + super(); + this.upstream = upstream; + this.bufferSize = bufferSize; + this.buffer = new RingBuffer(bufferSize); + } + summary() { + return `${this.upstream.summary()} -> Prefetch`; + } + /** + * Refill the prefetch buffer. Returns only after the buffer is full, or + * the upstream source is exhausted. + */ + refill() { + while (!this.buffer.isFull()) { + const v = this.upstream.next(); + this.buffer.push(v); + } + } + next() { + this.refill(); + return this.buffer.shift(); + } +}; +var ShuffleIterator = class extends PrefetchIterator { + constructor(upstream, windowSize, seed) { + super(upstream, windowSize); + this.upstream = upstream; + this.windowSize = windowSize; + this.upstreamExhausted = false; + this.random = seedrandom2.alea(seed || util_exports.now().toString()); + this.lastRead = Promise.resolve({ value: null, done: false }); + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + randomInt(max6) { + return Math.floor(this.random() * max6); + } + chooseIndex() { + return this.randomInt(this.buffer.length()); + } + async serialNext() { + if (!this.upstreamExhausted) { + this.refill(); + } + while (!this.buffer.isEmpty()) { + const chosenIndex = this.chooseIndex(); + const result = await this.buffer.shuffleExcise(chosenIndex); + if (result.done) { + this.upstreamExhausted = true; + } else { + this.refill(); + return result; + } + } + return { value: null, done: true }; + } +}; +var Dataset = class { + constructor() { + this.size = null; + } + // TODO(soergel): Make Datasets report whether repeated iterator() calls + // produce the same result (e.g., reading from a file) or different results + // (e.g., from the webcam). Currently we don't make this distinction but it + // could be important for the user to know. + // abstract isDeterministic(): boolean; + /** + * Groups elements into batches. + * + * It is assumed that each of the incoming dataset elements has the same + * structure -- i.e. the same set of keys at each location in an object + * hierarchy. For each key, the resulting `Dataset` provides a batched + * element collecting all of the incoming values for that key. + * + * * Incoming primitives are grouped into a 1-D Tensor. + * * Incoming Tensors are grouped into a new Tensor where the 0th axis is + * the batch dimension. + * * Incoming arrays are converted to Tensor and then batched. + * * A nested array is interpreted as an n-D Tensor, so the batched result + * has n+1 dimensions. + * * An array that cannot be converted to Tensor produces an error. + * + * If an array should not be batched as a unit, it should first be converted + * to an object with integer keys. + * + * Here are a few examples: + * + * Batch a dataset of numbers: + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4); + * await a.forEachAsync(e => e.print()); + * ``` + * + * Batch a dataset of arrays: + * ```js + * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4); + * await b.forEachAsync(e => e.print()); + * ``` + * + * Batch a dataset of objects: + * ```js + * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13}, + * {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17}, + * {a: 8, b: 18}]).batch(4); + * await c.forEachAsync(e => { + * console.log('{'); + * for(var key in e) { + * console.log(key+':'); + * e[key].print(); + * } + * console.log('}'); + * }) + * ``` + * + * @param batchSize The number of elements desired per batch. + * @param smallLastBatch Whether to emit the final batch when it has fewer + * than batchSize elements. Default true. + * @returns A `Dataset`, from which a stream of batches can be obtained. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + batch(batchSize, smallLastBatch = true) { + const base = this; + util_exports.assert(batchSize > 0, () => `batchSize needs to be positive, but it is + ${batchSize}`); + let size; + if (this.size === Infinity || this.size == null) { + size = this.size; + } else if (smallLastBatch) { + size = Math.ceil(this.size / batchSize); + } else { + size = Math.floor(this.size / batchSize); + } + return datasetFromIteratorFn(async () => { + return (await base.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat); + }, size); + } + /** + * Concatenates this `Dataset` with another. + * + * ```js + * const a = tf.data.array([1, 2, 3]); + * const b = tf.data.array([4, 5, 6]); + * const c = a.concatenate(b); + * await c.forEachAsync(e => console.log(e)); + * ``` + * + * @param dataset A `Dataset` to be concatenated onto this one. + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + concatenate(dataset) { + const base = this; + let size; + if (this.size === Infinity || dataset.size === Infinity) { + size = Infinity; + } else if (this.size != null && dataset.size != null) { + size = this.size + dataset.size; + } else { + size = null; + } + return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size); + } + /** + * Filters this dataset according to `predicate`. + * + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) + * .filter(x => x%2 === 0); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param predicate A function mapping a dataset element to a boolean or a + * `Promise` for one. + * + * @returns A `Dataset` of elements for which the predicate was true. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + filter(predicate) { + const base = this; + let size; + if (this.size === Infinity) { + size = Infinity; + } else { + size = null; + } + return datasetFromIteratorFn(async () => { + return (await base.iterator()).filter((x) => tidy(() => predicate(x))); + }, size); + } + /** + * Apply a function to every element of the dataset. + * + * After the function is applied to a dataset element, any Tensors contained + * within that element are disposed. + * + * ```js + * const a = tf.data.array([1, 2, 3]); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param f A function to apply to each dataset element. + * @returns A `Promise` that resolves after all elements have been processed. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + async forEachAsync(f) { + return (await this.iterator()).forEachAsync(f); + } + /** + * Maps this dataset through a 1-to-1 transform. + * + * ```js + * const a = tf.data.array([1, 2, 3]).map(x => x*x); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param transform A function mapping a dataset element to a transformed + * dataset element. + * + * @returns A `Dataset` of transformed elements. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + map(transform5) { + const base = this; + return datasetFromIteratorFn(async () => { + return (await base.iterator()).map((x) => tidy(() => transform5(x))); + }, this.size); + } + /** + * Maps this dataset through an async 1-to-1 transform. + * + * ```js + * const a = + * tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){ + * setTimeout(() => { + * resolve(x * x); + * }, Math.random()*1000 + 500); + * })); + * console.log(await a.toArray()); + * ``` + * + * @param transform A function mapping a dataset element to a `Promise` for a + * transformed dataset element. This transform is responsible for disposing + * any intermediate `Tensor`s, i.e. by wrapping its computation in + * `tf.tidy()`; that cannot be automated here (as it is in the synchronous + * `map()` case). + * + * @returns A `Dataset` of transformed elements. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + mapAsync(transform5) { + const base = this; + return datasetFromIteratorFn(async () => { + return (await base.iterator()).mapAsync(transform5); + }, this.size); + } + /** + * Creates a `Dataset` that prefetches elements from this dataset. + * + * @param bufferSize: An integer specifying the number of elements to be + * prefetched. + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + prefetch(bufferSize) { + if (bufferSize == null) { + throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified."); + } + const base = this; + return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size); + } + /** + * Repeats this dataset `count` times. + * + * NOTE: If this dataset is a function of global state (e.g. a random number + * generator), then different repetitions may produce different elements. + * + * ```js + * const a = tf.data.array([1, 2, 3]).repeat(3); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param count: (Optional) An integer, representing the number of times + * the dataset should be repeated. The default behavior (if `count` is + * `undefined` or negative) is for the dataset be repeated indefinitely. + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + repeat(count2) { + const base = this; + let size; + if (this.size != null && count2 > 0) { + size = this.size * count2; + } else if (count2 === 0) { + size = 0; + } else if (this.size != null && (count2 === void 0 || count2 < 0)) { + size = Infinity; + } else { + size = null; + } + return datasetFromIteratorFn(async () => { + const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false })); + return iteratorFromConcatenated(iteratorIterator.take(count2)); + }, size); + } + /** + * Creates a `Dataset` that skips `count` initial elements from this dataset. + * + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param count: The number of elements of this dataset that should be skipped + * to form the new dataset. If `count` is greater than the size of this + * dataset, the new dataset will contain no elements. If `count` + * is `undefined` or negative, skips the entire dataset. + * + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + skip(count2) { + const base = this; + let size; + if (this.size != null && count2 >= 0 && this.size >= count2) { + size = this.size - count2; + } else if (this.size != null && (this.size < count2 || count2 === void 0 || count2 < 0)) { + size = 0; + } else { + size = null; + } + return datasetFromIteratorFn(async () => (await base.iterator()).skip(count2), size); + } + /** + * Pseudorandomly shuffles the elements of this dataset. This is done in a + * streaming manner, by sampling from a given number of prefetched elements. + * + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param bufferSize: An integer specifying the number of elements from this + * dataset from which the new dataset will sample. + * @param seed: (Optional) An integer specifying the random seed that will + * be used to create the distribution. + * @param reshuffleEachIteration: (Optional) A boolean, which if true + * indicates that the dataset should be pseudorandomly reshuffled each time + * it is iterated over. If false, elements will be returned in the same + * shuffled order on each iteration. (Defaults to `true`.) + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + shuffle(bufferSize, seed, reshuffleEachIteration = true) { + if (bufferSize == null || bufferSize < 0) { + if (this.size == null) { + throw new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."); + } else { + throw new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`); + } + } + const base = this; + const random = seedrandom3.alea(seed || util_exports.now().toString()); + return datasetFromIteratorFn(async () => { + let seed2 = random.int32(); + if (reshuffleEachIteration) { + seed2 += random.int32(); + } + return (await base.iterator()).shuffle(bufferSize, seed2.toString()); + }, this.size); + } + /** + * Creates a `Dataset` with at most `count` initial elements from this + * dataset. + * + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param count: The number of elements of this dataset that should be taken + * to form the new dataset. If `count` is `undefined` or negative, or if + * `count` is greater than the size of this dataset, the new dataset will + * contain all elements of this dataset. + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + take(count2) { + const base = this; + let size; + if (this.size != null && this.size > count2) { + size = count2; + } else if (this.size != null && this.size <= count2) { + size = this.size; + } else { + size = null; + } + return datasetFromIteratorFn(async () => (await base.iterator()).take(count2), size); + } + /** + * Collect all elements of this dataset into an array. + * + * Obviously this will succeed only for small datasets that fit in memory. + * Useful for testing and generally should be avoided if possible. + * + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6]); + * console.log(await a.toArray()); + * ``` + * + * @returns A Promise for an array of elements, which will resolve + * when a new stream has been obtained and fully consumed. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + async toArray() { + if (this.size === Infinity) { + throw new Error("Can not convert infinite data stream to array."); + } + return (await this.iterator()).toArray(); + } + /** + * Collect all elements of this dataset into an array with prefetching 100 + * elements. This is useful for testing, because the prefetch changes the + * order in which the Promises are resolved along the processing pipeline. + * This may help expose bugs where results are dependent on the order of + * Promise resolution rather than on the logical order of the stream (i.e., + * due to hidden mutable state). + * + * @returns A Promise for an array of elements, which will resolve + * when a new stream has been obtained and fully consumed. + */ + async toArrayForTest() { + if (this.size === Infinity) { + throw new Error("Can not convert infinite data stream to array."); + } + return (await this.iterator()).toArrayForTest(); + } +}; +Dataset.MAX_BUFFER_SIZE = 1e4; +function datasetFromIteratorFn(iteratorFn, size = null) { + return new class extends Dataset { + constructor() { + super(...arguments); + this.size = size; + } + /* + * Provide a new stream of elements. Note this will also start new streams + * from any underlying `Dataset`s. + */ + async iterator() { + return iteratorFn(); + } + }(); +} +function array(items) { + return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length); +} +function zip(datasets) { + if (!isIterable2(datasets)) { + throw new Error("The argument to zip() must be an object or array."); + } + let size; + if (Array.isArray(datasets)) { + for (let i = 0; i < datasets.length; i++) { + size = size == null ? datasets[i].size : Math.min(size, datasets[i].size); + } + } else if (datasets instanceof Object) { + for (const ds in datasets) { + size = size == null ? datasets[ds].size : Math.min(size, datasets[ds].size); + } + } + return datasetFromIteratorFn(async () => { + const streams = await deepMapAndAwaitAll(datasets, (d) => { + if (d instanceof Dataset) { + return { value: d.iterator(), recurse: false }; + } else if (isIterable2(d)) { + return { value: null, recurse: true }; + } else { + throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives."); + } + }); + return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST); + }, size); +} +function deepBatchConcat(rows) { + if (rows === null) { + return null; + } + const exampleRow = rows[0]; + if (canTensorify(exampleRow)) { + const value = batchConcat(rows); + return { value, recurse: false }; + } + return { value: null, recurse: true }; +} +function batchConcat(arrays) { + if (arrays.length === 0) { + throw new Error("Can't make a batch of zero elements."); + } + if (arrays[0] instanceof Tensor) { + return stack(arrays); + } else { + return tensor(arrays); + } +} +var TextLineDataset = class extends Dataset { + /** + * Create a `TextLineDataset`. + * + * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream. + */ + constructor(input2) { + super(); + this.input = input2; + } + async iterator() { + const inputIterator = await this.input.iterator(); + const utf8Iterator = inputIterator.decodeUTF8(); + const lineIterator = utf8Iterator.split("\n").map((line) => { + if (line.endsWith("\r")) { + line = line.slice(0, -1); + } + return line; + }); + return lineIterator; + } +}; +var CODE_QUOTE = '"'; +var STATE_OUT = Symbol("out"); +var STATE_FIELD = Symbol("field"); +var STATE_QUOTE = Symbol("quote"); +var STATE_QUOTE_AFTER_QUOTE = Symbol("quoteafterquote"); +var STATE_WITHIN_QUOTE_IN_QUOTE = Symbol("quoteinquote"); +var CSVDataset = class extends Dataset { + /** + * Returns column names of the csv dataset. If `configuredColumnsOnly` is + * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is + * false and `columnNames` is provided, `columnNames`. If + * `configuredColumnsOnly` is false and `columnNames` is not provided, return + * all column names parsed from the csv file. For example usage please go to + * `tf.data.csv`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + async columnNames() { + if (!this.columnNamesValidated) { + await this.setColumnNames(); + } + return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames; + } + /* 1) If `columnNames` is provided as string[], use this string[] as output + * keys in corresponding order. The length must match the number of inferred + * columns if `hasHeader` is true . + * 2) If `columnNames` is not provided, parse header line as `columnNames` if + * hasHeader is true. If `hasHeader` is false, throw an error. + * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must + * exist in parsed `columnNames`. + */ + async setColumnNames() { + const columnNamesFromFile = await this.maybeReadHeaderLine(); + if (!this.fullColumnNames && !columnNamesFromFile) { + throw new Error("Column names must be provided if there is no header line."); + } else if (this.fullColumnNames && columnNamesFromFile) { + util_exports.assert(columnNamesFromFile.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 (" + columnNamesFromFile.length.toString() + ")."); + } + if (!this.fullColumnNames) { + this.fullColumnNames = columnNamesFromFile; + } + const counts = this.fullColumnNames.reduce((countAcc, name) => { + countAcc[name] = countAcc[name] + 1 || 1; + return countAcc; + }, {}); + const duplicateNames = Object.keys(counts).filter((name) => counts[name] > 1); + util_exports.assert(duplicateNames.length === 0, () => "Duplicate column names found: " + duplicateNames.toString()); + if (this.columnConfigs) { + for (const key of Object.keys(this.columnConfigs)) { + const index = this.fullColumnNames.indexOf(key); + if (index === -1) { + throw new Error('The key "' + key + '" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + ")."); + } + } + } + this.columnNamesValidated = true; + } + async maybeReadHeaderLine() { + if (this.hasHeader) { + const iter = await this.base.iterator(); + const firstElement = await iter.next(); + if (firstElement.done) { + throw new Error("No data was found for CSV parsing."); + } + const firstLine = firstElement.value; + const headers = this.parseRow(firstLine, false); + return headers; + } else { + return null; + } + } + /** + * Create a `CSVDataset`. + * + * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream. + * @param csvConfig (Optional) A CSVConfig object that contains configurations + * of reading and decoding from CSV file(s). + * + * hasHeader: (Optional) A boolean value that indicates whether the first + * row of provided CSV file is a header line with column names, and should + * not be included in the data. Defaults to `true`. + * + * columnNames: (Optional) A list of strings that corresponds to + * the CSV column names, in order. If provided, it ignores the column + * names inferred from the header row. If not provided, infers the column + * names from the first row of the records. If hasHeader is false and + * columnNames is not provided, this method throws an error. + * + * columnConfigs: (Optional) A dictionary whose key is column names, value + * is an object stating if this column is required, column's data type, + * default value, and if this column is label. If provided, keys must + * correspond to names provided in columnNames or inferred from the file + * header lines. If isLabel is true any column, returns an array of two + * items: the first item is a dict of features key/value pairs, the second + * item is a dict of labels key/value pairs. If no feature is marked as + * label, returns a dict of features only. + * + * configuredColumnsOnly (Optional) If true, only columns provided in + * columnConfigs will be parsed and provided during iteration. + * + * delimiter (Optional) The string used to parse each line of the input + * file. Defaults to `,`. + */ + constructor(input2, csvConfig) { + super(); + this.input = input2; + this.hasHeader = true; + this.fullColumnNames = null; + this.columnNamesValidated = false; + this.columnConfigs = null; + this.configuredColumnsOnly = false; + this.delimiter = ","; + this.delimWhitespace = false; + this.base = new TextLineDataset(input2); + if (!csvConfig) { + csvConfig = {}; + } + this.hasHeader = csvConfig.hasHeader === false ? false : true; + this.fullColumnNames = csvConfig.columnNames; + this.columnConfigs = csvConfig.columnConfigs; + this.configuredColumnsOnly = csvConfig.configuredColumnsOnly; + if (csvConfig.delimWhitespace) { + util_exports.assert(csvConfig.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true."); + this.delimWhitespace = true; + this.delimiter = " "; + } else { + this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ","; + } + } + async iterator() { + if (!this.columnNamesValidated) { + await this.setColumnNames(); + } + let lines = await this.base.iterator(); + if (this.hasHeader) { + lines = lines.skip(1); + } + return lines.map((x) => this.makeDataElement(x)); + } + makeDataElement(line) { + const values = this.parseRow(line); + const features = {}; + const labels = {}; + for (let i = 0; i < this.fullColumnNames.length; i++) { + const key = this.fullColumnNames[i]; + const config = this.columnConfigs ? this.columnConfigs[key] : null; + if (this.configuredColumnsOnly && !config) { + continue; + } else { + const value = values[i]; + let parsedValue = null; + if (value === "") { + if (config && config.default !== void 0) { + parsedValue = config.default; + } else if (config && (config.required || config.isLabel)) { + throw new Error(`Required column ${key} is empty in this line: ${line}`); + } else { + parsedValue = void 0; + } + } else { + const valueAsNum = Number(value); + if (isNaN(valueAsNum)) { + if (config && config.dtype === "bool") { + parsedValue = this.getBoolean(value); + } else { + parsedValue = value; + } + } else if (!config || !config.dtype) { + parsedValue = valueAsNum; + } else { + switch (config.dtype) { + case "float32": + parsedValue = valueAsNum; + break; + case "int32": + parsedValue = Math.floor(valueAsNum); + break; + case "bool": + parsedValue = this.getBoolean(value); + break; + default: + parsedValue = valueAsNum; + } + } + } + config && config.isLabel ? labels[key] = parsedValue : features[key] = parsedValue; + } + } + if (Object.keys(labels).length === 0) { + return features; + } else { + return { xs: features, ys: labels }; + } + } + getBoolean(value) { + if (value === "1" || value.toLowerCase() === "true") { + return 1; + } else { + return 0; + } + } + // adapted from https://beta.observablehq.com/@mbostock/streaming-csv + parseRow(line, validateElementCount = true) { + const result = []; + let readOffset = 0; + const readLength = line.length; + let currentState = STATE_OUT; + for (let i = 0; i < readLength; i++) { + switch (currentState) { + case STATE_OUT: + switch (line.charAt(i)) { + case CODE_QUOTE: + readOffset = i + 1; + currentState = STATE_QUOTE; + break; + case this.delimiter: + readOffset = i + 1; + if (this.delimiter === " " && this.delimWhitespace) { + break; + } + result.push(""); + currentState = STATE_OUT; + break; + default: + currentState = STATE_FIELD; + readOffset = i; + break; + } + break; + case STATE_FIELD: + switch (line.charAt(i)) { + case this.delimiter: + result.push(line.substring(readOffset, i)); + currentState = STATE_OUT; + readOffset = i + 1; + break; + default: + } + break; + case STATE_QUOTE: + switch (line.charAt(i)) { + case CODE_QUOTE: + currentState = STATE_QUOTE_AFTER_QUOTE; + break; + default: + } + break; + case STATE_QUOTE_AFTER_QUOTE: + switch (line.charAt(i)) { + case this.delimiter: + result.push(line.substring(readOffset, i - 1)); + currentState = STATE_OUT; + readOffset = i + 1; + break; + case CODE_QUOTE: + currentState = STATE_QUOTE; + break; + default: + currentState = STATE_WITHIN_QUOTE_IN_QUOTE; + break; + } + break; + case STATE_WITHIN_QUOTE_IN_QUOTE: + switch (line.charAt(i)) { + case CODE_QUOTE: + currentState = STATE_QUOTE; + break; + default: + } + break; + default: + } + } + if (currentState === STATE_QUOTE_AFTER_QUOTE) { + result.push(line.substring(readOffset, readLength - 1)); + } else { + result.push(line.substring(readOffset)); + } + if (validateElementCount && result.length !== this.fullColumnNames.length) { + throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`); + } + return result; + } +}; +var MicrophoneIterator = class _MicrophoneIterator extends LazyIterator { + constructor(microphoneConfig) { + super(); + this.microphoneConfig = microphoneConfig; + this.isClosed = false; + this.fftSize = microphoneConfig.fftSize || 1024; + const fftSizeLog2 = Math.log2(this.fftSize); + if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 || !Number.isInteger(fftSizeLog2)) { + throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`); + } + this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43; + this.sampleRateHz = microphoneConfig.sampleRateHz; + this.columnTruncateLength = microphoneConfig.columnTruncateLength || this.fftSize; + this.audioTrackConstraints = microphoneConfig.audioTrackConstraints; + this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0; + this.includeSpectrogram = microphoneConfig.includeSpectrogram === false ? false : true; + this.includeWaveform = microphoneConfig.includeWaveform === true ? true : false; + if (!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`; + } + // Construct a MicrophoneIterator and start the audio stream. + static async create(microphoneConfig = {}) { + if (!env().get("IS_BROWSER")) { + throw new Error("microphone API is only supported in browser environment."); + } + const microphoneIterator = new _MicrophoneIterator(microphoneConfig); + await microphoneIterator.start(); + return microphoneIterator; + } + // Start the audio stream and FFT. + async start() { + try { + this.stream = await navigator.mediaDevices.getUserMedia({ + audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints, + video: false + }); + } catch (e) { + throw new Error(`Error thrown while initializing video stream: ${e.message}`); + } + if (!this.stream) { + throw new Error("Could not obtain audio from microphone."); + } + const ctxConstructor = ( + // tslint:disable-next-line:no-any + window.AudioContext || window.webkitAudioContext + ); + this.audioContext = new ctxConstructor(); + if (!this.sampleRateHz) { + this.sampleRateHz = this.audioContext.sampleRate; + } else if (this.audioContext.sampleRate !== this.sampleRateHz) { + throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`); + } + const streamSource = this.audioContext.createMediaStreamSource(this.stream); + this.analyser = this.audioContext.createAnalyser(); + this.analyser.fftSize = this.fftSize * 2; + this.analyser.smoothingTimeConstant = this.smoothingTimeConstant; + streamSource.connect(this.analyser); + this.freqData = new Float32Array(this.fftSize); + this.timeData = new Float32Array(this.fftSize); + return; + } + async next() { + if (this.isClosed) { + return { value: null, done: true }; + } + let spectrogramTensor; + let waveformTensor; + const audioDataQueue = await this.getAudioData(); + if (this.includeSpectrogram) { + const freqData = this.flattenQueue(audioDataQueue.freqDataQueue); + spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]); + } + if (this.includeWaveform) { + const timeData = this.flattenQueue(audioDataQueue.timeDataQueue); + waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]); + } + return { + value: { "spectrogram": spectrogramTensor, "waveform": waveformTensor }, + done: false + }; + } + // Capture one result from the audio stream, and extract the value from + // iterator.next() result. + async capture() { + return (await this.next()).value; + } + async getAudioData() { + const freqDataQueue = []; + const timeDataQueue = []; + let currentFrames = 0; + return new Promise((resolve) => { + const intervalID = setInterval(() => { + if (this.includeSpectrogram) { + this.analyser.getFloatFrequencyData(this.freqData); + if (this.freqData[0] === -Infinity) { + resolve({ freqDataQueue, timeDataQueue }); + } + freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength)); + } + if (this.includeWaveform) { + this.analyser.getFloatTimeDomainData(this.timeData); + timeDataQueue.push(this.timeData.slice()); + } + if (++currentFrames === this.numFrames) { + clearInterval(intervalID); + resolve({ freqDataQueue, timeDataQueue }); + } + }, this.fftSize / this.sampleRateHz * 1e3); + }); + } + // Stop the audio stream and pause the iterator. + stop() { + if (!this.isClosed) { + this.isClosed = true; + this.analyser.disconnect(); + this.audioContext.close(); + if (this.stream != null && this.stream.getTracks().length > 0) { + this.stream.getTracks()[0].stop(); + } + } + } + // Override toArray() function to prevent collecting. + toArray() { + throw new Error("Can not convert infinite audio stream to array."); + } + // Return audio sampling rate in Hz + getSampleRate() { + return this.sampleRateHz; + } + flattenQueue(queue) { + const frameSize = queue[0].length; + const freqData = new Float32Array(queue.length * frameSize); + queue.forEach((data, i) => freqData.set(data, i * frameSize)); + return freqData; + } + getTensorFromAudioDataArray(freqData, shape) { + const vals = new Float32Array(util_exports.sizeFromShape(shape)); + vals.set(freqData, vals.length - freqData.length); + return tensor(vals, shape); + } +}; +var WebcamIterator = class _WebcamIterator extends LazyIterator { + constructor(webcamVideoElement, webcamConfig) { + super(); + this.webcamVideoElement = webcamVideoElement; + this.webcamConfig = webcamConfig; + this.isClosed = true; + this.resize = false; + if (this.needToResize()) { + this.resize = true; + this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth]; + this.cropBoxInd = tensor1d([0], "int32"); + if (this.webcamConfig.centerCrop) { + const widthCroppingRatio = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width; + const heightCroppingRatio = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height; + const widthCropStart = (1 - widthCroppingRatio) / 2; + const heightCropStart = (1 - heightCroppingRatio) / 2; + const widthCropEnd = widthCropStart + widthCroppingRatio; + const heightCropEnd = heightCroppingRatio + heightCropStart; + this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]); + } else { + this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]); + } + } + } + summary() { + return `webcam`; + } + // Construct a WebcamIterator and start it's video stream. + static async create(webcamVideoElement, webcamConfig = {}) { + if (!env().get("IS_BROWSER")) { + throw new Error("tf.data.webcam is only supported in browser environment."); + } + if (!webcamVideoElement) { + webcamVideoElement = document.createElement("video"); + if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) { + throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element."); + } + webcamVideoElement.width = webcamConfig.resizeWidth; + webcamVideoElement.height = webcamConfig.resizeHeight; + } + const webcamIterator = new _WebcamIterator(webcamVideoElement, webcamConfig); + await webcamIterator.start(); + return webcamIterator; + } + // Async function to start video stream. + async start() { + if (this.webcamConfig.facingMode) { + util_exports.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) { + e.message = `Error thrown while initializing video stream: ${e.message}`; + throw e; + } + if (!this.stream) { + throw new Error("Could not obtain video from webcam."); + } + try { + this.webcamVideoElement.srcObject = this.stream; + } catch (error) { + console.log(error); + this.webcamVideoElement.src = window.URL.createObjectURL(this.stream); + } + this.webcamVideoElement.play(); + this.isClosed = false; + return new Promise((resolve) => { + this.webcamVideoElement.onloadedmetadata = () => { + resolve(); + }; + }); + } + async next() { + if (this.isClosed) { + return { value: null, done: true }; + } + let img; + try { + img = browser_exports.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(img), done: false }; + } catch (e) { + throw new Error(`Error thrown cropping the video: ${e.message}`); + } finally { + img.dispose(); + } + } else { + return { value: img, done: false }; + } + } + needToResize() { + if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) { + return true; + } + return false; + } + // Cropping and resizing each frame based on config + cropAndResizeFrame(img) { + return tidy(() => { + const expandedImage = expandDims(cast(img, "float32"), 0); + let resizedImage; + resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear"); + const shape = resizedImage.shape; + return reshape(resizedImage, shape.slice(1)); + }); + } + // Capture one frame from the video stream, and extract the value from + // iterator.next() result. + async capture() { + return (await this.next()).value; + } + // Stop the video stream and pause webcam iterator. + stop() { + const tracks = this.stream.getTracks(); + tracks.forEach((track) => track.stop()); + try { + this.webcamVideoElement.srcObject = null; + } catch (error) { + console.log(error); + this.webcamVideoElement.src = null; + } + this.isClosed = true; + } + // Override toArray() function to prevent collecting. + toArray() { + throw new Error("Can not convert infinite video stream to array."); + } +}; +var DataSource = class { +}; +var StringIterator = class extends LazyIterator { + /** + * Splits a string stream on a given separator. + * + * It is assumed that the incoming chunk boundaries have no semantic meaning, + * so conceptually the incoming stream is treated simply as the concatenation + * of its elements. + * + * The outgoing stream provides chunks corresponding to the results of the + * standard string split() operation (even if such a chunk spanned incoming + * chunks). The separators are not included. + * + * A typical usage is to split a text file (represented as a stream with + * arbitrary chunk boundaries) into lines. + * + * @param upstream A readable stream of strings that can be treated as + * concatenated. + * @param separator A character to split on. + */ + split(separator) { + return new SplitIterator(this, separator); + } +}; +var SplitIterator = class extends StringIterator { + constructor(upstream, separator) { + super(); + this.upstream = upstream; + this.impl = new SplitIteratorImpl(upstream, separator); + } + summary() { + return this.impl.summary(); + } + async next() { + return this.impl.next(); + } +}; +var SplitIteratorImpl = class extends OneToManyIterator { + constructor(upstream, separator) { + super(); + this.upstream = upstream; + this.separator = separator; + this.carryover = ""; + } + summary() { + return `${this.upstream.summary()} -> Split('${this.separator}')`; + } + async pump() { + const chunkResult = await this.upstream.next(); + if (chunkResult.done) { + if (this.carryover === "") { + return false; + } + this.outputQueue.push(this.carryover); + this.carryover = ""; + return true; + } + const lines = chunkResult.value.split(this.separator); + lines[0] = this.carryover + lines[0]; + for (const line of lines.slice(0, -1)) { + this.outputQueue.push(line); + } + this.carryover = lines[lines.length - 1]; + return true; + } +}; +var ByteChunkIterator = class extends LazyIterator { + /** + * Decode a stream of UTF8-encoded byte arrays to a stream of strings. + * + * The byte arrays producetd from the ByteChunkIterator on which this is + * called will be interpreted as concatenated. No assumptions are made about + * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a + * character may span the boundary between chunks. This naturally happens, + * for instance, when reading fixed-size byte arrays from a file. + */ + decodeUTF8() { + return new Utf8Iterator(this); + } +}; +var Utf8Iterator = class extends StringIterator { + constructor(upstream) { + super(); + this.upstream = upstream; + this.impl = new Utf8IteratorImpl(upstream); + } + summary() { + return this.impl.summary(); + } + async next() { + return this.impl.next(); + } +}; +var Utf8IteratorImpl = class extends OneToManyIterator { + constructor(upstream) { + super(); + this.upstream = upstream; + if (env().get("IS_BROWSER")) { + this.decoder = new TextDecoder("utf-8"); + } else { + const { StringDecoder } = require_string_decoder(); + this.decoder = new StringDecoder("utf8"); + } + } + summary() { + return `${this.upstream.summary()} -> Utf8`; + } + async pump() { + const chunkResult = await this.upstream.next(); + let chunk; + if (chunkResult.done) { + return false; + } else { + chunk = chunkResult.value; + } + let text; + if (env().get("IS_BROWSER")) { + text = this.decoder.decode(chunk, { stream: true }); + } else { + text = this.decoder.write(Buffer.from(chunk.buffer)); + } + this.outputQueue.push(text); + return true; + } +}; +var FileChunkIterator = class extends ByteChunkIterator { + constructor(file, options = {}) { + super(); + this.file = file; + this.options = options; + util_exports.assert(file instanceof Uint8Array || (env().get("IS_BROWSER") ? file instanceof File || file instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now."); + this.offset = options.offset || 0; + this.chunkSize = options.chunkSize || 1024 * 1024; + } + summary() { + return `FileChunks ${this.file}`; + } + async next() { + if (this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size)) { + return { value: null, done: true }; + } + const chunk = new Promise((resolve, reject) => { + const end = this.offset + this.chunkSize; + if (this.file instanceof Uint8Array) { + resolve(new Uint8Array(this.file.slice(this.offset, end))); + } else { + const fileReader = new FileReader(); + fileReader.onload = (event) => { + let data = fileReader.result; + if (data instanceof ArrayBuffer) { + data = new Uint8Array(data); + } + if (!(data instanceof Uint8Array)) { + return reject(new TypeError("FileReader returned unknown type.")); + } + resolve(data); + }; + fileReader.onabort = (event) => { + return reject(new Error("Aborted")); + }; + fileReader.onerror = (event) => { + return reject(new Error(event.type)); + }; + const slice5 = this.file.slice(this.offset, end); + fileReader.readAsArrayBuffer(slice5); + } + this.offset = end; + }); + return { value: await chunk, done: false }; + } +}; +async function urlChunkIterator(url, options = {}, fetchFunc) { + let urlString; + let requestInit; + if (typeof url === "string") { + urlString = url; + } else { + urlString = url.url; + requestInit = getRequestInitFromRequest(url); + } + const response = await (fetchFunc || util_exports.fetch)(urlString, requestInit); + if (response.ok) { + const uint8Array = new Uint8Array(await response.arrayBuffer()); + return new FileChunkIterator(uint8Array, options); + } else { + throw new Error(response.statusText); + } +} +var getRequestInitFromRequest = (request) => { + const init2 = { + method: request.method, + headers: request.headers, + body: request.body, + mode: request.mode, + credentials: request.credentials, + cache: request.cache, + redirect: request.redirect, + referrer: request.referrer, + integrity: request.integrity + }; + return init2; +}; +function isLocalPath(source) { + return typeof source === "string" && source.slice(0, 7) === "file://"; +} +var FileDataSource = class extends DataSource { + /** + * Create a `FileDataSource`. + * + * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to + * read. Local file only works in node environment. + * @param options Options passed to the underlying `FileChunkIterator`s, + * such as {chunksize: 1024}. + */ + constructor(input2, options = {}) { + super(); + this.input = input2; + this.options = options; + } + async iterator() { + if (isLocalPath(this.input) && env().get("IS_NODE")) { + const fs = require_fs(); + this.input = fs.readFileSync(this.input.slice(7)); + } + return new FileChunkIterator(this.input, this.options); + } +}; +var URLDataSource = class extends DataSource { + /** + * Create a `URLDataSource`. + * + * @param url A source URL string, or a `Request` object. + * @param options Options passed to the underlying `FileChunkIterator`s, + * such as {chunksize: 1024}. + */ + constructor(url, fileOptions = {}) { + super(); + this.url = url; + this.fileOptions = fileOptions; + } + // TODO(soergel): provide appropriate caching options. Currently this + // will download the URL anew for each call to iterator(). Since we have + // to treat the downloaded file as a blob/buffer anyway, we may as well retain + // it-- but that raises GC issues. Also we may want a persistent disk cache. + async iterator() { + if (isLocalPath(this.url)) { + return new FileDataSource(this.url, this.fileOptions).iterator(); + } else { + return urlChunkIterator(this.url, this.fileOptions); + } + } +}; +function csv(source, csvConfig = {}) { + return new CSVDataset(new URLDataSource(source), csvConfig); +} +function func(f) { + const iter = iteratorFromFunction(f); + return datasetFromIteratorFn(async () => iter); +} +function generator(generator2) { + return datasetFromIteratorFn(async () => { + const gen = await generator2(); + return iteratorFromFunction(() => gen.next()); + }); +} +async function webcam(webcamVideoElement, webcamConfig) { + return WebcamIterator.create(webcamVideoElement, webcamConfig); +} +async function microphone(microphoneConfig) { + return MicrophoneIterator.create(microphoneConfig); +} +var version4 = "4.16.0"; +function assertNotComplex(tensor2, opName) { + if (!Array.isArray(tensor2)) { + tensor2 = [tensor2]; + } + tensor2.forEach((t) => { + if (t != null) { + util_exports.assert(t.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the CPU backend.`); + } + }); +} +var whereImpl2 = kernel_impls_exports.whereImpl; +var MathBackendCPU = class _MathBackendCPU extends KernelBackend { + nextDataId() { + return _MathBackendCPU.nextDataId++; + } + constructor() { + super(); + this.blockSize = 48; + this.firstUse = true; + this.data = new DataStorage(this, engine()); + } + write(values, shape, dtype) { + if (this.firstUse) { + this.firstUse = false; + if (env().get("IS_NODE")) { + backend_util_exports.warn("\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================"); + } + } + const dataId = { id: this.nextDataId() }; + this.data.set(dataId, { values, dtype, refCount: 1 }); + return dataId; + } + /** + * Create a data bucket in cpu backend. + * @param shape Shape of the `TensorInfo`. + * @param dtype DType of the `TensorInfo`. + * @param values The value of the `TensorInfo` stored as a flattened array. + */ + makeTensorInfo(shape, dtype, values) { + let outId; + if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) { + const encodedValues = values.map((d) => util_exports.encodeString(d)); + outId = this.write(encodedValues, shape, dtype); + } else { + outId = this.write(values, shape, dtype); + } + return { dataId: outId, shape, dtype }; + } + /** Return refCount of a `TensorData`. */ + refCount(dataId) { + if (this.data.has(dataId)) { + const tensorData = this.data.get(dataId); + return tensorData.refCount; + } + return 0; + } + /** Increase refCount of a `TensorData`. */ + incRef(dataId) { + const tensorData = this.data.get(dataId); + tensorData.refCount++; + } + /** Decrease refCount of a `TensorData`. */ + decRef(dataId) { + if (this.data.has(dataId)) { + const tensorData = this.data.get(dataId); + tensorData.refCount--; + } + } + move(dataId, values, shape, dtype, refCount) { + this.data.set(dataId, { values, dtype, refCount }); + } + numDataIds() { + return this.data.numDataIds(); + } + async read(dataId) { + return this.readSync(dataId); + } + readSync(dataId) { + const { dtype, complexTensorInfos } = this.data.get(dataId); + if (dtype === "complex64") { + const realValues = this.readSync(complexTensorInfos.real.dataId); + const imagValues = this.readSync(complexTensorInfos.imag.dataId); + return backend_util_exports.mergeRealAndImagArrays(realValues, imagValues); + } + return util_exports.convertBackendValuesAndArrayBuffer(this.data.get(dataId).values, dtype); + } + bufferSync(t) { + const data = this.readSync(t.dataId); + if (t.dtype === "string") { + try { + const strings = data.map((d) => util_exports.decodeString(d)); + return buffer(t.shape, t.dtype, strings); + } catch (_a) { + throw new Error("Failed to decode encoded string bytes into utf-8"); + } + } + return buffer(t.shape, t.dtype, data); + } + makeOutput(values, shape, dtype) { + return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this); + } + /** + * Dispose the memory if the dataId has 0 refCount. Return true if the memory + * is released or memory is not managed in this backend, false if memory is + * not cleared. + * @param dataId + * @oaram force Optional, remove the data regardless of refCount + */ + disposeData(dataId, force = false) { + if (this.data.has(dataId)) { + this.data.get(dataId).refCount--; + if (!force && this.data.get(dataId).refCount > 0) { + return false; + } + const { complexTensorInfos } = this.data.get(dataId); + if (complexTensorInfos != null) { + this.disposeData(complexTensorInfos.real.dataId, true); + this.disposeData(complexTensorInfos.imag.dataId, true); + } + this.data.delete(dataId); + } + return true; + } + disposeIntermediateTensorInfo(tensorInfo) { + this.disposeData(tensorInfo.dataId); + } + async time(f) { + const start = util_exports.now(); + f(); + const kernelMs = util_exports.now() - start; + return { kernelMs }; + } + memory() { + return { + // Unreliable due to automatic gc. The numbers above are cumulative. + unreliable: true, + reasons: ["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."] + }; + } + where(condition) { + assertNotComplex([condition], "where"); + const condVals = this.readSync(condition.dataId); + return whereImpl2(condition.shape, condVals); + } + dispose() { + } + floatPrecision() { + return 32; + } + /** Returns the smallest representable number. */ + epsilon() { + return super.epsilon(); + } +}; +MathBackendCPU.nextDataId = 0; +var shared_exports = {}; +__export2(shared_exports, { + addImpl: () => addImpl, + bincountImpl: () => bincountImpl, + bincountReduceImpl: () => bincountReduceImpl, + bitwiseAndImpl: () => bitwiseAndImpl, + castImpl: () => castImpl, + ceilImpl: () => ceilImpl, + concatImpl: () => concatImpl, + equalImpl: () => equalImpl, + expImpl: () => expImpl, + expm1Impl: () => expm1Impl, + floorDivImpl: () => floorDivImpl, + floorImpl: () => floorImpl, + gatherNdImpl: () => gatherNdImpl, + gatherV2Impl: () => gatherV2Impl, + greaterEqualImpl: () => greaterEqualImpl, + greaterImpl: () => greaterImpl, + lessEqualImpl: () => lessEqualImpl, + lessImpl: () => lessImpl, + linSpaceImpl: () => linSpaceImpl, + logImpl: () => logImpl, + maxImpl: () => maxImpl, + maximumImpl: () => maximumImpl, + minimumImpl: () => minimumImpl, + multiplyImpl: () => multiplyImpl, + negImpl: () => negImpl, + notEqualImpl: () => notEqualImpl, + prodImpl: () => prodImpl, + raggedGatherImpl: () => raggedGatherImpl, + raggedRangeImpl: () => raggedRangeImpl, + raggedTensorToTensorImpl: () => raggedTensorToTensorImpl, + rangeImpl: () => rangeImpl, + rsqrtImpl: () => rsqrtImpl, + scatterImpl: () => scatterImpl, + sigmoidImpl: () => sigmoidImpl, + simpleAbsImpl: () => simpleAbsImpl, + sliceImpl: () => sliceImpl, + sparseFillEmptyRowsImpl: () => sparseFillEmptyRowsImpl, + sparseReshapeImpl: () => sparseReshapeImpl, + sparseSegmentReductionImpl: () => sparseSegmentReductionImpl, + sqrtImpl: () => sqrtImpl, + squaredDifferenceImpl: () => squaredDifferenceImpl, + staticRegexReplaceImpl: () => staticRegexReplaceImpl, + stridedSliceImpl: () => stridedSliceImpl, + stringNGramsImpl: () => stringNGramsImpl, + stringSplitImpl: () => stringSplitImpl, + stringToHashBucketFastImpl: () => stringToHashBucketFastImpl, + subImpl: () => subImpl, + tileImpl: () => tileImpl, + topKImpl: () => topKImpl, + transposeImpl: () => transposeImpl, + uniqueImpl: () => uniqueImpl +}); +function simpleAbsImpl(vals) { + const resultValues = new Float32Array(vals.length); + for (let i = 0; i < vals.length; ++i) { + resultValues[i] = Math.abs(vals[i]); + } + return resultValues; +} +var abs2 = (args) => { + const { x } = args.inputs; + const cpuBackend = args.backend; + assertNotComplex(x, "abs"); + let resultValues = new Float32Array(util_exports.sizeFromShape(x.shape)); + const values = cpuBackend.data.get(x.dataId).values; + resultValues = simpleAbsImpl(values); + return cpuBackend.makeOutput(resultValues, x.shape, x.dtype); +}; +var absConfig = { + kernelName: Abs, + backendName: "cpu", + kernelFunc: abs2 +}; +function createSimpleBinaryKernelImpl(op2) { + return (aShape, bShape, aVals, bVals, dtype) => { + const newShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + const resultRank = newShape.length; + const resultStrides = util_exports.computeStrides(newShape); + const resultSize = util_exports.sizeFromShape(newShape); + const result = util_exports.getTypedArrayFromDType(dtype, resultSize); + const aRank = aShape.length; + const bRank = bShape.length; + const aStrides = util_exports.computeStrides(aShape); + const bStrides = util_exports.computeStrides(bShape); + const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape); + const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape); + if (aBroadcastDims.length + bBroadcastDims.length === 0) { + for (let i = 0; i < result.length; ++i) { + result[i] = op2(aVals[i % aVals.length], bVals[i % bVals.length]); + } + } else { + for (let i = 0; i < result.length; ++i) { + const loc = util_exports.indexToLoc(i, resultRank, resultStrides); + const aLoc = loc.slice(-aRank); + aBroadcastDims.forEach((d) => aLoc[d] = 0); + const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides); + const bLoc = loc.slice(-bRank); + bBroadcastDims.forEach((d) => bLoc[d] = 0); + const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides); + result[i] = op2(aVals[aIndex], bVals[bIndex]); + } + } + return [result, newShape]; + }; +} +function complex2(args) { + const { inputs, backend: backend2 } = args; + const { real: real4, imag: imag4 } = inputs; + const realVals = backend2.data.get(real4.dataId).values; + const imagVals = backend2.data.get(imag4.dataId).values; + const complexInfo = backend2.makeTensorInfo(real4.shape, "complex64"); + const complex4 = backend2.data.get(complexInfo.dataId); + complex4.complexTensorInfos = { + real: backend2.makeTensorInfo(real4.shape, "float32", realVals), + imag: backend2.makeTensorInfo(imag4.shape, "float32", imagVals) + }; + return complexInfo; +} +var complexConfig = { + kernelName: Complex, + backendName: "cpu", + kernelFunc: complex2 +}; +function zeros3(backend2, shape, dtype = "float32") { + if (dtype === "complex64") { + const real4 = zeros3(backend2, shape, "float32"); + const imag4 = zeros3(backend2, shape, "float32"); + return complex2({ inputs: { real: real4, imag: imag4 }, backend: backend2 }); + } + const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype); + return backend2.makeTensorInfo(shape, dtype, values); +} +function identity2(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + backend2.incRef(x.dataId); + return { dataId: x.dataId, shape: x.shape, dtype: x.dtype }; +} +var identityConfig = { + kernelName: Identity, + backendName: "cpu", + kernelFunc: identity2 +}; +function real2(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const real4 = backend2.data.get(input2.dataId).complexTensorInfos.real; + const realVal = backend2.data.get(real4.dataId).values; + return backend2.makeTensorInfo(real4.shape, real4.dtype, realVal); +} +var realConfig = { + kernelName: Real, + backendName: "cpu", + kernelFunc: real2 +}; +function castImpl(values, shape, inputType, dtype) { + if (dtype === "int32") { + const resultValues = Int32Array.from(values); + return [shape, "int32", resultValues]; + } + if (dtype === "bool") { + const zero = util_exports.toTypedArray([0], inputType); + const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0)(shape, [], values, zero, "bool"); + return [resultShape, "bool", resultData]; + } + throw new Error(`Error in Cast: failed to cast ${inputType} to ${dtype}`); +} +function cast3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dtype } = attrs; + if (dtype === "complex64") { + if (x.dtype === "complex64") { + return identity2({ inputs: { x }, backend: backend2 }); + } + const zerosTensorInfo = zeros3(backend2, x.shape, x.dtype); + const floatX = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } }); + const result = complex2({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(zerosTensorInfo); + backend2.disposeIntermediateTensorInfo(floatX); + return result; + } + if (x.dtype === "complex64") { + const realPart = real2({ inputs: { input: x }, backend: backend2 }); + const result = cast3({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } }); + backend2.disposeIntermediateTensorInfo(realPart); + return result; + } + if (!util_exports.hasEncodingLoss(x.dtype, dtype)) { + const result = identity2({ inputs: { x }, backend: backend2 }); + return { dataId: result.dataId, shape: result.shape, dtype }; + } + const values = backend2.data.get(x.dataId).values; + const [resultShape, resultType, resultData] = castImpl(values, x.shape, x.dtype, dtype); + return backend2.makeTensorInfo(resultShape, resultType, resultData); +} +var castConfig = { + kernelName: Cast, + backendName: "cpu", + kernelFunc: cast3 +}; +function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) { + if (complexImpl == null) { + return ({ inputs, backend: backend2 }) => { + const { a, b } = inputs; + const cpuBackend = backend2; + assertNotComplex([a, b], name); + const aVals = cpuBackend.data.get(a.dataId).values; + const bVals = cpuBackend.data.get(b.dataId).values; + const decodedAVals = a.dtype === "string" ? ( + // tslint:disable-next-line: no-any + backend_util_exports.fromUint8ToStringArray(aVals) + ) : aVals; + const decodedBVals = a.dtype === "string" ? ( + // tslint:disable-next-line: no-any + backend_util_exports.fromUint8ToStringArray(bVals) + ) : bVals; + const $dtype = dtype || a.dtype; + const [resultData, resultShape] = simpleImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype); + return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData); + }; + } + return ({ inputs, backend: backend2 }) => { + const { a, b } = inputs; + const cpuBackend = backend2; + if (a.dtype === "complex64" || b.dtype === "complex64") { + const $aComplex = cast3({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: "complex64" } }); + const $aComplexVals = cpuBackend.data.get($aComplex.dataId); + const aReal = $aComplexVals.complexTensorInfos.real; + const aImag = $aComplexVals.complexTensorInfos.imag; + const aRealVals = cpuBackend.data.get(aReal.dataId).values; + const aImagVals = cpuBackend.data.get(aImag.dataId).values; + const $bComplex = cast3({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: "complex64" } }); + const $bComplexVals = cpuBackend.data.get($bComplex.dataId); + const bReal = $bComplexVals.complexTensorInfos.real; + const bImag = $bComplexVals.complexTensorInfos.imag; + const bRealVals = cpuBackend.data.get(bReal.dataId).values; + const bImagVals = cpuBackend.data.get(bImag.dataId).values; + const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals); + const resultReal = cpuBackend.makeTensorInfo(resultShape, "float32", resultRealData); + const resultImag = cpuBackend.makeTensorInfo(resultShape, "float32", resultImagData); + const result = complex2({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend }); + cpuBackend.disposeIntermediateTensorInfo($aComplex); + cpuBackend.disposeIntermediateTensorInfo($bComplex); + cpuBackend.disposeIntermediateTensorInfo(resultReal); + cpuBackend.disposeIntermediateTensorInfo(resultImag); + return result; + } else { + const aVals = cpuBackend.data.get(a.dataId).values; + const bVals = cpuBackend.data.get(b.dataId).values; + const $dtype = dtype || a.dtype; + const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype); + return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData); + } + }; +} +function createComplexBinaryKernelImpl(op2) { + return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => { + const resultShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + const resultSize = util_exports.sizeFromShape(resultShape); + const resultRank = resultShape.length; + const resultStrides = util_exports.computeStrides(resultShape); + const resultRealVals = util_exports.getTypedArrayFromDType("float32", resultSize); + const resultImagVals = util_exports.getTypedArrayFromDType("float32", resultSize); + const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, resultShape); + const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, resultShape); + const aVals = backend_util_exports.mergeRealAndImagArrays(aRealVals, aImagVals); + const bVals = backend_util_exports.mergeRealAndImagArrays(bRealVals, bImagVals); + const aRank = aShape.length; + const aStrides = util_exports.computeStrides(aShape); + const bRank = bShape.length; + const bStrides = util_exports.computeStrides(bShape); + if (aBroadcastDims.length + bBroadcastDims.length === 0) { + for (let i = 0; i < resultRealVals.length; i++) { + const aIdx = i % aVals.length; + const bIdx = i % bVals.length; + const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]); + resultRealVals[i] = result.real; + resultImagVals[i] = result.imag; + } + } else { + for (let i = 0; i < resultRealVals.length; i++) { + const loc = util_exports.indexToLoc(i, resultRank, resultStrides); + const aLoc = loc.slice(-aRank); + aBroadcastDims.forEach((d) => aLoc[d] = 0); + const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides); + const bLoc = loc.slice(-bRank); + bBroadcastDims.forEach((d) => bLoc[d] = 0); + const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides); + const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]); + resultRealVals[i] = opResult.real; + resultImagVals[i] = opResult.imag; + } + } + return [resultRealVals, resultImagVals, resultShape]; + }; +} +var addImpl = createSimpleBinaryKernelImpl((a, b) => a + b); +var addComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => { + return { real: aReal + bReal, imag: aImag + bImag }; +}); +var add4 = binaryKernelFunc(Add, addImpl, addComplexImpl); +var addConfig = { + kernelName: Add, + backendName: "cpu", + kernelFunc: add4 +}; +function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) { + const weightsSize = util_exports.sizeFromShape(weightsShape); + const outVals = util_exports.makeZerosTypedArray(size, weightsDtype); + for (let i = 0; i < xVals.length; i++) { + const value = xVals[i]; + if (value < 0) { + throw new Error("Input x must be non-negative!"); + } + if (value >= size) { + continue; + } + if (weightsSize > 0) { + outVals[value] += weightsVals[i]; + } else { + outVals[value] += 1; + } + } + return outVals; +} +function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) { + const numRows = xBuf.shape[0]; + const numCols = xBuf.shape[1]; + const outBuf = buffer([numRows, size], weightsBuf.dtype); + for (let i = 0; i < numRows; i++) { + for (let j = 0; j < numCols; j++) { + const value = xBuf.get(i, j); + if (value < 0) { + throw new Error("Input x must be non-negative!"); + } + if (value >= size) { + continue; + } + if (binaryOutput) { + outBuf.set(1, i, value); + } else { + if (weightsBuf.size > 0) { + outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value); + } else { + outBuf.set(outBuf.get(i, value) + 1, i, value); + } + } + } + } + return outBuf; +} +var bitwiseAndImpl = createSimpleBinaryKernelImpl((a, b) => a & b); +var bitwiseAnd2 = binaryKernelFunc(BitwiseAnd, bitwiseAndImpl); +var bitwiseAndConfig = { + kernelName: BitwiseAnd, + backendName: "cpu", + kernelFunc: bitwiseAnd2 +}; +function createSimpleUnaryImpl(op2) { + return (values, dtype, attrs) => { + const newValues = util_exports.getArrayFromDType(dtype, values.length); + for (let i = 0; i < values.length; ++i) { + newValues[i] = op2(values[i], attrs); + } + return newValues; + }; +} +function unaryKernelFunc(name, op2, dtype) { + const impl = createSimpleUnaryImpl(op2); + return unaryKernelFuncFromImpl(name, impl, dtype); +} +function unaryKernelFuncFromImpl(name, unaryImpl, dtype) { + return ({ inputs, attrs, backend: backend2 }) => { + const { x } = inputs; + assertNotComplex(x, name); + const cpuBackend = backend2; + const values = cpuBackend.data.get(x.dataId).values; + let decoded; + if (x.dtype === "string") { + if (!Array.isArray(values)) { + throw new Error("String tensor's value was not an instance of Array"); + } + decoded = backend_util_exports.fromUint8ToStringArray(values); + } else { + decoded = values; + } + const $dtype = dtype || x.dtype; + const newValues = unaryImpl(decoded, $dtype, attrs); + return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues); + }; +} +var ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi)); +var ceil2 = unaryKernelFuncFromImpl(Ceil, ceilImpl); +var ceilConfig = { + kernelName: Ceil, + backendName: "cpu", + kernelFunc: ceil2 +}; +function concatImpl(inputs, outShape, dtype, simplyConcat) { + const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape)); + if (simplyConcat && dtype !== "string") { + let offset = 0; + inputs.forEach((input2) => { + const size = util_exports.sizeFromShape(input2.shape); + outVals.set(input2.vals, offset); + offset += size; + }); + } else { + let colOffset = 0; + inputs.forEach((input2) => { + const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals; + let tIdx = 0; + for (let row = 0; row < input2.shape[0]; ++row) { + const resIdx = row * outShape[1] + colOffset; + for (let col = 0; col < input2.shape[1]; ++col) { + outVals[resIdx + col] = decodedData[tIdx++]; + } + } + colOffset += input2.shape[1]; + }); + } + return outVals; +} +var equalImpl = createSimpleBinaryKernelImpl((a, b) => a === b ? 1 : 0); +var equal2 = binaryKernelFunc(Equal, equalImpl, null, "bool"); +var equalConfig = { + kernelName: Equal, + backendName: "cpu", + kernelFunc: equal2 +}; +var expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi)); +var exp2 = unaryKernelFuncFromImpl(Exp, expImpl, "float32"); +var expConfig = { + kernelName: Exp, + backendName: "cpu", + kernelFunc: exp2 +}; +var expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi)); +var expm12 = unaryKernelFuncFromImpl(Expm1, expm1Impl); +var expm1Config = { + kernelName: Expm1, + backendName: "cpu", + kernelFunc: expm12 +}; +var floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi)); +var floor2 = unaryKernelFuncFromImpl(Floor, floorImpl); +var floorConfig = { + kernelName: Floor, + backendName: "cpu", + kernelFunc: floor2 +}; +var floorDivImpl = createSimpleBinaryKernelImpl((a, b) => Math.floor(a / b)); +var floorDiv2 = binaryKernelFunc(FloorDiv, floorDivImpl, null, "int32"); +var floorDivConfig = { + kernelName: FloorDiv, + backendName: "cpu", + kernelFunc: floorDiv2 +}; +function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) { + const outBuf = buffer([numSlices, sliceSize], dtype); + for (let i = 0; i < numSlices; i++) { + const index = []; + let flattenIndex = 0; + for (let j = 0; j < sliceRank; j++) { + const dim = indicesData[i * sliceRank + j]; + flattenIndex += dim * strides[j]; + index.push(dim); + } + if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) { + throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`); + } + for (let k = 0; k < sliceSize; k++) { + outBuf.values[i * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k)); + } + } + return outBuf; +} +function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) { + const outBuf = buffer(flattenOutputShape, xBuf.dtype); + for (let i = 0; i < outBuf.size; ++i) { + const newLoc = outBuf.indexToLoc(i); + const originalLoc = newLoc.slice(); + const batchIdx = originalLoc[0]; + const indicesIdx = originalLoc[2]; + const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]); + originalLoc[2] = indicesBuf.values[indicesIndex]; + const originalIndex = xBuf.locToIndex(originalLoc); + if (0 <= originalIndex && originalIndex < xBuf.values.length) { + outBuf.values[i] = xBuf.values[originalIndex]; + } + } + return outBuf; +} +var greaterImpl = createSimpleBinaryKernelImpl((a, b) => a > b ? 1 : 0); +var greater3 = binaryKernelFunc(Greater, greaterImpl, null, "bool"); +var greaterConfig = { + kernelName: Greater, + backendName: "cpu", + kernelFunc: greater3 +}; +var greaterEqualImpl = createSimpleBinaryKernelImpl((a, b) => a >= b ? 1 : 0); +var greaterEqual2 = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null, "bool"); +var greaterEqualConfig = { + kernelName: GreaterEqual, + backendName: "cpu", + kernelFunc: greaterEqual2 +}; +var lessImpl = createSimpleBinaryKernelImpl((a, b) => a < b ? 1 : 0); +var less3 = binaryKernelFunc(Less, lessImpl, null, "bool"); +var lessConfig = { + kernelName: Less, + backendName: "cpu", + kernelFunc: less3 +}; +var lessEqualImpl = createSimpleBinaryKernelImpl((a, b) => a <= b ? 1 : 0); +var lessEqual2 = binaryKernelFunc(LessEqual, lessEqualImpl, null, "bool"); +var lessEqualConfig = { + kernelName: LessEqual, + backendName: "cpu", + kernelFunc: lessEqual2 +}; +function linSpaceImpl(start, stop, num) { + const step5 = (stop - start) / (num - 1); + const values = util_exports.makeZerosTypedArray(num, "float32"); + values[0] = start; + for (let i = 1; i < values.length; i++) { + values[i] = values[i - 1] + step5; + } + return values; +} +var logImpl = createSimpleUnaryImpl((xi) => Math.log(xi)); +var log3 = unaryKernelFuncFromImpl(Log, logImpl); +var logConfig = { + kernelName: Log, + backendName: "cpu", + kernelFunc: log3 +}; +function maxImpl(aVals, reduceSize, outShape, dtype) { + const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape)); + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let max6 = aVals[offset]; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + if (Number.isNaN(value) || value > max6) { + max6 = value; + } + } + vals[i] = max6; + } + return vals; +} +var maximumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.max(aValue, bValue)); +var maximum3 = binaryKernelFunc(Maximum, maximumImpl); +var maximumConfig = { + kernelName: Maximum, + backendName: "cpu", + kernelFunc: maximum3 +}; +var minimumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.min(aValue, bValue)); +var minimum3 = binaryKernelFunc(Minimum, minimumImpl); +var minimumConfig = { + kernelName: Minimum, + backendName: "cpu", + kernelFunc: minimum3 +}; +var multiplyImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue * bValue); +var multiplyComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => { + return { + real: aReal * bReal - aImag * bImag, + imag: aReal * bImag + aImag * bReal + }; +}); +var multiply2 = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl); +var multiplyConfig = { + kernelName: Multiply, + backendName: "cpu", + kernelFunc: multiply2 +}; +function negImpl(xVals, xShape, xDtype) { + const minusOne = util_exports.createScalarValue(-1, xDtype); + return multiplyImpl([], xShape, minusOne, xVals, xDtype); +} +function neg2(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + assertNotComplex(x, "neg"); + const xVals = backend2.data.get(x.dataId).values; + const [res, newShape] = negImpl(xVals, x.shape, x.dtype); + return backend2.makeTensorInfo(newShape, x.dtype, res); +} +var negConfig = { + kernelName: Neg, + backendName: "cpu", + kernelFunc: neg2 +}; +var notEqualImpl = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0); +var notEqual2 = binaryKernelFunc(NotEqual, notEqualImpl, null, "bool"); +var notEqualConfig = { + kernelName: NotEqual, + backendName: "cpu", + kernelFunc: notEqual2 +}; +function transposeImpl(xVals, xShape, dtype, perm, newShape) { + const xRank = xShape.length; + const xSize = util_exports.sizeFromShape(xShape); + const xStrides = util_exports.computeStrides(xShape); + const newStrides = util_exports.computeStrides(newShape); + const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape)); + for (let i = 0; i < xSize; ++i) { + const loc = util_exports.indexToLoc(i, xRank, xStrides); + const newLoc = new Array(loc.length); + for (let i2 = 0; i2 < newLoc.length; i2++) { + newLoc[i2] = loc[perm[i2]]; + } + const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides); + result[newIndex] = xVals[i]; + } + return result; +} +function transpose2(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x } = inputs; + const { perm } = attrs; + assertNotComplex(x, "transpose"); + const xRank = x.shape.length; + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[perm[i]]; + } + const values = backend2.data.get(x.dataId).values; + const result = transposeImpl(values, x.shape, x.dtype, perm, newShape); + const dataId = backend2.write(result, newShape, x.dtype); + return { dataId, shape: newShape, dtype: x.dtype }; +} +var transposeConfig = { + kernelName: Transpose, + backendName: "cpu", + kernelFunc: transpose2 +}; +function prodImpl(xShape, xDtype, xVals, reductionAxes) { + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, reductionAxes); + const outDtype = upcastType(xDtype, "int32"); + const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype); + const reduceSize = util_exports.sizeFromShape(reduceShape); + for (let i = 0; i < outVals.length; ++i) { + const offset = i * reduceSize; + let prod5 = 1; + for (let j = 0; j < reduceSize; ++j) { + prod5 *= xVals[offset + j]; + } + outVals[i] = prod5; + } + return { outVals, outShape, outDtype }; +} +function prod2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + assertNotComplex(x, "prod"); + const xRank = x.shape.length; + const axes = util_exports.parseAxisParam(axis, x.shape); + const permutation = backend_util_exports.getAxesPermutation(axes, xRank); + let reductionAxes = axes; + let permutedX = x; + const intermediateTensorInfos = []; + if (permutation != null) { + permutedX = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } }); + intermediateTensorInfos.push(permutedX); + reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank); + } + const xVals = backend2.data.get(permutedX.dataId).values; + const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes); + let resultShape = outShape; + if (keepDims) { + resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes); + } + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return backend2.makeTensorInfo(resultShape, outDtype, outVals); +} +var prodConfig = { + kernelName: Prod, + backendName: "cpu", + kernelFunc: prod2 +}; +function validateIndices(indices, indicesShape, numParams) { + indices.forEach((index, i) => { + if (index < 0 || index >= numParams) { + const locString = util_exports.indexToLoc(i, indicesShape.length, util_exports.computeStrides(indicesShape)).join(","); + throw new Error(`indices[${locString}] = ${index} is not in [0, ${numParams})`); + } + }); +} +function validateSplits(paramsNestedSplits, numParamsDenseValues) { + for (let dim = 0; dim < paramsNestedSplits.length; ++dim) { + const splits = paramsNestedSplits[dim]; + const lastSplit = dim === paramsNestedSplits.length - 1 ? numParamsDenseValues : paramsNestedSplits[dim + 1].length; + if (splits.length === 0) { + throw new Error("Ragged splits may not be empty"); + } + if (splits[0] < 0) { + throw new Error("Ragged splits must be non-negative"); + } + if (splits[splits.length - 1] > lastSplit) { + throw new Error("Ragged splits must not point past values"); + } + for (let i = 1; i < splits.length; ++i) { + if (splits[i - 1] > splits[i]) { + throw new Error("Ragged splits must be sorted in ascending order"); + } + } + } +} +function makeSplits(indices, indicesShape, paramsNestedSplits, numParamsDenseValues) { + const valueSlices = []; + let numValues = 0; + const numSplits = indicesShape.length - 1 + paramsNestedSplits.length; + const outSplits = new Array(numSplits).fill(null).map(() => [0]); + validateSplits(paramsNestedSplits, numParamsDenseValues); + let nrows = 1; + for (let dim = 0; dim < indicesShape.length - 1; ++dim) { + nrows *= indicesShape[dim]; + const rowLength = indicesShape[dim + 1]; + for (let i = 1; i < nrows + 1; ++i) { + outSplits[dim].push(i * rowLength); + } + } + for (let i = 0; i < indices.length; ++i) { + let start = indices[i]; + let limit = indices[i] + 1; + for (let dim = 0; dim < paramsNestedSplits.length; ++dim) { + const splits = paramsNestedSplits[dim]; + const outDim = dim + indicesShape.length - 1; + if (outDim >= 0) { + const outSplitsOutDim = outSplits[outDim]; + const delta = outSplitsOutDim[outSplitsOutDim.length - 1] - splits[start]; + for (let j = start; j < limit; ++j) { + outSplits[outDim].push(splits[j + 1] + delta); + } + } + start = splits[start]; + limit = splits[limit]; + } + if (limit !== start) { + valueSlices.push([start, limit]); + numValues += limit - start; + } + } + return { outSplits, valueSlices, numValues }; +} +function getSplits(outSplits) { + const splitsOut = []; + for (let i = 0; i < outSplits.length; ++i) { + const numSplits = outSplits[i].length; + const splits = util_exports.getArrayFromDType("int32", numSplits); + splitsOut.push(splits); + outSplits[i].forEach((value, j) => splits[j] = value); + } + return splitsOut; +} +function computeFlatOuterDims(orig, numOutDims) { + const outDims = orig.slice(0, numOutDims); + while (outDims.length < numOutDims) { + outDims.push(1); + } + for (let inDim = numOutDims; inDim < orig.length; inDim++) { + outDims[numOutDims - 1] *= orig[inDim]; + } + return outDims; +} +function writeValueSlices(paramsDenseValues, paramsDenseValuesShape, valueSlices, valueSize, values, valuesShape) { + const denseM = computeFlatOuterDims(paramsDenseValuesShape, 2)[1]; + const valuesM = computeFlatOuterDims(valuesShape, 2)[1]; + let outPos = 0; + for (const slice5 of valueSlices) { + for (let i = slice5[0]; i < slice5[1]; ++i) { + for (let j = 0; j < valueSize; ++j) { + values[outPos * valuesM + j] = paramsDenseValues[i * denseM + j]; + } + ++outPos; + } + } +} +function getValues(paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, valueSlices, numValues) { + const valuesShape = paramsDenseValuesShape.slice(); + valuesShape[0] = numValues; + const valuesOut = util_exports.getArrayFromDType(paramsDenseValuesDType, util_exports.sizeFromShape(valuesShape)); + const numElements = paramsDenseValues.length; + const valueSize = numElements === 0 ? 0 : numElements / paramsDenseValuesShape[0]; + writeValueSlices(paramsDenseValues, paramsDenseValuesShape, valueSlices, valueSize, valuesOut, valuesShape); + return [valuesOut, valuesShape]; +} +function raggedGatherImpl(paramsNestedSplits, paramsNestedSplitsShapes, paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, indices, indicesShape, outputRaggedRank) { + if (paramsNestedSplits.length === 0) { + throw new Error("paramsNestedSplits must be non empty"); + } + if (paramsNestedSplitsShapes[0].length === 0) { + throw new Error("Split tensors must not be scalars"); + } + const numParams = paramsNestedSplitsShapes[0][0] - 1; + validateIndices(indices, indicesShape, numParams); + if (paramsDenseValuesShape.length === 0) { + throw new Error("params.rank must be nonzero"); + } + const numParamsDenseValues = paramsDenseValuesShape[0]; + const { outSplits, valueSlices, numValues } = makeSplits(indices, indicesShape, paramsNestedSplits, numParamsDenseValues); + const outputNestedSplits = getSplits(outSplits); + const outputDenseValues = getValues(paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, valueSlices, numValues); + return [outputNestedSplits, outputDenseValues[0], outputDenseValues[1]]; +} +var INT32_MAX2 = 2147483647; +function raggedRangeImpl(starts, startsShape, startsDType, limits, limitsShape, deltas, deltasShape) { + if (startsShape.length > 1) { + throw new Error("starts must be a scalar or vector"); + } + if (limitsShape.length > 1) { + throw new Error("limits must be a scalar or vector"); + } + if (deltasShape.length > 1) { + throw new Error("deltas must be a scalar or vector"); + } + const broadcastStarts = startsShape.length === 0; + const broadcastLimits = limitsShape.length === 0; + const broadcastDeltas = deltasShape.length === 0; + const inSizes = []; + if (!broadcastStarts) { + inSizes.push(startsShape[0]); + } + if (!broadcastLimits) { + inSizes.push(limitsShape[0]); + } + if (!broadcastDeltas) { + inSizes.push(deltasShape[0]); + } + for (let i = 1; i < inSizes.length; ++i) { + if (inSizes[i] !== inSizes[i - 1]) { + throw new Error("starts, limits, and deltas must have the same shape"); + } + } + const nRows = inSizes.length === 0 ? 1 : inSizes[0]; + const rtNestedSplits = util_exports.getArrayFromDType("int32", nRows + 1); + rtNestedSplits[0] = 0; + for (let row = 0; row < nRows; ++row) { + const start = broadcastStarts ? starts[0] : starts[row]; + const limit = broadcastLimits ? limits[0] : limits[row]; + const delta = broadcastDeltas ? deltas[0] : deltas[row]; + if (delta === 0) { + throw new Error("Requires delta != 0"); + } + let size; + if (delta > 0 && limit < start || delta < 0 && limit > start) { + size = 0; + } else { + size = Math.ceil(Math.abs((limit - start) / delta)); + if (size > INT32_MAX2) { + throw new Error(`Requires ((limit - start) / delta) <= ${INT32_MAX2}`); + } + } + rtNestedSplits[row + 1] = rtNestedSplits[row] + size; + } + const nVals = rtNestedSplits[nRows]; + const rtDenseValues = util_exports.getArrayFromDType(startsDType, nVals); + let valueIndex = 0; + for (let row = 0; row < nRows; ++row) { + const rowSize = rtNestedSplits[row + 1] - rtNestedSplits[row]; + let value = broadcastStarts ? starts[0] : starts[row]; + const delta = broadcastDeltas ? deltas[0] : deltas[row]; + for (let i = 0; i < rowSize; ++i) { + rtDenseValues[valueIndex++] = value; + value += delta; + } + } + return [rtNestedSplits, rtDenseValues]; +} +var RowPartitionType2 = backend_util_exports.RowPartitionType; +var RaggedTensorToTensorOp = class _RaggedTensorToTensorOp { + constructor(shape, shapeShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypeStrings) { + this.shape = shape; + this.shapeShape = shapeShape; + this.values = values; + this.valuesShape = valuesShape; + this.valuesDType = valuesDType; + this.defaultValue = defaultValue; + this.defaultValueShape = defaultValueShape; + this.rowPartitionValues = rowPartitionValues; + this.rowPartitionValuesShapes = rowPartitionValuesShapes; + this.rowPartitionTypes = backend_util_exports.getRowPartitionTypesHelper(rowPartitionTypeStrings); + this.raggedRank = backend_util_exports.getRaggedRank(this.rowPartitionTypes); + } + getRowPartitionTypeByDimension(dimension) { + if (this.rowPartitionTypes[0] === RowPartitionType2.FIRST_DIM_SIZE) { + return this.rowPartitionTypes[dimension + 1]; + } else { + return this.rowPartitionTypes[dimension]; + } + } + // Returns the relationship between dimension and dimension + 1. + getRowPartitionTensor(dimension) { + if (this.rowPartitionTypes[0] === RowPartitionType2.FIRST_DIM_SIZE) { + return this.rowPartitionValues[dimension + 1]; + } else { + return this.rowPartitionValues[dimension]; + } + } + getMaxWidth(dimension) { + const rowPartitionTensor = this.getRowPartitionTensor(dimension - 1); + switch (this.getRowPartitionTypeByDimension(dimension - 1)) { + case RowPartitionType2.VALUE_ROWIDS: + return _RaggedTensorToTensorOp.getMaxWidthValueRowID(rowPartitionTensor); + case RowPartitionType2.ROW_SPLITS: + return _RaggedTensorToTensorOp.getMaxWidthRowSplit(rowPartitionTensor); + default: + throw new Error(`Cannot handle partition type ${RowPartitionType2[this.getRowPartitionTypeByDimension(dimension - 1)]}`); + } + } + static getMaxWidthRowSplit(rowSplit) { + const tensorLength = rowSplit.length; + if (tensorLength === 0 || tensorLength === 1) { + return 0; + } + let maxWidth = 0; + for (let i = 0; i < tensorLength - 1; ++i) { + const currentWidth = rowSplit[i + 1] - rowSplit[i]; + if (currentWidth > maxWidth) { + maxWidth = currentWidth; + } + } + return maxWidth; + } + static getMaxWidthValueRowID(valueRowIds) { + const indexLength = valueRowIds.length; + if (indexLength === 0) { + return 0; + } + let firstEqualIndex = 0; + let firstEqualIndexValue = valueRowIds[0]; + let maxWidth = 0; + for (let i = 1; i < indexLength; ++i) { + const value = valueRowIds[i]; + if (value !== firstEqualIndexValue) { + firstEqualIndexValue = value; + maxWidth = Math.max(i - firstEqualIndex, maxWidth); + firstEqualIndex = i; + } + } + return Math.max(indexLength - firstEqualIndex, maxWidth); + } + tensorShapeFromTensor(t, tShape, isPartial = true) { + if (tShape.length === 0) { + if (t[0] === -1) { + return []; + } + throw new Error(`The only valid scalar shape tensor is the fully unknown shape specified as -1.`); + } + return makeShape(t, isPartial); + } + calculateOutputSize(firstDim) { + const valueShape = this.valuesShape; + const defaultValueShape = this.defaultValueShape; + backend_util_exports.validateDefaultValueShape(defaultValueShape, valueShape); + const shape = this.tensorShapeFromTensor(this.shape, this.shapeShape); + const outputShape = backend_util_exports.combineRaggedTensorToTensorShapes(this.raggedRank, shape, valueShape); + const result = outputShape; + if (result[0] < 0) { + result[0] = firstDim; + } + for (let i = 1; i <= this.raggedRank; ++i) { + if (result[i] < 0) { + result[i] = this.getMaxWidth(i); + } + } + return result; + } + /** + * The outputIndex represents the index in the output tensor + * where the first element of a particular dimension would be written. + * If it is -1, it indicates that the index is out of scope. + * Example, given firstDimension = 10, firstDimensionOutput = 6, + * and outputIndexMultiplier = 100: + * result = [0 100 200 300 400 500 -1 -1 -1 -1] + * If firstDimensionOutput = 11 instead, then: + * result = [0 100 200 300 400 500 600 700 800 900] + */ + calculateFirstParentOutputIndex(firstDimension, outputIndexMultiplier, firstDimensionOutput) { + const minDimension = Math.min(firstDimension, firstDimensionOutput); + const result = []; + let currentOutputIndex = 0; + for (let i = 0; i < minDimension; ++i, currentOutputIndex += outputIndexMultiplier) { + result.push(currentOutputIndex); + } + for (let i = minDimension; i < firstDimension; ++i) { + result.push(-1); + } + util_exports.assert(result.length === firstDimension, () => "Final length of result must be equal to firstDimension."); + return result; + } + calculateOutputIndexRowSplit(rowSplit, parentOutputIndex, outputIndexMultiplier, outputSize) { + const rowSplitSize = rowSplit.length; + const result = []; + for (let i = 0; i < rowSplitSize - 1; ++i) { + const rowLength = rowSplit[i + 1] - rowSplit[i]; + let realLength = Math.min(outputSize, rowLength); + let parentOutputIndexCurrent = parentOutputIndex[i]; + if (parentOutputIndexCurrent === -1) { + realLength = 0; + } + for (let j = 0; j < realLength; ++j) { + result.push(parentOutputIndexCurrent); + parentOutputIndexCurrent += outputIndexMultiplier; + } + for (let j = 0; j < rowLength - realLength; ++j) { + result.push(-1); + } + } + if (rowSplitSize > 0 && result.length !== rowSplit[rowSplitSize - 1]) { + throw new Error("Invalid row split size."); + } + return result; + } + // Calculate the output index of the first element of a list. + // The parentOutputIndex is the same computation for the previous list. + // -1 indicates an element or list that is out of range. + // The outputIndexMultiplier is the number of output indices one moves + // forward for each column. + // E.g., given: + // valueRowIds:[0 1 2 2 2 3 5 5 6] + // parentOutputIndex:[1000 1100 2000 2100 -1 3000 4000] + // outputIndexMultiplier: 10 + // outputSize: 2 + // You get: + // result = [1000 1100 2000 2010 -1 2100 -1 -1 3000] + // result[0] = parentOutputIndex[valueRowIds[0]] + // result[1] = parentOutputIndex[valueRowIds[1]] + // result[2] = parentOutputIndex[valueRowIds[2]] + // result[3] = parentOutputIndex[valueRowIds[2] + 10] + // result[4] = -1 because it is the third element the size is 2. + // result[5] = parentOutputIndex[valueRowIds[3]] + // result[6] = -1 because parentOutputIndex[valueRowIds[6]] == -1 + // result[7] = -1 because parentOutputIndex[valueRowIds[6]] == -1 + // result[8] = parentOutputIndex[valueRowIds[7]] + calculateOutputIndexValueRowID(valueRowIds, parentOutputIndex, outputIndexMultiplier, outputSize) { + const indexSize = valueRowIds.length; + const result = []; + if (indexSize === 0) { + return []; + } + let currentOutputColumn = 0; + let currentValueRowId = valueRowIds[0]; + if (currentValueRowId >= parentOutputIndex.length) { + throw new Error(`Got currentValueRowId=${currentValueRowId}, which is not less than ${parentOutputIndex.length}`); + } + let currentOutputIndex = parentOutputIndex[currentValueRowId]; + result.push(currentOutputIndex); + for (let i = 1; i < indexSize; ++i) { + const nextValueRowId = valueRowIds[i]; + if (nextValueRowId === currentValueRowId) { + if (currentOutputIndex >= 0) { + ++currentOutputColumn; + if (currentOutputColumn < outputSize) { + currentOutputIndex += outputIndexMultiplier; + } else { + currentOutputIndex = -1; + } + } + } else { + currentOutputColumn = 0; + currentValueRowId = nextValueRowId; + if (nextValueRowId >= parentOutputIndex.length) { + throw new Error(`Got nextValueRowId=${nextValueRowId} which is not less than ${parentOutputIndex.length}`); + } + currentOutputIndex = parentOutputIndex[nextValueRowId]; + } + result.push(currentOutputIndex); + } + if (result.length !== valueRowIds.length) { + throw new Error("Invalid row ids."); + } + return result; + } + calculateOutputIndex(dimension, parentOutputIndex, outputIndexMultiplier, outputSize) { + const rowPartitionTensor = this.getRowPartitionTensor(dimension); + const partitionType = this.getRowPartitionTypeByDimension(dimension); + switch (partitionType) { + case RowPartitionType2.VALUE_ROWIDS: + return this.calculateOutputIndexValueRowID(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize); + case RowPartitionType2.ROW_SPLITS: + if (rowPartitionTensor.length - 1 > parentOutputIndex.length) { + throw new Error(`Row partition size is greater than output size: ${rowPartitionTensor.length - 1} > ${parentOutputIndex.length}`); + } + return this.calculateOutputIndexRowSplit(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize); + default: + throw new Error(`Unsupported partition type: ${RowPartitionType2[partitionType]}`); + } + } + getFirstDimensionSize() { + const firstPartitionTensor = this.rowPartitionValues[0]; + if (this.rowPartitionTypes.length === 0) { + throw new Error("No row_partition_types given."); + } + const firstPartitionType = this.rowPartitionTypes[0]; + switch (firstPartitionType) { + case RowPartitionType2.FIRST_DIM_SIZE: + return firstPartitionTensor[0]; + case RowPartitionType2.VALUE_ROWIDS: + throw new Error("Cannot handle VALUE_ROWIDS in first dimension."); + case RowPartitionType2.ROW_SPLITS: + return this.rowPartitionValuesShapes[0][0] - 1; + default: + throw new Error(`Cannot handle type ${RowPartitionType2[firstPartitionType]}`); + } + } + compute() { + const firstPartitionTensor = this.rowPartitionValues[0]; + if (firstPartitionTensor.length <= 0) { + throw new Error("Invalid first partition input. Tensor requires at least one element."); + } + const firstDimension = this.getFirstDimensionSize(); + const outputSize = this.calculateOutputSize(firstDimension); + const multiplier = new Array(this.raggedRank + 1); + multiplier[multiplier.length - 1] = 1; + for (let i = multiplier.length - 2; i >= 0; --i) { + multiplier[i] = multiplier[i + 1] * outputSize[i + 1]; + } + const outputShape = makeShape(outputSize, false); + const outputTensor = util_exports.getArrayFromDType(this.valuesDType, util_exports.sizeFromShape(outputShape)); + const fullSize = multiplier[0] * outputSize[0]; + if (fullSize > 0) { + let outputIndex = this.calculateFirstParentOutputIndex(firstDimension, multiplier[0], outputSize[0]); + for (let i = 1; i <= this.raggedRank; ++i) { + const newOutputIndex = this.calculateOutputIndex(i - 1, outputIndex, multiplier[i], outputSize[i]); + outputIndex = newOutputIndex; + } + this.setOutput(this.raggedRank, outputIndex, outputTensor, outputShape); + } + return [outputShape, outputTensor]; + } + setOutput(raggedRank, outputIndex, outputTensor, outputShape) { + if (outputTensor.length === 0) { + return; + } + const valuesBase = this.values; + const outputBase = outputTensor; + let elementShape = outputShape.slice(); + elementShape = elementShape.slice(raggedRank + 1); + const valueElementSize = util_exports.sizeFromShape(elementShape); + const outputIndexSize = outputIndex.length; + let defaultValue = this.defaultValue; + if (defaultValue.length !== valueElementSize && defaultValue.length !== 1) { + const srcShape = this.defaultValueShape; + tidy(() => { + const defaultValueTensor = reshape(defaultValue, srcShape); + const bCastDefault = broadcastTo(defaultValueTensor, elementShape); + defaultValue = bCastDefault.dataSync(); + }); + } + let srcStart = 0; + let dstStart = 0; + let dstEnd = 0; + for (let srcI = 0; srcI <= outputIndexSize; ++srcI) { + let dstI = srcI < outputIndexSize ? outputIndex[srcI] : -1; + if (dstI === dstEnd) { + ++dstEnd; + continue; + } + if (dstStart < dstEnd) { + const src = valuesBase.subarray(srcStart * valueElementSize); + const dst = outputBase.subarray(dstStart * valueElementSize); + const nVals = (dstEnd - dstStart) * valueElementSize; + copyArray(dst, src, nVals); + } + if (srcI >= outputIndexSize) { + const outputSize = outputTensor.length; + dstI = Math.floor(outputSize / valueElementSize); + } + if (dstI > dstEnd) { + if (this.defaultValue.length === 1) { + outputBase.subarray(dstEnd * valueElementSize, dstI * valueElementSize).fill(this.defaultValue[0]); + dstEnd = dstI; + } else { + while (dstI > dstEnd) { + const dst = outputBase.slice(dstEnd * valueElementSize); + copyArray(dst, defaultValue, valueElementSize); + ++dstEnd; + } + } + } + if (dstI < 0) { + srcStart = srcI + 1; + dstStart = dstEnd; + } else { + srcStart = srcI; + dstStart = dstEnd; + dstEnd = dstStart + 1; + } + } + } +}; +function copyArray(dst, src, size) { + for (let i = 0; i < size; i++) { + dst[i] = src[i]; + } +} +function makeShape(shape, isPartial) { + const out = []; + for (let dim of shape) { + if (dim < 0) { + if (!isPartial) { + throw new Error(`Dimension ${dim} must be >= 0`); + } + if (dim < -1) { + throw new Error(`Dimension ${dim} must be >= -1`); + } + dim = -1; + } + out.push(dim); + } + return out; +} +function raggedTensorToTensorImpl(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes) { + return new RaggedTensorToTensorOp(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes).compute(); +} +function rangeImpl(start, stop, step5, dtype) { + const sameStartStop = start === stop; + const increasingRangeNegativeStep = start < stop && step5 < 0; + const decreasingRangePositiveStep = stop < start && step5 > 1; + if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) { + return util_exports.makeZerosTypedArray(0, dtype); + } + const numElements = Math.abs(Math.ceil((stop - start) / step5)); + const values = util_exports.makeZerosTypedArray(numElements, dtype); + if (stop < start && step5 === 1) { + step5 = -1; + } + values[0] = start; + for (let i = 1; i < values.length; i++) { + values[i] = values[i - 1] + step5; + } + return values; +} +var rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi)); +var rsqrt2 = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl); +var rsqrtConfig = { + kernelName: Rsqrt, + backendName: "cpu", + kernelFunc: rsqrt2 +}; +function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) { + const flattenShape = [outputSize / sliceSize, sliceSize]; + const indicesData = indices.values; + const updatesData = updates.values; + if (outputSize === 0) { + return buffer(shape, updates.dtype); + } + const outBuf = defaultValue instanceof TensorBuffer ? defaultValue : buffer(flattenShape, updates.dtype); + if (typeof defaultValue === "string") { + outBuf.values.fill(defaultValue); + } else if (typeof defaultValue === "number") { + outBuf.values.fill(defaultValue); + } else if (typeof defaultValue === "boolean") { + outBuf.values.fill(+defaultValue); + } + for (let i = 0; i < numUpdates; i++) { + const index = []; + let flattenIndex = 0; + for (let j = 0; j < sliceRank; j++) { + const dim = indicesData[i * sliceRank + j]; + index.push(dim); + flattenIndex += dim * strides[j]; + } + if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) { + throw new Error(`Invalid indices: ${index} does not index into ${shape}`); + } + for (let k = 0; k < sliceSize; k++) { + if (sumDupeIndices) { + outBuf.values[flattenIndex * sliceSize + k] += updatesData[i * sliceSize + k]; + } else { + outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i * sliceSize + k]; + } + } + } + return outBuf; +} +var sigmoidImpl = createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi))); +var sigmoid2 = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi))); +var sigmoidConfig = { + kernelName: Sigmoid, + backendName: "cpu", + kernelFunc: sigmoid2 +}; +function sliceImpl(vals, begin, size, shape, dtype) { + const isContinous = slice_util_exports.isSliceContinous(shape, begin, size); + const length = util_exports.sizeFromShape(size); + const xStrides = util_exports.computeStrides(shape); + if (isContinous) { + const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides); + if (dtype === "string") { + return vals.slice(flatOffset, flatOffset + length); + } + return vals.subarray(flatOffset, flatOffset + length); + } + const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(vals) : vals; + const inBuf = buffer(shape, dtype, decodedData); + const outBuf = buffer(size, dtype); + for (let i = 0; i < outBuf.size; ++i) { + const outLoc = outBuf.indexToLoc(i); + const inLoc = outLoc.map((idx, j) => idx + begin[j]); + outBuf.set(inBuf.get(...inLoc), ...outLoc); + } + if (dtype === "string") { + return backend_util_exports.fromStringArrayToUint8(outBuf.values); + } + return outBuf.values; +} +function slice2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { begin, size } = attrs; + assertNotComplex(x, "slice"); + const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size); + slice_util_exports.assertParamsValid(x, $begin, $size); + const vals = backend2.data.get(x.dataId).values; + const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype); + return backend2.makeTensorInfo($size, x.dtype, outVals); +} +var sliceConfig = { + kernelName: Slice, + backendName: "cpu", + kernelFunc: slice2 +}; +function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) { + const indicesCount = indicesShape[0]; + const denseRows = denseShape[0]; + const emptyRowIndicator = new Array(denseRows); + const reverseIndexMap = new Array(indicesCount); + const rank = indicesShape[1]; + if (denseRows === 0) { + if (indicesCount !== 0) { + throw new Error(backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount)); + } + const outputIndices = util_exports.getArrayFromDType(indicesDType, 0); + const outputValues = util_exports.getArrayFromDType(valuesDType, 0); + return [ + outputIndices, + [0, rank], + outputValues, + emptyRowIndicator, + reverseIndexMap + ]; + } + let rowsAreOrdered = true; + let lastIndicesRow = 0; + const csrOffset = new Array(denseRows).fill(0); + for (let i = 0; i < indicesCount; ++i) { + const row = indices[i * rank]; + if (row < 0) { + throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row)); + } + if (row >= denseRows) { + throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows)); + } + ++csrOffset[row]; + rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow; + lastIndicesRow = row; + } + let allRowsFull = true; + for (let row = 0; row < denseRows; ++row) { + const rowEmpty = csrOffset[row] === 0; + emptyRowIndicator[row] = rowEmpty; + allRowsFull = allRowsFull && !rowEmpty; + csrOffset[row] = Math.max(csrOffset[row], 1); + if (row > 0) { + csrOffset[row] += csrOffset[row - 1]; + } + } + if (allRowsFull && rowsAreOrdered) { + const outputIndices = indices; + const outputValues = values; + for (let i = 0; i < indicesCount; ++i) { + reverseIndexMap[i] = i; + } + return [ + outputIndices, + [indicesCount, rank], + outputValues, + emptyRowIndicator, + reverseIndexMap + ]; + } else { + const fullIndicesCount = csrOffset[denseRows - 1]; + const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank); + const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount); + const filledCount = new Array(denseRows).fill(0); + for (let i = 0; i < indicesCount; ++i) { + const row = indices[i * rank]; + const offset = filledCount[row]; + const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset; + filledCount[row]++; + for (let j = 0; j < rank; ++j) { + outputIndices[outputI * rank + j] = indices[i * rank + j]; + } + outputValues[outputI] = values[i]; + reverseIndexMap[i] = outputI; + } + for (let row = 0; row < denseRows; ++row) { + const rowCount = filledCount[row]; + if (rowCount === 0) { + const startingIndex = row === 0 ? 0 : csrOffset[row - 1]; + outputIndices[startingIndex * rank + 0] = row; + for (let col = 1; col < rank; ++col) { + outputIndices[startingIndex * rank + col] = 0; + } + outputValues[startingIndex] = defaultValue; + } + } + return [ + outputIndices, + [fullIndicesCount, rank], + outputValues, + emptyRowIndicator, + reverseIndexMap + ]; + } +} +function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) { + const denseSize = util_exports.sizeFromShape(inputShape); + const nnz = inputIndicesShape[0]; + const outputRank = targetShape.length; + const outputShape = []; + let product = 1; + let unknownIndex = -1; + for (let d = 0; d < outputRank; ++d) { + const size = targetShape[d]; + if (size === -1) { + if (unknownIndex !== -1) { + throw new Error(backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d)); + } + unknownIndex = d; + outputShape.push(1); + } else { + if (size < 0) { + throw new Error(backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(d, size)); + } + product *= size; + outputShape.push(size); + } + } + if (unknownIndex !== -1) { + if (product <= 0) { + throw new Error(backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage()); + } + const missing = Math.trunc(denseSize / product); + if (product * missing !== denseSize) { + throw new Error(backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape)); + } + outputShape[unknownIndex] = missing; + } + const outputSize = util_exports.sizeFromShape(outputShape); + if (outputSize !== denseSize) { + throw new Error(backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape)); + } + const inputRank = inputShape.length; + const inputStrides = []; + if (inputRank > 0) { + inputStrides[inputRank - 1] = 1; + for (let d = inputRank - 2; d >= 0; --d) { + inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1]; + } + } + const outputStrides = []; + if (outputRank > 0) { + outputStrides[outputRank - 1] = 1; + for (let d = outputRank - 2; d >= 0; --d) { + outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1]; + } + } + const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank); + for (let i = 0; i < nnz; ++i) { + let id = 0; + for (let j = 0; j < inputRank; ++j) { + id += inputIndices[i * inputRank + j] * inputStrides[j]; + } + for (let j = 0; j < outputRank; ++j) { + newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]); + id %= outputStrides[j]; + } + } + return [newIndices, [nnz, outputRank], outputShape]; +} +function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) { + const numIndices = indices.length; + const inputFlat = [inputShape[0], input2.length / inputShape[0]]; + const numCol = inputFlat[1]; + const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0; + const outputRows = lastSegmentIdPlusOne; + if (outputRows < 0) { + throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + } + const outputShape = inputShape.slice(); + outputShape[0] = outputRows; + const outputLength = outputShape.reduce((product, value) => product * value, 1); + const output = util_exports.getArrayFromDType(inputDType, outputLength); + if (numIndices === 0) { + if (outputRows > 0) { + output.fill(defaultValue); + } + return [output, outputShape]; + } + if (outputRows <= 0) { + throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + } + let start = 0, end = 1; + let uninitializedIndex = 0; + let outIndex = segmentIds[start]; + while (true) { + let nextIndex = 0; + if (end < numIndices) { + nextIndex = segmentIds[end]; + if (outIndex === nextIndex) { + ++end; + continue; + } + if (outIndex >= nextIndex) { + throw new Error(backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage()); + } + } + if (outIndex < 0 || outIndex >= outputRows) { + throw new Error(backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows)); + } + if (outIndex > uninitializedIndex) { + output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol); + } + for (let i = start; i < end; ++i) { + const index = indices[i]; + if (index < 0 || index >= inputFlat[0]) { + throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0])); + } + for (let j = 0; j < numCol; j++) { + output[outIndex * numCol + j] += input2[index * numCol + j]; + } + } + if (isMean) { + for (let j = 0; j < numCol; j++) { + output[outIndex * numCol + j] /= end - start; + } + } + start = end; + ++end; + uninitializedIndex = outIndex + 1; + outIndex = nextIndex; + if (end > numIndices) { + break; + } + } + if (uninitializedIndex < outputRows) { + output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol); + } + return [output, outputShape]; +} +var sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi)); +var sqrt2 = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi)); +var sqrtConfig = { + kernelName: Sqrt, + backendName: "cpu", + kernelFunc: sqrt2 +}; +var squaredDifferenceImpl = createSimpleBinaryKernelImpl((a, b) => { + const diff = a - b; + return diff * diff; +}); +var squaredDifference2 = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl); +var squaredDifferenceConfig = { + kernelName: SquaredDifference, + backendName: "cpu", + kernelFunc: squaredDifference2 +}; +var staticRegexReplaceImpl = createSimpleUnaryImpl((x, attrs) => { + const { pattern, replaceGlobal, rewrite } = attrs; + return x.replace(new RegExp(pattern, replaceGlobal ? "g" : ""), rewrite); +}); +var staticRegexReplace2 = unaryKernelFuncFromImpl(StaticRegexReplace, staticRegexReplaceImpl); +var staticRegexReplaceConfig = { + kernelName: StaticRegexReplace, + backendName: "cpu", + kernelFunc: staticRegexReplace2 +}; +function stridedSliceImpl(outShape, xBuf, strides, begin) { + const outBuf = buffer(outShape, xBuf.dtype); + for (let i = 0; i < outBuf.size; i++) { + const loc = outBuf.indexToLoc(i); + const newLoc = new Array(loc.length); + for (let j = 0; j < newLoc.length; j++) { + newLoc[j] = loc[j] * strides[j] + begin[j]; + } + outBuf.set(xBuf.get(...newLoc), ...loc); + } + return outBuf; +} +var StringNGramsOp = class { + constructor(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) { + this.separator = util_exports.encodeString(separator); + this.nGramWidths = nGramWidths; + this.leftPad = util_exports.encodeString(leftPad); + this.rightPad = util_exports.encodeString(rightPad2); + this.padWidth = padWidth; + this.preserveShort = preserveShortSequences; + } + getPadWidth(nGramWidth) { + return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1); + } + getNumNGrams(length, nGramWidth) { + const padWidth = this.getPadWidth(nGramWidth); + return Math.max(0, length + 2 * padWidth - nGramWidth + 1); + } + createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) { + for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) { + const padWidth = this.getPadWidth(nGramWidth); + const leftPadding = Math.max(0, padWidth - nGramIndex); + const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1))); + const numTokens = nGramWidth - (leftPadding + rightPadding); + const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth); + let nGramSize = 0; + nGramSize += leftPadding * this.leftPad.length; + for (let n = 0; n < numTokens; ++n) { + nGramSize += data[dataStartIndex + n].length; + } + nGramSize += rightPadding * this.rightPad.length; + const numSeparators = leftPadding + rightPadding + numTokens - 1; + nGramSize += numSeparators * this.separator.length; + output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize); + const nGram = output[outputStartIndex + nGramIndex]; + let nextNGramIndex = 0; + const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value); + for (let n = 0; n < leftPadding; ++n) { + appendToNGram(this.leftPad); + appendToNGram(this.separator); + } + for (let n = 0; n < numTokens - 1; ++n) { + appendToNGram(data[dataStartIndex + n]); + appendToNGram(this.separator); + } + if (numTokens > 0) { + appendToNGram(data[dataStartIndex + numTokens - 1]); + for (let n = 0; n < rightPadding; ++n) { + appendToNGram(this.separator); + appendToNGram(this.rightPad); + } + } else { + for (let n = 0; n < rightPadding - 1; ++n) { + appendToNGram(this.rightPad); + appendToNGram(this.separator); + } + appendToNGram(this.rightPad); + } + } + } + // Data and splits together form the definition of the ragged tensor, + // where data is 1 dimensional and contains the values of the tensor + // and splits denotes the indices at which each row starts. + compute(data, splits) { + const inputDataSize = data.length; + const splitsSize = splits.length; + if (splitsSize > 0) { + let prevSplit = splits[0]; + if (prevSplit !== 0) { + throw new Error(`First split value must be 0, got ${prevSplit}`); + } + for (let i = 1; i < splitsSize; ++i) { + let validSplits = splits[i] >= prevSplit; + validSplits = validSplits && splits[i] <= inputDataSize; + if (!validSplits) { + throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`); + } + prevSplit = splits[i]; + } + if (prevSplit !== inputDataSize) { + throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`); + } + } + const numBatchItems = splitsSize - 1; + const nGramsSplits = util_exports.getArrayFromDType("int32", splitsSize); + if (inputDataSize === 0 || splitsSize === 0) { + const empty = new Array(inputDataSize); + for (let i = 0; i <= numBatchItems; ++i) { + nGramsSplits[i] = 0; + } + return [empty, nGramsSplits]; + } + nGramsSplits[0] = 0; + for (let i = 1; i <= numBatchItems; ++i) { + const length = splits[i] - splits[i - 1]; + let numNGrams = 0; + this.nGramWidths.forEach((nGramWidth) => { + numNGrams += this.getNumNGrams(length, nGramWidth); + }); + if (this.preserveShort && length > 0 && numNGrams === 0) { + numNGrams = 1; + } + nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams; + } + const nGrams = new Array(nGramsSplits[numBatchItems]); + for (let i = 0; i < numBatchItems; ++i) { + const splitIndex = splits[i]; + let outputStartIdx = nGramsSplits[i]; + this.nGramWidths.forEach((nGramWidth) => { + const length = splits[i + 1] - splits[i]; + const numNGrams = this.getNumNGrams(length, nGramWidth); + this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth); + outputStartIdx += numNGrams; + }); + if (this.preserveShort && outputStartIdx === nGramsSplits[i]) { + const dataLength = splits[i + 1] - splits[i]; + if (dataLength === 0) { + continue; + } + const nGramWidth = dataLength + 2 * this.padWidth; + const numNGrams = 1; + this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth); + } + } + return [nGrams, nGramsSplits]; + } +}; +function stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) { + return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences).compute(data, dataSplits); +} +function split3(str, delimiters, skipEmpty, result) { + if (!str.length) { + return; + } + if (delimiters.length === 0) { + for (let i = 0; i < str.length; ++i) { + result.push(str.subarray(i, i + 1)); + } + return; + } + if (delimiters.length === 1) { + const delimiter = delimiters[0]; + let f = str.indexOf(delimiter); + while (f !== -1) { + const token = str.subarray(0, f); + if (!skipEmpty || token.length !== 0) { + result.push(token); + } + str = str.subarray(f + 1); + f = str.indexOf(delimiter); + } + if (!skipEmpty || str.length !== 0) { + result.push(str); + } + return; + } + let tokenStart = 0; + for (let i = 0; i < str.length + 1; i++) { + if (i === str.length || delimiters.indexOf(str[i]) !== -1) { + const token = str.subarray(tokenStart, i); + if (!skipEmpty || token.length !== 0) { + result.push(token); + } + tokenStart = i + 1; + } + } +} +function stringSplitImpl(input2, delimiter, skipEmpty) { + const batchSize = input2.length; + const tokens = []; + let outputSize = 0; + let maxNumEntries = 0; + const numIndices = new Array(batchSize); + for (let i = 0; i < batchSize; ++i) { + const prevTokensLength = tokens.length; + split3(input2[i], delimiter, skipEmpty, tokens); + const nEntries = tokens.length - prevTokensLength; + numIndices[i] = nEntries; + outputSize += nEntries; + maxNumEntries = Math.max(maxNumEntries, nEntries); + } + const indices = util_exports.getArrayFromDType("int32", outputSize * 2); + const values = new Array(outputSize); + const shape = [batchSize, maxNumEntries]; + let c = 0; + for (let i = 0; i < batchSize; ++i) { + for (let j = 0; j < numIndices[i]; ++j) { + indices[c * 2] = i; + indices[c * 2 + 1] = j; + values[c] = tokens[c]; + ++c; + } + } + return [indices, values, shape]; +} +function stringToHashBucketFastImpl(input2, numBuckets) { + const output = util_exports.getArrayFromDType("int32", input2.length); + for (let i = 0; i < input2.length; ++i) { + output[i] = util_exports.fingerPrint64(input2[i]).modulo(numBuckets).getLowBitsUnsigned(); + } + return output; +} +var subImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue - bValue); +var subComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => { + return { real: aReal - bReal, imag: aImag - bImag }; +}); +var sub2 = binaryKernelFunc(Sub, subImpl, subComplexImpl); +var subConfig = { + kernelName: Sub, + backendName: "cpu", + kernelFunc: sub2 +}; +function tileImpl(xBuf, reps) { + const newShape = new Array(xBuf.rank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = xBuf.shape[i] * reps[i]; + } + const result = buffer(newShape, xBuf.dtype); + for (let i = 0; i < result.values.length; ++i) { + const newLoc = result.indexToLoc(i); + const originalLoc = new Array(xBuf.rank); + for (let j = 0; j < originalLoc.length; j++) { + originalLoc[j] = newLoc[j] % xBuf.shape[j]; + } + const originalIndex = xBuf.locToIndex(originalLoc); + result.values[i] = xBuf.values[originalIndex]; + } + return result; +} +var comparePair = (a, b) => { + const valueDiff = b.value - a.value; + return valueDiff === 0 ? a.index - b.index : valueDiff; +}; +function select(array2, k, left = 0, right = array2.length - 1) { + while (right > left) { + if (right - left > 600) { + const n = right - left + 1; + const i2 = k - left + 1; + const z = Math.log(n); + const s = 0.5 * Math.exp(2 * z / 3); + const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i2 - n / 2); + const newLeft = Math.max(left, Math.floor(k - i2 * s / n + sd)); + const newRight = Math.min(right, Math.floor(k + (n - i2) * s / n + sd)); + select(array2, k, newLeft, newRight); + } + const t = array2[k]; + let i = left; + let j = right; + util_exports.swap(array2, left, k); + if (comparePair(array2[right], t) > 0) { + util_exports.swap(array2, left, right); + } + while (i < j) { + util_exports.swap(array2, i, j); + i++; + j--; + while (comparePair(array2[i], t) < 0) { + i = i + 1; + } + while (comparePair(array2[j], t) > 0) { + j = j - 1; + } + } + if (comparePair(array2[left], t) === 0) { + util_exports.swap(array2, left, j); + } else { + j = j + 1; + util_exports.swap(array2, j, right); + } + if (j <= k) { + left = j + 1; + } + if (k <= j) { + right = j - 1; + } + } +} +function topKImpl(x, xShape, xDtype, k, sorted) { + const lastDim = xShape[xShape.length - 1]; + const [batch, size] = [x.length / lastDim, lastDim]; + const allTopKVals = util_exports.getTypedArrayFromDType(xDtype, batch * k); + const allTopKIndices = util_exports.getTypedArrayFromDType("int32", batch * k); + for (let b = 0; b < batch; b++) { + const offset = b * size; + const vals = x.subarray(offset, offset + size); + let valAndInd = new Array(vals.length); + vals.forEach((value, index) => valAndInd[index] = { value, index }); + if (k < valAndInd.length) { + select(valAndInd, k); + valAndInd = valAndInd.slice(0, k); + } + if (sorted) { + valAndInd.sort(comparePair); + } + const outOffset = b * k; + const topKVals = allTopKVals.subarray(outOffset, outOffset + k); + const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k); + for (let i = 0; i < k; i++) { + topKVals[i] = valAndInd[i].value; + topKIndices[i] = valAndInd[i].index; + } + } + const outputShape = xShape.slice(); + outputShape[outputShape.length - 1] = k; + return [ + buffer(outputShape, xDtype, allTopKVals), + buffer(outputShape, "int32", allTopKIndices) + ]; +} +function uniqueImpl(values, axis, shape, dtype) { + const $axis = util_exports.parseAxisParam(axis, shape)[0]; + const newShape = [1, shape[0], 1]; + for (let i = 0; i < $axis; i++) { + newShape[0] *= shape[i]; + } + newShape[1] = shape[$axis]; + for (let i = $axis + 1; i < shape.length; i++) { + newShape[2] *= shape[i]; + } + const uniqueElements = /* @__PURE__ */ new Map(); + const indices = new Int32Array(shape[$axis]); + const inputBuffer = new TensorBuffer(newShape, dtype, values); + const uniqueIndices = []; + const is1DTensor = newShape[0] === 1 && newShape[2] === 1; + for (let i = 0; i < shape[$axis]; i++) { + let element; + if (is1DTensor) { + element = values[i].toString(); + } else { + const axisValues = []; + for (let m = 0; m < newShape[0]; m++) { + for (let n = 0; n < newShape[2]; n++) { + axisValues.push(inputBuffer.get(m, i, n)); + } + } + element = axisValues.join(","); + } + const existingIndex = uniqueElements.get(element); + if (existingIndex != null) { + indices[i] = existingIndex; + } else { + const uniqueIndex = uniqueElements.size; + uniqueElements.set(element, uniqueIndex); + indices[i] = uniqueIndex; + uniqueIndices.push(i); + } + } + const outputTmpShape = newShape.slice(); + outputTmpShape[1] = uniqueElements.size; + const outputBuffer = new TensorBuffer(outputTmpShape, dtype); + uniqueIndices.forEach((uniqueElementIndex, i) => { + for (let m = 0; m < newShape[0]; m++) { + for (let n = 0; n < newShape[2]; n++) { + outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n); + } + } + }); + const outputShape = shape.slice(); + outputShape[$axis] = outputTmpShape[1]; + return { + outputValues: outputBuffer.values, + outputShape, + indices + }; +} +var version5 = "4.16.0"; +registerBackend( + "cpu", + () => new MathBackendCPU(), + 1 + /* priority */ +); +var elu4 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1); +var eluConfig = { + kernelName: Elu, + backendName: "cpu", + kernelFunc: elu4 +}; +function leakyRelu2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { alpha } = attrs; + assertNotComplex([x], "leakyRelu"); + const xSize = util_exports.sizeFromShape(x.shape); + const xVals = backend2.data.get(x.dataId).values; + const outVals = util_exports.getTypedArrayFromDType("float32", xSize); + for (let i = 0; i < xVals.length; i++) { + outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i]; + } + return backend2.makeTensorInfo(x.shape, "float32", outVals); +} +var leakyReluConfig = { + kernelName: LeakyRelu, + backendName: "cpu", + kernelFunc: leakyRelu2 +}; +var preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue); +function prelu3(args) { + const { inputs, backend: backend2 } = args; + const { x, alpha } = inputs; + assertNotComplex([x, alpha], "prelu"); + const aVals = backend2.data.get(x.dataId).values; + const bVals = backend2.data.get(alpha.dataId).values; + const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, "float32"); + return backend2.makeTensorInfo(resultShape, "float32", resultData); +} +var preluConfig = { + kernelName: Prelu, + backendName: "cpu", + kernelFunc: prelu3 +}; +var relu2 = unaryKernelFunc(Relu, (xi) => Math.max(0, xi)); +var reluConfig = { + kernelName: Relu, + backendName: "cpu", + kernelFunc: relu2 +}; +var relu62 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6)); +var relu6Config = { + kernelName: Relu6, + backendName: "cpu", + kernelFunc: relu62 +}; +function applyActivation2(backend2, x, activation2, preluActivationWeights, leakyreluAlpha) { + if (activation2 === "linear") { + return identity2({ inputs: { x }, backend: backend2 }); + } else if (activation2 === "relu") { + return relu2({ inputs: { x }, backend: backend2 }); + } else if (activation2 === "elu") { + return elu4({ inputs: { x }, backend: backend2 }); + } else if (activation2 === "relu6") { + return relu62({ inputs: { x }, backend: backend2 }); + } else if (activation2 === "prelu") { + return prelu3({ inputs: { x, alpha: preluActivationWeights }, backend: backend2 }); + } else if (activation2 === "leakyrelu") { + return leakyRelu2({ inputs: { x }, backend: backend2, attrs: { alpha: leakyreluAlpha } }); + } else if (activation2 === "sigmoid") { + return sigmoid2({ inputs: { x }, backend: backend2 }); + } + throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`); +} +function reshape3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { shape } = attrs; + const xSize = util_exports.sizeFromShape(x.shape); + const $shape = util_exports.inferFromImplicitShape(shape, xSize); + const $xSize = util_exports.sizeFromShape($shape); + util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`); + backend2.incRef(x.dataId); + const xData = backend2.data.get(x.dataId); + if (xData.complexTensorInfos != null) { + const real4 = xData.complexTensorInfos.real; + const imag4 = xData.complexTensorInfos.imag; + real4.shape = $shape; + imag4.shape = $shape; + } + return { dataId: x.dataId, shape: $shape, dtype: x.dtype }; +} +var reshapeConfig = { + kernelName: Reshape, + backendName: "cpu", + kernelFunc: reshape3 +}; +function batchMatMul(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b } = inputs; + const { transposeA, transposeB } = attrs; + assertNotComplex([a, b], "matMul"); + const aRank = a.shape.length; + const bRank = b.shape.length; + const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1]; + const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2]; + const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2]; + const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1]; + const outerDimsA = a.shape.slice(0, -2); + const outerDimsB = b.shape.slice(0, -2); + const batchDimA = util_exports.sizeFromShape(outerDimsA); + const batchDimB = util_exports.sizeFromShape(outerDimsB); + const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); + const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA]; + const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB]; + const a3d = reshape3({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } }); + const b3d = reshape3({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } }); + const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2]; + const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1]; + const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2]; + const batchDim = Math.max(batchDimA, batchDimB); + const a3dValues = backend2.data.get(a3d.dataId).values; + const b3dValues = backend2.data.get(b3d.dataId).values; + const a3dStrides = util_exports.computeStrides(a3d.shape); + const b3dStrides = util_exports.computeStrides(b3d.shape); + const [aBatch, aOuterStep, aInnerStep] = transposeA ? [a3dStrides[0], 1, a3dStrides[1]] : [a3dStrides[0], a3dStrides[1], 1]; + const [bInnerStep, bOuterStep, bBatch] = transposeB ? [1, b3dStrides[1], b3dStrides[0]] : [b3dStrides[1], 1, b3dStrides[0]]; + const size = leftDim * rightDim; + const result = buffer([batchDim, leftDim, rightDim], a3d.dtype); + const resVals = result.values; + const blockSize = backend2.blockSize; + for (let bi = 0; bi < batchDim; bi++) { + const batchIndexA = bi % batchDimA; + const batchIndexB = bi % batchDimB; + for (let i0 = 0; i0 < leftDim; i0 += blockSize) { + const iBlock = Math.min(i0 + blockSize, leftDim); + for (let j0 = 0; j0 < rightDim; j0 += blockSize) { + const jBlock = Math.min(j0 + blockSize, rightDim); + for (let k02 = 0; k02 < sharedDim; k02 += blockSize) { + const kBlock = Math.min(k02 + blockSize, sharedDim); + for (let i = i0; i < iBlock; i++) { + for (let j = j0; j < jBlock; j++) { + let sum6 = 0; + for (let k = k02; k < kBlock; k++) { + const aVal = ( + // tslint:disable-next-line: max-line-length + a3dValues[batchIndexA * aBatch + i * aOuterStep + k * aInnerStep] + ); + const bVal = ( + // tslint:disable-next-line: max-line-length + b3dValues[k * bInnerStep + j * bOuterStep + batchIndexB * bBatch] + ); + sum6 += aVal * bVal; + } + resVals[bi * size + (i * rightDim + j)] += sum6; + } + } + } + } + } + } + backend2.disposeIntermediateTensorInfo(a3d); + backend2.disposeIntermediateTensorInfo(b3d); + return backend2.makeTensorInfo(outShape, result.dtype, result.values); +} +var batchMatMulConfig = { + kernelName: BatchMatMul, + backendName: "cpu", + kernelFunc: batchMatMul +}; +function _fusedMatMul(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b, bias, preluActivationWeights } = inputs; + const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs; + let current; + let addRes; + let activationRes; + const intermediates = []; + const matMulRes = batchMatMul({ inputs: { a, b }, attrs: { transposeA, transposeB }, backend: backend2 }); + current = matMulRes; + if (bias) { + addRes = add4({ inputs: { a: current, b: bias }, backend: backend2 }); + intermediates.push(current); + current = addRes; + } + if (activation2) { + activationRes = applyActivation2(backend2, current, activation2, preluActivationWeights, leakyreluAlpha); + intermediates.push(current); + current = activationRes; + } + for (const i of intermediates) { + backend2.disposeIntermediateTensorInfo(i); + } + return current; +} +var _fusedMatMulConfig = { + kernelName: _FusedMatMul, + backendName: "cpu", + kernelFunc: _fusedMatMul +}; +var acos2 = unaryKernelFunc(Acos, (xi) => Math.acos(xi)); +var acosConfig = { + kernelName: Acos, + backendName: "cpu", + kernelFunc: acos2 +}; +var acosh2 = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi)); +var acoshConfig = { + kernelName: Acosh, + backendName: "cpu", + kernelFunc: acosh2 +}; +function addN2(args) { + const { inputs, backend: backend2 } = args; + const tensors = inputs; + assertNotComplex(inputs, "addN"); + const vals = tensors.map((t) => backend2.data.get(t.dataId).values); + const outBuf = buffer(tensors[0].shape, tensors[0].dtype); + const outVals = outBuf.values; + for (let i = 0; i < tensors.length; i++) { + const currVals = vals[i]; + for (let j = 0; j < outVals.length; j++) { + outVals[j] += currVals[j]; + } + } + return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); +} +var addNConfig = { + kernelName: AddN, + backendName: "cpu", + kernelFunc: addN2 +}; +function all2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + assertNotComplex(x, "all"); + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + if (permutedAxes != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("all", axes, $x.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype); + const aVals = backend2.data.get($x.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let all5 = aVals[offset]; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + all5 = all5 && value; + } + vals[i] = all5; + } + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo($x); + } + const result = backend2.makeTensorInfo(outShape, $x.dtype, vals); + if (keepDims) { + const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } }); + backend2.disposeIntermediateTensorInfo(result); + return reshapedResult; + } + return result; +} +var allConfig = { + kernelName: All, + backendName: "cpu", + kernelFunc: all2 +}; +function any2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + assertNotComplex(x, "any"); + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + if (permutedAxes != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("any", axes, $x.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype); + const aVals = backend2.data.get($x.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let anyVal = aVals[offset]; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + anyVal = anyVal || value; + } + vals[i] = anyVal; + } + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo($x); + } + const result = backend2.makeTensorInfo(outShape, $x.dtype, vals); + if (keepDims) { + const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } }); + backend2.disposeIntermediateTensorInfo(result); + return reshapedResult; + } + return result; +} +var anyConfig = { + kernelName: Any, + backendName: "cpu", + kernelFunc: any2 +}; +function argMax2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis } = attrs; + assertNotComplex(x, "argMax"); + let axes = util_exports.parseAxisParam(axis, x.shape); + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + const intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length); + } + axes = [axes[0]]; + backend_util_exports.assertAxesAreInnerMostDims("argMax", axes, $x.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes); + const outSize = util_exports.sizeFromShape(outShape); + const vals = util_exports.makeZerosTypedArray(outSize, "int32"); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const aVals = backend2.data.get($x.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let max6 = aVals[offset]; + let maxIndex = 0; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + if (value > max6) { + max6 = value; + maxIndex = j; + } + } + vals[i] = maxIndex; + } + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return backend2.makeTensorInfo(outShape, "int32", vals); +} +var argMaxConfig = { + kernelName: ArgMax, + backendName: "cpu", + kernelFunc: argMax2 +}; +function argMin2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis } = attrs; + assertNotComplex(x, "argMin"); + let axes = util_exports.parseAxisParam(axis, x.shape); + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + const intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length); + } + axes = [axes[0]]; + backend_util_exports.assertAxesAreInnerMostDims("argMin", axes, $x.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes); + const outSize = util_exports.sizeFromShape(outShape); + const vals = util_exports.makeZerosTypedArray(outSize, "int32"); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const aVals = backend2.data.get($x.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let min6 = aVals[offset]; + let minIndex = 0; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + if (value < min6) { + min6 = value; + minIndex = j; + } + } + vals[i] = minIndex; + } + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return backend2.makeTensorInfo(outShape, "int32", vals); +} +var argMinConfig = { + kernelName: ArgMin, + backendName: "cpu", + kernelFunc: argMin2 +}; +var asin2 = unaryKernelFunc(Asin, (xi) => Math.asin(xi)); +var asinConfig = { + kernelName: Asin, + backendName: "cpu", + kernelFunc: asin2 +}; +var asinh2 = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi)); +var asinhConfig = { + kernelName: Asinh, + backendName: "cpu", + kernelFunc: asinh2 +}; +var atan3 = unaryKernelFunc(Atan, (xi) => Math.atan(xi)); +var atanConfig = { + kernelName: Atan, + backendName: "cpu", + kernelFunc: atan3 +}; +var atan2Impl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.atan2(aValue, bValue)); +var atan22 = binaryKernelFunc(Atan2, atan2Impl); +var atan2Config = { + kernelName: Atan2, + backendName: "cpu", + kernelFunc: atan22 +}; +var atanh2 = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi)); +var atanhConfig = { + kernelName: Atanh, + backendName: "cpu", + kernelFunc: atanh2 +}; +function pool2(xValues, xShape, dtype, strides, convInfo, poolType) { + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY; + const output = buffer(convInfo.outShape, dtype); + const outputVals = output.values; + const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3]; + const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3]; + const outputColStrides = convInfo.outShape[3]; + for (let b = 0; b < convInfo.batchSize; ++b) { + const outputBatchOffset = b * outputBatchStrides; + const inputBatchOffset = b * strides[0]; + for (let d = 0; d < convInfo.inChannels; ++d) { + for (let yR = 0; yR < convInfo.outHeight; ++yR) { + const xRCorner = yR * strideHeight - padTop; + const xRMin = Math.max(0, xRCorner); + const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner); + const outputRowOffset = outputBatchOffset + yR * outputRowStrides; + for (let yC = 0; yC < convInfo.outWidth; ++yC) { + const xCCorner = yC * strideWidth - padLeft; + const xCMin = Math.max(0, xCCorner); + const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner); + let minMaxValue = initialValue; + let avgValue = 0; + let count2 = 0; + for (let xR = xRMin; xR < xRMax; xR += dilationHeight) { + const xROffset = inputBatchOffset + xR * strides[1]; + for (let xC = xCMin; xC < xCMax; xC += dilationWidth) { + const xCOffset = xROffset + xC * strides[2]; + const pixel = xValues[xCOffset + d]; + if (poolType === "max" && pixel > minMaxValue) { + minMaxValue = pixel; + } else if (poolType === "avg") { + avgValue += pixel; + count2++; + } + } + if (isNaN(minMaxValue)) { + break; + } + } + const outputOffset = outputRowOffset + yC * outputColStrides + d; + outputVals[outputOffset] = poolType === "avg" ? avgValue / count2 : minMaxValue; + } + } + } + } + return output; +} +function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) { + const maxPositions = buffer(convInfo.outShape, "int32"); + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const xBuf = buffer(xShape, dtype, xValues); + for (let b = 0; b < convInfo.batchSize; ++b) { + for (let d = 0; d < convInfo.inChannels; ++d) { + for (let yR = 0; yR < convInfo.outHeight; ++yR) { + const xRCorner = yR * strideHeight - padTop; + let xRMin = xRCorner; + while (xRMin < 0) { + xRMin += dilationHeight; + } + const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner); + for (let yC = 0; yC < convInfo.outWidth; ++yC) { + const xCCorner = yC * strideWidth - padLeft; + let xCMin = xCCorner; + while (xCMin < 0) { + xCMin += dilationWidth; + } + const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner); + let maxValue = Number.NEGATIVE_INFINITY; + let maxPosition = -1; + for (let xR = xRMin; xR < xRMax; xR += dilationHeight) { + const wR = xR - xRCorner; + for (let xC = xCMin; xC < xCMax; xC += dilationWidth) { + const wC = xC - xCCorner; + const pixel = xBuf.get(b, xR, xC, d); + if (pixel > maxValue) { + maxValue = pixel; + if (flattenPositions) { + maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d; + } else { + maxPosition = wR * effectiveFilterWidth + wC; + } + } + } + } + maxPositions.set(maxPosition, b, yR, yC, d); + } + } + } + } + return maxPositions; +} +function pool3d2(xValues, xShape, dtype, strides, convInfo, poolType) { + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = convInfo.padInfo.front; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY; + const output = buffer(convInfo.outShape, dtype); + const outputVals = output.values; + const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4]; + const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4]; + const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4]; + const outputColStrides = convInfo.outShape[4]; + for (let batch = 0; batch < convInfo.batchSize; ++batch) { + const outputBatchOffset = batch * outputBatchStrides; + const inputBatchOffset = batch * strides[0]; + for (let channel = 0; channel < convInfo.inChannels; ++channel) { + for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) { + const xDepthCorner = yDepth * strideDepth - padFront; + let xDepthMin = xDepthCorner; + while (xDepthMin < 0) { + xDepthMin += dilationDepth; + } + const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner); + const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides; + for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) { + const xRowCorner = yRow * strideHeight - padTop; + let xRowMin = xRowCorner; + while (xRowMin < 0) { + xRowMin += dilationHeight; + } + const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner); + const outputRowOffset = outputDepthOffset + yRow * outputRowStrides; + for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) { + const xColCorner = yCol * strideWidth - padLeft; + let xColMin = xColCorner; + while (xColMin < 0) { + xColMin += dilationWidth; + } + const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner); + const outputColOffset = outputRowOffset + yCol * outputColStrides; + let minMaxValue = initialValue; + let avgValue = 0; + let count2 = 0; + for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) { + const xDepthOffset = inputBatchOffset + xDepth * strides[1]; + for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) { + const xRowOffset = xDepthOffset + xRow * strides[2]; + for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) { + const xColOffset = xRowOffset + xCol * strides[3]; + const pixel = xValues[xColOffset + channel]; + if (poolType === "max" && pixel > minMaxValue) { + minMaxValue = pixel; + } else if (poolType === "avg") { + avgValue += pixel; + count2++; + } + if (isNaN(minMaxValue)) { + break; + } + } + if (isNaN(minMaxValue)) { + break; + } + } + if (isNaN(minMaxValue)) { + break; + } + } + const outputOffset = outputColOffset + channel; + outputVals[outputOffset] = poolType === "avg" ? avgValue / Math.max(count2, 1) : minMaxValue; + } + } + } + } + } + return output; +} +function maxPool3dPositions(xBuf, convInfo) { + const maxPositions = buffer(convInfo.outShape, "int32"); + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = convInfo.padInfo.front; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + for (let batch = 0; batch < convInfo.batchSize; ++batch) { + for (let channel = 0; channel < convInfo.inChannels; ++channel) { + for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) { + const xDepthCorner = yDepth * strideDepth - padFront; + let xDepthMin = xDepthCorner; + while (xDepthMin < 0) { + xDepthMin += dilationDepth; + } + const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner); + for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) { + const xRowCorner = yRow * strideHeight - padTop; + let xRowMin = xRowCorner; + while (xRowMin < 0) { + xRowMin += dilationHeight; + } + const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner); + for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) { + const xColCorner = yCol * strideWidth - padLeft; + let xColMin = xColCorner; + while (xColMin < 0) { + xColMin += dilationWidth; + } + const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner); + let maxValue = Number.NEGATIVE_INFINITY; + let maxPosition = -1; + for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) { + const wDepth = xDepth - xDepthCorner; + for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) { + const wRow = xRow - xRowCorner; + for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) { + const wCol = xCol - xColCorner; + const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel); + if (pixel >= maxValue) { + maxValue = pixel; + maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol; + } + } + } + } + maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel); + } + } + } + } + } + return maxPositions; +} +function avgPool2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + assertNotComplex(x, "avgPool"); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = 1; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + let res; + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) { + res = identity2({ inputs: { x }, backend: backend2 }); + } else { + const xValues = backend2.data.get(x.dataId).values; + const strides2 = util_exports.computeStrides(x.shape); + const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, "avg"); + res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values); + } + return res; +} +var avgPoolConfig = { + kernelName: AvgPool, + backendName: "cpu", + kernelFunc: avgPool2 +}; +function avgPool3D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs; + assertNotComplex(x, "avgPool3d"); + const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat); + const xValues = backend2.data.get(x.dataId).values; + const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "avg"); + return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values); +} +var avgPool3DConfig = { + kernelName: AvgPool3D, + backendName: "cpu", + kernelFunc: avgPool3D +}; +function avgPool3DGrad(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + assertNotComplex([dy, input2], "avgPool3DGrad"); + const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode); + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const filterDepth = convInfo.filterDepth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const dx = buffer(input2.shape, "float32"); + const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth); + const dyBuf = backend2.bufferSync(dy); + for (let batch = 0; batch < convInfo.batchSize; ++batch) { + for (let channel = 0; channel < convInfo.inChannels; ++channel) { + for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) { + for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) { + for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) { + const dyDepthCorner = dxDepth - padFront; + const dyRowCorner = dxRow - padTop; + const dyColCorner = dxCol - padLeft; + let dotProd = 0; + for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) { + const dyDepth = (dyDepthCorner + wDepth) / strideDepth; + if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) { + continue; + } + for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) { + const dyRow = (dyRowCorner + wRow) / strideHeight; + if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) { + continue; + } + for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) { + const dyCol = (dyColCorner + wCol) / strideWidth; + if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) { + continue; + } + const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel); + dotProd += pixel; + } + } + } + dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel); + } + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var avgPool3DGradConfig2 = { + kernelName: AvgPool3DGrad, + backendName: "cpu", + kernelFunc: avgPool3DGrad +}; +function avgPoolGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const x = input2; + assertNotComplex([dy, input2], "avgPoolGrad"); + const { filterSize, strides, pad: pad3 } = attrs; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3); + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const dx = buffer(x.shape, "float32"); + const avgMultiplier = 1 / (filterHeight * filterWidth); + const dyData = backend2.data.get(dy.dataId).values; + const dyBuf = buffer(dy.shape, "float32", dyData); + for (let b = 0; b < convInfo.batchSize; ++b) { + for (let d = 0; d < convInfo.inChannels; ++d) { + for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) { + for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) { + const dyRCorner = dxR - padTop; + const dyCCorner = dxC - padLeft; + let dotProd = 0; + for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) { + const dyR = (dyRCorner + wR) / strideHeight; + if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) { + continue; + } + for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) { + const dyC = (dyCCorner + wC) / strideWidth; + if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) { + continue; + } + const pixel = dyBuf.get(b, dyR, dyC, d); + dotProd += pixel; + } + } + dx.set(dotProd * avgMultiplier, b, dxR, dxC, d); + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var avgPoolGradConfig2 = { + kernelName: AvgPoolGrad, + backendName: "cpu", + kernelFunc: avgPoolGrad2 +}; +function batchNorm2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, scale: scale22, offset, mean: mean4, variance } = inputs; + util_exports.assert(mean4.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."); + util_exports.assert(offset == null || mean4.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."); + util_exports.assert(scale22 == null || mean4.shape.length === scale22.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); + assertNotComplex([x, mean4, variance, scale22, offset], "batchNorm"); + let { varianceEpsilon } = attrs; + if (varianceEpsilon == null) { + varianceEpsilon = 1e-3; + } + const xVals = backend2.data.get(x.dataId).values; + const mVals = backend2.data.get(mean4.dataId).values; + const varVals = backend2.data.get(variance.dataId).values; + const sVals = scale22 ? backend2.data.get(scale22.dataId).values : new Float32Array([1]); + const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]); + const outVals = new Float32Array(xVals.length); + const offValsLength = offVals.length; + const sValsLength = sVals.length; + const varValsLength = varVals.length; + const mValsLength = mVals.length; + let offi = 0; + let mi = 0; + let si = 0; + let vi = 0; + for (let i = 0; i < xVals.length; ++i) { + outVals[i] = offVals[offi++] + (xVals[i] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon); + if (offi >= offValsLength) { + offi = 0; + } + if (mi >= mValsLength) { + mi = 0; + } + if (si >= sValsLength) { + si = 0; + } + if (vi >= varValsLength) { + vi = 0; + } + } + return backend2.makeTensorInfo(x.shape, x.dtype, outVals); +} +var batchNormConfig = { + kernelName: FusedBatchNorm, + backendName: "cpu", + kernelFunc: batchNorm2 +}; +function batchToSpaceND2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, crops } = attrs; + assertNotComplex([x], "batchToSpaceND"); + const prod5 = blockShape.reduce((a, b) => a * b); + const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5); + const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length); + const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5); + const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length); + const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length); + const xReshaped = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } }); + const xTransposed = transpose2({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } }); + const xTransposedReshaped = reshape3({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } }); + const result = slice2({ + inputs: { x: xTransposedReshaped }, + backend: backend2, + attrs: { begin: sliceBeginCoords, size: sliceSize } + }); + backend2.disposeIntermediateTensorInfo(xReshaped); + backend2.disposeIntermediateTensorInfo(xTransposed); + backend2.disposeIntermediateTensorInfo(xTransposedReshaped); + return result; +} +var batchToSpaceNDConfig = { + kernelName: BatchToSpaceND, + backendName: "cpu", + kernelFunc: batchToSpaceND2 +}; +function bincount2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, weights } = inputs; + const { size } = attrs; + const xVals = backend2.data.get(x.dataId).values; + const weightsVals = backend2.data.get(weights.dataId).values; + const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size); + return backend2.makeTensorInfo([size], weights.dtype, outVals); +} +var bincountConfig = { + kernelName: Bincount, + backendName: "cpu", + kernelFunc: bincount2 +}; +function broadcastArgs2(args) { + const { inputs, backend: backend2 } = args; + const { s0, s1 } = inputs; + const s0Vals = backend2.data.get(s0.dataId).values; + const s1Vals = backend2.data.get(s1.dataId).values; + const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals)); + return backend2.makeTensorInfo([broadcastShape.length], "int32", Int32Array.from(broadcastShape)); +} +var broadcastArgsConfig = { + kernelName: BroadcastArgs, + backendName: "cpu", + kernelFunc: broadcastArgs2 +}; +var clipByValue2 = unaryKernelFunc(ClipByValue, (xi, attrs) => { + const clipAttrs = attrs; + if (xi > clipAttrs.clipValueMax) { + return clipAttrs.clipValueMax; + } + return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi; +}); +var clipByValueConfig = { + kernelName: ClipByValue, + backendName: "cpu", + kernelFunc: clipByValue2 +}; +var complexAbs = (args) => { + const { x } = args.inputs; + const cpuBackend = args.backend; + const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape)); + const complexVals = cpuBackend.data.get(x.dataId); + const real4 = complexVals.complexTensorInfos.real; + const imag4 = complexVals.complexTensorInfos.imag; + const realVals = cpuBackend.data.get(real4.dataId).values; + const imagVals = cpuBackend.data.get(imag4.dataId).values; + for (let i = 0; i < realVals.length; i++) { + const real5 = realVals[i]; + const imag5 = imagVals[i]; + resultValues[i] = Math.hypot(real5, imag5); + } + return cpuBackend.makeOutput(resultValues, x.shape, "float32"); +}; +var complexAbsConfig = { + kernelName: ComplexAbs, + backendName: "cpu", + kernelFunc: complexAbs +}; +function imag2(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const imag4 = backend2.data.get(input2.dataId).complexTensorInfos.imag; + const imagVal = backend2.data.get(imag4.dataId).values; + return backend2.makeTensorInfo(imag4.shape, imag4.dtype, imagVal); +} +var imagConfig = { + kernelName: Imag, + backendName: "cpu", + kernelFunc: imag2 +}; +function concat2(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0]; + const shapes = inputs.map((t) => t.shape); + backend_util_exports.assertParamsConsistent(shapes, $axis); + let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis); + if (util_exports.sizeFromShape(outShape) === 0) { + return backend2.makeTensorInfo(outShape, inputs[0].dtype, []); + } + const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0); + if ($inputs.length === 1) { + return identity2({ inputs: { x: $inputs[0] }, backend: backend2 }); + } + if ($inputs[0].dtype === "complex64") { + const reals = $inputs.map((t) => real2({ inputs: { input: t }, backend: backend2 })); + const imags = $inputs.map((t) => imag2({ inputs: { input: t }, backend: backend2 })); + const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } }); + const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } }); + const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 }); + reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r)); + imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i)); + backend2.disposeIntermediateTensorInfo(realConcated); + backend2.disposeIntermediateTensorInfo(imagConcated); + return result; + } + const inputs2D = $inputs.map((t) => { + const innerSize = util_exports.sizeFromShape(t.shape.slice($axis)); + const shape = [-1, innerSize]; + return reshape3({ inputs: { x: t }, backend: backend2, attrs: { shape } }); + }); + const inputsValShapes = inputs2D.map((t) => { + return { vals: backend2.data.get(t.dataId).values, shape: t.shape }; + }); + outShape = backend_util_exports.computeOutShape( + inputs2D.map((t) => t.shape), + 1 + /* axis */ + ); + const simplyConcat = inputs2D[0].shape[0] === 1; + const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat); + const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), $axis); + const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals); + inputs2D.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return outInfo; +} +var concatConfig = { + kernelName: Concat, + backendName: "cpu", + kernelFunc: concat2 +}; +function conv2D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs; + assertNotComplex([x, filter], "conv2d"); + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const padLeft = convInfo.padInfo.left; + const padTop = convInfo.padInfo.top; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const y = new TensorBuffer(convInfo.outShape, x.dtype); + const xStrides = util_exports.computeStrides(x.shape); + const filterStrides = util_exports.computeStrides(filter.shape); + const xBatchStride = xStrides[0]; + const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2]; + const xColStride = isChannelsLast ? xStrides[2] : 1; + const xChannelStride = isChannelsLast ? 1 : xStrides[1]; + const yBatchStride = y.strides[0]; + const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2]; + const yColStride = isChannelsLast ? y.strides[2] : 1; + const yChannelStride = isChannelsLast ? 1 : y.strides[1]; + const xVals = backend2.data.get(x.dataId).values; + const wVals = backend2.data.get(filter.dataId).values; + const yVals = y.values; + for (let b = 0; b < convInfo.batchSize; ++b) { + const xOffset1 = b * xBatchStride; + const yOffset1 = b * yBatchStride; + for (let yR = 0; yR < convInfo.outHeight; ++yR) { + const yOffset2 = yOffset1 + yR * yRowStride; + const xRCorner = yR * convInfo.strideHeight - padTop; + for (let wR = 0; wR < filterHeight; ++wR) { + const xR = xRCorner + wR * dilationHeight; + if (xR < 0 || xR >= convInfo.inHeight) { + continue; + } + const wOffset1 = wR * filterStrides[0]; + const xOffset2 = xOffset1 + xR * xRowStride; + for (let yC = 0; yC < convInfo.outWidth; ++yC) { + const yOffset3 = yOffset2 + yC * yColStride; + const xCCorner = yC * convInfo.strideWidth - padLeft; + for (let wC = 0; wC < filterWidth; ++wC) { + const xC = xCCorner + wC * dilationWidth; + if (xC < 0 || xC >= convInfo.inWidth) { + continue; + } + const wOffset2 = wOffset1 + wC * filterStrides[1]; + const xOffset3 = xOffset2 + xC * xColStride; + let wOffset3 = wOffset2; + for (let d1 = 0; d1 < convInfo.inChannels; ++d1) { + const xVal = xVals[xOffset3 + d1 * xChannelStride]; + for (let d2 = 0; d2 < convInfo.outChannels; ++d2) { + yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2]; + } + wOffset3 += convInfo.outChannels; + } + } + } + } + } + } + return backend2.makeTensorInfo(y.shape, y.dtype, yVals); +} +var conv2DConfig = { + kernelName: Conv2D, + backendName: "cpu", + kernelFunc: conv2D +}; +function conv2DBackpropFilter2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs; + assertNotComplex([x, dy], "conv2dBackpropFilter"); + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat); + const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const dW = new TensorBuffer(convInfo.filterShape, "float32"); + const leftPad = convInfo.padInfo.left; + const topPad = convInfo.padInfo.top; + const xVals = backend2.data.get(x.dataId).values; + const dyVals = backend2.data.get(dy.dataId).values; + const xBuf = new TensorBuffer(x.shape, x.dtype, xVals); + const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals); + for (let wR = 0; wR < filterHeight; ++wR) { + const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight)); + const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight); + for (let wC = 0; wC < filterWidth; ++wC) { + const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth)); + const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth); + for (let d1 = 0; d1 < convInfo.inChannels; ++d1) { + for (let d2 = 0; d2 < convInfo.outChannels; ++d2) { + let dotProd = 0; + for (let b = 0; b < convInfo.batchSize; ++b) { + for (let yR = yRMin; yR < yRMax; ++yR) { + const xR = wR + yR * strideHeight - topPad; + for (let yC = yCMin; yC < yCMax; ++yC) { + const xC = wC + yC * strideWidth - leftPad; + if (isChannelsLast) { + dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2); + } else { + dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC); + } + } + } + } + dW.set(dotProd, wR, wC, d1, d2); + } + } + } + } + return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values); +} +var conv2DBackpropFilterConfig = { + kernelName: Conv2DBackpropFilter, + backendName: "cpu", + kernelFunc: conv2DBackpropFilter2 +}; +function conv2DBackpropInput2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs; + assertNotComplex([dy, filter], "conv2dBackpropInput"); + const filterStrides = util_exports.computeStrides(filter.shape); + const dyStrides = util_exports.computeStrides(dy.shape); + let $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat); + const dx = new TensorBuffer(convInfo.inShape, "float32"); + const dxValues = dx.values; + const dyValues = backend2.data.get(dy.dataId).values; + const fltValues = backend2.data.get(filter.dataId).values; + const [fltS0, fltS1, fltS2] = filterStrides; + const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo; + $dataFormat = convInfo.dataFormat; + const topPad = filterHeight - 1 - convInfo.padInfo.top; + const leftPad = filterWidth - 1 - convInfo.padInfo.left; + const isChannelsLast = $dataFormat === "channelsLast"; + const xBatchStride = dx.strides[0]; + const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2]; + const xColStride = isChannelsLast ? dx.strides[2] : 1; + const xChannelStride = isChannelsLast ? 1 : dx.strides[1]; + const yBatchStride = dyStrides[0]; + const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2]; + const yColStride = isChannelsLast ? dyStrides[2] : 1; + const yChannelStride = isChannelsLast ? 1 : dyStrides[1]; + for (let b = 0; b < batchSize; ++b) { + for (let d1 = 0; d1 < inChannels; ++d1) { + for (let xR = 0; xR < inHeight; ++xR) { + const xRCorner = xR - topPad; + const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight)); + const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight); + for (let xC = 0; xC < inWidth; ++xC) { + const xCCorner = xC - leftPad; + const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth)); + const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth); + let dotProd = 0; + for (let yR = xRMin; yR < yRMax; ++yR) { + const wR = yR * strideHeight - xRCorner; + for (let yC = xCMin; yC < yCMax; ++yC) { + const wC = yC * strideWidth - xCCorner; + const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC; + const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1; + for (let d2 = 0; d2 < outChannels; ++d2) { + const pixel = dyValues[dyOffset + yChannelStride * d2]; + const weight = fltValues[fltOffset + d2]; + dotProd += pixel * weight; + } + } + } + const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1; + dxValues[dxOffset] = dotProd; + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var conv2DBackpropInputConfig = { + kernelName: Conv2DBackpropInput, + backendName: "cpu", + kernelFunc: conv2DBackpropInput2 +}; +function conv3D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + assertNotComplex([x, filter], "conv3d"); + const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3); + const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo; + const padFront = padInfo.front; + const padLeft = padInfo.left; + const padTop = padInfo.top; + const y = new TensorBuffer(convInfo.outShape, x.dtype); + const xVals = backend2.data.get(x.dataId).values; + const wVals = backend2.data.get(filter.dataId).values; + const yVals = y.values; + const xStrides = util_exports.computeStrides(x.shape); + const filterStrides = util_exports.computeStrides(filter.shape); + for (let b = 0; b < convInfo.batchSize; ++b) { + const xOffset1 = b * xStrides[0]; + const yOffset1 = b * y.strides[0]; + for (let yF = 0; yF < convInfo.outDepth; ++yF) { + const yOffset2 = yOffset1 + yF * y.strides[1]; + const xFCorner = yF * convInfo.strideDepth - padFront; + for (let wF = 0; wF < filterDepth; ++wF) { + const xF = xFCorner + wF * dilationDepth; + if (xF < 0 || xF >= convInfo.inDepth) { + continue; + } + const wOffset1 = wF * filterStrides[0]; + const xOffset2 = xOffset1 + xF * xStrides[1]; + for (let yR = 0; yR < convInfo.outHeight; ++yR) { + const yOffset3 = yOffset2 + yR * y.strides[2]; + const xRCorner = yR * convInfo.strideHeight - padTop; + for (let wR = 0; wR < filterHeight; ++wR) { + const xR = xRCorner + wR * dilationHeight; + if (xR < 0 || xR >= convInfo.inHeight) { + continue; + } + const wOffset2 = wOffset1 + wR * filterStrides[1]; + const xOffset3 = xOffset2 + xR * xStrides[2]; + for (let yC = 0; yC < convInfo.outWidth; ++yC) { + const yOffset4 = yOffset3 + yC * convInfo.outChannels; + const xCCorner = yC * convInfo.strideWidth - padLeft; + for (let wC = 0; wC < filterWidth; ++wC) { + const xC = xCCorner + wC * dilationWidth; + if (xC < 0 || xC >= convInfo.inWidth) { + continue; + } + const wOffset3 = wOffset2 + wC * filterStrides[2]; + const xOffset4 = xOffset3 + xC * convInfo.inChannels; + let wOffset4 = wOffset3; + for (let d1 = 0; d1 < convInfo.inChannels; ++d1) { + const xVal = xVals[xOffset4 + d1]; + for (let d2 = 0; d2 < convInfo.outChannels; ++d2) { + yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2]; + } + wOffset4 += convInfo.outChannels; + } + } + } + } + } + } + } + } + return backend2.makeTensorInfo(y.shape, y.dtype, y.values); +} +var conv3DConfig = { + kernelName: Conv3D, + backendName: "cpu", + kernelFunc: conv3D +}; +function conv3DBackpropFilterV2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, pad: pad3, filterShape } = attrs; + assertNotComplex([x, dy], "conv3dBackpropFilterV2"); + const xStrides = util_exports.computeStrides(x.shape); + const dyStrides = util_exports.computeStrides(dy.shape); + const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3); + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const filterDepth = convInfo.filterDepth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const dw = new TensorBuffer(convInfo.filterShape, "float32"); + const dwValues = dw.values; + const [dwS0, dwS1, dwS2, dwS3] = dw.strides; + const dyValues = backend2.data.get(dy.dataId).values; + const [dyS0, dyS1, dyS2, dyS3] = dyStrides; + const xValues = backend2.data.get(x.dataId).values; + const [xS0, xS1, xS2, xS3] = xStrides; + const frontPad = convInfo.padInfo.front; + const leftPad = convInfo.padInfo.left; + const topPad = convInfo.padInfo.top; + for (let wF = 0; wF < filterDepth; ++wF) { + const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth)); + const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth); + const wOffset1 = wF * dwS0; + for (let wR = 0; wR < filterHeight; ++wR) { + const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight)); + const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight); + const wOffset2 = wR * dwS1 + wOffset1; + for (let wC = 0; wC < filterWidth; ++wC) { + const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth)); + const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth); + const wOffset3 = wC * dwS2 + wOffset2; + for (let d1 = 0; d1 < convInfo.inChannels; ++d1) { + const wOffset4 = d1 * dwS3 + wOffset3; + for (let d2 = 0; d2 < convInfo.outChannels; ++d2) { + let dotProd = 0; + for (let b = 0; b < convInfo.batchSize; ++b) { + const xOffset1 = b * xS0; + const yOffset1 = b * dyS0; + for (let yF = yFMin; yF < yFMax; ++yF) { + const xF = wF + yF * strideDepth - frontPad; + const xOffset2 = xF * xS1 + xOffset1; + const yOffset2 = yF * dyS1 + yOffset1; + for (let yR = yRMin; yR < yRMax; ++yR) { + const xR = wR + yR * strideHeight - topPad; + const xOffset3 = xR * xS2 + xOffset2; + const yOffset3 = yR * dyS2 + yOffset2; + for (let yC = yCMin; yC < yCMax; ++yC) { + const xC = wC + yC * strideWidth - leftPad; + const xOffset4 = xC * xS3 + xOffset3; + const yOffset4 = yC * dyS3 + yOffset3; + dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2]; + } + } + } + } + dwValues[wOffset4 + d2] = dotProd; + } + } + } + } + } + return backend2.makeTensorInfo(dw.shape, dw.dtype, dw.values); +} +var conv3DBackpropFilterV2Config = { + kernelName: Conv3DBackpropFilterV2, + backendName: "cpu", + kernelFunc: conv3DBackpropFilterV2 +}; +function conv3DBackpropInputV2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { pad: pad3, strides, inputShape } = attrs; + assertNotComplex([dy], "conv3dBackpropInputV2"); + const dyStrides = util_exports.computeStrides(dy.shape); + const filterStrides = util_exports.computeStrides(filter.shape); + const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3); + const dx = new TensorBuffer(convInfo.inShape, "float32"); + const dxValues = dx.values; + const [dxS0, dxS1, dxS2, dxS3] = dx.strides; + const dyValues = backend2.data.get(dy.dataId).values; + const [dyS0, dyS1, dyS2, dyS3] = dyStrides; + const fltValues = backend2.data.get(filter.dataId).values; + const [fltS0, fltS1, fltS2, fltS3] = filterStrides; + const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo; + const frontPad = filterDepth - 1 - convInfo.padInfo.front; + const topPad = filterHeight - 1 - convInfo.padInfo.top; + const leftPad = filterWidth - 1 - convInfo.padInfo.left; + for (let b = 0; b < batchSize; ++b) { + for (let d1 = 0; d1 < inChannels; ++d1) { + for (let xF = 0; xF < inDepth; ++xF) { + const xFCorner = xF - frontPad; + const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth)); + const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth); + for (let xR = 0; xR < inHeight; ++xR) { + const xRCorner = xR - topPad; + const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight)); + const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight); + for (let xC = 0; xC < inWidth; ++xC) { + const xCCorner = xC - leftPad; + const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth)); + const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth); + let dotProd = 0; + for (let yF = xFMin; yF < yFMax; ++yF) { + const wF = yF * strideDepth - xFCorner; + for (let yR = xRMin; yR < yRMax; ++yR) { + const wR = yR * strideHeight - xRCorner; + for (let yC = xCMin; yC < yCMax; ++yC) { + const wC = yC * strideWidth - xCCorner; + const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC; + const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1; + for (let d2 = 0; d2 < outChannels; ++d2) { + const pixel = dyValues[dyOffset + d2]; + const weight = fltValues[fltOffset + d2]; + dotProd += pixel * weight; + } + } + } + } + dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd; + } + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var conv3DBackpropInputV2Config = { + kernelName: Conv3DBackpropInputV2, + backendName: "cpu", + kernelFunc: conv3DBackpropInputV2 +}; +var cos2 = unaryKernelFunc(Cos, (xi) => Math.cos(xi)); +var cosConfig = { + kernelName: Cos, + backendName: "cpu", + kernelFunc: cos2 +}; +var cosh2 = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi)); +var coshConfig = { + kernelName: Cosh, + backendName: "cpu", + kernelFunc: cosh2 +}; +function cropAndResize3(args) { + const { inputs, backend: backend2, attrs } = args; + const { image: image2, boxes, boxInd } = inputs; + const { cropSize, method, extrapolationValue } = attrs; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const numBoxes = boxes.shape[0]; + const [cropHeight, cropWidth] = cropSize; + const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], "float32"); + const boxVals = backend2.data.get(boxes.dataId).values; + const boxIndVals = backend2.data.get(boxInd.dataId).values; + const imageVals = backend2.data.get(image2.dataId).values; + const inStride = util_exports.computeStrides(image2.shape); + const outStride = util_exports.computeStrides(output.shape); + for (let b = 0; b < numBoxes; b++) { + const startInd = b * 4; + const y1 = boxVals[startInd]; + const x1 = boxVals[startInd + 1]; + const y2 = boxVals[startInd + 2]; + const x2 = boxVals[startInd + 3]; + const bInd = boxIndVals[b]; + if (bInd >= batch) { + continue; + } + const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0; + const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0; + for (let y = 0; y < cropHeight; y++) { + const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1); + if (yInd < 0 || yInd > imageHeight - 1) { + for (let x = 0; x < cropWidth; x++) { + for (let c = 0; c < numChannels; c++) { + const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = extrapolationValue; + } + } + continue; + } + if (method === "bilinear") { + const topInd = Math.floor(yInd); + const bottomInd = Math.ceil(yInd); + const yLerp = yInd - topInd; + for (let x = 0; x < cropWidth; x++) { + const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1); + if (xInd < 0 || xInd > imageWidth - 1) { + for (let c = 0; c < numChannels; c++) { + const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = extrapolationValue; + } + continue; + } + const leftInd = Math.floor(xInd); + const rightInd = Math.ceil(xInd); + const xLerp = xInd - leftInd; + for (let c = 0; c < numChannels; c++) { + let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0]; + const topLeft = imageVals[ind]; + ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0]; + const topRight = imageVals[ind]; + ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0]; + const bottomLeft = imageVals[ind]; + ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0]; + const bottomRight = imageVals[ind]; + const top = topLeft + (topRight - topLeft) * xLerp; + const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp; + ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = top + (bottom - top) * yLerp; + } + } + } else { + for (let x = 0; x < cropWidth; ++x) { + const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1); + if (xInd < 0 || xInd > imageWidth - 1) { + for (let c = 0; c < numChannels; c++) { + const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = extrapolationValue; + } + continue; + } + const closestX = Math.round(xInd); + const closestY = Math.round(yInd); + for (let c = 0; c < numChannels; c++) { + const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0]; + const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[outInd] = imageVals[inInd]; + } + } + } + } + } + return backend2.makeTensorInfo(output.shape, output.dtype, output.values); +} +var cropAndResizeConfig = { + kernelName: CropAndResize, + backendName: "cpu", + kernelFunc: cropAndResize3 +}; +function cumprod2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + assertNotComplex(x, "cumprod"); + const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length); + let $x = x; + if (permutation != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } }); + } + const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0]; + if (permutedAxis !== $x.shape.length - 1) { + throw new Error(`backend.cumprod in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`); + } + const resultDtype = upcastType($x.dtype, "int32"); + const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype); + const aVals = backend2.data.get($x.dataId).values; + const finalDim = $x.shape[$x.shape.length - 1]; + const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j; + for (let i = 0; i < aVals.length; i += finalDim) { + for (let j = 0; j < finalDim; j++) { + const idx = indexAdjuster(i, j); + if (j === 0) { + vals[idx] = exclusive ? 1 : aVals[idx]; + } else { + const prevIdx = indexAdjuster(i, j - 1); + vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx]; + } + } + } + const result = backend2.makeTensorInfo($x.shape, resultDtype, vals); + if (permutation != null) { + const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation); + const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } }); + backend2.disposeIntermediateTensorInfo(result); + backend2.disposeIntermediateTensorInfo($x); + return reverseTransposedResult; + } + return result; +} +var cumprodConfig = { + kernelName: Cumprod, + backendName: "cpu", + kernelFunc: cumprod2 +}; +function cumsum2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + assertNotComplex(x, "cumsum"); + const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length); + let $x = x; + if (permutation != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } }); + } + const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0]; + if (permutedAxis !== $x.shape.length - 1) { + throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`); + } + const resultDtype = upcastType($x.dtype, "int32"); + const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype); + const aVals = backend2.data.get($x.dataId).values; + const finalDim = $x.shape[$x.shape.length - 1]; + const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j; + for (let i = 0; i < aVals.length; i += finalDim) { + for (let j = 0; j < finalDim; j++) { + const idx = indexAdjuster(i, j); + if (j === 0) { + vals[idx] = exclusive ? 0 : aVals[idx]; + } else { + const prevIdx = indexAdjuster(i, j - 1); + vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx]; + } + } + } + const result = backend2.makeTensorInfo($x.shape, resultDtype, vals); + if (permutation != null) { + const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation); + const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } }); + backend2.disposeIntermediateTensorInfo(result); + backend2.disposeIntermediateTensorInfo($x); + return reverseTransposedResult; + } + return result; +} +var cumsumConfig = { + kernelName: Cumsum, + backendName: "cpu", + kernelFunc: cumsum2 +}; +function denseBincount2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, weights } = inputs; + const { size, binaryOutput } = attrs; + if (x.shape.length === 1) { + const xVals = backend2.data.get(x.dataId).values; + const weightsVals = backend2.data.get(weights.dataId).values; + const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size); + return backend2.makeTensorInfo([size], weights.dtype, outVals); + } else if (x.shape.length === 2) { + const xBuf = backend2.bufferSync(x); + const weightsBuf = backend2.bufferSync(weights); + const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput); + return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values); + } + throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`); +} +var denseBincountConfig = { + kernelName: DenseBincount, + backendName: "cpu", + kernelFunc: denseBincount2 +}; +function depthToSpace2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockSize, dataFormat } = attrs; + util_exports.assert(dataFormat === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`); + const batchSize = x.shape[0]; + const inputHeight = x.shape[1]; + const inputWidth = x.shape[2]; + const inputDepth = x.shape[3]; + const outputHeight = inputHeight * blockSize; + const outputWidth = inputWidth * blockSize; + const outputDepth = inputDepth / (blockSize * blockSize); + const xValues = backend2.data.get(x.dataId).values; + const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth); + let outputIdx = 0; + for (let b = 0; b < batchSize; ++b) { + for (let h = 0; h < outputHeight; ++h) { + const inH = Math.floor(h / blockSize); + const offsetH = h % blockSize; + for (let w = 0; w < outputWidth; ++w) { + const inW = Math.floor(w / blockSize); + const offsetW = w % blockSize; + const offsetD = (offsetH * blockSize + offsetW) * outputDepth; + for (let d = 0; d < outputDepth; ++d) { + const inD = d + offsetD; + const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b)); + result[outputIdx++] = xValues[inputIdx]; + } + } + } + } + return backend2.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result); +} +var depthToSpaceConfig = { + kernelName: DepthToSpace, + backendName: "cpu", + kernelFunc: depthToSpace2 +}; +function depthwiseConv2dNative(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations, dimRoundingMode } = attrs; + assertNotComplex([x, filter], "depthwiseConv2DNative"); + const xStrides = util_exports.computeStrides(x.shape); + const filterStrides = util_exports.computeStrides(filter.shape); + let $dilations = dilations; + if ($dilations == null) { + $dilations = [1, 1]; + } + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`); + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filter.shape, + strides, + $dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo; + const padLeft = padInfo.left; + const padTop = padInfo.top; + const chMul = convInfo.outChannels / convInfo.inChannels; + const y = new TensorBuffer(convInfo.outShape, x.dtype); + const xVals = backend2.data.get(x.dataId).values; + const wVals = backend2.data.get(filter.dataId).values; + const yVals = y.values; + for (let b = 0; b < convInfo.batchSize; ++b) { + const xOffset1 = b * xStrides[0]; + const yOffset1 = b * y.strides[0]; + for (let yR = 0; yR < convInfo.outHeight; ++yR) { + const yOffset2 = yOffset1 + yR * y.strides[1]; + const xRCorner = yR * convInfo.strideHeight - padTop; + for (let wR = 0; wR < filterHeight; ++wR) { + const xR = xRCorner + wR * dilationHeight; + if (xR < 0 || xR >= convInfo.inHeight) { + continue; + } + const wOffset1 = wR * filterStrides[0]; + const xOffset2 = xOffset1 + xR * xStrides[1]; + for (let yC = 0; yC < convInfo.outWidth; ++yC) { + const yOffset3 = yOffset2 + yC * y.strides[2]; + const xCCorner = yC * convInfo.strideWidth - padLeft; + for (let wC = 0; wC < filterWidth; ++wC) { + const xC = xCCorner + wC * dilationWidth; + if (xC < 0 || xC >= convInfo.inWidth) { + continue; + } + const wOffset2 = wOffset1 + wC * filterStrides[1]; + const xOffset3 = xOffset2 + xC * convInfo.inChannels; + let yOffset4 = yOffset3; + let wOffset3 = wOffset2; + for (let d1 = 0; d1 < convInfo.inChannels; ++d1) { + const xVal = xVals[xOffset3 + d1]; + for (let q = 0; q < chMul; ++q) { + yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q]; + } + yOffset4 += chMul; + wOffset3 += chMul; + } + } + } + } + } + } + return backend2.makeTensorInfo(y.shape, y.dtype, y.values); +} +var depthwiseConv2dNativeConfig = { + kernelName: DepthwiseConv2dNative, + backendName: "cpu", + kernelFunc: depthwiseConv2dNative +}; +function depthwiseConv2dNativeBackpropFilter2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs; + assertNotComplex([x, dy], "depthwiseConv2dNativeBackpropFilter"); + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filterShape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo; + const dW = new TensorBuffer(convInfo.filterShape, "float32"); + const leftPad = convInfo.padInfo.left; + const topPad = convInfo.padInfo.top; + const chMul = convInfo.outChannels / convInfo.inChannels; + const xVals = backend2.data.get(x.dataId).values; + const xBuf = new TensorBuffer(x.shape, x.dtype, xVals); + const dyVals = backend2.data.get(dy.dataId).values; + const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals); + for (let wR = 0; wR < filterHeight; ++wR) { + const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight)); + const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight); + for (let wC = 0; wC < filterWidth; ++wC) { + const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth)); + const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth); + for (let d2 = 0; d2 < convInfo.outChannels; ++d2) { + const d1 = Math.trunc(d2 / chMul); + const dm = d2 % chMul; + let dotProd = 0; + for (let b = 0; b < convInfo.batchSize; ++b) { + for (let yR = yRMin; yR < yRMax; ++yR) { + const xR = wR + yR * strideHeight - topPad; + for (let yC = yCMin; yC < yCMax; ++yC) { + const xC = wC + yC * strideWidth - leftPad; + dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2); + } + } + } + dW.set(dotProd, wR, wC, d1, dm); + } + } + } + return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values); +} +var depthwiseConv2dNativeBackpropFilterConfig = { + kernelName: DepthwiseConv2dNativeBackpropFilter, + backendName: "cpu", + kernelFunc: depthwiseConv2dNativeBackpropFilter2 +}; +function depthwiseConv2dNativeBackpropInput2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs; + assertNotComplex([dy, filter], "depthwiseConv2DNativeBackpropInput"); + const dyStrides = util_exports.computeStrides(dy.shape); + const filterStrides = util_exports.computeStrides(filter.shape); + const convInfo = backend_util_exports.computeConv2DInfo( + inputShape, + filter.shape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const dx = new TensorBuffer(convInfo.inShape, "float32"); + const dxValues = dx.values; + const [dxS0, dxS1, dxS2] = dx.strides; + const dyValues = backend2.data.get(dy.dataId).values; + const [dyS0, dyS1, dyS2] = dyStrides; + const fltValues = backend2.data.get(filter.dataId).values; + const [fltS0, fltS1, fltS2] = filterStrides; + const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo; + const topPad = filterHeight - 1 - convInfo.padInfo.top; + const leftPad = filterWidth - 1 - convInfo.padInfo.left; + const chMul = outChannels / inChannels; + for (let b = 0; b < batchSize; ++b) { + for (let d1 = 0; d1 < inChannels; ++d1) { + for (let xR = 0; xR < inHeight; ++xR) { + const xRCorner = xR - topPad; + const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight)); + const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight); + for (let xC = 0; xC < inWidth; ++xC) { + const xCCorner = xC - leftPad; + const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth)); + const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth); + let dotProd = 0; + for (let yR = xRMin; yR < yRMax; ++yR) { + const wR = yR * strideHeight - xRCorner; + for (let yC = xCMin; yC < yCMax; ++yC) { + const wC = yC * strideWidth - xCCorner; + const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC; + const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1; + for (let dm = 0; dm < chMul; ++dm) { + const d2 = d1 * chMul + dm; + const pixel = dyValues[dyOffset + d2]; + const weight = fltValues[fltOffset + dm]; + dotProd += pixel * weight; + } + } + } + dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd; + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var depthwiseConv2dNativeBackpropInputConfig = { + kernelName: DepthwiseConv2dNativeBackpropInput, + backendName: "cpu", + kernelFunc: depthwiseConv2dNativeBackpropInput2 +}; +function diag2(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + const xSize = util_exports.sizeFromShape(x.shape); + const xVals = backend2.data.get(x.dataId).values; + const outBuf = buffer([xSize, xSize], x.dtype); + const vals = outBuf.values; + for (let i = 0; i < xVals.length; i++) { + vals[i * xSize + i] = xVals[i]; + } + const outShape = [...x.shape, ...x.shape]; + return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values); +} +var diagConfig = { + kernelName: Diag, + backendName: "cpu", + kernelFunc: diag2 +}; +var dilation2DConfig = { + kernelName: Dilation2D, + backendName: "cpu", + kernelFunc: ({ inputs, backend: backend2, attrs }) => { + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + const cpuBackend = backend2; + const xVals = cpuBackend.data.get(x.dataId).values; + const xRank = x.shape.length; + const filterVals = cpuBackend.data.get(filter.dataId).values; + const filterRank = filter.shape.length; + const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + const outSize = util_exports.sizeFromShape(outShape); + const outRank = outShape.length; + const outputVals = util_exports.getArrayFromDType(x.dtype, outSize); + for (let b = 0; b < batchSize; ++b) { + for (let hOut = 0; hOut < outHeight; ++hOut) { + const hBeg = hOut * strideHeight - padInfo.top; + for (let wOut = 0; wOut < outWidth; ++wOut) { + const wBeg = wOut * strideWidth - padInfo.left; + for (let d = 0; d < inChannels; ++d) { + let curVal = Number.MIN_SAFE_INTEGER; + for (let h = 0; h < filterHeight; ++h) { + const hIn = hBeg + h * dilationHeight; + if (hIn >= 0 && hIn < inHeight) { + for (let w = 0; w < filterWidth; ++w) { + const wIn = wBeg + w * dilationWidth; + if (wIn >= 0 && wIn < inWidth) { + const xIndex = util_exports.locToIndex([b, hIn, wIn, d], xRank, util_exports.computeStrides(x.shape)); + const filterIndex = util_exports.locToIndex([h, w, d], filterRank, util_exports.computeStrides(filter.shape)); + const val = xVals[xIndex] + filterVals[filterIndex]; + if (val > curVal) { + curVal = val; + } + } + } + } + } + const outputIndex = util_exports.locToIndex([b, hOut, wOut, d], outRank, util_exports.computeStrides(outShape)); + outputVals[outputIndex] = curVal; + } + } + } + } + const dataId = cpuBackend.write(util_exports.toTypedArray(outputVals, x.dtype), outShape, x.dtype); + return { dataId, shape: outShape, dtype: x.dtype }; + } +}; +var dilation2DBackpropFilterConfig = { + kernelName: Dilation2DBackpropFilter, + backendName: "cpu", + kernelFunc: ({ inputs, backend: backend2, attrs }) => { + const { x, filter, dy } = inputs; + const { strides, pad: pad3, dilations } = attrs; + const cpuBackend = backend2; + const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values); + const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values); + const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`); + const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values); + const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype); + for (let b = 0; b < batchSize; ++b) { + for (let hOut = 0; hOut < outHeight; ++hOut) { + const hBeg = hOut * strideHeight - padInfo.top; + for (let wOut = 0; wOut < outWidth; ++wOut) { + const wBeg = wOut * strideWidth - padInfo.left; + for (let d = 0; d < inChannels; ++d) { + let curVal = Number.MIN_SAFE_INTEGER; + let hMax = 0; + let wMax = 0; + for (let h = 0; h < filterHeight; ++h) { + const hIn = hBeg + h * dilationHeight; + if (hIn >= 0 && hIn < inHeight) { + for (let w = 0; w < filterWidth; ++w) { + const wIn = wBeg + w * dilationWidth; + if (wIn >= 0 && wIn < inWidth) { + const val = $x[b][hIn][wIn][d] + $filter[h][w][d]; + if (val > curVal) { + curVal = val; + hMax = h; + wMax = w; + } + } + } + } + } + gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d]; + } + } + } + } + const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype); + return { dataId, shape: filter.shape, dtype: filter.dtype }; + } +}; +var dilation2DBackpropInputConfig = { + kernelName: Dilation2DBackpropInput, + backendName: "cpu", + kernelFunc: ({ inputs, backend: backend2, attrs }) => { + const { x, filter, dy } = inputs; + const { strides, pad: pad3, dilations } = attrs; + const cpuBackend = backend2; + const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values); + const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values); + const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`); + const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values); + const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype); + for (let b = 0; b < batchSize; ++b) { + for (let hOut = 0; hOut < outHeight; ++hOut) { + const hBeg = hOut * strideHeight - padInfo.top; + for (let wOut = 0; wOut < outWidth; ++wOut) { + const wBeg = wOut * strideWidth - padInfo.left; + for (let d = 0; d < inChannels; ++d) { + let curVal = Number.MIN_SAFE_INTEGER; + let hInMax = hBeg < 0 ? 0 : hBeg; + let wInMax = wBeg < 0 ? 0 : wBeg; + for (let h = 0; h < filterHeight; ++h) { + const hIn = hBeg + h * dilationHeight; + if (hIn >= 0 && hIn < inHeight) { + for (let w = 0; w < filterWidth; ++w) { + const wIn = wBeg + w * dilationWidth; + if (wIn >= 0 && wIn < inWidth) { + const val = $x[b][hIn][wIn][d] + $filter[h][w][d]; + if (val > curVal) { + curVal = val; + hInMax = hIn; + wInMax = wIn; + } + } + } + } + } + gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d]; + } + } + } + } + const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), x.shape, x.dtype); + return { dataId, shape: x.shape, dtype: x.dtype }; + } +}; +function draw2(args) { + const { inputs, backend: backend2, attrs } = args; + const { image: image2 } = inputs; + const { canvas, options } = attrs; + const { contextOptions, imageOptions } = options || {}; + const alpha = (imageOptions === null || imageOptions === void 0 ? void 0 : imageOptions.alpha) || 1; + const contextType = (contextOptions === null || contextOptions === void 0 ? void 0 : contextOptions.contextType) || "2d"; + if (contextType !== "2d") { + throw new Error(`Context type ${contextOptions.contextType} is not supported by the CPU backend.`); + } + const ctx = canvas.getContext(contextType, (contextOptions === null || contextOptions === void 0 ? void 0 : contextOptions.contextAttributes) || {}); + if (ctx == null) { + throw new Error(`Could not get the context with ${contextType} type.`); + } + const [height, width] = image2.shape.slice(0, 2); + const depth = image2.shape.length === 2 ? 1 : image2.shape[2]; + const data = backend2.data.get(image2.dataId).values; + const multiplier = image2.dtype === "float32" ? 255 : 1; + const bytes = new Uint8ClampedArray(width * height * 4); + for (let i = 0; i < height * width; ++i) { + const rgba = [0, 0, 0, 255 * alpha]; + for (let d = 0; d < depth; d++) { + const value = data[i * depth + d]; + if (image2.dtype === "float32") { + if (value < 0 || value > 1) { + throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`); + } + } else if (image2.dtype === "int32") { + if (value < 0 || value > 255) { + throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`); + } + } + if (depth === 1) { + rgba[0] = value * multiplier; + rgba[1] = value * multiplier; + rgba[2] = value * multiplier; + } else { + rgba[d] = value * multiplier; + } + } + const j = i * 4; + bytes[j + 0] = Math.round(rgba[0]); + bytes[j + 1] = Math.round(rgba[1]); + bytes[j + 2] = Math.round(rgba[2]); + bytes[j + 3] = Math.round(rgba[3]); + } + canvas.width = width; + canvas.height = height; + const imageData = new ImageData(bytes, width, height); + ctx.putImageData(imageData, 0, 0); + return image2; +} +var drawConfig = { + kernelName: Draw, + backendName: "cpu", + kernelFunc: draw2 +}; +function sum3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + assertNotComplex(x, "sum"); + let $x; + if (x.dtype === "bool") { + $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "int32" } }); + } else { + $x = identity2({ inputs: { x }, backend: backend2 }); + } + const xRank = $x.shape.length; + const axes = util_exports.parseAxisParam(axis, $x.shape); + const permutation = backend_util_exports.getAxesPermutation(axes, xRank); + let reductionAxes = axes; + let permutedX = $x; + if (permutation != null) { + permutedX = transpose2({ inputs: { x: $x }, backend: backend2, attrs: { perm: permutation } }); + reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, permutedX.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, reductionAxes); + const resultDtype = backend_util_exports.upcastType(permutedX.dtype, "int32"); + let result = zeros3(backend2, outShape, resultDtype); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const vals = backend2.data.get(result.dataId).values; + const aVals = backend2.data.get(permutedX.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let sum6 = 0; + for (let j = 0; j < reduceSize; ++j) { + sum6 += aVals[offset + j]; + } + vals[i] = sum6; + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes); + const oldResult = result; + result = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: newShape } }); + backend2.disposeIntermediateTensorInfo(oldResult); + } + backend2.disposeIntermediateTensorInfo($x); + if (permutation != null) { + backend2.disposeIntermediateTensorInfo(permutedX); + } + return result; +} +var sumConfig = { + kernelName: Sum, + backendName: "cpu", + kernelFunc: sum3 +}; +function einsum2(args) { + const { inputs, backend: backend2, attrs } = args; + const { equation } = attrs; + const tensors = inputs; + const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length); + backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors); + const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims); + const nSteps = steps.length; + let out = null; + let numDimsRemaining = allDims.length; + const tensorsToDispose = []; + for (let i = 0; i < nSteps; ++i) { + for (const idTerm of steps[i]) { + const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]); + let x; + if (backend_util_exports.isIdentityPermutation(perm)) { + x = tensors[idTerm]; + } else { + x = transpose2({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } }); + tensorsToDispose.push(x); + } + const targetShape = x.shape.slice(); + for (let k = 0; k < dimsToExpand.length; ++k) { + targetShape.splice(dimsToExpand[k], 0, 1); + } + if (!util_exports.arraysEqual(x.shape, targetShape)) { + x = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } }); + tensorsToDispose.push(x); + } + if (out === null) { + out = x; + } else { + out = multiply2({ inputs: { a: x, b: out }, backend: backend2 }); + tensorsToDispose.push(out); + } + } + if (i < nSteps - 1) { + if (path[i] >= 0) { + out = sum3({ + inputs: { x: out }, + backend: backend2, + attrs: { + axis: path[i] - (allDims.length - numDimsRemaining), + keepDims: false + } + }); + tensorsToDispose.push(out); + } + numDimsRemaining--; + } + } + for (const tensorInfo of tensorsToDispose) { + if (tensorInfo === out) { + continue; + } + backend2.disposeIntermediateTensorInfo(tensorInfo); + } + return out; +} +var einsumConfig = { + kernelName: Einsum, + backendName: "cpu", + kernelFunc: einsum2 +}; +function eluGrad(args) { + const { inputs, backend: backend2 } = args; + const { dy, y } = inputs; + assertNotComplex([dy, y], "eluGrad"); + const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape)); + const values = backend2.data.get(y.dataId).values; + const dyValues = backend2.data.get(dy.dataId).values; + for (let i = 0; i < values.length; ++i) { + const v = values[i]; + if (v >= 0) { + resultValues[i] = dyValues[i]; + } else { + resultValues[i] = dyValues[i] * (v + 1); + } + } + return backend2.makeTensorInfo(y.shape, "float32", resultValues); +} +var eluGradConfig2 = { + kernelName: EluGrad, + backendName: "cpu", + kernelFunc: eluGrad +}; +var p = backend_util_exports.ERF_P; +var a1 = backend_util_exports.ERF_A1; +var a2 = backend_util_exports.ERF_A2; +var a3 = backend_util_exports.ERF_A3; +var a4 = backend_util_exports.ERF_A4; +var a5 = backend_util_exports.ERF_A5; +var erf2 = unaryKernelFunc(Erf, (xi) => { + const sign4 = Math.sign(xi); + const v = Math.abs(xi); + const t = 1 / (1 + p * v); + return sign4 * (1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-v * v)); +}); +var erfConfig = { + kernelName: Erf, + backendName: "cpu", + kernelFunc: erf2 +}; +function expandDims3(args) { + const { inputs, backend: backend2, attrs } = args; + const { input: input2 } = inputs; + const { dim } = attrs; + const inputRank = input2.shape.length; + const newShape = input2.shape.slice(); + let $dim = dim; + if (dim < 0) { + util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`); + $dim = inputRank + dim + 1; + } + newShape.splice($dim, 0, 1); + return reshape3({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } }); +} +var expandDimsConfig = { + kernelName: ExpandDims, + backendName: "cpu", + kernelFunc: expandDims3 +}; +var realDivImpl = createSimpleBinaryKernelImpl((a, b) => a / b); +var div2 = binaryKernelFunc(RealDiv, realDivImpl); +var realDivConfig = { + kernelName: RealDiv, + backendName: "cpu", + kernelFunc: div2 +}; +function fftBatch(input2, inverse, cpuBackend) { + const inputShape = input2.shape; + const batch = inputShape[0]; + const innerDim = inputShape[1]; + const inputVals = cpuBackend.data.get(input2.dataId); + const real2D = inputVals.complexTensorInfos.real; + const imag2D = inputVals.complexTensorInfos.imag; + const resultShape = [batch, innerDim]; + const resultSize = util_exports.sizeFromShape(resultShape); + const resultReal = util_exports.getTypedArrayFromDType("float32", resultSize); + const resultImag = util_exports.getTypedArrayFromDType("float32", resultSize); + for (let b = 0; b < batch; b++) { + const r = slice2({ + inputs: { x: real2D }, + backend: cpuBackend, + attrs: { begin: [b, 0], size: [1, innerDim] } + }); + const i = slice2({ + inputs: { x: imag2D }, + backend: cpuBackend, + attrs: { begin: [b, 0], size: [1, innerDim] } + }); + const input3 = complex2({ inputs: { real: r, imag: i }, backend: cpuBackend }); + const { real: real4, imag: imag4 } = fftImpl(input3, inverse, cpuBackend); + const res = backend_util_exports.mergeRealAndImagArrays(real4, imag4); + for (let d = 0; d < innerDim; d++) { + const c = backend_util_exports.getComplexWithIndex(res, d); + resultReal[b * innerDim + d] = c.real; + resultImag[b * innerDim + d] = c.imag; + } + cpuBackend.disposeIntermediateTensorInfo(r); + cpuBackend.disposeIntermediateTensorInfo(i); + cpuBackend.disposeIntermediateTensorInfo(input3); + } + const $realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultReal); + const $imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultImag); + const result = complex2({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend }); + cpuBackend.disposeIntermediateTensorInfo($realInfo); + cpuBackend.disposeIntermediateTensorInfo($imagInfo); + return result; +} +function fftImpl(input2, inverse, cpuBackend) { + const inputSize = util_exports.sizeFromShape(input2.shape); + const inputVals = cpuBackend.data.get(input2.dataId); + const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values; + const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values; + if (isExponentOf2(inputSize)) { + const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend); + const resultShape = [input2.shape[0], input2.shape[1]]; + if (inverse) { + const realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.real); + const imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.imag); + const sizeInfo = cpuBackend.makeTensorInfo([], "float32", util_exports.createScalarValue(inputSize, "float32")); + const sizeInfoCopy = identity2({ inputs: { x: sizeInfo }, backend: cpuBackend }); + const divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend }); + const divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend }); + const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values; + const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values; + cpuBackend.disposeIntermediateTensorInfo(realInfo); + cpuBackend.disposeIntermediateTensorInfo(imagInfo); + cpuBackend.disposeIntermediateTensorInfo(sizeInfo); + cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy); + cpuBackend.disposeIntermediateTensorInfo(divRealInfo); + cpuBackend.disposeIntermediateTensorInfo(divImagInfo); + return { real: divRealVals, imag: divImagVals }; + } + return result; + } else { + const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals); + const rawOutput = fourierTransformByMatmul(data, inputSize, inverse); + return backend_util_exports.splitRealAndImagArrays(rawOutput); + } +} +function isExponentOf2(size) { + return (size & size - 1) === 0; +} +function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) { + if (size === 1) { + return { real: realVals, imag: imagVals }; + } + const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals); + const half = size / 2; + const evenComplex = backend_util_exports.complexWithEvenIndex(data); + const evenRealVals = evenComplex.real; + const evenImagVals = evenComplex.imag; + const evenShape = [evenRealVals.length]; + const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenRealVals); + const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenImagVals); + const evenTensorInfo = complex2({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend }); + const oddComplex = backend_util_exports.complexWithOddIndex(data); + const oddRealVals = oddComplex.real; + const oddImagVals = oddComplex.imag; + const oddShape = [oddRealVals.length]; + const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddRealVals); + const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddImagVals); + const oddTensorInfo = complex2({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend }); + const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend); + const $evenRealVals = $evenComplex.real; + const $evenImagVals = $evenComplex.imag; + const $evenShape = [$evenRealVals.length]; + const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenRealVals); + const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenImagVals); + const $evenTensorInfo = complex2({ + inputs: { real: $evenRealInfo, imag: $evenImagInfo }, + backend: cpuBackend + }); + const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend); + const $oddRealVals = $oddComplex.real; + const $oddImagVals = $oddComplex.imag; + const $oddShape = [$oddRealVals.length]; + const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddRealVals); + const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddImagVals); + const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend }); + const e = backend_util_exports.exponents(size, inverse); + const eShape = [e.real.length]; + const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.real); + const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.imag); + const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend }); + const exponentInfo = multiply2({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend }); + const addPart = add4({ + inputs: { a: $evenTensorInfo, b: exponentInfo }, + backend: cpuBackend + }); + const subPart = sub2({ + inputs: { a: $evenTensorInfo, b: exponentInfo }, + backend: cpuBackend + }); + const addPartReal = real2({ inputs: { input: addPart }, backend: cpuBackend }); + const subPartReal = real2({ inputs: { input: subPart }, backend: cpuBackend }); + const addPartImag = imag2({ inputs: { input: addPart }, backend: cpuBackend }); + const subPartImag = imag2({ inputs: { input: subPart }, backend: cpuBackend }); + const $real = concat2({ + inputs: [addPartReal, subPartReal], + backend: cpuBackend, + attrs: { axis: 0 } + }); + const $imag = concat2({ + inputs: [addPartImag, subPartImag], + backend: cpuBackend, + attrs: { axis: 0 } + }); + const $realVals = cpuBackend.data.get($real.dataId).values; + const $imagVals = cpuBackend.data.get($imag.dataId).values; + cpuBackend.disposeIntermediateTensorInfo(evenRealInfo); + cpuBackend.disposeIntermediateTensorInfo(evenImagInfo); + cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo); + cpuBackend.disposeIntermediateTensorInfo(oddRealInfo); + cpuBackend.disposeIntermediateTensorInfo(oddImagInfo); + cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo); + cpuBackend.disposeIntermediateTensorInfo($evenRealInfo); + cpuBackend.disposeIntermediateTensorInfo($evenImagInfo); + cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo); + cpuBackend.disposeIntermediateTensorInfo($oddRealInfo); + cpuBackend.disposeIntermediateTensorInfo($oddImagInfo); + cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo); + cpuBackend.disposeIntermediateTensorInfo(eRealInfo); + cpuBackend.disposeIntermediateTensorInfo(eImagInfo); + cpuBackend.disposeIntermediateTensorInfo(complexInfo); + cpuBackend.disposeIntermediateTensorInfo(exponentInfo); + cpuBackend.disposeIntermediateTensorInfo(addPart); + cpuBackend.disposeIntermediateTensorInfo(subPart); + cpuBackend.disposeIntermediateTensorInfo(addPartReal); + cpuBackend.disposeIntermediateTensorInfo(addPartImag); + cpuBackend.disposeIntermediateTensorInfo(subPartReal); + cpuBackend.disposeIntermediateTensorInfo(subPartImag); + cpuBackend.disposeIntermediateTensorInfo($real); + cpuBackend.disposeIntermediateTensorInfo($imag); + return { real: $realVals, imag: $imagVals }; +} +function fourierTransformByMatmul(data, size, inverse) { + const ret = new Float32Array(size * 2); + for (let r = 0; r < size; r++) { + let real4 = 0; + let imag4 = 0; + for (let c = 0; c < size; c++) { + const e = backend_util_exports.exponent(r * c, size, inverse); + const term = backend_util_exports.getComplexWithIndex(data, c); + real4 += term.real * e.real - term.imag * e.imag; + imag4 += term.real * e.imag + term.imag * e.real; + } + if (inverse) { + real4 /= size; + imag4 /= size; + } + backend_util_exports.assignToTypedArray(ret, real4, imag4, r); + } + return ret; +} +function fft2(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const inputSize = util_exports.sizeFromShape(input2.shape); + const innerDimensionSize = input2.shape[input2.shape.length - 1]; + const batch = inputSize / innerDimensionSize; + const input2D = reshape3({ + inputs: { x: input2 }, + backend: backend2, + attrs: { shape: [batch, innerDimensionSize] } + }); + const result = fftBatch(input2D, false, backend2); + const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } }); + backend2.disposeIntermediateTensorInfo(input2D); + backend2.disposeIntermediateTensorInfo(result); + return resultReshaped; +} +var fftConfig = { + kernelName: FFT, + backendName: "cpu", + kernelFunc: fft2 +}; +function fill2(args) { + const { backend: backend2, attrs } = args; + const { shape, value, dtype } = attrs; + const $dtype = dtype || util_exports.inferDtype(value); + const values = util_exports.getArrayFromDType($dtype, util_exports.sizeFromShape(shape)); + fillValues(values, value, $dtype); + return backend2.makeTensorInfo(shape, $dtype, values); +} +var fillConfig = { + kernelName: Fill, + backendName: "cpu", + kernelFunc: fill2 +}; +function fillValues(values, value, dtype) { + if (dtype === "string") { + values.fill(value); + } else { + values.fill(value); + } +} +var flipLeftRightConfig = { + kernelName: FlipLeftRight, + backendName: "cpu", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { image: image2 } = inputs; + const cpuBackend = backend2; + const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape)); + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const imageVals = cpuBackend.data.get(image2.dataId).values; + for (let batchIdx = 0; batchIdx < batch; batchIdx++) { + const batchOffset = batchIdx * imageWidth * imageHeight * numChannels; + for (let row = 0; row < imageHeight; row++) { + const rowOffset = row * (imageWidth * numChannels); + for (let col = 0; col < imageWidth; col++) { + const colOffset = col * numChannels; + for (let channel = 0; channel < numChannels; channel++) { + const coordX = Math.round(imageWidth - col - 1); + const outIdx = batchOffset + rowOffset + colOffset + channel; + let outputValue = imageVals[outIdx]; + if (coordX >= 0 && coordX < imageWidth) { + const rotatedColOffset = coordX * numChannels; + const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel; + outputValue = imageVals[imageIdx]; + } + output[outIdx] = outputValue; + } + } + } + } + const dataId = cpuBackend.write(output, image2.shape, image2.dtype); + return { dataId, shape: image2.shape, dtype: image2.dtype }; + } +}; +function fusedConv2D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + let result = conv2D({ + inputs: { x, filter }, + backend: backend2, + attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } + }); + if (bias) { + const resultOld = result; + if (dataFormat === "NCHW" && bias.shape.length === 1 && bias.shape[0] !== 1) { + const reshapedBias = reshape3({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } }); + result = add4({ inputs: { a: result, b: reshapedBias }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(reshapedBias); + } else { + result = add4({ inputs: { a: result, b: bias }, backend: backend2 }); + } + backend2.disposeIntermediateTensorInfo(resultOld); + } + if (activation2) { + const resultOld = result; + if (dataFormat === "NCHW" && activation2 === "prelu" && preluActivationWeights.shape.length === 1 && preluActivationWeights.shape[0] !== 1) { + const reshapedAlpha = reshape3({ + inputs: { x: preluActivationWeights }, + backend: backend2, + attrs: { shape: [preluActivationWeights.shape[0], 1, 1] } + }); + result = applyActivation2(backend2, result, activation2, reshapedAlpha, leakyreluAlpha); + backend2.disposeIntermediateTensorInfo(reshapedAlpha); + } else { + result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha); + } + backend2.disposeIntermediateTensorInfo(resultOld); + } + return result; +} +var fusedConv2DConfig = { + kernelName: FusedConv2D, + backendName: "cpu", + kernelFunc: fusedConv2D +}; +function fusedDepthwiseConv2D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + let result = depthwiseConv2dNative({ + inputs: { x, filter }, + backend: backend2, + attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } + }); + if (bias) { + const oldResult = result; + result = add4({ inputs: { a: result, b: bias }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(oldResult); + } + if (activation2) { + const oldResult = result; + result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha); + backend2.disposeIntermediateTensorInfo(oldResult); + } + return result; +} +var fusedDepthwiseConv2DConfig = { + kernelName: FusedDepthwiseConv2D, + backendName: "cpu", + kernelFunc: fusedDepthwiseConv2D +}; +function gatherNd(args) { + const { inputs, backend: backend2 } = args; + const { params, indices } = inputs; + const paramsSize = util_exports.sizeFromShape(params.shape); + const indicesShape = indices.shape; + const sliceRank = indicesShape[indicesShape.length - 1]; + const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices); + if (numSlices === 0) { + return backend2.makeTensorInfo(resultShape, params.dtype, []); + } + const indicesData = backend2.data.get(indices.dataId).values; + const paramsBuf = backend2.bufferSync(params); + const outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize); + return backend2.makeTensorInfo(resultShape, params.dtype, outBuf.values); +} +var gatherNdConfig = { + kernelName: GatherNd, + backendName: "cpu", + kernelFunc: gatherNd +}; +function gatherV2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, indices } = inputs; + const { axis, batchDims } = attrs; + assertNotComplex([x, indices], "gatherV2"); + const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0]; + const indicesVals = backend2.data.get(indices.dataId).values; + const axisDim = x.shape[parsedAxis]; + for (let i = 0; i < indicesVals.length; ++i) { + const index = indicesVals[i]; + util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`); + } + let $batchDims = batchDims; + if (batchDims == null) { + $batchDims = 0; + } + const indicesSize = util_exports.sizeFromShape(indices.shape); + const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims); + const flattenX = reshape3({ + inputs: { x }, + backend: backend2, + attrs: { + shape: [ + shapeInfo.batchSize, + shapeInfo.outerSize, + shapeInfo.dimSize, + shapeInfo.sliceSize + ] + } + }); + const flattenIndex = reshape3({ + inputs: { x: indices }, + backend: backend2, + attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] } + }); + const flattenOutputShape = [ + shapeInfo.batchSize, + shapeInfo.outerSize, + indicesSize / shapeInfo.batchSize, + shapeInfo.sliceSize + ]; + const indicesBuf = backend2.bufferSync(flattenIndex); + const xBuf = backend2.bufferSync(flattenX); + const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape); + backend2.disposeIntermediateTensorInfo(flattenX); + backend2.disposeIntermediateTensorInfo(flattenIndex); + return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values); +} +var gatherV2Config = { + kernelName: GatherV2, + backendName: "cpu", + kernelFunc: gatherV2 +}; +function ifft2(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const inputSize = util_exports.sizeFromShape(input2.shape); + const innerDimensionSize = input2.shape[input2.shape.length - 1]; + const batch = inputSize / innerDimensionSize; + const input2D = reshape3({ + inputs: { x: input2 }, + backend: backend2, + attrs: { shape: [batch, innerDimensionSize] } + }); + const result = fftBatch(input2D, true, backend2); + const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } }); + backend2.disposeIntermediateTensorInfo(input2D); + backend2.disposeIntermediateTensorInfo(result); + return resultReshaped; +} +var ifftConfig = { + kernelName: IFFT, + backendName: "cpu", + kernelFunc: ifft2 +}; +var isFinite3 = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, "bool"); +var isFiniteConfig = { + kernelName: IsFinite, + backendName: "cpu", + kernelFunc: isFinite3 +}; +var isInf2 = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, "bool"); +var isInfConfig = { + kernelName: IsInf, + backendName: "cpu", + kernelFunc: isInf2 +}; +var isNaN3 = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, "bool"); +var isNaNConfig = { + kernelName: IsNan, + backendName: "cpu", + kernelFunc: isNaN3 +}; +function linSpace(args) { + const { backend: backend2, attrs } = args; + const { start, stop, num } = attrs; + const outVals = linSpaceImpl(start, stop, num); + return backend2.makeTensorInfo([outVals.length], "float32", outVals); +} +var linSpaceConfig = { + kernelName: LinSpace, + backendName: "cpu", + kernelFunc: linSpace +}; +var log1p2 = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi)); +var log1pConfig = { + kernelName: Log1p, + backendName: "cpu", + kernelFunc: log1p2 +}; +var logicalAndImpl = createSimpleBinaryKernelImpl((a, b) => a && b); +var logicalAnd2 = binaryKernelFunc(LogicalAnd, logicalAndImpl, null, "bool"); +var logicalAndConfig = { + kernelName: LogicalAnd, + backendName: "cpu", + kernelFunc: logicalAnd2 +}; +var logicalNot2 = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, "bool"); +var logicalNotConfig = { + kernelName: LogicalNot, + backendName: "cpu", + kernelFunc: logicalNot2 +}; +var logicalOrImpl = createSimpleBinaryKernelImpl((a, b) => a || b); +var logicalOr2 = binaryKernelFunc(LogicalOr, logicalOrImpl, null, "bool"); +var logicalOrConfig = { + kernelName: LogicalOr, + backendName: "cpu", + kernelFunc: logicalOr2 +}; +function lRN(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + assertNotComplex(x, "LRN"); + const channels = x.shape[3]; + const maxD = channels - 1; + const xValues = backend2.data.get(x.dataId).values; + const size = util_exports.sizeFromShape(x.shape); + const result = new Float32Array(size); + function sumAcrossChannels(offset) { + const currentChannel = offset % channels; + let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius); + const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD); + let sum6 = 0; + for (; beginSumOffset <= endSumOffset; beginSumOffset++) { + const z = xValues[beginSumOffset]; + sum6 += z * z; + } + return sum6; + } + for (let offset = 0; offset < size; offset++) { + const sum6 = sumAcrossChannels(offset); + const val = xValues[offset] * Math.pow(bias + alpha * sum6, -beta); + result[offset] = val; + } + return backend2.makeTensorInfo(x.shape, x.dtype, result); +} +var LRNConfig = { + kernelName: LRN, + backendName: "cpu", + kernelFunc: lRN +}; +function lRNGrad(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, y, dy } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + assertNotComplex(dy, "LRNGrad"); + const dySize = util_exports.sizeFromShape(dy.shape); + const channels = dy.shape[3]; + const dyValues = backend2.data.get(dy.dataId).values; + const xValues = backend2.data.get(x.dataId).values; + const yValues = backend2.data.get(y.dataId).values; + const result = new Float32Array(dySize); + const size = dySize; + for (let offset = 0; offset < size; offset++) { + const currentChannel = offset % channels; + const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius); + const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1); + let norm2 = 0; + for (let k = depthBegin; k < depthEnd; k++) { + norm2 += Math.pow(xValues[k], 2); + } + norm2 = alpha * norm2 + bias; + for (let k = depthBegin; k < depthEnd; k++) { + let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm2; + if (offset === k) { + dyi += Math.pow(norm2, -beta); + } + dyi *= dyValues[offset]; + result[k] += dyi; + } + } + return backend2.makeTensorInfo(dy.shape, x.dtype, result); +} +var LRNGradConfig = { + kernelName: LRNGrad, + backendName: "cpu", + kernelFunc: lRNGrad +}; +function max3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { reductionIndices, keepDims } = attrs; + const cpuBackend = backend2; + let xShape = x.shape; + const xRank = xShape.length; + const origAxes = util_exports.parseAxisParam(reductionIndices, xShape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let xVals = cpuBackend.data.get(x.dataId).values; + if (permutedAxes != null) { + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = xShape[permutedAxes[i]]; + } + xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + xShape = newShape; + } + assertNotComplex(x, "max"); + backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank); + const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype); + const dataId = cpuBackend.write(result, maxOutShape, x.dtype); + let outShape = maxOutShape; + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes); + outShape = newShape; + } + return { dataId, shape: outShape, dtype: x.dtype }; +} +var maxConfig = { + kernelName: Max, + backendName: "cpu", + kernelFunc: max3 +}; +function maxPool2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + assertNotComplex(x, "maxPool"); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = 1; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + let res; + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) { + res = identity2({ inputs: { x }, backend: backend2 }); + } else { + const xValues = backend2.data.get(x.dataId).values; + const strides2 = util_exports.computeStrides(x.shape); + const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, "max"); + res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values); + } + return res; +} +var maxPoolConfig = { + kernelName: MaxPool, + backendName: "cpu", + kernelFunc: maxPool2 +}; +function maxPool3D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs; + assertNotComplex(x, "maxPool3d"); + const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat); + const xValues = backend2.data.get(x.dataId).values; + const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "max"); + return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values); +} +var maxPool3DConfig = { + kernelName: MaxPool3D, + backendName: "cpu", + kernelFunc: maxPool3D +}; +function maxPool3DGrad(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + assertNotComplex([dy, input2], "maxPool3DGrad"); + const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode); + const inputBuf = backend2.bufferSync(input2); + const maxPosBuf = maxPool3dPositions(inputBuf, convInfo); + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const dx = buffer(input2.shape, "float32"); + const dyBuf = backend2.bufferSync(dy); + for (let batch = 0; batch < convInfo.batchSize; ++batch) { + for (let channel = 0; channel < convInfo.inChannels; ++channel) { + for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) { + for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) { + for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) { + const dyDepthCorner = dxDepth - padFront; + const dyRowCorner = dxRow - padTop; + const dyColCorner = dxCol - padLeft; + let dotProd = 0; + for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) { + const dyDepth = (dyDepthCorner + wDepth) / strideDepth; + if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) { + continue; + } + for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) { + const dyRow = (dyRowCorner + wRow) / strideHeight; + if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) { + continue; + } + for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) { + const dyCol = (dyColCorner + wCol) / strideWidth; + if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) { + continue; + } + const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel); + const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol; + const mask = maxPos === curPos ? 1 : 0; + if (mask === 0) { + continue; + } + const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel); + dotProd += pixel * mask; + } + } + } + dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel); + } + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var maxPool3DGradConfig2 = { + kernelName: MaxPool3DGrad, + backendName: "cpu", + kernelFunc: maxPool3DGrad +}; +function maxPoolGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2, output } = inputs; + const x = input2; + assertNotComplex([input2, output], "maxPoolGrad"); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode); + const xValues = backend2.data.get(x.dataId).values; + const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values); + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const dx = buffer(x.shape, "float32"); + const dyData = backend2.data.get(dy.dataId).values; + const dyBuf = buffer(dy.shape, "float32", dyData); + for (let b = 0; b < convInfo.batchSize; ++b) { + for (let d = 0; d < convInfo.inChannels; ++d) { + for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) { + for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) { + const dyRCorner = dxR - padTop; + const dyCCorner = dxC - padLeft; + let dotProd = 0; + for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) { + const dyR = (dyRCorner + wR) / strideHeight; + if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) { + continue; + } + for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) { + const dyC = (dyCCorner + wC) / strideWidth; + if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) { + continue; + } + const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d); + const curPos = wR * effectiveFilterWidth + wC; + const mask = maxPos === curPos ? 1 : 0; + if (mask === 0) { + continue; + } + const pixel = dyBuf.get(b, dyR, dyC, d); + dotProd += pixel * mask; + } + } + dx.set(dotProd, b, dxR, dxC, d); + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var maxPoolGradConfig2 = { + kernelName: MaxPoolGrad, + backendName: "cpu", + kernelFunc: maxPoolGrad2 +}; +function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) { + const strides = util_exports.computeStrides(xShape); + const maxPools = pool2(xValues, xShape, dtype, strides, convInfo, "max"); + const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex); + return [maxPools.values, maxPositions.values]; +} +var maxPoolWithArgmaxConfig = { + kernelName: MaxPoolWithArgmax, + backendName: "cpu", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { x } = inputs; + const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs; + const cpuBackend = backend2; + assertNotComplex(x, "MaxPoolWithArgmax"); + const values = cpuBackend.data.get(x.dataId).values; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3); + const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo); + const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype); + const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype); + return [ + { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype }, + { dataId: indexesDataId, shape: convInfo.outShape, dtype: "int32" } + ]; + } +}; +function mean2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + const axes = util_exports.parseAxisParam(axis, x.shape); + const shapes = backend_util_exports.computeOutAndReduceShapes(x.shape, axes); + const reduceShape = shapes[1]; + const reduceSize = util_exports.sizeFromShape(reduceShape); + const toDispose = []; + const reduceSizeScalar = backend2.makeTensorInfo([], "float32", new Float32Array([reduceSize])); + toDispose.push(reduceSizeScalar); + const $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } }); + toDispose.push($x); + const res = div2({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend2 }); + toDispose.push(res); + const result = sum3({ inputs: { x: res }, backend: backend2, attrs: { axis, keepDims } }); + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var meanConfig = { + kernelName: Mean, + backendName: "cpu", + kernelFunc: mean2 +}; +function min3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + assertNotComplex(x, "min"); + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + if (permutedAxes != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("min", axes, $x.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype); + const aVals = backend2.data.get($x.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let min6 = aVals[offset]; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + if (Number.isNaN(value) || value < min6) { + min6 = value; + } + } + vals[i] = min6; + } + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo($x); + } + const result = backend2.makeTensorInfo(outShape, $x.dtype, vals); + if (keepDims) { + const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } }); + backend2.disposeIntermediateTensorInfo(result); + return reshapedResult; + } + return result; +} +var minConfig = { + kernelName: Min, + backendName: "cpu", + kernelFunc: min3 +}; +function mirrorPad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { paddings, mode } = attrs; + assertNotComplex(x, "mirrorPad"); + const outShape = paddings.map( + (p2, i) => p2[0] + x.shape[i] + p2[1] + /* afterPad */ + ); + const start = paddings.map((p2) => p2[0]); + const end = paddings.map((p2, i) => p2[0] + x.shape[i]); + const offset = mode === "reflect" ? 0 : 1; + const xVals = backend2.data.get(x.dataId).values; + const xRank = x.shape.length; + const xStrides = util_exports.computeStrides(x.shape); + const resultSize = util_exports.sizeFromShape(outShape); + const resultRank = outShape.length; + const resultStrides = util_exports.computeStrides(outShape); + const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize); + for (let i = 0; i < resultSize; i++) { + let coords2 = util_exports.indexToLoc(i, resultRank, resultStrides); + for (let i2 = 0; i2 < resultRank; i2++) { + if (coords2[i2] < start[i2]) { + coords2[i2] = start[i2] * 2 - coords2[i2] - offset; + } else if (coords2[i2] >= end[i2]) { + coords2[i2] = (end[i2] - 1) * 2 - coords2[i2] + offset; + } + } + coords2 = coords2.map((c, i2) => c - start[i2]); + const inIndex = util_exports.locToIndex(coords2, xRank, xStrides); + resVals[i] = xVals[inIndex]; + } + const outId = backend2.write(resVals, outShape, x.dtype); + return { dataId: outId, shape: outShape, dtype: x.dtype }; +} +var mirrorPadConfig = { + kernelName: MirrorPad, + backendName: "cpu", + kernelFunc: mirrorPad2 +}; +var modImpl = createSimpleBinaryKernelImpl((aValue, bValue) => { + const rem = aValue % bValue; + if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) { + return rem; + } else { + return (rem + bValue) % bValue; + } +}); +var mod2 = binaryKernelFunc(Mod, modImpl); +var modConfig = { + kernelName: Mod, + backendName: "cpu", + kernelFunc: mod2 +}; +var seedrandom4 = __toESM(require_seedrandom2()); +function softmax3(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { dim } = attrs; + const logitsRank = logits.shape.length; + let $dim = dim; + if ($dim === -1) { + $dim = logitsRank - 1; + } + if ($dim !== logitsRank - 1) { + throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`); + } + const axes = util_exports.parseAxisParam([$dim], logits.shape); + const maxLogit = max3({ + inputs: { x: logits }, + backend: backend2, + attrs: { reductionIndices: axes, keepDims: false } + }); + const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes); + const maxLogitReshaped = reshape3({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } }); + const a = sub2({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend2 }); + const b = exp2({ inputs: { x: a }, backend: backend2 }); + const sumExp = sum3({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } }); + const sumReshaped = reshape3({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } }); + const result = div2({ inputs: { a: b, b: sumReshaped }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(maxLogit); + backend2.disposeIntermediateTensorInfo(maxLogitReshaped); + backend2.disposeIntermediateTensorInfo(a); + backend2.disposeIntermediateTensorInfo(b); + backend2.disposeIntermediateTensorInfo(sumExp); + backend2.disposeIntermediateTensorInfo(sumReshaped); + return result; +} +var softmaxConfig = { + kernelName: Softmax, + backendName: "cpu", + kernelFunc: softmax3 +}; +function multinomial2(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { numSamples, seed, normalized } = attrs; + assertNotComplex(logits, "multinomial"); + const probabilities = normalized ? logits : softmax3({ inputs: { logits }, backend: backend2, attrs: { dim: -1 } }); + const batchSize = probabilities.shape[0]; + const numEvents = probabilities.shape[1]; + const probVals = backend2.data.get(probabilities.dataId).values; + const resShape = [batchSize, numSamples]; + const resVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape), "int32"); + for (let b = 0; b < batchSize; ++b) { + const offset = b * numEvents; + const cdf = new Float32Array(numEvents - 1); + cdf[0] = probVals[offset]; + for (let event = 1; event < cdf.length; ++event) { + cdf[event] = cdf[event - 1] + probVals[offset + event]; + } + const random = seedrandom4.alea(seed.toString()); + const outOffset = b * numSamples; + for (let sampleId = 0; sampleId < numSamples; ++sampleId) { + const r = random(); + resVals[outOffset + sampleId] = cdf.length; + for (let event = 0; event < cdf.length; event++) { + if (r < cdf[event]) { + resVals[outOffset + sampleId] = event; + break; + } + } + } + } + if (!normalized) { + backend2.disposeIntermediateTensorInfo(probabilities); + } + return backend2.makeTensorInfo(resShape, "int32", resVals); +} +var multinomialConfig = { + kernelName: Multinomial, + backendName: "cpu", + kernelFunc: multinomial2 +}; +var nonMaxSuppressionV3Impl2 = kernel_impls_exports.nonMaxSuppressionV3Impl; +function nonMaxSuppressionV3(args) { + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold } = attrs; + assertNotComplex(boxes, "NonMaxSuppression"); + const boxesVals = backend2.data.get(boxes.dataId).values; + const scoresVals = backend2.data.get(scores.dataId).values; + const { selectedIndices } = nonMaxSuppressionV3Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold); + return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)); +} +var nonMaxSuppressionV3Config = { + kernelName: NonMaxSuppressionV3, + backendName: "cpu", + kernelFunc: nonMaxSuppressionV3 +}; +var nonMaxSuppressionV4Impl2 = kernel_impls_exports.nonMaxSuppressionV4Impl; +function nonMaxSuppressionV4(args) { + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs; + assertNotComplex(boxes, "NonMaxSuppressionPadded"); + const boxesVals = backend2.data.get(boxes.dataId).values; + const scoresVals = backend2.data.get(scores.dataId).values; + const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize); + return [ + backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)), + backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs])) + ]; +} +var nonMaxSuppressionV4Config = { + kernelName: NonMaxSuppressionV4, + backendName: "cpu", + kernelFunc: nonMaxSuppressionV4 +}; +var nonMaxSuppressionV5Impl2 = kernel_impls_exports.nonMaxSuppressionV5Impl; +function nonMaxSuppressionV5(args) { + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs; + assertNotComplex(boxes, "NonMaxSuppressionWithScore"); + const boxesVals = backend2.data.get(boxes.dataId).values; + const scoresVals = backend2.data.get(scores.dataId).values; + const maxOutputSizeVal = maxOutputSize; + const iouThresholdVal = iouThreshold; + const scoreThresholdVal = scoreThreshold; + const softNmsSigmaVal = softNmsSigma; + const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl2(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal); + return [ + backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)), + backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores)) + ]; +} +var nonMaxSuppressionV5Config = { + kernelName: NonMaxSuppressionV5, + backendName: "cpu", + kernelFunc: nonMaxSuppressionV5 +}; +function oneHot2(args) { + const { inputs, backend: backend2, attrs } = args; + const { indices } = inputs; + const { dtype, depth, onValue, offValue } = attrs; + assertNotComplex(indices, "oneHot"); + const indicesSize = util_exports.sizeFromShape(indices.shape); + const res = new Float32Array(indicesSize * depth); + res.fill(offValue); + const indicesVal = backend2.data.get(indices.dataId).values; + for (let event = 0; event < indicesSize; ++event) { + if (indicesVal[event] >= 0 && indicesVal[event] < depth) { + res[event * depth + indicesVal[event]] = onValue; + } + } + return backend2.makeTensorInfo([...indices.shape, depth], dtype, res); +} +var oneHotConfig = { + kernelName: OneHot, + backendName: "cpu", + kernelFunc: oneHot2 +}; +function zerosLike2(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (x.dtype === "string") { + throw new Error("zerosLike is not supported for string tensors"); + } else if (x.dtype === "complex64") { + const realPart = real2({ inputs: { input: x }, backend: backend2 }); + const r = zerosLike2({ inputs: { x: realPart }, backend: backend2 }); + const imagPart = imag2({ inputs: { input: x }, backend: backend2 }); + const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 }); + const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(r); + backend2.disposeIntermediateTensorInfo(imagPart); + backend2.disposeIntermediateTensorInfo(i); + return result; + } else { + return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } }); + } +} +var zerosLikeConfig = { + kernelName: ZerosLike, + backendName: "cpu", + kernelFunc: zerosLike2 +}; +function onesLike2(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (x.dtype === "string") { + throw new Error("onesLike is not supported for string tensors"); + } else if (x.dtype === "complex64") { + const realPart = real2({ inputs: { input: x }, backend: backend2 }); + const r = onesLike2({ inputs: { x: realPart }, backend: backend2 }); + const imagPart = imag2({ inputs: { input: x }, backend: backend2 }); + const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 }); + const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(r); + backend2.disposeIntermediateTensorInfo(imagPart); + backend2.disposeIntermediateTensorInfo(i); + return result; + } else { + return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } }); + } +} +var onesLikeConfig = { + kernelName: OnesLike, + backendName: "cpu", + kernelFunc: onesLike2 +}; +function pack(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + if (inputs.length === 1) { + return expandDims3({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } }); + } + const shape = inputs[0].shape; + const dtype = inputs[0].dtype; + inputs.forEach((t) => { + util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes"); + util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes"); + }); + const intermediateTensorInfos = []; + const expandedTensors = inputs.map((t) => { + const expandedT = expandDims3({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } }); + intermediateTensorInfos.push(expandedT); + return expandedT; + }); + const result = concat2({ inputs: expandedTensors, backend: backend2, attrs: { axis } }); + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var packConfig = { + kernelName: Pack, + backendName: "cpu", + kernelFunc: pack +}; +function padV2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { paddings, constantValue } = attrs; + assertNotComplex(x, "pad"); + const outShape = paddings.map( + (p2, i) => p2[0] + x.shape[i] + p2[1] + /* afterPad */ + ); + const start = paddings.map((p2) => p2[0]); + const xVals = backend2.data.get(x.dataId).values; + const xSize = util_exports.sizeFromShape(x.shape); + const xRank = x.shape.length; + const xStrides = util_exports.computeStrides(x.shape); + const resultSize = util_exports.sizeFromShape(outShape); + const resultRank = outShape.length; + const resultStrides = util_exports.computeStrides(outShape); + const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize); + if (constantValue !== 0) { + resVals.fill(constantValue); + } + for (let i = 0; i < xSize; i++) { + const coords2 = util_exports.indexToLoc(i, xRank, xStrides); + const outCoords = coords2.map((c, i2) => c + start[i2]); + const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides); + resVals[outIndex] = xVals[i]; + } + const outId = backend2.write(resVals, outShape, x.dtype); + return { dataId: outId, shape: outShape, dtype: x.dtype }; +} +var padV2Config = { + kernelName: PadV2, + backendName: "cpu", + kernelFunc: padV2 +}; +var powImpl = createSimpleBinaryKernelImpl((a, b) => Math.pow(a, b)); +var pow2 = binaryKernelFunc(Pow, powImpl); +var powConfig = { + kernelName: Pow, + backendName: "cpu", + kernelFunc: pow2 +}; +function raggedGather2(args) { + const { inputs, backend: backend2, attrs } = args; + const { paramsNestedSplits, paramsDenseValues, indices } = inputs; + const { outputRaggedRank } = attrs; + const $paramsNestedSplits = paramsNestedSplits.map((t) => backend2.data.get(t.dataId).values); + const $paramsNestedSplitsShapes = paramsNestedSplits.map((t) => t.shape); + const $paramsDenseValues = backend2.data.get(paramsDenseValues.dataId).values; + const $indices = backend2.data.get(indices.dataId).values; + const [outputNestedSplits, outputDenseValues, outputDenseValuesShape] = raggedGatherImpl($paramsNestedSplits, $paramsNestedSplitsShapes, $paramsDenseValues, paramsDenseValues.shape, paramsDenseValues.dtype, $indices, indices.shape, outputRaggedRank); + const outputNestedSplitsTensors = outputNestedSplits.map((splits) => backend2.makeTensorInfo([splits.length], "int32", splits)); + const outputDenseValuesTensor = backend2.makeTensorInfo(outputDenseValuesShape, paramsDenseValues.dtype, outputDenseValues); + return outputNestedSplitsTensors.concat([outputDenseValuesTensor]); +} +var raggedGatherConfig = { + kernelName: RaggedGather, + backendName: "cpu", + kernelFunc: raggedGather2 +}; +function raggedRange2(args) { + const { inputs, backend: backend2 } = args; + const { starts, limits, deltas } = inputs; + const $starts = backend2.data.get(starts.dataId).values; + const $limits = backend2.data.get(limits.dataId).values; + const $deltas = backend2.data.get(deltas.dataId).values; + const [rtNestedSplitsData, rtDenseValuesData] = raggedRangeImpl($starts, starts.shape, starts.dtype, $limits, limits.shape, $deltas, deltas.shape); + const rtNestedSplits = backend2.makeTensorInfo([rtNestedSplitsData.length], "int32", rtNestedSplitsData); + const rtDenseValues = backend2.makeTensorInfo([rtDenseValuesData.length], starts.dtype, rtDenseValuesData); + return [rtNestedSplits, rtDenseValues]; +} +var raggedRangeConfig = { + kernelName: RaggedRange, + backendName: "cpu", + kernelFunc: raggedRange2 +}; +function raggedTensorToTensor2(args) { + const { inputs, backend: backend2, attrs } = args; + const { shape, values, defaultValue, rowPartitionTensors } = inputs; + const { rowPartitionTypes } = attrs; + const $shape = backend2.data.get(shape.dataId).values; + const $values = backend2.data.get(values.dataId).values; + const $defaultValue = backend2.data.get(defaultValue.dataId).values; + const $rowPartitionValues = rowPartitionTensors.map((t) => backend2.data.get(t.dataId).values); + const rowPartitionValuesShapes = rowPartitionTensors.map((t) => t.shape); + const [outputShape, output] = raggedTensorToTensorImpl($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes); + return backend2.makeTensorInfo(outputShape, values.dtype, output); +} +var raggedTensorToTensorConfig = { + kernelName: RaggedTensorToTensor, + backendName: "cpu", + kernelFunc: raggedTensorToTensor2 +}; +function range3(args) { + const { backend: backend2, attrs } = args; + const { start, stop, dtype, step: step5 } = attrs; + const values = rangeImpl(start, stop, step5, dtype); + return backend2.makeTensorInfo([values.length], dtype, values); +} +var rangeConfig = { + kernelName: Range, + backendName: "cpu", + kernelFunc: range3 +}; +var reciprocal2 = unaryKernelFunc(Reciprocal, (xi) => 1 / xi); +var reciprocalConfig = { + kernelName: Reciprocal, + backendName: "cpu", + kernelFunc: reciprocal2 +}; +function resizeBilinear3(args) { + const { inputs, backend: backend2, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + assertNotComplex(images, "resizeBilinear"); + const imagesStrides = util_exports.computeStrides(images.shape); + const [newHeight, newWidth] = size; + const [batch, oldHeight, oldWidth, numChannels] = images.shape; + const xValues = backend2.data.get(images.dataId).values; + const result = new Float32Array(util_exports.sizeFromShape([batch, newHeight, newWidth, numChannels])); + const effectiveInputSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutputSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + let outputIdx = 0; + const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0]; + const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1]; + for (let b = 0; b < batch; b++) { + for (let r = 0; r < newHeight; r++) { + let sourceFracRow; + if (halfPixelCenters) { + sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5; + } else { + sourceFracRow = effectiveRowSizeRatio * r; + } + const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow)); + const rowFrac = sourceFracRow - sourceRowFloor; + const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow)); + const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1]; + const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1]; + for (let c = 0; c < newWidth; c++) { + let sourceFracCol; + if (halfPixelCenters) { + sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5; + } else { + sourceFracCol = effectiveColSizeRatio * c; + } + const sourceColFloor = Math.max(0, Math.floor(sourceFracCol)); + const colFrac = sourceFracCol - sourceColFloor; + const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol)); + const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2]; + const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2]; + const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2]; + const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2]; + for (let d = 0; d < numChannels; d++) { + const topLeft = xValues[topLeftOffest + d]; + const bottomLeft = xValues[botLeftOffset + d]; + const topRight = xValues[topRightOffset + d]; + const bottomRight = xValues[botRightOffest + d]; + const top = topLeft + (topRight - topLeft) * colFrac; + const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac; + const newValue = top + (bottom - top) * rowFrac; + result[outputIdx++] = newValue; + } + } + } + } + return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], "float32", result); +} +var resizeBilinearConfig = { + kernelName: ResizeBilinear, + backendName: "cpu", + kernelFunc: resizeBilinear3 +}; +function resizeBilinearGrad(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + assertNotComplex([dy, images], "resizeBilinearGrad"); + const imagesStrides = util_exports.computeStrides(images.shape); + const [batch, xHeight, xWidth, depth] = images.shape; + const [, yHeight, yWidth] = dy.shape; + const output = new Float32Array(batch * xHeight * xWidth * depth); + const effectiveXSize = [ + alignCorners && yHeight > 1 ? xHeight - 1 : xHeight, + alignCorners && yWidth > 1 ? xWidth - 1 : xWidth + ]; + const effectiveYSize = [ + alignCorners && yHeight > 1 ? yHeight - 1 : yHeight, + alignCorners && yWidth > 1 ? yWidth - 1 : yWidth + ]; + const heightScale = effectiveXSize[0] / effectiveYSize[0]; + const widthScale = effectiveXSize[1] / effectiveYSize[1]; + const dyValues = backend2.data.get(dy.dataId).values; + let offset = 0; + for (let b = 0; b < batch; b++) { + const bOffset = b * imagesStrides[0]; + for (let r = 0; r < yHeight; r++) { + const dxR = r * heightScale; + const topDxRIndex = Math.floor(dxR); + const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1); + const topDxROffset = bOffset + topDxRIndex * imagesStrides[1]; + const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1]; + const dxRLerp = dxR - topDxRIndex; + const inverseDxRLerp = 1 - dxRLerp; + for (let c = 0; c < yWidth; c++) { + const dxC = c * widthScale; + const leftDxCIndex = Math.floor(dxC); + const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1); + const dxCLerp = dxC - leftDxCIndex; + const inverseDxCLerp = 1 - dxCLerp; + const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2]; + const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2]; + const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2]; + const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2]; + const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp; + const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp; + const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp; + const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp; + for (let d = 0; d < depth; d++) { + const dyVal = dyValues[offset++]; + output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp; + output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp; + output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp; + output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp; + } + } + } + } + return backend2.makeTensorInfo([batch, xWidth, xHeight, depth], "float32", output); +} +var resizeBilinearGradConfig2 = { + kernelName: ResizeBilinearGrad, + backendName: "cpu", + kernelFunc: resizeBilinearGrad +}; +function resizeNearestNeighbor2(args) { + const { inputs, backend: backend2, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + assertNotComplex(images, "resizeNearestNeighbor"); + const imagesStrides = util_exports.computeStrides(images.shape); + const [newHeight, newWidth] = size; + const [batch, oldHeight, oldWidth, numChannels] = images.shape; + const xValues = backend2.data.get(images.dataId).values; + const output = new Float32Array(batch * newHeight * newWidth * numChannels); + const effectiveInputSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutputSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0]; + const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1]; + let outputOffset = 0; + for (let b = 0; b < batch; b++) { + const batchOffset = b * imagesStrides[0]; + for (let r = 0; r < newHeight; r++) { + const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r + 0.5) : effectiveRowSizeRatio * r; + let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow)); + if (halfPixelCenters) { + sourceNearestRow = Math.max(0, sourceNearestRow); + } + const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1]; + for (let c = 0; c < newWidth; c++) { + const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c; + let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol)); + if (halfPixelCenters) { + sourceNearestCol = Math.max(0, sourceNearestCol); + } + const colOffset = rowOffset + sourceNearestCol * imagesStrides[2]; + for (let d = 0; d < numChannels; d++) { + const newVal = xValues[colOffset + d]; + output[outputOffset++] = newVal; + } + } + } + } + return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output); +} +var resizeNearestNeighborConfig = { + kernelName: ResizeNearestNeighbor, + backendName: "cpu", + kernelFunc: resizeNearestNeighbor2 +}; +function resizeNearestNeighborGrad(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + assertNotComplex([dy, images], "resizeNearestNeighborGrad"); + const imagesStrides = util_exports.computeStrides(images.shape); + const dyStrides = util_exports.computeStrides(dy.shape); + const [batch, xHeight, xWidth, depth] = images.shape; + const [, yHeight, yWidth] = dy.shape; + const output = new Float32Array(batch * xHeight * xWidth * depth); + const dyValues = backend2.data.get(dy.dataId).values; + const effectiveXSize = [ + alignCorners && yHeight > 1 ? xHeight - 1 : xHeight, + alignCorners && yWidth > 1 ? xWidth - 1 : xWidth + ]; + const effectiveYSize = [ + alignCorners && yHeight > 1 ? yHeight - 1 : yHeight, + alignCorners && yWidth > 1 ? yWidth - 1 : yWidth + ]; + const heightScale = effectiveXSize[0] / effectiveYSize[0]; + const widthScale = effectiveXSize[1] / effectiveYSize[1]; + const invHeightScale = 1 / heightScale; + const invWidthScale = 1 / widthScale; + const winHeight = Math.ceil(invHeightScale) * 2 + 2; + const winWidth = Math.ceil(invWidthScale) * 2 + 2; + for (let b = 0; b < batch; b++) { + const batchOffset = b * imagesStrides[0]; + for (let r = 0; r < xHeight; r++) { + const rowOffset = batchOffset + r * imagesStrides[1]; + const startRLerp = Math.floor(r * invHeightScale); + const startDyR = Math.floor(startRLerp - winHeight / 2); + for (let c = 0; c < xWidth; c++) { + const colOffset = rowOffset + c * imagesStrides[2]; + const startCLerp = Math.floor(c * invWidthScale); + const startDyC = Math.floor(startCLerp - winWidth / 2); + for (let d = 0; d < depth; d++) { + let accum = 0; + for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) { + const dyR = dyRIndex + startDyR; + if (dyR < 0 || dyR >= yHeight) { + continue; + } + const dyROffset = batchOffset + dyR * dyStrides[1]; + const sourceFracRow = dyR * heightScale; + const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow)); + if (r !== sourceNearestRow) { + continue; + } + for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) { + const dyC = dyCIndex + startDyC; + if (dyC < 0 || dyC >= yWidth) { + continue; + } + const dyCOffset = dyROffset + dyC * dyStrides[2]; + const sourceFracCol = dyC * widthScale; + const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol)); + if (c === sourceNearestCol) { + accum += dyValues[dyCOffset + d]; + } + } + } + output[colOffset + d] = accum; + } + } + } + } + return backend2.makeTensorInfo(images.shape, images.dtype, output); +} +var resizeNearestNeighborGradConfig2 = { + kernelName: ResizeNearestNeighborGrad, + backendName: "cpu", + kernelFunc: resizeNearestNeighborGrad +}; +function reverse2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dims } = attrs; + assertNotComplex(x, "reverse"); + const xRank = x.shape.length; + const $dims = util_exports.parseAxisParam(dims, x.shape); + if (xRank === 0) { + return identity2({ inputs: { x }, backend: backend2 }); + } + const outBuf = new TensorBuffer(x.shape, x.dtype); + const xBuf = backend2.bufferSync(x); + for (let i = 0; i < outBuf.size; i++) { + const outLoc = outBuf.indexToLoc(i); + const inLoc = outLoc.slice(); + $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]); + outBuf.set(xBuf.get(...inLoc), ...outLoc); + } + return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); +} +var reverseConfig = { + kernelName: Reverse, + backendName: "cpu", + kernelFunc: reverse2 +}; +var rotateWithOffsetConfig = { + kernelName: RotateWithOffset, + backendName: "cpu", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { image: image2 } = inputs; + const { radians, fillValue, center } = attrs; + const cpuBackend = backend2; + const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape)); + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth); + const fullOpacityValue = 255; + const sinFactor = Math.sin(radians); + const cosFactor = Math.cos(radians); + const imageVals = cpuBackend.data.get(image2.dataId).values; + for (let batchIdx = 0; batchIdx < batch; batchIdx++) { + const batchOffset = batchIdx * imageWidth * imageHeight * numChannels; + for (let row = 0; row < imageHeight; row++) { + const rowOffset = row * (imageWidth * numChannels); + for (let col = 0; col < imageWidth; col++) { + const colOffset = col * numChannels; + for (let channel = 0; channel < numChannels; channel++) { + const coords2 = [batch, row, col, channel]; + const x = coords2[2]; + const y = coords2[1]; + let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor; + let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor; + coordX = Math.round(coordX + centerX); + coordY = Math.round(coordY + centerY); + let outputValue = fillValue; + if (typeof fillValue !== "number") { + if (channel === 3) { + outputValue = fullOpacityValue; + } else { + outputValue = fillValue[channel]; + } + } + if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) { + const rotatedRowOffset = coordY * (imageWidth * numChannels); + const rotatedColOffset = coordX * numChannels; + const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel; + outputValue = imageVals[imageIdx]; + } + const outIdx = batchOffset + rowOffset + colOffset + channel; + output[outIdx] = outputValue; + } + } + } + } + const dataId = cpuBackend.write(output, image2.shape, image2.dtype); + return { dataId, shape: image2.shape, dtype: image2.dtype }; + } +}; +var round3 = unaryKernelFunc(Round, (xi) => { + const base = Math.floor(xi); + if (xi - base < 0.5) { + return Math.floor(xi); + } else if (xi - base > 0.5) { + return Math.ceil(xi); + } else { + if (base % 2 === 0) { + return base; + } else { + return base + 1; + } + } +}); +var roundConfig = { + kernelName: Round, + backendName: "cpu", + kernelFunc: round3 +}; +function scatterNd(args) { + const { inputs, backend: backend2, attrs } = args; + const { indices, updates } = inputs; + const { shape } = attrs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape); + const sumDupeIndices = true; + const indicesBuf = backend2.bufferSync(indices); + const updatesBuf = backend2.bufferSync(updates); + const outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0, sumDupeIndices); + return backend2.makeTensorInfo(shape, outBuf.dtype, outBuf.values); +} +var scatterNdConfig = { + kernelName: ScatterNd, + backendName: "cpu", + kernelFunc: scatterNd +}; +function lowerBound2(array2, value) { + let left = 0; + let right = array2.length; + let mid = 0; + while (left < right) { + mid = Math.floor((left + right) / 2); + if (array2[mid] < value) { + left = mid + 1; + } else { + right = mid; + } + } + return right; +} +function upperBound2(array2, value) { + let left = 0; + let right = array2.length; + let mid = 0; + while (left < right) { + mid = Math.floor((left + right) / 2); + if (array2[mid] <= value) { + left = mid + 1; + } else { + right = mid; + } + } + return right; +} +function searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues, side) { + const output = util_exports.getArrayFromDType("int32", batchSize * numValues); + for (let b = 0; b < batchSize; ++b) { + const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs); + const outputOffset = b * numValues; + for (let i = 0; i < numValues; ++i) { + output[outputOffset + i] = side === "left" ? lowerBound2(sortedInputsSlice, values[i + outputOffset]) : upperBound2(sortedInputsSlice, values[i + outputOffset]); + } + } + return output; +} +function searchSorted2(args) { + const { inputs, backend: backend2, attrs } = args; + const { sortedSequence, values } = inputs; + const { side } = attrs; + const $sortedSequence = backend2.data.get(sortedSequence.dataId).values; + const $values = backend2.data.get(values.dataId).values; + const output = searchSortedImpl($sortedSequence, $values, sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side); + return backend2.makeTensorInfo(values.shape, "int32", output); +} +var searchSortedConfig = { + kernelName: SearchSorted, + backendName: "cpu", + kernelFunc: searchSorted2 +}; +function select2(args) { + const { inputs, backend: backend2 } = args; + const { condition, t, e } = inputs; + assertNotComplex([condition, t, e], "select"); + const conditionRank = condition.shape.length; + const values = backend2.data.get(condition.dataId).values; + const tValues = backend2.data.get(t.dataId).values; + const eValues = backend2.data.get(e.dataId).values; + const resultDtype = upcastType(t.dtype, e.dtype); + const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape), resultDtype); + let index = 0; + const offset = conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1)); + for (let i = 0; i < values.length; i++) { + for (let j = 0; j < offset; j++) { + if (values[i] === 1) { + newValues[index++] = tValues[i]; + } else { + newValues[index++] = eValues[i]; + } + } + } + return backend2.makeTensorInfo(t.shape, resultDtype, newValues); +} +var selectConfig = { + kernelName: Select, + backendName: "cpu", + kernelFunc: select2 +}; +var scaleAlpha = backend_util_exports.SELU_SCALEALPHA; +var scale = backend_util_exports.SELU_SCALE; +var selu2 = unaryKernelFunc(Selu, (xi) => { + if (xi >= 0) { + return scale * xi; + } else { + return scaleAlpha * (Math.exp(xi) - 1); + } +}); +var seluConfig = { + kernelName: Selu, + backendName: "cpu", + kernelFunc: selu2 +}; +var sign2 = unaryKernelFunc(Sign, (xi) => { + if (xi < 0) { + return -1; + } else if (xi > 0) { + return 1; + } else { + return 0; + } +}); +var signConfig = { + kernelName: Sign, + backendName: "cpu", + kernelFunc: sign2 +}; +var sin2 = unaryKernelFunc(Sin, (xi) => Math.sin(xi)); +var sinConfig = { + kernelName: Sin, + backendName: "cpu", + kernelFunc: sin2 +}; +var sinh2 = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi)); +var sinhConfig = { + kernelName: Sinh, + backendName: "cpu", + kernelFunc: sinh2 +}; +var epsilon2 = 11920928955078125e-23; +var threshold2 = Math.log(epsilon2) + 2; +var softplus2 = unaryKernelFunc(Softplus, (xi) => { + const tooLarge = xi > -threshold2; + const tooSmall = xi < threshold2; + const expX = Math.exp(xi); + let result; + if (tooSmall) { + result = expX; + } else if (tooLarge) { + result = xi; + } else { + result = Math.log(1 + expX); + } + return result; +}); +var softplusConfig = { + kernelName: Softplus, + backendName: "cpu", + kernelFunc: softplus2 +}; +function spaceToBatchND2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, paddings } = attrs; + assertNotComplex([x], "spaceToBatchND"); + const prod5 = util_exports.sizeFromShape(blockShape); + const completePaddings = [[0, 0]]; + completePaddings.push(...paddings); + for (let i = 1 + blockShape.length; i < x.shape.length; ++i) { + completePaddings.push([0, 0]); + } + const paddedX = padV2Config.kernelFunc({ + inputs: { x }, + backend: backend2, + attrs: { paddings: completePaddings, constantValue: 0 } + }); + const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false); + const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false); + const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false); + const reshapeInputs = { x: paddedX }; + const reshapeAttrs = { shape: reshapedPaddedShape }; + const paddedXReshaped = reshape3({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs }); + const transposeInputs = { x: paddedXReshaped }; + const transposeAttrs = { perm: permutedReshapedPaddedPermutation }; + const paddedXT = transpose2({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs }); + const resultReshapeInputs = { x: paddedXT }; + const resultReshapeAttrs = { shape: flattenShape }; + const result = reshape3({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs }); + backend2.disposeIntermediateTensorInfo(paddedX); + backend2.disposeIntermediateTensorInfo(paddedXReshaped); + backend2.disposeIntermediateTensorInfo(paddedXT); + return result; +} +var spaceToBatchNDConfig = { + kernelName: SpaceToBatchND, + backendName: "cpu", + kernelFunc: spaceToBatchND2 +}; +function sparseFillEmptyRows2(args) { + const { inputs, backend: backend2 } = args; + const { indices, values, denseShape, defaultValue } = inputs; + if (denseShape.shape.length !== 1) { + throw new Error(`Dense shape must be a vector, saw: + ${denseShape.shape}`); + } + if (indices.shape.length !== 2) { + throw new Error(`Indices must be a matrix, saw: + ${indices.shape}`); + } + if (values.shape.length !== 1) { + throw new Error(`Values must be a vector, saw: + ${values.shape}`); + } + if (defaultValue.shape.length !== 0) { + throw new Error(`Default value must be a scalar, saw: + ${defaultValue.shape}`); + } + const $indices = backend2.data.get(indices.dataId).values; + const $values = backend2.data.get(values.dataId).values; + const $denseShape = backend2.data.get(denseShape.dataId).values; + const $defaultValue = backend2.data.get(defaultValue.dataId).values[0]; + const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue); + return [ + backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices), + backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues), + backend2.makeTensorInfo([emptyRowIndicator.length], "bool", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))), + backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap)) + ]; +} +var sparseFillEmptyRowsConfig = { + kernelName: SparseFillEmptyRows, + backendName: "cpu", + kernelFunc: sparseFillEmptyRows2 +}; +function sparseReshape2(args) { + const { inputs, backend: backend2 } = args; + const { inputIndices, inputShape, newShape } = inputs; + if (inputIndices.shape.length !== 2) { + throw new Error(`Input indices should be a matrix but received shape + ${inputIndices.shape}`); + } + if (inputShape.shape.length !== 1) { + throw new Error(`Input shape should be a vector but received shape + ${inputShape.shape}`); + } + if (newShape.shape.length !== 1) { + throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`); + } + const $inputShape = Array.from(backend2.data.get(inputShape.dataId).values); + const $inputIndices = backend2.data.get(inputIndices.dataId).values; + const targetShape = Array.from(backend2.data.get(newShape.dataId).values); + const [newIndices, indicesShape, outputShape] = sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape); + return [ + backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices), + backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape)) + ]; +} +var sparseReshapeConfig = { + kernelName: SparseReshape, + backendName: "cpu", + kernelFunc: sparseReshape2 +}; +function sparseSegmentMean2(args) { + const { inputs, backend: backend2 } = args; + const { data, indices, segmentIds } = inputs; + if (data.shape.length < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if (indices.shape.length !== 1) { + throw new Error(`Indices should be a vector but received shape + ${indices.shape}`); + } + if (segmentIds.shape.length !== 1) { + throw new Error(`Segment ids should be a vector but received shape + ${segmentIds.shape}`); + } + if (indices.shape[0] !== segmentIds.shape[0]) { + throw new Error(`segmentIds and indices should have same size.`); + } + const $data = backend2.data.get(data.dataId).values; + const $indices = backend2.data.get(indices.dataId).values; + const $segmentIds = backend2.data.get(segmentIds.dataId).values; + const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true); + return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData); +} +var sparseSegmentMeanConfig = { + kernelName: SparseSegmentMean, + backendName: "cpu", + kernelFunc: sparseSegmentMean2 +}; +function sparseSegmentSum2(args) { + const { inputs, backend: backend2 } = args; + const { data, indices, segmentIds } = inputs; + if (data.shape.length < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if (indices.shape.length !== 1) { + throw new Error(`Indices should be a vector but received shape + ${indices.shape}`); + } + if (segmentIds.shape.length !== 1) { + throw new Error(`Segment ids should be a vector but received shape + ${segmentIds.shape}`); + } + if (indices.shape[0] !== segmentIds.shape[0]) { + throw new Error(`segmentIds and indices should have same size.`); + } + const $data = backend2.data.get(data.dataId).values; + const $indices = backend2.data.get(indices.dataId).values; + const $segmentIds = backend2.data.get(segmentIds.dataId).values; + const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds); + return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData); +} +var sparseSegmentSumConfig = { + kernelName: SparseSegmentSum, + backendName: "cpu", + kernelFunc: sparseSegmentSum2 +}; +function sparseToDense2(args) { + const { inputs, backend: backend2, attrs } = args; + const { sparseIndices, sparseValues, defaultValue } = inputs; + const { outputShape } = attrs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape); + const sumDupeIndices = false; + const indicesBuf = backend2.bufferSync(sparseIndices); + let outBuf; + switch (sparseValues.dtype) { + case "bool": { + const updatesBuf = backend2.bufferSync(sparseValues); + const $defaultValue = Boolean(backend2.data.get(defaultValue.dataId).values[0]); + outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + break; + } + case "float32": { + const updatesBuf = backend2.bufferSync(sparseValues); + const $defaultValue = backend2.data.get(defaultValue.dataId).values[0]; + outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + break; + } + case "int32": { + const updatesBuf = backend2.bufferSync(sparseValues); + const $defaultValue = backend2.data.get(defaultValue.dataId).values[0]; + outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + break; + } + case "string": { + const updatesBuf = backend2.bufferSync(sparseValues); + const $defaultValue = util_exports.decodeString(backend2.data.get(defaultValue.dataId).values[0]); + outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + break; + } + default: + throw new Error(`Unsupported type ${sparseValues.dtype}`); + } + return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values); +} +var sparseToDenseConfig = { + kernelName: SparseToDense, + backendName: "cpu", + kernelFunc: sparseToDense2 +}; +function splitV(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { numOrSizeSplits, axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, x.shape)[0]; + const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis); + const begin = new Array(x.shape.length).fill(0); + const size = x.shape.slice(); + return splitSizes.map((s) => { + const sliceSize = [...size]; + sliceSize[$axis] = s; + const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } }); + begin[$axis] += s; + return sliceT; + }); +} +var splitVConfig = { + kernelName: SplitV, + backendName: "cpu", + kernelFunc: splitV +}; +var squareConfig = { + kernelName: Square, + backendName: "cpu", + kernelFunc: ({ inputs, backend: backend2 }) => { + const { x } = inputs; + const cpuBackend = backend2; + assertNotComplex(x, "square"); + const values = cpuBackend.data.get(x.dataId).values; + const newValues = new Float32Array(values.length); + for (let i = 0; i < values.length; ++i) { + const value = values[i]; + newValues[i] = value * value; + } + const dataId = cpuBackend.write(newValues, x.shape, x.dtype); + return { dataId, shape: x.shape, dtype: x.dtype }; + } +}; +var step2 = unaryKernelFunc(Step, (xi, attrs) => { + const stepAttrs = attrs; + if (isNaN(xi)) { + return NaN; + } else { + return xi > 0 ? 1 : stepAttrs.alpha; + } +}); +var stepConfig = { + kernelName: Step, + backendName: "cpu", + kernelFunc: step2 +}; +function stridedSlice2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs; + assertNotComplex(x, "stridedSlice"); + const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); + let result; + if (isIdentity) { + result = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); + } else if (sliceDim0 || isSimpleSlice) { + util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`); + const size = slice_util_exports.computeOutShape($begin, $end, $strides); + const sliced = slice2({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } }); + result = reshape3({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeIntermediateTensorInfo(sliced); + } else { + const xBuf = backend2.bufferSync(x); + const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin); + result = backend2.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values); + } + return result; +} +var stridedSliceConfig = { + kernelName: StridedSlice, + backendName: "cpu", + kernelFunc: stridedSlice2 +}; +function stringNGrams2(args) { + const { inputs, backend: backend2, attrs } = args; + const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs; + const { data, dataSplits } = inputs; + const $data = backend2.data.get(data.dataId).values; + const $dataSplits = backend2.data.get(dataSplits.dataId).values; + const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences); + return [ + backend2.makeTensorInfo([nGrams.length], "string", nGrams), + backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits) + ]; +} +var stringNGramsConfig = { + kernelName: StringNGrams, + backendName: "cpu", + kernelFunc: stringNGrams2 +}; +function stringSplit2(args) { + const { inputs, backend: backend2, attrs } = args; + const { skipEmpty } = attrs; + const { input: input2, delimiter } = inputs; + if (input2.dtype !== "string") { + throw new Error("Input must be of datatype string"); + } + if (input2.shape.length !== 1) { + throw new Error(`Input must be a vector, got shape: ${input2.shape}`); + } + if (delimiter.shape.length !== 0) { + throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`); + } + const $input = backend2.data.get(input2.dataId).values; + const $delimiter = backend2.data.get(delimiter.dataId).values[0]; + const [indices, values, shape] = stringSplitImpl($input, $delimiter, skipEmpty); + const outputSize = values.length; + return [ + backend2.makeTensorInfo([outputSize, 2], "int32", indices), + backend2.makeTensorInfo([outputSize], "string", values), + backend2.makeTensorInfo([2], "int32", new Int32Array(shape)) + ]; +} +var stringSplitConfig = { + kernelName: StringSplit, + backendName: "cpu", + kernelFunc: stringSplit2 +}; +function stringToHashBucketFast2(args) { + const { inputs, backend: backend2, attrs } = args; + const { numBuckets } = attrs; + const { input: input2 } = inputs; + if (input2.dtype !== "string") { + throw new Error("Input must be of datatype string"); + } + if (numBuckets <= 0) { + throw new Error(`Number of buckets must be at least 1`); + } + const $input = backend2.data.get(input2.dataId).values; + const output = stringToHashBucketFastImpl($input, numBuckets); + return backend2.makeTensorInfo(input2.shape, "int32", output); +} +var stringToHashBucketFastConfig = { + kernelName: StringToHashBucketFast, + backendName: "cpu", + kernelFunc: stringToHashBucketFast2 +}; +var tan2 = unaryKernelFunc(Tan, (xi) => Math.tan(xi)); +var tanConfig = { + kernelName: Tan, + backendName: "cpu", + kernelFunc: tan2 +}; +var tanh3 = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi)); +var tanhConfig = { + kernelName: Tanh, + backendName: "cpu", + kernelFunc: tanh3 +}; +function tensorScatterUpdate2(args) { + const { inputs, backend: backend2 } = args; + const { tensor: tensor2, indices, updates } = inputs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, tensor2.shape); + const sumDupeIndices = false; + const indicesBuf = backend2.bufferSync(indices); + const updatesBuf = backend2.bufferSync(updates); + const tensorBuf = backend2.bufferSync(tensor2); + const outBuf = scatterImpl(indicesBuf, updatesBuf, tensor2.shape, outputSize, sliceSize, numUpdates, sliceRank, strides, tensorBuf, sumDupeIndices); + return backend2.makeTensorInfo(tensor2.shape, outBuf.dtype, outBuf.values); +} +var tensorScatterUpdateConfig = { + kernelName: TensorScatterUpdate, + backendName: "cpu", + kernelFunc: tensorScatterUpdate2 +}; +function tile3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { reps } = attrs; + assertNotComplex(x, "tile"); + const outBuf = tileImpl(backend2.bufferSync(x), reps); + return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); +} +var tileConfig = { + kernelName: Tile, + backendName: "cpu", + kernelFunc: tile3 +}; +function topK(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { k, sorted } = attrs; + assertNotComplex(x, "topk"); + const xVals = backend2.data.get(x.dataId).values; + const [allTopKVals, allTopKIndices] = topKImpl(xVals, x.shape, x.dtype, k, sorted); + return [ + backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values), + backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values) + ]; +} +var topKConfig = { + kernelName: TopK, + backendName: "cpu", + kernelFunc: topK +}; +function transform2(args) { + const { inputs, attrs, backend: backend2 } = args; + const { image: image2, transforms } = inputs; + const { interpolation, fillMode, fillValue, outputShape } = attrs; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth]; + const outShape = [batch, outHeight, outWidth, numChannels]; + const inStrides = util_exports.computeStrides(image2.shape); + const batchInStride = inStrides[0]; + const rowInStride = inStrides[1]; + const colInStride = inStrides[2]; + const outStrides = util_exports.computeStrides(outShape); + const batchOutStride = outStrides[0]; + const rowOutStride = outStrides[1]; + const colOutStride = outStrides[2]; + const outVals = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(outShape)); + outVals.fill(fillValue); + const imageVals = backend2.data.get(image2.dataId).values; + const transformVals = backend2.data.get(transforms.dataId).values; + for (let b = 0; b < batch; ++b) { + const transform5 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8); + for (let outY = 0; outY < outHeight; ++outY) { + for (let outX = 0; outX < outWidth; ++outX) { + for (let channel = 0; channel < numChannels; ++channel) { + let val; + const projection = transform5[6] * outX + transform5[7] * outY + 1; + if (projection === 0) { + continue; + } + const inX = (transform5[0] * outX + transform5[1] * outY + transform5[2]) / projection; + const inY = (transform5[3] * outX + transform5[4] * outY + transform5[5]) / projection; + const x = mapCoord(inX, imageWidth, fillMode); + const y = mapCoord(inY, imageHeight, fillMode); + switch (interpolation) { + case "nearest": + val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue); + break; + case "bilinear": + val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue); + break; + default: + throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${interpolation}`); + } + const ind = b * batchOutStride + outY * rowOutStride + outX * colOutStride + channel; + outVals[ind] = val; + } + } + } + return backend2.makeTensorInfo(outShape, image2.dtype, outVals); + } + const dataId = backend2.write(outVals, outShape, image2.dtype); + return { dataId, shape: image2.shape, dtype: image2.dtype }; +} +var transformConfig = { + kernelName: Transform, + backendName: "cpu", + kernelFunc: transform2 +}; +function mapCoord(outCoord, len, mode) { + switch (mode) { + case "reflect": + return mapCoordReflect(outCoord, len); + case "wrap": + return mapCoordWrap(outCoord, len); + case "nearest": + return mapCoordNearest(outCoord, len); + case "constant": + default: + return mapCoordConstant(outCoord, len); + } +} +function mapCoordReflect(outCoord, len) { + let inCoord = outCoord; + if (inCoord < 0) { + if (len <= 1) { + inCoord = 0; + } else { + const sz2 = 2 * len; + if (inCoord < sz2) { + inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord; + } + inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1; + } + } else if (inCoord > len - 1) { + if (len <= 1) { + inCoord = 0; + } else { + const sz2 = 2 * len; + inCoord -= sz2 * Math.trunc(inCoord / sz2); + if (inCoord >= len) { + inCoord = sz2 - inCoord - 1; + } + } + } + return util_exports.clamp(0, inCoord, len - 1); +} +function mapCoordWrap(outCoord, len) { + let inCoord = outCoord; + if (inCoord < 0) { + if (len <= 1) { + inCoord = 0; + } else { + const sz = len - 1; + inCoord += len * (Math.trunc(-inCoord / sz) + 1); + } + } else if (inCoord > len - 1) { + if (len <= 1) { + inCoord = 0; + } else { + const sz = len - 1; + inCoord -= len * Math.trunc(inCoord / sz); + } + } + return util_exports.clamp(0, inCoord, len - 1); +} +function mapCoordConstant(outCoord, len) { + return outCoord; +} +function mapCoordNearest(outCoord, len) { + return util_exports.clamp(0, outCoord, len - 1); +} +function readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) { + const ind = batch * batchStride + y * rowStride + x * colStride + channel; + if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) { + return imageVals[ind]; + } else { + return fillValue; + } +} +function nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) { + const $y = Math.round(y); + const $x = Math.round(x); + return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue); +} +function bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) { + const yFloor = Math.floor(y); + const xFloor = Math.floor(x); + const yCeil = yFloor + 1; + const xCeil = xFloor + 1; + const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue); + const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue); + return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil; +} +function unique3(args) { + const { inputs, attrs, backend: backend2 } = args; + const { axis } = attrs; + const { x } = inputs; + assertNotComplex(x, "unique"); + const values = backend2.data.get(x.dataId).values; + const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype); + return [ + backend2.makeTensorInfo(outputShape, x.dtype, outputValues), + backend2.makeTensorInfo([indices.length], "int32", indices) + ]; +} +var uniqueConfig = { + kernelName: Unique, + backendName: "cpu", + kernelFunc: unique3 +}; +function unpack(args) { + const { inputs, backend: backend2, attrs } = args; + const { value } = inputs; + let { axis } = attrs; + if (axis < 0) { + axis += value.shape.length; + } + const valueRank = value.shape.length; + const num = value.shape[axis]; + const outShape = new Array(valueRank - 1); + let outIndex = 0; + for (let i = 0; i < valueRank; i++) { + if (i !== axis) { + outShape[outIndex++] = value.shape[i]; + } + } + const begin = new Array(valueRank).fill(0); + const size = value.shape.slice(); + size[axis] = 1; + const res = new Array(num); + for (let i = 0; i < res.length; i++) { + begin[axis] = i; + const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size } }); + res[i] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } }); + backend2.disposeIntermediateTensorInfo(tempRes); + } + return res; +} +var unpackConfig = { + kernelName: Unpack, + backendName: "cpu", + kernelFunc: unpack +}; +function unsortedSegmentSum2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, segmentIds } = inputs; + const { numSegments } = attrs; + assertNotComplex(x, "unsortedSegmentSum"); + const xRank = x.shape.length; + const segmentIdsRank = segmentIds.shape.length; + const res = []; + const intermediates = []; + const numIters = xRank - segmentIdsRank; + let $segmentIds = segmentIds; + for (let i = 0; i < numIters; ++i) { + const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i + 1 } }); + $segmentIds = expanded; + intermediates.push(expanded); + } + for (let i = 0; i < numSegments; ++i) { + const scalarValue = util_exports.createScalarValue(i, "int32"); + const segmentId = backend2.makeTensorInfo([], "int32", scalarValue); + const mask = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 }); + const maskCasted = cast3({ inputs: { x: mask }, backend: backend2, attrs: { dtype: "float32" } }); + const mul2 = multiply2({ inputs: { a: maskCasted, b: x }, backend: backend2 }); + const sumTensorInfo = sum3({ inputs: { x: mul2 }, backend: backend2, attrs: { axis: 0, keepDims: false } }); + res.push(sumTensorInfo); + intermediates.push(segmentId); + intermediates.push(mask); + intermediates.push(maskCasted); + intermediates.push(mul2); + intermediates.push(sumTensorInfo); + } + const result = pack({ inputs: res, backend: backend2, attrs: { axis: 0 } }); + intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var unsortedSegmentSumConfig = { + kernelName: UnsortedSegmentSum, + backendName: "cpu", + kernelFunc: unsortedSegmentSum2 +}; +var kernelConfigs = [ + _fusedMatMulConfig, + absConfig, + acosConfig, + acoshConfig, + addConfig, + addNConfig, + allConfig, + anyConfig, + argMaxConfig, + argMinConfig, + asinConfig, + asinhConfig, + atanConfig, + atan2Config, + atanhConfig, + avgPoolConfig, + avgPool3DConfig, + avgPool3DGradConfig2, + avgPoolGradConfig2, + batchMatMulConfig, + batchNormConfig, + batchToSpaceNDConfig, + bincountConfig, + bitwiseAndConfig, + broadcastArgsConfig, + castConfig, + ceilConfig, + clipByValueConfig, + complexConfig, + complexAbsConfig, + concatConfig, + conv2DConfig, + conv2DBackpropFilterConfig, + conv2DBackpropInputConfig, + conv3DConfig, + conv3DBackpropFilterV2Config, + conv3DBackpropInputV2Config, + cosConfig, + coshConfig, + cropAndResizeConfig, + cumprodConfig, + cumsumConfig, + denseBincountConfig, + depthToSpaceConfig, + depthwiseConv2dNativeConfig, + depthwiseConv2dNativeBackpropFilterConfig, + depthwiseConv2dNativeBackpropInputConfig, + diagConfig, + dilation2DConfig, + dilation2DBackpropFilterConfig, + dilation2DBackpropInputConfig, + drawConfig, + einsumConfig, + eluConfig, + eluGradConfig2, + equalConfig, + erfConfig, + expConfig, + expandDimsConfig, + expm1Config, + fftConfig, + fillConfig, + flipLeftRightConfig, + floorConfig, + floorDivConfig, + fusedConv2DConfig, + fusedDepthwiseConv2DConfig, + gatherNdConfig, + gatherV2Config, + greaterConfig, + greaterEqualConfig, + identityConfig, + ifftConfig, + imagConfig, + isFiniteConfig, + isInfConfig, + isNaNConfig, + leakyReluConfig, + lessConfig, + lessEqualConfig, + linSpaceConfig, + logConfig, + log1pConfig, + logicalAndConfig, + logicalNotConfig, + logicalOrConfig, + LRNConfig, + LRNGradConfig, + maxConfig, + maximumConfig, + maxPoolConfig, + maxPool3DConfig, + maxPool3DGradConfig2, + maxPoolGradConfig2, + maxPoolWithArgmaxConfig, + meanConfig, + minConfig, + minimumConfig, + mirrorPadConfig, + modConfig, + multinomialConfig, + multiplyConfig, + negConfig, + nonMaxSuppressionV3Config, + nonMaxSuppressionV4Config, + nonMaxSuppressionV5Config, + notEqualConfig, + oneHotConfig, + onesLikeConfig, + packConfig, + padV2Config, + powConfig, + preluConfig, + prodConfig, + raggedGatherConfig, + raggedRangeConfig, + raggedTensorToTensorConfig, + rangeConfig, + realConfig, + realDivConfig, + reciprocalConfig, + reluConfig, + relu6Config, + reshapeConfig, + resizeBilinearConfig, + resizeBilinearGradConfig2, + resizeNearestNeighborConfig, + resizeNearestNeighborGradConfig2, + reverseConfig, + rotateWithOffsetConfig, + roundConfig, + rsqrtConfig, + scatterNdConfig, + searchSortedConfig, + selectConfig, + seluConfig, + sigmoidConfig, + signConfig, + sinConfig, + sinhConfig, + sliceConfig, + softmaxConfig, + softplusConfig, + spaceToBatchNDConfig, + sparseFillEmptyRowsConfig, + sparseReshapeConfig, + sparseSegmentMeanConfig, + sparseSegmentSumConfig, + sparseToDenseConfig, + splitVConfig, + sqrtConfig, + squareConfig, + squaredDifferenceConfig, + staticRegexReplaceConfig, + stepConfig, + stridedSliceConfig, + stringNGramsConfig, + stringSplitConfig, + stringToHashBucketFastConfig, + subConfig, + sumConfig, + tanConfig, + tanhConfig, + tensorScatterUpdateConfig, + tileConfig, + topKConfig, + transformConfig, + transposeConfig, + uniqueConfig, + unpackConfig, + unsortedSegmentSumConfig, + zerosLikeConfig +]; +for (const kernelConfig of kernelConfigs) { + registerKernel(kernelConfig); +} +var webgl_util_exports = {}; +__export2(webgl_util_exports, { + assertNotComplex: () => assertNotComplex2, + bindCanvasToFramebuffer: () => bindCanvasToFramebuffer, + bindColorTextureToFramebuffer: () => bindColorTextureToFramebuffer, + bindTextureToProgramUniformSampler: () => bindTextureToProgramUniformSampler, + bindTextureUnit: () => bindTextureUnit, + bindVertexBufferToProgramAttribute: () => bindVertexBufferToProgramAttribute, + callAndCheck: () => callAndCheck, + canBeRepresented: () => canBeRepresented, + createFragmentShader: () => createFragmentShader, + createFramebuffer: () => createFramebuffer, + createProgram: () => createProgram, + createStaticIndexBuffer: () => createStaticIndexBuffer, + createStaticVertexBuffer: () => createStaticVertexBuffer, + createTexture: () => createTexture, + createVertexShader: () => createVertexShader, + getBatchDim: () => getBatchDim, + getExtensionOrThrow: () => getExtensionOrThrow, + getFramebufferErrorMessage: () => getFramebufferErrorMessage, + getMaxTexturesInShader: () => getMaxTexturesInShader, + getNumChannels: () => getNumChannels, + getProgramUniformLocation: () => getProgramUniformLocation, + getProgramUniformLocationOrThrow: () => getProgramUniformLocationOrThrow, + getRowsCols: () => getRowsCols, + getShapeAs3D: () => getShapeAs3D, + getTextureShapeFromLogicalShape: () => getTextureShapeFromLogicalShape, + getWebGLDisjointQueryTimerVersion: () => getWebGLDisjointQueryTimerVersion, + getWebGLErrorMessage: () => getWebGLErrorMessage, + getWebGLMaxTextureSize: () => getWebGLMaxTextureSize, + hasExtension: () => hasExtension, + isCapableOfRenderingToFloatTexture: () => isCapableOfRenderingToFloatTexture, + isDownloadFloatTextureEnabled: () => isDownloadFloatTextureEnabled, + isReshapeFree: () => isReshapeFree, + isWebGLFenceEnabled: () => isWebGLFenceEnabled, + isWebGLVersionEnabled: () => isWebGLVersionEnabled, + linkProgram: () => linkProgram, + logShaderSourceAndInfoLog: () => logShaderSourceAndInfoLog, + resetMaxTextureSize: () => resetMaxTextureSize, + resetMaxTexturesInShader: () => resetMaxTexturesInShader, + unbindColorTextureFromFramebuffer: () => unbindColorTextureFromFramebuffer, + unbindTextureUnit: () => unbindTextureUnit, + validateFramebuffer: () => validateFramebuffer, + validateProgram: () => validateProgram, + validateTextureSize: () => validateTextureSize +}); +var contexts = {}; +var WEBGL_ATTRIBUTES = { + alpha: false, + antialias: false, + premultipliedAlpha: false, + preserveDrawingBuffer: false, + depth: false, + stencil: false, + failIfMajorPerformanceCaveat: true +}; +function setWebGLContext(webGLVersion, gl) { + contexts[webGLVersion] = gl; +} +function getWebGLContext(webGLVersion, customCanvas) { + if (!(webGLVersion in contexts) || customCanvas != null) { + const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas); + if (newCtx !== null) { + contexts[webGLVersion] = newCtx; + } else { + console.log("Could not get context for WebGL version", webGLVersion); + return null; + } + } + const gl = contexts[webGLVersion]; + if (gl == null || gl.isContextLost()) { + delete contexts[webGLVersion]; + return getWebGLContext(webGLVersion); + } + gl.disable(gl.DEPTH_TEST); + gl.disable(gl.STENCIL_TEST); + gl.disable(gl.BLEND); + gl.disable(gl.DITHER); + gl.disable(gl.POLYGON_OFFSET_FILL); + gl.disable(gl.SAMPLE_COVERAGE); + gl.enable(gl.SCISSOR_TEST); + gl.enable(gl.CULL_FACE); + gl.cullFace(gl.BACK); + return contexts[webGLVersion]; +} +function createCanvas(webGLVersion) { + if (!env().getBool("IS_SAFARI") && typeof OffscreenCanvas !== "undefined" && webGLVersion === 2) { + return new OffscreenCanvas(300, 150); + } else if (typeof document !== "undefined") { + return document.createElement("canvas"); + } else { + throw new Error("Cannot create a canvas in this context"); + } +} +function getWebGLRenderingContext(webGLVersion, customCanvas) { + if (webGLVersion !== 1 && webGLVersion !== 2) { + throw new Error("Cannot get WebGL rendering context, WebGL is disabled."); + } + const canvas = customCanvas == null ? createCanvas(webGLVersion) : customCanvas; + canvas.addEventListener("webglcontextlost", (ev) => { + ev.preventDefault(); + delete contexts[webGLVersion]; + }, false); + if (env().getBool("SOFTWARE_WEBGL_ENABLED")) { + WEBGL_ATTRIBUTES.failIfMajorPerformanceCaveat = false; + } + if (webGLVersion === 1) { + return ( + // tslint:disable-next-line + canvas.getContext("webgl", WEBGL_ATTRIBUTES) || canvas.getContext("experimental-webgl", WEBGL_ATTRIBUTES) + ); + } + return canvas.getContext("webgl2", WEBGL_ATTRIBUTES); +} +var PackingScheme; +(function(PackingScheme2) { + PackingScheme2[PackingScheme2["DENSE"] = 0] = "DENSE"; + PackingScheme2[PackingScheme2["SHARED_BATCH"] = 1] = "SHARED_BATCH"; +})(PackingScheme || (PackingScheme = {})); +var TextureUsage; +(function(TextureUsage2) { + TextureUsage2[TextureUsage2["RENDER"] = 0] = "RENDER"; + TextureUsage2[TextureUsage2["UPLOAD"] = 1] = "UPLOAD"; + TextureUsage2[TextureUsage2["PIXELS"] = 2] = "PIXELS"; + TextureUsage2[TextureUsage2["DOWNLOAD"] = 3] = "DOWNLOAD"; +})(TextureUsage || (TextureUsage = {})); +var PhysicalTextureType; +(function(PhysicalTextureType2) { + PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16"; + PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32"; + PhysicalTextureType2[PhysicalTextureType2["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE"; + PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32"; + PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16"; +})(PhysicalTextureType || (PhysicalTextureType = {})); +function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) { + return [columns, rows]; +} +function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) { + return matrixSize * channelsPerTexture; +} +function getDenseTexShape(shape) { + const size = util_exports.sizeFromShape(shape); + const texelsNeeded = Math.ceil(size / 4); + return util_exports.sizeToSquarishShape(texelsNeeded); +} +function getPackedMatrixTextureShapeWidthHeight(rows, columns) { + return [ + Math.max(1, Math.ceil(columns / 2)), + Math.max(1, Math.ceil(rows / 2)) + ]; +} +function getPackedRGBAArraySizeFromMatrixShape(rows, columns) { + const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns); + return w * h * 4; +} +function getTextureConfig(gl, textureHalfFloatExtension) { + const glany = gl; + let internalFormatFloat; + let internalFormatHalfFloat; + let internalFormatPackedHalfFloat; + let internalFormatPackedFloat; + let textureFormatFloat; + let downloadTextureFormat; + let downloadUnpackNumChannels; + let defaultNumChannels; + let textureTypeHalfFloat; + let textureTypeFloat; + if (env().getNumber("WEBGL_VERSION") === 2) { + internalFormatFloat = glany.R32F; + internalFormatHalfFloat = glany.R16F; + internalFormatPackedHalfFloat = glany.RGBA16F; + internalFormatPackedFloat = glany.RGBA32F; + textureFormatFloat = glany.RED; + downloadUnpackNumChannels = 4; + defaultNumChannels = 1; + textureTypeHalfFloat = glany.HALF_FLOAT; + textureTypeFloat = glany.FLOAT; + downloadTextureFormat = glany.RGBA8; + } else { + internalFormatFloat = gl.RGBA; + internalFormatHalfFloat = gl.RGBA; + internalFormatPackedHalfFloat = gl.RGBA; + internalFormatPackedFloat = glany.RGBA; + textureFormatFloat = gl.RGBA; + downloadUnpackNumChannels = 4; + defaultNumChannels = 4; + textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null; + textureTypeFloat = gl.FLOAT; + downloadTextureFormat = gl.RGBA; + } + return { + internalFormatFloat, + internalFormatHalfFloat, + internalFormatPackedHalfFloat, + internalFormatPackedFloat, + textureFormatFloat, + downloadTextureFormat, + downloadUnpackNumChannels, + defaultNumChannels, + textureTypeHalfFloat, + textureTypeFloat + }; +} +function callAndCheck(gl, func2) { + const returnValue = func2(); + if (env().getBool("DEBUG")) { + checkWebGLError(gl); + } + return returnValue; +} +function checkWebGLError(gl) { + const error = gl.getError(); + if (error !== gl.NO_ERROR) { + throw new Error("WebGL Error: " + getWebGLErrorMessage(gl, error)); + } +} +var MIN_FLOAT16 = 596e-10; +var MAX_FLOAT16 = 65504; +function canBeRepresented(num) { + if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) { + return true; + } + return false; +} +function getWebGLErrorMessage(gl, status) { + switch (status) { + case gl.NO_ERROR: + return "NO_ERROR"; + case gl.INVALID_ENUM: + return "INVALID_ENUM"; + case gl.INVALID_VALUE: + return "INVALID_VALUE"; + case gl.INVALID_OPERATION: + return "INVALID_OPERATION"; + case gl.INVALID_FRAMEBUFFER_OPERATION: + return "INVALID_FRAMEBUFFER_OPERATION"; + case gl.OUT_OF_MEMORY: + return "OUT_OF_MEMORY"; + case gl.CONTEXT_LOST_WEBGL: + return "CONTEXT_LOST_WEBGL"; + default: + return `Unknown error code ${status}`; + } +} +function getExtensionOrThrow(gl, extensionName) { + return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension "' + extensionName + '" not supported on this browser.'); +} +function createVertexShader(gl, vertexShaderSource) { + const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), "Unable to create vertex WebGLShader."); + callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource)); + callAndCheck(gl, () => gl.compileShader(vertexShader)); + if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) { + console.log(gl.getShaderInfoLog(vertexShader)); + throw new Error("Failed to compile vertex shader."); + } + return vertexShader; +} +function createFragmentShader(gl, fragmentShaderSource) { + const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), "Unable to create fragment WebGLShader."); + callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource)); + callAndCheck(gl, () => gl.compileShader(fragmentShader)); + if (env().get("ENGINE_COMPILE_ONLY")) { + return fragmentShader; + } + if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) { + logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader)); + throw new Error("Failed to compile fragment shader."); + } + return fragmentShader; +} +var lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g; +function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) { + const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog); + if (lineNumberRegexResult == null) { + console.log(`Couldn't parse line number in error: ${shaderInfoLog}`); + console.log(shaderSource); + return; + } + const lineNumber = +lineNumberRegexResult[1]; + const shaderLines = shaderSource.split("\n"); + const pad3 = shaderLines.length.toString().length + 2; + const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line); + let maxLineLength = 0; + for (let i = 0; i < linesWithLineNumbers.length; i++) { + maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength); + } + const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1); + const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber); + const afterErrorLines = linesWithLineNumbers.slice(lineNumber); + console.log(beforeErrorLines.join("\n")); + console.log(shaderInfoLog.split("\n")[0]); + console.log(`%c ${util_exports.rightPad(errorLine[0], maxLineLength)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"); + console.log(afterErrorLines.join("\n")); +} +function createProgram(gl) { + return throwIfNull(gl, () => gl.createProgram(), "Unable to create WebGLProgram."); +} +function linkProgram(gl, program) { + callAndCheck(gl, () => gl.linkProgram(program)); + if (env().get("ENGINE_COMPILE_ONLY")) { + return; + } + if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) { + console.log(gl.getProgramInfoLog(program)); + throw new Error("Failed to link vertex and fragment shaders."); + } +} +function validateProgram(gl, program) { + callAndCheck(gl, () => gl.validateProgram(program)); + if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) { + console.log(gl.getProgramInfoLog(program)); + throw new Error("Shader program validation failed."); + } +} +function createStaticVertexBuffer(gl, data) { + const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer"); + callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2)); + callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW)); + return buffer2; +} +function createStaticIndexBuffer(gl, data) { + const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer"); + callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer2)); + callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW)); + return buffer2; +} +function getNumChannels() { + if (env().getNumber("WEBGL_VERSION") === 2) { + return 1; + } + return 4; +} +function createTexture(gl) { + return throwIfNull(gl, () => gl.createTexture(), "Unable to create WebGLTexture."); +} +function validateTextureSize(width, height) { + const maxTextureSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE"); + if (width <= 0 || height <= 0) { + const requested = `[${width}x${height}]`; + throw new Error("Requested texture size " + requested + " is invalid."); + } + if (width > maxTextureSize || height > maxTextureSize) { + const requested = `[${width}x${height}]`; + const max6 = `[${maxTextureSize}x${maxTextureSize}]`; + throw new Error("Requested texture size " + requested + " greater than WebGL maximum on this browser / GPU " + max6 + "."); + } +} +function createFramebuffer(gl) { + return throwIfNull(gl, () => gl.createFramebuffer(), "Unable to create WebGLFramebuffer."); +} +function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer2, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) { + const loc = gl.getAttribLocation(program, attribute); + if (loc === -1) { + return false; + } + callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2)); + callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes)); + callAndCheck(gl, () => gl.enableVertexAttribArray(loc)); + return true; +} +function bindTextureUnit(gl, texture, textureUnit) { + validateTextureUnit(gl, textureUnit); + callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit)); + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture)); +} +function unbindTextureUnit(gl, textureUnit) { + validateTextureUnit(gl, textureUnit); + callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit)); + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); +} +function getProgramUniformLocationOrThrow(gl, program, uniformName) { + return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform "' + uniformName + '" not present in program.'); +} +function getProgramUniformLocation(gl, program, uniformName) { + return gl.getUniformLocation(program, uniformName); +} +function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) { + callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit)); + callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit)); +} +function bindCanvasToFramebuffer(gl) { + callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null)); + callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height)); + callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height)); +} +function bindColorTextureToFramebuffer(gl, texture, framebuffer) { + callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer)); + callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0)); +} +function unbindColorTextureFromFramebuffer(gl, framebuffer) { + callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer)); + callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0)); +} +function validateFramebuffer(gl) { + const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER); + if (status !== gl.FRAMEBUFFER_COMPLETE) { + throw new Error("Error binding framebuffer: " + getFramebufferErrorMessage(gl, status)); + } +} +function getFramebufferErrorMessage(gl, status) { + switch (status) { + case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT: + return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT"; + case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT: + return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT"; + case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS: + return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS"; + case gl.FRAMEBUFFER_UNSUPPORTED: + return "FRAMEBUFFER_UNSUPPORTED"; + default: + return `unknown error ${status}`; + } +} +function throwIfNull(gl, returnTOrNull, failureMessage) { + const tOrNull = callAndCheck(gl, () => returnTOrNull()); + if (tOrNull == null) { + throw new Error(failureMessage); + } + return tOrNull; +} +function validateTextureUnit(gl, textureUnit) { + const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1; + const glTextureUnit = textureUnit + gl.TEXTURE0; + if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) { + const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`; + throw new Error(`textureUnit must be in ${textureUnitRange}.`); + } +} +function getBatchDim(shape, dimsToSkip = 2) { + return util_exports.sizeFromShape(shape.slice(0, shape.length - dimsToSkip)); +} +function getRowsCols(shape) { + if (shape.length === 0) { + throw Error("Cannot get rows and columns of an empty shape array."); + } + return [ + shape.length > 1 ? shape[shape.length - 2] : 1, + shape[shape.length - 1] + ]; +} +function getShapeAs3D(shape) { + let shapeAs3D = [1, 1, 1]; + const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1; + if (!isScalar) { + shapeAs3D = [getBatchDim(shape), ...getRowsCols(shape)]; + } + return shapeAs3D; +} +function getTextureShapeFromLogicalShape(logShape, isPacked = false) { + let maxTexSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE"); + let maxSizeForNarrowTex = env().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE"); + if (maxSizeForNarrowTex === Infinity && env().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE")) { + maxSizeForNarrowTex = maxTexSize / 2; + } + if (isPacked) { + maxTexSize = maxTexSize * 2; + maxSizeForNarrowTex = maxSizeForNarrowTex * 2; + logShape = logShape.map((d, i) => i >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i]) : logShape[i]); + if (logShape.length === 1) { + logShape = [2, logShape[0]]; + } + } + if (logShape.length !== 2) { + const squeezeResult = util_exports.squeezeShape(logShape); + logShape = squeezeResult.newShape; + } + let size = util_exports.sizeFromShape(logShape); + let textureShape = null; + if (logShape.length <= 1 && size <= maxTexSize) { + textureShape = [1, size]; + } else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) { + textureShape = logShape; + } else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) { + textureShape = [logShape[0] * logShape[1], logShape[2]]; + } else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) { + textureShape = [logShape[0], logShape[1] * logShape[2]]; + } else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) { + textureShape = [logShape[0] * logShape[1] * logShape[2], logShape[3]]; + } else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) { + textureShape = [logShape[0], logShape[1] * logShape[2] * logShape[3]]; + } + const isLongNarrowTex = textureShape != null && Math.max(...textureShape) > maxSizeForNarrowTex && Math.min(...textureShape) <= (isPacked ? 2 : 1) && Math.min(...textureShape) > 0; + if (textureShape == null || isLongNarrowTex) { + if (isPacked) { + const batchDim = getBatchDim(logShape); + let rows = 2, cols = 2; + if (logShape.length) { + [rows, cols] = getRowsCols(logShape); + } + size = batchDim * (rows / 2) * (cols / 2); + textureShape = util_exports.sizeToSquarishShape(size).map((d) => d * 2); + } else { + textureShape = util_exports.sizeToSquarishShape(size); + } + } + return textureShape; +} +function isEven(n) { + return n % 2 === 0; +} +function isReshapeFree(shape1, shape2) { + shape1 = shape1.slice(-2); + shape2 = shape2.slice(-2); + if (util_exports.arraysEqual(shape1, shape2)) { + return true; + } + if (!shape1.length || !shape2.length) { + return true; + } + if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) { + return true; + } + if (shape1.length !== shape2.length) { + const shape1Cols = shape1[shape1.length - 1]; + const shape2Cols = shape2[shape2.length - 1]; + if (shape1Cols === shape2Cols) { + return true; + } + if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) { + return true; + } + } + return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]); +} +var MAX_TEXTURE_SIZE; +var MAX_TEXTURES_IN_SHADER; +function getWebGLMaxTextureSize(webGLVersion) { + if (MAX_TEXTURE_SIZE == null) { + const gl = getWebGLContext(webGLVersion); + MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE); + } + return MAX_TEXTURE_SIZE; +} +function resetMaxTextureSize() { + MAX_TEXTURE_SIZE = null; +} +function resetMaxTexturesInShader() { + MAX_TEXTURES_IN_SHADER = null; +} +function getMaxTexturesInShader(webGLVersion) { + if (MAX_TEXTURES_IN_SHADER == null) { + const gl = getWebGLContext(webGLVersion); + MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS); + } + return Math.min(16, MAX_TEXTURES_IN_SHADER); +} +function getWebGLDisjointQueryTimerVersion(webGLVersion) { + if (webGLVersion === 0) { + return 0; + } + let queryTimerVersion; + const gl = getWebGLContext(webGLVersion); + if (hasExtension(gl, "EXT_disjoint_timer_query_webgl2") && webGLVersion === 2) { + queryTimerVersion = 2; + } else if (hasExtension(gl, "EXT_disjoint_timer_query")) { + queryTimerVersion = 1; + } else { + queryTimerVersion = 0; + } + return queryTimerVersion; +} +function hasExtension(gl, extensionName) { + const ext = gl.getExtension(extensionName); + return ext != null; +} +function isWebGLVersionEnabled(webGLVersion) { + try { + const gl = getWebGLContext(webGLVersion); + if (gl != null) { + return true; + } + } catch (e) { + console.log("Error when getting WebGL context: ", e); + return false; + } + return false; +} +function isCapableOfRenderingToFloatTexture(webGLVersion) { + if (webGLVersion === 0) { + return false; + } + const gl = getWebGLContext(webGLVersion); + if (webGLVersion === 1) { + if (!hasExtension(gl, "OES_texture_float")) { + return false; + } + } else { + if (!hasExtension(gl, "EXT_color_buffer_float")) { + return false; + } + } + const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl); + return isFrameBufferComplete; +} +function isDownloadFloatTextureEnabled(webGLVersion) { + if (webGLVersion === 0) { + return false; + } + const gl = getWebGLContext(webGLVersion); + if (webGLVersion === 1) { + if (!hasExtension(gl, "OES_texture_float")) { + return false; + } + if (!hasExtension(gl, "WEBGL_color_buffer_float")) { + return false; + } + } else { + if (hasExtension(gl, "EXT_color_buffer_float")) { + return createFloatTextureAndBindToFramebuffer(gl); + } + const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float"; + if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) { + const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT); + return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension); + } + return false; + } + const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl); + return isFrameBufferComplete; +} +function createFloatTextureAndBindToFramebuffer(gl) { + const texConfig = getTextureConfig(gl); + const texture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, texture); + const width = 1; + const height = 1; + gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null); + const frameBuffer = gl.createFramebuffer(); + gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer); + gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); + const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE; + gl.bindTexture(gl.TEXTURE_2D, null); + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + gl.deleteTexture(texture); + gl.deleteFramebuffer(frameBuffer); + return isFrameBufferComplete; +} +function createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension) { + const texConfig = getTextureConfig(gl, textureHalfFloatExtension); + const texture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, texture); + const width = 1; + const height = 1; + gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null); + const frameBuffer = gl.createFramebuffer(); + gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer); + gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); + const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE; + gl.bindTexture(gl.TEXTURE_2D, null); + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + gl.deleteTexture(texture); + gl.deleteFramebuffer(frameBuffer); + return isFrameBufferComplete; +} +function isWebGLFenceEnabled(webGLVersion) { + if (webGLVersion !== 2) { + return false; + } + const gl = getWebGLContext(webGLVersion); + const isEnabled = gl.fenceSync != null; + return isEnabled; +} +function assertNotComplex2(tensor2, opName) { + if (!Array.isArray(tensor2)) { + tensor2 = [tensor2]; + } + tensor2.forEach((t) => { + if (t != null) { + util_exports.assert(t.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the WebGL backend.`); + } + }); +} +var ENV5 = env(); +ENV5.registerFlag("HAS_WEBGL", () => ENV5.getNumber("WEBGL_VERSION") > 0); +ENV5.registerFlag("WEBGL_VERSION", () => { + if (isWebGLVersionEnabled(2)) { + return 2; + } else if (isWebGLVersionEnabled(1)) { + return 1; + } + return 0; +}); +ENV5.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false); +ENV5.registerFlag("WEBGL_BUFFER_SUPPORTED", () => ENV5.get("WEBGL_VERSION") === 2); +ENV5.registerFlag("WEBGL_CPU_FORWARD", () => true); +ENV5.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false); +ENV5.registerFlag("WEBGL_PACK", () => ENV5.getBool("HAS_WEBGL")); +ENV5.registerFlag("WEBGL_PACK_NORMALIZATION", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_CLIP", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_REDUCE", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_LAZILY_UNPACK", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_CONV_IM2COL", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => getWebGLMaxTextureSize(ENV5.getNumber("WEBGL_VERSION"))); +ENV5.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => getMaxTexturesInShader(ENV5.getNumber("WEBGL_VERSION"))); +ENV5.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => { + const webGLVersion = ENV5.getNumber("WEBGL_VERSION"); + if (webGLVersion === 0) { + return 0; + } + return getWebGLDisjointQueryTimerVersion(webGLVersion); +}); +ENV5.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => ENV5.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !device_util_exports.isMobile()); +ENV5.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => isCapableOfRenderingToFloatTexture(ENV5.getNumber("WEBGL_VERSION"))); +ENV5.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => { + return ENV5.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : ENV5.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"); +}); +ENV5.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => isDownloadFloatTextureEnabled(ENV5.getNumber("WEBGL_VERSION"))); +ENV5.registerFlag("WEBGL_FENCE_API_ENABLED", () => isWebGLFenceEnabled(ENV5.getNumber("WEBGL_VERSION"))); +ENV5.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => { + const useUniforms = ENV5.getBool("WEBGL_RENDER_FLOAT32_ENABLED"); + return useUniforms ? 4 : 0; +}); +ENV5.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => { + return -1; +}, (threshold3) => { + if (!(typeof threshold3 === "number")) { + throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${threshold3}.`); + } + if (threshold3 < 0 && threshold3 !== -1) { + throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold3}.`); + } +}); +ENV5.registerFlag("WEBGL_FLUSH_THRESHOLD", () => { + return device_util_exports.isMobile() ? 1 : -1; +}, (threshold3) => { + if (!(typeof threshold3 === "number")) { + throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${threshold3}.`); + } + if (threshold3 < 0 && threshold3 !== -1) { + throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold3}.`); + } +}); +ENV5.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128); +ENV5.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false); +ENV5.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5); +ENV5.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128); +ENV5.registerFlag("WEBGL_EXP_CONV", () => false); +ENV5.registerFlag("SOFTWARE_WEBGL_ENABLED", () => ENV5.getBool("IS_TEST")); +ENV5.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE", () => Infinity); +ENV5.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE", () => false); +ENV5.registerFlag("WEBGL2_ISNAN_CUSTOM", () => false); +ENV5.registerFlag("ENGINE_COMPILE_ONLY", () => false); +function getGlslDifferences() { + let version102; + let attribute; + let varyingVs; + let varyingFs; + let texture2D; + let output; + let defineOutput; + let defineSpecialNaN; + let defineSpecialInf; + let defineRound; + if (env().getNumber("WEBGL_VERSION") === 2) { + version102 = "#version 300 es"; + attribute = "in"; + varyingVs = "out"; + varyingFs = "in"; + texture2D = "texture"; + output = "outputColor"; + defineOutput = "out vec4 outputColor;"; + defineSpecialNaN = env().getBool("WEBGL2_ISNAN_CUSTOM") ? ` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -78,7 +53145,9 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram } #define isnan(value) isnan_custom(value) - `:"",l="",u=` + ` : ""; + defineSpecialInf = ``; + defineRound = ` #define round(value) newRound(value) int newRound(float value) { return int(floor(value + 0.5)); @@ -87,7 +53156,16 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram ivec4 newRound(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `):(e="",t="attribute",n="varying",a="varying",r="texture2D",s="gl_FragColor",i="",o=` + `; + } else { + version102 = ""; + attribute = "attribute"; + varyingVs = "varying"; + varyingFs = "varying"; + texture2D = "texture2D"; + output = "gl_FragColor"; + defineOutput = ""; + defineSpecialNaN = ` #define isnan(value) isnan_custom(value) bool isnan_custom(float val) { return (val > 0. || val < 1. || val == 0.) ? false : true; @@ -95,7 +53173,8 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram bvec4 isnan_custom(vec4 val) { return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w)); } - `,l=` + `; + defineSpecialInf = ` uniform float INFINITY; bool isinf(float val) { @@ -104,7 +53183,8 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram bvec4 isinf(vec4 val) { return equal(abs(val), vec4(INFINITY)); } - `,u=` + `; + defineRound = ` int round(float value) { return int(floor(value + 0.5)); } @@ -112,15 +53192,72 @@ Hi, looks like you are running TensorFlow.js in Node.js. 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 Zo(e,t,n="index"){let a=w.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 Of(e,t,n="index"){let a=w.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}; ${o};`}).join("")}function pJ(e,t){let n=e.length,a=e.map(s=>`${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 cJ(e,t,n="index"){let a=e.map((s,i)=>i),r=pJ(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=w.computeStrides(e).map(n=>n.toString());return` - int getFlatIndex(ivec3 coords) { - return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; + `; } -`}function j1(){return` + return { + version: version102, + attribute, + varyingVs, + varyingFs, + texture2D, + output, + defineOutput, + defineSpecialNaN, + defineSpecialInf, + defineRound + }; +} +function getLogicalCoordinatesFromFlatIndex(coords2, shape, index = "index") { + const strides = util_exports.computeStrides(shape); + return strides.map((stride, i) => { + const line1 = `int ${coords2[i]} = ${index} / ${stride}`; + const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${stride}` : `index -= ${coords2[i]} * ${stride}`; + return `${line1}; ${line2};`; + }).join(""); +} +function getOutputLogicalCoordinatesFromFlatIndexByUniform(coords2, shape, index = "index") { + const strides = util_exports.computeStrides(shape); + return strides.map((_, i) => { + const line1 = `int ${coords2[i]} = ${index} / outShapeStrides[${i}]`; + const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * outShapeStrides[${i}]` : `index -= ${coords2[i]} * outShapeStrides[${i}]`; + return `${line1}; ${line2};`; + }).join(""); +} +function symbolicallyComputeStrides(indicesArr, variableName) { + const numCoords = indicesArr.length; + const shape = indicesArr.map((d) => `${variableName}[${d}]`); + const strides = new Array(numCoords - 1); + strides[numCoords - 2] = shape[numCoords - 1]; + for (let i = numCoords - 3; i >= 0; --i) { + strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`; + } + return strides; +} +function getLogicalCoordinatesFromFlatIndexByUniform(coords2, variableName, index = "index") { + const indicesArray = coords2.map((_, i) => i); + const strides = symbolicallyComputeStrides(indicesArray, variableName); + return strides.map((_, i) => { + const line1 = `int ${coords2[i]} = ${index} / ${strides[i]}`; + const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${strides[i]}` : `index -= ${coords2[i]} * ${strides[i]}`; + return `${line1}; ${line2};`; + }).join(""); +} +function getFlatIndexFrom3D(shape) { + const strides = util_exports.computeStrides(shape).map((d) => d.toString()); + return ` + int getFlatIndex(ivec3 coords) { + return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z; + } +`; +} +function getFlatIndexFrom3DOutput() { + return ` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var EE=` +`; +} +var ENCODE_FLOAT_SNIPPET = ` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -159,27 +53296,211 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`,{getBroadcastDims:AE}=N;function dJ(e,t,n){let a=[];if(e.forEach(c=>{let h=w.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?`[${c.arrayIndex}]`:""};`)});let r=a.join(` -`),s=e.map(c=>hJ(c,t,n.packedInputs,n.enableShapeUniforms)).join(` -`),i=t.texShape,o=_n(),l=gJ(o),u,p,d=xJ(o);return t.isPacked?(u=mJ(t.logicalShape,i,n.enableShapeUniforms),p=yJ(o)):(u=fJ(t.logicalShape,i,n.enableShapeUniforms),p=bJ(o)),n.packedInputs&&(d+=IJ),[d,l,p,r,u,s,n.userCode].join(` -`)}function up(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return MJ(e,t);case 1:return OJ(e,t);case 2:return zJ(e,t);case 3:return BJ(e,t);case 4:return UJ(e,t);case 5:return GJ(e);case 6:return HJ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function FE(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return RJ(e);case 1:return PJ(e,t);case 2:return LJ(e,t);case 3:return WJ(e,t);default:return VJ(e,t)}}function hJ(e,t,n=!1,a){let r="";n?r+=FE(e,a):r+=up(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=qJ(e,t):r+=jJ(e,t)),r}function mJ(e,t,n){switch(e.length){case 0:return $E();case 1:return SJ(e,t,n);case 2:return $J(e,t,n);case 3:return TJ(e,t,n);default:return _J(e,t,n)}}function fJ(e,t,n){switch(e.length){case 0:return $E();case 1:return NJ(e,t,n);case 2:return DJ(e,t,n);case 3:return CJ(e,t,n);case 4:return EJ(e,t,n);case 5:return AJ(e,t);case 6:return FJ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function gJ(e){return` +`; +var { getBroadcastDims: getBroadcastDims2 } = backend_util_exports; +function makeShader(inputsInfo, outputShape, program) { + const prefixSnippets = []; + inputsInfo.forEach((x) => { + const size = util_exports.sizeFromShape(x.shapeInfo.logicalShape); + if (x.shapeInfo.isUniform) { + prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ""};`); + } else { + prefixSnippets.push(`uniform sampler2D ${x.name};`); + prefixSnippets.push(`uniform int offset${x.name};`); + } + if (program.enableShapeUniforms) { + const { uniformShape } = getUniformInfoFromShape(program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape); + switch (uniformShape.length) { + case 1: + prefixSnippets.push(`uniform int ${x.name}Shape;`); + break; + case 2: + prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`); + break; + case 3: + prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`); + break; + case 4: + prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`); + break; + default: + break; + } + prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`); + } + }); + if (program.enableShapeUniforms) { + switch (outputShape.logicalShape.length) { + case 1: + prefixSnippets.push(`uniform int outShape;`); + break; + case 2: + prefixSnippets.push(`uniform ivec2 outShape;`); + prefixSnippets.push(`uniform int outShapeStrides;`); + break; + case 3: + prefixSnippets.push(`uniform ivec3 outShape;`); + prefixSnippets.push(`uniform ivec2 outShapeStrides;`); + break; + case 4: + prefixSnippets.push(`uniform ivec4 outShape;`); + prefixSnippets.push(`uniform ivec3 outShapeStrides;`); + break; + default: + break; + } + prefixSnippets.push(`uniform ivec2 outTexShape;`); + } + if (program.customUniforms) { + program.customUniforms.forEach((d) => { + prefixSnippets.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`); + }); + } + const inputPrefixSnippet = prefixSnippets.join("\n"); + const inputSamplingSnippet = inputsInfo.map((x) => getInputSamplingSnippet(x, outputShape, program.packedInputs, program.enableShapeUniforms)).join("\n"); + const outTexShape = outputShape.texShape; + const glsl = getGlslDifferences(); + const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl); + let outputSamplingSnippet; + let floatTextureSetOutputSnippet; + let shaderPrefix = getShaderPrefix(glsl); + if (outputShape.isPacked) { + outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms); + floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl); + } else { + outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms); + floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl); + } + if (program.packedInputs) { + shaderPrefix += SHADER_PACKED_PREFIX; + } + const source = [ + shaderPrefix, + floatTextureSampleSnippet, + floatTextureSetOutputSnippet, + inputPrefixSnippet, + outputSamplingSnippet, + inputSamplingSnippet, + program.userCode + ].join("\n"); + return source; +} +function getSamplerFromInInfo(inInfo, enableShapeUniforms = false) { + const shape = inInfo.shapeInfo.logicalShape; + switch (shape.length) { + case 0: + return getSamplerScalar(inInfo, enableShapeUniforms); + case 1: + return getSampler1D(inInfo, enableShapeUniforms); + case 2: + return getSampler2D(inInfo, enableShapeUniforms); + case 3: + return getSampler3D(inInfo, enableShapeUniforms); + case 4: + return getSampler4D(inInfo, enableShapeUniforms); + case 5: + return getSampler5D(inInfo); + case 6: + return getSampler6D(inInfo); + default: + throw new Error(`${shape.length}-D input sampling is not yet supported`); + } +} +function getPackedSamplerFromInInfo(inInfo, enableShapeUniforms) { + const shape = inInfo.shapeInfo.logicalShape; + switch (shape.length) { + case 0: + return getPackedSamplerScalar(inInfo); + case 1: + return getPackedSampler1D(inInfo, enableShapeUniforms); + case 2: + return getPackedSampler2D(inInfo, enableShapeUniforms); + case 3: + return getPackedSampler3D(inInfo, enableShapeUniforms); + default: + return getPackedSamplerND(inInfo, enableShapeUniforms); + } +} +function getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false, enableShapeUniforms) { + let res = ""; + if (usesPackedTextures) { + res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms); + } else { + res += getSamplerFromInInfo(inInfo, enableShapeUniforms); + } + const inShape = inInfo.shapeInfo.logicalShape; + const outShape = outShapeInfo.logicalShape; + if (inShape.length <= outShape.length) { + if (usesPackedTextures) { + res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo); + } else { + res += getSamplerAtOutputCoords(inInfo, outShapeInfo); + } + } + return res; +} +function getPackedOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) { + switch (outShape.length) { + case 0: + return getOutputScalarCoords(); + case 1: + return getOutputPacked1DCoords(outShape, outTexShape, enableShapeUniforms); + case 2: + return getOutputPacked2DCoords(outShape, outTexShape, enableShapeUniforms); + case 3: + return getOutputPacked3DCoords(outShape, outTexShape, enableShapeUniforms); + default: + return getOutputPackedNDCoords(outShape, outTexShape, enableShapeUniforms); + } +} +function getOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) { + switch (outShape.length) { + case 0: + return getOutputScalarCoords(); + case 1: + return getOutput1DCoords(outShape, outTexShape, enableShapeUniforms); + case 2: + return getOutput2DCoords(outShape, outTexShape, enableShapeUniforms); + case 3: + return getOutput3DCoords(outShape, outTexShape, enableShapeUniforms); + case 4: + return getOutput4DCoords(outShape, outTexShape, enableShapeUniforms); + case 5: + return getOutput5DCoords(outShape, outTexShape); + case 6: + return getOutput6DCoords(outShape, outTexShape); + default: + throw new Error(`${outShape.length}-D output sampling is not yet supported`); + } +} +function getFloatTextureSampleSnippet(glsl) { + return ` float sampleTexture(sampler2D textureSampler, vec2 uv) { - return ${e.texture2D}(textureSampler, uv).r; + return ${glsl.texture2D}(textureSampler, uv).r; } - `}function bJ(e){return` + `; +} +function getFloatTextureSetRSnippet(glsl) { + return ` void setOutput(float val) { - ${e.output} = vec4(val, 0, 0, 0); + ${glsl.output} = vec4(val, 0, 0, 0); } - `}function yJ(e){return` + `; +} +function getFloatTextureSetRGBASnippet(glsl) { + return ` void setOutput(vec4 val) { - ${e.output} = val; + ${glsl.output} = val; } - `}function xJ(e){return`${e.version} + `; +} +function getShaderPrefix(glsl) { + const SHADER_PREFIX = `${glsl.version} precision highp float; precision highp int; precision highp sampler2D; - ${e.varyingFs} vec2 resultUV; - ${e.defineOutput} + ${glsl.varyingFs} vec2 resultUV; + ${glsl.defineOutput} const vec2 halfCR = vec2(0.5, 0.5); struct ivec5 @@ -202,9 +53523,9 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram }; uniform float NAN; - ${e.defineSpecialNaN} - ${e.defineSpecialInf} - ${e.defineRound} + ${glsl.defineSpecialNaN} + ${glsl.defineSpecialInf} + ${glsl.defineRound} int imod(int x, int y) { return x - y * (x / y); @@ -229,10 +53550,13 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${vJ} - ${wJ} - ${kJ} - `}var vJ=` + ${SAMPLE_1D_SNIPPET} + ${SAMPLE_2D_SNIPPET} + ${SAMPLE_3D_SNIPPET} + `; + return SHADER_PREFIX; +} +var SAMPLE_1D_SNIPPET = ` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -244,7 +53568,8 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,wJ=` +`; +var SAMPLE_2D_SNIPPET = ` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -252,7 +53577,8 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,kJ=` +`; +var SAMPLE_3D_SNIPPET = ` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -261,7 +53587,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,IJ=` +`; +var SHADER_PACKED_PREFIX = ` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -272,68 +53599,111 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function $E(){return` +`; +function getOutputScalarCoords() { + return ` int getOutputCoords() { return 0; } - `}function SJ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` + `; +} +function getOutputPacked1DCoords(shape, texShape, enableShapeUniforms) { + const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)]; + if (packedTexShape[0] === 1) { + if (enableShapeUniforms) { + return ` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } - `:` + `; + } + return ` int getOutputCoords() { - return 2 * int(resultUV.x * ${a[1]}.0); + return 2 * int(resultUV.x * ${packedTexShape[1]}.0); } - `:a[1]===1?n?` + `; + } + if (packedTexShape[1] === 1) { + if (enableShapeUniforms) { + return ` int getOutputCoords() { return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0)); } - `:` + `; + } + return ` int getOutputCoords() { - return 2 * int(resultUV.y * ${a[0]}.0); + return 2 * int(resultUV.y * ${packedTexShape[0]}.0); } - `:n?` + `; + } + if (enableShapeUniforms) { + return ` int getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y); } - `:` + `; + } + return ` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * - vec2(${a[0]}, ${a[1]})); - return 2 * (resTexRC.x * ${a[1]} + resTexRC.y); + vec2(${packedTexShape[0]}, ${packedTexShape[1]})); + return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y); } - `}function NJ(e,t,n){return t[0]===1?n?` + `; +} +function getOutput1DCoords(shape, texShape, enableShapeUniforms) { + if (texShape[0] === 1) { + if (enableShapeUniforms) { + return ` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } - `:` + `; + } + return ` int getOutputCoords() { - return int(resultUV.x * ${t[1]}.0); + return int(resultUV.x * ${texShape[1]}.0); } - `:t[1]===1?n?` + `; + } + if (texShape[1] === 1) { + if (enableShapeUniforms) { + return ` int getOutputCoords() { return int(resultUV.y * float(outTexShape[0])); } - `:` + `; + } + return ` int getOutputCoords() { - return int(resultUV.y * ${t[0]}.0); + return int(resultUV.y * ${texShape[0]}.0); } - `:n?` + `; + } + if (enableShapeUniforms) { + return ` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); return resTexRC.x * outTexShape[1] + resTexRC.y; } - `:` + `; + } + return ` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * - vec2(${t[0]}, ${t[1]})); - return resTexRC.x * ${t[1]} + resTexRC.y; + vec2(${texShape[0]}, ${texShape[1]})); + return resTexRC.x * ${texShape[1]} + resTexRC.y; } - `}function TJ(e,t,n){if(n)return` + `; +} +function getOutputPacked3DCoords(shape, texShape, enableShapeUniforms) { + if (enableShapeUniforms) { + return ` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -350,37 +53720,54 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),s=r*Math.ceil(e[1]/2);return` + `; + } + const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)]; + const texelsInLogicalRow = Math.ceil(shape[2] / 2); + const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2); + return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * - vec2(${a[0]}, ${a[1]})); - int index = resTexRC.x * ${a[1]} + resTexRC.y; + vec2(${packedTexShape[0]}, ${packedTexShape[1]})); + int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y; - int b = index / ${s}; - index -= b * ${s}; + int b = index / ${texelsInBatch}; + index -= b * ${texelsInBatch}; - int r = 2 * (index / ${r}); - int c = imod(index, ${r}) * 2; + int r = 2 * (index / ${texelsInLogicalRow}); + int c = imod(index, ${texelsInLogicalRow}) * 2; return ivec3(b, r, c); } - `}function CJ(e,t,n){if(n)return` + `; +} +function getOutput3DCoords(shape, texShape, enableShapeUniforms) { + if (enableShapeUniforms) { + const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], shape); + return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Of(["r","c","d"],e)} + ${coordsFromIndexSnippet2} return ivec3(r, c, d); } -`;let a=Zo(["r","c","d"],e);return` +`; + } + const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape); + return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * - vec2(${t[0]}, ${t[1]})); - int index = resTexRC.x * ${t[1]} + resTexRC.y; - ${a} + vec2(${texShape[0]}, ${texShape[1]})); + int index = resTexRC.x * ${texShape[1]} + resTexRC.y; + ${coordsFromIndexSnippet} return ivec3(r, c, d); } - `}function _J(e,t,n){if(n)return` + `; +} +function getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) { + if (enableShapeUniforms) { + return ` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -402,74 +53789,115 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec4(b2, b, r, c); } - `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),s=r*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` -`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,b)=>`coords.${d[b+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,f=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` + `; +} +function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) { + const texName = inputInfo.name; + const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1); + const funcName = "get" + texFuncSnippet + "AtOutCoords"; + const inRank = inputInfo.shapeInfo.logicalShape.length; + const outRank = outShapeInfo.logicalShape.length; + const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape); + const type = getCoordsDataType(outRank); + const rankDiff = outRank - inRank; + let coordsSnippet; + const fields = ["x", "y", "z", "w", "u", "v"]; + if (inRank === 0) { + coordsSnippet = ""; + } else if (outRank < 2 && broadcastDims.length >= 1) { + coordsSnippet = "coords = 0;"; + } else { + coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n"); + } + let unpackedCoordsSnippet = ""; + if (outRank < 2 && inRank > 0) { + unpackedCoordsSnippet = "coords"; + } else { + unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", "); + } + let output = `return outputValue;`; + const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape); + const isInputScalar = inSize === 1; + const outSize = util_exports.sizeFromShape(outShapeInfo.logicalShape); + const isOutputScalar = outSize === 1; + if (inRank === 1 && !isInputScalar && !isOutputScalar) { + output = ` return vec4(outputValue.xy, outputValue.xy); - `;else if(m&&!f)i===1?h=` + `; + } else if (isInputScalar && !isOutputScalar) { + if (outRank === 1) { + output = ` return vec4(outputValue.x, outputValue.x, 0., 0.); - `:h=` + `; + } else { + output = ` return vec4(outputValue.x); - `;else if(o.length){let g=s-2,b=s-1;o.indexOf(g)>-1&&o.indexOf(b)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(b)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return` - vec4 ${r}() { - ${l} coords = getOutputCoords(); - ${p} - vec4 outputValue = get${a}(${c}); - ${h} + `; } - `}function jJ(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&&w.arraysEqual(i,s))return` - float ${r}() { - return sampleTexture(${n}, resultUV); + } else if (broadcastDims.length) { + const rows = inRank - 2; + const cols = inRank - 1; + if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) { + output = `return vec4(outputValue.x);`; + } else if (broadcastDims.indexOf(rows) > -1) { + output = `return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);`; + } else if (broadcastDims.indexOf(cols) > -1) { + output = `return vec4(outputValue.xx, outputValue.zz);`; + } + } + return ` + vec4 ${funcName}() { + ${type} coords = getOutputCoords(); + ${coordsSnippet} + vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet}); + ${output} + } + `; +} +function getSamplerAtOutputCoords(inputInfo, outShapeInfo) { + const texName = inputInfo.name; + const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1); + const funcName = "get" + texFuncSnippet + "AtOutCoords"; + const outTexShape = outShapeInfo.texShape; + const inTexShape = inputInfo.shapeInfo.texShape; + const inRank = inputInfo.shapeInfo.logicalShape.length; + const outRank = outShapeInfo.logicalShape.length; + if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && util_exports.arraysEqual(inTexShape, outTexShape)) { + return ` + float ${funcName}() { + return sampleTexture(${texName}, resultUV); } - `;let u=ct(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.${h[f+d]} = 0;`).join(` -`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+d]}`).join(", "),` - float ${r}() { - ${u} coords = getOutputCoords(); - ${c} - return get${a}(${m}); + `; + } + const type = getCoordsDataType(outRank); + const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape); + const rankDiff = outRank - inRank; + let coordsSnippet; + const fields = ["x", "y", "z", "w", "u", "v"]; + if (inRank === 0) { + coordsSnippet = ""; + } else if (outRank < 2 && broadcastDims.length >= 1) { + coordsSnippet = "coords = 0;"; + } else { + coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n"); + } + let unpackedCoordsSnippet = ""; + if (outRank < 2 && inRank > 0) { + unpackedCoordsSnippet = "coords"; + } else { + unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", "); + } + return ` + float ${funcName}() { + ${type} coords = getOutputCoords(); + ${coordsSnippet} + return get${texFuncSnippet}(${unpackedCoordsSnippet}); } - `}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 ${e} is not yet supported`)}function K1(e,t,n){let{newShape:a,keptDims:r}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!w.arraysEqual(t,n)&&a.lengthe[n]).join(", ")}function KJ(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=dJ(r,i,t),l=lE(e.gl,o),u=e.createProgram(l);return G().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},DE(e,t,u)))}function DE(e,t,n){let a=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(n,"NAN",!1),G().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(n,"INFINITY",!1));let p=!1;for(let d of t.variableNames){let c={name:d,uniform:e.getUniformLocation(n,d,p),offset:e.getUniformLocation(n,`offset${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 jI(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(!w.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(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function XJ(e,t,n,a,r){t.program.enableShapeUniforms||(jI(t.inShapeInfos,n),jI([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),G().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=K1(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=w.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&w.arraysEqual(i.shape,l),b=w.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&w.arraysEqual(l,n.texData.texShape),v=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${b}_${y}_${g}_${c}_${h}_${m}_${v}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${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 ZJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Ic.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=_n();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + `; +} +function getCoordsDataType(rank) { + if (rank <= 1) { + return "int"; + } else if (rank === 2) { + return "ivec2"; + } else if (rank === 3) { + return "ivec3"; + } else if (rank === 4) { + return "ivec4"; + } else if (rank === 5) { + return "ivec5"; + } else if (rank === 6) { + return "ivec6"; + } else { + throw Error(`GPU for rank ${rank} is not yet supported`); + } +} +function getUniformInfoFromShape(isPacked, shape, texShape) { + const { newShape, keptDims } = util_exports.squeezeShape(shape); + const rank = shape.length; + const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1; + const squeezeShape2 = useSqueezePackedShape ? shape.slice(1) : newShape; + const useSqueezeShape = !isPacked && rank > 1 && !util_exports.arraysEqual(shape, texShape) && newShape.length < rank || useSqueezePackedShape; + const uniformShape = useSqueezeShape ? squeezeShape2 : shape; + return { useSqueezeShape, uniformShape, keptDims }; +} +function squeezeInputInfo(inInfo, squeezedShape) { + const newInputInfo = JSON.parse(JSON.stringify(inInfo)); + newInputInfo.shapeInfo.logicalShape = squeezedShape; + return newInputInfo; +} +function getSqueezedParams(params, keptDims) { + return keptDims.map((d) => params[d]).join(", "); +} +function compileProgram(gpgpu, program, inputs, output) { + const inputInfos = inputs.map((input2, i) => { + const shapeInfo = { + logicalShape: input2.shape, + texShape: input2.isUniform ? null : input2.texData.texShape, + isUniform: input2.isUniform, + isPacked: input2.isUniform ? false : input2.texData.isPacked, + flatOffset: null + }; + if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) { + shapeInfo.flatOffset = input2.texData.slice.flatOffset; + } + return { name: program.variableNames[i], shapeInfo }; + }); + const inShapeInfos = inputInfos.map((x) => x.shapeInfo); + const outShapeInfo = { + logicalShape: output.shape, + texShape: output.texData.texShape, + isUniform: false, + isPacked: output.texData.isPacked, + flatOffset: null + }; + const source = makeShader(inputInfos, outShapeInfo, program); + const fragmentShader = createFragmentShader(gpgpu.gl, source); + const webGLProgram = gpgpu.createProgram(fragmentShader); + if (!env().get("ENGINE_COMPILE_ONLY")) { + gpgpu.buildVao(webGLProgram); + return Object.assign({ + program, + fragmentShader, + source, + webGLProgram, + inShapeInfos, + outShapeInfo + }, getUniformLocations(gpgpu, program, webGLProgram)); + } else { + return { + program, + fragmentShader, + source, + webGLProgram, + inShapeInfos, + outShapeInfo, + variablesLocations: null, + customUniformLocations: null, + infLoc: null, + nanLoc: null, + outShapeLocation: null, + outShapeStridesLocation: null, + outTexShapeLocation: null + }; + } +} +function getUniformLocations(gpgpu, program, webGLProgram) { + const variablesLocations = []; + const customUniformLocations = []; + let outShapeLocation; + let outTexShapeLocation; + let outShapeStridesLocation; + let infLoc = null; + let nanLoc = null; + nanLoc = gpgpu.getUniformLocation(webGLProgram, "NAN", false); + if (env().getNumber("WEBGL_VERSION") === 1) { + infLoc = gpgpu.getUniformLocation(webGLProgram, "INFINITY", false); + } + const shouldThrow = false; + for (const varName of program.variableNames) { + const varLocs = { + name: varName, + uniform: gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow), + offset: gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow) + }; + if (program.enableShapeUniforms) { + varLocs.shape = gpgpu.getUniformLocation(webGLProgram, `${varName}Shape`, shouldThrow); + varLocs.texShape = gpgpu.getUniformLocation(webGLProgram, `${varName}TexShape`, shouldThrow); + } + variablesLocations.push(varLocs); + } + if (program.enableShapeUniforms) { + outShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outShape", shouldThrow); + outShapeStridesLocation = gpgpu.getUniformLocation(webGLProgram, "outShapeStrides", shouldThrow); + outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outTexShape", shouldThrow); + } + if (program.customUniforms) { + for (const d of program.customUniforms) { + customUniformLocations.push(gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow)); + } + } + return { + variablesLocations, + customUniformLocations, + infLoc, + nanLoc, + outShapeLocation, + outShapeStridesLocation, + outTexShapeLocation + }; +} +function validateBinaryAndProgram(shapeInfos, inputs) { + if (shapeInfos.length !== inputs.length) { + throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`); + } + shapeInfos.forEach((s, i) => { + const shapeA = s.logicalShape; + const input2 = inputs[i]; + const shapeB = input2.shape; + if (!util_exports.arraysEqual(shapeA, shapeB)) { + throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`); + } + if (s.isUniform && input2.isUniform) { + return; + } + const texShapeA = s.texShape; + const texShapeB = input2.isUniform ? null : input2.texData.texShape; + if (!util_exports.arraysEqual(texShapeA, texShapeB)) { + throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`); + } + }); +} +function runProgram(gpgpu, binary, inputs, output, customUniformValues) { + if (!binary.program.enableShapeUniforms) { + validateBinaryAndProgram(binary.inShapeInfos, inputs); + validateBinaryAndProgram([binary.outShapeInfo], [output]); + } + const outTex = output.texData.texture; + const outTexShape = output.texData.texShape; + if (output.texData.isPacked) { + gpgpu.setOutputPackedMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]); + } else { + gpgpu.setOutputMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]); + } + gpgpu.setProgram(binary.webGLProgram); + gpgpu.bindVertexArray(binary.webGLProgram.vao); + if (env().getNumber("WEBGL_VERSION") === 1) { + if (binary.infLoc !== null) { + gpgpu.gl.uniform1f(binary.infLoc, Infinity); + } + } + if (binary.nanLoc !== null) { + gpgpu.gl.uniform1f(binary.nanLoc, NaN); + } + for (let i = 0; i < inputs.length; ++i) { + const input2 = inputs[i]; + const { uniform: varLoc, offset: varOffsetLoc, shape: varShapeLoc, texShape: varTexShapeLoc } = binary.variablesLocations[i]; + if (varShapeLoc) { + const { uniformShape } = getUniformInfoFromShape(binary.program.packedInputs, input2.shape, input2.texData.texShape); + switch (uniformShape.length) { + case 1: + gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape)); + break; + case 2: + gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape)); + break; + case 3: + gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape)); + break; + case 4: + gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape)); + break; + default: + break; + } + } + if (varTexShapeLoc) { + gpgpu.gl.uniform2i(varTexShapeLoc, input2.texData.texShape[0], input2.texData.texShape[1]); + } + if (varLoc == null) { + continue; + } + if (input2.isUniform) { + if (util_exports.sizeFromShape(input2.shape) < 2) { + gpgpu.gl.uniform1f(varLoc, input2.uniformValues[0]); + } else { + let vals = input2.uniformValues; + if (!(vals instanceof Float32Array)) { + vals = new Float32Array(vals); + } + gpgpu.gl.uniform1fv(varLoc, vals); + } + continue; + } + if (input2.texData.slice != null && varOffsetLoc != null) { + gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset); + } + gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i); + } + const outShapeLoc = binary.outShapeLocation; + if (outShapeLoc) { + switch (output.shape.length) { + case 1: + gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape)); + break; + case 2: + gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape)); + break; + case 3: + gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape)); + break; + case 4: + gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape)); + break; + default: + break; + } + } + if (binary.outShapeStridesLocation) { + const strides = util_exports.computeStrides(output.shape); + switch (output.shape.length) { + case 2: + gpgpu.gl.uniform1iv(binary.outShapeStridesLocation, new Int32Array(strides)); + break; + case 3: + gpgpu.gl.uniform2iv(binary.outShapeStridesLocation, new Int32Array(strides)); + break; + case 4: + gpgpu.gl.uniform3iv(binary.outShapeStridesLocation, new Int32Array(strides)); + break; + default: + break; + } + } + if (binary.outTexShapeLocation) { + gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]); + } + if (binary.program.customUniforms && customUniformValues) { + for (let i = 0; i < binary.program.customUniforms.length; ++i) { + const d = binary.program.customUniforms[i]; + const customLoc = binary.customUniformLocations[i]; + const customValue = customUniformValues[i]; + if (d.type === "float") { + gpgpu.gl.uniform1fv(customLoc, customValue); + } else if (d.type === "vec2") { + gpgpu.gl.uniform2fv(customLoc, customValue); + } else if (d.type === "vec3") { + gpgpu.gl.uniform3fv(customLoc, customValue); + } else if (d.type === "vec4") { + gpgpu.gl.uniform4fv(customLoc, customValue); + } else if (d.type === "int") { + gpgpu.gl.uniform1iv(customLoc, customValue); + } else if (d.type === "ivec2") { + gpgpu.gl.uniform2iv(customLoc, customValue); + } else if (d.type === "ivec3") { + gpgpu.gl.uniform3iv(customLoc, customValue); + } else if (d.type === "ivec4") { + gpgpu.gl.uniform4iv(customLoc, customValue); + } else { + throw Error(`uniform type ${d.type} is not supported yet.`); + } + } + } + gpgpu.executeProgram(); +} +function makeShaderKey(program, inputs, output) { + let keyInputs = ""; + inputs.concat(output).forEach((x) => { + const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0; + if (program.enableShapeUniforms && !x.isUniform) { + const xTexShape = x.texData.texShape; + const { useSqueezeShape, uniformShape, keptDims } = getUniformInfoFromShape(program.packedInputs, x.shape, xTexShape); + let rank1 = "", rank2 = "", rank34 = ""; + if (uniformShape.length === 1 && program.packedInputs) { + const packedTexShape = [Math.ceil(xTexShape[0] / 2), Math.ceil(xTexShape[1] / 2)]; + rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`; + } else if (uniformShape.length === 2 && !program.packedInputs) { + rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`; + } else if (uniformShape.length > 2 && !program.packedInputs) { + const strides = util_exports.computeStrides(uniformShape); + rank34 = `${strides[0] === xTexShape[1]}_${strides[strides.length - 1] === xTexShape[1]}`; + } + const xRank = x.shape.length; + const isLogicalShapTexShapeEqual = uniformShape.length === 2 && util_exports.arraysEqual(x.shape, xTexShape); + const isScalar = util_exports.sizeFromShape(x.shape) === 1; + const broadcastDims = backend_util_exports.getBroadcastDims(x.shape, output.shape); + const isInOutTexShapeEqual = !program.packedInputs && xRank === output.shape.length && util_exports.arraysEqual(xTexShape, output.texData.texShape); + const isTexShapeGreaterThanOne = program.packedInputs || uniformShape.length > 2 ? "" : `${xTexShape[0] > 1}_${xTexShape[1] > 1}`; + keyInputs += `${xRank}_${isInOutTexShapeEqual}_${useSqueezeShape ? keptDims : ""}_${uniformShape.length}_${isScalar}_${broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`; + } else { + const texShape = x.isUniform ? "uniform" : x.texData.texShape; + keyInputs += `${x.shape}_${texShape}_${hasOffset}`; + } + }); + const keyUserCode = program.userCode; + let key = program.constructor.name; + key += "_" + keyInputs + "_" + keyUserCode + `${env().getNumber("WEBGL_VERSION")}`; + return key; +} +function useShapeUniforms(rank) { + return env().getBool("WEBGL_USE_SHAPES_UNIFORMS") && rank <= 4; +} +var DecodeMatrixProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = false; + this.packedOutput = true; + this.outPackingScheme = PackingScheme.DENSE; + this.customUniforms = [{ name: "texShape", type: "ivec2" }]; + const glsl = getGlslDifferences(); + this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + this.userCode = ` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Of(["r","c","d"],e):Zo(["r","c","d"],e)} + ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], outputShape) : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)} return ivec3(r, c, d); } @@ -1006,11 +55200,24 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result[i] = getA(rc.x, rc.y, rc.z); } - ${t.output} = result; + ${glsl.output} = result; } - `}},JJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Ic.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=_n();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + `; + } +}; +var DecodeMatrixPackedProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.outPackingScheme = PackingScheme.DENSE; + this.customUniforms = [{ name: "texShape", type: "ivec2" }]; + const glsl = getGlslDifferences(); + this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + this.userCode = ` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Of(["r","c","d"],e):Zo(["r","c","d"],e)} + ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], outputShape) : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)} return ivec3(r, c, d); } @@ -1026,52 +55233,117 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z)); } - ${t.output} = result; + ${glsl.output} = result; } - `}},QJ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=da.DOWNLOAD;let t=_n();this.outputShape=e,this.userCode=` - ${EE} + `; + } +}; +var EncodeFloatProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.outTexUsage = TextureUsage.DOWNLOAD; + const glsl = getGlslDifferences(); + this.outputShape = outputShape; + this.userCode = ` + ${ENCODE_FLOAT_SNIPPET} void main() { float x = getAAtOutCoords(); - ${t.output} = encode_float(x); + ${glsl.output} = encode_float(x); } - `}},e9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=da.DOWNLOAD;let t=_n();this.outputShape=e,this.userCode=` - ${EE} + `; + } +}; +var EncodeFloatPackedProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = false; + this.outTexUsage = TextureUsage.DOWNLOAD; + const glsl = getGlslDifferences(); + this.outputShape = outputShape; + this.userCode = ` + ${ENCODE_FLOAT_SNIPPET} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); - ${t.output} = encode_float(x); + ${glsl.output} = encode_float(x); } - `}},t9={R:0,G:1,B:2,A:3},KI=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let a=_n();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;iUE,createBufferFromOutputTexture:()=>qE,createFloat16MatrixTexture:()=>zE,createFloat16PackedMatrixTexture:()=>VE,createFloat32MatrixTexture:()=>LE,createIndexBuffer:()=>OE,createPackedMatrixTexture:()=>BE,createUnsignedBytesMatrixTexture:()=>WE,createVertexBuffer:()=>PE,createVertexShader:()=>ME,downloadByteEncodedFloatMatrixFromOutputTexture:()=>KE,downloadFloat32MatrixFromBuffer:()=>jE,downloadMatrixFromPackedOutputTexture:()=>YE,downloadPackedMatrixFromBuffer:()=>XE,getInternalFormatForFloat16MatrixTexture:()=>Y1,getInternalFormatForFloat16PackedMatrixTexture:()=>Q1,getInternalFormatForFloat32MatrixTexture:()=>X1,getInternalFormatForPackedMatrixTexture:()=>J1,getInternalFormatForUnsignedBytesMatrixTexture:()=>Z1,uploadDenseMatrixToTexture:()=>GE,uploadPixelDataToTexture:()=>HE});function ME(e){let t=_n(),n=`${t.version} + `; + } +}; +var gpgpu_util_exports = {}; +__export2(gpgpu_util_exports, { + bindVertexProgramAttributeStreams: () => bindVertexProgramAttributeStreams, + createBufferFromOutputTexture: () => createBufferFromOutputTexture, + createFloat16MatrixTexture: () => createFloat16MatrixTexture, + createFloat16PackedMatrixTexture: () => createFloat16PackedMatrixTexture, + createFloat32MatrixTexture: () => createFloat32MatrixTexture, + createIndexBuffer: () => createIndexBuffer, + createPackedMatrixTexture: () => createPackedMatrixTexture, + createUnsignedBytesMatrixTexture: () => createUnsignedBytesMatrixTexture, + createVertexBuffer: () => createVertexBuffer, + createVertexShader: () => createVertexShader2, + downloadByteEncodedFloatMatrixFromOutputTexture: () => downloadByteEncodedFloatMatrixFromOutputTexture, + downloadFloat32MatrixFromBuffer: () => downloadFloat32MatrixFromBuffer, + downloadMatrixFromPackedOutputTexture: () => downloadMatrixFromPackedOutputTexture, + downloadPackedMatrixFromBuffer: () => downloadPackedMatrixFromBuffer, + getInternalFormatForFloat16MatrixTexture: () => getInternalFormatForFloat16MatrixTexture, + getInternalFormatForFloat16PackedMatrixTexture: () => getInternalFormatForFloat16PackedMatrixTexture, + getInternalFormatForFloat32MatrixTexture: () => getInternalFormatForFloat32MatrixTexture, + getInternalFormatForPackedMatrixTexture: () => getInternalFormatForPackedMatrixTexture, + getInternalFormatForUnsignedBytesMatrixTexture: () => getInternalFormatForUnsignedBytesMatrixTexture, + uploadDenseMatrixToTexture: () => uploadDenseMatrixToTexture, + uploadPixelDataToTexture: () => uploadPixelDataToTexture +}); +function createVertexShader2(gl) { + const glsl = getGlslDifferences(); + const vertexShaderSource = `${glsl.version} precision highp float; - ${t.attribute} vec3 clipSpacePos; - ${t.attribute} vec2 uv; - ${t.varyingVs} vec2 resultUV; + ${glsl.attribute} vec3 clipSpacePos; + ${glsl.attribute} vec2 uv; + ${glsl.varyingVs} vec2 resultUV; void main() { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; - }`;return oE(e,n)}function PE(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return cE(e,t)}function OE(e){let t=new Uint16Array([0,1,2,2,1,3]);return dE(e,t)}function Ad(e,t,n,a,r,s){mE(t,n);let i=hE(e),o=e.TEXTURE_2D;return de(e,()=>e.bindTexture(o,i)),de(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),de(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),de(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),de(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),G().getNumber("WEBGL_VERSION")===1?de(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)):de(e,()=>e.texStorage2D(o,1,a,t,n)),de(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function X1(e){return e.internalFormatFloat}function LE(e,t,n,a){let[r,s]=Ed(t,n);return Ad(e,r,s,X1(a),a.textureFormatFloat,e.FLOAT)}function Y1(e){return e.internalFormatHalfFloat}function zE(e,t,n,a){let[r,s]=Ed(t,n);return Ad(e,r,s,Y1(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function Z1(e){return e.downloadTextureFormat}function WE(e,t,n,a){let[r,s]=Ed(t,n);return Ad(e,r,s,Z1(a),e.RGBA,e.UNSIGNED_BYTE)}function J1(e){return e.internalFormatPackedFloat}function BE(e,t,n,a){let[r,s]=op(t,n);return Ad(e,r,s,J1(a),e.RGBA,e.FLOAT)}function Q1(e){return e.internalFormatPackedHalfFloat}function VE(e,t,n,a){let[r,s]=op(t,n);return Ad(e,r,s,Q1(a),e.RGBA,a.textureTypeHalfFloat)}function UE(e,t,n){return de(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),rv(e,t,"clipSpacePos",n,3,20,0)&&rv(e,t,"uv",n,2,20,12)}function GE(e,t,n,a,r,s){de(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),G().getNumber("WEBGL_VERSION")===2?de(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,a,e.RGBA,o,i)):de(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),de(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function HE(e,t,n){de(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?G().getNumber("WEBGL_VERSION")===2?de(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):de(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):G().getNumber("WEBGL_VERSION")===2?de(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):de(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),de(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function qE(e,t,n,a){let r=e.createBuffer();de(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return de(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),de(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),de(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function jE(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function KE(e,t,n,a){let[r,s]=Ed(t,n),i=4,o=new Uint8Array(JZ(t*n,i));return de(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function XE(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(QZ(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 YE(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 Wh=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,rE(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=nc(this.gl,r),ha(this.gl,s))this.textureHalfFloatExtension=nc(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=nc(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=PE(this.gl),this.indexBuffer=OE(this.gl),this.framebuffer=fE(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. 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this.throwIfDisposed(),VE(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),BE(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,()=>KE(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return XE(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return jE(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=qE(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 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t=this.gl;de(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),UE(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&&Ph(this.gl,this.program),de(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?bE(this.gl,e,t):yE(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(),xE(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=op(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&&Ph(this.gl,this.program),ac(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 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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 w.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=a9(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)),w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Oh(this.gl,e,this.framebuffer),this.debug&&ac(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Oh(this.gl,this.outputTexture,this.framebuffer),this.debug&&ac(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;Oh(a,e,this.framebuffer),this.debug&&ac(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 a9(e){let t=0;for(;t`${e}.${n}`)}function In(e,t){return t===1?[e]:eA(e,t)}function X9(e,t){if(e===1)return"rc";let n="";for(let a=0;a gl.bindTexture(tex2d, texture)); + callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE)); + callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE)); + callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST)); + callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST)); + if (env().getNumber("WEBGL_VERSION") === 1) { + callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null)); + } else { + callAndCheck(gl, () => gl.texStorage2D(tex2d, 1, internalFormat, width, height)); + } + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); + return { texture, texShape: [height, width] }; +} +function getInternalFormatForFloat32MatrixTexture(textureConfig) { + return textureConfig.internalFormatFloat; +} +function createFloat32MatrixTexture(gl, rows, columns, textureConfig) { + const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns); + return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT); +} +function getInternalFormatForFloat16MatrixTexture(textureConfig) { + return textureConfig.internalFormatHalfFloat; +} +function createFloat16MatrixTexture(gl, rows, columns, textureConfig) { + const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns); + return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat); +} +function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) { + return textureConfig.downloadTextureFormat; +} +function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) { + const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns); + return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE); +} +function getInternalFormatForPackedMatrixTexture(textureConfig) { + return textureConfig.internalFormatPackedFloat; +} +function createPackedMatrixTexture(gl, rows, columns, textureConfig) { + const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns); + return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT); +} +function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) { + return textureConfig.internalFormatPackedHalfFloat; +} +function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) { + const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns); + return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat); +} +function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) { + const posOffset = 0; + const uvOffset = 3 * 4; + const stride = 3 * 4 + 2 * 4; + callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer)); + const success = bindVertexBufferToProgramAttribute(gl, program, "clipSpacePos", vertexBuffer, 3, stride, posOffset); + return success && bindVertexBufferToProgramAttribute(gl, program, "uv", vertexBuffer, 2, stride, uvOffset); +} +function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) { + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture)); + let dataForUpload, texelDataType, internalFormat; + if (data instanceof Uint8Array) { + dataForUpload = new Uint8Array(width * height * 4); + texelDataType = gl.UNSIGNED_BYTE; + internalFormat = gl.RGBA; + } else { + dataForUpload = new Float32Array(width * height * 4); + texelDataType = gl.FLOAT; + internalFormat = textureConfig.internalFormatPackedFloat; + } + dataForUpload.set(data); + if (env().getNumber("WEBGL_VERSION") === 2) { + callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload)); + } else { + callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload)); + } + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); +} +function uploadPixelDataToTexture(gl, texture, pixels) { + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture)); + if (pixels.data instanceof Uint8Array) { + if (env().getNumber("WEBGL_VERSION") === 2) { + callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data)); + } else { + callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data)); + } + } else { + if (env().getNumber("WEBGL_VERSION") === 2) { + callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels)); + } else { + callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels)); + } + } + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); +} +function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) { + const buffer2 = gl2.createBuffer(); + callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2)); + const bytesPerFloat = 4; + const valuesPerTexel = 4; + const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns; + callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ)); + callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0)); + callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null)); + return buffer2; +} +function downloadFloat32MatrixFromBuffer(gl, buffer2, size) { + const gl2 = gl; + const downloadTarget = new Float32Array(size); + gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2); + gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget); + gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null); + return downloadTarget; +} +function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) { + const [w, h] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns); + const numChannels = 4; + const downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels)); + callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget)); + return new Float32Array(downloadTarget.buffer); +} +function downloadPackedMatrixFromBuffer(gl, buffer2, batch, rows, cols, physicalRows, physicalCols, textureConfig) { + const gl2 = gl; + const downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols)); + gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2); + gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget); + gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null); + return downloadTarget; +} +function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) { + const packedRGBA = new Float32Array(physicalRows * physicalCols * 4); + callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA)); + return packedRGBA; +} +var GPGPUContext = class { + constructor(gl) { + this.outputTexture = null; + this.program = null; + this.disposed = false; + this.itemsToPoll = []; + const glVersion = env().getNumber("WEBGL_VERSION"); + if (gl != null) { + this.gl = gl; + setWebGLContext(glVersion, gl); + } else { + this.gl = getWebGLContext(glVersion); + } + gl = this.gl; + if (env().getNumber("WEBGL_VERSION") === 2) { + const gl2 = gl; + this.createVertexArray = () => { + return callAndCheck(gl2, () => gl2.createVertexArray()); + }; + this.bindVertexArray = (vao) => { + return callAndCheck(gl2, () => gl2.bindVertexArray(vao)); + }; + this.deleteVertexArray = (vao) => { + return callAndCheck(gl2, () => gl2.deleteVertexArray(vao)); + }; + this.getVertexArray = () => { + return callAndCheck(gl2, () => gl2.getParameter(gl2.VERTEX_ARRAY_BINDING)); + }; + } else if (gl != null) { + const ext = gl.getExtension("OES_vertex_array_object"); + if (ext == null) { + throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object."); + } + this.createVertexArray = () => { + return callAndCheck(gl, () => ext.createVertexArrayOES()); + }; + this.bindVertexArray = (vao) => { + return callAndCheck(gl, () => ext.bindVertexArrayOES(vao)); + }; + this.deleteVertexArray = (vao) => { + return callAndCheck(gl, () => ext.deleteVertexArrayOES(vao)); + }; + this.getVertexArray = () => { + return callAndCheck(gl, () => gl.getParameter(ext.VERTEX_ARRAY_BINDING_OES)); + }; + } + let COLOR_BUFFER_FLOAT = "WEBGL_color_buffer_float"; + const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float"; + this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"); + if (env().getNumber("WEBGL_VERSION") === 1) { + const TEXTURE_FLOAT = "OES_texture_float"; + const TEXTURE_HALF_FLOAT = "OES_texture_half_float"; + this.textureFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_FLOAT); + if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) { + this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT); + } else if (env().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."); + } + this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT); + if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) { + this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT); + } else if (env().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 { + COLOR_BUFFER_FLOAT = "EXT_color_buffer_float"; + if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) { + this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT); + } else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) { + this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT); + } else { + throw new Error("GL context does not support color renderable floats"); + } + } + this.vertexBuffer = createVertexBuffer(this.gl); + this.indexBuffer = createIndexBuffer(this.gl); + this.framebuffer = createFramebuffer(this.gl); + this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension); + } + get debug() { + return env().getBool("DEBUG"); + } + dispose() { + if (this.disposed) { + return; + } + if (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."); + } + if (this.outputTexture != null) { + console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing."); + } + const gl = this.gl; + callAndCheck(gl, () => gl.finish()); + callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null)); + callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer)); + callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null)); + callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null)); + callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer)); + this.disposed = true; + } + createFloat32MatrixTexture(rows, columns) { + this.throwIfDisposed(); + return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig); + } + createFloat16MatrixTexture(rows, columns) { + this.throwIfDisposed(); + return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig); + } + createUnsignedBytesMatrixTexture(rows, columns) { + this.throwIfDisposed(); + return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig); + } + uploadPixelDataToTexture(texture, pixels) { + this.throwIfDisposed(); + uploadPixelDataToTexture(this.gl, texture, pixels); + } + uploadDenseMatrixToTexture(texture, width, height, data) { + this.throwIfDisposed(); + uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig); + } + createFloat16PackedMatrixTexture(rows, columns) { + this.throwIfDisposed(); + return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig); + } + createPackedMatrixTexture(rows, columns) { + this.throwIfDisposed(); + return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig); + } + deleteMatrixTexture(texture) { + this.throwIfDisposed(); + if (this.outputTexture === texture) { + unbindColorTextureFromFramebuffer(this.gl, this.framebuffer); + this.outputTexture = null; + } + callAndCheck(this.gl, () => this.gl.deleteTexture(texture)); + } + downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) { + return this.downloadMatrixDriver(texture, () => downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig)); + } + downloadPackedMatrixFromBuffer(buffer2, batch, rows, columns, physicalRows, physicalCols) { + return downloadPackedMatrixFromBuffer(this.gl, buffer2, batch, rows, columns, physicalRows, physicalCols, this.textureConfig); + } + downloadFloat32MatrixFromBuffer(buffer2, size) { + return downloadFloat32MatrixFromBuffer(this.gl, buffer2, size); + } + createBufferFromTexture(texture, rows, columns) { + this.bindTextureToFrameBuffer(texture); + const result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig); + this.unbindTextureToFrameBuffer(); + return result; + } + createAndWaitForFence() { + const fenceContext = this.createFence(this.gl); + return this.pollFence(fenceContext); + } + createFence(gl) { + let query; + let isFencePassed; + if (env().getBool("WEBGL_FENCE_API_ENABLED")) { + const gl2 = gl; + const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0); + gl.flush(); + isFencePassed = () => { + const status = gl2.clientWaitSync(sync, 0, 0); + return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED; + }; + query = sync; + } else if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0) { + query = this.beginQuery(); + this.endQuery(); + isFencePassed = () => this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")); + } else { + isFencePassed = () => true; + } + return { query, isFencePassed }; + } + downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) { + return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols)); + } + createProgram(fragmentShader) { + this.throwIfDisposed(); + const gl = this.gl; + if (this.vertexShader == null) { + this.vertexShader = createVertexShader2(gl); + } + const program = createProgram(gl); + callAndCheck(gl, () => gl.attachShader(program, this.vertexShader)); + callAndCheck(gl, () => gl.attachShader(program, fragmentShader)); + linkProgram(gl, program); + const program2 = Object.assign(program, { vao: this.createVertexArray() }); + if (this.debug) { + validateProgram(gl, program2); + } + return program2; + } + buildVao(program) { + this.setProgram(program); + this.bindVertexArray(program.vao); + const gl = this.gl; + callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, this.indexBuffer)); + bindVertexProgramAttributeStreams(gl, program, this.vertexBuffer); + } + deleteProgram(program) { + this.throwIfDisposed(); + if (program === this.program) { + this.program = null; + } + if (program != null) { + callAndCheck(this.gl, () => this.gl.deleteProgram(program)); + this.deleteVertexArray(program.vao); + } + } + setProgram(program) { + this.throwIfDisposed(); + this.program = program; + if (this.program != null) { + if (this.debug) { + validateProgram(this.gl, this.program); + } + } + callAndCheck(this.gl, () => this.gl.useProgram(program)); + } + getUniformLocation(program, uniformName, shouldThrow = true) { + this.throwIfDisposed(); + if (shouldThrow) { + return getProgramUniformLocationOrThrow(this.gl, program, uniformName); + } else { + return getProgramUniformLocation(this.gl, program, uniformName); + } + } + getAttributeLocation(program, attribute) { + this.throwIfDisposed(); + return callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute)); + } + getUniformLocationNoThrow(program, uniformName) { + this.throwIfDisposed(); + return this.gl.getUniformLocation(program, uniformName); + } + setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) { + this.throwIfDisposed(); + this.throwIfNoProgram(); + bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit); + } + setOutputMatrixTexture(outputMatrixTexture, rows, columns) { + this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows); + } + setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) { + this.throwIfDisposed(); + const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns); + this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height); + } + setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) { + this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows); + } + setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) { + throw new Error("setOutputPackedMatrixWriteRegion not implemented."); + } + debugValidate() { + if (this.program != null) { + validateProgram(this.gl, this.program); + } + validateFramebuffer(this.gl); + } + executeProgram() { + this.throwIfDisposed(); + this.throwIfNoProgram(); + const gl = this.gl; + if (this.debug) { + const boundVao = this.getVertexArray(); + console.assert(boundVao === this.program.vao, "VAO changed between setProgram and executeProgram!"); + this.debugValidate(); + } + callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0)); + } + blockUntilAllProgramsCompleted() { + this.throwIfDisposed(); + callAndCheck(this.gl, () => this.gl.finish()); + } + getQueryTimerExtension() { + if (this.disjointQueryTimerExtension == null) { + this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query"); + } + return this.disjointQueryTimerExtension; + } + getQueryTimerExtensionWebGL2() { + return this.getQueryTimerExtension(); + } + getQueryTimerExtensionWebGL1() { + return this.getQueryTimerExtension(); + } + beginQuery() { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) { + const gl2 = this.gl; + const ext2 = this.getQueryTimerExtensionWebGL2(); + const query2 = gl2.createQuery(); + gl2.beginQuery(ext2.TIME_ELAPSED_EXT, query2); + return query2; + } + const ext = this.getQueryTimerExtensionWebGL1(); + const query = ext.createQueryEXT(); + ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query); + return query; + } + endQuery() { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) { + const gl2 = this.gl; + const ext2 = this.getQueryTimerExtensionWebGL2(); + gl2.endQuery(ext2.TIME_ELAPSED_EXT); + return; + } + const ext = this.getQueryTimerExtensionWebGL1(); + ext.endQueryEXT(ext.TIME_ELAPSED_EXT); + } + async waitForQueryAndGetTime(query) { + await util_exports.repeatedTry(() => this.disposed || // while testing contexts are created / disposed + // in rapid succession, so without this check we + // may poll for the query timer indefinitely + this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))); + return this.getQueryTime(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")); + } + getQueryTime(query, queryTimerVersion) { + if (queryTimerVersion === 0) { + return null; + } + if (queryTimerVersion === 2) { + const gl2 = this.gl; + const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT); + return timeElapsedNanos / 1e6; + } else { + const ext = this.getQueryTimerExtensionWebGL1(); + const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT); + return timeElapsedNanos / 1e6; + } + } + isQueryAvailable(query, queryTimerVersion) { + if (queryTimerVersion === 0) { + return true; + } + if (queryTimerVersion === 2) { + const gl2 = this.gl; + const ext = this.getQueryTimerExtensionWebGL2(); + const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE); + if (this.disjoint == null) { + this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT); + } + return available && !this.disjoint; + } else { + const ext = this.getQueryTimerExtensionWebGL1(); + const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT); + if (this.disjoint == null) { + this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT); + } + return available && !this.disjoint; + } + } + pollFence(fenceContext) { + return new Promise((resolve) => { + this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve()); + }); + } + pollItems() { + const index = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn)); + for (let i = 0; i <= index; ++i) { + const { resolveFn } = this.itemsToPoll[i]; + resolveFn(); + } + this.itemsToPoll = this.itemsToPoll.slice(index + 1); + } + addItemToPoll(isDoneFn, resolveFn) { + this.itemsToPoll.push({ isDoneFn, resolveFn }); + if (this.itemsToPoll.length > 1) { + return; + } + let scheduleFn = void 0; + if ("setTimeoutCustom" in env().platform) { + scheduleFn = env().platform.setTimeoutCustom.bind(env().platform); + } + util_exports.repeatedTry(() => { + this.pollItems(); + return this.itemsToPoll.length === 0; + }, () => 0, null, scheduleFn); + } + bindTextureToFrameBuffer(texture) { + this.throwIfDisposed(); + bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer); + if (this.debug) { + validateFramebuffer(this.gl); + } + } + unbindTextureToFrameBuffer() { + if (this.outputTexture != null) { + bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer); + if (this.debug) { + validateFramebuffer(this.gl); + } + } else { + unbindColorTextureFromFramebuffer(this.gl, this.framebuffer); + } + } + downloadMatrixDriver(texture, downloadAndDecode) { + this.bindTextureToFrameBuffer(texture); + const result = downloadAndDecode(); + this.unbindTextureToFrameBuffer(); + return result; + } + setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) { + this.throwIfDisposed(); + const gl = this.gl; + bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer); + if (this.debug) { + validateFramebuffer(gl); + } + this.outputTexture = outputMatrixTextureMaybePacked; + callAndCheck(gl, () => gl.viewport(0, 0, width, height)); + callAndCheck(gl, () => gl.scissor(0, 0, width, height)); + } + setOutputMatrixWriteRegionDriver(x, y, width, height) { + this.throwIfDisposed(); + callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height)); + } + 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 linearSearchLastTrue(arr) { + let i = 0; + for (; i < arr.length; ++i) { + const isDone = arr[i](); + if (!isDone) { + break; + } + } + return i - 1; +} +var { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, bitwiseAndImpl: bitwiseAndImplCPU, castImpl: castImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, raggedGatherImpl: raggedGatherImplCPU, raggedRangeImpl: raggedRangeImplCPU, raggedTensorToTensorImpl: raggedTensorToTensorImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, staticRegexReplaceImpl: staticRegexReplaceImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports; +function getVecChannels(name, rank) { + return ["x", "y", "z", "w", "u", "v"].slice(0, rank).map((d) => `${name}.${d}`); +} +function getChannels(name, rank) { + if (rank === 1) { + return [name]; + } + return getVecChannels(name, rank); +} +function getSourceCoords(rank, dims) { + if (rank === 1) { + return "rc"; + } + let coords2 = ""; + for (let i = 0; i < rank; i++) { + coords2 += dims[i]; + if (i < rank - 1) { + coords2 += ","; + } + } + return coords2; +} +var PackProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = false; + this.packedOutput = true; + this.outputShape = outputShape; + this.rank = outputShape.length; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + if (this.rank === 0) { + this.userCode = ` void main() { setOutput(vec4(getA(), 0., 0., 0.)); } - `;else{let t=In("rc",this.rank),n=ct(this.rank),a=this.getOutOfBoundsCondition(t),r=this.getSetup(t),s=this.getOutput(t);this.userCode=` + `; + } else { + const channels = getChannels("rc", this.rank); + const dtype = getCoordsDataType(this.rank); + const outOfBoundsCondition = this.getOutOfBoundsCondition(channels); + const setup76 = this.getSetup(channels); + const output = this.getOutput(channels); + this.userCode = ` void main() { - ${n} rc = getOutputCoords(); + ${dtype} rc = getOutputCoords(); - if(${a}) { + if(${outOfBoundsCondition}) { setOutput(vec4(0)); } else { - ${r} + ${setup76} - setOutput(vec4(${s})); + setOutput(vec4(${output})); } } - `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let a=0;a<=1;a++){let r=`${n===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let s=2;s ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`; + } + let cond = ""; + for (let i = this.rank - 2; i < this.rank; i++) { + cond += `${dims[i]} >= ${this.enableShapeUniforms ? `outShape[${i}]` : this.outputShape[i]}`; + if (i < this.rank - 1) { + cond += "||"; + } + } + return cond; + } + getSetup(dims) { + if (this.rank === 1) { + return ""; + } + const innerDims = dims.slice(-2); + const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1]; + const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2]; + return ` + int r = ${innerDims[0]}; + int c = ${innerDims[1]}; int rp1 = r + 1; int cp1 = c + 1; - bool cEdge = cp1 >= ${n}; - bool rEdge = rp1 >= ${a}; - `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${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+=` - ${r} - ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""} + bool cEdge = cp1 >= ${col}; + bool rEdge = rp1 >= ${row}; + `; + } + getOutput(dims) { + const sourceCoords = this.getSourceCoordsArr(dims); + if (this.rank === 1) { + const outShape = this.enableShapeUniforms ? "outShape" : this.outputShape[0]; + return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`; + } + return `getA(${sourceCoords[0]}), + cEdge ? 0. : getA(${sourceCoords[1]}), + rEdge ? 0. : getA(${sourceCoords[2]}), + rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`; + } +}; +var ReshapePackedProgram = class { + constructor(outputShape, inputShape) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [{ name: "inputShape", type: "ivec3" }]; + this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + let mainLoop = ``; + for (let i = 0; i < 4; i++) { + let thisRC = `thisRC = rc;`; + if (i % 2 === 1) { + thisRC += `thisRC.z += 1;`; + } + if (i > 1) { + thisRC += `thisRC.y += 1;`; + } + mainLoop += ` + ${thisRC} + ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""} int flatIndex = getFlatIndex(thisRC); ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex); vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z)); - result[${a}] = + result[${i}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); - ${a>0?"}":""} - `}this.userCode=` - ${Z9(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?j1():q1(e)} + ${i > 0 ? "}" : ""} + `; + } + this.userCode = ` + ${getReshapedInputCoords(inputShape, this.enableShapeUniforms)} + ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)} void main() { ivec3 rc = getOutputCoords(); @@ -1167,21 +56165,225 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 result = vec4(0.); ivec3 thisRC; - int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]}; - int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]}; + int rows = ${this.enableShapeUniforms ? "outShape[1]" : outputShape[1]}; + int cols = ${this.enableShapeUniforms ? "outShape[2]" : outputShape[2]}; - ${n} + ${mainLoop} setOutput(result); } - `}};function Z9(e,t){return` + `; + } +}; +function getReshapedInputCoords(shape, enableShapeUniforms) { + const coordsFromIndexSnippet = enableShapeUniforms ? getLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], "inputShape") : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape); + return ` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?cJ(["r","c","d"],"inputShape"):Zo(["r","c","d"],e)} + ${coordsFromIndexSnippet} return ivec3(r, c, d); } - `}var J9=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=YI(t,n),r=ZI(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=XI(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===dn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===dn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===dn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===dn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===dn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=YI(n,a),s=ZI(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=XI(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 XI(e,t,n,a,r){let s=eQ(t,a),i;if(r){let[l,u]=op(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 eQ(e,t){switch(e){case dn.PACKED_2X2_FLOAT32:return J1(t);case dn.PACKED_2X2_FLOAT16:return Q1(t);case dn.UNPACKED_FLOAT32:return X1(t);case dn.UNPACKED_FLOAT16:return Y1(t);case dn.PACKED_4X1_UNSIGNED_BYTE:return Z1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function tQ(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 YI(e,t){if(e===da.UPLOAD)return dn.PACKED_2X2_FLOAT32;if(e===da.RENDER||e==null)return tQ(t);if(e===da.DOWNLOAD||e===da.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 TextureManager = class { + constructor(gpgpu) { + this.gpgpu = gpgpu; + this.numUsedTextures = 0; + this.numFreeTextures = 0; + this._numBytesAllocated = 0; + this._numBytesFree = 0; + this.freeTextures = {}; + this.usedTextures = {}; + this.logEnabled = false; + } + acquireTexture(shapeRC, usage, isPacked) { + const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked); + const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked); + if (!(shapeKey in this.freeTextures)) { + this.freeTextures[shapeKey] = []; + } + if (!(shapeKey in this.usedTextures)) { + this.usedTextures[shapeKey] = []; + } + const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked); + if (this.freeTextures[shapeKey].length > 0) { + this.numFreeTextures--; + this.numUsedTextures++; + this._numBytesFree -= texBytes; + this.log(); + const newTexture2 = this.freeTextures[shapeKey].pop(); + this.usedTextures[shapeKey].push(newTexture2); + return newTexture2; + } + let newTexture; + if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) { + newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]); + } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) { + newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]); + } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) { + newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]); + } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) { + newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]); + } else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) { + newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]); + } + this.usedTextures[shapeKey].push(newTexture); + this.numUsedTextures++; + this._numBytesAllocated += texBytes; + this.log(); + return newTexture; + } + releaseTexture(texture, shape, logicalTexType, isPacked) { + if (this.freeTextures == null) { + return; + } + const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked); + const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked); + if (!(shapeKey in this.freeTextures)) { + this.freeTextures[shapeKey] = []; + } + const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked); + const deleteTexThreshold = env().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD"); + if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) { + this.gpgpu.deleteMatrixTexture(texture.texture); + this._numBytesAllocated -= texBytes; + } else { + this.freeTextures[shapeKey].push(texture); + this.numFreeTextures++; + this._numBytesFree += texBytes; + } + this.numUsedTextures--; + const texList = this.usedTextures[shapeKey]; + const texIndex = texList && texList.indexOf(texture); + if (texIndex == null || texIndex < 0) { + throw new Error("Cannot release a texture that was never provided by this texture manager"); + } + texList[texIndex] = texList[texList.length - 1]; + texList.pop(); + this.log(); + } + log() { + if (!this.logEnabled) { + return; + } + const total = this.numFreeTextures + this.numUsedTextures; + console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`); + const freeRatio = this._numBytesFree / this._numBytesAllocated; + console.log(`Bytes allocated: ${this._numBytesAllocated}`); + console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`); + } + get numBytesAllocated() { + return this._numBytesAllocated; + } + get numBytesFree() { + return this._numBytesFree; + } + getNumUsedTextures() { + return this.numUsedTextures; + } + getNumFreeTextures() { + return this.numFreeTextures; + } + dispose() { + if (this.freeTextures == null) { + return; + } + for (const texShape in this.freeTextures) { + this.freeTextures[texShape].forEach((tex) => { + this.gpgpu.deleteMatrixTexture(tex.texture); + }); + } + for (const texShape in this.usedTextures) { + this.usedTextures[texShape].forEach((tex) => { + this.gpgpu.deleteMatrixTexture(tex.texture); + }); + } + this.freeTextures = null; + this.usedTextures = null; + this.numUsedTextures = 0; + this.numFreeTextures = 0; + this._numBytesAllocated = 0; + this._numBytesFree = 0; + } +}; +function numBytesForInternalFormat(gl, internalFormat) { + const glany = gl; + if (internalFormat === glany.R32F) { + return 4; + } else if (internalFormat === glany.R16F) { + return 2; + } else if (internalFormat === glany.RGBA32F) { + return 16; + } else if (internalFormat === gl.RGBA) { + return 16; + } else if (internalFormat === glany.RGBA16F) { + return 8; + } else if (internalFormat === glany.RGBA8) { + return 4; + } + throw new Error(`Unknown internal format ${internalFormat}`); +} +function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) { + const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig); + let numElements; + if (isPacked) { + const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]); + numElements = packedWidth * packedHeight; + } else { + const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]); + numElements = width * height; + } + const bytesPerElement2 = numBytesForInternalFormat(gl, internalFormat); + return numElements * bytesPerElement2; +} +function internalFormatForPhysicalTexType(physicalTexType, textureConfig) { + switch (physicalTexType) { + case PhysicalTextureType.PACKED_2X2_FLOAT32: + return getInternalFormatForPackedMatrixTexture(textureConfig); + case PhysicalTextureType.PACKED_2X2_FLOAT16: + return getInternalFormatForFloat16PackedMatrixTexture(textureConfig); + case PhysicalTextureType.UNPACKED_FLOAT32: + return getInternalFormatForFloat32MatrixTexture(textureConfig); + case PhysicalTextureType.UNPACKED_FLOAT16: + return getInternalFormatForFloat16MatrixTexture(textureConfig); + case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE: + return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig); + default: + throw new Error(`Unknown physical texture type ${physicalTexType}`); + } +} +function getPhysicalTextureForRendering(isPacked) { + if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")) { + if (isPacked) { + return PhysicalTextureType.PACKED_2X2_FLOAT32; + } + return PhysicalTextureType.UNPACKED_FLOAT32; + } + if (isPacked) { + return PhysicalTextureType.PACKED_2X2_FLOAT16; + } + return PhysicalTextureType.UNPACKED_FLOAT16; +} +function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) { + if (logicalTexType === TextureUsage.UPLOAD) { + return PhysicalTextureType.PACKED_2X2_FLOAT32; + } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) { + return getPhysicalTextureForRendering(isPacked); + } else if (logicalTexType === TextureUsage.DOWNLOAD || logicalTexType === TextureUsage.PIXELS) { + return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE; + } + throw new Error(`Unknown logical texture type ${logicalTexType}`); +} +function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) { + return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`; +} +var UnaryOpProgram = class { + constructor(aShape, opSnippet) { + this.variableNames = ["A"]; + this.outputShape = aShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + this.userCode = ` float unaryOperation(float x) { - ${t} + ${opSnippet} } void main() { @@ -1190,11 +56392,23 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Ma="if (isnan(x)) return x;",nQ="return x;",JI="return abs(x);",aQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",rQ=Ma+` + `; + } +}; +var CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`; +var LINEAR = `return x;`; +var ABS = `return abs(x);`; +var ELU2 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`; +var RELU = CHECK_NAN_SNIPPET + ` return (x < 0.0) ? 0.0 : x; -`,sQ=Ma+` +`; +var RELU6 = CHECK_NAN_SNIPPET + ` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Zr="return x;",iQ="return 1.0 / (1.0 + exp(-1.0 * x));",oQ="return x;",lQ=` +`; +var CLONE = "return x;"; +var SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`; +var LINEAR2 = `return x;`; +var ELU3 = ` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1203,7 +56417,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,uQ=` +`; +var RELU2 = ` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1213,7 +56428,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,pQ=` +`; +var RELU62 = ` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1223,9 +56439,18 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,cQ="return 1.0 / (1.0 + exp(-1.0 * x));",ns=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` +`; +var SIGMOID2 = `return 1.0 / (1.0 + exp(-1.0 * x));`; +var UnaryOpPackedProgram = class { + constructor(aShape, opSnippet) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = aShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + this.userCode = ` vec4 unaryOperation(vec4 x) { - ${t} + ${opSnippet} } void main() { @@ -1234,19 +56459,930 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},dQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.length,n=In("rc",t),a=ct(t),r=X9(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `; + } +}; +var UnpackProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = false; + this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const rank = outputShape.length; + const channels = getChannels("rc", rank); + const dtype = getCoordsDataType(rank); + const sourceCoords = getSourceCoords(rank, channels); + const innerDims = channels.slice(-2); + const coords2 = rank <= 1 ? "rc" : `vec2(${innerDims.join(",")})`; + this.userCode = ` void main() { - ${a} rc = getOutputCoords(); - vec4 packedInput = getA(${r}); + ${dtype} rc = getOutputCoords(); + vec4 packedInput = getA(${sourceCoords}); - setOutput(getChannel(packedInput, ${i})); + setOutput(getChannel(packedInput, ${coords2})); } - `}},hQ=mr.whereImpl,mQ=1e-7,fQ=1e-4,gx={};function gQ(e){return e in gx||(gx[e]={}),gx[e]}var bQ=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),yQ=600;function xQ(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*yQ/1024/1024}var Lf=class extends Ec{nextDataId(){return Lf.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 Wh)t=e;else{let n=Ka(G().getNumber("WEBGL_VERSION"),e);t=new Wh(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ka(G().getNumber("WEBGL_VERSION"));t=new Wh(n),this.binaryCache=gQ(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new J9(this.gpgpu),this.numMBBeforeWarning=xQ(),this.texData=new xm(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=rc(t),u=new KI(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 ns(i,Zr):d=new rr(i,Zr);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=w.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+=w.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 ns(a,Zr):h=new rr(a,Zr);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,...Ah(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=w.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 ns(r,Zr):c=new rr(r,Zr);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=>w.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=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.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=w.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:w.now(),endMs:null}}endTimer(e){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.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=bQ){return G().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)0&&w.isString(n[0])){let r=n.map(s=>w.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 dQ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Y9(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 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=w.sizeFromShape(r),c=t[0]*t[1]*4;w.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=rc(r),o;a?o=new JJ(i):o=new ZJ(i);let l=!0,u=[t!=null?t:Ah(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===Ic.DENSE){let g=s!=null?s:Ah(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.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&&w.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&&!Sc(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=YJ(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")||XJ(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=w.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?mQ:fQ}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=w.now());let p=t.texShape;if(p==null&&(p=kE(n,o),t.texShape=p),r!=null){let d=rc(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=op(p[0],p[1])),o?c=new n9(d,f):c=new KI(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]],v=!0,I=this.runWebGLProgram(c,[b],a,x,v),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+=w.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=vQ(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]*w.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}=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=_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)}};Lf.nextDataId=0;function vQ(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 Lf,2);var kQ={forceHalfFloat:nA},tk=` + `; + } +}; +var whereImpl3 = kernel_impls_exports.whereImpl; +var EPSILON_FLOAT322 = 1e-7; +var EPSILON_FLOAT162 = 1e-4; +var binaryCaches = {}; +function getBinaryCache(webGLVersion) { + if (webGLVersion in binaryCaches) { + return binaryCaches[webGLVersion]; + } + binaryCaches[webGLVersion] = {}; + return binaryCaches[webGLVersion]; +} +var CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"); +var BEFORE_PAGING_CONSTANT = 600; +function numMBBeforeWarning() { + if (env().global.screen == null) { + return 1024; + } + return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024; +} +var MathBackendWebGL = class _MathBackendWebGL extends KernelBackend { + nextDataId() { + return _MathBackendWebGL.nextDataId++; + } + constructor(gpuResource) { + super(); + this.pendingRead = /* @__PURE__ */ new WeakMap(); + this.pendingDisposal = /* @__PURE__ */ new WeakSet(); + this.dataRefCount = /* @__PURE__ */ new WeakMap(); + this.numBytesInGPU = 0; + this.uploadWaitMs = 0; + this.downloadWaitMs = 0; + this.lastGlFlushTime = 0; + this.warnedAboutMemory = false; + this.pendingDeletes = 0; + this.disposed = false; + if (!env().getBool("HAS_WEBGL")) { + throw new Error("WebGL is not supported on this device"); + } + let newGPGPU; + if (gpuResource != null) { + if (gpuResource instanceof GPGPUContext) { + newGPGPU = gpuResource; + } else { + const gl = getWebGLContext(env().getNumber("WEBGL_VERSION"), gpuResource); + newGPGPU = new GPGPUContext(gl); + } + this.binaryCache = {}; + this.gpgpuCreatedLocally = false; + } else { + const gl = getWebGLContext(env().getNumber("WEBGL_VERSION")); + newGPGPU = new GPGPUContext(gl); + this.binaryCache = getBinaryCache(env().getNumber("WEBGL_VERSION")); + this.gpgpuCreatedLocally = true; + } + this.gpgpu = newGPGPU; + this.canvas = this.gpgpu.gl.canvas; + this.textureManager = new TextureManager(this.gpgpu); + this.numMBBeforeWarning = numMBBeforeWarning(); + this.texData = new DataStorage(this, engine()); + } + numDataIds() { + return this.texData.numDataIds() - this.pendingDeletes; + } + // Writes a new entry to the data store with a WebGL texture, and registers it + // to the texture manager. + writeTexture(texture, shape, dtype, texHeight, texWidth, channels) { + const input2 = this.makeTensorInfo(shape, dtype); + const inData = this.texData.get(input2.dataId); + inData.isPacked = false; + inData.texture = { texture, texShape: [texHeight, texWidth] }; + inData.texShape = [texHeight, texWidth]; + const shapeAs3D = getShapeAs3D(shape); + const program = new EncodeMatrixProgram(shapeAs3D, false, channels); + const output = this.runWebGLProgram(program, [input2], dtype, [[texHeight, texWidth]]); + output.shape = shape; + inData.texture = null; + this.disposeIntermediateTensorInfo(input2); + return output.dataId; + } + write(values, shape, dtype) { + if (env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || env().getBool("DEBUG")) { + this.checkNumericalProblems(values); + } + if (dtype === "complex64" && values != null) { + throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`); + } + const dataId = { id: this.nextDataId() }; + this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 }); + return dataId; + } + /** Return refCount of a `TensorData`. */ + refCount(dataId) { + if (this.texData.has(dataId)) { + const tensorData = this.texData.get(dataId); + return tensorData.refCount; + } + return 0; + } + /** Increase refCount of a `TextureData`. */ + incRef(dataId) { + const texData = this.texData.get(dataId); + texData.refCount++; + } + /** Decrease refCount of a `TextureData`. */ + decRef(dataId) { + if (this.texData.has(dataId)) { + const texData = this.texData.get(dataId); + texData.refCount--; + } + } + move(dataId, values, shape, dtype, refCount) { + if (env().getBool("DEBUG")) { + this.checkNumericalProblems(values); + } + if (dtype === "complex64") { + throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`); + } + this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount }); + } + disposeIntermediateTensorInfo(tensorInfo) { + this.disposeData(tensorInfo.dataId); + } + readSync(dataId) { + const texData = this.texData.get(dataId); + const { values, dtype, complexTensorInfos, slice: slice5, shape, isPacked } = texData; + if (slice5 != null) { + let program; + if (isPacked) { + program = new UnaryOpPackedProgram(shape, CLONE); + } else { + program = new UnaryOpProgram(shape, CLONE); + } + const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype); + const data = this.readSync(res.dataId); + this.disposeIntermediateTensorInfo(res); + return data; + } + if (values != null) { + return this.convertAndCacheOnCPU(dataId); + } + if (dtype === "string") { + return values; + } + const shouldTimeProgram = this.activeTimers != null; + let start; + if (shouldTimeProgram) { + start = util_exports.now(); + } + let result; + if (dtype === "complex64") { + const realValues = this.readSync(complexTensorInfos.real.dataId); + const imagValues = this.readSync(complexTensorInfos.imag.dataId); + result = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues); + } else { + result = this.getValuesFromTexture(dataId); + } + if (shouldTimeProgram) { + this.downloadWaitMs += util_exports.now() - start; + } + return this.convertAndCacheOnCPU(dataId, result); + } + async read(dataId) { + if (this.pendingRead.has(dataId)) { + const subscribers2 = this.pendingRead.get(dataId); + return new Promise((resolve) => subscribers2.push(resolve)); + } + const texData = this.texData.get(dataId); + const { values, shape, slice: slice5, dtype, complexTensorInfos, isPacked } = texData; + if (slice5 != null) { + let program; + if (isPacked) { + program = new UnaryOpPackedProgram(shape, CLONE); + } else { + program = new UnaryOpProgram(shape, CLONE); + } + const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype); + const data = this.read(res.dataId); + this.disposeIntermediateTensorInfo(res); + return data; + } + if (values != null) { + return this.convertAndCacheOnCPU(dataId); + } + if (env().getBool("DEBUG")) { + if (!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && env().getNumber("WEBGL_VERSION") === 2) { + throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`); + } + } + let buffer2 = null; + let tmpDownloadTarget; + if (dtype !== "complex64" && env().get("WEBGL_BUFFER_SUPPORTED")) { + tmpDownloadTarget = this.decode(dataId); + const tmpData = this.texData.get(tmpDownloadTarget.dataId); + buffer2 = this.gpgpu.createBufferFromTexture(tmpData.texture.texture, ...getDenseTexShape(shape)); + } + this.pendingRead.set(dataId, []); + if (dtype !== "complex64") { + await this.gpgpu.createAndWaitForFence(); + } + let vals; + if (dtype === "complex64") { + const ps = await Promise.all([ + this.read(complexTensorInfos.real.dataId), + this.read(complexTensorInfos.imag.dataId) + ]); + const realValues = ps[0]; + const imagValues = ps[1]; + vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues); + } else if (buffer2 == null) { + vals = this.getValuesFromTexture(dataId); + } else { + const size = util_exports.sizeFromShape(shape); + vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2, size); + } + if (tmpDownloadTarget != null) { + this.disposeIntermediateTensorInfo(tmpDownloadTarget); + } + if (buffer2 != null) { + const gl = this.gpgpu.gl; + callAndCheck(gl, () => gl.deleteBuffer(buffer2)); + } + const dTypeVals = this.convertAndCacheOnCPU(dataId, vals); + const subscribers = this.pendingRead.get(dataId); + this.pendingRead.delete(dataId); + subscribers.forEach((resolve) => resolve(dTypeVals)); + if (this.pendingDisposal.has(dataId)) { + this.pendingDisposal.delete(dataId); + if (this.disposeData(dataId)) { + engine().removeDataId(dataId, this); + } + this.pendingDeletes--; + } + return dTypeVals; + } + /** + * Read tensor to a new texture that is densely packed for ease of use. + * @param dataId The source tensor. + * @param options + * customTexShape: Optional. If set, will use the user defined texture + * shape to create the texture. + */ + readToGPU(dataId, options = {}) { + const texData = this.texData.get(dataId); + const { values, shape, slice: slice5, dtype, isPacked, texture } = texData; + if (dtype === "complex64") { + throw new Error("Does not support reading texture for complex64 dtype."); + } + if (slice5 != null) { + let program; + if (isPacked) { + program = new UnaryOpPackedProgram(shape, CLONE); + } else { + program = new UnaryOpProgram(shape, CLONE); + } + const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype); + const gpuResouorce = this.readToGPU(res, options); + this.disposeIntermediateTensorInfo(res); + return gpuResouorce; + } + if (texture == null) { + if (values != null) { + throw new Error("Data is not on GPU but on CPU."); + } else { + throw new Error("There is no data on GPU or CPU."); + } + } + const tmpTarget = this.decode(dataId, options.customTexShape); + const tensorRef = engine().makeTensorFromTensorInfo(tmpTarget); + const tmpData = this.texData.get(tmpTarget.dataId); + return Object.assign({ tensorRef }, tmpData.texture); + } + bufferSync(t) { + const data = this.readSync(t.dataId); + if (t.dtype === "string") { + try { + const strings = data.map((d) => util_exports.decodeString(d)); + return buffer(t.shape, t.dtype, strings); + } catch (_a) { + throw new Error("Failed to decode encoded string bytes into utf-8"); + } + } + return buffer(t.shape, t.dtype, data); + } + checkNumericalProblems(values) { + if (values == null) { + return; + } + for (let i = 0; i < values.length; i++) { + const num = values[i]; + if (!canBeRepresented(num)) { + if (env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")) { + throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`); + } + throw Error(`The value ${num} cannot be represented on this device.`); + } + } + } + getValuesFromTexture(dataId) { + const { shape, dtype, isPacked } = this.texData.get(dataId); + const size = util_exports.sizeFromShape(shape); + if (env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) { + const tmpTarget = this.decode(dataId); + const tmpData2 = this.texData.get(tmpTarget.dataId); + const vals2 = this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture.texture, ...getDenseTexShape(shape)).subarray(0, size); + this.disposeIntermediateTensorInfo(tmpTarget); + return vals2; + } + const shouldUsePackedProgram = env().getBool("WEBGL_PACK") && isPacked === true; + const outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape; + const program = shouldUsePackedProgram ? new EncodeFloatPackedProgram(outputShape) : new EncodeFloatProgram(outputShape); + const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], "float32"); + const tmpData = this.texData.get(output.dataId); + const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size); + this.disposeIntermediateTensorInfo(output); + return vals; + } + timerAvailable() { + return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0; + } + time(f) { + const oldActiveTimers = this.activeTimers; + const newActiveTimers = []; + let outerMostTime = false; + if (this.programTimersStack == null) { + this.programTimersStack = newActiveTimers; + outerMostTime = true; + } else { + this.activeTimers.push(newActiveTimers); + } + this.activeTimers = newActiveTimers; + f(); + const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null); + const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null); + this.activeTimers = oldActiveTimers; + if (outerMostTime) { + this.programTimersStack = null; + } + const res = { + uploadWaitMs: this.uploadWaitMs, + downloadWaitMs: this.downloadWaitMs, + kernelMs: null, + wallMs: null + // will be filled by the engine + }; + return (async () => { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { + const kernelMs = await Promise.all(flattenedActiveTimerQueries); + res["kernelMs"] = util_exports.sum(kernelMs); + res["getExtraProfileInfo"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", "); + } else { + res["kernelMs"] = { + error: "WebGL query timers are not supported in this environment." + }; + } + this.uploadWaitMs = 0; + this.downloadWaitMs = 0; + return res; + })(); + } + memory() { + return { + unreliable: false, + numBytesInGPU: this.numBytesInGPU, + numBytesInGPUAllocated: this.textureManager.numBytesAllocated, + numBytesInGPUFree: this.textureManager.numBytesFree + }; + } + startTimer() { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { + return this.gpgpu.beginQuery(); + } + return { startMs: util_exports.now(), endMs: null }; + } + endTimer(query) { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { + this.gpgpu.endQuery(); + return query; + } + query.endMs = util_exports.now(); + return query; + } + async getQueryTime(query) { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { + return this.gpgpu.waitForQueryAndGetTime(query); + } + const timerQuery = query; + return timerQuery.endMs - timerQuery.startMs; + } + /** + * Decrease the RefCount on the dataId and dispose the memory if the dataId + * has 0 refCount. If there are pending read on the data, the disposal would + * added to the pending delete queue. Return true if the dataId is removed + * from backend or the backend does not contain the dataId, false if the + * dataId is not removed. Memory may or may not be released even when dataId + * is removed, which also depends on dataRefCount, see `releaseGPU`. + * @param dataId + * @oaram force Optional, remove the data regardless of refCount + */ + disposeData(dataId, force = false) { + if (this.pendingDisposal.has(dataId)) { + return false; + } + if (!this.texData.has(dataId)) { + return true; + } + if (force) { + this.texData.get(dataId).refCount = 0; + } else { + this.texData.get(dataId).refCount--; + } + if (!force && this.texData.get(dataId).refCount > 0) { + return false; + } + if (this.pendingRead.has(dataId)) { + this.pendingDisposal.add(dataId); + this.pendingDeletes++; + return false; + } + this.releaseGPUData(dataId); + const { complexTensorInfos } = this.texData.get(dataId); + if (complexTensorInfos != null) { + this.disposeData(complexTensorInfos.real.dataId, force); + this.disposeData(complexTensorInfos.imag.dataId, force); + } + this.texData.delete(dataId); + return true; + } + releaseGPUData(dataId) { + const { texture, dtype, texShape, usage, isPacked, slice: slice5 } = this.texData.get(dataId); + const key = slice5 && slice5.origDataId || dataId; + const refCount = this.dataRefCount.get(key); + if (refCount > 1) { + this.dataRefCount.set(key, refCount - 1); + } else { + this.dataRefCount.delete(key); + if (texture != null) { + this.numBytesInGPU -= this.computeBytes(texShape, dtype); + this.textureManager.releaseTexture(texture, texShape, usage, isPacked); + } + } + const texData = this.texData.get(dataId); + texData.texture = null; + texData.texShape = null; + texData.isPacked = false; + texData.slice = null; + } + getTexture(dataId) { + this.uploadToGPU(dataId); + return this.texData.get(dataId).texture.texture; + } + /** + * Returns internal information for the specific data bucket. Used in unit + * tests. + */ + getDataInfo(dataId) { + return this.texData.get(dataId); + } + /* + Tests whether all the inputs to an op are small and on the CPU. This heuristic + determines when it would be faster to execute a kernel on the CPU. WebGL + kernels opt into running this check and forwarding when appropriate. + TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more + sustainable strategy for optimizing backend execution of ops. + */ + shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) { + return env().getBool("WEBGL_CPU_FORWARD") && inputs.every((input2) => this.texData.get(input2.dataId).texture == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold); + } + getGPGPUContext() { + return this.gpgpu; + } + where(condition) { + backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead"); + const condVals = condition.dataSync(); + return whereImpl3(condition.shape, condVals); + } + packedUnaryOp(x, op2, dtype) { + const program = new UnaryOpPackedProgram(x.shape, op2); + const outInfo = this.compileAndRun(program, [x], dtype); + return engine().makeTensorFromTensorInfo(outInfo); + } + // TODO(msoulanille) remove this once the backend has been modularized + // a copy is needed here to break a circular dependency. + // Also remove the op from unary_op. + abs(x) { + if (this.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") { + const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values); + return this.makeOutput(x.shape, x.dtype, outValues); + } + if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) { + return this.packedUnaryOp(x, ABS, x.dtype); + } + const program = new UnaryOpProgram(x.shape, ABS); + const outInfo = this.compileAndRun(program, [x]); + return engine().makeTensorFromTensorInfo(outInfo); + } + makeTensorInfo(shape, dtype, values) { + let dataId; + if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) { + const encodedValues = values.map((d) => util_exports.encodeString(d)); + dataId = this.write(encodedValues, shape, dtype); + } else { + dataId = this.write(values, shape, dtype); + } + this.texData.get(dataId).usage = null; + return { dataId, shape, dtype }; + } + makeOutput(shape, dtype, values) { + return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this); + } + unpackTensor(input2) { + const program = new UnpackProgram(input2.shape); + return this.runWebGLProgram(program, [input2], input2.dtype); + } + packTensor(input2) { + const program = new PackProgram(input2.shape); + const preventEagerUnpackingOutput = true; + return this.runWebGLProgram(program, [input2], input2.dtype, null, preventEagerUnpackingOutput); + } + packedReshape(input2, afterShape) { + const input3DShape = [ + getBatchDim(input2.shape), + ...getRowsCols(input2.shape) + ]; + const input3D = { + dtype: input2.dtype, + shape: input3DShape, + dataId: input2.dataId + }; + const afterShapeAs3D = [ + getBatchDim(afterShape), + ...getRowsCols(afterShape) + ]; + const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape); + const preventEagerUnpackingOfOutput = true; + const customValues = [input3DShape]; + const output = this.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput); + return { dataId: output.dataId, shape: afterShape, dtype: output.dtype }; + } + decode(dataId, customTexShape) { + const texData = this.texData.get(dataId); + const { isPacked, shape, dtype } = texData; + if (customTexShape != null) { + const size = util_exports.sizeFromShape(shape); + const texSize = customTexShape[0] * customTexShape[1] * 4; + util_exports.assert(size <= texSize, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."); + } + const shapeAs3D = getShapeAs3D(shape); + let program; + if (isPacked) { + program = new DecodeMatrixPackedProgram(shapeAs3D); + } else { + program = new DecodeMatrixProgram(shapeAs3D); + } + const preventEagerUnpackingOfOutput = true; + const customValues = [customTexShape != null ? customTexShape : getDenseTexShape(shapeAs3D)]; + const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, customValues, preventEagerUnpackingOfOutput, customTexShape); + return { dtype, shape, dataId: out.dataId }; + } + runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false, customTexShape) { + const output = this.makeTensorInfo(program.outputShape, outputDtype); + const outData = this.texData.get(output.dataId); + if (program.packedOutput) { + outData.isPacked = true; + } + if (program.outPackingScheme === PackingScheme.DENSE) { + const texelShape = customTexShape != null ? customTexShape : getDenseTexShape(program.outputShape); + outData.texShape = texelShape.map((d) => d * 2); + } + if (program.outTexUsage != null) { + outData.usage = program.outTexUsage; + } + if (util_exports.sizeFromShape(output.shape) === 0) { + outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0); + return output; + } + const dataToDispose = []; + const inputsData = inputs.map((input2) => { + if (input2.dtype === "complex64") { + throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`); + } + let texData = this.texData.get(input2.dataId); + if (texData.texture == null) { + if (!program.packedInputs && util_exports.sizeFromShape(input2.shape) <= env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM")) { + return { + shape: input2.shape, + texData: null, + isUniform: true, + uniformValues: texData.values + }; + } + if (program.packedInputs) { + texData.isPacked = true; + texData.shape = input2.shape; + } + } + this.uploadToGPU(input2.dataId); + if (!!texData.isPacked !== !!program.packedInputs) { + input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2); + dataToDispose.push(input2); + texData = this.texData.get(input2.dataId); + } else if (texData.isPacked && !isReshapeFree(texData.shape, input2.shape)) { + const savedInput = input2; + const targetShape = input2.shape; + input2.shape = texData.shape; + input2 = this.packedReshape(input2, targetShape); + dataToDispose.push(input2); + texData = this.texData.get(input2.dataId); + savedInput.shape = targetShape; + } + return { shape: input2.shape, texData, isUniform: false }; + }); + this.uploadToGPU(output.dataId); + const outputData = { shape: output.shape, texData: outData, isUniform: false }; + const key = makeShaderKey(program, inputsData, outputData); + const binary = this.getAndSaveBinary(key, () => { + return compileProgram(this.gpgpu, program, inputsData, outputData); + }); + const shouldTimeProgram = this.activeTimers != null; + let query; + if (shouldTimeProgram) { + query = this.startTimer(); + } + if (!env().get("ENGINE_COMPILE_ONLY")) { + runProgram(this.gpgpu, binary, inputsData, outputData, customUniformValues); + } + dataToDispose.forEach((info) => this.disposeIntermediateTensorInfo(info)); + if (shouldTimeProgram) { + query = this.endTimer(query); + this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) }); + } + const glFlushThreshold = env().getNumber("WEBGL_FLUSH_THRESHOLD"); + if (glFlushThreshold > 0) { + const time2 = util_exports.now(); + if (time2 - this.lastGlFlushTime > glFlushThreshold) { + this.gpgpu.gl.flush(); + this.lastGlFlushTime = time2; + } + } + if (!env().getBool("WEBGL_LAZILY_UNPACK") && outData.isPacked && preventEagerUnpackingOfOutput === false) { + const unpacked = this.unpackTensor(output); + this.disposeIntermediateTensorInfo(output); + return unpacked; + } + return output; + } + compileAndRun(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false) { + outputDtype = outputDtype || inputs[0].dtype; + const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput); + return outInfo; + } + getAndSaveBinary(key, getBinary) { + if (!(key in this.binaryCache)) { + this.binaryCache[key] = getBinary(); + } + return this.binaryCache[key]; + } + getTextureManager() { + return this.textureManager; + } + dispose() { + if (this.disposed) { + return; + } + if (!env().getBool("IS_TEST")) { + const allKeys = Object.keys(this.binaryCache); + allKeys.forEach((key) => { + this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram); + delete this.binaryCache[key]; + }); + } + this.textureManager.dispose(); + if (this.canvas != null && (typeof HTMLCanvasElement !== "undefined" && this.canvas instanceof HTMLCanvasElement)) { + this.canvas.remove(); + } else { + this.canvas = null; + } + if (this.gpgpuCreatedLocally) { + this.gpgpu.program = null; + this.gpgpu.dispose(); + } + this.disposed = true; + } + floatPrecision() { + if (this.floatPrecisionValue == null) { + this.floatPrecisionValue = tidy(() => { + if (!env().get("WEBGL_RENDER_FLOAT32_ENABLED")) { + const debugFlag = env().getBool("DEBUG"); + env().set("DEBUG", false); + const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0]; + env().set("DEBUG", debugFlag); + if (underflowCheckValue > 0) { + return 32; + } + } + return 16; + }); + } + return this.floatPrecisionValue; + } + /** Returns the smallest representable number. */ + epsilon() { + return this.floatPrecision() === 32 ? EPSILON_FLOAT322 : EPSILON_FLOAT162; + } + uploadToGPU(dataId) { + const texData = this.texData.get(dataId); + const { shape, dtype, values, texture, usage, isPacked } = texData; + if (texture != null) { + return; + } + const shouldTimeProgram = this.activeTimers != null; + let start; + if (shouldTimeProgram) { + start = util_exports.now(); + } + let texShape = texData.texShape; + if (texShape == null) { + texShape = getTextureShapeFromLogicalShape(shape, isPacked); + texData.texShape = texShape; + } + if (values != null) { + const shapeAs3D = getShapeAs3D(shape); + let program; + let width = texShape[1], height = texShape[0]; + const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray; + if (isPacked || !isByteArray) { + [width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]); + } + if (isPacked) { + program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray); + } else { + program = new EncodeMatrixProgram(shapeAs3D, isByteArray); + } + const tempDenseInputTexShape = isByteArray ? [height, width] : texShape; + const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype); + const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId); + if (isByteArray) { + tempDenseInputTexData.usage = TextureUsage.PIXELS; + } else { + tempDenseInputTexData.usage = TextureUsage.UPLOAD; + } + tempDenseInputTexData.texShape = tempDenseInputTexShape; + this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values); + const customValues = [[height, width]]; + const preventEagerUnpacking = true; + const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, customValues, preventEagerUnpacking); + const outputTexData = this.texData.get(encodedOutputTarget.dataId); + texData.texShape = outputTexData.texShape; + texData.isPacked = outputTexData.isPacked; + texData.usage = outputTexData.usage; + if (!env().get("ENGINE_COMPILE_ONLY")) { + texData.texture = outputTexData.texture; + texData.values = null; + this.texData.delete(encodedOutputTarget.dataId); + } else { + this.disposeData(encodedOutputTarget.dataId); + } + this.disposeIntermediateTensorInfo(tempDenseInputHandle); + if (shouldTimeProgram) { + this.uploadWaitMs += util_exports.now() - start; + } + } else { + const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked); + texData.texture = newTexture; + } + } + convertAndCacheOnCPU(dataId, float32Values) { + const texData = this.texData.get(dataId); + const { dtype } = texData; + if (float32Values != null) { + texData.values = float32ToTypedArray(float32Values, dtype); + } + return texData.values; + } + acquireTexture(texShape, texType, dtype, isPacked) { + this.numBytesInGPU += this.computeBytes(texShape, dtype); + if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) { + const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2); + this.warnedAboutMemory = true; + console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`); + } + return this.textureManager.acquireTexture(texShape, texType, isPacked); + } + computeBytes(shape, dtype) { + return shape[0] * shape[1] * util_exports.bytesPerElement(dtype); + } + checkCompileCompletion() { + for (const [, binary] of Object.entries(this.binaryCache)) { + this.checkCompletion_(binary); + } + } + async checkCompileCompletionAsync() { + const ps = []; + if (this.gpgpu.parallelCompilationExtension) { + for (const [, binary] of Object.entries(this.binaryCache)) { + ps.push(this.checkCompletionAsync_(binary)); + } + return Promise.all(ps); + } else { + for (const [, binary] of Object.entries(this.binaryCache)) { + const p2 = new Promise((resolve) => { + try { + this.checkCompletion_(binary); + resolve(true); + } catch (error) { + throw error; + } + }); + ps.push(p2); + } + return Promise.all(ps); + } + } + async checkCompletionAsync_(binary) { + if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) { + return this.checkCompletion_(binary); + } else { + await nextFrame(); + return this.checkCompletionAsync_(binary); + } + } + checkCompletion_(binary) { + if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) { + console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram)); + if (this.gpgpu.gl.getShaderParameter(binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) { + logShaderSourceAndInfoLog(binary.source, this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader)); + throw new Error("Failed to compile fragment shader."); + } + throw new Error("Failed to link vertex and fragment shaders."); + } + return true; + } + getUniformLocations() { + for (const binary of Object.values(this.binaryCache)) { + this.gpgpu.buildVao(binary.webGLProgram); + const { variablesLocations, customUniformLocations, infLoc, nanLoc, outShapeLocation, outShapeStridesLocation, outTexShapeLocation } = getUniformLocations(this.gpgpu, binary.program, binary.webGLProgram); + binary.variablesLocations = variablesLocations; + binary.customUniformLocations = customUniformLocations; + binary.infLoc = infLoc; + binary.nanLoc = nanLoc; + binary.outShapeLocation = outShapeLocation; + binary.outShapeStridesLocation = outShapeStridesLocation; + binary.outTexShapeLocation = outTexShapeLocation; + } + } + /** + * Create a TF.js tensor out of an existing WebGL texture. A new texture will + * be created. + */ + createTensorFromGPUData(values, shape, dtype) { + values.channels = values.channels || "RGBA"; + const { texture, height, width, channels } = values; + const backend2 = engine().backend; + if (!backend2.gpgpu.gl.isTexture(texture)) { + 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)'.`); + } + const dataId = backend2.writeTexture(texture, shape, dtype, height, width, channels); + return engine().makeTensorFromDataId(dataId, shape, dtype, backend2); + } +}; +MathBackendWebGL.nextDataId = 0; +function float32ToTypedArray(a, dtype) { + if (dtype === "float32" || dtype === "complex64") { + return a; + } else if (dtype === "int32" || dtype === "bool") { + const result = dtype === "int32" ? new Int32Array(a.length) : new Uint8Array(a.length); + for (let i = 0; i < result.length; ++i) { + result[i] = Math.round(a[i]); + } + return result; + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} +var version6 = "4.16.0"; +function forceHalfFloat() { + env().set("WEBGL_FORCE_F16_TEXTURES", true); +} +if (device_util_exports.isBrowser()) { + registerBackend( + "webgl", + () => new MathBackendWebGL(), + 2 + /* priority */ + ); +} +var webgl = { forceHalfFloat }; +var CHECK_NAN_SNIPPET2 = ` if (isnan(a)) return a; if (isnan(b)) return b; -`,ki=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` +`; +var BinaryOpProgram = class { + constructor(op2, aShape, bShape) { + this.variableNames = ["A", "B"]; + this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + this.userCode = ` float binaryOperation(float a, float b) { - ${e} + ${op2} } void main() { @@ -1254,44 +57390,80 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},Qo=` + `; + } +}; +var CHECK_NAN_SNIPPET_PACKED = ` 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; -`,hp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=vn(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=` +`; +var BinaryOpPackedProgram = class { + constructor(op2, aShape, bShape, checkOutOfBounds = false) { + this.variableNames = ["A", "B"]; + this.supportsBroadcasting = true; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + const rank = this.outputShape.length; + this.enableShapeUniforms = useShapeUniforms(rank); + let checkOutOfBoundsString = ""; + if (checkOutOfBounds) { + if (rank === 0 || util_exports.sizeFromShape(this.outputShape) === 1) { + checkOutOfBoundsString = ` result.y = 0.; result.z = 0.; result.w = 0.; - `;else if(s=` - ${ct(r)} coords = getOutputCoords(); - `,r===1)this.enableShapeUniforms?s+=` + `; + } else { + const dtype = getCoordsDataType(rank); + checkOutOfBoundsString = ` + ${dtype} coords = getOutputCoords(); + `; + if (rank === 1) { + if (this.enableShapeUniforms) { + checkOutOfBoundsString += ` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; result.w = 0.; - `:s+=` + `; + } else { + checkOutOfBoundsString += ` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; - `;else{let i=In("coords",r);this.enableShapeUniforms?s+=` + `; + } + } else { + const channels = getChannels("coords", rank); + if (this.enableShapeUniforms) { + checkOutOfBoundsString += ` bool nextRowOutOfBounds = - (${i[r-2]} + 1) >= outShape[${r} - 2]; + (${channels[rank - 2]} + 1) >= outShape[${rank} - 2]; bool nextColOutOfBounds = - (${i[r-1]} + 1) >= outShape[${r} - 1]; + (${channels[rank - 1]} + 1) >= outShape[${rank} - 1]; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; - `:s+=` + `; + } else { + checkOutOfBoundsString += ` bool nextRowOutOfBounds = - (${i[r-2]} + 1) >= ${this.outputShape[r-2]}; + (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]}; bool nextColOutOfBounds = - (${i[r-1]} + 1) >= ${this.outputShape[r-1]}; + (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]}; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; - `}this.userCode=` + `; + } + } + } + } + this.userCode = ` vec4 binaryOperation(vec4 a, vec4 b) { - ${e} + ${op2} } void main() { @@ -1299,41 +57471,259 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 b = getBAtOutCoords(); vec4 result = binaryOperation(a, b); - ${s} + ${checkOutOfBoundsString} 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 IQ={kernelName:eo,backendName:"webgl",kernelFunc:aa};function As(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 SQ={kernelName:km,backendName:"webgl",kernelFunc:As},aA="return (a < 0.) ? b * a : a;",rA=` + `; + } +}; +function identity3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + backend2.incRef(x.dataId); + return { dataId: x.dataId, shape: x.shape, dtype: x.dtype }; +} +var identityConfig2 = { + kernelName: Identity, + backendName: "webgl", + kernelFunc: identity3 +}; +function complex3(args) { + const { inputs, backend: backend2 } = args; + const { real: real4, imag: imag4 } = inputs; + const complexInfo = backend2.makeTensorInfo(real4.shape, "complex64"); + const complex4 = backend2.texData.get(complexInfo.dataId); + const realTensorInfo = identity3({ inputs: { x: real4 }, backend: backend2 }); + const imagTensorInfo = identity3({ inputs: { x: imag4 }, backend: backend2 }); + complex4.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo }; + return complexInfo; +} +var complexConfig2 = { + kernelName: Complex, + backendName: "webgl", + kernelFunc: complex3 +}; +var LEAKYRELU = `return (a < 0.) ? b * a : a;`; +var LEAKYRELU_PACKED = ` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function NQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(rA,r.shape,i.shape):new ki(aA,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var TQ={kernelName:ro,backendName:"webgl",kernelFunc:NQ},sA="return (a < 0.) ? b * a : a;",iA=` +`; +function leakyRelu3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { alpha } = attrs; + const $alpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(alpha, "float32")); + const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape); + const result = backend2.runWebGLProgram(program, [x, $alpha], "float32"); + backend2.disposeIntermediateTensorInfo($alpha); + return result; +} +var leakyReluConfig2 = { + kernelName: LeakyRelu, + backendName: "webgl", + kernelFunc: leakyRelu3 +}; +var PRELU = `return (a < 0.) ? b * a : a;`; +var PRELU_PACKED = ` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function CQ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(iA,a.shape,r.shape):new ki(sA,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var _Q={kernelName:wo,backendName:"webgl",kernelFunc:CQ},mp="if (isnan(x)) return x;";function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let 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 ns(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[v,I]=x,T={dataId:v.dataId,dtype:v.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},E=new ki(e,l.shape,u.shape);return p.runWebGLProgram(E,[T,C],ga(v.dtype,I.dtype))}),y=As({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),v=p.makeTensorInfo(x,d),I=p.texData.get(v.dataId);return I.values=y,v}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new hp(t,l.shape,u.shape,n):h=new ki(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Nc(e,t=!1){if(e==="linear")return t?oQ:nQ;if(e==="relu")return t?uQ:rQ;if(e==="elu")return t?lQ:aQ;if(e==="relu6")return t?pQ:sQ;if(e==="prelu")return t?iA:sA;if(e==="leakyrelu")return t?rA:aA;if(e==="sigmoid")return t?cQ:iQ;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 prelu4(args) { + const { inputs, backend: backend2 } = args; + const { x, alpha } = inputs; + const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) : new BinaryOpProgram(PRELU, x.shape, alpha.shape); + return backend2.runWebGLProgram(program, [x, alpha], "float32"); +} +var preluConfig2 = { + kernelName: Prelu, + backendName: "webgl", + kernelFunc: prelu4 +}; +var CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`; +function unaryKernelFunc2({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) { + return ({ inputs, backend: backend2 }) => { + const { x } = inputs; + const webglBackend = backend2; + const $dtype = dtype || x.dtype; + if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) { + const xData = webglBackend.texData.get(x.dataId); + const outValues = cpuKernelImpl(xData.values, $dtype); + return webglBackend.makeTensorInfo(x.shape, $dtype, outValues); + } + const shouldUsePackedProgram = env().getBool("WEBGL_PACK_UNARY_OPERATIONS") && packedOpSnippet != null; + let program; + if (shouldUsePackedProgram) { + program = new UnaryOpPackedProgram(x.shape, packedOpSnippet); + } else { + program = new UnaryOpProgram(x.shape, opSnippet); + } + return webglBackend.runWebGLProgram(program, [x], $dtype); + }; +} +function binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) { + return ({ inputs, backend: backend2 }) => { + const { a, b } = inputs; + const webglBackend = backend2; + if (supportsComplex && a.dtype === "complex64") { + const aData = webglBackend.texData.get(a.dataId); + const bData = webglBackend.texData.get(b.dataId); + const [real4, imag4] = [ + [aData.complexTensorInfos.real, bData.complexTensorInfos.real], + [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag] + ].map((complexParts) => { + const [aPart, bPart] = complexParts; + const aHandle = { + dataId: aPart.dataId, + dtype: aPart.dtype, + shape: a.shape + }; + const bHandle = { + dataId: bPart.dataId, + dtype: bPart.dtype, + shape: b.shape + }; + const program2 = new BinaryOpProgram(opSnippet, a.shape, b.shape); + return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype)); + }); + const complexOutput = complex3({ inputs: { real: real4, imag: imag4 }, backend: webglBackend }); + webglBackend.disposeIntermediateTensorInfo(real4); + webglBackend.disposeIntermediateTensorInfo(imag4); + return complexOutput; + } + const $dtype = dtype || upcastType(a.dtype, b.dtype); + if ((a.dtype === "string" || b.dtype === "string" || webglBackend.shouldExecuteOnCPU([a, b])) && cpuKernelImpl != null) { + const aVals = webglBackend.texData.get(a.dataId).values; + const bVals = webglBackend.texData.get(b.dataId).values; + const decodedAVals = a.dtype === "string" ? ( + // tslint:disable-next-line: no-any + backend_util_exports.fromUint8ToStringArray(aVals) + ) : aVals; + const decodedBVals = a.dtype === "string" ? ( + // tslint:disable-next-line: no-any + backend_util_exports.fromUint8ToStringArray(bVals) + ) : bVals; + const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype); + const out = webglBackend.makeTensorInfo(outShape, $dtype); + const outData = webglBackend.texData.get(out.dataId); + outData.values = outValues; + return out; + } + const shouldUsePackedProgram = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") && packedOpSnippet != null; + let program; + if (shouldUsePackedProgram) { + program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds); + } else { + program = new BinaryOpProgram(opSnippet, a.shape, b.shape); + } + return webglBackend.runWebGLProgram(program, [a, b], $dtype); + }; +} +function mapActivationToShaderProgram(activation2, packed = false) { + if (activation2 === "linear") { + if (packed) { + return LINEAR2; + } + return LINEAR; + } else if (activation2 === "relu") { + if (packed) { + return RELU2; + } + return RELU; + } else if (activation2 === "elu") { + if (packed) { + return ELU3; + } + return ELU2; + } else if (activation2 === "relu6") { + if (packed) { + return RELU62; + } + return RELU6; + } else if (activation2 === "prelu") { + if (packed) { + return PRELU_PACKED; + } + return PRELU; + } else if (activation2 === "leakyrelu") { + if (packed) { + return LEAKYRELU_PACKED; + } + return LEAKYRELU; + } else if (activation2 === "sigmoid") { + if (packed) { + return SIGMOID2; + } + return SIGMOID; + } + throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`); +} +var MatMulPackedProgram = class { + constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyreluActivation = false) { + this.variableNames = ["matrixA", "matrixB"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const sharedDim = transposeA ? aShape[1] : aShape[2]; + const sharedDimensionPacked = Math.ceil(sharedDim / 2); + const aSample = transposeA ? "i * 2, rc.y" : "rc.y, i * 2"; + const bSample = transposeB ? "rc.z, i * 2" : "i * 2, rc.z"; + const aSwizzle = transposeA ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"]; + const bSwizzle = transposeB ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"]; + let activationSnippet = "", applyActivationSnippet = ""; + if (activation2) { + if (hasPreluActivation) { + activationSnippet = `vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); - ${i} - }`:l?f=`vec4 activation(vec4 a) { + ${activation2} + }`; + } else if (hasLeakyreluActivation) { + activationSnippet = `vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); - ${i} - }`:f=`vec4 activation(vec4 x) { - ${i} - }`,g="result = activation(result);");let b=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let y="rc.x",x="rc.x";e[0]`The new shape (${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&&!Sc(r.shape,l)&&!(p.texture!==null&&Sc(p.shape,l))?AQ(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var FQ={kernelName:Ru,backendName:"webgl",kernelFunc:ce},nS=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${w.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` - if (inIdx < 0 || inIdx >= ${r}) { + `; + } +}; +var MUL = "return a * b;"; +function multiply3(args) { + const { inputs, backend: backend2 } = args; + const { a, b } = inputs; + const dtype = backend_util_exports.upcastType(a.dtype, b.dtype); + if (a.dtype === "complex64") { + const aData = backend2.texData.get(a.dataId); + const bData = backend2.texData.get(b.dataId); + const realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a.shape, b.shape); + const imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a.shape, b.shape); + const inputs2 = [ + { + dataId: aData.complexTensorInfos.real.dataId, + dtype: aData.complexTensorInfos.real.dtype, + shape: a.shape + }, + { + dataId: aData.complexTensorInfos.imag.dataId, + dtype: aData.complexTensorInfos.imag.dtype, + shape: a.shape + }, + { + dataId: bData.complexTensorInfos.real.dataId, + dtype: bData.complexTensorInfos.real.dtype, + shape: b.shape + }, + { + dataId: bData.complexTensorInfos.imag.dataId, + dtype: bData.complexTensorInfos.imag.dtype, + shape: b.shape + } + ]; + const realPart = backend2.runWebGLProgram(realProgram, inputs2, "float32"); + const imagPart = backend2.runWebGLProgram(imagProgram, inputs2, "float32"); + const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(imagPart); + return complexOutput; + } + if (backend2.shouldExecuteOnCPU([a, b])) { + const aData = backend2.texData.get(a.dataId); + const bData = backend2.texData.get(b.dataId); + const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype); + const out = backend2.makeTensorInfo(outShape, dtype); + const outData = backend2.texData.get(out.dataId); + outData.values = outValues; + return out; + } + let program; + if (env().getBool("WEBGL_PACK_BINARY_OPERATIONS")) { + program = new BinaryOpPackedProgram(MUL, a.shape, b.shape); + } else { + program = new BinaryOpProgram(MUL, a.shape, b.shape); + } + return backend2.runWebGLProgram(program, [a, b], dtype); +} +var multiplyConfig2 = { + kernelName: Multiply, + backendName: "webgl", + kernelFunc: multiply3 +}; +function packedReshape(input2, afterShape, backend2) { + const input3DShape = [ + getBatchDim(input2.shape), + ...getRowsCols(input2.shape) + ]; + const input3D = { + dtype: input2.dtype, + shape: input3DShape, + dataId: input2.dataId + }; + const afterShapeAs3D = [ + getBatchDim(afterShape), + ...getRowsCols(afterShape) + ]; + const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape); + const preventEagerUnpackingOfOutput = true; + const customValues = [input3DShape]; + const output = backend2.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput); + return { dataId: output.dataId, shape: afterShape, dtype: output.dtype }; +} +function reshape4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { shape } = attrs; + const webglBackend = backend2; + const xSize = util_exports.sizeFromShape(x.shape); + const $shape = util_exports.inferFromImplicitShape(shape, xSize); + const $xSize = util_exports.sizeFromShape($shape); + util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`); + const xTexData = webglBackend.texData.get(x.dataId); + if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) && !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) { + return packedReshape(x, $shape, webglBackend); + } + webglBackend.incRef(x.dataId); + return { dataId: x.dataId, shape: $shape, dtype: x.dtype }; +} +var reshapeConfig2 = { + kernelName: Reshape, + backendName: "webgl", + kernelFunc: reshape4 +}; +var MeanProgram = class { + constructor(reduceInfo, divisor) { + this.variableNames = ["x"]; + const { windowSize, batchSize, inSize, outSize } = reduceInfo; + this.outputShape = [batchSize, outSize]; + const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4; + const windowSizeVec4Remainder = windowSize % 4; + let updateSnippet = `sumValue += dot(values, ones);`; + if (divisor != null) { + const denominator = 1 / divisor; + updateSnippet = `sumValue += dot(values * ${util_exports.isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`; + } + let checkOutOfBounds = ""; + if (inSize % windowSize > 0) { + checkOutOfBounds = ` + if (inIdx < 0 || inIdx >= ${inSize}) { return 0.0; } - `),this.userCode=` + `; + } + this.userCode = ` const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float getValue(int batch, int inIdx) { - ${u} + ${checkOutOfBounds} return getX(batch, inIdx); } @@ -1377,11 +57899,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; - int inOffset = outIdx * ${n}; + int inOffset = outIdx * ${windowSize}; float sumValue = 0.0; - for (int i = 0; i < ${i}; i += 4) { + for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), @@ -1390,64 +57912,110 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getValue(batch, inIdx + 3) ); - ${l} + ${updateSnippet} } - int inIdx = inOffset + ${i}; - if (${o===1}) { + int inIdx = inOffset + ${windowSizeNearestVec4}; + if (${windowSizeVec4Remainder === 1}) { vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0); - ${l} - } else if (${o===2}) { + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), 0.0, 0.0); - ${l} - } else if (${o===3}) { + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), 0.0); - ${l} + ${updateSnippet} } setOutput(sumValue); } - `}},$Q=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"}) { + `; + } +}; +var ReduceProgram = class { + constructor(reduceInfo, reduceType) { + this.variableNames = ["x"]; + const { windowSize, batchSize, inSize, outSize } = reduceInfo; + this.outputShape = [batchSize, outSize]; + let initializationValue = "0.0"; + let compareOp = ``; + if (reduceType === "prod") { + initializationValue = "1.0"; + } else if (reduceType === "min") { + initializationValue = "1.0 / 1e-20"; + compareOp = `min`; + } else if (reduceType === "max") { + initializationValue = "-1.0 / 1e-20"; + compareOp = `max`; + } + let returnValue = `${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + if (reduceType === "sum") { + returnValue = `sumValue`; + } else if (reduceType === "prod") { + returnValue = `prodValue`; + } else if (reduceType === "all") { + returnValue = `allValue`; + } else if (reduceType === "any") { + returnValue = `anyValue`; + } + const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4; + const windowSizeVec4Remainder = windowSize % 4; + let updateSnippet = ` + if (${reduceType === "sum"}) { sumValue += dot(values, ones); - } else if (${t==="prod"}) { + } else if (${reduceType === "prod"}) { vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]); prodValue *= tmp[0] * tmp[1]; } else { - minMaxValue = ${o}(values, minMaxValue); - if (${t==="min"} || ${t==="max"}) { - minMaxValue = ${o}(values, minMaxValue); + minMaxValue = ${compareOp}(values, minMaxValue); + if (${reduceType === "min"} || ${reduceType === "max"}) { + minMaxValue = ${compareOp}(values, minMaxValue); bvec4 isNaN = isnan(values); if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) { minMaxValue = vec4(NAN); } } } - `,c="vec4";t==="all"?(i="1.0",d=` + `; + let vecType = `vec4`; + if (reduceType === "all") { + initializationValue = "1.0"; + updateSnippet = ` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); - `,c="bvec4"):t==="any"&&(i="0.0",d=` + `; + vecType = `bvec4`; + } else if (reduceType === "any") { + initializationValue = "0.0"; + updateSnippet = ` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); - `,c="bvec4");let h="";r%n>0&&(h=` - if (inIdx < 0 || inIdx >= ${r}) { + `; + vecType = `bvec4`; + } + let checkOutOfBounds = ""; + if (inSize % windowSize > 0) { + checkOutOfBounds = ` + if (inIdx < 0 || inIdx >= ${inSize}) { return initializationValue; } - `),this.userCode=` - const float initializationValue = ${i}; + `; + } + this.userCode = ` + const float initializationValue = ${initializationValue}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float getValue(int batch, int inIdx) { - ${h} + ${checkOutOfBounds} return getX(batch, inIdx); } @@ -1455,174 +58023,673 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; - int inOffset = outIdx * ${n}; + int inOffset = outIdx * ${windowSize}; - vec4 minMaxValue = vec4(${i}); + vec4 minMaxValue = vec4(${initializationValue}); float prodValue = 1.0; float sumValue = 0.0; float allValue = 1.0; float anyValue = 0.0; - for (int i = 0; i < ${u}; i += 4) { + for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) { int inIdx = inOffset + i; - ${c} values = ${c}( + ${vecType} values = ${vecType}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); - ${d} + ${updateSnippet} } - int inIdx = inOffset + ${u}; - if (${p===1}) { - ${c} values = ${c}( + int inIdx = inOffset + ${windowSizeNearestVec4}; + if (${windowSizeVec4Remainder === 1}) { + ${vecType} values = ${vecType}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); - ${d} - } else if (${p===2}) { - ${c} values = ${c}( + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 2}) { + ${vecType} values = ${vecType}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); - ${d} - } else if (${p===3}) { - ${c} values = ${c}( + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 3}) { + ${vecType} values = ${vecType}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); - ${d} + ${updateSnippet} } - setOutput(${l}); + setOutput(${returnValue}); } - `}};function DQ(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 el(e,t,n,a){let r=DQ(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=eA("rc",this.rank),s=new Array(this.rank);for(let u=0;u 6) { + throw Error(`Transpose for rank ${rank} is not yet supported`); + } + const originalOrder = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"]; + const switchedCoords = new Array(rank); + for (let i = 0; i < newDim.length; i++) { + switchedCoords[newDim[i]] = originalOrder[i]; + } + return switchedCoords.join(); +} +var TransposePackedProgram = class { + constructor(aShape, newDim) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + const outputShape = new Array(aShape.length); + for (let i = 0; i < outputShape.length; i++) { + outputShape[i] = aShape[newDim[i]]; + } + this.outputShape = outputShape; + this.rank = outputShape.length; + if (this.rank > 6) { + throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`); + } + const dtype = getCoordsDataType(this.rank); + const outputOrder = getVecChannels("rc", this.rank); + const switchedOrder = new Array(this.rank); + for (let i = 0; i < newDim.length; i++) { + switchedOrder[newDim[i]] = outputOrder[i]; + } + const innerDims = `vec2(${switchedOrder.slice(-2).join()})`; + const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`; + const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`; + this.userCode = ` + void main() { + ${dtype} rc = getOutputCoords(); vec4 result = vec4(0.); - result[0] = ${l}; - if(${o}) { - result[1] = ${l}; + result[0] = ${getc}; + if(${nextColumn}) { + result[1] = ${getc}; } - --${r[this.rank-1]}; - if(++${r[this.rank-2]} < ${n[this.rank-2]}) { - result[2] = ${l}; - if(${o}) { - result[3] = ${l}; + --${outputOrder[this.rank - 1]}; + if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) { + result[2] = ${getc}; + if(${nextColumn}) { + result[3] = ${getc}; } } setOutput(result); } - `}};function zf(e,t,n){let a=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PQ(e.shape,t):new RQ(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function OQ(e,t,n,a){let r=t,s=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=zf(e,l,a),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=N.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let m=w.sizeFromShape(c),f=w.sizeFromShape(e.shape)/m,g=ce({inputs:{x:p},attrs:{shape:[f,m]},backend:a}),b=Mm(e.dtype),y=el(g,b,"sum",a),x=ce({inputs:{x:y},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(y),u&&a.disposeIntermediateTensorInfo(p),x}function Wf(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return OQ(r,s,i,n)}var LQ={kernelName:Lo,backendName:"webgl",kernelFunc:Wf};function Sn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let p=0;p`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let v=n?[b,d,h]:[b,h,d],I=a?[y,m,c]:[y,c,m],T=ce({inputs:{x:e},backend:r,attrs:{shape:v}}),C=ce({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[T,C],F=Math.max(b,y),D=n?T.shape[1]:T.shape[2],$=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?Nc(l,!0):null,U=$||S||M||B!=null,H;if((h===1||m===1)&&D>lA&&U===!1){let K=T,Z=C;n&&(K=Sn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(Z=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=ce({inputs:{x:K},backend:r,attrs:{shape:[F,D,1]}}),E.push(ae));let te=m===1?2:1,re=Z;ee&&(re=ce({inputs:{x:Z},backend:r,attrs:{shape:[F,1,D]}}),E.push(re));let ie=nk({inputs:{a:ae,b:re},backend:r});H=Wf({inputs:{x:ie},backend:r,attrs:{axis:te,keepDims:!0}}),E.push(ie)}else{let K=ga(e.dtype,t.dtype),Z=new oA(v,I,[F,h,m],n,a,$,B,S,M),J=[T,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",w.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 WQ(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 mm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var BQ={kernelName:ii,backendName:"webgl",kernelFunc:WQ},aS="return abs(x);";function VQ(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 ns(a.shape,aS):r=new rr(a.shape,aS),n.runWebGLProgram(r,[a],a.dtype)}var UQ={kernelName:Yl,backendName:"webgl",kernelFunc:VQ},GQ=Ma+` + `; + } +}; +function transposeImpl2(x, perm, backend2) { + const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new TransposePackedProgram(x.shape, perm) : new TransposeProgram(x.shape, perm); + return backend2.runWebGLProgram(program, [x], x.dtype); +} +function sumImpl(x, axis, keepDims, backend2) { + const reductionIndices = axis; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + const sumInputIsTransposed = permutedAxes != null; + let sumInput = x; + if (sumInputIsTransposed) { + sumInput = transposeImpl2(x, permutedAxes, backend2); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank); + const [sumOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(sumInput.shape, axes); + let outShape = sumOutShape; + if (keepDims) { + outShape = backend_util_exports.expandShapeToKeepDim(sumOutShape, origAxes); + } + const inSize = util_exports.sizeFromShape(reduceShape); + const xSize = util_exports.sizeFromShape(x.shape); + const batchSize = xSize / inSize; + const reshapedInput = reshape4({ inputs: { x: sumInput }, attrs: { shape: [batchSize, inSize] }, backend: backend2 }); + const outType = sumOutType(x.dtype); + const reduced = reduce(reshapedInput, outType, "sum", backend2); + const out = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(reshapedInput); + backend2.disposeIntermediateTensorInfo(reduced); + if (sumInputIsTransposed) { + backend2.disposeIntermediateTensorInfo(sumInput); + } + return out; +} +function sum4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + return sumImpl(x, axis, keepDims, backend2); +} +var sumConfig2 = { + kernelName: Sum, + backendName: "webgl", + kernelFunc: sum4 +}; +function transpose3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { perm } = attrs; + const webglBackend = backend2; + const xRank = x.shape.length; + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[perm[i]]; + } + let out; + if (webglBackend.shouldExecuteOnCPU([x])) { + const xTexData = webglBackend.texData.get(x.dataId); + const values = xTexData.values; + const outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape); + out = webglBackend.makeTensorInfo(newShape, x.dtype); + const outData = webglBackend.texData.get(out.dataId); + outData.values = outValues; + } else { + out = transposeImpl2(x, perm, webglBackend); + } + return out; +} +var transposeConfig2 = { + kernelName: Transpose, + backendName: "webgl", + kernelFunc: transpose3 +}; +var MATMUL_SHARED_DIM_THRESHOLD = 1e3; +function batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) { + const aRank = a.shape.length; + const bRank = b.shape.length; + const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1]; + const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2]; + const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2]; + const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1]; + const outerDimsA = a.shape.slice(0, -2); + const outerDimsB = b.shape.slice(0, -2); + const batchDimA = util_exports.sizeFromShape(outerDimsA); + const batchDimB = util_exports.sizeFromShape(outerDimsB); + const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); + const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA]; + const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB]; + const a3d = reshape4({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } }); + const b3d = reshape4({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } }); + const intermediates = [a3d, b3d]; + const batchDim = Math.max(batchDimA, batchDimB); + const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2]; + const hasBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + const hasLeakyreluAlpha = activation2 === "leakyrelu"; + const fusedActivation = activation2 != null ? mapActivationToShaderProgram(activation2, true) : null; + const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null; + let out; + if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) { + let aVec = a3d; + let bVec = b3d; + if (transposeA) { + aVec = transpose3({ inputs: { x: a3d }, backend: backend2, attrs: { perm: [0, 2, 1] } }); + intermediates.push(aVec); + } + if (transposeB) { + bVec = transpose3({ inputs: { x: b3d }, backend: backend2, attrs: { perm: [0, 2, 1] } }); + intermediates.push(bVec); + } + const shouldReshapeA = outerShapeB !== 1; + const shouldReshapeB = outerShapeB === 1; + let aVec3d = aVec; + if (shouldReshapeA) { + aVec3d = reshape4({ + inputs: { x: aVec }, + backend: backend2, + attrs: { shape: [batchDim, sharedDim, 1] } + }); + intermediates.push(aVec3d); + } + const axis = outerShapeB === 1 ? 2 : 1; + let bVec3d = bVec; + if (shouldReshapeB) { + bVec3d = reshape4({ + inputs: { x: bVec }, + backend: backend2, + attrs: { shape: [batchDim, 1, sharedDim] } + }); + intermediates.push(bVec3d); + } + const product = multiply3({ inputs: { a: aVec3d, b: bVec3d }, backend: backend2 }); + out = sum4({ inputs: { x: product }, backend: backend2, attrs: { axis, keepDims: true } }); + intermediates.push(product); + } else { + const dtype = upcastType(a.dtype, b.dtype); + const program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + const inputs = [a3d, b3d]; + if (bias != null) { + inputs.push(bias); + } + if (hasPreluActivationWeights) { + inputs.push(preluActivationWeights); + } + if (hasLeakyreluAlpha) { + const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32")); + inputs.push($leakyreluAlpha); + intermediates.push($leakyreluAlpha); + } + out = backend2.runWebGLProgram(program, inputs, dtype); + } + const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } }); + intermediates.push(out); + for (const i of intermediates) { + backend2.disposeIntermediateTensorInfo(i); + } + return outReshaped; +} +function _fusedMatMul2(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b, bias, preluActivationWeights } = inputs; + const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs; + return batchMatMulImpl({ + a, + b, + transposeA, + transposeB, + backend: backend2, + bias, + preluActivationWeights, + leakyreluAlpha, + activation: activation2 + }); +} +var _fusedMatMulConfig2 = { + kernelName: _FusedMatMul, + backendName: "webgl", + kernelFunc: _fusedMatMul2 +}; +var ABS2 = `return abs(x);`; +function abs3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (backend2.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") { + const xData = backend2.texData.get(x.dataId); + const outValues = simpleAbsImplCPU(xData.values); + return backend2.makeTensorInfo(x.shape, x.dtype, outValues); + } + let program; + if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) { + program = new UnaryOpPackedProgram(x.shape, ABS2); + } else { + program = new UnaryOpProgram(x.shape, ABS2); + } + return backend2.runWebGLProgram(program, [x], x.dtype); +} +var absConfig2 = { + kernelName: Abs, + backendName: "webgl", + kernelFunc: abs3 +}; +var ACOS = CHECK_NAN_SNIPPET + ` if (abs(x) > 1.) { return NAN; } return acos(x); -`,HQ=Ze({opSnippet:GQ}),qQ={kernelName:Ni,backendName:"webgl",kernelFunc:HQ},jQ=Ma+` +`; +var acos3 = unaryKernelFunc2({ opSnippet: ACOS }); +var acosConfig2 = { + kernelName: Acos, + backendName: "webgl", + kernelFunc: acos3 +}; +var ACOSH = CHECK_NAN_SNIPPET + ` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,KQ=Ze({opSnippet:jQ}),XQ={kernelName:Ti,backendName:"webgl",kernelFunc:KQ},rS="return a + b;",YQ=mn({opSnippet:rS,packedOpSnippet:rS,supportsComplex:!0,cpuKernelImpl:r9}),ZQ={kernelName:ys,backendName:"webgl",kernelFunc:YQ},JQ=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${r}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`; +var acosh3 = unaryKernelFunc2({ opSnippet: ACOSH }); +var acoshConfig2 = { + kernelName: Acosh, + backendName: "webgl", + kernelFunc: acosh3 +}; +var ADD = "return a + b;"; +var addKernelFunc = binaryKernelFunc2({ + opSnippet: ADD, + packedOpSnippet: ADD, + supportsComplex: true, + cpuKernelImpl: addImplCPU +}); +var addConfig2 = { + kernelName: Add, + backendName: "webgl", + kernelFunc: addKernelFunc +}; +var AddNProgram = class { + constructor(outputShape, shapes) { + this.outputShape = []; + this.outputShape = outputShape; + this.variableNames = shapes.map((_, i) => `T${i}`); + const snippets = []; + this.variableNames.forEach((variable2) => { + snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`); + }); + const operation = this.variableNames.map((variable2) => { + return `v${variable2}`; + }).join(" + "); + this.userCode = ` void main() { - ${n.join(` - `)} + ${snippets.join("\n ")} - float result = ${a}; + float result = ${operation}; setOutput(result); } - `}},QQ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` + `; + } +}; +var AddNPackedProgram = class { + constructor(outputShape, shapes) { + this.outputShape = []; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = outputShape; + this.variableNames = shapes.map((_, i) => `T${i}`); + const snippets = []; + this.variableNames.forEach((variable2) => { + snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`); + }); + const operation = this.variableNames.map((variable2) => { + return `v${variable2}`; + }).join(" + "); + this.userCode = ` void main() { - ${n.join(` - `)} + ${snippets.join("\n ")} - vec4 result = ${a}; + vec4 result = ${operation}; setOutput(result); } - `}};function Bh(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=Bh({inputs:a.slice(0,o),backend:n}),u=Bh({inputs:a.slice(o),backend:n});return Bh({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 QQ(a[0].shape,s):new JQ(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var eee={kernelName:Ci,backendName:"webgl",kernelFunc:Bh};function tee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.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=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=el(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 nee={kernelName:Zl,backendName:"webgl",kernelFunc:tee};function aee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.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=w.sizeFromShape(h),f=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=el(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 ree={kernelName:Jl,backendName:"webgl",kernelFunc:aee},see=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 addN3(args) { + const { inputs, backend: backend2 } = args; + const tensors = inputs; + if (tensors.length === 1) { + return identity3({ inputs: { x: tensors[0] }, backend: backend2 }); + } + if (tensors.length > env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) { + const midIndex = Math.floor(tensors.length / 2); + const leftSide = addN3({ inputs: tensors.slice(0, midIndex), backend: backend2 }); + const rightSide = addN3({ inputs: tensors.slice(midIndex), backend: backend2 }); + return addN3({ inputs: [leftSide, rightSide], backend: backend2 }); + } + const dtype = tensors.map((t) => t.dtype).reduce((d1, d2) => upcastType(d1, d2)); + const shapes = tensors.map((t) => t.shape); + const usePackedOp = env().getBool("WEBGL_PACK"); + const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes); + return backend2.runWebGLProgram(program, tensors, dtype); +} +var addNConfig2 = { + kernelName: AddN, + backendName: "webgl", + kernelFunc: addN3 +}; +function all3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let permutedX = x; + if (permutedAxes != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("all", axes, xRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes); + const inSize = util_exports.sizeFromShape(reduceShape); + const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } }); + const reduced = reduce(a2D, a2D.dtype, "all", backend2); + let res; + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } }); + } else { + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } }); + } + backend2.disposeIntermediateTensorInfo(a2D); + backend2.disposeIntermediateTensorInfo(reduced); + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo(permutedX); + } + return res; +} +var allConfig2 = { + kernelName: All, + backendName: "webgl", + kernelFunc: all3 +}; +function any3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let permutedX = x; + if (permutedAxes != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("any", axes, xRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes); + const inSize = util_exports.sizeFromShape(reduceShape); + const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } }); + const reduced = reduce(a2D, a2D.dtype, "any", backend2); + let res; + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } }); + } else { + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } }); + } + backend2.disposeIntermediateTensorInfo(a2D); + backend2.disposeIntermediateTensorInfo(reduced); + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo(permutedX); + } + return res; +} +var anyConfig2 = { + kernelName: Any, + backendName: "webgl", + kernelFunc: any3 +}; +var ArgMinMaxProgram = class { + constructor(reduceInfo, op2, firstPass) { + this.variableNames = ["A"]; + const { windowSize, batchSize, outSize } = reduceInfo; + if (!firstPass) { + this.variableNames.push("bestIndicesA"); + } + this.outputShape = [batchSize, outSize]; + const compOp = op2 === "max" ? ">" : "<"; + const indexSnippet = firstPass ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));"; + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; - int inOffset = outIdx * ${a}; + int inOffset = outIdx * ${windowSize}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); - for (int i = 0; i < ${a}; i++) { - int inIdx = ${o}; + for (int i = 0; i < ${windowSize}; i++) { + int inIdx = ${indexSnippet}; float candidate = getA(batch, inIdx); - if (candidate ${i} bestValue) { + if (candidate ${compOp} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } - `}},iee=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${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); - ++${u[o-2]}; - ${C} sourceLocA = ${C}(${u.join()}, 0); - --${u[o-1]}; - ${C} sourceLocB = ${C}(${u.join()}, 0); - --${u[o-2]};`}else d=o,p=` - ${l} sourceLocR = coords; - ++${u[o-1]}; - ${l} sourceLocG = coords; - ++${u[o-2]}; - ${l} sourceLocA = coords; - --${u[o-1]}; - ${l} sourceLocB = coords; - --${u[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,d),h="."+c[d-1],m=c.map(C=>"int "+C),f=In("sourceLocR",d-1).concat("inIdx.r"),g=In("sourceLocG",d-1).concat("inIdx.g"),b=In("sourceLocB",d-1).concat("inIdx.b"),y=In("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":` - inIdx = round(vec4(getBestIndicesAChannel(${f.join()}), - getBestIndicesAChannel(${g.join()}), - getBestIndicesAChannel(${b.join()}), - getBestIndicesAChannel(${y.join()})));`,I=`vec4( - getAChannel(${f.join()}), - hasNextCol ? getAChannel(${g.join()}) : 0., - hasNextRow ? getAChannel(${b.join()}) : 0., - hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,T=a?"":` - float getBestIndicesAChannel(${m.join()}) { - return getChannel(getBestIndicesA(${c.join()}), - vec2(${c.slice(-2).join()})); - }`;this.userCode=` - float getAChannel(${m.join()}) { - return getChannel(getA(${c.join()}), - vec2(${c.slice(-2).join()})); + `; + } +}; +var ArgMinMaxPackedProgram = class { + constructor(shape, windowSize, op2, firstPass) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + util_exports.assert(shape.length > 2, () => `Packed arg${op2.charAt(0).toUpperCase() + op2.slice(1)} supports only inputs with rank above 2.`); + const inSize = shape[shape.length - 1]; + const outSize = Math.ceil(inSize / windowSize); + this.outputShape = shape.slice(0, -1); + if (outSize > 1) { + this.outputShape.push(outSize); + } + if (!firstPass) { + this.variableNames.push("bestIndicesA"); + } + const outShape = this.outputShape; + const rank = outShape.length; + const dtype = getCoordsDataType(rank); + const coords2 = getChannels("coords", rank); + let sourceLocSetup; + let sourceRank; + if (outSize === 1) { + sourceRank = rank + 1; + const sourceLocDType = getCoordsDataType(sourceRank); + sourceLocSetup = ` + ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords2.join()}, 0); + ++${coords2[rank - 1]}; + ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords2.join()}, 0); + ++${coords2[rank - 2]}; + ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords2.join()}, 0); + --${coords2[rank - 1]}; + ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords2.join()}, 0); + --${coords2[rank - 2]};`; + } else { + sourceRank = rank; + sourceLocSetup = ` + ${dtype} sourceLocR = coords; + ++${coords2[rank - 1]}; + ${dtype} sourceLocG = coords; + ++${coords2[rank - 2]}; + ${dtype} sourceLocA = coords; + --${coords2[rank - 1]}; + ${dtype} sourceLocB = coords; + --${coords2[rank - 2]};`; + } + const channels = ["x", "y", "z", "w", "u", "v"].slice(0, sourceRank); + const inChannel = "." + channels[sourceRank - 1]; + const intChannels = channels.map((x) => "int " + x); + const srcRCoords = getChannels("sourceLocR", sourceRank - 1).concat("inIdx.r"); + const srcGCoords = getChannels("sourceLocG", sourceRank - 1).concat("inIdx.g"); + const srcBCoords = getChannels("sourceLocB", sourceRank - 1).concat("inIdx.b"); + const srcACoords = getChannels("sourceLocA", sourceRank - 1).concat("inIdx.a"); + const compOp = op2 === "max" ? "greaterThan" : "lessThan"; + const fetchCandidateIdx = firstPass ? "" : ` + inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}), + getBestIndicesAChannel(${srcGCoords.join()}), + getBestIndicesAChannel(${srcBCoords.join()}), + getBestIndicesAChannel(${srcACoords.join()})));`; + const fetchValue = `vec4( + getAChannel(${srcRCoords.join()}), + hasNextCol ? getAChannel(${srcGCoords.join()}) : 0., + hasNextRow ? getAChannel(${srcBCoords.join()}) : 0., + hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`; + const getBestIndicesAChannelSnippet = firstPass ? "" : ` + float getBestIndicesAChannel(${intChannels.join()}) { + return getChannel(getBestIndicesA(${channels.join()}), + vec2(${channels.slice(-2).join()})); + }`; + this.userCode = ` + float getAChannel(${intChannels.join()}) { + return getChannel(getA(${channels.join()}), + vec2(${channels.slice(-2).join()})); } - ${T} + ${getBestIndicesAChannelSnippet} void main() { - ${l} coords = getOutputCoords(); - bool hasNextCol = ${u[o-1]} < ${i[o-1]-1}; - bool hasNextRow = ${u[o-2]} < ${i[o-2]-1}; - ${p} - ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h}, - sourceLocB${h}, sourceLocA${h}) * ${t}; + ${dtype} coords = getOutputCoords(); + bool hasNextCol = ${coords2[rank - 1]} < ${outShape[rank - 1] - 1}; + bool hasNextRow = ${coords2[rank - 2]} < ${outShape[rank - 2] - 1}; + ${sourceLocSetup} + ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel}, + sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); - vec4 bestValue = ${I}; + vec4 bestValue = ${fetchValue}; - for (int i = 0; i < ${t}; i++) { + for (int i = 0; i < ${windowSize}; i++) { inIdx = srcIdx; - ${v} - vec4 candidate = ${I}; + ${fetchCandidateIdx} + vec4 candidate = ${fetchValue}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( - vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); + vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); bestValue = vec4(replace.x ? candidate.x : bestValue.x, replace.y ? candidate.y : bestValue.y, @@ -1633,27 +58700,197 @@ return log(x + sqrt(x * x - 1.0));`,KQ=Ze({opSnippet:jQ}),XQ={kernelName:Ti,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 see(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 iee(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=w.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 oee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.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=cA(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var lee={kernelName:Ql,backendName:"webgl",kernelFunc:oee};function uee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.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=cA(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var pee={kernelName:eu,backendName:"webgl",kernelFunc:uee},cee=Ma+` + `; + } +}; +function argReduce(backend2, x, reduceType, bestIndicesA = null) { + let batchSize = x.shape[0]; + let inSize = x.shape[1]; + if (bestIndicesA != null) { + batchSize = bestIndicesA.shape[0]; + inSize = bestIndicesA.shape[1]; + } + const windowSize = backend_util_exports.computeOptimalWindowSize(inSize); + const reduceInfo = { windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize) }; + const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null); + const inputs = [x]; + if (bestIndicesA != null) { + inputs.push(bestIndicesA); + } + const output = backend2.runWebGLProgram(program, inputs, "int32"); + if (output.shape[1] === 1) { + return output; + } + const result = argReduce(backend2, x, reduceType, output); + backend2.disposeIntermediateTensorInfo(output); + return result; +} +function argReducePacked(backend2, x, reduceType, bestIndicesA = null) { + const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape; + const inSize = inShape[inShape.length - 1]; + const windowSize = backend_util_exports.computeOptimalWindowSize(inSize); + const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null); + const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA]; + const output = backend2.runWebGLProgram(program, inputs, "int32"); + if (output.shape.length === x.shape.length) { + const result = argReducePacked(backend2, x, reduceType, output); + backend2.disposeIntermediateTensorInfo(output); + return result; + } + return output; +} +function argMinMaxReduce(backend2, x, axis, reduceType) { + const axes = [axis]; + backend_util_exports.assertAxesAreInnerMostDims("arg" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length); + if (!env().getBool("WEBGL_PACK_REDUCE") || x.shape.length <= 2) { + const intermediateTensorInfos = []; + const xtexData = backend2.texData.get(x.dataId); + const xIsPacked = xtexData !== null && xtexData.isPacked; + let xUnPacked = x; + if (xIsPacked) { + xUnPacked = backend2.unpackTensor(x); + intermediateTensorInfos.push(xUnPacked); + } + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xUnPacked.shape, axes); + const inSize = util_exports.sizeFromShape(reduceShape); + const a2D = reshape4({ inputs: { x: xUnPacked }, backend: backend2, attrs: { shape: [-1, inSize] } }); + intermediateTensorInfos.push(a2D); + const reduced = argReduce(backend2, a2D, reduceType); + intermediateTensorInfos.push(reduced); + const reshaped = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } }); + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return reshaped; + } + return argReducePacked(backend2, x, reduceType); +} +function argMax3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis } = attrs; + let axes = util_exports.parseAxisParam(axis, x.shape); + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + const intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("argMax", [axes[0]], $x.shape.length); + const out = argMinMaxReduce(backend2, $x, axes[0], "max"); + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return out; +} +var argMaxConfig2 = { + kernelName: ArgMax, + backendName: "webgl", + kernelFunc: argMax3 +}; +function argMin3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis } = attrs; + let axes = util_exports.parseAxisParam(axis, x.shape); + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + const intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("argMin", [axes[0]], $x.shape.length); + const out = argMinMaxReduce(backend2, $x, axes[0], "min"); + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return out; +} +var argMinConfig2 = { + kernelName: ArgMin, + backendName: "webgl", + kernelFunc: argMin3 +}; +var ASIN = CHECK_NAN_SNIPPET + ` if (abs(x) > 1.) { return NAN; } return asin(x); -`,dee=Ze({opSnippet:cee}),hee={kernelName:_i,backendName:"webgl",kernelFunc:dee},mee=Ma+"return log(x + sqrt(x * x + 1.0));",fee=Ze({opSnippet:mee}),gee={kernelName:Ei,backendName:"webgl",kernelFunc:fee},bee=Ma+` +`; +var asin3 = unaryKernelFunc2({ opSnippet: ASIN }); +var asinConfig2 = { + kernelName: Asin, + backendName: "webgl", + kernelFunc: asin3 +}; +var ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`; +var asinh3 = unaryKernelFunc2({ opSnippet: ASINH }); +var asinhConfig2 = { + kernelName: Asinh, + backendName: "webgl", + kernelFunc: asinh3 +}; +var ATAN = CHECK_NAN_SNIPPET + ` return atan(x); -`,yee=Ze({opSnippet:bee}),xee={kernelName:Ai,backendName:"webgl",kernelFunc:yee},vee=tk+` +`; +var atan4 = unaryKernelFunc2({ opSnippet: ATAN }); +var atanConfig2 = { + kernelName: Atan, + backendName: "webgl", + kernelFunc: atan4 +}; +var ATAN2 = CHECK_NAN_SNIPPET2 + ` return atan(a, b); -`,wee=` +`; +var ATAN2_PACKED = ` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - `+Qo+` + ` + CHECK_NAN_SNIPPET_PACKED + ` return result; -`,kee=mn({opSnippet:vee,packedOpSnippet:wee}),Iee={kernelName:$i,backendName:"webgl",kernelFunc:kee},See=Ma+` +`; +var atan23 = binaryKernelFunc2({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED }); +var atan2Config2 = { + kernelName: Atan2, + backendName: "webgl", + kernelFunc: atan23 +}; +var ATANH = CHECK_NAN_SNIPPET + ` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernelName:Fi,backendName:"webgl",kernelFunc:Nee},Tc=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}); +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`; +var atanh3 = unaryKernelFunc2({ opSnippet: ATANH }); +var atanhConfig2 = { + kernelName: Atanh, + backendName: "webgl", + kernelFunc: atanh3 +}; +var Pool2DProgram = class { + constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) { + this.variableNames = ["x"]; + if (poolType === "avg" && computePositions) { + throw new Error("Cannot compute positions for average pool."); + } + const filterWidth = convInfo.filterWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + this.outputShape = convInfo.outShape; + const isAvgPool = poolType === "avg"; + const batchFlattenPositionStr = `((batch * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`; + const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`; + let initializationValue = "0.0"; + if (!isAvgPool) { + initializationValue = "-1.0 / 1e-20"; + } + if (computePositions) { + const compareOp2 = ">="; + this.userCode = ` + const ivec2 strides = ivec2(${strideHeight}, ${strideWidth}); + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); @@ -1671,19 +58908,19 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel int minMaxPosition = 0; float avgValue = 0.0; - for (int wR = 0; wR < ${p}; - wR += ${l}) { + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { int xR = xRCorner + wR; - if (xR < 0 || xR >= ${e.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${d}; - wC += ${u}) { + for (int wC = 0; wC < ${effectiveFilterWidth}; + wC += ${dilationWidth}) { int xC = xCCorner + wC; - if (xC < 0 || xC >= ${e.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } @@ -1693,31 +58930,42 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${C} currMinMaxValue) { + if (value ${compareOp2} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; - minMaxPosition = ${a?r?f:g:`wR * ${d} + wC`}; + minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`}; } } } setOutput(float(minMaxPosition)); } - `;return}let y="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / max(count, 1.0)");let v=Math.floor(s/4)*4,I=s%4,T=` - if (${m}) { + `; + return; + } + const compareOp = "max"; + let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + if (poolType === "avg") { + returnValue = `avgValue / max(count, 1.0)`; + } + const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4; + const filterWidthVec4Remainder = filterWidth % 4; + const updateSnippet = ` + if (${isAvgPool}) { avgValue += dot(values, ones); } else { - minMaxValue = ${y}(values, minMaxValue); + minMaxValue = ${compareOp}(values, minMaxValue); } - `;this.userCode=` - const ivec2 strides = ivec2(${i}, ${o}); - const ivec2 pads = ivec2(${c}, ${h}); - const float initializationValue = ${b}; + `; + this.userCode = ` + const ivec2 strides = ivec2(${strideHeight}, ${strideWidth}); + const ivec2 pads = ivec2(${padTop}, ${padLeft}); + const float initializationValue = ${initializationValue}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xR, int xC, int d) { - if (xC < 0 || xC >= ${e.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { return initializationValue; } count += 1.0; @@ -1735,33 +58983,33 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined - vec4 minMaxValue = vec4(${b}); + vec4 minMaxValue = vec4(${initializationValue}); float avgValue = 0.0; count = 0.0; - for (int wR = 0; wR < ${p}; - wR += ${l}) { + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { int xR = xRCorner + wR; - if (xR < 0 || xR >= ${e.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${v}; wC += 4) { - int xC = xCCorner + wC * ${u}; + for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) { + int xC = xCCorner + wC * ${dilationWidth}; vec4 values = vec4( getValue(batch, xR, xC, d), - getValue(batch, xR, xC + ${u}, d), - getValue(batch, xR, xC + 2 * ${u}, d), - getValue(batch, xR, xC + 3 * ${u}, d) + getValue(batch, xR, xC + ${dilationWidth}, d), + getValue(batch, xR, xC + 2 * ${dilationWidth}, d), + getValue(batch, xR, xC + 3 * ${dilationWidth}, d) ); - ${T} + ${updateSnippet} } - int xC = xCCorner + ${v}; - if (${I===1}) { + int xC = xCCorner + ${filterWidthNearestVec4}; + if (${filterWidthVec4Remainder === 1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, @@ -1769,33 +59017,63 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel initializationValue ); - ${T} - } else if (${I===2}) { + ${updateSnippet} + } else if (${filterWidthVec4Remainder === 2}) { vec4 values = vec4( getValue(batch, xR, xC, d), - getValue(batch, xR, xC + ${u}, d), + getValue(batch, xR, xC + ${dilationWidth}, d), initializationValue, initializationValue ); - ${T} - } else if (${I===3}) { + ${updateSnippet} + } else if (${filterWidthVec4Remainder === 3}) { vec4 values = vec4( getValue(batch, xR, xC, d), - getValue(batch, xR, xC + ${u}, d), - getValue(batch, xR, xC + 2 * ${u}, d), + getValue(batch, xR, xC + ${dilationWidth}, d), + getValue(batch, xR, xC + 2 * ${dilationWidth}, d), initializationValue ); - ${T} + ${updateSnippet} } } - setOutput(${x}); + setOutput(${returnValue}); } - `}},ak=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let F=">=";this.userCode=` + `; + } +}; +var Pool3DProgram = class { + constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) { + this.variableNames = ["x"]; + if (poolType === "avg" && computePositions) { + throw new Error("Cannot compute positions for average pool."); + } + const filterWidth = convInfo.filterWidth; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = convInfo.padInfo.front; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + this.outputShape = convInfo.outShape; + const isAvgPool = poolType === "avg"; + let initializationValue = "0.0"; + if (!isAvgPool) { + initializationValue = "-1.0 / 1e-20"; + } + if (computePositions) { + const compareOp2 = ">="; + this.userCode = ` const ivec3 strides = - ivec3(${i}, ${o}, ${l}); - const ivec3 pads = ivec3(${f}, ${g}, ${b}); + ivec3(${strideDepth}, ${strideHeight}, ${strideWidth}); + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); void main() { ivec5 coords = getOutputCoords(); @@ -1813,27 +59091,27 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel float minMaxValueFound = 0.0; int minMaxPosition = 0; - for (int wD = 0; wD < ${c}; - wD += ${u}) { + for (int wD = 0; wD < ${effectiveFilterDepth}; + wD += ${dilationDepth}) { int xD = xDCorner + wD; - if (xD < 0 || xD >= ${e.inDepth}) { + if (xD < 0 || xD >= ${convInfo.inDepth}) { continue; } - for (int wR = 0; wR < ${h}; - wR += ${p}) { + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { int xR = xRCorner + wR; - if (xR < 0 || xR >= ${e.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${m}; - wC += ${d}) { + for (int wC = 0; wC < ${effectiveFilterWidth}; + wC += ${dilationWidth}) { int xC = xCCorner + wC; - if (xC < 0 || xC >= ${e.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } @@ -1843,34 +59121,45 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${F} currMinMaxValue) { + if (value ${compareOp2} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; - minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} + - wR * ${m} + wC`}; + minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} + + wR * ${effectiveFilterWidth} + wC`}; } } } } setOutput(float(minMaxPosition)); } - `;return}let v="max",I=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(I="avgValue / max(count, 1.0)");let T=Math.floor(s/4)*4,C=s%4,E=` - if (${y}) { + `; + return; + } + const compareOp = "max"; + let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + if (poolType === "avg") { + returnValue = `avgValue / max(count, 1.0)`; + } + const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4; + const filterWidthVec4Remainder = filterWidth % 4; + const updateSnippet = ` + if (${isAvgPool}) { avgValue += dot(values, ones); } else { - minMaxValue = ${v}(values, minMaxValue); + minMaxValue = ${compareOp}(values, minMaxValue); } - `;this.userCode=` + `; + this.userCode = ` const ivec3 strides = - ivec3(${i}, ${o}, ${l}); - const ivec3 pads = ivec3(${f}, ${g}, ${b}); - const float initializationValue = ${x}; + ivec3(${strideDepth}, ${strideHeight}, ${strideWidth}); + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); + const float initializationValue = ${initializationValue}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xD, int xR, int xC, int ch) { - if (xC < 0 || xC >= ${e.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { return initializationValue; } count += 1.0; @@ -1889,41 +59178,41 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined - vec4 minMaxValue = vec4(${x}); + vec4 minMaxValue = vec4(${initializationValue}); float avgValue = 0.0; count = 0.0; - for (int wD = 0; wD < ${c}; - wD += ${u}) { + for (int wD = 0; wD < ${effectiveFilterDepth}; + wD += ${dilationDepth}) { int xD = xDCorner + wD; - if (xD < 0 || xD >= ${e.inDepth}) { + if (xD < 0 || xD >= ${convInfo.inDepth}) { continue; } - for (int wR = 0; wR < ${h}; - wR += ${p}) { + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { int xR = xRCorner + wR; - if (xR < 0 || xR >= ${e.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${T}; wC += 4) { - int xC = xCCorner + wC * ${d}; + for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) { + int xC = xCCorner + wC * ${dilationWidth}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), - getValue(batch, xD, xR, xC + ${d}, ch), - getValue(batch, xD, xR, xC + 2 * ${d}, ch), - getValue(batch, xD, xR, xC + 3 * ${d}, ch) + getValue(batch, xD, xR, xC + ${dilationWidth}, ch), + getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch), + getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch) ); - ${E} + ${updateSnippet} } - int xC = xCCorner + ${T}; - if (${C===1}) { + int xC = xCCorner + ${filterWidthNearestVec4}; + if (${filterWidthVec4Remainder === 1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, @@ -1931,33 +59220,84 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel initializationValue ); - ${E} - } else if (${C===2}) { + ${updateSnippet} + } else if (${filterWidthVec4Remainder === 2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), - getValue(batch, xD, xR, xC + ${d}, ch), + getValue(batch, xD, xR, xC + ${dilationWidth}, ch), initializationValue, initializationValue ); - ${E} - } else if (${C===3}) { + ${updateSnippet} + } else if (${filterWidthVec4Remainder === 3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), - getValue(batch, xD, xR, xC + ${d}, ch), - getValue(batch, xD, xR, xC + 2 * ${d}, ch), + getValue(batch, xD, xR, xC + ${dilationWidth}, ch), + getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch), initializationValue ); - ${E} + ${updateSnippet} } } } - setOutput(${I}); + setOutput(${returnValue}); } - `}};function Cee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;lp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.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&&w.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Tc(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var _ee={kernelName:Di,backendName:"webgl",kernelFunc:Cee};function Eee(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 Aee={kernelName:tu,backendName:"webgl",kernelFunc:Eee},Fee=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}); + `; + } +}; +function avgPool3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + assertNotComplex2(x, "avgPool"); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = 1; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) { + return identity3({ inputs: { x }, backend: backend2 }); + } + const avgPoolProgram = new Pool2DProgram(convInfo, "avg", false); + return backend2.runWebGLProgram(avgPoolProgram, [x], "float32"); +} +var avgPoolConfig2 = { + kernelName: AvgPool, + backendName: "webgl", + kernelFunc: avgPool3 +}; +function avgPool3D2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs; + const dilations = [1, 1, 1]; + const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat); + const avgPoolProgram = new Pool3DProgram(convInfo, "avg", false); + return backend2.runWebGLProgram(avgPoolProgram, [x], "float32"); +} +var avgPool3DConfig2 = { + kernelName: AvgPool3D, + backendName: "webgl", + kernelFunc: avgPool3D2 +}; +var AvgPool2DBackpropProgram = class { + constructor(convInfo) { + this.variableNames = ["dy"]; + this.outputShape = convInfo.inShape; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const avgMultiplier = 1 / (filterHeight * filterWidth); + this.userCode = ` + const ivec2 pads = ivec2(${padTop}, ${padLeft}); + const float avgMultiplier = float(${avgMultiplier}); void main() { ivec4 coords = getOutputCoords(); @@ -1971,20 +59311,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${o}; - wR += ${s}) { - float dyR = float(dyRCorner + wR) / ${a}.0; + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - for (int wC = 0; wC < ${l}; - wC+= ${i}) { - float dyC = float(dyCCorner + wC) / ${r}.0; + for (int wC = 0; wC < ${effectiveFilterWidth}; + wC+= ${dilationWidth}) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } @@ -1997,9 +59337,32 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(dotProd); } - `}},$ee=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}); + `; + } +}; +var AvgPool3DBackpropProgram = class { + constructor(convInfo) { + this.variableNames = ["dy"]; + this.outputShape = convInfo.inShape; + const filterDepth = convInfo.filterDepth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth); + this.userCode = ` + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); + const float avgMultiplier = float(${avgMultiplier}); void main() { ivec5 coords = getOutputCoords(); @@ -2016,30 +59379,30 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wD = 0; wD < ${p}; - wD += ${o}) { - float dyD = float(dyDCorner + wD) / ${r}.0; + for (int wD = 0; wD < ${effectiveFilterDepth}; + wD += ${dilationDepth}) { + float dyD = float(dyDCorner + wD) / ${strideDepth}.0; - if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { + if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); - for (int wR = 0; wR < ${d}; - wR += ${l}) { - float dyR = float(dyRCorner + wR) / ${s}.0; + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - for (int wC = 0; wC < ${c}; - wC += ${u}) { - float dyC = float(dyCCorner + wC) / ${i}.0; + for (int wC = 0; wC < ${effectiveFilterWidth}; + wC += ${dilationWidth}) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } @@ -2053,77 +59416,465 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(dotProd); } - `}};function Dee(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 $ee(c);return n.runWebGLProgram(h,[r],i.dtype)}var Ree={kernelName:$c,backendName:"webgl",kernelFunc:Dee};function Mee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;lp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=new Fee(p);return n.runWebGLProgram(d,[r],i.dtype)}var Pee={kernelName:Fc,backendName:"webgl",kernelFunc:Mee};function Oee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return mm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Lee={kernelName:Ri,backendName:"webgl",kernelFunc:Oee},zee=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 avgPool3DGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const x = input2; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = [1, 1, 1]; + const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo); + return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype); +} +var avgPool3DGradConfig3 = { + kernelName: AvgPool3DGrad, + backendName: "webgl", + kernelFunc: avgPool3DGrad2 +}; +function avgPoolGrad3(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const x = input2; + assertNotComplex2([dy, input2], "avgPoolGrad"); + const { filterSize, strides, pad: pad3 } = attrs; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3); + const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo); + return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype); +} +var avgPoolGradConfig3 = { + kernelName: AvgPoolGrad, + backendName: "webgl", + kernelFunc: avgPoolGrad3 +}; +function batchMatMul2(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b } = inputs; + const { transposeA, transposeB } = attrs; + return batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2 }); +} +var batchMatMulConfig2 = { + kernelName: BatchMatMul, + backendName: "webgl", + kernelFunc: batchMatMul2 +}; +var BatchNormProgram = class { + constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) { + this.outputShape = []; + this.variableNames = ["x", "mean", "variance"]; + backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape); + backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape); + let offsetSnippet = "0.0"; + if (offsetShape != null) { + backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape); + this.variableNames.push("offset"); + offsetSnippet = "getOffsetAtOutCoords()"; + } + let scaleSnippet = "1.0"; + if (scaleShape != null) { + backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape); + this.variableNames.push("scale"); + scaleSnippet = "getScaleAtOutCoords()"; + } + this.outputShape = xShape; + this.userCode = ` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); - float offset = ${i}; - float scale = ${o}; - float inv = scale * inversesqrt(variance + float(${s})); + float offset = ${offsetSnippet}; + float scale = ${scaleSnippet}; + float inv = scale * inversesqrt(variance + float(${varianceEpsilon})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},Wee=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=` + `; + } +}; +var BatchNormPackedProgram = class { + constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) { + this.packedInputs = true; + this.packedOutput = true; + this.variableNames = ["x", "mean", "variance"]; + backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape); + backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape); + let offsetSnippet = "vec4(0.0)"; + if (offsetShape != null) { + backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape); + this.variableNames.push("offset"); + offsetSnippet = "getOffsetAtOutCoords()"; + } + let scaleSnippet = "vec4(1.0)"; + if (scaleShape != null) { + backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape); + this.variableNames.push("scale"); + scaleSnippet = "getScaleAtOutCoords()"; + } + this.outputShape = xShape; + this.userCode = ` void main() { - vec4 offset = ${i}; - vec4 scale = ${o}; + vec4 offset = ${offsetSnippet}; + vec4 scale = ${scaleSnippet}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); - vec4 inv = scale * inversesqrt(variance + vec4(${s})); + vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon})); setOutput((x - mean) * inv + offset); } - `}},Bee=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new Wee(a.shape,r.shape,s.shape,p,d,l):new zee(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},Vee={kernelName:Ji,backendName:"webgl",kernelFunc:Bee},Uee=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ct(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Gee(this.rank),a,r=e.map((s,i)=>`sourceLoc.${lv[i]} = start[${i}] + coords.${lv[i]};`);a=` - ${t} sourceLoc; - ${t} coords = getOutputCoords(); - ${r.join(` -`)} - `,this.userCode=` + `; + } +}; +var batchNorm3 = ({ inputs, backend: backend2, attrs }) => { + const { x, mean: mean4, variance, offset, scale: scale22 } = inputs; + util_exports.assert(mean4.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."); + util_exports.assert(offset == null || mean4.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."); + util_exports.assert(scale22 == null || mean4.shape.length === scale22.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); + let { varianceEpsilon } = attrs; + if (varianceEpsilon == null) { + varianceEpsilon = 1e-3; + } + const finalInputs = [x, mean4, variance]; + let offsetShape = null; + if (offset != null) { + offsetShape = offset.shape; + finalInputs.push(offset); + } + let scaleShape = null; + if (scale22 != null) { + scaleShape = scale22.shape; + finalInputs.push(scale22); + } + const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new BatchNormPackedProgram(x.shape, mean4.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean4.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon); + const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype); + return output; +}; +var batchNormConfig2 = { + kernelName: FusedBatchNorm, + backendName: "webgl", + kernelFunc: batchNorm3 +}; +var SliceProgram = class { + constructor(destSize) { + this.variableNames = ["source"]; + this.outputShape = destSize; + this.rank = destSize.length; + const dtype = getCoordsDataType(this.rank); + this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; + const sourceCoords = getCoords(this.rank); + let body; + const coordSum = destSize.map((_, i) => { + return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`; + }); + body = ` + ${dtype} sourceLoc; + ${dtype} coords = getOutputCoords(); + ${coordSum.join("\n")} + `; + this.userCode = ` void main() { - ${a} - setOutput(getSource(${n})); + ${body} + setOutput(getSource(${sourceCoords})); } - `}},lv=["x","y","z","w","u","v"];function Gee(e){if(e===1)return"sourceLoc";if(e<=6)return lv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Hee=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ct(this.rank),n=In("coords",this.rank),a=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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]}; - result.y = ${s}; - --${a[this.rank-1]}; + `; + } +}; +var coords = ["x", "y", "z", "w", "u", "v"]; +function getCoords(rank) { + if (rank === 1) { + return "sourceLoc"; + } else if (rank <= 6) { + return coords.slice(0, rank).map((x) => "sourceLoc." + x).join(","); + } else { + throw Error(`Slicing for rank ${rank} is not yet supported`); + } +} +var SlicePackedProgram = class { + constructor(destSize) { + this.variableNames = ["source"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = destSize; + this.rank = destSize.length; + this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; + const dtype = getCoordsDataType(this.rank); + const coords2 = getChannels("coords", this.rank); + const sourceLoc = getChannels("sourceLoc", this.rank); + const innerDims = this.rank === 1 ? "sourceLoc" : `vec2(${sourceLoc.slice(-2).join()})`; + const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`; + const upperRow = ` + result.x = ${getChannel}; + if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) { + ++${sourceLoc[this.rank - 1]}; + result.y = ${getChannel}; + --${sourceLoc[this.rank - 1]}; } - `,o=this.rank===1?"":` - --${n[this.rank-1]}; - if (++${n[this.rank-2]} < ${e[this.rank-2]}) { - ++${a[this.rank-2]}; - result.z = ${s}; - if (++${n[this.rank-1]} < ${e[this.rank-1]}) { - ++${a[this.rank-1]}; - result.w = ${s}; + `; + const lowerRow = this.rank === 1 ? "" : ` + --${coords2[this.rank - 1]}; + if (++${coords2[this.rank - 2]} < ${destSize[this.rank - 2]}) { + ++${sourceLoc[this.rank - 2]}; + result.z = ${getChannel}; + if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) { + ++${sourceLoc[this.rank - 1]}; + result.w = ${getChannel}; } } - `,l=this.rank<=4?`sourceLoc = coords + - ${t}(${e.map((u,p)=>`start[${p}]`).join()});`:e.map((u,p)=>`${a[p]} = ${n[p]} + start[${p}];`).join(` -`);this.userCode=` + `; + const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords + + ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` : destSize.map((_, i) => `${sourceLoc[i]} = ${coords2[i]} + start[${i}];`).join("\n"); + this.userCode = ` void main() { - ${t} coords = getOutputCoords(); - ${t} sourceLoc; - ${l} + ${dtype} coords = getOutputCoords(); + ${dtype} sourceLoc; + ${sourceLocSetup} vec4 result = vec4(0.); - ${i} - ${o} + ${upperRow} + ${lowerRow} setOutput(result); } - `}};function qee(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=Xt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function fp(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Xt.parseSliceParams(r,s,i);if(Xt.assertParamsValid(r,o,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=P9(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Xt.isSliceContinous(r.shape,o,l);if(u||!p){let d=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Hee(l):new Uee(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),qee(r,o,l,n)}var jee={kernelName:Bu,backendName:"webgl",kernelFunc:fp},Kee=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;w.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=fp({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},Xee={kernelName:nu,backendName:"webgl",kernelFunc:Kee};function Yee(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 Zee={kernelName:au,backendName:"webgl",kernelFunc:Yee},Jee=` + `; + } +}; +function shallowSlice(x, begin, size, backend2) { + const xTexData = backend2.texData.get(x.dataId); + const t = backend2.makeTensorInfo(size, x.dtype); + const newTexData = backend2.texData.get(t.dataId); + Object.assign(newTexData, xTexData); + newTexData.refCount = 1; + newTexData.shape = size; + newTexData.dtype = x.dtype; + let flatOffset = slice_util_exports.computeFlatOffset(begin, util_exports.computeStrides(x.shape)); + if (xTexData.slice) { + flatOffset += xTexData.slice.flatOffset; + } + newTexData.slice = { + flatOffset, + // Point to the original dataId, which is used to do ref counting. + origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId + }; + const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1; + backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1); + return t; +} +function slice3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { begin, size } = attrs; + const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size); + slice_util_exports.assertParamsValid(x, $begin, $size); + if (util_exports.sizeFromShape($size) === 0) { + return backend2.makeTensorInfo($size, x.dtype, []); + } + if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string") { + const xTexData = backend2.texData.get(x.dataId); + const outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype); + return backend2.makeTensorInfo($size, x.dtype, outValues); + } + const { isPacked } = backend2.texData.get(x.dataId); + const isContinous = slice_util_exports.isSliceContinous(x.shape, $begin, $size); + if (isPacked || !isContinous) { + const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new SlicePackedProgram($size) : new SliceProgram($size); + const customValues = [$begin]; + return backend2.runWebGLProgram(program, [x], x.dtype, customValues); + } + backend2.uploadToGPU(x.dataId); + return shallowSlice(x, $begin, $size, backend2); +} +var sliceConfig2 = { + kernelName: Slice, + backendName: "webgl", + kernelFunc: slice3 +}; +var batchToSpaceND3 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, crops } = attrs; + util_exports.assert(x.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"); + const prod5 = blockShape.reduce((a, b) => a * b); + const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5); + const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length); + const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5); + const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length); + const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length); + const toDispose = []; + const reshapedIntermediate = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } }); + const transposedIntermediate = transpose3({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } }); + const reshapedIntermediate2 = reshape4({ + inputs: { x: transposedIntermediate }, + backend: backend2, + attrs: { shape: reshapedPermuted } + }); + const sliced = slice3({ + inputs: { x: reshapedIntermediate2 }, + backend: backend2, + attrs: { begin: sliceBeginCoords, size: sliceSize } + }); + toDispose.push(reshapedIntermediate); + toDispose.push(transposedIntermediate); + toDispose.push(reshapedIntermediate2); + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return sliced; +}; +var batchToSpaceNDConfig2 = { + kernelName: BatchToSpaceND, + backendName: "webgl", + kernelFunc: batchToSpaceND3 +}; +function bincount3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, weights } = inputs; + const { size } = attrs; + const xVals = backend2.readSync(x.dataId); + const weightsVals = backend2.readSync(weights.dataId); + const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size); + return backend2.makeTensorInfo([size], weights.dtype, outVals); +} +var bincountConfig2 = { + kernelName: Bincount, + backendName: "webgl", + kernelFunc: bincount3 +}; +var BITWISEAND = ` int r = int(a.r) & int(b.r); int g = int(a.g) & int(b.g); int rb = int(a.b) & int(b.b); int ra = int(a.a) & int(b.a); return vec4(r, g, rb, ra); -`,Qee=` +`; +var BITWISEAND_UNPACKED = ` return float(int(a.r) & int(b.r)); -`;function ete(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=G().getNumber("WEBGL_VERSION");if(n.shouldExecuteOnCPU([a,r])||i===1){let l=n.texData.get(a.dataId).values,u=n.texData.get(r.dataId).values,[p,d]=i9(a.shape,r.shape,l,u,a.dtype),c=n.makeTensorInfo(d,a.dtype),h=n.texData.get(c.dataId);return h.values=p,c}let o;return s?o=new hp(Jee,a.shape,r.shape,!1):o=new ki(Qee,a.shape,r.shape),n.runWebGLProgram(o,[a,r],a.dtype)}var tte={kernelName:ru,backendName:"webgl",kernelFunc:ete};function nte(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 ate={kernelName:Dc,backendName:"webgl",kernelFunc:nte},rte="return float(a != b);",dA=mn({opSnippet:rte,cpuKernelImpl:C9,dtype:"bool"}),ste={kernelName:_u,backendName:"webgl",kernelFunc:dA};function Fd(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 ite={kernelName:Dm,backendName:"webgl",kernelFunc:Fd},ote="return float(int(x));";function lte(e,t){let n=new rr(e.shape,ote),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 aa({inputs:{x:r},backend:n});let i=Nt(r.shape),o=uv({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=As({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Fd({inputs:{input:r},backend:n}),o=uv({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!w.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]=o9(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return lte(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",w.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 ute={kernelName:Mi,backendName:"webgl",kernelFunc:uv},sS="return ceil(x);",pte=Ze({opSnippet:sS,packedOpSnippet:sS,cpuKernelImpl:l9}),cte={kernelName:Pi,backendName:"webgl",kernelFunc:pte},dte=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` +`; +function bitwiseAnd3(args) { + const { inputs, backend: backend2 } = args; + const { a, b } = inputs; + const shouldUsePackedProgram = env().getBool("WEBGL_PACK_BINARY_OPERATIONS"); + const versionNumber = env().getNumber("WEBGL_VERSION"); + if (backend2.shouldExecuteOnCPU([a, b]) || versionNumber === 1) { + const aVals = backend2.texData.get(a.dataId).values; + const bVals = backend2.texData.get(b.dataId).values; + const [outValues, outShape] = bitwiseAndImplCPU(a.shape, b.shape, aVals, bVals, a.dtype); + const out = backend2.makeTensorInfo(outShape, a.dtype); + const outData = backend2.texData.get(out.dataId); + outData.values = outValues; + return out; + } + let program; + if (shouldUsePackedProgram) { + program = new BinaryOpPackedProgram(BITWISEAND, a.shape, b.shape, false); + } else { + program = new BinaryOpProgram(BITWISEAND_UNPACKED, a.shape, b.shape); + } + return backend2.runWebGLProgram(program, [a, b], a.dtype); +} +var bitwiseAndConfig2 = { + kernelName: BitwiseAnd, + backendName: "webgl", + kernelFunc: bitwiseAnd3 +}; +function broadcastArgs3(args) { + const { inputs, backend: backend2 } = args; + const { s0, s1 } = inputs; + const s0Vals = backend2.readSync(s0.dataId); + const s1Vals = backend2.readSync(s1.dataId); + const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals)); + return backend2.makeTensorInfo([broadcastShape.length], "int32", Int32Array.from(broadcastShape)); +} +var broadcastArgsConfig2 = { + kernelName: BroadcastArgs, + backendName: "webgl", + kernelFunc: broadcastArgs3 +}; +var NOT_EQUAL = `return float(a != b);`; +var notEqual3 = binaryKernelFunc2({ opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: "bool" }); +var notEqualConfig2 = { + kernelName: NotEqual, + backendName: "webgl", + kernelFunc: notEqual3 +}; +function real3(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const inputData = backend2.texData.get(input2.dataId); + return identity3({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 }); +} +var realConfig2 = { + kernelName: Real, + backendName: "webgl", + kernelFunc: real3 +}; +var TO_INT = `return float(int(x));`; +function int(input2, backend2) { + const program = new UnaryOpProgram(input2.shape, TO_INT); + const output = backend2.runWebGLProgram(program, [input2], "int32"); + return { dataId: output.dataId, shape: output.shape, dtype: output.dtype }; +} +function cast4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dtype } = attrs; + if (dtype === "complex64") { + if (x.dtype === "complex64") { + return identity3({ inputs: { x }, backend: backend2 }); + } + const zerosTensor = zeros(x.shape); + const floatX = cast4({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } }); + const result = complex3({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 }); + zerosTensor.dispose(); + backend2.disposeIntermediateTensorInfo(floatX); + return result; + } + if (x.dtype === "complex64") { + const realPart = real3({ inputs: { input: x }, backend: backend2 }); + const result = cast4({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } }); + backend2.disposeIntermediateTensorInfo(realPart); + return result; + } + if (!util_exports.hasEncodingLoss(x.dtype, dtype)) { + const result = identity3({ inputs: { x }, backend: backend2 }); + return { dataId: result.dataId, shape: result.shape, dtype }; + } + if (backend2.shouldExecuteOnCPU([x])) { + const values = backend2.texData.get(x.dataId).values; + const [resultShape, resultType, resultData] = castImplCPU(values, x.shape, x.dtype, dtype); + return backend2.makeTensorInfo(resultShape, resultType, resultData); + } + if (dtype === "int32") { + return int(x, backend2); + } + if (dtype === "bool") { + const zerosTensorInfo = backend2.makeTensorInfo([], "bool", util_exports.getTypedArrayFromDType("bool", 1)); + const binaryInputs = { a: x, b: zerosTensorInfo }; + const result = notEqual3({ inputs: binaryInputs, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(zerosTensorInfo); + return result; + } + throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`); +} +var castConfig2 = { + kernelName: Cast, + backendName: "webgl", + kernelFunc: cast4 +}; +var CEIL = `return ceil(x);`; +var ceil3 = unaryKernelFunc2({ opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU }); +var ceilConfig2 = { + kernelName: Ceil, + backendName: "webgl", + kernelFunc: ceil3 +}; +var ClipProgram = class { + constructor(aShape) { + this.variableNames = ["A"]; + this.customUniforms = [ + { name: "minVal", type: "float" }, + { name: "maxVal", type: "float" } + ]; + this.outputShape = aShape; + this.userCode = ` void main() { float value = getAAtOutCoords(); @@ -2134,7 +59885,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel setOutput(clamp(value, minVal, maxVal)); } - `}},hte=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=` + `; + } +}; +var ClipPackedProgram = class { + constructor(aShape) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [ + { name: "minVal", type: "float" }, + { name: "maxVal", type: "float" } + ]; + this.outputShape = aShape; + this.userCode = ` void main() { vec4 value = getAAtOutCoords(); @@ -2145,7 +59909,32 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function mte(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 hte(r.shape):o=new dte(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var fte={kernelName:xs,backendName:"webgl",kernelFunc:mte},gte=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `; + } +}; +function clipByValue3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { clipValueMin, clipValueMax } = attrs; + let program; + if (env().getBool("WEBGL_PACK_CLIP")) { + program = new ClipPackedProgram(x.shape); + } else { + program = new ClipProgram(x.shape); + } + const customValues = [[clipValueMin], [clipValueMax]]; + return backend2.runWebGLProgram(program, [x], x.dtype, customValues); +} +var clipByValueConfig2 = { + kernelName: ClipByValue, + backendName: "webgl", + kernelFunc: clipByValue3 +}; +var ComplexAbsProgram = class { + constructor(shape) { + this.variableNames = ["real", "imag"]; + this.outputShape = shape; + this.userCode = ` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2158,96 +59947,339 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function iS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function bte(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new gte(a.shape),i=[iS(a,r.complexTensorInfos.real),iS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var yte={kernelName:Rc,backendName:"webgl",kernelFunc:bte},xte=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}`); + const offsets = new Array(shapes.length - 1); + offsets[0] = shapes[0][1]; + for (let i = 1; i < offsets.length; i++) { + offsets[i] = offsets[i - 1] + shapes[i][1]; + } + const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`]; + for (let i = 1; i < offsets.length; i++) { + const shift = offsets[i - 1]; + snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`); + } + const lastIndex = offsets.length; + const lastShift = offsets[offsets.length - 1]; + snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`); + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int yR = coords.x; int yC = coords.y; - ${n.join(` - `)} + ${snippets.join("\n ")} } - `}},vte=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ct(a),s=In("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m `T${i}`); + const offsets = new Array(shapes.length - 1); + offsets[0] = shapes[0][axis]; + for (let i = 1; i < offsets.length; i++) { + offsets[i] = offsets[i - 1] + shapes[i][axis]; + } + const channel = channels[axis]; + const lastChannels = channels.slice(-2); + const allChannels = channels.join(); + let getValueSnippet = `if (${channel} < ${offsets[0]}) { return getChannel( - getT0(${p}), vec2(${u.join()})); - }`;for(let m=1;m= ${o[m-1]}) { + getT0(${allChannels}), vec2(${lastChannels.join()})); + }`; + for (let i = 1; i < offsets.length; i++) { + const shift2 = offsets[i - 1]; + getValueSnippet += ` + if (${channel} < ${offsets[i]} && ${channel} >= ${offsets[i - 1]}) { return getChannel( - getT${m}(${$h(i,l,f)}), - vec2(${$h(u,l,f)})); - }`}let c=o.length,h=o[o.length-1];d+=` + getT${i}(${shiftedChannels(channels, channel, shift2)}), + vec2(${shiftedChannels(lastChannels, channel, shift2)})); + }`; + } + const lastIndex = offsets.length; + const shift = offsets[offsets.length - 1]; + getValueSnippet += ` return getChannel( - getT${c}(${$h(i,l,h)}), - vec2(${$h(u,l,h)}));`,this.userCode=` - float getValue(${i.map(m=>"int "+m)}) { - ${d} + getT${lastIndex}(${shiftedChannels(channels, channel, shift)}), + vec2(${shiftedChannels(lastChannels, channel, shift)}));`; + this.userCode = ` + float getValue(${channels.map((x) => "int " + x)}) { + ${getValueSnippet} } void main() { - ${r} coords = getOutputCoords(); - vec4 result = vec4(getValue(${s}), 0., 0., 0.); + ${dtype} coords = getOutputCoords(); + vec4 result = vec4(getValue(${coords2}), 0., 0., 0.); - ${s[a-1]} = ${s[a-1]} + 1; - if (${s[a-1]} < ${n[a-1]}) { - result.g = getValue(${s}); + ${coords2[rank - 1]} = ${coords2[rank - 1]} + 1; + if (${coords2[rank - 1]} < ${shape[rank - 1]}) { + result.g = getValue(${coords2}); } - ${s[a-2]} = ${s[a-2]} + 1; - if (${s[a-2]} < ${n[a-2]}) { - result.a = getValue(${s}); + ${coords2[rank - 2]} = ${coords2[rank - 2]} + 1; + if (${coords2[rank - 2]} < ${shape[rank - 2]}) { + result.a = getValue(${coords2}); } - ${s[a-1]} = ${s[a-1]} - 1; - if (${s[a-2]} < ${n[a-2]} && - ${s[a-1]} < ${n[a-1]}) { - result.b = getValue(${s}); + ${coords2[rank - 1]} = ${coords2[rank - 1]} - 1; + if (${coords2[rank - 2]} < ${shape[rank - 2]} && + ${coords2[rank - 1]} < ${shape[rank - 1]}) { + result.b = getValue(${coords2}); } setOutput(result); } - `}};function $h(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Bf(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 wte={kernelName:Em,backendName:"webgl",kernelFunc:Bf};function sc(e,t,n){let a=e[0].dtype;if(a==="complex64"){let h=e.map(y=>Fd({inputs:{input:y},backend:n})),m=e.map(y=>Bf({inputs:{input:y},backend:n})),f=sc(h,t,n),g=sc(m,t,n),b=As({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(v=>{let I=[-1,w.sizeFromShape(v.shape.slice(t))];return ce({inputs:{x:v},backend:n,attrs:{shape:I}})}),m=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape})),f=N.computeOutShape(h.map(v=>v.shape),1),g=h[0].shape[0]===1,b=u9(m,f,a,g),y=N.computeOutShape(e.map(v=>v.shape),t),x=n.makeTensorInfo(y,a,b);return h.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}let s=e.filter(h=>w.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,Zr):new ns(e[0].shape,Zr);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}=kte(s,t,n),p=new xte(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 kte(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,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function hA(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.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(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>w.sizeFromShape(u.shape)>0);return l.length===1?aa({inputs:{x:l[0]},backend:n}):sc(l,s,n)}var Ite={kernelName:su,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 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="",v="";n&&(a?x=`float activation(float a) { + `; + } +}; +function shiftedChannels(channels, channel, shift) { + const channelIdx = channels.indexOf(channel); + const res = channels.map((c, idx) => { + if (idx === channelIdx) { + return `${c} - ${shift}`; + } else { + return c; + } + }); + return res.join(); +} +function imag3(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const inputData = backend2.texData.get(input2.dataId); + return identity3({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 }); +} +var imagConfig2 = { + kernelName: Imag, + backendName: "webgl", + kernelFunc: imag3 +}; +function concatImpl2(inputs, axis, backend2) { + const dtype = inputs[0].dtype; + if (dtype === "complex64") { + const reals = inputs.map((t) => real3({ inputs: { input: t }, backend: backend2 })); + const imags = inputs.map((t) => imag3({ inputs: { input: t }, backend: backend2 })); + const realConcated = concatImpl2(reals, axis, backend2); + const imagConcated = concatImpl2(imags, axis, backend2); + const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 }); + reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r)); + imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i)); + backend2.disposeIntermediateTensorInfo(realConcated); + backend2.disposeIntermediateTensorInfo(imagConcated); + return result2; + } + let runOnCpu = backend2.shouldExecuteOnCPU(inputs); + if (dtype === "string") { + runOnCpu = true; + } + if (runOnCpu) { + const tensors2D2 = inputs.map((t) => { + const innerSize = util_exports.sizeFromShape(t.shape.slice(axis)); + const shape = [-1, innerSize]; + return reshape4({ inputs: { x: t }, backend: backend2, attrs: { shape } }); + }); + const inputsValShapes = tensors2D2.map((t) => { + return { vals: backend2.readSync(t.dataId), shape: t.shape }; + }); + const outShape2 = backend_util_exports.computeOutShape( + tensors2D2.map((t) => t.shape), + 1 + /* axis */ + ); + const simplyConcat = tensors2D2[0].shape[0] === 1; + const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat); + const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis); + const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals); + tensors2D2.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return outInfo; + } + const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0); + const shouldPack = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && $inputs[0].shape.length > 1; + if ($inputs.length === 1) { + const program2 = shouldPack ? new UnaryOpProgram(inputs[0].shape, CLONE) : new UnaryOpPackedProgram(inputs[0].shape, CLONE); + return backend2.runWebGLProgram(program2, inputs, dtype); + } + const maxTexturesInShader = env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER"); + if ($inputs.length > maxTexturesInShader) { + const reducedInputs = []; + for (let i = 0; i < $inputs.length; i += maxTexturesInShader) { + const subArray = $inputs.slice(i, i + maxTexturesInShader); + reducedInputs.push(concatImpl2(subArray, axis, backend2)); + } + const result2 = concatImpl2(reducedInputs, axis, backend2); + for (const i of reducedInputs) { + backend2.disposeIntermediateTensorInfo(i); + } + return result2; + } + if (shouldPack) { + const program2 = new ConcatPackedProgram($inputs.map((t) => t.shape), axis); + return backend2.runWebGLProgram(program2, $inputs, dtype); + } + const { tensors2D, outShape } = computeTensors2D($inputs, axis, backend2); + const program = new ConcatProgram(tensors2D.map((t) => t.shape)); + const result = backend2.runWebGLProgram(program, tensors2D, dtype); + tensors2D.forEach((r) => backend2.disposeIntermediateTensorInfo(r)); + const reshapedResult = reshape4({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(result); + return reshapedResult; +} +function computeTensors2D(inputs, axis, backend2) { + const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis); + const tensors2D = inputs.map((x) => reshape4({ + inputs: { x }, + attrs: { shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))] }, + backend: backend2 + })); + return { tensors2D, outShape }; +} +function concat3(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0]; + const shapes = inputs.map((t) => t.shape); + backend_util_exports.assertParamsConsistent(shapes, $axis); + const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis); + if (util_exports.sizeFromShape(outShape) === 0) { + return backend2.makeTensorInfo(outShape, inputs[0].dtype, []); + } + const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0); + if ($inputs.length === 1) { + return identity3({ inputs: { x: $inputs[0] }, backend: backend2 }); + } + return concatImpl2($inputs, $axis, backend2); +} +var concatConfig2 = { + kernelName: Concat, + backendName: "webgl", + kernelFunc: concat3 +}; +var Conv2DProgram = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) { + this.variableNames = ["x", "W"]; + this.outputShape = convInfo.outShape; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4; + const inputDepthVec4Remainder = convInfo.inChannels % 4; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const rowDim = isChannelsLast ? 1 : 2; + const colDim = isChannelsLast ? 2 : 3; + const channelDim = isChannelsLast ? 3 : 1; + let activationSnippet = "", applyActivationSnippet = ""; + if (activation2) { + if (hasPreluActivationWeights) { + activationSnippet = `float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); - ${n} - }`:r?x=`float activation(float a) { + ${activation2} + }`; + } else if (hasLeakyreluAlpha) { + activationSnippet = `float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); - ${n} - }`:x=` + ${activation2} + }`; + } else { + activationSnippet = ` float activation(float x) { - ${n} + ${activation2} } - `,v="result = activation(result);");let I=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` - ${x} + `; + } + applyActivationSnippet = `result = activation(result);`; + } + const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : ""; + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivationWeights) { + this.variableNames.push("preluActivationWeights"); + } + if (hasLeakyreluAlpha) { + this.variableNames.push("leakyreluAlpha"); + } + this.userCode = ` + ${activationSnippet} - const ivec2 strides = ivec2(${o}, ${l}); - const ivec2 pads = ivec2(${s}, ${i}); + const ivec2 strides = ivec2(${strideHeight}, ${strideWidth}); + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; - int d2 = coords[${y}]; + int d2 = coords[${channelDim}]; ivec2 xRCCorner = - ivec2(coords[${g}], coords[${b}]) * strides - pads; + ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${d}; wR++) { - int xR = xRCorner + wR * ${u}; + for (int wR = 0; wR < ${filterHeight}; wR++) { + int xR = xRCorner + wR * ${dilationHeight}; - if (xR < 0 || xR >= ${e.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${c}; wC++) { - int xC = xCCorner + wC * ${p}; + for (int wC = 0; wC < ${filterWidth}; wC++) { + int xC = xCCorner + wC * ${dilationWidth}; - if (xC < 0 || xC >= ${e.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } - for (int d1 = 0; d1 < ${h}; d1 += 4) { + for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), @@ -2255,7 +60287,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel getW(wR, wC, d1 + 3, d2) ); - if (${f}) { + if (${isChannelsLast}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), @@ -2274,57 +60306,57 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } } - if (${m===1}) { + if (${inputDepthVec4Remainder === 1}) { - if (${f}) { + if (${isChannelsLast}) { dotProd += - getX(batch, xR, xC, ${h}) * - getW(wR, wC, ${h}, d2); + getX(batch, xR, xC, ${inputDepthNearestVec4}) * + getW(wR, wC, ${inputDepthNearestVec4}, d2); } else { dotProd += - getX(batch, ${h}, xR, xC) * - getW(wR, wC, ${h}, d2); + getX(batch, ${inputDepthNearestVec4}, xR, xC) * + getW(wR, wC, ${inputDepthNearestVec4}, d2); } - } else if (${m===2}) { + } else if (${inputDepthVec4Remainder === 2}) { vec2 wValues = vec2( - getW(wR, wC, ${h}, d2), - getW(wR, wC, ${h} + 1, d2) + getW(wR, wC, ${inputDepthNearestVec4}, d2), + getW(wR, wC, ${inputDepthNearestVec4} + 1, d2) ); - if (${f}) { + if (${isChannelsLast}) { vec2 xValues = vec2( - getX(batch, xR, xC, ${h}), - getX(batch, xR, xC, ${h} + 1) + getX(batch, xR, xC, ${inputDepthNearestVec4}), + getX(batch, xR, xC, ${inputDepthNearestVec4} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( - getX(batch, ${h}, xR, xC), - getX(batch, ${h} + 1, xR, xC) + getX(batch, ${inputDepthNearestVec4}, xR, xC), + getX(batch, ${inputDepthNearestVec4} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } - } else if (${m===3}) { + } else if (${inputDepthVec4Remainder === 3}) { vec3 wValues = vec3( - getW(wR, wC, ${h}, d2), - getW(wR, wC, ${h} + 1, d2), - getW(wR, wC, ${h} + 2, d2) + getW(wR, wC, ${inputDepthNearestVec4}, d2), + getW(wR, wC, ${inputDepthNearestVec4} + 1, d2), + getW(wR, wC, ${inputDepthNearestVec4} + 2, d2) ); - if (${f}) { + if (${isChannelsLast}) { vec3 xValues = vec3( - getX(batch, xR, xC, ${h}), - getX(batch, xR, xC, ${h} + 1), - getX(batch, xR, xC, ${h} + 2) + getX(batch, xR, xC, ${inputDepthNearestVec4}), + getX(batch, xR, xC, ${inputDepthNearestVec4} + 1), + getX(batch, xR, xC, ${inputDepthNearestVec4} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( - getX(batch, ${h}, xR, xC), - getX(batch, ${h} + 1, xR, xC), - getX(batch, ${h} + 2, xR, xC) + getX(batch, ${inputDepthNearestVec4}, xR, xC), + getX(batch, ${inputDepthNearestVec4} + 1, xR, xC), + getX(batch, ${inputDepthNearestVec4} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } @@ -2334,13 +60366,34 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } float result = dotProd; - ${I} - ${v} + ${addBiasSnippet} + ${applyActivationSnippet} setOutput(result); } - `}},Ste=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` - const ivec3 strides = ivec3(${r}, ${s}, ${i}); - const ivec3 pads = ivec3(${t}, ${n}, ${a}); + `; + } +}; +var Conv3DProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "W"]; + this.outputShape = convInfo.outShape; + const padFront = convInfo.padInfo.front; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const filterDepth = convInfo.filterDepth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4; + const inputDepthVec4Remainder = convInfo.inChannels % 4; + this.userCode = ` + const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth}); + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); void main() { ivec5 coords = getOutputCoords(); @@ -2356,28 +60409,28 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; - for (int wF = 0; wF < ${p}; wF++) { - int xF = xFCorner + wF * ${o}; + for (int wF = 0; wF < ${filterDepth}; wF++) { + int xF = xFCorner + wF * ${dilationDepth}; - if (xF < 0 || xF >= ${e.inDepth}) { + if (xF < 0 || xF >= ${convInfo.inDepth}) { continue; } - for (int wR = 0; wR < ${d}; wR++) { - int xR = xRCorner + wR * ${l}; + for (int wR = 0; wR < ${filterHeight}; wR++) { + int xR = xRCorner + wR * ${dilationHeight}; - if (xR < 0 || xR >= ${e.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${c}; wC++) { - int xC = xCCorner + wC * ${u}; + for (int wC = 0; wC < ${filterWidth}; wC++) { + int xC = xCCorner + wC * ${dilationWidth}; - if (xC < 0 || xC >= ${e.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } - for (int d1 = 0; d1 < ${h}; d1 += 4) { + for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), @@ -2394,30 +60447,30 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel dotProd += dot(xValues, wValues); } - if (${m===1}) { + if (${inputDepthVec4Remainder === 1}) { dotProd += - getX(batch, xF, xR, xC, ${h}) * - getW(wF, wR, wC, ${h}, d2); - } else if (${m===2}) { + getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) * + getW(wF, wR, wC, ${inputDepthNearestVec4}, d2); + } else if (${inputDepthVec4Remainder === 2}) { vec2 xValues = vec2( - getX(batch, xF, xR, xC, ${h}), - getX(batch, xF, xR, xC, ${h} + 1) + getX(batch, xF, xR, xC, ${inputDepthNearestVec4}), + getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1) ); vec2 wValues = vec2( - getW(wF, wR, wC, ${h}, d2), - getW(wF, wR, wC, ${h} + 1, d2) + getW(wF, wR, wC, ${inputDepthNearestVec4}, d2), + getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2) ); dotProd += dot(xValues, wValues); - } else if (${m===3}) { + } else if (${inputDepthVec4Remainder === 3}) { vec3 xValues = vec3( - getX(batch, xF, xR, xC, ${h}), - getX(batch, xF, xR, xC, ${h} + 1), - getX(batch, xF, xR, xC, ${h} + 2) + getX(batch, xF, xR, xC, ${inputDepthNearestVec4}), + getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1), + getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2) ); vec3 wValues = vec3( - getW(wF, wR, wC, ${h}, d2), - getW(wF, wR, wC, ${h} + 1, d2), - getW(wF, wR, wC, ${h} + 2, d2) + getW(wF, wR, wC, ${inputDepthNearestVec4}, d2), + getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2), + getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2) ); dotProd += dot(xValues, wValues); } @@ -2426,41 +60479,82 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } 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=` + `; + } +}; +var Conv2DPackedProgram = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) { + this.variableNames = ["x", "W"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [ + { name: "pads", type: "ivec2" }, + { name: "strides", type: "ivec2" }, + { name: "dilations", type: "ivec2" }, + { name: "inDims", type: "ivec2" } + ]; + this.outputShape = convInfo.outShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const padLeft = convInfo.padInfo.left; + const strideWidth = convInfo.strideWidth; + const dilationWidth = convInfo.dilationWidth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const texelsAcross = filterWidth; + let mainLoop = ` int xR; int xC; int xCOffset; - vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f=0 && xR < inDims[0]) { - `;for(let f=0;f<(p+1)/2;f++){let g=f*2;if(d+=` - xC = xCCorner + ${g*o}; - `,i===1){if(g= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) { - xTexelC${g} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${g}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${g}Ready = 1; + xTexelC${colIndex}Ready = 1; } - `,o===1&&g>0?d+=` - xC${g} = vec4(xTexelC${g-2}.zw, xTexelC${g}.xy); - `:d+=` + `; + if (dilationWidth === 1 && colIndex > 0) { + mainLoop += ` + xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy); + `; + } else { + mainLoop += ` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { @@ -2472,137 +60566,206 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel previous.zw = vec2(0.0); } - xC${g} = vec4(previous.zw, xTexelC${g}.xy); + xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy); } else { - xC${g} = vec4(0.0, 0.0, xTexelC${g}.xy); + xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy); } - `):d+=` - if (xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) { - xTexelC${g} = getX(batch, xR, xC, d1); + `; + } + } else { + mainLoop += ` + if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { - xTexelC${g}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${g}Ready = 1; + xTexelC${colIndex}Ready = 1; } - xC${g} = xTexelC${g}; - `,g+1= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { - xTexelC${g+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${g+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${g+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - `,o>1?d+=` + `; + if (dilationWidth > 1) { + mainLoop += ` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); - xC${g+1} = vec4(previous.zw, xTexelC${g+1}.xy); + xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy); } else { - xC${g+1} = vec4(0.0, 0.0, xTexelC${g+1}.xy); + xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy); } - `:d+=` - xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.xy); - `):b===1?d+=` - xC${g+1} = xTexelC${g}; - `:d+=` - xCOffset = xC + ${b}; + `; + } else { + mainLoop += ` + xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy); + `; + } + } else { + if (nextTexelOffset === 1) { + mainLoop += ` + xC${colIndex + 1} = xTexelC${colIndex}; + `; + } else { + mainLoop += ` + xCOffset = xC + ${nextTexelOffset}; - if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { - xTexelC${g+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { - xTexelC${g+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${g+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${g+1} = xTexelC${g+1}; - `}}else g= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) { - xTexelC${g} = getX(batch, xR, xCOffset, d1); + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${g}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${g}Ready = 1; + xTexelC${colIndex}Ready = 1; } - if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${g+1}Ready == 0) { - xTexelC${g+1} = getX(batch, xR, xC + 1, d1); + if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { - xTexelC${g+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${g+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${g} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw); - `,g+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } - xC${g+1} = vec4(xTexelC${g+1}.xy, final.xy); - `)):(d+=` - if(xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) { - xTexelC${g} = getX(batch, xR, xC, d1); + xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy); + `; + } + } else { + mainLoop += ` + if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { - xTexelC${g}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${g}Ready = 1; + xTexelC${colIndex}Ready = 1; } xCOffset = xC + strides[1]; - if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { - xTexelC${g+1} = getX(batch, xR, xCOffset, d1); + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { - xTexelC${g+1}.zw = vec2(0.); + xTexelC${colIndex + 1}.zw = vec2(0.); } - xTexelC${g+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${g} = vec4( - xTexelC${g}.xy, xTexelC${g+1}.xy); - `,g+1= 0) { + if(d0 < inputShape[${rowDim}] && d0 >= 0) { // Use custom imod instead mod. On Intel GPU, mod may generate // unexpected value. // https://github.com/tensorflow/tfjs/issues/5447 @@ -2638,25 +60829,28 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / inChannels); - if(d1 < inputShape[${i}] && d1 >= 0) { + if(d1 < inputShape[${colDim}] && d1 >= 0) { ch = imod(pos, inChannels); - if (${r}) { + if (${isChannelsLast}) { innerDims = vec2(d1, ch); - result[${u*2+p}] = getChannel( + result[${row * 2 + col}] = getChannel( getA(rc.x, d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); - result[${u*2+p}] = getChannel( + result[${row * 2 + col}] = getChannel( getA(rc.x, ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } - `;this.userCode=` + `; + } + } + this.userCode = ` void main() { ivec3 rc = getOutputCoords(); @@ -2665,11 +60859,249 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; - ${l} + ${unrolled} - ${a.output} = result; + ${glsl.output} = result; } - `}};function fm(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 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=fm(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=fm(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&&w.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},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Sc(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let T=mm({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);w.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,g=aa({inputs:{x:T},backend:a}),g.shape=n.outShape,b.push(T)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),v=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=mm({a:h?x:v,b:h?v:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(v),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function bA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,v=[];if(s!=null){let K=fm(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),v.push(s))}if(r!=null){let K=fm(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),v.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(I);let T=new Nte(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=a.runWebGLProgram(T,[e],"float32",C),F=ce({inputs:{x:E},backend:a,attrs:{shape:b}});v.push(E),v.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?Nc(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",w.createScalarValue(i,"float32"));U.push(K),v.push(K)}let H=a.runWebGLProgram(B,U,"float32"),j=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});v.push(H);for(let K of v)a.disposeIntermediateTensorInfo(K);return j}function Tte(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 Cte={kernelName:Oi,backendName:"webgl",kernelFunc:Tte},_te=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 getShapeForBatchMatMul(shape, isChannelsLast) { + const length = shape.length; + if (length >= 3) { + return isChannelsLast ? [ + ...shape.slice(0, -3), + shape[length - 3] * shape[length - 2], + shape[length - 1] + /* channel */ + ] : [ + ...shape.slice(0, -3), + shape[length - 3], + shape[length - 2] * shape[length - 1] + /* height * width */ + ]; + } else if (!isChannelsLast && length === 1 && shape[0] > 1) { + return [shape[0], 1]; + } else { + return null; + } +} +function conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) { + const xShape = x.shape; + const xTexData = backend2.texData.get(x.dataId); + const sharedMatMulDim = convInfo.inChannels; + const outerShapeX = xShape[0] * xShape[1] * xShape[2]; + const outerShapeFilter = convInfo.outChannels; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const transposeA = false; + const transposeB = false; + let out; + const intermediates = []; + if (preluActivationWeights != null) { + const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast); + if (targetShape != null) { + preluActivationWeights = reshape4({ + inputs: { x: preluActivationWeights }, + backend: backend2, + attrs: { shape: targetShape } + }); + intermediates.push(preluActivationWeights); + } + } + if (bias != null) { + const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast); + if (targetShape != null) { + bias = reshape4({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } }); + intermediates.push(bias); + } + } + const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD; + const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked && isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 && util_exports.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3)); + if (canOptimize) { + const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1); + const xReshaped = { + dataId: x.dataId, + shape: [1, targetShape, convInfo.inChannels], + dtype: x.dtype + }; + const originalXTexDataShape = xTexData.shape; + xTexData.shape = xTexData.shape.slice(); + xTexData.shape[xTexData.shape.length - 2]++; + util_exports.assert(isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`); + const filterReshaped = reshape4({ + inputs: { x: filter }, + backend: backend2, + attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] } + }); + intermediates.push(filterReshaped); + const pointwiseConv = batchMatMulImpl({ + a: xReshaped, + b: filterReshaped, + backend: backend2, + transposeA, + transposeB, + bias, + activation: activation2, + preluActivationWeights, + leakyreluAlpha + }); + const pointwiseConvTexData = backend2.texData.get(pointwiseConv.dataId); + util_exports.assert(pointwiseConvTexData.isPacked, () => "batchMatMul result is expected to be packed"); + xTexData.shape = originalXTexDataShape; + pointwiseConvTexData.shape = convInfo.outShape; + out = identity3({ inputs: { x: pointwiseConv }, backend: backend2 }); + out.shape = convInfo.outShape; + intermediates.push(pointwiseConv); + } else { + const numCols = convInfo.outHeight * convInfo.outWidth; + const xReshaped = reshape4({ + inputs: { x }, + backend: backend2, + attrs: { + shape: isChannelsLast ? [convInfo.batchSize, numCols, convInfo.inChannels] : [convInfo.batchSize, convInfo.inChannels, numCols] + } + }); + const filterReshaped = reshape4({ + inputs: { x: filter }, + backend: backend2, + attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] } + }); + const result = batchMatMulImpl({ + a: isChannelsLast ? xReshaped : filterReshaped, + b: isChannelsLast ? filterReshaped : xReshaped, + transposeA: !isChannelsLast, + transposeB, + backend: backend2, + bias, + activation: activation2, + preluActivationWeights, + leakyreluAlpha + }); + out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } }); + intermediates.push(xReshaped); + intermediates.push(filterReshaped); + intermediates.push(result); + } + for (const i of intermediates) { + backend2.disposeIntermediateTensorInfo(i); + } + return out; +} +function conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) { + const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo; + const isChannelsLast = dataFormat === "channelsLast"; + const sharedDim = filterWidth * filterHeight * inChannels; + const numCols = outHeight * outWidth; + const x2ColShape = [convInfo.batchSize, sharedDim, numCols]; + const transposeA = true; + const transposeB = false; + const intermediates = []; + if (preluActivationWeights != null) { + const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast); + if (targetShape != null) { + preluActivationWeights = reshape4({ + inputs: { x: preluActivationWeights }, + backend: backend2, + attrs: { shape: targetShape } + }); + intermediates.push(preluActivationWeights); + } + } + if (bias != null) { + const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast); + if (targetShape != null) { + bias = reshape4({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } }); + intermediates.push(bias); + } + } + const w2Row = reshape4({ + inputs: { x: filter }, + backend: backend2, + attrs: { shape: [1, sharedDim, util_exports.sizeFromShape(filter.shape) / sharedDim] } + }); + intermediates.push(w2Row); + const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo); + const customValues = [ + x.shape, + [convInfo.padInfo.top, convInfo.padInfo.left], + [convInfo.strideHeight, convInfo.strideWidth], + [convInfo.dilationHeight, convInfo.dilationWidth], + [convInfo.inChannels], + [convInfo.filterWidth * convInfo.inChannels], + [convInfo.outWidth] + ]; + const im2Col = backend2.runWebGLProgram(im2ColProgram, [x], "float32", customValues); + const im2ColReshaped = reshape4({ inputs: { x: im2Col }, backend: backend2, attrs: { shape: x2ColShape } }); + intermediates.push(im2Col); + intermediates.push(im2ColReshaped); + const hasBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + const hasLeakyreluAlpha = activation2 === "leakyrelu"; + const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null; + const matmulProgram = new MatMulPackedProgram(isChannelsLast ? im2ColReshaped.shape : w2Row.shape, isChannelsLast ? w2Row.shape : im2ColReshaped.shape, isChannelsLast ? [convInfo.batchSize, numCols, convInfo.outChannels] : [convInfo.batchSize, convInfo.outChannels, numCols], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + const inputs = isChannelsLast ? [im2ColReshaped, w2Row] : [w2Row, im2ColReshaped]; + if (bias) { + inputs.push(bias); + } + if (hasPreluActivationWeights) { + inputs.push(preluActivationWeights); + } + if (hasLeakyreluAlpha) { + const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32")); + inputs.push($leakyreluAlpha); + intermediates.push($leakyreluAlpha); + } + const product = backend2.runWebGLProgram(matmulProgram, inputs, "float32"); + const out = reshape4({ inputs: { x: product }, backend: backend2, attrs: { shape: convInfo.outShape } }); + intermediates.push(product); + for (const i of intermediates) { + backend2.disposeIntermediateTensorInfo(i); + } + return out; +} +function conv2d4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); + let out; + if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) { + out = conv2dByMatMul({ x, filter, convInfo, backend: backend2 }); + } else if (convInfo.strideWidth <= 2 && $dataFormat === "channelsLast" && env().getBool("WEBGL_EXP_CONV")) { + const program = new Conv2DPackedProgram(convInfo); + const customValues = [ + [convInfo.padInfo.top, convInfo.padInfo.left], + [convInfo.strideHeight, convInfo.strideWidth], + [convInfo.dilationHeight, convInfo.dilationWidth], + [convInfo.inHeight, convInfo.inWidth] + ]; + out = backend2.runWebGLProgram(program, [x, filter], "float32", customValues); + } else if (env().getBool("WEBGL_CONV_IM2COL")) { + out = conv2dWithIm2Row({ x, filter, convInfo, backend: backend2 }); + } else { + const program = new Conv2DProgram(convInfo); + out = backend2.runWebGLProgram(program, [x, filter], "float32"); + } + const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } }); + backend2.disposeIntermediateTensorInfo(out); + return outReshaped; +} +var conv2DConfig2 = { + kernelName: Conv2D, + backendName: "webgl", + kernelFunc: conv2d4 +}; +var Conv2DDerFilterProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "dy"]; + this.outputShape = convInfo.filterShape; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2681,24 +61113,24 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int b = 0; b < ${e.batchSize}; b++) { - for (int yR = 0; yR < ${e.outHeight}; yR++) { - int xR = wR + yR * ${t} - ${a}; + for (int b = 0; b < ${convInfo.batchSize}; b++) { + for (int yR = 0; yR < ${convInfo.outHeight}; yR++) { + int xR = wR + yR * ${strideHeight} - ${padTop}; - if (xR < 0 || xR >= ${e.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int yC = 0; yC < ${e.outWidth}; yC++) { - int xC = wC + yC * ${n} - ${r}; + for (int yC = 0; yC < ${convInfo.outWidth}; yC++) { + int xC = wC + yC * ${strideWidth} - ${padLeft}; - if (xC < 0 || xC >= ${e.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } - ${s?`float dyValue = getDy(b, yR, yC, d2); + ${isChannelsLast ? `float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); - dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC); + dotProd += (xValue * dyValue);` : `float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue);`} } @@ -2706,45 +61138,62 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(dotProd); } - `}},Ete=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` - const ivec2 pads = ivec2(${i}, ${o}); + `; + } +}; +var Conv2DDerInputProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "W"]; + this.outputShape = convInfo.inShape; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const padTop = filterHeight - 1 - convInfo.padInfo.top; + const padLeft = filterWidth - 1 - convInfo.padInfo.left; + const rowDim = isChannelsLast ? 1 : 2; + const colDim = isChannelsLast ? 2 : 3; + const channelDim = isChannelsLast ? 3 : 1; + this.userCode = ` + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; - int d1 = coords[${p}]; + int d1 = coords[${channelDim}]; - ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads; + ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${t}; wR++) { - float dyR = float(dyRCorner + wR) / ${a}.0; + for (int wR = 0; wR < ${filterHeight}; wR++) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - int wRPerm = ${t} - 1 - wR; + int wRPerm = ${filterHeight} - 1 - wR; - for (int wC = 0; wC < ${n}; wC++) { - float dyC = float(dyCCorner + wC) / ${r}.0; + for (int wC = 0; wC < ${filterWidth}; wC++) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); - int wCPerm = ${n} - 1 - wC; + int wCPerm = ${filterWidth} - 1 - wC; - for (int d2 = 0; d2 < ${e.outChannels}; d2++) { + for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) { - if (${s}) { + if (${isChannelsLast}) { float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; @@ -2759,7 +61208,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(dotProd); } - `}},Ate=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=` + `; + } +}; +var Conv3DDerFilterProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "dy"]; + this.outputShape = convInfo.filterShape; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const padFront = convInfo.padInfo.front; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + this.userCode = ` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2770,25 +61232,25 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel float dotProd = 0.0; - for (int b = 0; b < ${e.batchSize}; b++) { - for (int yF = 0; yF < ${e.outDepth}; yF++) { - int xF = wF + yF * ${t} - ${r}; + for (int b = 0; b < ${convInfo.batchSize}; b++) { + for (int yF = 0; yF < ${convInfo.outDepth}; yF++) { + int xF = wF + yF * ${strideDepth} - ${padFront}; - if (xF < 0 || xF >= ${e.inDepth}) { + if (xF < 0 || xF >= ${convInfo.inDepth}) { continue; } - for (int yR = 0; yR < ${e.outHeight}; yR++) { - int xR = wR + yR * ${n} - ${s}; + for (int yR = 0; yR < ${convInfo.outHeight}; yR++) { + int xR = wR + yR * ${strideHeight} - ${padTop}; - if (xR < 0 || xR >= ${e.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int yC = 0; yC < ${e.outWidth}; yC++) { - int xC = wC + yC * ${a} - ${i}; + for (int yC = 0; yC < ${convInfo.outWidth}; yC++) { + int xC = wC + yC * ${strideWidth} - ${padLeft}; - if (xC < 0 || xC >= ${e.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } @@ -2801,8 +61263,24 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(dotProd); } - `}},Fte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` - const ivec3 pads = ivec3(${o}, ${l}, ${u}); + `; + } +}; +var Conv3DDerInputProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "W"]; + this.outputShape = convInfo.inShape; + const filterDepth = convInfo.filterDepth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const padFront = filterDepth - 1 - convInfo.padInfo.front; + const padTop = filterHeight - 1 - convInfo.padInfo.top; + const padLeft = filterWidth - 1 - convInfo.padInfo.left; + this.userCode = ` + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); void main() { ivec5 coords = getOutputCoords(); @@ -2816,39 +61294,39 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel int dyCCorner = dyCorner.z; float dotProd = 0.0; - for (int wF = 0; wF < ${t}; wF++) { - float dyF = float(dyFCorner + wF) / ${r}.0; + for (int wF = 0; wF < ${filterDepth}; wF++) { + float dyF = float(dyFCorner + wF) / ${strideDepth}.0; - if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { + if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); - int wFPerm = ${t} - 1 - wF; + int wFPerm = ${filterDepth} - 1 - wF; - for (int wR = 0; wR < ${n}; wR++) { - float dyR = float(dyRCorner + wR) / ${s}.0; + for (int wR = 0; wR < ${filterHeight}; wR++) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - int wRPerm = ${n} - 1 - wR; + int wRPerm = ${filterHeight} - 1 - wR; - for (int wC = 0; wC < ${a}; wC++) { - float dyC = float(dyCCorner + wC) / ${i}.0; + for (int wC = 0; wC < ${filterWidth}; wC++) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); - int wCPerm = ${a} - 1 - wC; + int wCPerm = ${filterWidth} - 1 - wC; - for (int d2 = 0; d2 < ${e.outChannels}; d2++) { + for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; @@ -2858,8 +61336,39 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(dotProd); } - `}};function $te(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new _te(c);return n.runWebGLProgram(h,[r,s],"float32")}var Dte={kernelName:Im,backendName:"webgl",kernelFunc:$te},Rte=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}); + `; + } +}; +function conv2DBackpropFilter3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat); + const program = new Conv2DDerFilterProgram(convInfo); + return backend2.runWebGLProgram(program, [x, dy], "float32"); +} +var conv2DBackpropFilterConfig2 = { + kernelName: Conv2DBackpropFilter, + backendName: "webgl", + kernelFunc: conv2DBackpropFilter3 +}; +var Conv2DDerInputPackedProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "W"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [ + { name: "strides", type: "vec2" } + ]; + this.outputShape = convInfo.inShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = filterHeight - 1 - convInfo.padInfo.top; + const padLeft = filterWidth - 1 - convInfo.padInfo.left; + this.userCode = ` + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); @@ -2871,29 +61380,29 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel int dyCCorner = dyCorner.y; vec4 result = vec4(0.); - for (int wR = 0; wR < ${t}; wR++) { + for (int wR = 0; wR < ${filterHeight}; wR++) { float dyR = float(dyRCorner + wR) / strides[0]; - if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - int wRPerm = ${t} - 1 - wR; + int wRPerm = ${filterHeight} - 1 - wR; - for (int wC = 0; wC < ${n}; wC++) { - int wCPerm = ${n} - 1 - wC; + for (int wC = 0; wC < ${filterWidth}; wC++) { + int wCPerm = ${filterWidth} - 1 - wC; float dyC = float(dyCCorner + wC) / strides[1]; - bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0) + bool idyCVal = (dyC >= 0.0) && (dyC < ${convInfo.outWidth}.0) && (fract(dyC) == 0.0); int idyC = int(dyC); float dyC2 = float(dyCCorner + wC + 1) / strides[1]; - bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0) + bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${convInfo.outWidth}.0) && (fract(dyC2) == 0.0); int idyC2 = int(dyC2); if (idyCVal && idyCVal2) { - for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) { + for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC, d2); vec4 dySample2 = (idyC / 2 == idyC2 / 2) ? @@ -2910,7 +61419,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel dot(dyValue, wValue.zw)); } } else if (idyCVal) { - for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) { + for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC, d2); vec2 dyValue = mod(float(idyC), 2.) == 0. ? @@ -2919,7 +61428,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel dot(dyValue, wValue.zw)); } } else if (idyCVal2) { - for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) { + for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC2, d2); vec2 dyValue = mod(float(idyC2), 2.) == 0. ? @@ -2932,19 +61441,126 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(result); } - `}};function Mte(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 Rte(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new Ete(c);return n.runWebGLProgram(h,[r,s],"float32")}}var Pte={kernelName:Li,backendName:"webgl",kernelFunc:Mte};function Ote(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 Ste(u);return n.runWebGLProgram(p,[r,s],"float32")}var Lte={kernelName:zi,backendName:"webgl",kernelFunc:Ote};function zte(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 Ate(u);return n.runWebGLProgram(p,[r,s],"float32")}var Wte={kernelName:iu,backendName:"webgl",kernelFunc:zte};function Bte(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 Fte(u);return n.runWebGLProgram(p,[r,s],"float32")}var Vte={kernelName:ou,backendName:"webgl",kernelFunc:Bte},Ute=mp+` + `; + } +}; +function conv2DBackpropInput3(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat); + if (env().getBool("WEBGL_PACK_CONV2DTRANSPOSE") && $dataFormat === "channelsLast") { + const customValues = [ + [convInfo.strideHeight, convInfo.strideWidth] + ]; + const program = new Conv2DDerInputPackedProgram(convInfo); + return backend2.runWebGLProgram(program, [dy, filter], "float32", customValues); + } else { + const program = new Conv2DDerInputProgram(convInfo); + return backend2.runWebGLProgram(program, [dy, filter], "float32"); + } +} +var conv2DBackpropInputConfig2 = { + kernelName: Conv2DBackpropInput, + backendName: "webgl", + kernelFunc: conv2DBackpropInput3 +}; +function conv3D2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3); + const program = new Conv3DProgram(convInfo); + return backend2.runWebGLProgram(program, [x, filter], "float32"); +} +var conv3DConfig2 = { + kernelName: Conv3D, + backendName: "webgl", + kernelFunc: conv3D2 +}; +function conv3DBackpropFilterV22(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, pad: pad3, filterShape } = attrs; + const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3); + const program = new Conv3DDerFilterProgram(convInfo); + return backend2.runWebGLProgram(program, [x, dy], "float32"); +} +var conv3DBackpropFilterV2Config2 = { + kernelName: Conv3DBackpropFilterV2, + backendName: "webgl", + kernelFunc: conv3DBackpropFilterV22 +}; +function conv3DBackpropInput2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { pad: pad3, strides, inputShape } = attrs; + const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3); + const program = new Conv3DDerInputProgram(convInfo); + return backend2.runWebGLProgram(program, [dy, filter], "float32"); +} +var conv3DBackpropInputConfig = { + kernelName: Conv3DBackpropInputV2, + backendName: "webgl", + kernelFunc: conv3DBackpropInput2 +}; +var COS = CHECK_NAN_SNIPPET_UNARY + ` return cos(x); -`,Gte=` +`; +var COS_PACKED = ` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${Qo} + ${CHECK_NAN_SNIPPET_PACKED} return result; -`,Hte=Ze({opSnippet:Ute,packedOpSnippet:Gte}),qte={kernelName:Wi,backendName:"webgl",kernelFunc:Hte},jte=` +`; +var cos3 = unaryKernelFunc2({ opSnippet: COS, packedOpSnippet: COS_PACKED }); +var cosConfig2 = { + kernelName: Cos, + backendName: "webgl", + kernelFunc: cos3 +}; +var COSH = ` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,Kte=Ze({opSnippet:jte}),Xte={kernelName:Bi,backendName:"webgl",kernelFunc:Kte},Yte=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${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) * ${h}`],[y,x,v]=d>1?[`${(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(${y}); +`; +var cosh3 = unaryKernelFunc2({ opSnippet: COSH }); +var coshConfig2 = { + kernelName: Cosh, + backendName: "webgl", + kernelFunc: cosh3 +}; +var CropAndResizeProgram = class { + constructor(imageShape, boxShape, cropSize, method, extrapolationValue) { + this.variableNames = ["Image", "Boxes", "BoxInd"]; + this.outputShape = []; + const [batch, imageHeight, imageWidth, depth] = imageShape; + const [numBoxes] = boxShape; + const [cropHeight, cropWidth] = cropSize; + this.outputShape = [numBoxes, cropHeight, cropWidth, depth]; + const methodId = method === "bilinear" ? 1 : 0; + const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`]; + const [heightRatio, heightScale, inY] = cropHeight > 1 ? [ + `${(imageHeight - 1) / (cropHeight - 1)}`, + "(y2-y1) * height_ratio", + `y1*${inputHeightFloat} + float(y)*(height_scale)` + ] : [ + "0.0", + "0.0", + `0.5 * (y1+y2) * ${inputHeightFloat}` + ]; + const [widthRatio, widthScale, inX] = cropWidth > 1 ? [ + `${(imageWidth - 1) / (cropWidth - 1)}`, + "(x2-x1) * width_ratio", + `x1*${inputWidthFloat} + float(x)*(width_scale)` + ] : [ + "0.0", + "0.0", + `0.5 * (x1+x2) * ${inputWidthFloat}` + ]; + this.userCode = ` + const float height_ratio = float(${heightRatio}); + const float width_ratio = float(${widthRatio}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2960,26 +61576,26 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // get image in batch index int bInd = round(getBoxInd(b)); - if(bInd < 0 || bInd >= ${s}) { + if(bInd < 0 || bInd >= ${batch}) { return; } - float height_scale = ${g}; - float width_scale = ${x}; + float height_scale = ${heightScale}; + float width_scale = ${widthScale}; - float in_y = ${b}; - if( in_y < 0.0 || in_y > ${h} ) { - setOutput(float(${r})); + float in_y = ${inY}; + if( in_y < 0.0 || in_y > ${inputHeightFloat} ) { + setOutput(float(${extrapolationValue})); return; } - float in_x = ${v}; - if( in_x < 0.0 || in_x > ${m} ) { - setOutput(float(${r})); + float in_x = ${inX}; + if( in_x < 0.0 || in_x > ${inputWidthFloat} ) { + setOutput(float(${extrapolationValue})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); - if(${c} == 1) { + if(${methodId} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); @@ -3003,20 +61619,174 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel setOutput(newValue); } } - `}},Zte=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 Yte(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},Jte={kernelName:uu,backendName:"webgl",kernelFunc:Zte},Cc;(function(e){e.Prod="*",e.Sum="+"})(Cc||(Cc={}));var oS=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===Cc.Prod?"1.0":"0.0",i=n?s:`getX(${lS(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 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` + `; + } +}; +var cropAndResize4 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { image: image2, boxes, boxInd } = inputs; + const { cropSize, method, extrapolationValue } = attrs; + const program = new CropAndResizeProgram(image2.shape, boxes.shape, cropSize, method, extrapolationValue); + return backend2.runWebGLProgram(program, [image2, boxes, boxInd], "float32"); +}; +var cropAndResizeConfig2 = { + kernelName: CropAndResize, + backendName: "webgl", + kernelFunc: cropAndResize4 +}; +var CumOpType; +(function(CumOpType2) { + CumOpType2["Prod"] = "*"; + CumOpType2["Sum"] = "+"; +})(CumOpType || (CumOpType = {})); +var CumProgram = class { + constructor(op2, outputShape, exclusive, reverse5) { + this.op = op2; + this.outputShape = outputShape; + this.variableNames = ["x"]; + this.customUniforms = [{ name: "index", type: "float" }]; + const rank = this.outputShape.length; + const initVal = this.op === CumOpType.Prod ? "1.0" : "0.0"; + const val = exclusive ? initVal : `getX(${getCoords2(rank, "coords", this.op)})`; + const length = this.outputShape[this.outputShape.length - 1]; + let condition = ""; + let idxString = ""; + if (exclusive) { + condition = reverse5 ? `end != ${length - 1}` : "end != 0"; + idxString = reverse5 ? "end + 1" : "end - 1"; + } else { + condition = reverse5 ? `end + pow2 < ${length}` : "end >= pow2"; + idxString = reverse5 ? "end + pow2" : "end - pow2"; + } + this.userCode = ` void main() { - ${ct(r)} coords = getOutputCoords(); - int end = ${uS(r,"coords",this.op)}; - float val = ${i}; + ${getCoordsDataType(rank)} coords = getOutputCoords(); + int end = ${getFinalCoord(rank, "coords", this.op)}; + float val = ${val}; int pow2 = int(pow(2.0, index)); - if (${l}) { - int idx = ${u}; - ${uS(r,"coords",this.op)} = idx; - val ${this.op}= getX(${lS(r,"coords",this.op)}); + if (${condition}) { + int idx = ${idxString}; + ${getFinalCoord(rank, "coords", this.op)} = idx; + val ${this.op}= getX(${getCoords2(rank, "coords", this.op)}); } setOutput(val); } - `}};function lS(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 ${e} is not yet supported`)}function uS(e,t,n){if(e===1)return`${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 ${e} is not yet supported`)}function yA(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=Sn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=aa({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new oS(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new oS(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=Sn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function Qte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return yA(Cc.Prod,r,n,s,i,o)}var ene={kernelName:lu,backendName:"webgl",kernelFunc:Qte};function tne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return yA(Cc.Sum,r,n,s,i,o)}var nne={kernelName:Vi,backendName:"webgl",kernelFunc:tne};function ane(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=ZE(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=s9(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var rne={kernelName:Mc,backendName:"webgl",kernelFunc:ane},sne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `; + } +}; +function getCoords2(rank, name, op2) { + if (rank === 1) { + return `${name}`; + } else if (rank === 2) { + return `${name}.x, ${name}.y`; + } else if (rank === 3) { + return `${name}.x, ${name}.y, ${name}.z`; + } else if (rank === 4) { + return `${name}.x, ${name}.y, ${name}.z, ${name}.w`; + } else { + throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`); + } +} +function getFinalCoord(rank, name, op2) { + if (rank === 1) { + return `${name}`; + } else if (rank === 2) { + return `${name}.y`; + } else if (rank === 3) { + return `${name}.z`; + } else if (rank === 4) { + return `${name}.w`; + } else { + throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`); + } +} +function cumImpl(op2, x, backend2, axis, exclusive, reverse5) { + const xRank = x.shape.length; + const permutation = backend_util_exports.getAxesPermutation([axis], xRank); + let permutedX = x; + if (permutation != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } }); + } + const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0]; + if (permutedAxis !== xRank - 1) { + throw new Error(`WebGL cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`); + } + const size = permutedX.shape[permutedAxis]; + let result = identity3({ inputs: { x: permutedX }, backend: backend2 }); + for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) { + const program = new CumProgram(op2, permutedX.shape, false, reverse5); + const customValues = [[i]]; + const prevResult = result; + result = backend2.runWebGLProgram(program, [result], result.dtype, customValues); + backend2.disposeIntermediateTensorInfo(prevResult); + } + if (exclusive) { + const program = new CumProgram(op2, permutedX.shape, exclusive, reverse5); + const prevResult = result; + result = backend2.runWebGLProgram(program, [result], result.dtype); + backend2.disposeIntermediateTensorInfo(prevResult); + } + if (permutation != null) { + const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation); + const reverseTransposedResult = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } }); + backend2.disposeIntermediateTensorInfo(result); + backend2.disposeIntermediateTensorInfo(permutedX); + return reverseTransposedResult; + } + return result; +} +function cumprod3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + return cumImpl(CumOpType.Prod, x, backend2, axis, exclusive, reverse5); +} +var cumprodConfig2 = { + kernelName: Cumprod, + backendName: "webgl", + kernelFunc: cumprod3 +}; +function cumsum3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + return cumImpl(CumOpType.Sum, x, backend2, axis, exclusive, reverse5); +} +var cumsumConfig2 = { + kernelName: Cumsum, + backendName: "webgl", + kernelFunc: cumsum3 +}; +function denseBincount3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, weights } = inputs; + const { size, binaryOutput } = attrs; + if (x.shape.length === 1) { + const xVals = backend2.readSync(x.dataId); + const weightsVals = backend2.readSync(weights.dataId); + const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size); + return backend2.makeTensorInfo([size], weights.dtype, outVals); + } else if (x.shape.length === 2) { + const xBuf = backend2.bufferSync(x); + const weightsBuf = backend2.bufferSync(weights); + const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput); + return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values); + } + throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`); +} +var denseBincountConfig2 = { + kernelName: DenseBincount, + backendName: "webgl", + kernelFunc: denseBincount3 +}; +var DepthToSpaceProgram = class { + constructor(outputShape, blockSize, dataFormat) { + this.variableNames = ["x"]; + this.outputShape = []; + this.outputShape = outputShape; + this.blockSize = blockSize; + this.dataFormat = dataFormat; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3024,37 +61794,130 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel int w = ${this.getWidthCoordString()}; int d = ${this.getDepthCoordString()}; - int in_h = h / ${t}; - int offset_h = imod(h, ${t}); - int in_w = w / ${t}; - int offset_w = imod(w, ${t}); - int offset_d = (offset_h * ${t} + offset_w) * + int in_h = h / ${blockSize}; + int offset_h = imod(h, ${blockSize}); + int in_w = w / ${blockSize}; + int offset_w = imod(w, ${blockSize}); + int offset_d = (offset_h * ${blockSize} + offset_w) * ${this.getOutputDepthSize()}; int in_d = d + offset_d; float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ine(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 sne(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var one={kernelName:pu,backendName:"webgl",kernelFunc:ine},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() { + if (this.dataFormat === "NHWC") { + return `coords[1]`; + } else { + return `coords[2]`; + } + } + getWidthCoordString() { + if (this.dataFormat === "NHWC") { + return `coords[2]`; + } else { + return `coords[3]`; + } + } + getDepthCoordString() { + if (this.dataFormat === "NHWC") { + return `coords[3]`; + } else { + return `coords[1]`; + } + } + getOutputDepthSize() { + if (this.dataFormat === "NHWC") { + return this.outputShape[3]; + } else { + return this.outputShape[1]; + } + } + getInputSamplingString() { + if (this.dataFormat === "NHWC") { + return `getX(b, in_h, in_w, in_d)`; + } else { + return `getX(b, in_d, in_h, in_w)`; + } + } +}; +function depthToSpace3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockSize, dataFormat } = attrs; + const batchSize = x.shape[0]; + const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2]; + const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3]; + const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1]; + const outputHeight = inputHeight * blockSize; + const outputWidth = inputWidth * blockSize; + const outputDepth = inputDepth / (blockSize * blockSize); + const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth]; + const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat); + return backend2.runWebGLProgram(program, [x], x.dtype); +} +var depthToSpaceConfig2 = { + kernelName: DepthToSpace, + backendName: "webgl", + kernelFunc: depthToSpace3 +}; +var DepthwiseConv2DProgram = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) { + this.variableNames = ["x", "W"]; + this.customUniforms = [ + { name: "pads", type: "ivec2" }, + { name: "strides", type: "ivec2" }, + { name: "dilations", type: "ivec2" }, + { name: "inDims", type: "ivec2" } + ]; + this.outputShape = convInfo.outShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const channelMul = convInfo.outChannels / convInfo.inChannels; + let activationSnippet = "", applyActivationSnippet = ""; + if (activation2) { + if (hasPreluActivation) { + activationSnippet = `float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); - ${n} - }`:r?l=`float activation(float a) { + ${activation2} + }`; + } else if (hasLeakyReluAlpha) { + activationSnippet = `float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); - ${n} - }`:l=` + ${activation2} + }`; + } else { + activationSnippet = ` float activation(float x) { - ${n} + ${activation2} } - `,u="result = activation(result);");let p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` - ${l} + `; + } + applyActivationSnippet = `result = activation(result);`; + } + const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : ""; + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivation) { + this.variableNames.push("preluActivationWeights"); + } + if (hasLeakyReluAlpha) { + this.variableNames.push("leakyreluAlpha"); + } + this.userCode = ` + ${activationSnippet} void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; - int d1 = d2 / ${o}; - int q = d2 - d1 * ${o}; + int d1 = d2 / ${channelMul}; + int q = d2 - d1 * ${channelMul}; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; @@ -3063,14 +61926,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations. - for (int wR = 0; wR < ${s}; wR++) { + for (int wR = 0; wR < ${filterHeight}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } - for (int wC = 0; wC < ${i}; wC++) { + for (int wC = 0; wC < ${filterWidth}; wC++) { int xC = xCCorner + wC * dilations[1]; if (xC < 0 || xC >= inDims[1]) { @@ -3084,44 +61947,86 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } float result = dotProd; - ${p} - ${u} + ${addBiasSnippet} + ${applyActivationSnippet} 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=` + `; + } +}; +var DepthwiseConvPacked2DProgram = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) { + this.variableNames = ["x", "W"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [ + { name: "pads", type: "ivec2" }, + { name: "strides", type: "ivec2" }, + { name: "dilations", type: "ivec2" }, + { name: "inDims", type: "ivec2" } + ]; + this.outputShape = convInfo.outShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const channelMul = convInfo.outChannels / convInfo.inChannels; + const padLeft = convInfo.padInfo.left; + const strideWidth = convInfo.strideWidth; + const dilationWidth = convInfo.dilationWidth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const texelsAcross = filterWidth; + let mainLoop = ` int xR; int xC; int xCOffset; - vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { - `;for(let g=0;g<(d+1)/2;g++){let b=g*2;if(c+=` - xC = xCCorner + ${b*l}; - `,o===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { - xTexelC${b} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${b}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${b}Ready = 1; + xTexelC${colIndex}Ready = 1; } - `,l===1&&b>0?c+=` - xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy); - `:c+=` + `; + if (dilationWidth === 1 && colIndex > 0) { + mainLoop += ` + xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy); + `; + } else { + mainLoop += ` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { @@ -3133,174 +62038,292 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel previous.zw = vec2(0.0); } - xC${b} = vec4(previous.zw, xTexelC${b}.xy); + xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy); } else { - xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy); + xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy); } - `):c+=` - if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { - xTexelC${b} = getX(batch, xR, xC, d1); + `; + } + } else { + mainLoop += ` + if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { - xTexelC${b}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${b}Ready = 1; + xTexelC${colIndex}Ready = 1; } - xC${b} = xTexelC${b}; - `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { - xTexelC${b+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${b+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${b+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - `,l>1?c+=` + `; + if (dilationWidth > 1) { + mainLoop += ` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); - xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy); + xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy); } else { - xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy); + xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy); } - `:c+=` - xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy); - `):y===1?c+=` - xC${b+1} = xTexelC${b}; - `:c+=` - xCOffset = xC + ${y}; + `; + } else { + mainLoop += ` + xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy); + `; + } + } else { + if (nextTexelOffset === 1) { + mainLoop += ` + xC${colIndex + 1} = xTexelC${colIndex}; + `; + } else { + mainLoop += ` + xCOffset = xC + ${nextTexelOffset}; - if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { - xTexelC${b+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { - xTexelC${b+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${b+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${b+1} = xTexelC${b+1}; - `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { - xTexelC${b} = getX(batch, xR, xCOffset, d1); + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${b}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${b}Ready = 1; + xTexelC${colIndex}Ready = 1; } - if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) { - xTexelC${b+1} = getX(batch, xR, xC + 1, d1); + if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { - xTexelC${b+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${b+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw); - `,b+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } - xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy); - `)):(c+=` - if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { - xTexelC${b} = getX(batch, xR, xC, d1); + xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy); + `; + } + } else { + mainLoop += ` + if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { - xTexelC${b}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${b}Ready = 1; + xTexelC${colIndex}Ready = 1; } xCOffset = xC + strides[1]; - if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { - xTexelC${b+1} = getX(batch, xR, xCOffset, d1); + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { - xTexelC${b+1}.zw = vec2(0.); + xTexelC${colIndex + 1}.zw = vec2(0.); } - xTexelC${b+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${b} = vec4( - xTexelC${b}.xy, xTexelC${b+1}.xy); - `,b+1`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 une={kernelName:Ui,backendName:"webgl",kernelFunc:lne},pne=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 depthwiseConv2dNative2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations, dimRoundingMode } = attrs; + let $dilations = dilations; + if ($dilations == null) { + $dilations = [1, 1]; + } + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`); + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filter.shape, + strides, + $dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + let program; + if (env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) { + program = new DepthwiseConvPacked2DProgram(convInfo); + } else { + program = new DepthwiseConv2DProgram(convInfo); + } + const customValues = [ + [convInfo.padInfo.top, convInfo.padInfo.left], + [convInfo.strideHeight, convInfo.strideWidth], + [convInfo.dilationHeight, convInfo.dilationWidth], + [convInfo.inHeight, convInfo.inWidth] + ]; + return backend2.runWebGLProgram(program, [x, filter], "float32", customValues); +} +var depthwiseConv2dNativeConfig2 = { + kernelName: DepthwiseConv2dNative, + backendName: "webgl", + kernelFunc: depthwiseConv2dNative2 +}; +var DepthwiseConv2DDerFilterProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "dy"]; + this.outputShape = convInfo.filterShape; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const channelMul = convInfo.outChannels / convInfo.inChannels; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int dm = coords.w; - int d2 = d1 * ${s} + dm; + int d2 = d1 * ${channelMul} + dm; float dotProd = 0.0; // TO DO: Vec4 over the batch size - for (int b = 0; b < ${e.batchSize}; b++) { - for (int yR = 0; yR < ${e.outHeight}; yR++) { - int xR = wR + yR * ${t} - ${a}; + for (int b = 0; b < ${convInfo.batchSize}; b++) { + for (int yR = 0; yR < ${convInfo.outHeight}; yR++) { + int xR = wR + yR * ${strideHeight} - ${padTop}; - if (xR < 0 || xR >= ${e.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int yC = 0; yC < ${e.outWidth}; yC++) { - int xC = wC + yC * ${n} - ${r}; + for (int yC = 0; yC < ${convInfo.outWidth}; yC++) { + int xC = wC + yC * ${strideWidth} - ${padLeft}; - if (xC < 0 || xC >= ${e.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } @@ -3312,8 +62335,22 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(dotProd); } - `}},cne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` - const ivec2 pads = ivec2(${s}, ${i}); + `; + } +}; +var DepthwiseConv2DDerInputProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "W"]; + this.outputShape = convInfo.inShape; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const padTop = filterHeight - 1 - convInfo.padInfo.top; + const padLeft = filterWidth - 1 - convInfo.padInfo.left; + const channelMul = convInfo.outChannels / convInfo.inChannels; + this.userCode = ` + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); @@ -3325,30 +62362,30 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel float dotProd = 0.0; - for (int wR = 0; wR < ${t}; wR++) { - float dyR = float(dyRCorner + wR) / ${a}.0; + for (int wR = 0; wR < ${filterHeight}; wR++) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - int wRPerm = ${t} - 1 - wR; + int wRPerm = ${filterHeight} - 1 - wR; - for (int wC = 0; wC < ${n}; wC++) { - float dyC = float(dyCCorner + wC) / ${r}.0; + for (int wC = 0; wC < ${filterWidth}; wC++) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); - int wCPerm = ${n} - 1 - wC; + int wCPerm = ${filterWidth} - 1 - wC; // TO DO: Vec4 over the channelMul - for (int dm = 0; dm < ${o}; dm++) { - int d2 = d1 * ${o} + dm; + for (int dm = 0; dm < ${channelMul}; dm++) { + int d2 = d1 * ${channelMul} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; @@ -3357,15 +62394,93 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(dotProd); } - `}};function dne(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 pne(d);return n.runWebGLProgram(c,[r,s],"float32")}var hne={kernelName:Sm,backendName:"webgl",kernelFunc:dne};function mne(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 cne(d);return n.runWebGLProgram(c,[r,s],"float32")}var fne={kernelName:Nm,backendName:"webgl",kernelFunc:mne},gne=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `; + } +}; +function depthwiseConv2dNativeBackpropFilter3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs; + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filterShape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const program = new DepthwiseConv2DDerFilterProgram(convInfo); + return backend2.runWebGLProgram(program, [x, dy], "float32"); +} +var depthwiseConv2dNativeBackpropFilterConfig2 = { + kernelName: DepthwiseConv2dNativeBackpropFilter, + backendName: "webgl", + kernelFunc: depthwiseConv2dNativeBackpropFilter3 +}; +function depthwiseConv2dNativeBackpropInput3(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs; + const convInfo = backend_util_exports.computeConv2DInfo( + inputShape, + filter.shape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const program = new DepthwiseConv2DDerInputProgram(convInfo); + return backend2.runWebGLProgram(program, [dy, filter], "float32"); +} +var depthwiseConv2dNativeBackpropInputConfig2 = { + kernelName: DepthwiseConv2dNativeBackpropInput, + backendName: "webgl", + kernelFunc: depthwiseConv2dNativeBackpropInput3 +}; +var DiagProgram = class { + constructor(size) { + this.variableNames = ["X"]; + this.outputShape = [size, size]; + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function bne(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new gne(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 yne={kernelName:Pc,backendName:"webgl",kernelFunc:bne},xne=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}, ${d}); + `; + } +}; +function diag3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + const outShape = [...x.shape, ...x.shape]; + const xSize = util_exports.sizeFromShape(x.shape); + const flat = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [xSize] } }); + const program = new DiagProgram(xSize); + const res = backend2.runWebGLProgram(program, [flat], flat.dtype); + const out = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } }); + backend2.disposeIntermediateTensorInfo(flat); + backend2.disposeIntermediateTensorInfo(res); + return out; +} +var diagConfig2 = { + kernelName: Diag, + backendName: "webgl", + kernelFunc: diag3 +}; +var Dilation2DProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "W"]; + this.outputShape = convInfo.outShape; + const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo; + const { top: padTop, left: padLeft } = padInfo; + this.userCode = ` + const ivec2 strides = ivec2(${strideHeight}, ${strideWidth}); + const ivec2 pads = ivec2(${padTop}, ${padLeft}); const float neg_infinity = -3.4e38; void main() { @@ -3378,14 +62493,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; - for (int h = 0; h < ${i}; h++) { - int hIn = hBeg + h * ${l}; + for (int h = 0; h < ${filterHeight}; h++) { + int hIn = hBeg + h * ${dilationHeight}; - if (hIn >= 0 && hIn < ${t}) { - for (int w = 0; w < ${o}; w++) { - int wIn = wBeg + w * ${u}; + if (hIn >= 0 && hIn < ${inHeight}) { + for (int w = 0; w < ${filterWidth}; w++) { + int wIn = wBeg + w * ${dilationWidth}; - if (wIn >= 0 && wIn < ${n}) { + if (wIn >= 0 && wIn < ${inWidth}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); @@ -3401,7 +62516,92 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel float result = curVal; setOutput(result); } - `}};function vne(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 xne(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 wne={kernelName:Gi,backendName:"webgl",kernelFunc:vne};function kne(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=Wf({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 Ine={kernelName:Tm,backendName:"webgl",kernelFunc:kne},Sne="return (x >= 0.0) ? x : (exp(x) - 1.0);",Nne=` + `; + } +}; +function dilation2D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + const convInfo = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + let out; + const program = new Dilation2DProgram(convInfo); + out = backend2.runWebGLProgram(program, [x, filter], "float32"); + const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } }); + backend2.disposeIntermediateTensorInfo(out); + return outReshaped; +} +var dilation2DConfig2 = { + kernelName: Dilation2D, + backendName: "webgl", + kernelFunc: dilation2D +}; +function einsum3(args) { + const { inputs, backend: backend2, attrs } = args; + const { equation } = attrs; + const tensors = inputs; + const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length); + backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors); + const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims); + const nSteps = steps.length; + let out = null; + let numDimsRemaining = allDims.length; + const tensorsToDispose = []; + for (let i = 0; i < nSteps; ++i) { + for (const idTerm of steps[i]) { + const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]); + let x; + if (backend_util_exports.isIdentityPermutation(perm)) { + x = tensors[idTerm]; + } else { + x = transpose3({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } }); + tensorsToDispose.push(x); + } + const targetShape = x.shape.slice(); + for (let k = 0; k < dimsToExpand.length; ++k) { + targetShape.splice(dimsToExpand[k], 0, 1); + } + if (!util_exports.arraysEqual(x.shape, targetShape)) { + x = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } }); + tensorsToDispose.push(x); + } + if (out === null) { + out = x; + } else { + out = multiply3({ inputs: { a: x, b: out }, backend: backend2 }); + tensorsToDispose.push(out); + } + } + if (i < nSteps - 1) { + if (path[i] >= 0) { + out = sum4({ + inputs: { x: out }, + backend: backend2, + attrs: { + axis: path[i] - (allDims.length - numDimsRemaining), + keepDims: false + } + }); + tensorsToDispose.push(out); + } + numDimsRemaining--; + } + } + for (const tensorInfo of tensorsToDispose) { + if (tensorInfo === out) { + continue; + } + backend2.disposeIntermediateTensorInfo(tensorInfo); + } + return out; +} +var einsumConfig2 = { + kernelName: Einsum, + backendName: "webgl", + kernelFunc: einsum3 +}; +var ELU4 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`; +var ELU_PACKED = ` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3410,29 +62610,70 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,Tne=Ze({opSnippet:Sne,packedOpSnippet:Nne}),Cne={kernelName:qi,backendName:"webgl",kernelFunc:Tne},_ne="return (b >= 0.0) ? a : a * (b + 1.0);",Ene=` +`; +var elu5 = unaryKernelFunc2({ opSnippet: ELU4, packedOpSnippet: ELU_PACKED }); +var eluConfig2 = { + kernelName: Elu, + backendName: "webgl", + kernelFunc: elu5 +}; +var ELU_DER = `return (b >= 0.0) ? a : a * (b + 1.0);`; +var ELU_DER_PACKED = ` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,Ane=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(Ene,a.shape,r.shape):new ki(_ne,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},Fne={kernelName:cu,backendName:"webgl",kernelFunc:Ane},$ne=` +`; +var eluGrad2 = (args) => { + const { inputs, backend: backend2 } = args; + const { dy, y } = inputs; + const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) : new BinaryOpProgram(ELU_DER, dy.shape, y.shape); + return backend2.runWebGLProgram(program, [dy, y], dy.dtype); +}; +var eluGradConfig3 = { + kernelName: EluGrad, + backendName: "webgl", + kernelFunc: eluGrad2 +}; +var PACKED_EQUAL = ` return vec4(equal(a, b)); -`,Dne="return float(a == b);",Rne=mn({opSnippet:Dne,packedOpSnippet:$ne,dtype:"bool",cpuKernelImpl:p9}),Mne={kernelName:du,backendName:"webgl",kernelFunc:Rne},Pne=` +`; +var EQUAL = `return float(a == b);`; +var equal3 = binaryKernelFunc2({ + opSnippet: EQUAL, + packedOpSnippet: PACKED_EQUAL, + dtype: "bool", + cpuKernelImpl: equalImplCPU +}); +var equalConfig2 = { + kernelName: Equal, + backendName: "webgl", + kernelFunc: equal3 +}; +var ERF = ` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. - float p = ${N.ERF_P}; - float a1 = ${N.ERF_A1}; - float a2 = ${N.ERF_A2}; - float a3 = ${N.ERF_A3}; - float a4 = ${N.ERF_A4}; - float a5 = ${N.ERF_A5}; + float p = ${backend_util_exports.ERF_P}; + float a1 = ${backend_util_exports.ERF_A1}; + float a2 = ${backend_util_exports.ERF_A2}; + float a3 = ${backend_util_exports.ERF_A3}; + float a4 = ${backend_util_exports.ERF_A4}; + float a5 = ${backend_util_exports.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,One=Ze({opSnippet:Pne}),Lne={kernelName:ji,backendName:"webgl",kernelFunc:One},zne=mp+` +`; +var erf3 = unaryKernelFunc2({ opSnippet: ERF }); +var erfConfig2 = { + kernelName: Erf, + backendName: "webgl", + kernelFunc: erf3 +}; +var EXP = CHECK_NAN_SNIPPET_UNARY + ` return exp(x); -`,Wne=` +`; +var EXP_PACKED = ` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3441,21 +62682,74 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel result.a = isNaN.a ? x.a : result.a; return result; -`,wA=Ze({opSnippet:zne,packedOpSnippet:Wne,cpuKernelImpl:c9,dtype:"float32"}),Bne={kernelName:Ki,backendName:"webgl",kernelFunc:wA};function pv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var Vne={kernelName:hu,backendName:"webgl",kernelFunc:pv},pS="return exp(x) - 1.0;",Une=Ze({opSnippet:pS,packedOpSnippet:pS,cpuKernelImpl:d9}),Gne={kernelName:Xi,backendName:"webgl",kernelFunc:Une},cS=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}; +`; +var exp3 = unaryKernelFunc2({ + opSnippet: EXP, + packedOpSnippet: EXP_PACKED, + cpuKernelImpl: expImplCPU, + dtype: "float32" +}); +var expConfig2 = { + kernelName: Exp, + backendName: "webgl", + kernelFunc: exp3 +}; +function expandDims4(args) { + const { inputs, attrs, backend: backend2 } = args; + const { dim } = attrs; + const { input: input2 } = inputs; + const inputRank = input2.shape.length; + const newShape = input2.shape.slice(); + let $dim = dim; + if (dim < 0) { + util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`); + $dim = inputRank + dim + 1; + } + newShape.splice($dim, 0, 1); + return reshape4({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } }); +} +var expandDimsConfig2 = { + kernelName: ExpandDims, + backendName: "webgl", + kernelFunc: expandDims4 +}; +var EXPM1 = `return exp(x) - 1.0;`; +var expm13 = unaryKernelFunc2({ opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU }); +var expm1Config2 = { + kernelName: Expm1, + backendName: "webgl", + kernelFunc: expm13 +}; +var FFTProgram = class { + constructor(component, inputShape, inverse) { + this.variableNames = ["real", "imag"]; + const innerDim = inputShape[1]; + this.outputShape = inputShape; + const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`; + const resultDenominator = inverse ? `${innerDim}.0` : "1.0"; + let opString; + if (component === "real") { + opString = "return real * expR - imag * expI;"; + } else if (component === "imag") { + opString = "return real * expI + imag * expR;"; + } else { + throw new Error(`FFT component must be either "real" or "imag", got ${component}.`); + } + this.userCode = ` + const float exponentMultiplier = ${exponentMultiplierSnippet}; float unaryOpComplex(float real, float expR, float imag, float expI) { - ${i} + ${opString} } float mulMatDFT(int batch, int index) { - float indexRatio = float(index) / float(${a}); + float indexRatio = float(index) / float(${innerDim}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; - for (int i = 0; i < ${a}; i++) { + for (int i = 0; i < ${innerDim}; i++) { // x = (-2|2 * PI / N) * index * i; float x = exponentMultiplierTimesIndexRatio * float(i); float expR = cos(x); @@ -3464,7 +62758,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel float imag = getImag(batch, i); result += - unaryOpComplex(real, expR, imag, expI) / ${s}; + unaryOpComplex(real, expR, imag, expI) / ${resultDenominator}; } return result; @@ -3474,26 +62768,126 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function kA(e,t,n){let a=n.texData.get(e.dataId),r=w.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 cS("real",l,t),p=new cS("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=As({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 Hne(e){let{inputs:t,backend:n}=e,{input:a}=t;return kA(a,!1,n)}var qne={kernelName:Cm,backendName:"webgl",kernelFunc:Hne},jne=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `; + } +}; +function fftImpl2(x, inverse, backend2) { + const xData = backend2.texData.get(x.dataId); + const inputSize = util_exports.sizeFromShape(x.shape); + const innerDimensionSize = x.shape[x.shape.length - 1]; + const batch = inputSize / innerDimensionSize; + const input2D = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [batch, innerDimensionSize] } }); + const xShape = input2D.shape; + const realProgram = new FFTProgram("real", xShape, inverse); + const imagProgram = new FFTProgram("imag", xShape, inverse); + const inputs = [ + { + dataId: xData.complexTensorInfos.real.dataId, + dtype: xData.complexTensorInfos.real.dtype, + shape: xShape + }, + { + dataId: xData.complexTensorInfos.imag.dataId, + dtype: xData.complexTensorInfos.imag.dtype, + shape: xShape + } + ]; + const realPart = backend2.runWebGLProgram(realProgram, inputs, "float32"); + const imagPart = backend2.runWebGLProgram(imagProgram, inputs, "float32"); + const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(imagPart); + const complexOutputReshaped = reshape4({ inputs: { x: complexOutput }, backend: backend2, attrs: { shape: x.shape } }); + backend2.disposeIntermediateTensorInfo(input2D); + backend2.disposeIntermediateTensorInfo(complexOutput); + return complexOutputReshaped; +} +function fft3(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + return fftImpl2(input2, false, backend2); +} +var fftConfig2 = { + kernelName: FFT, + backendName: "webgl", + kernelFunc: fft3 +}; +var FillProgram = class { + constructor(shape, value) { + this.outputShape = []; + this.customUniforms = [{ name: "value", type: "float" }]; + this.variableNames = ["x"]; + this.outputShape = shape; + this.userCode = ` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function $d(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new jne(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Kne={kernelName:Oc,backendName:"webgl",kernelFunc:$d},Xne=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `; + } +}; +function fill3(args) { + const { backend: backend2, attrs } = args; + const { shape, value } = attrs; + let { dtype } = attrs; + dtype = dtype || util_exports.inferDtype(value); + if (dtype === "string") { + const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape)); + values.fill(value); + return backend2.makeTensorInfo(shape, dtype, values); + } else { + const program = new FillProgram(shape, value); + const customValues = [[value]]; + return backend2.runWebGLProgram(program, [], dtype, customValues); + } +} +var fillConfig2 = { + kernelName: Fill, + backendName: "webgl", + kernelFunc: fill3 +}; +var FlipLeftRightProgram = class { + constructor(imageShape) { + this.variableNames = ["Image"]; + this.outputShape = []; + const imageWidth = imageShape[2]; + this.outputShape = imageShape; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; - int coordX = ${t} - x - 1; + int coordX = ${imageWidth} - x - 1; float outputValue; - if(coordX >= 0 && coordX < ${t}) { + if(coordX >= 0 && coordX < ${imageWidth}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } - `}},Yne={kernelName:mu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Xne(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},dS="return floor(x);",Zne=Ze({opSnippet:dS,packedOpSnippet:dS,cpuKernelImpl:h9}),Jne={kernelName:Yi,backendName:"webgl",kernelFunc:Zne},Qne=` + `; + } +}; +var flipLeftRightConfig2 = { + kernelName: FlipLeftRight, + backendName: "webgl", + kernelFunc: ({ inputs, backend: backend2 }) => { + const { image: image2 } = inputs; + const webglBackend = backend2; + const program = new FlipLeftRightProgram(image2.shape); + const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype); + return output; + } +}; +var FLOOR = `return floor(x);`; +var floor3 = unaryKernelFunc2({ opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU }); +var floorConfig2 = { + kernelName: Floor, + backendName: "webgl", + kernelFunc: floor3 +}; +var INT_DIV = ` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3503,7 +62897,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } else { return NAN; } -`,eae=` +`; +var INT_DIV_PACKED = ` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3524,15 +62919,28 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,tae=mn({opSnippet:Qne,packedOpSnippet:eae,dtype:"int32"}),nae={kernelName:Zi,backendName:"webgl",kernelFunc:tae},aae=class{constructor(e){this.variableNames=["A"];let t=_n(),[n,a]=e;this.outputShape=e,this.userCode=` +`; +var floorDiv3 = binaryKernelFunc2({ opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: "int32" }); +var floorDivConfig2 = { + kernelName: FloorDiv, + backendName: "webgl", + kernelFunc: floorDiv3 +}; +var FromPixelsProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + const glsl = getGlslDifferences(); + const [height, width] = outputShape; + this.outputShape = outputShape; + this.userCode = ` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; - vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0); + vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0); - vec4 values = ${t.texture2D}(A, uv); + vec4 values = ${glsl.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; @@ -3546,7 +62954,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel setOutput(floor(value * 255.0 + 0.5)); } - `}},rae=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=_n(),[n,a]=e;this.outputShape=e,this.userCode=` + `; + } +}; +var FromPixelsPackedProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = false; + this.packedOutput = true; + const glsl = getGlslDifferences(); + const [height, width] = outputShape; + this.outputShape = outputShape; + this.userCode = ` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3561,8 +62980,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / - vec2(${a}.0, ${n}.0); - vec4 values = ${t.texture2D}(A, uv); + vec2(${width}.0, ${height}.0); + vec4 values = ${glsl.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; @@ -3578,41 +62997,451 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } } - ${t.output} = result; + ${glsl.output} = result; } - `}},sae={kernelName:Hh,backendName:"webgl",kernelFunc:iae},kl,bx=G().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function iae(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],d=[u,l,s];if(o||i){let f=G().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(kl==null||f!==bx)&&(bx=f,kl=document.createElement("canvas").getContext("2d",{willReadFrequently:bx})),kl.canvas.width=l,kl.canvas.height=u,kl.drawImage(r,0,0,l,u),r=kl.canvas}let c=n.makeTensorInfo(p,"int32");n.texData.get(c.dataId).usage=da.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=G().getBool("WEBGL_PACK")?new rae(d):new aae(d),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function oae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=N.convertConv2DDataFormat(p),g=N.computeConv2DInfo(r.shape,s.shape,l,d,u,c,!1,f),b,y=[],x=i!=null,v=o!=null,I=h==="leakyrelu",T=()=>{let E=[r,s],F=(D,$)=>{if($==="NCHW"&&D.shape.length===1&&D.shape[0]!==1){let S=ce({inputs:{x:D},backend:n,attrs:{shape:[D.shape[0],1,1]}});return y.push(S),S}return D};if(x&&E.push(F(i,p)),v&&E.push(F(o,p)),I){let D=n.makeTensorInfo([],"float32",w.createScalarValue(m,"float32"));E.push(D),y.push(D)}return E};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=gA({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(g.strideWidth<=2&&f==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let E=h?Nc(h,!0):null,F=new fA(g,x,E,v,I),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=T();b=n.runWebGLProgram(F,$,"float32",D)}else if(G().getBool("WEBGL_CONV_IM2COL"))b=bA({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let E=h?Nc(h,!1):null,F=new mA(g,x,E,v,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 lae={kernelName:oi,backendName:"webgl",kernelFunc:oae};function uae(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]),w.assert(N.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=N.computeConv2DInfo(r.shape,s.shape,l,f,u,d,!0),b=G().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=c?Nc(c,b):null,x=[r,s],v=i!=null,I=o!=null,T=c==="leakyrelu";if(v&&x.push(i),I&&x.push(o),T){let D=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));x.push(D),m.push(D)}let C;b?C=new vA(g,v,y,I,T):C=new xA(g,v,y,I,T);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=n.runWebGLProgram(C,x,"float32",E);return m.forEach(D=>n.disposeIntermediateTensorInfo(D)),F}var pae={kernelName:li,backendName:"webgl",kernelFunc:uae},cae=class{constructor(e,t,n,a){this.sliceDim=e,this.strides=t,this.paramsShape=a,this.variableNames=["x","indices"],this.outputShape=n;let r=ct(n.length),s=` - int index;`;for(let i=0;i { + const inputs2 = [x, filter]; + const alignInputWithDataFormat = (input2, dataFormat2) => { + if (dataFormat2 === "NCHW" && input2.shape.length === 1 && input2.shape[0] !== 1) { + const alignedInput = reshape4({ + inputs: { x: input2 }, + backend: backend2, + attrs: { shape: [input2.shape[0], 1, 1] } + }); + intermediates.push(alignedInput); + return alignedInput; + } + return input2; + }; + if (hasBias) { + inputs2.push(alignInputWithDataFormat(bias, dataFormat)); + } + if (hasPreluActivationWeights) { + inputs2.push(alignInputWithDataFormat(preluActivationWeights, dataFormat)); + } + if (hasLeakyreluAlpha) { + const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32")); + inputs2.push($leakyreluAlpha); + intermediates.push($leakyreluAlpha); + } + return inputs2; + }; + if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) { + out = conv2dByMatMul({ + x, + filter, + convInfo, + backend: backend2, + bias, + activation: activation2, + preluActivationWeights, + leakyreluAlpha + }); + } else if (convInfo.strideWidth <= 2 && $dataFormat === "channelsLast" && env().getBool("WEBGL_EXP_CONV")) { + const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null; + const program = new Conv2DPackedProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + const customValues = [ + [convInfo.padInfo.top, convInfo.padInfo.left], + [convInfo.strideHeight, convInfo.strideWidth], + [convInfo.dilationHeight, convInfo.dilationWidth], + [convInfo.inHeight, convInfo.inWidth] + ]; + const inputs2 = prepareInputs(); + out = backend2.runWebGLProgram(program, inputs2, "float32", customValues); + } else if (env().getBool("WEBGL_CONV_IM2COL")) { + out = conv2dWithIm2Row({ + x, + filter, + convInfo, + backend: backend2, + bias, + activation: activation2, + preluActivationWeights, + leakyreluAlpha + }); + } else { + const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, false) : null; + const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + const inputs2 = prepareInputs(); + out = backend2.runWebGLProgram(program, inputs2, "float32"); + } + const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } }); + intermediates.push(out); + intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return outReshaped; +} +var fusedConv2DConfig2 = { + kernelName: FusedConv2D, + backendName: "webgl", + kernelFunc: fusedConv2d +}; +function fusedDepthwiseConv2D2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const intermediates = []; + let $dilations = dilations; + if ($dilations == null) { + $dilations = [1, 1]; + } + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`); + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filter.shape, + strides, + $dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const shouldPackDepthwiseConv = env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1; + const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, shouldPackDepthwiseConv) : null; + const programInputs = [x, filter]; + const hasBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + const hasLeakyreluAlpha = activation2 === "leakyrelu"; + if (hasBias) { + programInputs.push(bias); + } + if (hasPreluActivationWeights) { + programInputs.push(preluActivationWeights); + } + if (hasLeakyreluAlpha) { + const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32")); + programInputs.push($leakyreluAlpha); + intermediates.push($leakyreluAlpha); + } + let program; + if (shouldPackDepthwiseConv) { + program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + } else { + program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + } + const customValues = [ + [convInfo.padInfo.top, convInfo.padInfo.left], + [convInfo.strideHeight, convInfo.strideWidth], + [convInfo.dilationHeight, convInfo.dilationWidth], + [convInfo.inHeight, convInfo.inWidth] + ]; + const result = backend2.runWebGLProgram(program, programInputs, "float32", customValues); + intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var fusedDepthwiseConv2DConfig2 = { + kernelName: FusedDepthwiseConv2D, + backendName: "webgl", + kernelFunc: fusedDepthwiseConv2D2 +}; +var GatherNDProgram = class { + constructor(sliceDim, strides, shape, paramsShape) { + this.sliceDim = sliceDim; + this.strides = strides; + this.paramsShape = paramsShape; + this.variableNames = ["x", "indices"]; + this.outputShape = shape; + const dtype = getCoordsDataType(shape.length); + let mainLoop = ` + int index;`; + for (let j = 0; j < this.sliceDim; j++) { + mainLoop += ` + index = round(getIndices(coords[0], ${j})); out_of_bounds = out_of_bounds || index < 0; - out_of_bounds = out_of_bounds || index >= ${this.paramsShape[i]}; - flattenIndex += index * ${this.strides[i]};`;this.userCode=` + out_of_bounds = out_of_bounds || index >= ${this.paramsShape[j]}; + flattenIndex += index * ${this.strides[j]};`; + } + this.userCode = ` void main() { - ${r} coords = getOutputCoords(); + ${dtype} coords = getOutputCoords(); int flattenIndex = 0; bool out_of_bounds = false; - ${s} + ${mainLoop} setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1])); } - `}};function dae(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=w.sizeFromShape(a.shape),[l,u,p,d]=N.prepareAndValidate(a,r),c=ce({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),h=ce({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape)/p,p]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(a),x=m9(b,y,a.dtype,u,i,p,d,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new cae(i,d,[u,p],a.shape),f=n.runWebGLProgram(m,[h,c],h.dtype),g=ce({inputs:{x:f},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),g}var hae={kernelName:gu,backendName:"webgl",kernelFunc:dae},mae=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ct(this.rank),a=fae(e,2);this.userCode=` + `; + } +}; +function gatherNd2(args) { + const { inputs, backend: backend2 } = args; + const { params, indices } = inputs; + const indicesShape = indices.shape; + const sliceRank = indicesShape[indicesShape.length - 1]; + const paramsSize = util_exports.sizeFromShape(params.shape); + const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices); + const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } }); + const flattenX = reshape4({ + inputs: { x: params }, + backend: backend2, + attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] } + }); + if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === "string") { + const indicesData = backend2.readSync(indices.dataId); + const paramsBuf = backend2.bufferSync(params); + const outValue = gatherNdImplCPU(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize); + return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values); + } + const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize], params.shape); + const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype); + const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } }); + backend2.disposeIntermediateTensorInfo(flattenIndices); + backend2.disposeIntermediateTensorInfo(flattenX); + backend2.disposeIntermediateTensorInfo(res); + return reshaped; +} +var gatherNdConfig2 = { + kernelName: GatherNd, + backendName: "webgl", + kernelFunc: gatherNd2 +}; +var GatherProgram = class { + constructor(aShape, outputShape) { + this.variableNames = ["A", "indices"]; + this.outputShape = outputShape; + this.rank = outputShape.length; + const dtype = getCoordsDataType(this.rank); + const sourceCoords = getSourceCoords2(aShape, 2); + this.userCode = ` void main() { - ${n} resRC = getOutputCoords(); + ${dtype} resRC = getOutputCoords(); int index = int(getIndices(resRC.x, resRC.z)); - float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0; - setOutput(inBounds * getA(${a})); + float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0; + setOutput(inBounds * getA(${sourceCoords})); } - `}};function fae(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r=0,()=>`GatherV2: the index value ${I} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=w.sizeFromShape(s.shape),d=[],c=ce({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=ce({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,p/u.batchSize]}});d.push(c),d.push(h);let m=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let y=n.bufferSync(h),x=n.bufferSync(c),v=f9(x,y,m);return d.forEach(I=>n.disposeIntermediateTensorInfo(I)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new mae(c.shape,m),g=n.runWebGLProgram(f,[c,h],c.dtype);d.push(g);let b=ce({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var gae={kernelName:fu,backendName:"webgl",kernelFunc:IA},bae="return float(a > b);",yae=` + `; + } +}; +function getSourceCoords2(aShape, axis) { + const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"]; + const sourceCoords = []; + for (let i = 0; i < aShape.length; i++) { + if (i === 2) { + sourceCoords.push("index"); + } else { + sourceCoords.push(`${currentCoords[i]}`); + } + } + return sourceCoords.join(); +} +function gatherV22(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, indices } = inputs; + const { axis, batchDims } = attrs; + const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0]; + if (env().get("DEBUG")) { + const indicesVals = backend2.readSync(indices.dataId); + const axisDim = x.shape[parsedAxis]; + for (let i = 0; i < indicesVals.length; ++i) { + const index = indicesVals[i]; + util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`); + } + } + const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims); + const indicesSize = util_exports.sizeFromShape(indices.shape); + const toDispose = []; + const flattenX = reshape4({ + inputs: { x }, + backend: backend2, + attrs: { + shape: [ + shapeInfo.batchSize, + shapeInfo.outerSize, + shapeInfo.dimSize, + shapeInfo.sliceSize + ] + } + }); + const flattenIndex = reshape4({ + inputs: { x: indices }, + backend: backend2, + attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] } + }); + toDispose.push(flattenX); + toDispose.push(flattenIndex); + const flattenOutputShape = [ + shapeInfo.batchSize, + shapeInfo.outerSize, + indicesSize / shapeInfo.batchSize, + shapeInfo.sliceSize + ]; + if (backend2.shouldExecuteOnCPU([x, indices]) || x.dtype === "string") { + const indicesBuf = backend2.bufferSync(flattenIndex); + const xBuf = backend2.bufferSync(flattenX); + const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape); + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values); + } + const program = new GatherProgram(flattenX.shape, flattenOutputShape); + const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype); + toDispose.push(res); + const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } }); + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return reshaped; +} +var gatherV2Config2 = { + kernelName: GatherV2, + backendName: "webgl", + kernelFunc: gatherV22 +}; +var GREATER = `return float(a > b);`; +var GREATER_PACKED = ` return vec4(greaterThan(a, b)); -`,xae=mn({opSnippet:bae,packedOpSnippet:yae,cpuKernelImpl:g9,dtype:"bool"}),vae={kernelName:bu,backendName:"webgl",kernelFunc:xae},wae="return float(a >= b);",kae=` +`; +var greater4 = binaryKernelFunc2({ + opSnippet: GREATER, + packedOpSnippet: GREATER_PACKED, + cpuKernelImpl: greaterImplCPU, + dtype: "bool" +}); +var greaterConfig2 = { + kernelName: Greater, + backendName: "webgl", + kernelFunc: greater4 +}; +var GREATER_EQUAL = `return float(a >= b);`; +var GREATER_EQUAL_PACKED = ` return vec4(greaterThanEqual(a, b)); -`,Iae=mn({opSnippet:wae,packedOpSnippet:kae,dtype:"bool",cpuKernelImpl:b9}),Sae={kernelName:Qi,backendName:"webgl",kernelFunc:Iae};function Nae(e){let{inputs:t,backend:n}=e,{input:a}=t;return kA(a,!0,n)}var Tae={kernelName:_m,backendName:"webgl",kernelFunc:Nae},Cae="return float(!isnan(x) && !isinf(x));",_ae=Ze({opSnippet:Cae,dtype:"bool"}),Eae={kernelName:to,backendName:"webgl",kernelFunc:_ae},Aae="return float(isinf(x));",Fae=Ze({opSnippet:Aae,dtype:"bool"}),$ae={kernelName:no,backendName:"webgl",kernelFunc:Fae},Dae="return float(isnan(x));",Rae=Ze({opSnippet:Dae,dtype:"bool"}),Mae={kernelName:ao,backendName:"webgl",kernelFunc:Rae},Pae="return float(a < b);",Oae=` +`; +var greaterEqual3 = binaryKernelFunc2({ + opSnippet: GREATER_EQUAL, + packedOpSnippet: GREATER_EQUAL_PACKED, + dtype: "bool", + cpuKernelImpl: greaterEqualImplCPU +}); +var greaterEqualConfig2 = { + kernelName: GreaterEqual, + backendName: "webgl", + kernelFunc: greaterEqual3 +}; +function ifft3(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + return fftImpl2(input2, true, backend2); +} +var ifftConfig2 = { + kernelName: IFFT, + backendName: "webgl", + kernelFunc: ifft3 +}; +var IS_FINITE = `return float(!isnan(x) && !isinf(x));`; +var isFinite4 = unaryKernelFunc2({ opSnippet: IS_FINITE, dtype: "bool" }); +var isFiniteConfig2 = { + kernelName: IsFinite, + backendName: "webgl", + kernelFunc: isFinite4 +}; +var IS_INF = `return float(isinf(x));`; +var isInf3 = unaryKernelFunc2({ opSnippet: IS_INF, dtype: "bool" }); +var isInfConfig2 = { + kernelName: IsInf, + backendName: "webgl", + kernelFunc: isInf3 +}; +var IS_NAN = `return float(isnan(x));`; +var isNaN4 = unaryKernelFunc2({ opSnippet: IS_NAN, dtype: "bool" }); +var isNaNConfig2 = { + kernelName: IsNan, + backendName: "webgl", + kernelFunc: isNaN4 +}; +var LESS = `return float(a < b);`; +var LESS_PACKED = ` return vec4(lessThan(a, b)); -`,Lae=mn({opSnippet:Pae,packedOpSnippet:Oae,cpuKernelImpl:y9,dtype:"bool"}),zae={kernelName:yu,backendName:"webgl",kernelFunc:Lae},Wae="return float(a <= b);",Bae=` +`; +var less4 = binaryKernelFunc2({ + opSnippet: LESS, + packedOpSnippet: LESS_PACKED, + cpuKernelImpl: lessImplCPU, + dtype: "bool" +}); +var lessConfig2 = { + kernelName: Less, + backendName: "webgl", + kernelFunc: less4 +}; +var LESS_EQUAL = `return float(a <= b);`; +var LESS_EQUAL_PACKED = ` return vec4(lessThanEqual(a, b)); -`,Vae=mn({opSnippet:Wae,packedOpSnippet:Bae,cpuKernelImpl:x9,dtype:"bool"}),Uae={kernelName:xu,backendName:"webgl",kernelFunc:Vae};function Gae(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=v9(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var Hae={kernelName:vu,backendName:"webgl",kernelFunc:Gae},qae=mp+` +`; +var lessEqual3 = binaryKernelFunc2({ + opSnippet: LESS_EQUAL, + packedOpSnippet: LESS_EQUAL_PACKED, + cpuKernelImpl: lessEqualImplCPU, + dtype: "bool" +}); +var lessEqualConfig2 = { + kernelName: LessEqual, + backendName: "webgl", + kernelFunc: lessEqual3 +}; +function linSpace2(args) { + const { backend: backend2, attrs } = args; + const { start, stop, num } = attrs; + const outVals = linSpaceImplCPU(start, stop, num); + return backend2.makeTensorInfo([outVals.length], "float32", outVals); +} +var linSpaceConfig2 = { + kernelName: LinSpace, + backendName: "webgl", + kernelFunc: linSpace2 +}; +var LOG = CHECK_NAN_SNIPPET_UNARY + ` return x < 0.0 ? 0./0. : log(x); -`,jae=` +`; +var LOG_PACKED = ` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -3620,18 +63449,75 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel 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; -`,Kae=Ze({opSnippet:qae,packedOpSnippet:jae,cpuKernelImpl:w9}),Xae={kernelName:so,backendName:"webgl",kernelFunc:Kae},Yae=mp+` +`; +var log4 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU }); +var logConfig2 = { + kernelName: Log, + backendName: "webgl", + kernelFunc: log4 +}; +var LOG1P = CHECK_NAN_SNIPPET_UNARY + ` return log(1.0 + x); -`,Zae=Ze({opSnippet:Yae}),Jae={kernelName:io,backendName:"webgl",kernelFunc:Zae},Qae="return float(a >= 1.0 && b >= 1.0);",ere=` +`; +var log1p3 = unaryKernelFunc2({ opSnippet: LOG1P }); +var log1pConfig2 = { + kernelName: Log1p, + backendName: "webgl", + kernelFunc: log1p3 +}; +var LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`; +var LOGICAL_AND_PACKED = ` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,tre=mn({opSnippet:Qae,packedOpSnippet:ere,dtype:"bool"}),nre={kernelName:wu,backendName:"webgl",kernelFunc:tre},are="return float(!(x >= 1.0));",rre=Ze({opSnippet:are}),sre={kernelName:ku,backendName:"webgl",kernelFunc:rre},ire="return float(a >= 1.0 || b >= 1.0);",ore=` +`; +var logicalAnd3 = binaryKernelFunc2({ + opSnippet: LOGICAL_AND, + packedOpSnippet: LOGICAL_AND_PACKED, + dtype: "bool" +}); +var logicalAndConfig2 = { + kernelName: LogicalAnd, + backendName: "webgl", + kernelFunc: logicalAnd3 +}; +var LOGICAL_NOT = `return float(!(x >= 1.0));`; +var logicalNot3 = unaryKernelFunc2({ opSnippet: LOGICAL_NOT }); +var logicalNotConfig2 = { + kernelName: LogicalNot, + backendName: "webgl", + kernelFunc: logicalNot3 +}; +var LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`; +var LOGICAL_OR_PACKED = ` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,lre=mn({opSnippet:ire,packedOpSnippet:ore,dtype:"bool"}),ure={kernelName:Iu,backendName:"webgl",kernelFunc:lre},pre=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=` +`; +var logicalOr3 = binaryKernelFunc2({ opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: "bool" }); +var logicalOrConfig2 = { + kernelName: LogicalOr, + backendName: "webgl", + kernelFunc: logicalOr3 +}; +var LRNProgram = class { + constructor(xShape, radius, bias, alpha, beta) { + this.variableNames = ["x"]; + this.outputShape = []; + const rad = radius; + const maxD = xShape[3] - 1; + this.outputShape = xShape; + let powOperator; + const basis = `float(${bias}) + float(${alpha}) * sum`; + if (beta === 0.5) { + powOperator = `inversesqrt(${basis})`; + } else if (beta === 1) { + powOperator = `1.0/(${basis})`; + } else { + powOperator = `exp(log(${basis}) * float(-${beta}));`; + } + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3640,17 +63526,38 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; - for (int j = -${s}; j <= ${s}; j++) { + for (int j = -${rad}; j <= ${rad}; j++) { int idx = d + j; - if (idx >= 0 && idx <= ${i}) { + if (idx >= 0 && idx <= ${maxD}) { float z = getX(b, r, c, idx); sum += z * z; } } - float val = x * ${o}; + float val = x * ${powOperator}; setOutput(val); } - `}},cre=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + `; + } +}; +var LRNPackedProgram = class { + constructor(xShape, radius, bias, alpha, beta) { + this.variableNames = ["x"]; + this.outputShape = []; + this.packedInputs = true; + this.packedOutput = true; + const rad = radius; + const maxD = xShape[3] - 1; + this.outputShape = xShape; + let powOperator; + const basis = `float(${bias}) + float(${alpha}) * sum`; + if (beta === 0.5) { + powOperator = `inversesqrt(${basis})`; + } else if (beta === 1) { + powOperator = `1.0/(${basis})`; + } else { + powOperator = `exp(log(${basis}) * float(-${beta}));`; + } + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3674,7 +63581,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); - int firstChannel = d - ${s}; + int firstChannel = d - ${rad}; vec2 cache = vec2(0.); if(firstChannel >= 0){ vec4 firstChannelFrag = getX(b, r, c, firstChannel); @@ -3685,10 +63592,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } ivec2 depth = ivec2(d, d + 1); - for (int j = - ${s}; j <= ${s}; j++) { + for (int j = - ${rad}; j <= ${rad}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); - bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i})); + bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD})); bool depthInRange = aboveLowerBound.x && belowUpperBound.x; bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; @@ -3709,10 +63616,35 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel sum += z * z; } } - vec4 result = xAtOutputCoords * ${o}; + vec4 result = xAtOutputCoords * ${powOperator}; setOutput(result); } - `}},dre=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 cre(r.shape,s,i,o,l):new pre(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},hre={kernelName:oo,backendName:"webgl",kernelFunc:dre},mre=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=` + `; + } +}; +var lrn = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) : new LRNProgram(x.shape, depthRadius, bias, alpha, beta); + return backend2.runWebGLProgram(program, [x], x.dtype); +}; +var LRNConfig2 = { + kernelName: LRN, + backendName: "webgl", + kernelFunc: lrn +}; +var LRNGradProgram = class { + constructor(inputShape, depthRadius, bias, alpha, beta) { + this.variableNames = ["inputImage", "outputImage", "dy"]; + this.outputShape = []; + this.outputShape = inputShape; + this.depth = inputShape[3]; + this.depthRadius = depthRadius; + this.bias = bias; + this.alpha = alpha; + this.beta = beta; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3721,9 +63653,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { - int depthBegin = int(max(0.0, float(d - ${t}))); + int depthBegin = int(max(0.0, float(d - ${depthRadius}))); int depthEnd = int(min(float(${this.depth}), - float(d + ${t} + 1))); + float(d + ${depthRadius} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; @@ -3741,19 +63673,19 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } } - norm = float(${a}) * norm + float(${n}); + norm = float(${alpha}) * norm + float(${bias}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ - float dyi = -2.0 * float(${a}) - * float(${r}) + float dyi = -2.0 * float(${alpha}) + * float(${beta}) * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { - dyi += pow(norm, -1.0 * ${r}); + dyi += pow(norm, -1.0 * ${beta}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); @@ -3767,17 +63699,155 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(result); } - `}},fre=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 mre(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},gre={kernelName:Su,backendName:"webgl",kernelFunc:fre};function bre(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=el(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=w.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 { + const { inputs, backend: backend2, attrs } = args; + const { x, y, dy } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta); + return backend2.runWebGLProgram(program, [x, y, dy], x.dtype); +}; +var LRNGradConfig2 = { + kernelName: LRNGrad, + backendName: "webgl", + kernelFunc: lrnGrad +}; +function maxImpl2(x, reduceShape, outShape, backend2) { + const inSize = util_exports.sizeFromShape(reduceShape); + const xSize = util_exports.sizeFromShape(x.shape); + const batchSize = xSize / inSize; + const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 }); + const reduced = reduce(reshapedInput, x.dtype, "max", backend2); + const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(reshapedInput); + backend2.disposeIntermediateTensorInfo(reduced); + return reshapedOutput; +} +function max4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { reductionIndices, keepDims } = attrs; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + const maxInputIsTransposed = permutedAxes != null; + const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]); + let maxInput = x; + if (maxInputIsTransposed) { + if (shouldExecuteOnCPU) { + const xTexData = backend2.texData.get(maxInput.dataId); + const values = xTexData.values; + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[permutedAxes[i]]; + } + const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape); + maxInput = backend2.makeTensorInfo(newShape, x.dtype); + const maxInputData = backend2.texData.get(maxInput.dataId); + maxInputData.values = maxInputValues; + } else { + maxInput = transposeImpl2(x, permutedAxes, backend2); + } + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank); + const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(maxInput.shape, axes); + let outShape = maxOutShape; + if (keepDims) { + outShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes); + } + let out; + if (shouldExecuteOnCPU) { + const xTexData = backend2.texData.get(maxInput.dataId); + const values = xTexData.values; + const outValues = maxImplCPU(values, util_exports.sizeFromShape(reduceShape), outShape, x.dtype); + out = backend2.makeTensorInfo(outShape, x.dtype); + const outData = backend2.texData.get(out.dataId); + outData.values = outValues; + } else { + out = maxImpl2(maxInput, reduceShape, outShape, backend2); + } + if (maxInputIsTransposed) { + backend2.disposeIntermediateTensorInfo(maxInput); + } + return out; +} +var maxConfig2 = { + kernelName: Max, + backendName: "webgl", + kernelFunc: max4 +}; +var MAXIMUM = CHECK_NAN_SNIPPET2 + ` return max(a, b); -`,vre=` +`; +var MAXIMUM_PACKED = ` vec4 result = vec4(max(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); - `+Qo+` + ` + CHECK_NAN_SNIPPET_PACKED + ` return result; -`,wre=mn({opSnippet:xre,packedOpSnippet:vre,cpuKernelImpl:I9}),kre={kernelName:uo,backendName:"webgl",kernelFunc:wre};function Ire(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;lp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.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&&w.arraysEqual(p.inShape,p.outShape))return aa({inputs:{x:r},backend:n});let d=new Tc(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Sre={kernelName:po,backendName:"webgl",kernelFunc:Ire};function Nre(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 Tre={kernelName:Nu,backendName:"webgl",kernelFunc:Nre},Cre=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}); +`; +var maximum4 = binaryKernelFunc2({ + opSnippet: MAXIMUM, + packedOpSnippet: MAXIMUM_PACKED, + cpuKernelImpl: maximumImplCPU +}); +var maximumConfig2 = { + kernelName: Maximum, + backendName: "webgl", + kernelFunc: maximum4 +}; +function maxPool3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + assertNotComplex2(x, "maxPool"); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = 1; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) { + return identity3({ inputs: { x }, backend: backend2 }); + } + const maxPoolProgram = new Pool2DProgram(convInfo, "max", false); + return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype); +} +var maxPoolConfig2 = { + kernelName: MaxPool, + backendName: "webgl", + kernelFunc: maxPool3 +}; +function maxPool3d2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs; + const dilations = [1, 1, 1]; + const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat); + const maxPoolProgram = new Pool3DProgram(convInfo, "max", false); + return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype); +} +var maxPool3DConfig2 = { + kernelName: MaxPool3D, + backendName: "webgl", + kernelFunc: maxPool3d2 +}; +var MaxPool2DBackpropProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "maxPos"]; + this.outputShape = convInfo.inShape; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1; + this.userCode = ` + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); @@ -3791,30 +63861,30 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${r}; - wR += ${a}) { - float dyR = float(dyRCorner + wR) / ${t}.0; + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - for (int wC = 0; wC < ${s}; wC++) { - float dyC = float(dyCCorner + wC) / ${n}.0; + for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); - int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d)); + int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. - int curPosValue = wR * ${s} + wC; + int curPosValue = wR * ${effectiveFilterWidth} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; @@ -3822,8 +63892,28 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(dotProd); } - `}},_re=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` - const ivec3 pads = ivec3(${p}, ${d}, ${c}); + `; + } +}; +var MaxPool3DBackpropProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "maxPos"]; + this.outputShape = convInfo.inShape; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1; + this.userCode = ` + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); void main() { ivec5 coords = getOutputCoords(); @@ -3840,44 +63930,44 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wD = 0; wD < ${o}; - wD += ${r}) { - float dyD = float(dyDCorner + wD) / ${t}.0; + for (int wD = 0; wD < ${effectiveFilterDepth}; + wD += ${dilationDepth}) { + float dyD = float(dyDCorner + wD) / ${strideDepth}.0; - if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { + if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); - for (int wR = 0; wR < ${l}; - wR += ${s}) { - float dyR = float(dyRCorner + wR) / ${n}.0; + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - for (int wC = 0; wC < ${u}; - wC += ${i}) { - float dyC = float(dyCCorner + wC) / ${a}.0; + for (int wC = 0; wC < ${effectiveFilterWidth}; + wC += ${dilationWidth}) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); - int maxPosValue = ${h} - + int maxPosValue = ${lastIndex} - int(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. int curPosValue = - wD * ${l} * ${u} + - wR * ${u} + wC; + wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} + + wR * ${effectiveFilterWidth} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; @@ -3886,107 +63976,359 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } setOutput(dotProd); } - `}};function Ere(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 ak(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new _re(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Are={kernelName:zc,backendName:"webgl",kernelFunc:Ere};function Fre(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;lp([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 Tc(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Cre(c),b=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),b}var $re={kernelName:Lc,backendName:"webgl",kernelFunc:Fre};function Dre(e,t,n,a){let r=new Tc(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Tc(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Rre={kernelName:Wc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];w.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]=Dre(a,o,p,l);return[d,c]}};function Mre(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=el(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var Pre={kernelName:co,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=w.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,v=new Array(o);for(let C=0;C { + const { x } = inputs; + const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs; + const webglBackend = backend2; + util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`); + const dilations = [1, 1]; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3); + const [result, indexes] = maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, webglBackend); + return [result, indexes]; + } +}; +function meanImpl(x, reduceShape, outShape, backend2) { + const inSize = util_exports.sizeFromShape(reduceShape); + const xSize = util_exports.sizeFromShape(x.shape); + const batchSize = xSize / inSize; + const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 }); + const reduced = reduce(reshapedInput, "float32", "mean", backend2); + const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(reshapedInput); + backend2.disposeIntermediateTensorInfo(reduced); + return reshapedOutput; +} +var meanConfig2 = { + kernelName: Mean, + backendName: "webgl", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { x } = inputs; + const { keepDims, axis } = attrs; + const webglBackend = backend2; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + const meanInputIsTransposed = permutedAxes != null; + const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]); + const intermediates = []; + let meanInput = x; + if (meanInputIsTransposed) { + if (shouldExecuteOnCPU) { + const xTexData = webglBackend.texData.get(meanInput.dataId); + const values = xTexData.values; + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[permutedAxes[i]]; + } + const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape); + meanInput = webglBackend.makeTensorInfo(newShape, x.dtype); + const meanInputData = webglBackend.texData.get(meanInput.dataId); + meanInputData.values = meanInputValues; + } else { + meanInput = transposeImpl2(x, permutedAxes, webglBackend); + } + intermediates.push(meanInput); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank); + const [meanOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(meanInput.shape, axes); + let outShape = meanOutShape; + if (keepDims) { + outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes); + } + const out = meanImpl(meanInput, reduceShape, outShape, webglBackend); + for (const i of intermediates) { + webglBackend.disposeIntermediateTensorInfo(i); + } + return out; + } +}; +function min4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let permutedX = x; + if (permutedAxes != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("min", axes, xRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes); + const inSize = util_exports.sizeFromShape(reduceShape); + const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } }); + const reduced = reduce(a2D, a2D.dtype, "min", backend2); + let res; + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } }); + } else { + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } }); + } + backend2.disposeIntermediateTensorInfo(a2D); + backend2.disposeIntermediateTensorInfo(reduced); + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo(permutedX); + } + return res; +} +var minConfig2 = { + kernelName: Min, + backendName: "webgl", + kernelFunc: min4 +}; +var MINIMUM = CHECK_NAN_SNIPPET2 + ` return min(a, b); -`,Wre=` +`; +var MINIMUM_PACKED = ` vec4 result = vec4(min(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); - `+Qo+` + ` + CHECK_NAN_SNIPPET_PACKED + ` return result; -`,Bre=mn({opSnippet:zre,packedOpSnippet:Wre,cpuKernelImpl:S9}),Vre={kernelName:mo,backendName:"webgl",kernelFunc:Bre},Ure=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}; +`; +var minimum4 = binaryKernelFunc2({ + opSnippet: MINIMUM, + packedOpSnippet: MINIMUM_PACKED, + cpuKernelImpl: minimumImplCPU +}); +var minimumConfig2 = { + kernelName: Minimum, + backendName: "webgl", + kernelFunc: minimum4 +}; +var MirrorPadProgram = class { + constructor(xShape, paddings, mode) { + this.variableNames = ["x"]; + this.outputShape = paddings.map( + (p2, i) => p2[0] + xShape[i] + p2[1] + /* afterPad */ + ); + const rank = xShape.length; + const dtype = getCoordsDataType(rank); + const start = paddings.map((p2) => p2[0]).join(","); + const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(","); + const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank); + const offset = mode === "reflect" ? 0 : 1; + if (rank === 1) { + this.userCode = ` + int start = ${start}; + int end = ${end}; void main() { int outC = getOutputCoords(); if (outC < start) { - outC = start * 2 - outC - ${l}; + outC = start * 2 - outC - ${offset}; } else if(outC >= end) { - outC = (end - 1) * 2 - outC + ${l}; + outC = (end - 1) * 2 - outC + ${offset}; } setOutput(getX(outC - start)); } - `;return}this.userCode=` - ${r} start = ${r}(${s}); - ${r} end = ${r}(${i}); + `; + return; + } + this.userCode = ` + ${dtype} start = ${dtype}(${start}); + ${dtype} end = ${dtype}(${end}); void main() { - ${r} outC = getOutputCoords(); - for (int i = 0; i < ${a}; i++) { + ${dtype} outC = getOutputCoords(); + for (int i = 0; i < ${rank}; i++) { if (outC[i] < start[i]) { - outC[i] = start[i] * 2 - outC[i] - ${l}; + outC[i] = start[i] * 2 - outC[i] - ${offset}; } else if(outC[i] >= end[i]) { - outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; + outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset}; } } - ${r} coords = outC - start; - setOutput(getX(${o})); + ${dtype} coords = outC - start; + setOutput(getX(${unpackedCoords})); } - `}},Gre=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=` - ${r} source = rc; + `; + } +}; +var MirrorPadPackedProgram = class { + constructor(xShape, paddings, mode) { + this.variableNames = ["x"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = paddings.map( + (p2, i) => p2[0] + xShape[i] + p2[1] + /* afterPad */ + ); + const rank = xShape.length; + const dtype = getCoordsDataType(rank); + const start = paddings.map((p2) => p2[0]).join(","); + const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(","); + const coords2 = getChannels("rc", rank); + const source = getChannels("source", rank); + const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`; + const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`; + const offset = mode === "reflect" ? 0 : 1; + let mainLoop = ""; + if (rank === 1) { + const padSetup = ` + ${dtype} source = rc; if (source < start) { - source = start * 2 - source - ${d}; + source = start * 2 - source - ${offset}; } else if (source >= end) { - source = (end - 1) * 2 - source + ${d}; + source = (end - 1) * 2 - source + ${offset}; } source -= start; - `;c=` - ${r} rc = outputLoc; - ${h} - result[0] = getChannel(getX(${l.join()}), ${p}); - ${o[a-1]} += 1; - if(${u}) { - ${h} - result[1] = getChannel(getX(${l.join()}), ${p}); + `; + mainLoop = ` + ${dtype} rc = outputLoc; + ${padSetup} + result[0] = getChannel(getX(${source.join()}), ${innerDims}); + ${coords2[rank - 1]} += 1; + if(${cLimit}) { + ${padSetup} + result[1] = getChannel(getX(${source.join()}), ${innerDims}); } - `}else{let h=` - ${r} source = rc; - ${r} lt = ${r}(lessThan(source, start)); - ${r} gte = ${r}(greaterThanEqual(source, end)); - ${r} orig = 1 - (lt + gte); + `; + } else { + const padSetup = ` + ${dtype} source = rc; + ${dtype} lt = ${dtype}(lessThan(source, start)); + ${dtype} gte = ${dtype}(greaterThanEqual(source, end)); + ${dtype} orig = 1 - (lt + gte); source = orig * source + - lt * (start * 2 - source - ${d}) + - gte * ((end - 1) * 2 - source + ${d}); + lt * (start * 2 - source - ${offset}) + + gte * ((end - 1) * 2 - source + ${offset}); source -= start; - `;c=` - ${r} rc = outputLoc; - ${h} - result[0] = getChannel(getX(${l.join()}), ${p}); - ${o[a-1]} += 1; - if(${u}) { - ${h} - result[1] = getChannel(getX(${l.join()}), ${p}); + `; + mainLoop = ` + ${dtype} rc = outputLoc; + ${padSetup} + result[0] = getChannel(getX(${source.join()}), ${innerDims}); + ${coords2[rank - 1]} += 1; + if(${cLimit}) { + ${padSetup} + result[1] = getChannel(getX(${source.join()}), ${innerDims}); } rc = outputLoc; - ${o[a-2]} += 1; - if(${o[a-2]} < ${this.outputShape[a-2]}) { - ${h} - result[2] = getChannel(getX(${l.join()}), ${p}); - ${o[a-1]} += 1; - if(${u}) { - ${h} - result[3] = getChannel(getX(${l.join()}), ${p}); + ${coords2[rank - 2]} += 1; + if(${coords2[rank - 2]} < ${this.outputShape[rank - 2]}) { + ${padSetup} + result[2] = getChannel(getX(${source.join()}), ${innerDims}); + ${coords2[rank - 1]} += 1; + if(${cLimit}) { + ${padSetup} + result[3] = getChannel(getX(${source.join()}), ${innerDims}); } } - `}this.userCode=` - const ${r} start = ${r}(${s}); - const ${r} end = ${r}(${i}); + `; + } + this.userCode = ` + const ${dtype} start = ${dtype}(${start}); + const ${dtype} end = ${dtype}(${end}); void main() { - ${r} outputLoc = getOutputCoords(); + ${dtype} outputLoc = getOutputCoords(); vec4 result = vec4(0.); - ${c} + ${mainLoop} setOutput(result); } - `}},Hre=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Gre(a.shape,r,s):new Ure(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},qre={kernelName:fo,backendName:"webgl",kernelFunc:Hre},jre=`if (b == 0.0) return NAN; - return mod(a, b);`,Kre=` + `; + } +}; +var mirrorPadKernelFunc = ({ inputs, backend: backend2, attrs }) => { + const { x } = inputs; + const { paddings, mode } = attrs; + const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new MirrorPadPackedProgram(x.shape, paddings, mode) : new MirrorPadProgram(x.shape, paddings, mode); + const output = backend2.runWebGLProgram(program, [x], x.dtype); + return output; +}; +var mirrorPadConfig2 = { + kernelName: MirrorPad, + backendName: "webgl", + kernelFunc: mirrorPadKernelFunc +}; +var MOD = `if (b == 0.0) return NAN; + return mod(a, b);`; +var MOD_PACKED = ` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+Qo+` + ` + CHECK_NAN_SNIPPET_PACKED + ` return result; -`,Xre=mn({opSnippet:jre,packedOpSnippet:Kre}),Yre={kernelName:go,backendName:"webgl",kernelFunc:Xre},Zre=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` +`; +var mod3 = binaryKernelFunc2({ + opSnippet: MOD, + packedOpSnippet: MOD_PACKED +}); +var modConfig2 = { + kernelName: Mod, + backendName: "webgl", + kernelFunc: mod3 +}; +var MultinomialProgram = class { + constructor(batchSize, numOutcomes, numSamples) { + this.variableNames = ["probs"]; + this.customUniforms = [{ name: "seed", type: "float" }]; + this.outputShape = [batchSize, numSamples]; + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3994,7 +64336,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel float r = random(seed); float cdf = 0.0; - for (int i = 0; i < ${t-1}; i++) { + for (int i = 0; i < ${numOutcomes - 1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { @@ -4004,13 +64346,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Nee=Ze({opSnippet:See}),Tee={kernel } // If no other event happened, last event happened. - setOutput(float(${t-1})); + setOutput(float(${numOutcomes - 1})); } - `}},Jre=` + `; + } +}; +var DIV = ` if (a == b) { return 1.0; }; -return a / b;`,Qre=` +return a / b;`; +var DIV_PACKED = ` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4028,9 +64374,79 @@ return a / b;`,Qre=` } return result; -`,NA=mn({opSnippet:Jre,packedOpSnippet:Qre,checkOutOfBounds:!0}),ese={kernelName:Hi,backendName:"webgl",kernelFunc:NA},hS="return a - b;",TA=mn({opSnippet:hS,packedOpSnippet:hS,supportsComplex:!0,cpuKernelImpl:H9}),tse={kernelName:Bo,backendName:"webgl",kernelFunc:TA};function CA(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.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=Wf({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 nse={kernelName:zo,backendName:"webgl",kernelFunc:CA};function ase(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 Zre(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var rse={kernelName:Tu,backendName:"webgl",kernelFunc:ase},sse=Ma+` +`; +var realDiv = binaryKernelFunc2({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true }); +var realDivConfig2 = { + kernelName: RealDiv, + backendName: "webgl", + kernelFunc: realDiv +}; +var SUB = "return a - b;"; +var sub3 = binaryKernelFunc2({ + opSnippet: SUB, + packedOpSnippet: SUB, + supportsComplex: true, + cpuKernelImpl: subImplCPU +}); +var subConfig2 = { + kernelName: Sub, + backendName: "webgl", + kernelFunc: sub3 +}; +function softmax4(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { dim } = attrs; + const axes = util_exports.parseAxisParam([dim], logits.shape); + const maxLogit = max4({ + inputs: { x: logits }, + backend: backend2, + attrs: { reductionIndices: axes, keepDims: false } + }); + const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes); + const maxLogitsReshaped = reshape4({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } }); + const a = sub3({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 }); + const b = exp3({ inputs: { x: a }, backend: backend2 }); + const sumExp = sum4({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } }); + const sumExpReshaped = reshape4({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } }); + const res = realDiv({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(maxLogit); + backend2.disposeIntermediateTensorInfo(maxLogitsReshaped); + backend2.disposeIntermediateTensorInfo(a); + backend2.disposeIntermediateTensorInfo(b); + backend2.disposeIntermediateTensorInfo(sumExp); + backend2.disposeIntermediateTensorInfo(sumExpReshaped); + return res; +} +var softmaxConfig2 = { + kernelName: Softmax, + backendName: "webgl", + kernelFunc: softmax4 +}; +function multinomial3(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { numSamples, seed, normalized } = attrs; + const probs = normalized ? logits : softmax4({ inputs: { logits }, backend: backend2, attrs: { dim: logits.shape.length - 1 } }); + const batchSize = probs.shape[0]; + const numOutcomes = probs.shape[1]; + const program = new MultinomialProgram(batchSize, numOutcomes, numSamples); + const customValues = [[seed]]; + const res = backend2.runWebGLProgram(program, [probs], "int32", customValues); + if (!normalized) { + backend2.disposeIntermediateTensorInfo(probs); + } + return res; +} +var multinomialConfig2 = { + kernelName: Multinomial, + backendName: "webgl", + kernelFunc: multinomial3 +}; +var NEG = CHECK_NAN_SNIPPET + ` return -x; -`,ise=` +`; +var NEG_PACKED = ` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4040,16 +64456,218 @@ return a / b;`,Qre=` result.a = isNaN.a ? x.a : result.a; return result; -`;function ose(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=T9(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ns(a.shape,ise):r=new rr(a.shape,sse),n.runWebGLProgram(r,[a],a.dtype)}var lse={kernelName:Cu,backendName:"webgl",kernelFunc:ose},use=mr.nonMaxSuppressionV3Impl;function pse(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}=use(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var cse={kernelName:Eu,backendName:"webgl",kernelFunc:pse},dse=mr.nonMaxSuppressionV4Impl;function hse(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}=dse(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var mse={kernelName:Au,backendName:"webgl",kernelFunc:hse},fse=mr.nonMaxSuppressionV5Impl;function gse(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}=fse(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var bse={kernelName:Fu,backendName:"webgl",kernelFunc:gse},yse=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`; +function neg3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (backend2.shouldExecuteOnCPU([x])) { + const xData = backend2.texData.get(x.dataId); + const [outValues, newShape] = negImplCPU(xData.values, x.shape, x.dtype); + return backend2.makeTensorInfo(newShape, x.dtype, outValues); + } + let program; + if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) { + program = new UnaryOpPackedProgram(x.shape, NEG_PACKED); + } else { + program = new UnaryOpProgram(x.shape, NEG); + } + return backend2.runWebGLProgram(program, [x], x.dtype); +} +var negConfig2 = { + kernelName: Neg, + backendName: "webgl", + kernelFunc: neg3 +}; +var nonMaxSuppressionV3Impl3 = kernel_impls_exports.nonMaxSuppressionV3Impl; +function nonMaxSuppressionV32(args) { + backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold } = attrs; + const boxesVals = backend2.readSync(boxes.dataId); + const scoresVals = backend2.readSync(scores.dataId); + const { selectedIndices } = nonMaxSuppressionV3Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold); + return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)); +} +var nonMaxSuppressionV3Config2 = { + kernelName: NonMaxSuppressionV3, + backendName: "webgl", + kernelFunc: nonMaxSuppressionV32 +}; +var nonMaxSuppressionV4Impl3 = kernel_impls_exports.nonMaxSuppressionV4Impl; +function nonMaxSuppressionV42(args) { + backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs; + const boxesVals = backend2.readSync(boxes.dataId); + const scoresVals = backend2.readSync(scores.dataId); + const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize); + return [ + backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)), + backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs])) + ]; +} +var nonMaxSuppressionV4Config2 = { + kernelName: NonMaxSuppressionV4, + backendName: "webgl", + kernelFunc: nonMaxSuppressionV42 +}; +var nonMaxSuppressionV5Impl3 = kernel_impls_exports.nonMaxSuppressionV5Impl; +function nonMaxSuppressionV52(args) { + backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs; + const boxesVals = backend2.readSync(boxes.dataId); + const scoresVals = backend2.readSync(scores.dataId); + const maxOutputSizeVal = maxOutputSize; + const iouThresholdVal = iouThreshold; + const scoreThresholdVal = scoreThreshold; + const softNmsSigmaVal = softNmsSigma; + const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl3(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal); + return [ + backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)), + backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores)) + ]; +} +var nonMaxSuppressionV5Config2 = { + kernelName: NonMaxSuppressionV5, + backendName: "webgl", + kernelFunc: nonMaxSuppressionV52 +}; +var OneHotProgram = class { + constructor(numIndices, depth, onValue, offValue) { + this.variableNames = ["indices"]; + this.outputShape = [numIndices, depth]; + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); - setOutput(mix(float(${a}), float(${n}), + setOutput(mix(float(${offValue}), float(${onValue}), float(index == coords.y))); } - `}},xse=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=w.sizeFromShape(r.shape),p=new yse(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},vse={kernelName:yo,backendName:"webgl",kernelFunc:xse};function gm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Fd({inputs:{input:a},backend:n}),s=gm({inputs:{x:r},backend:n}),i=Bf({inputs:{input:a},backend:n}),o=gm({inputs:{x:i},backend:n}),l=As({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $d({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var wse={kernelName:Yu,backendName:"webgl",kernelFunc:gm};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=Fd({inputs:{input:a},backend:n}),s=_A({inputs:{x:r},backend:n}),i=Bf({inputs:{input:a},backend:n}),o=gm({inputs:{x:i},backend:n}),l=As({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return $d({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var kse={kernelName:$u,backendName:"webgl",kernelFunc:_A};function Ise(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=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.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=hA({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Sse={kernelName:Du,backendName:"webgl",kernelFunc:Ise},Nse=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}; + `; + } +}; +var oneHot3 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { indices } = inputs; + const { dtype, depth, onValue, offValue } = attrs; + const indicesSize = util_exports.sizeFromShape(indices.shape); + const program = new OneHotProgram(indicesSize, depth, onValue, offValue); + const reshaped = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [indicesSize] } }); + const result = backend2.runWebGLProgram(program, [reshaped], dtype); + backend2.disposeIntermediateTensorInfo(reshaped); + const outShape = [...indices.shape, depth]; + const out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outShape } }); + backend2.disposeIntermediateTensorInfo(result); + return out; +}; +var oneHotConfig2 = { + kernelName: OneHot, + backendName: "webgl", + kernelFunc: oneHot3 +}; +function zerosLike3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (x.dtype === "complex64") { + const realPart = real3({ inputs: { input: x }, backend: backend2 }); + const r = zerosLike3({ inputs: { x: realPart }, backend: backend2 }); + const imagPart = imag3({ inputs: { input: x }, backend: backend2 }); + const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 }); + const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(r); + backend2.disposeIntermediateTensorInfo(imagPart); + backend2.disposeIntermediateTensorInfo(i); + return result; + } else { + return fill3({ + attrs: { + shape: x.shape, + dtype: x.dtype, + value: x.dtype === "string" ? "" : 0 + }, + backend: backend2 + }); + } +} +var zerosLikeConfig2 = { + kernelName: ZerosLike, + backendName: "webgl", + kernelFunc: zerosLike3 +}; +function onesLike3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (x.dtype === "string") { + throw new Error("onesLike is not supported under string dtype"); + } else if (x.dtype === "complex64") { + const realPart = real3({ inputs: { input: x }, backend: backend2 }); + const r = onesLike3({ inputs: { x: realPart }, backend: backend2 }); + const imagPart = imag3({ inputs: { input: x }, backend: backend2 }); + const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 }); + const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(r); + backend2.disposeIntermediateTensorInfo(imagPart); + backend2.disposeIntermediateTensorInfo(i); + return result; + } else { + return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 }); + } +} +var onesLikeConfig2 = { + kernelName: OnesLike, + backendName: "webgl", + kernelFunc: onesLike3 +}; +function pack2(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + if (inputs.length === 1) { + return expandDims4({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } }); + } + const shape = inputs[0].shape; + const dtype = inputs[0].dtype; + inputs.forEach((t) => { + util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes"); + util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes"); + }); + const intermediateTensorInfos = []; + const expandedTensors = inputs.map((t) => { + const expandedT = expandDims4({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } }); + intermediateTensorInfos.push(expandedT); + return expandedT; + }); + const result = concat3({ inputs: expandedTensors, backend: backend2, attrs: { axis } }); + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var packConfig2 = { + kernelName: Pack, + backendName: "webgl", + kernelFunc: pack2 +}; +var PadProgram = class { + constructor(xShape, paddings, constantValue) { + this.variableNames = ["x"]; + this.customUniforms = [{ name: "value", type: "float" }]; + this.outputShape = paddings.map( + (p2, i) => p2[0] + xShape[i] + p2[1] + /* afterPad */ + ); + const rank = xShape.length; + const type = getCoordsDataType(rank); + const start = paddings.map((p2) => p2[0]).join(","); + const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(","); + const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank); + if (rank === 1) { + this.userCode = ` + int start = ${start}; + int end = ${end}; void main() { int outC = getOutputCoords(); @@ -4059,44 +64677,106 @@ return a / b;`,Qre=` setOutput(getX(outC - start)); } } - `;return}this.userCode=` - ${r} start = ${r}(${s}); - ${r} end = ${r}(${i}); + `; + return; + } + this.userCode = ` + ${type} start = ${type}(${start}); + ${type} end = ${type}(${end}); void main() { - ${r} outC = getOutputCoords(); + ${type} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { - ${r} coords = outC - start; - setOutput(getX(${o})); + ${type} coords = outC - start; + setOutput(getX(${unpackedCoords})); } } - `}},Tse=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(${u}) { - `,a===1?"":`} + `; + } +}; +var PadPackedProgram = class { + constructor(xShape, paddings, constantValue) { + this.variableNames = ["x"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [{ name: "value", type: "float" }]; + this.outputShape = paddings.map( + (p2, i) => p2[0] + xShape[i] + p2[1] + /* afterPad */ + ); + const rank = xShape.length; + const dtype = getCoordsDataType(rank); + const start = paddings.map((p2) => p2[0]).join(","); + const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(","); + const coords2 = getChannels("rc", rank); + const source = getChannels("source", rank); + const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`; + const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`; + const componentSetup = [ + `${dtype} rc = outputLoc;`, + `${coords2[rank - 1]} += 1; + if(${cLimit}) { + `, + rank === 1 ? "" : `} rc = outputLoc; - ${o[a-2]} += 1; - if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1; - if(${u}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))"; + let mainLoop = ""; + for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) { + mainLoop += ` + ${componentSetup[i]} + if (${paddingArea}) { + result[${i}] = float(value); } else { - ${r} source = rc - start; - result[${m}] = getChannel(getX(${l.join()}), ${p}); + ${dtype} source = rc - start; + result[${i}] = getChannel(getX(${source.join()}), ${innerDims}); } - `;h+=a===1?"} ":"}}",this.userCode=` - const ${r} start = ${r}(${s}); - const ${r} end = ${r}(${i}); + `; + } + mainLoop += rank === 1 ? `} ` : `}}`; + this.userCode = ` + const ${dtype} start = ${dtype}(${start}); + const ${dtype} end = ${dtype}(${end}); void main() { - ${r} outputLoc = getOutputCoords(); + ${dtype} outputLoc = getOutputCoords(); vec4 result = vec4(0.); - ${h} + ${mainLoop} setOutput(result); } - `}},EA=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return $d({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Tse(r.shape,s,i):new Nse(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},Cse={kernelName:xo,backendName:"webgl",kernelFunc:EA},_se=` + `; + } +}; +var padV22 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { paddings, constantValue } = attrs; + if (util_exports.sizeFromShape(x.shape) === 0) { + const outputShape = paddings.map( + (p2, i) => p2[0] + x.shape[i] + p2[1] + /* afterPad */ + ); + return fill3({ + backend: backend2, + attrs: { shape: outputShape, value: constantValue, dtype: x.dtype } + }); + } + const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new PadPackedProgram(x.shape, paddings, constantValue) : new PadProgram(x.shape, paddings, constantValue); + const customValues = [[constantValue]]; + return backend2.runWebGLProgram(program, [x], x.dtype, customValues); +}; +var padV2Config2 = { + kernelName: PadV2, + backendName: "webgl", + kernelFunc: padV22 +}; +var POW = ` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4105,7 +64785,8 @@ return a / b;`,Qre=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,Ese=` +`; +var POW_PACKED = ` // 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); @@ -4121,11 +64802,132 @@ return a / b;`,Qre=` 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+` + ` + CHECK_NAN_SNIPPET_PACKED + ` return result; -`,Ase=mn({opSnippet:_se,packedOpSnippet:Ese}),Fse={kernelName:vo,backendName:"webgl",kernelFunc:Ase};function $se(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=w.parseAxisParam(s,r.shape),p=u,d=N.getAxesPermutation(p,o),c=r;d!=null&&(c=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=N.getInnerMostAxes(p.length,o),l.push(c)),N.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:b}=_9(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=w.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Mm(r.dtype),x=el(b,y,"prod",n);h=ce({inputs:{x},backend:n,attrs:{shape:m}}),l.push(b),l.push(x)}if(i){l.push(h);let m=N.expandShapeToKeepDim(h.shape,u);h=ce({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Dse={kernelName:ko,backendName:"webgl",kernelFunc:$se};function Rse(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]=E9(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 Mse={kernelName:Am,backendName:"webgl",kernelFunc:Rse};function Pse(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 Ose={kernelName:Fm,backendName:"webgl",kernelFunc:Pse};function Lse(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]=F9(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var zse={kernelName:$m,backendName:"webgl",kernelFunc:Lse},AA=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=$9(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Wse={kernelName:Bc,backendName:"webgl",kernelFunc:AA},Bse="return 1.0 / x;",Vse=Ze({opSnippet:Bse}),Use={kernelName:Io,backendName:"webgl",kernelFunc:Vse},Gse=Ma+` +`; +var pow3 = binaryKernelFunc2({ opSnippet: POW, packedOpSnippet: POW_PACKED }); +var powConfig2 = { + kernelName: Pow, + backendName: "webgl", + kernelFunc: pow3 +}; +function prod3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + const xRank = x.shape.length; + const toDispose = []; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let permutedX = x; + if (permutedAxes != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + toDispose.push(permutedX); + } + backend_util_exports.assertAxesAreInnerMostDims("prod", axes, xRank); + let res; + if (backend2.shouldExecuteOnCPU([permutedX])) { + const xVals = backend2.texData.get(permutedX.dataId).values; + const { outVals, outShape, outDtype } = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes); + res = backend2.makeTensorInfo(outShape, outDtype, outVals); + } else { + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes); + const inSize = util_exports.sizeFromShape(reduceShape); + const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } }); + const outputDType = sumOutType(x.dtype); + const reduced = reduce(a2D, outputDType, "prod", backend2); + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } }); + toDispose.push(a2D); + toDispose.push(reduced); + } + if (keepDims) { + toDispose.push(res); + const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes); + res = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: newShape } }); + } + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return res; +} +var prodConfig2 = { + kernelName: Prod, + backendName: "webgl", + kernelFunc: prod3 +}; +function raggedGather3(args) { + const { inputs, backend: backend2, attrs } = args; + const { paramsNestedSplits, paramsDenseValues, indices } = inputs; + const { outputRaggedRank } = attrs; + const $paramsNestedSplits = paramsNestedSplits.map((t) => backend2.readSync(t.dataId)); + const $paramsNestedSplitsShapes = paramsNestedSplits.map((t) => t.shape); + const $paramsDenseValues = backend2.readSync(paramsDenseValues.dataId); + const $indices = backend2.readSync(indices.dataId); + const [outputNestedSplits, outputDenseValues, outputDenseValuesShape] = raggedGatherImplCPU($paramsNestedSplits, $paramsNestedSplitsShapes, $paramsDenseValues, paramsDenseValues.shape, paramsDenseValues.dtype, $indices, indices.shape, outputRaggedRank); + const outputNestedSplitsTensors = outputNestedSplits.map((splits) => backend2.makeTensorInfo([splits.length], "int32", splits)); + const outputDenseValuesTensor = backend2.makeTensorInfo(outputDenseValuesShape, paramsDenseValues.dtype, outputDenseValues); + return outputNestedSplitsTensors.concat([outputDenseValuesTensor]); +} +var raggedGatherConfig2 = { + kernelName: RaggedGather, + backendName: "webgl", + kernelFunc: raggedGather3 +}; +function raggedRange3(args) { + const { inputs, backend: backend2 } = args; + const { starts, limits, deltas } = inputs; + const $starts = backend2.readSync(starts.dataId); + const $limits = backend2.readSync(limits.dataId); + const $deltas = backend2.readSync(deltas.dataId); + const [rtNestedSplitsData, rtDenseValuesData] = raggedRangeImplCPU($starts, starts.shape, starts.dtype, $limits, limits.shape, $deltas, deltas.shape); + const rtNestedSplits = backend2.makeTensorInfo([rtNestedSplitsData.length], "int32", rtNestedSplitsData); + const rtDenseValues = backend2.makeTensorInfo([rtDenseValuesData.length], starts.dtype, rtDenseValuesData); + return [rtNestedSplits, rtDenseValues]; +} +var raggedRangeConfig2 = { + kernelName: RaggedRange, + backendName: "webgl", + kernelFunc: raggedRange3 +}; +function raggedTensorToTensor3(args) { + const { inputs, backend: backend2, attrs } = args; + const { shape, values, defaultValue, rowPartitionTensors } = inputs; + const { rowPartitionTypes } = attrs; + const $shape = backend2.readSync(shape.dataId); + const $values = backend2.readSync(values.dataId); + const $defaultValue = backend2.readSync(defaultValue.dataId); + const $rowPartitionValues = rowPartitionTensors.map((t) => backend2.readSync(t.dataId)); + const rowPartitionValuesShapes = rowPartitionTensors.map((t) => t.shape); + const [outputShape, output] = raggedTensorToTensorImplCPU($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes); + return backend2.makeTensorInfo(outputShape, values.dtype, output); +} +var raggedTensorToTensorConfig2 = { + kernelName: RaggedTensorToTensor, + backendName: "webgl", + kernelFunc: raggedTensorToTensor3 +}; +var range4 = (args) => { + const { backend: backend2, attrs } = args; + const { start, stop, step: step5, dtype } = attrs; + const values = rangeImplCPU(start, stop, step5, dtype); + return backend2.makeTensorInfo([values.length], dtype, values); +}; +var rangeConfig2 = { + kernelName: Range, + backendName: "webgl", + kernelFunc: range4 +}; +var RECIPROCAL = `return 1.0 / x;`; +var reciprocal3 = unaryKernelFunc2({ opSnippet: RECIPROCAL }); +var reciprocalConfig2 = { + kernelName: Reciprocal, + backendName: "webgl", + kernelFunc: reciprocal3 +}; +var RELU3 = CHECK_NAN_SNIPPET + ` return (x < 0.0) ? 0.0 : x; -`,Hse=` +`; +var RELU_PACKED = ` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4135,9 +64937,17 @@ return a / b;`,Qre=` result.a = isNaN.a ? x.a : result.a; return result; -`,qse=Ze({opSnippet:Gse,packedOpSnippet:Hse}),jse={kernelName:So,backendName:"webgl",kernelFunc:qse},Kse=Ma+` +`; +var relu3 = unaryKernelFunc2({ opSnippet: RELU3, packedOpSnippet: RELU_PACKED }); +var reluConfig2 = { + kernelName: Relu, + backendName: "webgl", + kernelFunc: relu3 +}; +var RELU63 = CHECK_NAN_SNIPPET + ` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Xse=` +`; +var RELU6_PACKED = ` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4147,11 +64957,38 @@ return a / b;`,Qre=` result.a = isNaN.a ? x.a : result.a; return result; -`,Yse=Ze({opSnippet:Kse,packedOpSnippet:Xse}),Zse={kernelName:Co,backendName:"webgl",kernelFunc:Yse},Jse=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=` +`; +var relu63 = unaryKernelFunc2({ opSnippet: RELU63, packedOpSnippet: RELU6_PACKED }); +var relu6Config2 = { + kernelName: Relu6, + backendName: "webgl", + kernelFunc: relu63 +}; +var ResizeBilinearProgram = class { + constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) { + this.variableNames = ["A"]; + this.outputShape = []; + const [batch, oldHeight, oldWidth, depth] = inputShape; + this.outputShape = [batch, newHeight, newWidth, depth]; + const effectiveInSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + let sourceFracIndexRC; + if (halfPixelCenters) { + sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)`; + } else { + sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`; + } + this.userCode = ` const vec2 effectiveInputOverOutputRatioRC = vec2( - ${u[0]/p[0]}, - ${u[1]/p[1]}); - const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); + ${effectiveInSize[0] / effectiveOutSize[0]}, + ${effectiveInSize[1] / effectiveOutSize[1]}); + const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0); void main() { ivec4 coords = getOutputCoords(); @@ -4160,7 +64997,7 @@ return a / b;`,Qre=` ivec2 yRC = coords.yz; // Fractional source index. - vec2 sourceFracIndexRC = ${d}; + vec2 sourceFracIndexRC = ${sourceFracIndexRC}; // Compute the four integer indices. ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0))); @@ -4180,13 +65017,38 @@ return a / b;`,Qre=` setOutput(newValue); } - `}},Qse=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=` + `; + } +}; +var ResizeBilinearPackedProgram = class { + constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = []; + const [batch, oldHeight, oldWidth, depth] = inputShape; + this.outputShape = [batch, newHeight, newWidth, depth]; + const effectiveInSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + let sourceFracIndexRC; + if (halfPixelCenters) { + sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)`; + } else { + sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`; + } + this.userCode = ` const vec3 effectiveInputOverOutputRatioRC = vec3( - ${u[0]/p[0]}, - ${u[1]/p[1]}, - ${u[1]/p[1]}); - const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, - ${o}.0); + ${effectiveInSize[0] / effectiveOutSize[0]}, + ${effectiveInSize[1] / effectiveOutSize[1]}, + ${effectiveInSize[1] / effectiveOutSize[1]}); + const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0, + ${oldWidth}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); @@ -4200,7 +65062,7 @@ return a / b;`,Qre=` ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. - vec3 sourceFracIndexRC = ${d}; + vec3 sourceFracIndexRC = ${sourceFracIndexRC}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); @@ -4208,8 +65070,8 @@ return a / b;`,Qre=` min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); // Should we calculate next column and row elements in 2x2 packed cell. - bool hasNextCol = d < ${l-1}; - bool hasNextRow = coords.z < ${n-1}; + bool hasNextCol = d < ${depth - 1}; + bool hasNextRow = coords.z < ${newWidth - 1}; // In parallel, construct four corners for all four components in // packed 2x2 cell. @@ -4257,7 +65119,44 @@ return a / b;`,Qre=` setOutput(newValue); } - `}};function eie(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 Qse(r.shape,l,u,s,i):new Jse(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var tie={kernelName:To,backendName:"webgl",kernelFunc:eie},nie=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 resizeBilinear4(args) { + const { inputs, backend: backend2, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + const [newHeight, newWidth] = size; + const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters); + return backend2.runWebGLProgram(program, [images], "float32"); +} +var resizeBilinearConfig2 = { + kernelName: ResizeBilinear, + backendName: "webgl", + kernelFunc: resizeBilinear4 +}; +var ResizeBilinearBackpropProgram = class { + constructor(dyShape, inputShape, alignCorners) { + this.variableNames = ["dy"]; + this.outputShape = []; + this.outputShape = inputShape; + const [, xHeight, xWidth] = inputShape; + const [, yHeight, yWidth] = dyShape; + const effectiveXSize = [ + alignCorners && yHeight > 1 ? xHeight - 1 : xHeight, + alignCorners && yWidth > 1 ? xWidth - 1 : xWidth + ]; + const effectiveYSize = [ + alignCorners && yHeight > 1 ? yHeight - 1 : yHeight, + alignCorners && yWidth > 1 ? yWidth - 1 : yWidth + ]; + const heightScale = effectiveXSize[0] / effectiveYSize[0]; + const widthScale = effectiveXSize[1] / effectiveYSize[1]; + const invHeightScale = 1 / heightScale; + const invWidthScale = 1 / widthScale; + const winHeight = Math.ceil(invHeightScale) * 2 + 2; + const winWidth = Math.ceil(invWidthScale) * 2 + 2; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4267,14 +65166,14 @@ return a / b;`,Qre=` float accumulator = 0.0; - const float heightScale = float(${u}); - const float widthScale = float(${p}); + const float heightScale = float(${heightScale}); + const float widthScale = float(${widthScale}); - const float invHeightScale = float(${d}); - const float invWidthScale = float(${c}); + const float invHeightScale = float(${invHeightScale}); + const float invWidthScale = float(${invWidthScale}); - const int winHeight = int(${h}); - const int winWidth = int(${m}); + const int winHeight = int(${winHeight}); + const int winWidth = int(${winWidth}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); @@ -4288,7 +65187,7 @@ return a / b;`,Qre=` int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy - if (dyR < 0 || dyR >= ${s}) { + if (dyR < 0 || dyR >= ${yHeight}) { continue; } @@ -4296,19 +65195,19 @@ return a / b;`,Qre=` int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy - if (dyC < 0 || dyC >= ${i}) { + if (dyC < 0 || dyC >= ${yWidth}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); - int bottomDxRIndex = int(min(ceil(dxR), ${a-1}.0)); + int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0)); float dxRLerp = dxR - float(topDxRIndex); float inverseDxRLerp = 1.0 - dxRLerp; float dxC = float(dyC) * widthScale; int leftDxCIndex = int(floor(dxC)); - int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0)); + int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; @@ -4338,11 +65237,47 @@ return a / b;`,Qre=` setOutput(accumulator); } - `}};function aie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new nie(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var rie={kernelName:Pu,backendName:"webgl",kernelFunc:aie},sie=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 resizeBilinearGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + const program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners); + return backend2.runWebGLProgram(program, [dy], dy.dtype); +} +var resizeBilinearGradConfig3 = { + kernelName: ResizeBilinearGrad, + backendName: "webgl", + kernelFunc: resizeBilinearGrad2 +}; +var ResizeNearestNeighborProgram = class { + constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) { + this.variableNames = ["A"]; + this.outputShape = []; + const [batch, oldHeight, oldWidth, depth] = inputShape; + this.outputShape = [batch, newHeight, newWidth, depth]; + const effectiveInSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + const roundBase = alignCorners ? "0.5" : "0.0"; + let sourceFracIndexRC; + if (halfPixelCenters) { + sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`; + } else { + sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`; + } + this.userCode = ` const vec2 effectiveInputOverOutputRatioRC = vec2( - ${u[0]/p[0]}, - ${u[1]/p[1]}); - const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); + ${effectiveInSize[0] / effectiveOutSize[0]}, + ${effectiveInSize[1] / effectiveOutSize[1]}); + const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0); void main() { ivec4 coords = getOutputCoords(); @@ -4351,22 +65286,48 @@ return a / b;`,Qre=` ivec2 yRC = coords.yz; // Fractional source index. - vec2 sourceFracIndexRC = ${c}; + vec2 sourceFracIndexRC = ${sourceFracIndexRC}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( - min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); + min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } - `}},iie=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=` + `; + } +}; +var ResizeNearestNeighborPackedProgram = class { + constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = []; + const [batch, oldHeight, oldWidth, depth] = inputShape; + this.outputShape = [batch, newHeight, newWidth, depth]; + const effectiveInSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + const roundBase = alignCorners ? "0.5" : "0.0"; + let sourceFracIndexRC; + if (halfPixelCenters) { + sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))`; + } else { + sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`; + } + this.userCode = ` const vec3 effectiveInputOverOutputRatioRC = vec3( - ${u[0]/p[0]}, - ${u[1]/p[1]}, - ${u[1]/p[1]}); - const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, - ${o}.0); + ${effectiveInSize[0] / effectiveOutSize[0]}, + ${effectiveInSize[1] / effectiveOutSize[1]}, + ${effectiveInSize[1] / effectiveOutSize[1]}); + const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0, + ${oldWidth}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); @@ -4380,15 +65341,15 @@ return a / b;`,Qre=` ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. - vec3 sourceFracIndexRC = ${c}; + vec3 sourceFracIndexRC = ${sourceFracIndexRC}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( - min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); + min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase}))); // Should we calculate next column and row elements in 2x2 packed cell. - bool hasNextCol = d < ${l-1}; - bool hasNextRow = coords.z < ${n-1}; + bool hasNextCol = d < ${depth - 1}; + bool hasNextRow = coords.z < ${newWidth - 1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), @@ -4401,7 +65362,44 @@ return a / b;`,Qre=` setOutput(newValue); } - `}};function oie(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 iie(r.shape,l,u,s,i):new sie(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var lie={kernelName:No,backendName:"webgl",kernelFunc:oie},uie=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 resizeNearestNeighbor3(args) { + const { inputs, backend: backend2, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + const [newHeight, newWidth] = size; + const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeNearestNeighborPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters); + return backend2.runWebGLProgram(program, [images], images.dtype); +} +var resizeNearestNeighborConfig2 = { + kernelName: ResizeNearestNeighbor, + backendName: "webgl", + kernelFunc: resizeNearestNeighbor3 +}; +var ResizeNearestNeigborBackpropProgram = class { + constructor(dyShape, inputShape, alignCorners) { + this.variableNames = ["dy"]; + this.outputShape = []; + this.outputShape = inputShape; + const [, xHeight, xWidth] = inputShape; + const [, yHeight, yWidth] = dyShape; + const effectiveXSize = [ + alignCorners && yHeight > 1 ? xHeight - 1 : xHeight, + alignCorners && yWidth > 1 ? xWidth - 1 : xWidth + ]; + const effectiveYSize = [ + alignCorners && yHeight > 1 ? yHeight - 1 : yHeight, + alignCorners && yWidth > 1 ? yWidth - 1 : yWidth + ]; + const heightScale = effectiveXSize[0] / effectiveYSize[0]; + const widthScale = effectiveXSize[1] / effectiveYSize[1]; + const invHeightScale = 1 / heightScale; + const invWidthScale = 1 / widthScale; + const winHeight = Math.ceil(invHeightScale) * 2 + 2; + const winWidth = Math.ceil(invWidthScale) * 2 + 2; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4411,14 +65409,14 @@ return a / b;`,Qre=` float accumulator = 0.0; - const float heightScale = float(${u}); - const float widthScale = float(${p}); + const float heightScale = float(${heightScale}); + const float widthScale = float(${widthScale}); - const float invHeightScale = float(${d}); - const float invWidthScale = float(${c}); + const float invHeightScale = float(${invHeightScale}); + const float invWidthScale = float(${invWidthScale}); - const int winHeight = int(${h}); - const int winWidth = int(${m}); + const int winHeight = int(${winHeight}); + const int winWidth = int(${winWidth}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); @@ -4432,7 +65430,7 @@ return a / b;`,Qre=` int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy - if (dyR < 0 || dyR >= ${s}) { + if (dyR < 0 || dyR >= ${yHeight}) { continue; } @@ -4440,26 +65438,26 @@ return a / b;`,Qre=` int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy - if (dyC < 0 || dyC >= ${i}) { + if (dyC < 0 || dyC >= ${yWidth}) { continue; } float sourceFracRow = - float(${o[0]}) * - (float(dyR) / float(${l[0]})); + float(${effectiveXSize[0]}) * + (float(dyR) / float(${effectiveYSize[0]})); float sourceFracCol = - float(${o[1]}) * - (float(dyC) / float(${l[1]})); + float(${effectiveXSize[1]}) * + (float(dyC) / float(${effectiveYSize[1]})); int sourceNearestRow = int(min( - float(int(${a}) - 1), - ${n} ? float(round(sourceFracRow)) : + float(int(${xHeight}) - 1), + ${alignCorners} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( - float(int(${r}) - 1), - ${n} ? float(round(sourceFracCol)) : + float(int(${xWidth}) - 1), + ${alignCorners} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { @@ -4471,47 +65469,166 @@ return a / b;`,Qre=` setOutput(accumulator); } - `}};function pie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new uie(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var cie={kernelName:Mu,backendName:"webgl",kernelFunc:pie},die=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `; + } +}; +function resizeNearestNeighborGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + const program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners); + return backend2.runWebGLProgram(program, [dy], dy.dtype); +} +var resizeNearestNeighborGradConfig3 = { + kernelName: ResizeNearestNeighborGrad, + backendName: "webgl", + kernelFunc: resizeNearestNeighborGrad2 +}; +var ReverseProgram = class { + constructor(xShape, axis) { + this.variableNames = ["x"]; + const rank = xShape.length; + if (rank > 4) { + throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`); + } + this.outputShape = xShape; + if (rank === 1) { + this.userCode = ` void main() { int coord = getOutputCoords(); - setOutput(getX(${e[0]} - coord - 1)); + setOutput(getX(${xShape[0]} - coord - 1)); } - `;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=ct(n);this.userCode=` - void main() { - ${s} coords = getOutputCoords(); - setOutput(getX(${r})); + `; + return; + } + const getInCoord = (i) => { + if (axis.indexOf(i) !== -1 && xShape[i] !== 1) { + return `${xShape[i]} - coords[${i}] - 1`; } - `}},hie=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 < ${this.outputShape[n-2]}`,i=ct(n);n===1?this.userCode=` + return `coords[${i}]`; + }; + const inCoords = xShape.map((_, i) => getInCoord(i)).join(","); + const type = getCoordsDataType(rank); + this.userCode = ` + void main() { + ${type} coords = getOutputCoords(); + setOutput(getX(${inCoords})); + } + `; + } +}; +var ReversePackedProgram = class { + constructor(xShape, axis) { + this.variableNames = ["x"]; + this.packedInputs = true; + this.packedOutput = true; + const rank = xShape.length; + if (rank > 4) { + throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`); + } + this.outputShape = xShape; + const channels = getChannels("rc", rank); + const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`; + const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`; + const type = getCoordsDataType(rank); + if (rank === 1) { + this.userCode = ` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); - result.r = getChannel(getX(${e[0]} - rc - 1), - ${e[0]} - rc - 1); - if(${r}){ - result.g = getChannel(getX(${e[0]} - (rc + 1) - 1), - ${e[0]} - (rc + 1) - 1); + result.r = getChannel(getX(${xShape[0]} - rc - 1), + ${xShape[0]} - rc - 1); + if(${nextColumn}){ + result.g = getChannel(getX(${xShape[0]} - (rc + 1) - 1), + ${xShape[0]} - (rc + 1) - 1); } setOutput(result); } - `:this.userCode=` + `; + } else { + this.userCode = ` void main() { - ${i} rc = getOutputCoords(); + ${type} rc = getOutputCoords(); vec4 result = vec4(0.); - result.r = ${o(a.slice())}; - if(${r}){ - result.g = ${l(a.slice())}; + result.r = ${getR(channels.slice())}; + if(${nextColumn}){ + result.g = ${getG(channels.slice())}; } - if(${s}) { - result.b = ${u(a.slice())}; - if(${r}) { - result.a = ${p(a.slice())}; + if(${nextRow}) { + result.b = ${getB(channels.slice())}; + if(${nextColumn}) { + result.a = ${getA(channels.slice())}; } } setOutput(result); } - `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function mie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return aa({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hie(r.shape,o):new die(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var fie={kernelName:_o,backendName:"webgl",kernelFunc:mie},gie=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=` + `; + } + function getR(channels2) { + return getChannel(channels2); + } + function getG(channels2) { + channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`; + return getChannel(channels2); + } + function getB(channels2) { + channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`; + return getChannel(channels2); + } + function getA(channels2) { + channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`; + channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`; + return getChannel(channels2); + } + function getChannel(channels2) { + const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels2)); + const inCoords = inCoordsArray.join(","); + const innerDims = inCoordsArray.slice(-2).join(","); + return `getChannel(getX(${inCoords}), vec2(${innerDims}))`; + } + function getInCoord(i, channels1) { + if (axis.indexOf(i) !== -1 && xShape[i] !== 1) { + return `${xShape[i]} - ${channels1[i]} - 1`; + } else { + return `${channels1[i]}`; + } + } + } +}; +function reverse3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dims } = attrs; + const xRank = x.shape.length; + const $dims = util_exports.parseAxisParam(dims, x.shape); + if (xRank === 0) { + return identity3({ inputs: { x }, backend: backend2 }); + } + const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new ReversePackedProgram(x.shape, $dims) : new ReverseProgram(x.shape, $dims); + return backend2.runWebGLProgram(program, [x], x.dtype); +} +var reverseConfig2 = { + kernelName: Reverse, + backendName: "webgl", + kernelFunc: reverse3 +}; +var RotateProgram = class { + constructor(imageShape, fillValue) { + this.variableNames = ["Image"]; + this.outputShape = []; + this.customUniforms = [{ name: "params", type: "vec4" }]; + const imageHeight = imageShape[1]; + const imageWidth = imageShape[2]; + this.outputShape = imageShape; + let fillSnippet = ""; + if (typeof fillValue === "number") { + fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`; + } else { + fillSnippet = ` + vec3 fill = vec3(${fillValue.join(",")}); + float outputValue = fill[coords[3]];`; + } + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -4522,13 +65639,30 @@ return a / b;`,Qre=` (float(y) - params[1]) * params[3]; int coordX = int(round(coordXFloat + params[0])); int coordY = int(round(coordYFloat + params[1])); - ${r} - if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) { + ${fillSnippet} + if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } - `}},bie={kernelName:Zu,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new gie(a.shape,s),[u,p]=N.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},yie=` + `; + } +}; +var rotateWithOffsetConfig2 = { + kernelName: RotateWithOffset, + backendName: "webgl", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { image: image2 } = inputs; + const { radians, fillValue, center } = attrs; + const webglBackend = backend2; + const program = new RotateProgram(image2.shape, fillValue); + const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]); + const customValues = [[centerX, centerY, Math.sin(radians), Math.cos(radians)]]; + const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype, customValues); + return output; + } +}; +var ROUND = ` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4543,45 +65677,117 @@ return a / b;`,Qre=` return base + 1.0; } } -`,xie=Ze({opSnippet:yie}),vie={kernelName:Eo,backendName:"webgl",kernelFunc:xie},wie="return inversesqrt(x);",kie=Ze({opSnippet:wie,cpuKernelImpl:D9}),Iie={kernelName:Ao,backendName:"webgl",kernelFunc:kie},rk=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=ct(r.length),u=ct(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${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}); +`; +var round4 = unaryKernelFunc2({ opSnippet: ROUND }); +var roundConfig2 = { + kernelName: Round, + backendName: "webgl", + kernelFunc: round4 +}; +var RSQRT = `return inversesqrt(x);`; +var rsqrt3 = unaryKernelFunc2({ opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU }); +var rsqrtConfig2 = { + kernelName: Rsqrt, + backendName: "webgl", + kernelFunc: rsqrt3 +}; +var ScatterProgram = class { + constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true, defaultIsTensor = false) { + this.variableNames = ["updates", "indices", "defaultValue"]; + this.outputShape = shape; + const stridesType = getCoordsDataType(strides.length); + const dtype = getCoordsDataType(shape.length); + let indicesString = ""; + if (indicesRank === 1) { + indicesString = "i"; + } else if (indicesRank === 2) { + indicesString = "i, j"; + } + const indicesSnippet = `getIndices(${indicesString})`; + let updatesString = ""; + if (updatesRank === 1) { + updatesString = "i"; + } else if (updatesRank === 2) { + updatesString = "i, coords[1]"; + } + const updatesSnippet = `getUpdates(${updatesString})`; + let defaultValuesString = ""; + if (defaultIsTensor) { + defaultValuesString = "coords[0], coords[1]"; + } + const defaultValueSnippet = `getDefaultValue(${defaultValuesString})`; + const strideString = sliceDim > 1 ? "strides[j]" : "strides"; + this.userCode = ` + ${stridesType} strides = ${stridesType}(${strides}); void main() { - ${u} coords = getOutputCoords(); + ${dtype} coords = getOutputCoords(); float sum = 0.0; bool found = false; - for (int i = 0; i < ${e}; i++) { + for (int i = 0; i < ${updateSize}; i++) { int flattenedIndex = 0; - for (int j = 0; j < ${t}; j++) { - int index = round(${d}); - flattenedIndex += index * ${g}; + for (int j = 0; j < ${sliceDim}; j++) { + int index = round(${indicesSnippet}); + flattenedIndex += index * ${strideString}; } if (flattenedIndex == coords[0]) { - sum += ${h}; + sum += ${updatesSnippet}; found = true; } } - setOutput(mix(${f}, sum, float(found))); + setOutput(mix(${defaultValueSnippet}, sum, float(found))); } - `}},Sie=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=ct(r.length),u=ct(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${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}); + `; + } +}; +var ScatterPackedProgram = class { + constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true, defaultIsTensor = false) { + this.variableNames = ["updates", "indices", "defaultValue"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = shape; + const stridesType = getCoordsDataType(strides.length); + const dtype = getCoordsDataType(shape.length); + let indicesString = ""; + if (indicesRank === 1) { + indicesString = "i"; + } else if (indicesRank === 2) { + indicesString = "i, j"; + } + const indicesSnippet = `getIndices(${indicesString})`; + let updatesString = ""; + if (updatesRank === 1) { + updatesString = "i"; + } else if (updatesRank === 2) { + updatesString = "i, coords[1]"; + } + const updatesSnippet = `getUpdates(${updatesString})`; + let defaultValuesString = ""; + if (defaultIsTensor) { + defaultValuesString = "coords[0], coords[1]"; + } + const defaultValueSnippet = `getDefaultValue(${defaultValuesString})`; + const strideString = sliceDim > 1 ? "strides[j]" : "strides"; + const strideString2 = sliceDim > 1 ? "strides[j + 1]" : "strides"; + this.userCode = ` + ${stridesType} strides = ${stridesType}(${strides}); void main() { - ${u} coords = getOutputCoords(); + ${dtype} coords = getOutputCoords(); vec4 sum = vec4(0.); vec4 found = vec4(0.); - for (int i = 0; i < ${e}; i+=2) { + for (int i = 0; i < ${updateSize}; i+=2) { ivec2 flattenedIndex = ivec2(0); - for (int j = 0; j < ${t}; j+=2) { - ivec4 index = round(${d}); - flattenedIndex += index.xz * ${g}; - if (j + 1 < ${t}) { - flattenedIndex += index.yw * ${b}; + for (int j = 0; j < ${sliceDim}; j+=2) { + ivec4 index = round(${indicesSnippet}); + flattenedIndex += index.xz * ${strideString}; + if (j + 1 < ${sliceDim}) { + flattenedIndex += index.yw * ${strideString2}; } } if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] || flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) { - vec4 updVals = ${h}; + vec4 updVals = ${updatesSnippet}; if (flattenedIndex[0] == coords[0]) { sum.xy += updVals.xy; found.xy = vec2(1.); @@ -4598,16 +65804,59 @@ return a / b;`,Qre=` } } } - setOutput(mix(${f}, sum, found)); + setOutput(mix(${defaultValueSnippet}, sum, found)); } - `}};function Nie(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 Sie(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 Tie={kernelName:Ou,backendName:"webgl",kernelFunc:Nie},Cie=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 scatterNd2(args) { + const { inputs, backend: backend2, attrs } = args; + const { indices, updates } = inputs; + const { shape } = attrs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape); + const flattenShape = [outputSize / sliceSize, sliceSize]; + if (outputSize === 0) { + return backend2.makeTensorInfo(shape, indices.dtype); + } + const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } }); + const flattenX = reshape4({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } }); + const defaultValue = backend2.makeTensorInfo([], "float32", new Float32Array([0])); + let program; + if (env().getBool("WEBGL_PACK")) { + program = new ScatterPackedProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape); + } else { + program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape); + } + const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype); + const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape } }); + backend2.disposeIntermediateTensorInfo(flattenIndices); + backend2.disposeIntermediateTensorInfo(flattenX); + backend2.disposeIntermediateTensorInfo(res); + backend2.disposeIntermediateTensorInfo(defaultValue); + return reshaped; +} +var scatterNdConfig2 = { + kernelName: ScatterNd, + backendName: "webgl", + kernelFunc: scatterNd2 +}; +var SearchSortedProgram = class { + constructor(batchSize, numInputs, numValues, side) { + this.variableNames = ["sortedSequence", "values"]; + this.customUniforms = [{ name: "numInputs", type: "int" }]; + this.outputShape = [batchSize, numValues]; + const webGL2LoopHead = "while (left < right) {"; + const webGL1LoopHead = `for (int i = 0; i < ${Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`; + const loopHead = env().getNumber("WEBGL_VERSION") === 2 ? webGL2LoopHead : webGL1LoopHead; + const boundComparator = side === "left" ? "<" : "<="; + this.userCode = ` int findBound(int batch, float value) { int left = 0; int right = numInputs; int mid; - ${i} + ${loopHead} mid = (left + right) / 2; - if (getSortedSequence(batch, mid) ${o} value) { + if (getSortedSequence(batch, mid) ${boundComparator} value) { left = mid + 1; } else { right = mid; @@ -4625,25 +65874,89 @@ return a / b;`,Qre=` setOutput(float(findBound(batch, value))); } - `}};function _ie(e){let{inputs:t,backend:n,attrs:a}=e,{sortedSequence:r,values:s}=t,{side:i}=a,o=new Cie(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return n.runWebGLProgram(o,[r,s],"int32",l)}var Eie={kernelName:zu,backendName:"webgl",kernelFunc:_ie},Aie=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 1.0) { - setOutput(getA(${r})); - } else { - setOutput(getB(${r})); + `; + } +}; +function searchSorted3(args) { + const { inputs, backend: backend2, attrs } = args; + const { sortedSequence, values } = inputs; + const { side } = attrs; + const program = new SearchSortedProgram(sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side); + const customValues = [[sortedSequence.shape[1]]]; + return backend2.runWebGLProgram(program, [sortedSequence, values], "int32", customValues); +} +var searchSortedConfig2 = { + kernelName: SearchSorted, + backendName: "webgl", + kernelFunc: searchSorted3 +}; +var SelectProgram = class { + constructor(cRank, shape, rank) { + this.variableNames = ["c", "a", "b"]; + this.outputShape = shape; + let cCoords; + let abCoords; + if (rank > 4) { + throw Error(`Where for rank ${rank} is not yet supported`); + } + if (rank === 1) { + abCoords = `resRC`; + cCoords = `resRC`; + } else { + const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"]; + const cCoordVars = []; + const abCoordVars = []; + for (let i = 0; i < shape.length; i++) { + abCoordVars.push(`${currentCoords[i]}`); + if (i < cRank) { + cCoordVars.push(`${currentCoords[i]}`); } } - `}};function Fie(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new Aie(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ga(r.dtype,s.dtype))}var $ie={kernelName:Wu,backendName:"webgl",kernelFunc:Fie},Die=` + cCoords = cCoordVars.join(); + abCoords = abCoordVars.join(); + } + const dtype = getCoordsDataType(rank); + this.userCode = ` + void main() { + ${dtype} resRC = getOutputCoords(); + float cVal = getC(${cCoords}); + if (cVal >= 1.0) { + setOutput(getA(${abCoords})); + } else { + setOutput(getB(${abCoords})); + } + } + `; + } +}; +function select3(args) { + const { inputs, backend: backend2 } = args; + const { condition, t, e } = inputs; + const program = new SelectProgram(condition.shape.length, t.shape, t.shape.length); + return backend2.runWebGLProgram(program, [condition, t, e], upcastType(t.dtype, e.dtype)); +} +var selectConfig2 = { + kernelName: Select, + backendName: "webgl", + kernelFunc: select3 +}; +var SELU = ` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 - float scaleAlpha = ${N.SELU_SCALEALPHA}; - float scale = ${N.SELU_SCALE}; + float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA}; + float scale = ${backend_util_exports.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,Rie=Ze({opSnippet:Die}),Mie={kernelName:Fo,backendName:"webgl",kernelFunc:Rie},Pie=mp+` +`; +var selu3 = unaryKernelFunc2({ opSnippet: SELU }); +var seluConfig2 = { + kernelName: Selu, + backendName: "webgl", + kernelFunc: selu3 +}; +var SIGMOID3 = CHECK_NAN_SNIPPET_UNARY + ` return 1.0 / (1.0 + exp(-1.0 * x)); -`,Oie=` +`; +var SIGMOID_PACKED = ` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4653,20 +65966,53 @@ return a / b;`,Qre=` result.a = isNaN.a ? x.a : result.a; return result; -`,Lie=Ze({opSnippet:Pie,packedOpSnippet:Oie,cpuKernelImpl:M9}),zie={kernelName:Mo,backendName:"webgl",kernelFunc:Lie},Wie=` +`; +var sigmoid3 = unaryKernelFunc2({ + opSnippet: SIGMOID3, + packedOpSnippet: SIGMOID_PACKED, + cpuKernelImpl: sigmoidImplCPU +}); +var sigmoidConfig2 = { + kernelName: Sigmoid, + backendName: "webgl", + kernelFunc: sigmoid3 +}; +var SIGN = ` if (isnan(x)) { return 0.0; } return sign(x); -`,Bie=Ze({opSnippet:Wie}),Vie={kernelName:Ro,backendName:"webgl",kernelFunc:Bie},Uie=mp+` +`; +var sign3 = unaryKernelFunc2({ opSnippet: SIGN }); +var signConfig2 = { + kernelName: Sign, + backendName: "webgl", + kernelFunc: sign3 +}; +var SIN = CHECK_NAN_SNIPPET_UNARY + ` return sin(x); -`,Gie=` +`; +var SIN_PACKED = ` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${Qo} + ${CHECK_NAN_SNIPPET_PACKED} return result; -`,Hie=Ze({opSnippet:Uie,packedOpSnippet:Gie}),qie={kernelName:$o,backendName:"webgl",kernelFunc:Hie},jie=` +`; +var sin3 = unaryKernelFunc2({ opSnippet: SIN, packedOpSnippet: SIN_PACKED }); +var sinConfig2 = { + kernelName: Sin, + backendName: "webgl", + kernelFunc: sin3 +}; +var SINH = ` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,Kie=Ze({opSnippet:jie}),Xie={kernelName:Do,backendName:"webgl",kernelFunc:Kie},Yie=` +`; +var sinh3 = unaryKernelFunc2({ opSnippet: SINH }); +var sinhConfig2 = { + kernelName: Sinh, + backendName: "webgl", + kernelFunc: sinh3 +}; +var SOFTPLUS = ` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4686,33 +66032,499 @@ return a / b;`,Qre=` result = log(exp_x + 1.0); } return result; -`,Zie=Ze({opSnippet:Yie}),Jie={kernelName:Po,backendName:"webgl",kernelFunc:Zie},Qie=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;w.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},eoe={kernelName:Vu,backendName:"webgl",kernelFunc:Qie};function toe(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: - ${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw: - ${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw: - ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw: - ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=n.readSync(i.dataId)[0],[d,c,h,m,f]=O9(o,a.shape,a.dtype,l,r.dtype,u,p);return[n.makeTensorInfo(c,a.dtype,d),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var noe={kernelName:Vc,backendName:"webgl",kernelFunc:toe};function aoe(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=L9(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var roe={kernelName:Gu,backendName:"webgl",kernelFunc:aoe};function soe(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape - ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=QE(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(p,a.dtype,u)}var ioe={kernelName:Uc,backendName:"webgl",kernelFunc:soe};function ooe(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape - ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=QE(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var loe={kernelName:Gc,backendName:"webgl",kernelFunc:ooe};function uoe(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=N.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let b=n.bufferSync(r),y=n.bufferSync(s),x=w.decodeString(n.readSync(i.dataId)[0]),v=R9(b,y,o,c,p,u,l,d,x,h);return n.makeTensorInfo(o,v.dtype,v.values)}let m=new rk(u,l,r.shape.length,s.shape.length,d,[c,1],h),f=n.runWebGLProgram(m,[s,r,i],s.dtype),g=ce({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),g}var poe={kernelName:Hu,backendName:"webgl",kernelFunc:uoe};function coe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=N.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=fp({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var doe={kernelName:Uu,backendName:"webgl",kernelFunc:coe},mS="return sqrt(x);",hoe=Ze({opSnippet:mS,packedOpSnippet:mS,cpuKernelImpl:z9}),moe={kernelName:Oo,backendName:"webgl",kernelFunc:hoe},foe="return x * x;",goe=Ze({opSnippet:foe}),boe={kernelName:Hc,backendName:"webgl",kernelFunc:goe},fS="return (a - b) * (a - b);",yoe=mn({opSnippet:fS,packedOpSnippet:fS}),xoe={kernelName:Wo,backendName:"webgl",kernelFunc:yoe};function voe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;if(r.dtype!=="string")throw new Error("Input must be of datatype string");let s=n.readSync(r.dataId),i=N.fromUint8ToStringArray(s),o=W9(i,"string",a);return n.makeTensorInfo(r.shape,"string",o)}var woe={kernelName:qc,backendName:"webgl",kernelFunc:voe};function koe({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ma+` - return x > 0.0 ? 1.0 : float(${t.alpha}); - `,s=new rr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Ioe={kernelName:ws,backendName:"webgl",kernelFunc:koe},Soe=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=` - ${r} begin = ${r}(${e}); - ${r} strides = ${r}(${t}); +`; +var softplus3 = unaryKernelFunc2({ opSnippet: SOFTPLUS }); +var softplusConfig2 = { + kernelName: Softplus, + backendName: "webgl", + kernelFunc: softplus3 +}; +var spaceToBatchND3 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, paddings } = attrs; + util_exports.assert(x.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"); + const prod5 = blockShape.reduce((a, b) => a * b); + const completePaddings = [[0, 0]]; + completePaddings.push(...paddings); + for (let i = 1 + blockShape.length; i < x.shape.length; ++i) { + completePaddings.push([0, 0]); + } + const toDispose = []; + const paddedX = padV22({ + inputs: { x }, + backend: backend2, + attrs: { paddings: completePaddings, constantValue: 0 } + }); + const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false); + const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false); + const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false); + const reshapedPaddedX = reshape4({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } }); + const paddedXT = transpose3({ + inputs: { x: reshapedPaddedX }, + backend: backend2, + attrs: { perm: permutedReshapedPaddedPermutation } + }); + const result = reshape4({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } }); + toDispose.push(paddedX); + toDispose.push(reshapedPaddedX); + toDispose.push(paddedXT); + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +}; +var spaceToBatchNDConfig2 = { + kernelName: SpaceToBatchND, + backendName: "webgl", + kernelFunc: spaceToBatchND3 +}; +function sparseFillEmptyRows3(args) { + const { inputs, backend: backend2 } = args; + const { indices, values, denseShape, defaultValue } = inputs; + if (denseShape.shape.length !== 1) { + throw new Error(`Dense shape must be a vector, saw: + ${denseShape.shape}`); + } + if (indices.shape.length !== 2) { + throw new Error(`Indices must be a matrix, saw: + ${indices.shape}`); + } + if (values.shape.length !== 1) { + throw new Error(`Values must be a vector, saw: + ${values.shape}`); + } + if (defaultValue.shape.length !== 0) { + throw new Error(`Default value must be a scalar, saw: + ${defaultValue.shape}`); + } + const $indices = backend2.readSync(indices.dataId); + const $values = backend2.readSync(values.dataId); + const $denseShape = backend2.readSync(denseShape.dataId); + const $defaultValue = backend2.readSync(defaultValue.dataId)[0]; + const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue); + return [ + backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices), + backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues), + backend2.makeTensorInfo([emptyRowIndicator.length], "bool", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))), + backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap)) + ]; +} +var sparseFillEmptyRowsConfig2 = { + kernelName: SparseFillEmptyRows, + backendName: "webgl", + kernelFunc: sparseFillEmptyRows3 +}; +function sparseReshape3(args) { + const { inputs, backend: backend2 } = args; + const { inputIndices, inputShape, newShape } = inputs; + if (inputIndices.shape.length !== 2) { + throw new Error(`Input indices should be a matrix but received shape ${inputIndices.shape}`); + } + if (inputShape.shape.length !== 1) { + throw new Error(`Input shape should be a vector but received shape ${inputShape.shape}`); + } + if (newShape.shape.length !== 1) { + throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`); + } + const $inputShape = Array.from(backend2.readSync(inputShape.dataId)); + const $inputIndices = backend2.readSync(inputIndices.dataId); + const targetShape = Array.from(backend2.readSync(newShape.dataId)); + const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape); + return [ + backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices), + backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape)) + ]; +} +var sparseReshapeConfig2 = { + kernelName: SparseReshape, + backendName: "webgl", + kernelFunc: sparseReshape3 +}; +function sparseSegmentMean3(args) { + const { inputs, backend: backend2 } = args; + const { data, indices, segmentIds } = inputs; + if (data.shape.length < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if (indices.shape.length !== 1) { + throw new Error(`Indices should be a vector but received shape + ${indices.shape}`); + } + if (segmentIds.shape.length !== 1) { + throw new Error(`Segment ids should be a vector but received shape + ${segmentIds.shape}`); + } + const $data = backend2.readSync(data.dataId); + const $indices = backend2.readSync(indices.dataId); + const $segmentIds = backend2.readSync(segmentIds.dataId); + const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds, true); + return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData); +} +var sparseSegmentMeanConfig2 = { + kernelName: SparseSegmentMean, + backendName: "webgl", + kernelFunc: sparseSegmentMean3 +}; +function sparseSegmentSum3(args) { + const { inputs, backend: backend2 } = args; + const { data, indices, segmentIds } = inputs; + if (data.shape.length < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if (indices.shape.length !== 1) { + throw new Error(`Indices should be a vector but received shape + ${indices.shape}`); + } + if (segmentIds.shape.length !== 1) { + throw new Error(`Segment ids should be a vector but received shape + ${segmentIds.shape}`); + } + const $data = backend2.readSync(data.dataId); + const $indices = backend2.readSync(indices.dataId); + const $segmentIds = backend2.readSync(segmentIds.dataId); + const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds); + return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData); +} +var sparseSegmentSumConfig2 = { + kernelName: SparseSegmentSum, + backendName: "webgl", + kernelFunc: sparseSegmentSum3 +}; +function sparseToDense3(args) { + const { inputs, backend: backend2, attrs } = args; + const { sparseIndices, sparseValues, defaultValue } = inputs; + const { outputShape } = attrs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape); + const sumDupeIndices = false; + if (sparseValues.dtype === "string") { + const indicesBuf = backend2.bufferSync(sparseIndices); + const updatesBuf = backend2.bufferSync(sparseValues); + const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]); + const outBuf = scatterImplCPU(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values); + } + const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices); + const res = backend2.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype); + const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } }); + backend2.disposeIntermediateTensorInfo(res); + return reshaped; +} +var sparseToDenseConfig2 = { + kernelName: SparseToDense, + backendName: "webgl", + kernelFunc: sparseToDense3 +}; +function splitV2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { numOrSizeSplits, axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, x.shape)[0]; + const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis); + const xRank = x.shape.length; + const begin = new Array(xRank).fill(0); + const size = x.shape.slice(); + return splitSizes.map((s) => { + const sliceSize = [...size]; + sliceSize[$axis] = s; + const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } }); + begin[$axis] += s; + return sliceT; + }); +} +var splitVConfig2 = { + kernelName: SplitV, + backendName: "webgl", + kernelFunc: splitV2 +}; +var SQRT = `return sqrt(x);`; +var sqrt3 = unaryKernelFunc2({ opSnippet: SQRT, packedOpSnippet: SQRT, cpuKernelImpl: sqrtImplCPU }); +var sqrtConfig2 = { + kernelName: Sqrt, + backendName: "webgl", + kernelFunc: sqrt3 +}; +var SQUARE = `return x * x;`; +var square3 = unaryKernelFunc2({ opSnippet: SQUARE }); +var squareConfig2 = { + kernelName: Square, + backendName: "webgl", + kernelFunc: square3 +}; +var SQUARED_DIFFERENCE = "return (a - b) * (a - b);"; +var squaredDifference3 = binaryKernelFunc2({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE }); +var squaredDifferenceConfig2 = { + kernelName: SquaredDifference, + backendName: "webgl", + kernelFunc: squaredDifference3 +}; +function staticRegexReplace3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + if (x.dtype !== "string") { + throw new Error("Input must be of datatype string"); + } + const $x = backend2.readSync(x.dataId); + const stringInput = backend_util_exports.fromUint8ToStringArray($x); + const output = staticRegexReplaceImplCPU(stringInput, "string", attrs); + return backend2.makeTensorInfo(x.shape, "string", output); +} +var staticRegexReplaceConfig2 = { + kernelName: StaticRegexReplace, + backendName: "webgl", + kernelFunc: staticRegexReplace3 +}; +function step3({ inputs, attrs, backend: backend2 }) { + const { x } = inputs; + const opSnippet = CHECK_NAN_SNIPPET + ` + return x > 0.0 ? 1.0 : float(${attrs.alpha}); + `; + const program = new UnaryOpProgram(x.shape, opSnippet); + return backend2.runWebGLProgram(program, [x], x.dtype); +} +var stepConfig2 = { + kernelName: Step, + backendName: "webgl", + kernelFunc: step3 +}; +var StridedSliceProgram = class { + constructor(begin, strides, size) { + this.variableNames = ["x"]; + this.outputShape = size; + const rank = size.length; + const inputDtype = getCoordsDataType(size.length); + const dtype = getCoordsDataType(size.length); + let newCoords = ""; + if (rank === 1) { + newCoords = "coords * strides + begin"; + } else { + let outputAxis = 0; + newCoords = size.map((_, i) => { + outputAxis++; + return size.length === 1 ? `coords * strides[${i}] + begin[${i}]` : `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`; + }).join(","); + } + this.userCode = ` + ${inputDtype} begin = ${inputDtype}(${begin}); + ${inputDtype} strides = ${inputDtype}(${strides}); void main() { - ${s} coords = getOutputCoords(); - setOutput(getX(${i})); + ${dtype} coords = getOutputCoords(); + setOutput(getX(${newCoords})); } - `}};function Noe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:b,begin:y,end:x,strides:v}=Xt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=ce({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Xt.computeOutShape(y,x,v),E=fp({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});I=ce({inputs:{x:E},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),E=Le(r.shape,r.dtype,C),F=B9(h,E,v,y);I=n.makeTensorInfo(m,r.dtype,F.values)}else{let C=new Soe(y,v,h);I=n.runWebGLProgram(C,[r],r.dtype)}let T=ce({inputs:{x:I},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(I),T}var Toe={kernelName:qu,backendName:"webgl",kernelFunc:Noe};function Coe(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]=V9(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var _oe={kernelName:jc,backendName:"webgl",kernelFunc:Coe};function Eoe(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]=U9(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 Aoe={kernelName:Kc,backendName:"webgl",kernelFunc:Eoe};function Foe(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=G9(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var $oe={kernelName:Xc,backendName:"webgl",kernelFunc:Foe},Doe="return tan(x);",Roe=Ze({opSnippet:Doe}),Moe={kernelName:Vo,backendName:"webgl",kernelFunc:Roe},Poe=` + `; + } +}; +function stridedSlice3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs; + const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); + let result; + if (isIdentity) { + result = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); + } else if (sliceDim0 || isSimpleSlice) { + util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`); + const size = slice_util_exports.computeOutShape($begin, $end, $strides); + const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } }); + result = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeIntermediateTensorInfo(sliced); + } else { + const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]); + if (shouldExecuteOnCPU) { + const values = backend2.readSync(x.dataId); + const xBuf = buffer(x.shape, x.dtype, values); + const resultValues = stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin); + result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values); + } else { + const program = new StridedSliceProgram($begin, $strides, finalShapeSparse); + result = backend2.runWebGLProgram(program, [x], x.dtype); + } + } + const resultReshaped = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeIntermediateTensorInfo(result); + return resultReshaped; +} +var stridedSliceConfig2 = { + kernelName: StridedSlice, + backendName: "webgl", + kernelFunc: stridedSlice3 +}; +function stringNGrams3(args) { + const { inputs, backend: backend2, attrs } = args; + const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs; + const { data, dataSplits } = inputs; + const $data = backend2.readSync(data.dataId); + const $dataSplits = backend2.readSync(dataSplits.dataId); + const [nGrams, nGramsSplits] = stringNGramsImplCPU($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences); + return [ + backend2.makeTensorInfo([nGrams.length], "string", nGrams), + backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits) + ]; +} +var stringNGramsConfig2 = { + kernelName: StringNGrams, + backendName: "webgl", + kernelFunc: stringNGrams3 +}; +function stringSplit3(args) { + const { inputs, backend: backend2, attrs } = args; + const { skipEmpty } = attrs; + const { input: input2, delimiter } = inputs; + if (input2.dtype !== "string") { + throw new Error("Input must be of datatype string"); + } + if (input2.shape.length !== 1) { + throw new Error(`Input must be a vector, got shape: ${input2.shape}`); + } + if (delimiter.shape.length !== 0) { + throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`); + } + const $input = backend2.readSync(input2.dataId); + const $delimiter = backend2.readSync(delimiter.dataId)[0]; + const [indices, values, shape] = stringSplitImplCPU($input, $delimiter, skipEmpty); + const outputSize = values.length; + return [ + backend2.makeTensorInfo([outputSize, 2], "int32", indices), + backend2.makeTensorInfo([outputSize], "string", values), + backend2.makeTensorInfo([2], "int32", new Int32Array(shape)) + ]; +} +var stringSplitConfig2 = { + kernelName: StringSplit, + backendName: "webgl", + kernelFunc: stringSplit3 +}; +function stringToHashBucketFast3(args) { + const { inputs, backend: backend2, attrs } = args; + const { numBuckets } = attrs; + const { input: input2 } = inputs; + if (input2.dtype !== "string") { + throw new Error("Input must be of datatype string"); + } + if (numBuckets <= 0) { + throw new Error(`Number of buckets must be at least 1`); + } + const $input = backend2.readSync(input2.dataId); + const output = stringToHashBucketFastImplCPU($input, numBuckets); + return backend2.makeTensorInfo(input2.shape, "int32", output); +} +var stringToHashBucketFastConfig2 = { + kernelName: StringToHashBucketFast, + backendName: "webgl", + kernelFunc: stringToHashBucketFast3 +}; +var TAN = `return tan(x);`; +var tan3 = unaryKernelFunc2({ opSnippet: TAN }); +var tanConfig2 = { + kernelName: Tan, + backendName: "webgl", + kernelFunc: tan3 +}; +var TANH = ` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); -`,Ooe=Ze({opSnippet:Poe}),Loe={kernelName:Uo,backendName:"webgl",kernelFunc:Ooe};function zoe(e){let{inputs:t,backend:n,attrs:a}=e,{tensor:r,indices:s,updates:i}=t,{}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=N.calculateShapes(i,s,r.shape),c=[d/u,u];if(d===0)return n.makeTensorInfo(r.shape,s.dtype);let 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 rk(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 Woe={kernelName:Lu,backendName:"webgl",kernelFunc:zoe},Boe=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=>w.decodeString(d)):o,u=Le(r.shape,r.dtype,l),p=q9(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Boe(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var Uoe={kernelName:vs,backendName:"webgl",kernelFunc:FA},Goe=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 getSourceCoords3(aShape) { + const rank = aShape.length; + if (rank > 5) { + throw Error(`Tile for rank ${rank} is not yet supported`); + } + if (rank === 1) { + return `imod(resRC, ${aShape[0]})`; + } + const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"]; + const sourceCoords = []; + for (let i = 0; i < aShape.length; i++) { + sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`); + } + return sourceCoords.join(); +} +function tile4(params) { + const { inputs, backend: backend2, attrs } = params; + const { x } = inputs; + const { reps } = attrs; + if (x.dtype === "string" || x.shape.length > 5) { + const data = backend2.readSync(x.dataId); + const value = x.dtype === "string" ? data.map((d) => util_exports.decodeString(d)) : data; + const buf = buffer(x.shape, x.dtype, value); + const outBuf = tileImplCPU(buf, reps); + return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); + } + const program = new TileProgram(x.shape, reps); + const output = backend2.runWebGLProgram(program, [x], x.dtype); + return output; +} +var tileConfig2 = { + kernelName: Tile, + backendName: "webgl", + kernelFunc: tile4 +}; +var SwapProgram = class { + /** + * @param shape desired output shape (can be larger than input shape, output + * will be padded with -Infinity) + */ + constructor(shape) { + 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 = shape; + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4752,7 +66564,22 @@ return a / b;`,Qre=` setOutput(float(i1)); } } - `}},Hoe=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=` + `; + } +}; +var MergeProgram = class { + /** + * @param shape desired output shape (must be half of the input size) + */ + constructor(shape) { + this.variableNames = ["x", "indices"]; + this.customUniforms = [ + { name: "n", type: "int" }, + { name: "firstPass", type: "int" }, + { name: "k", type: "int" } + ]; + this.outputShape = shape; + this.userCode = ` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); @@ -4786,10 +66613,139 @@ return a / b;`,Qre=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function qs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function gS(e){let t=1;for(;tl){let F=n.readSync(r.dataId),[D,$]=j9(F,u,r.dtype,s,i);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,$d({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=w.sizeFromShape(u)/p,f=ce({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&qs(n,h);let g=gS(s),b=gS(p),y=null,x=()=>y===null?[f,f]:[f,y],v=(F,D,$)=>{let S=x(),M=new Goe($),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;$/=2)v(D,$,[m,b])}for(let F=b;F>g;F/=2){let D=x(),$=new Hoe([m,F/2]),S=[[p],[y===null?1:0],[g]],M=y;y=n.runWebGLProgram($,D,"int32",S),qs(n,M);let B=g/2,U=B*2;for(let H=B;H>=1;H/=2)v(U,H,y.shape)}let I=y;y=fp({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,s]}}),qs(n,I);let T=IA({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 joe={kernelName:ju,backendName:"webgl",kernelFunc:qoe},Koe=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 disposeIntermediateTensorInfoOrNull(backend2, tensorInfo) { + if (tensorInfo !== null) { + backend2.disposeIntermediateTensorInfo(tensorInfo); + } +} +function roundUpToPow2(num) { + let pow22 = 1; + while (pow22 < num) { + pow22 *= 2; + } + return pow22; +} +function topK2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { k, sorted } = attrs; + const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"); + const TOPK_K_CPU_HANDOFF_THRESHOLD = env().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"); + const xShape = x.shape; + const lastDim = xShape[xShape.length - 1]; + if (backend2.shouldExecuteOnCPU([x]) || lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD || k > TOPK_K_CPU_HANDOFF_THRESHOLD) { + const xVals = backend2.readSync(x.dataId); + const [allTopKVals, allTopKIndices] = topKImplCPU(xVals, xShape, x.dtype, k, sorted); + return [ + backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values), + backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values) + ]; + } + if (k === 0) { + xShape[xShape.length - 1] = 0; + return [ + backend2.makeTensorInfo(xShape, x.dtype, []), + backend2.makeTensorInfo(xShape, "int32", []) + ]; + } + if (lastDim === 1) { + return [ + x, + fill3({ attrs: { shape: xShape, dtype: "int32", value: 0 }, backend: backend2 }) + ]; + } + const xtexData = backend2.texData.get(x.dataId); + const xIsPacked = xtexData !== null && xtexData.isPacked; + const xUnPacked = xIsPacked ? backend2.unpackTensor(x) : x; + const xSize = util_exports.sizeFromShape(xShape); + const batch = xSize / lastDim; + const x2D = reshape4({ inputs: { x: xUnPacked }, attrs: { shape: [batch, lastDim] }, backend: backend2 }); + if (xIsPacked) { + disposeIntermediateTensorInfoOrNull(backend2, xUnPacked); + } + const kPow2 = roundUpToPow2(k); + const lastDimPow2 = roundUpToPow2(lastDim); + let indices = null; + const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices]; + const runSwap = (dir, inc, shape) => { + const inputs2 = getInputs(); + const program = new SwapProgram(shape); + const fistPass = indices === null ? 1 : 0; + const customValues = [[lastDim], [fistPass], [Number.NEGATIVE_INFINITY], [dir], [inc]]; + const prevIndices2 = indices; + indices = backend2.runWebGLProgram(program, inputs2, "int32", customValues); + disposeIntermediateTensorInfoOrNull(backend2, prevIndices2); + }; + for (let len = 1; len < kPow2; len *= 2) { + const dir = len * 2; + for (let inc = len; inc >= 1; inc /= 2) { + runSwap(dir, inc, [batch, lastDimPow2]); + } + } + for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) { + const inputs2 = getInputs(); + const mergeProgram = new MergeProgram([batch, indicesSize / 2]); + const firstPass = indices === null ? 1 : 0; + const customValues = [[lastDim], [firstPass], [kPow2]]; + const prevIndices2 = indices; + indices = backend2.runWebGLProgram(mergeProgram, inputs2, "int32", customValues); + disposeIntermediateTensorInfoOrNull(backend2, prevIndices2); + const len = kPow2 / 2; + const dir = len * 2; + for (let inc = len; inc >= 1; inc /= 2) { + runSwap(dir, inc, indices.shape); + } + } + let prevIndices = indices; + indices = slice3({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } }); + disposeIntermediateTensorInfoOrNull(backend2, prevIndices); + let values = gatherV22({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } }); + disposeIntermediateTensorInfoOrNull(backend2, x2D); + const newShape = xShape.slice(0, -1); + newShape.push(k); + prevIndices = indices; + indices = reshape4({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 }); + disposeIntermediateTensorInfoOrNull(backend2, prevIndices); + const prevValues = values; + values = reshape4({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 }); + disposeIntermediateTensorInfoOrNull(backend2, prevValues); + return [values, indices]; +} +var topKConfig2 = { + kernelName: TopK, + backendName: "webgl", + kernelFunc: topK2 +}; +var TransformProgram = class { + constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) { + this.variableNames = ["Image", "Transforms"]; + this.outputShape = outShape; + const interpolationModeId = interpolation === "nearest" ? 1 : 2; + let fillModeId; + switch (fillMode) { + case "constant": + fillModeId = 1; + break; + case "reflect": + fillModeId = 2; + break; + case "wrap": + fillModeId = 3; + break; + case "nearest": + fillModeId = 4; + break; + default: + fillModeId = 1; + break; + } + this.userCode = ` float mapCoord(float outCoord, float len) { float inCoord = outCoord; - if(${o} == 2) { + if(${fillModeId} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; @@ -4813,7 +66769,7 @@ return a / b;`,Qre=` } } return clamp(inCoord, 0.0, len - 1.0); - } else if (${o} == 3) { + } else if (${fillModeId} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; @@ -4830,7 +66786,7 @@ return a / b;`,Qre=` } } return clamp(inCoord, 0.0, len - 1.0); - } else if (${o} == 4) { + } else if (${fillModeId} == 4) { return clamp(outCoord, 0.0, len - 1.0); } else { return outCoord; @@ -4840,10 +66796,10 @@ return a / b;`,Qre=` float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; - if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) { + if (0 <= coordY && coordY < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) { outputValue = getImage(batch, coordY, coordX, channel); } else { - outputValue = float(${r}); + outputValue = float(${fillValue}); } return outputValue; } @@ -4867,14 +66823,14 @@ return a / b;`,Qre=` float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { - outputValue = float(${r}); + outputValue = float(${fillValue}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; - float mapX = mapCoord(inX, float(${t})); - float mapY = mapCoord(inY, float(${e})); + float mapX = mapCoord(inX, float(${imageWidth})); + float mapY = mapCoord(inY, float(${imageHeight})); - if (${i} == 1) { + if (${interpolationModeId} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, @@ -4898,26 +66854,126 @@ return a / b;`,Qre=` } setOutput(outputValue); } - `}};function Xoe(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 Koe(d,c,i,o,l,g);return n.runWebGLProgram(b,[r,s],"float32")}var Yoe={kernelName:Ku,backendName:"webgl",kernelFunc:Xoe};function Zoe(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;lp(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=K9(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var Joe={kernelName:Yc,backendName:"webgl",kernelFunc:Zoe};function Qoe(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let f=0;fn.disposeIntermediateTensorInfo(f)),m}var ele={kernelName:Xu,backendName:"webgl",kernelFunc:Qoe},tle=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,p=n%4,d=` + `; + } +}; +function transform3(args) { + const { inputs, backend: backend2, attrs } = args; + const { image: image2, transforms } = inputs; + const { interpolation, fillMode, fillValue, outputShape } = attrs; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth]; + const outShape = [ + batch, + outHeight, + outWidth, + numChannels + ]; + const program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape); + return backend2.runWebGLProgram(program, [image2, transforms], "float32"); +} +var transformConfig2 = { + kernelName: Transform, + backendName: "webgl", + kernelFunc: transform3 +}; +function unique4(args) { + const { inputs, attrs, backend: backend2 } = args; + const { axis } = attrs; + const { x } = inputs; + assertNotComplex2(x, "unique"); + console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded"); + const values = backend2.readSync(x.dataId); + const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype); + return [ + backend2.makeTensorInfo(outputShape, x.dtype, outputValues), + backend2.makeTensorInfo([indices.length], "int32", indices) + ]; +} +var uniqueConfig2 = { + kernelName: Unique, + backendName: "webgl", + kernelFunc: unique4 +}; +function unpack2(args) { + const { inputs, backend: backend2, attrs } = args; + const { value } = inputs; + let { axis } = attrs; + if (axis < 0) { + axis += value.shape.length; + } + const x = value; + const xRank = x.shape.length; + const num = value.shape[axis]; + const outShape = new Array(xRank - 1); + let outIndex = 0; + for (let i = 0; i < xRank; i++) { + if (i !== axis) { + outShape[outIndex++] = x.shape[i]; + } + } + const toDispose = []; + const begin = new Array(xRank).fill(0); + const size = x.shape.slice(); + size[axis] = 1; + const res = new Array(num); + for (let i = 0; i < res.length; i++) { + begin[axis] = i; + const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size } }); + const reshaped = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } }); + res[i] = reshaped; + toDispose.push(sliced); + } + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return res; +} +var unpackConfig2 = { + kernelName: Unpack, + backendName: "webgl", + kernelFunc: unpack2 +}; +var SegmentOpProgram = class { + constructor(segOpInfo, segOpType) { + this.variableNames = ["x", "segmentIds"]; + const windowSize = segOpInfo.windowSize; + const batchSize = segOpInfo.batchSize; + const inSize = segOpInfo.inSize; + const numSegments = segOpInfo.numSegments; + const outSize = numSegments * Math.ceil(inSize / windowSize); + this.outputShape = [batchSize, outSize]; + const initializationValue = "0.0"; + const returnValue = `sumValue`; + const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4; + const windowSizeVec4Remainder = windowSize % 4; + const updateSnippet = ` sumValue += dot(values, segFilter); - `,c="";r%n>0&&(c=` - if (inIdx < 0 || inIdx >= ${r}) { + `; + let checkValueOutOfBounds = ""; + if (inSize % windowSize > 0) { + checkValueOutOfBounds = ` + if (inIdx < 0 || inIdx >= ${inSize}) { return initializationValue; } - `);let h="";r%n>0&&(h=` - if (inIdx < 0 || inIdx >= ${r}) { + `; + } + let checkSegmentIdOutOfBounds = ""; + if (inSize % windowSize > 0) { + checkSegmentIdOutOfBounds = ` + if (inIdx < 0 || inIdx >= ${inSize}) { return -1.0; } - `),this.userCode=` - const float initializationValue = ${o}; + `; + } + this.userCode = ` + const float initializationValue = ${initializationValue}; float getValue(int batch, int inIdx) { - ${c} + ${checkValueOutOfBounds} return getX(batch, inIdx); } float getSegmentIdAtIndex(int inIdx) { - ${h} + ${checkSegmentIdOutOfBounds} return getSegmentIds(inIdx); } @@ -4926,12 +66982,12 @@ return a / b;`,Qre=` int batch = coords[0]; int outIdx = coords[1]; int inOffset = int(floor(float(outIdx) / float( - ${s})) * float(${n})); - int currentSeg = int(mod(float(outIdx), float(${s}))); + ${numSegments})) * float(${windowSize})); + int currentSeg = int(mod(float(outIdx), float(${numSegments}))); float sumValue = 0.0; - for (int i = 0; i < ${u}; i += 4) { + for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), @@ -4947,11 +67003,11 @@ return a / b;`,Qre=` int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 ); - ${d} + ${updateSnippet} } - int inIdx = inOffset + ${u}; - if (${p===1}) { + int inIdx = inOffset + ${windowSizeNearestVec4}; + if (${windowSizeVec4Remainder === 1}) { vec4 values = vec4( getValue(batch, inIdx), initializationValue, @@ -4968,8 +67024,8 @@ return a / b;`,Qre=` 0 ); - ${d} - } else if (${p===2}) { + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), @@ -4984,8 +67040,8 @@ return a / b;`,Qre=` 0 ); - ${d} - } else if (${p===3}) { + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), @@ -5000,11 +67056,10024 @@ return a / b;`,Qre=` 0 ); - ${d} + ${updateSnippet} } - setOutput(${l}); + setOutput(${returnValue}); } - `}};function nle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,p=N.getAxesPermutation([u],o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=N.getInnerMostAxes(1,o)[0]);let c=N.segment_util.computeOutShape(d.shape,u,i),h=w.sizeFromShape([d.shape[u]]),m=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Mm(r.dtype),g=(v,I,T,C,E)=>{let F=v.shape[0],D=v.shape[1],$=N.segment_util.segOpComputeOptimalWindowSize(D,E),S={windowSize:$,inSize:D,batchSize:F,numSegments:E},M=new tle(S,I),B=n.compileAndRun(M,[v,T],C);if(l.push(B),B.shape[1]===E)return B;let U=AA({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),H=FA({inputs:{x:U},backend:n,attrs:{reps:[D/$]}});return 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ale={kernelName:Zc,backendName:"webgl",kernelFunc:nle},rle=[BQ,UQ,qQ,XQ,ZQ,eee,nee,ree,lee,pee,hee,gee,xee,Iee,Tee,_ee,Aee,Ree,Pee,Lee,Vee,Xee,Zee,tte,ate,ute,cte,fte,SQ,yte,Ite,Cte,Dte,Pte,Lte,Wte,Vte,qte,Xte,Jte,ene,nne,rne,one,une,hne,fne,yne,wne,Ine,Cne,Fne,Mne,Lne,Bne,Vne,Gne,qne,Kne,Yne,Jne,nae,sae,lae,pae,hae,gae,vae,Sae,IQ,Tae,wte,Eae,$ae,Mae,TQ,zae,Uae,Hae,Xae,Jae,nre,sre,ure,hre,gre,yre,kre,Sre,Tre,Are,$re,Rre,Pre,Lre,Vre,qre,Yre,rse,EQ,lse,cse,mse,bse,ste,vse,kse,Sse,Cse,Fse,_Q,Dse,Mse,Ose,zse,Wse,ite,ese,Use,jse,Zse,FQ,tie,rie,lie,cie,fie,bie,vie,Iie,Tie,Eie,$ie,Mie,zie,Vie,qie,Xie,jee,nse,Jie,eoe,noe,roe,ioe,loe,poe,doe,moe,boe,xoe,woe,Ioe,Toe,_oe,Aoe,$oe,tse,LQ,Moe,Loe,Woe,Uoe,joe,Yoe,zQ,Joe,ele,ale,wse];for(let e of rle)Jc(e);var Qe;(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"})(Qe||(Qe={}));var _c;(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"})(_c||(_c={}));var $A;function sle(e){$A=e.wasm.cwrap(ii,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function ile(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 ${E.shape.length}.`);m=E.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=_c[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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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(w.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,Qe[u.dtype],y),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var cle=!0,dle=Ht(ys,cle),DA;function hle(e){DA=e.wasm.cwrap(Ci,null,["array","number","number","number"])}function mle(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return DA(s,r.length,Qe[a.dtype],i),a}var fle={kernelName:Ci,backendName:"wasm",setupFunc:hle,kernelFunc:mle};function Vf(e){let{inputs:{x:t},backend:n}=e;if(t.dtype==="string")return bn(n.readSync(t.dataId),t.shape,t.dtype);let a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var gle={kernelName:eo,backendName:"wasm",kernelFunc:Vf},RA;function ble(e){RA=e.wasm.cwrap(Er,null,["number","array","number","number","number","array","number"])}function gs(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=xle(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var vle={kernelName:Er,backendName:"wasm",kernelFunc:gs,setupFunc:ble};function Fs(e,t,n){let a=e.shape,r=e.shape.length,s=w.parseAxisParam(t,a),i=s,o=N.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let c=0;c`new shape: ${i}, old shape: ${a.shape}. 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tue={kernelName:nu,backendName:"wasm",kernelFunc:eue},UA;function nue(e){UA=e.wasm.cwrap(au,null,["number","number","boolean","number","number","number"])}function aue(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 UA(p(r),i,o,p(s),Qe[s.dtype],p(u)),u}var rue={kernelName:au,backendName:"wasm",setupFunc:nue,kernelFunc:aue},sue=!0,iue=Ht(ru,sue);function oue(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 lue={kernelName:Dc,backendName:"wasm",kernelFunc:oue};function $s(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var uue={kernelName:Mi,backendName:"wasm",kernelFunc:$s},pue=Xe(Pi),GA;function cue(e){GA=e.wasm.cwrap(xs,null,["number","number","number","number"])}function due(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 GA(o,s,i,u),l}var hue={kernelName:xs,backendName:"wasm",setupFunc:cue,kernelFunc:due};function HA(e){let{inputs:t,backend:n}=e,a=w.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=>w.sizeFromShape(h.shape)>0);if(i.length===1)return Vf({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(w.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let v=[-1,w.sizeFromShape(x.shape.slice(a))];return 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b=N.getUndoAxesPermutation(u);g=gs({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Oue={kernelName:lu,backendName:"wasm",setupFunc:Mue,kernelFunc:Pue},QA;function Lue(e){QA=e.wasm.cwrap(Vi,null,["number","number","number","number","number","number"])}function zue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;w.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=gs({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;QA(m,i?1:0,o?1:0,h,f,Qe[r.dtype]);let g=c;if(u!==null){let b=N.getUndoAxesPermutation(u);g=gs({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Wue={kernelName:Vi,backendName:"wasm",setupFunc:Lue,kernelFunc:zue},eF;function Bue(e){eF=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function Vue(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 eF(d(r),new Uint8Array(new Int32Array(r.shape).buffer),r.shape.length,i,l,d(s),Qe[s.dtype],o,d(p)),p}var Uue={kernelName:Mc,backendName:"wasm",setupFunc:Bue,kernelFunc:Vue},tF;function Gue(e){tF=e.wasm.cwrap(pu,null,["number","number","number","array","number","array","array","number","number"])}function Hue(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(w.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return tF(g,s,i==="NHWC"?1:0,b,r.shape.length-1,y,x,m.length,v),f}var que={kernelName:pu,backendName:"wasm",setupFunc:Gue,kernelFunc:Hue},nF;function jue(e){nF=e.wasm.cwrap(Ui,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Kue(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,v=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 nF(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,b,y,x,D,v,I,T,C,E,F,S),$}var Xue={kernelName:Ui,backendName:"wasm",setupFunc:jue,kernelFunc:Kue},aF;function Yue(e){aF=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function Zue(e){let{inputs:t,backend:n}=e,{x:a}=t,r=w.sizeFromShape(a.shape),s=n.makeOutput([...a.shape,...a.shape],a.dtype);return aF(n.dataIdMap.get(a.dataId).id,Qe[a.dtype],r,n.dataIdMap.get(s.dataId).id),s}var Jue={kernelName:Pc,backendName:"wasm",setupFunc:Yue,kernelFunc:Zue},rF;function Que(e){rF=e.wasm.cwrap(Gi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function epe(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 rF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(p.dataId).id,Qe[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 tpe={kernelName:Gi,backendName:"wasm",setupFunc:Que,kernelFunc:epe},sF;function npe(e){sF=e.wasm.cwrap(Fl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ape(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 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,Qe[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 rpe={kernelName:Fl,backendName:"wasm",setupFunc:npe,kernelFunc:ape},iF;function spe(e){iF=e.wasm.cwrap(Al,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ipe(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 ${i.dtype}`);let p=N.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return iF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var ope={kernelName:Al,backendName:"wasm",setupFunc:spe,kernelFunc:ipe},lpe=Xe(qi),oF;function upe(e){oF=e.wasm.cwrap(cu,null,["number","number","number"])}function ppe(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return oF(i(r),i(a),i(s)),s}var cpe={kernelName:cu,backendName:"wasm",setupFunc:upe,kernelFunc:ppe},dpe=!1,hpe=Ht(du,dpe,"bool"),mpe=Xe(ji),fpe=Xe(Ki,"float32");function dv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Wn({inputs:{x:r},backend:a,attrs:{shape:o}})}var gpe={kernelName:hu,backendName:"wasm",kernelFunc:dv},bpe=Xe(Xi,"float32");function lF(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var ype={kernelName:Oc,backendName:"wasm",kernelFunc:lF},uF;function xpe(e){uF=e.wasm.cwrap(mu,null,["number","number","number","number","number","number"])}function vpe(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return uF(s,o,l,u,p,i),r}var wpe={kernelName:mu,backendName:"wasm",kernelFunc:vpe,setupFunc:xpe},kpe=Xe(Yi),Ipe=!1,Spe=Ht(Zi,Ipe),pF;function Npe(e){pF=e.wasm.cwrap(Ji,null,["number","number","number","number","number","number","number"])}function Tpe(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return pF(p,d,c,h,m,r,g),f}var Cpe={kernelName:Ji,backendName:"wasm",setupFunc:Npe,kernelFunc:Tpe},cF;function _pe(e){cF=e.wasm.cwrap(oi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Epe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=_c[h];if(g==null)throw new Error(`${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,v=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 (${x})`);v=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=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:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return cF(b,j,K,Z,y,I,T,v,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Ape={kernelName:oi,backendName:"wasm",setupFunc:_pe,kernelFunc:Epe},dF;function Fpe(e){dF=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 $pe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=_c[h];if(g==null)throw new Error(`${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,v=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 (${x})`);v=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=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:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return dF(b,j,K,Z,y,I,T,v,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Dpe={kernelName:li,backendName:"wasm",setupFunc:Fpe,kernelFunc:$pe},hF;function Rpe(e){hF=e.wasm.cwrap(gu,null,["number","number","number","number","number","number","array","number"])}function Mpe(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 hF(c,Qe[a.dtype],h,i,d,o,m,f),u}var Ppe={kernelName:gu,backendName:"wasm",setupFunc:Rpe,kernelFunc:Mpe},mF;function Ope(e){mF=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Lpe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.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=w.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(w.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,v=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer);return mF(y,Qe[r.dtype],I,b,x,d.batchSize,T,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var zpe={kernelName:fu,backendName:"wasm",setupFunc:Ope,kernelFunc:Lpe},Wpe=!1,Bpe=Ht(bu,Wpe,"bool"),Vpe=!1,Upe=Ht(Qi,Vpe,"bool"),Gpe=Xe(to,"bool"),Hpe=Xe(no,"bool"),qpe=Xe(ao,"bool"),fF;function jpe(e){fF=e.wasm.cwrap(ro,null,["number","number","number","number"])}function Kpe(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(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;fF(r,Qe[t.dtype],n,i)}return s}var Xpe={kernelName:ro,backendName:"wasm",setupFunc:jpe,kernelFunc:Kpe},Ype=!1,Zpe=Ht(yu,Ype,"bool"),Jpe=!1,Qpe=Ht(xu,Jpe,"bool"),gF;function ece(e){gF=e.wasm.cwrap(vu,null,["number","number","number","number"])}function tce(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return gF(n.dataIdMap.get(o.dataId).id,a,r,i),o}var nce={kernelName:vu,backendName:"wasm",setupFunc:ece,kernelFunc:tce},ace=Xe(so),rce=Xe(io),sce=!1,ice=Ht(wu,sce,"bool"),oce=Xe(ku),lce=!1,uce=Ht(Iu,lce,"bool"),pce=!1,cce=Ht(OS,pce,"bool"),bF;function dce(e){bF=e.wasm.cwrap(oo,null,["number","number","number","number","number","number","number"])}function hce(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 bF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var mce={kernelName:oo,backendName:"wasm",setupFunc:dce,kernelFunc:hce},yF;function fce(e){yF=e.wasm.cwrap(Su,null,["number","number","number","number","number","number","number","number","number"])}function gce(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 yF(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 bce={kernelName:Su,backendName:"wasm",setupFunc:fce,kernelFunc:gce},xF;function yce(e){xF=e.wasm.cwrap(lo,null,["number","number","number","number"])}function xce(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}=Fs(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=w.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;xF(o,Qe[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=N.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var vce={kernelName:lo,backendName:"wasm",setupFunc:yce,kernelFunc:xce},wce=!1,kce=Ht(uo,wce),vF;function Ice(e){vF=e.wasm.cwrap(po,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Sce(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),E=a.dataIdMap.get(C.dataId).id;return vF(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,b,y,x,v,I,T,E),C}var Nce={kernelName:po,backendName:"wasm",setupFunc:Ice,kernelFunc:Sce},wF;function Tce(e){wF=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 Cce(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 wF(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 _ce={kernelName:Nu,backendName:"wasm",setupFunc:Tce,kernelFunc:Cce},kF;function Ece(e){kF=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 Ace(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 kF(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 Fce={kernelName:zc,backendName:"wasm",setupFunc:Ece,kernelFunc:Ace},IF;function $ce(e){IF=e.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dce(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=N.computePool2DInfo(s.shape,i,o,1,l,u),d=n.makeOutput(s.shape,s.dtype);return IF(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),d}var Rce={kernelName:Lc,backendName:"wasm",setupFunc:$ce,kernelFunc:Dce},SF;function Mce(e){SF=e.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","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,includeBatchInIndex:l}=a;w.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];w.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,[1,1],o),d=n.makeOutput(p.outShape,r.dtype),c=n.makeOutput(p.outShape,"int32");return SF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,n.dataIdMap.get(c.dataId).id,Qe[r.dtype],l,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),[d,c]}var Oce={kernelName:Wc,backendName:"wasm",setupFunc:Mce,kernelFunc:Pce},NF;function Lce(e){NF=e.wasm.cwrap(co,null,["number, number, number"])}function zce(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}=Fs(i,r,t),m=d;if(h){let v=t.dataIdMap.get(p.dataId).id;v!==o&&(u=p,l=v,m=N.getInnerMostAxes(m.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=N.computeOutAndReduceShapes(u.shape,m),b=w.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=$s({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(f,"float32");if(w.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;NF(l,b,v)}if(h&&t.disposeData(p.dataId),s){let v=N.expandShapeToKeepDim(x.shape,c);x.shape=v}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var Wce={kernelName:co,backendName:"wasm",setupFunc:Lce,kernelFunc:zce},TF;function Bce(e){TF=e.wasm.cwrap(ho,null,["number","number","number","number"])}function Vce(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}=Fs(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=w.sizeFromShape(g),y=t.makeOutput(f,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;TF(l,Qe[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:ho,backendName:"wasm",setupFunc:Bce,kernelFunc:Vce},Gce=!1,Hce=Ht(mo,Gce),hv;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(hv||(hv={}));var CF;function qce(e){CF=e.wasm.cwrap(fo,null,["number","array","number","number","array","array","number","number"])}function jce(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 CF(i,u,t.shape.length,Qe[t.dtype],c,h,hv[r],l),o}var Kce={kernelName:fo,backendName:"wasm",kernelFunc:jce,setupFunc:qce},_F;function Xce(e){_F=e.wasm.cwrap(zo,null,["number","number","number","number"])}function EF(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=w.sizeFromShape(n.shape)/o;return w.sizeFromShape(s.shape)===0||_F(r,i,o,l),s}var Yce={kernelName:zo,backendName:"wasm",setupFunc:Xce,kernelFunc:EF},AF;function Zce(e){AF=e.wasm.cwrap(Tu,null,["number","number","number","number","number","number"])}function Jce(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 ${r.dtype}`);let l=o?r:EF({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),[u,p]=l.shape,d=n.makeOutput([u,s],"int32");return AF(n.dataIdMap.get(l.dataId).id,u,p,s,i,n.dataIdMap.get(d.dataId).id),o||n.disposeData(l.dataId),d}var Qce={kernelName:Tu,backendName:"wasm",setupFunc:Zce,kernelFunc:Jce},ede=Ht(go,!0),tde=!0,nde=Ht(bo,tde),ade=Xe(Cu);function sk(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var FF;function rde(e){FF=e.wasm.cwrap(Eu,"number",["number","number","number","number","number"])}function sde(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=FF(u,p,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=sk(t,d);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var ide={kernelName:Eu,backendName:"wasm",setupFunc:rde,kernelFunc:sde},$F;function ode(e){$F=e.wasm.cwrap(Au,"number",["number","number","number","number","number","bool"])}function lde(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=$F(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 ude={kernelName:Au,backendName:"wasm",setupFunc:ode,kernelFunc:lde},DF;function pde(e){DF=e.wasm.cwrap(Fu,"number",["number","number","number","number","number","number"])}function cde(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=DF(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 dde={kernelName:Fu,backendName:"wasm",setupFunc:pde,kernelFunc:cde},hde=!1,mde=Ht(_u,hde,"bool"),RF;function fde(e){RF=e.wasm.cwrap(yo,null,["number","number","number","number","number"])}function gde(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 RF(d,i,o,l,p),u}var bde={kernelName:yo,backendName:"wasm",setupFunc:fde,kernelFunc:gde};function yde(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var xde={kernelName:$u,backendName:"wasm",kernelFunc:yde};function vde(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return dv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching 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PF={kernelName:xo,backendName:"wasm",kernelFunc:Ide,setupFunc:kde},Sde=!1,Nde=Ht(vo,Sde),OF;function Tde(e){OF=e.wasm.cwrap(wo,null,["number","number","number"])}function Cde(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=s,l=a,u=l;l.dtype!=="float32"&&(u=$s({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 OF(o,i,d),l.dtype!=="float32"&&n.disposeData(u.dataId),p}var _de={kernelName:wo,backendName:"wasm",setupFunc:Tde,kernelFunc:Cde},LF;function Ede(e){LF=e.wasm.cwrap(ko,null,["number","number","number","number"])}function Ade(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}=Fs(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=w.sizeFromShape(g),y=t.makeOutput(f,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;LF(l,b,Qe[y.dtype],x)}if(h&&t.disposeData(p.dataId),s){let x=N.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var Fde={kernelName:ko,backendName:"wasm",setupFunc:Ede,kernelFunc:Ade},$de=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=R1(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Dde={kernelName:Bc,backendName:"wasm",kernelFunc:$de},Rde=!0,Mde=Ht(Hi,Rde),Pde=Xe(Io),Ode=Xe(So),Lde=Xe(Co),zF;function zde(e){zF=e.wasm.cwrap(To,null,["number","number","number","number","number","number","number","number","number","number"])}function Wde(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=$s({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let b=f.id,y=t.makeOutput(m,"float32");if(w.sizeFromShape(r.shape)===0)return y;let x=t.dataIdMap.get(y.dataId).id;return zF(b,p,d,c,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),y}var Bde={kernelName:To,backendName:"wasm",setupFunc:zde,kernelFunc:Wde},WF;function Vde(e){WF=e.wasm.cwrap(Pu,null,["number","number","number","array","array","boolean"])}function Ude(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=$s({backend:n,inputs:{x:r},attrs:{dtype:"float32"}}),l=n.dataIdMap.get(u.dataId)),WF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&n.disposeData(u.dataId),o}var Gde={kernelName:Pu,backendName:"wasm",setupFunc:Vde,kernelFunc:Ude},BF;function Hde(e){BF=e.wasm.cwrap(No,null,["number","number","number","number","number","number","number","number","number","number"])}function qde(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.makeOutput(m,"float32");if(w.sizeFromShape(r.shape)===0)return f;let g=t.dataIdMap.get(r.dataId),b;g.dtype!=="float32"&&(b=$s({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),g=t.dataIdMap.get(b.dataId));let y=g.id,x=t.dataIdMap.get(f.dataId).id;return BF(y,p,d,c,h,l,u,s?1:0,i?1:0,x),b!=null&&t.disposeData(b.dataId),f}var jde={kernelName:No,backendName:"wasm",setupFunc:Hde,kernelFunc:qde},VF;function Kde(e){VF=e.wasm.cwrap(Mu,null,["number","number","number","array","array","boolean"])}function Xde(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=$s({backend:n,inputs:{x:r},attrs:{dtype:"float32"}}),l=n.dataIdMap.get(u.dataId)),VF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&n.disposeData(u.dataId),o}var Yde={kernelName:Mu,backendName:"wasm",setupFunc:Kde,kernelFunc:Xde},UF;function Zde(e){UF=e.wasm.cwrap(_o,null,["number","array","number","array","number","number"])}function Jde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=w.parseAxisParam(s,r.shape);if(r.shape.length===0)return Vf({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(o.dataId).id,p=new Uint8Array(new Int32Array(i).buffer),d=new Uint8Array(new 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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(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof Ur?this.faceFeatureExtractor.forwardInput(n):n;return Bd(r.as2D(r.shape[0],-1),a.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return T$(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=ng(n);return this.faceFeatureExtractor.loadFromWeightMap(a),C$(r)}extractParams(n){let a=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*a+r,i=n.slice(0,n.length-s),o=n.slice(n.length-s);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(o)}};var 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p=i(`Prediction/BoxPredictor_${u}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${u}/box_encoding_predictor`),d=i(`Prediction/BoxPredictor_${u}/ClassPredictor`,`prediction_layer/box_predictor_${u}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function H$(e){let 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hfe(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 mfe(e,t){let{sizes:n,centers:a}=hfe(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 K$(e,t,n){return P(()=>{let a=e.shape[0],r=mfe(W(Ln(n.extra_dim,[a,1,1]),[-1,4]),W(e,[-1,4]));r=W(r,[a,r.shape[0]/a,4]);let s=ma(Ue(t,[0,0,1],[-1,-1,-1])),i=Ue(s,[0,0,0],[-1,-1,1]);i=W(i,[a,i.shape[1]]);let o=pt(r),l=pt(i);return{boxes:o,scores:l}})}function sl(e,t){return P(()=>{let n=e.shape[0],a=W(rl(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(rl(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function X$(e,t,n){return P(()=>{let 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var il=class extends fn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return P(()=>{let a=se(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=q$(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=X$(s.out,s.conv11,n.prediction_layer);return K$(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await kt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Oa(n),s=await kt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,I]=[Math.max(0,b[x][0]),Math.min(1,b[x][2])].map(E=>E*g),[T,C]=[Math.max(0,b[x][1]),Math.min(1,b[x][3])].map(E=>E*f);return new wt(p[x],new yp(T,v,C-T,I-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return H$(t)}extractParams(t){return G$(t)}};function ffe(e){let t=new il;return t.extractWeights(e),t}function ECe(e){return ffe(e)}var Y$=class extends il{};var Z$=.4,J$=[new Pe(.738768,.874946),new Pe(2.42204,2.65704),new Pe(4.30971,7.04493),new Pe(10.246,4.59428),new Pe(12.6868,11.8741)],Q$=[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)],eD=[117.001,114.697,97.404],tD="tiny_yolov2_model",nD="tiny_yolov2_separable_conv_model";var cg=e=>typeof e=="number";function aD(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(!cg(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=>cg(t.x)&&cg(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(cg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Rp(e){return P(()=>{let t=z(e,ve(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Gr(e,t){return P(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Rt(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Rp(n)})}function Hr(e,t){return P(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Cs(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Rp(n)})}function gfe(e,t){let n=Np(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${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,`${l}/bn`);return{conv:u,bn:p}}let s=Tp(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function rD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=Fn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=gfe(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),T=u(h,m,"conv2"),C=u(m,f,"conv3"),E=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:T,conv3:C,conv4:E,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),E=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,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: ${s().length}`);return{params:p,paramMappings:i}}function bfe(e,t){let n=ia(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:u}}let i=Cp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function sD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=bfe(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return An(e,n),{params:i,paramMappings:n}}var xr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Nk=class extends fn{constructor(n){super("TinyYolov2");aD(n),this._config=n}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(n,a){let r=Gr(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=Gr(r,a.conv6),r=Gr(r,a.conv7),rl(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?Rp(rl(n,a.conv0,"valid",!1)):Hr(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=a.conv6?Hr(r,a.conv6):r,r=a.conv7?Hr(r,a.conv7):r,rl(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return P(()=>{let s=se(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?yr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await kt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new xr(a),i=await kt(n),o=await this.forwardInput(i,r),l=P(()=>pt(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(b=>b.box),c=p.map(b=>b.score),h=p.map(b=>b.classScore),m=p.map(b=>this.config.classes[b.label]);return h$(d.map(b=>b.rescale(r)),c,this.config.iouThreshold,!0).map(b=>new Ds(c[b],h[b],m[b],d[b],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return sD(n,this.config)}extractParams(n){let a=this.config.filterSizes||Nk.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 ${r} filterSizes in config`);return rD(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let y=n.reshape([p,p,d,this.boxEncodingSize]),x=y.slice([0,0,0,0],[p,p,d,4]),v=y.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Xa(y.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):ve(0);return[x,v,I]}),f=[],g=await h.array(),b=await c.array();for(let y=0;yr){let T=(x+Hf(b[y][x][v][0]))/p*l,C=(y+Hf(b[y][x][v][1]))/p*u,E=Math.exp(b[y][x][v][2])*this.config.anchors[v].x/p*l,F=Math.exp(b[y][x][v][3])*this.config.anchors[v].y/p*u,D=T-E/2,$=C-F/2,S={row:y,col:x,anchor:v},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new bp(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)}},ol=Nk;ol.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Mp=class extends ol{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:Z$,classes:["face"],...t?{anchors:Q$,meanRgb:eD}:{anchors:J$,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?nD:tD}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function v_e(e,t=!0){let n=new Mp(t);return n.extractWeights(e),n}var dg=class extends xr{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 ll(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Ap(l)?r(l):l.detection),i=a||(t instanceof Te?await Ld(t,s):await Od(t,s)),o=await n(i);return i.forEach(l=>l instanceof Te&&l.dispose()),o}async function Pp(e,t,n,a,r){return ll([e],t,async s=>n(s[0]),a,r)}var iD=.4,oD=[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)],lD=[117.001,114.697,97.404];var Op=class extends ol{constructor(){let t={withSeparableConvs:!0,iouThreshold:iD,classes:["face"],anchors:oD,meanRgb:lD,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 il,tinyFaceDetector:new Op,tinyYolov2:new Mp,faceLandmark68Net:new $p,faceLandmark68TinyNet:new ug,faceRecognitionNet:new Dp,faceExpressionNet:new ag,ageGenderNet:new og},yfe=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),Y_e=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),Z_e=(e,t)=>rt.tinyYolov2.locateFaces(e,t),xfe=e=>rt.faceLandmark68Net.detectLandmarks(e),J_e=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),Q_e=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),eEe=e=>rt.faceExpressionNet.predictExpressions(e),tEe=e=>rt.ageGenderNet.predictAgeAndGender(e),vfe=e=>rt.ssdMobilenetv1.load(e),nEe=e=>rt.tinyFaceDetector.load(e),aEe=e=>rt.tinyYolov2.load(e),rEe=e=>rt.faceLandmark68Net.load(e),sEe=e=>rt.faceLandmark68TinyNet.load(e),iEe=e=>rt.faceRecognitionNet.load(e),oEe=e=>rt.faceExpressionNet.load(e),lEe=e=>rt.ageGenderNet.load(e),uEe=vfe,pEe=yfe,cEe=xfe;var hg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},ul=class extends hg{async run(){let t=await this.parentTask,n=await ll(t,this.input,async a=>Promise.all(a.map(r=>rt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>bk(a,n[r]))}withAgeAndGender(){return new cl(this,this.input)}},pl=class extends hg{async run(){let t=await this.parentTask;if(!t)return;let n=await Pp(t,this.input,a=>rt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return bk(t,n)}withAgeAndGender(){return new dl(this,this.input)}},Ps=class extends ul{withAgeAndGender(){return new Ls(this,this.input)}withFaceDescriptors(){return new Ws(this,this.input)}},Os=class extends pl{withAgeAndGender(){return new zs(this,this.input)}withFaceDescriptor(){return new Bs(this,this.input)}};var mg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},cl=class extends mg{async run(){let t=await this.parentTask,n=await ll(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 Ik(Sk(a,i,o),s)})}withFaceExpressions(){return new ul(this,this.input)}},dl=class extends mg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Pp(t,this.input,s=>rt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Ik(Sk(t,a,r),n)}withFaceExpressions(){return new pl(this,this.input)}},Ls=class extends cl{withFaceExpressions(){return new Ps(this,this.input)}withFaceDescriptors(){return new Ws(this,this.input)}},zs=class extends dl{withFaceExpressions(){return new Os(this,this.input)}withFaceDescriptor(){return new Bs(this,this.input)}};var fg=class extends La{constructor(n,a){super();this.parentTask=n;this.input=a}},Ws=class extends fg{async run(){let t=await this.parentTask;return(await ll(t,this.input,a=>Promise.all(a.map(r=>rt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>kk(t[r],a))}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new Ls(this,this.input)}},Bs=class extends fg{async run(){let t=await this.parentTask;if(!t)return;let n=await Pp(t,this.input,a=>rt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return kk(t,n)}withFaceExpressions(){return new Os(this,this.input)}withAgeAndGender(){return new zs(this,this.input)}};var gg=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}},bg=class extends gg{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Te?await Ld(this.input,n):await Od(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)=>Vd(i,r[o]))}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new Ls(this,this.input)}withFaceDescriptors(){return new Ws(this,this.input)}},yg=class extends gg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Te?await Ld(this.input,[n]):await Od(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Te&&s.dispose()),Vd(t,r)}withFaceExpressions(){return new Os(this,this.input)}withAgeAndGender(){return new zs(this,this.input)}withFaceDescriptor(){return new Bs(this,this.input)}};var xg=class extends La{constructor(n,a=new Oa){super();this.input=n;this.options=a}},Gd=class extends xg{async run(){let{input:t,options:n}=this,a;if(n instanceof dg)a=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Oa)a=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof xr)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=>vp({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new bg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ul(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new cl(this.runAndExtendWithFaceDetections(),this.input)}},vg=class extends xg{async run(){let t=await new Gd(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?vp({},n):void 0)})}withFaceLandmarks(t=!1){return new yg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new pl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new dl(this.runAndExtendWithFaceDetection(),this.input)}};function lAe(e,t=new Oa){return new vg(e,t)}function Tk(e,t=new Oa){return new Gd(e,t)}async function wfe(e,t){return Tk(e,new Oa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function mAe(e,t={}){return Tk(e,new xr(t)).withFaceLandmarks().withFaceDescriptors()}var fAe=wfe;function uD(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 wg=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 Wr)return i;if(i instanceof Float32Array)return new Wr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Wr(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=>uD(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Dd(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Wr.fromJSON(a));return new wg(n,t.distanceThreshold)}};function DAe(e){let t=new Op;return t.extractWeights(e),t}function kfe(e,t){let{width:n,height:a}=new En(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>kfe(r,{width:n,height:a}));if(Ap(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Vd(vp(e,r),s)}return Br(e)?vp(e,e.detection.forSize(n,a)):e instanceof ka||e instanceof wt?e.forSize(n,a):e}var GAe=F$;export{og as AgeGenderNet,bp as BoundingBox,dt as Box,La as ComposableTask,Ws as ComputeAllFaceDescriptorsTask,fg as ComputeFaceDescriptorsTaskBase,Bs as ComputeSingleFaceDescriptorTask,bg as DetectAllFaceLandmarksTask,Gd as DetectAllFacesTask,gg as DetectFaceLandmarksTaskBase,xg as DetectFacesTaskBase,yg as DetectSingleFaceLandmarksTask,vg as DetectSingleFaceTask,En as Dimensions,_$ as FACE_EXPRESSION_LABELS,wt as FaceDetection,Y$ as FaceDetectionNet,ag as FaceExpressionNet,Ms as FaceExpressions,$p as FaceLandmark68Net,ug as FaceLandmark68TinyNet,z$ as FaceLandmarkNet,ka as FaceLandmarks,f$ as FaceLandmarks5,xp as FaceLandmarks68,Dd as FaceMatch,wg as FaceMatcher,Dp as FaceRecognitionNet,xk as Gender,Rd as LabeledBox,Wr as LabeledFaceDescriptors,Ur as NetInput,fn as NeuralNetwork,Ds as ObjectDetection,Pe as Point,g$ as PredictedBox,yp as Rect,il as SsdMobilenetv1,Oa as SsdMobilenetv1Options,Op as TinyFaceDetector,dg as TinyFaceDetectorOptions,Mp as TinyYolov2,xr as TinyYolov2Options,fAe as allFaces,wfe as allFacesSsdMobilenetv1,mAe as allFacesTinyYolov2,b$ as awaitMediaLoaded,y$ as bufferToImage,Q_e as computeFaceDescriptor,Sp as createCanvas,Xf as createCanvasFromMedia,ECe as createFaceDetectionNet,E2e as createFaceRecognitionNet,ffe as createSsdMobilenetv1,DAe as createTinyFaceDetector,v_e as createTinyYolov2,Tk as detectAllFaces,xfe as detectFaceLandmarks,J_e as detectFaceLandmarksTiny,cEe as detectLandmarks,lAe as detectSingleFace,A$ as draw,at as env,uD as euclideanDistance,Ik as extendWithAge,kk as extendWithFaceDescriptor,vp as extendWithFaceDetection,bk as extendWithFaceExpressions,Vd as extendWithFaceLandmarks,Sk as extendWithGender,Ld as extractFaceTensors,Od as extractFaces,zke as fetchImage,w$ as fetchJson,Gke as fetchNetWeights,Rs as fetchOrThrow,Yke as fetchVideo,sa as getContext2dOrThrow,Ip as getMediaDimensions,x$ as imageTensorToCanvas,v$ as imageToSquare,e0e as inverseSigmoid,c$ as iou,gk as isMediaElement,Kf as isMediaLoaded,D2e as isWithAge,Br as isWithFaceDetection,E$ as isWithFaceExpressions,Ap as isWithFaceLandmarks,O2e as isWithGender,lEe as loadAgeGenderModel,uEe as loadFaceDetectionModel,oEe as loadFaceExpressionModel,rEe as loadFaceLandmarkModel,sEe as loadFaceLandmarkTinyModel,iEe as loadFaceRecognitionModel,vfe as loadSsdMobilenetv1Model,nEe as loadTinyFaceDetectorModel,aEe as loadTinyYolov2Model,I$ as loadWeightMap,pEe as locateFaces,aIe as matchDimensions,d$ as minBbox,rt as nets,h$ as nonMaxSuppression,yr as normalize,m$ as padToSquare,tEe as predictAgeAndGender,eEe as recognizeFaceExpressions,kfe as resizeResults,wp as resolveInput,Jwe as shuffleArray,Hf as sigmoid,yfe as ssdMobilenetv1,ze as tf,Y_e as tinyFaceDetector,Z_e as tinyYolov2,kt as toNetInput,p$ as utils,aD as validateConfig,GAe as version}; + `; + } +}; +function unsortedSegmentSum3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, segmentIds } = inputs; + const { numSegments } = attrs; + const xRank = x.shape.length; + const toDispose = []; + let axis = 0; + const permutation = backend_util_exports.getAxesPermutation([axis], xRank); + let permutedX = x; + if (permutation != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } }); + toDispose.push(permutedX); + axis = backend_util_exports.getInnerMostAxes(1, xRank)[0]; + } + const outShape = backend_util_exports.segment_util.computeOutShape(permutedX.shape, axis, numSegments); + const inSize = util_exports.sizeFromShape([permutedX.shape[axis]]); + const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } }); + toDispose.push(a2D); + const outputDType = sumOutType(x.dtype); + const segOpCompute = (x2, segOpType, segmentIds2, dtype, numSegments2) => { + const batchSize = x2.shape[0]; + const inSize2 = x2.shape[1]; + const windowSize = backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2, numSegments2); + const segOpInfo = { windowSize, inSize: inSize2, batchSize, numSegments: numSegments2 }; + const program = new SegmentOpProgram(segOpInfo, segOpType); + const output = backend2.compileAndRun(program, [x2, segmentIds2], dtype); + toDispose.push(output); + if (output.shape[1] === numSegments2) { + return output; + } + const rangeInfo = range4({ + backend: backend2, + attrs: { start: 0, stop: numSegments2, step: 1, dtype: "float32" } + }); + const tileInfo = tile4({ + inputs: { x: rangeInfo }, + backend: backend2, + attrs: { reps: [inSize2 / windowSize] } + }); + toDispose.push(rangeInfo); + toDispose.push(tileInfo); + const result2 = segOpCompute(output, segOpType, tileInfo, dtype, numSegments2); + return result2; + }; + const segOpResult = segOpCompute(a2D, "unsortedSegmentSum", segmentIds, outputDType, numSegments); + const reshaped = reshape4({ inputs: { x: segOpResult }, backend: backend2, attrs: { shape: outShape } }); + let result = reshaped; + if (permutation != null) { + toDispose.push(reshaped); + const perm = backend_util_exports.getUndoAxesPermutation(permutation); + result = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm } }); + } + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var unsortedSegmentSumConfig2 = { + kernelName: UnsortedSegmentSum, + backendName: "webgl", + kernelFunc: unsortedSegmentSum3 +}; +var kernelConfigs2 = [ + _fusedMatMulConfig2, + absConfig2, + acosConfig2, + acoshConfig2, + addConfig2, + addNConfig2, + allConfig2, + anyConfig2, + argMaxConfig2, + argMinConfig2, + asinConfig2, + asinhConfig2, + atanConfig2, + atan2Config2, + atanhConfig2, + avgPoolConfig2, + avgPool3DConfig2, + avgPool3DGradConfig3, + avgPoolGradConfig3, + batchMatMulConfig2, + batchNormConfig2, + batchToSpaceNDConfig2, + bincountConfig2, + bitwiseAndConfig2, + broadcastArgsConfig2, + castConfig2, + ceilConfig2, + clipByValueConfig2, + complexConfig2, + complexAbsConfig2, + concatConfig2, + conv2DConfig2, + conv2DBackpropFilterConfig2, + conv2DBackpropInputConfig2, + conv3DConfig2, + conv3DBackpropFilterV2Config2, + conv3DBackpropInputConfig, + cosConfig2, + coshConfig2, + cropAndResizeConfig2, + cumprodConfig2, + cumsumConfig2, + denseBincountConfig2, + depthToSpaceConfig2, + depthwiseConv2dNativeConfig2, + depthwiseConv2dNativeBackpropFilterConfig2, + depthwiseConv2dNativeBackpropInputConfig2, + diagConfig2, + dilation2DConfig2, + einsumConfig2, + eluConfig2, + eluGradConfig3, + equalConfig2, + erfConfig2, + expConfig2, + expandDimsConfig2, + expm1Config2, + fftConfig2, + fillConfig2, + flipLeftRightConfig2, + floorConfig2, + floorDivConfig2, + fromPixelsConfig, + fusedConv2DConfig2, + fusedDepthwiseConv2DConfig2, + gatherNdConfig2, + gatherV2Config2, + greaterConfig2, + greaterEqualConfig2, + identityConfig2, + ifftConfig2, + imagConfig2, + isFiniteConfig2, + isInfConfig2, + isNaNConfig2, + leakyReluConfig2, + lessConfig2, + lessEqualConfig2, + linSpaceConfig2, + logConfig2, + log1pConfig2, + logicalAndConfig2, + logicalNotConfig2, + logicalOrConfig2, + LRNConfig2, + LRNGradConfig2, + maxConfig2, + maximumConfig2, + maxPoolConfig2, + maxPool3DConfig2, + maxPool3DGradConfig3, + maxPoolGradConfig3, + maxPoolWithArgmaxConfig2, + meanConfig2, + minConfig2, + minimumConfig2, + mirrorPadConfig2, + modConfig2, + multinomialConfig2, + multiplyConfig2, + negConfig2, + nonMaxSuppressionV3Config2, + nonMaxSuppressionV4Config2, + nonMaxSuppressionV5Config2, + notEqualConfig2, + oneHotConfig2, + onesLikeConfig2, + packConfig2, + padV2Config2, + powConfig2, + preluConfig2, + prodConfig2, + raggedGatherConfig2, + raggedRangeConfig2, + raggedTensorToTensorConfig2, + rangeConfig2, + realConfig2, + realDivConfig2, + reciprocalConfig2, + reluConfig2, + relu6Config2, + reshapeConfig2, + resizeBilinearConfig2, + resizeBilinearGradConfig3, + resizeNearestNeighborConfig2, + resizeNearestNeighborGradConfig3, + reverseConfig2, + rotateWithOffsetConfig2, + roundConfig2, + rsqrtConfig2, + scatterNdConfig2, + searchSortedConfig2, + selectConfig2, + seluConfig2, + sigmoidConfig2, + signConfig2, + sinConfig2, + sinhConfig2, + sliceConfig2, + softmaxConfig2, + softplusConfig2, + spaceToBatchNDConfig2, + sparseFillEmptyRowsConfig2, + sparseReshapeConfig2, + sparseSegmentMeanConfig2, + sparseSegmentSumConfig2, + sparseToDenseConfig2, + splitVConfig2, + sqrtConfig2, + squareConfig2, + squaredDifferenceConfig2, + staticRegexReplaceConfig2, + stepConfig2, + stridedSliceConfig2, + stringNGramsConfig2, + stringSplitConfig2, + stringToHashBucketFastConfig2, + subConfig2, + sumConfig2, + tanConfig2, + tanhConfig2, + tensorScatterUpdateConfig2, + tileConfig2, + topKConfig2, + transformConfig2, + transposeConfig2, + uniqueConfig2, + unpackConfig2, + unsortedSegmentSumConfig2, + zerosLikeConfig2 +]; +for (const kernelConfig of kernelConfigs2) { + registerKernel(kernelConfig); +} +var CppDType; +(function(CppDType2) { + CppDType2[CppDType2["float32"] = 0] = "float32"; + CppDType2[CppDType2["int32"] = 1] = "int32"; + CppDType2[CppDType2["bool"] = 2] = "bool"; + CppDType2[CppDType2["string"] = 3] = "string"; + CppDType2[CppDType2["complex64"] = 4] = "complex64"; +})(CppDType || (CppDType = {})); +var FusableActivation; +(function(FusableActivation2) { + FusableActivation2[FusableActivation2["linear"] = 0] = "linear"; + FusableActivation2[FusableActivation2["relu"] = 1] = "relu"; + FusableActivation2[FusableActivation2["relu6"] = 2] = "relu6"; + FusableActivation2[FusableActivation2["prelu"] = 3] = "prelu"; + FusableActivation2[FusableActivation2["leakyrelu"] = 4] = "leakyrelu"; + FusableActivation2[FusableActivation2["sigmoid"] = 5] = "sigmoid"; + FusableActivation2[FusableActivation2["elu"] = 6] = "elu"; +})(FusableActivation || (FusableActivation = {})); +var wasmFusedMatMul; +function setup(backend2) { + wasmFusedMatMul = backend2.wasm.cwrap(_FusedMatMul, null, [ + "number", + "array", + "number", + "number", + "array", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // out_id + ]); +} +function fusedBatchMatMul(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b, bias, preluActivationWeights } = inputs; + if (a.dtype !== "float32" || b.dtype !== "float32") { + throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`); + } + const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs; + const aId = backend2.dataIdMap.get(a.dataId).id; + const bId = backend2.dataIdMap.get(b.dataId).id; + let biasId = 0; + if (bias != null) { + const biasData = backend2.dataIdMap.get(bias.dataId); + if (biasData.shape.length !== 1) { + throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`); + } + biasId = biasData.id; + } + const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id; + const fusedActivation = FusableActivation[activation2]; + if (fusedActivation == null) { + throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`); + } + const leftDim = transposeA ? a.shape[2] : a.shape[1]; + const rightDim = transposeB ? b.shape[1] : b.shape[2]; + const batchDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + const out = backend2.makeOutput([...batchDims, leftDim, rightDim], a.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer); + const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer); + wasmFusedMatMul(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, leakyreluAlpha || 0, outId); + return out; +} +var _fusedMatMulConfig3 = { + kernelName: _FusedMatMul, + backendName: "wasm", + setupFunc: setup, + kernelFunc: fusedBatchMatMul +}; +function createUnaryKernelConfig(kernelName, outType) { + let wasmFunc8; + function setupFunc3(backend2) { + wasmFunc8 = backend2.wasm.cwrap(kernelName, null, [ + "number", + "number", + "number" + // out_id + ]); + } + function kernelFunc3(args) { + const { backend: backend2, inputs: { x } } = args; + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(x.shape, outType || x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + if (util_exports.sizeFromShape(out.shape) === 0) { + return out; + } + wasmFunc8(xId, CppDType[x.dtype], outId); + return out; + } + return { kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3 }; +} +var absConfig3 = createUnaryKernelConfig(Abs); +var acosConfig3 = createUnaryKernelConfig(Acos); +var acoshConfig3 = createUnaryKernelConfig(Acosh); +function createBinaryKernelConfig(kernelName, supportsFullBroadcast20, dtype) { + let wasmFunc8; + function setupFunc3(backend2) { + wasmFunc8 = backend2.wasm.cwrap(kernelName, null, [ + "number", + "array", + "number", + "number", + "array", + "number", + "number", + "number" + // out_id + ]); + } + function kernelFunc3(args) { + const { backend: backend2, inputs } = args; + const { a, b } = inputs; + const aId = backend2.dataIdMap.get(a.dataId).id; + const bId = backend2.dataIdMap.get(b.dataId).id; + const outputType = dtype != null ? dtype : a.dtype; + const newShape = backend_util_exports.assertAndGetBroadcastShape(a.shape, b.shape); + const out = backend2.makeOutput(newShape, outputType); + if (util_exports.sizeFromShape(newShape) === 0) { + return out; + } + const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer); + const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + const kernelFunc4 = () => wasmFunc8(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a.dtype], outId); + kernelFunc4(); + return out; + } + return { kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3 }; +} +var supportsFullBroadcast = true; +var addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast); +var wasmFunc; +function setupFunc(backend2) { + wasmFunc = backend2.wasm.cwrap(AddN, null, [ + "array", + "number", + "number", + "number" + // out_id + ]); +} +function addn(args) { + const { inputs, backend: backend2 } = args; + const out = backend2.makeOutput(inputs[0].shape, inputs[0].dtype); + if (util_exports.sizeFromShape(out.shape) === 0) { + return out; + } + const inputIds = inputs.map((x) => backend2.dataIdMap.get(x.dataId).id); + const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId); + return out; +} +var addNConfig3 = { + kernelName: AddN, + backendName: "wasm", + setupFunc, + kernelFunc: addn +}; +function identity4(args) { + const { inputs: { x }, backend: backend2 } = args; + if (x.dtype === "string") { + return tensor(backend2.readSync(x.dataId), x.shape, x.dtype); + } + const out = backend2.makeOutput(x.shape, x.dtype); + const inVals = backend2.typedArrayFromHeap(x); + const outVals = backend2.typedArrayFromHeap(out); + outVals.set(inVals); + return out; +} +var identityConfig3 = { + kernelName: Identity, + backendName: "wasm", + kernelFunc: identity4 +}; +var wasmTranspose; +function setup2(backend2) { + wasmTranspose = backend2.wasm.cwrap(Transpose, null, [ + "number", + "array", + "number", + "number", + "number", + "array", + "number" + // perm.length + ]); +} +function transpose4(args) { + const { inputs, backend: backend2, attrs } = args; + const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm); + let permIsNoOp = true; + for (let i = 0; i < perm.length; i++) { + if (perm[i] !== i) { + permIsNoOp = false; + } + } + const outShape = computeOutShape4(inputs.x.shape, attrs.perm); + const x = { + dataId: inputs.x.dataId, + shape: reducedShape, + dtype: inputs.x.dtype + }; + if (permIsNoOp) { + const cloned = identity4({ inputs, backend: backend2 }); + cloned.shape = outShape; + return cloned; + } + const out = backend2.makeOutput(outShape, x.dtype); + const xId = backend2.dataIdMap.get(x.dataId).id; + const outId = backend2.dataIdMap.get(out.dataId).id; + const permBytes = new Uint8Array(new Int32Array(perm).buffer); + const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length); + return out; +} +function computeOutShape4(inShape, perm) { + const outShape = new Array(inShape.length); + for (let i = 0; i < outShape.length; i++) { + outShape[i] = inShape[perm[i]]; + } + return outShape; +} +function removeOneSizeDims(shape, perm) { + const newShape = []; + const newPerm = []; + for (let i = 0; i < shape.length; ++i) { + if (shape[i] !== 1) { + newShape.push(shape[i]); + } + if (shape[perm[i]] !== 1) { + newPerm.push(perm[i]); + } + } + for (let i = 0; i < newPerm.length; ++i) { + let minValIdx = -1; + for (let j = 0; j < newPerm.length; ++j) { + if (newPerm[j] >= i && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) { + minValIdx = j; + } + } + newPerm[minValIdx] = i; + } + return [newShape, newPerm]; +} +var transposeConfig3 = { + kernelName: Transpose, + backendName: "wasm", + kernelFunc: transpose4, + setupFunc: setup2 +}; +function permuteAxesAndTranspose(x, axis, backend2) { + const xShape = x.shape; + const xRank = x.shape.length; + const originalAxes = util_exports.parseAxisParam(axis, xShape); + let axes = originalAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let xTransposed = null; + let inputWasTransposed = false; + if (permutedAxes != null) { + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = xShape[permutedAxes[i]]; + } + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 }); + const xId = backend2.dataIdMap.get(x.dataId).id; + const transposedId = backend2.dataIdMap.get(xTransposed.dataId).id; + if (transposedId !== xId) { + inputWasTransposed = true; + } + } + return { transposed: xTransposed, originalAxes, axes, inputWasTransposed }; +} +var wasmAll; +function setup3(backend2) { + wasmAll = backend2.wasm.cwrap(All, null, ["number, number, number"]); +} +function all4(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + input2 = transposed; + inputId = transposedId; + } + const inputRank = input2.shape.length; + backend_util_exports.assertAxesAreInnerMostDims("all", axes, inputRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, x.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmAll(inputId, reduceSize, outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var allConfig3 = { + kernelName: All, + backendName: "wasm", + setupFunc: setup3, + kernelFunc: all4 +}; +var wasmAny; +function setup4(backend2) { + wasmAny = backend2.wasm.cwrap(Any, null, ["number, number, number"]); +} +function any4(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + input2 = transposed; + inputId = transposedId; + } + const inputRank = input2.shape.length; + backend_util_exports.assertAxesAreInnerMostDims("any", axes, inputRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, x.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmAny(inputId, reduceSize, outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var anyConfig3 = { + kernelName: Any, + backendName: "wasm", + setupFunc: setup4, + kernelFunc: any4 +}; +function createArgMinMaxKernelConfig(kernelName) { + let wasmFunc8; + function setupFunc3(backend2) { + wasmFunc8 = backend2.wasm.cwrap(kernelName, null, [ + "number", + "number", + "number", + "number", + "number" + // out_id + ]); + } + function kernelFunc3(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + if (transposedId !== xId) { + input2 = transposed; + inputId = transposedId; + } + } + const outShape = input2.shape.slice(0, -1); + const out = backend2.makeOutput(outShape, "int32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + const outerSize = util_exports.sizeFromShape(out.shape); + const innerSize = input2.shape[axes[0]]; + wasmFunc8(inputId, CppDType[input2.dtype], outerSize, innerSize, outId); + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + return out; + } + return { + kernelName, + backendName: "wasm", + setupFunc: setupFunc3, + kernelFunc: kernelFunc3 + }; +} +var argMaxConfig3 = createArgMinMaxKernelConfig(ArgMax); +var argMinConfig3 = createArgMinMaxKernelConfig(ArgMin); +var asinConfig3 = createUnaryKernelConfig(Asin); +var asinhConfig3 = createUnaryKernelConfig(Asinh); +var atanConfig3 = createUnaryKernelConfig(Atan); +var atan2Config3 = createBinaryKernelConfig( + Atan2, + /*supportsFullBroadcast=*/ + false +); +var atanhConfig3 = createUnaryKernelConfig(Atanh); +var wasmAvgPool; +function setup5(backend2) { + wasmAvgPool = backend2.wasm.cwrap(AvgPool, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function avgPool4(args) { + const { inputs, attrs, backend: backend2 } = args; + const x = inputs.x; + const xId = backend2.dataIdMap.get(x.dataId).id; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const channels = convInfo.inChannels; + if (convInfo.dataFormat !== "channelsLast") { + throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`); + } + if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) { + throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId); + return out; +} +var avgPoolConfig3 = { + kernelName: AvgPool, + backendName: "wasm", + setupFunc: setup5, + kernelFunc: avgPool4 +}; +var wasmAvgPool3D; +function setup6(backend2) { + wasmAvgPool3D = backend2.wasm.cwrap("AvgPool3D", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function avgPool3D3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs; + const convInfo = backend_util_exports.computePool3DInfo( + x.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3, + dimRoundingMode, + dataFormat + ); + const out = backend2.makeOutput(convInfo.outShape, x.dtype); + wasmAvgPool3D( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(out.dataId).id, + convInfo.batchSize, + // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inDepth, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outDepth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideDepth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationDepth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterDepth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.front, + convInfo.padInfo.top, + convInfo.padInfo.left + ); + return out; +} +var avgPool3DConfig3 = { + kernelName: AvgPool3D, + backendName: "wasm", + setupFunc: setup6, + kernelFunc: avgPool3D3 +}; +var wasmAvgPool3DGrad; +function setup7(backend2) { + wasmAvgPool3DGrad = backend2.wasm.cwrap("AvgPool3DGrad", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // filterWidth + ]); +} +function avgPool3DGrad3(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool3DInfo( + input2.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3, + dimRoundingMode + ); + const dx = backend2.makeOutput(input2.shape, input2.dtype); + wasmAvgPool3DGrad( + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(dx.dataId).id, + convInfo.batchSize, + // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inDepth, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outDepth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideDepth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationDepth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterDepth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.front, + convInfo.padInfo.top, + convInfo.padInfo.left, + convInfo.filterDepth, + convInfo.filterHeight, + convInfo.filterWidth + ); + return dx; +} +var avgPool3DGradConfig4 = { + kernelName: AvgPool3DGrad, + backendName: "wasm", + setupFunc: setup7, + kernelFunc: avgPool3DGrad3 +}; +var wasmAvgPoolGrad; +function setup8(backend2) { + wasmAvgPoolGrad = backend2.wasm.cwrap("AvgPoolGrad", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // filterWidth + ]); +} +function avgPoolGrad4(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3 } = attrs; + const convInfo = backend_util_exports.computePool2DInfo( + input2.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3 + ); + const dx = backend2.makeOutput(input2.shape, input2.dtype); + wasmAvgPoolGrad( + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(dx.dataId).id, + convInfo.batchSize, + // Since Pool ops (AvgPool and MaxPool) support 2D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.top, + convInfo.padInfo.left, + convInfo.filterHeight, + convInfo.filterWidth + ); + return dx; +} +var avgPoolGradConfig4 = { + kernelName: AvgPoolGrad, + backendName: "wasm", + setupFunc: setup8, + kernelFunc: avgPoolGrad4 +}; +function reshape5(args) { + const { inputs, attrs } = args; + const { x } = inputs; + const { shape } = attrs; + const xSize = util_exports.sizeFromShape(x.shape); + const $shape = util_exports.inferFromImplicitShape(shape, xSize); + util_exports.assert(xSize === util_exports.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`); + args.backend.incRef(x.dataId); + return { dataId: x.dataId, shape: $shape, dtype: x.dtype }; +} +var reshapeConfig3 = { + kernelName: Reshape, + backendName: "wasm", + kernelFunc: reshape5 +}; +var wasmBatchMatMul; +function setup9(backend2) { + wasmBatchMatMul = backend2.wasm.cwrap(BatchMatMul, null, [ + "number", + "array", + "number", + "number", + "array", + "number", + "number", + "number", + "number" + // out_id + ]); +} +function batchMatMul3(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b } = inputs; + const { transposeA, transposeB } = attrs; + if (a.dtype !== "float32" || b.dtype !== "float32") { + throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`); + } + const aRank = a.shape.length; + const bRank = b.shape.length; + const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1]; + const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2]; + const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2]; + const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1]; + const outerDimsA = a.shape.slice(0, -2); + const outerDimsB = b.shape.slice(0, -2); + const batchDimA = util_exports.sizeFromShape(outerDimsA); + const batchDimB = util_exports.sizeFromShape(outerDimsB); + const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); + const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA]; + const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB]; + const a3d = reshape5({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } }); + const b3d = reshape5({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } }); + const a3dId = backend2.dataIdMap.get(a3d.dataId).id; + const b3dId = backend2.dataIdMap.get(b3d.dataId).id; + const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1]; + const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2]; + const batchDim = Math.max(batchDimA, batchDimB); + const out = backend2.makeOutput([batchDim, leftDim, rightDim], a3d.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer); + const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer); + wasmBatchMatMul(a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes, b3d.shape.length, transposeA, transposeB, outId); + backend2.disposeData(a3d.dataId); + backend2.disposeData(b3d.dataId); + out.shape = outShape; + return out; +} +var batchMatMulConfig3 = { + kernelName: BatchMatMul, + backendName: "wasm", + setupFunc: setup9, + kernelFunc: batchMatMul3 +}; +function slice4(args) { + const { inputs: { x }, attrs: { begin, size }, backend: backend2 } = args; + const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size); + const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_); + const xVals = backend2.readSync(x.dataId); + const out = backend2.makeOutput(size_, x.dtype); + const xStrides = util_exports.computeStrides(x.shape); + const outData = backend2.dataIdMap.get(out.dataId); + if (isContinous) { + const flatOffset = slice_util_exports.computeFlatOffset(begin_, xStrides); + if (x.dtype === "string") { + outData.stringBytes = xVals.slice(flatOffset, flatOffset + util_exports.sizeFromShape(size_)); + } else { + const outVals2 = backend2.typedArrayFromHeap(out); + outVals2.set(xVals.subarray(flatOffset, flatOffset + util_exports.sizeFromShape(size_))); + } + return out; + } + if (x.dtype === "string") { + const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype); + outData.stringBytes = res; + return out; + } + const outVals = backend2.typedArrayFromHeap(out); + const rank = x.shape.length; + if (rank === 2) { + slice2d2(xVals, xStrides[0], outVals, begin_, size_); + } else if (rank === 3) { + slice3d2(xVals, xStrides[0], xStrides[1], outVals, begin_, size_); + } else if (rank === 4) { + slice4d2(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_); + } else { + const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype); + outVals.set(res); + } + return out; +} +function slice2d2(xVals, xStride, outVals, begin, size) { + let outOffset = 0; + const beginI = begin[0]; + const beginJ = begin[1]; + const endI = beginI + size[0]; + for (let i = beginI; i < endI; i++) { + const xOffset = i * xStride + beginJ; + outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset); + outOffset += size[1]; + } +} +function slice3d2(xVals, xStride1, xStride2, outVals, begin, size) { + let outOffset = 0; + const beginI = begin[0]; + const beginJ = begin[1]; + const beginK = begin[2]; + const endI = beginI + size[0]; + const endJ = beginJ + size[1]; + for (let i = beginI; i < endI; i++) { + for (let j = beginJ; j < endJ; j++) { + const xOffset = i * xStride1 + j * xStride2 + beginK; + outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset); + outOffset += size[2]; + } + } +} +function slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size) { + let outOffset = 0; + const beginI = begin[0]; + const beginJ = begin[1]; + const beginK = begin[2]; + const endI = beginI + size[0]; + const endJ = beginJ + size[1]; + const endK = beginK + size[2]; + const beginL = begin[3]; + for (let i = beginI; i < endI; i++) { + for (let j = beginJ; j < endJ; j++) { + for (let k = beginK; k < endK; k++) { + const xOffset = i * xStride1 + j * xStride2 + k * xStride3 + beginL; + outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset); + outOffset += size[3]; + } + } + } +} +var sliceConfig3 = { + kernelName: Slice, + backendName: "wasm", + kernelFunc: slice4 +}; +function batchToSpaceND4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, crops } = attrs; + const prod5 = blockShape.reduce((a, b) => a * b); + const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5); + const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length); + const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5); + const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length); + const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length); + const xReshaped = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } }); + const xTransposed = transpose4({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } }); + const xTransposedReshaped = reshape5({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } }); + const result = slice4({ + inputs: { x: xTransposedReshaped }, + backend: backend2, + attrs: { begin: sliceBeginCoords, size: sliceSize } + }); + backend2.disposeData(xReshaped.dataId); + backend2.disposeData(xTransposed.dataId); + backend2.disposeData(xTransposedReshaped.dataId); + return result; +} +var batchToSpaceNDConfig3 = { + kernelName: BatchToSpaceND, + backendName: "wasm", + kernelFunc: batchToSpaceND4 +}; +var wasmBincount; +function setup10(backend2) { + wasmBincount = backend2.wasm.cwrap(Bincount, null, [ + "number", + "number", + "boolean", + "number", + "number", + "number" + // outId + ]); +} +function bincount4(args) { + const { backend: backend2, inputs, attrs } = args; + const { x, weights } = inputs; + const { size } = attrs; + const hasWeights = weights.shape.reduce((p2, v) => p2 * v, 1) !== 0; + const outShape = x.shape.length === 1 ? [size] : [x.shape[0], size]; + const out = backend2.makeOutput(outShape, weights.dtype); + function tensorId(x2) { + return backend2.dataIdMap.get(x2.dataId).id; + } + wasmBincount(tensorId(x), size, hasWeights, tensorId(weights), CppDType[weights.dtype], tensorId(out)); + return out; +} +var bincountConfig3 = { + kernelName: Bincount, + backendName: "wasm", + setupFunc: setup10, + kernelFunc: bincount4 +}; +var supportsFullBroadcast2 = true; +var bitwiseAndConfig3 = createBinaryKernelConfig(BitwiseAnd, supportsFullBroadcast2); +function broadcastArgs4(args) { + const { inputs, backend: backend2 } = args; + const { s0, s1 } = inputs; + const s0Vals = backend2.typedArrayFromHeap(s0); + const s1Vals = backend2.typedArrayFromHeap(s1); + const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals)); + return backend2.makeOutput( + [broadcastShape.length], + "int32", + /*memoryOffset=*/ + void 0, + /*values=*/ + new Int32Array(broadcastShape) + ); +} +var broadcastArgsConfig3 = { + kernelName: BroadcastArgs, + backendName: "wasm", + kernelFunc: broadcastArgs4 +}; +function cast5(args) { + const { inputs: { x }, attrs: { dtype }, backend: backend2 } = args; + const out = backend2.makeOutput(x.shape, dtype); + const inVals = backend2.typedArrayFromHeap(x); + const outVals = backend2.typedArrayFromHeap(out); + outVals.set(inVals); + return out; +} +var castConfig3 = { + kernelName: Cast, + backendName: "wasm", + kernelFunc: cast5 +}; +var ceilConfig3 = createUnaryKernelConfig(Ceil); +var wasmClip; +function setup11(backend2) { + wasmClip = backend2.wasm.cwrap(ClipByValue, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function clip(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { clipValueMin, clipValueMax } = attrs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(x.shape, x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmClip(xId, clipValueMin, clipValueMax, outId); + return out; +} +var clipByValueConfig3 = { + kernelName: ClipByValue, + backendName: "wasm", + setupFunc: setup11, + kernelFunc: clip +}; +function concat4(args) { + const { inputs, backend: backend2 } = args; + const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0]; + const shapes = inputs.map((t) => t.shape); + backend_util_exports.assertParamsConsistent(shapes, axis); + let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis); + const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0); + if ($inputs.length === 1) { + return identity4({ inputs: { x: $inputs[0] }, backend: backend2 }); + } + const out = backend2.makeOutput(outShape, inputs[0].dtype); + if (util_exports.sizeFromShape(outShape) === 0) { + return out; + } + if ($inputs[0].dtype === "string") { + const inputs2D = $inputs.map((t) => { + const innerSize = util_exports.sizeFromShape(t.shape.slice(axis)); + const shape = [-1, innerSize]; + return reshape5({ inputs: { x: t }, backend: backend2, attrs: { shape } }); + }); + const inputsValShapes = inputs2D.map((t) => { + return { vals: backend2.readSync(t.dataId), shape: t.shape }; + }); + outShape = backend_util_exports.computeOutShape( + inputs2D.map((t) => t.shape), + 1 + /* axis */ + ); + const simplyConcat = inputs2D[0].shape[0] === 1; + const outVals2 = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat); + const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), axis); + out.shape = finalOutShape; + const outData = backend2.dataIdMap.get(out.dataId); + outData.stringBytes = backend_util_exports.fromStringArrayToUint8(outVals2); + inputs2D.forEach((t) => backend2.disposeData(t.dataId)); + return out; + } + const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis)); + let sumInnerDims = 0; + const innerDims = $inputs.map((input2) => { + const innerDim = util_exports.sizeFromShape(input2.shape.slice(axis)); + sumInnerDims += innerDim; + return innerDim; + }); + const inVals = $inputs.map((input2) => backend2.typedArrayFromHeap(input2)); + const outVals = backend2.typedArrayFromHeap(out); + for (let b = 0; b < batchDim; b++) { + let outOffset = b * sumInnerDims; + for (let i = 0; i < inVals.length; i++) { + const innerDim = innerDims[i]; + const inOffset = b * innerDim; + const vals = inVals[i].subarray(inOffset, inOffset + innerDim); + outVals.set(vals, outOffset); + outOffset += innerDim; + } + } + return out; +} +var concatConfig3 = { + kernelName: Concat, + backendName: "wasm", + kernelFunc: concat4 +}; +var wasmConv2d; +function setup12(backend2) { + wasmConv2d = backend2.wasm.cwrap(Conv2D, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function conv2d5(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x, filter } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const filterId = backend2.dataIdMap.get(filter.dataId).id; + const { strides, dilations, pad: pad3, dimRoundingMode, dataFormat } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const inputChannels = convInfo.inChannels; + const outputChannels = convInfo.outChannels; + const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0; + if (convInfo.dataFormat !== "channelsLast") { + throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId); + return out; +} +var conv2DConfig3 = { + kernelName: Conv2D, + backendName: "wasm", + setupFunc: setup12, + kernelFunc: conv2d5 +}; +var wasmConv2DBackpropInput; +function setup13(backend2) { + wasmConv2DBackpropInput = backend2.wasm.cwrap(Conv2DBackpropInput, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function conv2DBackpropInput4(args) { + const { backend: backend2, inputs, attrs } = args; + const { dy, filter } = inputs; + const { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape } = attrs; + const dilations = 1; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); + const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo; + const topPad = filterHeight - 1 - convInfo.padInfo.top; + const leftPad = filterWidth - 1 - convInfo.padInfo.left; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const dxStrides = util_exports.computeStrides(convInfo.inShape); + const dyStrides = util_exports.computeStrides(dy.shape); + const [fltS0, fltS1, fltS2] = util_exports.computeStrides(filter.shape); + const xBatchStride = dxStrides[0]; + const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2]; + const xColStride = isChannelsLast ? dxStrides[2] : 1; + const xChannelStride = isChannelsLast ? 1 : dxStrides[1]; + const yBatchStride = dyStrides[0]; + const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2]; + const yColStride = isChannelsLast ? dyStrides[2] : 1; + const yChannelStride = isChannelsLast ? 1 : dyStrides[1]; + const out = backend2.makeOutput(convInfo.inShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + const dyId = backend2.dataIdMap.get(dy.dataId).id; + const filterId = backend2.dataIdMap.get(filter.dataId).id; + wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId); + return out; +} +var conv2DBackpropInputConfig3 = { + kernelName: Conv2DBackpropInput, + backendName: "wasm", + setupFunc: setup13, + kernelFunc: conv2DBackpropInput4 +}; +var wasmConv3D; +function setup14(backend2) { + wasmConv3D = backend2.wasm.cwrap(Conv3D, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function conv3D3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + if (x.dtype !== "float32") { + throw new Error(`Tensor x must have dtype float32, got ${x.dtype}`); + } + if (filter.dtype !== "float32") { + throw new Error(`Tensor filter must have dtype float32, got ${filter.dtype}`); + } + const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3); + const out = backend2.makeOutput(convInfo.outShape, x.dtype); + wasmConv3D(backend2.dataIdMap.get(x.dataId).id, backend2.dataIdMap.get(filter.dataId).id, backend2.dataIdMap.get(out.dataId).id, convInfo.batchSize, convInfo.inDepth, convInfo.inHeight, convInfo.inWidth, convInfo.inChannels, convInfo.outDepth, convInfo.outHeight, convInfo.outWidth, convInfo.outChannels, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationDepth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left); + return out; +} +var conv3DConfig3 = { + kernelName: Conv3D, + backendName: "wasm", + setupFunc: setup14, + kernelFunc: conv3D3 +}; +var wasmConv3DBackpropFilterV2; +function setup15(backend2) { + wasmConv3DBackpropFilterV2 = backend2.wasm.cwrap(Conv3DBackpropFilterV2, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function conv3DBackpropFilterV23(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, pad: pad3, filterShape } = attrs; + if (x.dtype !== "float32") { + throw new Error(`Tensor dy must have dtype float32, got ${x.dtype}`); + } + if (dy.dtype !== "float32") { + throw new Error(`Tensor filter must have dtype float32, got ${dy.dtype}`); + } + const convInfo = backend_util_exports.computeConv3DInfo( + x.shape, + filterShape, + strides, + /*dilations=*/ + 1, + pad3 + ); + const dw = backend2.makeOutput(convInfo.filterShape, dy.dtype); + wasmConv3DBackpropFilterV2(backend2.dataIdMap.get(x.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dw.dataId).id, convInfo.batchSize, convInfo.inDepth, convInfo.inHeight, convInfo.inWidth, convInfo.inChannels, convInfo.outDepth, convInfo.outHeight, convInfo.outWidth, convInfo.outChannels, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationDepth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left); + return dw; +} +var conv3DBackpropFilterV2Config3 = { + kernelName: Conv3DBackpropFilterV2, + backendName: "wasm", + setupFunc: setup15, + kernelFunc: conv3DBackpropFilterV23 +}; +var wasmConv3DBackpropInputV2; +function setup16(backend2) { + wasmConv3DBackpropInputV2 = backend2.wasm.cwrap(Conv3DBackpropInputV2, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function conv3DBackpropInputV22(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { pad: pad3, strides, inputShape } = attrs; + if (dy.dtype !== "float32") { + throw new Error(`Tensor dy must have dtype float32, got ${dy.dtype}`); + } + if (filter.dtype !== "float32") { + throw new Error(`Tensor filter must have dtype float32, got ${filter.dtype}`); + } + const convInfo = backend_util_exports.computeConv3DInfo( + inputShape, + filter.shape, + strides, + /*dilations=*/ + 1, + pad3 + ); + const dx = backend2.makeOutput(convInfo.inShape, dy.dtype); + wasmConv3DBackpropInputV2(backend2.dataIdMap.get(filter.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dx.dataId).id, convInfo.batchSize, convInfo.inDepth, convInfo.inHeight, convInfo.inWidth, convInfo.inChannels, convInfo.outDepth, convInfo.outHeight, convInfo.outWidth, convInfo.outChannels, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationDepth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left); + return dx; +} +var conv3DBackpropInputV2Config2 = { + kernelName: Conv3DBackpropInputV2, + backendName: "wasm", + setupFunc: setup16, + kernelFunc: conv3DBackpropInputV22 +}; +var cosConfig3 = createUnaryKernelConfig(Cos); +var coshConfig3 = createUnaryKernelConfig(Cosh); +var InterpolationMethod; +(function(InterpolationMethod2) { + InterpolationMethod2[InterpolationMethod2["bilinear"] = 0] = "bilinear"; + InterpolationMethod2[InterpolationMethod2["nearest"] = 1] = "nearest"; +})(InterpolationMethod || (InterpolationMethod = {})); +var wasmCropAndResize; +function setup17(backend2) { + wasmCropAndResize = backend2.wasm.cwrap(CropAndResize, null, [ + "number", + "number", + "number", + "number", + "array", + "number", + "number", + "number", + "number", + "number" + // out id + ]); +} +function cropAndResize5(args) { + const { backend: backend2, inputs, attrs } = args; + const { method, extrapolationValue, cropSize } = attrs; + const { image: image2, boxes, boxInd } = inputs; + const numBoxes = boxes.shape[0]; + const [cropHeight, cropWidth] = cropSize; + const outShape = [numBoxes, cropHeight, cropWidth, image2.shape[3]]; + let imagesData = backend2.dataIdMap.get(image2.dataId); + let castedData; + if (image2.dtype !== "float32") { + castedData = cast5({ backend: backend2, inputs: { x: image2 }, attrs: { dtype: "float32" } }); + imagesData = backend2.dataIdMap.get(castedData.dataId); + } + const imagesId = imagesData.id; + const boxesId = backend2.dataIdMap.get(boxes.dataId).id; + const boxIndId = backend2.dataIdMap.get(boxInd.dataId).id; + const out = backend2.makeOutput(outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + const imagesShapeBytes = new Uint8Array(new Int32Array(image2.shape).buffer); + wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId); + if (castedData != null) { + backend2.disposeData(castedData.dataId); + } + return out; +} +var cropAndResizeConfig3 = { + kernelName: CropAndResize, + backendName: "wasm", + setupFunc: setup17, + kernelFunc: cropAndResize5 +}; +var wasmCumprod; +function setup18(backend2) { + wasmCumprod = backend2.wasm.cwrap(Cumprod, null, [ + "number", + "number", + "number", + "number", + "number", + "number" + // dtype + ]); +} +function cumprod4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + const xRank = x.shape.length; + util_exports.assert(x.dtype === "float32" || x.dtype === "int32", () => `cumprod does not support ${x.dtype} tensors in the WASM backend`); + const permutation = backend_util_exports.getAxesPermutation([axis], xRank); + let permutedX = x; + if (permutation !== null) { + permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 }); + } + const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0]; + backend_util_exports.assertAxesAreInnerMostDims("cumprod", [permutedAxis], xRank); + const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype); + const finalDim = permutedX.shape[permutedAxis]; + const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id; + const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id; + wasmCumprod(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]); + let out = permutedOut; + if (permutation !== null) { + const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation); + out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 }); + backend2.disposeData(permutedX.dataId); + backend2.disposeData(permutedOut.dataId); + } + return out; +} +var cumprodConfig3 = { + kernelName: Cumprod, + backendName: "wasm", + setupFunc: setup18, + kernelFunc: cumprod4 +}; +var wasmCumsum; +function setup19(backend2) { + wasmCumsum = backend2.wasm.cwrap(Cumsum, null, [ + "number", + "number", + "number", + "number", + "number", + "number" + // dtype + ]); +} +function cumsum4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + const xRank = x.shape.length; + util_exports.assert(x.dtype === "float32" || x.dtype === "int32", () => `cumsum does not support ${x.dtype} tensors in the WASM backend`); + const permutation = backend_util_exports.getAxesPermutation([axis], xRank); + let permutedX = x; + if (permutation !== null) { + permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 }); + } + const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0]; + backend_util_exports.assertAxesAreInnerMostDims("cumsum", [permutedAxis], xRank); + const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype); + const finalDim = permutedX.shape[permutedAxis]; + const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id; + const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id; + wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]); + let out = permutedOut; + if (permutation !== null) { + const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation); + out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 }); + backend2.disposeData(permutedX.dataId); + backend2.disposeData(permutedOut.dataId); + } + return out; +} +var cumsumConfig3 = { + kernelName: Cumsum, + backendName: "wasm", + setupFunc: setup19, + kernelFunc: cumsum4 +}; +var wasmDenseBincount; +function setup20(backend2) { + wasmDenseBincount = backend2.wasm.cwrap("DenseBincount", null, [ + "number", + "array", + "number", + "number", + "boolean", + "number", + "number", + "boolean", + "number" + // outId + ]); +} +function denseBincount4(args) { + const { backend: backend2, inputs, attrs } = args; + const { x, weights } = inputs; + const { size, binaryOutput } = attrs; + const hasWeights = weights.shape.reduce((p2, v) => p2 * v, 1) !== 0; + const outShape = x.shape.length === 1 ? [size] : [x.shape[0], size]; + const out = backend2.makeOutput(outShape, weights.dtype); + function tensorId(x2) { + return backend2.dataIdMap.get(x2.dataId).id; + } + wasmDenseBincount(tensorId(x), new Uint8Array(new Int32Array(x.shape).buffer), x.shape.length, size, hasWeights, tensorId(weights), CppDType[weights.dtype], binaryOutput, tensorId(out)); + return out; +} +var denseBincountConfig3 = { + kernelName: DenseBincount, + backendName: "wasm", + setupFunc: setup20, + kernelFunc: denseBincount4 +}; +var wasmDepthToSpace; +function setup21(backend2) { + wasmDepthToSpace = backend2.wasm.cwrap(DepthToSpace, null, [ + "number", + "number", + "number", + "array", + "number", + "array", + "array", + "number", + "number" + // outId + ]); +} +function depthToSpace4(args) { + const { backend: backend2, inputs, attrs } = args; + const { x } = inputs; + const { blockSize, dataFormat } = attrs; + const batchSize = x.shape[0]; + const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2]; + const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3]; + const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1]; + const outputHeight = inputHeight * blockSize; + const outputWidth = inputWidth * blockSize; + const outputDepth = inputDepth / (blockSize * blockSize); + const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth]; + const out = backend2.makeOutput(outputShape, "float32"); + const xData = backend2.dataIdMap.get(x.dataId); + const xId = xData.id; + const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer); + const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer); + const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + const channelsLast = dataFormat === "NHWC" ? 1 : 0; + wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId); + return out; +} +var depthToSpaceConfig3 = { + kernelName: DepthToSpace, + backendName: "wasm", + setupFunc: setup21, + kernelFunc: depthToSpace4 +}; +var wasmDepthwiseConv2d; +function setup22(backend2) { + wasmDepthwiseConv2d = backend2.wasm.cwrap(DepthwiseConv2dNative, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function depthwiseConv2d5(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x, filter } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const filterId = backend2.dataIdMap.get(filter.dataId).id; + const { strides, dilations, pad: pad3, dimRoundingMode } = attrs; + const $dilations = dilations == null ? [1, 1] : dilations; + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filter.shape, + strides, + $dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const inputChannels = convInfo.inChannels; + const outputChannels = convInfo.outChannels; + const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0; + if (convInfo.dataFormat !== "channelsLast") { + throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId); + return out; +} +var depthwiseConv2dNativeConfig3 = { + kernelName: DepthwiseConv2dNative, + backendName: "wasm", + setupFunc: setup22, + kernelFunc: depthwiseConv2d5 +}; +var wasmDiag; +function setup23(backend2) { + wasmDiag = backend2.wasm.cwrap("Diag", null, [ + "number", + "number", + "number", + "number" + // outId + ]); +} +function diag4(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + const xSize = util_exports.sizeFromShape(x.shape); + const out = backend2.makeOutput([...x.shape, ...x.shape], x.dtype); + wasmDiag(backend2.dataIdMap.get(x.dataId).id, CppDType[x.dtype], xSize, backend2.dataIdMap.get(out.dataId).id); + return out; +} +var diagConfig3 = { + kernelName: Diag, + backendName: "wasm", + setupFunc: setup23, + kernelFunc: diag4 +}; +var wasmDilation2D; +function setup24(backend2) { + wasmDilation2D = backend2.wasm.cwrap(Dilation2D, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function dilation2D2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + if (x.dtype !== filter.dtype) { + throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${x.dtype} and ${filter.dtype}`); + } + const dilationInfo = backend_util_exports.computeDilation2DInfo( + x.shape, + filter.shape, + strides, + pad3, + /*dataFormat=*/ + "NHWC", + dilations + ); + const out = backend2.makeOutput(dilationInfo.outShape, x.dtype); + wasmDilation2D( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(filter.dataId).id, + backend2.dataIdMap.get(out.dataId).id, + CppDType[x.dtype], + dilationInfo.batchSize, + /*depth=*/ + dilationInfo.inChannels, + dilationInfo.inHeight, + dilationInfo.inWidth, + dilationInfo.outHeight, + dilationInfo.outWidth, + dilationInfo.strideHeight, + dilationInfo.strideWidth, + dilationInfo.dilationHeight, + dilationInfo.dilationWidth, + dilationInfo.filterHeight, + dilationInfo.filterWidth, + dilationInfo.padInfo.top, + dilationInfo.padInfo.left + ); + return out; +} +var dilation2DConfig3 = { + kernelName: Dilation2D, + backendName: "wasm", + setupFunc: setup24, + kernelFunc: dilation2D2 +}; +var wasmDilation2DBackpropFilter; +function setup25(backend2) { + wasmDilation2DBackpropFilter = backend2.wasm.cwrap(Dilation2DBackpropFilter, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function dilation2DBackpropFilter(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, dy } = inputs; + const { strides, pad: pad3, dilations } = attrs; + if (x.dtype !== filter.dtype || x.dtype !== dy.dtype) { + throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${x.dtype}, ${filter.dtype}, and ${dy.dtype}`); + } + const dilationInfo = backend_util_exports.computeDilation2DInfo( + x.shape, + filter.shape, + strides, + pad3, + /*dataFormat=*/ + "NHWC", + dilations + ); + const gradients = backend2.makeOutput(filter.shape, filter.dtype); + wasmDilation2DBackpropFilter( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(filter.dataId).id, + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(gradients.dataId).id, + CppDType[x.dtype], + dilationInfo.batchSize, + /*depth=*/ + dilationInfo.inChannels, + dilationInfo.inHeight, + dilationInfo.inWidth, + dilationInfo.outHeight, + dilationInfo.outWidth, + dilationInfo.strideHeight, + dilationInfo.strideWidth, + dilationInfo.dilationHeight, + dilationInfo.dilationWidth, + dilationInfo.filterHeight, + dilationInfo.filterWidth, + dilationInfo.padInfo.top, + dilationInfo.padInfo.left + ); + return gradients; +} +var dilation2DBackpropFilterConfig2 = { + kernelName: Dilation2DBackpropFilter, + backendName: "wasm", + setupFunc: setup25, + kernelFunc: dilation2DBackpropFilter +}; +var wasmDilation2DBackpropInput; +function setup26(backend2) { + wasmDilation2DBackpropInput = backend2.wasm.cwrap(Dilation2DBackpropInput, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function dilation2DBackpropInput(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, dy } = inputs; + const { strides, pad: pad3, dilations } = attrs; + if (x.dtype !== filter.dtype || x.dtype !== dy.dtype) { + throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${x.dtype}, ${filter.dtype}, and ${dy.dtype}`); + } + const dilationInfo = backend_util_exports.computeDilation2DInfo( + x.shape, + filter.shape, + strides, + pad3, + /*dataFormat=*/ + "NHWC", + dilations + ); + const gradients = backend2.makeOutput(x.shape, x.dtype); + wasmDilation2DBackpropInput( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(filter.dataId).id, + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(gradients.dataId).id, + CppDType[x.dtype], + dilationInfo.batchSize, + /*depth=*/ + dilationInfo.inChannels, + dilationInfo.inHeight, + dilationInfo.inWidth, + dilationInfo.outHeight, + dilationInfo.outWidth, + dilationInfo.strideHeight, + dilationInfo.strideWidth, + dilationInfo.dilationHeight, + dilationInfo.dilationWidth, + dilationInfo.filterHeight, + dilationInfo.filterWidth, + dilationInfo.padInfo.top, + dilationInfo.padInfo.left + ); + return gradients; +} +var dilation2DBackpropInputConfig2 = { + kernelName: Dilation2DBackpropInput, + backendName: "wasm", + setupFunc: setup26, + kernelFunc: dilation2DBackpropInput +}; +var eluConfig3 = createUnaryKernelConfig(Elu); +var wasmEluGrad; +function setup27(backend2) { + wasmEluGrad = backend2.wasm.cwrap(EluGrad, null, [ + "number", + "number", + "number" + // outId + ]); +} +function eluGrad3(args) { + const { inputs, backend: backend2 } = args; + const { dy, y } = inputs; + const out = backend2.makeOutput(y.shape, "float32"); + const tensorId = (x) => { + return backend2.dataIdMap.get(x.dataId).id; + }; + wasmEluGrad(tensorId(y), tensorId(dy), tensorId(out)); + return out; +} +var eluGradConfig4 = { + kernelName: EluGrad, + backendName: "wasm", + setupFunc: setup27, + kernelFunc: eluGrad3 +}; +var supportsFullBroadcast3 = false; +var equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast3, "bool"); +var erfConfig3 = createUnaryKernelConfig(Erf); +var expConfig3 = createUnaryKernelConfig(Exp, "float32"); +function expandDims5(args) { + const { inputs, attrs, backend: backend2 } = args; + const { input: input2 } = inputs; + const { dim } = attrs; + const inputRank = input2.shape.length; + const newShape = input2.shape.slice(); + let $dim = dim; + if (dim < 0) { + util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`); + $dim = inputRank + dim + 1; + } + newShape.splice($dim, 0, 1); + return reshape5({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } }); +} +var expandDimsConfig3 = { + kernelName: ExpandDims, + backendName: "wasm", + kernelFunc: expandDims5 +}; +var expm1Config3 = createUnaryKernelConfig(Expm1, "float32"); +function fill4(args) { + const { attrs: { shape, value }, backend: backend2 } = args; + let { attrs: { dtype } } = args; + dtype = dtype || util_exports.inferDtype(value); + const out = backend2.makeOutput(shape, dtype); + const outVals = backend2.typedArrayFromHeap(out); + outVals.fill(value); + return out; +} +var fillConfig3 = { + kernelName: Fill, + backendName: "wasm", + kernelFunc: fill4 +}; +var wasmFlipLeftRight; +function setup28(backend2) { + wasmFlipLeftRight = backend2.wasm.cwrap(FlipLeftRight, null, [ + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function flipLeftRight2(args) { + const { inputs, backend: backend2 } = args; + const { image: image2 } = inputs; + const out = backend2.makeOutput(image2.shape, image2.dtype); + const imageId = backend2.dataIdMap.get(image2.dataId).id; + const outId = backend2.dataIdMap.get(out.dataId).id; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId); + return out; +} +var flipLeftRightConfig3 = { + kernelName: FlipLeftRight, + backendName: "wasm", + kernelFunc: flipLeftRight2, + setupFunc: setup28 +}; +var floorConfig3 = createUnaryKernelConfig(Floor); +var supportsFullBroadcast4 = false; +var floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast4); +var wasmBatchNorm; +function setup29(backend2) { + wasmBatchNorm = backend2.wasm.cwrap(FusedBatchNorm, null, ["number", "number", "number", "number", "number", "number", "number"]); +} +function fusedBatchNorm(args) { + const { backend: backend2, inputs, attrs } = args; + const { varianceEpsilon } = attrs; + const { x, mean: mean4, variance, offset, scale: scale22 } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const meanId = backend2.dataIdMap.get(mean4.dataId).id; + const varianceId = backend2.dataIdMap.get(variance.dataId).id; + const offsetId = offset != null ? backend2.dataIdMap.get(offset.dataId).id : 0; + const scaleId = scale22 != null ? backend2.dataIdMap.get(scale22.dataId).id : 0; + const out = backend2.makeOutput(x.shape, x.dtype); + if (util_exports.sizeFromShape(x.shape) === 0) { + return out; + } + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId); + return out; +} +var fusedBatchNormConfig = { + kernelName: FusedBatchNorm, + backendName: "wasm", + setupFunc: setup29, + kernelFunc: fusedBatchNorm +}; +var wasmFusedConv2d; +function setup30(backend2) { + wasmFusedConv2d = backend2.wasm.cwrap(FusedConv2D, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function fusedConv2d2(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode); + const fusedActivation = FusableActivation[activation2]; + if (fusedActivation == null) { + throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`); + } + const xId = backend2.dataIdMap.get(x.dataId).id; + const filterId = backend2.dataIdMap.get(filter.dataId).id; + const outputChannels = convInfo.outChannels; + let biasId = 0; + if (bias != null) { + const biasData = backend2.dataIdMap.get(bias.dataId); + if (biasData.shape.length !== 1) { + throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`); + } + if (biasData.shape[0] !== outputChannels) { + throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`); + } + biasId = biasData.id; + } + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const inputChannels = convInfo.inChannels; + const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0; + const batchSize = convInfo.batchSize; + const inHeight = convInfo.inHeight; + const inWidth = convInfo.inWidth; + if (dataFormat !== "NHWC") { + throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id; + wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId); + return out; +} +var fusedConv2DConfig3 = { + kernelName: FusedConv2D, + backendName: "wasm", + setupFunc: setup30, + kernelFunc: fusedConv2d2 +}; +var wasmFusedDepthwiseConv2d; +function setup31(backend2) { + wasmFusedDepthwiseConv2d = backend2.wasm.cwrap(FusedDepthwiseConv2D, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function fusedDepthwiseConv2d(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filter.shape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const fusedActivation = FusableActivation[activation2]; + if (fusedActivation == null) { + throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`); + } + const xId = backend2.dataIdMap.get(x.dataId).id; + const filterId = backend2.dataIdMap.get(filter.dataId).id; + const outputChannels = convInfo.outChannels; + let biasId = 0; + if (bias != null) { + const biasData = backend2.dataIdMap.get(bias.dataId); + if (biasData.shape.length !== 1) { + throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`); + } + if (biasData.shape[0] !== outputChannels) { + throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`); + } + biasId = biasData.id; + } + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const inputChannels = convInfo.inChannels; + const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0; + const batchSize = convInfo.batchSize; + const inHeight = convInfo.inHeight; + const inWidth = convInfo.inWidth; + if (dataFormat !== "NHWC") { + throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id; + wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId); + return out; +} +var fusedDepthwiseConv2DConfig3 = { + kernelName: FusedDepthwiseConv2D, + backendName: "wasm", + setupFunc: setup31, + kernelFunc: fusedDepthwiseConv2d +}; +var wasmGatherNd; +function setup32(backend2) { + wasmGatherNd = backend2.wasm.cwrap(GatherNd, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number" + // outId + ]); +} +function gatherNd3(args) { + const { backend: backend2, inputs } = args; + const { params, indices } = inputs; + const [resultShape, numSlices, sliceSize, strides] = gather_nd_util_exports.prepareAndValidate(params, indices); + const out = backend2.makeOutput(resultShape, params.dtype); + if (numSlices === 0) { + return out; + } + const indicesShape = indices.shape; + const sliceRank = indicesShape[indicesShape.length - 1]; + const xData = backend2.dataIdMap.get(params.dataId); + const xId = xData.id; + const indicesData = backend2.dataIdMap.get(indices.dataId); + const indicesId = indicesData.id; + const stridesBytes = new Uint8Array(new Int32Array(strides).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId); + return out; +} +var gatherNdConfig3 = { + kernelName: GatherNd, + backendName: "wasm", + setupFunc: setup32, + kernelFunc: gatherNd3 +}; +var wasmGather; +function setup33(backend2) { + wasmGather = backend2.wasm.cwrap("Gather", null, [ + "number", + "number", + "array", + "number", + "number", + "number", + "array", + "number" + // outId + ]); +} +function gatherV23(args) { + const { backend: backend2, inputs, attrs } = args; + const { x, indices } = inputs; + const { axis, batchDims } = attrs; + const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0]; + const indicesVals = backend2.readSync(indices.dataId); + const axisDim = x.shape[parsedAxis]; + for (let i = 0; i < indicesVals.length; ++i) { + const index = indicesVals[i]; + util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`); + } + const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims); + const flattenX = reshape5({ + inputs: { x }, + attrs: { + shape: [ + shapeInfo.batchSize, + shapeInfo.outerSize, + shapeInfo.dimSize, + shapeInfo.sliceSize + ] + }, + backend: backend2 + }); + const indicesSize = util_exports.sizeFromShape(indices.shape); + const flattenIndex = reshape5({ + inputs: { x: indices }, + attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }, + backend: backend2 + }); + const flattenOutputShape = [ + shapeInfo.batchSize, + shapeInfo.outerSize, + indicesSize / shapeInfo.batchSize, + shapeInfo.sliceSize + ]; + const out = backend2.makeOutput(flattenOutputShape, x.dtype); + if (util_exports.sizeFromShape(x.shape) === 0) { + return out; + } + const stridesSize = flattenX.shape.length - 1; + const xData = backend2.dataIdMap.get(flattenX.dataId); + const xId = xData.id; + const indicesData = backend2.dataIdMap.get(flattenIndex.dataId); + const indicesId = indicesData.id; + const outId = backend2.dataIdMap.get(out.dataId).id; + const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer); + const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer); + wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, shapeInfo.batchSize, outStridesBytes, outId); + backend2.disposeData(flattenX.dataId); + backend2.disposeData(flattenIndex.dataId); + out.shape = shapeInfo.outputShape; + return out; +} +var gatherV2Config3 = { + kernelName: GatherV2, + backendName: "wasm", + setupFunc: setup33, + kernelFunc: gatherV23 +}; +var supportsFullBroadcast5 = false; +var greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast5, "bool"); +var supportsFullBroadcast6 = false; +var greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast6, "bool"); +var isFiniteConfig3 = createUnaryKernelConfig(IsFinite, "bool"); +var isInfConfig3 = createUnaryKernelConfig(IsInf, "bool"); +var isNaNConfig3 = createUnaryKernelConfig(IsNan, "bool"); +var wasmFunc2; +function setupFunc2(backend2) { + wasmFunc2 = backend2.wasm.cwrap(LeakyRelu, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function leakyRelu4(args) { + const { inputs: { x }, attrs: { alpha }, backend: backend2 } = args; + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(x.shape, "float32"); + if (util_exports.sizeFromShape(x.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmFunc2(xId, CppDType[x.dtype], alpha, outId); + } + return out; +} +var leakyReluConfig3 = { + kernelName: LeakyRelu, + backendName: "wasm", + setupFunc: setupFunc2, + kernelFunc: leakyRelu4 +}; +var supportsFullBroadcast7 = false; +var lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast7, "bool"); +var supportsFullBroadcast8 = false; +var lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast8, "bool"); +var wasmLinSpace; +function setup34(backend2) { + wasmLinSpace = backend2.wasm.cwrap(LinSpace, null, [ + "number", + "number", + "number", + "number" + // num + ]); +} +function linSpace3(args) { + const { attrs, backend: backend2 } = args; + const { start, stop, num } = attrs; + const numInt = Math.floor(num); + const out = backend2.makeOutput([numInt], "float32"); + wasmLinSpace(backend2.dataIdMap.get(out.dataId).id, start, stop, numInt); + return out; +} +var linSpaceConfig3 = { + kernelName: LinSpace, + backendName: "wasm", + setupFunc: setup34, + kernelFunc: linSpace3 +}; +var logConfig3 = createUnaryKernelConfig(Log); +var log1pConfig3 = createUnaryKernelConfig(Log1p); +var supportsFullBroadcast9 = false; +var logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast9, "bool"); +var logicalNotConfig3 = createUnaryKernelConfig(LogicalNot); +var supportsFullBroadcast10 = false; +var logicalOrConfig3 = createBinaryKernelConfig(LogicalOr, supportsFullBroadcast10, "bool"); +var supportsFullBroadcast11 = false; +var logicalXorConfig = createBinaryKernelConfig(LogicalXor, supportsFullBroadcast11, "bool"); +var wasmLRN; +function setup35(backend2) { + wasmLRN = backend2.wasm.cwrap(LRN, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // beta + ]); +} +function lrn2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + if (x.dtype !== "float32") { + throw new Error("LRN error: x must have dtype float32"); + } + const out = backend2.makeOutput(x.shape, x.dtype); + wasmLRN( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(out.dataId).id, + /*channels=*/ + x.shape[3], + depthRadius, + bias, + alpha, + beta + ); + return out; +} +var lrnConfig = { + kernelName: LRN, + backendName: "wasm", + setupFunc: setup35, + kernelFunc: lrn2 +}; +var wasmLRNGrad; +function setup36(backend2) { + wasmLRNGrad = backend2.wasm.cwrap(LRNGrad, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // beta + ]); +} +function lrnGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, y, dy } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + if (x.dtype !== "float32" || y.dtype !== "float32" || dy.dtype !== "float32") { + throw new Error("LRNGrad error: x, y, and dy must have dtype float32"); + } + const dx = backend2.makeOutput(x.shape, x.dtype); + wasmLRNGrad( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(y.dataId).id, + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(dx.dataId).id, + /*channels=*/ + dy.shape[3], + depthRadius, + bias, + alpha, + beta + ); + return dx; +} +var lrnGradConfig2 = { + kernelName: LRNGrad, + backendName: "wasm", + setupFunc: setup36, + kernelFunc: lrnGrad2 +}; +var wasmMax; +function setup37(backend2) { + wasmMax = backend2.wasm.cwrap(Max, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function max5(args) { + const { backend: backend2, inputs, attrs } = args; + const { reductionIndices: axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + input2 = transposed; + inputId = transposedId; + } + const inputRank = input2.shape.length; + backend_util_exports.assertAxesAreInnerMostDims("max", axes, inputRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, x.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmMax(inputId, CppDType[x.dtype], reduceSize, outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var maxConfig3 = { + kernelName: Max, + backendName: "wasm", + setupFunc: setup37, + kernelFunc: max5 +}; +var supportsFullBroadcast12 = false; +var maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast12); +var wasmMaxPool; +function setup38(backend2) { + wasmMaxPool = backend2.wasm.cwrap(MaxPool, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function maxPool4(args) { + const { inputs, attrs, backend: backend2 } = args; + const x = inputs.x; + const xId = backend2.dataIdMap.get(x.dataId).id; + util_exports.assert(x.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${x.dtype}.`); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const inputChannels = convInfo.inChannels; + const outputChannels = convInfo.outChannels; + if (convInfo.dataFormat !== "channelsLast") { + throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId); + return out; +} +var maxPoolConfig3 = { + kernelName: MaxPool, + backendName: "wasm", + setupFunc: setup38, + kernelFunc: maxPool4 +}; +var wasmMaxPool3D; +function setup39(backend2) { + wasmMaxPool3D = backend2.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" + // padLeft + ]); +} +function maxPool3D2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs; + const convInfo = backend_util_exports.computePool3DInfo( + x.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3, + dimRoundingMode, + dataFormat + ); + const out = backend2.makeOutput(convInfo.outShape, x.dtype); + wasmMaxPool3D( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(out.dataId).id, + convInfo.batchSize, + // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inDepth, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outDepth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideDepth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationDepth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterDepth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.front, + convInfo.padInfo.top, + convInfo.padInfo.left + ); + return out; +} +var maxPool3DConfig3 = { + kernelName: MaxPool3D, + backendName: "wasm", + setupFunc: setup39, + kernelFunc: maxPool3D2 +}; +var wasmMaxPool3DGrad; +function setup40(backend2) { + wasmMaxPool3DGrad = backend2.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" + // padLeft + ]); +} +function maxPool3DGrad3(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool3DInfo( + input2.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3, + dimRoundingMode + ); + const dx = backend2.makeOutput(input2.shape, input2.dtype); + wasmMaxPool3DGrad( + backend2.dataIdMap.get(input2.dataId).id, + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(dx.dataId).id, + convInfo.batchSize, + // Since Pool3D ops (MaxPool3D and MaxPool3D) support 3D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inDepth, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outDepth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideDepth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationDepth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterDepth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.front, + convInfo.padInfo.top, + convInfo.padInfo.left + ); + return dx; +} +var maxPool3DGradConfig4 = { + kernelName: MaxPool3DGrad, + backendName: "wasm", + setupFunc: setup40, + kernelFunc: maxPool3DGrad3 +}; +var wasmMaxPoolGrad; +function setup41(backend2) { + wasmMaxPoolGrad = backend2.wasm.cwrap("MaxPoolGrad", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function maxPoolGrad4(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool2DInfo( + input2.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3, + dimRoundingMode + ); + const dx = backend2.makeOutput(input2.shape, input2.dtype); + wasmMaxPoolGrad( + backend2.dataIdMap.get(input2.dataId).id, + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(dx.dataId).id, + convInfo.batchSize, + // Since Pool ops (MaxPool and MaxPool) support 2D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.top, + convInfo.padInfo.left + ); + return dx; +} +var maxPoolGradConfig4 = { + kernelName: MaxPoolGrad, + backendName: "wasm", + setupFunc: setup41, + kernelFunc: maxPoolGrad4 +}; +var wasmMaxPoolWithArgmax; +function setup42(backend2) { + wasmMaxPoolWithArgmax = backend2.wasm.cwrap("MaxPoolWithArgmax", null, [ + "number", + "number", + "number", + "number", + "boolean", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function maxPoolWithArgmax2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs; + util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`); + const dilations = [1, 1]; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3); + const pooled = backend2.makeOutput(convInfo.outShape, x.dtype); + const indexes = backend2.makeOutput(convInfo.outShape, "int32"); + wasmMaxPoolWithArgmax(backend2.dataIdMap.get(x.dataId).id, backend2.dataIdMap.get(pooled.dataId).id, backend2.dataIdMap.get(indexes.dataId).id, CppDType[x.dtype], includeBatchInIndex, convInfo.batchSize, convInfo.inChannels, convInfo.inHeight, convInfo.inWidth, convInfo.outHeight, convInfo.outWidth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth, convInfo.padInfo.top, convInfo.padInfo.left); + return [pooled, indexes]; +} +var maxPoolWithArgmaxConfig3 = { + kernelName: MaxPoolWithArgmax, + backendName: "wasm", + setupFunc: setup42, + kernelFunc: maxPoolWithArgmax2 +}; +var wasmMean; +function setup43(backend2) { + wasmMean = backend2.wasm.cwrap(Mean, null, ["number, number, number"]); +} +function mean3(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + let reductionAxes = axes; + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + if (transposedId !== xId) { + input2 = transposed; + inputId = transposedId; + reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length); + } + } + backend_util_exports.assertAxesAreInnerMostDims("mean", reductionAxes, input2.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + let castedInput = input2; + if (input2.dtype !== "float32") { + castedInput = cast5({ backend: backend2, inputs: { x: input2 }, attrs: { dtype: "float32" } }); + inputId = backend2.dataIdMap.get(castedInput.dataId).id; + } + const out = backend2.makeOutput(outShape, "float32"); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmMean(inputId, reduceSize, outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + if (input2.dtype !== "float32") { + backend2.disposeData(castedInput.dataId); + } + return out; +} +var meanConfig3 = { + kernelName: Mean, + backendName: "wasm", + setupFunc: setup43, + kernelFunc: mean3 +}; +var wasmMin; +function setup44(backend2) { + wasmMin = backend2.wasm.cwrap(Min, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function min5(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + if (transposedId !== xId) { + input2 = transposed; + inputId = transposedId; + } + } + const inputRank = input2.shape.length; + backend_util_exports.assertAxesAreInnerMostDims("min", axes, inputRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, input2.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmMin(inputId, CppDType[x.dtype], reduceSize, outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var minConfig3 = { + kernelName: Min, + backendName: "wasm", + setupFunc: setup44, + kernelFunc: min5 +}; +var supportsFullBroadcast13 = false; +var minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast13); +var MirrorPaddingMode; +(function(MirrorPaddingMode2) { + MirrorPaddingMode2[MirrorPaddingMode2["reflect"] = 0] = "reflect"; + MirrorPaddingMode2[MirrorPaddingMode2["symmetric"] = 1] = "symmetric"; +})(MirrorPaddingMode || (MirrorPaddingMode = {})); +var wasmMirrorPad; +function setup45(backend2) { + wasmMirrorPad = backend2.wasm.cwrap(MirrorPad, null, [ + "number", + "array", + "number", + "number", + "array", + "array", + "number", + "number" + // outId + ]); +} +function mirrorPad3(args) { + const { inputs: { x }, backend: backend2, attrs: { paddings, mode } } = args; + const outShape = paddings.map( + (p2, i) => p2[0] + x.shape[i] + p2[1] + /* afterPad */ + ); + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(outShape, x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]); + const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]); + const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer); + const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer); + wasmMirrorPad(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, MirrorPaddingMode[mode], outId); + return out; +} +var mirrorPadConfig3 = { + kernelName: MirrorPad, + backendName: "wasm", + kernelFunc: mirrorPad3, + setupFunc: setup45 +}; +var wasmFunc3; +function setup46(backend2) { + wasmFunc3 = backend2.wasm.cwrap(Softmax, null, [ + "number", + "number", + "number", + "number" + // batch + ]); +} +function softmax5(args) { + const { backend: backend2, inputs: { logits }, attrs: { dim } } = args; + const xId = backend2.dataIdMap.get(logits.dataId).id; + const out = backend2.makeOutput(logits.shape, logits.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const channels = logits.shape[dim]; + const batch = util_exports.sizeFromShape(logits.shape) / channels; + if (util_exports.sizeFromShape(out.shape) === 0) { + return out; + } + wasmFunc3(xId, outId, channels, batch); + return out; +} +var softmaxConfig3 = { + kernelName: Softmax, + backendName: "wasm", + setupFunc: setup46, + kernelFunc: softmax5 +}; +var wasmMultinomial; +function setup47(backend2) { + wasmMultinomial = backend2.wasm.cwrap(Multinomial, null, [ + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function multinomial4(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { numSamples, seed, normalized } = attrs; + if (logits.dtype !== "float32") { + throw new Error(`Tensor logits must have dtype float32, got ${logits.dtype}`); + } + const probabilities = normalized ? logits : softmax5({ + inputs: { logits }, + backend: backend2, + attrs: { dim: logits.shape.length - 1 } + }); + const [batchSize, numEvents] = probabilities.shape; + const out = backend2.makeOutput([batchSize, numSamples], "int32"); + wasmMultinomial(backend2.dataIdMap.get(probabilities.dataId).id, batchSize, numEvents, numSamples, seed, backend2.dataIdMap.get(out.dataId).id); + if (!normalized) { + backend2.disposeData(probabilities.dataId); + } + return out; +} +var multinomialConfig3 = { + kernelName: Multinomial, + backendName: "wasm", + setupFunc: setup47, + kernelFunc: multinomial4 +}; +var modConfig3 = createBinaryKernelConfig( + Mod, + /*supportsFullBroadcast=*/ + true +); +var supportsFullBroadcast14 = true; +var multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast14); +var negConfig3 = createUnaryKernelConfig(Neg); +function parseResultStruct(backend2, resOffset) { + const result = new Int32Array(backend2.wasm.HEAPU8.buffer, resOffset, 4); + const pSelectedIndices = result[0]; + const selectedSize = result[1]; + const pSelectedScores = result[2]; + const pValidOutputs = result[3]; + backend2.wasm._free(resOffset); + return { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs }; +} +var wasmFunc4; +function setup48(backend2) { + wasmFunc4 = backend2.wasm.cwrap( + NonMaxSuppressionV3, + "number", + // Result* + [ + "number", + "number", + "number", + "number", + "number" + // scoreThreshold + ] + ); +} +function kernelFunc(args) { + const { backend: backend2, inputs, attrs } = args; + const { iouThreshold, maxOutputSize, scoreThreshold } = attrs; + const { boxes, scores } = inputs; + const boxesId = backend2.dataIdMap.get(boxes.dataId).id; + const scoresId = backend2.dataIdMap.get(scores.dataId).id; + const resOffset = wasmFunc4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold); + const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset); + backend2.wasm._free(pSelectedScores); + backend2.wasm._free(pValidOutputs); + const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices); + return selectedIndicesTensor; +} +var nonMaxSuppressionV3Config3 = { + kernelName: NonMaxSuppressionV3, + backendName: "wasm", + setupFunc: setup48, + kernelFunc +}; +var wasmFunc5; +function setup49(backend2) { + wasmFunc5 = backend2.wasm.cwrap( + NonMaxSuppressionV4, + "number", + // Result* + [ + "number", + "number", + "number", + "number", + "number", + "bool" + // padToMaxOutputSize + ] + ); +} +function nonMaxSuppressionV43(args) { + const { backend: backend2, inputs, attrs } = args; + const { iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize } = attrs; + const { boxes, scores } = inputs; + const boxesId = backend2.dataIdMap.get(boxes.dataId).id; + const scoresId = backend2.dataIdMap.get(scores.dataId).id; + const resOffset = wasmFunc5(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize); + const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset); + backend2.wasm._free(pSelectedScores); + const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices); + const validOutputsTensor = backend2.makeOutput([], "int32", pValidOutputs); + return [selectedIndicesTensor, validOutputsTensor]; +} +var nonMaxSuppressionV4Config3 = { + kernelName: NonMaxSuppressionV4, + backendName: "wasm", + setupFunc: setup49, + kernelFunc: nonMaxSuppressionV43 +}; +var wasmFunc6; +function setup50(backend2) { + wasmFunc6 = backend2.wasm.cwrap( + NonMaxSuppressionV5, + "number", + // Result* + [ + "number", + "number", + "number", + "number", + "number", + "number" + // softNmsSigma + ] + ); +} +function kernelFunc2(args) { + const { backend: backend2, inputs, attrs } = args; + const { iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma } = attrs; + const { boxes, scores } = inputs; + const boxesId = backend2.dataIdMap.get(boxes.dataId).id; + const scoresId = backend2.dataIdMap.get(scores.dataId).id; + const resOffset = wasmFunc6(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset); + backend2.wasm._free(pValidOutputs); + const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices); + const selectedScoresTensor = backend2.makeOutput([selectedSize], "float32", pSelectedScores); + return [selectedIndicesTensor, selectedScoresTensor]; +} +var nonMaxSuppressionV5Config3 = { + kernelName: NonMaxSuppressionV5, + backendName: "wasm", + setupFunc: setup50, + kernelFunc: kernelFunc2 +}; +var supportsFullBroadcast15 = false; +var notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast15, "bool"); +var wasmOneHot; +function setup51(backend2) { + wasmOneHot = backend2.wasm.cwrap(OneHot, null, [ + "number", + "number", + "number", + "number", + "number" + // out_id + ]); +} +function oneHot4(args) { + const { inputs, backend: backend2, attrs } = args; + const { indices } = inputs; + const { dtype, depth, onValue, offValue } = attrs; + const out = backend2.makeOutput([...indices.shape, depth], dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const indicesData = backend2.dataIdMap.get(indices.dataId); + const indicesId = indicesData.id; + wasmOneHot(indicesId, depth, onValue, offValue, outId); + return out; +} +var oneHotConfig3 = { + kernelName: OneHot, + backendName: "wasm", + setupFunc: setup51, + kernelFunc: oneHot4 +}; +function onesLike4(args) { + const { inputs: { x }, backend: backend2 } = args; + const out = backend2.makeOutput(x.shape, x.dtype); + const outVals = backend2.typedArrayFromHeap(out); + outVals.fill(1); + return out; +} +var onesLikeConfig3 = { + kernelName: OnesLike, + backendName: "wasm", + kernelFunc: onesLike4 +}; +function pack3(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + if (inputs.length === 1) { + return expandDims5({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } }); + } + const shape = inputs[0].shape; + const dtype = inputs[0].dtype; + inputs.forEach((t) => { + util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes"); + util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes"); + }); + const intermediateTensorInfos = []; + const expandedTensors = inputs.map((t) => { + const expandedT = expandDims5({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } }); + intermediateTensorInfos.push(expandedT); + return expandedT; + }); + const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } }); + intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId)); + return result; +} +var packConfig3 = { + kernelName: Pack, + backendName: "wasm", + kernelFunc: pack3 +}; +var wasmPadV2; +function setup52(backend2) { + wasmPadV2 = backend2.wasm.cwrap(PadV2, null, [ + "number", + "array", + "number", + "number", + "array", + "array", + "number", + "number" + // outId + ]); +} +function pad2(args) { + const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args; + const outShape = paddings.map( + (p2, i) => p2[0] + x.shape[i] + p2[1] + /* afterPad */ + ); + if (util_exports.sizeFromShape(x.shape) === 0) { + return fill4({ + backend: backend2, + attrs: { shape: outShape, value: constantValue, dtype: x.dtype } + }); + } + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(outShape, x.dtype); + const outTensorData = backend2.dataIdMap.get(out.dataId); + const outId = outTensorData.id; + const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]); + const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]); + const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer); + const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer); + wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId); + return out; +} +var padV2Config3 = { + kernelName: PadV2, + backendName: "wasm", + kernelFunc: pad2, + setupFunc: setup52 +}; +var supportsFullBroadcast16 = false; +var powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast16); +var wasmPrelu; +function setup53(backend2) { + wasmPrelu = backend2.wasm.cwrap(Prelu, null, [ + "number", + "number", + "number" + // out_id + ]); +} +function prelu5(args) { + const { inputs, backend: backend2 } = args; + const { x, alpha } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const weightsId = backend2.dataIdMap.get(alpha.dataId).id; + let inputId = xId; + const input2 = x; + let castedInput = input2; + if (input2.dtype !== "float32") { + castedInput = cast5({ backend: backend2, inputs: { x }, attrs: { dtype: "float32" } }); + inputId = backend2.dataIdMap.get(castedInput.dataId).id; + } + const out = backend2.makeOutput(x.shape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmPrelu(inputId, weightsId, outId); + if (input2.dtype !== "float32") { + backend2.disposeData(castedInput.dataId); + } + return out; +} +var preluConfig3 = { + kernelName: Prelu, + backendName: "wasm", + setupFunc: setup53, + kernelFunc: prelu5 +}; +var wasmProd; +function setup54(backend2) { + wasmProd = backend2.wasm.cwrap(Prod, null, [ + "number", + "number", + "number", + "number" + ]); +} +function prod4(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + let reductionAxes = axes; + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + if (transposedId !== xId) { + input2 = transposed; + inputId = transposedId; + reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length); + } + } + backend_util_exports.assertAxesAreInnerMostDims("prod", reductionAxes, input2.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, input2.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmProd(inputId, reduceSize, CppDType[out.dtype], outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var prodConfig3 = { + kernelName: Prod, + backendName: "wasm", + setupFunc: setup54, + kernelFunc: prod4 +}; +var range5 = (args) => { + const { backend: backend2, attrs } = args; + const { start, stop, step: step5, dtype } = attrs; + const values = rangeImpl(start, stop, step5, dtype); + const out = backend2.makeOutput([values.length], dtype); + const outVals = backend2.typedArrayFromHeap(out); + outVals.set(values); + return out; +}; +var rangeConfig3 = { + kernelName: Range, + backendName: "wasm", + kernelFunc: range5 +}; +var supportsFullBroadcast17 = true; +var realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast17); +var reciprocalConfig3 = createUnaryKernelConfig(Reciprocal); +var reluConfig3 = createUnaryKernelConfig(Relu); +var relu6Config3 = createUnaryKernelConfig(Relu6); +var wasmResizeBilinear; +function setup55(backend2) { + wasmResizeBilinear = backend2.wasm.cwrap(ResizeBilinear, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function resizeBilinear5(args) { + const { backend: backend2, inputs, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + const [newHeight, newWidth] = size; + const [batch, oldHeight, oldWidth, numChannels] = images.shape; + const outShape = [batch, newHeight, newWidth, numChannels]; + let xData = backend2.dataIdMap.get(images.dataId); + let castedData; + if (xData.dtype !== "float32") { + castedData = cast5({ backend: backend2, inputs: { x: images }, attrs: { dtype: "float32" } }); + xData = backend2.dataIdMap.get(castedData.dataId); + } + const xId = xData.id; + const out = backend2.makeOutput(outShape, "float32"); + if (util_exports.sizeFromShape(images.shape) === 0) { + return out; + } + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId); + if (castedData != null) { + backend2.disposeData(castedData.dataId); + } + return out; +} +var resizeBilinearConfig3 = { + kernelName: ResizeBilinear, + backendName: "wasm", + setupFunc: setup55, + kernelFunc: resizeBilinear5 +}; +var wasmResizeBilinearGrad; +function setup56(backend2) { + wasmResizeBilinearGrad = backend2.wasm.cwrap(ResizeBilinearGrad, null, [ + "number", + "number", + "number", + "array", + "array", + "boolean" + // alignCorners + ]); +} +function resizeBilinearGrad3(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + const dx = backend2.makeOutput(images.shape, "float32"); + let xData = backend2.dataIdMap.get(images.dataId); + let castedData; + if (xData.dtype !== "float32") { + castedData = cast5({ + backend: backend2, + inputs: { x: images }, + attrs: { dtype: "float32" } + }); + xData = backend2.dataIdMap.get(castedData.dataId); + } + wasmResizeBilinearGrad(backend2.dataIdMap.get(images.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dx.dataId).id, new Uint8Array(new Int32Array(images.shape).buffer), new Uint8Array(new Int32Array(dy.shape).buffer), alignCorners); + if (castedData != null) { + backend2.disposeData(castedData.dataId); + } + return dx; +} +var resizeBilinearGradConfig4 = { + kernelName: ResizeBilinearGrad, + backendName: "wasm", + setupFunc: setup56, + kernelFunc: resizeBilinearGrad3 +}; +var wasmResizeNearestNeighbor; +function setup57(backend2) { + wasmResizeNearestNeighbor = backend2.wasm.cwrap(ResizeNearestNeighbor, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function resizeNearestNeighbor4(args) { + const { backend: backend2, inputs, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + const [newHeight, newWidth] = size; + const [batch, oldHeight, oldWidth, numChannels] = images.shape; + const outShape = [batch, newHeight, newWidth, numChannels]; + const out = backend2.makeOutput(outShape, "float32"); + if (util_exports.sizeFromShape(images.shape) === 0) { + return out; + } + let xData = backend2.dataIdMap.get(images.dataId); + let castedData; + if (xData.dtype !== "float32") { + castedData = cast5({ + backend: backend2, + inputs: { x: images }, + attrs: { dtype: "float32" } + }); + xData = backend2.dataIdMap.get(castedData.dataId); + } + const xId = xData.id; + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmResizeNearestNeighbor(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId); + if (castedData != null) { + backend2.disposeData(castedData.dataId); + } + return out; +} +var resizeNearestNeighborConfig3 = { + kernelName: ResizeNearestNeighbor, + backendName: "wasm", + setupFunc: setup57, + kernelFunc: resizeNearestNeighbor4 +}; +var wasmResizeNearestNeighborGrad; +function setup58(backend2) { + wasmResizeNearestNeighborGrad = backend2.wasm.cwrap(ResizeNearestNeighborGrad, null, [ + "number", + "number", + "number", + "array", + "array", + "boolean" + // alignCorners + ]); +} +function resizeNearestNeighborGrad3(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + const dx = backend2.makeOutput(images.shape, "float32"); + let xData = backend2.dataIdMap.get(images.dataId); + let castedData; + if (xData.dtype !== "float32") { + castedData = cast5({ + backend: backend2, + inputs: { x: images }, + attrs: { dtype: "float32" } + }); + xData = backend2.dataIdMap.get(castedData.dataId); + } + wasmResizeNearestNeighborGrad(backend2.dataIdMap.get(images.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dx.dataId).id, new Uint8Array(new Int32Array(images.shape).buffer), new Uint8Array(new Int32Array(dy.shape).buffer), alignCorners); + if (castedData != null) { + backend2.disposeData(castedData.dataId); + } + return dx; +} +var resizeNearestNeighborGradConfig4 = { + kernelName: ResizeNearestNeighborGrad, + backendName: "wasm", + setupFunc: setup58, + kernelFunc: resizeNearestNeighborGrad3 +}; +var wasmReverse; +function setup59(backend2) { + wasmReverse = backend2.wasm.cwrap(Reverse, null, [ + "number", + "array", + "number", + "array", + "number", + "number" + // out_id + ]); +} +function reverse4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dims } = attrs; + const axes = util_exports.parseAxisParam(dims, x.shape); + if (x.shape.length === 0) { + return identity4({ inputs: { x }, backend: backend2 }); + } + const out = backend2.makeOutput(x.shape, x.dtype); + const xId = backend2.dataIdMap.get(x.dataId).id; + const outId = backend2.dataIdMap.get(out.dataId).id; + const axesBytes = new Uint8Array(new Int32Array(axes).buffer); + const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId); + const reshaped = reshape5({ inputs: { x: out }, attrs: { shape: x.shape }, backend: backend2 }); + backend2.disposeData(out.dataId); + return reshaped; +} +var reverseConfig3 = { + kernelName: Reverse, + backendName: "wasm", + kernelFunc: reverse4, + setupFunc: setup59 +}; +var wasmRotate; +function setup60(backend2) { + wasmRotate = backend2.wasm.cwrap(RotateWithOffset, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number", + "number" + // outId + ]); +} +function rotateWithOffset2(args) { + const { inputs, backend: backend2, attrs } = args; + const { image: image2 } = inputs; + const { radians, fillValue, center } = attrs; + const out = backend2.makeOutput(image2.shape, image2.dtype); + const imageId = backend2.dataIdMap.get(image2.dataId).id; + const outId = backend2.dataIdMap.get(out.dataId).id; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth); + const fillIsBlack = fillValue === 0; + const fullOpacityValue = 255; + const fillValues2 = typeof fillValue === "number" ? [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] : [...fillValue, fullOpacityValue]; + const fillBytes = new Uint8Array(new Int32Array(fillValues2).buffer); + wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues2.length, outId); + return out; +} +var rotateWithOffsetConfig3 = { + kernelName: RotateWithOffset, + backendName: "wasm", + kernelFunc: rotateWithOffset2, + setupFunc: setup60 +}; +var roundConfig3 = createUnaryKernelConfig(Round); +var rsqrtConfig3 = createUnaryKernelConfig(Rsqrt); +var wasmScatterNd; +function setup61(backend2) { + wasmScatterNd = backend2.wasm.cwrap(ScatterNd, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number", + "number" + // outId + ]); +} +function scatterNd3(args) { + const { backend: backend2, inputs, attrs } = args; + const { indices, updates } = inputs; + const { shape } = attrs; + const out = backend2.makeOutput(shape, updates.dtype); + if (util_exports.sizeFromShape(shape) === 0) { + return out; + } + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = scatter_nd_util_exports.calculateShapes(updates, indices, shape); + const indicesData = backend2.dataIdMap.get(indices.dataId); + const indicesId = indicesData.id; + const updatesData = backend2.dataIdMap.get(updates.dataId); + const updatesId = updatesData.id; + const stridesBytes = new Uint8Array(new Int32Array(strides).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId); + return out; +} +var scatterNdConfig3 = { + kernelName: ScatterNd, + backendName: "wasm", + setupFunc: setup61, + kernelFunc: scatterNd3 +}; +var wasmSearchSorted; +function setup62(backend2) { + wasmSearchSorted = backend2.wasm.cwrap(SearchSorted, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "bool", + "number" + // outId + ]); +} +function searchSorted4(args) { + const { inputs, backend: backend2, attrs } = args; + const { sortedSequence, values } = inputs; + const { side } = attrs; + if (sortedSequence.dtype !== values.dtype) { + throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${sortedSequence.dtype} and ${values.dtype}`); + } + const out = backend2.makeOutput(values.shape, "int32"); + function tensorId(x) { + return backend2.dataIdMap.get(x.dataId).id; + } + wasmSearchSorted( + tensorId(sortedSequence), + tensorId(values), + /*batchSize=*/ + sortedSequence.shape[0], + /*sequenceSize=*/ + sortedSequence.shape[1], + /*valuesSize=*/ + values.shape[1], + /*dtype=*/ + CppDType[sortedSequence.dtype], + /*isSideLeft=*/ + side === "left", + tensorId(out) + ); + return out; +} +var searchSortedConfig3 = { + kernelName: SearchSorted, + backendName: "wasm", + setupFunc: setup62, + kernelFunc: searchSorted4 +}; +var wasmSelect; +function setup63(backend2) { + wasmSelect = backend2.wasm.cwrap("SelectV2", null, [ + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function select4(args) { + const { inputs, backend: backend2 } = args; + const { condition, t, e } = inputs; + const conditionId = backend2.dataIdMap.get(condition.dataId).id; + const tId = backend2.dataIdMap.get(t.dataId).id; + const eId = backend2.dataIdMap.get(e.dataId).id; + const out = backend2.makeOutput(t.shape, t.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const cRank = condition.shape.length; + const tRank = t.shape.length; + const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1)); + wasmSelect(conditionId, tId, eId, offset, outId); + return out; +} +var selectConfig3 = { + kernelName: Select, + backendName: "wasm", + kernelFunc: select4, + setupFunc: setup63 +}; +var seluConfig3 = createUnaryKernelConfig(Selu); +var wasmFunc7; +function setup64(backend2) { + wasmFunc7 = backend2.wasm.cwrap(Sigmoid, null, ["number", "number"]); +} +function sigmoid4(args) { + const { backend: backend2, inputs: { x } } = args; + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(x.shape, x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + if (util_exports.sizeFromShape(out.shape) === 0) { + return out; + } + wasmFunc7(xId, outId); + return out; +} +var sigmoidConfig3 = { + kernelName: "Sigmoid", + backendName: "wasm", + setupFunc: setup64, + kernelFunc: sigmoid4 +}; +var signConfig3 = createUnaryKernelConfig(Sign); +var sinConfig3 = createUnaryKernelConfig(Sin); +var sinhConfig3 = createUnaryKernelConfig(Sinh); +var softplusConfig3 = createUnaryKernelConfig(Softplus); +function spaceToBatchND4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, paddings } = attrs; + const prod5 = util_exports.sizeFromShape(blockShape); + const completePaddings = [[0, 0]]; + completePaddings.push(...paddings); + for (let i = 1 + blockShape.length; i < x.shape.length; ++i) { + completePaddings.push([0, 0]); + } + const paddedX = padV2Config3.kernelFunc({ + inputs: { x }, + backend: backend2, + attrs: { paddings: completePaddings, constantValue: 0 } + }); + const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false); + const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false); + const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false); + const reshapeInputs = { x: paddedX }; + const reshapeAttrs = { shape: reshapedPaddedShape }; + const paddedXReshaped = reshape5({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs }); + const transposeInputs = { x: paddedXReshaped }; + const transposeAttrs = { perm: permutedReshapedPaddedPermutation }; + const paddedXT = transpose4({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs }); + const resultReshapeInputs = { x: paddedXT }; + const resultReshapeAttrs = { shape: flattenShape }; + const result = reshape5({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs }); + backend2.disposeData(paddedX.dataId); + backend2.disposeData(paddedXReshaped.dataId); + backend2.disposeData(paddedXT.dataId); + return result; +} +var spaceToBatchNDConfig3 = { + kernelName: SpaceToBatchND, + backendName: "wasm", + kernelFunc: spaceToBatchND4 +}; +var wasmSparseFillEmptyRows; +function setup65(backend2) { + wasmSparseFillEmptyRows = backend2.wasm.cwrap("SparseFillEmptyRows", "number", [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // exceptionValuesId + ]); +} +function sparseFillEmptyRows4(args) { + const { backend: backend2, inputs } = args; + const { indices, values, denseShape, defaultValue } = inputs; + const indicesCount = indices.shape[0]; + const rank = indices.shape[1]; + const denseRows = backend2.readSync(denseShape.dataId)[0]; + const maxOutputIndicesShape = [indicesCount + denseRows, rank]; + const indicesId = backend2.dataIdMap.get(indices.dataId).id; + const valuesId = backend2.dataIdMap.get(values.dataId).id; + const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id; + const outputIndices = backend2.makeOutput(maxOutputIndicesShape, indices.dtype); + const outputIndicesId = backend2.dataIdMap.get(outputIndices.dataId).id; + const outputValues = backend2.makeOutput(maxOutputIndicesShape.slice(0, 1), values.dtype); + const outputValuesId = backend2.dataIdMap.get(outputValues.dataId).id; + const emptyRowIndicator = backend2.makeOutput([denseRows], "bool"); + const emptyRowIndicatorId = backend2.dataIdMap.get(emptyRowIndicator.dataId).id; + const reverseIndexMap = backend2.makeOutput([indicesCount], indices.dtype); + const reverseIndexMapId = backend2.dataIdMap.get(reverseIndexMap.dataId).id; + const exceptionValues = backend2.makeOutput([4], "int32"); + const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id; + const outputRows = wasmSparseFillEmptyRows(indicesId, valuesId, CppDType[values.dtype], indicesCount, denseRows, rank, defaultValueId, outputIndicesId, outputValuesId, emptyRowIndicatorId, reverseIndexMapId, exceptionValuesId); + const exceptionValuesArray = backend2.readSync(exceptionValues.dataId); + let exceptionMessage; + switch (exceptionValuesArray[0]) { + case 1: { + exceptionMessage = backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(exceptionValuesArray[1]); + break; + } + case 2: { + exceptionMessage = backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + } + case 3: + exceptionMessage = backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]); + break; + default: + exceptionMessage = ""; + } + backend2.disposeData(exceptionValues.dataId); + if (exceptionMessage) { + backend2.disposeData(outputIndices.dataId); + backend2.disposeData(outputValues.dataId); + backend2.disposeData(emptyRowIndicator.dataId); + backend2.disposeData(reverseIndexMap.dataId); + throw new Error(exceptionMessage); + } + let resizedIndices = outputIndices; + let resizedValues = outputValues; + if (outputRows !== maxOutputIndicesShape[0]) { + resizedIndices = slice4({ + inputs: { x: outputIndices }, + attrs: { begin: 0, size: [outputRows, rank] }, + backend: backend2 + }); + resizedValues = slice4({ + inputs: { x: outputValues }, + attrs: { begin: 0, size: outputRows }, + backend: backend2 + }); + backend2.disposeData(outputIndices.dataId); + backend2.disposeData(outputValues.dataId); + } + return [resizedIndices, resizedValues, emptyRowIndicator, reverseIndexMap]; +} +var sparseFillEmptyRowsConfig3 = { + kernelName: SparseFillEmptyRows, + backendName: "wasm", + setupFunc: setup65, + kernelFunc: sparseFillEmptyRows4 +}; +var wasmSparseReshape; +function setup66(backend2) { + wasmSparseReshape = backend2.wasm.cwrap(SparseReshape, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // exceptionValuesId + ]); +} +function sparseReshape4(args) { + const { backend: backend2, inputs } = args; + const { inputIndices, inputShape, newShape } = inputs; + if (inputIndices.shape.length !== 2) { + throw new Error(`Input indices should be a matrix but received shape + ${inputIndices.shape}`); + } + if (inputShape.shape.length !== 1) { + throw new Error(`Input shape should be a vector but received shape + ${inputShape.shape}`); + } + if (newShape.shape.length !== 1) { + throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`); + } + const inputIndicesId = backend2.dataIdMap.get(inputIndices.dataId).id; + const inputShapeId = backend2.dataIdMap.get(inputShape.dataId).id; + const newShapeId = backend2.dataIdMap.get(newShape.dataId).id; + const nnz = inputIndices.shape[0]; + const outputRank = util_exports.sizeFromShape(newShape.shape); + const newIndices = backend2.makeOutput([nnz, outputRank], inputIndices.dtype); + const newIndicesId = backend2.dataIdMap.get(newIndices.dataId).id; + const outputShape = backend2.makeOutput([outputRank], newShape.dtype); + const outputShapeId = backend2.dataIdMap.get(outputShape.dataId).id; + const exceptionValues = backend2.makeOutput([3], "int32"); + const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id; + wasmSparseReshape(inputIndicesId, inputShapeId, newShapeId, nnz, newIndicesId, outputShapeId, exceptionValuesId); + const exceptionValuesArray = backend2.readSync(exceptionValues.dataId); + let exceptionMessage; + switch (exceptionValuesArray[0]) { + case 0: { + exceptionMessage = backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + } + case 1: { + exceptionMessage = backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + } + case 2: + exceptionMessage = backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage(); + break; + case 3: { + const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId)); + exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShapeValues, outputShapeValues); + break; + } + case 4: { + const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId)); + exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShapeValues, outputShapeValues); + break; + } + default: + exceptionMessage = ""; + } + backend2.disposeData(exceptionValues.dataId); + if (exceptionMessage) { + backend2.disposeData(newIndices.dataId); + backend2.disposeData(outputShape.dataId); + throw new Error(exceptionMessage); + } + return [newIndices, outputShape]; +} +var sparseReshapeConfig3 = { + kernelName: SparseReshape, + backendName: "wasm", + setupFunc: setup66, + kernelFunc: sparseReshape4 +}; +var wasmSparseSegmentReduction; +function setup67(backend2) { + wasmSparseSegmentReduction = backend2.wasm.cwrap("SparseSegmentReduction", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // defaultValue + ]); +} +function sparseSegmentReduction(args, isMean) { + const { backend: backend2, inputs } = args; + const { data, indices, segmentIds } = inputs; + const numIndices = indices.shape[0]; + const segmentIdsBack = backend2.readSync(segmentIds.dataId, numIndices - 1, numIndices)[0]; + const lastSegmentIdPlusOne = numIndices > 0 ? segmentIdsBack + 1 : 0; + const outputRows = lastSegmentIdPlusOne; + if (outputRows < 0) { + throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + } + const outputShape = data.shape.slice(); + outputShape[0] = outputRows; + const dataId = backend2.dataIdMap.get(data.dataId).id; + const indicesId = backend2.dataIdMap.get(indices.dataId).id; + const segmentIdsId = backend2.dataIdMap.get(segmentIds.dataId).id; + const output = backend2.makeOutput(outputShape, data.dtype); + const outputId = backend2.dataIdMap.get(output.dataId).id; + const exceptionValues = backend2.makeOutput([4], "int32"); + const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id; + wasmSparseSegmentReduction(dataId, CppDType[data.dtype], data.shape[0], indicesId, segmentIdsId, outputId, exceptionValuesId, isMean, 0); + const exceptionValuesArray = backend2.readSync(exceptionValues.dataId); + let exceptionMessage; + switch (exceptionValuesArray[0]) { + case 0: { + exceptionMessage = backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage(); + break; + } + case 1: { + exceptionMessage = backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage(); + break; + } + case 2: + exceptionMessage = backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + case 3: + exceptionMessage = backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]); + break; + default: + exceptionMessage = ""; + } + backend2.disposeData(exceptionValues.dataId); + if (exceptionMessage) { + backend2.disposeData(output.dataId); + throw new Error(exceptionMessage); + } + return output; +} +function sparseSegmentMean4(args) { + return sparseSegmentReduction(args, true); +} +var sparseSegmentMeanConfig3 = { + kernelName: SparseSegmentMean, + backendName: "wasm", + setupFunc: setup67, + kernelFunc: sparseSegmentMean4 +}; +function sparseSegmentSum4(args) { + return sparseSegmentReduction(args, false); +} +var sparseSegmentSumConfig3 = { + kernelName: SparseSegmentSum, + backendName: "wasm", + setupFunc: setup67, + kernelFunc: sparseSegmentSum4 +}; +var wasmSparseToDense; +function setup68(backend2) { + wasmSparseToDense = backend2.wasm.cwrap(SparseToDense, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number", + "number" + // outId + ]); +} +function sparseToDense4(args) { + const { backend: backend2, inputs, attrs } = args; + const { sparseIndices, sparseValues, defaultValue } = inputs; + const { outputShape } = attrs; + const out = backend2.makeOutput(outputShape, defaultValue.dtype); + if (util_exports.sizeFromShape(outputShape) === 0) { + return out; + } + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape); + const sparseIndicesId = backend2.dataIdMap.get(sparseIndices.dataId).id; + const sparseValuesId = backend2.dataIdMap.get(sparseValues.dataId).id; + const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id; + const stridesBytes = new Uint8Array(new Int32Array(strides).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmSparseToDense(sparseIndicesId, sparseValuesId, sparseValues.shape.length, defaultValueId, CppDType[defaultValue.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId); + return out; +} +var sparseToDenseConfig3 = { + kernelName: SparseToDense, + backendName: "wasm", + setupFunc: setup68, + kernelFunc: sparseToDense4 +}; +function splitV3(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x } = inputs; + const { numOrSizeSplits, axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, x.shape)[0]; + const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis); + const begin = new Array(x.shape.length).fill(0); + const size = x.shape.slice(); + return splitSizes.map((s) => { + const xSliceSize = [...size]; + xSliceSize[$axis] = s; + const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 }); + begin[$axis] += s; + return xSlice; + }); +} +var splitVConfig3 = { + kernelName: SplitV, + backendName: "wasm", + kernelFunc: splitV3 +}; +var sqrtConfig3 = createUnaryKernelConfig(Sqrt); +var squareConfig3 = createUnaryKernelConfig(Square); +var supportsFullBroadcast18 = true; +var squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast18); +var wasmStep; +function setup69(backend2) { + wasmStep = backend2.wasm.cwrap(Step, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function step4(args) { + const { backend: backend2, inputs, attrs } = args; + const { alpha } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(x.shape, x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmStep(xId, alpha, CppDType[x.dtype], outId); + return out; +} +var stepConfig3 = { + kernelName: Step, + backendName: "wasm", + setupFunc: setup69, + kernelFunc: step4 +}; +var wasmStridedSlice; +function setup70(backend2) { + wasmStridedSlice = backend2.wasm.cwrap(StridedSlice, null, [ + "number", + "array", + "number", + "array", + "array", + "array", + "array", + "array", + "number", + "number" + // outId + ]); +} +function stridedSlice4(args) { + const { backend: backend2, inputs, attrs } = args; + const { x } = inputs; + const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs; + const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); + let result; + if (isIdentity) { + result = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); + } else if (sliceDim0 || isSimpleSlice) { + util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`); + const size = slice_util_exports.computeOutShape($begin, $end, $strides); + const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } }); + result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeData(sliced.dataId); + } else { + const out = backend2.makeOutput(finalShapeSparse, "float32"); + const xId = backend2.dataIdMap.get(x.dataId).id; + const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer); + const beginBytes = new Uint8Array(new Int32Array($begin).buffer); + const endBytes = new Uint8Array(new Int32Array($end).buffer); + const stridesBytes = new Uint8Array(new Int32Array($strides).buffer); + const outputShapeBytes = new Uint8Array(new Int32Array(finalShapeSparse).buffer); + const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(finalShapeSparse)).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmStridedSlice(xId, xStridesBytes, x.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, finalShapeSparse.length, outId); + result = reshape5({ inputs: { x: out }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeData(out.dataId); + } + return result; +} +var stridedSliceConfig3 = { + kernelName: StridedSlice, + backendName: "wasm", + setupFunc: setup70, + kernelFunc: stridedSlice4 +}; +function stringNGrams4(args) { + const { backend: backend2, inputs, attrs } = args; + const { data, dataSplits } = inputs; + const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs; + const $data = backend2.readSync(data.dataId); + const $dataSplits = backend2.readSync(dataSplits.dataId); + const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences); + const nGramsOut = backend2.makeOutput([nGrams.length], "string"); + const nGramsOutData = backend2.dataIdMap.get(nGramsOut.dataId); + nGramsOutData.stringBytes = nGrams; + const nGramsSplitsOut = backend2.makeOutput(dataSplits.shape, "int32"); + const nGramsSplitsOutVals = backend2.typedArrayFromHeap(nGramsSplitsOut); + nGramsSplitsOutVals.set(nGramsSplits); + return [nGramsOut, nGramsSplitsOut]; +} +var stringNGramsConfig3 = { + kernelName: StringNGrams, + backendName: "wasm", + kernelFunc: stringNGrams4 +}; +function stringSplit4(args) { + const { backend: backend2, inputs, attrs } = args; + const { input: input2, delimiter } = inputs; + const { skipEmpty } = attrs; + const inputVals = backend2.readSync(input2.dataId); + const delimiterVals = backend2.readSync(delimiter.dataId); + const [indices, values, shape] = stringSplitImpl(inputVals, delimiterVals[0], skipEmpty); + const outputSize = values.length; + const indicesOut = backend2.makeOutput([outputSize, 2], "int32"); + const indicesOutVals = backend2.typedArrayFromHeap(indicesOut); + indicesOutVals.set(indices); + const valuesOut = backend2.makeOutput([outputSize], "string"); + const valuesOutData = backend2.dataIdMap.get(valuesOut.dataId); + valuesOutData.stringBytes = values; + const shapeOut = backend2.makeOutput([2], "int32"); + const shapeOutVals = backend2.typedArrayFromHeap(shapeOut); + shapeOutVals.set(shape); + return [indicesOut, valuesOut, shapeOut]; +} +var stringSplitConfig3 = { + kernelName: StringSplit, + backendName: "wasm", + kernelFunc: stringSplit4 +}; +function stringToHashBucketFast4(args) { + const { backend: backend2, inputs, attrs } = args; + const { input: input2 } = inputs; + const { numBuckets } = attrs; + const inputVals = backend2.readSync(input2.dataId); + const values = stringToHashBucketFastImpl(inputVals, numBuckets); + const out = backend2.makeOutput(input2.shape, "int32"); + const outVals = backend2.typedArrayFromHeap(out); + outVals.set(values); + return out; +} +var stringToHashBucketFastConfig3 = { + kernelName: StringToHashBucketFast, + backendName: "wasm", + kernelFunc: stringToHashBucketFast4 +}; +var supportsFullBroadcast19 = true; +var subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast19); +var wasmSum; +function setup71(backend2) { + wasmSum = backend2.wasm.cwrap(Sum, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function sum5(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + let reductionAxes = axes; + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + if (transposedId !== xId) { + input2 = transposed; + inputId = transposedId; + reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length); + } + } + backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, input2.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, input2.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmSum(inputId, reduceSize, CppDType[out.dtype], outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var sumConfig3 = { + kernelName: Sum, + backendName: "wasm", + setupFunc: setup71, + kernelFunc: sum5 +}; +var tanConfig3 = createUnaryKernelConfig(Tan); +var tanhConfig3 = createUnaryKernelConfig(Tanh); +var wasmTensorScatterUpdate; +function setup72(backend2) { + wasmTensorScatterUpdate = backend2.wasm.cwrap(TensorScatterUpdate, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number", + "number", + "number" + // tensorId + ]); +} +function tensorScatterUpdate4(args) { + const { backend: backend2, inputs, attrs } = args; + const { tensor: tensor2, indices, updates } = inputs; + const {} = attrs; + const out = backend2.makeOutput(tensor2.shape, tensor2.dtype); + if (util_exports.sizeFromShape(tensor2.shape) === 0) { + return out; + } + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = scatter_nd_util_exports.calculateShapes(updates, indices, tensor2.shape); + const indicesData = backend2.dataIdMap.get(indices.dataId); + const indicesId = indicesData.id; + const updatesData = backend2.dataIdMap.get(updates.dataId); + const updatesId = updatesData.id; + const tensorData = backend2.dataIdMap.get(tensor2.dataId); + const tensorId = tensorData.id; + const stridesBytes = new Uint8Array(new Int32Array(strides).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmTensorScatterUpdate(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId, tensorId); + return out; +} +var tensorScatterUpdateConfig3 = { + kernelName: TensorScatterUpdate, + backendName: "wasm", + setupFunc: setup72, + kernelFunc: tensorScatterUpdate4 +}; +var wasmTile; +function setup73(backend2) { + wasmTile = backend2.wasm.cwrap(Tile, null, [ + "number", + "array", + "number", + "array", + "number", + "number" + // out_id + ]); +} +function tile5(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const { reps } = attrs; + const newShape = new Array(x.shape.length); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[i] * reps[i]; + } + const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer); + const out = backend2.makeOutput(newShape, x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId); + return out; +} +var tileConfig3 = { + kernelName: Tile, + backendName: "wasm", + setupFunc: setup73, + kernelFunc: tile5 +}; +var wasmTopK; +function setup74(backend2) { + wasmTopK = backend2.wasm.cwrap(TopK, null, [ + "number", + "array", + "number", + "number", + "number", + "bool", + "number", + "number" + // outIndicesId + ]); +} +var topk2 = ({ inputs, backend: backend2, attrs }) => { + const { x } = inputs; + const { k, sorted } = attrs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + const outputShape = x.shape.slice(); + outputShape[outputShape.length - 1] = k; + const outValues = backend2.makeOutput(outputShape, x.dtype); + const outValuesId = backend2.dataIdMap.get(outValues.dataId).id; + const outIndices = backend2.makeOutput(outputShape, "int32"); + const outIndicesId = backend2.dataIdMap.get(outIndices.dataId).id; + wasmTopK(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted, outValuesId, outIndicesId); + return [outValues, outIndices]; +}; +var topKConfig3 = { + kernelName: TopK, + backendName: "wasm", + setupFunc: setup74, + kernelFunc: topk2 +}; +var wasmTransform; +function setup75(backend2) { + wasmTransform = backend2.wasm.cwrap(Transform, null, [ + "number", + "number", + "bool", + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number", + "array", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function transform4(args) { + const { backend: backend2, inputs, attrs } = args; + const { image: image2, transforms } = inputs; + const { interpolation, fillMode, fillValue, outputShape } = attrs; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth]; + const outShape = [ + batch, + outHeight, + outWidth, + numChannels + ]; + const inputStrides = new Uint8Array(new Int32Array(util_exports.computeStrides(image2.shape)).buffer); + const outputStrides = new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer); + const out = backend2.makeOutput(outShape, image2.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const imageData = backend2.dataIdMap.get(image2.dataId); + const imageId = imageData.id; + const transformsData = backend2.dataIdMap.get(transforms.dataId); + const transformsId = transformsData.id; + const interpolationModeId = interpolation === "nearest" ? 1 : 2; + let fillModeId; + switch (fillMode) { + case "constant": + fillModeId = 1; + break; + case "reflect": + fillModeId = 2; + break; + case "wrap": + fillModeId = 3; + break; + case "nearest": + fillModeId = 4; + break; + default: + fillModeId = 1; + break; + } + wasmTransform(imageId, transformsId, transforms.shape[0] > 1, batch, outHeight, outWidth, numChannels, imageWidth, imageHeight, inputStrides, image2.shape.length - 1, outputStrides, outShape.length - 1, interpolationModeId, fillModeId, fillValue, outId); + return out; +} +var transformConfig3 = { + kernelName: Transform, + backendName: "wasm", + setupFunc: setup75, + kernelFunc: transform4 +}; +function unique5(args) { + const { inputs, attrs, backend: backend2 } = args; + const { axis } = attrs; + const { x } = inputs; + const { outputValues, outputShape, indices } = uniqueImpl(backend2.readSync(x.dataId), axis, x.shape, x.dtype); + return [ + backend2.makeOutput( + outputShape, + x.dtype, + /*memoryOffset=*/ + void 0, + outputValues + ), + backend2.makeOutput( + [indices.length], + "int32", + /*memoryOffset=*/ + void 0, + indices + ) + ]; +} +var uniqueConfig3 = { + kernelName: Unique, + backendName: "wasm", + kernelFunc: unique5 +}; +function unpack3(args) { + const { inputs, backend: backend2, attrs } = args; + const { value } = inputs; + let { axis } = attrs; + if (axis < 0) { + axis += value.shape.length; + } + const numOutputs = value.shape[axis]; + const rank = value.shape.length; + const outShape = new Array(rank - 1); + let outIndex = 0; + for (let i = 0; i < rank; i++) { + if (i !== axis) { + outShape[outIndex++] = value.shape[i]; + } + } + const outs = new Array(numOutputs); + const begin = new Array(rank).fill(0); + const size = value.shape.slice(); + size[axis] = 1; + for (let i = 0; i < outs.length; i++) { + begin[axis] = i; + outs[i] = slice4({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 }); + } + return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape })); +} +var unpackConfig3 = { + kernelName: Unpack, + backendName: "wasm", + kernelFunc: unpack3 +}; +function zerosLike4(args) { + const { inputs: { x }, backend: backend2 } = args; + const out = backend2.makeOutput(x.shape, x.dtype); + const outVals = backend2.typedArrayFromHeap(out); + outVals.fill(0); + return out; +} +var zerosLikeConfig3 = { + kernelName: ZerosLike, + backendName: "wasm", + kernelFunc: zerosLike4 +}; +var kernelConfigs3 = [ + _fusedMatMulConfig3, + absConfig3, + acosConfig3, + acoshConfig3, + addConfig3, + addNConfig3, + allConfig3, + anyConfig3, + argMaxConfig3, + argMinConfig3, + asinConfig3, + asinhConfig3, + atanConfig3, + atan2Config3, + atanhConfig3, + avgPoolConfig3, + avgPoolGradConfig4, + avgPool3DConfig3, + avgPool3DGradConfig4, + batchMatMulConfig3, + batchToSpaceNDConfig3, + bincountConfig3, + bitwiseAndConfig3, + broadcastArgsConfig3, + castConfig3, + ceilConfig3, + clipByValueConfig3, + concatConfig3, + conv2DConfig3, + conv2DBackpropInputConfig3, + conv3DConfig3, + conv3DBackpropFilterV2Config3, + conv3DBackpropInputV2Config2, + cosConfig3, + coshConfig3, + cropAndResizeConfig3, + cumprodConfig3, + cumsumConfig3, + denseBincountConfig3, + depthToSpaceConfig3, + depthwiseConv2dNativeConfig3, + diagConfig3, + dilation2DConfig3, + dilation2DBackpropFilterConfig2, + dilation2DBackpropInputConfig2, + eluConfig3, + eluGradConfig4, + equalConfig3, + erfConfig3, + expConfig3, + expandDimsConfig3, + expm1Config3, + fillConfig3, + flipLeftRightConfig3, + floorConfig3, + floorDivConfig3, + fusedBatchNormConfig, + fusedConv2DConfig3, + fusedDepthwiseConv2DConfig3, + gatherNdConfig3, + gatherV2Config3, + greaterConfig3, + greaterEqualConfig3, + identityConfig3, + isFiniteConfig3, + isInfConfig3, + isNaNConfig3, + leakyReluConfig3, + lessConfig3, + lessEqualConfig3, + linSpaceConfig3, + log1pConfig3, + logConfig3, + logicalAndConfig3, + logicalNotConfig3, + logicalOrConfig3, + logicalXorConfig, + lrnConfig, + lrnGradConfig2, + maxConfig3, + maximumConfig3, + maxPoolConfig3, + maxPool3DConfig3, + maxPool3DGradConfig4, + maxPoolGradConfig4, + maxPoolWithArgmaxConfig3, + meanConfig3, + minConfig3, + minimumConfig3, + mirrorPadConfig3, + multinomialConfig3, + modConfig3, + multiplyConfig3, + negConfig3, + nonMaxSuppressionV3Config3, + nonMaxSuppressionV4Config3, + nonMaxSuppressionV5Config3, + notEqualConfig3, + oneHotConfig3, + onesLikeConfig3, + packConfig3, + padV2Config3, + powConfig3, + preluConfig3, + prodConfig3, + rangeConfig3, + realDivConfig3, + reciprocalConfig3, + reluConfig3, + relu6Config3, + reshapeConfig3, + resizeBilinearConfig3, + resizeBilinearGradConfig4, + resizeNearestNeighborConfig3, + resizeNearestNeighborGradConfig4, + reverseConfig3, + rotateWithOffsetConfig3, + roundConfig3, + rsqrtConfig3, + scatterNdConfig3, + searchSortedConfig3, + selectConfig3, + seluConfig3, + sigmoidConfig3, + signConfig3, + sinConfig3, + sinhConfig3, + sliceConfig3, + softmaxConfig3, + softplusConfig3, + spaceToBatchNDConfig3, + sparseFillEmptyRowsConfig3, + sparseReshapeConfig3, + sparseSegmentMeanConfig3, + sparseSegmentSumConfig3, + sparseToDenseConfig3, + splitVConfig3, + sqrtConfig3, + squareConfig3, + squaredDifferenceConfig3, + stepConfig3, + stridedSliceConfig3, + stringNGramsConfig3, + stringSplitConfig3, + stringToHashBucketFastConfig3, + subConfig3, + sumConfig3, + tanConfig3, + tanhConfig3, + tensorScatterUpdateConfig3, + tileConfig3, + topKConfig3, + transformConfig3, + transposeConfig3, + uniqueConfig3, + unpackConfig3, + zerosLikeConfig3 +]; +for (const kernelConfig of kernelConfigs3) { + registerKernel(kernelConfig); +} +var ENV6 = env(); +ENV6.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => { + try { + return WebAssembly.validate(new Uint8Array([ + 0, + 97, + 115, + 109, + 1, + 0, + 0, + 0, + 1, + 4, + 1, + 96, + 0, + 0, + 3, + 2, + 1, + 0, + 10, + 9, + 1, + 7, + 0, + 65, + 0, + 253, + 15, + 26, + 11 + ])); + } catch (e) { + return false; + } +}); +ENV6.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { + if (ENV6.get("IS_NODE")) { + return false; + } + try { + new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)); + return WebAssembly.validate(new Uint8Array([ + 0, + 97, + 115, + 109, + 1, + 0, + 0, + 0, + 1, + 4, + 1, + 96, + 0, + 0, + 3, + 2, + 1, + 0, + 5, + 4, + 1, + 3, + 1, + 1, + 10, + 11, + 1, + 9, + 0, + 65, + 0, + 254, + 16, + 2, + 0, + 26, + 11 + ])); + } catch (e) { + return false; + } +}); +var wasmFactoryThreadedSimd_import = __toESM(require_tfjs_backend_wasm_threaded_simd()); +var import_tfjs_backend_wasm_threaded_simd_worker = __toESM(require_tfjs_backend_wasm_threaded_simd_worker()); +var wasmFactory_import = __toESM(require_tfjs_backend_wasm()); +var wasmFactoryThreadedSimd = wasmFactoryThreadedSimd_import.default || wasmFactoryThreadedSimd_import; +var wasmFactory = wasmFactory_import.default || wasmFactory_import; +var BackendWasm = class extends KernelBackend { + constructor(wasm) { + super(); + this.wasm = wasm; + this.dataIdNextNumber = 1; + this.wasm.tfjs.initWithThreadsCount(threadsCount); + actualThreadsCount = this.wasm.tfjs.getThreadsCount(); + this.dataIdMap = new DataStorage(this, engine()); + } + write(values, shape, dtype) { + const dataId = { id: this.dataIdNextNumber++ }; + this.move(dataId, values, shape, dtype, 1); + return dataId; + } + numDataIds() { + return this.dataIdMap.numDataIds(); + } + async time(f) { + const start = util_exports.now(); + f(); + const kernelMs = util_exports.now() - start; + return { kernelMs }; + } + move(dataId, values, shape, dtype, refCount) { + const id = this.dataIdNextNumber++; + if (dtype === "string") { + const stringBytes = values; + this.dataIdMap.set(dataId, { id, stringBytes, shape, dtype, memoryOffset: null, refCount }); + return; + } + const size = util_exports.sizeFromShape(shape); + const numBytes = size * util_exports.bytesPerElement(dtype); + const memoryOffset = this.wasm._malloc(numBytes) >>> 0; + this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount }); + this.wasm.tfjs.registerTensor(id, size, memoryOffset); + if (values != null) { + this.wasm.HEAPU8.set(new Uint8Array(values.buffer, values.byteOffset, numBytes), memoryOffset); + } + } + async read(dataId) { + return this.readSync(dataId); + } + readSync(dataId, start, end) { + const { memoryOffset, dtype, shape, stringBytes } = this.dataIdMap.get(dataId); + if (dtype === "string") { + if ((start == null || start === 0) && (end == null || end >= stringBytes.length)) { + return stringBytes; + } + return stringBytes.slice(start, end); + } + start = start || 0; + end = end || util_exports.sizeFromShape(shape); + const bytesPerElement2 = util_exports.bytesPerElement(dtype); + const bytes = this.wasm.HEAPU8.slice(memoryOffset + start * bytesPerElement2, memoryOffset + end * bytesPerElement2); + return typedArrayFromBuffer(bytes.buffer, dtype); + } + /** + * Dispose the memory if the dataId has 0 refCount. Return true if the memory + * is released, false otherwise. + * @param dataId + * @oaram force Optional, remove the data regardless of refCount + */ + disposeData(dataId, force = false) { + if (this.dataIdMap.has(dataId)) { + const data = this.dataIdMap.get(dataId); + data.refCount--; + if (!force && data.refCount > 0) { + return false; + } + this.wasm._free(data.memoryOffset); + this.wasm.tfjs.disposeData(data.id); + this.dataIdMap.delete(dataId); + } + return true; + } + /** Return refCount of a `TensorData`. */ + refCount(dataId) { + if (this.dataIdMap.has(dataId)) { + const tensorData = this.dataIdMap.get(dataId); + return tensorData.refCount; + } + return 0; + } + incRef(dataId) { + const data = this.dataIdMap.get(dataId); + if (data != null) { + data.refCount++; + } + } + floatPrecision() { + return 32; + } + // Returns the memory offset of a tensor. Useful for debugging and unit + // testing. + getMemoryOffset(dataId) { + return this.dataIdMap.get(dataId).memoryOffset; + } + dispose() { + this.wasm.tfjs.dispose(); + if ("PThread" in this.wasm) { + this.wasm.PThread.terminateAllThreads(); + } + this.wasm = null; + } + memory() { + return { unreliable: false }; + } + /** + * Make a tensor info for the output of an op. If `memoryOffset` is not + * present, this method allocates memory on the WASM heap. If `memoryOffset` + * is present, the memory was allocated elsewhere (in c++) and we just record + * the pointer where that memory lives. + */ + makeOutput(shape, dtype, memoryOffset, values) { + let dataId; + if (memoryOffset == null) { + dataId = this.write(values !== null && values !== void 0 ? values : null, shape, dtype); + } else { + const id = this.dataIdNextNumber++; + dataId = { id }; + this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount: 1 }); + const size = util_exports.sizeFromShape(shape); + this.wasm.tfjs.registerTensor(id, size, memoryOffset); + } + return { dataId, shape, dtype }; + } + typedArrayFromHeap({ shape, dtype, dataId }) { + const buffer2 = this.wasm.HEAPU8.buffer; + const { memoryOffset } = this.dataIdMap.get(dataId); + const size = util_exports.sizeFromShape(shape); + switch (dtype) { + case "float32": + return new Float32Array(buffer2, memoryOffset, size); + case "int32": + return new Int32Array(buffer2, memoryOffset, size); + case "bool": + return new Uint8Array(buffer2, memoryOffset, size); + default: + throw new Error(`Unknown dtype ${dtype}`); + } + } +}; +function createInstantiateWasmFunc(path) { + return (imports, callback) => { + util_exports.fetch(path, { credentials: "same-origin" }).then((response) => { + if (!response["ok"]) { + imports.env.a(`failed to load wasm binary file at '${path}'`); + } + response.arrayBuffer().then((binary) => { + WebAssembly.instantiate(binary, imports).then((output) => { + callback(output.instance, output.module); + }); + }); + }); + return {}; + }; +} +function getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) { + if (wasmPath != null) { + return wasmPath; + } + let path = "tfjs-backend-wasm.wasm"; + if (simdSupported && threadsSupported) { + path = "tfjs-backend-wasm-threaded-simd.wasm"; + } else if (simdSupported) { + path = "tfjs-backend-wasm-simd.wasm"; + } + if (wasmFileMap != null) { + if (wasmFileMap[path] != null) { + return wasmFileMap[path]; + } + } + return wasmModuleFolder + path; +} +async function init() { + const [simdSupported, threadsSupported] = await Promise.all([ + env().getAsync("WASM_HAS_SIMD_SUPPORT"), + env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT") + ]); + return new Promise((resolve, reject) => { + const factoryConfig = {}; + factoryConfig.locateFile = (path, prefix) => { + if (path.endsWith(".worker.js")) { + const response = import_tfjs_backend_wasm_threaded_simd_worker.wasmWorkerContents.replace(/\n/g, "\\n"); + const blob = new Blob([response], { type: "application/javascript" }); + return URL.createObjectURL(blob); + } + if (path.endsWith(".wasm")) { + return getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : prefix); + } + return prefix + path; + }; + if (customFetch) { + factoryConfig.instantiateWasm = createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : "")); + } + let initialized = false; + factoryConfig.onAbort = () => { + if (initialized) { + return; + } + if (initAborted) { + return; + } + initAborted = true; + const rejectMsg = "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"; + reject({ message: rejectMsg }); + }; + let wasm; + if (threadsSupported && simdSupported && wasmPath == null) { + factoryConfig.mainScriptUrlOrBlob = new Blob([`var WasmBackendModuleThreadedSimd = ` + wasmFactoryThreadedSimd.toString()], { type: "text/javascript" }); + wasm = wasmFactoryThreadedSimd(factoryConfig); + } else { + wasm = wasmFactory(factoryConfig); + } + wasm.then((module) => { + initialized = true; + initAborted = false; + const voidReturnType = null; + module.tfjs = { + init: module.cwrap("init", null, []), + initWithThreadsCount: module.cwrap("init_with_threads_count", null, ["number"]), + getThreadsCount: module.cwrap("get_threads_count", "number", []), + registerTensor: module.cwrap("register_tensor", null, [ + "number", + "number", + "number" + // memoryOffset + ]), + disposeData: module.cwrap("dispose_data", voidReturnType, ["number"]), + dispose: module.cwrap("dispose", voidReturnType, []) + }; + resolve({ wasm: module }); + }).catch(reject); + }); +} +function typedArrayFromBuffer(buffer2, dtype) { + switch (dtype) { + case "float32": + return new Float32Array(buffer2); + case "int32": + return new Int32Array(buffer2); + case "bool": + return new Uint8Array(buffer2); + default: + throw new Error(`Unknown dtype ${dtype}`); + } +} +var wasmBinaryNames = [ + "tfjs-backend-wasm.wasm", + "tfjs-backend-wasm-simd.wasm", + "tfjs-backend-wasm-threaded-simd.wasm" +]; +var wasmPath = null; +var wasmPathPrefix = null; +var wasmFileMap = {}; +var initAborted = false; +var customFetch = false; +function setWasmPath(path, usePlatformFetch = false) { + deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."); + if (initAborted) { + throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`"); + } + wasmPath = path; + customFetch = usePlatformFetch; +} +function setWasmPaths(prefixOrFileMap, usePlatformFetch = false) { + if (initAborted) { + throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`"); + } + if (typeof prefixOrFileMap === "string") { + wasmPathPrefix = prefixOrFileMap; + } else { + wasmFileMap = prefixOrFileMap; + const missingPaths = wasmBinaryNames.filter((name) => wasmFileMap[name] == null); + if (missingPaths.length > 0) { + throw new Error(`There were no entries found for the following binaries: ${missingPaths.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.`); + } + } + customFetch = usePlatformFetch; +} +var threadsCount = -1; +var actualThreadsCount = -1; +function setThreadsCount(numThreads) { + threadsCount = numThreads; +} +function getThreadsCount() { + if (actualThreadsCount === -1) { + throw new Error(`WASM backend not initialized.`); + } + return actualThreadsCount; +} +var version8 = "4.16.0"; +var WASM_PRIORITY = 2; +registerBackend("wasm", async () => { + const { wasm } = await init(); + return new BackendWasm(wasm); +}, WASM_PRIORITY); +var version9 = "4.16.0"; +var version22 = "4.16.0"; +var version32 = "4.16.0"; +var version42 = "4.16.0"; +var version52 = "4.16.0"; +var version62 = { + // tfjs: tfjsVersion, + tfjs: version9, + "tfjs-core": version9, + // 'tfjs-data': tfjsDataVersion, + // 'tfjs-layers': tfjsLayersVersion, + "tfjs-converter": version22, + "tfjs-backend-cpu": version32, + "tfjs-backend-webgl": version42, + "tfjs-backend-wasm": version52 +}; + +// src/draw/index.ts +var draw_exports = {}; +__export(draw_exports, { + AnchorPosition: () => AnchorPosition, + DrawBox: () => DrawBox, + DrawBoxOptions: () => DrawBoxOptions, + DrawFaceLandmarks: () => DrawFaceLandmarks, + DrawFaceLandmarksOptions: () => DrawFaceLandmarksOptions, + DrawTextField: () => DrawTextField, + DrawTextFieldOptions: () => DrawTextFieldOptions, + drawContour: () => drawContour, + drawDetections: () => drawDetections, + drawFaceExpressions: () => drawFaceExpressions, + drawFaceLandmarks: () => drawFaceLandmarks +}); + +// src/draw/drawContour.ts +function drawContour(ctx, points, isClosed = false) { + ctx.beginPath(); + points.slice(1).forEach(({ x, y }, prevIdx) => { + const from = points[prevIdx]; + ctx.moveTo(from.x, from.y); + ctx.lineTo(x, y); + }); + if (isClosed) { + const from = points[points.length - 1]; + const to = points[0]; + if (!from || !to) { + return; + } + ctx.moveTo(from.x, from.y); + ctx.lineTo(to.x, to.y); + } + ctx.stroke(); +} + +// src/utils/index.ts +var utils_exports = {}; +__export(utils_exports, { + computeReshapedDimensions: () => computeReshapedDimensions, + getCenterPoint: () => getCenterPoint, + isDimensions: () => isDimensions, + isEven: () => isEven2, + isFloat: () => isFloat, + isTensor: () => isTensor, + isTensor1D: () => isTensor1D, + isTensor2D: () => isTensor2D, + isTensor3D: () => isTensor3D, + isTensor4D: () => isTensor4D, + isValidNumber: () => isValidNumber, + isValidProbablitiy: () => isValidProbablitiy, + range: () => range6, + round: () => round5 +}); + +// src/classes/Dimensions.ts +var Dimensions = class _Dimensions { + constructor(width, height) { + if (!isValidNumber(width) || !isValidNumber(height)) { + throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`); + } + this._width = width; + this._height = height; + } + get width() { + return this._width; + } + get height() { + return this._height; + } + reverse() { + return new _Dimensions(1 / this.width, 1 / this.height); + } +}; + +// src/utils/index.ts +function isTensor(tensor2, dim) { + return tensor2 instanceof Tensor && tensor2.shape.length === dim; +} +function isTensor1D(tensor2) { + return isTensor(tensor2, 1); +} +function isTensor2D(tensor2) { + return isTensor(tensor2, 2); +} +function isTensor3D(tensor2) { + return isTensor(tensor2, 3); +} +function isTensor4D(tensor2) { + return isTensor(tensor2, 4); +} +function isFloat(num) { + return num % 1 !== 0; +} +function isEven2(num) { + return num % 2 === 0; +} +function round5(num, prec = 2) { + const f = 10 ** prec; + return Math.floor(num * f) / f; +} +function isDimensions(obj) { + return obj && obj.width && obj.height; +} +function computeReshapedDimensions({ width, height }, inputSize) { + const scale3 = inputSize / Math.max(height, width); + return new Dimensions(Math.round(width * scale3), Math.round(height * scale3)); +} +function getCenterPoint(pts) { + return pts.reduce((sum6, pt) => sum6.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length)); +} +function range6(num, start, step5) { + return Array(num).fill(0).map((_, i) => start + i * step5); +} +function isValidNumber(num) { + return !!num && num !== Infinity && num !== -Infinity && !Number.isNaN(num) || num === 0; +} +function isValidProbablitiy(num) { + return isValidNumber(num) && num >= 0 && num <= 1; +} + +// src/classes/Point.ts +var Point = class _Point { + constructor(x, y) { + this._x = x; + this._y = y; + } + get x() { + return this._x; + } + get y() { + return this._y; + } + add(pt) { + return new _Point(this.x + pt.x, this.y + pt.y); + } + sub(pt) { + return new _Point(this.x - pt.x, this.y - pt.y); + } + mul(pt) { + return new _Point(this.x * pt.x, this.y * pt.y); + } + div(pt) { + return new _Point(this.x / pt.x, this.y / pt.y); + } + abs() { + return new _Point(Math.abs(this.x), Math.abs(this.y)); + } + magnitude() { + return Math.sqrt(this.x ** 2 + this.y ** 2); + } + floor() { + return new _Point(Math.floor(this.x), Math.floor(this.y)); + } +}; + +// src/classes/Box.ts +var Box = class _Box { + static isRect(rect) { + return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber); + } + static assertIsValidBox(box, callee, allowNegativeDimensions = false) { + if (!_Box.isRect(box)) { + throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`); + } + if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) { + throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`); + } + } + constructor(_box, allowNegativeDimensions = true) { + const box = _box || {}; + const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber); + const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber); + if (!isRect && !isBbox) { + throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`); + } + const [x, y, width, height] = isRect ? [box.x, box.y, box.width, box.height] : [box.left, box.top, box.right - box.left, box.bottom - box.top]; + _Box.assertIsValidBox({ + x, + y, + width, + height + }, "Box.constructor", allowNegativeDimensions); + this._x = x; + this._y = y; + this._width = width; + this._height = height; + } + 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 Point(this.left, this.top); + } + get topRight() { + return new Point(this.right, this.top); + } + get bottomLeft() { + return new Point(this.left, this.bottom); + } + get bottomRight() { + return new Point(this.right, this.bottom); + } + round() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.round(val)); + return new _Box({ + x, + y, + width, + height + }); + } + floor() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.floor(val)); + return new _Box({ + x, + y, + width, + height + }); + } + toSquare() { + let { + x, + y, + width, + height + } = this; + const diff = Math.abs(width - height); + if (width < height) { + x -= diff / 2; + width += diff; + } + if (height < width) { + y -= diff / 2; + height += diff; + } + return new _Box({ x, y, width, height }); + } + rescale(s) { + const scaleX = isDimensions(s) ? s.width : s; + const scaleY = isDimensions(s) ? s.height : s; + return new _Box({ + x: this.x * scaleX, + y: this.y * scaleY, + width: this.width * scaleX, + height: this.height * scaleY + }); + } + pad(padX, padY) { + const [x, y, width, height] = [ + this.x - padX / 2, + this.y - padY / 2, + this.width + padX, + this.height + padY + ]; + return new _Box({ x, y, width, height }); + } + clipAtImageBorders(imgWidth, imgHeight) { + const { x, y, right, bottom } = this; + const clippedX = Math.max(x, 0); + const clippedY = Math.max(y, 0); + const newWidth = right - clippedX; + const newHeight = bottom - clippedY; + const clippedWidth = Math.min(newWidth, imgWidth - clippedX); + const clippedHeight = Math.min(newHeight, imgHeight - clippedY); + return new _Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight }).floor(); + } + shift(sx, sy) { + const { width, height } = this; + const x = this.x + sx; + const y = this.y + sy; + return new _Box({ x, y, width, height }); + } + padAtBorders(imageHeight, imageWidth) { + const w = this.width + 1; + const h = this.height + 1; + const dx = 1; + const dy = 1; + let edx = w; + let edy = h; + let x = this.left; + let y = this.top; + let ex = this.right; + let ey = this.bottom; + if (ex > imageWidth) { + edx = -ex + imageWidth + w; + ex = imageWidth; + } + if (ey > imageHeight) { + edy = -ey + imageHeight + h; + ey = imageHeight; + } + if (x < 1) { + edy = 2 - x; + x = 1; + } + if (y < 1) { + edy = 2 - y; + y = 1; + } + return { dy, edy, dx, edx, y, ey, x, ex, w, h }; + } + calibrate(region) { + return new _Box({ + left: this.left + region.left * this.width, + top: this.top + region.top * this.height, + right: this.right + region.right * this.width, + bottom: this.bottom + region.bottom * this.height + }).toSquare().round(); + } +}; + +// src/classes/BoundingBox.ts +var BoundingBox = class extends Box { + constructor(left, top, right, bottom, allowNegativeDimensions = false) { + super({ left, top, right, bottom }, allowNegativeDimensions); + } +}; + +// src/classes/ObjectDetection.ts +var ObjectDetection = class _ObjectDetection { + constructor(score, classScore, className, relativeBox, imageDims) { + this._imageDims = new Dimensions(imageDims.width, imageDims.height); + this._score = score; + this._classScore = classScore; + this._className = className; + this._box = new Box(relativeBox).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 Box(this._box).rescale(this.imageDims.reverse()); + } + forSize(width, height) { + return new _ObjectDetection( + this.score, + this.classScore, + this.className, + this.relativeBox, + { width, height } + ); + } +}; + +// src/classes/FaceDetection.ts +var FaceDetection = class _FaceDetection extends ObjectDetection { + constructor(score, relativeBox, imageDims) { + super(score, score, "", relativeBox, imageDims); + } + forSize(width, height) { + const { score, relativeBox, imageDims } = super.forSize(width, height); + return new _FaceDetection(score, relativeBox, imageDims); + } +}; + +// src/ops/iou.ts +function iou(box1, box2, isIOU = true) { + const width = Math.max(0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left)); + const height = Math.max(0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top)); + const interSection = width * height; + return isIOU ? interSection / (box1.area + box2.area - interSection) : interSection / Math.min(box1.area, box2.area); +} + +// src/ops/minBbox.ts +function minBbox(pts) { + const xs = pts.map((pt) => pt.x); + const ys = pts.map((pt) => pt.y); + const minX = xs.reduce((min6, x) => x < min6 ? x : min6, Infinity); + const minY = ys.reduce((min6, y) => y < min6 ? y : min6, Infinity); + const maxX = xs.reduce((max6, x) => max6 < x ? x : max6, 0); + const maxY = ys.reduce((max6, y) => max6 < y ? y : max6, 0); + return new BoundingBox(minX, minY, maxX, maxY); +} + +// src/ops/nonMaxSuppression.ts +function nonMaxSuppression2(boxes, scores, iouThreshold, isIOU = true) { + let indicesSortedByScore = scores.map((score, boxIndex) => ({ score, boxIndex })).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex); + const pick = []; + while (indicesSortedByScore.length > 0) { + const curr = indicesSortedByScore.pop(); + pick.push(curr); + const indices = indicesSortedByScore; + const outputs = []; + for (let i = 0; i < indices.length; i++) { + const idx = indices[i]; + const currBox = boxes[curr]; + const idxBox = boxes[idx]; + outputs.push(iou(currBox, idxBox, isIOU)); + } + indicesSortedByScore = indicesSortedByScore.filter( + (_, j) => outputs[j] <= iouThreshold + ); + } + return pick; +} + +// src/ops/normalize.ts +function normalize(x, meanRgb) { + return tidy(() => { + const [r, g, b] = meanRgb; + const avg_r = fill([...x.shape.slice(0, 3), 1], r, "float32"); + const avg_g = fill([...x.shape.slice(0, 3), 1], g, "float32"); + const avg_b = fill([...x.shape.slice(0, 3), 1], b, "float32"); + const avg_rgb = concat([avg_r, avg_g, avg_b], 3); + return sub(x, avg_rgb); + }); +} + +// src/ops/padToSquare.ts +function padToSquare(imgTensor, isCenterImage = false) { + return tidy(() => { + const [height, width] = imgTensor.shape.slice(1); + if (height === width) + return imgTensor; + const dimDiff = Math.abs(height - width); + const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1)); + const paddingAxis = height > width ? 2 : 1; + const createPaddingTensor = (paddingAmountLocal) => { + const paddingTensorShape = imgTensor.shape.slice(); + paddingTensorShape[paddingAxis] = paddingAmountLocal; + return fill(paddingTensorShape, 0, "float32"); + }; + const paddingTensorAppend = createPaddingTensor(paddingAmount); + const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis]; + const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null; + const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend].filter((t) => !!t).map((t) => cast(t, "float32")); + return concat(tensorsToStack, paddingAxis); + }); +} + +// src/ops/shuffleArray.ts +function shuffleArray(inputArray) { + const array2 = inputArray.slice(); + for (let i = array2.length - 1; i > 0; i--) { + const j = Math.floor(Math.random() * (i + 1)); + const x = array2[i]; + array2[i] = array2[j]; + array2[j] = x; + } + return array2; +} + +// src/ops/index.ts +function sigmoid5(x) { + return 1 / (1 + Math.exp(-x)); +} +function inverseSigmoid(x) { + return Math.log(x / (1 - x)); +} + +// src/classes/Rect.ts +var Rect = class extends Box { + constructor(x, y, width, height, allowNegativeDimensions = false) { + super({ x, y, width, height }, allowNegativeDimensions); + } +}; + +// src/classes/FaceLandmarks.ts +var relX = 0.5; +var relY = 0.43; +var relScale = 0.45; +var FaceLandmarks = class { + constructor(relativeFaceLandmarkPositions, imgDims, shift = new Point(0, 0)) { + const { width, height } = imgDims; + this._imgDims = new Dimensions(width, height); + this._shift = shift; + this._positions = relativeFaceLandmarkPositions.map( + (pt) => pt.mul(new Point(width, height)).add(shift) + ); + } + get shift() { + return new Point(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( + (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)) + ); + } + forSize(width, height) { + return new this.constructor( + this.relativePositions, + { width, height } + ); + } + shiftBy(x, y) { + return new this.constructor( + this.relativePositions, + this._imgDims, + new Point(x, y) + ); + } + shiftByPoint(pt) { + return this.shiftBy(pt.x, pt.y); + } + /** + * Aligns the face landmarks after face detection from the relative positions of the faces + * bounding box, or it's current shift. This function should be used to align the face images + * after face detection has been performed, before they are passed to the face recognition net. + * This will make the computed face descriptor more accurate. + * + * @param detection (optional) The bounding box of the face or the face detection result. If + * no argument was passed the position of the face landmarks are assumed to be relative to + * it's current shift. + * @returns The bounding box of the aligned face. + */ + align(detection, options = {}) { + if (detection) { + const box = detection instanceof FaceDetection ? detection.box.floor() : new Box(detection); + return this.shiftBy(box.x, box.y).align(null, options); + } + const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options }; + if (useDlibAlignment) { + return this.alignDlib(); + } + return this.alignMinBbox(minBoxPadding); + } + alignDlib() { + const centers = this.getRefPointsForAlignment(); + const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers; + const distToMouth = (pt) => mouthCenter.sub(pt).magnitude(); + const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2; + const size = Math.floor(eyeToMouthDist / relScale); + const refPoint = getCenterPoint(centers); + const x = Math.floor(Math.max(0, refPoint.x - relX * size)); + const y = Math.floor(Math.max(0, refPoint.y - relY * size)); + return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y)); + } + alignMinBbox(padding) { + const box = minBbox(this.positions); + return box.pad(box.width * padding, box.height * padding); + } + getRefPointsForAlignment() { + throw new Error("getRefPointsForAlignment not implemented by base class"); + } +}; + +// src/classes/FaceLandmarks5.ts +var FaceLandmarks5 = class extends FaceLandmarks { + getRefPointsForAlignment() { + const pts = this.positions; + return [ + pts[0], + pts[1], + getCenterPoint([pts[3], pts[4]]) + ]; + } +}; + +// src/classes/FaceLandmarks68.ts +var FaceLandmarks68 = class extends FaceLandmarks { + 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(getCenterPoint); + } +}; + +// src/classes/FaceMatch.ts +var FaceMatch = class { + constructor(label, distance) { + this._label = label; + this._distance = distance; + } + get label() { + return this._label; + } + get distance() { + return this._distance; + } + toString(withDistance = true) { + return `${this.label}${withDistance ? ` (${round5(this.distance)})` : ""}`; + } +}; + +// src/classes/LabeledBox.ts +var LabeledBox = class extends Box { + static assertIsValidLabeledBox(box, callee) { + Box.assertIsValidBox(box, callee); + if (!isValidNumber(box.label)) { + throw new Error(`${callee} - expected property label (${box.label}) to be a number`); + } + } + constructor(box, label) { + super(box); + this._label = label; + } + get label() { + return this._label; + } +}; + +// src/classes/LabeledFaceDescriptors.ts +var LabeledFaceDescriptors = class _LabeledFaceDescriptors { + constructor(label, descriptors) { + if (!(typeof label === "string")) { + throw new Error("LabeledFaceDescriptors - constructor expected label to be a string"); + } + if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) { + throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array"); + } + this._label = label; + this._descriptors = descriptors; + } + get label() { + return this._label; + } + get descriptors() { + return this._descriptors; + } + toJSON() { + return { + label: this.label, + descriptors: this.descriptors.map((d) => Array.from(d)) + }; + } + static fromJSON(json20) { + const descriptors = json20.descriptors.map((d) => new Float32Array(d)); + return new _LabeledFaceDescriptors(json20.label, descriptors); + } +}; + +// src/classes/PredictedBox.ts +var PredictedBox = class extends LabeledBox { + static assertIsValidPredictedBox(box, callee) { + LabeledBox.assertIsValidLabeledBox(box, callee); + if (!isValidProbablitiy(box.score) || !isValidProbablitiy(box.classScore)) { + throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`); + } + } + constructor(box, label, score, classScore) { + super(box, label); + this._score = score; + this._classScore = classScore; + } + get score() { + return this._score; + } + get classScore() { + return this._classScore; + } +}; + +// src/factories/WithFaceDetection.ts +function isWithFaceDetection(obj) { + return obj.detection instanceof FaceDetection; +} +function extendWithFaceDetection(sourceObj, detection) { + const extension = { detection }; + return { ...sourceObj, ...extension }; +} + +// src/env/createBrowserEnv.ts +function createBrowserEnv() { + const fetch4 = window.fetch; + if (!fetch4) + throw new Error("fetch - missing fetch implementation for browser environment"); + const readFile = () => { + throw new Error("readFile - filesystem not available for browser environment"); + }; + return { + Canvas: HTMLCanvasElement, + CanvasRenderingContext2D, + Image: HTMLImageElement, + ImageData, + Video: HTMLVideoElement, + createCanvasElement: () => document.createElement("canvas"), + createImageElement: () => document.createElement("img"), + createVideoElement: () => document.createElement("video"), + fetch: fetch4, + readFile + }; +} + +// src/env/isNodejs.ts +function isNodejs() { + return typeof global === "object" && typeof process !== "undefined" && process.versions != null && process.versions.node != null; +} + +// src/env/createFileSystem.ts +function createFileSystem(fs) { + let requireFsError = ""; + if (!fs && isNodejs()) { + try { + fs = __require("fs"); + } catch (err) { + requireFsError = err.toString(); + } + } + const readFile = fs ? (filePath) => new Promise((resolve, reject) => { + fs.readFile(filePath, (err, buffer2) => err ? reject(err) : resolve(buffer2)); + }) : () => { + throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); + }; + return { readFile }; +} + +// src/env/createNodejsEnv.ts +function createNodejsEnv() { + const Canvas = global["Canvas"] || global.HTMLCanvasElement; + const Image = global.Image || global.HTMLImageElement; + const Video = global["Video"] || global.HTMLVideoElement; + const createCanvasElement = () => { + if (Canvas) + return new Canvas(); + throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment"); + }; + const createImageElement = () => { + if (Image) + return new Image(); + throw new Error("createImageElement - missing Image implementation for nodejs environment"); + }; + const createVideoElement2 = () => { + if (Video) + return new Video(); + throw new Error("createVideoElement - missing Video implementation for nodejs environment"); + }; + const fetch4 = global.fetch; + const fileSystem = createFileSystem(); + return { + Canvas: Canvas || class { + }, + CanvasRenderingContext2D: global.CanvasRenderingContext2D || class { + }, + Image: Image || class { + }, + ImageData: global.ImageData || class { + }, + Video: global.HTMLVideoElement || class { + }, + createCanvasElement, + createImageElement, + createVideoElement: createVideoElement2, + fetch: fetch4, + ...fileSystem + }; +} + +// src/env/isBrowser.ts +function isBrowser2() { + return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined"; +} + +// src/env/index.ts +var environment; +function getEnv() { + if (!environment) { + throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()"); + } + return environment; +} +function setEnv(env3) { + environment = env3; +} +function initialize() { + if (isBrowser2()) + return setEnv(createBrowserEnv()); + if (isNodejs()) + return setEnv(createNodejsEnv()); + return null; +} +function monkeyPatch(env3) { + if (!environment) { + initialize(); + } + if (!environment) { + throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()"); + } + const { Canvas = environment.Canvas, Image = environment.Image } = env3; + environment.Canvas = Canvas; + environment.Image = Image; + environment.createCanvasElement = env3.createCanvasElement || (() => new Canvas()); + environment.createImageElement = env3.createImageElement || (() => new Image()); + environment.ImageData = env3.ImageData || environment.ImageData; + environment.Video = env3.Video || environment.Video; + environment.fetch = env3.fetch || environment.fetch; + environment.readFile = env3.readFile || environment.readFile; +} +var env2 = { + getEnv, + setEnv, + initialize, + createBrowserEnv, + createFileSystem, + createNodejsEnv, + monkeyPatch, + isBrowser: isBrowser2, + isNodejs +}; +initialize(); + +// src/dom/resolveInput.ts +function resolveInput(arg) { + if (!env2.isNodejs() && typeof arg === "string") { + return document.getElementById(arg); + } + return arg; +} + +// src/dom/getContext2dOrThrow.ts +function getContext2dOrThrow(canvasArg) { + const { Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2 } = env2.getEnv(); + if (canvasArg instanceof CanvasRenderingContext2D2) + return canvasArg; + const canvas = resolveInput(canvasArg); + if (!(canvas instanceof Canvas)) + throw new Error("resolveContext2d - expected canvas to be of instance of Canvas"); + const ctx = canvas.getContext("2d", { willReadFrequently: true }); + if (!ctx) + throw new Error("resolveContext2d - canvas 2d context is null"); + return ctx; +} + +// src/draw/DrawTextField.ts +var AnchorPosition = /* @__PURE__ */ ((AnchorPosition2) => { + AnchorPosition2["TOP_LEFT"] = "TOP_LEFT"; + AnchorPosition2["TOP_RIGHT"] = "TOP_RIGHT"; + AnchorPosition2["BOTTOM_LEFT"] = "BOTTOM_LEFT"; + AnchorPosition2["BOTTOM_RIGHT"] = "BOTTOM_RIGHT"; + return AnchorPosition2; +})(AnchorPosition || {}); +var DrawTextFieldOptions = class { + constructor(options = {}) { + const { + anchorPosition, + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = options; + this.anchorPosition = anchorPosition || "TOP_LEFT" /* TOP_LEFT */; + this.backgroundColor = backgroundColor || "rgba(0, 0, 0, 0.5)"; + this.fontColor = fontColor || "rgba(255, 255, 255, 1)"; + this.fontSize = fontSize || 14; + this.fontStyle = fontStyle || "Georgia"; + this.padding = padding || 4; + } +}; +var DrawTextField = class _DrawTextField { + constructor(text, anchor, options = {}) { + this.text = typeof text === "string" ? [text] : text instanceof _DrawTextField ? text.text : text; + this.anchor = anchor; + this.options = new DrawTextFieldOptions(options); + } + measureWidth(ctx) { + const { padding } = this.options; + return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + 2 * padding; + } + measureHeight() { + const { fontSize, padding } = this.options; + return this.text.length * fontSize + 2 * padding; + } + getUpperLeft(ctx, canvasDims) { + const { anchorPosition } = this.options; + const isShiftLeft = anchorPosition === "BOTTOM_RIGHT" /* BOTTOM_RIGHT */ || anchorPosition === "TOP_RIGHT" /* TOP_RIGHT */; + const isShiftTop = anchorPosition === "BOTTOM_LEFT" /* BOTTOM_LEFT */ || anchorPosition === "BOTTOM_RIGHT" /* BOTTOM_RIGHT */; + const textFieldWidth = this.measureWidth(ctx); + const textFieldHeight = this.measureHeight(); + const x = isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x; + const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y; + if (canvasDims) { + const { width, height } = canvasDims; + const newX = Math.max(Math.min(x, width - textFieldWidth), 0); + const newY = Math.max(Math.min(y, height - textFieldHeight), 0); + return { x: newX, y: newY }; + } + return { x, y }; + } + draw(canvasArg) { + const canvas = resolveInput(canvasArg); + const ctx = getContext2dOrThrow(canvas); + const { + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = this.options; + ctx.font = `${fontSize}px ${fontStyle}`; + const maxTextWidth = this.measureWidth(ctx); + const textHeight = this.measureHeight(); + ctx.fillStyle = backgroundColor; + const upperLeft = this.getUpperLeft(ctx, canvas); + ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight); + ctx.fillStyle = fontColor; + this.text.forEach((textLine, i) => { + const x = padding + upperLeft.x; + const y = padding + upperLeft.y + (i + 1) * fontSize; + ctx.fillText(textLine, x, y); + }); + } +}; + +// src/draw/DrawBox.ts +var DrawBoxOptions = class { + constructor(options = {}) { + const { + boxColor, + lineWidth, + label, + drawLabelOptions + } = options; + this.boxColor = boxColor || "rgba(0, 0, 255, 1)"; + this.lineWidth = lineWidth || 2; + this.label = label; + const defaultDrawLabelOptions = { + anchorPosition: "BOTTOM_LEFT" /* BOTTOM_LEFT */, + backgroundColor: this.boxColor + }; + this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions }); + } +}; +var DrawBox = class { + constructor(box, options = {}) { + this.box = new Box(box); + this.options = new DrawBoxOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { boxColor, lineWidth } = this.options; + const { + x, + y, + width, + height + } = this.box; + ctx.strokeStyle = boxColor; + ctx.lineWidth = lineWidth; + ctx.strokeRect(x, y, width, height); + const { label } = this.options; + if (label) { + new DrawTextField([label], { x: x - lineWidth / 2, y }, this.options.drawLabelOptions).draw(canvasArg); + } + } +}; + +// src/draw/drawDetections.ts +function drawDetections(canvasArg, detections) { + const detectionsArray = Array.isArray(detections) ? detections : [detections]; + detectionsArray.forEach((det) => { + const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0; + const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det); + const label = score ? `${round5(score)}` : void 0; + new DrawBox(box, { label }).draw(canvasArg); + }); +} + +// src/dom/isMediaLoaded.ts +function isMediaLoaded(media) { + const { Image, Video } = env2.getEnv(); + return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3; +} + +// src/dom/awaitMediaLoaded.ts +function awaitMediaLoaded(media) { + return new Promise((resolve, reject) => { + if (media instanceof env2.getEnv().Canvas || isMediaLoaded(media)) + resolve(null); + function onError(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + reject(e); + } + function onLoad(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + resolve(e); + } + media.addEventListener("load", onLoad); + media.addEventListener("error", onError); + }); +} + +// src/dom/bufferToImage.ts +function bufferToImage(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToImage - expected buf to be of type: Blob")); + const reader = new FileReader(); + reader.onload = () => { + if (typeof reader.result !== "string") + reject(new Error("bufferToImage - expected reader.result to be a string, in onload")); + const img = env2.getEnv().createImageElement(); + img.onload = () => resolve(img); + img.onerror = reject; + img.src = reader.result; + }; + reader.onerror = reject; + reader.readAsDataURL(buf); + }); +} + +// src/dom/getMediaDimensions.ts +function getMediaDimensions(input2) { + const { Image, Video } = env2.getEnv(); + if (input2 instanceof Image) { + return new Dimensions(input2.naturalWidth, input2.naturalHeight); + } + if (input2 instanceof Video) { + return new Dimensions(input2.videoWidth, input2.videoHeight); + } + return new Dimensions(input2.width, input2.height); +} + +// src/dom/createCanvas.ts +function createCanvas2({ width, height }) { + const { createCanvasElement } = env2.getEnv(); + const canvas = createCanvasElement(); + canvas.width = width; + canvas.height = height; + return canvas; +} +function createCanvasFromMedia(media, dims) { + const { ImageData: ImageData2 } = env2.getEnv(); + if (!(media instanceof ImageData2) && !isMediaLoaded(media)) { + throw new Error("createCanvasFromMedia - media has not finished loading yet"); + } + const { width, height } = dims || getMediaDimensions(media); + const canvas = createCanvas2({ width, height }); + if (media instanceof ImageData2) { + getContext2dOrThrow(canvas).putImageData(media, 0, 0); + } else { + getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height); + } + return canvas; +} + +// src/dom/imageTensorToCanvas.ts +async function imageTensorToCanvas(imgTensor, canvas) { + const targetCanvas = canvas || env2.getEnv().createCanvasElement(); + const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0); + const imgTensor3D = tidy(() => imgTensor.as3D(height, width, numChannels).toInt()); + await browser_exports.toPixels(imgTensor3D, targetCanvas); + imgTensor3D.dispose(); + return targetCanvas; +} + +// src/dom/isMediaElement.ts +function isMediaElement(input2) { + const { Image, Canvas, Video } = env2.getEnv(); + return input2 instanceof Image || input2 instanceof Canvas || input2 instanceof Video; +} + +// src/dom/imageToSquare.ts +function imageToSquare(input2, inputSize, centerImage = false) { + const { Image, Canvas } = env2.getEnv(); + if (!(input2 instanceof Image || input2 instanceof Canvas)) { + throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement"); + } + if (inputSize <= 0) + return createCanvas2({ width: 1, height: 1 }); + const dims = getMediaDimensions(input2); + const scale3 = inputSize / Math.max(dims.height, dims.width); + const width = scale3 * dims.width; + const height = scale3 * dims.height; + const targetCanvas = createCanvas2({ width: inputSize, height: inputSize }); + const inputCanvas = input2 instanceof Canvas ? input2 : createCanvasFromMedia(input2); + const offset = Math.abs(width - height) / 2; + const dx = centerImage && width < height ? offset : 0; + const dy = centerImage && height < width ? offset : 0; + if (inputCanvas.width > 0 && inputCanvas.height > 0) + getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height); + return targetCanvas; +} + +// src/dom/NetInput.ts +var NetInput = class { + constructor(inputs, treatAsBatchInput = false) { + this._imageTensors = []; + this._canvases = []; + this._treatAsBatchInput = false; + this._inputDimensions = []; + this._inputSize = 0; + if (!Array.isArray(inputs)) { + throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`); + } + this._treatAsBatchInput = treatAsBatchInput; + this._batchSize = inputs.length; + inputs.forEach((input2, idx) => { + if (isTensor3D(input2)) { + this._imageTensors[idx] = input2; + this._inputDimensions[idx] = input2.shape; + return; + } + if (isTensor4D(input2)) { + const batchSize = input2.shape[0]; + if (batchSize !== 1) { + throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + this._imageTensors[idx] = input2; + this._inputDimensions[idx] = input2.shape.slice(1); + return; + } + const canvas = input2 instanceof env2.getEnv().Canvas ? input2 : createCanvasFromMedia(input2); + this._canvases[idx] = canvas; + this._inputDimensions[idx] = [canvas.height, canvas.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 range6(this.batchSize, 0, 1).map( + (_, batchIdx) => this.getReshapedInputDimensions(batchIdx) + ); + } + getInput(batchIdx) { + return this.canvases[batchIdx] || this.imageTensors[batchIdx]; + } + getInputDimensions(batchIdx) { + return this._inputDimensions[batchIdx]; + } + getInputHeight(batchIdx) { + return this._inputDimensions[batchIdx][0]; + } + getInputWidth(batchIdx) { + return this._inputDimensions[batchIdx][1]; + } + getReshapedInputDimensions(batchIdx) { + if (typeof this.inputSize !== "number") { + throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet"); + } + const width = this.getInputWidth(batchIdx); + const height = this.getInputHeight(batchIdx); + return computeReshapedDimensions({ width, height }, this.inputSize); + } + /** + * Create a batch tensor from all input canvases and tensors + * with size [batchSize, inputSize, inputSize, 3]. + * + * @param inputSize Height and width of the tensor. + * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on + * both sides of the minor dimension oof the image. + * @returns The batch tensor. + */ + toBatchTensor(inputSize, isCenterInputs = true) { + this._inputSize = inputSize; + return tidy(() => { + const inputTensors = range6(this.batchSize, 0, 1).map((batchIdx) => { + const input2 = this.getInput(batchIdx); + if (input2 instanceof Tensor) { + let imgTensor = isTensor4D(input2) ? input2 : expandDims(input2); + imgTensor = padToSquare(imgTensor, isCenterInputs); + if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) { + imgTensor = image.resizeBilinear(imgTensor, [inputSize, inputSize], false, false); + } + return imgTensor.as3D(inputSize, inputSize, 3); + } + if (input2 instanceof env2.getEnv().Canvas) { + return browser_exports.fromPixels(imageToSquare(input2, inputSize, isCenterInputs)); + } + throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input2}`); + }); + const batchTensor = stack(inputTensors.map((t) => cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3); + return batchTensor; + }); + } +}; + +// src/dom/toNetInput.ts +async function toNetInput(inputs) { + if (inputs instanceof NetInput) + return inputs; + const inputArgArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArgArray.length) + throw new Error("toNetInput - empty array passed as input"); + const getIdxHint = (idx) => Array.isArray(inputs) ? ` at input index ${idx}:` : ""; + const inputArray = inputArgArray.map(resolveInput); + inputArray.forEach((input2, i) => { + if (!isMediaElement(input2) && !isTensor3D(input2) && !isTensor4D(input2)) { + if (typeof inputArgArray[i] === "string") + throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`); + throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`); + } + if (isTensor4D(input2)) { + const batchSize = input2.shape[0]; + if (batchSize !== 1) + throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + }); + await Promise.all(inputArray.map((input2) => isMediaElement(input2) && awaitMediaLoaded(input2))); + return new NetInput(inputArray, Array.isArray(inputs)); +} + +// src/dom/extractFaces.ts +async function extractFaces(input2, detections) { + const { Canvas } = env2.getEnv(); + let canvas = input2; + if (!(input2 instanceof Canvas)) { + const netInput = await toNetInput(input2); + if (netInput.batchSize > 1) + throw new Error("extractFaces - batchSize > 1 not supported"); + const tensorOrCanvas = netInput.getInput(0); + canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas); + } + const ctx = getContext2dOrThrow(canvas); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det).map((box) => box.clipAtImageBorders(canvas.width, canvas.height)); + return boxes.map(({ x, y, width, height }) => { + const faceImg = createCanvas2({ width, height }); + if (width > 0 && height > 0) + getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0); + return faceImg; + }); +} + +// src/dom/extractFaceTensors.ts +async function extractFaceTensors(imageTensor, detections) { + if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) { + throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D"); + } + if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) { + throw new Error("extractFaceTensors - batchSize > 1 not supported"); + } + return tidy(() => { + const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight)); + const faceTensors = boxes.filter((box) => box.width > 0 && box.height > 0).map(({ x, y, width, height }) => slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])); + return faceTensors; + }); +} + +// src/dom/fetchOrThrow.ts +async function fetchOrThrow(url, init2) { + const { fetch: fetch4 } = env2.getEnv(); + const res = await fetch4(url, init2); + if (!(res.status < 400)) { + throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`); + } + return res; +} + +// src/dom/fetchImage.ts +async function fetchImage(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("image/")) { + throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToImage(blob); +} + +// src/dom/fetchJson.ts +async function fetchJson(uri) { + return (await fetchOrThrow(uri)).json(); +} + +// src/dom/fetchNetWeights.ts +async function fetchNetWeights(uri) { + return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer()); +} + +// src/dom/bufferToVideo.ts +function bufferToVideo(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToVideo - expected buf to be of type: Blob")); + const video = env2.getEnv().createVideoElement(); + video.oncanplay = () => resolve(video); + video.onerror = reject; + video.playsInline = true; + video.muted = true; + video.src = URL.createObjectURL(buf); + video.play(); + }); +} + +// src/dom/fetchVideo.ts +async function fetchVideo(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("video/")) { + throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToVideo(blob); +} + +// src/common/getModelUris.ts +function getModelUris(uri, defaultModelName) { + const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`; + if (!uri) { + return { + modelBaseUri: "", + manifestUri: defaultManifestFilename + }; + } + if (uri === "/") { + return { + modelBaseUri: "/", + manifestUri: `/${defaultManifestFilename}` + }; + } + const protocol = uri.startsWith("http://") ? "http://" : uri.startsWith("https://") ? "https://" : ""; + uri = uri.replace(protocol, ""); + const parts = uri.split("/").filter((s) => s); + const manifestFile = uri.endsWith(".json") ? parts[parts.length - 1] : defaultManifestFilename; + let modelBaseUri = protocol + (uri.endsWith(".json") ? parts.slice(0, parts.length - 1) : parts).join("/"); + modelBaseUri = uri.startsWith("/") ? `/${modelBaseUri}` : modelBaseUri; + return { + modelBaseUri, + manifestUri: modelBaseUri === "/" ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}` + }; +} + +// src/dom/loadWeightMap.ts +async function loadWeightMap(uri, defaultModelName) { + const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName); + const manifest = await fetchJson(manifestUri); + return io_exports.loadWeights(manifest, modelBaseUri); +} + +// src/dom/matchDimensions.ts +function matchDimensions(input2, reference, useMediaDimensions = false) { + const { width, height } = useMediaDimensions ? getMediaDimensions(reference) : reference; + input2.width = width; + input2.height = height; + return { width, height }; +} + +// src/NeuralNetwork.ts +var NeuralNetwork = class { + constructor(name) { + this._params = void 0; + this._paramMappings = []; + this._name = name; + } + get params() { + return this._params; + } + get paramMappings() { + return this._paramMappings; + } + get isLoaded() { + return !!this.params; + } + getParamFromPath(paramPath) { + const { obj, objProp } = this.traversePropertyPath(paramPath); + return obj[objProp]; + } + reassignParamFromPath(paramPath, tensor2) { + const { obj, objProp } = this.traversePropertyPath(paramPath); + obj[objProp].dispose(); + obj[objProp] = tensor2; + } + getParamList() { + return this._paramMappings.map(({ paramPath }) => ({ + path: paramPath, + tensor: this.getParamFromPath(paramPath) + })); + } + getTrainableParams() { + return this.getParamList().filter((param) => param.tensor instanceof Variable); + } + getFrozenParams() { + return this.getParamList().filter((param) => !(param.tensor instanceof Variable)); + } + variable() { + this.getFrozenParams().forEach(({ path, tensor: tensor2 }) => { + this.reassignParamFromPath(path, tensor2.variable()); + }); + } + freeze() { + this.getTrainableParams().forEach(({ path, tensor: variable2 }) => { + const tensor2 = tensor(variable2.dataSync()); + variable2.dispose(); + this.reassignParamFromPath(path, tensor2); + }); + } + dispose(throwOnRedispose = true) { + this.getParamList().forEach((param) => { + if (throwOnRedispose && param.tensor.isDisposed) { + throw new Error(`param tensor has already been disposed for path ${param.path}`); + } + param.tensor.dispose(); + }); + this._params = void 0; + } + serializeParams() { + return new Float32Array( + this.getParamList().map(({ tensor: tensor2 }) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)) + ); + } + async load(weightsOrUrl) { + if (weightsOrUrl instanceof Float32Array) { + this.extractWeights(weightsOrUrl); + return; + } + await this.loadFromUri(weightsOrUrl); + } + async loadFromUri(uri) { + if (uri && typeof uri !== "string") { + throw new Error(`${this._name}.loadFromUri - expected model uri`); + } + const weightMap = await loadWeightMap(uri, this.getDefaultModelName()); + this.loadFromWeightMap(weightMap); + } + async loadFromDisk(filePath) { + if (filePath && typeof filePath !== "string") { + throw new Error(`${this._name}.loadFromDisk - expected model file path`); + } + const { readFile } = env2.getEnv(); + const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName()); + const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => typeof buf === "string" ? Buffer.from(buf) : buf.buffer))); + const loadWeights2 = io_exports.weightsLoaderFactory(fetchWeightsFromDisk); + const manifest = JSON.parse((await readFile(manifestUri)).toString()); + const weightMap = await loadWeights2(manifest, modelBaseUri); + this.loadFromWeightMap(weightMap); + } + loadFromWeightMap(weightMap) { + const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap); + this._paramMappings = paramMappings; + this._params = params; + } + extractWeights(weights) { + const { paramMappings, params } = this.extractParams(weights); + this._paramMappings = paramMappings; + this._params = params; + } + traversePropertyPath(paramPath) { + if (!this.params) { + throw new Error("traversePropertyPath - model has no loaded params"); + } + const result = paramPath.split("/").reduce((res, objProp2) => { + if (!res.nextObj.hasOwnProperty(objProp2)) { + throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`); + } + return { obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2] }; + }, { nextObj: this.params }); + const { obj, objProp } = result; + if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) { + throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`); + } + return { obj, objProp }; + } +}; + +// src/common/depthwiseSeparableConv.ts +function depthwiseSeparableConv(x, params, stride) { + return tidy(() => { + let out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same"); + out = add2(out, params.bias); + return out; + }); +} + +// src/faceFeatureExtractor/denseBlock.ts +function denseBlock3(x, denseBlockParams, isFirstLayer = false) { + return tidy(() => { + const out1 = relu( + isFirstLayer ? add2( + conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), + denseBlockParams.conv0.bias + ) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]) + ); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = relu(add2(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + return relu(add2(out1, add2(out2, out3))); + }); +} +function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) { + return tidy(() => { + const out1 = relu( + isFirstLayer ? add2( + conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), + denseBlockParams.conv0.bias + ) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]) + ); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = relu(add2(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + const in4 = relu(add2(out1, add2(out2, out3))); + const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]); + return relu(add2(out1, add2(out2, add2(out3, out4)))); + }); +} + +// src/common/convLayer.ts +function convLayer(x, params, padding = "same", withRelu = false) { + return tidy(() => { + const out = add2( + conv2d(x, params.filters, [1, 1], padding), + params.bias + ); + return withRelu ? relu(out) : out; + }); +} + +// src/common/disposeUnusedWeightTensors.ts +function disposeUnusedWeightTensors(weightMap, paramMappings) { + Object.keys(weightMap).forEach((path) => { + if (!paramMappings.some((pm) => pm.originalPath === path)) { + weightMap[path].dispose(); + } + }); +} + +// src/common/extractConvParamsFactory.ts +function extractConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, filterSize, mappedPrefix) => { + const filters = tensor4d( + extractWeights(channelsIn * channelsOut * filterSize * filterSize), + [filterSize, filterSize, channelsIn, channelsOut] + ); + const bias = tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { filters, bias }; + }; +} + +// src/common/extractFCParamsFactory.ts +function extractFCParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]); + const fc_bias = tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/weights` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { + weights: fc_weights, + bias: fc_bias + }; + }; +} + +// src/common/types.ts +var SeparableConvParams = class { + // eslint-disable-next-line no-useless-constructor + constructor(depthwise_filter, pointwise_filter, bias) { + this.depthwise_filter = depthwise_filter; + this.pointwise_filter = pointwise_filter; + this.bias = bias; + } +}; + +// src/common/extractSeparableConvParamsFactory.ts +function extractSeparableConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]); + const pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]); + const bias = tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/depthwise_filter` }, + { paramPath: `${mappedPrefix}/pointwise_filter` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return new SeparableConvParams( + depthwise_filter, + pointwise_filter, + bias + ); + }; +} +function loadSeparableConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4); + const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return new SeparableConvParams( + depthwise_filter, + pointwise_filter, + bias + ); + }; +} + +// src/common/extractWeightEntryFactory.ts +function extractWeightEntryFactory(weightMap, paramMappings) { + return (originalPath, paramRank, mappedPath) => { + const tensor2 = weightMap[originalPath]; + if (!isTensor(tensor2, paramRank)) { + throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`); + } + paramMappings.push( + { originalPath, paramPath: mappedPath || originalPath } + ); + return tensor2; + }; +} + +// src/common/extractWeightsFactory.ts +function extractWeightsFactory(weights) { + let remainingWeights = weights; + function extractWeights(numWeights) { + const ret = remainingWeights.slice(0, numWeights); + remainingWeights = remainingWeights.slice(numWeights); + return ret; + } + function getRemainingWeights() { + return remainingWeights; + } + return { + extractWeights, + getRemainingWeights + }; +} + +// src/faceFeatureExtractor/extractorsFactory.ts +function extractorsFactory(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`) : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`); + const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`); + const conv22 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`); + return { conv0, conv1, conv2: conv22 }; + } + function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const { conv0, conv1, conv2: conv22 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer); + const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParams.ts +function extractParams(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock4Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock4Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock4Params(32, 64, "dense1"); + const dense2 = extractDenseBlock4Params(64, 128, "dense2"); + const dense3 = extractDenseBlock4Params(128, 256, "dense3"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { + dense0, + dense1, + dense2, + dense3 + } + }; +} + +// src/common/loadConvParamsFactory.ts +function loadConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { filters, bias }; + }; +} + +// src/faceFeatureExtractor/loadParamsFactory.ts +function loadParamsFactory(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractDenseBlock3Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + return { conv0, conv1, conv2: conv22 }; + } + function extractDenseBlock4Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + const conv3 = extractSeparableConvParams(`${prefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap(weightMap) { + const paramMappings = []; + const { + extractDenseBlock4Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock4Params("dense0", true), + dense1: extractDenseBlock4Params("dense1"), + dense2: extractDenseBlock4Params("dense2"), + dense3: extractDenseBlock4Params("dense3") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceFeatureExtractor/FaceFeatureExtractor.ts +var FaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("FaceFeatureExtractor"); + } + forwardInput(input2) { + const { params } = this; + if (!params) { + throw new Error("FaceFeatureExtractor - load model before inference"); + } + return tidy(() => { + const batchTensor = cast(input2.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock4(normalized, params.dense0, true); + out = denseBlock4(out, params.dense1); + out = denseBlock4(out, params.dense2); + out = denseBlock4(out, params.dense3); + out = avgPool(out, [7, 7], [2, 2], "valid"); + return out; + }); + } + async forward(input2) { + return this.forwardInput(await toNetInput(input2)); + } + getDefaultModelName() { + return "face_feature_extractor_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap(weightMap); + } + extractParams(weights) { + return extractParams(weights); + } +}; + +// src/common/fullyConnectedLayer.ts +function fullyConnectedLayer(x, params) { + return tidy(() => add2( + matMul(x, params.weights), + params.bias + )); +} + +// src/faceProcessor/extractParams.ts +function extractParams2(weights, channelsIn, channelsOut) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const fc = extractFCParams(channelsIn, channelsOut, "fc"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { fc } + }; +} + +// src/faceProcessor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap2(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { weights, bias }; + } + const params = { + fc: extractFcParams("fc") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceProcessor/util.ts +function seperateWeightMaps(weightMap) { + const featureExtractorMap = {}; + const classifierMap = {}; + Object.keys(weightMap).forEach((key) => { + const map = key.startsWith("fc") ? classifierMap : featureExtractorMap; + map[key] = weightMap[key]; + }); + return { featureExtractorMap, classifierMap }; +} + +// src/faceProcessor/FaceProcessor.ts +var FaceProcessor = class extends NeuralNetwork { + constructor(_name, faceFeatureExtractor) { + super(_name); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input2) { + const { params } = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tidy(() => { + const bottleneckFeatures = input2 instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input2) : input2; + return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc); + }); + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const { params, paramMappings } = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams2(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut()); + } + extractParamsFromWeightMap(weightMap) { + const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap2(classifierMap); + } + extractParams(weights) { + const cIn = this.getClassifierChannelsIn(); + const cOut = this.getClassifierChannelsOut(); + const classifierWeightSize = cOut * cIn + cOut; + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceExpressionNet/FaceExpressions.ts +var FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"]; +var FaceExpressions = class { + constructor(probabilities) { + this.neutral = 0; + this.happy = 0; + this.sad = 0; + this.angry = 0; + this.fearful = 0; + this.disgusted = 0; + this.surprised = 0; + if (probabilities.length !== 7) { + throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`); + } + FACE_EXPRESSION_LABELS.forEach((expression, idx) => { + this[expression] = probabilities[idx]; + }); + } + asSortedArray() { + return FACE_EXPRESSION_LABELS.map((expression) => ({ expression, probability: this[expression] })).sort((e0, e1) => e1.probability - e0.probability); + } +}; + +// src/faceExpressionNet/FaceExpressionNet.ts +var FaceExpressionNet = class extends FaceProcessor { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceExpressionNet", faceFeatureExtractor); + } + forwardInput(input2) { + return tidy(() => softmax(this.runNet(input2))); + } + async forward(input2) { + return this.forwardInput(await toNetInput(input2)); + } + async predictExpressions(input2) { + const netInput = await toNetInput(input2); + const out = await this.forwardInput(netInput); + const probabilitesByBatch = await Promise.all(unstack(out).map(async (t) => { + const data = t.dataSync(); + t.dispose(); + return data; + })); + out.dispose(); + const predictionsByBatch = probabilitesByBatch.map((probabilites) => new FaceExpressions(probabilites)); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "face_expression_model"; + } + getClassifierChannelsIn() { + return 256; + } + getClassifierChannelsOut() { + return 7; + } +}; + +// src/factories/WithFaceExpressions.ts +function isWithFaceExpressions(obj) { + return obj.expressions instanceof FaceExpressions; +} +function extendWithFaceExpressions(sourceObj, expressions) { + const extension = { expressions }; + return { ...sourceObj, ...extension }; +} + +// src/draw/drawFaceExpressions.ts +function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, textFieldAnchor) { + const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]; + faceExpressionsArray.forEach((e) => { + const expr = e instanceof FaceExpressions ? e : isWithFaceExpressions(e) ? e.expressions : void 0; + if (!expr) { + throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof"); + } + const sorted = expr.asSortedArray(); + const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence); + const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0); + const drawTextField = new DrawTextField( + resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round5(exprLocal.probability)})`), + anchor + ); + drawTextField.draw(canvasArg); + }); +} + +// src/factories/WithFaceLandmarks.ts +function isWithFaceLandmarks(obj) { + return isWithFaceDetection(obj) && obj["landmarks"] instanceof FaceLandmarks && obj["unshiftedLandmarks"] instanceof FaceLandmarks && obj["alignedRect"] instanceof FaceDetection; +} +function calculateFaceAngle(mesh) { + const degrees = (radians) => radians * 180 / Math.PI; + const calcLengthBetweenTwoPoints = (a, b) => Math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2); + const angle = { + roll: void 0, + pitch: void 0, + yaw: void 0 + }; + const calcYaw = (leftPoint, midPoint, rightPoint) => { + const leftToMidpoint = Math.floor(leftPoint.x - midPoint.x); + const rightToMidpoint = Math.floor(midPoint.x - rightPoint.x); + return leftToMidpoint - rightToMidpoint; + }; + const calcRoll = (lever, pivot) => { + const hypotenuse = Math.hypot(pivot.x - lever.x, pivot.y - lever.y); + const opposite = pivot.y - lever.y; + const angleInRadians = Math.asin(opposite / hypotenuse); + const angleInDegrees = degrees(angleInRadians); + const normalizeAngle = Math.floor(90 - angleInDegrees); + const tiltDirection = pivot.x - lever.x < 0 ? -1 : 1; + const result = normalizeAngle * tiltDirection; + return result; + }; + const calcPitch = (leftPoint, midPoint, rightPoint) => { + const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint); + const baseCoords = new Point((leftPoint.x + rightPoint.x) / 2, (leftPoint.y + rightPoint.y) / 2); + const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords); + const angleInRadians = Math.atan(midToBaseLength / base); + const angleInDegrees = Math.floor(degrees(angleInRadians)); + const direction = baseCoords.y - midPoint.y < 0 ? -1 : 1; + const result = angleInDegrees * direction; + return result; + }; + if (!mesh || !mesh.positions || mesh.positions.length !== 68) + return angle; + const pt = mesh.positions; + angle.roll = calcRoll(pt[27], pt[66]); + angle.pitch = calcPitch(pt[14], pt[30], pt[2]); + angle.yaw = calcYaw(pt[14], pt[33], pt[2]); + return angle; +} +function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) { + const { box: shift } = sourceObj.detection; + const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y); + const rect = landmarks.align(); + const { imageDims } = sourceObj.detection; + const alignedRect = new FaceDetection( + sourceObj.detection.score, + rect.rescale(imageDims.reverse()), + imageDims + ); + const angle = calculateFaceAngle(unshiftedLandmarks); + const extension = { landmarks, unshiftedLandmarks, alignedRect, angle }; + return { ...sourceObj, ...extension }; +} + +// src/draw/DrawFaceLandmarks.ts +var DrawFaceLandmarksOptions = class { + constructor(options = {}) { + const { + drawLines = true, + drawPoints = true, + lineWidth, + lineColor, + pointSize, + pointColor + } = options; + this.drawLines = drawLines; + this.drawPoints = drawPoints; + this.lineWidth = lineWidth || 1; + this.pointSize = pointSize || 2; + this.lineColor = lineColor || "rgba(0, 255, 255, 1)"; + this.pointColor = pointColor || "rgba(255, 0, 255, 1)"; + } +}; +var DrawFaceLandmarks = class { + constructor(faceLandmarks, options = {}) { + this.faceLandmarks = faceLandmarks; + this.options = new DrawFaceLandmarksOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { + drawLines, + drawPoints, + lineWidth, + lineColor, + pointSize, + pointColor + } = this.options; + if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) { + ctx.strokeStyle = lineColor; + ctx.lineWidth = lineWidth; + drawContour(ctx, this.faceLandmarks.getJawOutline()); + drawContour(ctx, this.faceLandmarks.getLeftEyeBrow()); + drawContour(ctx, this.faceLandmarks.getRightEyeBrow()); + drawContour(ctx, this.faceLandmarks.getNose()); + drawContour(ctx, this.faceLandmarks.getLeftEye(), true); + drawContour(ctx, this.faceLandmarks.getRightEye(), true); + drawContour(ctx, this.faceLandmarks.getMouth(), true); + } + if (drawPoints) { + ctx.strokeStyle = pointColor; + ctx.fillStyle = pointColor; + const drawPoint = (pt) => { + ctx.beginPath(); + ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI); + ctx.fill(); + }; + this.faceLandmarks.positions.forEach(drawPoint); + } + } +}; +function drawFaceLandmarks(canvasArg, faceLandmarks) { + const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]; + faceLandmarksArray.forEach((f) => { + const landmarks = f instanceof FaceLandmarks ? f : isWithFaceLandmarks(f) ? f.landmarks : void 0; + if (!landmarks) { + throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof"); + } + new DrawFaceLandmarks(landmarks).draw(canvasArg); + }); +} + +// package.json +var version7 = "1.7.12"; + +// src/xception/extractParams.ts +function extractorsFactory2(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`); + return { separable_conv0, separable_conv1, expansion_conv }; + } + function extractMainBlockParams(channels, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`); + return { separable_conv0, separable_conv1, separable_conv2 }; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParams3(weights, numMainBlocks) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = extractorsFactory2(extractWeights, paramMappings); + const entry_flow_conv_in = extractConvParams(3, 32, 3, "entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, "entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, "entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range6(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams(256, 512, "exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { entry_flow, middle_flow, exit_flow } + }; +} + +// src/xception/extractParamsFromWeightMap.ts +function loadParamsFactory2(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractReductionBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`); + return { separable_conv0, separable_conv1, expansion_conv }; + } + function extractMainBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`); + return { separable_conv0, separable_conv1, separable_conv2 }; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParamsFromWeightMap3(weightMap, numMainBlocks) { + const paramMappings = []; + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = loadParamsFactory2(weightMap, paramMappings); + const entry_flow_conv_in = extractConvParams("entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams("entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams("entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range6(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams("exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }; +} + +// src/xception/TinyXception.ts +function conv(x, params, stride) { + return add2(conv2d(x, params.filters, stride, "same"), params.bias); +} +function reductionBlock(x, params, isActivateInput = true) { + let out = isActivateInput ? relu(x) : x; + out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]); + out = maxPool(out, [3, 3], [2, 2], "same"); + out = add2(out, conv(x, params.expansion_conv, [2, 2])); + return out; +} +function mainBlock(x, params) { + let out = depthwiseSeparableConv(relu(x), params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]); + out = depthwiseSeparableConv(relu(out), params.separable_conv2, [1, 1]); + out = add2(out, x); + return out; +} +var TinyXception = class extends NeuralNetwork { + constructor(numMainBlocks) { + super("TinyXception"); + this._numMainBlocks = numMainBlocks; + } + forwardInput(input2) { + const { params } = this; + if (!params) { + throw new Error("TinyXception - load model before inference"); + } + return tidy(() => { + const batchTensor = cast(input2.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = relu(conv(normalized, params.entry_flow.conv_in, [2, 2])); + out = reductionBlock(out, params.entry_flow.reduction_block_0, false); + out = reductionBlock(out, params.entry_flow.reduction_block_1); + range6(this._numMainBlocks, 0, 1).forEach((idx) => { + out = mainBlock(out, params.middle_flow[`main_block_${idx}`]); + }); + out = reductionBlock(out, params.exit_flow.reduction_block); + out = relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1])); + return out; + }); + } + async forward(input2) { + return this.forwardInput(await toNetInput(input2)); + } + getDefaultModelName() { + return "tiny_xception_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap3(weightMap, this._numMainBlocks); + } + extractParams(weights) { + return extractParams3(weights, this._numMainBlocks); + } +}; + +// src/ageGenderNet/extractParams.ts +function extractParams4(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const age = extractFCParams(512, 1, "fc/age"); + const gender = extractFCParams(512, 2, "fc/gender"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { fc: { age, gender } } + }; +} + +// src/ageGenderNet/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap4(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { weights, bias }; + } + const params = { + fc: { + age: extractFcParams("fc/age"), + gender: extractFcParams("fc/gender") + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/ageGenderNet/types.ts +var Gender = /* @__PURE__ */ ((Gender2) => { + Gender2["FEMALE"] = "female"; + Gender2["MALE"] = "male"; + return Gender2; +})(Gender || {}); + +// src/ageGenderNet/AgeGenderNet.ts +var AgeGenderNet = class extends NeuralNetwork { + constructor(faceFeatureExtractor = new TinyXception(2)) { + super("AgeGenderNet"); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input2) { + const { params } = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tidy(() => { + const bottleneckFeatures = input2 instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input2) : input2; + const pooled = avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1); + const age = fullyConnectedLayer(pooled, params.fc.age).as1D(); + const gender = fullyConnectedLayer(pooled, params.fc.gender); + return { age, gender }; + }); + } + forwardInput(input2) { + return tidy(() => { + const { age, gender } = this.runNet(input2); + return { age, gender: softmax(gender) }; + }); + } + async forward(input2) { + return this.forwardInput(await toNetInput(input2)); + } + async predictAgeAndGender(input2) { + const netInput = await toNetInput(input2); + const out = await this.forwardInput(netInput); + const ages = unstack(out.age); + const genders = unstack(out.gender); + const ageAndGenderTensors = ages.map((ageTensor, i) => ({ + ageTensor, + genderTensor: genders[i] + })); + const predictionsByBatch = await Promise.all( + ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => { + const age = ageTensor.dataSync()[0]; + const probMale = genderTensor.dataSync()[0]; + const isMale = probMale > 0.5; + const gender = isMale ? "male" /* MALE */ : "female" /* FEMALE */; + const genderProbability = isMale ? probMale : 1 - probMale; + ageTensor.dispose(); + genderTensor.dispose(); + return { age, gender, genderProbability }; + }) + ); + out.age.dispose(); + out.gender.dispose(); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "age_gender_model"; + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const { params, paramMappings } = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams4(weights); + } + extractParamsFromWeightMap(weightMap) { + const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap4(classifierMap); + } + extractParams(weights) { + const classifierWeightSize = 512 * 1 + 1 + (512 * 2 + 2); + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68NetBase.ts +var FaceLandmark68NetBase = class extends FaceProcessor { + postProcess(output, inputSize, originalDimensions) { + const inputDimensions = originalDimensions.map(({ width, height }) => { + const scale3 = inputSize / Math.max(height, width); + return { + width: width * scale3, + height: height * scale3 + }; + }); + const batchSize = inputDimensions.length; + return tidy(() => { + const createInterleavedTensor = (fillX, fillY) => stack([fill([68], fillX, "float32"), fill([68], fillY, "float32")], 1).as2D(1, 136).as1D(); + const getPadding2 = (batchIdx, cond) => { + const { width, height } = inputDimensions[batchIdx]; + return cond(width, height) ? Math.abs(width - height) / 2 : 0; + }; + const getPaddingX = (batchIdx) => getPadding2(batchIdx, (w, h) => w < h); + const getPaddingY = (batchIdx) => getPadding2(batchIdx, (w, h) => h < w); + const landmarkTensors = output.mul(fill([batchSize, 136], inputSize, "float32")).sub(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor( + getPaddingX(batchIdx), + getPaddingY(batchIdx) + )))).div(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor( + inputDimensions[batchIdx].width, + inputDimensions[batchIdx].height + )))); + return landmarkTensors; + }); + } + forwardInput(input2) { + return tidy(() => { + const out = this.runNet(input2); + return this.postProcess( + out, + input2.inputSize, + input2.inputDimensions.map(([height, width]) => ({ height, width })) + ); + }); + } + async forward(input2) { + return this.forwardInput(await toNetInput(input2)); + } + async detectLandmarks(input2) { + const netInput = await toNetInput(input2); + const landmarkTensors = tidy( + () => unstack(this.forwardInput(netInput)) + ); + const landmarksForBatch = await Promise.all(landmarkTensors.map( + async (landmarkTensor, batchIdx) => { + const landmarksArray = Array.from(landmarkTensor.dataSync()); + const xCoords = landmarksArray.filter((_, i) => isEven2(i)); + const yCoords = landmarksArray.filter((_, i) => !isEven2(i)); + return new FaceLandmarks68( + Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])), + { + height: netInput.getInputHeight(batchIdx), + width: netInput.getInputWidth(batchIdx) + } + ); + } + )); + landmarkTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? landmarksForBatch : landmarksForBatch[0]; + } + getClassifierChannelsOut() { + return 136; + } +}; + +// src/faceLandmarkNet/FaceLandmark68Net.ts +var FaceLandmark68Net = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceLandmark68Net", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_model"; + } + getClassifierChannelsIn() { + return 256; + } +}; + +// src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts +function extractParamsFromWeightMapTiny(weightMap) { + const paramMappings = []; + const { + extractDenseBlock3Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock3Params("dense0", true), + dense1: extractDenseBlock3Params("dense1"), + dense2: extractDenseBlock3Params("dense2") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceFeatureExtractor/extractParamsTiny.ts +function extractParamsTiny(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock3Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock3Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock3Params(32, 64, "dense1"); + const dense2 = extractDenseBlock3Params(64, 128, "dense2"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { dense0, dense1, dense2 } + }; +} + +// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts +var TinyFaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("TinyFaceFeatureExtractor"); + } + forwardInput(input2) { + const { params } = this; + if (!params) { + throw new Error("TinyFaceFeatureExtractor - load model before inference"); + } + return tidy(() => { + const batchTensor = cast(input2.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock3(normalized, params.dense0, true); + out = denseBlock3(out, params.dense1); + out = denseBlock3(out, params.dense2); + out = avgPool(out, [14, 14], [2, 2], "valid"); + return out; + }); + } + async forward(input2) { + return this.forwardInput(await toNetInput(input2)); + } + getDefaultModelName() { + return "face_feature_extractor_tiny_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMapTiny(weightMap); + } + extractParams(weights) { + return extractParamsTiny(weights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68TinyNet.ts +var FaceLandmark68TinyNet = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) { + super("FaceLandmark68TinyNet", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_tiny_model"; + } + getClassifierChannelsIn() { + return 128; + } +}; + +// src/faceLandmarkNet/index.ts +var FaceLandmarkNet = class extends FaceLandmark68Net { +}; + +// src/faceRecognitionNet/scaleLayer.ts +function scale2(x, params) { + return add2(mul(x, params.weights), params.biases); +} + +// src/faceRecognitionNet/convLayer.ts +function convLayer2(x, params, strides, withRelu, padding = "same") { + const { filters, bias } = params.conv; + let out = conv2d(x, filters, strides, padding); + out = add2(out, bias); + out = scale2(out, params.scale); + return withRelu ? relu(out) : out; +} +function conv2(x, params) { + return convLayer2(x, params, [1, 1], true); +} +function convNoRelu(x, params) { + return convLayer2(x, params, [1, 1], false); +} +function convDown(x, params) { + return convLayer2(x, params, [2, 2], true, "valid"); +} + +// src/faceRecognitionNet/extractParams.ts +function extractorsFactory3(extractWeights, paramMappings) { + function extractFilterValues(numFilterValues, numFilters, filterSize) { + const weights = extractWeights(numFilterValues); + const depth = weights.length / (numFilters * filterSize * filterSize); + if (isFloat(depth)) { + throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`); + } + return tidy( + () => transpose( + tensor4d(weights, [numFilters, depth, filterSize, filterSize]), + [2, 3, 1, 0] + ) + ); + } + function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const filters = extractFilterValues(numFilterValues, numFilters, filterSize); + const bias = tensor1d(extractWeights(numFilters)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { filters, bias }; + } + function extractScaleLayerParams(numWeights, mappedPrefix) { + const weights = tensor1d(extractWeights(numWeights)); + const biases = tensor1d(extractWeights(numWeights)); + paramMappings.push( + { paramPath: `${mappedPrefix}/weights` }, + { paramPath: `${mappedPrefix}/biases` } + ); + return { + weights, + biases + }; + } + function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const conv3 = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`); + const scale3 = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`); + return { conv: conv3, scale: scale3 }; + } + function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) { + const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`); + const conv22 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`); + return { conv1, conv2: conv22 }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParams5(weights) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory3(extractWeights, paramMappings); + const conv32_down = extractConvLayerParams(4704, 32, 7, "conv32_down"); + const conv32_1 = extractResidualLayerParams(9216, 32, 3, "conv32_1"); + const conv32_2 = extractResidualLayerParams(9216, 32, 3, "conv32_2"); + const conv32_3 = extractResidualLayerParams(9216, 32, 3, "conv32_3"); + const conv64_down = extractResidualLayerParams(36864, 64, 3, "conv64_down", true); + const conv64_1 = extractResidualLayerParams(36864, 64, 3, "conv64_1"); + const conv64_2 = extractResidualLayerParams(36864, 64, 3, "conv64_2"); + const conv64_3 = extractResidualLayerParams(36864, 64, 3, "conv64_3"); + const conv128_down = extractResidualLayerParams(147456, 128, 3, "conv128_down", true); + const conv128_1 = extractResidualLayerParams(147456, 128, 3, "conv128_1"); + const conv128_2 = extractResidualLayerParams(147456, 128, 3, "conv128_2"); + const conv256_down = extractResidualLayerParams(589824, 256, 3, "conv256_down", true); + const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1"); + const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2"); + const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out"); + const fc = tidy( + () => transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]) + ); + paramMappings.push({ paramPath: "fc" }); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + return { params, paramMappings }; +} + +// src/faceRecognitionNet/extractParamsFromWeightMap.ts +function extractorsFactory4(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractScaleLayerParams(prefix) { + const weights = extractWeightEntry(`${prefix}/scale/weights`, 1); + const biases = extractWeightEntry(`${prefix}/scale/biases`, 1); + return { weights, biases }; + } + function extractConvLayerParams(prefix) { + const filters = extractWeightEntry(`${prefix}/conv/filters`, 4); + const bias = extractWeightEntry(`${prefix}/conv/bias`, 1); + const scale3 = extractScaleLayerParams(prefix); + return { conv: { filters, bias }, scale: scale3 }; + } + function extractResidualLayerParams(prefix) { + return { + conv1: extractConvLayerParams(`${prefix}/conv1`), + conv2: extractConvLayerParams(`${prefix}/conv2`) + }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParamsFromWeightMap5(weightMap) { + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory4(weightMap, paramMappings); + const conv32_down = extractConvLayerParams("conv32_down"); + const conv32_1 = extractResidualLayerParams("conv32_1"); + const conv32_2 = extractResidualLayerParams("conv32_2"); + const conv32_3 = extractResidualLayerParams("conv32_3"); + const conv64_down = extractResidualLayerParams("conv64_down"); + const conv64_1 = extractResidualLayerParams("conv64_1"); + const conv64_2 = extractResidualLayerParams("conv64_2"); + const conv64_3 = extractResidualLayerParams("conv64_3"); + const conv128_down = extractResidualLayerParams("conv128_down"); + const conv128_1 = extractResidualLayerParams("conv128_1"); + const conv128_2 = extractResidualLayerParams("conv128_2"); + const conv256_down = extractResidualLayerParams("conv256_down"); + const conv256_1 = extractResidualLayerParams("conv256_1"); + const conv256_2 = extractResidualLayerParams("conv256_2"); + const conv256_down_out = extractResidualLayerParams("conv256_down_out"); + const { fc } = weightMap; + paramMappings.push({ originalPath: "fc", paramPath: "fc" }); + if (!isTensor2D(fc)) { + throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceRecognitionNet/residualLayer.ts +function residual(x, params) { + let out = conv2(x, params.conv1); + out = convNoRelu(out, params.conv2); + out = add2(out, x); + out = relu(out); + return out; +} +function residualDown(x, params) { + let out = convDown(x, params.conv1); + out = convNoRelu(out, params.conv2); + let pooled = avgPool(x, 2, 2, "valid"); + const zeros4 = zeros(pooled.shape); + const isPad = pooled.shape[3] !== out.shape[3]; + const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]; + if (isAdjustShape) { + const padShapeX = [...out.shape]; + padShapeX[1] = 1; + const zerosW = zeros(padShapeX); + out = concat([out, zerosW], 1); + const padShapeY = [...out.shape]; + padShapeY[2] = 1; + const zerosH = zeros(padShapeY); + out = concat([out, zerosH], 2); + } + pooled = isPad ? concat([pooled, zeros4], 3) : pooled; + out = add2(pooled, out); + out = relu(out); + return out; +} + +// src/faceRecognitionNet/FaceRecognitionNet.ts +var FaceRecognitionNet = class extends NeuralNetwork { + constructor() { + super("FaceRecognitionNet"); + } + forwardInput(input2) { + const { params } = this; + if (!params) { + throw new Error("FaceRecognitionNet - load model before inference"); + } + return tidy(() => { + const batchTensor = cast(input2.toBatchTensor(150, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = convDown(normalized, params.conv32_down); + out = maxPool(out, 3, 2, "valid"); + out = residual(out, params.conv32_1); + out = residual(out, params.conv32_2); + out = residual(out, params.conv32_3); + out = residualDown(out, params.conv64_down); + out = residual(out, params.conv64_1); + out = residual(out, params.conv64_2); + out = residual(out, params.conv64_3); + out = residualDown(out, params.conv128_down); + out = residual(out, params.conv128_1); + out = residual(out, params.conv128_2); + out = residualDown(out, params.conv256_down); + out = residual(out, params.conv256_1); + out = residual(out, params.conv256_2); + out = residualDown(out, params.conv256_down_out); + const globalAvg = out.mean([1, 2]); + const fullyConnected = matMul(globalAvg, params.fc); + return fullyConnected; + }); + } + async forward(input2) { + return this.forwardInput(await toNetInput(input2)); + } + async computeFaceDescriptor(input2) { + var _a; + if ((_a = input2 == null ? void 0 : input2.shape) == null ? void 0 : _a.some((dim) => dim <= 0)) + return new Float32Array(128); + const netInput = await toNetInput(input2); + const faceDescriptorTensors = tidy(() => unstack(this.forwardInput(netInput))); + const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())); + faceDescriptorTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0]; + } + getDefaultModelName() { + return "face_recognition_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap5(weightMap); + } + extractParams(weights) { + return extractParams5(weights); + } +}; + +// src/faceRecognitionNet/index.ts +function createFaceRecognitionNet(weights) { + const net = new FaceRecognitionNet(); + net.extractWeights(weights); + return net; +} + +// src/factories/WithFaceDescriptor.ts +function extendWithFaceDescriptor(sourceObj, descriptor) { + const extension = { descriptor }; + return { ...sourceObj, ...extension }; +} + +// src/factories/WithAge.ts +function isWithAge(obj) { + return typeof obj.age === "number"; +} +function extendWithAge(sourceObj, age) { + const extension = { age }; + return { ...sourceObj, ...extension }; +} + +// src/factories/WithGender.ts +function isWithGender(obj) { + return (obj.gender === "male" /* MALE */ || obj.gender === "female" /* FEMALE */) && isValidProbablitiy(obj.genderProbability); +} +function extendWithGender(sourceObj, gender, genderProbability) { + const extension = { gender, genderProbability }; + return { ...sourceObj, ...extension }; +} + +// src/ssdMobilenetv1/extractParams.ts +function extractorsFactory5(extractWeights, paramMappings) { + function extractDepthwiseConvParams(numChannels, mappedPrefix) { + const filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]); + const batch_norm_scale = tensor1d(extractWeights(numChannels)); + const batch_norm_offset = tensor1d(extractWeights(numChannels)); + const batch_norm_mean = tensor1d(extractWeights(numChannels)); + const batch_norm_variance = tensor1d(extractWeights(numChannels)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/batch_norm_scale` }, + { paramPath: `${mappedPrefix}/batch_norm_offset` }, + { paramPath: `${mappedPrefix}/batch_norm_mean` }, + { paramPath: `${mappedPrefix}/batch_norm_variance` } + ); + return { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }; + } + function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) { + const filters = tensor4d( + extractWeights(channelsIn * channelsOut * filterSize * filterSize), + [filterSize, filterSize, channelsIn, channelsOut] + ); + const bias = tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}` } + ); + return { filters, bias }; + } + function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) { + const { + filters, + bias + } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true); + return { + filters, + batch_norm_offset: bias + }; + } + function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) { + const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`); + const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`); + return { depthwise_conv, pointwise_conv }; + } + function extractMobilenetV1Params() { + const conv_0 = extractPointwiseConvParams(3, 32, 3, "mobilenetv1/conv_0"); + const conv_1 = extractConvPairParams(32, 64, "mobilenetv1/conv_1"); + const conv_2 = extractConvPairParams(64, 128, "mobilenetv1/conv_2"); + const conv_3 = extractConvPairParams(128, 128, "mobilenetv1/conv_3"); + const conv_4 = extractConvPairParams(128, 256, "mobilenetv1/conv_4"); + const conv_5 = extractConvPairParams(256, 256, "mobilenetv1/conv_5"); + const conv_6 = extractConvPairParams(256, 512, "mobilenetv1/conv_6"); + const conv_7 = extractConvPairParams(512, 512, "mobilenetv1/conv_7"); + const conv_8 = extractConvPairParams(512, 512, "mobilenetv1/conv_8"); + const conv_9 = extractConvPairParams(512, 512, "mobilenetv1/conv_9"); + const conv_10 = extractConvPairParams(512, 512, "mobilenetv1/conv_10"); + const conv_11 = extractConvPairParams(512, 512, "mobilenetv1/conv_11"); + const conv_12 = extractConvPairParams(512, 1024, "mobilenetv1/conv_12"); + const conv_13 = extractConvPairParams(1024, 1024, "mobilenetv1/conv_13"); + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + conv_8, + conv_9, + conv_10, + conv_11, + conv_12, + conv_13 + }; + } + function extractPredictionLayerParams() { + const conv_0 = extractPointwiseConvParams(1024, 256, 1, "prediction_layer/conv_0"); + const conv_1 = extractPointwiseConvParams(256, 512, 3, "prediction_layer/conv_1"); + const conv_2 = extractPointwiseConvParams(512, 128, 1, "prediction_layer/conv_2"); + const conv_3 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_3"); + const conv_4 = extractPointwiseConvParams(256, 128, 1, "prediction_layer/conv_4"); + const conv_5 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_5"); + const conv_6 = extractPointwiseConvParams(256, 64, 1, "prediction_layer/conv_6"); + const conv_7 = extractPointwiseConvParams(64, 128, 3, "prediction_layer/conv_7"); + const box_encoding_0_predictor = extractConvParams(512, 12, 1, "prediction_layer/box_predictor_0/box_encoding_predictor"); + const class_predictor_0 = extractConvParams(512, 9, 1, "prediction_layer/box_predictor_0/class_predictor"); + const box_encoding_1_predictor = extractConvParams(1024, 24, 1, "prediction_layer/box_predictor_1/box_encoding_predictor"); + const class_predictor_1 = extractConvParams(1024, 18, 1, "prediction_layer/box_predictor_1/class_predictor"); + const box_encoding_2_predictor = extractConvParams(512, 24, 1, "prediction_layer/box_predictor_2/box_encoding_predictor"); + const class_predictor_2 = extractConvParams(512, 18, 1, "prediction_layer/box_predictor_2/class_predictor"); + const box_encoding_3_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_3/box_encoding_predictor"); + const class_predictor_3 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_3/class_predictor"); + const box_encoding_4_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_4/box_encoding_predictor"); + const class_predictor_4 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_4/class_predictor"); + const box_encoding_5_predictor = extractConvParams(128, 24, 1, "prediction_layer/box_predictor_5/box_encoding_predictor"); + const class_predictor_5 = extractConvParams(128, 18, 1, "prediction_layer/box_predictor_5/class_predictor"); + const box_predictor_0 = { + box_encoding_predictor: box_encoding_0_predictor, + class_predictor: class_predictor_0 + }; + const box_predictor_1 = { + box_encoding_predictor: box_encoding_1_predictor, + class_predictor: class_predictor_1 + }; + const box_predictor_2 = { + box_encoding_predictor: box_encoding_2_predictor, + class_predictor: class_predictor_2 + }; + const box_predictor_3 = { + box_encoding_predictor: box_encoding_3_predictor, + class_predictor: class_predictor_3 + }; + const box_predictor_4 = { + box_encoding_predictor: box_encoding_4_predictor, + class_predictor: class_predictor_4 + }; + const box_predictor_5 = { + box_encoding_predictor: box_encoding_5_predictor, + class_predictor: class_predictor_5 + }; + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + box_predictor_0, + box_predictor_1, + box_predictor_2, + box_predictor_3, + box_predictor_4, + box_predictor_5 + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParams6(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory5(extractWeights, paramMappings); + const mobilenetv1 = extractMobilenetV1Params(); + const prediction_layer = extractPredictionLayerParams(); + const extra_dim = tensor3d( + extractWeights(5118 * 4), + [1, 5118, 4] + ); + const output_layer = { + extra_dim + }; + paramMappings.push({ paramPath: "output_layer/extra_dim" }); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + params: { + mobilenetv1, + prediction_layer, + output_layer + }, + paramMappings + }; +} + +// src/ssdMobilenetv1/extractParamsFromWeightMap.ts +function extractorsFactory6(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractPointwiseConvParams(prefix, idx, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`); + const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`); + return { filters, batch_norm_offset }; + } + function extractConvPairParams(idx) { + const mappedPrefix = `mobilenetv1/conv_${idx}`; + const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`; + const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`; + const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`; + const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`); + const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`); + const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`); + const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`); + const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`); + return { + depthwise_conv: { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }, + pointwise_conv: extractPointwiseConvParams("MobilenetV1", idx, mappedPrefixPointwiseConv) + }; + } + function extractMobilenetV1Params() { + return { + conv_0: extractPointwiseConvParams("MobilenetV1", 0, "mobilenetv1/conv_0"), + conv_1: extractConvPairParams(1), + conv_2: extractConvPairParams(2), + conv_3: extractConvPairParams(3), + conv_4: extractConvPairParams(4), + conv_5: extractConvPairParams(5), + conv_6: extractConvPairParams(6), + conv_7: extractConvPairParams(7), + conv_8: extractConvPairParams(8), + conv_9: extractConvPairParams(9), + conv_10: extractConvPairParams(10), + conv_11: extractConvPairParams(11), + conv_12: extractConvPairParams(12), + conv_13: extractConvPairParams(13) + }; + } + function extractConvParams(prefix, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`); + const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`); + return { filters, bias }; + } + function extractBoxPredictorParams(idx) { + const box_encoding_predictor = extractConvParams( + `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, + `prediction_layer/box_predictor_${idx}/box_encoding_predictor` + ); + const class_predictor = extractConvParams( + `Prediction/BoxPredictor_${idx}/ClassPredictor`, + `prediction_layer/box_predictor_${idx}/class_predictor` + ); + return { box_encoding_predictor, class_predictor }; + } + function extractPredictionLayerParams() { + return { + conv_0: extractPointwiseConvParams("Prediction", 0, "prediction_layer/conv_0"), + conv_1: extractPointwiseConvParams("Prediction", 1, "prediction_layer/conv_1"), + conv_2: extractPointwiseConvParams("Prediction", 2, "prediction_layer/conv_2"), + conv_3: extractPointwiseConvParams("Prediction", 3, "prediction_layer/conv_3"), + conv_4: extractPointwiseConvParams("Prediction", 4, "prediction_layer/conv_4"), + conv_5: extractPointwiseConvParams("Prediction", 5, "prediction_layer/conv_5"), + conv_6: extractPointwiseConvParams("Prediction", 6, "prediction_layer/conv_6"), + conv_7: extractPointwiseConvParams("Prediction", 7, "prediction_layer/conv_7"), + box_predictor_0: extractBoxPredictorParams(0), + box_predictor_1: extractBoxPredictorParams(1), + box_predictor_2: extractBoxPredictorParams(2), + box_predictor_3: extractBoxPredictorParams(3), + box_predictor_4: extractBoxPredictorParams(4), + box_predictor_5: extractBoxPredictorParams(5) + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParamsFromWeightMap6(weightMap) { + const paramMappings = []; + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory6(weightMap, paramMappings); + const extra_dim = weightMap["Output/extra_dim"]; + paramMappings.push({ originalPath: "Output/extra_dim", paramPath: "output_layer/extra_dim" }); + if (!isTensor3D(extra_dim)) { + throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`); + } + const params = { + mobilenetv1: extractMobilenetV1Params(), + prediction_layer: extractPredictionLayerParams(), + output_layer: { + extra_dim + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/ssdMobilenetv1/pointwiseConvLayer.ts +function pointwiseConvLayer(x, params, strides) { + return tidy(() => { + let out = conv2d(x, params.filters, strides, "same"); + out = add2(out, params.batch_norm_offset); + return clipByValue(out, 0, 6); + }); +} + +// src/ssdMobilenetv1/mobileNetV1.ts +var epsilon3 = 0.0010000000474974513; +function depthwiseConvLayer(x, params, strides) { + return tidy(() => { + let out = depthwiseConv2d(x, params.filters, strides, "same"); + out = batchNorm( + out, + params.batch_norm_mean, + params.batch_norm_variance, + params.batch_norm_offset, + params.batch_norm_scale, + epsilon3 + ); + return clipByValue(out, 0, 6); + }); +} +function getStridesForLayerIdx(layerIdx) { + return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1]; +} +function mobileNetV1(x, params) { + return tidy(() => { + let conv11; + let out = pointwiseConvLayer(x, params.conv_0, [2, 2]); + const convPairParams = [ + params.conv_1, + params.conv_2, + params.conv_3, + params.conv_4, + params.conv_5, + params.conv_6, + params.conv_7, + params.conv_8, + params.conv_9, + params.conv_10, + params.conv_11, + params.conv_12, + params.conv_13 + ]; + convPairParams.forEach((param, i) => { + const layerIdx = i + 1; + const depthwiseConvStrides = getStridesForLayerIdx(layerIdx); + out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides); + out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]); + if (layerIdx === 11) + conv11 = out; + }); + if (conv11 === null) { + throw new Error("mobileNetV1 - output of conv layer 11 is null"); + } + return { + out, + conv11 + }; + }); +} + +// src/ssdMobilenetv1/nonMaxSuppression.ts +function IOU(boxes, i, j) { + const boxesData = boxes.arraySync(); + const yminI = Math.min(boxesData[i][0], boxesData[i][2]); + const xminI = Math.min(boxesData[i][1], boxesData[i][3]); + const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]); + const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]); + const yminJ = Math.min(boxesData[j][0], boxesData[j][2]); + const xminJ = Math.min(boxesData[j][1], boxesData[j][3]); + const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]); + const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]); + const areaI = (ymaxI - yminI) * (xmaxI - xminI); + const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); + if (areaI <= 0 || areaJ <= 0) + return 0; + const intersectionYmin = Math.max(yminI, yminJ); + const intersectionXmin = Math.max(xminI, xminJ); + const intersectionYmax = Math.min(ymaxI, ymaxJ); + const intersectionXmax = Math.min(xmaxI, xmaxJ); + const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0); + return intersectionArea / (areaI + areaJ - intersectionArea); +} +function nonMaxSuppression3(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + const numBoxes = boxes.shape[0]; + const outputSize = Math.min(maxOutputSize, numBoxes); + const candidates = scores.map((score, boxIndex) => ({ score, boxIndex })).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score); + const suppressFunc = (x) => x <= iouThreshold ? 1 : 0; + const selected = []; + candidates.forEach((c) => { + if (selected.length >= outputSize) + return; + const originalScore = c.score; + for (let j = selected.length - 1; j >= 0; --j) { + const iou2 = IOU(boxes, c.boxIndex, selected[j]); + if (iou2 === 0) + continue; + c.score *= suppressFunc(iou2); + if (c.score <= scoreThreshold) + break; + } + if (originalScore === c.score) { + selected.push(c.boxIndex); + } + }); + return selected; +} + +// src/ssdMobilenetv1/outputLayer.ts +function getCenterCoordinatesAndSizesLayer(x) { + const vec = unstack(transpose(x, [1, 0])); + const sizes = [ + sub(vec[2], vec[0]), + sub(vec[3], vec[1]) + ]; + const centers = [ + add2(vec[0], div(sizes[0], 2)), + add2(vec[1], div(sizes[1], 2)) + ]; + return { sizes, centers }; +} +function decodeBoxesLayer(x0, x1) { + const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0); + const vec = unstack(transpose(x1, [1, 0])); + const div0_out = div(mul(exp(div(vec[2], 5)), sizes[0]), 2); + const add0_out = add2(mul(div(vec[0], 10), sizes[0]), centers[0]); + const div1_out = div(mul(exp(div(vec[3], 5)), sizes[1]), 2); + const add1_out = add2(mul(div(vec[1], 10), sizes[1]), centers[1]); + return transpose( + stack([ + sub(add0_out, div0_out), + sub(add1_out, div1_out), + add2(add0_out, div0_out), + add2(add1_out, div1_out) + ]), + [1, 0] + ); +} +function outputLayer(boxPredictions, classPredictions, params) { + return tidy(() => { + const batchSize = boxPredictions.shape[0]; + let boxes = decodeBoxesLayer( + reshape(tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), + reshape(boxPredictions, [-1, 4]) + ); + boxes = reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]); + const scoresAndClasses = sigmoid(slice(classPredictions, [0, 0, 1], [-1, -1, -1])); + let scores = slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]); + scores = reshape(scores, [batchSize, scores.shape[1]]); + const boxesByBatch = unstack(boxes); + const scoresByBatch = unstack(scores); + return { boxes: boxesByBatch, scores: scoresByBatch }; + }); +} + +// src/ssdMobilenetv1/boxPredictionLayer.ts +function boxPredictionLayer(x, params) { + return tidy(() => { + const batchSize = x.shape[0]; + const boxPredictionEncoding = reshape( + convLayer(x, params.box_encoding_predictor), + [batchSize, -1, 1, 4] + ); + const classPrediction = reshape( + convLayer(x, params.class_predictor), + [batchSize, -1, 3] + ); + return { boxPredictionEncoding, classPrediction }; + }); +} + +// src/ssdMobilenetv1/predictionLayer.ts +function predictionLayer(x, conv11, params) { + return tidy(() => { + const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]); + const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]); + const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]); + const conv3 = pointwiseConvLayer(conv22, params.conv_3, [2, 2]); + const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]); + const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]); + const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]); + const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]); + const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0); + const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1); + const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2); + const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3); + const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4); + const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5); + const boxPredictions = concat([ + boxPrediction0.boxPredictionEncoding, + boxPrediction1.boxPredictionEncoding, + boxPrediction2.boxPredictionEncoding, + boxPrediction3.boxPredictionEncoding, + boxPrediction4.boxPredictionEncoding, + boxPrediction5.boxPredictionEncoding + ], 1); + const classPredictions = concat([ + boxPrediction0.classPrediction, + boxPrediction1.classPrediction, + boxPrediction2.classPrediction, + boxPrediction3.classPrediction, + boxPrediction4.classPrediction, + boxPrediction5.classPrediction + ], 1); + return { + boxPredictions, + classPredictions + }; + }); +} + +// src/ssdMobilenetv1/SsdMobilenetv1Options.ts +var SsdMobilenetv1Options = class { + constructor({ minConfidence, maxResults } = {}) { + this._name = "SsdMobilenetv1Options"; + this._minConfidence = minConfidence || 0.5; + this._maxResults = maxResults || 100; + if (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; + } +}; + +// src/ssdMobilenetv1/SsdMobilenetv1.ts +var SsdMobilenetv1 = class extends NeuralNetwork { + constructor() { + super("SsdMobilenetv1"); + } + forwardInput(input2) { + const { params } = this; + if (!params) + throw new Error("SsdMobilenetv1 - load model before inference"); + return tidy(() => { + const batchTensor = cast(input2.toBatchTensor(512, false), "float32"); + const x = sub(div(batchTensor, 127.5), 1); + const features = mobileNetV1(x, params.mobilenetv1); + const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer); + return outputLayer(boxPredictions, classPredictions, params.output_layer); + }); + } + async forward(input2) { + return this.forwardInput(await toNetInput(input2)); + } + async locateFaces(input2, options = {}) { + const { maxResults, minConfidence } = new SsdMobilenetv1Options(options); + const netInput = await toNetInput(input2); + const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput); + const boxes = _boxes[0]; + const scores = _scores[0]; + for (let i = 1; i < _boxes.length; i++) { + _boxes[i].dispose(); + _scores[i].dispose(); + } + const scoresData = Array.from(scores.dataSync()); + const iouThreshold = 0.5; + const indices = nonMaxSuppression3(boxes, scoresData, maxResults, iouThreshold, minConfidence); + const reshapedDims = netInput.getReshapedInputDimensions(0); + const inputSize = netInput.inputSize; + const padX = inputSize / reshapedDims.width; + const padY = inputSize / reshapedDims.height; + const boxesData = boxes.arraySync(); + const results = indices.map((idx) => { + const [top, bottom] = [ + Math.max(0, boxesData[idx][0]), + Math.min(1, boxesData[idx][2]) + ].map((val) => val * padY); + const [left, right] = [ + Math.max(0, boxesData[idx][1]), + Math.min(1, boxesData[idx][3]) + ].map((val) => val * padX); + return new FaceDetection( + scoresData[idx], + new Rect(left, top, right - left, bottom - top), + { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) } + ); + }); + boxes.dispose(); + scores.dispose(); + return results; + } + getDefaultModelName() { + return "ssd_mobilenetv1_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap6(weightMap); + } + extractParams(weights) { + return extractParams6(weights); + } +}; + +// src/ssdMobilenetv1/index.ts +function createSsdMobilenetv1(weights) { + const net = new SsdMobilenetv1(); + net.extractWeights(weights); + return net; +} +function createFaceDetectionNet(weights) { + return createSsdMobilenetv1(weights); +} +var FaceDetectionNet = class extends SsdMobilenetv1 { +}; + +// src/tinyYolov2/const.ts +var IOU_THRESHOLD = 0.4; +var BOX_ANCHORS = [ + new Point(0.738768, 0.874946), + new Point(2.42204, 2.65704), + new Point(4.30971, 7.04493), + new Point(10.246, 4.59428), + new Point(12.6868, 11.8741) +]; +var BOX_ANCHORS_SEPARABLE = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404]; +var DEFAULT_MODEL_NAME2 = "tiny_yolov2_model"; +var DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model"; + +// src/tinyYolov2/config.ts +var isNumber2 = (arg) => typeof arg === "number"; +function validateConfig(config) { + if (!config) { + throw new Error(`invalid config: ${config}`); + } + if (typeof config.withSeparableConvs !== "boolean") { + throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`); + } + if (!isNumber2(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1) { + throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`); + } + if (!Array.isArray(config.classes) || !config.classes.length || !config.classes.every((c) => typeof c === "string")) { + throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`); + } + if (!Array.isArray(config.anchors) || !config.anchors.length || !config.anchors.map((a) => a || {}).every((a) => isNumber2(a.x) && isNumber2(a.y))) { + throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`); + } + if (config.meanRgb && (!Array.isArray(config.meanRgb) || config.meanRgb.length !== 3 || !config.meanRgb.every(isNumber2))) { + throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`); + } +} + +// src/tinyYolov2/leaky.ts +function leaky(x) { + return tidy(() => { + const min6 = mul(x, scalar(0.10000000149011612)); + return add2(relu(sub(x, min6)), min6); + }); +} + +// src/tinyYolov2/convWithBatchNorm.ts +function convWithBatchNorm(x, params) { + return tidy(() => { + let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = conv2d(out, params.conv.filters, [1, 1], "valid"); + out = sub(out, params.bn.sub); + out = mul(out, params.bn.truediv); + out = add2(out, params.conv.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/depthwiseSeparableConv.ts +function depthwiseSeparableConv2(x, params) { + return tidy(() => { + let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid"); + out = add2(out, params.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/extractParams.ts +function extractorsFactory7(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + function extractBatchNormParams(size, mappedPrefix) { + const sub4 = tensor1d(extractWeights(size)); + const truediv = tensor1d(extractWeights(size)); + paramMappings.push( + { paramPath: `${mappedPrefix}/sub` }, + { paramPath: `${mappedPrefix}/truediv` } + ); + return { sub: sub4, truediv }; + } + function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) { + const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`); + const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`); + return { conv: conv3, bn }; + } + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParams7(weights, config, boxEncodingSize, filterSizes) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory7(extractWeights, paramMappings); + let params; + if (config.withSeparableConvs) { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = config.isFirstLayerConv2d ? extractConvParams(s0, s1, 3, "conv0") : extractSeparableConvParams(s0, s1, "conv0"); + const conv1 = extractSeparableConvParams(s1, s2, "conv1"); + const conv22 = extractSeparableConvParams(s2, s3, "conv2"); + const conv3 = extractSeparableConvParams(s3, s4, "conv3"); + const conv4 = extractSeparableConvParams(s4, s5, "conv4"); + const conv5 = extractSeparableConvParams(s5, s6, "conv5"); + const conv6 = s7 ? extractSeparableConvParams(s6, s7, "conv6") : void 0; + const conv7 = s8 ? extractSeparableConvParams(s7, s8, "conv7") : void 0; + const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } else { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = extractConvWithBatchNormParams(s0, s1, "conv0"); + const conv1 = extractConvWithBatchNormParams(s1, s2, "conv1"); + const conv22 = extractConvWithBatchNormParams(s2, s3, "conv2"); + const conv3 = extractConvWithBatchNormParams(s3, s4, "conv3"); + const conv4 = extractConvWithBatchNormParams(s4, s5, "conv4"); + const conv5 = extractConvWithBatchNormParams(s5, s6, "conv5"); + const conv6 = extractConvWithBatchNormParams(s6, s7, "conv6"); + const conv7 = extractConvWithBatchNormParams(s7, s8, "conv7"); + const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { params, paramMappings }; +} + +// src/tinyYolov2/extractParamsFromWeightMap.ts +function extractorsFactory8(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractBatchNormParams(prefix) { + const sub4 = extractWeightEntry(`${prefix}/sub`, 1); + const truediv = extractWeightEntry(`${prefix}/truediv`, 1); + return { sub: sub4, truediv }; + } + function extractConvParams(prefix) { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { filters, bias }; + } + function extractConvWithBatchNormParams(prefix) { + const conv3 = extractConvParams(`${prefix}/conv`); + const bn = extractBatchNormParams(`${prefix}/bn`); + return { conv: conv3, bn }; + } + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParamsFromWeightMap7(weightMap, config) { + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory8(weightMap, paramMappings); + let params; + if (config.withSeparableConvs) { + const numFilters = config.filterSizes && config.filterSizes.length || 9; + params = { + conv0: config.isFirstLayerConv2d ? extractConvParams("conv0") : extractSeparableConvParams("conv0"), + conv1: extractSeparableConvParams("conv1"), + conv2: extractSeparableConvParams("conv2"), + conv3: extractSeparableConvParams("conv3"), + conv4: extractSeparableConvParams("conv4"), + conv5: extractSeparableConvParams("conv5"), + conv6: numFilters > 7 ? extractSeparableConvParams("conv6") : void 0, + conv7: numFilters > 8 ? extractSeparableConvParams("conv7") : void 0, + conv8: extractConvParams("conv8") + }; + } else { + params = { + conv0: extractConvWithBatchNormParams("conv0"), + conv1: extractConvWithBatchNormParams("conv1"), + conv2: extractConvWithBatchNormParams("conv2"), + conv3: extractConvWithBatchNormParams("conv3"), + conv4: extractConvWithBatchNormParams("conv4"), + conv5: extractConvWithBatchNormParams("conv5"), + conv6: extractConvWithBatchNormParams("conv6"), + conv7: extractConvWithBatchNormParams("conv7"), + conv8: extractConvParams("conv8") + }; + } + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/tinyYolov2/TinyYolov2Options.ts +var TinyYolov2Options = class { + constructor({ inputSize, scoreThreshold } = {}) { + this._name = "TinyYolov2Options"; + this._inputSize = inputSize || 416; + this._scoreThreshold = scoreThreshold || 0.5; + if (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; + } +}; + +// src/tinyYolov2/TinyYolov2Base.ts +var _TinyYolov2Base = class _TinyYolov2Base extends NeuralNetwork { + constructor(config) { + super("TinyYolov2"); + validateConfig(config); + this._config = config; + } + 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(x, params) { + let out = convWithBatchNorm(x, params.conv0); + out = maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv1); + out = maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv2); + out = maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv3); + out = maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv4); + out = maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv5); + out = maxPool(out, [2, 2], [1, 1], "same"); + out = convWithBatchNorm(out, params.conv6); + out = convWithBatchNorm(out, params.conv7); + return convLayer(out, params.conv8, "valid", false); + } + runMobilenet(x, params) { + let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv2(x, params.conv0); + out = maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv1); + out = maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv2); + out = maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv3); + out = maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv4); + out = maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv5); + out = maxPool(out, [2, 2], [1, 1], "same"); + out = params.conv6 ? depthwiseSeparableConv2(out, params.conv6) : out; + out = params.conv7 ? depthwiseSeparableConv2(out, params.conv7) : out; + return convLayer(out, params.conv8, "valid", false); + } + forwardInput(input2, inputSize) { + const { params } = this; + if (!params) { + throw new Error("TinyYolov2 - load model before inference"); + } + return tidy(() => { + let batchTensor = cast(input2.toBatchTensor(inputSize, false), "float32"); + batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor; + batchTensor = batchTensor.div(255); + return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params); + }); + } + async forward(input2, inputSize) { + return this.forwardInput(await toNetInput(input2), inputSize); + } + async detect(input2, forwardParams = {}) { + const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams); + const netInput = await toNetInput(input2); + const out = await this.forwardInput(netInput, inputSize); + const out0 = tidy(() => unstack(out)[0].expandDims()); + const inputDimensions = { + width: netInput.getInputWidth(0), + height: netInput.getInputHeight(0) + }; + const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold); + out.dispose(); + out0.dispose(); + const boxes = results.map((res) => res.box); + const scores = results.map((res) => res.score); + const classScores = results.map((res) => res.classScore); + const classNames = results.map((res) => this.config.classes[res.label]); + const indices = nonMaxSuppression2( + boxes.map((box) => box.rescale(inputSize)), + scores, + this.config.iouThreshold, + true + ); + const detections = indices.map((idx) => new ObjectDetection( + scores[idx], + classScores[idx], + classNames[idx], + boxes[idx], + inputDimensions + )); + return detections; + } + getDefaultModelName() { + return ""; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap7(weightMap, this.config); + } + extractParams(weights) { + const filterSizes = this.config.filterSizes || _TinyYolov2Base.DEFAULT_FILTER_SIZES; + const numFilters = filterSizes ? filterSizes.length : void 0; + if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) { + throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`); + } + return extractParams7(weights, this.config, this.boxEncodingSize, filterSizes); + } + async extractBoxes(outputTensor, inputBlobDimensions, scoreThreshold) { + const { width, height } = inputBlobDimensions; + const inputSize = Math.max(width, height); + const correctionFactorX = inputSize / width; + const correctionFactorY = inputSize / height; + const numCells = outputTensor.shape[1]; + const numBoxes = this.config.anchors.length; + const [boxesTensor, scoresTensor, classScoresTensor] = tidy(() => { + const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]); + const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]); + const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]); + const classScores = this.withClassScores ? softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : scalar(0); + return [boxes, scores, classScores]; + }); + const results = []; + const scoresData = await scoresTensor.array(); + const boxesData = await boxesTensor.array(); + for (let row = 0; row < numCells; row++) { + for (let col = 0; col < numCells; col++) { + for (let anchor = 0; anchor < numBoxes; anchor++) { + const score = sigmoid5(scoresData[row][col][anchor][0]); + if (!scoreThreshold || score > scoreThreshold) { + const ctX = (col + sigmoid5(boxesData[row][col][anchor][0])) / numCells * correctionFactorX; + const ctY = (row + sigmoid5(boxesData[row][col][anchor][1])) / numCells * correctionFactorY; + const widthLocal = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX; + const heightLocal = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY; + const x = ctX - widthLocal / 2; + const y = ctY - heightLocal / 2; + const pos = { row, col, anchor }; + const { classScore, label } = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : { classScore: 1, label: 0 }; + results.push({ + box: new BoundingBox(x, y, x + widthLocal, y + heightLocal), + score, + classScore: score * classScore, + label, + ...pos + }); + } + } + } + } + boxesTensor.dispose(); + scoresTensor.dispose(); + classScoresTensor.dispose(); + return results; + } + async extractPredictedClass(classesTensor, pos) { + const { row, col, anchor } = pos; + const classesData = await classesTensor.array(); + return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({ + classScore, + label + })).reduce((max6, curr) => max6.classScore > curr.classScore ? max6 : curr); + } +}; +_TinyYolov2Base.DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024]; +var TinyYolov2Base = _TinyYolov2Base; + +// src/tinyYolov2/TinyYolov2.ts +var TinyYolov2 = class extends TinyYolov2Base { + constructor(withSeparableConvs = true) { + const config = { + withSeparableConvs, + iouThreshold: IOU_THRESHOLD, + classes: ["face"], + ...withSeparableConvs ? { + anchors: BOX_ANCHORS_SEPARABLE, + meanRgb: MEAN_RGB_SEPARABLE + } : { + anchors: BOX_ANCHORS, + withClassScores: true + } + }; + super(config); + } + get withSeparableConvs() { + return this.config.withSeparableConvs; + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input2, forwardParams) { + const objectDetections = await this.detect(input2, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight })); + } + getDefaultModelName() { + return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME2; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/tinyYolov2/index.ts +function createTinyYolov2(weights, withSeparableConvs = true) { + const net = new TinyYolov2(withSeparableConvs); + net.extractWeights(weights); + return net; +} + +// src/tinyFaceDetector/TinyFaceDetectorOptions.ts +var TinyFaceDetectorOptions = class extends TinyYolov2Options { + constructor() { + super(...arguments); + this._name = "TinyFaceDetectorOptions"; + } +}; + +// src/globalApi/ComposableTask.ts +var ComposableTask = class { + // eslint-disable-next-line no-unused-vars + async then(onfulfilled) { + return onfulfilled(await this.run()); + } + async run() { + throw new Error("ComposableTask - run is not implemented"); + } +}; + +// src/globalApi/extractFacesAndComputeResults.ts +async function extractAllFacesAndComputeResults(parentResults, input2, computeResults, extractedFaces, getRectForAlignment = ({ alignedRect }) => alignedRect) { + const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection); + const faces = extractedFaces || (input2 instanceof Tensor ? await extractFaceTensors(input2, faceBoxes) : await extractFaces(input2, faceBoxes)); + const results = await computeResults(faces); + faces.forEach((f) => f instanceof Tensor && f.dispose()); + return results; +} +async function extractSingleFaceAndComputeResult(parentResult, input2, computeResult, extractedFaces, getRectForAlignment) { + return extractAllFacesAndComputeResults( + [parentResult], + input2, + async (faces) => computeResult(faces[0]), + extractedFaces, + getRectForAlignment + ); +} + +// src/tinyFaceDetector/const.ts +var IOU_THRESHOLD2 = 0.4; +var BOX_ANCHORS2 = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB = [117.001, 114.697, 97.404]; + +// src/tinyFaceDetector/TinyFaceDetector.ts +var TinyFaceDetector = class extends TinyYolov2Base { + constructor() { + const config = { + withSeparableConvs: true, + iouThreshold: IOU_THRESHOLD2, + classes: ["face"], + anchors: BOX_ANCHORS2, + meanRgb: MEAN_RGB, + isFirstLayerConv2d: true, + filterSizes: [3, 16, 32, 64, 128, 256, 512] + }; + super(config); + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input2, forwardParams) { + const objectDetections = await this.detect(input2, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight })); + } + getDefaultModelName() { + return "tiny_face_detector_model"; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/globalApi/nets.ts +var nets = { + ssdMobilenetv1: new SsdMobilenetv1(), + tinyFaceDetector: new TinyFaceDetector(), + tinyYolov2: new TinyYolov2(), + faceLandmark68Net: new FaceLandmark68Net(), + faceLandmark68TinyNet: new FaceLandmark68TinyNet(), + faceRecognitionNet: new FaceRecognitionNet(), + faceExpressionNet: new FaceExpressionNet(), + ageGenderNet: new AgeGenderNet() +}; +var ssdMobilenetv1 = (input2, options) => nets.ssdMobilenetv1.locateFaces(input2, options); +var tinyFaceDetector = (input2, options) => nets.tinyFaceDetector.locateFaces(input2, options); +var tinyYolov2 = (input2, options) => nets.tinyYolov2.locateFaces(input2, options); +var detectFaceLandmarks = (input2) => nets.faceLandmark68Net.detectLandmarks(input2); +var detectFaceLandmarksTiny = (input2) => nets.faceLandmark68TinyNet.detectLandmarks(input2); +var computeFaceDescriptor = (input2) => nets.faceRecognitionNet.computeFaceDescriptor(input2); +var recognizeFaceExpressions = (input2) => nets.faceExpressionNet.predictExpressions(input2); +var predictAgeAndGender = (input2) => nets.ageGenderNet.predictAgeAndGender(input2); +var loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url); +var loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url); +var loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url); +var loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url); +var loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url); +var loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url); +var loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url); +var loadAgeGenderModel = (url) => nets.ageGenderNet.load(url); +var loadFaceDetectionModel = loadSsdMobilenetv1Model; +var locateFaces = ssdMobilenetv1; +var detectLandmarks = detectFaceLandmarks; + +// src/globalApi/PredictFaceExpressionsTask.ts +var PredictFaceExpressionsTaskBase = class extends ComposableTask { + constructor(parentTask, input2, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input2; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResults = await this.parentTask; + const faceExpressionsByFace = await extractAllFacesAndComputeResults( + parentResults, + this.input, + async (faces) => Promise.all( + faces.map((face) => nets.faceExpressionNet.predictExpressions(face)) + ), + this.extractedFaces + ); + return parentResults.map( + (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]) + ); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const faceExpressions = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.faceExpressionNet.predictExpressions(face), + this.extractedFaces + ); + return extendWithFaceExpressions(parentResult, faceExpressions); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask(this, this.input); + } +}; +var PredictAllFaceExpressionsWithFaceAlignmentTask = class extends PredictAllFaceExpressionsTask { + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsWithFaceAlignmentTask = class extends PredictSingleFaceExpressionsTask { + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/PredictAgeAndGenderTask.ts +var PredictAgeAndGenderTaskBase = class extends ComposableTask { + constructor(parentTask, input2, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input2; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResults = await this.parentTask; + const ageAndGenderByFace = await extractAllFacesAndComputeResults( + parentResults, + this.input, + async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face))), + this.extractedFaces + ); + return parentResults.map((parentResult, i) => { + const { age, gender, genderProbability } = ageAndGenderByFace[i]; + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + }); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.ageGenderNet.predictAgeAndGender(face), + this.extractedFaces + ); + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask(this, this.input); + } +}; +var PredictAllAgeAndGenderWithFaceAlignmentTask = class extends PredictAllAgeAndGenderTask { + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderWithFaceAlignmentTask = class extends PredictSingleAgeAndGenderTask { + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/ComputeFaceDescriptorsTasks.ts +var ComputeFaceDescriptorsTaskBase = class extends ComposableTask { + constructor(parentTask, input2) { + super(); + this.parentTask = parentTask; + this.input = input2; + } +}; +var ComputeAllFaceDescriptorsTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResults = await this.parentTask; + const descriptors = await extractAllFacesAndComputeResults( + parentResults, + this.input, + (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face))), + null, + (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }) + ); + return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor)); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; +var ComputeSingleFaceDescriptorTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const descriptor = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.faceRecognitionNet.computeFaceDescriptor(face), + null, + // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow + (parentResult2) => parentResult2.landmarks.align(null, { useDlibAlignment: true }) + ); + return extendWithFaceDescriptor(parentResult, descriptor); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; + +// src/globalApi/DetectFaceLandmarksTasks.ts +var DetectFaceLandmarksTaskBase = class extends ComposableTask { + constructor(parentTask, input2, useTinyLandmarkNet) { + super(); + this.parentTask = parentTask; + this.input = input2; + this.useTinyLandmarkNet = useTinyLandmarkNet; + } + get landmarkNet() { + return this.useTinyLandmarkNet ? nets.faceLandmark68TinyNet : nets.faceLandmark68Net; + } +}; +var DetectAllFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResults = await this.parentTask; + const detections = parentResults.map((res) => res.detection); + const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections); + const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))); + faces.forEach((f) => f instanceof Tensor && f.dispose()); + const result = parentResults.filter((_parentResult, i) => faceLandmarksByFace[i]).map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i])); + return result; + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var DetectSingleFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const { detection } = parentResult; + const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]); + const landmarks = await this.landmarkNet.detectLandmarks(faces[0]); + faces.forEach((f) => f instanceof Tensor && f.dispose()); + return extendWithFaceLandmarks(parentResult, landmarks); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/DetectFacesTasks.ts +var DetectFacesTaskBase = class extends ComposableTask { + // eslint-disable-next-line no-unused-vars + constructor(input2, options = new SsdMobilenetv1Options()) { + super(); + this.input = input2; + this.options = options; + } +}; +var DetectAllFacesTask = class extends DetectFacesTaskBase { + async run() { + const { input: input2, options } = this; + let result; + if (options instanceof TinyFaceDetectorOptions) + result = nets.tinyFaceDetector.locateFaces(input2, options); + else if (options instanceof SsdMobilenetv1Options) + result = nets.ssdMobilenetv1.locateFaces(input2, options); + else if (options instanceof TinyYolov2Options) + result = nets.tinyYolov2.locateFaces(input2, options); + else + throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options"); + return result; + } + runAndExtendWithFaceDetections() { + return new Promise((resolve, reject) => { + this.run().then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection)))).catch((err) => reject(err)); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectAllFaceLandmarksTask( + this.runAndExtendWithFaceDetections(), + this.input, + useTinyLandmarkNet + ); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask( + this.runAndExtendWithFaceDetections(), + this.input + ); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask( + this.runAndExtendWithFaceDetections(), + this.input + ); + } +}; +var DetectSingleFaceTask = class extends DetectFacesTaskBase { + async run() { + const faceDetections = await new DetectAllFacesTask(this.input, this.options); + let faceDetectionWithHighestScore = faceDetections[0]; + faceDetections.forEach((faceDetection) => { + if (faceDetection.score > faceDetectionWithHighestScore.score) + faceDetectionWithHighestScore = faceDetection; + }); + return faceDetectionWithHighestScore; + } + runAndExtendWithFaceDetection() { + return new Promise(async (resolve) => { + const detection = await this.run(); + resolve(detection ? extendWithFaceDetection({}, detection) : void 0); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectSingleFaceLandmarksTask( + this.runAndExtendWithFaceDetection(), + this.input, + useTinyLandmarkNet + ); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask( + this.runAndExtendWithFaceDetection(), + this.input + ); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask( + this.runAndExtendWithFaceDetection(), + this.input + ); + } +}; + +// src/globalApi/detectFaces.ts +function detectSingleFace(input2, options = new SsdMobilenetv1Options()) { + return new DetectSingleFaceTask(input2, options); +} +function detectAllFaces(input2, options = new SsdMobilenetv1Options()) { + return new DetectAllFacesTask(input2, options); +} + +// src/globalApi/allFaces.ts +async function allFacesSsdMobilenetv1(input2, minConfidence) { + return detectAllFaces(input2, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {})).withFaceLandmarks().withFaceDescriptors(); +} +async function allFacesTinyYolov2(input2, forwardParams = {}) { + return detectAllFaces(input2, new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors(); +} +var allFaces = allFacesSsdMobilenetv1; + +// src/euclideanDistance.ts +function euclideanDistance(arr1, arr2) { + if (arr1.length !== arr2.length) + throw new Error("euclideanDistance: arr1.length !== arr2.length"); + const desc1 = Array.from(arr1); + const desc2 = Array.from(arr2); + return Math.sqrt( + desc1.map((val, i) => val - desc2[i]).reduce((res, diff) => res + diff * diff, 0) + ); +} + +// src/globalApi/FaceMatcher.ts +var FaceMatcher = class _FaceMatcher { + constructor(inputs, distanceThreshold = 0.6) { + this._distanceThreshold = distanceThreshold; + const inputArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArray.length) + throw new Error("FaceRecognizer.constructor - expected atleast one input"); + let count2 = 1; + const createUniqueLabel = () => `person ${count2++}`; + this._labeledDescriptors = inputArray.map((desc) => { + if (desc instanceof LabeledFaceDescriptors) + return desc; + if (desc instanceof Float32Array) + return new LabeledFaceDescriptors(createUniqueLabel(), [desc]); + if (desc.descriptor && desc.descriptor instanceof Float32Array) + return new LabeledFaceDescriptors(createUniqueLabel(), [desc.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(queryDescriptor, descriptors) { + return descriptors.map((d) => euclideanDistance(d, queryDescriptor)).reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1); + } + matchDescriptor(queryDescriptor) { + return this.labeledDescriptors.map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors))).reduce((best, curr) => best.distance < curr.distance ? best : curr); + } + findBestMatch(queryDescriptor) { + const bestMatch = this.matchDescriptor(queryDescriptor); + return bestMatch.distance < this._distanceThreshold ? bestMatch : new FaceMatch("unknown", bestMatch.distance); + } + toJSON() { + return { + distanceThreshold: this._distanceThreshold, + labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()) + }; + } + static fromJSON(json20) { + const labeledDescriptors = json20.labeledDescriptors.map((ld) => LabeledFaceDescriptors.fromJSON(ld)); + return new _FaceMatcher(labeledDescriptors, json20.distanceThreshold); + } +}; + +// src/tinyFaceDetector/index.ts +function createTinyFaceDetector(weights) { + const net = new TinyFaceDetector(); + net.extractWeights(weights); + return net; +} + +// src/resizeResults.ts +function resizeResults(results, dimensions) { + const { width, height } = new Dimensions(dimensions.width, dimensions.height); + if (width <= 0 || height <= 0) { + throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`); + } + if (Array.isArray(results)) { + return results.map((obj) => resizeResults(obj, { width, height })); + } + if (isWithFaceLandmarks(results)) { + const resizedDetection = results.detection.forSize(width, height); + const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height); + return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks); + } + if (isWithFaceDetection(results)) { + return extendWithFaceDetection(results, results.detection.forSize(width, height)); + } + if (results instanceof FaceLandmarks || results instanceof FaceDetection) { + return results.forSize(width, height); + } + return results; +} + +// src/index.ts +var version10 = version7; +export { + 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, + createCanvas2 as createCanvas, + createCanvasFromMedia, + createFaceDetectionNet, + createFaceRecognitionNet, + createSsdMobilenetv1, + createTinyFaceDetector, + createTinyYolov2, + detectAllFaces, + detectFaceLandmarks, + detectFaceLandmarksTiny, + detectLandmarks, + detectSingleFace, + draw_exports as draw, + env2 as 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, + nonMaxSuppression2 as nonMaxSuppression, + normalize, + padToSquare, + predictAgeAndGender, + recognizeFaceExpressions, + resizeResults, + resolveInput, + shuffleArray, + sigmoid5 as sigmoid, + ssdMobilenetv1, + tfjs_esm_exports as tf, + tinyFaceDetector, + tinyYolov2, + toNetInput, + utils_exports as utils, + validateConfig, + version10 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 b1bdb7b..bc04c55 100644 --- a/dist/face-api.esm.js.map +++ b/dist/face-api.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["tfjs.esm.js", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/isNodejs.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/bufferToVideo.ts", "../src/dom/fetchVideo.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", 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E0;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(E0||(E0={}));var A0;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(A0||(A0={}));var D0;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(D0||(D0={}));var hK={float32:A0,int32:_0,bool:E0,complex64:D0};function ur(r,t){if(r===\"string\"||t===\"string\"){if(r===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${t}`)}return hK[r][t]}function xc(r){return ur(r,\"int32\")}function ox(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function sx(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function jt(r,t){if(r.dtype===t.dtype)return[r,t];let e=ur(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function $0(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function gK(r,t){return t.some(e=>e.id===r.id)}function ph(r){let t=[];return X_(r,t,new Set),t}function X_(r,t,e){if(r==null)return;if(r instanceof Ot){t.push(r);return}if(!xK(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),X_(s,t,e))}}function xK(r){return Array.isArray(r)||typeof r==\"object\"}function R0(r){return r.kernelName!=null}var ix=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(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},Iu=class{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ix}async 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){Jg(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 ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof Uo)&&typeof n.then==\"function\"){let o=++this.pendingBackendInitId,s=n.then(i=>o(othis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return Iu.nextTensorId++}nextVariableId(){return Iu.nextVariableId++}clone(t){let e=T.runKernel(bo,{x:t}),n={x:t},o=i=>({x:()=>{let a=\"float32\",u={x:i},l={dtype:a};return T.runKernel(xo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(ih(t,this.backendName)!=null))throw new Error(`Kernel '${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=R0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(R0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=ih(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=R0(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=b0(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\"&&Ho(t[0])&&(s=t.map(u=>wu(u)));let i=o.write(s,e,n),a=new Ot(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n===\"string\"){let u=this.state.tensorInfo.get(i),l=h0(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 Ot(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 gl(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*Pp(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 gl||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 n=t.size*Pp(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,n,o,s,i){let a={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:s},u=b0(t);u!=null&&(o=u.gradFunc),o!=null&&(a.gradient=l=>(l=l.map((c,p)=>{if(c==null){let m=n[p],f=Lp(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(l.length>1?l:l[0],s,i))),this.state.activeTape.push(a)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=ph(t),n=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(_(e.length>0,()=>\"gradients() received an empty list of xs.\"),n!=null&&n.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",t));_(s instanceof Ot,()=>\"The result y returned by f() must be a tensor.\");let i=V_(this.state.activeTape,e,s);if(!o&&i.length===0&&e.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let a={};a[s.id]=n==null?yK(s.shape):n,G_(a,i,l=>this.tidy(l),bK);let u=e.map(l=>a[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:u}})}customGrad(t){return _(Ai(t),()=>\"The f passed in customGrad(f) must be a function.\"),(...e)=>{_(e.every(a=>a instanceof Ot),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let n,o={};e.forEach((a,u)=>{o[u]=a});let s=(a,u)=>(n=t(...e,u),_(n.value instanceof Ot,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),_(Ai(n.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),n.value),i=(a,u)=>{let l=n.gradFunc(a,u),c=Array.isArray(l)?l:[l];_(c.length===e.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),_(c.every(m=>m instanceof Ot),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:i,inputs:o})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=gc(),n=await this.backend.time(t);return n.wallMs=gc()-e,n}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new ix;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Iu.nextTensorId=0;Iu.nextVariableId=0;function yK(r){let t=eh(te(r),\"float32\");return T.makeTensor(t,r,\"float32\")}function F0(){let r=y0();if(r._tfengine==null){let t=new rh(r);r._tfengine=new Iu(t)}return I_(r._tfengine.ENV),q_(()=>r._tfengine),r._tfengine}var T=F0();function bK(r,t){let e={a:r,b:t};return T.runKernel(ao,e)}var Cu={};Kt(Cu,{isBrowser:()=>P0,isMobile:()=>CK,mockIsMobile:()=>IK});function wK(){return typeof navigator!=\"undefined\"&&navigator!=null}var O0;function IK(r){O0=r}function CK(r){if(O0!==void 0)return O0;if(r||wK()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let t=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!t){let e=r;return 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s={x:C(r,\"x\",\"cumsum\")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(ls,s,i)}var dm=k({cumsum_:Kj});function jj(r,t,e,n=!1){let o=C(r,\"x\",\"denseBincount\"),s=C(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: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return T.runKernel(eu,i,a)}var gh=k({denseBincount_:jj});function Xj(r,t,e=\"NHWC\"){let n=C(r,\"x\",\"depthToSpace\",\"float32\"),o=e===\"NHWC\"?n.shape[1]:n.shape[2],s=e===\"NHWC\"?n.shape[2]:n.shape[3],i=e===\"NHWC\"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: 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i=R(n,[-1,1]),a=Bt(e,i);return R(a,[a.size])}else{let i=R(n,[n.shape[0],n.shape[1]]);return Bt(e,i)}}var zx=k({dot_:n6});function o6(r,...t){let e=t.map((o,s)=>C(o,`tensors${s}`,\"einsum\")),n={equation:r};return T.runKernel(Wp,e,n)}var AE=k({einsum_:o6});function s6(r){let e={x:C(r,\"x\",\"elu\",\"float32\")};return T.runKernel(ms,e)}var ca=k({elu_:s6});function i6(r,t){let e=C(r,\"x\",\"ensureShape\",\"string_or_numeric\");if(!c0(e.shape,t))throw new Error(`EnsureShape: Shape of tensor ${e.shape} is not compatible with expected shape ${t}`);return r}var DE=k({ensureShape_:i6});function a6(r){let t=C(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(fs,e)}var Bx=k({erf_:a6});function Z0(r,t){for(let e=0;er[s]);return[e,o]}function ko(r,t){let e=t.map(n=>1);return $E(r,e,t)}function l6(r,t,e){_(Z0(t,e),()=>`${r} supports only inner-most axes for now. 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T.runKernel(Bp,p,m)}var $m=k({conv2DBackpropFilter_:j5});function _c(r,t,e){if(e==null||e===\"linear\")return r;if(e===\"relu\")return $(r,To(t));throw new Error(`Cannot compute gradient for fused activation ${e}.`)}function Ec(r,t){let e=t,n=ye(r.shape,t.shape);return n.length>0&&(e=pt(e,n)),R(e,r.shape)}function Ac(r,t,e,n){if(t===\"linear\")return r;if(t===\"relu\")return Mr(r);if(t===\"elu\")return ca(r);if(t===\"relu6\")return ym(r);if(t===\"prelu\")return Ru(r,e);if(t===\"leakyrelu\")return _u(r,n);if(t===\"sigmoid\")return en(r);throw new Error(`Unknown fused activation ${t}.`)}var Dc=(r,t)=>!(r>0)||t===\"linear\";function X5({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\",Dc(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}'`);let g=wc(f.shape,m.shape,e,s,n,i),x;a!=null&&(x=C(a,\"bias\",\"fused conv2d\"),[x]=jt(x,p),o===\"NHWC\"?Mt(g.outShape,x.shape):(_(x.shape.length<=1,()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`),_(x.shape.length===0||x.shape[0]===g.outChannels||x.shape[0]===1,()=>`Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`)));let b;if(l!=null){let E=l.shape;if(_(E.length<=1||E.length===3,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${E.length}.`),E.length===1)_(E[0]===1||E[0]===g.outChannels,()=>`Error in fused conv2d: PReLU activation weights (${E}) is not compatible with the number of output channels (${g.outChannels}).`);else if(E.length===3)try{Mt(E,g.outShape)}catch(A){let D=`Error in fused conv2d: PReLU activation weights (${E}) is not compatible with the output shape of the conv2d (${g.outShape}).`;throw Error(D)}b=C(l,\"prelu weights\",\"fused conv2d\")}let w=(E,A)=>{_(o===\"NHWC\",()=>`Error in gradient of fused conv2D: got dataFormat of ${o} but only NHWC is currently supported.`);let[D,F,P,V]=A,G=_c(E,P,u);_(co(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. 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Got strides ${e} and dilations '${s}'`),Se(\"fused depthwiseConv2d\",n,i);let h=wc(f.shape,m.shape,e,s,n,i,!0),g;a!=null&&(g=C(a,\"bias\",\"fused conv2d\"),[g]=jt(g,p),Mt(h.outShape,g.shape));let x;l!=null&&(x=C(l,\"prelu weights\",\"fused depthwiseConv2d\"));let b=(N,E)=>{_(co(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=_c(N,F,u),G=by(D.shape,V,A,e,n,s,i),W=yy(D,V,A.shape,e,n,s,i);if(P!=null){let q=Ec(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?fn((E,A,D)=>{let F=T.runKernel(Qi,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):fn((E,A,D,F)=>{let P=T.runKernel(Qi,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 $A=k({fusedDepthwiseConv2d_:J5});function Q5({a:r,b:t,transposeA:e=!1,transposeB:n=!1,bias:o,activation:s=\"linear\",preluActivationWeights:i,leakyreluAlpha:a=.2}){if(Dc(T.state.gradientDepth,s)===!1){let V=Bt(r,t,e,n);return o!=null&&(V=Y(V,o)),Ac(V,s,i,a)}let u=C(r,\"a\",\"fused matMul\"),l=C(t,\"b\",\"fused matMul\");[u,l]=jt(u,l);let c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=te(d),x=te(h);_(c===p,()=>`Error in fused matMul: inner shapes (${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=C(o,\"bias\",\"fused matMul\"),[E]=jt(E,u),Mt(w,E.shape));let A;i!=null&&(A=C(i,\"prelu weights\",\"fused matMul\"));let D=(V,G)=>{let[W,q,H,K]=G,X=_c(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=Ec(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?fn((G,W,q)=>{let H=T.runKernel(Zi,F,P);return q([G,W,H]),{value:R(H,w),gradFunc:D}})(I,N):fn((G,W,q,H)=>{let K=T.runKernel(Zi,F,P);return H([G,W,K,q]),{value:R(K,w),gradFunc:D}})(I,N,E)}var RA=k({fusedMatMul_:Q5});function t8(r){return Ih(r,.54,.46)}var FA=k({hammingWindow_:t8});function e8(r){return Ih(r,.5,.5)}var wy=k({hannWindow_:e8});function r8(r,t,e,n=!1,o=0){let s=0,i=[];for(;s+t<=r.size;)i.push(Pt(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(Ba,c,p)}var PA=k({cropAndResize_:o8});function s8(r){let t=C(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(Ua,e,{})}var MA=k({flipLeftRight_:s8});function i8(r){let t=C(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,Or(t,o)}var LA=k({grayscaleToRGB_:i8});function a8(r,t,e=0,n=.5){let o=C(r,\"image\",\"rotateWithOffset\",\"float32\");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(hl,s,i)}var zA=k({rotateWithOffset_:a8});function _o(r,t,e,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=r.shape[0];return e=Math.min(e,i),_(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${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(GA);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=m8(r,b,p[N]);if(E>=n){I=!0;break}if(g.score=g.score*f8(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&&VA(l,g,GA))}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 m8(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 f8(r,t,e){let n=Math.exp(t*e*e);return e<=r?n:0}function GA(r,t){return r.score-t.score||r.score===t.score&&t.boxIndex-r.boxIndex}async function d8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY){let s=C(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=C(t,\"scores\",\"nonMaxSuppressionAsync\"),a=_o(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}=Cy(l,c,e,n,o);return s!==r&&s.dispose(),i!==t&&i.dispose(),Ke(p,\"int32\")}var WA=d8;function h8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,\"boxes\",\"nonMaxSuppression\"),a=C(t,\"scores\",\"nonMaxSuppression\"),u=_o(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(ol,l,c);return{selectedIndices:p[0],selectedScores:p[1]}}var UA=k({nonMaxSuppressionWithScore_:h8});async function g8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=C(t,\"scores\",\"nonMaxSuppressionAsync\"),u=_o(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}=Sy(c,p,e,n,o,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Ke(m,\"int32\"),selectedScores:Ke(f)}}var HA=g8;function x8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,\"boxes\",\"nonMaxSuppression\"),a=C(t,\"scores\",\"nonMaxSuppression\"),u=_o(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(nl,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var qA=k({nonMaxSuppressionPadded_:x8});async function y8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=C(t,\"scores\",\"nonMaxSuppressionAsync\"),u=_o(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,[m,f]=await Promise.all([i.data(),a.data()]),{selectedIndices:d,validOutputs:h}=vy(m,f,l,c,p,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Ke(d,\"int32\"),validOutputs:ft(h,\"int32\")}}var KA=y8;function b8(r,t,e=!1,n=!1){let o=C(r,\"images\",\"resizeBilinear\");_(o.rank===3||o.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${o.rank}.`),_(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),_(n===!1||e===!1,()=>\"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.\");let s=o,i=!1;o.rank===3&&(i=!0,s=R(o,[1,o.shape[0],o.shape[1],o.shape[2]]));let[]=t,a={images:s},u={alignCorners:e,halfPixelCenters:n,size:t},l=T.runKernel(Us,a,u);return i?R(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var Ny=k({resizeBilinear_:b8});function 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t2=k({absoluteDifference_:_8});function E8(r,t,e,n,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"cosineDistance\"),i=C(t,\"predictions\",\"cosineDistance\"),a=null;n!=null&&(a=C(n,\"weights\",\"cosineDistance\")),Re(s.shape,i.shape,\"Error in cosineDistance: \");let u=ft(1),l=lt(u,pt($(s,i),e,!0));return qr(l,a,o)}var e2=k({cosineDistance_:E8});function A8(r,t,e,n=Ze.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"hingeLoss\"),s=C(t,\"predictions\",\"hingeLoss\"),i=null;e!=null&&(i=C(e,\"weights\",\"hingeLoss\")),Re(o.shape,s.shape,\"Error in hingeLoss: \");let a=ft(1);o=lt($(ft(2),o),a);let u=Mr(lt(a,$(o,s)));return qr(u,i,n)}var r2=k({hingeLoss_:A8});function D8(r,t,e,n=1,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"huberLoss\"),i=C(t,\"predictions\",\"huberLoss\"),a=null;e!=null&&(a=C(e,\"weights\",\"huberLoss\")),Re(s.shape,i.shape,\"Error in huberLoss: \");let u=ft(n),l=Ee(lt(i,s)),c=mo(l,u),p=lt(l,c),m=Y($(ft(.5),Wt(c)),$(u,p));return qr(m,a,o)}var 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s=C(r,\"multiClassLabels\",\"sigmoidCrossEntropy\"),i=C(t,\"logits\",\"sigmoidCrossEntropy\"),a=null;if(e!=null&&(a=C(e,\"weights\",\"sigmoidCrossEntropy\")),Re(s.shape,i.shape,\"Error in sigmoidCrossEntropy: \"),n>0){let l=ft(n),c=ft(1),p=ft(.5);s=Y($(s,lt(c,l)),$(p,l))}let u=F8(s,i);return qr(u,a,o)}var i2=k({sigmoidCrossEntropy_:O8});function P8(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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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 Rc=class extends Kr{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:`${n}/accumulator`,variable:B(()=>No(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let u=Y(a,Wt(i));a.assign(u);let l=Y($(ct(i,Ne(Y(u,T.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Tt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var Fc=class extends Kr{static get className(){return\"Adam\"}constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=ft(e).variable(),this.accBeta2=ft(n).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=lt(1,this.accBeta1),o=lt(1,this.accBeta2);e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:B(()=>vt(a).variable(u))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${s}/v`,variable:B(()=>vt(a).variable(u))});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedSecondMoment[i].variable,m=Y($(c,this.beta1),$(l,1-this.beta1)),f=Y($(p,this.beta2),$(Wt(l),1-this.beta2)),d=ct(m,n),h=ct(f,o);c.assign(m),p.assign(f);let g=Y($(ct(d,Y(Ne(h),this.epsilon)),-this.learningRate),a);a.assign(g)}),this.accBeta1.assign($(this.accBeta1,this.beta1)),this.accBeta2.assign($(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Tt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),B(()=>{this.accBeta1.assign(pn(this.beta1,this.iterations_+1)),this.accBeta2.assign(pn(this.beta2,this.iterations_+1))});let e=t.length/2,n=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var Oc=class extends Kr{static get className(){return\"Adamax\"}constructor(t,e,n,o=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=ft(0).variable(),this.accBeta1=ft(e).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=lt(1,this.accBeta1),o=ct(-this.learningRate,Y($(this.iteration,this.decay),1));e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:vt(a).variable(u)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${s}/v`,variable:vt(a).variable(u)});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedWeightedInfNorm[i].variable,m=Y($(c,this.beta1),$(l,1-this.beta1)),f=$(p,this.beta2),d=Ee(l),h=_n(f,d);c.assign(m),p.assign(h);let g=Y($(ct(o,n),ct(m,Y(h,this.epsilon))),a);a.assign(g)}),this.iteration.assign(Y(this.iteration,1)),this.accBeta1.assign($(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Tt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(t){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var Sl=class extends Kr{static get className(){return\"SGD\"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=T.registeredVariables[n];B(()=>{let a=Y($(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=$e(ft(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}};var Pc=class extends Sl{static get className(){return\"Momentum\"}constructor(t,e,n=!1){super(t),this.learningRate=t,this.momentum=e,this.useNesterov=n,this.accumulations=[],this.m=ft(this.momentum)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulations[o]==null&&(this.accumulations[o]={originalName:`${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 Mc=class extends Kr{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),$(Wt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=Y($(p,this.decay),$(a,1-this.decay)),f=ct($(a,this.learningRate),Ne(lt(c,Y(Wt(m),this.epsilon)))),d=Y($(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=lt(s,d);s.assign(h)}else{let p=Y($(u,this.decay),$(Wt(a),1-this.decay)),m=Y($(l,this.momentum),ct($(a,this.learningRate),Ne(Y(p,this.epsilon))));u.assign(p),l.assign(m);let f=lt(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Tt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Tt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Tt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,n=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};var Z8=[$c,Rc,Fc,Oc,Pc,Mc,Sl];function g2(){for(let r of Z8)dN(r)}var 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browser.\");t.startsWith(Nl.URL_SCHEME)&&(t=t.slice(Nl.URL_SCHEME.length)),(t==null||t.length===0)&&(t=J8),this.modelJsonFileName=t+Q8,this.weightDataFileName=t+tY}async save(t){if(typeof document==\"undefined\")throw new Error(\"Browser downloads are not supported in this environment since `document` is not present\");let e=vr.join(t.weightData),n=window.URL.createObjectURL(new Blob([e],{type:\"application/octet-stream\"}));if(t.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");{let o=[{paths:[\"./\"+this.weightDataFileName],weights:t.weightSpecs}],s=ux(t,o),i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:\"application/json\"})),a=this.modelJsonAnchor==null?document.createElement(\"a\"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=i,await x2(()=>a.dispatchEvent(new MouseEvent(\"click\"))),t.weightData!=null){let 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kY(r,t){let e=r[0].length;r.forEach((o,s)=>{_(o.length===e,()=>`Error in concat${e}D: rank of tensors[${s}] must be the same as the rank of the rest (${e})`)}),_(t>=0&&t`Error in concat${e}D: axis must be between 0 and ${e-1}.`);let n=r[0];r.forEach((o,s)=>{for(let i=0;i`Error in concat${e}D: Shape of tensors[${s}] (${o}) does not match the shape of the rest (${n}) along the non-concatenated axis ${s}.`)})}function TY(r,t){let e=r[0].slice();for(let n=1;n=0)if(a>=0){if(a!==s)throw new Error(`rt input.shape and shape=${t} are incompatible: rt input.shape[${o+r}] = ${s} but shape[${o+r}] = ${a}`)}else n[i]=s}return n}function EY(r){let t={FIRST_DIM_SIZE:ga.FIRST_DIM_SIZE,VALUE_ROWIDS:ga.VALUE_ROWIDS,ROW_LENGTHS:ga.ROW_LENGTHS,ROW_SPLITS:ga.ROW_SPLITS,ROW_LIMITS:ga.ROW_LIMITS,ROW_STARTS:ga.ROW_STARTS},e=[];for(let n of r)if(n in t)e.push(t[n]);else break;return e}function AY(r){return r.length===0?0:r[0]===ga.FIRST_DIM_SIZE?r.length-1:r.length}function DY(r,t){if(r==null||t==null)return;let e=r.length,n=t.length;if(e>=n)throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${t}, are incompatible: defaultValue.rank = ${e} must be less than ragged tensor input flatValues.rank = ${n})`);for(let o=0;o=0&&i>=0&&s!==1&&s!==i)throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${t} are incompatible: defaultValue.shape[${o-r.length}] = ${s} but ragged tensor input.flatValues.shape[${o-r.length}] = ${i}`)}}var $y=30;function $Y(r){return r<=$y?r:Mp(r,Math.floor(Math.sqrt(r)))}function RY(r,t,e){let n=e*(typeof r==\"number\"?r:r[0]),o=t*(typeof r==\"number\"?r:r[1]);return[n,o]}function FY(r,t,e,n=!0){let o=[];if(n)o=o.concat(t.slice(0)),o.push(r[0]/e),o=o.concat(r.slice(1));else{o=o.concat(r[0]);let s=t.length;for(let i=0;i=t*2+1||i%2===1?s.push(i):o.push(i);n.push(...o),n.push(0),n.push(...s)}return n}function PY(r,t,e,n=!0){let o=[];n?o.push(r[0]/e):o.push(r[0]*e);for(let 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cD={kernelName:tu,inputsToSave:[\"x\"],gradFunc:Ry.gradFunc};var pD={kernelName:Mi,saveAllInputs:!0,gradFunc:(r,t,e)=>{let n=t.map(u=>u.shape),{axis:o}=e,s=fr(o,t[0].shape)[0],i=n.map(u=>u[s]);return gr(r,i,s).map(u=>()=>u)}};var mD={kernelName:ns,inputsToSave:[\"x\",\"filter\"],gradFunc:(r,t,e)=>{let[n,o]=t,{dilations:s,strides:i,pad:a,dataFormat:u}=e;return _(co(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. 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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 Ia))throw new kt(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=Cn(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}}};Ia.className=\"Sequential\";J.registerClass(Ia);function JZ(r){return new jn(r)}function QZ(r){return new Ia(r)}function KN(r){return qy(r)}function tJ(r,t){In.registerCallbackConstructor(r,t)}var on=class extends J.Serializable{getConfig(){return{}}},fb=class extends on{apply(t,e=1){return tR(t,e)}};fb.className=\"elu\";J.registerClass(fb);var db=class extends on{apply(t){return Im(t)}};db.className=\"selu\";J.registerClass(db);var hb=class extends on{apply(t){return Mr(t)}};hb.className=\"relu\";J.registerClass(hb);var gb=class extends on{apply(t){return B(()=>mo(6,Mr(t)))}};gb.className=\"relu6\";J.registerClass(gb);var xb=class extends on{apply(t){return t}};xb.className=\"linear\";J.registerClass(xb);var yb=class extends on{apply(t){return en(t)}};yb.className=\"sigmoid\";J.registerClass(yb);var bb=class extends on{apply(t){return rR(t)}};bb.className=\"hardSigmoid\";J.registerClass(bb);var wb=class extends on{apply(t){return pi(t)}};wb.className=\"softplus\";J.registerClass(wb);var Ib=class extends on{apply(t){return eR(t)}};Ib.className=\"softsign\";J.registerClass(Ib);var Cb=class extends on{apply(t){return ia(t)}};Cb.className=\"tanh\";J.registerClass(Cb);var nf=class extends on{apply(t,e=-1){return Fu(t,e)}};nf.className=\"softmax\";J.registerClass(nf);var vb=class extends on{apply(t,e=-1){return hm(t,e)}};vb.className=\"logSoftmax\";J.registerClass(vb);var Sb=class extends on{apply(t,e=1){return B(()=>$(en($(t,e)),t))}};Sb.className=\"swish\";J.registerClass(Sb);var Nb=class extends on{apply(t){return B(()=>$(t,ia(pi(t))))}};Nb.className=\"mish\";J.registerClass(Nb);function yi(r){return r.getClassName()}function jN(r,t={}){return xa(r,J.SerializationMap.getMap().classNameMap,t,\"activation\")}function bi(r){if(r==null){let t={};return t.className=\"linear\",t.config={},jN(t)}if(typeof r==\"string\"){let t={};return t.className=r,t.config={},jN(t)}else return r instanceof on?r:jN(r)}function XN(r){if(r!=null&&typeof r!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var kb=class extends J.Serializable{},Wu=class extends kb{constructor(t){super(),XN(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,pt($(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,pt($(this.l2,Vc(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Wu.className=\"L1L2\";J.registerClass(Wu);function MR(r){return XN(r),new Wu({l1:r!=null?r.l1:null,l2:0})}function LR(r){return XN(r),new Wu({l2:r!=null?r.l2:null,l1:0})}var OR={l1l2:\"L1L2\"};function me(r){return Fm(r)}function PR(r,t={}){return xa(r,J.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function Ce(r){if(r==null)return null;if(typeof r==\"string\"){let e={className:r in OR?OR[r]:r,config:{}};return PR(e)}else return r instanceof kb?r:PR(r)}var of=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=Mr(t);return this.maxValue!=null&&(n=Sr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};of.className=\"ReLU\";J.registerClass(of);var sf=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 _u(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};sf.className=\"LeakyReLU\";J.registerClass(sf);var af=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 YN(r,t){return B(()=>(Oe(t),t===\"channelsFirst\"?Vt(r,[0,2,3,4,1]):r))}function rJ(r,t,e,n=1,o=\"valid\",s,i=1){return B(()=>{if(s==null&&(s=yn()),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 kt(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let a=cm(r,t,n,o===\"same\"?\"same\":\"valid\",\"NWC\",i);return e!=null&&(a=bn(a,e)),a})}function zR(r,t,e,n=[1,1],o=\"valid\",s,i,a=null){return B(()=>{if(s==null&&(s=yn()),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 kt(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=Lu.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 nJ(r,t,e,n=[1,1,1],o=\"valid\",s,i){return B(()=>{if(s==null&&(s=yn()),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 ${r.rank}.`);let a=YN(r,s);if(o===\"causal\")throw new kt(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return a=Rx(a,t,n,o===\"same\"?\"same\":\"valid\",\"NDHWC\",i),e!=null&&(a=bn(a,e)),s===\"channelsFirst\"&&(a=Vt(a,[0,4,1,2,3])),a})}var Jc=class extends _t{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",Jc.verifyArgs(e),this.rank=t,Qe(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new kt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Uu(e.kernelSize,t,\"kernelSize\"),this.strides=Uu(e.strides==null?1:e.strides,t,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,gn(this.padding),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Oe(this.dataFormat),this.activation=bi(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=Uu(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(fo(\"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:yi(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}},Hu=class extends Jc{constructor(t,e){super(t,e),this.kernel=null,Hu.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=zR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=rJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=zR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=nJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new kt(\"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)}`)}},Dl=class extends Hu{constructor(t){super(2,t),Dl.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)}.`)}};Dl.className=\"Conv2D\";J.registerClass(Dl);var $l=class extends Hu{constructor(t){super(3,t),$l.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 ${JSON.stringify(t.kernelSize)}.`)}};$l.className=\"Conv3D\";J.registerClass($l);var pf=class extends Dl{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=wi(u,m,c,this.padding),h=wi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Vt(n,[0,2,3,1]));let x=mm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=bn(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]=wi(e[o],u,i,this.padding),e[s]=wi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};pf.className=\"Conv2DTranspose\";J.registerClass(pf);var mf=class extends $l{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=wi(l,h,m,this.padding),w=wi(c,g,f,this.padding),I=wi(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=Ox(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=bn(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]=wi(e[o],c,a,this.padding),e[s]=wi(e[s],p,u,this.padding),e[i]=wi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};mf.className=\"Conv3DTranspose\";J.registerClass(mf);var Tb=class extends Hu{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 kt(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(t=Vt(t,[0,2,3,1])),n=Cm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=bn(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 ff=class extends Tb{constructor(t){super(2,t)}};ff.className=\"SeparableConv2D\";J.registerClass(ff);var qu=class extends Hu{constructor(t){super(1,t),qu.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 ${JSON.stringify(t.kernelSize)}.`)}};qu.className=\"Conv1D\";J.registerClass(qu);var df=class extends _t{constructor(t){super(t),typeof t.cropping==\"number\"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]==\"number\"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?\"channelsLast\":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat===\"channelsFirst\"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat===\"channelsLast\"){let n=Ah(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ah(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ah(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ah(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};df.className=\"Cropping2D\";J.registerClass(df);var hf=class extends _t{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?\"nearest\":t.interpolation,j$(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\"?hn.resizeNearestNeighbor(n,[s,i]):hn.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\"?hn.resizeNearestNeighbor(n,[s,i]):hn.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}};hf.className=\"UpSampling2D\";J.registerClass(hf);function oJ(r,t,e=[1,1],n=\"valid\",o,s){return B(()=>{o==null&&(o=yn()),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=ua(i,t,e,n===\"same\"?\"same\":\"valid\",\"NHWC\",s),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}var gf=class extends Jc{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=oJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=bn(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=An(e,this.kernelSize[0],this.padding,this.strides[0]),i=An(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}};gf.className=\"DepthwiseConv2D\";J.registerClass(gf);function ZN(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 JN(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 ${u}D.`);let l=[1,0].concat(xn(2,u));if(t=Vt(t,l),s!=null)throw new kt(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),o!=null&&(o=Q(Q(o,\"bool\"),\"float32\"),o.rank===u-1&&(o=ar(o,-1)),o=Vt(o,l)),n&&(t=hr(t,0),o!=null&&(o=hr(o,0)));let c=[],p,m=e,f=t.shape[0],d=xr(t),h;o!=null&&(h=xr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=lt(Ir(N),N),A=Y($(w[0],N),$(m[0],E)),D=m.map((F,P)=>Y($(w[1][P],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var Dn=class extends _t{constructor(t){super(t);let e;if(t.cell==null)throw new z(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(t.cell)?e=new ep({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ie({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return xn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Hy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${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 En(\"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=ZN(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 nn){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=JN((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=_l(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()===Dn.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=Cn(o,n);return new t(Object.assign(e,{cell:s}))}};Dn.className=\"RNN\";J.registerClass(Dn);var Rl=class extends _t{},Qc=class extends Rl{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=bi(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=Bc([1,gi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Bc([1,gi([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 ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0Ir(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0Ir(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Fo($(t,i),this.kernel.read()):s=Fo(t,this.kernel.read()),this.bias!=null&&(s=bn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=Y(s,Fo(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:yi(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)}};Qc.className=\"SimpleRNNCell\";J.registerClass(Qc);var xf=class extends Dn{constructor(t){t.cell=new Qc(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)}};xf.className=\"SimpleRNN\";J.registerClass(xf);var tp=class extends Rl{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=bi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=bi(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=Bc([1,gi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Bc([1,gi([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)}};yf.className=\"GRU\";J.registerClass(yf);var Fl=class extends Rl{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=bi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=bi(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=Bc([1,gi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Bc([1,gi([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 wn{apply(u,l){let c=s.apply([i]),p=new Vu().apply([i]),m=s.apply([i*2]);return MN(MN(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 ${t.length}.`);let o=t[1],s=t[2];t=t[0],0Ir(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0Ir(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};bf.className=\"LSTM\";J.registerClass(bf);var ep=class extends Rl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{hi(`RNNCell_${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(Cn(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 $h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Uy(t(),e),a=()=>Bu(i,t,n);return!o||o<=1?$e(a().clone()):Array(o).fill(void 0).map(a).map(l=>$e(l.clone()))}var sJ=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 En(\"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=An(l,o[0],s,i[0],a[0]),m=An(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};_b.className=\"ConvRNN2D\";var rp=class extends Fl{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=Uu(n,2,\"kernelSize\"),this.kernelSize.forEach(u=>Qe(u,\"kernelSize\")),this.strides=Uu(o||1,2,\"strides\"),this.strides.forEach(u=>Qe(u,\"strides\")),this.padding=s||\"valid\",gn(this.padding),this.dataFormat=i||\"channelsLast\",Oe(this.dataFormat),this.dilationRate=Uu(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 wn{apply(m,f){let d=l.apply([c]),h=dr([c]),g=l.apply([c*2]);return Pm([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=sJ(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=Tn(t,e,this.strides,o||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?bn(s,n,this.dataFormat):s}recurrentConv(t,e){return Tn(t,e,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};rp.className=\"ConvLSTM2DCell\";J.registerClass(rp);var wf=class extends _b{constructor(t){let e=new rp(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};wf.className=\"ConvLSTM2D\";J.registerClass(wf);var np=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()}};np.className=\"Dropout\";J.registerClass(np);var If=class extends np{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};If.className=\"SpatialDropout1D\";J.registerClass(If);var Cf=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=bi(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=Fo(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Fo(n,this.kernel.read()),this.bias!=null&&(s=bn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:yi(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}};Cf.className=\"Dense\";J.registerClass(Cf);var vf=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 ${t.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[t[0],Ro(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:yi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};Sf.className=\"Activation\";J.registerClass(Sf);var Nf=class extends _t{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),Z$(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};Nf.className=\"RepeatVector\";J.registerClass(Nf);var kf=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}};kf.className=\"Reshape\";J.registerClass(kf);var Tf=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=xn(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}};Tf.className=\"Permute\";J.registerClass(Tf);var _f=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 bc(mi(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=-1,s=!0,i=bc(mi(n,this.maskValue),o,s);return $(n,Q(i,n.dtype))})}};_f.className=\"Masking\";J.registerClass(_f);var Ef=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),mi(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=rn(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}};Ef.className=\"Embedding\";J.registerClass(Ef);var Pl=class extends _t{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new kt}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: ${JSON.stringify(t)}.`);let n=t[0]==null?null:t[0].slice(1);for(let s=1;ss.length);t.indexOf(null)===-1&&$o(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=gi(o);for(let i of t){let a=i.rank;for(let u=0;u1){let c=xn(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(xn(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 ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:ar(o,0));let n=e[0];for(let o=1;o{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0];for(let n=1;n{let e=t[0];for(let n=1;n1)throw new z(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \"+JSON.stringify(t))}mergeFunction(t){return B(()=>Pm(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new z(\"A `Concatenate` layer should be called on a list of inputs.\");let e=t,n=e[0].slice(),o=this.axis<0?n.length+this.axis:this.axis;for(let s of e.slice(1)){if(n[o]==null||s[o]==null){n[o]=null;break}n[o]+=s[o]}return n}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new z(\"`mask` should be an array for Concatenate\");if(!Array.isArray(t))throw new z(\"`inputs` should be an array for Concatenate\");if(e.length!==t.length)throw new z(`Mismatch in the length of mask (${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 kt(\"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 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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])),iJ(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 kt(\"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}};Pf.className=\"Dot\";J.registerClass(Pf);var Mf=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 Bu(()=>Y(Mm(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Mf.className=\"GaussianNoise\";J.registerClass(Mf);var Lf=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?Bu(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Mm(n.shape,1,s))},()=>n,e.training||!1):n})}};Lf.className=\"GaussianDropout\";J.registerClass(Lf);var zf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return Bu(()=>{let s=St(t),i=1.6732632423543772,a=1.0507009873554805,u=-i*a,l=mn(Hn(n),this.rate);l=rn(l,\"float32\");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=Y($(s,l),$(Y(l,-1),u));return Y($(m,c),p)},()=>St(t),e.training||!1)}return t})}};zf.className=\"AlphaDropout\";J.registerClass(zf);function Gh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=Sx(r,t,e,n,o,s);else if(r.rank===3)i=Nx(r,t,e,n,o,s);else if(r.rank===4)i=kx(r,t,e,n,o,s);else throw new kt(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function aJ(r,t,e,n,o=.001){return B(()=>{let s=vc(r,n),i=s.mean,a=s.variance;return[Gh(r,i,a,e,t,o),i,a]})}function lJ(r,t,e,n,o=.001){return B(()=>{let s=vc(r,n),i=s.mean,a=s.variance,u=[];for(let d of 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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 ${JSON.stringify(t)}.`);this.inputSpec=[new Ie({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=xn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=Ao(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,xn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),I=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return Gh(o,b,w,I,N,this.epsilon)}else return Gh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=uJ(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,I)=>{B(()=>{let N=1-I,E=b.read(),A=$(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}};Bf.className=\"BatchNormalization\";J.registerClass(Bf);var Vf=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: ${s}`);if(this.axis.length!==$o(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}=vc(n,this.axis,!0),l=Ao(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=yn()),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(()=>cJ(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Gf.className=\"ZeroPadding2D\";J.registerClass(Gf);function Fb(r,t,e,n,o,s){return B(()=>{Oe(o),RN(s),gn(n),e==null&&(e=[1,1]),n==null&&(n=\"valid\"),o==null&&(o=yn()),s==null&&(s=\"max\"),r=Bh(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=Du(r,t,e,a):i=Su(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),RN(s),gn(n),e==null&&(e=[1,1,1]),n==null&&(n=\"valid\"),o==null&&(o=yn()),s==null&&(s=\"max\"),r=YN(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=Jx(r,t,e,a):i=vx(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,gn(this.padding),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=An(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=_l(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return qn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Wf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,\"max\")}};Wf.className=\"MaxPooling1D\";J.registerClass(Wf);var Uf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,\"avg\")}};Uf.className=\"AveragePooling1D\";J.registerClass(Uf);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),gn(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=An(e,this.poolSize[0],this.padding,this.strides[0]),n=An(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}},Hf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,\"max\")}};Hf.className=\"MaxPooling2D\";J.registerClass(Hf);var qf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,\"avg\")}};qf.className=\"AveragePooling2D\";J.registerClass(qf);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),gn(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=An(e,this.poolSize[0],this.padding,this.strides[0]),n=An(n,this.poolSize[1],this.padding,this.strides[1]),o=An(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}},Kf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),BR(t,e,n,o,s,\"max\")}};Kf.className=\"MaxPooling3D\";J.registerClass(Kf);var jf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),BR(t,e,n,o,s,\"avg\")}};jf.className=\"AveragePooling3D\";J.registerClass(jf);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 kt}},Xf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return ke(n,1)})}};Xf.className=\"GlobalAveragePooling1D\";J.registerClass(Xf);var Yf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Nr(n,1)})}};Yf.className=\"GlobalMaxPooling1D\";J.registerClass(Yf);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 kt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Zf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?ke(n,[1,2]):ke(n,[2,3])})}};Zf.className=\"GlobalAveragePooling2D\";J.registerClass(Zf);var Jf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Nr(n,[1,2]):Nr(n,[2,3])})}};Jf.className=\"GlobalMaxPooling2D\";J.registerClass(Jf);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=Cn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Qf=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 ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),JN((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Qf.className=\"TimeDistributed\";J.registerClass(Qf);function pJ(r){ya(q$,\"BidirectionalMergeMode\",r)}var mJ=\"concat\",td=class extends Ob{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=Cn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=Cn(o),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?mJ:t.mergeMode,pJ(this.mergeMode),t.weights)throw new kt(\"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()):Tr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=ZN(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 kt(\"Support for constants in Bidirectional layers is not implemented yet.\");let u=i[0]instanceof nn;for(let l of i)if(l instanceof nn!==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=Pm([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){hi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),hi(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=Cn(e.layer);if(delete e.layer,e.numConstants!=null)throw new kt(\"Deserialization of a Bidirectional layer with numConstants present is not supported yet.\");let o=e;return o.layer=n,new t(o)}};td.className=\"Bidirectional\";J.registerClass(td);var ed=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=rn(t,\"float32\")),Y($(t,this.scale),this.offset)))}};ed.className=\"Rescaling\";J.registerClass(ed);var{resizeBilinear:fJ,cropAndResize:dJ}=hn,rd=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=fJ(t,[e,n]);return rn(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}};rd.className=\"CenterCrop\";J.registerClass(rd);function VR(r,t,e,n){let o=St(r);if(o.dtype!==\"int32\"&&(o=rn(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 $(u,n);throw new z(\"When outputMode is 'tfIdf', weights must be provided.\")}var nd=class extends _t{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode=\"multiHot\"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode===\"oneHot\"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!==\"int32\"&&(t=rn(t,\"int32\"));let n;if(typeof e.countWeights!=\"undefined\"){if(this.outputMode!==\"count\")throw new z(`countWeights is not used when outputMode !== count.\n Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Nr(t),s=bl(t),i=Fe(this.numTokens,o).bufferSync().get(0),a=mn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return VR(t,this.outputMode,this.numTokens,n)})}};nd.className=\"CategoryEncoding\";J.registerClass(nd);var gJ=[\"bilinear\",\"nearest\"],GR=new Set(gJ),od=class extends _t{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(GR.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 hn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation===\"nearest\")return hn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...GR]} are supported`)})}};od.className=\"Resizing\";J.registerClass(od);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 xJ=[\"bilinear\",\"nearest\"],WR=new Set(xJ),sd=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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Got: ${this.factor}`);if(this.widthUpper{let n=St(t);this.imgHeight=n.shape[n.shape.length-3];let o=n.shape[n.shape.length-2];this.widthFactor=Hn([1],1+this.widthLower,1+this.widthUpper,\"float32\",this.randomGenerator.next());let s=this.widthFactor.dataSync()[0]*o;s=Math.round(s);let i=[this.imgHeight,s];switch(this.interpolation){case\"bilinear\":return hn.resizeBilinear(t,i);case\"nearest\":return hn.resizeNearestNeighbor(t,i);default:throw new Error(`Interpolation is ${this.interpolation}\n but only ${[...WR]} are supported`)}})}};sd.className=\"RandomWidth\";J.registerClass(sd);function yJ(r){return new xi(r)}function bJ(r){return new lf(r)}function wJ(r){return new of(r)}function IJ(r){return new sf(r)}function CJ(r){return new af(r)}function vJ(r){return new cf(r)}function SJ(r){return new uf(r)}function NJ(r){return new qu(r)}function kJ(r){return new Dl(r)}function TJ(r){return new pf(r)}function _J(r){return new $l(r)}function EJ(r){return new mf(r)}function AJ(r){return new ff(r)}function DJ(r){return new df(r)}function $J(r){return new hf(r)}function RJ(r){return new gf(r)}function FJ(r){return new Sf(r)}function OJ(r){return new Cf(r)}function PJ(r){return new np(r)}function MJ(r){return new If(r)}function LJ(r){return new vf(r)}function zJ(r){return new Nf(r)}function BJ(r){return new kf(r)}function VJ(r){return new Tf(r)}function GJ(r){return new Ef(r)}function WJ(r){return new Af(r)}function UJ(r){return new $f(r)}function HJ(r){return new Of(r)}function qJ(r){return new Rf(r)}function KJ(r){return new Ff(r)}function jJ(r){return new Df(r)}function XJ(r){return new Pf(r)}function YJ(r){return new Bf(r)}function ZJ(r){return new Vf(r)}function JJ(r){return new Gf(r)}function QN(r){return new Uf(r)}function QJ(r){return QN(r)}function t9(r){return QN(r)}function tk(r){return new qf(r)}function e9(r){return tk(r)}function r9(r){return tk(r)}function ek(r){return new jf(r)}function n9(r){return ek(r)}function o9(r){return ek(r)}function s9(r){return new Xf(r)}function i9(r){return new Zf(r)}function UR(r){return new Yf(r)}function HR(r){return new Jf(r)}function qR(r){return new Wf(r)}function KR(r){return new Hf(r)}function a9(r){return new Kf(r)}function l9(r){return new yf(r)}function u9(r){return new tp(r)}function c9(r){return new bf(r)}function p9(r){return new Fl(r)}function m9(r){return new xf(r)}function f9(r){return new Qc(r)}function d9(r){return new wf(r)}function h9(r){return new rp(r)}function g9(r){return new Dn(r)}function x9(r){return new ep(r)}function y9(r){return new td(r)}function b9(r){return new Qf(r)}var w9=UR,I9=HR,C9=qR,v9=KR;function S9(r){return new Mf(r)}function N9(r){return new Lf(r)}function k9(r){return new zf(r)}function T9(r){return new _f(r)}function _9(r){return new ed(r)}function E9(r){return new rd(r)}function A9(r){return new od(r)}function D9(r){return new nd(r)}function $9(r){return new sd(r)}var XR={};Kt(XR,{MAPE:()=>W9,MSE:()=>q9,binaryAccuracy:()=>R9,binaryCrossentropy:()=>F9,categoricalAccuracy:()=>P9,categoricalCrossentropy:()=>M9,cosineProximity:()=>B9,mape:()=>U9,meanAbsoluteError:()=>V9,meanAbsolutePercentageError:()=>G9,meanSquaredError:()=>H9,mse:()=>K9,precision:()=>L9,recall:()=>z9,sparseCategoricalAccuracy:()=>O9});function R9(r,t){return Ph(r,t)}function F9(r,t){return nb(r,t)}function O9(r,t){return ob(r,t)}function P9(r,t){return Mh(r,t)}function M9(r,t){return Lh(r,t)}function L9(r,t){return VN(r,t)}function z9(r,t){return yR(r,t)}function B9(r,t){return Oh(r,t)}function V9(r,t){return Jm(r,t)}function G9(r,t){return Gu(r,t)}function W9(r,t){return Gu(r,t)}function U9(r,t){return Gu(r,t)}function H9(r,t){return wa(r,t)}function q9(r,t){return wa(r,t)}function K9(r,t){return wa(r,t)}var YR={};Kt(YR,{modelFromJSON:()=>RR});var ZR={};Kt(ZR,{l1:()=>X9,l1l2:()=>j9,l2:()=>Y9});function j9(r){return new Wu(r)}function X9(r){return MR(r)}function Y9(r){return LR(r)}var Mb=class extends Al{constructor(){super(...arguments),this.model=null}setModel(t){if(!(t instanceof jn))throw new Error(\"model must be a LayersModel, not some other Container\");this.model=t}};function Pb(r,t){return rt}var Lb=class extends Mb{constructor(t){if(super(),t==null&&(t={}),t.restoreBestWeights)throw new kt(\"restoreBestWeights = True is not implemented in EarlyStopping yet.\");this.monitor=t.monitor||\"val_loss\",this.minDelta=Math.abs(t.minDelta||0),this.patience=t.patience||0,this.verbose=t.verbose||0,this.mode=t.mode||\"auto\",this.baseline=t.baseline,[\"auto\",\"min\",\"max\"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. 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o=v(\"strides\",r,t,e),s=v(\"pad\",r,t,e),i=v(\"kernelSize\",r,t,e),a=v(\"includeBatchInIndex\",r,t,e),{result:u,indexes:l}=n.maxPoolWithArgmax(v(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s,a);return[u,l]}case\"AvgPool3D\":{let o=v(\"strides\",r,t,e),s=v(\"pad\",r,t,e),i=v(\"kernelSize\",r,t,e);return[n.avgPool3d(v(\"x\",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case\"MaxPool3D\":{let o=v(\"strides\",r,t,e),s=v(\"pad\",r,t,e),i=v(\"kernelSize\",r,t,e);return[n.maxPool3d(v(\"x\",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case\"Dilation2D\":{let o=v(\"strides\",r,t,e),s=v(\"pad\",r,t,e),i=v(\"dilations\",r,t,e),a=o[1],u=o[2],l=i[1],c=i[2];return[n.dilation2d(v(\"x\",r,t,e),v(\"filter\",r,t,e),[a,u],s,[l,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var fF=(r,t,e,n=ae)=>{switch(r.op){case\"Fill\":{let o=v(\"shape\",r,t,e),s=v(\"dtype\",r,t,e),i=v(\"value\",r,t,e);return[n.fill(o,i,s)]}case\"LinSpace\":{let 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v(\"x\",r,t,e).map(c=>n.tensor1d(c.shape));case\"Size\":return[n.scalar(v(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[n.scalar(v(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[n.scalar(1)];case\"Print\":let i=v(\"x\",r,t,e),a=v(\"data\",r,t,e),u=v(\"message\",r,t,e),l=v(\"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=v(\"keyDType\",r,t,e),i=v(\"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=v(\"tableHandle\",r,t,e,n),s=v(\"keys\",r,t,e),i=v(\"values\",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let o=v(\"tableHandle\",r,t,e,n),s=v(\"keys\",r,t,e),i=v(\"defaultValue\",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let o=v(\"tableHandle\",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var yF=(r,t,e,n=ae)=>{switch(r.op){case\"ResizeBilinear\":{let o=v(\"images\",r,t,e),s=v(\"size\",r,t,e),i=v(\"alignCorners\",r,t,e),a=v(\"halfPixelCenters\",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case\"ResizeNearestNeighbor\":{let o=v(\"images\",r,t,e),s=v(\"size\",r,t,e),i=v(\"alignCorners\",r,t,e),a=v(\"halfPixelCenters\",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case\"CropAndResize\":{let o=v(\"image\",r,t,e),s=v(\"boxes\",r,t,e),i=v(\"boxInd\",r,t,e),a=v(\"cropSize\",r,t,e),u=v(\"method\",r,t,e),l=v(\"extrapolationValue\",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case\"ImageProjectiveTransformV3\":{let o=v(\"images\",r,t,e),s=v(\"transforms\",r,t,e),i=v(\"outputShape\",r,t,e),a=v(\"fillValue\",r,t,e),u=v(\"interpolation\",r,t,e),l=v(\"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 bF=(r,t,e,n=ae)=>{switch(r.op){case\"Equal\":return[n.equal(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"NotEqual\":return[n.notEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Greater\":return[n.greater(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"GreaterEqual\":return[n.greaterEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Less\":return[n.less(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LessEqual\":return[n.lessEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LogicalAnd\":return[n.logicalAnd(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LogicalNot\":return[n.logicalNot(v(\"a\",r,t,e))];case\"LogicalOr\":return[n.logicalOr(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[n.where(v(\"condition\",r,t,e),v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"BitwiseAnd\":return[n.bitwiseAnd(v(\"a\",r,t,e),v(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var wF=(r,t,e,n=ae)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[n.matMul(v(\"a\",r,t,e),v(\"b\",r,t,e),v(\"transposeA\",r,t,e),v(\"transposeB\",r,t,e))];case\"Einsum\":return[n.einsum(v(\"equation\",r,t,e),...v(\"tensors\",r,t,e))];case\"Transpose\":return[n.transpose(v(\"x\",r,t,e),v(\"perm\",r,t,e))];case\"_FusedMatMul\":let[o,s]=v(\"fusedOps\",r,t,e),i=o===\"biasadd\",a=s===\"prelu\",u=v(\"numArgs\",r,t,e),l=v(\"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]=v(\"args\",r,t,e);return[n.fused.matMul({a:v(\"a\",r,t,e),b:v(\"b\",r,t,e),transposeA:v(\"transposeA\",r,t,e),transposeB:v(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case\"MatrixBandPart\":return[n.linalg.bandPart(v(\"a\",r,t,e),v(\"numLower\",r,t,e),v(\"numUpper\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var IF=(r,t,e,n=ae)=>{switch(r.op){case\"EuclideanNorm\":return[n.euclideanNorm(v(\"x\",r,t,e),v(\"axis\",r,t,e),v(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[n.batchNorm(v(\"x\",r,t,e),v(\"mean\",r,t,e),v(\"variance\",r,t,e),v(\"offset\",r,t,e),v(\"scale\",r,t,e),v(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[n.batchNorm(v(\"x\",r,t,e),v(\"mean\",r,t,e),v(\"variance\",r,t,e),v(\"offset\",r,t,e),v(\"scale\",r,t,e),v(\"epsilon\",r,t,e))];case\"LRN\":return[n.localResponseNormalization(v(\"x\",r,t,e),v(\"radius\",r,t,e),v(\"bias\",r,t,e),v(\"alpha\",r,t,e),v(\"beta\",r,t,e))];case\"Softmax\":return[n.softmax(v(\"x\",r,t,e))];case\"LogSoftmax\":return[n.logSoftmax(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var CF=(r,t,e,n=ae)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v(\"paramsNestedSplits\",r,t,e),v(\"paramsDenseValues\",r,t,e),v(\"indices\",r,t,e),v(\"outputRaggedRank\",r,t,e));return o.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v(\"starts\",r,t,e),v(\"limits\",r,t,e),v(\"splits\",r,t,e));return[o,s]}case\"RaggedTensorToTensor\":return[n.raggedTensorToTensor(v(\"shape\",r,t,e),v(\"values\",r,t,e),v(\"defaultValue\",r,t,e),v(\"rowPartitionTensors\",r,t,e),v(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var vF=(r,t,e,n=ae)=>{switch(r.op){case\"Max\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.max(v(\"x\",r,t,e),a,u)]}case\"Mean\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.mean(v(\"x\",r,t,e),a,u)]}case\"Min\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.min(v(\"x\",r,t,e),a,u)]}case\"Sum\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.sum(v(\"x\",r,t,e),a,u)]}case\"All\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.all(v(\"x\",r,t,e),a,u)]}case\"Any\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.any(v(\"x\",r,t,e),a,u)]}case\"ArgMax\":{let a=v(\"axis\",r,t,e);return[n.argMax(v(\"x\",r,t,e),a)]}case\"ArgMin\":{let a=v(\"axis\",r,t,e);return[n.argMin(v(\"x\",r,t,e),a)]}case\"Prod\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.prod(v(\"x\",r,t,e),a,u)]}case\"Cumprod\":{let a=v(\"axis\",r,t,e),u=v(\"exclusive\",r,t,e),l=v(\"reverse\",r,t,e);return[n.cumprod(v(\"x\",r,t,e),a,u,l)]}case\"Cumsum\":{let a=v(\"axis\",r,t,e),u=v(\"exclusive\",r,t,e),l=v(\"reverse\",r,t,e);return[n.cumsum(v(\"x\",r,t,e),a,u,l)]}case\"Bincount\":let o=v(\"x\",r,t,e),s=v(\"weights\",r,t,e),i=v(\"size\",r,t,e);return[n.bincount(o,s,i)];case\"DenseBincount\":{let a=v(\"x\",r,t,e),u=v(\"weights\",r,t,e),l=v(\"size\",r,t,e),c=v(\"binaryOutput\",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var SF=(r,t,e,n=ae)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let o=v(\"n\",r,t,e),s=v(\"axis\",r,t,e),i=v(\"tensors\",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case\"Gather\":{let o=v(\"x\",r,t,e),s=v(\"indices\",r,t,e);return[n.gather(o,n.cast(s,\"int32\"),0)]}case\"GatherV2\":{let o=v(\"axis\",r,t,e),s=v(\"batchDims\",r,t,e),i=v(\"x\",r,t,e),a=v(\"indices\",r,t,e);return[n.gather(i,n.cast(a,\"int32\"),o,s)]}case\"Reverse\":{let o=v(\"dims\",r,t,e),s=[];for(let a=0;a{let o=v(\"axis\",r,t,e),s=v(\"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=v(\"axis\",r,t,e),s=v(\"tensor\",r,t,e);return n.unstack(s,o)}case\"Tile\":{let o=v(\"reps\",r,t,e);return[n.tile(v(\"x\",r,t,e),o)]}case\"Split\":case\"SplitV\":{let o=v(\"axis\",r,t,e),s=v(\"numOrSizeSplits\",r,t,e),i=v(\"x\",r,t,e);return n.split(i,s,o)}case\"ScatterNd\":{let o=v(\"indices\",r,t,e),s=v(\"values\",r,t,e),i=v(\"shape\",r,t,e);return[n.scatterND(o,s,i)]}case\"GatherNd\":{let o=v(\"x\",r,t,e),s=v(\"indices\",r,t,e);return[n.gatherND(o,s)]}case\"SparseToDense\":{let o=v(\"sparseIndices\",r,t,e),s=v(\"outputShape\",r,t,e),i=v(\"sparseValues\",r,t,e),a=v(\"defaultValue\",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case\"TensorScatterUpdate\":{let o=v(\"indices\",r,t,e),s=v(\"values\",r,t,e),i=v(\"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=ae)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v(\"indices\",r,t,e),v(\"values\",r,t,e),v(\"denseShape\",r,t,e),v(\"defaultValue\",r,t,e));return[o,s,i,a]}case\"SparseReshape\":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v(\"inputIndices\",r,t,e),v(\"inputShape\",r,t,e),v(\"newShape\",r,t,e));return[o,s]}case\"SparseSegmentMean\":return[n.sparse.sparseSegmentMean(v(\"data\",r,t,e),v(\"indices\",r,t,e),v(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[n.sparse.sparseSegmentSum(v(\"data\",r,t,e),v(\"indices\",r,t,e),v(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var kF=(r,t,e,n=ae)=>{switch(r.op){case\"FFT\":return[n.fft(v(\"x\",r,t,e))];case\"IFFT\":return[n.ifft(v(\"x\",r,t,e))];case\"RFFT\":return[n.rfft(v(\"x\",r,t,e))];case\"IRFFT\":return[n.irfft(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var TF=(r,t,e,n=ae)=>{switch(r.op){case\"StaticRegexReplace\":return[n.string.staticRegexReplace(v(\"input\",r,t,e),v(\"pattern\",r,t,e),v(\"rewrite\",r,t,e),v(\"replaceGlobal\",r,t,e))];case\"StringNGrams\":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v(\"data\",r,t,e),v(\"dataSplits\",r,t,e),v(\"separator\",r,t,e),v(\"nGramWidths\",r,t,e),v(\"leftPad\",r,t,e),v(\"rightPad\",r,t,e),v(\"padWidth\",r,t,e),v(\"preserveShortSequences\",r,t,e));return[o,s]}case\"StringSplit\":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v(\"input\",r,t,e),v(\"delimiter\",r,t,e),v(\"skipEmpty\",r,t,e));return[o,s,i]}case\"StringToHashBucketFast\":return[n.string.stringToHashBucketFast(v(\"input\",r,t,e),v(\"numBuckets\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _F=(r,t,e,n=ae)=>{switch(r.op){case\"Cast\":return[n.cast(v(\"x\",r,t,e),v(\"dtype\",r,t,e))];case\"ExpandDims\":{let o=v(\"axis\",r,t,e);return[n.expandDims(v(\"x\",r,t,e),o)]}case\"Squeeze\":{let o=v(\"axis\",r,t,e);return[n.squeeze(v(\"x\",r,t,e),o)]}case\"Reshape\":return[n.reshape(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"EnsureShape\":return[n.ensureShape(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"MirrorPad\":return[n.mirrorPad(v(\"x\",r,t,e),v(\"padding\",r,t,e),v(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[n.pad(v(\"x\",r,t,e),v(\"padding\",r,t,e),v(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let o=v(\"blockShape\",r,t,e),s=v(\"paddings\",r,t,e);return[n.spaceToBatchND(v(\"x\",r,t,e),o,s)]}case\"BatchToSpaceND\":{let o=v(\"blockShape\",r,t,e),s=v(\"crops\",r,t,e);return[n.batchToSpaceND(v(\"x\",r,t,e),o,s)]}case\"DepthToSpace\":{let o=v(\"blockSize\",r,t,e),s=v(\"dataFormat\",r,t,e).toUpperCase();return[n.depthToSpace(v(\"x\",r,t,e),o,s)]}case\"BroadcastTo\":return[n.broadcastTo(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"BroadcastArgs\":return[n.broadcastArgs(v(\"s0\",r,t,e),v(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Nk(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case\"arithmetic\":return o(()=>nF(i,a,u));case\"basic_math\":return o(()=>oF(i,a,u));case\"control\":return cF(i,a,u);case\"convolution\":return o(()=>mF(i,a,u));case\"creation\":return o(()=>fF(i,a,u));case\"dynamic\":return dF(i,a,u);case\"evaluation\":return o(()=>hF(i,a,u));case\"image\":return o(()=>yF(i,a,u));case\"graph\":return o(()=>gF(i,a,u));case\"logical\":return o(()=>bF(i,a,u));case\"matrices\":return o(()=>wF(i,a,u));case\"normalization\":return o(()=>IF(i,a,u));case\"ragged\":return o(()=>CF(i,a,u));case\"reduction\":return o(()=>vF(i,a,u));case\"slice_join\":return o(()=>SF(i,a,u));case\"sparse\":return o(()=>NF(i,a,u));case\"spectral\":return o(()=>kF(i,a,u));case\"string\":return o(()=>TF(i,a,u));case\"transformation\":return o(()=>_F(i,a,u));case\"hash_table\":return xF(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 kk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>vn(m)[0]));n=n||[];let c=new Set(n.map(m=>vn(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Ku(m)||jQ(m)||XQ(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 EF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>vn(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=WQ(d,u);return UQ(h,u),h}function WQ(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 ad=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function UQ(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 ad(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new ad(`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 ad(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new ad(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function AF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Ku(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new op(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+\"--\"+o.join(this.SEPARATOR)}compile(t,e){let n=kk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${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=EF(this.graph,n),u=AF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return $e(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[vn(m)[0]]),s=e.map(m=>vn(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Kh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=vn(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=Nk(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. 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Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m=\"\";if(p.node.op===\"Enter\"&&v(\"isConstant\",p.node,o,n)&&([m]=Ii(p.node.name,n)),o[p.node.name]==null){let f=Nk(p.node,o,n,this._resourceManager);m||([m]=Ii(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=Ii(a.name,n);s[u]||!i.has(a.name)||(a.op===\"Merge\"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=vn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${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 ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=vn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=vn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var ew=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var YQ=\"?tfjs-format=file\",ZQ=\"model.json\",jh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Lr){this.modelUrl=t,this.loadOptions=e,this.version=\"n/a\",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new ew}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else 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s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new op(qh.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=qh.Instance.transformGraph(t.modelInitializer);this.initializer=new op(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t==\"string\"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than 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ZF={};Kt(ZF,{CSVDataset:()=>cd,Dataset:()=>vi,FileDataSource:()=>hd,TextLineDataset:()=>ud,URLDataSource:()=>gd,array:()=>VF,csv:()=>qF,func:()=>KF,generator:()=>jF,microphone:()=>YF,version_data:()=>Kk,webcam:()=>XF,zip:()=>GF});var BF=Xl(bh());var MF=Xl(bh());function $F(r,t){return rw(r,t)}function rw(r,t,e=new Map,n=new Set){if(r==null)return null;if(typeof Blob==\"function\"&&r instanceof Blob)return r.slice();if(n.has(r))throw new Error(\"Circular references are not supported.\");if(e.has(r))return e.get(r);let o=t(r);if(o.recurse&&o.value!==null)throw new Error(\"A deep map function may not return both a value and recurse=true.\");if(o.recurse)if(ju(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let a=r[i],u=rw(a,t,e,n);s[i]=u}return n.delete(r),r.__proto__&&(s.__proto__=r.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return e.set(r,o.value),o.value}function RF(r,t=_k){return FF(r,t)}function FF(r,t,e=new Set){let 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ld{constructor(){super(sp.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 Fk(this,t,e)}columnMajorBatch(t,e=!0,n=_k){return this.rowMajorBatch(t,e).map(s=>RF(s,n))}concatenate(t,e){return new sw(Vk([this,t]),e)}take(t){return t<0||t==null?this:new Rk(this,t)}skip(t){return t<0||t==null?this:new $k(this,t)}prefetch(t){return new iw(this,t)}shuffle(t,e){return new Bk(this,t,e)}serial(){return new Dk(this)}},Ek=class extends tr{constructor(t){super(),this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:PF(t),done:!1}}},Ak=class extends tr{constructor(t){super(),this.nextFn=t}summary(){return\"Function call\"}async 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tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}},Ok=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Pk=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=So.getTensorsInContainer(t.value),n=this.transform(t.value),o=So.getTensorsInContainer(n);for(let s of e)So.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Mk=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=So.getTensorsInContainer(t.value),n=await this.transform(t.value),o=So.getTensorsInContainer(n);for(let s of e)So.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},ip=class extends tr{constructor(){super(),this.outputQueue=new sp,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}}},Lk=class extends ip{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=So.getTensorsInContainer(t.value),n=this.transform(t.value),o=So.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)So.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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nw(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Ll.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 pd(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 md=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=fi([i,s,u,a],[1,4])}else this.cropBox=fi([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 md(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: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ar(Q(t,\"float32\"),0),n;n=hn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,\"bilinear\");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error(\"Can not convert infinite video stream to array.\")}};var fd=class{};var Zh=class extends tr{split(t){return new Wk(this,t)}},Wk=class extends Zh{constructor(t,e){super(),this.upstream=t,this.impl=new Uk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Uk=class extends ip{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=\"\"}summary(){return`${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 Hk(this)}},Hk=class extends Zh{constructor(t){super(),this.upstream=t,this.impl=new qk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},qk=class extends ip{constructor(t){if(super(),this.upstream=t,L().get(\"IS_BROWSER\"))this.decoder=new 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eet(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=wt(i.shape,\"float32\"),E=1/(f*d),A=e.data.get(o.dataId).values,D=wt(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 lP={kernelName:Zl,backendName:\"cpu\",kernelFunc:eet};function ret(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=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=Bo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var cP={kernelName:Pi,backendName:\"cpu\",kernelFunc:net};function oet(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=bd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var pP={kernelName:Oa,backendName:\"cpu\",kernelFunc:oet};function set(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 mP={kernelName:Ql,backendName:\"cpu\",kernelFunc:set};var iet=At(yo,(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 Zr({inputs:{x:u[0]},backend:e});if(u[0].dtype===\"complex64\"){let h=u.map(I=>Mo({inputs:{input:I},backend:e})),g=u.map(I=>va({inputs:{input:I},backend:e})),x=Yu({inputs:h,backend:e,attrs:{axis:s}}),b=Yu({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=ap(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 gP={kernelName:Mi,backendName:\"cpu\",kernelFunc:Yu};function _T(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],Ct=et+mt*D;for(let Rt=0;Rt=m.inWidth)continue;let ge=gt+qt*E[1],re=Ct+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 Ct=0;Ct=l.inWidth)continue;let ce=mt+Ht*F[2],ge=gt+qt*l.inChannels,re=ce;for(let xe=0;xeMath.cos(r)),vP={kernelName:is,backendName:\"cpu\",kernelFunc:det};var het=At(as,r=>Math.cosh(r)),SP={kernelName:as,backendName:\"cpu\",kernelFunc:het};function get(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=wt([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,Ct=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 OP={kernelName:ou,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 ${ou}, 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 PP={kernelName:nu,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 ${nu}, 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 Net(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||\"2d\";if(c!==\"2d\")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype===\"float32\"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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Mnt(r,t,e){switch(r.length){case 0:return DL();case 1:return Knt(r,t,e);case 2:return eot(r,t,e);case 3:return Xnt(r,t,e);case 4:return Znt(r,t,e);case 5:return Jnt(r,t);case 6:return Qnt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function Lnt(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function znt(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function Bnt(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function Vnt(r){return`${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 ${Wnt}\n ${Unt}\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`,Wnt=`\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`,Unt=`\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`,Hnt=`\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 DL(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function qnt(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 Knt(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 * ${t[1]} + resTexRC.y;\n }\n `}function jnt(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx 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getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.x * ${t[1]} + resTexRC.y;\n ${n}\n return ivec3(r, c, d);\n }\n `}function Ynt(r,t,e){if(e)return`\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a=\"\",u=\"b, r, c\";for(let l=2;l=1?c=\"coords = 0;\":c=a.map(b=>`coords.${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 `}function hot(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`\n float ${o}() {\n return sampleTexture(${e}, resultUV);\n }\n `;let l=zt(u),c=_L(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.${f[h+p]} = 0;`).join(`\n`);let d=\"\";return u<2&&a>0?d=\"coords\":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(\", \"),`\n float ${o}() {\n ${l} coords = getOutputCoords();\n ${m}\n return get${n}(${d});\n }\n `}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 ${r} is not yet supported`)}function zw(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.lengthr[e]).join(\", \")}function RL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=EL(o,i,t),u=zT(r.gl,a),l=r.createProgram(u);return L().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},n1(r,t,l)))}function n1(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,\"NAN\",!1),L().getNumber(\"WEBGL_VERSION\")===1&&(u=r.getUniformLocation(e,\"INFINITY\",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${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 $L(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 ${u} must match`)})}function FL(r,t,e,n,o){t.program.enableShapeUniforms||($L(t.inShapeInfos,e),$L([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=zw(r.packedInputs,i.shape,u),m=\"\",f=\"\",d=\"\";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${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=Zu.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 Gw=class{constructor(t){this.variableNames=[\"A\"],this.outTexUsage=Jr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=`\n ${Lw}\n\n void main() {\n float x = getAAtOutCoords();\n ${e.output} = encode_float(x);\n }\n `}};var Ww=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Jr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=`\n ${Lw}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${e.output} = encode_float(x);\n }\n `}};var yot={R:0,G:1,B:2,A:3},cg=class{constructor(t,e=!1,n=\"RGBA\"){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let o=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let s=\"result\";e&&(s=\"floor(result * 255. + 0.5)\");let i=\"\";for(let a=0;am1,createBufferFromOutputTexture:()=>h1,createFloat16MatrixTexture:()=>l1,createFloat16PackedMatrixTexture:()=>p1,createFloat32MatrixTexture:()=>a1,createIndexBuffer:()=>i1,createPackedMatrixTexture:()=>c1,createUnsignedBytesMatrixTexture:()=>u1,createVertexBuffer:()=>s1,createVertexShader:()=>o1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>x1,downloadFloat32MatrixFromBuffer:()=>g1,downloadMatrixFromPackedOutputTexture:()=>b1,downloadPackedMatrixFromBuffer:()=>y1,getInternalFormatForFloat16MatrixTexture:()=>qw,getInternalFormatForFloat16PackedMatrixTexture:()=>Xw,getInternalFormatForFloat32MatrixTexture:()=>Hw,getInternalFormatForPackedMatrixTexture:()=>jw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Kw,uploadDenseMatrixToTexture:()=>f1,uploadPixelDataToTexture:()=>d1});function o1(r){let t=We(),e=`${t.version}\n precision highp float;\n ${t.attribute} vec3 clipSpacePos;\n ${t.attribute} vec2 uv;\n ${t.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = vec4(clipSpacePos, 1);\n resultUV = uv;\n }`;return LT(r,e)}function s1(r){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return GT(r,t)}function i1(r){let t=new Uint16Array([0,1,2,2,1,3]);return WT(r,t)}function pg(r,t,e,n,o,s){HT(t,e);let i=UT(r),a=r.TEXTURE_2D;return ht(r,()=>r.bindTexture(a,i)),ht(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),ht(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),ht(r,()=>r.texParameteri(a,r.TEXTURE_MIN_FILTER,r.NEAREST)),ht(r,()=>r.texParameteri(a,r.TEXTURE_MAG_FILTER,r.NEAREST)),L().getNumber(\"WEBGL_VERSION\")===1?ht(r,()=>r.texImage2D(a,0,n,t,e,0,o,s,null)):ht(r,()=>r.texStorage2D(a,1,n,t,e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:i,texShape:[e,t]}}function Hw(r){return r.internalFormatFloat}function a1(r,t,e,n){let[o,s]=xp(t,e);return pg(r,o,s,Hw(n),n.textureFormatFloat,r.FLOAT)}function qw(r){return r.internalFormatHalfFloat}function l1(r,t,e,n){let[o,s]=xp(t,e);return pg(r,o,s,qw(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Kw(r){return r.downloadTextureFormat}function u1(r,t,e,n){let[o,s]=xp(t,e);return pg(r,o,s,Kw(n),r.RGBA,r.UNSIGNED_BYTE)}function jw(r){return r.internalFormatPackedFloat}function c1(r,t,e,n){let[o,s]=Sa(t,e);return pg(r,o,s,jw(n),r.RGBA,r.FLOAT)}function Xw(r){return r.internalFormatPackedHalfFloat}function p1(r,t,e,n){let[o,s]=Sa(t,e);return pg(r,o,s,Xw(n),r.RGBA,n.textureTypeHalfFloat)}function m1(r,t,e){return ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,e)),Ow(r,t,\"clipSpacePos\",e,3,20,0)&&Ow(r,t,\"uv\",e,2,20,12)}function f1(r,t,e,n,o,s){ht(r,()=>r.bindTexture(r.TEXTURE_2D,t));let i,a,u;o instanceof Uint8Array?(i=new Uint8Array(e*n*4),a=r.UNSIGNED_BYTE,u=r.RGBA):(i=new Float32Array(e*n*4),a=r.FLOAT,u=s.internalFormatPackedFloat),i.set(o),L().getNumber(\"WEBGL_VERSION\")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,e,n,r.RGBA,a,i)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,u,e,n,0,r.RGBA,a,i)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function d1(r,t,e){ht(r,()=>r.bindTexture(r.TEXTURE_2D,t)),e.data instanceof Uint8Array?L().getNumber(\"WEBGL_VERSION\")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,e.width,e.height,r.RGBA,r.UNSIGNED_BYTE,e.data)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,e.width,e.height,0,r.RGBA,r.UNSIGNED_BYTE,e.data)):L().getNumber(\"WEBGL_VERSION\")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,e)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function h1(r,t,e,n){let o=r.createBuffer();ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));let a=4*4*t*e;return ht(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,a,r.STREAM_READ)),ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,0)),ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function g1(r,t,e){let n=r,o=new Float32Array(e);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function x1(r,t,e,n){let[o,s]=xp(t,e),i=4,a=new Uint8Array(IL(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function y1(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array(CL(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function b1(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var wp=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=L().getNumber(\"WEBGL_VERSION\");if(t!=null?(this.gl=t,FT(e,t)):this.gl=Yn(e),t=this.gl,L().getNumber(\"WEBGL_VERSION\")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let 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=Nd(this.gl,s),Zn(this.gl,i))this.textureHalfFloatExtension=Nd(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),Zn(this.gl,o))this.colorBufferHalfFloatExtension=Nd(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\",Zn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Zn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=s1(this.gl),this.indexBuffer=i1(this.gl),this.framebuffer=qT(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(),a1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),l1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),u1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),d1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),f1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),p1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),c1(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,()=>x1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return y1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return g1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=h1(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 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e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),m1(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?KT(this.gl,t,e):jT(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(),XT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=Sa(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),kd(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=Nd(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=bot(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&&kd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ug(this.gl,this.outputTexture,this.framebuffer),this.debug&&kd(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&&kd(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 bot(r){let t=0;for(;t`${r}.${e}`)}function er(r,t){return t===1?[r]:I1(r,t)}function Tz(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 Pd=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 ${wot(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Ad():Ed(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 wot(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?TL([\"r\",\"c\",\"d\"],\"inputShape\"):ki([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var tI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=Ez(e,n),s=Az(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=_z(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===zr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===zr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===zr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===zr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===zr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=Ez(n,o),i=Az(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=_z(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${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 Iot(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 _z(r,t,e,n,o){let s=Cot(t,n),i;if(o){let[u,l]=Sa(r[0],r[1]);i=u*l}else{let[u,l]=xp(r[0],r[1]);i=u*l}let a=Iot(e,s);return i*a}function Cot(r,t){switch(r){case zr.PACKED_2X2_FLOAT32:return jw(t);case zr.PACKED_2X2_FLOAT16:return Xw(t);case zr.UNPACKED_FLOAT32:return Hw(t);case zr.UNPACKED_FLOAT16:return qw(t);case zr.PACKED_4X1_UNSIGNED_BYTE:return Kw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function vot(r){return L().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?zr.PACKED_2X2_FLOAT32:zr.UNPACKED_FLOAT32:r?zr.PACKED_2X2_FLOAT16:zr.UNPACKED_FLOAT16}function Ez(r,t){if(r===Jr.UPLOAD)return zr.PACKED_2X2_FLOAT32;if(r===Jr.RENDER||r==null)return vot(t);if(r===Jr.DOWNLOAD||r===Jr.PIXELS)return zr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function Az(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var Br=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;\",Dz=\"return x;\",C1=\"return abs(x);\";var $z=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",Rz=yr+`\n return (x < 0.0) ? 0.0 : x;\n`,Fz=yr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Na=\"return x;\",Oz=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var Mz=\"return x;\",Lz=`\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`,zz=`\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`,Bz=`\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`,Vz=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Fn=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 ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var eI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let e=t.length,n=er(\"rc\",e),o=zt(e),s=Tz(e,n),i=n.slice(-2),a=e<=1?\"rc\":`vec2(${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 Not=jr.whereImpl,kot=1e-7,Tot=1e-4,rI={};function _ot(r){return r in rI||(rI[r]={}),rI[r]}var Eot=L().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),Aot=600;function Dot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Aot/1024/1024}var Qu=class extends Uo{nextDataId(){return Qu.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 wp)e=t;else{let n=Yn(L().getNumber(\"WEBGL_VERSION\"),t);e=new wp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Yn(L().getNumber(\"WEBGL_VERSION\"));e=new wp(n),this.binaryCache=_ot(L().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new tI(this.gpgpu),this.numMBBeforeWarning=Dot(),this.texData=new Da(this,Wn())}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=Td(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:Jr.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:Jr.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 Fn(a,Na):m=new Br(a,Na);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 Fn(o,Na):d=new Br(o,Na);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)&&Wn().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 Fn(s,Na):f=new Br(s,Na);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=Wn().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 wt(t.shape,t.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return wt(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=Eot){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 Wn().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=[Vl(t.shape),...Gl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Vl(e),...Gl(e)],i=new Pd(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=Td(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===Zu.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&&!Ju(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=OL(t,c,p),f=this.getAndSaveBinary(m,()=>RL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get(\"ENGINE_COMPILE_ONLY\")||FL(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?kot:Tot}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=YT(n,u),e.texShape=p),s!=null){let m=Td(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=Sa(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=Jr.PIXELS:w.usage=Jr.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=$ot(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}=n1(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=Wn().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 Wn().makeTensorFromDataId(l,e,n,u)}};Qu.nextDataId=0;function $ot(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 Qu,2);var JDe={forceHalfFloat:Wz};var Md=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var On=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 Qn=`\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 Jn=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 Uz={kernelName:bo,backendName:\"webgl\",kernelFunc:rr};function Pn(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 Hz={kernelName:zp,backendName:\"webgl\",kernelFunc:Pn};var v1=\"return (a < 0.) ? b * a : a;\",S1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Rot(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 Jn(S1,o.shape,i.shape):new On(v1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],\"float32\");return e.disposeIntermediateTensorInfo(i),u}var qz={kernelName:vs,backendName:\"webgl\",kernelFunc:Rot};var N1=\"return (a < 0.) ? b * a : a;\",k1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Fot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Jn(k1,n.shape,o.shape):new On(N1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],\"float32\")}var Kz={kernelName:zs,backendName:\"webgl\",kernelFunc:Fot};var Vo=\"if (isnan(x)) return x;\";function It({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 Fn(i.shape,t):c=new Br(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 On(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Pn({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 Jn(t,u.shape,l.shape,e):f=new On(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Wl(r,t=!1){if(r===\"linear\")return t?Mz:Dz;if(r===\"relu\")return t?zz:Rz;if(r===\"elu\")return t?Lz:$z;if(r===\"relu6\")return t?Bz:Fz;if(r===\"prelu\")return t?k1:N1;if(r===\"leakyrelu\")return t?S1:v1;if(r===\"sigmoid\")return t?Vz:Oz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Ld=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&&!Ju(o.shape,u)&&!(c.texture!==null&&Ju(c.shape,u))?Yz(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var Zz={kernelName:Ui,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]), 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 Pot(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 to(r,t,e,n){let o=Pot(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 ${this.rank} is not yet supported.`);let o=zt(this.rank),s=I1(\"rc\",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u===\"leakyrelu\",H=u!=null?Wl(u,!0):null,K=G||W||q||H!=null,X;if((f===1||d===1)&&V>_1&&K===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,at=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(ot));let it=d===1?2:1,mt=nt;at&&(mt=rt({inputs:{x:nt},backend:o,attrs:{shape:[P,1,V]}}),F.push(mt));let gt=fg({inputs:{a:ot,b:mt},backend:o});X=Cp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new Ld(N,E,[P,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let at=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));st.push(at),F.push(at)}X=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:X},backend:o,attrs:{shape:I}});F.push(X);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function Lot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return vp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var e3={kernelName:Zi,backendName:\"webgl\",kernelFunc:Lot};var r3=\"return abs(x);\";function zot(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!==\"complex64\"){let s=e.texData.get(n.dataId),i=Zw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new Fn(n.shape,r3):o=new Br(n.shape,r3),e.runWebGLProgram(o,[n],n.dtype)}var n3={kernelName:$i,backendName:\"webgl\",kernelFunc:zot};var Bot=yr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,Vot=It({opSnippet:Bot}),o3={kernelName:qo,backendName:\"webgl\",kernelFunc:Vot};var Got=yr+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,Wot=It({opSnippet:Got}),s3={kernelName:Ko,backendName:\"webgl\",kernelFunc:Wot};var i3=\"return a + b;\",Uot=ue({opSnippet:i3,packedOpSnippet:i3,supportsComplex:!0,cpuKernelImpl:PL}),a3={kernelName:ao,backendName:\"webgl\",kernelFunc:Uot};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=`\n void main() {\n ${n.join(`\n `)}\n\n float result = ${o};\n setOutput(result);\n }\n `}};var aI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${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=`\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 l3={kernelName:jo,backendName:\"webgl\",kernelFunc:lI};function Hot(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=to(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 u3={kernelName:Ra,backendName:\"webgl\",kernelFunc:Hot};function qot(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=to(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 c3={kernelName:Fa,backendName:\"webgl\",kernelFunc:qot};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 p3(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=p3(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function m3(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=m3(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=p3(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 m3(r,t,n)}function Kot(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 f3={kernelName:Ri,backendName:\"webgl\",kernelFunc:Kot};function jot(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 d3={kernelName:Fi,backendName:\"webgl\",kernelFunc:jot};var Xot=yr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,Yot=It({opSnippet:Xot}),h3={kernelName:Xo,backendName:\"webgl\",kernelFunc:Yot};var Zot=yr+\"return log(x + sqrt(x * x + 1.0));\",Jot=It({opSnippet:Zot}),g3={kernelName:Yo,backendName:\"webgl\",kernelFunc:Jot};var Qot=yr+`\n return atan(x);\n`,tst=It({opSnippet:Qot}),x3={kernelName:Zo,backendName:\"webgl\",kernelFunc:tst};var est=Md+`\n return atan(a, b);\n`,rst=`\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 `+Qn+`\n return result;\n`,nst=ue({opSnippet:est,packedOpSnippet:rst}),y3={kernelName:Qo,backendName:\"webgl\",kernelFunc:nst};var ost=yr+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelName:Jo,backendName:\"webgl\",kernelFunc:sst};var Ti=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 `}},ec=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 ist(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ni(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 Ti(c,\"avg\",!1);return e.runWebGLProgram(p,[o],\"float32\")}var w3={kernelName:ts,backendName:\"webgl\",kernelFunc:ist};function ast(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 ec(p,\"avg\",!1);return e.runWebGLProgram(m,[o],\"float32\")}var I3={kernelName:Oi,backendName:\"webgl\",kernelFunc:ast};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 lst(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:Jl,backendName:\"webgl\",kernelFunc:lst};function ust(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;Ni([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 v3={kernelName:Zl,backendName:\"webgl\",kernelFunc:ust};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return vp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var S3={kernelName:es,backendName:\"webgl\",kernelFunc:cst};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 pst=({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)},N3={kernelName:ys,backendName:\"webgl\",kernelFunc:pst};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=mst(this.rank),o,s=t.map((i,a)=>`sourceLoc.${E1[a]} = start[${a}] + coords.${E1[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 `}},E1=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function mst(r){if(r===1)return\"sourceLoc\";if(r<=6)return E1.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]} < 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u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function _i(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=dz(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),fst(o,a,u,e)}var k3={kernelName:qi,backendName:\"webgl\",kernelFunc:_i};var dst=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 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i=e.texData.get(o.dataId).values,[a,u,l]=zL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s===\"int32\")return R3(o,e);if(s===\"bool\"){let i=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),u=A1({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 F3={kernelName:xo,backendName:\"webgl\",kernelFunc:D1};var O3=\"return ceil(x);\",Cst=It({opSnippet:O3,packedOpSnippet:O3,cpuKernelImpl:BL}),P3={kernelName:rs,backendName:\"webgl\",kernelFunc:Cst};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 vst(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 M3={kernelName:yo,backendName:\"webgl\",kernelFunc:vst};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 L3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Sst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new wI(n.shape),i=[L3(n,o.complexTensorInfos.real),L3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var z3={kernelName:tu,backendName:\"webgl\",kernelFunc:Sst};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 Sp(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 B3={kernelName:qp,backendName:\"webgl\",kernelFunc:Sp};function zd(r,t,e){let n=r[0].dtype;if(n===\"complex64\"){let f=r.map(b=>Ul({inputs:{input:b},backend:e})),d=r.map(b=>Sp({inputs:{input:b},backend:e})),h=zd(f,t,e),g=zd(d,t,e),x=Pn({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=VL(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 Br(r[0].shape,Na):new Fn(r[0].shape,Na);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}=Nst(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 Nst(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 $1(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}):zd(u,s,e)}var V3={kernelName:Mi,backendName:\"webgl\",kernelFunc:$1};var Bd=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 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.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>_1)&&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(Ju(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=vp({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=vp({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?Wl(a,!0):null,q=new Ld(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 kst(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 Vd(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 Bd(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:ns,backendName:\"webgl\",kernelFunc:kst};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 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Tst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new EI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var W3={kernelName:Bp,backendName:\"webgl\",kernelFunc:Tst};var RI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=t.inShape,this.enableShapeUniforms=de(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${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 _st(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 U3={kernelName:os,backendName:\"webgl\",kernelFunc:_st};function Est(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 H3={kernelName:ss,backendName:\"webgl\",kernelFunc:Est};function Ast(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 q3={kernelName:Ma,backendName:\"webgl\",kernelFunc:Ast};function Dst(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 K3={kernelName:La,backendName:\"webgl\",kernelFunc:Dst};var $st=Vo+`\n return cos(x);\n`,Rst=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Qn}\n return result;\n`,Fst=It({opSnippet:$st,packedOpSnippet:Rst}),j3={kernelName:is,backendName:\"webgl\",kernelFunc:Fst};var Ost=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,Pst=It({opSnippet:Ost}),X3={kernelName:as,backendName:\"webgl\",kernelFunc:Pst};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(${f} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var Mst=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new FI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],\"float32\")},Y3={kernelName:Ba,backendName:\"webgl\",kernelFunc:Mst};var Np;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Np||(Np={}));var hg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,i=this.op===Np.Prod?\"1.0\":\"0.0\",a=n?i:`getX(${Z3(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 = ${J3(s,\"coords\",this.op)};\n float val = ${a};\n int pow2 = int(pow(2.0, index));\n if (${l}) {\n int idx = ${c};\n ${J3(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${Z3(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function Z3(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 ${r} is not yet supported`)}function J3(r,t,e){if(r===1)return`${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=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new hg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new hg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Lst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(Np.Prod,o,e,s,i,a)}var Q3={kernelName:za,backendName:\"webgl\",kernelFunc:Lst};function zst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(Np.Sum,o,e,s,i,a)}var tB={kernelName:ls,backendName:\"webgl\",kernelFunc:zst};function Bst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Yw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=ML(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var eB={kernelName:eu,backendName:\"webgl\",kernelFunc:Bst};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 Vst(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 rB={kernelName:Va,backendName:\"webgl\",kernelFunc:Vst};var Gd=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 Wd=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 Wd(p):m=new Gd(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 nB={kernelName:us,backendName:\"webgl\",kernelFunc:Gst};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 * ${u} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Wst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new MI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var oB={kernelName:Vp,backendName:\"webgl\",kernelFunc:Wst};function Ust(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new LI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var sB={kernelName:Gp,backendName:\"webgl\",kernelFunc:Ust};var zI=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function Hst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new zI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var iB={kernelName:ru,backendName:\"webgl\",kernelFunc:Hst};var BI=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`\n const ivec2 strides = ivec2(${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 qst(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 aB={kernelName:cs,backendName:\"webgl\",kernelFunc:qst};function Kst(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=Cp({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 lB={kernelName:Wp,backendName:\"webgl\",kernelFunc:Kst};var jst=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",Xst=`\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`,Yst=It({opSnippet:jst,packedOpSnippet:Xst}),uB={kernelName:ms,backendName:\"webgl\",kernelFunc:Yst};var Zst=\"return (b >= 0.0) ? a : a * (b + 1.0);\",Jst=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,Qst=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Jn(Jst,n.shape,o.shape):new On(Zst,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},cB={kernelName:Ga,backendName:\"webgl\",kernelFunc:Qst};var tit=`\n return vec4(equal(a, b));\n`,eit=\"return float(a == b);\",rit=ue({opSnippet:eit,packedOpSnippet:tit,dtype:\"bool\",cpuKernelImpl:GL}),pB={kernelName:Wa,backendName:\"webgl\",kernelFunc:rit};var nit=`\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 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,oit=It({opSnippet:nit}),mB={kernelName:fs,backendName:\"webgl\",kernelFunc:oit};var sit=Vo+`\n return exp(x);\n`,iit=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,R1=It({opSnippet:sit,packedOpSnippet:iit,cpuKernelImpl:WL,dtype:\"float32\"}),fB={kernelName:ds,backendName:\"webgl\",kernelFunc:R1};function VI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var dB={kernelName:Li,backendName:\"webgl\",kernelFunc:VI};var hB=\"return exp(x) - 1.0;\",ait=It({opSnippet:hB,packedOpSnippet:hB,cpuKernelImpl:UL}),gB={kernelName:hs,backendName:\"webgl\",kernelFunc:ait};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=Pn({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 lit(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!1,e)}var xB={kernelName:Up,backendName:\"webgl\",kernelFunc:lit};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 Hl(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 yB={kernelName:su,backendName:\"webgl\",kernelFunc:Hl};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 < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var bB={kernelName:Ua,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new UI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var wB=\"return floor(x);\",uit=It({opSnippet:wB,packedOpSnippet:wB,cpuKernelImpl:HL}),IB={kernelName:gs,backendName:\"webgl\",kernelFunc:uit};var cit=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,pit=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,mit=ue({opSnippet:cit,packedOpSnippet:pit,dtype:\"int32\"}),CB={kernelName:xs,backendName:\"webgl\",kernelFunc:mit};var HI=class{constructor(t){this.variableNames=[\"A\"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${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 vB={kernelName:oh,backendName:\"webgl\",kernelFunc:fit},Ud,F1=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function fit(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\");(Ud==null||h!==F1)&&(F1=h,Ud=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:F1})),Ud.canvas.width=u,Ud.canvas.height=l,Ud.drawImage(o,0,0,u,l),o=Ud.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=Jr.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 dit(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?Wl(f,!0):null,F=new Vd(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?Wl(f,!1):null,F=new Bd(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 SB={kernelName:Ji,backendName:\"webgl\",kernelFunc:dit};function hit(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?Wl(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 Wd(g,I,b,N,E):A=new Gd(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 NB={kernelName:Qi,backendName:\"webgl\",kernelFunc:hit};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 git(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=qL(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 kB={kernelName:Ha,backendName:\"webgl\",kernelFunc:git};var jI=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=xit(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 xit(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, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype===\"string\"){let b=e.bufferSync(f),w=e.bufferSync(m),I=KL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new jI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var TB={kernelName:zi,backendName:\"webgl\",kernelFunc:O1};var yit=\"return float(a > b);\",bit=`\n return vec4(greaterThan(a, b));\n`,wit=ue({opSnippet:yit,packedOpSnippet:bit,cpuKernelImpl:jL,dtype:\"bool\"}),_B={kernelName:qa,backendName:\"webgl\",kernelFunc:wit};var Iit=\"return float(a >= b);\",Cit=`\n return vec4(greaterThanEqual(a, b));\n`,vit=ue({opSnippet:Iit,packedOpSnippet:Cit,dtype:\"bool\",cpuKernelImpl:XL}),EB={kernelName:bs,backendName:\"webgl\",kernelFunc:vit};function Sit(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!0,e)}var AB={kernelName:Hp,backendName:\"webgl\",kernelFunc:Sit};var Nit=\"return float(!isnan(x) && !isinf(x));\",kit=It({opSnippet:Nit,dtype:\"bool\"}),DB={kernelName:ws,backendName:\"webgl\",kernelFunc:kit};var Tit=\"return float(isinf(x));\",_it=It({opSnippet:Tit,dtype:\"bool\"}),$B={kernelName:Is,backendName:\"webgl\",kernelFunc:_it};var Eit=\"return float(isnan(x));\",Ait=It({opSnippet:Eit,dtype:\"bool\"}),RB={kernelName:Cs,backendName:\"webgl\",kernelFunc:Ait};var Dit=\"return float(a < b);\",$it=`\n return vec4(lessThan(a, b));\n`,Rit=ue({opSnippet:Dit,packedOpSnippet:$it,cpuKernelImpl:YL,dtype:\"bool\"}),FB={kernelName:Ka,backendName:\"webgl\",kernelFunc:Rit};var Fit=\"return float(a <= b);\",Oit=`\n return vec4(lessThanEqual(a, b));\n`,Pit=ue({opSnippet:Fit,packedOpSnippet:Oit,cpuKernelImpl:ZL,dtype:\"bool\"}),OB={kernelName:ja,backendName:\"webgl\",kernelFunc:Pit};function Mit(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=JL(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var PB={kernelName:Xa,backendName:\"webgl\",kernelFunc:Mit};var Lit=Vo+`\n return x < 0.0 ? 0./0. : log(x);\n`,zit=`\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`,Bit=It({opSnippet:Lit,packedOpSnippet:zit,cpuKernelImpl:QL}),MB={kernelName:Ss,backendName:\"webgl\",kernelFunc:Bit};var Vit=Vo+`\n return log(1.0 + x);\n`,Git=It({opSnippet:Vit}),LB={kernelName:Ns,backendName:\"webgl\",kernelFunc:Git};var Wit=\"return float(a >= 1.0 && b >= 1.0);\",Uit=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,Hit=ue({opSnippet:Wit,packedOpSnippet:Uit,dtype:\"bool\"}),zB={kernelName:Ya,backendName:\"webgl\",kernelFunc:Hit};var qit=\"return float(!(x >= 1.0));\",Kit=It({opSnippet:qit}),BB={kernelName:Za,backendName:\"webgl\",kernelFunc:Kit};var jit=\"return float(a >= 1.0 || b >= 1.0);\",Xit=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,Yit=ue({opSnippet:jit,packedOpSnippet:Xit,dtype:\"bool\"}),VB={kernelName:Ja,backendName:\"webgl\",kernelFunc:Yit};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 Zit=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)},GB={kernelName:ks,backendName:\"webgl\",kernelFunc:Zit};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 Jit=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)},WB={kernelName:Qa,backendName:\"webgl\",kernelFunc:Jit};function UB(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=to(a,r.dtype,\"max\",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function P1(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 Ti(c,\"max\",!1);return e.runWebGLProgram(p,[o],o.dtype)}var KB={kernelName:Es,backendName:\"webgl\",kernelFunc:rat};function nat(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 ec(p,\"max\",!1);return e.runWebGLProgram(m,[o],o.dtype)}var jB={kernelName:Bi,backendName:\"webgl\",kernelFunc:nat};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 oat(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 ec(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 XB={kernelName:au,backendName:\"webgl\",kernelFunc:oat};function sat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;Ni([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 Ti(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 YB={kernelName:iu,backendName:\"webgl\",kernelFunc:sat};function ZB(r,t,e,n){let o=new Ti(e,\"max\",!1),s=n.runWebGLProgram(o,[r],\"float32\");o=new Ti(e,\"max\",!0,!0,t);let i=n.runWebGLProgram(o,[r],\"float32\");return[s,i]}var JB={kernelName:lu,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 Hl({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)},bV={kernelName:Ms,backendName:\"webgl\",kernelFunc:B1};var _at=`\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`,Eat=`\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 `+Qn+`\n return result;\n`,Aat=ue({opSnippet:_at,packedOpSnippet:Eat}),wV={kernelName:Ls,backendName:\"webgl\",kernelFunc:Aat};function Dat(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}=iz(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=xc(o.dtype),w=to(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 IV={kernelName:Bs,backendName:\"webgl\",kernelFunc:Dat};function $at(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=az(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var CV={kernelName:Kp,backendName:\"webgl\",kernelFunc:$at};function Rat(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]=lz(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 vV={kernelName:jp,backendName:\"webgl\",kernelFunc:Rat};function Fat(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]=uz(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var SV={kernelName:Xp,backendName:\"webgl\",kernelFunc:Fat};var V1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=cz(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},NV={kernelName:uu,backendName:\"webgl\",kernelFunc:V1};var Oat=\"return 1.0 / x;\",Pat=It({opSnippet:Oat}),kV={kernelName:Vs,backendName:\"webgl\",kernelFunc:Pat};var Mat=yr+`\n return (x < 0.0) ? 0.0 : x;\n`,Lat=`\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`,zat=It({opSnippet:Mat,packedOpSnippet:Lat}),TV={kernelName:Gs,backendName:\"webgl\",kernelFunc:zat};var Bat=yr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Vat=`\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`,Gat=It({opSnippet:Bat,packedOpSnippet:Vat}),_V={kernelName:Hs,backendName:\"webgl\",kernelFunc:Gat};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 Wat(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 EV={kernelName:Us,backendName:\"webgl\",kernelFunc:Wat};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 Uat(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 AV={kernelName:il,backendName:\"webgl\",kernelFunc:Uat};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 < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function Hat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new cC(o.shape,u,l,s,i):new uC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var DV={kernelName:Ws,backendName:\"webgl\",kernelFunc:Hat};var pC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${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 qat(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 $V={kernelName:sl,backendName:\"webgl\",kernelFunc:qat};var mC=class{constructor(t,e){this.variableNames=[\"x\"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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 Kat(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 RV={kernelName:qs,backendName:\"webgl\",kernelFunc:Kat};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 < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var FV={kernelName:hl,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new dC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var jat=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n 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setOutput(mix(${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 Jat(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 rc(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 MV={kernelName:al,backendName:\"webgl\",kernelFunc:Jat};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 zV={kernelName:Hi,backendName:\"webgl\",kernelFunc:tlt};var elt=`\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`,rlt=It({opSnippet:elt}),BV={kernelName:Xs,backendName:\"webgl\",kernelFunc:rlt};var nlt=Vo+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,olt=`\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`,slt=It({opSnippet:nlt,packedOpSnippet:olt,cpuKernelImpl:fz}),VV={kernelName:Qs,backendName:\"webgl\",kernelFunc:slt};var ilt=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,alt=It({opSnippet:ilt}),GV={kernelName:Js,backendName:\"webgl\",kernelFunc:alt};var llt=Vo+`\n return sin(x);\n`,ult=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Qn}\n return result;\n`,clt=It({opSnippet:llt,packedOpSnippet:ult}),WV={kernelName:Ys,backendName:\"webgl\",kernelFunc:clt};var plt=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,mlt=It({opSnippet:plt}),UV={kernelName:Zs,backendName:\"webgl\",kernelFunc:mlt};var flt=`\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`,dlt=It({opSnippet:flt}),HV={kernelName:ti,backendName:\"webgl\",kernelFunc:dlt};var hlt=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},qV={kernelName:Ki,backendName:\"webgl\",kernelFunc:hlt};function glt(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]=hz(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 KV={kernelName:cu,backendName:\"webgl\",kernelFunc:glt};function xlt(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]=gz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var jV={kernelName:cl,backendName:\"webgl\",kernelFunc:xlt};function ylt(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 ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=Jw(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var XV={kernelName:pu,backendName:\"webgl\",kernelFunc:ylt};function blt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at 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s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=yz(i,\"string\",n);return e.makeTensorInfo(o.shape,\"string\",a)}var oG={kernelName:cc,backendName:\"webgl\",kernelFunc:klt};function Tlt({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=yr+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new Br(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var sG={kernelName:wo,backendName:\"webgl\",kernelFunc:Tlt};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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Dlt(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=Cz(i,o);return e.makeTensorInfo(s.shape,\"int32\",a)}var uG={kernelName:gu,backendName:\"webgl\",kernelFunc:Dlt};var $lt=\"return tan(x);\",Rlt=It({opSnippet:$lt}),cG={kernelName:ii,backendName:\"webgl\",kernelFunc:Rlt};var Flt=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,Olt=It({opSnippet:Flt}),pG={kernelName:ai,backendName:\"webgl\",kernelFunc:Olt};function Plt(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 rc(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 mG={kernelName:ll,backendName:\"webgl\",kernelFunc:Plt};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=wt(o.shape,o.dtype,l),p=Sz(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 fG={kernelName:lo,backendName:\"webgl\",kernelFunc:G1};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 kp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function dG(r){let t=1;for(;tu){let P=e.readSync(o.dataId),[V,G]=Nz(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,Hl({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&&kp(e,f);let x=dG(s),b=dG(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),kp(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),kp(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=_i({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),kp(e,E);let A=O1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});kp(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),kp(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),kp(e,F),[A,w]}var hG={kernelName:fl,backendName:\"webgl\",kernelFunc:Llt};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 zlt(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 gG={kernelName:dl,backendName:\"webgl\",kernelFunc:zlt};function Blt(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;Ni(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}=kz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],\"int32\",l)]}var xG={kernelName:xu,backendName:\"webgl\",kernelFunc:Blt};function Vlt(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 yG={kernelName:Xi,backendName:\"webgl\",kernelFunc:Vlt};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 Glt(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=xc(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=V1({backend:e,attrs:{start:0,stop:D,step:1,dtype:\"float32\"}}),K=G1({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 bG={kernelName:yu,backendName:\"webgl\",kernelFunc:Glt};var Wlt=[e3,n3,o3,s3,a3,l3,u3,c3,f3,d3,h3,g3,x3,y3,b3,w3,I3,C3,v3,S3,N3,T3,_3,E3,A3,F3,P3,M3,Hz,z3,V3,G3,W3,U3,H3,q3,K3,j3,X3,Y3,Q3,tB,eB,rB,nB,oB,sB,iB,aB,lB,uB,cB,pB,mB,fB,dB,gB,xB,yB,bB,IB,CB,vB,SB,NB,kB,TB,_B,EB,Uz,AB,B3,DB,$B,RB,qz,FB,OB,PB,MB,LB,zB,BB,VB,GB,WB,HB,qB,KB,jB,XB,YB,JB,tV,eV,rV,nV,oV,uV,Xz,cV,pV,mV,fV,D3,dV,xV,yV,bV,wV,Kz,IV,CV,vV,SV,NV,$3,sV,kV,TV,_V,Zz,EV,AV,DV,$V,RV,FV,OV,PV,MV,LV,zV,BV,VV,GV,WV,UV,k3,lV,HV,qV,KV,jV,XV,YV,ZV,JV,tG,eG,nG,oG,sG,iG,aG,lG,uG,aV,Qz,cG,pG,mG,fG,hG,gG,t3,xG,yG,bG,hV];for(let r of Wlt)pc(r);var Nt;(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\"})(Nt||(Nt={}));var nc;(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\"})(nc||(nc={}));var wG;function Ult(r){wG=r.wasm.cwrap(Zi,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Hlt(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 ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=nc[c];if(g==null)throw new Error(`${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=Hr.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 wG(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var IG={kernelName:Zi,backendName:\"wasm\",setupFunc:Ult,kernelFunc:Hlt};function yt(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,Nt[a.dtype],c),l}return{kernelName:r,backendName:\"wasm\",setupFunc:n,kernelFunc:o}}var CG=yt($i);var vG=yt(qo);var SG=yt(Ko);function ee(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,Nt[l.dtype],b))(),h}return{kernelName:r,backendName:\"wasm\",setupFunc:o,kernelFunc:s}}var qlt=!0,NG=ee(ao,qlt);var kG;function Klt(r){kG=r.wasm.cwrap(jo,null,[\"array\",\"number\",\"number\",\"number\"])}function jlt(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 kG(s,o.length,Nt[n.dtype],i),n}var TG={kernelName:jo,backendName:\"wasm\",setupFunc:Klt,kernelFunc:jlt};function Tp(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 _G={kernelName:bo,backendName:\"wasm\",kernelFunc:Tp};var EG;function Xlt(r){EG=r.wasm.cwrap(uo,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function go(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=Zlt(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 AG={kernelName:uo,backendName:\"wasm\",kernelFunc:go,setupFunc:Xlt};function Sn(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=cp(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)mut(u,c[0],m,s,i);else if(f===3)fut(u,c[0],c[1],m,s,i);else if(f===4)dut(u,c[0],c[1],c[2],m,s,i);else{let d=cp(u,s,i,t.shape,t.dtype);m.set(d)}return l}function mut(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=go({inputs:{x:f},backend:e,attrs:{perm:l}}),h=mr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Go({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 tW={kernelName:Pi,backendName:\"wasm\",kernelFunc:hut};var eW;function gut(r){eW=r.wasm.cwrap(Oa,null,[\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\"])}function xut(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 eW(c(o),i,a,c(s),Nt[s.dtype],c(l)),l}var rW={kernelName:Oa,backendName:\"wasm\",setupFunc:gut,kernelFunc:xut};var yut=!0,nW=ee(Pa,yut);function but(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 oW={kernelName:Ql,backendName:\"wasm\",kernelFunc:but};function Mn(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 sW={kernelName:xo,backendName:\"wasm\",kernelFunc:Mn};var iW=yt(rs);var aW;function wut(r){aW=r.wasm.cwrap(yo,null,[\"number\",\"number\",\"number\",\"number\"])}function Iut(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 aW(a,s,i,l),u}var lW={kernelName:yo,backendName:\"wasm\",setupFunc:wut,kernelFunc:Iut};function W1(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 Tp({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 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x=S.getUndoAxesPermutation(l);g=go({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var NW={kernelName:za,backendName:\"wasm\",setupFunc:Fut,kernelFunc:Out};var kW;function Put(r){kW=r.wasm.cwrap(ls,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Mut(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=go({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;kW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=go({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var TW={kernelName:ls,backendName:\"wasm\",setupFunc:Put,kernelFunc:Mut};var _W;function Lut(r){_W=r.wasm.cwrap(\"DenseBincount\",null,[\"number\",\"array\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"boolean\",\"number\"])}function zut(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 _W(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),Nt[s.dtype],a,p(c)),c}var EW={kernelName:eu,backendName:\"wasm\",setupFunc:Lut,kernelFunc:zut};var AW;function But(r){AW=r.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function Vut(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 AW(x,s,i===\"NHWC\"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var DW={kernelName:Va,backendName:\"wasm\",setupFunc:But,kernelFunc:Vut};var $W;function Gut(r){$W=r.wasm.cwrap(us,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Wut(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 $W(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 RW={kernelName:us,backendName:\"wasm\",setupFunc:Gut,kernelFunc:Wut};var FW;function Uut(r){FW=r.wasm.cwrap(\"Diag\",null,[\"number\",\"number\",\"number\",\"number\"])}function Hut(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 FW(e.dataIdMap.get(n.dataId).id,Nt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var OW={kernelName:ru,backendName:\"wasm\",setupFunc:Uut,kernelFunc:Hut};var PW;function qut(r){PW=r.wasm.cwrap(cs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Kut(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 LW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[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 zW={kernelName:ou,backendName:\"wasm\",setupFunc:jut,kernelFunc:Xut};var BW;function Yut(r){BW=r.wasm.cwrap(nu,null,[\"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,{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 ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(o.shape,o.dtype);return BW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var VW={kernelName:nu,backendName:\"wasm\",setupFunc:Yut,kernelFunc:Zut};var GW=yt(ms);var WW;function Jut(r){WW=r.wasm.cwrap(Ga,null,[\"number\",\"number\",\"number\"])}function Qut(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,\"float32\"),i=a=>e.dataIdMap.get(a.dataId).id;return WW(i(o),i(n),i(s)),s}var UW={kernelName:Ga,backendName:\"wasm\",setupFunc:Jut,kernelFunc:Qut};var tct=!1,HW=ee(Wa,tct,\"bool\");var qW=yt(fs);var KW=yt(ds,\"float32\");function NC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var jW={kernelName:Li,backendName:\"wasm\",kernelFunc:NC};var XW=yt(hs,\"float32\");function H1(r){let{attrs:{shape:t,value:e,dtype:n},backend:o}=r,s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var YW={kernelName:su,backendName:\"wasm\",kernelFunc:H1};var ZW;function ect(r){ZW=r.wasm.cwrap(Ua,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function rct(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return ZW(s,a,u,l,c,i),o}var JW={kernelName:Ua,backendName:\"wasm\",kernelFunc:rct,setupFunc:ect};var QW=yt(gs);var nct=!1,tU=ee(xs,nct);var eU;function oct(r){eU=r.wasm.cwrap(ys,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function sct(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return eU(c,p,m,f,d,o,g),h}var rU={kernelName:ys,backendName:\"wasm\",setupFunc:oct,kernelFunc:sct};var nU;function ict(r){nU=r.wasm.cwrap(Ji,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function act(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=nc[f];if(g==null)throw new Error(`${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 nU(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 oU={kernelName:Ji,backendName:\"wasm\",setupFunc:ict,kernelFunc:act};var sU;function lct(r){sU=r.wasm.cwrap(Qi,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 uct(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=nc[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 sU(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 iU={kernelName:Qi,backendName:\"wasm\",setupFunc:lct,kernelFunc:uct};var aU;function cct(r){aU=r.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function pct(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 aU(f,Nt[n.dtype],h,i,p,a,g,x),l}var lU={kernelName:Ha,backendName:\"wasm\",setupFunc:cct,kernelFunc:pct};var uU;function mct(r){uU=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function fct(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 uU(w,Nt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var cU={kernelName:zi,backendName:\"wasm\",setupFunc:mct,kernelFunc:fct};var dct=!1,pU=ee(qa,dct,\"bool\");var hct=!1,mU=ee(bs,hct,\"bool\");var fU=yt(ws,\"bool\");var dU=yt(Is,\"bool\");var hU=yt(Cs,\"bool\");var gU;function gct(r){gU=r.wasm.cwrap(vs,null,[\"number\",\"number\",\"number\",\"number\"])}function xct(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;gU(o,Nt[t.dtype],e,i)}return s}var xU={kernelName:vs,backendName:\"wasm\",setupFunc:gct,kernelFunc:xct};var yct=!1,yU=ee(Ka,yct,\"bool\");var bct=!1,bU=ee(ja,bct,\"bool\");var wU;function wct(r){wU=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\",\"number\"])}function Ict(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],\"float32\");return wU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var IU={kernelName:Xa,backendName:\"wasm\",setupFunc:wct,kernelFunc:Ict};var CU=yt(Ss);var vU=yt(Ns);var Cct=!1,SU=ee(Ya,Cct,\"bool\");var NU=yt(Za);var vct=!1,kU=ee(Ja,vct,\"bool\");var Sct=!1,TU=ee(v_,Sct,\"bool\");var _U;function Nct(r){_U=r.wasm.cwrap(ks,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function kct(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 _U(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var EU={kernelName:ks,backendName:\"wasm\",setupFunc:Nct,kernelFunc:kct};var AU;function Tct(r){AU=r.wasm.cwrap(Qa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function _ct(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 AU(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:Qa,backendName:\"wasm\",setupFunc:Tct,kernelFunc:_ct};var $U;function Ect(r){$U=r.wasm.cwrap(Ts,null,[\"number\",\"number\",\"number\",\"number\"])}function Act(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}=Sn(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;$U(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var RU={kernelName:Ts,backendName:\"wasm\",setupFunc:Ect,kernelFunc:Act};var Dct=!1,FU=ee(_s,Dct);var OU;function $ct(r){OU=r.wasm.cwrap(Es,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Rct(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 OU(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 PU={kernelName:Es,backendName:\"wasm\",setupFunc:$ct,kernelFunc:Rct};var MU;function Fct(r){MU=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 Oct(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 MU(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:Bi,backendName:\"wasm\",setupFunc:Fct,kernelFunc:Oct};var zU;function Pct(r){zU=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 Mct(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 zU(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 BU={kernelName:au,backendName:\"wasm\",setupFunc:Pct,kernelFunc:Mct};var VU;function Lct(r){VU=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function zct(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.computePool2DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return VU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),p}var GU={kernelName:iu,backendName:\"wasm\",setupFunc:Lct,kernelFunc:zct};var WU;function Bct(r){WU=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Vct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,includeBatchInIndex:u}=n;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let l=[1,1];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,[1,1],a),p=e.makeOutput(c.outShape,o.dtype),m=e.makeOutput(c.outShape,\"int32\");return WU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,e.dataIdMap.get(m.dataId).id,Nt[o.dtype],u,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[p,m]}var UU={kernelName:lu,backendName:\"wasm\",setupFunc:Bct,kernelFunc:Vct};var HU;function Gct(r){HU=r.wasm.cwrap(As,null,[\"number, number, number\"])}function Wct(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}=Sn(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=Mn({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;HU(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 qU={kernelName:As,backendName:\"wasm\",setupFunc:Gct,kernelFunc:Wct};var KU;function Uct(r){KU=r.wasm.cwrap(Ds,null,[\"number\",\"number\",\"number\",\"number\"])}function Hct(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}=Sn(i,o,t);if(f){let 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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.`)}n_=t}var jH=-1,t_=-1;function Smt(r){jH=r}function Nmt(){if(t_===-1)throw new Error(\"WASM backend not initialized.\");return t_}var kmt=\"4.7.0\";var Tmt=2;im(\"wasm\",async()=>{let{wasm:r}=await KH();return new Ig(r)},Tmt);var XH=\"4.7.0\",_mt=\"4.7.0\",Emt=\"4.7.0\",Amt=\"4.7.0\",Dmt=\"4.7.0\",$mt={tfjs:XH,\"tfjs-core\":XH,\"tfjs-converter\":_mt,\"tfjs-backend-cpu\":Emt,\"tfjs-backend-webgl\":Amt,\"tfjs-backend-wasm\":Dmt};export{$i as Abs,qo as Acos,Ko as Acosh,$c as AdadeltaOptimizer,Rc as AdagradOptimizer,Fc as AdamOptimizer,Oc as AdamaxOptimizer,ao as Add,jo as AddN,Ra as All,Fa as Any,Ri as ArgMax,Fi as ArgMin,Xo as Asin,Yo as Asinh,Zo as Atan,Qo as Atan2,Jo as Atanh,ts as AvgPool,Oi as AvgPool3D,Jl as AvgPool3DGrad,Zl as AvgPoolGrad,Ig as BackendWasm,es as BatchMatMul,Pi as BatchToSpaceND,Oa as Bincount,Pa as BitwiseAnd,Ql as BroadcastArgs,C_ as BroadcastTo,Mb as Callback,Yy as CallbackList,xo as Cast,rs as Ceil,yo as ClipByValue,zp as Complex,tu as ComplexAbs,Mi as Concat,ns as Conv2D,Bp as Conv2DBackpropFilter,os as Conv2DBackpropInput,ss as Conv3D,Ma as Conv3DBackpropFilterV2,La as Conv3DBackpropInputV2,is as Cos,as as Cosh,Ba as CropAndResize,za as Cumprod,ls as Cumsum,Jy as CustomCallback,Da as DataStorage,eu as DenseBincount,Va as DepthToSpace,us as DepthwiseConv2dNative,Vp as DepthwiseConv2dNativeBackpropFilter,Gp as DepthwiseConv2dNativeBackpropInput,ru as Diag,cs as Dilation2D,ou as Dilation2DBackpropFilter,nu as Dilation2DBackpropInput,Zg as Draw,g0 as ENV,Lb as EarlyStopping,Wp as Einsum,ms as Elu,Ga as EluGrad,rh as Environment,Wa as Equal,fs as Erf,ds as Exp,Li as ExpandDims,hs as Expm1,Up as FFT,su as Fill,Ua as FlipLeftRight,gs as Floor,xs as FloorDiv,oh as FromPixels,ys as FusedBatchNorm,Ji as FusedConv2D,Qi as FusedDepthwiseConv2D,wp as GPGPUContext,Ha as GatherNd,zi as GatherV2,jh as GraphModel,qa as Greater,bs as GreaterEqual,Zy as History,Hp as IFFT,bo as Identity,qp as Imag,Ie as InputSpec,ws as IsFinite,Is as IsInf,Cs as IsNan,Uo as KernelBackend,ks as LRN,Qa as LRNGrad,Dh as LayerVariable,jn as LayersModel,vs as LeakyRelu,Ka as Less,ja as LessEqual,Xa as LinSpace,Ss as Log,Ns as Log1p,S_ as LogSoftmax,Ya as LogicalAnd,Za as LogicalNot,Ja as LogicalOr,v_ as LogicalXor,Lmt as LowerBound,Xu as MathBackendCPU,Qu as MathBackendWebGL,zmt as MatrixBandPart,Ts as Max,Es as MaxPool,Bi as MaxPool3D,au as MaxPool3DGrad,iu as MaxPoolGrad,lu as MaxPoolWithArgmax,_s as Maximum,As as Mean,Ds as Min,$s as Minimum,Rs as MirrorPad,Fs as Mod,Pc as MomentumOptimizer,tl as Multinomial,Os as Multiply,Vi as Neg,rl as NonMaxSuppressionV3,nl as NonMaxSuppressionV4,ol as NonMaxSuppressionV5,el as NotEqual,M0 as OP_SCOPE_SUFFIX,Ps as OneHot,Gi as OnesLike,Kr as Optimizer,Nh as OptimizerConstructors,Wi as Pack,Ms as PadV2,Bmt as Pool,Ls as Pow,zs as Prelu,Bs as Prod,Mc as RMSPropOptimizer,Dn as RNN,Kp as RaggedGather,jp as RaggedRange,Xp as RaggedTensorToTensor,uu as Range,T0 as Rank,Yp as Real,ps as RealDiv,Vs as Reciprocal,Ze as Reduction,Gs as Relu,Hs as Relu6,Ui as Reshape,Us as ResizeBilinear,il as ResizeBilinearGrad,Ws as ResizeNearestNeighbor,sl as ResizeNearestNeighborGrad,qs as Reverse,hl as RotateWithOffset,Ks as Round,js as Rsqrt,Sl as SGDOptimizer,al as ScatterNd,ul as SearchSorted,Hi as Select,Xs as Selu,Ia as Sequential,Qs as Sigmoid,Js as Sign,Ys as Sin,Zs as Sinh,qi as Slice,ni as Softmax,ti as Softplus,Ki as SpaceToBatchND,cu as SparseFillEmptyRows,cl as SparseReshape,pu as SparseSegmentMean,mu as SparseSegmentSum,pl as SparseToDense,ji as SplitV,ei as Sqrt,fu as Square,oi as SquaredDifference,cc as StaticRegexReplace,wo as Step,ml as StridedSlice,du as StringNGrams,hu as StringSplit,gu as StringToHashBucketFast,si as Sub,ri as Sum,nn as SymbolicTensor,ii as Tan,ai as Tanh,Ot as Tensor,le as TensorBuffer,ll as TensorScatterUpdate,lo as Tile,fl as TopK,dl as Transform,uo as Transpose,xu as Unique,Xi as Unpack,yu as UnsortedSegmentSum,Vmt as UpperBound,gl as Variable,Yi as ZerosLike,Zi as _FusedMatMul,Ee as abs,hx as acos,gx as acosh,Y as add,IE as addN,lm as all,bc as any,oa as argMax,xx as argMin,yx as asin,bx as asinh,wx as atan,Ix as atan2,Cx as atanh,Su as avgPool,vx as avgPool3d,wE as backend,S as backend_util,SE as basicLSTMCell,aa as batchNorm,Sx as batchNorm2d,Nx as batchNorm3d,kx as batchNorm4d,Nu as batchToSpaceND,Tx as bincount,kE as bitwiseAnd,F5 as booleanMaskAsync,TE as broadcastArgs,la as broadcastTo,Hr as broadcast_util,Ay as browser,wt as buffer,J9 as callbacks,Q as cast,_x as ceil,Sr as clipByValue,cn as clone,kn as complex,ie as concat,Ex as concat1d,Ax as concat2d,Dx as concat3d,$x as concat4d,fR as constraints,cm as conv1d,Tn as conv2d,mm as conv2dTranspose,Rx as conv3d,Ox as conv3dTranspose,jmt as copyRegisteredKernels,ku as cos,fm as cosh,Ih as cosineWindow,Ic as cumprod,dm as cumsum,fn as customGrad,ZF as data,gh as denseBincount,K0 as deprecationWarn,Px as depthToSpace,ua as depthwiseConv2d,rQ as deregisterOp,Cu as device_util,_E as diag,Mx as dilation2d,uht as disableDeprecationWarnings,Tt as dispose,cht as disposeVariables,ct as div,Lx as divNoNan,zx as dot,cN as dropout,AE as einsum,ca as elu,lht as enableDebugMode,aht as enableProdMode,pN as enclosingPowerOfTwo,Wn as engine,DE as ensureShape,L as env,Fr as equal,Bx as erf,Vx as euclideanNorm,ir as exp,ar as expandDims,Gx as expm1,Cc as eye,Ou as fft,No as fill,ght as findBackend,xht as findBackendFactory,pa as floor,am as floorDiv,Wz as forceHalfFloat,Lu as fused,ma as gather,U5 as gatherND,Dy as gather_util,dht as getBackend,b0 as getGradient,ih as getKernel,Jg as getKernelsForBackend,Nmt as getThreadsCount,w1 as gpgpu_util,M6 as grad,L6 as grads,Fe as greater,mn as greaterEqual,vl as ifft,Tu as imag,hn as image,K5 as inTopKAsync,dR as initializers,KN as input,Lr as io,Tm as irfft,Wx as isFinite,Ux as isInf,Hx as isNaN,$e as keep,jr as kernel_impls,jR as layers,_u as leakyRelu,Il as less,Un as lessEqual,fN as linalg,FE as linspace,JQ as loadGraphModel,QQ as loadGraphModelSync,FR as loadLayersModel,qx as localResponseNormalization,kr as log,Eu as log1p,Xx as logSigmoid,hm as logSoftmax,gm as logSumExp,Pr as logicalAnd,Au as logicalNot,xm as logicalOr,Yx as logicalXor,j8 as losses,OE as lowerBound,Bt as matMul,k2 as math,Nr as max,Du as maxPool,Jx as maxPool3d,PE as maxPoolWithArgmax,_n as maximum,ke as mean,fh as memory,ME as meshgrid,XR as metrics,bl as min,mo as minimum,Qx as mirrorPad,ty as mod,JZ as model,YR as models,vc as moments,M5 as movingAverage,$ as mul,LE as multiRNNCell,zE as multinomial,Ut as neg,kh as nextFrame,wl as norm,mi as notEqual,fa as oneHot,dr as ones,Ir as onesLike,k as op,BE as outerProduct,dn as pad,VE as pad1d,GE as pad2d,WE as pad3d,UE as pad4d,ey as pool,pn as pow,Ru as prelu,dx as print,ry as prod,pht as profile,HE as raggedGather,qE as raggedRange,KE as raggedTensorToTensor,jE as rand,hA as randomGamma,kc as randomNormal,gA as randomStandardNormal,Hn as randomUniform,xA as randomUniformInt,da as range,fht as ready,Cl as real,ly as reciprocal,im as registerBackend,tJ as registerCallbackConstructor,k_ as registerGradient,pc as registerKernel,eQ as registerOp,ZR as regularizers,Mr as relu,ym as relu6,hht as removeBackend,R as reshape,hr as reverse,yA as reverse1d,bA as reverse2d,wA as reverse3d,IA as reverse4d,Pu as rfft,bm as round,wm as rsqrt,ft as scalar,z5 as scatterND,Mu as scatter_util,yh as searchSorted,Im as selu,Cm as separableConv2d,QZ as sequential,J as serialization,WK as setBackend,yht as setPlatform,Smt as setThreadsCount,Cmt as setWasmPath,vmt as setWasmPaths,FT as setWebGLContext,CA as setdiff1dAsync,Nw as shared,en as sigmoid,uy as sign,K8 as signal,vm as sin,Sm as sinh,Pt as slice,Nm as slice1d,wh as slice2d,km as slice3d,Tc as slice4d,ze as slice_util,Fu as softmax,pi as softplus,$u as spaceToBatchND,X8 as sparse,G5 as sparseToDense,q8 as spectral,gr as split,Ne as sqrt,Wt as square,_m as squaredDifference,qn as squeeze,qe as stack,To as step,cy as stridedSlice,Y8 as string,lt as sub,pt as sum,xc as sumOutType,py as tan,ia as tanh,sr as tensor,Ke as tensor1d,fi as tensor2d,my as tensor3d,vA as tensor4d,SA as tensor5d,NA as tensor6d,TA as tensorScatterUpdate,So as tensor_util,dA as test_util,B as tidy,Or as tile,mht as time,fy as topk,zc as train,Vt as transpose,Am as truncatedNormal,dy as unique,Kmt as unregisterGradient,qmt as unregisterKernel,Dm as unsortedSegmentSum,xr as unstack,ur as upcastType,_A as upperBound,y as util,z6 as valueAndGrad,B6 as valueAndGrads,hy as variable,Kx as variableGrads,$mt as version,DF as version_converter,B2 as version_core,MO as version_cpu,ef as version_layers,kmt as version_wasm,Gz as version_webgl,JDe as webgl,_d as webgl_util,be as where,xy 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 if (canvasArg instanceof CanvasRenderingContext2D) return canvasArg;\n const canvas = resolveInput(canvasArg);\n if (!(canvas instanceof Canvas)) throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n const ctx = canvas.getContext('2d', { willReadFrequently: true });\n if (!ctx) throw new Error('resolveContext2d - canvas 2d context is null');\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\": \"1.7.12\",\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\": \"^7.35.2\",\n \"@tensorflow/tfjs\": \"^4.7.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.7.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.7.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.7.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.7.0\",\n \"@tensorflow/tfjs-converter\": \"^4.7.0\",\n \"@tensorflow/tfjs-core\": \"^4.7.0\",\n \"@tensorflow/tfjs-data\": \"^4.7.0\",\n \"@tensorflow/tfjs-layers\": \"^4.7.0\",\n \"@tensorflow/tfjs-node\": \"^4.7.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.7.0\",\n \"@types/node\": \"^20.3.0\",\n \"@types/offscreencanvas\": \"^2019.7.0\",\n \"@typescript-eslint/eslint-plugin\": \"^5.59.9\",\n \"@typescript-eslint/parser\": \"^5.59.9\",\n \"@vladmandic/build\": \"^0.9.2\",\n \"@vladmandic/pilogger\": \"^0.4.8\",\n \"esbuild\": \"^0.18.1\",\n \"eslint\": \"^8.42.0\",\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\": \"^5.0.1\",\n \"seedrandom\": \"^3.0.5\",\n \"tslib\": \"^2.5.3\",\n \"typedoc\": \"^0.24.8\",\n \"typescript\": \"5.1.3\"\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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for (var name in all5)\n __defProp(target, name, { get: all5[name], enumerable: true });\n};\nvar __copyProps = (to, from, except, desc) => {\n if (from && typeof from === \"object\" || typeof from === \"function\") {\n for (let key of __getOwnPropNames(from))\n if (!__hasOwnProp.call(to, key) && key !== except)\n __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });\n }\n return to;\n};\nvar __toESM = (mod4, isNodeMode, target) => (target = mod4 != null ? __create(__getProtoOf(mod4)) : {}, __copyProps(\n // If the importer is in node compatibility mode or this is not an ESM\n // file that has been converted to a CommonJS file using a Babel-\n // compatible transform (i.e. \"__esModule\" has not been set), then set\n // \"default\" to the CommonJS \"module.exports\" for node compatibility.\n isNodeMode || !mod4 || !mod4.__esModule ? __defProp(target, \"default\", { value: mod4, enumerable: true }) : target,\n mod4\n));\n\n// node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js\nvar require_long = __commonJS({\n \"node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js\"(exports, module) {\n \"use strict\";\n module.exports = Long2;\n var wasm = null;\n try {\n wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([\n 0,\n 97,\n 115,\n 109,\n 1,\n 0,\n 0,\n 0,\n 1,\n 13,\n 2,\n 96,\n 0,\n 1,\n 127,\n 96,\n 4,\n 127,\n 127,\n 127,\n 127,\n 1,\n 127,\n 3,\n 7,\n 6,\n 0,\n 1,\n 1,\n 1,\n 1,\n 1,\n 6,\n 6,\n 1,\n 127,\n 1,\n 65,\n 0,\n 11,\n 7,\n 50,\n 6,\n 3,\n 109,\n 117,\n 108,\n 0,\n 1,\n 5,\n 100,\n 105,\n 118,\n 95,\n 115,\n 0,\n 2,\n 5,\n 100,\n 105,\n 118,\n 95,\n 117,\n 0,\n 3,\n 5,\n 114,\n 101,\n 109,\n 95,\n 115,\n 0,\n 4,\n 5,\n 114,\n 101,\n 109,\n 95,\n 117,\n 0,\n 5,\n 8,\n 103,\n 101,\n 116,\n 95,\n 104,\n 105,\n 103,\n 104,\n 0,\n 0,\n 10,\n 191,\n 1,\n 6,\n 4,\n 0,\n 35,\n 0,\n 11,\n 36,\n 1,\n 1,\n 126,\n 32,\n 0,\n 173,\n 32,\n 1,\n 173,\n 66,\n 32,\n 134,\n 132,\n 32,\n 2,\n 173,\n 32,\n 3,\n 173,\n 66,\n 32,\n 134,\n 132,\n 126,\n 34,\n 4,\n 66,\n 32,\n 135,\n 167,\n 36,\n 0,\n 32,\n 4,\n 167,\n 11,\n 36,\n 1,\n 1,\n 126,\n 32,\n 0,\n 173,\n 32,\n 1,\n 173,\n 66,\n 32,\n 134,\n 132,\n 32,\n 2,\n 173,\n 32,\n 3,\n 173,\n 66,\n 32,\n 134,\n 132,\n 127,\n 34,\n 4,\n 66,\n 32,\n 135,\n 167,\n 36,\n 0,\n 32,\n 4,\n 167,\n 11,\n 36,\n 1,\n 1,\n 126,\n 32,\n 0,\n 173,\n 32,\n 1,\n 173,\n 66,\n 32,\n 134,\n 132,\n 32,\n 2,\n 173,\n 32,\n 3,\n 173,\n 66,\n 32,\n 134,\n 132,\n 128,\n 34,\n 4,\n 66,\n 32,\n 135,\n 167,\n 36,\n 0,\n 32,\n 4,\n 167,\n 11,\n 36,\n 1,\n 1,\n 126,\n 32,\n 0,\n 173,\n 32,\n 1,\n 173,\n 66,\n 32,\n 134,\n 132,\n 32,\n 2,\n 173,\n 32,\n 3,\n 173,\n 66,\n 32,\n 134,\n 132,\n 129,\n 34,\n 4,\n 66,\n 32,\n 135,\n 167,\n 36,\n 0,\n 32,\n 4,\n 167,\n 11,\n 36,\n 1,\n 1,\n 126,\n 32,\n 0,\n 173,\n 32,\n 1,\n 173,\n 66,\n 32,\n 134,\n 132,\n 32,\n 2,\n 173,\n 32,\n 3,\n 173,\n 66,\n 32,\n 134,\n 132,\n 130,\n 34,\n 4,\n 66,\n 32,\n 135,\n 167,\n 36,\n 0,\n 32,\n 4,\n 167,\n 11\n ])), {}).exports;\n } catch (e) {\n }\n function Long2(low, high, unsigned) {\n this.low = low | 0;\n this.high = high | 0;\n this.unsigned = !!unsigned;\n }\n Long2.prototype.__isLong__;\n Object.defineProperty(Long2.prototype, \"__isLong__\", { value: true });\n function isLong(obj) {\n return (obj && obj[\"__isLong__\"]) === true;\n }\n Long2.isLong = isLong;\n var INT_CACHE = {};\n var UINT_CACHE = {};\n function fromInt(value, unsigned) {\n var obj, cachedObj, cache;\n if (unsigned) {\n value >>>= 0;\n if (cache = 0 <= value && value < 256) {\n cachedObj = UINT_CACHE[value];\n if (cachedObj)\n return cachedObj;\n }\n obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);\n if (cache)\n UINT_CACHE[value] = obj;\n return obj;\n } else {\n value |= 0;\n if (cache = -128 <= value && value < 128) {\n cachedObj = INT_CACHE[value];\n if (cachedObj)\n return cachedObj;\n }\n obj = fromBits(value, value < 0 ? -1 : 0, false);\n if (cache)\n INT_CACHE[value] = obj;\n return obj;\n }\n }\n Long2.fromInt = fromInt;\n function fromNumber(value, unsigned) {\n if (isNaN(value))\n return unsigned ? UZERO : ZERO;\n if (unsigned) {\n if (value < 0)\n return UZERO;\n if (value >= TWO_PWR_64_DBL)\n return MAX_UNSIGNED_VALUE;\n } else {\n if (value <= -TWO_PWR_63_DBL)\n return MIN_VALUE;\n if (value + 1 >= TWO_PWR_63_DBL)\n return MAX_VALUE;\n }\n if (value < 0)\n return fromNumber(-value, unsigned).neg();\n return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned);\n }\n Long2.fromNumber = fromNumber;\n function fromBits(lowBits, highBits, unsigned) {\n return new Long2(lowBits, highBits, unsigned);\n }\n Long2.fromBits = fromBits;\n var pow_dbl = Math.pow;\n function fromString(str, unsigned, radix) {\n if (str.length === 0)\n throw Error(\"empty string\");\n if (str === \"NaN\" || str === \"Infinity\" || str === \"+Infinity\" || str === \"-Infinity\")\n return ZERO;\n if (typeof unsigned === \"number\") {\n radix = unsigned, unsigned = false;\n } else {\n unsigned = !!unsigned;\n }\n radix = radix || 10;\n if (radix < 2 || 36 < radix)\n throw RangeError(\"radix\");\n var p2;\n if ((p2 = str.indexOf(\"-\")) > 0)\n throw Error(\"interior hyphen\");\n else if (p2 === 0) {\n return fromString(str.substring(1), unsigned, radix).neg();\n }\n var radixToPower = fromNumber(pow_dbl(radix, 8));\n var result = ZERO;\n for (var i = 0; i < str.length; i += 8) {\n var size = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size), radix);\n if (size < 8) {\n var power = fromNumber(pow_dbl(radix, size));\n result = result.mul(power).add(fromNumber(value));\n } else {\n result = result.mul(radixToPower);\n result = result.add(fromNumber(value));\n }\n }\n result.unsigned = unsigned;\n return result;\n }\n Long2.fromString = fromString;\n function fromValue(val, unsigned) {\n if (typeof val === \"number\")\n return fromNumber(val, unsigned);\n if (typeof val === \"string\")\n return fromString(val, unsigned);\n return fromBits(val.low, val.high, typeof unsigned === \"boolean\" ? unsigned : val.unsigned);\n }\n Long2.fromValue = fromValue;\n var TWO_PWR_16_DBL = 1 << 16;\n var TWO_PWR_24_DBL = 1 << 24;\n var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;\n var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;\n var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;\n var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);\n var ZERO = fromInt(0);\n Long2.ZERO = ZERO;\n var UZERO = fromInt(0, true);\n Long2.UZERO = UZERO;\n var ONE = fromInt(1);\n Long2.ONE = ONE;\n var UONE = fromInt(1, true);\n Long2.UONE = UONE;\n var NEG_ONE = fromInt(-1);\n Long2.NEG_ONE = NEG_ONE;\n var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false);\n Long2.MAX_VALUE = MAX_VALUE;\n var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true);\n Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;\n var MIN_VALUE = fromBits(0, 2147483648 | 0, false);\n Long2.MIN_VALUE = MIN_VALUE;\n var LongPrototype = Long2.prototype;\n LongPrototype.toInt = function toInt() {\n return this.unsigned ? this.low >>> 0 : this.low;\n };\n LongPrototype.toNumber = function toNumber() {\n if (this.unsigned)\n return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0);\n return this.high * TWO_PWR_32_DBL + (this.low >>> 0);\n };\n LongPrototype.toString = function toString(radix) {\n radix = radix || 10;\n if (radix < 2 || 36 < radix)\n throw RangeError(\"radix\");\n if (this.isZero())\n return \"0\";\n if (this.isNegative()) {\n if (this.eq(MIN_VALUE)) {\n var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this);\n return div3.toString(radix) + rem1.toInt().toString(radix);\n } else\n return \"-\" + this.neg().toString(radix);\n }\n var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this;\n var result = \"\";\n while (true) {\n var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix);\n rem = remDiv;\n if (rem.isZero())\n return digits + result;\n else {\n while (digits.length < 6)\n digits = \"0\" + digits;\n result = \"\" + digits + result;\n }\n }\n };\n LongPrototype.getHighBits = function getHighBits() {\n return this.high;\n };\n LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {\n return this.high >>> 0;\n };\n LongPrototype.getLowBits = function getLowBits() {\n return this.low;\n };\n LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {\n return this.low >>> 0;\n };\n LongPrototype.getNumBitsAbs = function getNumBitsAbs() {\n if (this.isNegative())\n return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();\n var val = this.high != 0 ? this.high : this.low;\n for (var bit = 31; bit > 0; bit--)\n if ((val & 1 << bit) != 0)\n break;\n return this.high != 0 ? bit + 33 : bit + 1;\n };\n LongPrototype.isZero = function isZero() {\n return this.high === 0 && this.low === 0;\n };\n LongPrototype.eqz = LongPrototype.isZero;\n LongPrototype.isNegative = function isNegative() {\n return !this.unsigned && this.high < 0;\n };\n LongPrototype.isPositive = function isPositive() {\n return this.unsigned || this.high >= 0;\n };\n LongPrototype.isOdd = function isOdd() {\n return (this.low & 1) === 1;\n };\n LongPrototype.isEven = function isEven2() {\n return (this.low & 1) === 0;\n };\n LongPrototype.equals = function equals(other) {\n if (!isLong(other))\n other = fromValue(other);\n if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1)\n return false;\n return this.high === other.high && this.low === other.low;\n };\n LongPrototype.eq = LongPrototype.equals;\n LongPrototype.notEquals = function notEquals(other) {\n return !this.eq(\n /* validates */\n other\n );\n };\n LongPrototype.neq = LongPrototype.notEquals;\n LongPrototype.ne = LongPrototype.notEquals;\n LongPrototype.lessThan = function lessThan(other) {\n return this.comp(\n /* validates */\n other\n ) < 0;\n };\n LongPrototype.lt = LongPrototype.lessThan;\n LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {\n return this.comp(\n /* validates */\n other\n ) <= 0;\n };\n LongPrototype.lte = LongPrototype.lessThanOrEqual;\n LongPrototype.le = LongPrototype.lessThanOrEqual;\n LongPrototype.greaterThan = function greaterThan(other) {\n return this.comp(\n /* validates */\n other\n ) > 0;\n };\n LongPrototype.gt = LongPrototype.greaterThan;\n LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {\n return this.comp(\n /* validates */\n other\n ) >= 0;\n };\n LongPrototype.gte = LongPrototype.greaterThanOrEqual;\n LongPrototype.ge = LongPrototype.greaterThanOrEqual;\n LongPrototype.compare = function compare(other) {\n if (!isLong(other))\n other = fromValue(other);\n if (this.eq(other))\n return 0;\n var thisNeg = this.isNegative(), otherNeg = other.isNegative();\n if (thisNeg && !otherNeg)\n return -1;\n if (!thisNeg && otherNeg)\n return 1;\n if (!this.unsigned)\n return this.sub(other).isNegative() ? -1 : 1;\n return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1;\n };\n LongPrototype.comp = LongPrototype.compare;\n LongPrototype.negate = function negate() {\n if (!this.unsigned && this.eq(MIN_VALUE))\n return MIN_VALUE;\n return this.not().add(ONE);\n };\n LongPrototype.neg = LongPrototype.negate;\n LongPrototype.add = function add5(addend) {\n if (!isLong(addend))\n addend = fromValue(addend);\n var a48 = this.high >>> 16;\n var a32 = this.high & 65535;\n var a16 = this.low >>> 16;\n var a00 = this.low & 65535;\n var b48 = addend.high >>> 16;\n var b32 = addend.high & 65535;\n var b16 = addend.low >>> 16;\n var b00 = addend.low & 65535;\n var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\n c00 += a00 + b00;\n c16 += c00 >>> 16;\n c00 &= 65535;\n c16 += a16 + b16;\n c32 += c16 >>> 16;\n c16 &= 65535;\n c32 += a32 + b32;\n c48 += c32 >>> 16;\n c32 &= 65535;\n c48 += a48 + b48;\n c48 &= 65535;\n return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);\n };\n LongPrototype.subtract = function subtract(subtrahend) {\n if (!isLong(subtrahend))\n subtrahend = fromValue(subtrahend);\n return this.add(subtrahend.neg());\n };\n LongPrototype.sub = LongPrototype.subtract;\n LongPrototype.multiply = function multiply4(multiplier) {\n if (this.isZero())\n return ZERO;\n if (!isLong(multiplier))\n multiplier = fromValue(multiplier);\n if (wasm) {\n var low = wasm.mul(\n this.low,\n this.high,\n multiplier.low,\n multiplier.high\n );\n return fromBits(low, wasm.get_high(), this.unsigned);\n }\n if (multiplier.isZero())\n return ZERO;\n if (this.eq(MIN_VALUE))\n return multiplier.isOdd() ? MIN_VALUE : ZERO;\n if (multiplier.eq(MIN_VALUE))\n return this.isOdd() ? MIN_VALUE : ZERO;\n if (this.isNegative()) {\n if (multiplier.isNegative())\n return this.neg().mul(multiplier.neg());\n else\n return this.neg().mul(multiplier).neg();\n } else if (multiplier.isNegative())\n return this.mul(multiplier.neg()).neg();\n if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))\n return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);\n var a48 = this.high >>> 16;\n var a32 = this.high & 65535;\n var a16 = this.low >>> 16;\n var a00 = this.low & 65535;\n var b48 = multiplier.high >>> 16;\n var b32 = multiplier.high & 65535;\n var b16 = multiplier.low >>> 16;\n var b00 = multiplier.low & 65535;\n var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\n c00 += a00 * b00;\n c16 += c00 >>> 16;\n c00 &= 65535;\n c16 += a16 * b00;\n c32 += c16 >>> 16;\n c16 &= 65535;\n c16 += a00 * b16;\n c32 += c16 >>> 16;\n c16 &= 65535;\n c32 += a32 * b00;\n c48 += c32 >>> 16;\n c32 &= 65535;\n c32 += a16 * b16;\n c48 += c32 >>> 16;\n c32 &= 65535;\n c32 += a00 * b32;\n c48 += c32 >>> 16;\n c32 &= 65535;\n c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;\n c48 &= 65535;\n return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);\n };\n LongPrototype.mul = LongPrototype.multiply;\n LongPrototype.divide = function divide(divisor) {\n if (!isLong(divisor))\n divisor = fromValue(divisor);\n if (divisor.isZero())\n throw Error(\"division by zero\");\n if (wasm) {\n if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) {\n return this;\n }\n var low = (this.unsigned ? wasm.div_u : wasm.div_s)(\n this.low,\n this.high,\n divisor.low,\n divisor.high\n );\n return fromBits(low, wasm.get_high(), this.unsigned);\n }\n if (this.isZero())\n return this.unsigned ? UZERO : ZERO;\n var approx, rem, res;\n if (!this.unsigned) {\n if (this.eq(MIN_VALUE)) {\n if (divisor.eq(ONE) || divisor.eq(NEG_ONE))\n return MIN_VALUE;\n else if (divisor.eq(MIN_VALUE))\n return ONE;\n else {\n var halfThis = this.shr(1);\n approx = halfThis.div(divisor).shl(1);\n if (approx.eq(ZERO)) {\n return divisor.isNegative() ? ONE : NEG_ONE;\n } else {\n rem = this.sub(divisor.mul(approx));\n res = approx.add(rem.div(divisor));\n return res;\n }\n }\n } else if (divisor.eq(MIN_VALUE))\n return this.unsigned ? UZERO : ZERO;\n if (this.isNegative()) {\n if (divisor.isNegative())\n return this.neg().div(divisor.neg());\n return this.neg().div(divisor).neg();\n } else if (divisor.isNegative())\n return this.div(divisor.neg()).neg();\n res = ZERO;\n } else {\n if (!divisor.unsigned)\n divisor = divisor.toUnsigned();\n if (divisor.gt(this))\n return UZERO;\n if (divisor.gt(this.shru(1)))\n return UONE;\n res = UZERO;\n }\n rem = this;\n while (rem.gte(divisor)) {\n approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));\n var log22 = Math.ceil(Math.log(approx) / Math.LN2), delta = log22 <= 48 ? 1 : pow_dbl(2, log22 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor);\n while (approxRem.isNegative() || approxRem.gt(rem)) {\n approx -= delta;\n approxRes = fromNumber(approx, this.unsigned);\n approxRem = approxRes.mul(divisor);\n }\n if (approxRes.isZero())\n approxRes = ONE;\n res = res.add(approxRes);\n rem = rem.sub(approxRem);\n }\n return res;\n };\n LongPrototype.div = LongPrototype.divide;\n LongPrototype.modulo = function modulo(divisor) {\n if (!isLong(divisor))\n divisor = fromValue(divisor);\n if (wasm) {\n var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(\n this.low,\n this.high,\n divisor.low,\n divisor.high\n );\n return fromBits(low, wasm.get_high(), this.unsigned);\n }\n return this.sub(this.div(divisor).mul(divisor));\n };\n LongPrototype.mod = LongPrototype.modulo;\n LongPrototype.rem = LongPrototype.modulo;\n LongPrototype.not = function not() {\n return fromBits(~this.low, ~this.high, this.unsigned);\n };\n LongPrototype.and = function and(other) {\n if (!isLong(other))\n other = fromValue(other);\n return fromBits(this.low & other.low, this.high & other.high, this.unsigned);\n };\n LongPrototype.or = function or(other) {\n if (!isLong(other))\n other = fromValue(other);\n return fromBits(this.low | other.low, this.high | other.high, this.unsigned);\n };\n LongPrototype.xor = function xor(other) {\n if (!isLong(other))\n other = fromValue(other);\n return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);\n };\n LongPrototype.shiftLeft = function shiftLeft(numBits) {\n if (isLong(numBits))\n numBits = numBits.toInt();\n if ((numBits &= 63) === 0)\n return this;\n else if (numBits < 32)\n return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned);\n else\n return fromBits(0, this.low << numBits - 32, this.unsigned);\n };\n LongPrototype.shl = LongPrototype.shiftLeft;\n LongPrototype.shiftRight = function shiftRight(numBits) {\n if (isLong(numBits))\n numBits = numBits.toInt();\n if ((numBits &= 63) === 0)\n return this;\n else if (numBits < 32)\n return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned);\n else\n return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned);\n };\n LongPrototype.shr = LongPrototype.shiftRight;\n LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {\n if (isLong(numBits))\n numBits = numBits.toInt();\n numBits &= 63;\n if (numBits === 0)\n return this;\n else {\n var high = this.high;\n if (numBits < 32) {\n var low = this.low;\n return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned);\n } else if (numBits === 32)\n return fromBits(high, 0, this.unsigned);\n else\n return fromBits(high >>> numBits - 32, 0, this.unsigned);\n }\n };\n LongPrototype.shru = LongPrototype.shiftRightUnsigned;\n LongPrototype.shr_u = LongPrototype.shiftRightUnsigned;\n LongPrototype.toSigned = function toSigned() {\n if (!this.unsigned)\n return this;\n return fromBits(this.low, this.high, false);\n };\n LongPrototype.toUnsigned = function toUnsigned() {\n if (this.unsigned)\n return this;\n return fromBits(this.low, this.high, true);\n };\n LongPrototype.toBytes = function toBytes(le) {\n return le ? this.toBytesLE() : this.toBytesBE();\n };\n LongPrototype.toBytesLE = function toBytesLE() {\n var hi = this.high, lo = this.low;\n return [\n lo & 255,\n lo >>> 8 & 255,\n lo >>> 16 & 255,\n lo >>> 24,\n hi & 255,\n hi >>> 8 & 255,\n hi >>> 16 & 255,\n hi >>> 24\n ];\n };\n LongPrototype.toBytesBE = function toBytesBE() {\n var hi = this.high, lo = this.low;\n return [\n hi >>> 24,\n hi >>> 16 & 255,\n hi >>> 8 & 255,\n hi & 255,\n lo >>> 24,\n lo >>> 16 & 255,\n lo >>> 8 & 255,\n lo & 255\n ];\n };\n Long2.fromBytes = function fromBytes(bytes, unsigned, le) {\n return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned);\n };\n Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) {\n return new Long2(\n bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24,\n bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24,\n unsigned\n );\n };\n Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) {\n return new Long2(\n bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7],\n bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3],\n unsigned\n );\n };\n }\n});\n\n// (disabled):node_modules/.pnpm/node-fetch@2.6.13/node_modules/node-fetch/browser.js\nvar require_browser = __commonJS({\n \"(disabled):node_modules/.pnpm/node-fetch@2.6.13/node_modules/node-fetch/browser.js\"() {\n \"use strict\";\n }\n});\n\n// (disabled):util\nvar require_util = __commonJS({\n \"(disabled):util\"() {\n \"use strict\";\n }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js\nvar require_alea = __commonJS({\n \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js\"(exports, module) {\n \"use strict\";\n (function(global2, module2, define2) {\n function Alea(seed) {\n var me = this, mash = Mash();\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n me.c = 1;\n me.s0 = mash(\" \");\n me.s1 = mash(\" \");\n me.s2 = mash(\" \");\n me.s0 -= mash(seed);\n if (me.s0 < 0) {\n me.s0 += 1;\n }\n me.s1 -= mash(seed);\n if (me.s1 < 0) {\n me.s1 += 1;\n }\n me.s2 -= mash(seed);\n if (me.s2 < 0) {\n me.s2 += 1;\n }\n mash = null;\n }\n function copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n }\n function impl(seed, opts) {\n var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;\n prng.int32 = function() {\n return xg.next() * 4294967296 | 0;\n };\n prng.double = function() {\n return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;\n };\n prng.quick = prng;\n if (state) {\n if (typeof state == \"object\")\n copy(state, xg);\n prng.state = function() {\n return copy(xg, {});\n };\n }\n return prng;\n }\n function Mash() {\n var n = 4022871197;\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 4294967296;\n }\n return (n >>> 0) * 23283064365386963e-26;\n };\n return mash;\n }\n if (module2 && module2.exports) {\n module2.exports = impl;\n } else if (define2 && define2.amd) {\n define2(function() {\n return impl;\n });\n } else {\n this.alea = impl;\n }\n })(\n exports,\n typeof module == \"object\" && module,\n // present in node.js\n typeof define == \"function\" && define\n // present with an AMD loader\n );\n }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js\nvar require_xor128 = __commonJS({\n \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js\"(exports, module) {\n \"use strict\";\n (function(global2, module2, define2) {\n function XorGen(seed) {\n var me = this, strseed = \"\";\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.next = function() {\n var t = me.x ^ me.x << 11;\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;\n };\n if (seed === (seed | 0)) {\n me.x = seed;\n } else {\n strseed += seed;\n }\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n }\n function copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n }\n function impl(seed, opts) {\n var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n return (xg.next() >>> 0) / 4294967296;\n };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof state == \"object\")\n copy(state, xg);\n prng.state = function() {\n return copy(xg, {});\n };\n }\n return prng;\n }\n if (module2 && module2.exports) {\n module2.exports = impl;\n } else if (define2 && define2.amd) {\n define2(function() {\n return impl;\n });\n } else {\n this.xor128 = impl;\n }\n })(\n exports,\n typeof module == \"object\" && module,\n // present in node.js\n typeof define == \"function\" && define\n // present with an AMD loader\n );\n }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js\nvar require_xorwow = __commonJS({\n \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js\"(exports, module) {\n \"use strict\";\n (function(global2, module2, define2) {\n function XorGen(seed) {\n var me = this, strseed = \"\";\n me.next = function() {\n var t = me.x ^ me.x >>> 2;\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n me.w = me.v;\n return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;\n };\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n if (seed === (seed | 0)) {\n me.x = seed;\n } else {\n strseed += seed;\n }\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n }\n function copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n }\n function impl(seed, opts) {\n var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n return (xg.next() >>> 0) / 4294967296;\n };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof state == \"object\")\n copy(state, xg);\n prng.state = function() {\n return copy(xg, {});\n };\n }\n return prng;\n }\n if (module2 && module2.exports) {\n module2.exports = impl;\n } else if (define2 && define2.amd) {\n define2(function() {\n return impl;\n });\n } else {\n this.xorwow = impl;\n }\n })(\n exports,\n typeof module == \"object\" && module,\n // present in node.js\n typeof define == \"function\" && define\n // present with an AMD loader\n );\n }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js\nvar require_xorshift7 = __commonJS({\n \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js\"(exports, module) {\n \"use strict\";\n (function(global2, module2, define2) {\n function XorGen(seed) {\n var me = this;\n me.next = function() {\n var X = me.x, i = me.i, t, v, w;\n t = X[i];\n t ^= t >>> 7;\n v = t ^ t << 24;\n t = X[i + 1 & 7];\n v ^= t ^ t >>> 10;\n t = X[i + 3 & 7];\n v ^= t ^ t >>> 3;\n t = X[i + 4 & 7];\n v ^= t ^ t << 7;\n t = X[i + 7 & 7];\n t = t ^ t << 13;\n v ^= t ^ t << 9;\n X[i] = v;\n me.i = i + 1 & 7;\n return v;\n };\n function init2(me2, seed2) {\n var j, w, X = [];\n if (seed2 === (seed2 | 0)) {\n w = X[0] = seed2;\n } else {\n seed2 = \"\" + seed2;\n for (j = 0; j < seed2.length; ++j) {\n X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;\n }\n }\n while (X.length < 8)\n X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j)\n ;\n if (j == 8)\n w = X[7] = -1;\n else\n w = X[j];\n me2.x = X;\n me2.i = 0;\n for (j = 256; j > 0; --j) {\n me2.next();\n }\n }\n init2(me, seed);\n }\n function copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n }\n function impl(seed, opts) {\n if (seed == null)\n seed = +/* @__PURE__ */ new Date();\n var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n return (xg.next() >>> 0) / 4294967296;\n };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x)\n copy(state, xg);\n prng.state = function() {\n return copy(xg, {});\n };\n }\n return prng;\n }\n if (module2 && module2.exports) {\n module2.exports = impl;\n } else if (define2 && define2.amd) {\n define2(function() {\n return impl;\n });\n } else {\n this.xorshift7 = impl;\n }\n })(\n exports,\n typeof module == \"object\" && module,\n // present in node.js\n typeof define == \"function\" && define\n // present with an AMD loader\n );\n }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js\nvar require_xor4096 = __commonJS({\n \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js\"(exports, module) {\n \"use strict\";\n (function(global2, module2, define2) {\n function XorGen(seed) {\n var me = this;\n me.next = function() {\n var w = me.w, X = me.X, i = me.i, t, v;\n me.w = w = w + 1640531527 | 0;\n v = X[i + 34 & 127];\n t = X[i = i + 1 & 127];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n v = X[i] = v ^ t;\n me.i = i;\n return v + (w ^ w >>> 16) | 0;\n };\n function init2(me2, seed2) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed2 === (seed2 | 0)) {\n v = seed2;\n seed2 = null;\n } else {\n seed2 = seed2 + \"\\0\";\n v = 0;\n limit = Math.max(limit, seed2.length);\n }\n for (i = 0, j = -32; j < limit; ++j) {\n if (seed2)\n v ^= seed2.charCodeAt((j + 32) % seed2.length);\n if (j === 0)\n w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = w + 1640531527 | 0;\n t = X[j & 127] ^= v + w;\n i = 0 == t ? i + 1 : 0;\n }\n }\n if (i >= 128) {\n X[(seed2 && seed2.length || 0) & 127] = -1;\n }\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[i + 34 & 127];\n t = X[i = i + 1 & 127];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n me2.w = w;\n me2.X = X;\n me2.i = i;\n }\n init2(me, seed);\n }\n function copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n }\n ;\n function impl(seed, opts) {\n if (seed == null)\n seed = +/* @__PURE__ */ new Date();\n var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n return (xg.next() >>> 0) / 4294967296;\n };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X)\n copy(state, xg);\n prng.state = function() {\n return copy(xg, {});\n };\n }\n return prng;\n }\n if (module2 && module2.exports) {\n module2.exports = impl;\n } else if (define2 && define2.amd) {\n define2(function() {\n return impl;\n });\n } else {\n this.xor4096 = impl;\n }\n })(\n exports,\n // window object or global\n typeof module == \"object\" && module,\n // present in node.js\n typeof define == \"function\" && define\n // present with an AMD loader\n );\n }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js\nvar require_tychei = __commonJS({\n \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js\"(exports, module) {\n \"use strict\";\n (function(global2, module2, define2) {\n function XorGen(seed) {\n var me = this, strseed = \"\";\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = b << 25 ^ b >>> 7 ^ c;\n c = c - d | 0;\n d = d << 24 ^ d >>> 8 ^ a;\n a = a - b | 0;\n me.b = b = b << 20 ^ b >>> 12 ^ c;\n me.c = c = c - d | 0;\n me.d = d << 16 ^ c >>> 16 ^ a;\n return me.a = a - b | 0;\n };\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n if (seed === Math.floor(seed)) {\n me.a = seed / 4294967296 | 0;\n me.b = seed | 0;\n } else {\n strseed += seed;\n }\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n }\n function copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n }\n ;\n function impl(seed, opts) {\n var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n return (xg.next() >>> 0) / 4294967296;\n };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof state == \"object\")\n copy(state, xg);\n prng.state = function() {\n return copy(xg, {});\n };\n }\n return prng;\n }\n if (module2 && module2.exports) {\n module2.exports = impl;\n } else if (define2 && define2.amd) {\n define2(function() {\n return impl;\n });\n } else {\n this.tychei = impl;\n }\n })(\n exports,\n typeof module == \"object\" && module,\n // present in node.js\n typeof define == \"function\" && define\n // present with an AMD loader\n );\n }\n});\n\n// (disabled):crypto\nvar require_crypto = __commonJS({\n \"(disabled):crypto\"() {\n \"use strict\";\n }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js\nvar require_seedrandom = __commonJS({\n \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js\"(exports, module) {\n \"use strict\";\n (function(global2, pool3, math) {\n var width = 256, chunks = 6, digits = 52, rngname = \"random\", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;\n function seedrandom5(seed, options, callback) {\n var key = [];\n options = options == true ? { entropy: true } : options || {};\n var shortseed = mixkey(flatten4(\n options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed,\n 3\n ), key);\n var arc4 = new ARC4(key);\n var prng = function() {\n var n = arc4.g(chunks), d = startdenom, x = 0;\n while (n < significance) {\n n = (n + x) * width;\n d *= width;\n x = arc4.g(1);\n }\n while (n >= overflow) {\n n /= 2;\n d /= 2;\n x >>>= 1;\n }\n return (n + x) / d;\n };\n prng.int32 = function() {\n return arc4.g(4) | 0;\n };\n prng.quick = function() {\n return arc4.g(4) / 4294967296;\n };\n prng.double = prng;\n mixkey(tostring(arc4.S), pool3);\n return (options.pass || callback || function(prng2, seed2, is_math_call, state) {\n if (state) {\n if (state.S) {\n copy(state, arc4);\n }\n prng2.state = function() {\n return copy(arc4, {});\n };\n }\n if (is_math_call) {\n math[rngname] = prng2;\n return seed2;\n } else\n return prng2;\n })(\n prng,\n shortseed,\n \"global\" in options ? options.global : this == math,\n options.state\n );\n }\n function ARC4(key) {\n var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n if (!keylen) {\n key = [keylen++];\n }\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];\n s[j] = t;\n }\n (me.g = function(count2) {\n var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;\n while (count2--) {\n t2 = s2[i2 = mask & i2 + 1];\n r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];\n }\n me.i = i2;\n me.j = j2;\n return r;\n })(width);\n }\n function copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n }\n ;\n function flatten4(obj, depth) {\n var result = [], typ = typeof obj, prop;\n if (depth && typ == \"object\") {\n for (prop in obj) {\n try {\n result.push(flatten4(obj[prop], depth - 1));\n } catch (e) {\n }\n }\n }\n return result.length ? result : typ == \"string\" ? obj : obj + \"\\0\";\n }\n function mixkey(seed, key) {\n var stringseed = seed + \"\", smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);\n }\n return tostring(key);\n }\n function autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global2.crypto || global2.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global2.navigator, plugins = browser && browser.plugins;\n return [+/* @__PURE__ */ new Date(), global2, plugins, global2.screen, tostring(pool3)];\n }\n }\n function tostring(a) {\n return String.fromCharCode.apply(0, a);\n }\n mixkey(math.random(), pool3);\n if (typeof module == \"object\" && module.exports) {\n module.exports = seedrandom5;\n try {\n nodecrypto = require_crypto();\n } catch (ex) {\n }\n } else if (typeof define == \"function\" && define.amd) {\n define(function() {\n return seedrandom5;\n });\n } else {\n math[\"seed\" + rngname] = seedrandom5;\n }\n })(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n typeof self !== \"undefined\" ? self : exports,\n [],\n // pool: entropy pool starts empty\n Math\n // math: package containing random, pow, and seedrandom\n );\n }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js\nvar require_seedrandom2 = __commonJS({\n \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js\"(exports, module) {\n \"use strict\";\n var alea5 = require_alea();\n var xor128 = require_xor128();\n var xorwow = require_xorwow();\n var xorshift7 = require_xorshift7();\n var xor4096 = require_xor4096();\n var tychei = require_tychei();\n var sr = require_seedrandom();\n sr.alea = alea5;\n sr.xor128 = xor128;\n sr.xorwow = xorwow;\n sr.xorshift7 = xorshift7;\n sr.xor4096 = xor4096;\n sr.tychei = tychei;\n module.exports = sr;\n }\n});\n\n// (disabled):node_modules/.pnpm/string_decoder@1.3.0/node_modules/string_decoder/lib/string_decoder.js\nvar require_string_decoder = __commonJS({\n \"(disabled):node_modules/.pnpm/string_decoder@1.3.0/node_modules/string_decoder/lib/string_decoder.js\"() {\n \"use strict\";\n }\n});\n\n// (disabled):fs\nvar require_fs = __commonJS({\n \"(disabled):fs\"() {\n \"use strict\";\n }\n});\n\n// (disabled):path\nvar require_path = __commonJS({\n \"(disabled):path\"() {\n \"use strict\";\n }\n});\n\n// (disabled):worker_threads\nvar require_worker_threads = __commonJS({\n \"(disabled):worker_threads\"() {\n \"use strict\";\n }\n});\n\n// (disabled):perf_hooks\nvar require_perf_hooks = __commonJS({\n \"(disabled):perf_hooks\"() {\n \"use strict\";\n }\n});\n\n// (disabled):os\nvar require_os = __commonJS({\n \"(disabled):os\"() {\n \"use strict\";\n }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js\nvar require_tfjs_backend_wasm_threaded_simd = __commonJS({\n \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js\"(exports, module) {\n \"use strict\";\n var WasmBackendModuleThreadedSimd2 = (() => {\n var _scriptDir = typeof document !== \"undefined\" && document.currentScript ? document.currentScript.src : void 0;\n if (typeof __filename !== \"undefined\")\n _scriptDir = _scriptDir || __filename;\n return function(WasmBackendModuleThreadedSimd3) {\n WasmBackendModuleThreadedSimd3 = WasmBackendModuleThreadedSimd3 || {};\n function GROWABLE_HEAP_I8() {\n if (wasmMemory.buffer != buffer2) {\n updateGlobalBufferAndViews(wasmMemory.buffer);\n }\n return HEAP8;\n }\n function GROWABLE_HEAP_U8() {\n if (wasmMemory.buffer != buffer2) {\n updateGlobalBufferAndViews(wasmMemory.buffer);\n }\n return HEAPU8;\n }\n function GROWABLE_HEAP_I16() {\n if (wasmMemory.buffer != buffer2) {\n updateGlobalBufferAndViews(wasmMemory.buffer);\n }\n return HEAP16;\n }\n function GROWABLE_HEAP_I32() {\n if (wasmMemory.buffer != buffer2) {\n updateGlobalBufferAndViews(wasmMemory.buffer);\n }\n return HEAP32;\n }\n function GROWABLE_HEAP_U32() {\n if (wasmMemory.buffer != buffer2) {\n updateGlobalBufferAndViews(wasmMemory.buffer);\n }\n return HEAPU32;\n }\n function GROWABLE_HEAP_F32() {\n if (wasmMemory.buffer != buffer2) {\n updateGlobalBufferAndViews(wasmMemory.buffer);\n }\n return HEAPF32;\n }\n function GROWABLE_HEAP_F64() {\n if (wasmMemory.buffer != buffer2) {\n updateGlobalBufferAndViews(wasmMemory.buffer);\n }\n return HEAPF64;\n }\n var Module = typeof WasmBackendModuleThreadedSimd3 != \"undefined\" ? WasmBackendModuleThreadedSimd3 : {};\n var readyPromiseResolve, readyPromiseReject;\n Module[\"ready\"] = new Promise(function(resolve, reject) {\n readyPromiseResolve = resolve;\n readyPromiseReject = reject;\n });\n var beforeListeners;\n if (typeof process !== \"undefined\" && process.listeners) {\n beforeListeners = { uncaughtException: process.listeners(\"uncaughtException\"), unhandledRejection: process.listeners(\"unhandledRejection\") };\n }\n var moduleOverrides = Object.assign({}, Module);\n var arguments_ = [];\n var thisProgram = \"./this.program\";\n var quit_ = (status, toThrow) => {\n throw toThrow;\n };\n var ENVIRONMENT_IS_WEB = typeof window == \"object\";\n var ENVIRONMENT_IS_WORKER = typeof importScripts == \"function\";\n var ENVIRONMENT_IS_NODE = typeof process == \"object\" && typeof process.versions == \"object\" && typeof process.versions.node == \"string\";\n var ENVIRONMENT_IS_PTHREAD = Module[\"ENVIRONMENT_IS_PTHREAD\"] || false;\n var scriptDirectory = \"\";\n function locateFile(path) {\n if (Module[\"locateFile\"]) {\n return Module[\"locateFile\"](path, scriptDirectory);\n }\n return scriptDirectory + path;\n }\n var read_, readAsync, readBinary, setWindowTitle;\n function logExceptionOnExit(e) {\n if (e instanceof ExitStatus)\n return;\n let toLog = e;\n err(\"exiting due to exception: \" + toLog);\n }\n if (ENVIRONMENT_IS_NODE) {\n var fs = require_fs();\n var nodePath = require_path();\n if (ENVIRONMENT_IS_WORKER) {\n scriptDirectory = nodePath.dirname(scriptDirectory) + \"/\";\n } else {\n scriptDirectory = __dirname + \"/\";\n }\n read_ = (filename, binary) => {\n filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename);\n return fs.readFileSync(filename, binary ? void 0 : \"utf8\");\n };\n readBinary = (filename) => {\n var ret = read_(filename, true);\n if (!ret.buffer) {\n ret = new Uint8Array(ret);\n }\n return ret;\n };\n readAsync = (filename, onload, onerror) => {\n filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename);\n fs.readFile(filename, function(err2, data) {\n if (err2)\n onerror(err2);\n else\n onload(data.buffer);\n });\n };\n if (process[\"argv\"].length > 1) {\n thisProgram = process[\"argv\"][1].replace(/\\\\/g, \"/\");\n }\n arguments_ = process[\"argv\"].slice(2);\n process[\"on\"](\"uncaughtException\", function(ex) {\n if (!(ex instanceof ExitStatus)) {\n throw ex;\n }\n });\n process[\"on\"](\"unhandledRejection\", function(reason) {\n throw reason;\n });\n quit_ = (status, toThrow) => {\n if (keepRuntimeAlive()) {\n process[\"exitCode\"] = status;\n throw toThrow;\n }\n logExceptionOnExit(toThrow);\n process[\"exit\"](status);\n };\n Module[\"inspect\"] = function() {\n return \"[Emscripten Module object]\";\n };\n let nodeWorkerThreads;\n try {\n nodeWorkerThreads = require_worker_threads();\n } catch (e) {\n console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');\n throw e;\n }\n global.Worker = nodeWorkerThreads.Worker;\n } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {\n if (ENVIRONMENT_IS_WORKER) {\n scriptDirectory = self.location.href;\n } else if (typeof document != \"undefined\" && document.currentScript) {\n scriptDirectory = document.currentScript.src;\n }\n if (typeof _scriptDir !== \"undefined\" && _scriptDir) {\n scriptDirectory = _scriptDir;\n }\n if (scriptDirectory.indexOf(\"blob:\") !== 0) {\n scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, \"\").lastIndexOf(\"/\") + 1);\n } else {\n scriptDirectory = \"\";\n }\n if (!ENVIRONMENT_IS_NODE) {\n read_ = (url) => {\n var xhr = new XMLHttpRequest();\n xhr.open(\"GET\", url, false);\n xhr.send(null);\n return xhr.responseText;\n };\n if (ENVIRONMENT_IS_WORKER) {\n readBinary = (url) => {\n var xhr = new XMLHttpRequest();\n xhr.open(\"GET\", url, false);\n xhr.responseType = \"arraybuffer\";\n xhr.send(null);\n return new Uint8Array(xhr.response);\n };\n }\n readAsync = (url, onload, onerror) => {\n var xhr = new XMLHttpRequest();\n xhr.open(\"GET\", url, true);\n xhr.responseType = \"arraybuffer\";\n xhr.onload = () => {\n if (xhr.status == 200 || xhr.status == 0 && xhr.response) {\n onload(xhr.response);\n return;\n }\n onerror();\n };\n xhr.onerror = onerror;\n xhr.send(null);\n };\n }\n setWindowTitle = (title) => document.title = title;\n } else {\n }\n if (ENVIRONMENT_IS_NODE) {\n if (typeof performance == \"undefined\") {\n global.performance = require_perf_hooks().performance;\n }\n }\n var defaultPrint = console.log.bind(console);\n var defaultPrintErr = console.warn.bind(console);\n if (ENVIRONMENT_IS_NODE) {\n defaultPrint = (str) => fs.writeSync(1, str + \"\\n\");\n defaultPrintErr = (str) => fs.writeSync(2, str + \"\\n\");\n }\n var out = Module[\"print\"] || defaultPrint;\n var err = Module[\"printErr\"] || defaultPrintErr;\n Object.assign(Module, moduleOverrides);\n moduleOverrides = null;\n if (Module[\"arguments\"])\n arguments_ = Module[\"arguments\"];\n if (Module[\"thisProgram\"])\n thisProgram = Module[\"thisProgram\"];\n if (Module[\"quit\"])\n quit_ = Module[\"quit\"];\n var POINTER_SIZE = 4;\n var Atomics_load = Atomics.load;\n var Atomics_store = Atomics.store;\n var Atomics_compareExchange = Atomics.compareExchange;\n var wasmBinary;\n if (Module[\"wasmBinary\"])\n wasmBinary = Module[\"wasmBinary\"];\n var noExitRuntime = Module[\"noExitRuntime\"] || true;\n if (typeof WebAssembly != \"object\") {\n abort(\"no native wasm support detected\");\n }\n var wasmMemory;\n var wasmModule;\n var ABORT = false;\n var EXITSTATUS;\n function assert3(condition, text) {\n if (!condition) {\n abort(text);\n }\n }\n var UTF8Decoder = typeof TextDecoder != \"undefined\" ? new TextDecoder(\"utf8\") : void 0;\n function UTF8ArrayToString(heapOrArray, idx, maxBytesToRead) {\n idx >>>= 0;\n var endIdx = idx + maxBytesToRead;\n var endPtr = idx;\n while (heapOrArray[endPtr] && !(endPtr >= endIdx))\n ++endPtr;\n if (endPtr - idx > 16 && heapOrArray.buffer && UTF8Decoder) {\n return UTF8Decoder.decode(heapOrArray.buffer instanceof SharedArrayBuffer ? heapOrArray.slice(idx, endPtr) : heapOrArray.subarray(idx, endPtr));\n }\n var str = \"\";\n while (idx < endPtr) {\n var u0 = heapOrArray[idx++];\n if (!(u0 & 128)) {\n str += String.fromCharCode(u0);\n continue;\n }\n var u1 = heapOrArray[idx++] & 63;\n if ((u0 & 224) == 192) {\n str += String.fromCharCode((u0 & 31) << 6 | u1);\n continue;\n }\n var u2 = heapOrArray[idx++] & 63;\n if ((u0 & 240) == 224) {\n u0 = (u0 & 15) << 12 | u1 << 6 | u2;\n } else {\n u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heapOrArray[idx++] & 63;\n }\n if (u0 < 65536) {\n str += String.fromCharCode(u0);\n } else {\n var ch = u0 - 65536;\n str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);\n }\n }\n return str;\n }\n function UTF8ToString(ptr, maxBytesToRead) {\n ptr >>>= 0;\n return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : \"\";\n }\n function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {\n outIdx >>>= 0;\n if (!(maxBytesToWrite > 0))\n return 0;\n var startIdx = outIdx;\n var endIdx = outIdx + maxBytesToWrite - 1;\n for (var i = 0; i < str.length; ++i) {\n var u = str.charCodeAt(i);\n if (u >= 55296 && u <= 57343) {\n var u1 = str.charCodeAt(++i);\n u = 65536 + ((u & 1023) << 10) | u1 & 1023;\n }\n if (u <= 127) {\n if (outIdx >= endIdx)\n break;\n heap[outIdx++ >>> 0] = u;\n } else if (u <= 2047) {\n if (outIdx + 1 >= endIdx)\n break;\n heap[outIdx++ >>> 0] = 192 | u >> 6;\n heap[outIdx++ >>> 0] = 128 | u & 63;\n } else if (u <= 65535) {\n if (outIdx + 2 >= endIdx)\n break;\n heap[outIdx++ >>> 0] = 224 | u >> 12;\n heap[outIdx++ >>> 0] = 128 | u >> 6 & 63;\n heap[outIdx++ >>> 0] = 128 | u & 63;\n } else {\n if (outIdx + 3 >= endIdx)\n break;\n heap[outIdx++ >>> 0] = 240 | u >> 18;\n heap[outIdx++ >>> 0] = 128 | u >> 12 & 63;\n heap[outIdx++ >>> 0] = 128 | u >> 6 & 63;\n heap[outIdx++ >>> 0] = 128 | u & 63;\n }\n }\n heap[outIdx >>> 0] = 0;\n return outIdx - startIdx;\n }\n function stringToUTF8(str, outPtr, maxBytesToWrite) {\n return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite);\n }\n var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;\n if (ENVIRONMENT_IS_PTHREAD) {\n buffer2 = Module[\"buffer\"];\n }\n function updateGlobalBufferAndViews(buf) {\n buffer2 = buf;\n Module[\"HEAP8\"] = HEAP8 = new Int8Array(buf);\n Module[\"HEAP16\"] = HEAP16 = new Int16Array(buf);\n Module[\"HEAP32\"] = HEAP32 = new Int32Array(buf);\n Module[\"HEAPU8\"] = HEAPU8 = new Uint8Array(buf);\n Module[\"HEAPU16\"] = HEAPU16 = new Uint16Array(buf);\n Module[\"HEAPU32\"] = HEAPU32 = new Uint32Array(buf);\n Module[\"HEAPF32\"] = HEAPF32 = new Float32Array(buf);\n Module[\"HEAPF64\"] = HEAPF64 = new Float64Array(buf);\n }\n var INITIAL_MEMORY = Module[\"INITIAL_MEMORY\"] || 16777216;\n if (ENVIRONMENT_IS_PTHREAD) {\n wasmMemory = Module[\"wasmMemory\"];\n buffer2 = Module[\"buffer\"];\n } else {\n if (Module[\"wasmMemory\"]) {\n wasmMemory = Module[\"wasmMemory\"];\n } else {\n wasmMemory = new WebAssembly.Memory({ \"initial\": INITIAL_MEMORY / 65536, \"maximum\": 4294967296 / 65536, \"shared\": true });\n if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) {\n err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");\n if (ENVIRONMENT_IS_NODE) {\n err(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and/or recent version)\");\n }\n throw Error(\"bad memory\");\n }\n }\n }\n if (wasmMemory) {\n buffer2 = wasmMemory.buffer;\n }\n INITIAL_MEMORY = buffer2.byteLength;\n updateGlobalBufferAndViews(buffer2);\n var wasmTable;\n var __ATPRERUN__ = [];\n var __ATINIT__ = [];\n var __ATPOSTRUN__ = [];\n var runtimeInitialized = false;\n function keepRuntimeAlive() {\n return noExitRuntime;\n }\n function preRun() {\n if (Module[\"preRun\"]) {\n if (typeof Module[\"preRun\"] == \"function\")\n Module[\"preRun\"] = [Module[\"preRun\"]];\n while (Module[\"preRun\"].length) {\n addOnPreRun(Module[\"preRun\"].shift());\n }\n }\n callRuntimeCallbacks(__ATPRERUN__);\n }\n function initRuntime() {\n runtimeInitialized = true;\n if (ENVIRONMENT_IS_PTHREAD)\n return;\n callRuntimeCallbacks(__ATINIT__);\n }\n function postRun() {\n if (ENVIRONMENT_IS_PTHREAD)\n return;\n if (Module[\"postRun\"]) {\n if (typeof Module[\"postRun\"] == \"function\")\n Module[\"postRun\"] = [Module[\"postRun\"]];\n while (Module[\"postRun\"].length) {\n addOnPostRun(Module[\"postRun\"].shift());\n }\n }\n callRuntimeCallbacks(__ATPOSTRUN__);\n }\n function addOnPreRun(cb) {\n __ATPRERUN__.unshift(cb);\n }\n function addOnInit(cb) {\n __ATINIT__.unshift(cb);\n }\n function addOnPostRun(cb) {\n __ATPOSTRUN__.unshift(cb);\n }\n var runDependencies = 0;\n var runDependencyWatcher = null;\n var dependenciesFulfilled = null;\n function addRunDependency(id) {\n runDependencies++;\n if (Module[\"monitorRunDependencies\"]) {\n Module[\"monitorRunDependencies\"](runDependencies);\n }\n }\n function removeRunDependency(id) {\n runDependencies--;\n if (Module[\"monitorRunDependencies\"]) {\n Module[\"monitorRunDependencies\"](runDependencies);\n }\n if (runDependencies == 0) {\n if (runDependencyWatcher !== null) {\n clearInterval(runDependencyWatcher);\n runDependencyWatcher = null;\n }\n if (dependenciesFulfilled) {\n var callback = dependenciesFulfilled;\n dependenciesFulfilled = null;\n callback();\n }\n }\n }\n function abort(what) {\n if (Module[\"onAbort\"]) {\n Module[\"onAbort\"](what);\n }\n what = \"Aborted(\" + what + \")\";\n err(what);\n ABORT = true;\n EXITSTATUS = 1;\n what += \". Build with -sASSERTIONS for more info.\";\n var e = new WebAssembly.RuntimeError(what);\n readyPromiseReject(e);\n throw e;\n }\n var dataURIPrefix = \"data:application/octet-stream;base64,\";\n function isDataURI(filename) {\n return filename.startsWith(dataURIPrefix);\n }\n function isFileURI(filename) {\n return filename.startsWith(\"file://\");\n }\n var wasmBinaryFile;\n wasmBinaryFile = \"tfjs-backend-wasm-threaded-simd.wasm\";\n if (!isDataURI(wasmBinaryFile)) {\n wasmBinaryFile = locateFile(wasmBinaryFile);\n }\n function getBinary(file) {\n try {\n if (file == wasmBinaryFile && wasmBinary) {\n return new Uint8Array(wasmBinary);\n }\n if (readBinary) {\n return readBinary(file);\n }\n throw \"both async and sync fetching of the wasm failed\";\n } catch (err2) {\n abort(err2);\n }\n }\n function getBinaryPromise() {\n if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {\n if (typeof fetch == \"function\" && !isFileURI(wasmBinaryFile)) {\n return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n if (!response[\"ok\"]) {\n throw \"failed to load wasm binary file at '\" + wasmBinaryFile + \"'\";\n }\n return response[\"arrayBuffer\"]();\n }).catch(function() {\n return getBinary(wasmBinaryFile);\n });\n } else {\n if (readAsync) {\n return new Promise(function(resolve, reject) {\n readAsync(wasmBinaryFile, function(response) {\n resolve(new Uint8Array(response));\n }, reject);\n });\n }\n }\n }\n return Promise.resolve().then(function() {\n return getBinary(wasmBinaryFile);\n });\n }\n function createWasm() {\n var info = { \"env\": asmLibraryArg, \"wasi_snapshot_preview1\": asmLibraryArg };\n function receiveInstance(instance, module2) {\n var exports3 = instance.exports;\n Module[\"asm\"] = exports3;\n registerTLSInit(Module[\"asm\"][\"_emscripten_tls_init\"]);\n wasmTable = Module[\"asm\"][\"__indirect_function_table\"];\n addOnInit(Module[\"asm\"][\"__wasm_call_ctors\"]);\n wasmModule = module2;\n if (!ENVIRONMENT_IS_PTHREAD) {\n var numWorkersToLoad = PThread.unusedWorkers.length;\n PThread.unusedWorkers.forEach(function(w) {\n PThread.loadWasmModuleToWorker(w, function() {\n if (!--numWorkersToLoad)\n removeRunDependency(\"wasm-instantiate\");\n });\n });\n }\n }\n if (!ENVIRONMENT_IS_PTHREAD) {\n addRunDependency(\"wasm-instantiate\");\n }\n function receiveInstantiationResult(result) {\n receiveInstance(result[\"instance\"], result[\"module\"]);\n }\n function instantiateArrayBuffer(receiver) {\n return getBinaryPromise().then(function(binary) {\n return WebAssembly.instantiate(binary, info);\n }).then(function(instance) {\n return instance;\n }).then(receiver, function(reason) {\n err(\"failed to asynchronously prepare wasm: \" + reason);\n abort(reason);\n });\n }\n function instantiateAsync() {\n if (!wasmBinary && typeof WebAssembly.instantiateStreaming == \"function\" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && !ENVIRONMENT_IS_NODE && typeof fetch == \"function\") {\n return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n var result = WebAssembly.instantiateStreaming(response, info);\n return result.then(receiveInstantiationResult, function(reason) {\n err(\"wasm streaming compile failed: \" + reason);\n err(\"falling back to ArrayBuffer instantiation\");\n return instantiateArrayBuffer(receiveInstantiationResult);\n });\n });\n } else {\n return instantiateArrayBuffer(receiveInstantiationResult);\n }\n }\n if (Module[\"instantiateWasm\"]) {\n try {\n var exports2 = Module[\"instantiateWasm\"](info, receiveInstance);\n return exports2;\n } catch (e) {\n err(\"Module.instantiateWasm callback failed with error: \" + e);\n readyPromiseReject(e);\n }\n }\n instantiateAsync().catch(readyPromiseReject);\n return {};\n }\n var tempDouble;\n var tempI64;\n var ASM_CONSTS = {};\n function ExitStatus(status) {\n this.name = \"ExitStatus\";\n this.message = \"Program terminated with exit(\" + status + \")\";\n this.status = status;\n }\n function killThread(pthread_ptr) {\n var worker = PThread.pthreads[pthread_ptr];\n delete PThread.pthreads[pthread_ptr];\n worker.terminate();\n __emscripten_thread_free_data(pthread_ptr);\n PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);\n worker.pthread_ptr = 0;\n }\n function cancelThread(pthread_ptr) {\n var worker = PThread.pthreads[pthread_ptr];\n worker.postMessage({ \"cmd\": \"cancel\" });\n }\n function cleanupThread(pthread_ptr) {\n var worker = PThread.pthreads[pthread_ptr];\n assert3(worker);\n PThread.returnWorkerToPool(worker);\n }\n function spawnThread(threadParams) {\n var worker = PThread.getNewWorker();\n if (!worker) {\n return 6;\n }\n PThread.runningWorkers.push(worker);\n PThread.pthreads[threadParams.pthread_ptr] = worker;\n worker.pthread_ptr = threadParams.pthread_ptr;\n var msg = { \"cmd\": \"run\", \"start_routine\": threadParams.startRoutine, \"arg\": threadParams.arg, \"pthread_ptr\": threadParams.pthread_ptr };\n worker.runPthread = () => {\n if (ENVIRONMENT_IS_NODE) {\n worker.ref();\n }\n worker.postMessage(msg, threadParams.transferList);\n delete worker.runPthread;\n };\n if (worker.loaded) {\n worker.runPthread();\n }\n return 0;\n }\n var SYSCALLS = { varargs: void 0, get: function() {\n SYSCALLS.varargs += 4;\n var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >>> 2];\n return ret;\n }, getStr: function(ptr) {\n var ret = UTF8ToString(ptr);\n return ret;\n } };\n function _proc_exit(code) {\n if (ENVIRONMENT_IS_PTHREAD)\n return _emscripten_proxy_to_main_thread_js(1, 1, code);\n EXITSTATUS = code;\n if (!keepRuntimeAlive()) {\n PThread.terminateAllThreads();\n if (Module[\"onExit\"])\n Module[\"onExit\"](code);\n ABORT = true;\n }\n quit_(code, new ExitStatus(code));\n }\n function exitJS(status, implicit) {\n EXITSTATUS = status;\n if (!implicit) {\n if (ENVIRONMENT_IS_PTHREAD) {\n exitOnMainThread(status);\n throw \"unwind\";\n } else {\n }\n }\n _proc_exit(status);\n }\n var _exit = exitJS;\n function handleException(e) {\n if (e instanceof ExitStatus || e == \"unwind\") {\n return EXITSTATUS;\n }\n quit_(1, e);\n }\n var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], pthreads: {}, init: function() {\n if (ENVIRONMENT_IS_PTHREAD) {\n PThread.initWorker();\n } else {\n PThread.initMainThread();\n }\n }, initMainThread: function() {\n var pthreadPoolSize = 8;\n while (pthreadPoolSize--) {\n PThread.allocateUnusedWorker();\n }\n }, initWorker: function() {\n noExitRuntime = false;\n }, setExitStatus: function(status) {\n EXITSTATUS = status;\n }, terminateAllThreads: function() {\n for (var worker of Object.values(PThread.pthreads)) {\n PThread.returnWorkerToPool(worker);\n }\n for (var worker of PThread.unusedWorkers) {\n worker.terminate();\n }\n PThread.unusedWorkers = [];\n }, returnWorkerToPool: function(worker) {\n var pthread_ptr = worker.pthread_ptr;\n delete PThread.pthreads[pthread_ptr];\n PThread.unusedWorkers.push(worker);\n PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);\n worker.pthread_ptr = 0;\n if (ENVIRONMENT_IS_NODE) {\n worker.unref();\n }\n __emscripten_thread_free_data(pthread_ptr);\n }, receiveObjectTransfer: function(data) {\n }, threadInitTLS: function() {\n PThread.tlsInitFunctions.forEach((f) => f());\n }, loadWasmModuleToWorker: function(worker, onFinishedLoading) {\n worker.onmessage = (e) => {\n var d = e[\"data\"];\n var cmd = d[\"cmd\"];\n if (worker.pthread_ptr)\n PThread.currentProxiedOperationCallerThread = worker.pthread_ptr;\n if (d[\"targetThread\"] && d[\"targetThread\"] != _pthread_self()) {\n var targetWorker = PThread.pthreads[d.targetThread];\n if (targetWorker) {\n targetWorker.postMessage(d, d[\"transferList\"]);\n } else {\n err('Internal error! Worker sent a message \"' + cmd + '\" to target pthread ' + d[\"targetThread\"] + \", but that thread no longer exists!\");\n }\n PThread.currentProxiedOperationCallerThread = void 0;\n return;\n }\n if (cmd === \"processProxyingQueue\") {\n executeNotifiedProxyingQueue(d[\"queue\"]);\n } else if (cmd === \"spawnThread\") {\n spawnThread(d);\n } else if (cmd === \"cleanupThread\") {\n cleanupThread(d[\"thread\"]);\n } else if (cmd === \"killThread\") {\n killThread(d[\"thread\"]);\n } else if (cmd === \"cancelThread\") {\n cancelThread(d[\"thread\"]);\n } else if (cmd === \"loaded\") {\n worker.loaded = true;\n if (ENVIRONMENT_IS_NODE) {\n worker.unref();\n }\n if (onFinishedLoading)\n onFinishedLoading(worker);\n if (worker.runPthread) {\n worker.runPthread();\n }\n } else if (cmd === \"print\") {\n out(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n } else if (cmd === \"printErr\") {\n err(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n } else if (cmd === \"alert\") {\n alert(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n } else if (d.target === \"setimmediate\") {\n worker.postMessage(d);\n } else if (cmd === \"callHandler\") {\n Module[d[\"handler\"]](...d[\"args\"]);\n } else if (cmd) {\n err(\"worker sent an unknown command \" + cmd);\n }\n PThread.currentProxiedOperationCallerThread = void 0;\n };\n worker.onerror = (e) => {\n var message = \"worker sent an error!\";\n err(message + \" \" + e.filename + \":\" + e.lineno + \": \" + e.message);\n throw e;\n };\n if (ENVIRONMENT_IS_NODE) {\n worker.on(\"message\", function(data) {\n worker.onmessage({ data });\n });\n worker.on(\"error\", function(e) {\n worker.onerror(e);\n });\n worker.on(\"detachedExit\", function() {\n });\n }\n var handlers = [];\n var knownHandlers = [\"onExit\", \"onAbort\", \"print\", \"printErr\"];\n for (var handler of knownHandlers) {\n if (Module.hasOwnProperty(handler)) {\n handlers.push(handler);\n }\n }\n worker.postMessage({ \"cmd\": \"load\", \"handlers\": handlers, \"urlOrBlob\": Module[\"mainScriptUrlOrBlob\"] || _scriptDir, \"wasmMemory\": wasmMemory, \"wasmModule\": wasmModule });\n }, allocateUnusedWorker: function() {\n var worker;\n var pthreadMainJs = locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");\n worker = new Worker(pthreadMainJs);\n PThread.unusedWorkers.push(worker);\n }, getNewWorker: function() {\n if (PThread.unusedWorkers.length == 0) {\n PThread.allocateUnusedWorker();\n PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]);\n }\n return PThread.unusedWorkers.pop();\n } };\n Module[\"PThread\"] = PThread;\n function callRuntimeCallbacks(callbacks2) {\n while (callbacks2.length > 0) {\n callbacks2.shift()(Module);\n }\n }\n function establishStackSpace() {\n var pthread_ptr = _pthread_self();\n var stackTop = GROWABLE_HEAP_I32()[pthread_ptr + 52 >>> 2];\n var stackSize = GROWABLE_HEAP_I32()[pthread_ptr + 56 >>> 2];\n var stackMax = stackTop - stackSize;\n _emscripten_stack_set_limits(stackTop, stackMax);\n stackRestore(stackTop);\n }\n Module[\"establishStackSpace\"] = establishStackSpace;\n function exitOnMainThread(returnCode) {\n if (ENVIRONMENT_IS_PTHREAD)\n return _emscripten_proxy_to_main_thread_js(2, 0, returnCode);\n try {\n _exit(returnCode);\n } catch (e) {\n handleException(e);\n }\n }\n var wasmTableMirror = [];\n function getWasmTableEntry(funcPtr) {\n var func2 = wasmTableMirror[funcPtr];\n if (!func2) {\n if (funcPtr >= wasmTableMirror.length)\n wasmTableMirror.length = funcPtr + 1;\n wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);\n }\n return func2;\n }\n function invokeEntryPoint(ptr, arg) {\n var result = getWasmTableEntry(ptr)(arg);\n if (keepRuntimeAlive()) {\n PThread.setExitStatus(result);\n } else {\n __emscripten_thread_exit(result);\n }\n }\n Module[\"invokeEntryPoint\"] = invokeEntryPoint;\n function registerTLSInit(tlsInitFunc) {\n PThread.tlsInitFunctions.push(tlsInitFunc);\n }\n function ___emscripten_init_main_thread_js(tb) {\n __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1, !ENVIRONMENT_IS_WEB);\n PThread.threadInitTLS();\n }\n function ___emscripten_thread_cleanup(thread) {\n if (!ENVIRONMENT_IS_PTHREAD)\n cleanupThread(thread);\n else\n postMessage({ \"cmd\": \"cleanupThread\", \"thread\": thread });\n }\n function pthreadCreateProxied(pthread_ptr, attr, startRoutine, arg) {\n if (ENVIRONMENT_IS_PTHREAD)\n return _emscripten_proxy_to_main_thread_js(3, 1, pthread_ptr, attr, startRoutine, arg);\n return ___pthread_create_js(pthread_ptr, attr, startRoutine, arg);\n }\n function ___pthread_create_js(pthread_ptr, attr, startRoutine, arg) {\n if (typeof SharedArrayBuffer == \"undefined\") {\n err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");\n return 6;\n }\n var transferList = [];\n var error = 0;\n if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) {\n return pthreadCreateProxied(pthread_ptr, attr, startRoutine, arg);\n }\n if (error)\n return error;\n var threadParams = { startRoutine, pthread_ptr, arg, transferList };\n if (ENVIRONMENT_IS_PTHREAD) {\n threadParams.cmd = \"spawnThread\";\n postMessage(threadParams, transferList);\n return 0;\n }\n return spawnThread(threadParams);\n }\n function __emscripten_default_pthread_stack_size() {\n return 65536;\n }\n var nowIsMonotonic = true;\n function __emscripten_get_now_is_monotonic() {\n return nowIsMonotonic;\n }\n function executeNotifiedProxyingQueue(queue) {\n Atomics.store(GROWABLE_HEAP_I32(), queue >> 2, 1);\n if (_pthread_self()) {\n __emscripten_proxy_execute_task_queue(queue);\n }\n Atomics.compareExchange(GROWABLE_HEAP_I32(), queue >> 2, 1, 0);\n }\n Module[\"executeNotifiedProxyingQueue\"] = executeNotifiedProxyingQueue;\n function __emscripten_notify_task_queue(targetThreadId, currThreadId, mainThreadId, queue) {\n if (targetThreadId == currThreadId) {\n setTimeout(() => executeNotifiedProxyingQueue(queue));\n } else if (ENVIRONMENT_IS_PTHREAD) {\n postMessage({ \"targetThread\": targetThreadId, \"cmd\": \"processProxyingQueue\", \"queue\": queue });\n } else {\n var worker = PThread.pthreads[targetThreadId];\n if (!worker) {\n return;\n }\n worker.postMessage({ \"cmd\": \"processProxyingQueue\", \"queue\": queue });\n }\n return 1;\n }\n function __emscripten_set_offscreencanvas_size(target, width, height) {\n return -1;\n }\n function _abort() {\n abort(\"\");\n }\n function warnOnce(text) {\n if (!warnOnce.shown)\n warnOnce.shown = {};\n if (!warnOnce.shown[text]) {\n warnOnce.shown[text] = 1;\n if (ENVIRONMENT_IS_NODE)\n text = \"warning: \" + text;\n err(text);\n }\n }\n function _emscripten_check_blocking_allowed() {\n if (ENVIRONMENT_IS_NODE)\n return;\n if (ENVIRONMENT_IS_WORKER)\n return;\n warnOnce(\"Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread\");\n }\n function _emscripten_date_now() {\n return Date.now();\n }\n function getHeapMax() {\n return 4294901760;\n }\n function _emscripten_get_heap_max() {\n return getHeapMax();\n }\n var _emscripten_get_now;\n if (ENVIRONMENT_IS_NODE) {\n _emscripten_get_now = () => {\n var t = process[\"hrtime\"]();\n return t[0] * 1e3 + t[1] / 1e6;\n };\n } else\n _emscripten_get_now = () => performance.timeOrigin + performance.now();\n function _emscripten_memcpy_big(dest, src, num) {\n GROWABLE_HEAP_U8().copyWithin(dest >>> 0, src >>> 0, src + num >>> 0);\n }\n function _emscripten_num_logical_cores() {\n if (ENVIRONMENT_IS_NODE)\n return require_os().cpus().length;\n return navigator[\"hardwareConcurrency\"];\n }\n function withStackSave(f) {\n var stack2 = stackSave();\n var ret = f();\n stackRestore(stack2);\n return ret;\n }\n function _emscripten_proxy_to_main_thread_js(index, sync) {\n var numCallArgs = arguments.length - 2;\n var outerArgs = arguments;\n return withStackSave(() => {\n var serializedNumCallArgs = numCallArgs;\n var args = stackAlloc(serializedNumCallArgs * 8);\n var b = args >> 3;\n for (var i = 0; i < numCallArgs; i++) {\n var arg = outerArgs[2 + i];\n GROWABLE_HEAP_F64()[b + i >>> 0] = arg;\n }\n return _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync);\n });\n }\n var _emscripten_receive_on_main_thread_js_callArgs = [];\n function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) {\n _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;\n var b = args >> 3;\n for (var i = 0; i < numCallArgs; i++) {\n _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i >>> 0];\n }\n var isEmAsmConst = index < 0;\n var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1];\n return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs);\n }\n function emscripten_realloc_buffer(size) {\n try {\n wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);\n updateGlobalBufferAndViews(wasmMemory.buffer);\n return 1;\n } catch (e) {\n }\n }\n function _emscripten_resize_heap(requestedSize) {\n var oldSize = GROWABLE_HEAP_U8().length;\n requestedSize = requestedSize >>> 0;\n if (requestedSize <= oldSize) {\n return false;\n }\n var maxHeapSize = getHeapMax();\n if (requestedSize > maxHeapSize) {\n return false;\n }\n let alignUp = (x, multiple) => x + (multiple - x % multiple) % multiple;\n for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {\n var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);\n overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);\n var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));\n var replacement = emscripten_realloc_buffer(newSize);\n if (replacement) {\n return true;\n }\n }\n return false;\n }\n function _emscripten_unwind_to_js_event_loop() {\n throw \"unwind\";\n }\n function _fd_close(fd) {\n if (ENVIRONMENT_IS_PTHREAD)\n return _emscripten_proxy_to_main_thread_js(4, 1, fd);\n return 52;\n }\n function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {\n if (ENVIRONMENT_IS_PTHREAD)\n return _emscripten_proxy_to_main_thread_js(5, 1, fd, offset_low, offset_high, whence, newOffset);\n return 70;\n }\n var printCharBuffers = [null, [], []];\n function printChar(stream, curr) {\n var buffer3 = printCharBuffers[stream];\n if (curr === 0 || curr === 10) {\n (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));\n buffer3.length = 0;\n } else {\n buffer3.push(curr);\n }\n }\n function _fd_write(fd, iov, iovcnt, pnum) {\n if (ENVIRONMENT_IS_PTHREAD)\n return _emscripten_proxy_to_main_thread_js(6, 1, fd, iov, iovcnt, pnum);\n var num = 0;\n for (var i = 0; i < iovcnt; i++) {\n var ptr = GROWABLE_HEAP_U32()[iov >>> 2];\n var len = GROWABLE_HEAP_U32()[iov + 4 >>> 2];\n iov += 8;\n for (var j = 0; j < len; j++) {\n printChar(fd, GROWABLE_HEAP_U8()[ptr + j >>> 0]);\n }\n num += len;\n }\n GROWABLE_HEAP_U32()[pnum >>> 2] = num;\n return 0;\n }\n function getCFunc(ident) {\n var func2 = Module[\"_\" + ident];\n return func2;\n }\n function writeArrayToMemory(array2, buffer3) {\n GROWABLE_HEAP_I8().set(array2, buffer3 >>> 0);\n }\n function ccall(ident, returnType, argTypes, args, opts) {\n var toC = { \"string\": (str) => {\n var ret2 = 0;\n if (str !== null && str !== void 0 && str !== 0) {\n var len = (str.length << 2) + 1;\n ret2 = stackAlloc(len);\n stringToUTF8(str, ret2, len);\n }\n return ret2;\n }, \"array\": (arr) => {\n var ret2 = stackAlloc(arr.length);\n writeArrayToMemory(arr, ret2);\n return ret2;\n } };\n function convertReturnValue(ret2) {\n if (returnType === \"string\") {\n return UTF8ToString(ret2);\n }\n if (returnType === \"boolean\")\n return Boolean(ret2);\n return ret2;\n }\n var func2 = getCFunc(ident);\n var cArgs = [];\n var stack2 = 0;\n if (args) {\n for (var i = 0; i < args.length; i++) {\n var converter = toC[argTypes[i]];\n if (converter) {\n if (stack2 === 0)\n stack2 = stackSave();\n cArgs[i] = converter(args[i]);\n } else {\n cArgs[i] = args[i];\n }\n }\n }\n var ret = func2.apply(null, cArgs);\n function onDone(ret2) {\n if (stack2 !== 0)\n stackRestore(stack2);\n return convertReturnValue(ret2);\n }\n ret = onDone(ret);\n return ret;\n }\n function cwrap(ident, returnType, argTypes, opts) {\n argTypes = argTypes || [];\n var numericArgs = argTypes.every((type) => type === \"number\" || type === \"boolean\");\n var numericRet = returnType !== \"string\";\n if (numericRet && numericArgs && !opts) {\n return getCFunc(ident);\n }\n return function() {\n return ccall(ident, returnType, argTypes, arguments, opts);\n };\n }\n PThread.init();\n var proxiedFunctionTable = [null, _proc_exit, exitOnMainThread, pthreadCreateProxied, _fd_close, _fd_seek, _fd_write];\n var asmLibraryArg = { \"__emscripten_init_main_thread_js\": ___emscripten_init_main_thread_js, \"__emscripten_thread_cleanup\": ___emscripten_thread_cleanup, \"__pthread_create_js\": ___pthread_create_js, \"_emscripten_default_pthread_stack_size\": __emscripten_default_pthread_stack_size, \"_emscripten_get_now_is_monotonic\": __emscripten_get_now_is_monotonic, \"_emscripten_notify_task_queue\": __emscripten_notify_task_queue, \"_emscripten_set_offscreencanvas_size\": __emscripten_set_offscreencanvas_size, \"abort\": _abort, \"emscripten_check_blocking_allowed\": _emscripten_check_blocking_allowed, \"emscripten_date_now\": _emscripten_date_now, \"emscripten_get_heap_max\": _emscripten_get_heap_max, \"emscripten_get_now\": _emscripten_get_now, \"emscripten_memcpy_big\": _emscripten_memcpy_big, \"emscripten_num_logical_cores\": _emscripten_num_logical_cores, \"emscripten_receive_on_main_thread_js\": _emscripten_receive_on_main_thread_js, \"emscripten_resize_heap\": _emscripten_resize_heap, \"emscripten_unwind_to_js_event_loop\": _emscripten_unwind_to_js_event_loop, \"exit\": _exit, \"fd_close\": _fd_close, \"fd_seek\": _fd_seek, \"fd_write\": _fd_write, \"memory\": wasmMemory || Module[\"wasmMemory\"] };\n var asm = createWasm();\n var ___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = function() {\n return (___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = Module[\"asm\"][\"__wasm_call_ctors\"]).apply(null, arguments);\n };\n var _init = Module[\"_init\"] = function() {\n return (_init = Module[\"_init\"] = Module[\"asm\"][\"init\"]).apply(null, arguments);\n };\n var _init_with_threads_count = Module[\"_init_with_threads_count\"] = function() {\n return (_init_with_threads_count = Module[\"_init_with_threads_count\"] = Module[\"asm\"][\"init_with_threads_count\"]).apply(null, arguments);\n };\n var _get_threads_count = Module[\"_get_threads_count\"] = function() {\n return (_get_threads_count = Module[\"_get_threads_count\"] = Module[\"asm\"][\"get_threads_count\"]).apply(null, arguments);\n };\n var _register_tensor = Module[\"_register_tensor\"] = function() {\n return (_register_tensor = Module[\"_register_tensor\"] = Module[\"asm\"][\"register_tensor\"]).apply(null, arguments);\n };\n var _dispose_data = Module[\"_dispose_data\"] = function() {\n return (_dispose_data = Module[\"_dispose_data\"] = Module[\"asm\"][\"dispose_data\"]).apply(null, arguments);\n };\n var _dispose = Module[\"_dispose\"] = function() {\n return (_dispose = Module[\"_dispose\"] = Module[\"asm\"][\"dispose\"]).apply(null, arguments);\n };\n var _Abs = Module[\"_Abs\"] = function() {\n return (_Abs = Module[\"_Abs\"] = Module[\"asm\"][\"Abs\"]).apply(null, arguments);\n };\n var _Acos = Module[\"_Acos\"] = function() {\n return (_Acos = Module[\"_Acos\"] = Module[\"asm\"][\"Acos\"]).apply(null, arguments);\n };\n var _Acosh = Module[\"_Acosh\"] = function() {\n return (_Acosh = Module[\"_Acosh\"] = Module[\"asm\"][\"Acosh\"]).apply(null, arguments);\n };\n var _Add = Module[\"_Add\"] = function() {\n return (_Add = Module[\"_Add\"] = Module[\"asm\"][\"Add\"]).apply(null, arguments);\n };\n var _AddN = Module[\"_AddN\"] = function() {\n return (_AddN = Module[\"_AddN\"] = Module[\"asm\"][\"AddN\"]).apply(null, arguments);\n };\n var _All = Module[\"_All\"] = function() {\n return (_All = Module[\"_All\"] = Module[\"asm\"][\"All\"]).apply(null, arguments);\n };\n var _Any = Module[\"_Any\"] = function() {\n return (_Any = Module[\"_Any\"] = Module[\"asm\"][\"Any\"]).apply(null, arguments);\n };\n var _ArgMax = Module[\"_ArgMax\"] = function() {\n return (_ArgMax = Module[\"_ArgMax\"] = Module[\"asm\"][\"ArgMax\"]).apply(null, arguments);\n };\n var _ArgMin = Module[\"_ArgMin\"] = function() {\n return (_ArgMin = Module[\"_ArgMin\"] = Module[\"asm\"][\"ArgMin\"]).apply(null, arguments);\n };\n var _Asin = Module[\"_Asin\"] = function() {\n return (_Asin = Module[\"_Asin\"] = Module[\"asm\"][\"Asin\"]).apply(null, arguments);\n };\n var _Asinh = Module[\"_Asinh\"] = function() {\n return (_Asinh = Module[\"_Asinh\"] = Module[\"asm\"][\"Asinh\"]).apply(null, arguments);\n };\n var _Atan = Module[\"_Atan\"] = function() {\n return (_Atan = Module[\"_Atan\"] = Module[\"asm\"][\"Atan\"]).apply(null, arguments);\n };\n var _Atan2 = Module[\"_Atan2\"] = function() {\n return (_Atan2 = Module[\"_Atan2\"] = Module[\"asm\"][\"Atan2\"]).apply(null, arguments);\n };\n var _Atanh = Module[\"_Atanh\"] = function() {\n return (_Atanh = Module[\"_Atanh\"] = Module[\"asm\"][\"Atanh\"]).apply(null, arguments);\n };\n var _AvgPool = Module[\"_AvgPool\"] = function() {\n return (_AvgPool = Module[\"_AvgPool\"] = Module[\"asm\"][\"AvgPool\"]).apply(null, arguments);\n };\n var _AvgPool3D = Module[\"_AvgPool3D\"] = function() {\n return (_AvgPool3D = Module[\"_AvgPool3D\"] = Module[\"asm\"][\"AvgPool3D\"]).apply(null, arguments);\n };\n var _AvgPool3DGrad = Module[\"_AvgPool3DGrad\"] = function() {\n return (_AvgPool3DGrad = Module[\"_AvgPool3DGrad\"] = Module[\"asm\"][\"AvgPool3DGrad\"]).apply(null, arguments);\n };\n var _AvgPoolGrad = Module[\"_AvgPoolGrad\"] = function() {\n return (_AvgPoolGrad = Module[\"_AvgPoolGrad\"] = Module[\"asm\"][\"AvgPoolGrad\"]).apply(null, arguments);\n };\n var _BatchMatMul = Module[\"_BatchMatMul\"] = function() {\n return (_BatchMatMul = Module[\"_BatchMatMul\"] = Module[\"asm\"][\"BatchMatMul\"]).apply(null, arguments);\n };\n var _Bincount = Module[\"_Bincount\"] = function() {\n return (_Bincount = Module[\"_Bincount\"] = Module[\"asm\"][\"Bincount\"]).apply(null, arguments);\n };\n var _BitwiseAnd = Module[\"_BitwiseAnd\"] = function() {\n return (_BitwiseAnd = Module[\"_BitwiseAnd\"] = Module[\"asm\"][\"BitwiseAnd\"]).apply(null, arguments);\n };\n var _Ceil = Module[\"_Ceil\"] = function() {\n return (_Ceil = Module[\"_Ceil\"] = Module[\"asm\"][\"Ceil\"]).apply(null, arguments);\n };\n var _ClipByValue = Module[\"_ClipByValue\"] = function() {\n return (_ClipByValue = Module[\"_ClipByValue\"] = Module[\"asm\"][\"ClipByValue\"]).apply(null, arguments);\n };\n var _Conv2D = Module[\"_Conv2D\"] = function() {\n return (_Conv2D = Module[\"_Conv2D\"] = Module[\"asm\"][\"Conv2D\"]).apply(null, arguments);\n };\n var _Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = function() {\n return (_Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null, arguments);\n };\n var _Conv3D = Module[\"_Conv3D\"] = function() {\n return (_Conv3D = Module[\"_Conv3D\"] = Module[\"asm\"][\"Conv3D\"]).apply(null, arguments);\n };\n var _Conv3DBackpropFilterV2 = Module[\"_Conv3DBackpropFilterV2\"] = function() {\n return (_Conv3DBackpropFilterV2 = Module[\"_Conv3DBackpropFilterV2\"] = Module[\"asm\"][\"Conv3DBackpropFilterV2\"]).apply(null, arguments);\n };\n var _Conv3DBackpropInputV2 = Module[\"_Conv3DBackpropInputV2\"] = function() {\n return (_Conv3DBackpropInputV2 = Module[\"_Conv3DBackpropInputV2\"] = Module[\"asm\"][\"Conv3DBackpropInputV2\"]).apply(null, arguments);\n };\n var _Cos = Module[\"_Cos\"] = function() {\n return (_Cos = Module[\"_Cos\"] = Module[\"asm\"][\"Cos\"]).apply(null, arguments);\n };\n var _Cosh = Module[\"_Cosh\"] = function() {\n return (_Cosh = Module[\"_Cosh\"] = Module[\"asm\"][\"Cosh\"]).apply(null, arguments);\n };\n var _CropAndResize = Module[\"_CropAndResize\"] = function() {\n return (_CropAndResize = Module[\"_CropAndResize\"] = Module[\"asm\"][\"CropAndResize\"]).apply(null, arguments);\n };\n var _Cumprod = Module[\"_Cumprod\"] = function() {\n return (_Cumprod = Module[\"_Cumprod\"] = Module[\"asm\"][\"Cumprod\"]).apply(null, arguments);\n };\n var _Cumsum = Module[\"_Cumsum\"] = function() {\n return (_Cumsum = Module[\"_Cumsum\"] = Module[\"asm\"][\"Cumsum\"]).apply(null, arguments);\n };\n var _DenseBincount = Module[\"_DenseBincount\"] = function() {\n return (_DenseBincount = Module[\"_DenseBincount\"] = Module[\"asm\"][\"DenseBincount\"]).apply(null, arguments);\n };\n var _DepthToSpace = Module[\"_DepthToSpace\"] = function() {\n return (_DepthToSpace = Module[\"_DepthToSpace\"] = Module[\"asm\"][\"DepthToSpace\"]).apply(null, arguments);\n };\n var _DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = function() {\n return (_DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null, arguments);\n };\n var _Diag = Module[\"_Diag\"] = function() {\n return (_Diag = Module[\"_Diag\"] = Module[\"asm\"][\"Diag\"]).apply(null, arguments);\n };\n var _Dilation2D = Module[\"_Dilation2D\"] = function() {\n return (_Dilation2D = Module[\"_Dilation2D\"] = Module[\"asm\"][\"Dilation2D\"]).apply(null, arguments);\n };\n var _Dilation2DBackpropFilter = Module[\"_Dilation2DBackpropFilter\"] = function() {\n return (_Dilation2DBackpropFilter = Module[\"_Dilation2DBackpropFilter\"] = Module[\"asm\"][\"Dilation2DBackpropFilter\"]).apply(null, arguments);\n };\n var _Dilation2DBackpropInput = Module[\"_Dilation2DBackpropInput\"] = function() {\n return (_Dilation2DBackpropInput = Module[\"_Dilation2DBackpropInput\"] = Module[\"asm\"][\"Dilation2DBackpropInput\"]).apply(null, arguments);\n };\n var _Elu = Module[\"_Elu\"] = function() {\n return (_Elu = Module[\"_Elu\"] = Module[\"asm\"][\"Elu\"]).apply(null, arguments);\n };\n var _EluGrad = Module[\"_EluGrad\"] = function() {\n return (_EluGrad = Module[\"_EluGrad\"] = Module[\"asm\"][\"EluGrad\"]).apply(null, arguments);\n };\n var _Equal = Module[\"_Equal\"] = function() {\n return (_Equal = Module[\"_Equal\"] = Module[\"asm\"][\"Equal\"]).apply(null, arguments);\n };\n var _Erf = Module[\"_Erf\"] = function() {\n return (_Erf = Module[\"_Erf\"] = Module[\"asm\"][\"Erf\"]).apply(null, arguments);\n };\n var _Exp = Module[\"_Exp\"] = function() {\n return (_Exp = Module[\"_Exp\"] = Module[\"asm\"][\"Exp\"]).apply(null, arguments);\n };\n var _Expm1 = Module[\"_Expm1\"] = function() {\n return (_Expm1 = Module[\"_Expm1\"] = Module[\"asm\"][\"Expm1\"]).apply(null, arguments);\n };\n var _FlipLeftRight = Module[\"_FlipLeftRight\"] = function() {\n return (_FlipLeftRight = Module[\"_FlipLeftRight\"] = Module[\"asm\"][\"FlipLeftRight\"]).apply(null, arguments);\n };\n var _Floor = Module[\"_Floor\"] = function() {\n return (_Floor = Module[\"_Floor\"] = Module[\"asm\"][\"Floor\"]).apply(null, arguments);\n };\n var _FloorDiv = Module[\"_FloorDiv\"] = function() {\n return (_FloorDiv = Module[\"_FloorDiv\"] = Module[\"asm\"][\"FloorDiv\"]).apply(null, arguments);\n };\n var _FusedBatchNorm = Module[\"_FusedBatchNorm\"] = function() {\n return (_FusedBatchNorm = Module[\"_FusedBatchNorm\"] = Module[\"asm\"][\"FusedBatchNorm\"]).apply(null, arguments);\n };\n var _FusedConv2D = Module[\"_FusedConv2D\"] = function() {\n return (_FusedConv2D = Module[\"_FusedConv2D\"] = Module[\"asm\"][\"FusedConv2D\"]).apply(null, arguments);\n };\n var _FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = function() {\n return (_FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null, arguments);\n };\n var _Gather = Module[\"_Gather\"] = function() {\n return (_Gather = Module[\"_Gather\"] = Module[\"asm\"][\"Gather\"]).apply(null, arguments);\n };\n var _GatherNd = Module[\"_GatherNd\"] = function() {\n return (_GatherNd = Module[\"_GatherNd\"] = Module[\"asm\"][\"GatherNd\"]).apply(null, arguments);\n };\n var _Greater = Module[\"_Greater\"] = function() {\n return (_Greater = Module[\"_Greater\"] = Module[\"asm\"][\"Greater\"]).apply(null, arguments);\n };\n var _GreaterEqual = Module[\"_GreaterEqual\"] = function() {\n return (_GreaterEqual = Module[\"_GreaterEqual\"] = Module[\"asm\"][\"GreaterEqual\"]).apply(null, arguments);\n };\n var _IsFinite = Module[\"_IsFinite\"] = function() {\n return (_IsFinite = Module[\"_IsFinite\"] = Module[\"asm\"][\"IsFinite\"]).apply(null, arguments);\n };\n var _IsInf = Module[\"_IsInf\"] = function() {\n return (_IsInf = Module[\"_IsInf\"] = Module[\"asm\"][\"IsInf\"]).apply(null, arguments);\n };\n var _IsNan = Module[\"_IsNan\"] = function() {\n return (_IsNan = Module[\"_IsNan\"] = Module[\"asm\"][\"IsNan\"]).apply(null, arguments);\n };\n var _LRN = Module[\"_LRN\"] = function() {\n return (_LRN = Module[\"_LRN\"] = Module[\"asm\"][\"LRN\"]).apply(null, arguments);\n };\n var _LRNGrad = Module[\"_LRNGrad\"] = function() {\n return (_LRNGrad = Module[\"_LRNGrad\"] = Module[\"asm\"][\"LRNGrad\"]).apply(null, arguments);\n };\n var _LeakyRelu = Module[\"_LeakyRelu\"] = function() {\n return (_LeakyRelu = Module[\"_LeakyRelu\"] = Module[\"asm\"][\"LeakyRelu\"]).apply(null, arguments);\n };\n var _Less = Module[\"_Less\"] = function() {\n return (_Less = Module[\"_Less\"] = Module[\"asm\"][\"Less\"]).apply(null, arguments);\n };\n var _LessEqual = Module[\"_LessEqual\"] = function() {\n return (_LessEqual = Module[\"_LessEqual\"] = Module[\"asm\"][\"LessEqual\"]).apply(null, arguments);\n };\n var _LinSpace = Module[\"_LinSpace\"] = function() {\n return (_LinSpace = Module[\"_LinSpace\"] = Module[\"asm\"][\"LinSpace\"]).apply(null, arguments);\n };\n var _Log = Module[\"_Log\"] = function() {\n return (_Log = Module[\"_Log\"] = Module[\"asm\"][\"Log\"]).apply(null, arguments);\n };\n var _Log1p = Module[\"_Log1p\"] = function() {\n return (_Log1p = Module[\"_Log1p\"] = Module[\"asm\"][\"Log1p\"]).apply(null, arguments);\n };\n var _LogicalAnd = Module[\"_LogicalAnd\"] = function() {\n return (_LogicalAnd = Module[\"_LogicalAnd\"] = Module[\"asm\"][\"LogicalAnd\"]).apply(null, arguments);\n };\n var _LogicalNot = Module[\"_LogicalNot\"] = function() {\n return (_LogicalNot = Module[\"_LogicalNot\"] = Module[\"asm\"][\"LogicalNot\"]).apply(null, arguments);\n };\n var _LogicalOr = Module[\"_LogicalOr\"] = function() {\n return (_LogicalOr = Module[\"_LogicalOr\"] = Module[\"asm\"][\"LogicalOr\"]).apply(null, arguments);\n };\n var _LogicalXor = Module[\"_LogicalXor\"] = function() {\n return 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arguments);\n };\n var _SparseReshape = Module[\"_SparseReshape\"] = function() {\n return (_SparseReshape = Module[\"_SparseReshape\"] = Module[\"asm\"][\"SparseReshape\"]).apply(null, arguments);\n };\n var _SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = function() {\n return (_SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = Module[\"asm\"][\"SparseSegmentReduction\"]).apply(null, arguments);\n };\n var _SparseToDense = Module[\"_SparseToDense\"] = function() {\n return (_SparseToDense = Module[\"_SparseToDense\"] = Module[\"asm\"][\"SparseToDense\"]).apply(null, arguments);\n };\n var _Sqrt = Module[\"_Sqrt\"] = function() {\n return (_Sqrt = Module[\"_Sqrt\"] = Module[\"asm\"][\"Sqrt\"]).apply(null, arguments);\n };\n var _Square = Module[\"_Square\"] = function() {\n return (_Square = Module[\"_Square\"] = Module[\"asm\"][\"Square\"]).apply(null, arguments);\n };\n var _SquaredDifference = Module[\"_SquaredDifference\"] = function() {\n return 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arguments);\n };\n var _malloc = Module[\"_malloc\"] = function() {\n return (_malloc = Module[\"_malloc\"] = Module[\"asm\"][\"malloc\"]).apply(null, arguments);\n };\n var _free = Module[\"_free\"] = function() {\n return (_free = Module[\"_free\"] = Module[\"asm\"][\"free\"]).apply(null, arguments);\n };\n var __emscripten_tls_init = Module[\"__emscripten_tls_init\"] = function() {\n return (__emscripten_tls_init = Module[\"__emscripten_tls_init\"] = Module[\"asm\"][\"_emscripten_tls_init\"]).apply(null, arguments);\n };\n var _pthread_self = Module[\"_pthread_self\"] = function() {\n return (_pthread_self = Module[\"_pthread_self\"] = Module[\"asm\"][\"pthread_self\"]).apply(null, arguments);\n };\n var ___errno_location = Module[\"___errno_location\"] = function() {\n return (___errno_location = Module[\"___errno_location\"] = Module[\"asm\"][\"__errno_location\"]).apply(null, arguments);\n };\n var __emscripten_thread_init = Module[\"__emscripten_thread_init\"] = function() {\n 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Module[\"asm\"][\"emscripten_main_browser_thread_id\"]).apply(null, arguments);\n };\n var _emscripten_run_in_main_runtime_thread_js = Module[\"_emscripten_run_in_main_runtime_thread_js\"] = function() {\n return (_emscripten_run_in_main_runtime_thread_js = Module[\"_emscripten_run_in_main_runtime_thread_js\"] = Module[\"asm\"][\"emscripten_run_in_main_runtime_thread_js\"]).apply(null, arguments);\n };\n var _emscripten_dispatch_to_thread_ = Module[\"_emscripten_dispatch_to_thread_\"] = function() {\n return (_emscripten_dispatch_to_thread_ = Module[\"_emscripten_dispatch_to_thread_\"] = Module[\"asm\"][\"emscripten_dispatch_to_thread_\"]).apply(null, arguments);\n };\n var __emscripten_proxy_execute_task_queue = Module[\"__emscripten_proxy_execute_task_queue\"] = function() {\n return (__emscripten_proxy_execute_task_queue = Module[\"__emscripten_proxy_execute_task_queue\"] = Module[\"asm\"][\"_emscripten_proxy_execute_task_queue\"]).apply(null, arguments);\n };\n var 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Module[\"stackRestore\"] = Module[\"asm\"][\"stackRestore\"]).apply(null, arguments);\n };\n var stackAlloc = Module[\"stackAlloc\"] = function() {\n return (stackAlloc = Module[\"stackAlloc\"] = Module[\"asm\"][\"stackAlloc\"]).apply(null, arguments);\n };\n var dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = function() {\n return (dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = Module[\"asm\"][\"dynCall_iijjiiii\"]).apply(null, arguments);\n };\n var dynCall_jiji = Module[\"dynCall_jiji\"] = function() {\n return (dynCall_jiji = Module[\"dynCall_jiji\"] = Module[\"asm\"][\"dynCall_jiji\"]).apply(null, arguments);\n };\n Module[\"keepRuntimeAlive\"] = keepRuntimeAlive;\n Module[\"wasmMemory\"] = wasmMemory;\n Module[\"cwrap\"] = cwrap;\n Module[\"ExitStatus\"] = ExitStatus;\n Module[\"PThread\"] = PThread;\n var calledRun;\n dependenciesFulfilled = function runCaller() {\n if (!calledRun)\n run();\n if (!calledRun)\n dependenciesFulfilled = runCaller;\n };\n function run(args) {\n args = args || arguments_;\n if (runDependencies > 0) {\n return;\n }\n if (ENVIRONMENT_IS_PTHREAD) {\n readyPromiseResolve(Module);\n initRuntime();\n startWorker(Module);\n return;\n }\n preRun();\n if (runDependencies > 0) {\n return;\n }\n function doRun() {\n if (calledRun)\n return;\n calledRun = true;\n Module[\"calledRun\"] = true;\n if (ABORT)\n return;\n initRuntime();\n readyPromiseResolve(Module);\n if (Module[\"onRuntimeInitialized\"])\n Module[\"onRuntimeInitialized\"]();\n postRun();\n }\n if (Module[\"setStatus\"]) {\n Module[\"setStatus\"](\"Running...\");\n setTimeout(function() {\n setTimeout(function() {\n Module[\"setStatus\"](\"\");\n }, 1);\n doRun();\n }, 1);\n } else {\n doRun();\n }\n }\n if (Module[\"preInit\"]) {\n if (typeof Module[\"preInit\"] == \"function\")\n Module[\"preInit\"] = [Module[\"preInit\"]];\n while (Module[\"preInit\"].length > 0) {\n Module[\"preInit\"].pop()();\n }\n }\n run();\n var listenersAdded;\n if (beforeListeners) {\n listenersAdded = { uncaughtException: process.listeners(\"uncaughtException\").filter(function(listener) {\n return !beforeListeners.uncaughtException.indexOf(listener) > -1;\n }), unhandledRejection: process.listeners(\"unhandledRejection\").filter(function(listener) {\n return !beforeListeners.unhandledRejection.indexOf(listener) > -1;\n }) };\n }\n var actualModule;\n if (typeof WasmBackendModule !== \"undefined\") {\n actualModule = WasmBackendModule;\n } else if (typeof WasmBackendModuleThreadedSimd3 !== \"undefined\") {\n actualModule = WasmBackendModuleThreadedSimd3;\n } else {\n throw new Error(\"Could not find wasm module in post.js\");\n }\n if (listenersAdded) {\n var tmpDispose = actualModule[\"_dispose\"];\n actualModule[\"_dispose\"] = function() {\n tmpDispose();\n listenersAdded.uncaughtException.forEach(function(listener) {\n process.removeListener(\"uncaughtException\", listener);\n });\n listenersAdded.unhandledRejection.forEach(function(listener) {\n process.removeListener(\"unhandledRejection\", listener);\n });\n };\n }\n return WasmBackendModuleThreadedSimd3.ready;\n };\n })();\n if (typeof exports === \"object\" && typeof module === \"object\")\n module.exports = WasmBackendModuleThreadedSimd2;\n else if (typeof define === \"function\" && define[\"amd\"])\n define([], function() {\n return WasmBackendModuleThreadedSimd2;\n });\n else if (typeof exports === \"object\")\n exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd2;\n }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js\nvar require_tfjs_backend_wasm_threaded_simd_worker = __commonJS({\n \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js\"(exports, module) {\n \"use strict\";\n module.exports.wasmWorkerContents = `\"use strict\";var Module={};var ENVIRONMENT_IS_NODE=typeof process==\"object\"&&typeof process.versions==\"object\"&&typeof process.versions.node==\"string\";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require(\"worker_threads\");var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",data=>onmessage({data:data}));var fs=require(\"fs\");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,\"utf8\")+\"//# sourceURL=\"+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+\"\n\");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;self.alert=threadAlert;Module[\"instantiateWasm\"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);receiveInstance(instance);Module[\"wasmModule\"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.startWorker=instance=>{Module=instance;postMessage({\"cmd\":\"loaded\"})};self.onmessage=e=>{try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:\"callHandler\",handler:handler,args:[...arguments]})}}Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob==\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module)}else if(e.data.cmd===\"run\"){Module[\"__emscripten_thread_init\"](e.data.pthread_ptr,0,0,1);Module[\"establishStackSpace\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].threadInitTLS();if(!initializedJS){pendingNotifiedProxyingQueues.forEach(queue=>{Module[\"executeNotifiedProxyingQueue\"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"keepRuntimeAlive\"]()){}else{Module[\"__emscripten_thread_exit\"](ex.status)}}else{throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"__emscripten_thread_exit\"](-1)}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processProxyingQueue\"){if(initializedJS){Module[\"executeNotifiedProxyingQueue\"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){if(Module[\"__emscripten_thread_crashed\"]){Module[\"__emscripten_thread_crashed\"]()}throw ex}};`;\n }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js\nvar require_tfjs_backend_wasm = __commonJS({\n \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js\"(exports, module) {\n \"use strict\";\n var WasmBackendModule2 = (() => {\n var _scriptDir = typeof document !== \"undefined\" && document.currentScript ? document.currentScript.src : void 0;\n if (typeof __filename !== \"undefined\")\n _scriptDir = _scriptDir || __filename;\n return function(WasmBackendModule3) {\n WasmBackendModule3 = WasmBackendModule3 || {};\n var Module = typeof WasmBackendModule3 != \"undefined\" ? WasmBackendModule3 : {};\n var readyPromiseResolve, readyPromiseReject;\n Module[\"ready\"] = new Promise(function(resolve, reject) {\n readyPromiseResolve = resolve;\n readyPromiseReject = reject;\n });\n var beforeListeners;\n if (typeof process !== \"undefined\" && process.listeners) {\n beforeListeners = { uncaughtException: process.listeners(\"uncaughtException\"), unhandledRejection: process.listeners(\"unhandledRejection\") };\n }\n var moduleOverrides = Object.assign({}, Module);\n var arguments_ = [];\n var thisProgram = \"./this.program\";\n var quit_ = (status, toThrow) => {\n throw toThrow;\n };\n var ENVIRONMENT_IS_WEB = typeof window == \"object\";\n var ENVIRONMENT_IS_WORKER = typeof importScripts == \"function\";\n var ENVIRONMENT_IS_NODE = typeof process == \"object\" && typeof process.versions == \"object\" && typeof process.versions.node == \"string\";\n var scriptDirectory = \"\";\n function locateFile(path) {\n if (Module[\"locateFile\"]) {\n return Module[\"locateFile\"](path, scriptDirectory);\n }\n return scriptDirectory + path;\n }\n var read_, readAsync, readBinary, setWindowTitle;\n function logExceptionOnExit(e) {\n if (e instanceof ExitStatus)\n return;\n let toLog = e;\n err(\"exiting due to exception: \" + toLog);\n }\n if (ENVIRONMENT_IS_NODE) {\n var fs = require_fs();\n var nodePath = require_path();\n if (ENVIRONMENT_IS_WORKER) {\n scriptDirectory = nodePath.dirname(scriptDirectory) + \"/\";\n } else {\n scriptDirectory = __dirname + \"/\";\n }\n read_ = (filename, binary) => {\n filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename);\n return fs.readFileSync(filename, binary ? void 0 : \"utf8\");\n };\n readBinary = (filename) => {\n var ret = read_(filename, true);\n if (!ret.buffer) {\n ret = new Uint8Array(ret);\n }\n return ret;\n };\n readAsync = (filename, onload, onerror) => {\n filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename);\n fs.readFile(filename, function(err2, data) {\n if (err2)\n onerror(err2);\n else\n onload(data.buffer);\n });\n };\n if (process[\"argv\"].length > 1) {\n thisProgram = process[\"argv\"][1].replace(/\\\\/g, \"/\");\n }\n arguments_ = process[\"argv\"].slice(2);\n process[\"on\"](\"uncaughtException\", function(ex) {\n if (!(ex instanceof ExitStatus)) {\n throw ex;\n }\n });\n process[\"on\"](\"unhandledRejection\", function(reason) {\n throw reason;\n });\n quit_ = (status, toThrow) => {\n if (keepRuntimeAlive()) {\n process[\"exitCode\"] = status;\n throw toThrow;\n }\n logExceptionOnExit(toThrow);\n process[\"exit\"](status);\n };\n Module[\"inspect\"] = function() {\n return \"[Emscripten Module object]\";\n };\n } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {\n if (ENVIRONMENT_IS_WORKER) {\n scriptDirectory = self.location.href;\n } else if (typeof document != \"undefined\" && document.currentScript) {\n scriptDirectory = document.currentScript.src;\n }\n if (_scriptDir) {\n scriptDirectory = _scriptDir;\n }\n if (scriptDirectory.indexOf(\"blob:\") !== 0) {\n scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, \"\").lastIndexOf(\"/\") + 1);\n } else {\n scriptDirectory = \"\";\n }\n {\n read_ = (url) => {\n var xhr = new XMLHttpRequest();\n xhr.open(\"GET\", url, false);\n xhr.send(null);\n return xhr.responseText;\n };\n if (ENVIRONMENT_IS_WORKER) {\n readBinary = (url) => {\n var xhr = new XMLHttpRequest();\n xhr.open(\"GET\", url, false);\n xhr.responseType = \"arraybuffer\";\n xhr.send(null);\n return new Uint8Array(xhr.response);\n };\n }\n readAsync = (url, onload, onerror) => {\n var xhr = new XMLHttpRequest();\n xhr.open(\"GET\", url, true);\n xhr.responseType = \"arraybuffer\";\n xhr.onload = () => {\n if (xhr.status == 200 || xhr.status == 0 && xhr.response) {\n onload(xhr.response);\n return;\n }\n onerror();\n };\n xhr.onerror = onerror;\n xhr.send(null);\n };\n }\n setWindowTitle = (title) => document.title = title;\n } else {\n }\n var out = Module[\"print\"] || console.log.bind(console);\n var err = Module[\"printErr\"] || console.warn.bind(console);\n Object.assign(Module, moduleOverrides);\n moduleOverrides = null;\n if (Module[\"arguments\"])\n arguments_ = Module[\"arguments\"];\n if (Module[\"thisProgram\"])\n thisProgram = Module[\"thisProgram\"];\n if (Module[\"quit\"])\n quit_ = Module[\"quit\"];\n var POINTER_SIZE = 4;\n var wasmBinary;\n if (Module[\"wasmBinary\"])\n wasmBinary = Module[\"wasmBinary\"];\n var noExitRuntime = Module[\"noExitRuntime\"] || true;\n if (typeof WebAssembly != \"object\") {\n abort(\"no native wasm support detected\");\n }\n var wasmMemory;\n var ABORT = false;\n var EXITSTATUS;\n function assert3(condition, text) {\n if (!condition) {\n abort(text);\n }\n }\n var UTF8Decoder = typeof TextDecoder != \"undefined\" ? new TextDecoder(\"utf8\") : void 0;\n function UTF8ArrayToString(heapOrArray, idx, maxBytesToRead) {\n idx >>>= 0;\n var endIdx = idx + maxBytesToRead;\n var endPtr = idx;\n while (heapOrArray[endPtr] && !(endPtr >= endIdx))\n ++endPtr;\n if (endPtr - idx > 16 && heapOrArray.buffer && UTF8Decoder) {\n return UTF8Decoder.decode(heapOrArray.subarray(idx, endPtr));\n }\n var str = \"\";\n while (idx < endPtr) {\n var u0 = heapOrArray[idx++];\n if (!(u0 & 128)) {\n str += String.fromCharCode(u0);\n continue;\n }\n var u1 = heapOrArray[idx++] & 63;\n if ((u0 & 224) == 192) {\n str += String.fromCharCode((u0 & 31) << 6 | u1);\n continue;\n }\n var u2 = heapOrArray[idx++] & 63;\n if ((u0 & 240) == 224) {\n u0 = (u0 & 15) << 12 | u1 << 6 | u2;\n } else {\n u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heapOrArray[idx++] & 63;\n }\n if (u0 < 65536) {\n str += String.fromCharCode(u0);\n } else {\n var ch = u0 - 65536;\n str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);\n }\n }\n return str;\n }\n function UTF8ToString(ptr, maxBytesToRead) {\n ptr >>>= 0;\n return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : \"\";\n }\n function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {\n outIdx >>>= 0;\n if (!(maxBytesToWrite > 0))\n return 0;\n var startIdx = outIdx;\n var endIdx = outIdx + maxBytesToWrite - 1;\n for (var i = 0; i < str.length; ++i) {\n var u = str.charCodeAt(i);\n if (u >= 55296 && u <= 57343) {\n var u1 = str.charCodeAt(++i);\n u = 65536 + ((u & 1023) << 10) | u1 & 1023;\n }\n if (u <= 127) {\n if (outIdx >= endIdx)\n break;\n heap[outIdx++ >>> 0] = u;\n } else if (u <= 2047) {\n if (outIdx + 1 >= endIdx)\n break;\n heap[outIdx++ >>> 0] = 192 | u >> 6;\n heap[outIdx++ >>> 0] = 128 | u & 63;\n } else if (u <= 65535) {\n if (outIdx + 2 >= endIdx)\n break;\n heap[outIdx++ >>> 0] = 224 | u >> 12;\n heap[outIdx++ >>> 0] = 128 | u >> 6 & 63;\n heap[outIdx++ >>> 0] = 128 | u & 63;\n } else {\n if (outIdx + 3 >= endIdx)\n break;\n heap[outIdx++ >>> 0] = 240 | u >> 18;\n heap[outIdx++ >>> 0] = 128 | u >> 12 & 63;\n heap[outIdx++ >>> 0] = 128 | u >> 6 & 63;\n heap[outIdx++ >>> 0] = 128 | u & 63;\n }\n }\n heap[outIdx >>> 0] = 0;\n return outIdx - startIdx;\n }\n function stringToUTF8(str, outPtr, maxBytesToWrite) {\n return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite);\n }\n var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;\n function updateGlobalBufferAndViews(buf) {\n buffer2 = buf;\n Module[\"HEAP8\"] = HEAP8 = new Int8Array(buf);\n Module[\"HEAP16\"] = HEAP16 = new Int16Array(buf);\n Module[\"HEAP32\"] = HEAP32 = new Int32Array(buf);\n Module[\"HEAPU8\"] = HEAPU8 = new Uint8Array(buf);\n Module[\"HEAPU16\"] = HEAPU16 = new Uint16Array(buf);\n Module[\"HEAPU32\"] = HEAPU32 = new Uint32Array(buf);\n Module[\"HEAPF32\"] = HEAPF32 = new Float32Array(buf);\n Module[\"HEAPF64\"] = HEAPF64 = new Float64Array(buf);\n }\n var INITIAL_MEMORY = Module[\"INITIAL_MEMORY\"] || 16777216;\n var wasmTable;\n var __ATPRERUN__ = [];\n var __ATINIT__ = [];\n var __ATPOSTRUN__ = [];\n var runtimeInitialized = false;\n function keepRuntimeAlive() {\n return noExitRuntime;\n }\n function preRun() {\n if (Module[\"preRun\"]) {\n if (typeof Module[\"preRun\"] == \"function\")\n Module[\"preRun\"] = [Module[\"preRun\"]];\n while (Module[\"preRun\"].length) {\n addOnPreRun(Module[\"preRun\"].shift());\n }\n }\n callRuntimeCallbacks(__ATPRERUN__);\n }\n function initRuntime() {\n runtimeInitialized = true;\n callRuntimeCallbacks(__ATINIT__);\n }\n function postRun() {\n if (Module[\"postRun\"]) {\n if (typeof Module[\"postRun\"] == \"function\")\n Module[\"postRun\"] = [Module[\"postRun\"]];\n while (Module[\"postRun\"].length) {\n addOnPostRun(Module[\"postRun\"].shift());\n }\n }\n callRuntimeCallbacks(__ATPOSTRUN__);\n }\n function addOnPreRun(cb) {\n __ATPRERUN__.unshift(cb);\n }\n function addOnInit(cb) {\n __ATINIT__.unshift(cb);\n }\n function addOnPostRun(cb) {\n __ATPOSTRUN__.unshift(cb);\n }\n var runDependencies = 0;\n var runDependencyWatcher = null;\n var dependenciesFulfilled = null;\n function addRunDependency(id) {\n runDependencies++;\n if (Module[\"monitorRunDependencies\"]) {\n Module[\"monitorRunDependencies\"](runDependencies);\n }\n }\n function removeRunDependency(id) {\n runDependencies--;\n if (Module[\"monitorRunDependencies\"]) {\n Module[\"monitorRunDependencies\"](runDependencies);\n }\n if (runDependencies == 0) {\n if (runDependencyWatcher !== null) {\n clearInterval(runDependencyWatcher);\n runDependencyWatcher = null;\n }\n if (dependenciesFulfilled) {\n var callback = dependenciesFulfilled;\n dependenciesFulfilled = null;\n callback();\n }\n }\n }\n function abort(what) {\n if (Module[\"onAbort\"]) {\n Module[\"onAbort\"](what);\n }\n what = \"Aborted(\" + what + \")\";\n err(what);\n ABORT = true;\n EXITSTATUS = 1;\n what += \". Build with -sASSERTIONS for more info.\";\n var e = new WebAssembly.RuntimeError(what);\n readyPromiseReject(e);\n throw e;\n }\n var dataURIPrefix = \"data:application/octet-stream;base64,\";\n function isDataURI(filename) {\n return filename.startsWith(dataURIPrefix);\n }\n function isFileURI(filename) {\n return filename.startsWith(\"file://\");\n }\n var wasmBinaryFile;\n wasmBinaryFile = \"tfjs-backend-wasm.wasm\";\n if (!isDataURI(wasmBinaryFile)) {\n wasmBinaryFile = locateFile(wasmBinaryFile);\n }\n function getBinary(file) {\n try {\n if (file == wasmBinaryFile && wasmBinary) {\n return new Uint8Array(wasmBinary);\n }\n if (readBinary) {\n return readBinary(file);\n }\n throw \"both async and sync fetching of the wasm failed\";\n } catch (err2) {\n abort(err2);\n }\n }\n function getBinaryPromise() {\n if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {\n if (typeof fetch == \"function\" && !isFileURI(wasmBinaryFile)) {\n return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n if (!response[\"ok\"]) {\n throw \"failed to load wasm binary file at '\" + wasmBinaryFile + \"'\";\n }\n return response[\"arrayBuffer\"]();\n }).catch(function() {\n return getBinary(wasmBinaryFile);\n });\n } else {\n if (readAsync) {\n return new Promise(function(resolve, reject) {\n readAsync(wasmBinaryFile, function(response) {\n resolve(new Uint8Array(response));\n }, reject);\n });\n }\n }\n }\n return Promise.resolve().then(function() {\n return getBinary(wasmBinaryFile);\n });\n }\n function createWasm() {\n var info = { \"env\": asmLibraryArg, \"wasi_snapshot_preview1\": asmLibraryArg };\n function receiveInstance(instance, module2) {\n var exports3 = instance.exports;\n Module[\"asm\"] = exports3;\n wasmMemory = Module[\"asm\"][\"memory\"];\n updateGlobalBufferAndViews(wasmMemory.buffer);\n wasmTable = Module[\"asm\"][\"__indirect_function_table\"];\n addOnInit(Module[\"asm\"][\"__wasm_call_ctors\"]);\n removeRunDependency(\"wasm-instantiate\");\n }\n addRunDependency(\"wasm-instantiate\");\n function receiveInstantiationResult(result) {\n receiveInstance(result[\"instance\"]);\n }\n function instantiateArrayBuffer(receiver) {\n return getBinaryPromise().then(function(binary) {\n return WebAssembly.instantiate(binary, info);\n }).then(function(instance) {\n return instance;\n }).then(receiver, function(reason) {\n err(\"failed to asynchronously prepare wasm: \" + reason);\n abort(reason);\n });\n }\n function instantiateAsync() {\n if (!wasmBinary && typeof WebAssembly.instantiateStreaming == \"function\" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && !ENVIRONMENT_IS_NODE && typeof fetch == \"function\") {\n return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n var result = WebAssembly.instantiateStreaming(response, info);\n return result.then(receiveInstantiationResult, function(reason) {\n err(\"wasm streaming compile failed: \" + reason);\n err(\"falling back to ArrayBuffer instantiation\");\n return instantiateArrayBuffer(receiveInstantiationResult);\n });\n });\n } else {\n return instantiateArrayBuffer(receiveInstantiationResult);\n }\n }\n if (Module[\"instantiateWasm\"]) {\n try {\n var exports2 = Module[\"instantiateWasm\"](info, receiveInstance);\n return exports2;\n } catch (e) {\n err(\"Module.instantiateWasm callback failed with error: \" + e);\n readyPromiseReject(e);\n }\n }\n instantiateAsync().catch(readyPromiseReject);\n return {};\n }\n var tempDouble;\n var tempI64;\n function ExitStatus(status) {\n this.name = \"ExitStatus\";\n this.message = \"Program terminated with exit(\" + status + \")\";\n this.status = status;\n }\n function callRuntimeCallbacks(callbacks2) {\n while (callbacks2.length > 0) {\n callbacks2.shift()(Module);\n }\n }\n function _abort() {\n abort(\"\");\n }\n function getHeapMax() {\n return 4294901760;\n }\n function _emscripten_get_heap_max() {\n return getHeapMax();\n }\n function _emscripten_memcpy_big(dest, src, num) {\n HEAPU8.copyWithin(dest >>> 0, src >>> 0, src + num >>> 0);\n }\n function emscripten_realloc_buffer(size) {\n try {\n wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);\n updateGlobalBufferAndViews(wasmMemory.buffer);\n return 1;\n } catch (e) {\n }\n }\n function _emscripten_resize_heap(requestedSize) {\n var oldSize = HEAPU8.length;\n requestedSize = requestedSize >>> 0;\n var maxHeapSize = getHeapMax();\n if (requestedSize > maxHeapSize) {\n return false;\n }\n let alignUp = (x, multiple) => x + (multiple - x % multiple) % multiple;\n for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {\n var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);\n overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);\n var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));\n var replacement = emscripten_realloc_buffer(newSize);\n if (replacement) {\n return true;\n }\n }\n return false;\n }\n var SYSCALLS = { varargs: void 0, get: function() {\n SYSCALLS.varargs += 4;\n var ret = HEAP32[SYSCALLS.varargs - 4 >>> 2];\n return ret;\n }, getStr: function(ptr) {\n var ret = UTF8ToString(ptr);\n return ret;\n } };\n function _fd_close(fd) {\n return 52;\n }\n function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {\n return 70;\n }\n var printCharBuffers = [null, [], []];\n function printChar(stream, curr) {\n var buffer3 = printCharBuffers[stream];\n if (curr === 0 || curr === 10) {\n (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));\n buffer3.length = 0;\n } else {\n buffer3.push(curr);\n }\n }\n function _fd_write(fd, iov, iovcnt, pnum) {\n var num = 0;\n for (var i = 0; i < iovcnt; i++) {\n var ptr = HEAPU32[iov >>> 2];\n var len = HEAPU32[iov + 4 >>> 2];\n iov += 8;\n for (var j = 0; j < len; j++) {\n printChar(fd, HEAPU8[ptr + j >>> 0]);\n }\n num += len;\n }\n HEAPU32[pnum >>> 2] = num;\n return 0;\n }\n function getCFunc(ident) {\n var func2 = Module[\"_\" + ident];\n return func2;\n }\n function writeArrayToMemory(array2, buffer3) {\n HEAP8.set(array2, buffer3 >>> 0);\n }\n function ccall(ident, returnType, argTypes, args, opts) {\n var toC = { \"string\": (str) => {\n var ret2 = 0;\n if (str !== null && str !== void 0 && str !== 0) {\n var len = (str.length << 2) + 1;\n ret2 = stackAlloc(len);\n stringToUTF8(str, ret2, len);\n }\n return ret2;\n }, \"array\": (arr) => {\n var ret2 = stackAlloc(arr.length);\n writeArrayToMemory(arr, ret2);\n return ret2;\n } };\n function convertReturnValue(ret2) {\n if (returnType === \"string\") {\n return UTF8ToString(ret2);\n }\n if (returnType === \"boolean\")\n return Boolean(ret2);\n return ret2;\n }\n var func2 = getCFunc(ident);\n var cArgs = [];\n var stack2 = 0;\n if (args) {\n for (var i = 0; i < args.length; i++) {\n var converter = toC[argTypes[i]];\n if (converter) {\n if (stack2 === 0)\n stack2 = stackSave();\n cArgs[i] = converter(args[i]);\n } else {\n cArgs[i] = args[i];\n }\n }\n }\n var ret = func2.apply(null, cArgs);\n function onDone(ret2) {\n if (stack2 !== 0)\n stackRestore(stack2);\n return convertReturnValue(ret2);\n }\n ret = onDone(ret);\n return ret;\n }\n function cwrap(ident, returnType, argTypes, opts) {\n argTypes = argTypes || [];\n var numericArgs = argTypes.every((type) => type === \"number\" || type === \"boolean\");\n var numericRet = returnType !== \"string\";\n if (numericRet && numericArgs && !opts) {\n return getCFunc(ident);\n }\n return function() {\n return ccall(ident, returnType, argTypes, arguments, opts);\n };\n }\n var asmLibraryArg = { \"abort\": _abort, \"emscripten_get_heap_max\": _emscripten_get_heap_max, \"emscripten_memcpy_big\": _emscripten_memcpy_big, \"emscripten_resize_heap\": _emscripten_resize_heap, \"fd_close\": _fd_close, \"fd_seek\": _fd_seek, \"fd_write\": _fd_write };\n var asm = createWasm();\n var ___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = function() {\n return (___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = Module[\"asm\"][\"__wasm_call_ctors\"]).apply(null, arguments);\n };\n var _init = Module[\"_init\"] = function() {\n return (_init = Module[\"_init\"] = Module[\"asm\"][\"init\"]).apply(null, arguments);\n };\n var _init_with_threads_count = Module[\"_init_with_threads_count\"] = function() {\n return (_init_with_threads_count = Module[\"_init_with_threads_count\"] = Module[\"asm\"][\"init_with_threads_count\"]).apply(null, arguments);\n };\n var _get_threads_count = Module[\"_get_threads_count\"] = function() {\n return (_get_threads_count = Module[\"_get_threads_count\"] = Module[\"asm\"][\"get_threads_count\"]).apply(null, arguments);\n };\n var _register_tensor = Module[\"_register_tensor\"] = function() {\n return (_register_tensor = Module[\"_register_tensor\"] = Module[\"asm\"][\"register_tensor\"]).apply(null, arguments);\n };\n var _dispose_data = Module[\"_dispose_data\"] = function() {\n return (_dispose_data = Module[\"_dispose_data\"] = Module[\"asm\"][\"dispose_data\"]).apply(null, arguments);\n };\n var _dispose = Module[\"_dispose\"] = function() {\n return (_dispose = Module[\"_dispose\"] = Module[\"asm\"][\"dispose\"]).apply(null, arguments);\n };\n var _Abs = Module[\"_Abs\"] = function() {\n return (_Abs = Module[\"_Abs\"] = Module[\"asm\"][\"Abs\"]).apply(null, arguments);\n };\n var _Acos = Module[\"_Acos\"] = function() {\n return (_Acos = Module[\"_Acos\"] = Module[\"asm\"][\"Acos\"]).apply(null, arguments);\n };\n var _Acosh = Module[\"_Acosh\"] = function() {\n return (_Acosh = Module[\"_Acosh\"] = Module[\"asm\"][\"Acosh\"]).apply(null, arguments);\n };\n var _Add = Module[\"_Add\"] = function() {\n return (_Add = Module[\"_Add\"] = Module[\"asm\"][\"Add\"]).apply(null, arguments);\n };\n var _AddN = Module[\"_AddN\"] = function() {\n return (_AddN = Module[\"_AddN\"] = Module[\"asm\"][\"AddN\"]).apply(null, arguments);\n };\n var _All = Module[\"_All\"] = function() {\n return (_All = Module[\"_All\"] = Module[\"asm\"][\"All\"]).apply(null, arguments);\n };\n var _Any = Module[\"_Any\"] = function() {\n return (_Any = Module[\"_Any\"] = Module[\"asm\"][\"Any\"]).apply(null, arguments);\n };\n var _ArgMax = Module[\"_ArgMax\"] = function() {\n return (_ArgMax = Module[\"_ArgMax\"] = Module[\"asm\"][\"ArgMax\"]).apply(null, arguments);\n };\n var _ArgMin = Module[\"_ArgMin\"] = function() {\n return (_ArgMin = Module[\"_ArgMin\"] = Module[\"asm\"][\"ArgMin\"]).apply(null, arguments);\n };\n var _Asin = Module[\"_Asin\"] = function() {\n return (_Asin = Module[\"_Asin\"] = Module[\"asm\"][\"Asin\"]).apply(null, arguments);\n };\n var _Asinh = Module[\"_Asinh\"] = function() {\n return (_Asinh = Module[\"_Asinh\"] = Module[\"asm\"][\"Asinh\"]).apply(null, arguments);\n };\n var _Atan = Module[\"_Atan\"] = function() {\n return (_Atan = Module[\"_Atan\"] = Module[\"asm\"][\"Atan\"]).apply(null, arguments);\n };\n var _Atan2 = Module[\"_Atan2\"] = function() {\n return (_Atan2 = Module[\"_Atan2\"] = Module[\"asm\"][\"Atan2\"]).apply(null, arguments);\n };\n var _Atanh = Module[\"_Atanh\"] = function() {\n return (_Atanh = Module[\"_Atanh\"] = Module[\"asm\"][\"Atanh\"]).apply(null, arguments);\n };\n var _AvgPool = Module[\"_AvgPool\"] = function() {\n return (_AvgPool = Module[\"_AvgPool\"] = Module[\"asm\"][\"AvgPool\"]).apply(null, arguments);\n };\n var _AvgPool3D = Module[\"_AvgPool3D\"] = function() {\n return (_AvgPool3D = Module[\"_AvgPool3D\"] = Module[\"asm\"][\"AvgPool3D\"]).apply(null, arguments);\n };\n var _AvgPool3DGrad = Module[\"_AvgPool3DGrad\"] = function() {\n return (_AvgPool3DGrad = Module[\"_AvgPool3DGrad\"] = Module[\"asm\"][\"AvgPool3DGrad\"]).apply(null, arguments);\n };\n var _AvgPoolGrad = Module[\"_AvgPoolGrad\"] = function() {\n return (_AvgPoolGrad = Module[\"_AvgPoolGrad\"] = Module[\"asm\"][\"AvgPoolGrad\"]).apply(null, arguments);\n };\n var _BatchMatMul = Module[\"_BatchMatMul\"] = function() {\n return (_BatchMatMul = Module[\"_BatchMatMul\"] = Module[\"asm\"][\"BatchMatMul\"]).apply(null, arguments);\n };\n var _Bincount = Module[\"_Bincount\"] = function() {\n return (_Bincount = Module[\"_Bincount\"] = Module[\"asm\"][\"Bincount\"]).apply(null, arguments);\n };\n var _BitwiseAnd = Module[\"_BitwiseAnd\"] = function() {\n return (_BitwiseAnd = Module[\"_BitwiseAnd\"] = Module[\"asm\"][\"BitwiseAnd\"]).apply(null, arguments);\n };\n var _Ceil = Module[\"_Ceil\"] = function() {\n return (_Ceil = Module[\"_Ceil\"] = Module[\"asm\"][\"Ceil\"]).apply(null, arguments);\n };\n var _ClipByValue = Module[\"_ClipByValue\"] = function() {\n return (_ClipByValue = Module[\"_ClipByValue\"] = Module[\"asm\"][\"ClipByValue\"]).apply(null, arguments);\n };\n var _Conv2D = Module[\"_Conv2D\"] = function() {\n return (_Conv2D = Module[\"_Conv2D\"] = Module[\"asm\"][\"Conv2D\"]).apply(null, arguments);\n };\n var _Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = function() {\n return (_Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null, arguments);\n };\n var _Conv3D = Module[\"_Conv3D\"] = function() {\n return (_Conv3D = Module[\"_Conv3D\"] = Module[\"asm\"][\"Conv3D\"]).apply(null, arguments);\n };\n var _Conv3DBackpropFilterV2 = Module[\"_Conv3DBackpropFilterV2\"] = function() {\n return (_Conv3DBackpropFilterV2 = Module[\"_Conv3DBackpropFilterV2\"] = Module[\"asm\"][\"Conv3DBackpropFilterV2\"]).apply(null, arguments);\n };\n var _Conv3DBackpropInputV2 = Module[\"_Conv3DBackpropInputV2\"] = function() {\n return (_Conv3DBackpropInputV2 = Module[\"_Conv3DBackpropInputV2\"] = Module[\"asm\"][\"Conv3DBackpropInputV2\"]).apply(null, arguments);\n };\n var _Cos = Module[\"_Cos\"] = function() {\n return (_Cos = Module[\"_Cos\"] = Module[\"asm\"][\"Cos\"]).apply(null, arguments);\n };\n var _Cosh = Module[\"_Cosh\"] = function() {\n return (_Cosh = Module[\"_Cosh\"] = Module[\"asm\"][\"Cosh\"]).apply(null, arguments);\n };\n var _CropAndResize = Module[\"_CropAndResize\"] = function() {\n return (_CropAndResize = Module[\"_CropAndResize\"] = Module[\"asm\"][\"CropAndResize\"]).apply(null, arguments);\n };\n var _Cumprod = Module[\"_Cumprod\"] = function() {\n return (_Cumprod = Module[\"_Cumprod\"] = Module[\"asm\"][\"Cumprod\"]).apply(null, arguments);\n };\n var _Cumsum = Module[\"_Cumsum\"] = function() {\n return (_Cumsum = Module[\"_Cumsum\"] = Module[\"asm\"][\"Cumsum\"]).apply(null, arguments);\n };\n var _DenseBincount = Module[\"_DenseBincount\"] = function() {\n return (_DenseBincount = Module[\"_DenseBincount\"] = Module[\"asm\"][\"DenseBincount\"]).apply(null, arguments);\n };\n var _DepthToSpace = Module[\"_DepthToSpace\"] = function() {\n return (_DepthToSpace = Module[\"_DepthToSpace\"] = Module[\"asm\"][\"DepthToSpace\"]).apply(null, arguments);\n };\n var _DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = function() {\n return (_DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null, arguments);\n };\n var _Diag = Module[\"_Diag\"] = function() {\n return (_Diag = Module[\"_Diag\"] = Module[\"asm\"][\"Diag\"]).apply(null, arguments);\n };\n var _Dilation2D = Module[\"_Dilation2D\"] = function() {\n return (_Dilation2D = Module[\"_Dilation2D\"] = Module[\"asm\"][\"Dilation2D\"]).apply(null, arguments);\n };\n var _Dilation2DBackpropFilter = Module[\"_Dilation2DBackpropFilter\"] = function() {\n return (_Dilation2DBackpropFilter = Module[\"_Dilation2DBackpropFilter\"] = Module[\"asm\"][\"Dilation2DBackpropFilter\"]).apply(null, arguments);\n };\n var _Dilation2DBackpropInput = Module[\"_Dilation2DBackpropInput\"] = function() {\n return (_Dilation2DBackpropInput = Module[\"_Dilation2DBackpropInput\"] = Module[\"asm\"][\"Dilation2DBackpropInput\"]).apply(null, arguments);\n };\n var _Elu = Module[\"_Elu\"] = function() {\n return (_Elu = Module[\"_Elu\"] = Module[\"asm\"][\"Elu\"]).apply(null, arguments);\n };\n var _EluGrad = Module[\"_EluGrad\"] = function() {\n return (_EluGrad = Module[\"_EluGrad\"] = Module[\"asm\"][\"EluGrad\"]).apply(null, arguments);\n };\n var _Equal = Module[\"_Equal\"] = function() {\n return (_Equal = Module[\"_Equal\"] = Module[\"asm\"][\"Equal\"]).apply(null, arguments);\n };\n var _Erf = Module[\"_Erf\"] = function() {\n return (_Erf = Module[\"_Erf\"] = Module[\"asm\"][\"Erf\"]).apply(null, arguments);\n };\n var _Exp = Module[\"_Exp\"] = function() {\n return (_Exp = Module[\"_Exp\"] = Module[\"asm\"][\"Exp\"]).apply(null, arguments);\n };\n var _Expm1 = Module[\"_Expm1\"] = function() {\n return (_Expm1 = Module[\"_Expm1\"] = Module[\"asm\"][\"Expm1\"]).apply(null, arguments);\n };\n var _FlipLeftRight = Module[\"_FlipLeftRight\"] = function() {\n return (_FlipLeftRight = Module[\"_FlipLeftRight\"] = Module[\"asm\"][\"FlipLeftRight\"]).apply(null, arguments);\n };\n var _Floor = Module[\"_Floor\"] = function() {\n return (_Floor = Module[\"_Floor\"] = Module[\"asm\"][\"Floor\"]).apply(null, arguments);\n };\n var _FloorDiv = Module[\"_FloorDiv\"] = function() {\n return (_FloorDiv = Module[\"_FloorDiv\"] = Module[\"asm\"][\"FloorDiv\"]).apply(null, arguments);\n };\n var _FusedBatchNorm = Module[\"_FusedBatchNorm\"] = function() {\n return (_FusedBatchNorm = Module[\"_FusedBatchNorm\"] = Module[\"asm\"][\"FusedBatchNorm\"]).apply(null, arguments);\n };\n var _FusedConv2D = Module[\"_FusedConv2D\"] = function() {\n return (_FusedConv2D = Module[\"_FusedConv2D\"] = Module[\"asm\"][\"FusedConv2D\"]).apply(null, arguments);\n };\n var _FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = function() {\n return (_FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null, arguments);\n };\n var _Gather = Module[\"_Gather\"] = function() {\n return (_Gather = Module[\"_Gather\"] = Module[\"asm\"][\"Gather\"]).apply(null, arguments);\n };\n var _GatherNd = Module[\"_GatherNd\"] = function() {\n return (_GatherNd = Module[\"_GatherNd\"] = Module[\"asm\"][\"GatherNd\"]).apply(null, arguments);\n };\n var _Greater = Module[\"_Greater\"] = function() {\n return (_Greater = Module[\"_Greater\"] = Module[\"asm\"][\"Greater\"]).apply(null, arguments);\n };\n var _GreaterEqual = Module[\"_GreaterEqual\"] = function() {\n return (_GreaterEqual = Module[\"_GreaterEqual\"] = Module[\"asm\"][\"GreaterEqual\"]).apply(null, arguments);\n };\n var _IsFinite = Module[\"_IsFinite\"] = function() {\n return (_IsFinite = Module[\"_IsFinite\"] = Module[\"asm\"][\"IsFinite\"]).apply(null, arguments);\n };\n var _IsInf = Module[\"_IsInf\"] = function() {\n return (_IsInf = Module[\"_IsInf\"] = Module[\"asm\"][\"IsInf\"]).apply(null, arguments);\n };\n var _IsNan = Module[\"_IsNan\"] = function() {\n return (_IsNan = Module[\"_IsNan\"] = Module[\"asm\"][\"IsNan\"]).apply(null, arguments);\n };\n var _LRN = Module[\"_LRN\"] = function() {\n return (_LRN = Module[\"_LRN\"] = Module[\"asm\"][\"LRN\"]).apply(null, arguments);\n };\n var _LRNGrad = Module[\"_LRNGrad\"] = function() {\n return (_LRNGrad = Module[\"_LRNGrad\"] = Module[\"asm\"][\"LRNGrad\"]).apply(null, arguments);\n };\n var _LeakyRelu = Module[\"_LeakyRelu\"] = function() {\n return (_LeakyRelu = Module[\"_LeakyRelu\"] = Module[\"asm\"][\"LeakyRelu\"]).apply(null, arguments);\n };\n var _Less = Module[\"_Less\"] = function() {\n return (_Less = Module[\"_Less\"] = Module[\"asm\"][\"Less\"]).apply(null, arguments);\n };\n var _LessEqual = Module[\"_LessEqual\"] = function() {\n return (_LessEqual = Module[\"_LessEqual\"] = Module[\"asm\"][\"LessEqual\"]).apply(null, arguments);\n };\n var _LinSpace = Module[\"_LinSpace\"] = function() {\n return (_LinSpace = Module[\"_LinSpace\"] = Module[\"asm\"][\"LinSpace\"]).apply(null, arguments);\n };\n var _Log = Module[\"_Log\"] = function() {\n return (_Log = Module[\"_Log\"] = Module[\"asm\"][\"Log\"]).apply(null, arguments);\n };\n var _Log1p = Module[\"_Log1p\"] = function() {\n return (_Log1p = Module[\"_Log1p\"] = Module[\"asm\"][\"Log1p\"]).apply(null, arguments);\n };\n var _LogicalAnd = Module[\"_LogicalAnd\"] = function() {\n return (_LogicalAnd = Module[\"_LogicalAnd\"] = Module[\"asm\"][\"LogicalAnd\"]).apply(null, arguments);\n };\n var _LogicalNot = Module[\"_LogicalNot\"] = function() {\n return (_LogicalNot = Module[\"_LogicalNot\"] = Module[\"asm\"][\"LogicalNot\"]).apply(null, arguments);\n };\n var _LogicalOr = Module[\"_LogicalOr\"] = function() {\n return (_LogicalOr = Module[\"_LogicalOr\"] = Module[\"asm\"][\"LogicalOr\"]).apply(null, arguments);\n };\n var _LogicalXor = Module[\"_LogicalXor\"] = function() {\n return (_LogicalXor = Module[\"_LogicalXor\"] = Module[\"asm\"][\"LogicalXor\"]).apply(null, arguments);\n };\n var _Max = Module[\"_Max\"] = function() {\n return (_Max = Module[\"_Max\"] = Module[\"asm\"][\"Max\"]).apply(null, arguments);\n };\n var _MaxPool = Module[\"_MaxPool\"] = function() {\n return (_MaxPool = Module[\"_MaxPool\"] = Module[\"asm\"][\"MaxPool\"]).apply(null, arguments);\n };\n var _MaxPool3D = Module[\"_MaxPool3D\"] = function() {\n return (_MaxPool3D = Module[\"_MaxPool3D\"] = Module[\"asm\"][\"MaxPool3D\"]).apply(null, arguments);\n };\n var _MaxPool3DGrad = Module[\"_MaxPool3DGrad\"] = function() {\n return (_MaxPool3DGrad = Module[\"_MaxPool3DGrad\"] = Module[\"asm\"][\"MaxPool3DGrad\"]).apply(null, arguments);\n };\n var _MaxPoolGrad = Module[\"_MaxPoolGrad\"] = function() {\n return (_MaxPoolGrad = Module[\"_MaxPoolGrad\"] = Module[\"asm\"][\"MaxPoolGrad\"]).apply(null, arguments);\n };\n var _MaxPoolWithArgmax = Module[\"_MaxPoolWithArgmax\"] = function() {\n return (_MaxPoolWithArgmax = Module[\"_MaxPoolWithArgmax\"] = Module[\"asm\"][\"MaxPoolWithArgmax\"]).apply(null, arguments);\n };\n var _Maximum = Module[\"_Maximum\"] = function() {\n return (_Maximum = Module[\"_Maximum\"] = Module[\"asm\"][\"Maximum\"]).apply(null, arguments);\n };\n var _Mean = Module[\"_Mean\"] = function() {\n return (_Mean = Module[\"_Mean\"] = Module[\"asm\"][\"Mean\"]).apply(null, arguments);\n };\n var _Min = Module[\"_Min\"] = function() {\n return (_Min = Module[\"_Min\"] = Module[\"asm\"][\"Min\"]).apply(null, arguments);\n };\n var _Minimum = Module[\"_Minimum\"] = function() {\n return (_Minimum = Module[\"_Minimum\"] = Module[\"asm\"][\"Minimum\"]).apply(null, arguments);\n };\n var _MirrorPad = Module[\"_MirrorPad\"] = function() {\n return (_MirrorPad = Module[\"_MirrorPad\"] = Module[\"asm\"][\"MirrorPad\"]).apply(null, arguments);\n };\n var _Mod = Module[\"_Mod\"] = function() {\n return (_Mod = Module[\"_Mod\"] = Module[\"asm\"][\"Mod\"]).apply(null, arguments);\n };\n var _Multinomial = Module[\"_Multinomial\"] = function() {\n return (_Multinomial = Module[\"_Multinomial\"] = Module[\"asm\"][\"Multinomial\"]).apply(null, arguments);\n };\n var _Multiply = Module[\"_Multiply\"] = function() {\n return (_Multiply = Module[\"_Multiply\"] = Module[\"asm\"][\"Multiply\"]).apply(null, arguments);\n };\n var _Neg = Module[\"_Neg\"] = function() {\n return (_Neg = Module[\"_Neg\"] = Module[\"asm\"][\"Neg\"]).apply(null, arguments);\n };\n var _NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = function() {\n return (_NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null, arguments);\n };\n var _NonMaxSuppressionV4 = Module[\"_NonMaxSuppressionV4\"] = function() {\n return (_NonMaxSuppressionV4 = Module[\"_NonMaxSuppressionV4\"] = Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null, arguments);\n };\n var _NonMaxSuppressionV5 = Module[\"_NonMaxSuppressionV5\"] = function() {\n return (_NonMaxSuppressionV5 = Module[\"_NonMaxSuppressionV5\"] = Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null, arguments);\n };\n var _NotEqual = Module[\"_NotEqual\"] = function() {\n return (_NotEqual = Module[\"_NotEqual\"] = Module[\"asm\"][\"NotEqual\"]).apply(null, arguments);\n };\n var _OneHot = Module[\"_OneHot\"] = function() {\n return (_OneHot = Module[\"_OneHot\"] = Module[\"asm\"][\"OneHot\"]).apply(null, arguments);\n };\n var _PadV2 = Module[\"_PadV2\"] = function() {\n return (_PadV2 = Module[\"_PadV2\"] = Module[\"asm\"][\"PadV2\"]).apply(null, arguments);\n };\n var _Pow = Module[\"_Pow\"] = function() {\n return (_Pow = Module[\"_Pow\"] = Module[\"asm\"][\"Pow\"]).apply(null, arguments);\n };\n var _Prelu = Module[\"_Prelu\"] = function() {\n return (_Prelu = Module[\"_Prelu\"] = Module[\"asm\"][\"Prelu\"]).apply(null, arguments);\n };\n var _Prod = Module[\"_Prod\"] = function() {\n return (_Prod = Module[\"_Prod\"] = Module[\"asm\"][\"Prod\"]).apply(null, arguments);\n };\n var _RealDiv = Module[\"_RealDiv\"] = function() {\n return (_RealDiv = Module[\"_RealDiv\"] = Module[\"asm\"][\"RealDiv\"]).apply(null, arguments);\n };\n var _Reciprocal = Module[\"_Reciprocal\"] = function() {\n return (_Reciprocal = Module[\"_Reciprocal\"] = Module[\"asm\"][\"Reciprocal\"]).apply(null, arguments);\n };\n var _Relu = Module[\"_Relu\"] = function() {\n return (_Relu = Module[\"_Relu\"] = Module[\"asm\"][\"Relu\"]).apply(null, arguments);\n };\n var _Relu6 = Module[\"_Relu6\"] = function() {\n return (_Relu6 = Module[\"_Relu6\"] = Module[\"asm\"][\"Relu6\"]).apply(null, arguments);\n };\n var _ResizeBilinear = Module[\"_ResizeBilinear\"] = function() {\n return (_ResizeBilinear = Module[\"_ResizeBilinear\"] = Module[\"asm\"][\"ResizeBilinear\"]).apply(null, arguments);\n };\n var _ResizeBilinearGrad = Module[\"_ResizeBilinearGrad\"] = function() {\n return (_ResizeBilinearGrad = Module[\"_ResizeBilinearGrad\"] = Module[\"asm\"][\"ResizeBilinearGrad\"]).apply(null, arguments);\n };\n var _ResizeNearestNeighbor = Module[\"_ResizeNearestNeighbor\"] = function() {\n return (_ResizeNearestNeighbor = Module[\"_ResizeNearestNeighbor\"] = Module[\"asm\"][\"ResizeNearestNeighbor\"]).apply(null, arguments);\n };\n var _ResizeNearestNeighborGrad = Module[\"_ResizeNearestNeighborGrad\"] = function() {\n return (_ResizeNearestNeighborGrad = Module[\"_ResizeNearestNeighborGrad\"] = Module[\"asm\"][\"ResizeNearestNeighborGrad\"]).apply(null, arguments);\n };\n var _Reverse = Module[\"_Reverse\"] = function() {\n return (_Reverse = Module[\"_Reverse\"] = Module[\"asm\"][\"Reverse\"]).apply(null, arguments);\n };\n var _RotateWithOffset = Module[\"_RotateWithOffset\"] = function() {\n return (_RotateWithOffset = Module[\"_RotateWithOffset\"] = Module[\"asm\"][\"RotateWithOffset\"]).apply(null, arguments);\n };\n var _Round = Module[\"_Round\"] = function() {\n return (_Round = Module[\"_Round\"] = Module[\"asm\"][\"Round\"]).apply(null, arguments);\n };\n var _Rsqrt = Module[\"_Rsqrt\"] = function() {\n return (_Rsqrt = Module[\"_Rsqrt\"] = Module[\"asm\"][\"Rsqrt\"]).apply(null, arguments);\n };\n var _ScatterNd = Module[\"_ScatterNd\"] = function() {\n return (_ScatterNd = Module[\"_ScatterNd\"] = Module[\"asm\"][\"ScatterNd\"]).apply(null, arguments);\n };\n var _SearchSorted = Module[\"_SearchSorted\"] = function() {\n return (_SearchSorted = Module[\"_SearchSorted\"] = Module[\"asm\"][\"SearchSorted\"]).apply(null, arguments);\n };\n var _SelectV2 = Module[\"_SelectV2\"] = function() {\n return (_SelectV2 = Module[\"_SelectV2\"] = Module[\"asm\"][\"SelectV2\"]).apply(null, arguments);\n };\n var _Selu = Module[\"_Selu\"] = function() {\n return (_Selu = Module[\"_Selu\"] = Module[\"asm\"][\"Selu\"]).apply(null, arguments);\n };\n var _Sigmoid = Module[\"_Sigmoid\"] = function() {\n return (_Sigmoid = Module[\"_Sigmoid\"] = Module[\"asm\"][\"Sigmoid\"]).apply(null, arguments);\n };\n var _Sign = Module[\"_Sign\"] = function() {\n return (_Sign = Module[\"_Sign\"] = Module[\"asm\"][\"Sign\"]).apply(null, arguments);\n };\n var _Sin = Module[\"_Sin\"] = function() {\n return (_Sin = Module[\"_Sin\"] = Module[\"asm\"][\"Sin\"]).apply(null, arguments);\n };\n var _Sinh = Module[\"_Sinh\"] = function() {\n return (_Sinh = Module[\"_Sinh\"] = Module[\"asm\"][\"Sinh\"]).apply(null, arguments);\n };\n var _Softmax = Module[\"_Softmax\"] = function() {\n return (_Softmax = Module[\"_Softmax\"] = Module[\"asm\"][\"Softmax\"]).apply(null, arguments);\n };\n var _Softplus = Module[\"_Softplus\"] = function() {\n return (_Softplus = Module[\"_Softplus\"] = Module[\"asm\"][\"Softplus\"]).apply(null, arguments);\n };\n var _SparseFillEmptyRows = Module[\"_SparseFillEmptyRows\"] = function() {\n return (_SparseFillEmptyRows = Module[\"_SparseFillEmptyRows\"] = Module[\"asm\"][\"SparseFillEmptyRows\"]).apply(null, arguments);\n };\n var _SparseReshape = Module[\"_SparseReshape\"] = function() {\n return (_SparseReshape = Module[\"_SparseReshape\"] = Module[\"asm\"][\"SparseReshape\"]).apply(null, arguments);\n };\n var _SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = function() {\n return (_SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = Module[\"asm\"][\"SparseSegmentReduction\"]).apply(null, arguments);\n };\n var _SparseToDense = Module[\"_SparseToDense\"] = function() {\n return (_SparseToDense = Module[\"_SparseToDense\"] = Module[\"asm\"][\"SparseToDense\"]).apply(null, arguments);\n };\n var _Sqrt = Module[\"_Sqrt\"] = function() {\n return (_Sqrt = Module[\"_Sqrt\"] = Module[\"asm\"][\"Sqrt\"]).apply(null, arguments);\n };\n var _Square = Module[\"_Square\"] = function() {\n return (_Square = Module[\"_Square\"] = Module[\"asm\"][\"Square\"]).apply(null, arguments);\n };\n var _SquaredDifference = Module[\"_SquaredDifference\"] = function() {\n return (_SquaredDifference = Module[\"_SquaredDifference\"] = Module[\"asm\"][\"SquaredDifference\"]).apply(null, arguments);\n };\n var _Step = Module[\"_Step\"] = function() {\n return (_Step = Module[\"_Step\"] = Module[\"asm\"][\"Step\"]).apply(null, arguments);\n };\n var _StridedSlice = Module[\"_StridedSlice\"] = function() {\n return (_StridedSlice = Module[\"_StridedSlice\"] = Module[\"asm\"][\"StridedSlice\"]).apply(null, arguments);\n };\n var _Sub = Module[\"_Sub\"] = function() {\n return (_Sub = Module[\"_Sub\"] = Module[\"asm\"][\"Sub\"]).apply(null, arguments);\n };\n var _Sum = Module[\"_Sum\"] = function() {\n return (_Sum = Module[\"_Sum\"] = Module[\"asm\"][\"Sum\"]).apply(null, arguments);\n };\n var _Tan = Module[\"_Tan\"] = function() {\n return (_Tan = Module[\"_Tan\"] = Module[\"asm\"][\"Tan\"]).apply(null, arguments);\n };\n var _Tanh = Module[\"_Tanh\"] = function() {\n return (_Tanh = Module[\"_Tanh\"] = Module[\"asm\"][\"Tanh\"]).apply(null, arguments);\n };\n var _TensorScatterUpdate = Module[\"_TensorScatterUpdate\"] = function() {\n return (_TensorScatterUpdate = Module[\"_TensorScatterUpdate\"] = Module[\"asm\"][\"TensorScatterUpdate\"]).apply(null, arguments);\n };\n var _Tile = Module[\"_Tile\"] = function() {\n return (_Tile = Module[\"_Tile\"] = Module[\"asm\"][\"Tile\"]).apply(null, arguments);\n };\n var _TopK = Module[\"_TopK\"] = function() {\n return (_TopK = Module[\"_TopK\"] = Module[\"asm\"][\"TopK\"]).apply(null, arguments);\n };\n var _Transform = Module[\"_Transform\"] = function() {\n return (_Transform = Module[\"_Transform\"] = Module[\"asm\"][\"Transform\"]).apply(null, arguments);\n };\n var _Transpose = Module[\"_Transpose\"] = function() {\n return (_Transpose = Module[\"_Transpose\"] = Module[\"asm\"][\"Transpose\"]).apply(null, arguments);\n };\n var __FusedMatMul = Module[\"__FusedMatMul\"] = function() {\n return (__FusedMatMul = Module[\"__FusedMatMul\"] = Module[\"asm\"][\"_FusedMatMul\"]).apply(null, arguments);\n };\n var _malloc = Module[\"_malloc\"] = function() {\n return (_malloc = Module[\"_malloc\"] = Module[\"asm\"][\"malloc\"]).apply(null, arguments);\n };\n var _free = Module[\"_free\"] = function() {\n return (_free = Module[\"_free\"] = Module[\"asm\"][\"free\"]).apply(null, arguments);\n };\n var ___errno_location = Module[\"___errno_location\"] = function() {\n return (___errno_location = Module[\"___errno_location\"] = Module[\"asm\"][\"__errno_location\"]).apply(null, arguments);\n };\n var stackSave = Module[\"stackSave\"] = function() {\n return (stackSave = Module[\"stackSave\"] = Module[\"asm\"][\"stackSave\"]).apply(null, arguments);\n };\n var stackRestore = Module[\"stackRestore\"] = function() {\n return (stackRestore = Module[\"stackRestore\"] = Module[\"asm\"][\"stackRestore\"]).apply(null, arguments);\n };\n var stackAlloc = Module[\"stackAlloc\"] = function() {\n return (stackAlloc = Module[\"stackAlloc\"] = Module[\"asm\"][\"stackAlloc\"]).apply(null, arguments);\n };\n var dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = function() {\n return (dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = Module[\"asm\"][\"dynCall_iijjiiii\"]).apply(null, arguments);\n };\n var dynCall_jiji = Module[\"dynCall_jiji\"] = function() {\n return (dynCall_jiji = Module[\"dynCall_jiji\"] = Module[\"asm\"][\"dynCall_jiji\"]).apply(null, arguments);\n };\n Module[\"cwrap\"] = cwrap;\n var calledRun;\n dependenciesFulfilled = function runCaller() {\n if (!calledRun)\n run();\n if (!calledRun)\n dependenciesFulfilled = runCaller;\n };\n function run(args) {\n args = args || arguments_;\n if (runDependencies > 0) {\n return;\n }\n preRun();\n if (runDependencies > 0) {\n return;\n }\n function doRun() {\n if (calledRun)\n return;\n calledRun = true;\n Module[\"calledRun\"] = true;\n if (ABORT)\n return;\n initRuntime();\n readyPromiseResolve(Module);\n if (Module[\"onRuntimeInitialized\"])\n Module[\"onRuntimeInitialized\"]();\n postRun();\n }\n if (Module[\"setStatus\"]) {\n Module[\"setStatus\"](\"Running...\");\n setTimeout(function() {\n setTimeout(function() {\n Module[\"setStatus\"](\"\");\n }, 1);\n doRun();\n }, 1);\n } else {\n doRun();\n }\n }\n if (Module[\"preInit\"]) {\n if (typeof Module[\"preInit\"] == \"function\")\n Module[\"preInit\"] = [Module[\"preInit\"]];\n while (Module[\"preInit\"].length > 0) {\n Module[\"preInit\"].pop()();\n }\n }\n run();\n var listenersAdded;\n if (beforeListeners) {\n listenersAdded = { uncaughtException: process.listeners(\"uncaughtException\").filter(function(listener) {\n return !beforeListeners.uncaughtException.indexOf(listener) > -1;\n }), unhandledRejection: process.listeners(\"unhandledRejection\").filter(function(listener) {\n return !beforeListeners.unhandledRejection.indexOf(listener) > -1;\n }) };\n }\n var actualModule;\n if (typeof WasmBackendModule3 !== \"undefined\") {\n actualModule = WasmBackendModule3;\n } else if (typeof WasmBackendModuleThreadedSimd !== \"undefined\") {\n actualModule = WasmBackendModuleThreadedSimd;\n } else {\n throw new Error(\"Could not find wasm module in post.js\");\n }\n if (listenersAdded) {\n var tmpDispose = actualModule[\"_dispose\"];\n actualModule[\"_dispose\"] = function() {\n tmpDispose();\n listenersAdded.uncaughtException.forEach(function(listener) {\n process.removeListener(\"uncaughtException\", listener);\n });\n listenersAdded.unhandledRejection.forEach(function(listener) {\n process.removeListener(\"unhandledRejection\", listener);\n });\n };\n }\n return WasmBackendModule3.ready;\n };\n })();\n if (typeof exports === \"object\" && typeof module === \"object\")\n module.exports = WasmBackendModule2;\n else if (typeof define === \"function\" && define[\"amd\"])\n define([], function() {\n return WasmBackendModule2;\n });\n else if (typeof exports === \"object\")\n exports[\"WasmBackendModule\"] = WasmBackendModule2;\n }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js\nvar EPSILON_FLOAT32 = 1e-7;\nvar EPSILON_FLOAT16 = 1e-4;\nvar DataStorage = class {\n constructor(backend2, dataMover) {\n this.backend = backend2;\n this.dataMover = dataMover;\n this.data = /* @__PURE__ */ new WeakMap();\n this.dataIdsCount = 0;\n }\n get(dataId) {\n if (!this.data.has(dataId)) {\n this.dataMover.moveData(this.backend, dataId);\n }\n return this.data.get(dataId);\n }\n set(dataId, value) {\n this.dataIdsCount++;\n this.data.set(dataId, value);\n }\n has(dataId) {\n return this.data.has(dataId);\n }\n delete(dataId) {\n this.dataIdsCount--;\n return this.data.delete(dataId);\n }\n numDataIds() {\n return this.dataIdsCount;\n }\n};\nvar KernelBackend = class {\n refCount(dataId) {\n return notYetImplemented(\"refCount\");\n }\n incRef(dataId) {\n return notYetImplemented(\"incRef\");\n }\n timerAvailable() {\n return true;\n }\n time(f) {\n return notYetImplemented(\"time\");\n }\n read(dataId) {\n return notYetImplemented(\"read\");\n }\n readSync(dataId) {\n return notYetImplemented(\"readSync\");\n }\n readToGPU(dataId, options) {\n return notYetImplemented(\"readToGPU\");\n }\n numDataIds() {\n return notYetImplemented(\"numDataIds\");\n }\n disposeData(dataId, force) {\n return notYetImplemented(\"disposeData\");\n }\n write(values, shape, dtype) {\n return notYetImplemented(\"write\");\n }\n move(dataId, values, shape, dtype, refCount) {\n return notYetImplemented(\"move\");\n }\n createTensorFromGPUData(values, shape, dtype) {\n return notYetImplemented(\"createTensorFromGPUData\");\n }\n memory() {\n return notYetImplemented(\"memory\");\n }\n /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n floatPrecision() {\n return notYetImplemented(\"floatPrecision\");\n }\n /** Returns the smallest representable number. */\n epsilon() {\n return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n }\n dispose() {\n return notYetImplemented(\"dispose\");\n }\n};\nfunction notYetImplemented(kernelName) {\n throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/util_base.js\nfunction shuffle(array2) {\n let counter = array2.length;\n let index = 0;\n while (counter > 0) {\n index = Math.random() * counter | 0;\n counter--;\n swap(array2, counter, index);\n }\n}\nfunction shuffleCombo(array2, array22) {\n if (array2.length !== array22.length) {\n throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`);\n }\n let counter = array2.length;\n let index = 0;\n while (counter > 0) {\n index = Math.random() * counter | 0;\n counter--;\n swap(array2, counter, index);\n swap(array22, counter, index);\n }\n}\nfunction clamp(min6, x, max6) {\n return Math.max(min6, Math.min(x, max6));\n}\nfunction nearestLargerEven(val) {\n return val % 2 === 0 ? val : val + 1;\n}\nfunction swap(object, left, right) {\n const temp = object[left];\n object[left] = object[right];\n object[right] = temp;\n}\nfunction sum(arr) {\n let sum6 = 0;\n for (let i = 0; i < arr.length; i++) {\n sum6 += arr[i];\n }\n return sum6;\n}\nfunction randUniform(a, b) {\n const r = Math.random();\n return b * r + (1 - r) * a;\n}\nfunction distSquared(a, b) {\n let result = 0;\n for (let i = 0; i < a.length; i++) {\n const diff = Number(a[i]) - Number(b[i]);\n result += diff * diff;\n }\n return result;\n}\nfunction assert(expr, msg) {\n if (!expr) {\n throw new Error(typeof msg === \"string\" ? msg : msg());\n }\n}\nfunction assertShapesMatch(shapeA, shapeB, errorMessagePrefix = \"\") {\n assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nfunction assertNonNull(a) {\n assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\nfunction sizeFromShape(shape) {\n if (shape.length === 0) {\n return 1;\n }\n let size = shape[0];\n for (let i = 1; i < shape.length; i++) {\n size *= shape[i];\n }\n return size;\n}\nfunction isScalarShape(shape) {\n return shape.length === 0;\n}\nfunction arraysEqualWithNull(n1, n2) {\n if (n1 === n2) {\n return true;\n }\n if (n1 == null || n2 == null) {\n return false;\n }\n if (n1.length !== n2.length) {\n return false;\n }\n for (let i = 0; i < n1.length; i++) {\n if (n1[i] !== null && n2[i] !== null && n1[i] !== n2[i]) {\n return false;\n }\n }\n return true;\n}\nfunction arraysEqual(n1, n2) {\n if (n1 === n2) {\n return true;\n }\n if (n1 == null || n2 == null) {\n return false;\n }\n if (n1.length !== n2.length) {\n return false;\n }\n for (let i = 0; i < n1.length; i++) {\n if (n1[i] !== n2[i]) {\n return false;\n }\n }\n return true;\n}\nfunction isInt(a) {\n return a % 1 === 0;\n}\nfunction tanh(x) {\n if (Math.tanh != null) {\n return Math.tanh(x);\n }\n if (x === Infinity) {\n return 1;\n } else if (x === -Infinity) {\n return -1;\n } else {\n const e2x = Math.exp(2 * x);\n return (e2x - 1) / (e2x + 1);\n }\n}\nfunction sizeToSquarishShape(size) {\n const width = Math.ceil(Math.sqrt(size));\n return [width, Math.ceil(size / width)];\n}\nfunction createShuffledIndices(n) {\n const shuffledIndices = new Uint32Array(n);\n for (let i = 0; i < n; ++i) {\n shuffledIndices[i] = i;\n }\n shuffle(shuffledIndices);\n return shuffledIndices;\n}\nfunction rightPad(a, size) {\n if (size <= a.length) {\n return a;\n }\n return a + \" \".repeat(size - a.length);\n}\nfunction repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter, scheduleFn) {\n return new Promise((resolve, reject) => {\n let tryCount = 0;\n const tryFn = () => {\n if (checkFn()) {\n resolve();\n return;\n }\n tryCount++;\n const nextBackoff = delayFn(tryCount);\n if (maxCounter != null && tryCount >= maxCounter) {\n reject();\n return;\n }\n if (scheduleFn != null) {\n scheduleFn(tryFn, nextBackoff);\n } else {\n setTimeout(tryFn, nextBackoff);\n }\n };\n tryFn();\n });\n}\nfunction inferFromImplicitShape(shape, size) {\n let shapeProd = 1;\n let implicitIdx = -1;\n for (let i = 0; i < shape.length; ++i) {\n if (shape[i] >= 0) {\n shapeProd *= shape[i];\n } else if (shape[i] === -1) {\n if (implicitIdx !== -1) {\n throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);\n }\n implicitIdx = i;\n } else if (shape[i] < 0) {\n throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n }\n }\n if (implicitIdx === -1) {\n if (size > 0 && size !== shapeProd) {\n throw Error(`Size(${size}) must match the product of shape ${shape}`);\n }\n return shape;\n }\n if (shapeProd === 0) {\n throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);\n }\n if (size % shapeProd !== 0) {\n throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);\n }\n const newShape = shape.slice();\n newShape[implicitIdx] = size / shapeProd;\n return newShape;\n}\nfunction parseAxisParam(axis, shape) {\n const rank = shape.length;\n axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);\n assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);\n return axis.map((a) => a < 0 ? rank + a : a);\n}\nfunction squeezeShape(shape, axis) {\n const newShape = [];\n const keptDims = [];\n const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();\n let j = 0;\n for (let i = 0; i < shape.length; ++i) {\n if (axes != null) {\n if (axes[j] === i && shape[i] !== 1) {\n throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n }\n if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n if (axes[j] <= i) {\n j++;\n }\n }\n if (shape[i] !== 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n }\n return { newShape, keptDims };\n}\nfunction getTypedArrayFromDType(dtype, size) {\n return getArrayFromDType(dtype, size);\n}\nfunction getArrayFromDType(dtype, size) {\n let values = null;\n if (dtype == null || dtype === \"float32\") {\n values = new Float32Array(size);\n } else if (dtype === \"int32\") {\n values = new Int32Array(size);\n } else if (dtype === \"bool\") {\n values = new Uint8Array(size);\n } else if (dtype === \"string\") {\n values = new Array(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values;\n}\nfunction checkConversionForErrors(vals, dtype) {\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i];\n if (isNaN(num) || !isFinite(num)) {\n throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n }\n }\n}\nfunction isValidDtype(dtype) {\n return dtype === \"bool\" || dtype === \"complex64\" || dtype === \"float32\" || dtype === \"int32\" || dtype === \"string\";\n}\nfunction hasEncodingLoss(oldType, newType) {\n if (newType === \"complex64\") {\n return false;\n }\n if (newType === \"float32\" && oldType !== \"complex64\") {\n return false;\n }\n if (newType === \"int32\" && oldType !== \"float32\" && oldType !== \"complex64\") {\n return false;\n }\n if (newType === \"bool\" && oldType === \"bool\") {\n return false;\n }\n return true;\n}\nfunction bytesPerElement(dtype) {\n if (dtype === \"float32\" || dtype === \"int32\") {\n return 4;\n } else if (dtype === \"complex64\") {\n return 8;\n } else if (dtype === \"bool\") {\n return 1;\n } else {\n throw new Error(`Unknown dtype ${dtype}`);\n }\n}\nfunction bytesFromStringArray(arr) {\n if (arr == null) {\n return 0;\n }\n let bytes = 0;\n arr.forEach((x) => bytes += x.length);\n return bytes;\n}\nfunction isString(value) {\n return typeof value === \"string\" || value instanceof String;\n}\nfunction isBoolean(value) {\n return typeof value === \"boolean\";\n}\nfunction isNumber(value) {\n return typeof value === \"number\";\n}\nfunction inferDtype(values) {\n if (Array.isArray(values)) {\n return inferDtype(values[0]);\n }\n if (values instanceof Float32Array) {\n return \"float32\";\n } else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) {\n return \"int32\";\n } else if (isNumber(values)) {\n return \"float32\";\n } else if (isString(values)) {\n return \"string\";\n } else if (isBoolean(values)) {\n return \"bool\";\n }\n return \"float32\";\n}\nfunction isFunction(f) {\n return !!(f && f.constructor && f.call && f.apply);\n}\nfunction nearestDivisor(size, start) {\n for (let i = start; i < size; ++i) {\n if (size % i === 0) {\n return i;\n }\n }\n return size;\n}\nfunction computeStrides(shape) {\n const rank = shape.length;\n if (rank < 2) {\n return [];\n }\n const strides = new Array(rank - 1);\n strides[rank - 2] = shape[rank - 1];\n for (let i = rank - 3; i >= 0; --i) {\n strides[i] = strides[i + 1] * shape[i + 1];\n }\n return strides;\n}\nfunction createNestedArray(offset, shape, a, isComplex = false) {\n const ret = new Array();\n if (shape.length === 1) {\n const d = shape[0] * (isComplex ? 2 : 1);\n for (let i = 0; i < d; i++) {\n ret[i] = a[offset + i];\n }\n } else {\n const d = shape[0];\n const rest = shape.slice(1);\n const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n for (let i = 0; i < d; i++) {\n ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);\n }\n }\n return ret;\n}\nfunction toNestedArray(shape, a, isComplex = false) {\n if (shape.length === 0) {\n return a[0];\n }\n const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n if (size === 0) {\n return [];\n }\n if (size !== a.length) {\n throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? \" for a complex tensor\" : \"\"}.`);\n }\n return createNestedArray(0, shape, a, isComplex);\n}\nfunction convertBackendValuesAndArrayBuffer(data, dtype) {\n if (Array.isArray(data)) {\n return data;\n }\n if (dtype === \"float32\") {\n return data instanceof Float32Array ? data : new Float32Array(data);\n } else if (dtype === \"int32\") {\n return data instanceof Int32Array ? data : new Int32Array(data);\n } else if (dtype === \"bool\" || dtype === \"string\") {\n return Uint8Array.from(new Int32Array(data));\n } else {\n throw new Error(`Unknown dtype ${dtype}`);\n }\n}\nfunction makeOnesTypedArray(size, dtype) {\n const array2 = makeZerosTypedArray(size, dtype);\n for (let i = 0; i < array2.length; i++) {\n array2[i] = 1;\n }\n return array2;\n}\nfunction makeZerosTypedArray(size, dtype) {\n if (dtype == null || dtype === \"float32\" || dtype === \"complex64\") {\n return new Float32Array(size);\n } else if (dtype === \"int32\") {\n return new Int32Array(size);\n } else if (dtype === \"bool\") {\n return new Uint8Array(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\nfunction makeZerosNestedTypedArray(shape, dtype) {\n const size = shape.reduce((prev, curr) => prev * curr, 1);\n if (dtype == null || dtype === \"float32\") {\n return toNestedArray(shape, new Float32Array(size));\n } else if (dtype === \"int32\") {\n return toNestedArray(shape, new Int32Array(size));\n } else if (dtype === \"bool\") {\n return toNestedArray(shape, new Uint8Array(size));\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\nfunction assertNonNegativeIntegerDimensions(shape) {\n shape.forEach((dimSize) => {\n assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);\n });\n}\nfunction locToIndex(locs, rank, strides) {\n if (rank === 0) {\n return 0;\n } else if (rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += strides[i] * locs[i];\n }\n return index;\n}\nfunction indexToLoc(index, rank, strides) {\n if (rank === 0) {\n return [];\n } else if (rank === 1) {\n return [index];\n }\n const locs = new Array(rank);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / strides[i]);\n index -= locs[i] * strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n}\nfunction isPromise(object) {\n return object && object.then && typeof object.then === \"function\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/environment.js\nvar TENSORFLOWJS_FLAGS_PREFIX = \"tfjsflags\";\nvar Environment = class {\n // tslint:disable-next-line: no-any\n constructor(global2) {\n this.global = global2;\n this.flags = {};\n this.flagRegistry = {};\n this.urlFlags = {};\n this.getQueryParams = getQueryParams;\n this.populateURLFlags();\n }\n setPlatform(platformName, platform) {\n if (this.platform != null) {\n if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platformName}.`);\n }\n }\n this.platformName = platformName;\n this.platform = platform;\n }\n registerFlag(flagName, evaluationFn, setHook) {\n this.flagRegistry[flagName] = { evaluationFn, setHook };\n if (this.urlFlags[flagName] != null) {\n const flagValue = this.urlFlags[flagName];\n if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n }\n this.set(flagName, flagValue);\n }\n }\n async getAsync(flagName) {\n if (flagName in this.flags) {\n return this.flags[flagName];\n }\n this.flags[flagName] = await this.evaluateFlag(flagName);\n return this.flags[flagName];\n }\n get(flagName) {\n if (flagName in this.flags) {\n return this.flags[flagName];\n }\n const flagValue = this.evaluateFlag(flagName);\n if (isPromise(flagValue)) {\n throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);\n }\n this.flags[flagName] = flagValue;\n return this.flags[flagName];\n }\n getNumber(flagName) {\n return this.get(flagName);\n }\n getBool(flagName) {\n return this.get(flagName);\n }\n getString(flagName) {\n return this.get(flagName);\n }\n getFlags() {\n return this.flags;\n }\n // For backwards compatibility.\n get features() {\n return this.flags;\n }\n set(flagName, value) {\n if (this.flagRegistry[flagName] == null) {\n throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n }\n this.flags[flagName] = value;\n if (this.flagRegistry[flagName].setHook != null) {\n this.flagRegistry[flagName].setHook(value);\n }\n }\n evaluateFlag(flagName) {\n if (this.flagRegistry[flagName] == null) {\n throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n }\n return this.flagRegistry[flagName].evaluationFn();\n }\n setFlags(flags) {\n this.flags = Object.assign({}, flags);\n }\n reset() {\n this.flags = {};\n this.urlFlags = {};\n this.populateURLFlags();\n }\n populateURLFlags() {\n if (typeof this.global === \"undefined\" || typeof this.global.location === \"undefined\" || typeof this.global.location.search === \"undefined\") {\n return;\n }\n const urlParams = this.getQueryParams(this.global.location.search);\n if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(\",\");\n keyValues.forEach((keyValue) => {\n const [key, value] = keyValue.split(\":\");\n this.urlFlags[key] = parseValue(key, value);\n });\n }\n }\n};\nfunction getQueryParams(queryString) {\n const params = {};\n queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n decodeParam(params, t[0], t[1]);\n return t.join(\"=\");\n });\n return params;\n}\nfunction decodeParam(params, name, value) {\n params[decodeURIComponent(name)] = decodeURIComponent(value || \"\");\n}\nfunction parseValue(flagName, value) {\n const lowerCaseValue = value.toLowerCase();\n if (lowerCaseValue === \"true\" || lowerCaseValue === \"false\") {\n return lowerCaseValue === \"true\";\n } else if (`${+lowerCaseValue}` === lowerCaseValue) {\n return +lowerCaseValue;\n } else {\n return value;\n }\n}\nfunction env() {\n return ENV;\n}\nvar ENV = null;\nfunction setEnvironmentGlobal(environment) {\n ENV = environment;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/global_util.js\nvar globalNameSpace;\nfunction getGlobalNamespace() {\n if (globalNameSpace == null) {\n let ns;\n if (typeof window !== \"undefined\") {\n ns = window;\n } else if (typeof global !== \"undefined\") {\n ns = global;\n } else if (typeof process !== \"undefined\") {\n ns = process;\n } else if (typeof self !== \"undefined\") {\n ns = self;\n } else {\n throw new Error(\"Could not find a global object\");\n }\n globalNameSpace = ns;\n }\n return globalNameSpace;\n}\nfunction getGlobalMap() {\n const ns = getGlobalNamespace();\n if (ns._tfGlobals == null) {\n ns._tfGlobals = /* @__PURE__ */ new Map();\n }\n return ns._tfGlobals;\n}\nfunction getGlobal(key, init2) {\n const globalMap = getGlobalMap();\n if (globalMap.has(key)) {\n return globalMap.get(key);\n } else {\n const singleton = init2();\n globalMap.set(key, singleton);\n return globalMap.get(key);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js\nvar Abs = \"Abs\";\nvar Acos = \"Acos\";\nvar Acosh = \"Acosh\";\nvar Add = \"Add\";\nvar AddN = \"AddN\";\nvar All = \"All\";\nvar Any = \"Any\";\nvar ArgMax = \"ArgMax\";\nvar ArgMin = \"ArgMin\";\nvar Asin = \"Asin\";\nvar Asinh = \"Asinh\";\nvar Atan = \"Atan\";\nvar Atanh = \"Atanh\";\nvar Atan2 = \"Atan2\";\nvar AvgPool = \"AvgPool\";\nvar AvgPoolGrad = \"AvgPoolGrad\";\nvar AvgPool3D = \"AvgPool3D\";\nvar AvgPool3DGrad = \"AvgPool3DGrad\";\nvar BatchMatMul = \"BatchMatMul\";\nvar BatchToSpaceND = \"BatchToSpaceND\";\nvar Bincount = \"Bincount\";\nvar BitwiseAnd = \"BitwiseAnd\";\nvar BroadcastTo = \"BroadcastTo\";\nvar BroadcastArgs = \"BroadcastArgs\";\nvar Cast = \"Cast\";\nvar Ceil = \"Ceil\";\nvar ClipByValue = \"ClipByValue\";\nvar Complex = \"Complex\";\nvar ComplexAbs = \"ComplexAbs\";\nvar Concat = \"Concat\";\nvar Conv2D = \"Conv2D\";\nvar Conv2DBackpropFilter = \"Conv2DBackpropFilter\";\nvar Conv2DBackpropInput = \"Conv2DBackpropInput\";\nvar Conv3D = \"Conv3D\";\nvar Conv3DBackpropFilterV2 = \"Conv3DBackpropFilterV2\";\nvar Conv3DBackpropInputV2 = \"Conv3DBackpropInputV2\";\nvar Cos = \"Cos\";\nvar Cosh = \"Cosh\";\nvar Cumprod = \"Cumprod\";\nvar Cumsum = \"Cumsum\";\nvar CropAndResize = \"CropAndResize\";\nvar DenseBincount = \"DenseBincount\";\nvar DepthToSpace = \"DepthToSpace\";\nvar DepthwiseConv2dNative = \"DepthwiseConv2dNative\";\nvar DepthwiseConv2dNativeBackpropFilter = \"DepthwiseConv2dNativeBackpropFilter\";\nvar DepthwiseConv2dNativeBackpropInput = \"DepthwiseConv2dNativeBackpropInput\";\nvar Diag = \"Diag\";\nvar Dilation2D = \"Dilation2D\";\nvar Dilation2DBackpropInput = \"Dilation2DBackpropInput\";\nvar Dilation2DBackpropFilter = \"Dilation2DBackpropFilter\";\nvar Draw = \"Draw\";\nvar RealDiv = \"RealDiv\";\nvar Einsum = \"Einsum\";\nvar Elu = \"Elu\";\nvar EluGrad = \"EluGrad\";\nvar Erf = \"Erf\";\nvar Equal = \"Equal\";\nvar Exp = \"Exp\";\nvar ExpandDims = \"ExpandDims\";\nvar Expm1 = \"Expm1\";\nvar FFT = \"FFT\";\nvar Fill = \"Fill\";\nvar FlipLeftRight = \"FlipLeftRight\";\nvar Floor = \"Floor\";\nvar FloorDiv = \"FloorDiv\";\nvar FusedBatchNorm = \"FusedBatchNorm\";\nvar GatherV2 = \"GatherV2\";\nvar GatherNd = \"GatherNd\";\nvar Greater = \"Greater\";\nvar GreaterEqual = \"GreaterEqual\";\nvar Identity = \"Identity\";\nvar IFFT = \"IFFT\";\nvar Imag = \"Imag\";\nvar IsFinite = \"IsFinite\";\nvar IsInf = \"IsInf\";\nvar IsNan = \"IsNan\";\nvar LeakyRelu = \"LeakyRelu\";\nvar Less = \"Less\";\nvar LessEqual = \"LessEqual\";\nvar LinSpace = \"LinSpace\";\nvar Log = \"Log\";\nvar Log1p = \"Log1p\";\nvar LogicalAnd = \"LogicalAnd\";\nvar LogicalNot = \"LogicalNot\";\nvar LogicalOr = \"LogicalOr\";\nvar LogicalXor = \"LogicalXor\";\nvar LogSoftmax = \"LogSoftmax\";\nvar LowerBound = \"LowerBound\";\nvar LRN = \"LRN\";\nvar LRNGrad = \"LRNGrad\";\nvar MatrixBandPart = \"MatrixBandPart\";\nvar Max = \"Max\";\nvar Maximum = \"Maximum\";\nvar MaxPool = \"MaxPool\";\nvar MaxPoolGrad = \"MaxPoolGrad\";\nvar MaxPool3D = \"MaxPool3D\";\nvar MaxPool3DGrad = \"MaxPool3DGrad\";\nvar MaxPoolWithArgmax = \"MaxPoolWithArgmax\";\nvar Mean = \"Mean\";\nvar Min = \"Min\";\nvar Minimum = \"Minimum\";\nvar MirrorPad = \"MirrorPad\";\nvar Mod = \"Mod\";\nvar Multinomial = \"Multinomial\";\nvar Multiply = \"Multiply\";\nvar Neg = \"Neg\";\nvar NotEqual = \"NotEqual\";\nvar NonMaxSuppressionV3 = \"NonMaxSuppressionV3\";\nvar NonMaxSuppressionV4 = \"NonMaxSuppressionV4\";\nvar NonMaxSuppressionV5 = \"NonMaxSuppressionV5\";\nvar OnesLike = \"OnesLike\";\nvar OneHot = \"OneHot\";\nvar Pack = \"Pack\";\nvar PadV2 = \"PadV2\";\nvar Pool = \"Pool\";\nvar Pow = \"Pow\";\nvar Prelu = \"Prelu\";\nvar Prod = \"Prod\";\nvar RaggedGather = \"RaggedGather\";\nvar RaggedRange = \"RaggedRange\";\nvar RaggedTensorToTensor = \"RaggedTensorToTensor\";\nvar Range = \"Range\";\nvar Real = \"Real\";\nvar Reciprocal = \"Reciprocal\";\nvar Relu = \"Relu\";\nvar Reshape = \"Reshape\";\nvar ResizeNearestNeighbor = \"ResizeNearestNeighbor\";\nvar ResizeNearestNeighborGrad = \"ResizeNearestNeighborGrad\";\nvar ResizeBilinear = \"ResizeBilinear\";\nvar ResizeBilinearGrad = \"ResizeBilinearGrad\";\nvar Relu6 = \"Relu6\";\nvar Reverse = \"Reverse\";\nvar Round = \"Round\";\nvar Rsqrt = \"Rsqrt\";\nvar ScatterNd = \"ScatterNd\";\nvar TensorScatterUpdate = \"TensorScatterUpdate\";\nvar SearchSorted = \"SearchSorted\";\nvar Select = \"Select\";\nvar Selu = \"Selu\";\nvar Slice = \"Slice\";\nvar Sin = \"Sin\";\nvar Sinh = \"Sinh\";\nvar Sign = \"Sign\";\nvar Sigmoid = \"Sigmoid\";\nvar Softplus = \"Softplus\";\nvar Sqrt = \"Sqrt\";\nvar Sum = \"Sum\";\nvar SpaceToBatchND = \"SpaceToBatchND\";\nvar SplitV = \"SplitV\";\nvar Softmax = \"Softmax\";\nvar SparseFillEmptyRows = \"SparseFillEmptyRows\";\nvar SparseReshape = \"SparseReshape\";\nvar SparseSegmentMean = \"SparseSegmentMean\";\nvar SparseSegmentSum = \"SparseSegmentSum\";\nvar SparseToDense = \"SparseToDense\";\nvar SquaredDifference = \"SquaredDifference\";\nvar Square = \"Square\";\nvar StaticRegexReplace = \"StaticRegexReplace\";\nvar StridedSlice = \"StridedSlice\";\nvar StringNGrams = \"StringNGrams\";\nvar StringSplit = \"StringSplit\";\nvar StringToHashBucketFast = \"StringToHashBucketFast\";\nvar Sub = \"Sub\";\nvar Tan = \"Tan\";\nvar Tanh = \"Tanh\";\nvar Tile = \"Tile\";\nvar TopK = \"TopK\";\nvar Transform = \"Transform\";\nvar Transpose = \"Transpose\";\nvar Unique = \"Unique\";\nvar Unpack = \"Unpack\";\nvar UnsortedSegmentSum = \"UnsortedSegmentSum\";\nvar UpperBound = \"UpperBound\";\nvar ZerosLike = \"ZerosLike\";\nvar Step = \"Step\";\nvar FromPixels = \"FromPixels\";\nvar RotateWithOffset = \"RotateWithOffset\";\nvar _FusedMatMul = \"_FusedMatMul\";\nvar FusedConv2D = \"FusedConv2D\";\nvar FusedDepthwiseConv2D = \"FusedDepthwiseConv2D\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/log.js\nfunction warn(...msg) {\n if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n console.warn(...msg);\n }\n}\nfunction log(...msg) {\n if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n console.log(...msg);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js\nvar kernelRegistry = getGlobal(\"kernelRegistry\", () => /* @__PURE__ */ new Map());\nvar gradRegistry = getGlobal(\"gradRegistry\", () => /* @__PURE__ */ new Map());\nfunction getKernel(kernelName, backendName) {\n const key = makeKey(kernelName, backendName);\n return kernelRegistry.get(key);\n}\nfunction getGradient(kernelName) {\n return gradRegistry.get(kernelName);\n}\nfunction getKernelsForBackend(backendName) {\n const it = kernelRegistry.entries();\n const result = [];\n while (true) {\n const { done, value } = it.next();\n if (done) {\n break;\n }\n const [key, config] = value;\n const [backend2] = key.split(\"_\");\n if (backend2 === backendName) {\n result.push(config);\n }\n }\n return result;\n}\nfunction registerKernel(config) {\n const { kernelName, backendName } = config;\n const key = makeKey(kernelName, backendName);\n if (kernelRegistry.has(key)) {\n warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);\n }\n kernelRegistry.set(key, config);\n}\nfunction registerGradient(config) {\n const { kernelName } = config;\n if (gradRegistry.has(kernelName)) {\n if (env().getBool(\"DEBUG\")) {\n warn(`Overriding the gradient for '${kernelName}'`);\n }\n }\n gradRegistry.set(kernelName, config);\n}\nfunction unregisterKernel(kernelName, backendName) {\n const key = makeKey(kernelName, backendName);\n if (!kernelRegistry.has(key)) {\n throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);\n }\n kernelRegistry.delete(key);\n}\nfunction unregisterGradient(kernelName) {\n if (!gradRegistry.has(kernelName)) {\n throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n }\n gradRegistry.delete(kernelName);\n}\nfunction copyRegisteredKernels(registeredBackendName, newBackendName) {\n const kernels = getKernelsForBackend(registeredBackendName);\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n registerKernel(newKernelConfig);\n });\n}\nfunction makeKey(kernelName, backendName) {\n return `${backendName}_${kernelName}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/util.js\nvar util_exports = {};\n__export(util_exports, {\n arraysEqual: () => arraysEqual,\n arraysEqualWithNull: () => arraysEqualWithNull,\n assert: () => assert,\n assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,\n assertNonNull: () => assertNonNull,\n assertShapesMatch: () => assertShapesMatch,\n bytesFromStringArray: () => bytesFromStringArray,\n bytesPerElement: () => bytesPerElement,\n checkConversionForErrors: () => checkConversionForErrors,\n clamp: () => clamp,\n computeStrides: () => computeStrides,\n convertBackendValuesAndArrayBuffer: () => convertBackendValuesAndArrayBuffer,\n createScalarValue: () => createScalarValue,\n createShuffledIndices: () => createShuffledIndices,\n decodeString: () => decodeString,\n distSquared: () => distSquared,\n encodeString: () => encodeString,\n fetch: () => fetch3,\n fingerPrint64: () => fingerPrint64,\n flatten: () => flatten,\n getArrayFromDType: () => getArrayFromDType,\n getTypedArrayFromDType: () => getTypedArrayFromDType,\n hasEncodingLoss: () => hasEncodingLoss,\n hexToLong: () => hexToLong,\n indexToLoc: () => indexToLoc,\n inferDtype: () => inferDtype,\n inferFromImplicitShape: () => inferFromImplicitShape,\n isBoolean: () => isBoolean,\n isFunction: () => isFunction,\n isInt: () => isInt,\n isNumber: () => isNumber,\n isPromise: () => isPromise,\n isScalarShape: () => isScalarShape,\n isString: () => isString,\n isTypedArray: () => isTypedArray,\n isValidDtype: () => isValidDtype,\n locToIndex: () => locToIndex,\n makeOnesTypedArray: () => makeOnesTypedArray,\n makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,\n makeZerosTypedArray: () => makeZerosTypedArray,\n nearestDivisor: () => nearestDivisor,\n nearestLargerEven: () => nearestLargerEven,\n now: () => now,\n parseAxisParam: () => parseAxisParam,\n randUniform: () => randUniform,\n repeatedTry: () => repeatedTry,\n rightPad: () => rightPad,\n shuffle: () => shuffle,\n shuffleCombo: () => shuffleCombo,\n sizeFromShape: () => sizeFromShape,\n sizeToSquarishShape: () => sizeToSquarishShape,\n squeezeShape: () => squeezeShape,\n sum: () => sum,\n swap: () => swap,\n tanh: () => tanh,\n toNestedArray: () => toNestedArray,\n toTypedArray: () => toTypedArray\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/platforms/is_typed_array_browser.js\nfunction isTypedArrayBrowser(a) {\n return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array || a instanceof Uint8ClampedArray;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/hash_util.js\nvar LongExports = __toESM(require_long());\nvar Long = (\n // tslint:disable-next-line\n LongExports.default || LongExports\n);\nfunction hexToLong(hex) {\n return Long.fromString(hex, true, 16);\n}\nvar k0 = hexToLong(\"c3a5c85c97cb3127\");\nvar k1 = hexToLong(\"b492b66fbe98f273\");\nvar k2 = hexToLong(\"9ae16a3b2f90404f\");\nfunction shiftMix(val) {\n return val.xor(val.shru(47));\n}\nfunction fetch2(s, offset, numBytes) {\n const bytes = s.slice(offset, offset + numBytes);\n return Long.fromBytes(Array.from(bytes), true, true);\n}\nfunction fetch64(s, offset) {\n return fetch2(s, offset, 8);\n}\nfunction fetch32(s, offset) {\n return fetch2(s, offset, 4);\n}\nfunction rotate64(val, shift) {\n return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));\n}\nfunction hashLen16(u, v, mul2 = hexToLong(\"9ddfea08eb382d69\")) {\n let a = u.xor(v).mul(mul2);\n a = a.xor(a.shru(47));\n let b = v.xor(a).mul(mul2);\n b = b.xor(b.shru(47));\n b = b.mul(mul2);\n return b;\n}\nfunction weakHashLen32WithSeeds(w, x, y, z, a, b) {\n a = a.add(w);\n b = rotate64(b.add(a).add(z), 21);\n const c = a;\n a = a.add(x);\n a = a.add(y);\n b = b.add(rotate64(a, 44));\n return [a.add(z), b.add(c)];\n}\nfunction weakHashLen32WithSeedsStr(s, offset, a, b) {\n return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b);\n}\nfunction hashLen0to16(s, len = s.length) {\n if (len >= 8) {\n const mul2 = k2.add(len * 2);\n const a = fetch64(s, 0).add(k2);\n const b = fetch64(s, len - 8);\n const c = rotate64(b, 37).mul(mul2).add(a);\n const d = rotate64(a, 25).add(b).mul(mul2);\n return hashLen16(c, d, mul2);\n }\n if (len >= 4) {\n const mul2 = k2.add(len * 2);\n const a = fetch32(s, 0);\n return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul2);\n }\n if (len > 0) {\n const a = s[0];\n const b = s[len >> 1];\n const c = s[len - 1];\n const y = a + (b << 8);\n const z = len + (c << 2);\n return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);\n }\n return k2;\n}\nfunction hashLen17to32(s, len = s.length) {\n const mul2 = k2.add(len * 2);\n const a = fetch64(s, 0).mul(k1);\n const b = fetch64(s, 8);\n const c = fetch64(s, len - 8).mul(mul2);\n const d = fetch64(s, len - 16).mul(k2);\n return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul2);\n}\nfunction hashLen33to64(s, len = s.length) {\n const mul2 = k2.add(len * 2);\n const a = fetch64(s, 0).mul(k2);\n const b = fetch64(s, 8);\n const c = fetch64(s, len - 8).mul(mul2);\n const d = fetch64(s, len - 16).mul(k2);\n const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);\n const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul2);\n const e = fetch64(s, 16).mul(mul2);\n const f = fetch64(s, 24);\n const g = y.add(fetch64(s, len - 32)).mul(mul2);\n const h = z.add(fetch64(s, len - 24)).mul(mul2);\n return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul2);\n}\nfunction fingerPrint64(s, len = s.length) {\n const seed = Long.fromNumber(81, true);\n if (len <= 32) {\n if (len <= 16) {\n return hashLen0to16(s, len);\n } else {\n return hashLen17to32(s, len);\n }\n } else if (len <= 64) {\n return hashLen33to64(s, len);\n }\n let x = seed;\n let y = seed.mul(k1).add(113);\n let z = shiftMix(y.mul(k2).add(113)).mul(k2);\n let v = [Long.UZERO, Long.UZERO];\n let w = [Long.UZERO, Long.UZERO];\n x = x.mul(k2).add(fetch64(s, 0));\n let offset = 0;\n const end = (len - 1 >> 6) * 64;\n const last64 = end + (len - 1 & 63) - 63;\n do {\n x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);\n y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);\n x = x.xor(w[1]);\n y = y.add(v[0]).add(fetch64(s, offset + 40));\n z = rotate64(z.add(w[0]), 33).mul(k1);\n v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));\n w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));\n [z, x] = [x, z];\n offset += 64;\n } while (offset !== end);\n const mul2 = k1.add(z.and(255).shl(1));\n offset = last64;\n w[0] = w[0].add(len - 1 & 63);\n v[0] = v[0].add(w[0]);\n w[0] = w[0].add(v[0]);\n x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul2);\n y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul2);\n x = x.xor(w[1].mul(9));\n y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));\n z = rotate64(z.add(w[0]), 33).mul(mul2);\n v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul2), x.add(w[0]));\n w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));\n [z, x] = [x, z];\n return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/util.js\nfunction createScalarValue(value, dtype) {\n if (dtype === \"string\") {\n return encodeString(value);\n }\n return toTypedArray([value], dtype);\n}\nfunction noConversionNeeded(a, dtype) {\n return a instanceof Float32Array && dtype === \"float32\" || a instanceof Int32Array && dtype === \"int32\" || a instanceof Uint8Array && dtype === \"bool\";\n}\nfunction toTypedArray(a, dtype) {\n if (dtype === \"string\") {\n throw new Error(\"Cannot convert a string[] to a TypedArray\");\n }\n if (Array.isArray(a)) {\n a = flatten(a);\n }\n if (env().getBool(\"DEBUG\")) {\n checkConversionForErrors(a, dtype);\n }\n if (noConversionNeeded(a, dtype)) {\n return a;\n }\n if (dtype == null || dtype === \"float32\" || dtype === \"complex64\") {\n return new Float32Array(a);\n } else if (dtype === \"int32\") {\n return new Int32Array(a);\n } else if (dtype === \"bool\") {\n const bool = new Uint8Array(a.length);\n for (let i = 0; i < bool.length; ++i) {\n if (Math.round(a[i]) !== 0) {\n bool[i] = 1;\n }\n }\n return bool;\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\nfunction now() {\n return env().platform.now();\n}\nfunction fetch3(path, requestInits) {\n return env().platform.fetch(path, requestInits);\n}\nfunction encodeString(s, encoding = \"utf-8\") {\n encoding = encoding || \"utf-8\";\n return env().platform.encode(s, encoding);\n}\nfunction decodeString(bytes, encoding = \"utf-8\") {\n encoding = encoding || \"utf-8\";\n return env().platform.decode(bytes, encoding);\n}\nfunction isTypedArray(a) {\n if (env().platform.isTypedArray != null) {\n return env().platform.isTypedArray(a);\n } else {\n return isTypedArrayBrowser(a);\n }\n}\nfunction flatten(arr, result = [], skipTypedArray = false) {\n if (result == null) {\n result = [];\n }\n if (typeof arr === \"boolean\" || typeof arr === \"number\" || typeof arr === \"string\" || isPromise(arr) || arr == null || isTypedArray(arr) && skipTypedArray) {\n result.push(arr);\n } else if (Array.isArray(arr) || isTypedArray(arr)) {\n for (let i = 0; i < arr.length; ++i) {\n flatten(arr[i], result, skipTypedArray);\n }\n } else {\n let maxIndex = -1;\n for (const key of Object.keys(arr)) {\n if (/^([1-9]+[0-9]*|0)$/.test(key)) {\n maxIndex = Math.max(maxIndex, Number(key));\n }\n }\n for (let i = 0; i <= maxIndex; i++) {\n flatten(arr[i], result, skipTypedArray);\n }\n }\n return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/profiler.js\nvar Profiler = class {\n constructor(backendTimer, logger) {\n this.backendTimer = backendTimer;\n this.logger = logger;\n if (logger == null) {\n this.logger = new Logger();\n }\n }\n profileKernel(kernelName, inputs, f) {\n let outputs;\n const holdResultWrapperFn = () => {\n outputs = f();\n };\n let timer;\n const start = now();\n if (this.backendTimer.timerAvailable()) {\n timer = this.backendTimer.time(holdResultWrapperFn);\n } else {\n holdResultWrapperFn();\n for (const output of outputs) {\n output.dataSync();\n }\n timer = Promise.resolve({ kernelMs: now() - start });\n }\n if (env().getBool(\"CHECK_COMPUTATION_FOR_ERRORS\")) {\n for (let i = 0; i < outputs.length; i++) {\n const output = outputs[i];\n output.data().then((tensorVals) => {\n checkComputationForErrors(tensorVals, output.dtype, kernelName);\n });\n }\n }\n const kernelProfile = {\n kernelName,\n outputs,\n inputs,\n timeMs: timer.then((timing) => timing.kernelMs),\n extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : \"\")\n };\n return kernelProfile;\n }\n logKernelProfile(kernelProfile) {\n const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n outputs.forEach((result) => {\n Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {\n this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n });\n });\n }\n};\nfunction checkComputationForErrors(vals, dtype, kernelName) {\n if (dtype !== \"float32\") {\n return false;\n }\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i];\n if (isNaN(num) || !isFinite(num)) {\n console.warn(`Found ${num} in the result of '${kernelName}'`);\n return true;\n }\n }\n return false;\n}\nvar Logger = class {\n logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n const time2 = typeof timeMs === \"number\" ? rightPad(`${timeMs}ms`, 9) : timeMs[\"error\"];\n const paddedName = rightPad(name, 25);\n const rank = result.rank;\n const size = result.size;\n const shape = rightPad(result.shape.toString(), 14);\n let inputShapesDescription = \"\";\n for (const name2 in inputs) {\n const input2 = inputs[name2];\n if (input2 != null) {\n const inputShape = input2.shape || result.shape;\n const inputRank = inputShape.length;\n inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : \"\"} `;\n }\n }\n console.log(`%c${paddedName}\t%c${time2}\t%c${rank}D ${shape}\t%c${size}\t%c${inputShapesDescription}\t%c${extraInfo}`, \"font-weight:bold\", \"color:red\", \"color:blue\", \"color: orange\", \"color: green\", \"color: steelblue\");\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tape.js\nfunction getFilteredNodesXToY(tape, xs, y) {\n const tensorsFromX = {};\n const nodesFromX = {};\n for (let i = 0; i < xs.length; i++) {\n tensorsFromX[xs[i].id] = true;\n }\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n for (const inputName in nodeInputs) {\n const input2 = nodeInputs[inputName];\n let anyInputFromX = false;\n for (let j = 0; j < xs.length; j++) {\n if (tensorsFromX[input2.id]) {\n node.outputs.forEach((output) => tensorsFromX[output.id] = true);\n anyInputFromX = true;\n nodesFromX[node.id] = true;\n break;\n }\n }\n if (anyInputFromX) {\n break;\n }\n }\n }\n const tensorsLeadToY = {};\n tensorsLeadToY[y.id] = true;\n const nodesToY = {};\n for (let i = tape.length - 1; i >= 0; i--) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n for (let j = 0; j < node.outputs.length; j++) {\n if (tensorsLeadToY[node.outputs[j].id]) {\n for (const inputName in nodeInputs) {\n tensorsLeadToY[nodeInputs[inputName].id] = true;\n nodesToY[node.id] = true;\n }\n break;\n }\n }\n }\n const filteredTape = [];\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n if (nodesFromX[node.id] && nodesToY[node.id]) {\n const prunedInputs = {};\n for (const inputName in node.inputs) {\n const nodeInput = node.inputs[inputName];\n if (tensorsFromX[nodeInput.id]) {\n prunedInputs[inputName] = nodeInput;\n }\n }\n const prunedNode = Object.assign({}, node);\n prunedNode.inputs = prunedInputs;\n prunedNode.outputs = node.outputs;\n filteredTape.push(prunedNode);\n }\n }\n return filteredTape;\n}\nfunction backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) {\n for (let i = filteredTape.length - 1; i >= 0; i--) {\n const node = filteredTape[i];\n const dys = [];\n node.outputs.forEach((o) => {\n const gradTensor = tensorAccumulatedGradientMap[o.id];\n if (gradTensor != null) {\n dys.push(gradTensor);\n } else {\n dys.push(null);\n }\n });\n if (node.gradient == null) {\n throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);\n }\n const inputGradients = node.gradient(dys);\n for (const inputName in node.inputs) {\n if (!(inputName in inputGradients)) {\n throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);\n }\n const dx = tidy2(() => inputGradients[inputName]());\n if (dx.dtype !== \"float32\") {\n throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n }\n const x = node.inputs[inputName];\n if (!arraysEqual(dx.shape, x.shape)) {\n throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);\n }\n if (tensorAccumulatedGradientMap[x.id] == null) {\n tensorAccumulatedGradientMap[x.id] = dx;\n } else {\n const curGradient = tensorAccumulatedGradientMap[x.id];\n tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx);\n curGradient.dispose();\n }\n }\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js\nvar FORMAT_LIMIT_NUM_VALS = 20;\nvar FORMAT_NUM_FIRST_LAST_VALS = 3;\nvar FORMAT_NUM_SIG_DIGITS = 7;\nfunction tensorToString(vals, shape, dtype, verbose) {\n const strides = computeStrides(shape);\n const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n const rank = shape.length;\n const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n const lines = [\"Tensor\"];\n if (verbose) {\n lines.push(` dtype: ${dtype}`);\n lines.push(` rank: ${rank}`);\n lines.push(` shape: [${shape}]`);\n lines.push(` values:`);\n }\n lines.push(valsLines.map((l) => \" \" + l).join(\"\\n\"));\n return lines.join(\"\\n\");\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n const n = sizeFromShape(shape);\n const numCols = strides[strides.length - 1];\n const padPerCol = new Array(numCols).fill(0);\n const rank = shape.length;\n const valuesOrTuples = dtype === \"complex64\" ? createComplexTuples(vals) : vals;\n if (rank > 1) {\n for (let row = 0; row < n / numCols; row++) {\n const offset = row * numCols;\n for (let j = 0; j < numCols; j++) {\n padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n }\n }\n }\n return padPerCol;\n}\nfunction valToString(val, pad3, dtype) {\n let valStr;\n if (Array.isArray(val)) {\n valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n } else if (isString(val)) {\n valStr = `'${val}'`;\n } else if (dtype === \"bool\") {\n valStr = boolNumToString(val);\n } else {\n valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n }\n return rightPad(valStr, pad3);\n}\nfunction boolNumToString(v) {\n return v === 0 ? \"false\" : \"true\";\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n const storagePerElement = dtype === \"complex64\" ? 2 : 1;\n const size = shape[0];\n const rank = shape.length;\n if (rank === 0) {\n if (dtype === \"complex64\") {\n const complexTuple = createComplexTuples(vals);\n return [valToString(complexTuple[0], 0, dtype)];\n }\n if (dtype === \"bool\") {\n return [boolNumToString(vals[0])];\n }\n return [vals[0].toString()];\n }\n if (rank === 1) {\n if (size > FORMAT_LIMIT_NUM_VALS) {\n const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n let firstVals = Array.from(vals.slice(0, firstValsSize));\n let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n if (dtype === \"complex64\") {\n firstVals = createComplexTuples(firstVals);\n lastVals = createComplexTuples(lastVals);\n }\n return [\n \"[\" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(\", \") + \", ..., \" + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(\", \") + \"]\"\n ];\n }\n const displayVals = dtype === \"complex64\" ? createComplexTuples(vals) : Array.from(vals);\n return [\n \"[\" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(\", \") + \"]\"\n ];\n }\n const subshape = shape.slice(1);\n const substrides = strides.slice(1);\n const stride = strides[0] * storagePerElement;\n const lines = [];\n if (size > FORMAT_LIMIT_NUM_VALS) {\n for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end),\n subshape,\n dtype,\n substrides,\n padPerCol,\n false\n /* isLast */\n ));\n }\n lines.push(\"...\");\n for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end),\n subshape,\n dtype,\n substrides,\n padPerCol,\n i === size - 1\n /* isLast */\n ));\n }\n } else {\n for (let i = 0; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end),\n subshape,\n dtype,\n substrides,\n padPerCol,\n i === size - 1\n /* isLast */\n ));\n }\n }\n const sep = rank === 2 ? \",\" : \"\";\n lines[0] = \"[\" + (size > 0 ? lines[0] + sep : \"\");\n for (let i = 1; i < lines.length - 1; i++) {\n lines[i] = \" \" + lines[i] + sep;\n }\n let newLineSep = \",\\n\";\n for (let i = 2; i < rank; i++) {\n newLineSep += \"\\n\";\n }\n lines[lines.length - 1] = \" \" + lines[lines.length - 1] + \"]\" + (isLast ? \"\" : newLineSep);\n return lines;\n}\nfunction createComplexTuples(vals) {\n const complexTuples = [];\n for (let i = 0; i < vals.length; i += 2) {\n complexTuples.push([vals[i], vals[i + 1]]);\n }\n return complexTuples;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tensor.js\nvar TensorBuffer = class {\n constructor(shape, dtype, values) {\n this.dtype = dtype;\n this.shape = shape.slice();\n this.size = sizeFromShape(shape);\n if (values != null) {\n const n = values.length;\n assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);\n }\n if (dtype === \"complex64\") {\n throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);\n }\n this.values = values || getArrayFromDType(dtype, this.size);\n this.strides = computeStrides(shape);\n }\n /**\n * Sets a value in the buffer at a given location.\n *\n * @param value The value to set.\n * @param locs The location indices.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n set(value, ...locs) {\n if (locs.length === 0) {\n locs = [0];\n }\n assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);\n const index = this.locToIndex(locs);\n this.values[index] = value;\n }\n /**\n * Returns the value in the buffer at the provided location.\n *\n * @param locs The location indices.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n get(...locs) {\n if (locs.length === 0) {\n locs = [0];\n }\n let i = 0;\n for (const loc of locs) {\n if (loc < 0 || loc >= this.shape[i]) {\n const msg = `Requested out of range element at ${locs}. Buffer shape=${this.shape}`;\n throw new Error(msg);\n }\n i++;\n }\n let index = locs[locs.length - 1];\n for (let i2 = 0; i2 < locs.length - 1; ++i2) {\n index += this.strides[i2] * locs[i2];\n }\n return this.values[index];\n }\n locToIndex(locs) {\n if (this.rank === 0) {\n return 0;\n } else if (this.rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return index;\n }\n indexToLoc(index) {\n if (this.rank === 0) {\n return [];\n } else if (this.rank === 1) {\n return [index];\n }\n const locs = new Array(this.shape.length);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / this.strides[i]);\n index -= locs[i] * this.strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n }\n get rank() {\n return this.shape.length;\n }\n /**\n * Creates an immutable `tf.Tensor` object from the buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n toTensor() {\n return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n }\n};\nvar trackerFn = null;\nvar opHandler = null;\nvar deprecationWarningFn = null;\nfunction setTensorTracker(fn) {\n trackerFn = fn;\n}\nfunction setOpHandler(handler) {\n opHandler = handler;\n}\nfunction setDeprecationWarningFn(fn) {\n deprecationWarningFn = fn;\n}\nvar Tensor = class {\n constructor(shape, dtype, dataId, id) {\n this.kept = false;\n this.isDisposedInternal = false;\n this.shape = shape.slice();\n this.dtype = dtype || \"float32\";\n this.size = sizeFromShape(shape);\n this.strides = computeStrides(shape);\n this.dataId = dataId;\n this.id = id;\n this.rankType = this.rank < 5 ? this.rank.toString() : \"higher\";\n }\n get rank() {\n return this.shape.length;\n }\n /**\n * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async buffer() {\n const vals = await this.data();\n return opHandler.buffer(this.shape, this.dtype, vals);\n }\n /**\n * Returns a `tf.TensorBuffer` that holds the underlying data.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n bufferSync() {\n return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n }\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * asynchronously.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async array() {\n const vals = await this.data();\n return toNestedArray(this.shape, vals, this.dtype === \"complex64\");\n }\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * synchronously.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n arraySync() {\n return toNestedArray(this.shape, this.dataSync(), this.dtype === \"complex64\");\n }\n /**\n * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n * promise of `TypedArray` that resolves when the computation has finished.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async data() {\n this.throwIfDisposed();\n const data = trackerFn().read(this.dataId);\n if (this.dtype === \"string\") {\n const bytes = await data;\n try {\n return bytes.map((b) => decodeString(b));\n } catch (_a) {\n throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\");\n }\n }\n return data;\n }\n /**\n * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()`\n * and `data()`, this method prevents data from being downloaded to CPU.\n *\n * For WebGL backend, the data will be stored on a densely packed texture.\n * This means that the texture will use the RGBA channels to store value.\n *\n * For WebGPU backend, the data will be stored on a buffer. There is no\n * parameter, so can not use a user-defined size to create the buffer.\n *\n * @param options:\n * For WebGL,\n * - customTexShape: Optional. If set, will use the user defined\n * texture shape to create the texture.\n *\n * @returns For WebGL backend, a GPUData contains the new texture and\n * its information.\n * {\n * tensorRef: The tensor that is associated with this texture,\n * texture: WebGLTexture,\n * texShape: [number, number] // [height, width]\n * }\n *\n * For WebGPU backend, a GPUData contains the new buffer.\n * {\n * tensorRef: The tensor that is associated with this buffer,\n * buffer: GPUBuffer,\n * }\n *\n * Remember to dispose the GPUData after it is used by\n * `res.tensorRef.dispose()`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dataToGPU(options) {\n this.throwIfDisposed();\n return trackerFn().readToGPU(this.dataId, options);\n }\n /**\n * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n * UI thread until the values are ready, which can cause performance issues.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dataSync() {\n this.throwIfDisposed();\n const data = trackerFn().readSync(this.dataId);\n if (this.dtype === \"string\") {\n try {\n return data.map((b) => decodeString(b));\n } catch (_a) {\n throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\");\n }\n }\n return data;\n }\n /** Returns the underlying bytes of the tensor's data. */\n async bytes() {\n this.throwIfDisposed();\n const data = await trackerFn().read(this.dataId);\n if (this.dtype === \"string\") {\n return data;\n } else {\n return new Uint8Array(data.buffer);\n }\n }\n /**\n * Disposes `tf.Tensor` from memory.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dispose() {\n if (this.isDisposed) {\n return;\n }\n if (this.kerasMask) {\n this.kerasMask.dispose();\n }\n trackerFn().disposeTensor(this);\n this.isDisposedInternal = true;\n }\n get isDisposed() {\n return this.isDisposedInternal;\n }\n throwIfDisposed() {\n if (this.isDisposed) {\n throw new Error(`Tensor is disposed.`);\n }\n }\n /**\n * Prints the `tf.Tensor`. See `tf.print` for details.\n *\n * @param verbose Whether to print verbose information about the tensor,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n print(verbose = false) {\n return opHandler.print(this, verbose);\n }\n /**\n * Returns a copy of the tensor. See `tf.clone` for details.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n clone() {\n this.throwIfDisposed();\n return opHandler.clone(this);\n }\n /**\n * Returns a human-readable description of the tensor. Useful for logging.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n toString(verbose = false) {\n const vals = this.dataSync();\n return tensorToString(vals, this.shape, this.dtype, verbose);\n }\n cast(dtype) {\n this.throwIfDisposed();\n return opHandler.cast(this, dtype);\n }\n variable(trainable = true, name, dtype) {\n this.throwIfDisposed();\n return trackerFn().makeVariable(this, trainable, name, dtype);\n }\n};\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n value: (instance) => {\n return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;\n }\n});\nfunction getGlobalTensorClass() {\n return getGlobal(\"Tensor\", () => {\n return Tensor;\n });\n}\ngetGlobalTensorClass();\nvar Variable = class extends Tensor {\n constructor(initialValue, trainable, name, tensorId) {\n super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n this.trainable = trainable;\n this.name = name;\n }\n /**\n * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n * the same shape and dtype as the old `tf.Tensor`.\n *\n * @param newValue New tensor to be assigned to this variable.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n assign(newValue) {\n if (newValue.dtype !== this.dtype) {\n throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);\n }\n if (!arraysEqual(newValue.shape, this.shape)) {\n throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);\n }\n trackerFn().disposeTensor(this);\n this.dataId = newValue.dataId;\n trackerFn().incRef(\n this,\n null\n /* backend */\n );\n }\n dispose() {\n trackerFn().disposeVariable(this);\n this.isDisposedInternal = true;\n }\n};\nObject.defineProperty(Variable, Symbol.hasInstance, {\n value: (instance) => {\n return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;\n }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js\nvar tensor_util_exports = {};\n__export(tensor_util_exports, {\n assertTypesMatch: () => assertTypesMatch,\n getTensorsInContainer: () => getTensorsInContainer,\n isTensorInList: () => isTensorInList,\n makeTypesMatch: () => makeTypesMatch\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/types.js\nvar Rank;\n(function(Rank2) {\n Rank2[\"R0\"] = \"R0\";\n Rank2[\"R1\"] = \"R1\";\n Rank2[\"R2\"] = \"R2\";\n Rank2[\"R3\"] = \"R3\";\n Rank2[\"R4\"] = \"R4\";\n Rank2[\"R5\"] = \"R5\";\n Rank2[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\nvar UpcastInt32AndMap;\n(function(UpcastInt32AndMap2) {\n UpcastInt32AndMap2[\"float32\"] = \"float32\";\n UpcastInt32AndMap2[\"int32\"] = \"int32\";\n UpcastInt32AndMap2[\"bool\"] = \"int32\";\n UpcastInt32AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function(UpcastBoolAndMap2) {\n UpcastBoolAndMap2[\"float32\"] = \"float32\";\n UpcastBoolAndMap2[\"int32\"] = \"int32\";\n UpcastBoolAndMap2[\"bool\"] = \"bool\";\n UpcastBoolAndMap2[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function(UpcastFloat32AndMap2) {\n UpcastFloat32AndMap2[\"float32\"] = \"float32\";\n UpcastFloat32AndMap2[\"int32\"] = \"float32\";\n UpcastFloat32AndMap2[\"bool\"] = \"float32\";\n UpcastFloat32AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function(UpcastComplex64AndMap2) {\n UpcastComplex64AndMap2[\"float32\"] = \"complex64\";\n UpcastComplex64AndMap2[\"int32\"] = \"complex64\";\n UpcastComplex64AndMap2[\"bool\"] = \"complex64\";\n UpcastComplex64AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nvar upcastTypeMap = {\n \"float32\": UpcastFloat32AndMap,\n \"int32\": UpcastInt32AndMap,\n \"bool\": UpcastBoolAndMap,\n \"complex64\": UpcastComplex64AndMap\n};\nfunction upcastType(typeA, typeB) {\n if (typeA === \"string\" || typeB === \"string\") {\n if (typeA === \"string\" && typeB === \"string\") {\n return \"string\";\n }\n throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n }\n return upcastTypeMap[typeA][typeB];\n}\nfunction sumOutType(type) {\n return upcastType(type, \"int32\");\n}\nfunction isWebGLData(values) {\n return values != null && typeof values === \"object\" && \"texture\" in values && values.texture instanceof WebGLTexture;\n}\nfunction isWebGPUData(values) {\n return typeof GPUBuffer !== \"undefined\" && values != null && typeof values === \"object\" && \"buffer\" in values && values.buffer instanceof GPUBuffer;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js\nfunction makeTypesMatch(a, b) {\n if (a.dtype === b.dtype) {\n return [a, b];\n }\n const dtype = upcastType(a.dtype, b.dtype);\n return [a.cast(dtype), b.cast(dtype)];\n}\nfunction assertTypesMatch(a, b) {\n assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`);\n}\nfunction isTensorInList(tensor2, tensorList) {\n return tensorList.some((x) => x.id === tensor2.id);\n}\nfunction getTensorsInContainer(result) {\n const list = [];\n const seen = /* @__PURE__ */ new Set();\n walkTensorContainer(result, list, seen);\n return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n if (container == null) {\n return;\n }\n if (container instanceof Tensor) {\n list.push(container);\n return;\n }\n if (!isIterable(container)) {\n return;\n }\n const iterable = container;\n for (const k in iterable) {\n const val = iterable[k];\n if (!seen.has(val)) {\n seen.add(val);\n walkTensorContainer(val, list, seen);\n }\n }\n}\nfunction isIterable(obj) {\n return Array.isArray(obj) || typeof obj === \"object\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/engine.js\nfunction isRegisteredKernelInvocation(kernelInvocation) {\n return kernelInvocation.kernelName != null;\n}\nvar EngineState = class {\n constructor() {\n this.registeredVariables = {};\n this.nextTapeNodeId = 0;\n this.numBytes = 0;\n this.numTensors = 0;\n this.numStringTensors = 0;\n this.numDataBuffers = 0;\n this.gradientDepth = 0;\n this.kernelDepth = 0;\n this.scopeStack = [];\n this.numDataMovesStack = [];\n this.nextScopeId = 0;\n this.tensorInfo = /* @__PURE__ */ new WeakMap();\n this.profiling = false;\n this.activeProfile = {\n newBytes: 0,\n newTensors: 0,\n peakBytes: 0,\n kernels: [],\n result: null,\n get kernelNames() {\n return Array.from(new Set(this.kernels.map((k) => k.name)));\n }\n };\n }\n dispose() {\n for (const variableName in this.registeredVariables) {\n this.registeredVariables[variableName].dispose();\n }\n }\n};\nvar Engine = class _Engine {\n constructor(ENV7) {\n this.ENV = ENV7;\n this.registry = {};\n this.registryFactory = {};\n this.pendingBackendInitId = 0;\n this.state = new EngineState();\n }\n async ready() {\n if (this.pendingBackendInit != null) {\n return this.pendingBackendInit.then(() => {\n });\n }\n if (this.backendInstance != null) {\n return;\n }\n const sortedBackends = this.getSortedBackends();\n for (let i = 0; i < sortedBackends.length; i++) {\n const backendName = sortedBackends[i];\n const success = await this.initializeBackend(backendName).success;\n if (success) {\n await this.setBackend(backendName);\n return;\n }\n }\n throw new Error(`Could not initialize any backends, all backend initializations failed.`);\n }\n get backend() {\n if (this.pendingBackendInit != null) {\n throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);\n }\n if (this.backendInstance == null) {\n const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n if (asyncInit) {\n throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);\n }\n this.setBackend(name);\n }\n return this.backendInstance;\n }\n backendNames() {\n return Object.keys(this.registryFactory);\n }\n findBackend(backendName) {\n if (!(backendName in this.registry)) {\n if (backendName in this.registryFactory) {\n const { asyncInit } = this.initializeBackend(backendName);\n if (asyncInit) {\n return null;\n }\n } else {\n return null;\n }\n }\n return this.registry[backendName];\n }\n findBackendFactory(backendName) {\n if (!(backendName in this.registryFactory)) {\n return null;\n }\n return this.registryFactory[backendName].factory;\n }\n registerBackend(backendName, factory, priority = 1) {\n if (backendName in this.registryFactory) {\n warn(`${backendName} backend was already registered. Reusing existing backend factory.`);\n return false;\n }\n this.registryFactory[backendName] = { factory, priority };\n return true;\n }\n async setBackend(backendName) {\n if (this.registryFactory[backendName] == null) {\n throw new Error(`Backend name '${backendName}' not found in registry`);\n }\n this.backendName = backendName;\n if (this.registry[backendName] == null) {\n this.backendInstance = null;\n const { success, asyncInit } = this.initializeBackend(backendName);\n const result = asyncInit ? await success : success;\n if (!result) {\n return false;\n }\n }\n this.backendInstance = this.registry[backendName];\n this.setupRegisteredKernels();\n this.profiler = new Profiler(this.backendInstance);\n return true;\n }\n setupRegisteredKernels() {\n const kernels = getKernelsForBackend(this.backendName);\n kernels.forEach((kernel) => {\n if (kernel.setupFunc != null) {\n kernel.setupFunc(this.backendInstance);\n }\n });\n }\n disposeRegisteredKernels(backendName) {\n const kernels = getKernelsForBackend(backendName);\n kernels.forEach((kernel) => {\n if (kernel.disposeFunc != null) {\n kernel.disposeFunc(this.registry[backendName]);\n }\n });\n }\n /**\n * Initializes a backend by looking up the backend name in the factory\n * registry and calling the factory method. Returns a boolean representing\n * whether the initialization of the backend suceeded. Throws an error if\n * there is no backend in the factory registry.\n */\n initializeBackend(backendName) {\n const registryFactoryEntry = this.registryFactory[backendName];\n if (registryFactoryEntry == null) {\n throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n }\n try {\n const backend2 = registryFactoryEntry.factory();\n if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === \"function\") {\n const promiseId = ++this.pendingBackendInitId;\n const success = backend2.then((backendInstance) => {\n if (promiseId < this.pendingBackendInitId) {\n return false;\n }\n this.registry[backendName] = backendInstance;\n this.pendingBackendInit = null;\n return true;\n }).catch((err) => {\n if (promiseId < this.pendingBackendInitId) {\n return false;\n }\n this.pendingBackendInit = null;\n warn(`Initialization of backend ${backendName} failed`);\n warn(err.stack || err.message);\n return false;\n });\n this.pendingBackendInit = success;\n return { success, asyncInit: true };\n } else {\n this.registry[backendName] = backend2;\n return { success: true, asyncInit: false };\n }\n } catch (err) {\n warn(`Initialization of backend ${backendName} failed`);\n warn(err.stack || err.message);\n return { success: false, asyncInit: false };\n }\n }\n removeBackend(backendName) {\n if (!(backendName in this.registryFactory)) {\n throw new Error(`${backendName} backend not found in registry`);\n }\n if (this.backendName === backendName && this.pendingBackendInit != null) {\n this.pendingBackendInitId++;\n }\n if (backendName in this.registry) {\n this.disposeRegisteredKernels(backendName);\n this.registry[backendName].dispose();\n delete this.registry[backendName];\n }\n delete this.registryFactory[backendName];\n if (this.backendName === backendName) {\n this.pendingBackendInit = null;\n this.backendName = null;\n this.backendInstance = null;\n }\n }\n getSortedBackends() {\n if (Object.keys(this.registryFactory).length === 0) {\n throw new Error(\"No backend found in registry.\");\n }\n return Object.keys(this.registryFactory).sort((a, b) => {\n return this.registryFactory[b].priority - this.registryFactory[a].priority;\n });\n }\n initializeBackendsAndReturnBest() {\n const sortedBackends = this.getSortedBackends();\n for (let i = 0; i < sortedBackends.length; i++) {\n const backendName = sortedBackends[i];\n const { success, asyncInit } = this.initializeBackend(backendName);\n if (asyncInit || success) {\n return { name: backendName, asyncInit };\n }\n }\n throw new Error(`Could not initialize any backends, all backend initializations failed.`);\n }\n moveData(backend2, dataId) {\n const info = this.state.tensorInfo.get(dataId);\n const srcBackend = info.backend;\n const values = this.readSync(dataId);\n const refCount = srcBackend.refCount(dataId);\n srcBackend.disposeData(dataId, true);\n info.backend = backend2;\n backend2.move(dataId, values, info.shape, info.dtype, refCount);\n if (this.shouldCheckForMemLeaks()) {\n this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n }\n }\n tidy(nameOrFn, fn) {\n let name = null;\n if (fn == null) {\n if (typeof nameOrFn !== \"function\") {\n throw new Error(\"Please provide a function to tidy()\");\n }\n fn = nameOrFn;\n } else {\n if (typeof nameOrFn !== \"string\" && !(nameOrFn instanceof String)) {\n throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");\n }\n if (typeof fn !== \"function\") {\n throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");\n }\n name = nameOrFn;\n }\n let result;\n return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n result = fn();\n if (result instanceof Promise) {\n console.error(\"Cannot return a Promise inside of tidy.\");\n }\n return result;\n });\n }\n scopedRun(start, end, f) {\n start();\n try {\n const res = f();\n end();\n return res;\n } catch (ex) {\n end();\n throw ex;\n }\n }\n nextTensorId() {\n return _Engine.nextTensorId++;\n }\n nextVariableId() {\n return _Engine.nextVariableId++;\n }\n /**\n * This method is called instead of the public-facing tensor.clone() when\n * saving a tensor for backwards pass. It makes sure to add the clone\n * operation to the tape regardless of being called inside a kernel\n * execution.\n */\n clone(x) {\n const y = ENGINE.runKernel(Identity, { x });\n const inputs = { x };\n const grad2 = (dy) => ({\n x: () => {\n const dtype = \"float32\";\n const gradInputs = { x: dy };\n const attrs = { dtype };\n return ENGINE.runKernel(\n Cast,\n gradInputs,\n // tslint:disable-next-line: no-unnecessary-type-assertion\n attrs\n );\n }\n });\n const saved = [];\n this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});\n return y;\n }\n /**\n * Execute a kernel with the given name and return the output tensor.\n *\n * @param kernelName The name of the kernel to execute.\n * @param inputs A map of input names to tensors.\n * @param attrs A map of attribute names to their values. An attribute is a\n * primitive (non-tensor) input to the kernel.\n * @param inputsToSave A list of tensors, inputs to save for the backprop\n * computation.\n * @param outputsToSave A list of booleans, specifying which output to save\n * for the backprop computation. These are booleans since the output\n * tensors are not visible to the user.\n */\n runKernel(kernelName, inputs, attrs) {\n if (this.backendName == null) {\n this.backend;\n }\n const hasKernel = getKernel(kernelName, this.backendName) != null;\n if (!hasKernel) {\n throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);\n }\n return this.runKernelFunc({ kernelName, inputs, attrs });\n }\n shouldCheckForMemLeaks() {\n return this.ENV.getBool(\"IS_TEST\");\n }\n checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n const numDataIdsAfter = this.backend.numDataIds();\n let numOutputDataIds = 0;\n outInfos.forEach((info) => {\n numOutputDataIds += info.dtype === \"complex64\" ? 3 : 1;\n });\n const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n if (dataIdsLeaked > 0) {\n throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);\n }\n }\n /**\n * Internal helper method to execute a kernel Func\n *\n * Use `runKernel` to execute kernels from outside of engine.\n */\n runKernelFunc(kernelParams) {\n let outputs;\n let saved = [];\n const isTapeOn = this.isTapeOn();\n const startingBytecount = this.state.numBytes;\n const startingNumTensors = this.state.numTensors;\n if (this.shouldCheckForMemLeaks()) {\n this.state.numDataMovesStack.push(0);\n }\n let kernelFunc3;\n if (this.backendName == null) {\n this.backend;\n }\n let out;\n const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : \"\";\n if (isRegisteredKernelInvocation(kernelParams)) {\n const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams;\n if (this.backendName == null) {\n this.backend;\n }\n const kernel = getKernel(kernelName, this.backendName);\n assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);\n kernelFunc3 = () => {\n const numDataIdsBefore = this.backend.numDataIds();\n out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend });\n const outInfos = Array.isArray(out) ? out : [out];\n if (this.shouldCheckForMemLeaks()) {\n this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n }\n const outTensors = outInfos.map((outInfo) => {\n if (outInfo.rank != null) {\n return outInfo;\n }\n return this.makeTensorFromTensorInfo(outInfo);\n });\n if (isTapeOn) {\n const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors);\n saved = this.saveTensorsForBackwardMode(tensorsToSave);\n }\n return outTensors;\n };\n } else {\n const { forwardFunc } = kernelParams;\n const saveFunc = (tensors) => {\n if (!isTapeOn) {\n return;\n }\n saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));\n };\n kernelFunc3 = () => {\n const numDataIdsBefore = this.backend.numDataIds();\n out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n const outs = Array.isArray(out) ? out : [out];\n if (this.shouldCheckForMemLeaks()) {\n this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);\n }\n return outs;\n };\n }\n const { inputs, attrs } = kernelParams;\n const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc;\n let kernelProfile;\n this.scopedRun(\n // Stop recording to a tape when running a kernel.\n () => this.state.kernelDepth++,\n () => this.state.kernelDepth--,\n () => {\n if (!this.ENV.getBool(\"DEBUG\") && !this.state.profiling) {\n outputs = kernelFunc3();\n } else {\n kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3());\n if (this.ENV.getBool(\"DEBUG\")) {\n this.profiler.logKernelProfile(kernelProfile);\n }\n outputs = kernelProfile.outputs;\n }\n }\n );\n if (isTapeOn) {\n this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);\n }\n if (this.state.profiling) {\n this.state.activeProfile.kernels.push({\n name: kernelOrScopeName,\n bytesAdded: this.state.numBytes - startingBytecount,\n totalBytesSnapshot: this.state.numBytes,\n tensorsAdded: this.state.numTensors - startingNumTensors,\n totalTensorsSnapshot: this.state.numTensors,\n inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),\n outputShapes: outputs.map((item) => item.shape),\n kernelTimeMs: kernelProfile.timeMs,\n extraInfo: kernelProfile.extraInfo\n });\n }\n return Array.isArray(out) ? outputs : outputs[0];\n }\n /**\n * Saves tensors used in forward mode for use in backward mode.\n *\n * @param tensors the list of tensors to save.\n */\n saveTensorsForBackwardMode(tensors) {\n const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));\n return saved;\n }\n /**\n * Returns a list of tensors to save for a given gradient calculation.\n *\n * @param kernelName name of kernel to look up gradient for.\n * @param inputs a map of input tensors.\n * @param outputs an array of output tensors from forward mode of kernel.\n */\n getTensorsForGradient(kernelName, inputs, outputs) {\n const gradConfig = getGradient(kernelName);\n if (gradConfig != null) {\n const inputsToSave = gradConfig.inputsToSave || [];\n const outputsToSave = gradConfig.outputsToSave || [];\n let inputTensorsToSave;\n if (gradConfig.saveAllInputs) {\n assert(Array.isArray(inputs), () => \"saveAllInputs is true, expected inputs to be an array.\");\n inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n } else {\n inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n }\n const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n return inputTensorsToSave.concat(outputTensorsToSave);\n }\n return [];\n }\n /**\n * Internal method used by public APIs for tensor creation. Makes a new\n * tensor with the provided shape, dtype and values. It always\n * creates a new data id and writes the values to the underlying backend.\n */\n makeTensor(values, shape, dtype, backend2) {\n if (values == null) {\n throw new Error(\"Values passed to engine.makeTensor() are null\");\n }\n dtype = dtype || \"float32\";\n backend2 = backend2 || this.backend;\n let backendVals = values;\n if (dtype === \"string\" && isString(values[0])) {\n backendVals = values.map((d) => encodeString(d));\n }\n const dataId = backend2.write(backendVals, shape, dtype);\n const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n this.trackTensor(t, backend2);\n if (dtype === \"string\") {\n const info = this.state.tensorInfo.get(dataId);\n const newBytes = bytesFromStringArray(backendVals);\n this.state.numBytes += newBytes - info.bytes;\n info.bytes = newBytes;\n }\n return t;\n }\n /**\n * Internal method used by backends. Makes a new tensor\n * that is a wrapper around an existing data id. It doesn't create\n * a new data id, only increments the ref count used in memory tracking.\n * @deprecated\n */\n makeTensorFromDataId(dataId, shape, dtype, backend2) {\n dtype = dtype || \"float32\";\n const tensorInfo = { dataId, shape, dtype };\n return this.makeTensorFromTensorInfo(tensorInfo, backend2);\n }\n /**\n * Internal method used by backends. Makes a new tensor that is a wrapper\n * around an existing data id in TensorInfo. It doesn't create a new data id,\n * only increments the ref count used in memory tracking.\n */\n makeTensorFromTensorInfo(tensorInfo, backend2) {\n const { dataId, shape, dtype } = tensorInfo;\n const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n this.trackTensor(t, backend2);\n return t;\n }\n makeVariable(initialValue, trainable = true, name, dtype) {\n name = name || this.nextVariableId().toString();\n if (dtype != null && dtype !== initialValue.dtype) {\n initialValue = initialValue.cast(dtype);\n }\n const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n if (this.state.registeredVariables[v.name] != null) {\n throw new Error(`Variable with name ${v.name} was already registered`);\n }\n this.state.registeredVariables[v.name] = v;\n this.incRef(v, this.backend);\n return v;\n }\n trackTensor(a, backend2) {\n this.state.numTensors++;\n if (a.dtype === \"string\") {\n this.state.numStringTensors++;\n }\n let bytes = 0;\n if (a.dtype !== \"complex64\" && a.dtype !== \"string\") {\n bytes = a.size * bytesPerElement(a.dtype);\n }\n this.state.numBytes += bytes;\n if (!this.state.tensorInfo.has(a.dataId)) {\n this.state.numDataBuffers++;\n this.state.tensorInfo.set(a.dataId, {\n backend: backend2 || this.backend,\n dtype: a.dtype,\n shape: a.shape,\n bytes\n });\n }\n if (!(a instanceof Variable)) {\n this.track(a);\n }\n }\n // Track the tensor by dataId and increase the refCount for the dataId in the\n // backend.\n // TODO(pyu10055): This is currently used by makeVariable method, to increase\n // refCount on the backend for the dataId. It can potentially be replaced with\n // Identity op indead of calling backend directly.\n incRef(a, backend2) {\n this.trackTensor(a, backend2);\n this.backend.incRef(a.dataId);\n }\n removeDataId(dataId, backend2) {\n if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) {\n this.state.tensorInfo.delete(dataId);\n this.state.numDataBuffers--;\n }\n }\n disposeTensor(a) {\n if (!this.state.tensorInfo.has(a.dataId)) {\n return;\n }\n const info = this.state.tensorInfo.get(a.dataId);\n this.state.numTensors--;\n if (a.dtype === \"string\") {\n this.state.numStringTensors--;\n this.state.numBytes -= info.bytes;\n }\n if (a.dtype !== \"complex64\" && a.dtype !== \"string\") {\n const bytes = a.size * bytesPerElement(a.dtype);\n this.state.numBytes -= bytes;\n }\n if (info.backend.disposeData(a.dataId)) {\n this.removeDataId(a.dataId, info.backend);\n }\n }\n disposeVariables() {\n for (const varName in this.state.registeredVariables) {\n const v = this.state.registeredVariables[varName];\n this.disposeVariable(v);\n }\n }\n disposeVariable(v) {\n this.disposeTensor(v);\n if (this.state.registeredVariables[v.name] != null) {\n delete this.state.registeredVariables[v.name];\n }\n }\n memory() {\n const info = this.backend.memory();\n info.numTensors = this.state.numTensors;\n info.numDataBuffers = this.state.numDataBuffers;\n info.numBytes = this.state.numBytes;\n if (this.state.numStringTensors > 0) {\n info.unreliable = true;\n if (info.reasons == null) {\n info.reasons = [];\n }\n info.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\");\n }\n return info;\n }\n async profile(query) {\n this.state.profiling = true;\n const startBytes = this.state.numBytes;\n const startNumTensors = this.state.numTensors;\n this.state.activeProfile.kernels = [];\n this.state.activeProfile.result = await query();\n this.state.profiling = false;\n this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));\n this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;\n for (const kernel of this.state.activeProfile.kernels) {\n kernel.kernelTimeMs = await kernel.kernelTimeMs;\n kernel.extraInfo = await kernel.extraInfo;\n }\n return this.state.activeProfile;\n }\n isTapeOn() {\n return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n }\n addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n const gradConfig = getGradient(kernelName);\n if (gradConfig != null) {\n gradientsFunc = gradConfig.gradFunc;\n }\n if (gradientsFunc != null) {\n tapeNode.gradient = (dys) => {\n dys = dys.map((dy, i) => {\n if (dy == null) {\n const output = outputs[i];\n const vals = makeZerosTypedArray(output.size, output.dtype);\n return this.makeTensor(vals, output.shape, output.dtype);\n }\n return dy;\n });\n return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n };\n }\n this.state.activeTape.push(tapeNode);\n }\n keep(result) {\n result.kept = true;\n return result;\n }\n startTape() {\n if (this.state.gradientDepth === 0) {\n this.state.activeTape = [];\n }\n this.state.gradientDepth++;\n }\n endTape() {\n this.state.gradientDepth--;\n }\n /**\n * Start a scope. Use this with endScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n startScope(name) {\n const scopeInfo = {\n track: [],\n name: \"unnamed scope\",\n id: this.state.nextScopeId++\n };\n if (name) {\n scopeInfo.name = name;\n }\n this.state.scopeStack.push(scopeInfo);\n this.state.activeScope = scopeInfo;\n }\n /**\n * End a scope. Use this with startScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n endScope(result) {\n const tensorsToTrackInParent = getTensorsInContainer(result);\n const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id));\n for (let i = 0; i < this.state.activeScope.track.length; i++) {\n const tensor2 = this.state.activeScope.track[i];\n if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) {\n tensor2.dispose();\n }\n }\n const oldScope = this.state.scopeStack.pop();\n this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];\n tensorsToTrackInParent.forEach((tensor2) => {\n if (!tensor2.kept && tensor2.scopeId === oldScope.id) {\n this.track(tensor2);\n }\n });\n }\n /**\n * Returns gradients of `f` with respect to each of the `xs`. The gradients\n * returned are of the same length as `xs`, but some might be null if `f`\n * was not a function of that `x`. It also takes optional dy to multiply the\n * gradient, which defaults to `1`.\n */\n gradients(f, xs, dy, allowNoGradients = false) {\n assert(xs.length > 0, () => \"gradients() received an empty list of xs.\");\n if (dy != null && dy.dtype !== \"float32\") {\n throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n }\n const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy(\"forward\", f));\n assert(y instanceof Tensor, () => \"The result y returned by f() must be a tensor.\");\n const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");\n }\n return this.tidy(\"backward\", () => {\n const accumulatedGradientMap = {};\n accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;\n backpropagateGradients(\n accumulatedGradientMap,\n filteredTape,\n // Pass the tidy function to avoid circular dep with `tape.ts`.\n (f2) => this.tidy(f2),\n // Pass an add function to avoide a circular dep with `tape.ts`.\n add\n );\n const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);\n if (this.state.gradientDepth === 0) {\n this.state.activeTape.forEach((node) => {\n for (const tensor2 of node.saved) {\n tensor2.dispose();\n }\n });\n this.state.activeTape = null;\n }\n return { value: y, grads: grads2 };\n });\n }\n customGrad(f) {\n assert(isFunction(f), () => \"The f passed in customGrad(f) must be a function.\");\n return (...inputs) => {\n assert(inputs.every((t) => t instanceof Tensor), () => \"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");\n let res;\n const inputMap = {};\n inputs.forEach((input2, i) => {\n inputMap[i] = input2;\n });\n const forwardFunc = (_, save) => {\n res = f(...[...inputs, save]);\n assert(res.value instanceof Tensor, () => \"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\");\n assert(isFunction(res.gradFunc), () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\");\n return res.value;\n };\n const backwardsFunc = (dy, saved) => {\n const gradRes = res.gradFunc(dy, saved);\n const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];\n assert(grads2.length === inputs.length, () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\");\n assert(grads2.every((t) => t instanceof Tensor), () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");\n const gradMap = {};\n grads2.forEach((grad2, i) => {\n gradMap[i] = () => grad2;\n });\n return gradMap;\n };\n return this.runKernelFunc({\n forwardFunc,\n backwardsFunc,\n inputs: inputMap\n });\n };\n }\n readSync(dataId) {\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.readSync(dataId);\n }\n read(dataId) {\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.read(dataId);\n }\n readToGPU(dataId, options) {\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.readToGPU(dataId, options);\n }\n async time(query) {\n const start = now();\n const timingInfo = await this.backend.time(query);\n timingInfo.wallMs = now() - start;\n return timingInfo;\n }\n /**\n * Tracks a Tensor in the current scope to be automatically cleaned up\n * when the current scope ends, and returns the value.\n *\n * @param result The Tensor to track in the current scope.\n */\n track(result) {\n if (this.state.activeScope != null) {\n result.scopeId = this.state.activeScope.id;\n this.state.activeScope.track.push(result);\n }\n return result;\n }\n get registeredVariables() {\n return this.state.registeredVariables;\n }\n /**\n * Resets the engine state. Removes all backends but does not remove\n * registered backend factories.\n */\n reset() {\n this.pendingBackendInitId++;\n this.state.dispose();\n this.ENV.reset();\n this.state = new EngineState();\n for (const backendName in this.registry) {\n this.disposeRegisteredKernels(backendName);\n this.registry[backendName].dispose();\n delete this.registry[backendName];\n }\n this.backendName = null;\n this.backendInstance = null;\n this.pendingBackendInit = null;\n }\n};\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n const values = makeOnesTypedArray(sizeFromShape(shape), \"float32\");\n return ENGINE.makeTensor(values, shape, \"float32\");\n}\nfunction getOrMakeEngine() {\n const ns = getGlobalNamespace();\n if (ns._tfengine == null) {\n const environment = new Environment(ns);\n ns._tfengine = new Engine(environment);\n }\n setEnvironmentGlobal(ns._tfengine.ENV);\n setTensorTracker(() => ns._tfengine);\n return ns._tfengine;\n}\nvar ENGINE = getOrMakeEngine();\nfunction add(a, b) {\n const inputs = { a, b };\n return ENGINE.runKernel(Add, inputs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/device_util.js\nvar device_util_exports = {};\n__export(device_util_exports, {\n isBrowser: () => isBrowser,\n isMobile: () => isMobile,\n mockIsMobile: () => mockIsMobile\n});\nfunction _isNavigatorDefined() {\n return typeof navigator !== \"undefined\" && navigator != null;\n}\nvar isMobileMockValue;\nfunction mockIsMobile(value) {\n isMobileMockValue = value;\n}\nfunction isMobile(nav) {\n if (isMobileMockValue !== void 0) {\n return isMobileMockValue;\n }\n if (nav || _isNavigatorDefined()) {\n if (!nav) {\n nav = navigator;\n }\n if (nav.product === \"ReactNative\") {\n return true;\n }\n const a = nav.userAgent || nav.vendor || // tslint:disable-next-line:no-any\n (typeof window !== \"undefined\" ? window.opera : \"\");\n if (!a) {\n const navAny = nav;\n return navAny.userAgentData && navAny.userAgentData.mobile;\n }\n return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || // tslint:disable-next-line:max-line-length\n /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i.test(a.substr(0, 4));\n }\n return false;\n}\nfunction isBrowser() {\n return typeof window !== \"undefined\" && window.document != null || //@ts-ignore\n typeof WorkerGlobalScope !== \"undefined\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/flags.js\nvar ENV2 = env();\nENV2.registerFlag(\"DEBUG\", () => false, (debugValue) => {\n if (debugValue) {\n console.warn(\"Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.\");\n }\n});\nENV2.registerFlag(\"IS_BROWSER\", () => isBrowser());\nENV2.registerFlag(\"IS_NODE\", () => typeof process !== \"undefined\" && typeof process.versions !== \"undefined\" && typeof process.versions.node !== \"undefined\");\nENV2.registerFlag(\"IS_CHROME\", () => typeof navigator !== \"undefined\" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));\nENV2.registerFlag(\"IS_SAFARI\", () => typeof navigator !== \"undefined\" && navigator != null && navigator.userAgent != null && /Safari/.test(navigator.userAgent) && /Apple/.test(navigator.vendor));\nENV2.registerFlag(\"PROD\", () => false);\nENV2.registerFlag(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\", () => ENV2.getBool(\"DEBUG\"));\nENV2.registerFlag(\"DEPRECATION_WARNINGS_ENABLED\", () => true);\nENV2.registerFlag(\"IS_TEST\", () => false);\nENV2.registerFlag(\"CHECK_COMPUTATION_FOR_ERRORS\", () => ENV2.getBool(\"DEBUG\"));\nENV2.registerFlag(\"WRAP_TO_IMAGEBITMAP\", () => false);\nENV2.registerFlag(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\", () => false);\nENV2.registerFlag(\"USE_SETTIMEOUTCUSTOM\", () => false);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js\nfunction inferShape(val, dtype) {\n let firstElem = val;\n if (isTypedArray(val)) {\n return dtype === \"string\" ? [] : [val.length];\n }\n if (isWebGLData(val)) {\n const usedChannels = val.channels || \"RGBA\";\n return [val.height, val.width * usedChannels.length];\n } else if (isWebGPUData(val)) {\n return [val.buffer.size / (dtype == null ? 4 : bytesPerElement(dtype))];\n }\n if (!Array.isArray(val)) {\n return [];\n }\n const shape = [];\n while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== \"string\") {\n shape.push(firstElem.length);\n firstElem = firstElem[0];\n }\n if (Array.isArray(val) && env().getBool(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\")) {\n deepAssertShapeConsistency(val, shape, []);\n }\n return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n indices = indices || [];\n if (!Array.isArray(val) && !isTypedArray(val)) {\n assert(shape.length === 0, () => `Element arr[${indices.join(\"][\")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);\n return;\n }\n assert(shape.length > 0, () => `Element arr[${indices.join(\"][\")}] should be a primitive, but is an array of ${val.length} elements`);\n assert(val.length === shape[0], () => `Element arr[${indices.join(\"][\")}] should have ${shape[0]} elements, but has ${val.length} elements`);\n const subShape = shape.slice(1);\n for (let i = 0; i < val.length; ++i) {\n deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n if (expectedDtype === \"string_or_numeric\") {\n return;\n }\n if (expectedDtype == null) {\n throw new Error(`Expected dtype cannot be null.`);\n }\n if (expectedDtype !== \"numeric\" && expectedDtype !== actualDType || expectedDtype === \"numeric\" && actualDType === \"string\") {\n throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n }\n}\nfunction convertToTensor(x, argName, functionName, parseAsDtype = \"numeric\") {\n if (x instanceof getGlobalTensorClass()) {\n assertDtype(parseAsDtype, x.dtype, argName, functionName);\n return x;\n }\n let inferredDtype = inferDtype(x);\n if (inferredDtype !== \"string\" && [\"bool\", \"int32\", \"float32\"].indexOf(parseAsDtype) >= 0) {\n inferredDtype = parseAsDtype;\n }\n assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== \"number\" && typeof x !== \"boolean\" && typeof x !== \"string\") {\n const type = x == null ? \"null\" : x.constructor.name;\n throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);\n }\n const inferredShape = inferShape(x, inferredDtype);\n if (!isTypedArray(x) && !Array.isArray(x)) {\n x = [x];\n }\n const skipTypedArray = true;\n const values = inferredDtype !== \"string\" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);\n return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nfunction convertToTensorArray(arg, argName, functionName, parseAsDtype = \"numeric\") {\n if (!Array.isArray(arg)) {\n throw new Error(`Argument ${argName} passed to ${functionName} must be a \\`Tensor[]\\` or \\`TensorLike[]\\``);\n }\n const tensors = arg;\n return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js\nvar OP_SCOPE_SUFFIX = \"__op\";\nfunction op(f) {\n const keys = Object.keys(f);\n if (keys.length !== 1) {\n throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);\n }\n let opName = keys[0];\n const fn = f[opName];\n if (opName.endsWith(\"_\")) {\n opName = opName.substring(0, opName.length - 1);\n }\n opName = opName + OP_SCOPE_SUFFIX;\n const f2 = (...args) => {\n ENGINE.startScope(opName);\n try {\n const result = fn(...args);\n if (isPromise(result)) {\n console.error(\"Cannot return a Promise inside of tidy.\");\n }\n ENGINE.endScope(result);\n return result;\n } catch (ex) {\n ENGINE.endScope(null);\n throw ex;\n }\n };\n Object.defineProperty(f2, \"name\", { value: opName, configurable: true });\n return f2;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js\nfunction complex_(real4, imag4) {\n const $real = convertToTensor(real4, \"real\", \"complex\");\n const $imag = convertToTensor(imag4, \"imag\", \"complex\");\n assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);\n const inputs = { real: $real, imag: $imag };\n return ENGINE.runKernel(Complex, inputs);\n}\nvar complex = op({ complex_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js\nfunction makeTensor(values, shape, inferredShape, dtype) {\n if (dtype == null) {\n dtype = inferDtype(values);\n } else if (dtype === \"complex64\") {\n throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);\n }\n if (isWebGPUData(values) || isWebGLData(values)) {\n if (dtype !== \"float32\" && dtype !== \"int32\") {\n throw new Error(`Creating tensor from GPU data only supports 'float32'|'int32' dtype, while the dtype is ${dtype}.`);\n }\n return ENGINE.backend.createTensorFromGPUData(values, shape || inferredShape, dtype);\n }\n if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== \"number\" && typeof values !== \"boolean\" && typeof values !== \"string\") {\n throw new Error(\"values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray\");\n }\n if (shape != null) {\n assertNonNegativeIntegerDimensions(shape);\n const providedSize = sizeFromShape(shape);\n const inferredSize = sizeFromShape(inferredShape);\n assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);\n for (let i = 0; i < inferredShape.length; ++i) {\n const inferred = inferredShape[i];\n const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true;\n assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);\n }\n }\n if (!isTypedArray(values) && !Array.isArray(values)) {\n values = [values];\n }\n shape = shape || inferredShape;\n values = dtype !== \"string\" ? toTypedArray(values, dtype) : flatten(values, [], true);\n return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js\nfunction tensor(values, shape, dtype) {\n const inferredShape = inferShape(values, dtype);\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/types.js\nvar DTYPE_VALUE_SIZE_MAP = {\n \"float32\": 4,\n \"float16\": 2,\n \"int32\": 4,\n \"uint16\": 2,\n \"uint8\": 1,\n \"bool\": 1,\n \"complex64\": 8\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/composite_array_buffer.js\nvar CompositeArrayBuffer = class _CompositeArrayBuffer {\n /**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers An array of ArrayBuffers to concatenate, or a single\n * ArrayBuffer.\n * @returns Result of concatenating `buffers` in order.\n */\n static join(buffers) {\n return new _CompositeArrayBuffer(buffers).slice();\n }\n constructor(buffers) {\n this.shards = [];\n this.previousShardIndex = 0;\n if (buffers == null) {\n return;\n }\n if (!(buffers instanceof Array)) {\n buffers = [buffers];\n }\n buffers = buffers.map((bufferOrTypedArray) => {\n if (isTypedArray(bufferOrTypedArray)) {\n return bufferOrTypedArray.buffer;\n }\n return bufferOrTypedArray;\n });\n if (buffers.length === 0) {\n return;\n }\n this.bufferUniformSize = buffers[0].byteLength;\n let start = 0;\n for (let i = 0; i < buffers.length; i++) {\n const buffer2 = buffers[i];\n if (i !== buffers.length - 1 && buffer2.byteLength !== this.bufferUniformSize) {\n this.bufferUniformSize = void 0;\n }\n const end = start + buffer2.byteLength;\n this.shards.push({ buffer: buffer2, start, end });\n start = end;\n }\n if (this.shards.length === 0) {\n this.byteLength = 0;\n }\n this.byteLength = this.shards[this.shards.length - 1].end;\n }\n slice(start = 0, end = this.byteLength) {\n if (this.shards.length === 0) {\n return new ArrayBuffer(0);\n }\n start = isNaN(Number(start)) ? 0 : start;\n end = isNaN(Number(end)) ? 0 : end;\n start = Math.max(0, start);\n end = Math.min(this.byteLength, end);\n if (end <= start) {\n return new ArrayBuffer(0);\n }\n const startShardIndex = this.findShardForByte(start);\n if (startShardIndex === -1) {\n throw new Error(`Could not find start shard for byte ${start}`);\n }\n const size = end - start;\n const outputBuffer = new ArrayBuffer(size);\n const outputArray = new Uint8Array(outputBuffer);\n let sliced = 0;\n for (let i = startShardIndex; i < this.shards.length; i++) {\n const shard = this.shards[i];\n const globalStart = start + sliced;\n const localStart = globalStart - shard.start;\n const outputStart = sliced;\n const globalEnd = Math.min(end, shard.end);\n const localEnd = globalEnd - shard.start;\n const outputSlice = new Uint8Array(shard.buffer, localStart, localEnd - localStart);\n outputArray.set(outputSlice, outputStart);\n sliced += outputSlice.length;\n if (end < shard.end) {\n break;\n }\n }\n return outputBuffer;\n }\n /**\n * Get the index of the shard that contains the byte at `byteIndex`.\n */\n findShardForByte(byteIndex) {\n if (this.shards.length === 0 || byteIndex < 0 || byteIndex >= this.byteLength) {\n return -1;\n }\n if (this.bufferUniformSize != null) {\n this.previousShardIndex = Math.floor(byteIndex / this.bufferUniformSize);\n return this.previousShardIndex;\n }\n function check(shard) {\n if (byteIndex < shard.start) {\n return -1;\n }\n if (byteIndex >= shard.end) {\n return 1;\n }\n return 0;\n }\n if (check(this.shards[this.previousShardIndex]) === 0) {\n return this.previousShardIndex;\n }\n const index = search(this.shards, check);\n if (index === -1) {\n return -1;\n }\n this.previousShardIndex = index;\n return this.previousShardIndex;\n }\n};\nfunction search(sortedArray, compare) {\n let min6 = 0;\n let max6 = sortedArray.length;\n while (min6 <= max6) {\n const middle = Math.floor((max6 - min6) / 2) + min6;\n const side = compare(sortedArray[middle]);\n if (side === 0) {\n return middle;\n } else if (side < 0) {\n max6 = middle;\n } else {\n min6 = middle + 1;\n }\n }\n return -1;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/globals.js\nfunction enableProdMode() {\n env().set(\"PROD\", true);\n}\nfunction enableDebugMode() {\n env().set(\"DEBUG\", true);\n}\nfunction disableDeprecationWarnings() {\n env().set(\"DEPRECATION_WARNINGS_ENABLED\", false);\n console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\nfunction deprecationWarn(msg) {\n if (env().getBool(\"DEPRECATION_WARNINGS_ENABLED\")) {\n console.warn(msg + \" You can disable deprecation warnings with tf.disableDeprecationWarnings().\");\n }\n}\nsetDeprecationWarningFn(deprecationWarn);\nfunction disposeVariables() {\n ENGINE.disposeVariables();\n}\nfunction engine() {\n return ENGINE;\n}\nfunction memory() {\n return ENGINE.memory();\n}\nfunction profile(f) {\n return ENGINE.profile(f);\n}\nfunction tidy(nameOrFn, fn) {\n return ENGINE.tidy(nameOrFn, fn);\n}\nfunction dispose(container) {\n const tensors = getTensorsInContainer(container);\n tensors.forEach((tensor2) => tensor2.dispose());\n}\nfunction keep(result) {\n return ENGINE.keep(result);\n}\nfunction time(f) {\n return ENGINE.time(f);\n}\nfunction setBackend(backendName) {\n return ENGINE.setBackend(backendName);\n}\nfunction ready() {\n return ENGINE.ready();\n}\nfunction getBackend() {\n return ENGINE.backendName;\n}\nfunction removeBackend(name) {\n ENGINE.removeBackend(name);\n}\nfunction findBackend(name) {\n return ENGINE.findBackend(name);\n}\nfunction findBackendFactory(name) {\n return ENGINE.findBackendFactory(name);\n}\nfunction registerBackend(name, factory, priority = 1) {\n return ENGINE.registerBackend(name, factory, priority);\n}\nfunction backend() {\n return ENGINE.backend;\n}\nfunction setPlatform(platformName, platform) {\n env().setPlatform(platformName, platform);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js\nvar NUM_BYTES_STRING_LENGTH = 4;\nasync function encodeWeights(tensors, group) {\n const specs = [];\n const dataPromises = [];\n const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors);\n for (let i = 0; i < names.length; ++i) {\n const name = names[i];\n const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n if (t.dtype !== \"float32\" && t.dtype !== \"int32\" && t.dtype !== \"bool\" && t.dtype !== \"string\" && t.dtype !== \"complex64\") {\n throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n }\n const spec = { name, shape: t.shape, dtype: t.dtype };\n if (t.dtype === \"string\") {\n const utf8bytes = new Promise(async (resolve) => {\n const vals = await t.bytes();\n const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;\n const bytes = new Uint8Array(totalNumBytes);\n let offset = 0;\n for (let i2 = 0; i2 < vals.length; i2++) {\n const val = vals[i2];\n const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n bytes.set(bytesOfLength, offset);\n offset += NUM_BYTES_STRING_LENGTH;\n bytes.set(val, offset);\n offset += val.length;\n }\n resolve(bytes);\n });\n dataPromises.push(utf8bytes);\n } else {\n dataPromises.push(t.data());\n }\n if (group != null) {\n spec.group = group;\n }\n specs.push(spec);\n }\n const tensorValues = await Promise.all(dataPromises);\n return { data: concatenateTypedArrays(tensorValues), specs };\n}\nfunction decodeWeights(weightData, specs) {\n const compositeBuffer = new CompositeArrayBuffer(weightData);\n const out = {};\n let offset = 0;\n for (const spec of specs) {\n const byteLength = getWeightBytelength(spec, (start, end) => {\n return compositeBuffer.slice(offset + start, offset + end);\n });\n out[spec.name] = decodeWeight(spec, compositeBuffer.slice(offset, offset + byteLength));\n offset += byteLength;\n }\n return out;\n}\nfunction getWeightBytelength(spec, slice5) {\n const size = sizeFromShape(spec.shape);\n let bytesPerValue;\n if (\"quantization\" in spec) {\n const quantization = spec.quantization;\n bytesPerValue = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n } else if (spec.dtype === \"string\") {\n let byteLength = 0;\n for (let i = 0; i < size; i++) {\n byteLength += NUM_BYTES_STRING_LENGTH + new Uint32Array(slice5(byteLength, byteLength + NUM_BYTES_STRING_LENGTH))[0];\n }\n return byteLength;\n } else {\n bytesPerValue = DTYPE_VALUE_SIZE_MAP[spec.dtype];\n }\n return size * bytesPerValue;\n}\nasync function getWeightBytelengthAsync(spec, slice5) {\n const size = sizeFromShape(spec.shape);\n let bytesPerValue;\n if (\"quantization\" in spec) {\n const quantization = spec.quantization;\n bytesPerValue = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n } else if (spec.dtype === \"string\") {\n let byteLength = 0;\n for (let i = 0; i < size; i++) {\n byteLength += NUM_BYTES_STRING_LENGTH + new Uint32Array(await slice5(byteLength, byteLength + NUM_BYTES_STRING_LENGTH))[0];\n }\n return byteLength;\n } else {\n bytesPerValue = DTYPE_VALUE_SIZE_MAP[spec.dtype];\n }\n return size * bytesPerValue;\n}\nfunction decodeWeight(spec, byteBuffer) {\n const name = spec.name;\n const dtype = spec.dtype;\n const shape = spec.shape;\n const size = sizeFromShape(shape);\n let values;\n let offset = 0;\n if (\"quantization\" in spec) {\n const quantization = spec.quantization;\n if (quantization.dtype === \"uint8\" || quantization.dtype === \"uint16\") {\n if (!(\"min\" in quantization && \"scale\" in quantization)) {\n throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);\n }\n } else if (quantization.dtype === \"float16\") {\n if (dtype !== \"float32\") {\n throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);\n }\n } else {\n throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);\n }\n const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n const quantizedArray = quantization.dtype === \"uint8\" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);\n if (dtype === \"float32\") {\n if (quantization.dtype === \"uint8\" || quantization.dtype === \"uint16\") {\n values = new Float32Array(quantizedArray.length);\n for (let i = 0; i < quantizedArray.length; i++) {\n const v = quantizedArray[i];\n values[i] = v * quantization.scale + quantization.min;\n }\n } else if (quantization.dtype === \"float16\") {\n const float16Decode = getFloat16Decoder();\n values = float16Decode(quantizedArray);\n } else {\n throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);\n }\n } else if (dtype === \"int32\") {\n if (quantization.dtype !== \"uint8\" && quantization.dtype !== \"uint16\") {\n throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);\n }\n values = new Int32Array(quantizedArray.length);\n for (let i = 0; i < quantizedArray.length; i++) {\n const v = quantizedArray[i];\n values[i] = Math.round(v * quantization.scale + quantization.min);\n }\n } else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n offset += size * quantizationSizeFactor;\n } else if (dtype === \"string\") {\n const size2 = sizeFromShape(spec.shape);\n values = [];\n for (let i = 0; i < size2; i++) {\n const byteLength = new Uint32Array(byteBuffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n offset += NUM_BYTES_STRING_LENGTH;\n const bytes = new Uint8Array(byteBuffer.slice(offset, offset + byteLength));\n values.push(bytes);\n offset += byteLength;\n }\n } else {\n const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n if (dtype === \"float32\") {\n values = new Float32Array(byteBuffer);\n } else if (dtype === \"int32\") {\n values = new Int32Array(byteBuffer);\n } else if (dtype === \"bool\") {\n values = new Uint8Array(byteBuffer);\n } else if (dtype === \"complex64\") {\n values = new Float32Array(byteBuffer);\n const real4 = new Float32Array(values.length / 2);\n const image2 = new Float32Array(values.length / 2);\n for (let i = 0; i < real4.length; i++) {\n real4[i] = values[i * 2];\n image2[i] = values[i * 2 + 1];\n }\n const realTensor = tensor(real4, shape, \"float32\");\n const imageTensor = tensor(image2, shape, \"float32\");\n const complexTensor = complex(realTensor, imageTensor);\n realTensor.dispose();\n imageTensor.dispose();\n return complexTensor;\n } else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n offset += size * dtypeFactor;\n }\n return tensor(values, shape, dtype);\n}\nasync function readToLength(reader, initialData, length) {\n let data = new Uint8Array(initialData);\n while (data.byteLength < length) {\n const { done, value } = await reader.read();\n if (done && value == null) {\n const missing = length - data.byteLength;\n throw new Error(`Reader is done but ${missing} bytes are still expected`);\n }\n const newData = new Uint8Array(data.length + value.byteLength);\n newData.set(data, 0);\n newData.set(new Uint8Array(value), data.length);\n data = newData;\n }\n return data.buffer;\n}\nasync function decodeWeightsStream(weightStream, specs) {\n const tensors = {};\n const reader = weightStream.getReader();\n let data = new ArrayBuffer(0);\n for (const spec of specs) {\n const byteLength = await getWeightBytelengthAsync(spec, async (start, end) => {\n data = await readToLength(reader, data, end);\n return data.slice(start, end);\n });\n data = await readToLength(reader, data, byteLength);\n const tensorData = data.slice(0, byteLength);\n data = data.slice(byteLength);\n const weightTensor = decodeWeight(spec, tensorData);\n tensors[spec.name] = weightTensor;\n if (getBackend() === \"webgpu\") {\n const b = backend();\n if (\"uploadToGPU\" in b && sizeFromShape(weightTensor.shape) >= env().get(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\")) {\n b.uploadToGPU(weightTensor.dataId);\n }\n }\n }\n return tensors;\n}\nfunction concatenateTypedArrays(xs) {\n if (xs === null) {\n throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n }\n let totalByteLength = 0;\n const normalizedXs = [];\n xs.forEach((x) => {\n totalByteLength += x.byteLength;\n normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));\n if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {\n throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n }\n });\n const y = new Uint8Array(totalByteLength);\n let offset = 0;\n normalizedXs.forEach((x) => {\n y.set(new Uint8Array(x.buffer), offset);\n offset += x.byteLength;\n });\n return y.buffer;\n}\nvar useNodeBuffer = typeof Buffer !== \"undefined\" && (typeof Blob === \"undefined\" || typeof atob === \"undefined\" || typeof btoa === \"undefined\");\nfunction stringByteLength(str) {\n if (useNodeBuffer) {\n return Buffer.byteLength(str, \"utf8\");\n }\n return new Blob([str]).size;\n}\nfunction arrayBufferToBase64String(buffer2) {\n if (useNodeBuffer) {\n return Buffer.from(buffer2).toString(\"base64\");\n }\n const buf = new Uint8Array(buffer2);\n let s = \"\";\n for (let i = 0, l = buf.length; i < l; i++) {\n s += String.fromCharCode(buf[i]);\n }\n return btoa(s);\n}\nfunction base64StringToArrayBuffer(str) {\n if (useNodeBuffer) {\n const buf = Buffer.from(str, \"base64\");\n return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n }\n const s = atob(str);\n const buffer2 = new Uint8Array(s.length);\n for (let i = 0; i < s.length; ++i) {\n buffer2.set([s.charCodeAt(i)], i);\n }\n return buffer2.buffer;\n}\nfunction concatenateArrayBuffers(buffers) {\n return CompositeArrayBuffer.join(buffers);\n}\nfunction basename(path) {\n const SEPARATOR = \"/\";\n path = path.trim();\n while (path.endsWith(SEPARATOR)) {\n path = path.slice(0, path.length - 1);\n }\n const items = path.split(SEPARATOR);\n return items[items.length - 1];\n}\nfunction getModelJSONForModelArtifacts(artifacts, manifest) {\n const result = {\n modelTopology: artifacts.modelTopology,\n format: artifacts.format,\n generatedBy: artifacts.generatedBy,\n convertedBy: artifacts.convertedBy,\n weightsManifest: manifest\n };\n if (artifacts.signature != null) {\n result.signature = artifacts.signature;\n }\n if (artifacts.userDefinedMetadata != null) {\n result.userDefinedMetadata = artifacts.userDefinedMetadata;\n }\n if (artifacts.modelInitializer != null) {\n result.modelInitializer = artifacts.modelInitializer;\n }\n if (artifacts.initializerSignature != null) {\n result.initializerSignature = artifacts.initializerSignature;\n }\n if (artifacts.trainingConfig != null) {\n result.trainingConfig = artifacts.trainingConfig;\n }\n return result;\n}\nfunction getModelArtifactsForJSONSync(modelJSON, weightSpecs, weightData) {\n const modelArtifacts = {\n modelTopology: modelJSON.modelTopology,\n format: modelJSON.format,\n generatedBy: modelJSON.generatedBy,\n convertedBy: modelJSON.convertedBy\n };\n if (modelJSON.trainingConfig != null) {\n modelArtifacts.trainingConfig = modelJSON.trainingConfig;\n }\n if (modelJSON.weightsManifest != null) {\n if (!weightSpecs) {\n throw new Error(\"modelJSON has weightsManifest but weightSpecs is null\");\n }\n if (!weightData) {\n throw new Error(\"modelJSON has weightsManifest but weightData is null\");\n }\n modelArtifacts.weightSpecs = weightSpecs;\n modelArtifacts.weightData = weightData;\n }\n if (modelJSON.signature != null) {\n modelArtifacts.signature = modelJSON.signature;\n }\n if (modelJSON.userDefinedMetadata != null) {\n modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;\n }\n if (modelJSON.modelInitializer != null) {\n modelArtifacts.modelInitializer = modelJSON.modelInitializer;\n }\n if (modelJSON.initializerSignature != null) {\n modelArtifacts.initializerSignature = modelJSON.initializerSignature;\n }\n return modelArtifacts;\n}\nasync function getModelArtifactsForJSON(modelJSON, loadWeights2) {\n let weightSpecs;\n let weightData;\n if (modelJSON.weightsManifest != null) {\n [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest);\n }\n return getModelArtifactsForJSONSync(modelJSON, weightSpecs, weightData);\n}\nfunction getModelArtifactsInfoForJSON(modelArtifacts) {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\"Expected JSON model topology, received ArrayBuffer.\");\n }\n return {\n dateSaved: /* @__PURE__ */ new Date(),\n modelTopologyType: \"JSON\",\n modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n weightDataBytes: modelArtifacts.weightData == null ? 0 : new CompositeArrayBuffer(modelArtifacts.weightData).byteLength\n };\n}\nfunction getWeightSpecs(weightsManifest) {\n const weightSpecs = [];\n for (const entry of weightsManifest) {\n weightSpecs.push(...entry.weights);\n }\n return weightSpecs;\n}\nfunction computeFloat16MantisaTable() {\n const convertMantissa = (i) => {\n let m = i << 13;\n let e = 0;\n while ((m & 8388608) === 0) {\n e -= 8388608;\n m <<= 1;\n }\n m &= ~8388608;\n e += 947912704;\n return m | e;\n };\n const mantisaTable = new Uint32Array(2048);\n mantisaTable[0] = 0;\n for (let i = 1; i < 1024; i++) {\n mantisaTable[i] = convertMantissa(i);\n }\n for (let i = 1024; i < 2048; i++) {\n mantisaTable[i] = 939524096 + (i - 1024 << 13);\n }\n return mantisaTable;\n}\nfunction computeFloat16ExponentTable() {\n const exponentTable = new Uint32Array(64);\n exponentTable[0] = 0;\n exponentTable[31] = 1199570944;\n exponentTable[32] = 2147483648;\n exponentTable[63] = 3347054592;\n for (let i = 1; i < 31; i++) {\n exponentTable[i] = i << 23;\n }\n for (let i = 33; i < 63; i++) {\n exponentTable[i] = 2147483648 + (i - 32 << 23);\n }\n return exponentTable;\n}\nfunction computeFloat16OffsetTable() {\n const offsetTable = new Uint32Array(64);\n for (let i = 0; i < 64; i++) {\n offsetTable[i] = 1024;\n }\n offsetTable[0] = offsetTable[32] = 0;\n return offsetTable;\n}\nfunction getFloat16Decoder() {\n const mantisaTable = computeFloat16MantisaTable();\n const exponentTable = computeFloat16ExponentTable();\n const offsetTable = computeFloat16OffsetTable();\n return (quantizedArray) => {\n const buffer2 = new ArrayBuffer(4 * quantizedArray.length);\n const bufferUint32View = new Uint32Array(buffer2);\n for (let index = 0; index < quantizedArray.length; index++) {\n const float16Bits = quantizedArray[index];\n const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];\n bufferUint32View[index] = float32Bits;\n }\n return new Float32Array(buffer2);\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js\nvar IORouterRegistry = class _IORouterRegistry {\n constructor() {\n this.saveRouters = [];\n this.loadRouters = [];\n }\n static getInstance() {\n if (_IORouterRegistry.instance == null) {\n _IORouterRegistry.instance = new _IORouterRegistry();\n }\n return _IORouterRegistry.instance;\n }\n /**\n * Register a save-handler router.\n *\n * @param saveRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `save` method defined or `null`.\n */\n static registerSaveRouter(saveRouter) {\n _IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n }\n /**\n * Register a load-handler router.\n *\n * @param loadRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `load` method defined or `null`.\n */\n static registerLoadRouter(loadRouter) {\n _IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n }\n /**\n * Look up IOHandler for saving, given a URL-like string.\n *\n * @param url\n * @returns If only one match is found, an instance of IOHandler with the\n * `save` method defined. If no match is found, `null`.\n * @throws Error, if more than one match is found.\n */\n static getSaveHandlers(url) {\n return _IORouterRegistry.getHandlers(url, \"save\");\n }\n /**\n * Look up IOHandler for loading, given a URL-like string.\n *\n * @param url\n * @param loadOptions Optional, custom load options.\n * @returns All valid handlers for `url`, given the currently registered\n * handler routers.\n */\n static getLoadHandlers(url, loadOptions) {\n return _IORouterRegistry.getHandlers(url, \"load\", loadOptions);\n }\n static getHandlers(url, handlerType, loadOptions) {\n const validHandlers = [];\n const routers = handlerType === \"load\" ? _IORouterRegistry.getInstance().loadRouters : _IORouterRegistry.getInstance().saveRouters;\n routers.forEach((router) => {\n const handler = router(url, loadOptions);\n if (handler !== null) {\n validHandlers.push(handler);\n }\n });\n return validHandlers;\n }\n};\nvar registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nvar registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nvar getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nvar getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js\nvar DATABASE_NAME = \"tensorflowjs\";\nvar DATABASE_VERSION = 1;\nvar MODEL_STORE_NAME = \"models_store\";\nvar INFO_STORE_NAME = \"model_info_store\";\nfunction getIndexedDBFactory() {\n if (!env().getBool(\"IS_BROWSER\")) {\n throw new Error(\"Failed to obtain IndexedDB factory because the current environmentis not a web browser.\");\n }\n const theWindow = typeof window === \"undefined\" ? self : window;\n const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB;\n if (factory == null) {\n throw new Error(\"The current browser does not appear to support IndexedDB.\");\n }\n return factory;\n}\nfunction setUpDatabase(openRequest) {\n const db = openRequest.result;\n db.createObjectStore(MODEL_STORE_NAME, { keyPath: \"modelPath\" });\n db.createObjectStore(INFO_STORE_NAME, { keyPath: \"modelPath\" });\n}\nvar BrowserIndexedDB = class {\n constructor(modelPath) {\n this.indexedDB = getIndexedDBFactory();\n if (modelPath == null || !modelPath) {\n throw new Error(\"For IndexedDB, modelPath must not be null, undefined or empty.\");\n }\n this.modelPath = modelPath;\n }\n async save(modelArtifacts) {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");\n }\n return this.databaseAction(this.modelPath, modelArtifacts);\n }\n async load() {\n return this.databaseAction(this.modelPath);\n }\n /**\n * Perform database action to put model artifacts into or read model artifacts\n * from IndexedDB object store.\n *\n * Whether the action is put or get depends on whether `modelArtifacts` is\n * specified. If it is specified, the action will be put; otherwise the action\n * will be get.\n *\n * @param modelPath A unique string path for the model.\n * @param modelArtifacts If specified, it will be the model artifacts to be\n * stored in IndexedDB.\n * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n * of `ModelArtifacts`, if the action is get.\n */\n databaseAction(modelPath, modelArtifacts) {\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n if (modelArtifacts == null) {\n const modelTx = db.transaction(MODEL_STORE_NAME, \"readonly\");\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const getRequest = modelStore.get(this.modelPath);\n getRequest.onsuccess = () => {\n if (getRequest.result == null) {\n db.close();\n return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));\n } else {\n resolve(getRequest.result.modelArtifacts);\n }\n };\n getRequest.onerror = (error) => {\n db.close();\n return reject(getRequest.error);\n };\n modelTx.oncomplete = () => db.close();\n } else {\n modelArtifacts.weightData = CompositeArrayBuffer.join(modelArtifacts.weightData);\n const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n const infoTx = db.transaction(INFO_STORE_NAME, \"readwrite\");\n let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n let putInfoRequest;\n try {\n putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n } catch (error) {\n return reject(error);\n }\n let modelTx;\n putInfoRequest.onsuccess = () => {\n modelTx = db.transaction(MODEL_STORE_NAME, \"readwrite\");\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n let putModelRequest;\n try {\n putModelRequest = modelStore.put({\n modelPath: this.modelPath,\n modelArtifacts,\n modelArtifactsInfo\n });\n } catch (error) {\n return reject(error);\n }\n putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n putModelRequest.onerror = (error) => {\n infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const deleteInfoRequest = infoStore.delete(this.modelPath);\n deleteInfoRequest.onsuccess = () => {\n db.close();\n return reject(putModelRequest.error);\n };\n deleteInfoRequest.onerror = (error2) => {\n db.close();\n return reject(putModelRequest.error);\n };\n };\n };\n putInfoRequest.onerror = (error) => {\n db.close();\n return reject(putInfoRequest.error);\n };\n infoTx.oncomplete = () => {\n if (modelTx == null) {\n db.close();\n } else {\n modelTx.oncomplete = () => db.close();\n }\n };\n }\n };\n openRequest.onerror = (error) => reject(openRequest.error);\n });\n }\n};\nBrowserIndexedDB.URL_SCHEME = \"indexeddb://\";\nvar indexedDBRouter = (url) => {\n if (!env().getBool(\"IS_BROWSER\")) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\nfunction browserIndexedDB(modelPath) {\n return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key;\n}\nvar BrowserIndexedDBManager = class {\n constructor() {\n this.indexedDB = getIndexedDBFactory();\n }\n async listModels() {\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n const tx = db.transaction(INFO_STORE_NAME, \"readonly\");\n const store = tx.objectStore(INFO_STORE_NAME);\n const getAllInfoRequest = store.getAll();\n getAllInfoRequest.onsuccess = () => {\n const out = {};\n for (const item of getAllInfoRequest.result) {\n out[item.modelPath] = item.modelArtifactsInfo;\n }\n resolve(out);\n };\n getAllInfoRequest.onerror = (error) => {\n db.close();\n return reject(getAllInfoRequest.error);\n };\n tx.oncomplete = () => db.close();\n };\n openRequest.onerror = (error) => reject(openRequest.error);\n });\n }\n async removeModel(path) {\n path = maybeStripScheme(path);\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n const infoTx = db.transaction(INFO_STORE_NAME, \"readwrite\");\n const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const getInfoRequest = infoStore.get(path);\n let modelTx;\n getInfoRequest.onsuccess = () => {\n if (getInfoRequest.result == null) {\n db.close();\n return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));\n } else {\n const deleteInfoRequest = infoStore.delete(path);\n const deleteModelData = () => {\n modelTx = db.transaction(MODEL_STORE_NAME, \"readwrite\");\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const deleteModelRequest = modelStore.delete(path);\n deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n deleteModelRequest.onerror = (error) => reject(getInfoRequest.error);\n };\n deleteInfoRequest.onsuccess = deleteModelData;\n deleteInfoRequest.onerror = (error) => {\n deleteModelData();\n db.close();\n return reject(getInfoRequest.error);\n };\n }\n };\n getInfoRequest.onerror = (error) => {\n db.close();\n return reject(getInfoRequest.error);\n };\n infoTx.oncomplete = () => {\n if (modelTx == null) {\n db.close();\n } else {\n modelTx.oncomplete = () => db.close();\n }\n };\n };\n openRequest.onerror = (error) => reject(openRequest.error);\n });\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js\nvar PATH_SEPARATOR = \"/\";\nvar PATH_PREFIX = \"tensorflowjs_models\";\nvar INFO_SUFFIX = \"info\";\nvar MODEL_TOPOLOGY_SUFFIX = \"model_topology\";\nvar WEIGHT_SPECS_SUFFIX = \"weight_specs\";\nvar WEIGHT_DATA_SUFFIX = \"weight_data\";\nvar MODEL_METADATA_SUFFIX = \"model_metadata\";\nfunction getModelKeys(path) {\n return {\n info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n };\n}\nfunction removeItems(keys) {\n for (const key of Object.values(keys)) {\n window.localStorage.removeItem(key);\n }\n}\nfunction getModelPathFromKey(key) {\n const items = key.split(PATH_SEPARATOR);\n if (items.length < 3) {\n throw new Error(`Invalid key format: ${key}`);\n }\n return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme2(key) {\n return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key;\n}\nvar BrowserLocalStorage = class {\n constructor(modelPath) {\n if (!env().getBool(\"IS_BROWSER\") || typeof window === \"undefined\" || typeof window.localStorage === \"undefined\") {\n throw new Error(\"The current environment does not support local storage.\");\n }\n this.LS = window.localStorage;\n if (modelPath == null || !modelPath) {\n throw new Error(\"For local storage, modelPath must not be null, undefined or empty.\");\n }\n this.modelPath = modelPath;\n this.keys = getModelKeys(this.modelPath);\n }\n /**\n * Save model artifacts to browser local storage.\n *\n * See the documentation to `browserLocalStorage` for details on the saved\n * artifacts.\n *\n * @param modelArtifacts The model artifacts to be stored.\n * @returns An instance of SaveResult.\n */\n async save(modelArtifacts) {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");\n } else {\n const topology = JSON.stringify(modelArtifacts.modelTopology);\n const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData);\n try {\n this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n this.LS.setItem(this.keys.topology, topology);\n this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(weightBuffer));\n const metadata = {\n format: modelArtifacts.format,\n generatedBy: modelArtifacts.generatedBy,\n convertedBy: modelArtifacts.convertedBy,\n signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0,\n userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0,\n modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0,\n initializerSignature: modelArtifacts.initializerSignature != null ? modelArtifacts.initializerSignature : void 0,\n trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0\n };\n this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));\n return { modelArtifactsInfo };\n } catch (err) {\n removeItems(this.keys);\n throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n }\n }\n }\n /**\n * Load a model from local storage.\n *\n * See the documentation to `browserLocalStorage` for details on the saved\n * artifacts.\n *\n * @returns The loaded model (if loading succeeds).\n */\n async load() {\n const info = JSON.parse(this.LS.getItem(this.keys.info));\n if (info == null) {\n throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n }\n if (info.modelTopologyType !== \"JSON\") {\n throw new Error(\"BrowserLocalStorage does not support loading non-JSON model topology yet.\");\n }\n const out = {};\n const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n if (topology == null) {\n throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);\n }\n out.modelTopology = topology;\n const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n if (weightSpecs == null) {\n throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);\n }\n out.weightSpecs = weightSpecs;\n const metadataString = this.LS.getItem(this.keys.modelMetadata);\n if (metadataString != null) {\n const metadata = JSON.parse(metadataString);\n out.format = metadata.format;\n out.generatedBy = metadata.generatedBy;\n out.convertedBy = metadata.convertedBy;\n if (metadata.signature != null) {\n out.signature = metadata.signature;\n }\n if (metadata.userDefinedMetadata != null) {\n out.userDefinedMetadata = metadata.userDefinedMetadata;\n }\n if (metadata.modelInitializer != null) {\n out.modelInitializer = metadata.modelInitializer;\n }\n if (metadata.initializerSignature != null) {\n out.initializerSignature = metadata.initializerSignature;\n }\n if (metadata.trainingConfig != null) {\n out.trainingConfig = metadata.trainingConfig;\n }\n }\n const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n if (weightDataBase64 == null) {\n throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);\n }\n out.weightData = base64StringToArrayBuffer(weightDataBase64);\n return out;\n }\n};\nBrowserLocalStorage.URL_SCHEME = \"localstorage://\";\nvar localStorageRouter = (url) => {\n if (!env().getBool(\"IS_BROWSER\")) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\nfunction browserLocalStorage(modelPath) {\n return new BrowserLocalStorage(modelPath);\n}\nvar BrowserLocalStorageManager = class {\n constructor() {\n assert(env().getBool(\"IS_BROWSER\"), () => \"Current environment is not a web browser\");\n assert(typeof window === \"undefined\" || typeof window.localStorage !== \"undefined\", () => \"Current browser does not appear to support localStorage\");\n this.LS = window.localStorage;\n }\n async listModels() {\n const out = {};\n const prefix = PATH_PREFIX + PATH_SEPARATOR;\n const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n for (let i = 0; i < this.LS.length; ++i) {\n const key = this.LS.key(i);\n if (key.startsWith(prefix) && key.endsWith(suffix)) {\n const modelPath = getModelPathFromKey(key);\n out[modelPath] = JSON.parse(this.LS.getItem(key));\n }\n }\n return out;\n }\n async removeModel(path) {\n path = maybeStripScheme2(path);\n const keys = getModelKeys(path);\n if (this.LS.getItem(keys.info) == null) {\n throw new Error(`Cannot find model at path '${path}'`);\n }\n const info = JSON.parse(this.LS.getItem(keys.info));\n removeItems(keys);\n return info;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js\nvar URL_SCHEME_SUFFIX = \"://\";\nvar ModelStoreManagerRegistry = class _ModelStoreManagerRegistry {\n constructor() {\n this.managers = {};\n }\n static getInstance() {\n if (_ModelStoreManagerRegistry.instance == null) {\n _ModelStoreManagerRegistry.instance = new _ModelStoreManagerRegistry();\n }\n return _ModelStoreManagerRegistry.instance;\n }\n /**\n * Register a save-handler router.\n *\n * @param saveRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `save` method defined or `null`.\n */\n static registerManager(scheme, manager) {\n assert(scheme != null, () => \"scheme must not be undefined or null.\");\n if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n }\n assert(scheme.length > 0, () => \"scheme must not be an empty string.\");\n const registry = _ModelStoreManagerRegistry.getInstance();\n assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n registry.managers[scheme] = manager;\n }\n static getManager(scheme) {\n const manager = _ModelStoreManagerRegistry.getInstance().managers[scheme];\n if (manager == null) {\n throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n }\n return manager;\n }\n static getSchemes() {\n return Object.keys(_ModelStoreManagerRegistry.getInstance().managers);\n }\n};\nfunction parseURL(url) {\n if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(\",\")}`);\n }\n return {\n scheme: url.split(URL_SCHEME_SUFFIX)[0],\n path: url.split(URL_SCHEME_SUFFIX)[1]\n };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);\n const loadHandler = loadHandlers[0];\n const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);\n assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);\n const saveHandler = saveHandlers[0];\n const sourceScheme = parseURL(sourceURL).scheme;\n const sourcePath = parseURL(sourceURL).path;\n const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n const modelArtifacts = await loadHandler.load();\n if (deleteSource && sameMedium) {\n await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);\n }\n const saveResult = await saveHandler.save(modelArtifacts);\n if (deleteSource && !sameMedium) {\n await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);\n }\n return saveResult.modelArtifactsInfo;\n}\nasync function listModels() {\n const schemes = ModelStoreManagerRegistry.getSchemes();\n const out = {};\n for (const scheme of schemes) {\n const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n for (const path in schemeOut) {\n const url = scheme + URL_SCHEME_SUFFIX + path;\n out[url] = schemeOut[path];\n }\n }\n return out;\n}\nasync function removeModel(url) {\n const schemeAndPath = parseURL(url);\n const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n return manager.removeModel(schemeAndPath.path);\n}\nasync function copyModel(sourceURL, destURL) {\n const deleteSource = false;\n return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nasync function moveModel(sourceURL, destURL) {\n const deleteSource = true;\n return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js\nvar PlatformBrowser = class {\n constructor() {\n this.messageName = \"setTimeoutCustom\";\n this.functionRefs = [];\n this.handledMessageCount = 0;\n this.hasEventListener = false;\n }\n fetch(path, init2) {\n return fetch(path, init2);\n }\n now() {\n return performance.now();\n }\n encode(text, encoding) {\n if (encoding !== \"utf-8\" && encoding !== \"utf8\") {\n throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n }\n if (this.textEncoder == null) {\n this.textEncoder = new TextEncoder();\n }\n return this.textEncoder.encode(text);\n }\n decode(bytes, encoding) {\n return new TextDecoder(encoding).decode(bytes);\n }\n // If the setTimeout nesting level is greater than 5 and timeout is less\n // than 4ms, timeout will be clamped to 4ms, which hurts the perf.\n // Interleaving window.postMessage and setTimeout will trick the browser and\n // avoid the clamp.\n setTimeoutCustom(functionRef, delay) {\n if (typeof window === \"undefined\" || !env().getBool(\"USE_SETTIMEOUTCUSTOM\")) {\n setTimeout(functionRef, delay);\n return;\n }\n this.functionRefs.push(functionRef);\n setTimeout(() => {\n window.postMessage({ name: this.messageName, index: this.functionRefs.length - 1 }, \"*\");\n }, delay);\n if (!this.hasEventListener) {\n this.hasEventListener = true;\n window.addEventListener(\"message\", (event) => {\n if (event.source === window && event.data.name === this.messageName) {\n event.stopPropagation();\n const functionRef2 = this.functionRefs[event.data.index];\n functionRef2();\n this.handledMessageCount++;\n if (this.handledMessageCount === this.functionRefs.length) {\n this.functionRefs = [];\n this.handledMessageCount = 0;\n }\n }\n }, true);\n }\n }\n isTypedArray(a) {\n return isTypedArrayBrowser(a);\n }\n};\nif (env().get(\"IS_BROWSER\")) {\n env().setPlatform(\"browser\", new PlatformBrowser());\n try {\n ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n } catch (err) {\n }\n try {\n ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n } catch (err) {\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js\nvar getNodeFetch = {\n // tslint:disable-next-line:no-require-imports\n importFetch: () => require_browser()\n};\nvar systemFetch;\nvar PlatformNode = class {\n constructor() {\n this.util = require_util();\n this.textEncoder = new this.util.TextEncoder();\n }\n fetch(path, requestInits) {\n if (env().global.fetch != null) {\n return env().global.fetch(path, requestInits);\n }\n if (systemFetch == null) {\n systemFetch = getNodeFetch.importFetch();\n }\n return systemFetch(path, requestInits);\n }\n now() {\n const time2 = process.hrtime();\n return time2[0] * 1e3 + time2[1] / 1e6;\n }\n encode(text, encoding) {\n if (encoding !== \"utf-8\" && encoding !== \"utf8\") {\n throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n }\n return this.textEncoder.encode(text);\n }\n decode(bytes, encoding) {\n if (bytes.length === 0) {\n return \"\";\n }\n return new this.util.TextDecoder(encoding).decode(bytes);\n }\n isTypedArray(a) {\n return this.util.types.isFloat32Array(a) || this.util.types.isInt32Array(a) || this.util.types.isUint8Array(a) || this.util.types.isUint8ClampedArray(a);\n }\n};\nif (env().get(\"IS_NODE\") && !env().get(\"IS_BROWSER\")) {\n env().setPlatform(\"node\", new PlatformNode());\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js\nfunction buffer(shape, dtype = \"float32\", values) {\n dtype = dtype || \"float32\";\n assertNonNegativeIntegerDimensions(shape);\n return new TensorBuffer(shape, dtype, values);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js\nfunction cast_(x, dtype) {\n const $x = convertToTensor(x, \"x\", \"cast\");\n if (!isValidDtype(dtype)) {\n throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n }\n if (dtype === \"string\" && $x.dtype !== \"string\" || dtype !== \"string\" && $x.dtype === \"string\") {\n throw new Error(\"Only strings can be casted to strings\");\n }\n const inputs = { x: $x };\n const attrs = { dtype };\n return ENGINE.runKernel(Cast, inputs, attrs);\n}\nvar cast = op({ cast_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js\nfunction clone_(x) {\n const $x = convertToTensor(x, \"x\", \"clone\", \"string_or_numeric\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Identity, inputs);\n}\nvar clone = op({ clone_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/print.js\nfunction print(x, verbose = false) {\n console.log(x.toString(verbose));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js\ngetOrMakeEngine();\nvar opHandler2 = {\n buffer,\n cast,\n clone,\n print\n};\nsetOpHandler(opHandler2);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/add.js\nfunction add_(a, b) {\n let $a = convertToTensor(a, \"a\", \"add\");\n let $b = convertToTensor(b, \"b\", \"add\");\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Add, inputs);\n}\nvar add2 = op({ add_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js\nfunction floorDiv_(a, b) {\n let $a = convertToTensor(a, \"a\", \"floorDiv\");\n let $b = convertToTensor(b, \"b\", \"floorDiv\");\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(FloorDiv, inputs);\n}\nvar floorDiv = op({ floorDiv_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/div.js\nfunction div_(a, b) {\n let $a = convertToTensor(a, \"a\", \"div\");\n let $b = convertToTensor(b, \"b\", \"div\");\n [$a, $b] = makeTypesMatch($a, $b);\n if ($a.dtype === \"int32\" && $b.dtype === \"int32\") {\n return floorDiv($a, $b);\n }\n const inputs = { a: $a, b: $b };\n const attrs = {};\n return ENGINE.runKernel(RealDiv, inputs, attrs);\n}\nvar div = op({ div_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/mul.js\nfunction mul_(a, b) {\n let $a = convertToTensor(a, \"a\", \"mul\");\n let $b = convertToTensor(b, \"b\", \"mul\");\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Multiply, inputs);\n}\nvar mul = op({ mul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/abs.js\nfunction abs_(x) {\n const $x = convertToTensor(x, \"x\", \"abs\");\n if ($x.dtype === \"complex64\") {\n const inputs = { x: $x };\n return ENGINE.runKernel(ComplexAbs, inputs);\n } else {\n const inputs = { x: $x };\n return ENGINE.runKernel(Abs, inputs);\n }\n}\nvar abs = op({ abs_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/acos.js\nfunction acos_(x) {\n const $x = convertToTensor(x, \"x\", \"acos\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Acos, inputs);\n}\nvar acos = op({ acos_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js\nfunction acosh_(x) {\n const $x = convertToTensor(x, \"x\", \"acosh\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Acosh, inputs);\n}\nvar acosh = op({ acosh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js\nfunction addN_(tensors) {\n assert(Array.isArray(tensors), () => \"The argument passed to tf.addN() must be a list of tensors\");\n assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);\n const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, \"addN\"));\n const firstTensor = $tensors[0];\n $tensors.forEach((t) => {\n if (t.dtype !== firstTensor.dtype) {\n throw new Error(\"All tensors passed to tf.addN() must have the same dtype\");\n }\n });\n $tensors.forEach((t) => {\n if (!arraysEqual(t.shape, firstTensor.shape)) {\n throw new Error(\"All tensors passed to tf.addN() must have the same shape\");\n }\n });\n const inputs = $tensors;\n return ENGINE.runKernel(AddN, inputs);\n}\nvar addN = op({ addN_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/all.js\nfunction all_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, \"x\", \"all\", \"bool\");\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(All, inputs, attrs);\n}\nvar all = op({ all_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/any.js\nfunction any_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, \"x\", \"any\", \"bool\");\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Any, inputs, attrs);\n}\nvar any = op({ any_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js\nfunction argMax_(x, axis = 0) {\n const $x = convertToTensor(x, \"x\", \"argMax\");\n const inputs = { x: $x };\n const attrs = { axis };\n return ENGINE.runKernel(ArgMax, inputs, attrs);\n}\nvar argMax = op({ argMax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js\nfunction argMin_(x, axis = 0) {\n const $x = convertToTensor(x, \"x\", \"argMin\");\n const inputs = { x: $x };\n const attrs = { axis };\n return ENGINE.runKernel(ArgMin, inputs, attrs);\n}\nvar argMin = op({ argMin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/asin.js\nfunction asin_(x) {\n const $x = convertToTensor(x, \"x\", \"asin\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Asin, inputs);\n}\nvar asin = op({ asin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js\nfunction asinh_(x) {\n const $x = convertToTensor(x, \"x\", \"asinh\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Asinh, inputs);\n}\nvar asinh = op({ asinh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan.js\nfunction atan_(x) {\n const $x = convertToTensor(x, \"x\", \"atan\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Atan, inputs);\n}\nvar atan = op({ atan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js\nfunction atan2_(a, b) {\n let $a = convertToTensor(a, \"a\", \"atan2\");\n let $b = convertToTensor(b, \"b\", \"atan2\");\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Atan2, inputs);\n}\nvar atan2 = op({ atan2_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js\nfunction atanh_(x) {\n const $x = convertToTensor(x, \"x\", \"atanh\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Atanh, inputs);\n}\nvar atanh = op({ atanh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js\nfunction computeDilation2DInfo(inputShape, filterShape, strides, pad3, dataFormat = \"NHWC\", dilations) {\n const inputChannels = inputShape[3];\n const $filterShape = [...filterShape, inputChannels];\n const $dataFormat = convertConv2DDataFormat(dataFormat);\n return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad3, null, null, $dataFormat);\n}\nfunction computePool2DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = \"channelsLast\") {\n const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n let filterShape;\n if (dataFormat === \"channelsLast\") {\n filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n } else if (dataFormat === \"channelsFirst\") {\n filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n } else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n return computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, false, dataFormat);\n}\nfunction computePool3DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = \"NDHWC\") {\n const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n let filterShape;\n let $dataFormat;\n if (dataFormat === \"NDHWC\") {\n $dataFormat = \"channelsLast\";\n filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n } else if (dataFormat === \"NCDHW\") {\n $dataFormat = \"channelsFirst\";\n filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n } else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n return computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, false, $dataFormat, roundingMode);\n}\nfunction computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, depthwise = false, dataFormat = \"channelsLast\") {\n let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n if (dataFormat === \"channelsLast\") {\n [batchSize, inHeight, inWidth, inChannels] = inShape;\n } else if (dataFormat === \"channelsFirst\") {\n [batchSize, inChannels, inHeight, inWidth] = inShape;\n } else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n const [filterHeight, filterWidth, , filterChannels] = filterShape;\n const [strideHeight, strideWidth] = parseTupleParam(strides);\n const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n let outShape;\n if (dataFormat === \"channelsFirst\") {\n outShape = [batchSize, outChannels, outHeight, outWidth];\n } else if (dataFormat === \"channelsLast\") {\n outShape = [batchSize, outHeight, outWidth, outChannels];\n }\n return {\n batchSize,\n dataFormat,\n inHeight,\n inWidth,\n inChannels,\n outHeight,\n outWidth,\n outChannels,\n padInfo,\n strideHeight,\n strideWidth,\n filterHeight,\n filterWidth,\n effectiveFilterHeight,\n effectiveFilterWidth,\n dilationHeight,\n dilationWidth,\n inShape,\n outShape,\n filterShape\n };\n}\nfunction computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, depthwise = false, dataFormat = \"channelsLast\", roundingMode) {\n let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n if (dataFormat === \"channelsLast\") {\n [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n } else if (dataFormat === \"channelsFirst\") {\n [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n } else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n let outShape;\n if (dataFormat === \"channelsFirst\") {\n outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n } else if (dataFormat === \"channelsLast\") {\n outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n }\n return {\n batchSize,\n dataFormat,\n inDepth,\n inHeight,\n inWidth,\n inChannels,\n outDepth,\n outHeight,\n outWidth,\n outChannels,\n padInfo,\n strideDepth,\n strideHeight,\n strideWidth,\n filterDepth,\n filterHeight,\n filterWidth,\n effectiveFilterDepth,\n effectiveFilterHeight,\n effectiveFilterWidth,\n dilationDepth,\n dilationHeight,\n dilationWidth,\n inShape,\n outShape,\n filterShape\n };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n if (zeroPad == null) {\n zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n }\n const inputRows = inShape[0];\n const inputCols = inShape[1];\n const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, filterShape, outChannels, strides, zeroPad, roundingMode) {\n if (zeroPad == null) {\n zeroPad = computeDefaultPad(inShape, filterShape[0], strides[0]);\n }\n const outShape = [0, 0, 0, outChannels];\n for (let index = 0; index < 3; index++) {\n if (inShape[index] + 2 * zeroPad >= filterShape[index]) {\n outShape[index] = round((inShape[index] - filterShape[index] + 2 * zeroPad) / strides[index] + 1, roundingMode);\n }\n }\n return outShape;\n}\nfunction computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n if (typeof param === \"number\") {\n return [param, param, param];\n }\n if (param.length === 2) {\n return [param[0], param[1], 1];\n }\n return param;\n}\nfunction parse3TupleParam(param) {\n return typeof param === \"number\" ? [param, param, param] : param;\n}\nfunction getEffectiveFilterSize(filterSize, dilation) {\n if (dilation <= 1) {\n return filterSize;\n }\n return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n let padInfo;\n let outHeight;\n let outWidth;\n if (typeof pad3 === \"number\") {\n const padType = pad3 === 0 ? \"VALID\" : \"NUMBER\";\n padInfo = { top: pad3, bottom: pad3, left: pad3, right: pad3, type: padType };\n const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad3, roundingMode);\n outHeight = outShape[0];\n outWidth = outShape[1];\n } else if (pad3 === \"same\") {\n outHeight = Math.ceil(inHeight / strideHeight);\n outWidth = Math.ceil(inWidth / strideWidth);\n const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n const top = Math.floor(padAlongHeight / 2);\n const bottom = padAlongHeight - top;\n const left = Math.floor(padAlongWidth / 2);\n const right = padAlongWidth - left;\n padInfo = { top, bottom, left, right, type: \"SAME\" };\n } else if (pad3 === \"valid\") {\n padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: \"VALID\" };\n outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n } else if (typeof pad3 === \"object\") {\n const top = dataFormat === \"channelsLast\" ? pad3[1][0] : pad3[2][0];\n const bottom = dataFormat === \"channelsLast\" ? pad3[1][1] : pad3[2][1];\n const left = dataFormat === \"channelsLast\" ? pad3[2][0] : pad3[3][0];\n const right = dataFormat === \"channelsLast\" ? pad3[2][1] : pad3[3][1];\n const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? \"VALID\" : \"EXPLICIT\";\n padInfo = { top, bottom, left, right, type: padType };\n outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n } else {\n throw Error(`Unknown padding parameter: ${pad3}`);\n }\n return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n let padInfo;\n let outDepth;\n let outHeight;\n let outWidth;\n if (pad3 === \"valid\") {\n pad3 = 0;\n }\n if (typeof pad3 === \"number\") {\n const padType = pad3 === 0 ? \"VALID\" : \"NUMBER\";\n padInfo = {\n top: pad3,\n bottom: pad3,\n left: pad3,\n right: pad3,\n front: pad3,\n back: pad3,\n type: padType\n };\n const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], [filterDepth, filterHeight, filterWidth], 1, [strideDepth, strideHeight, strideWidth], pad3, roundingMode);\n outDepth = outShape[0];\n outHeight = outShape[1];\n outWidth = outShape[2];\n } else if (pad3 === \"same\") {\n outDepth = Math.ceil(inDepth / strideDepth);\n outHeight = Math.ceil(inHeight / strideHeight);\n outWidth = Math.ceil(inWidth / strideWidth);\n const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n const front = Math.floor(padAlongDepth / 2);\n const back = padAlongDepth - front;\n const top = Math.floor(padAlongHeight / 2);\n const bottom = padAlongHeight - top;\n const left = Math.floor(padAlongWidth / 2);\n const right = padAlongWidth - left;\n padInfo = { top, bottom, left, right, front, back, type: \"SAME\" };\n } else {\n throw Error(`Unknown padding parameter: ${pad3}`);\n }\n return { padInfo, outDepth, outHeight, outWidth };\n}\nfunction round(value, roundingMode) {\n if (!roundingMode) {\n return Math.trunc(value);\n }\n switch (roundingMode) {\n case \"round\":\n return Math.round(value);\n case \"ceil\":\n return Math.ceil(value);\n case \"floor\":\n return Math.floor(value);\n default:\n throw new Error(`Unknown roundingMode ${roundingMode}`);\n }\n}\nfunction tupleValuesAreOne(param) {\n const [dimA, dimB, dimC] = parseTupleParam(param);\n return dimA === 1 && dimB === 1 && dimC === 1;\n}\nfunction eitherStridesOrDilationsAreOne(strides, dilations) {\n return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\nfunction stridesOrDilationsArePositive(values) {\n return parseTupleParam(values).every((value) => value > 0);\n}\nfunction convertConv2DDataFormat(dataFormat) {\n if (dataFormat === \"NHWC\") {\n return \"channelsLast\";\n } else if (dataFormat === \"NCHW\") {\n return \"channelsFirst\";\n } else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n}\nfunction checkPadOnDimRoundingMode(opDesc, pad3, dimRoundingMode) {\n if (dimRoundingMode != null) {\n if (typeof pad3 === \"string\") {\n throw Error(`Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);\n } else if (typeof pad3 === \"number\") {\n assert(isInt(pad3), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);\n } else if (typeof pad3 === \"object\") {\n pad3.forEach((p2) => {\n p2.forEach((v) => {\n assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${v}.`);\n });\n });\n } else {\n throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad3}`);\n }\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js\nfunction reshape_(x, shape) {\n const $x = convertToTensor(x, \"x\", \"reshape\", \"string_or_numeric\");\n const inputs = { x: $x };\n const attrs = { shape };\n return ENGINE.runKernel(Reshape, inputs, attrs);\n}\nvar reshape = op({ reshape_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js\nfunction avgPool_(x, filterSize, strides, pad3, dimRoundingMode) {\n const $x = convertToTensor(x, \"x\", \"avgPool\", \"float32\");\n const dilations = 1;\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n checkPadOnDimRoundingMode(\"avgPool\", pad3, dimRoundingMode);\n const inputs = { x: x4D };\n const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n let res = ENGINE.runKernel(AvgPool, inputs, attrs);\n res = cast(res, $x.dtype);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar avgPool = op({ avgPool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js\nfunction avgPool3d_(x, filterSize, strides, pad3, dimRoundingMode, dataFormat = \"NDHWC\") {\n const $x = convertToTensor(x, \"x\", \"avgPool3d\", \"float32\");\n let x5D = $x;\n let reshapedTo5D = false;\n if ($x.rank === 4) {\n reshapedTo5D = true;\n x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n }\n assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n assert(dataFormat === \"NDHWC\", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);\n assert(typeof strides === \"number\" && strides > 0 || Array.isArray(strides) && strides[0] > 0 && strides[1] > 0 && strides[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${strides}'`);\n checkPadOnDimRoundingMode(\"avgPool3d\", pad3, dimRoundingMode);\n const inputs = { x: x5D };\n const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat };\n let res = ENGINE.runKernel(AvgPool3D, inputs, attrs);\n res = cast(res, x5D.dtype);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nvar avgPool3d = op({ avgPool3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat.js\nfunction concat_(tensors, axis = 0) {\n assert(tensors.length >= 1, () => \"Pass at least one tensor to concat\");\n const $tensors = convertToTensorArray(tensors, \"tensors\", \"concat\", \"string_or_numeric\");\n if ($tensors[0].dtype === \"complex64\") {\n $tensors.forEach((tensor2) => {\n if (tensor2.dtype !== \"complex64\") {\n throw new Error(`Cannot concatenate complex64 tensors with a tensor\n with dtype ${tensor2.dtype}. `);\n }\n });\n }\n if ($tensors.length === 1) {\n return clone($tensors[0]);\n }\n const inputs = $tensors;\n const attr = { axis };\n return ENGINE.runKernel(Concat, inputs, attr);\n}\nvar concat = op({ concat_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n let $a = convertToTensor(a, \"a\", \"matMul\");\n let $b = convertToTensor(b, \"b\", \"matMul\");\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n const attrs = { transposeA, transposeB };\n return ENGINE.runKernel(BatchMatMul, inputs, attrs);\n}\nvar matMul = op({ matMul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js\nfunction sigmoid_(x) {\n const $x = convertToTensor(x, \"x\", \"sigmoid\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Sigmoid, inputs);\n}\nvar sigmoid = op({ sigmoid_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js\nfunction slice_(x, begin, size) {\n const $x = convertToTensor(x, \"x\", \"slice\", \"string_or_numeric\");\n if ($x.rank === 0) {\n throw new Error(\"Slicing scalar is not possible\");\n }\n const inputs = { x: $x };\n const attrs = { begin, size };\n return ENGINE.runKernel(Slice, inputs, attrs);\n}\nvar slice = op({ slice_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js\nfunction tanh_(x) {\n const $x = convertToTensor(x, \"x\", \"tanh\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Tanh, inputs);\n}\nvar tanh2 = op({ tanh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n const $forgetBias = convertToTensor(forgetBias, \"forgetBias\", \"basicLSTMCell\");\n const $lstmKernel = convertToTensor(lstmKernel, \"lstmKernel\", \"basicLSTMCell\");\n const $lstmBias = convertToTensor(lstmBias, \"lstmBias\", \"basicLSTMCell\");\n const $data = convertToTensor(data, \"data\", \"basicLSTMCell\");\n const $c = convertToTensor(c, \"c\", \"basicLSTMCell\");\n const $h = convertToTensor(h, \"h\", \"basicLSTMCell\");\n const combined = concat([$data, $h], 1);\n const weighted = matMul(combined, $lstmKernel);\n const res = add2(weighted, $lstmBias);\n const batchSize = res.shape[0];\n const sliceCols = res.shape[1] / 4;\n const sliceSize = [batchSize, sliceCols];\n const i = slice(res, [0, 0], sliceSize);\n const j = slice(res, [0, sliceCols], sliceSize);\n const f = slice(res, [0, sliceCols * 2], sliceSize);\n const o = slice(res, [0, sliceCols * 3], sliceSize);\n const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));\n const newH = mul(tanh2(newC), sigmoid(o));\n return [newC, newH];\n}\nvar basicLSTMCell = op({ basicLSTMCell_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js\nfunction batchToSpaceND_(x, blockShape, crops) {\n const $x = convertToTensor(x, \"x\", \"batchToSpaceND\");\n const prod5 = blockShape.reduce((a, b) => a * b);\n assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length ${blockShape.length}`);\n assert($x.shape[0] % prod5 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(\" * \")} === ${prod5}`);\n const inputs = { x: $x };\n const attrs = { blockShape, crops };\n return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);\n}\nvar batchToSpaceND = op({ batchToSpaceND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js\nfunction xAs4D(x) {\n let x4D;\n if (x.rank === 0 || x.rank === 1) {\n x4D = reshape(x, [1, 1, 1, x.size]);\n } else if (x.rank === 2) {\n x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n } else if (x.rank === 3) {\n x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n } else {\n x4D = x;\n }\n return x4D;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js\nfunction batchNorm_(x, mean4, variance, offset, scale2, varianceEpsilon) {\n if (varianceEpsilon == null) {\n varianceEpsilon = 1e-3;\n }\n const $x = convertToTensor(x, \"x\", \"batchNorm\");\n const $mean = convertToTensor(mean4, \"mean\", \"batchNorm\");\n const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n let $scale;\n if (scale2 != null) {\n $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n }\n assert($mean.rank === $variance.rank, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n assert($offset == null || $mean.rank === $offset.rank, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n assert($scale == null || $mean.rank === $scale.rank, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n const x4D = xAs4D($x);\n const inputs = {\n x: x4D,\n scale: $scale,\n offset: $offset,\n mean: $mean,\n variance: $variance\n };\n const attrs = { varianceEpsilon };\n const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);\n return reshape(res, $x.shape);\n}\nvar batchNorm = op({ batchNorm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js\nfunction batchNorm2d_(x, mean4, variance, offset, scale2, varianceEpsilon) {\n const $x = convertToTensor(x, \"x\", \"batchNorm\");\n const $mean = convertToTensor(mean4, \"mean\", \"batchNorm\");\n const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n let $scale;\n if (scale2 != null) {\n $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n }\n assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);\n assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);\n assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);\n if ($scale != null) {\n assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);\n }\n return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm2d = op({ batchNorm2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js\nfunction batchNorm3d_(x, mean4, variance, offset, scale2, varianceEpsilon) {\n const $x = convertToTensor(x, \"x\", \"batchNorm\");\n const $mean = convertToTensor(mean4, \"mean\", \"batchNorm\");\n const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n let $scale;\n if (scale2 != null) {\n $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n }\n assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);\n assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);\n assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);\n if ($scale != null) {\n assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);\n }\n return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm3d = op({ batchNorm3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js\nfunction batchNorm4d_(x, mean4, variance, offset, scale2, varianceEpsilon) {\n const $x = convertToTensor(x, \"x\", \"batchNorm\");\n const $mean = convertToTensor(mean4, \"mean\", \"batchNorm\");\n const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n let $scale;\n if (scale2 != null) {\n $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n }\n let $offset;\n if (offset != null) {\n $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n }\n assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);\n assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);\n assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);\n if ($scale != null) {\n assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);\n }\n return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm4d = op({ batchNorm4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/bincount.js\nfunction bincount_(x, weights, size) {\n const $x = convertToTensor(x, \"x\", \"bincount\");\n const $weights = convertToTensor(weights, \"weights\", \"bincount\");\n assert($x.dtype === \"int32\", () => `Error in bincount: input dtype must be int32, but got ${$x.dtype}`);\n assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);\n const inputs = { x: $x, weights: $weights };\n const attrs = { size };\n return ENGINE.runKernel(Bincount, inputs, attrs);\n}\nvar bincount = op({ bincount_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/bitwise_and.js\nfunction bitwiseAnd_(x, y) {\n const $x = convertToTensor(x, \"x\", \"bitwiseAnd\");\n const $y = convertToTensor(y, \"y\", \"bitwiseAnd\");\n if (!arraysEqual($x.shape, $y.shape)) {\n throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${$x.shape}, y: ${$y.shape}`);\n }\n if ($x.dtype !== \"int32\" || $y.dtype !== \"int32\") {\n throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${$x.dtype} and type of y: ${$y.dtype}`);\n }\n const inputs = { a: $x, b: $y };\n return ENGINE.runKernel(BitwiseAnd, inputs);\n}\nvar bitwiseAnd = op({ bitwiseAnd_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_args.js\nfunction broadcastArgs_(s0, s1) {\n const shape1Input = convertToTensor(s0, \"s0\", \"broadcastArgs\", \"int32\");\n const shape2Input = convertToTensor(s1, \"s1\", \"broadcastArgs\", \"int32\");\n if (shape1Input.rank !== 1) {\n throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${shape1Input.rank}`);\n }\n if (shape2Input.rank !== 1) {\n throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${shape2Input.rank}`);\n }\n const inputs = { s0: shape1Input, s1: shape2Input };\n return ENGINE.runKernel(BroadcastArgs, inputs);\n}\nvar broadcastArgs = op({ broadcastArgs_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js\nfunction broadcastTo_(x, shape) {\n let input2 = convertToTensor(x, \"broadcastTo\", \"x\");\n const xShape = input2.shape;\n assertNonNegativeIntegerDimensions(shape);\n if (shape.length < input2.rank) {\n throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);\n }\n if (shape.length > input2.rank) {\n const newShape = input2.shape.slice();\n while (newShape.length < shape.length) {\n newShape.unshift(1);\n }\n input2 = reshape(input2, newShape);\n }\n const inputShape = input2.shape;\n const reps = Array.from(shape);\n for (let i = shape.length - 1; i >= 0; i--) {\n if (inputShape[i] === shape[i]) {\n reps[i] = 1;\n } else if (input2.shape[i] !== 1) {\n throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n }\n }\n const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0);\n if (axes.length === 0) {\n return clone(input2);\n }\n const inputs = { x: input2 };\n const attrs = { reps };\n return ENGINE.runKernel(Tile, inputs, attrs);\n}\nvar broadcastTo = op({ broadcastTo_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js\nfunction ceil_(x) {\n const $x = convertToTensor(x, \"x\", \"ceil\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Ceil, inputs);\n}\nvar ceil = op({ ceil_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fill.js\nfunction fill(shape, value, dtype) {\n assertNonNegativeIntegerDimensions(shape);\n dtype = dtype || inferDtype(value);\n const attrs = { shape, value, dtype };\n return ENGINE.runKernel(Fill, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n const $x = convertToTensor(x, \"x\", \"clipByValue\");\n assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);\n if (clipValueMin === clipValueMax) {\n return fill($x.shape, clipValueMin, $x.dtype);\n }\n const inputs = { x: $x };\n const attrs = { clipValueMin, clipValueMax };\n return ENGINE.runKernel(ClipByValue, inputs, attrs);\n}\nvar clipByValue = op({ clipByValue_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js\nfunction concat1d_(tensors) {\n return concat(\n tensors,\n 0\n /* axis */\n );\n}\nvar concat1d = op({ concat1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js\nfunction concat2d_(tensors, axis) {\n return concat(tensors, axis);\n}\nvar concat2d = op({ concat2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js\nfunction concat3d_(tensors, axis) {\n return concat(tensors, axis);\n}\nvar concat3d = op({ concat3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js\nfunction concat4d_(tensors, axis) {\n return concat(tensors, axis);\n}\nvar concat4d = op({ concat4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js\nfunction conv2d_(x, filter, strides, pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode) {\n const $x = convertToTensor(x, \"x\", \"conv2d\", \"float32\");\n const $filter = convertToTensor(filter, \"filter\", \"conv2d\", \"float32\");\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n checkPadOnDimRoundingMode(\"conv2d\", pad3, dimRoundingMode);\n const inDepth = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n assert(stridesOrDilationsArePositive(dilations), () => \"Error in conv2D: Dilated rates should be larger than 0.\");\n assert(stridesOrDilationsArePositive(strides), () => \"Error in conv2D: Strides should be larger than 0.\");\n const inputs = { x: x4D, filter: $filter };\n const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode };\n const res = ENGINE.runKernel(Conv2D, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar conv2d = op({ conv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js\nfunction conv1d_(x, filter, stride, pad3, dataFormat = \"NWC\", dilation = 1, dimRoundingMode) {\n const $x = convertToTensor(x, \"x\", \"conv1d\");\n const $filter = convertToTensor(filter, \"filter\", \"conv1d\");\n let x3D = $x;\n let reshapedTo3D = false;\n if ($x.rank === 2) {\n reshapedTo3D = true;\n x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n }\n assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);\n checkPadOnDimRoundingMode(\"conv1d\", pad3, dimRoundingMode);\n assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);\n assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);\n assert(stridesOrDilationsArePositive(dilation), () => \"Error in conv1D: Dilated rates should be larger than 0.\");\n assert(stridesOrDilationsArePositive(stride), () => \"Error in conv1D: Stride should be larger than 0.\");\n assert(dataFormat === \"NWC\", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n const strides = [1, stride];\n const dilations = [1, dilation];\n const conv2dDataFormat = \"NHWC\";\n const res = conv2d(input4D, filter4D, strides, pad3, conv2dDataFormat, dilations, dimRoundingMode);\n if (reshapedTo3D) {\n return reshape(res, [res.shape[2], res.shape[3]]);\n }\n return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nvar conv1d = op({ conv1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad3, dataFormat = \"NHWC\", dimRoundingMode) {\n assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);\n let xShape4D = xShape;\n let dy4D = dy;\n let reshapedTo4D = false;\n if (dy.rank === 3) {\n reshapedTo4D = true;\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n }\n assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);\n assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);\n assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);\n const inDepth = dataFormat === \"NHWC\" ? xShape4D[3] : xShape4D[1];\n const outDepth = dataFormat === \"NHWC\" ? dy4D.shape[3] : dy4D.shape[1];\n assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);\n assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);\n checkPadOnDimRoundingMode(\"conv2dDerInput\", pad3, dimRoundingMode);\n const inputs = { dy: dy4D, filter };\n const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape: xShape4D };\n const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar conv2DBackpropInput = op({ conv2DBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad3, dimRoundingMode) {\n const $x = convertToTensor(x, \"x\", \"conv2dTranspose\");\n const $filter = convertToTensor(filter, \"filter\", \"conv2dTranspose\");\n return conv2DBackpropInput(outputShape, $x, $filter, strides, pad3, \"NHWC\", dimRoundingMode);\n}\nvar conv2dTranspose = op({ conv2dTranspose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js\nfunction conv3d_(x, filter, strides, pad3, dataFormat = \"NDHWC\", dilations = [1, 1, 1]) {\n const $x = convertToTensor(x, \"x\", \"conv3d\");\n const $filter = convertToTensor(filter, \"filter\", \"conv3d\");\n let x5D = $x;\n let reshapedTo5D = false;\n if ($x.rank === 4) {\n reshapedTo5D = true;\n x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n }\n assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);\n assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n assert(dataFormat === \"NDHWC\", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n assert(stridesOrDilationsArePositive(dilations), () => \"Error in conv3D: Dilated rates should be larger than 0.\");\n assert(stridesOrDilationsArePositive(strides), () => \"Error in conv3D: Strides should be larger than 0.\");\n const inputs = { x: x5D, filter: $filter };\n const attrs = { strides, pad: pad3, dataFormat, dilations };\n const res = ENGINE.runKernel(Conv3D, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nvar conv3d = op({ conv3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad3) {\n assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);\n let xShape5D = xShape;\n let dy5D = dy;\n let reshapedTo5D = false;\n if (dy.rank === 4) {\n reshapedTo5D = true;\n dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n }\n const inDepth = xShape5D[4];\n const outDepth = dy5D.shape[4];\n assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);\n assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);\n assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);\n assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);\n assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);\n const inputs = { dy: dy5D, filter };\n const attrs = { pad: pad3, strides, inputShape: xShape5D };\n const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nvar conv3DBackpropInput = op({ conv3DBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad3) {\n const $x = convertToTensor(x, \"x\", \"conv3dTranspose\");\n const $filter = convertToTensor(filter, \"filter\", \"conv3dTranspose\");\n return conv3DBackpropInput(outputShape, $x, $filter, strides, pad3);\n}\nvar conv3dTranspose = op({ conv3dTranspose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/cos.js\nfunction cos_(x) {\n const $x = convertToTensor(x, \"x\", \"cos\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Cos, inputs);\n}\nvar cos = op({ cos_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js\nfunction cosh_(x) {\n const $x = convertToTensor(x, \"x\", \"cosh\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Cosh, inputs);\n}\nvar cosh = op({ cosh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumprod.js\nfunction cumprod_(x, axis = 0, exclusive = false, reverse5 = false) {\n const $x = convertToTensor(x, \"x\", \"cumprod\");\n const inputs = { x: $x };\n const attrs = { axis, exclusive, reverse: reverse5 };\n return ENGINE.runKernel(Cumprod, inputs, attrs);\n}\nvar cumprod = op({ cumprod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js\nfunction cumsum_(x, axis = 0, exclusive = false, reverse5 = false) {\n const $x = convertToTensor(x, \"x\", \"cumsum\");\n const inputs = { x: $x };\n const attrs = { axis, exclusive, reverse: reverse5 };\n return ENGINE.runKernel(Cumsum, inputs, attrs);\n}\nvar cumsum = op({ cumsum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/dense_bincount.js\nfunction denseBincount_(x, weights, size, binaryOutput = false) {\n const $x = convertToTensor(x, \"x\", \"denseBincount\");\n const $weights = convertToTensor(weights, \"weights\", \"denseBincount\");\n assert($x.dtype === \"int32\", () => `Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`);\n assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`);\n assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);\n const inputs = { x: $x, weights: $weights };\n const attrs = { size, binaryOutput };\n return ENGINE.runKernel(DenseBincount, inputs, attrs);\n}\nvar denseBincount = op({ denseBincount_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js\nfunction depthToSpace_(x, blockSize, dataFormat = \"NHWC\") {\n const $x = convertToTensor(x, \"x\", \"depthToSpace\", \"float32\");\n const inputHeight = dataFormat === \"NHWC\" ? $x.shape[1] : $x.shape[2];\n const inputWidth = dataFormat === \"NHWC\" ? $x.shape[2] : $x.shape[3];\n const inputDepth = dataFormat === \"NHWC\" ? $x.shape[3] : $x.shape[1];\n assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n ${inputHeight} and ${blockSize} for depthToSpace with input shape\n ${$x.shape}`);\n assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n ${inputWidth} and ${blockSize} for depthToSpace with input shape\n ${$x.shape}`);\n assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n const inputs = { x: $x };\n const attrs = { blockSize, dataFormat };\n return ENGINE.runKernel(DepthToSpace, inputs, attrs);\n}\nvar depthToSpace = op({ depthToSpace_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js\nfunction depthwiseConv2d_(x, filter, strides, pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode) {\n const $x = convertToTensor(x, \"x\", \"depthwiseConv2d\", \"float32\");\n const $filter = convertToTensor(filter, \"filter\", \"depthwiseConv2d\", \"float32\");\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n const inChannels = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n assert(inChannels === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${inChannels}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);\n checkPadOnDimRoundingMode(\"depthwiseConv2d\", pad3, dimRoundingMode);\n const inputs = { x: x4D, filter: $filter };\n const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode };\n const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar depthwiseConv2d = op({ depthwiseConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/diag.js\nfunction diag_(x) {\n const $x = convertToTensor(x, \"x\", \"diag\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Diag, inputs);\n}\nvar diag = op({ diag_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js\nfunction dilation2d_(x, filter, strides, pad3, dilations = [1, 1], dataFormat = \"NHWC\") {\n const $x = convertToTensor(x, \"x\", \"dilation2d\");\n const $filter = convertToTensor(filter, \"filter\", \"dilation2d\");\n assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);\n assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);\n assert(dataFormat === \"NHWC\", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n reshapedTo4D = true;\n }\n assert(x4D.shape[3] === $filter.shape[2], () => `Error in dilation2d: input and filter must have the same depth: ${x4D.shape[3]} vs ${$filter.shape[2]}`);\n const inputs = { x: x4D, filter: $filter };\n const attrs = { strides, pad: pad3, dilations };\n const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar dilation2d = op({ dilation2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js\nvar broadcast_util_exports = {};\n__export(broadcast_util_exports, {\n assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,\n getBroadcastDims: () => getBroadcastDims,\n getReductionAxes: () => getReductionAxes\n});\nfunction getBroadcastDims(inShape, outShape) {\n const inRank = inShape.length;\n const dims = [];\n for (let i = 0; i < inRank; i++) {\n const dim = inRank - 1 - i;\n const a = inShape[dim] || 1;\n const b = outShape[outShape.length - 1 - i] || 1;\n if (b > 1 && a === 1) {\n dims.unshift(dim);\n }\n }\n return dims;\n}\nfunction getReductionAxes(inShape, outShape) {\n const result = [];\n for (let i = 0; i < outShape.length; i++) {\n const inDim = inShape[inShape.length - i - 1];\n const outAxis = outShape.length - i - 1;\n const outDim = outShape[outAxis];\n if (inDim == null || inDim === 1 && outDim > 1) {\n result.unshift(outAxis);\n }\n }\n return result;\n}\nfunction assertAndGetBroadcastShape(shapeA, shapeB) {\n const l = Math.max(shapeA.length, shapeB.length);\n const result = new Array(l);\n for (let i = 0; i < l; i++) {\n let a = shapeA[shapeA.length - i - 1];\n if (a == null) {\n a = 1;\n }\n let b = shapeB[shapeB.length - i - 1];\n if (b == null) {\n b = 1;\n }\n if (a === 1) {\n result[l - i - 1] = b;\n } else if (b === 1) {\n result[l - i - 1] = a;\n } else if (a !== b) {\n const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;\n throw Error(errMsg);\n } else {\n result[l - i - 1] = a;\n }\n }\n return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/equal.js\nfunction equal_(a, b) {\n let $a = convertToTensor(a, \"a\", \"equal\", \"string_or_numeric\");\n let $b = convertToTensor(b, \"b\", \"equal\", \"string_or_numeric\");\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Equal, inputs);\n}\nvar equal = op({ equal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/where.js\nfunction where_(condition, a, b) {\n const $a = convertToTensor(a, \"a\", \"where\");\n const $b = convertToTensor(b, \"b\", \"where\");\n const $condition = convertToTensor(condition, \"condition\", \"where\", \"bool\");\n const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);\n const $broadcastedCondition = broadcastTo($condition, broadcastShape);\n const $broadcastedA = broadcastTo($a, broadcastShape);\n const $broadcastedB = broadcastTo($b, broadcastShape);\n const inputs = {\n condition: $broadcastedCondition,\n t: $broadcastedA,\n e: $broadcastedB\n };\n return ENGINE.runKernel(Select, inputs);\n}\nvar where = op({ where_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js\nfunction zerosLike_(x) {\n const $x = convertToTensor(x, \"x\", \"zerosLike\");\n const inputs = { x: $x };\n return ENGINE.runKernel(ZerosLike, inputs);\n}\nvar zerosLike = op({ zerosLike_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js\nfunction divNoNan_(a, b) {\n let $a = convertToTensor(a, \"a\", \"div\");\n let $b = convertToTensor(b, \"b\", \"div\");\n [$a, $b] = makeTypesMatch($a, $b);\n const divResult = div($a, $b);\n const zeros4 = zerosLike(divResult);\n const bEqualsZero = equal($b, zeros4);\n return where(bEqualsZero, zeros4, divResult);\n}\nvar divNoNan = op({ divNoNan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/dot.js\nfunction dot_(t1, t2) {\n const $t1 = convertToTensor(t1, \"t1\", \"dot\");\n const $t2 = convertToTensor(t2, \"t2\", \"dot\");\n assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);\n const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];\n const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];\n assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);\n if ($t1.rank === 1 && $t2.rank === 1) {\n const t12D = reshape($t1, [1, -1]);\n const t22D = reshape($t2, [-1, 1]);\n const t1t2 = matMul(t12D, t22D);\n return reshape(t1t2, []);\n } else if ($t1.rank === 1 && $t2.rank === 2) {\n const t12D = reshape($t1, [1, -1]);\n const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n const t1t2 = matMul(t12D, t22D);\n return reshape(t1t2, [t1t2.size]);\n } else if ($t1.rank === 2 && $t2.rank === 1) {\n const t22D = reshape($t2, [-1, 1]);\n const t1t2 = matMul($t1, t22D);\n return reshape(t1t2, [t1t2.size]);\n } else {\n const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n const t1t2 = matMul($t1, t22D);\n return t1t2;\n }\n}\nvar dot = op({ dot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/einsum.js\nfunction einsum_(equation, ...tensors) {\n const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, \"einsum\"));\n const attrs = { equation };\n return ENGINE.runKernel(Einsum, $tensors, attrs);\n}\nvar einsum = op({ einsum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/elu.js\nfunction elu_(x) {\n const $x = convertToTensor(x, \"x\", \"elu\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Elu, inputs);\n}\nvar elu = op({ elu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ensure_shape.js\nfunction ensureShape_(x, shape) {\n const $x = convertToTensor(x, \"x\", \"ensureShape\", \"string_or_numeric\");\n if (!arraysEqualWithNull($x.shape, shape)) {\n throw new Error(`EnsureShape: Shape of tensor ${$x.shape} is not compatible with expected shape ${shape}`);\n }\n return x;\n}\nvar ensureShape = op({ ensureShape_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf.js\nfunction erf_(x) {\n let $x = convertToTensor(x, \"x\", \"erf\");\n assert($x.dtype === \"int32\" || $x.dtype === \"float32\", () => \"Input dtype must be `int32` or `float32`.\");\n if ($x.dtype === \"int32\") {\n $x = cast($x, \"float32\");\n }\n const inputs = { x: $x };\n return ENGINE.runKernel(Erf, inputs);\n}\nvar erf = op({ erf_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js\nfunction axesAreInnerMostDims(axes, rank) {\n for (let i = 0; i < axes.length; ++i) {\n if (axes[axes.length - i - 1] !== rank - 1 - i) {\n return false;\n }\n }\n return true;\n}\nfunction combineLocations(outputLoc, reduceLoc, axes) {\n const rank = outputLoc.length + reduceLoc.length;\n const loc = [];\n let outIdx = 0;\n let reduceIdx = 0;\n for (let dim = 0; dim < rank; dim++) {\n if (axes.indexOf(dim) === -1) {\n loc.push(outputLoc[outIdx++]);\n } else {\n loc.push(reduceLoc[reduceIdx++]);\n }\n }\n return loc;\n}\nfunction computeOutAndReduceShapes(aShape, axes) {\n const outShape = [];\n const rank = aShape.length;\n for (let dim = 0; dim < rank; dim++) {\n if (axes.indexOf(dim) === -1) {\n outShape.push(aShape[dim]);\n }\n }\n const reduceShape = axes.map((dim) => aShape[dim]);\n return [outShape, reduceShape];\n}\nfunction expandShapeToKeepDim(shape, axes) {\n const reduceSubShape = axes.map((x) => 1);\n return combineLocations(shape, reduceSubShape, axes);\n}\nfunction assertAxesAreInnerMostDims(msg, axes, rank) {\n assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);\n}\nfunction getAxesPermutation(axes, rank) {\n if (axesAreInnerMostDims(axes, rank)) {\n return null;\n }\n const result = [];\n for (let i = 0; i < rank; ++i) {\n if (axes.indexOf(i) === -1) {\n result.push(i);\n }\n }\n axes.forEach((axis) => result.push(axis));\n return result;\n}\nfunction getUndoAxesPermutation(axes) {\n return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);\n}\nfunction getInnerMostAxes(numAxes, rank) {\n const res = [];\n for (let i = rank - numAxes; i < rank; ++i) {\n res.push(i);\n }\n return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max.js\nfunction max_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, \"x\", \"max\");\n const inputs = { x: $x };\n const attrs = { reductionIndices: axis, keepDims };\n return ENGINE.runKernel(Max, inputs, attrs);\n}\nvar max = op({ max_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/min.js\nfunction min_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, \"x\", \"min\");\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Min, inputs, attrs);\n}\nvar min = op({ min_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pow.js\nfunction pow_(base, exp4) {\n let $base = convertToTensor(base, \"base\", \"pow\");\n let $exp = convertToTensor(exp4, \"exp\", \"pow\");\n [$base, $exp] = makeTypesMatch($base, $exp);\n const inputs = { a: $base, b: $exp };\n return ENGINE.runKernel(Pow, inputs);\n}\nvar pow = op({ pow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js\nfunction scalar(value, dtype) {\n if ((isTypedArray(value) && dtype !== \"string\" || Array.isArray(value)) && dtype !== \"complex64\") {\n throw new Error(\"Error creating a new Scalar: value must be a primitive (number|boolean|string)\");\n }\n if (dtype === \"string\" && isTypedArray(value) && !(value instanceof Uint8Array)) {\n throw new Error(\"When making a scalar from encoded string, the value must be `Uint8Array`.\");\n }\n const shape = [];\n const inferredShape = [];\n return makeTensor(value, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js\nfunction sqrt_(x) {\n const $x = convertToTensor(x, \"x\", \"sqrt\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Sqrt, inputs);\n}\nvar sqrt = op({ sqrt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/square.js\nfunction square_(x) {\n const $x = convertToTensor(x, \"x\", \"square\");\n const attrs = {};\n return ENGINE.runKernel(\"Square\", { x: $x }, attrs);\n}\nvar square = op({ square_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sum.js\nfunction sum_(x, axis = null, keepDims = false) {\n let $x = convertToTensor(x, \"x\", \"sum\");\n if ($x.dtype === \"bool\") {\n $x = cast($x, \"int32\");\n }\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Sum, inputs, attrs);\n}\nvar sum2 = op({ sum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js\nfunction norm_(x, ord = \"euclidean\", axis = null, keepDims = false) {\n x = convertToTensor(x, \"x\", \"norm\");\n const norm2 = normImpl(x, ord, axis);\n let keepDimsShape = norm2.shape;\n if (keepDims) {\n const axes = parseAxisParam(axis, x.shape);\n keepDimsShape = expandShapeToKeepDim(norm2.shape, axes);\n }\n return reshape(norm2, keepDimsShape);\n}\nfunction normImpl(x, p2, axis = null) {\n if (x.rank === 0) {\n return abs(x);\n }\n if (x.rank !== 1 && axis === null) {\n return normImpl(reshape(x, [-1]), p2, axis);\n }\n if (x.rank === 1 || typeof axis === \"number\" || Array.isArray(axis) && axis.length === 1) {\n if (p2 === 1) {\n return sum2(abs(x), axis);\n }\n if (p2 === Infinity) {\n return max(abs(x), axis);\n }\n if (p2 === -Infinity) {\n return min(abs(x), axis);\n }\n if (p2 === \"euclidean\" || p2 === 2) {\n return sqrt(sum2(pow(abs(x), scalar(2, \"int32\")), axis));\n }\n throw new Error(`Error in norm: invalid ord value: ${p2}`);\n }\n if (Array.isArray(axis) && axis.length === 2) {\n if (p2 === 1) {\n return max(sum2(abs(x), axis[0]), axis[1] - 1);\n }\n if (p2 === Infinity) {\n return max(sum2(abs(x), axis[1]), axis[0]);\n }\n if (p2 === -Infinity) {\n return min(sum2(abs(x), axis[1]), axis[0]);\n }\n if (p2 === \"fro\" || p2 === \"euclidean\") {\n return sqrt(sum2(square(x), axis));\n }\n throw new Error(`Error in norm: invalid ord value: ${p2}`);\n }\n throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nvar norm = op({ norm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/euclidean_norm.js\nfunction euclideanNorm_(x, axis = null, keepDims = false) {\n return norm(x, \"euclidean\", axis, keepDims);\n}\nvar euclideanNorm = op({ euclideanNorm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/exp.js\nfunction exp_(x) {\n const $x = convertToTensor(x, \"x\", \"exp\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Exp, inputs);\n}\nvar exp = op({ exp_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js\nfunction expandDims_(x, axis = 0) {\n const $x = convertToTensor(x, \"x\", \"expandDims\", \"string_or_numeric\");\n assert(axis <= $x.rank, () => \"Axis must be <= rank of the tensor\");\n const inputs = { input: $x };\n const attrs = { dim: axis };\n return ENGINE.runKernel(ExpandDims, inputs, attrs);\n}\nvar expandDims = op({ expandDims_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js\nfunction expm1_(x) {\n const $x = convertToTensor(x, \"x\", \"expm1\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Expm1, inputs);\n}\nvar expm1 = op({ expm1_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tile.js\nfunction tile_(x, reps) {\n const $x = convertToTensor(x, \"x\", \"tile\", \"string_or_numeric\");\n assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);\n const inputs = { x: $x };\n const attrs = { reps };\n return ENGINE.runKernel(Tile, inputs, attrs);\n}\nvar tile = op({ tile_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/eye.js\nfunction eye_(numRows, numColumns, batchShape, dtype = \"float32\") {\n if (numColumns == null) {\n numColumns = numRows;\n }\n const buff = buffer([numRows, numColumns], dtype);\n const n = numRows <= numColumns ? numRows : numColumns;\n for (let i = 0; i < n; ++i) {\n buff.set(1, i, i);\n }\n const out = reshape(buff.toTensor(), [numRows, numColumns]);\n if (batchShape == null) {\n return out;\n } else {\n if (batchShape.length === 1) {\n return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n } else if (batchShape.length === 2) {\n return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n } else if (batchShape.length === 3) {\n return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n batchShape[0],\n batchShape[1],\n batchShape[2],\n 1,\n 1\n ]);\n } else {\n throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);\n }\n }\n}\nvar eye = op({ eye_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/floor.js\nfunction floor_(x) {\n const $x = convertToTensor(x, \"x\", \"floor\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Floor, inputs);\n}\nvar floor = op({ floor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather.js\nfunction gather_(x, indices, axis = 0, batchDims = 0) {\n const $x = convertToTensor(x, \"x\", \"gather\");\n const $indices = convertToTensor(indices, \"indices\", \"gather\", \"int32\");\n const inputs = { x: $x, indices: $indices };\n const attrs = { axis, batchDims };\n return ENGINE.runKernel(GatherV2, inputs, attrs);\n}\nvar gather = op({ gather_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater.js\nfunction greater_(a, b) {\n let $a = convertToTensor(a, \"a\", \"greater\", \"string_or_numeric\");\n let $b = convertToTensor(b, \"b\", \"greater\", \"string_or_numeric\");\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Greater, inputs);\n}\nvar greater = op({ greater_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js\nfunction greaterEqual_(a, b) {\n let $a = convertToTensor(a, \"a\", \"greaterEqual\", \"string_or_numeric\");\n let $b = convertToTensor(b, \"b\", \"greaterEqual\", \"string_or_numeric\");\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(GreaterEqual, inputs);\n}\nvar greaterEqual = op({ greaterEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/imag.js\nfunction imag_(input2) {\n const $input = convertToTensor(input2, \"input\", \"imag\");\n const inputs = { input: $input };\n return ENGINE.runKernel(Imag, inputs);\n}\nvar imag = op({ imag_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js\nfunction isFinite_(x) {\n const $x = convertToTensor(x, \"x\", \"isFinite\");\n const inputs = { x: $x };\n return ENGINE.runKernel(IsFinite, inputs);\n}\nvar isFinite2 = op({ isFinite_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js\nfunction isInf_(x) {\n const $x = convertToTensor(x, \"x\", \"isInf\");\n const inputs = { x: $x };\n return ENGINE.runKernel(IsInf, inputs);\n}\nvar isInf = op({ isInf_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js\nfunction isNaN_(x) {\n const $x = convertToTensor(x, \"x\", \"isNaN\");\n const inputs = { x: $x };\n return ENGINE.runKernel(IsNan, inputs);\n}\nvar isNaN2 = op({ isNaN_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js\nfunction leakyRelu_(x, alpha = 0.2) {\n const $x = convertToTensor(x, \"x\", \"leakyRelu\");\n const inputs = { x: $x };\n const attrs = { alpha };\n return ENGINE.runKernel(LeakyRelu, inputs, attrs);\n}\nvar leakyRelu = op({ leakyRelu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/less.js\nfunction less_(a, b) {\n let $a = convertToTensor(a, \"a\", \"less\", \"string_or_numeric\");\n let $b = convertToTensor(b, \"b\", \"less\", \"string_or_numeric\");\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Less, inputs);\n}\nvar less = op({ less_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js\nfunction lessEqual_(a, b) {\n let $a = convertToTensor(a, \"a\", \"lessEqual\", \"string_or_numeric\");\n let $b = convertToTensor(b, \"b\", \"lessEqual\", \"string_or_numeric\");\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(LessEqual, inputs);\n}\nvar lessEqual = op({ lessEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js\nfunction linspace(start, stop, num) {\n if (num <= 0) {\n throw new Error(\"The number of values should be positive.\");\n }\n const attrs = { start, stop, num };\n return ENGINE.runKernel(LinSpace, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n const $x = convertToTensor(x, \"x\", \"localResponseNormalization\");\n assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n rank ${$x.rank}.`);\n assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n const inputs = { x: x4D };\n const attrs = { depthRadius, bias, alpha, beta };\n const res = ENGINE.runKernel(LRN, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n } else {\n return res;\n }\n}\nvar localResponseNormalization = op({ localResponseNormalization_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/log.js\nfunction log_(x) {\n const $x = convertToTensor(x, \"x\", \"log\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Log, inputs);\n}\nvar log2 = op({ log_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js\nfunction log1p_(x) {\n const $x = convertToTensor(x, \"x\", \"log1p\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Log1p, inputs);\n}\nvar log1p = op({ log1p_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients.js\nfunction grad(f) {\n assert(isFunction(f), () => \"The f passed in grad(f) must be a function\");\n return (x, dy) => {\n const $x = convertToTensor(x, \"x\", \"tf.grad\", \"string_or_numeric\");\n const $dy = dy != null ? convertToTensor(dy, \"dy\", \"tf.grad\") : null;\n return ENGINE.tidy(() => {\n const { value, grads: grads2 } = ENGINE.gradients(() => f($x), [$x], $dy);\n if ($dy != null) {\n assertShapesMatch(value.shape, $dy.shape, \"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)\");\n }\n checkGrads(grads2);\n return grads2[0];\n });\n };\n}\nfunction grads(f) {\n assert(isFunction(f), () => \"The f passed in grads(f) must be a function\");\n return (args, dy) => {\n assert(Array.isArray(args), () => \"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s\");\n const $args = convertToTensorArray(args, \"args\", \"tf.grads\", \"string_or_numeric\");\n const $dy = dy != null ? convertToTensor(dy, \"dy\", \"tf.grads\") : null;\n return ENGINE.tidy(() => {\n const { value, grads: grads2 } = ENGINE.gradients(() => f(...$args), $args, $dy);\n if ($dy != null) {\n assertShapesMatch(value.shape, $dy.shape, \"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])\");\n }\n checkGrads(grads2);\n return grads2;\n });\n };\n}\nfunction valueAndGrad(f) {\n assert(isFunction(f), () => \"The f passed in valueAndGrad(f) must be a function\");\n return (x, dy) => {\n assert(x instanceof Tensor, () => \"The x passed in valueAndGrad(f)(x) must be a tensor\");\n assert(dy == null || dy instanceof Tensor, () => \"The dy passed in valueAndGrad(f)(x, dy) must be a tensor\");\n const { grads: grads2, value } = ENGINE.gradients(() => f(x), [x], dy);\n checkGrads(grads2);\n return { grad: grads2[0], value };\n };\n}\nfunction valueAndGrads(f) {\n assert(isFunction(f), () => \"The f passed in valueAndGrads(f) must be a function\");\n return (args, dy) => {\n assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => \"The args passed in valueAndGrads(f)(args) must be array of tensors\");\n assert(dy == null || dy instanceof Tensor, () => \"The dy passed in valueAndGrads(f)(args, dy) must be a tensor\");\n const res = ENGINE.gradients(() => f(...args), args, dy);\n if (dy != null) {\n assertShapesMatch(res.value.shape, dy.shape, \"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])\");\n }\n checkGrads(res.grads);\n return res;\n };\n}\nfunction variableGrads(f, varList) {\n assert(isFunction(f), () => \"The f passed in variableGrads(f) must be a function\");\n assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => \"The varList passed in variableGrads(f, varList) must be an array of variables\");\n const specifiedVarList = varList != null;\n if (!specifiedVarList) {\n varList = [];\n for (const varName in ENGINE.registeredVariables) {\n varList.push(ENGINE.registeredVariables[varName]);\n }\n }\n const specifiedNonTrainable = specifiedVarList ? varList.filter((variable2) => !variable2.trainable) : null;\n const originalVarCount = varList.length;\n varList = varList.filter((variable2) => variable2.trainable);\n assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);\n const allowNoGradients = true;\n const { value, grads: grads2 } = ENGINE.gradients(f, varList, null, allowNoGradients);\n assert(grads2.some((g) => g != null), () => \"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().\");\n assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);\n const namedGrads = {};\n varList.forEach((v, i) => {\n if (grads2[i] != null) {\n namedGrads[v.name] = grads2[i];\n }\n });\n if (specifiedNonTrainable != null) {\n specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);\n }\n return { value, grads: namedGrads };\n}\nfunction customGrad(f) {\n return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads2) {\n const numNullGradients = grads2.filter((g) => g == null).length;\n if (numNullGradients > 0) {\n throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n the f you passed encloses all operations that lead from x to y.`);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/neg.js\nfunction neg_(x) {\n const $x = convertToTensor(x, \"x\", \"neg\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Neg, inputs);\n}\nvar neg = op({ neg_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js\nfunction softplus_(x) {\n const $x = convertToTensor(x, \"x\", \"softplus\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Softplus, inputs);\n}\nvar softplus = op({ softplus_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js\nfunction logSigmoid_(x) {\n const $x = convertToTensor(x, \"x\", \"logSigmoid\");\n const customOp = customGrad((x2) => {\n const value = neg(softplus(neg(x2)));\n const gradFunc = (dy) => {\n const derX = mul(dy, sigmoid(neg(x2)));\n return derX;\n };\n return { value, gradFunc };\n });\n return customOp($x);\n}\nvar logSigmoid = op({ logSigmoid_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sub.js\nfunction sub_(a, b) {\n let $a = convertToTensor(a, \"a\", \"sub\");\n let $b = convertToTensor(b, \"b\", \"sub\");\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Sub, inputs);\n}\nvar sub = op({ sub_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js\nfunction logSoftmax_(logits, axis = -1) {\n const $logits = convertToTensor(logits, \"logits\", \"logSoftmax\");\n if (axis === -1) {\n axis = $logits.rank - 1;\n }\n if (axis !== $logits.rank - 1) {\n throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);\n }\n const customOp = customGrad((logits2, save) => {\n const keepDims = true;\n const xMax = max(logits2, axis, true);\n const shifted = sub(logits2, xMax);\n const value = sub(cast(shifted, \"float32\"), log2(sum2(exp(shifted), axis, keepDims)));\n save([value]);\n const gradFunc = (dy, saved) => {\n const [value2] = saved;\n const keepDims2 = true;\n const softmax6 = exp(value2);\n return sub(dy, mul(sum2(dy, axis, keepDims2), softmax6));\n };\n return { value, gradFunc };\n });\n return customOp($logits);\n}\nvar logSoftmax = op({ logSoftmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js\nfunction logSumExp_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, \"x\", \"logSumExp\");\n const axes = parseAxisParam(axis, $x.shape);\n const xMax = max(\n $x,\n axes,\n true\n /* keepDims */\n );\n const a = sub($x, xMax);\n const b = exp(a);\n const c = sum2(b, axes);\n const d = log2(c);\n const res = add2(reshape(xMax, d.shape), d);\n if (keepDims) {\n const newShape = expandShapeToKeepDim(res.shape, axes);\n return reshape(res, newShape);\n }\n return res;\n}\nvar logSumExp = op({ logSumExp_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js\nfunction logicalAnd_(a, b) {\n const $a = convertToTensor(a, \"a\", \"logicalAnd\", \"bool\");\n const $b = convertToTensor(b, \"b\", \"logicalAnd\", \"bool\");\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(LogicalAnd, inputs);\n}\nvar logicalAnd = op({ logicalAnd_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js\nfunction logicalNot_(x) {\n const $x = convertToTensor(x, \"x\", \"logicalNot\", \"bool\");\n const inputs = { x: $x };\n return ENGINE.runKernel(LogicalNot, inputs);\n}\nvar logicalNot = op({ logicalNot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js\nfunction logicalOr_(a, b) {\n const $a = convertToTensor(a, \"a\", \"logicalOr\", \"bool\");\n const $b = convertToTensor(b, \"b\", \"logicalOr\", \"bool\");\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(LogicalOr, inputs);\n}\nvar logicalOr = op({ logicalOr_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js\nfunction logicalXor_(a, b) {\n const $a = convertToTensor(a, \"a\", \"logicalXor\", \"bool\");\n const $b = convertToTensor(b, \"b\", \"logicalXor\", \"bool\");\n assertAndGetBroadcastShape($a.shape, $b.shape);\n return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nvar logicalXor = op({ logicalXor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/search_sorted.js\nvar INT32_MAX = 2147483648;\nfunction searchSorted_(sortedSequence, values, side = \"left\") {\n const $sortedSequence = convertToTensor(sortedSequence, \"sortedSequence\", \"searchSorted\");\n const $values = convertToTensor(values, \"values\", \"searchSorted\");\n const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1];\n const valuesSize = $values.shape[$values.shape.length - 1];\n const $sortedSequence2D = reshape($sortedSequence, [-1, sequenceSize]);\n const $values2D = reshape($values, [-1, valuesSize]);\n if ($sortedSequence2D.rank < 2) {\n throw new Error(`Sorted input argument must be at least 2-dimensional`);\n }\n if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) {\n throw new Error(`Leading dimension of 'sortedSequence' and 'values' must match.`);\n }\n if (sizeFromShape($values2D.shape) >= INT32_MAX) {\n throw new Error(`values tensor size must less than ${INT32_MAX}`);\n }\n if ($sortedSequence2D.shape[1] >= INT32_MAX) {\n throw new Error(`trailing dim_size must less than ${INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`);\n }\n const inputs = {\n sortedSequence: $sortedSequence2D,\n values: $values2D\n };\n const attrs = { side };\n return ENGINE.runKernel(SearchSorted, inputs, attrs);\n}\nvar searchSorted = op({ searchSorted_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/lower_bound.js\nfunction lowerBound(sortedSequence, values) {\n return searchSorted(sortedSequence, values, \"left\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js\nfunction maxPool_(x, filterSize, strides, pad3, dimRoundingMode) {\n const $x = convertToTensor(x, \"x\", \"maxPool\");\n const dilations = 1;\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n checkPadOnDimRoundingMode(\"maxPool\", pad3, dimRoundingMode);\n const inputs = { x: x4D };\n const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n const res = ENGINE.runKernel(MaxPool, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar maxPool = op({ maxPool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad3, dimRoundingMode, dataFormat = \"NDHWC\") {\n const $x = convertToTensor(x, \"x\", \"maxPool3d\");\n let x5D = $x;\n let reshapedTo5D = false;\n if ($x.rank === 4) {\n reshapedTo5D = true;\n x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n }\n assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n assert(dataFormat === \"NDHWC\", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);\n checkPadOnDimRoundingMode(\"maxPool3d\", pad3, dimRoundingMode);\n const inputs = { x: x5D };\n const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat };\n const res = ENGINE.runKernel(MaxPool3D, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nvar maxPool3d = op({ maxPool3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad3, includeBatchInIndex = false) {\n const $x = convertToTensor(x, \"x\", \"maxPoolWithArgmax\");\n const inputs = { x: $x };\n const attrs = { filterSize, strides, pad: pad3, includeBatchInIndex };\n const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n return { result: result[0], indexes: result[1] };\n}\nvar maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js\nfunction maximum_(a, b) {\n let $a = convertToTensor(a, \"a\", \"maximum\");\n let $b = convertToTensor(b, \"b\", \"maximum\");\n [$a, $b] = makeTypesMatch($a, $b);\n if ($a.dtype === \"bool\") {\n $a = cast($a, \"int32\");\n $b = cast($b, \"int32\");\n }\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Maximum, inputs);\n}\nvar maximum = op({ maximum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/mean.js\nfunction mean_(x, axis = null, keepDims = false) {\n const $x = convertToTensor(x, \"x\", \"mean\");\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Mean, inputs, attrs);\n}\nvar mean = op({ mean_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js\nfunction zeros(shape, dtype = \"float32\") {\n assertNonNegativeIntegerDimensions(shape);\n if (dtype === \"complex64\") {\n const real4 = zeros(shape, \"float32\");\n const imag4 = zeros(shape, \"float32\");\n return complex(real4, imag4);\n }\n const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones.js\nfunction ones2(shape, dtype = \"float32\") {\n assertNonNegativeIntegerDimensions(shape);\n if (dtype === \"complex64\") {\n const real4 = ones2(shape, \"float32\");\n const imag4 = zeros(shape, \"float32\");\n return complex(real4, imag4);\n }\n const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/meshgrid.js\nfunction meshgrid(x, y, { indexing = \"xy\" } = {}) {\n if (indexing !== \"xy\" && indexing !== \"ij\") {\n throw new TypeError(`${indexing} is not a valid third argument to meshgrid`);\n }\n if (x === void 0) {\n return [];\n }\n let $x = convertToTensor(x, \"x\", \"meshgrid\", x instanceof Tensor ? x.dtype : \"float32\");\n if (y === void 0) {\n return [$x];\n }\n let $y = convertToTensor(y, \"y\", \"meshgrid\", y instanceof Tensor ? y.dtype : \"float32\");\n const w = sizeFromShape($x.shape);\n const h = sizeFromShape($y.shape);\n if (indexing === \"xy\") {\n $x = reshape($x, [1, -1]);\n $y = reshape($y, [-1, 1]);\n return [\n matMul(ones2([h, 1], $x.dtype), $x),\n matMul($y, ones2([1, w], $y.dtype))\n ];\n }\n $x = reshape($x, [-1, 1]);\n $y = reshape($y, [1, -1]);\n return [\n matMul($x, ones2([1, h], $x.dtype)),\n matMul(ones2([w, 1], $y.dtype), $y)\n ];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js\nfunction minimum_(a, b) {\n let $a = convertToTensor(a, \"a\", \"minimum\");\n let $b = convertToTensor(b, \"b\", \"minimum\");\n [$a, $b] = makeTypesMatch($a, $b);\n if ($a.dtype === \"bool\") {\n $a = cast($a, \"int32\");\n $b = cast($b, \"int32\");\n }\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Minimum, inputs);\n}\nvar minimum = op({ minimum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/mirror_pad.js\nfunction mirrorPad_(x, paddings, mode) {\n assert(mode === \"reflect\" || mode === \"symmetric\", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);\n const $x = convertToTensor(x, \"x\", \"mirrorPad\");\n if ($x.rank === 0) {\n throw new Error(\"mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad\");\n }\n assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);\n const shapeOffset = mode === \"reflect\" ? 1 : 0;\n for (let i = 0; i < $x.rank; i++) {\n assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);\n assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset && paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);\n }\n const attrs = { paddings, mode };\n const inputs = { x: $x };\n return ENGINE.runKernel(MirrorPad, inputs, attrs);\n}\nvar mirrorPad = op({ mirrorPad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/mod.js\nfunction mod_(a, b) {\n let $a = convertToTensor(a, \"a\", \"mod\");\n let $b = convertToTensor(b, \"b\", \"mod\");\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(Mod, inputs);\n}\nvar mod = op({ mod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/moments.js\nfunction moments_(x, axis = null, keepDims = false) {\n x = convertToTensor(x, \"x\", \"moments\");\n const axes = parseAxisParam(axis, x.shape);\n const xMean = mean(x, axes, keepDims);\n let keepDimsShape = xMean.shape;\n if (!keepDims) {\n keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n }\n const devSquared = square(sub(cast(x, \"float32\"), reshape(xMean, keepDimsShape)));\n const variance = mean(devSquared, axes, keepDims);\n return { mean: xMean, variance };\n}\nvar moments = op({ moments_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js\nfunction multiRNNCell_(lstmCells, data, c, h) {\n const $data = convertToTensor(data, \"data\", \"multiRNNCell\");\n const $c = convertToTensorArray(c, \"c\", \"multiRNNCell\");\n const $h = convertToTensorArray(h, \"h\", \"multiRNNCell\");\n let input2 = $data;\n const newStates = [];\n for (let i = 0; i < lstmCells.length; i++) {\n const output = lstmCells[i](input2, $c[i], $h[i]);\n newStates.push(output[0]);\n newStates.push(output[1]);\n input2 = output[1];\n }\n const newC = [];\n const newH = [];\n for (let i = 0; i < newStates.length; i += 2) {\n newC.push(newStates[i]);\n newH.push(newStates[i + 1]);\n }\n return [newC, newH];\n}\nvar multiRNNCell = op({ multiRNNCell_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n const $logits = convertToTensor(logits, \"logits\", \"multinomial\");\n const numOutcomes = $logits.size;\n const origRank = $logits.rank;\n if (numOutcomes < 2) {\n throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);\n }\n if (origRank > 2) {\n throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n }\n seed = seed || Math.random();\n const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n const inputs = { logits: logits2D };\n const attrs = { numSamples, seed, normalized };\n const res = ENGINE.runKernel(Multinomial, inputs, attrs);\n return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nvar multinomial = op({ multinomial_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js\nfunction notEqual_(a, b) {\n let $a = convertToTensor(a, \"a\", \"notEqual\", \"string_or_numeric\");\n let $b = convertToTensor(b, \"b\", \"notEqual\", \"string_or_numeric\");\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n return ENGINE.runKernel(NotEqual, inputs);\n}\nvar notEqual = op({ notEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0, dtype = \"int32\") {\n if (depth < 2) {\n throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n }\n const $indices = convertToTensor(indices, \"indices\", \"oneHot\", \"int32\");\n const inputs = { indices: $indices };\n const attrs = { dtype, depth, onValue, offValue };\n return ENGINE.runKernel(OneHot, inputs, attrs);\n}\nvar oneHot = op({ oneHot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js\nfunction onesLike_(x) {\n const $x = convertToTensor(x, \"x\", \"onesLike\");\n const inputs = { x: $x };\n return ENGINE.runKernel(OnesLike, inputs);\n}\nvar onesLike = op({ onesLike_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js\nfunction outerProduct_(v1, v2) {\n const $v1 = convertToTensor(v1, \"v1\", \"outerProduct\");\n const $v2 = convertToTensor(v2, \"v2\", \"outerProduct\");\n assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);\n const v12D = reshape($v1, [-1, 1]);\n const v22D = reshape($v2, [1, -1]);\n return matMul(v12D, v22D);\n}\nvar outerProduct = op({ outerProduct_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad.js\nfunction pad_(x, paddings, constantValue = 0) {\n const $x = convertToTensor(x, \"x\", \"pad\");\n if ($x.rank === 0) {\n throw new Error(\"pad(scalar) is not defined. Pass non-scalar to pad\");\n }\n const attrs = { paddings, constantValue };\n const inputs = { x: $x };\n return ENGINE.runKernel(PadV2, inputs, attrs);\n}\nvar pad = op({ pad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js\nfunction pad1d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 2, () => \"Invalid number of paddings. Must be length of 2.\");\n return pad(x, [paddings], constantValue);\n}\nvar pad1d = op({ pad1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js\nfunction pad2d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n return pad(x, paddings, constantValue);\n}\nvar pad2d = op({ pad2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js\nfunction pad3d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n return pad(x, paddings, constantValue);\n}\nvar pad3d = op({ pad3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js\nfunction pad4d_(x, paddings, constantValue = 0) {\n assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n return pad(x, paddings, constantValue);\n}\nvar pad4d = op({ pad4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js\nfunction spaceToBatchND_(x, blockShape, paddings) {\n const $x = convertToTensor(x, \"x\", \"spaceToBatchND\");\n assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n assert($x.shape.reduce((a, b, i) => {\n if (i > 0 && i <= blockShape.length) {\n return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;\n }\n return a;\n }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n const inputs = { x: $x };\n const attrs = { blockShape, paddings };\n return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);\n}\nvar spaceToBatchND = op({ spaceToBatchND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js\nfunction pool_(input2, windowShape, poolingType, pad3, dilations, strides, dimRoundingMode) {\n if (dilations == null) {\n dilations = [1, 1];\n }\n if (strides == null) {\n strides = 1;\n }\n if (pad3 === 0) {\n pad3 = \"valid\";\n }\n const $x = convertToTensor(input2, \"x\", \"maxPool\");\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad3);\n const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n let basePadding;\n if (pad3 === \"same\") {\n basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n } else {\n basePadding = [[0, 0], [0, 0]];\n }\n const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n const convertedPad = isDilationOne ? pad3 : \"valid\";\n const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n const forwardOp = poolingType === \"avg\" ? () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode);\n const y = forwardOp();\n const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n const padStart = basePadding.map((b) => b[0]);\n const origPadEnd = basePadding.map((b) => b[1]);\n const fullInputShape = inputShape.concat(padStart, origPadEnd);\n const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n return [paddings, crops];\n}\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n const dilatedFilterShape = filterShape.map((s, i) => {\n return s + (s - 1) * (dilation[i] - 1);\n });\n const padExtraShape = dilatedFilterShape.map((s) => s - 1);\n const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2));\n const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n return padExtraShape.map((_, i) => {\n return [padExtraStart[i], padExtraEnd[i]];\n });\n}\nvar pool = op({ pool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js\nfunction prelu_(x, alpha) {\n const $x = convertToTensor(x, \"x\", \"prelu\");\n const $alpha = convertToTensor(alpha, \"alpha\", \"prelu\");\n const inputs = { x: $x, alpha: $alpha };\n return ENGINE.runKernel(Prelu, inputs);\n}\nvar prelu = op({ prelu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/prod.js\nfunction prod_(x, axis = null, keepDims = false) {\n let $x = convertToTensor(x, \"x\", \"prod\");\n if ($x.dtype === \"bool\") {\n $x = cast($x, \"int32\");\n }\n const inputs = { x: $x };\n const attrs = { axis, keepDims };\n return ENGINE.runKernel(Prod, inputs, attrs);\n}\nvar prod = op({ prod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_gather.js\nfunction raggedGather_(paramsNestedSplits, paramsDenseValues, indices, outputRaggedRank) {\n const $paramsNestedSplits = paramsNestedSplits.map((t, i) => convertToTensor(t, `tensors${i}`, \"raggedGather\", \"int32\"));\n const $paramsDenseValues = convertToTensor(paramsDenseValues, \"paramsDenseValues\", \"raggedGather\");\n const $indices = convertToTensor(indices, \"indices\", \"raggedGather\", \"int32\");\n const inputs = {\n paramsNestedSplits: $paramsNestedSplits,\n paramsDenseValues: $paramsDenseValues,\n indices: $indices\n };\n const attrs = { outputRaggedRank };\n const result = ENGINE.runKernel(RaggedGather, inputs, attrs);\n return {\n outputNestedSplits: result.slice(0, result.length - 1),\n outputDenseValues: result[result.length - 1]\n };\n}\nvar raggedGather = op({ raggedGather_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_range.js\nfunction raggedRange_(starts, limits, deltas) {\n const $starts = convertToTensor(starts, \"starts\", \"raggedRange\");\n const $limits = convertToTensor(limits, \"limits\", \"raggedRange\", $starts.dtype);\n const $deltas = convertToTensor(deltas, \"deltas\", \"raggedRange\", $starts.dtype);\n const inputs = {\n starts: $starts,\n limits: $limits,\n deltas: $deltas\n };\n const result = ENGINE.runKernel(RaggedRange, inputs);\n return {\n rtNestedSplits: result[0],\n rtDenseValues: result[1]\n };\n}\nvar raggedRange = op({ raggedRange_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_tensor_to_tensor.js\nfunction raggedTensorToTensor_(shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes) {\n const $shape = convertToTensor(shape, \"shape\", \"raggedTensorToTensor\", \"int32\");\n const $values = convertToTensor(values, \"values\", \"raggedTensorToTensor\");\n const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"raggedTensorToTensor\", $values.dtype);\n const $rowPartitionTensors = rowPartitionTensors.map((t, i) => convertToTensor(t, `tensors${i}`, \"raggedTensorToTensor\", \"int32\"));\n const inputs = {\n shape: $shape,\n values: $values,\n defaultValue: $defaultValue,\n rowPartitionTensors: $rowPartitionTensors\n };\n const attrs = { rowPartitionTypes };\n return ENGINE.runKernel(RaggedTensorToTensor, inputs, attrs);\n}\nvar raggedTensorToTensor = op({ raggedTensorToTensor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js\nfunction rand_(shape, randFunction, dtype) {\n assertNonNegativeIntegerDimensions(shape);\n const size = sizeFromShape(shape);\n let values = null;\n if (dtype == null || dtype === \"float32\") {\n values = new Float32Array(size);\n } else if (dtype === \"int32\") {\n values = new Int32Array(size);\n } else if (dtype === \"bool\") {\n values = new Uint8Array(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n for (let i = 0; i < size; i++) {\n values[i] = randFunction();\n }\n return ENGINE.makeTensor(values, shape, dtype);\n}\nvar rand = op({ rand_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js\nvar seedrandom = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/test_util.js\nvar test_util_exports = {};\n__export(test_util_exports, {\n TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,\n createVideoElement: () => createVideoElement,\n encodeStrings: () => encodeStrings,\n expectArrayBuffersEqual: () => expectArrayBuffersEqual,\n expectArraysClose: () => expectArraysClose,\n expectArraysEqual: () => expectArraysEqual,\n expectNumbersClose: () => expectNumbersClose,\n expectPromiseToFail: () => expectPromiseToFail,\n expectValuesInRange: () => expectValuesInRange,\n play: () => play,\n testEpsilon: () => testEpsilon\n});\nvar TEST_EPSILON_FLOAT32 = 1e-3;\nvar TEST_EPSILON_FLOAT16 = 0.1;\nfunction expectArraysClose(actual, expected, epsilon3) {\n if (epsilon3 == null) {\n epsilon3 = testEpsilon();\n }\n return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon3));\n}\nfunction testEpsilon() {\n return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n let checkClassType = true;\n if (isTypedArray(actual) || isTypedArray(expected)) {\n checkClassType = false;\n }\n if (isTypedArray(actual) && isTypedArray(expected)) {\n checkClassType = true;\n }\n if (checkClassType) {\n const aType = actual.constructor.name;\n const bType = expected.constructor.name;\n if (aType !== bType) {\n throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);\n }\n }\n if (Array.isArray(actual) && Array.isArray(expected)) {\n const actualShape = inferShape(actual);\n const expectedShape = inferShape(expected);\n if (!arraysEqual(actualShape, expectedShape)) {\n throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n }\n }\n const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);\n if (actualFlat.length !== expectedFlat.length) {\n throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.\nActual: ${actualFlat}.\nExpected: ${expectedFlat}.`);\n }\n for (let i = 0; i < expectedFlat.length; ++i) {\n const a = actualFlat[i];\n const e = expectedFlat[i];\n if (!predicate(a, e)) {\n throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\nActual: ${actualFlat}.\nExpected: ${expectedFlat}.`);\n }\n }\n if (typeof expect !== \"undefined\") {\n expect().nothing();\n }\n}\nfunction expectPromiseToFail(fn, done) {\n fn().then(() => done.fail(), () => done());\n if (typeof expect !== \"undefined\") {\n expect().nothing();\n }\n}\nfunction expectArraysEqual(actual, expected) {\n const exp4 = typeof expected === \"string\" || typeof expected === \"number\" || typeof expected === \"boolean\" ? [expected] : expected;\n if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {\n return expectArraysPredicate(actual, exp4, (a, b) => a == b);\n }\n return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nfunction expectNumbersClose(a, e, epsilon3) {\n if (epsilon3 == null) {\n epsilon3 = testEpsilon();\n }\n if (!areClose(a, e, epsilon3)) {\n throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n }\n if (typeof expect !== \"undefined\") {\n expect().nothing();\n }\n}\nfunction areClose(a, e, epsilon3) {\n if (!isFinite(a) && !isFinite(e)) {\n return true;\n }\n if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon3) {\n return false;\n }\n return true;\n}\nfunction expectValuesInRange(actual, low, high) {\n for (let i = 0; i < actual.length; i++) {\n if (actual[i] < low || actual[i] > high) {\n throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n }\n }\n}\nfunction expectArrayBuffersEqual(actual, expected) {\n const actualArray = new Float32Array(actual);\n const expectedArray = new Float32Array(expected);\n if (actualArray.length !== expectedArray.length) {\n throw new Error(`Expected ArrayBuffer to be of length ${expectedArray.length}, but it was ${actualArray.length}`);\n }\n for (let i = 0; i < expectedArray.length; i++) {\n if (actualArray[i] !== expectedArray[i]) {\n throw new Error(`Expected ArrayBuffer value at ${i} to be ${expectedArray[i]} but got ${actualArray[i]} instead`);\n }\n }\n}\nfunction encodeStrings(a) {\n for (let i = 0; i < a.length; i++) {\n const val = a[i];\n if (Array.isArray(val)) {\n encodeStrings(val);\n } else {\n a[i] = encodeString(val);\n }\n }\n return a;\n}\nfunction createVideoElement(source) {\n const video = document.createElement(\"video\");\n if (\"playsInline\" in video) {\n video.playsInline = true;\n }\n video.muted = true;\n video.loop = true;\n video.style.position = \"fixed\";\n video.style.left = \"0px\";\n video.style.top = \"0px\";\n video.preload = \"auto\";\n video.appendChild(source);\n return new Promise((resolve) => {\n video.addEventListener(\"loadeddata\", (_) => resolve(video));\n video.load();\n });\n}\nasync function play(video) {\n await video.play();\n if (\"requestVideoFrameCallback\" in video) {\n await new Promise((resolve) => {\n video.requestVideoFrameCallback(resolve);\n });\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js\nvar MPRandGauss = class {\n constructor(mean4, stdDeviation, dtype, truncated, seed) {\n this.mean = mean4;\n this.stdDev = stdDeviation;\n this.dtype = dtype;\n this.nextVal = NaN;\n this.truncated = truncated;\n if (this.truncated) {\n this.upper = this.mean + this.stdDev * 2;\n this.lower = this.mean - this.stdDev * 2;\n }\n const seedValue = seed ? seed : Math.random();\n this.random = seedrandom.alea(seedValue.toString());\n }\n /** Returns next sample from a Gaussian distribution. */\n nextValue() {\n if (!isNaN(this.nextVal)) {\n const value = this.nextVal;\n this.nextVal = NaN;\n return value;\n }\n let resultX, resultY;\n let isValid = false;\n while (!isValid) {\n let v1, v2, s;\n do {\n v1 = 2 * this.random() - 1;\n v2 = 2 * this.random() - 1;\n s = v1 * v1 + v2 * v2;\n } while (s >= 1 || s === 0);\n const mul2 = Math.sqrt(-2 * Math.log(s) / s);\n resultX = this.mean + this.stdDev * v1 * mul2;\n resultY = this.mean + this.stdDev * v2 * mul2;\n if (!this.truncated || this.isValidTruncated(resultX)) {\n isValid = true;\n }\n }\n if (!this.truncated || this.isValidTruncated(resultY)) {\n this.nextVal = this.convertValue(resultY);\n }\n return this.convertValue(resultX);\n }\n /** Handles proper rounding for non-floating-point numbers. */\n convertValue(value) {\n if (this.dtype == null || this.dtype === \"float32\") {\n return value;\n }\n return Math.round(value);\n }\n /** Returns true if less than 2-standard-deviations from the mean. */\n isValidTruncated(value) {\n return value <= this.upper && value >= this.lower;\n }\n};\nvar RandGamma = class {\n constructor(alpha, beta, dtype, seed) {\n this.alpha = alpha;\n this.beta = 1 / beta;\n this.dtype = dtype;\n const seedValue = seed ? seed : Math.random();\n this.randu = seedrandom.alea(seedValue.toString());\n this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n if (alpha < 1) {\n this.d = alpha + 2 / 3;\n } else {\n this.d = alpha - 1 / 3;\n }\n this.c = 1 / Math.sqrt(9 * this.d);\n }\n /** Returns next sample from a gamma distribution. */\n nextValue() {\n let x2, v0, v1, x, u, v;\n while (true) {\n do {\n x = this.randn.nextValue();\n v = 1 + this.c * x;\n } while (v <= 0);\n v *= v * v;\n x2 = x * x;\n v0 = 1 - 0.331 * x2 * x2;\n v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));\n u = this.randu();\n if (u < v0 || Math.log(u) < v1) {\n break;\n }\n }\n v = 1 / this.beta * this.d * v;\n if (this.alpha < 1) {\n v *= Math.pow(this.randu(), 1 / this.alpha);\n }\n return this.convertValue(v);\n }\n /** Handles proper rounding for non-floating-point numbers. */\n convertValue(value) {\n if (this.dtype === \"float32\") {\n return value;\n }\n return Math.round(value);\n }\n};\nvar UniformRandom = class {\n constructor(min6 = 0, max6 = 1, dtype, seed) {\n this.canReturnFloat = () => this.dtype == null || this.dtype === \"float32\";\n this.min = min6;\n this.range = max6 - min6;\n this.dtype = dtype;\n if (seed == null) {\n seed = Math.random();\n }\n if (typeof seed === \"number\") {\n seed = seed.toString();\n }\n if (!this.canReturnFloat() && this.range <= 1) {\n throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);\n }\n this.random = seedrandom.alea(seed);\n }\n convertValue(value) {\n if (this.canReturnFloat()) {\n return value;\n }\n return Math.round(value);\n }\n nextValue() {\n return this.convertValue(this.min + this.range * this.random());\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js\nfunction randomGamma_(shape, alpha, beta = 1, dtype = \"float32\", seed) {\n assertNonNegativeIntegerDimensions(shape);\n if (beta == null) {\n beta = 1;\n }\n if (dtype == null) {\n dtype = \"float32\";\n }\n if (dtype !== \"float32\" && dtype !== \"int32\") {\n throw new Error(`Unsupported data type ${dtype}`);\n }\n const rgamma = new RandGamma(alpha, beta, dtype, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = rgamma.nextValue();\n }\n return res.toTensor();\n}\nvar randomGamma = op({ randomGamma_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js\nfunction randomNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) {\n assertNonNegativeIntegerDimensions(shape);\n if (dtype != null && dtype === \"bool\") {\n throw new Error(`Unsupported data type ${dtype}`);\n }\n const randGauss = new MPRandGauss(mean4, stdDev, dtype, false, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = randGauss.nextValue();\n }\n return res.toTensor();\n}\nvar randomNormal = op({ randomNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_standard_normal.js\nfunction randomStandardNormal_(shape, dtype, seed) {\n if (dtype != null && dtype === \"bool\") {\n throw new Error(`Unsupported data type ${dtype}`);\n }\n return randomNormal(shape, 0, 1, dtype, seed);\n}\nvar randomStandardNormal = op({ randomStandardNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = \"float32\", seed) {\n assertNonNegativeIntegerDimensions(shape);\n const res = buffer(shape, dtype);\n const random = new UniformRandom(minval, maxval, null, seed);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = random.nextValue();\n }\n return res.toTensor();\n}\nvar randomUniform = op({ randomUniform_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform_int.js\nfunction randomUniformInt_(shape, minval, maxval, seed) {\n return randomUniform(shape, minval, maxval, \"int32\", seed);\n}\nvar randomUniformInt = op({ randomUniformInt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/range.js\nfunction range(start, stop, step5 = 1, dtype = \"float32\") {\n if (step5 === 0) {\n throw new Error(\"Cannot have a step of zero\");\n }\n const attrs = { start, stop, step: step5, dtype };\n return ENGINE.runKernel(Range, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/real.js\nfunction real_(input2) {\n const $input = convertToTensor(input2, \"input\", \"real\");\n const inputs = { input: $input };\n return ENGINE.runKernel(Real, inputs);\n}\nvar real = op({ real_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js\nfunction reciprocal_(x) {\n const $x = convertToTensor(x, \"x\", \"reciprocal\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Reciprocal, inputs);\n}\nvar reciprocal = op({ reciprocal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu.js\nfunction relu_(x) {\n const $x = convertToTensor(x, \"x\", \"relu\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Relu, inputs);\n}\nvar relu = op({ relu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js\nfunction relu6_(x) {\n const $x = convertToTensor(x, \"x\", \"relu6\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Relu6, inputs);\n}\nvar relu6 = op({ relu6_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js\nfunction reverse_(x, axis) {\n const $x = convertToTensor(x, \"x\", \"reverse\");\n const inputs = { x: $x };\n const attrs = { dims: axis };\n return ENGINE.runKernel(Reverse, inputs, attrs);\n}\nvar reverse = op({ reverse_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js\nfunction reverse1d_(x) {\n const $x = convertToTensor(x, \"x\", \"reverse\");\n assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n return reverse($x, 0);\n}\nvar reverse1d = op({ reverse1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js\nfunction reverse2d_(x, axis) {\n const $x = convertToTensor(x, \"x\", \"reverse\");\n assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n return reverse($x, axis);\n}\nvar reverse2d = op({ reverse2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js\nfunction reverse3d_(x, axis) {\n const $x = convertToTensor(x, \"x\", \"reverse\");\n assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n return reverse($x, axis);\n}\nvar reverse3d = op({ reverse3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js\nfunction reverse4d_(x, axis) {\n const $x = convertToTensor(x, \"x\", \"reverse\");\n assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n return reverse($x, axis);\n}\nvar reverse4d = op({ reverse4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/round.js\nfunction round_(x) {\n const $x = convertToTensor(x, \"x\", \"round\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Round, inputs);\n}\nvar round2 = op({ round_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js\nfunction rsqrt_(x) {\n const $x = convertToTensor(x, \"x\", \"rsqrt\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Rsqrt, inputs);\n}\nvar rsqrt = op({ rsqrt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu.js\nfunction selu_(x) {\n const $x = convertToTensor(x, \"x\", \"selu\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Selu, inputs);\n}\nvar selu = op({ selu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad3, dilation = [1, 1], dataFormat = \"NHWC\") {\n const $x = convertToTensor(x, \"x\", \"separableConv2d\");\n const $depthwiseFilter = convertToTensor(depthwiseFilter, \"depthwiseFilter\", \"separableConv2d\");\n const $pointwiseFilter = convertToTensor(pointwiseFilter, \"pointwiseFilter\", \"separableConv2d\");\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n if (dataFormat === \"NCHW\") {\n throw new Error(\"separableConv2d currently does not support dataFormat NCHW; only NHWC is supported\");\n }\n assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);\n assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);\n assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n const inChannels = $depthwiseFilter.shape[2];\n const channelMultiplier = $depthwiseFilter.shape[3];\n assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);\n const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad3, dataFormat, dilation);\n const pointwiseStride = 1;\n const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, \"valid\", dataFormat);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar separableConv2d = op({ separableConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js\nasync function setdiff1dAsync_(x, y) {\n const $x = convertToTensor(x, \"x\", \"setdiff1d\");\n const $y = convertToTensor(y, \"y\", \"setdiff1d\");\n assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n const xVals = await $x.data();\n const yVals = await $y.data();\n const ySet = new Set(yVals);\n let outputSize = 0;\n for (let i = 0; i < xVals.length; i++) {\n if (!ySet.has(xVals[i])) {\n outputSize++;\n }\n }\n const buffer2 = new TensorBuffer([outputSize], $x.dtype);\n const indices = new TensorBuffer([outputSize], \"int32\");\n for (let i = 0, p2 = 0; i < xVals.length; i++) {\n if (!ySet.has(xVals[i])) {\n buffer2.values[p2] = xVals[i];\n indices.values[p2] = i;\n p2++;\n }\n }\n return [buffer2.toTensor(), indices.toTensor()];\n}\nvar setdiff1dAsync = setdiff1dAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sign.js\nfunction sign_(x) {\n const $x = convertToTensor(x, \"x\", \"sign\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Sign, inputs);\n}\nvar sign = op({ sign_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sin.js\nfunction sin_(x) {\n const $x = convertToTensor(x, \"x\", \"sin\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Sin, inputs);\n}\nvar sin = op({ sin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js\nfunction sinh_(x) {\n const $x = convertToTensor(x, \"x\", \"sinh\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Sinh, inputs);\n}\nvar sinh = op({ sinh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js\nfunction slice1d_(x, begin, size) {\n const $x = convertToTensor(x, \"x\", \"slice1d\");\n assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, [begin], [size]);\n}\nvar slice1d = op({ slice1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js\nfunction slice2d_(x, begin, size) {\n const $x = convertToTensor(x, \"x\", \"slice2d\");\n assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\nvar slice2d = op({ slice2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js\nfunction slice3d_(x, begin, size) {\n const $x = convertToTensor(x, \"x\", \"slice3d\");\n assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\nvar slice3d = op({ slice3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js\nfunction slice4d_(x, begin, size) {\n const $x = convertToTensor(x, \"x\", \"slice4d\");\n assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\nvar slice4d = op({ slice4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js\nfunction softmax_(logits, dim = -1) {\n const $logits = convertToTensor(logits, \"logits\", \"softmax\", \"float32\");\n if (dim === -1) {\n dim = $logits.rank - 1;\n }\n if (dim !== $logits.rank - 1) {\n throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);\n }\n const inputs = { logits: $logits };\n const attrs = { dim };\n return ENGINE.runKernel(Softmax, inputs, attrs);\n}\nvar softmax = op({ softmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js\nfunction fft_(input2) {\n assert(input2.dtype === \"complex64\", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);\n const inputs = { input: input2 };\n return ENGINE.runKernel(FFT, inputs);\n}\nvar fft = op({ fft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js\nfunction ifft_(input2) {\n assert(input2.dtype === \"complex64\", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);\n const inputs = { input: input2 };\n return ENGINE.runKernel(IFFT, inputs);\n}\nvar ifft = op({ ifft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js\nfunction irfft_(input2) {\n const innerDimensionSize = input2.shape[input2.shape.length - 1];\n const batch = input2.size / innerDimensionSize;\n let ret;\n if (innerDimensionSize <= 2) {\n const complexInput = reshape(input2, [batch, innerDimensionSize]);\n ret = ifft(complexInput);\n } else {\n const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n const realInput = reshape(real(input2), [batch, innerDimensionSize]);\n const imagInput = reshape(imag(input2), [batch, innerDimensionSize]);\n const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n const r = concat([realInput, realConjugate], 1);\n const i = concat([imagInput, imagConjugate], 1);\n const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n ret = ifft(complexInput);\n }\n ret = real(ret);\n if (input2.rank === 3 && input2.shape[0] !== 0) {\n const temp = ret;\n const batch2 = input2.shape[0];\n ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);\n temp.dispose();\n }\n return ret;\n}\nvar irfft = op({ irfft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/split.js\nfunction split_(x, numOrSizeSplits, axis = 0) {\n const $x = convertToTensor(x, \"x\", \"split\");\n const inputs = { x: $x };\n const attr = { numOrSizeSplits, axis };\n return ENGINE.runKernel(SplitV, inputs, attr);\n}\nvar split = op({ split_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js\nfunction rfft_(input2, fftLength) {\n assert(input2.dtype === \"float32\", () => `The dtype for rfft() must be real value but got ${input2.dtype}`);\n let innerDimensionSize = input2.shape[input2.shape.length - 1];\n const batch = input2.size / innerDimensionSize;\n let adjustedInput;\n if (fftLength != null && fftLength < innerDimensionSize) {\n const begin = input2.shape.map((v) => 0);\n const size = input2.shape.map((v) => v);\n size[input2.shape.length - 1] = fftLength;\n adjustedInput = slice(input2, begin, size);\n innerDimensionSize = fftLength;\n } else if (fftLength != null && fftLength > innerDimensionSize) {\n const zerosShape = input2.shape.map((v) => v);\n zerosShape[input2.shape.length - 1] = fftLength - innerDimensionSize;\n adjustedInput = concat([input2, zeros(zerosShape)], input2.shape.length - 1);\n innerDimensionSize = fftLength;\n } else {\n adjustedInput = input2;\n }\n const zerosInput = zerosLike(adjustedInput);\n const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n const ret = fft(complexInput);\n const half = Math.floor(innerDimensionSize / 2) + 1;\n const realValues = real(ret);\n const imagValues = imag(ret);\n const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n const outputShape = adjustedInput.shape.slice();\n outputShape[adjustedInput.shape.length - 1] = half;\n return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nvar rfft = op({ rfft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js\nfunction squaredDifference_(a, b) {\n let $a = convertToTensor(a, \"a\", \"squaredDifference\");\n let $b = convertToTensor(b, \"b\", \"squaredDifference\");\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n const inputs = { a: $a, b: $b };\n const attrs = {};\n return ENGINE.runKernel(SquaredDifference, inputs, attrs);\n}\nvar squaredDifference = op({ squaredDifference_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js\nfunction squeeze_(x, axis) {\n const $x = convertToTensor(x, \"x\", \"squeeze\", \"string_or_numeric\");\n return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nvar squeeze = op({ squeeze_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/stack.js\nfunction stack_(tensors, axis = 0) {\n const $tensors = convertToTensorArray(tensors, \"tensors\", \"stack\", \"string_or_numeric\");\n assert($tensors.length >= 1, () => \"Pass at least one tensor to tf.stack\");\n if ($tensors.length > 0) {\n assert(axis <= $tensors[0].rank, () => \"Axis must be <= rank of the tensor\");\n }\n const inputs = $tensors;\n const attrs = { axis };\n return ENGINE.runKernel(Pack, inputs, attrs);\n}\nvar stack = op({ stack_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/step.js\nfunction step_(x, alpha = 0) {\n const $x = convertToTensor(x, \"x\", \"step\");\n const inputs = { x: $x };\n const attrs = { alpha };\n return ENGINE.runKernel(Step, inputs, attrs);\n}\nvar step = op({ step_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n const $x = convertToTensor(x, \"x\", \"stridedSlice\", \"string_or_numeric\");\n const inputs = { x: $x };\n const attrs = {\n begin,\n end,\n strides,\n beginMask,\n endMask,\n ellipsisMask,\n newAxisMask,\n shrinkAxisMask\n };\n return ENGINE.runKernel(StridedSlice, inputs, attrs);\n}\nvar stridedSlice = op({ stridedSlice_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tan.js\nfunction tan_(x) {\n const $x = convertToTensor(x, \"x\", \"tan\", \"float32\");\n const inputs = { x: $x };\n return ENGINE.runKernel(Tan, inputs);\n}\nvar tan = op({ tan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js\nfunction tensor1d(values, dtype) {\n assertNonNull(values);\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 1) {\n throw new Error(\"tensor1d() requires values to be a flat/TypedArray\");\n }\n const shape = null;\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js\nfunction tensor2d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 2) {\n throw new Error(\"tensor2d() requires shape to have two numbers\");\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n throw new Error(\"tensor2d() requires values to be number[][] or flat/TypedArray\");\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\"tensor2d() requires shape to be provided when `values` are a flat/TypedArray\");\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js\nfunction tensor3d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 3) {\n throw new Error(\"tensor3d() requires shape to have three numbers\");\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n throw new Error(\"tensor3d() requires values to be number[][][] or flat/TypedArray\");\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\"tensor3d() requires shape to be provided when `values` are a flat array\");\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js\nfunction tensor4d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 4) {\n throw new Error(\"tensor4d() requires shape to have four numbers\");\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n throw new Error(\"tensor4d() requires values to be number[][][][] or flat/TypedArray\");\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\"tensor4d() requires shape to be provided when `values` are a flat array\");\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js\nfunction tensor5d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 5) {\n throw new Error(\"tensor5d() requires shape to have five numbers\");\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n throw new Error(\"tensor5d() requires values to be number[][][][][] or flat/TypedArray\");\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\"tensor5d() requires shape to be provided when `values` are a flat array\");\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js\nfunction tensor6d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 6) {\n throw new Error(\"tensor6d() requires shape to have six numbers\");\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n throw new Error(\"tensor6d() requires values to be number[][][][][][] or flat/TypedArray\");\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\"tensor6d() requires shape to be provided when `values` are a flat array\");\n }\n shape = shape || inferredShape;\n return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js\nvar scatter_nd_util_exports = {};\n__export(scatter_nd_util_exports, {\n calculateShapes: () => calculateShapes,\n validateInput: () => validateInput,\n validateUpdateShape: () => validateUpdateShape\n});\nfunction validateUpdateShape(shape, indices, updates) {\n const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;\n const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;\n const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n if (updates.rank < batchDim) {\n throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n }\n if (shape.length < sliceDim + (updates.rank - batchDim)) {\n throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n }\n if (updates.rank !== batchDim + shape.length - sliceDim) {\n throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n }\n for (let d = 0; d < batchDim; ++d) {\n if (updates.shape[d] !== indices.shape[d]) {\n throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n }\n }\n for (let d = 0; d < updates.rank - batchDim; ++d) {\n if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n }\n }\n}\nfunction validateInput(updates, indices, shape) {\n if (indices.rank < 1) {\n throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);\n }\n if (updates.rank < 1) {\n throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);\n }\n if (indices.dtype !== \"int32\") {\n throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n }\n if (shape.length < 1) {\n throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n }\n if (shape.length === 0) {\n if (indices.size === 0) {\n throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n }\n if (updates.size === 0) {\n throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n }\n }\n validateUpdateShape(shape, indices, updates);\n}\nfunction calculateShapes(updates, indices, shape) {\n const indicesRank = indices.shape.length;\n const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;\n const totalNd = shape.length;\n let sliceSize = 1;\n for (let i = sliceRank; i < totalNd; ++i) {\n sliceSize *= shape[i];\n }\n const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;\n const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n const outputSize = sizeFromShape(shape);\n return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_scatter_update.js\nfunction tensorScatterUpdate_(tensor2, indices, updates) {\n const $tensor = convertToTensor(tensor2, \"tensor\", \"tensorScatterupdate\");\n const $indices = convertToTensor(indices, \"indices\", \"tensorScatterupdate\", \"int32\");\n const $updates = convertToTensor(updates, \"updates\", \"tensorScatterupdate\");\n validateInput($updates, $indices, $tensor.shape);\n if ($tensor.dtype !== $updates.dtype) {\n throw new Error(`tensor and updates must have the same dtype, instead they are ${$tensor.dtype} and ${$updates.dtype}.`);\n }\n const inputs = {\n tensor: $tensor,\n indices: $indices,\n updates: $updates\n };\n const attrs = {};\n return ENGINE.runKernel(TensorScatterUpdate, inputs, attrs);\n}\nvar tensorScatterUpdate = op({ tensorScatterUpdate_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js\nfunction topk_(x, k = 1, sorted = true) {\n const $x = convertToTensor(x, \"x\", \"topk\");\n if ($x.rank === 0) {\n throw new Error(\"topk() expects the input to be of rank 1 or higher\");\n }\n const lastDim = $x.shape[$x.shape.length - 1];\n if (k < 0) {\n throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);\n }\n if (k > lastDim) {\n throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);\n }\n const inputs = { x: $x };\n const attrs = { k, sorted };\n const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs);\n return { values, indices };\n}\nvar topk = op({ topk_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js\nfunction truncatedNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) {\n assertNonNegativeIntegerDimensions(shape);\n if (dtype != null && dtype === \"bool\") {\n throw new Error(`Unsupported data type $ { dtype }`);\n }\n const randGauss = new MPRandGauss(mean4, stdDev, dtype, true, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = randGauss.nextValue();\n }\n return res.toTensor();\n}\nvar truncatedNormal = op({ truncatedNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/unique.js\nfunction unique_(x, axis = 0) {\n const $x = convertToTensor(x, \"x\", \"unique\", \"string_or_numeric\");\n assert($x.rank > 0, () => \"The input tensor must be at least 1D\");\n const inputs = { x: $x };\n const attrs = { axis };\n const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n return { values, indices };\n}\nvar unique = op({ unique_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n const $x = convertToTensor(x, \"x\", \"unsortedSegmentSum\");\n const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"unsortedSegmentSum\", \"int32\");\n assert(isInt(numSegments), () => \"numSegments must be of dtype int\");\n const inputs = { x: $x, segmentIds: $segmentIds };\n const attrs = { numSegments };\n return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);\n}\nvar unsortedSegmentSum = op({ unsortedSegmentSum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js\nfunction unstack_(x, axis = 0) {\n const $x = convertToTensor(x, \"x\", \"unstack\", \"string_or_numeric\");\n assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n const inputs = { value: $x };\n const attrs = { axis };\n return ENGINE.runKernel(Unpack, inputs, attrs);\n}\nvar unstack = op({ unstack_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/upper_bound.js\nfunction upperBound(sortedSequence, values) {\n return searchSorted(sortedSequence, values, \"right\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/variable.js\nfunction variable(initialValue, trainable = true, name, dtype) {\n return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js\nfunction whereImpl(condShape, condVals) {\n const indices = [];\n for (let i = 0; i < condVals.length; i++) {\n if (condVals[i]) {\n indices.push(i);\n }\n }\n const inBuffer = buffer(condShape, \"int32\");\n const out = buffer([indices.length, condShape.length], \"int32\");\n for (let i = 0; i < indices.length; i++) {\n const loc = inBuffer.indexToLoc(indices[i]);\n const offset = i * condShape.length;\n out.values.set(loc, offset);\n }\n return out.toTensor();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js\nasync function whereAsync_(condition) {\n const $condition = convertToTensor(condition, \"condition\", \"whereAsync\", \"bool\");\n const vals = await $condition.data();\n const res = whereImpl($condition.shape, vals);\n if (condition !== $condition) {\n $condition.dispose();\n }\n return res;\n}\nvar whereAsync = whereAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js\nasync function booleanMaskAsync_(tensor2, mask, axis) {\n const $tensor = convertToTensor(tensor2, \"tensor\", \"boolMask\");\n const $mask = convertToTensor(mask, \"mask\", \"boolMask\", \"bool\");\n const axisFrom = axis == null ? 0 : axis;\n const maskDim = $mask.rank;\n const tensorShape = $tensor.shape;\n assert(maskDim > 0, () => \"mask cannot be scalar\");\n assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n let leadingSize = 1;\n for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n leadingSize *= tensorShape[i];\n }\n const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n const reshapedTensor = reshape($tensor, targetTensorShape);\n const reshapedMask = reshape($mask, [-1]);\n const positivePositions = await whereAsync(reshapedMask);\n const indices = squeeze(positivePositions, [1]);\n const res = gather(reshapedTensor, indices, axisFrom);\n if (tensor2 !== $tensor) {\n $tensor.dispose();\n }\n if (mask !== $mask) {\n $mask.dispose();\n }\n indices.dispose();\n reshapedTensor.dispose();\n reshapedMask.dispose();\n positivePositions.dispose();\n return res;\n}\nvar booleanMaskAsync = booleanMaskAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js\nfunction transpose_(x, perm, conjugate) {\n const $x = convertToTensor(x, \"x\", \"transpose\");\n if (perm == null) {\n perm = $x.shape.map((s, i) => i).reverse();\n }\n assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);\n perm.forEach((axis) => {\n assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);\n });\n if ($x.rank <= 1) {\n return $x.clone();\n }\n const inputs = { x: $x };\n const attrs = { perm };\n if ($x.dtype === \"complex64\") {\n return tidy(() => {\n let $real = real($x);\n let $imag = imag($x);\n $real = ENGINE.runKernel(Transpose, { x: $real }, attrs);\n $imag = ENGINE.runKernel(Transpose, { x: $imag }, attrs);\n if (conjugate) {\n $imag = neg($imag);\n }\n return complex($real, $imag);\n });\n }\n return ENGINE.runKernel(Transpose, inputs, attrs);\n}\nvar transpose = op({ transpose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js\nfunction movingAverage_(v, x, decay, step5, zeroDebias = true) {\n const $v = convertToTensor(v, \"v\", \"movingAverage\");\n const $x = convertToTensor(x, \"x\", \"movingAverage\");\n const $decay = convertToTensor(decay, \"decay\", \"movingAverage\");\n assertTypesMatch($v, $x);\n assert(arraysEqual($v.shape, $x.shape), () => \"Shape mismatch in v and x\");\n const one = scalar(1);\n const oneMinusDecay = sub(one, $decay);\n let update = mul(sub($x, $v), oneMinusDecay);\n if (zeroDebias) {\n assert(step5 != null, () => \"When using zeroDebias: true, step is required.\");\n const $step = convertToTensor(step5, \"step\", \"movingAverage\");\n update = div(update, sub(one, pow($decay, $step)));\n }\n return add2($v, update);\n}\nvar movingAverage = op({ movingAverage_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js\nfunction scatterND_(indices, updates, shape) {\n assertNonNegativeIntegerDimensions(shape);\n const $indices = convertToTensor(indices, \"indices\", \"scatterND\", \"int32\");\n const $updates = convertToTensor(updates, \"updates\", \"scatterND\");\n validateInput($updates, $indices, shape);\n const inputs = { indices: $indices, updates: $updates };\n const attrs = { shape };\n return ENGINE.runKernel(ScatterNd, inputs, attrs);\n}\nvar scatterND = op({ scatterND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js\nfunction validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {\n if (sparseIndices.dtype !== \"int32\") {\n throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);\n }\n if (sparseIndices.rank > 2) {\n throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);\n }\n const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n if (outputShape.length !== numDims) {\n throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);\n }\n const numValues = sparseValues.size;\n if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {\n throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);\n }\n if (sparseValues.dtype !== defaultValues.dtype) {\n throw new Error(\"sparseValues.dtype must match defaultValues.dtype\");\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n assertNonNegativeIntegerDimensions(outputShape);\n const $sparseIndices = convertToTensor(sparseIndices, \"sparseIndices\", \"sparseToDense\", \"int32\");\n const $sparseValues = convertToTensor(sparseValues, \"sparseValues\", \"sparseToDense\", \"string_or_numeric\");\n const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"sparseToDense\", $sparseValues.dtype);\n validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);\n const inputs = {\n sparseIndices: $sparseIndices,\n sparseValues: $sparseValues,\n defaultValue: $defaultValue\n };\n const attrs = { outputShape };\n return ENGINE.runKernel(SparseToDense, inputs, attrs);\n}\nvar sparseToDense = op({ sparseToDense_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js\nfunction gatherND_(x, indices) {\n const $indices = convertToTensor(indices, \"indices\", \"gatherND\", \"int32\");\n const $x = convertToTensor(x, \"x\", \"gatherND\", \"string_or_numeric\");\n const inputs = { params: $x, indices: $indices };\n return ENGINE.runKernel(GatherNd, inputs);\n}\nvar gatherND = op({ gatherND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js\nfunction getNoiseShape(x, noiseShape) {\n if (noiseShape == null) {\n return x.shape.slice();\n }\n if (arraysEqual(x.shape, noiseShape)) {\n return noiseShape;\n }\n if (x.shape.length === noiseShape.length) {\n const newDimension = [];\n for (let i = 0; i < x.shape.length; i++) {\n if (noiseShape[i] == null && x.shape[i] != null) {\n newDimension.push(x.shape[i]);\n } else {\n newDimension.push(noiseShape[i]);\n }\n }\n return newDimension;\n }\n return noiseShape;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js\nfunction dropout_(x, rate, noiseShape, seed) {\n const $x = convertToTensor(x, \"x\", \"dropout\");\n assert($x.dtype === \"float32\", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);\n assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n if (rate === 0) {\n return x instanceof Tensor ? $x.clone() : $x;\n }\n const $noiseShape = getNoiseShape($x, noiseShape);\n const keepProb = 1 - rate;\n const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, \"float32\", seed), keepProb)), keepProb);\n return mul($x, multiplier);\n}\nvar dropout = op({ dropout_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js\nfunction enclosingPowerOfTwo(value) {\n return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));\n}\nfunction cosineWindow(windowLength, a, b) {\n const even = 1 - windowLength % 2;\n const newValues = new Float32Array(windowLength);\n for (let i = 0; i < windowLength; ++i) {\n const cosArg = 2 * Math.PI * i / (windowLength + even - 1);\n newValues[i] = a - b * Math.cos(cosArg);\n }\n return tensor1d(newValues, \"float32\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js\nasync function inTopKAsync_(predictions, targets, k = 1) {\n const $predictions = convertToTensor(predictions, \"predictions\", \"inTopK\");\n const $targets = convertToTensor(targets, \"targets\", \"inTopK\");\n assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);\n assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);\n assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);\n const lastDim = $predictions.shape[$predictions.shape.length - 1];\n assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);\n const predictionsVals = await $predictions.data();\n const targetsVals = await $targets.data();\n const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n const precision3 = getTypedArrayFromDType(\"bool\", batch);\n for (let b = 0; b < batch; b++) {\n const offset = b * size;\n const vals = predictionsVals.subarray(offset, offset + size);\n const valAndInd = [];\n for (let i = 0; i < vals.length; i++) {\n valAndInd.push({ value: vals[i], index: i });\n }\n valAndInd.sort((a, b2) => b2.value - a.value);\n precision3[b] = 0;\n for (let i = 0; i < k; i++) {\n if (valAndInd[i].index === targetsVals[b]) {\n precision3[b] = 1;\n break;\n }\n }\n }\n if (predictions !== $predictions) {\n $predictions.dispose();\n }\n if (targets !== $targets) {\n $targets.dispose();\n }\n return tensor(precision3, $targets.shape, \"bool\");\n}\nvar inTopKAsync = inTopKAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js\nvar fused_ops_exports = {};\n__export(fused_ops_exports, {\n conv2d: () => conv2d2,\n depthwiseConv2d: () => depthwiseConv2d2,\n matMul: () => matMul2\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad3, dataFormat = \"NHWC\", dimRoundingMode) {\n let x4D = x;\n if (x.rank === 3) {\n x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n }\n let dy4D = dy;\n if (dy4D.rank === 3) {\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);\n assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);\n assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);\n const inDepth = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n const outDepth = dataFormat === \"NHWC\" ? dy4D.shape[3] : dy4D.shape[1];\n assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);\n assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);\n checkPadOnDimRoundingMode(\"conv2dDerFilter\", pad3, dimRoundingMode);\n const inputs = { x: x4D, dy: dy4D };\n const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape };\n return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);\n}\nvar conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js\nfunction getFusedDyActivation(dy, y, activation2) {\n if (activation2 == null || activation2 === \"linear\") {\n return dy;\n }\n if (activation2 === \"relu\") {\n return mul(dy, step(y));\n }\n throw new Error(`Cannot compute gradient for fused activation ${activation2}.`);\n}\nfunction getFusedBiasGradient(bias, dyActivation) {\n let res = dyActivation;\n const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);\n if (reduceAxes.length > 0) {\n res = sum2(res, reduceAxes);\n }\n return reshape(res, bias.shape);\n}\nfunction applyActivation(x, activation2, preluActivationWeights, leakyreluAlpha) {\n if (activation2 === \"linear\") {\n return x;\n } else if (activation2 === \"relu\") {\n return relu(x);\n } else if (activation2 === \"elu\") {\n return elu(x);\n } else if (activation2 === \"relu6\") {\n return relu6(x);\n } else if (activation2 === \"prelu\") {\n return prelu(x, preluActivationWeights);\n } else if (activation2 === \"leakyrelu\") {\n return leakyRelu(x, leakyreluAlpha);\n } else if (activation2 === \"sigmoid\") {\n return sigmoid(x);\n }\n throw new Error(`Unknown fused activation ${activation2}.`);\n}\nvar shouldFuse = (gradientDepth, activation2) => {\n const gradientMode = gradientDepth > 0;\n return !gradientMode || activation2 === \"linear\";\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js\nfunction fusedConv2d_({ x, filter, strides, pad: pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha }) {\n activation2 = activation2 || \"linear\";\n if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n assert(dataFormat === \"NHWC\", () => `Error in fused conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);\n let result = conv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n if (bias != null) {\n result = add2(result, bias);\n }\n return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n }\n const $x = convertToTensor(x, \"x\", \"conv2d\", \"float32\");\n const $filter = convertToTensor(filter, \"filter\", \"conv2d\", \"float32\");\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n checkPadOnDimRoundingMode(\"fused conv2d\", pad3, dimRoundingMode);\n const inputChannels = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) must match input depth for filter ${$filter.shape[2]}.`);\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode);\n let $bias;\n if (bias != null) {\n $bias = convertToTensor(bias, \"bias\", \"fused conv2d\");\n [$bias] = makeTypesMatch($bias, $x);\n if (dataFormat === \"NHWC\") {\n assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n } else {\n assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${$bias.shape.length}.`);\n assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not compatible with the number of output channels (${convInfo.outChannels})`);\n }\n }\n let $preluActivationWeights;\n if (preluActivationWeights != null) {\n const alphaShape = preluActivationWeights.shape;\n assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${alphaShape.length}.`);\n if (alphaShape.length === 1) {\n assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the number of output channels (${convInfo.outChannels}).`);\n } else if (alphaShape.length === 3) {\n try {\n assertAndGetBroadcastShape(alphaShape, convInfo.outShape);\n } catch (e) {\n const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`;\n throw Error(errMsg);\n }\n }\n $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused conv2d\");\n }\n const grad2 = (dy, saved) => {\n assert(dataFormat === \"NHWC\", () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n const [$filter2, x4D2, y, $bias2] = saved;\n const dyActivation = getFusedDyActivation(dy, y, activation2);\n assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3);\n const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3);\n const der = [xDer, filterDer];\n if ($bias2 != null) {\n const biasDer = getFusedBiasGradient($bias2, dyActivation);\n der.push(biasDer);\n }\n return der;\n };\n const inputs = {\n x: x4D,\n filter: $filter,\n bias: $bias,\n preluActivationWeights: $preluActivationWeights\n };\n const attrs = {\n strides,\n pad: pad3,\n dataFormat,\n dilations,\n dimRoundingMode,\n activation: activation2,\n leakyreluAlpha\n };\n if (bias == null) {\n const customOp = customGrad((x4D2, filter2, save) => {\n let res = (\n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(FusedConv2D, inputs, attrs)\n );\n save([filter2, x4D2, res]);\n if (reshapedTo4D) {\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad2 };\n });\n return customOp(x4D, $filter);\n } else {\n const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {\n let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);\n save([filter2, x4D2, res, bias2]);\n if (reshapedTo4D) {\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad2 };\n });\n return customOpWithBias(x4D, $filter, $bias);\n }\n}\nvar conv2d2 = op({ fusedConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad3, dilations = [1, 1], dimRoundingMode) {\n let x4D = x;\n if (x.rank === 3) {\n x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n }\n let dy4D = dy;\n if (dy4D.rank === 3) {\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n }\n const inputs = { x: x4D, dy: dy4D };\n const attrs = { strides, pad: pad3, dimRoundingMode, dilations, filterShape };\n return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);\n}\nvar depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad3, dilations = [1, 1], dimRoundingMode) {\n let dy4D = dy;\n let reshapedTo4D = false;\n if (dy.rank === 3) {\n reshapedTo4D = true;\n dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n }\n const inputs = { dy: dy4D, filter };\n const attrs = { strides, pad: pad3, dimRoundingMode, dilations, inputShape: xShape };\n const res = (\n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs)\n );\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad: pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha }) {\n if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n let result = depthwiseConv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n if (bias != null) {\n result = add2(result, bias);\n }\n return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n }\n const $x = convertToTensor(x, \"x\", \"depthwiseConv2d\", \"float32\");\n const $filter = convertToTensor(filter, \"filter\", \"depthwiseConv2d\", \"float32\");\n let x4D = $x;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n }\n assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);\n if (dilations == null) {\n dilations = [1, 1];\n }\n assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n checkPadOnDimRoundingMode(\"fused depthwiseConv2d\", pad3, dimRoundingMode);\n const convInfo = computeConv2DInfo(\n x4D.shape,\n $filter.shape,\n strides,\n dilations,\n pad3,\n dimRoundingMode,\n true\n /* depthwise */\n );\n let $bias;\n if (bias != null) {\n $bias = convertToTensor(bias, \"bias\", \"fused conv2d\");\n [$bias] = makeTypesMatch($bias, $x);\n assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n }\n let $preluActivationWeights;\n if (preluActivationWeights != null) {\n $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused depthwiseConv2d\");\n }\n const grad2 = (dy, saved) => {\n assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);\n const [$filter2, x4D2, y, bias2] = saved;\n const dyActivation = getFusedDyActivation(dy, y, activation2);\n const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3, dilations, dimRoundingMode);\n const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3, dilations, dimRoundingMode);\n if (bias2 != null) {\n const biasDer = getFusedBiasGradient($bias, dyActivation);\n return [xDer, filterDer, biasDer];\n }\n return [xDer, filterDer];\n };\n const inputs = {\n x: x4D,\n filter: $filter,\n bias: $bias,\n preluActivationWeights: $preluActivationWeights\n };\n const attrs = {\n strides,\n pad: pad3,\n dataFormat,\n dilations,\n dimRoundingMode,\n activation: activation2,\n leakyreluAlpha\n };\n if (bias == null) {\n const customOp = customGrad((x4D2, filter2, save) => {\n let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n save([filter2, x4D2, res]);\n if (reshapedTo4D) {\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad2 };\n });\n return customOp(x4D, $filter);\n } else {\n const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {\n let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n save([filter2, x4D2, res, bias2]);\n if (reshapedTo4D) {\n res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return { value: res, gradFunc: grad2 };\n });\n return customOpWithBias(x4D, $filter, $bias);\n }\n}\nvar depthwiseConv2d2 = op({ fusedDepthwiseConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha = 0.2 }) {\n if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n let result = matMul(a, b, transposeA, transposeB);\n if (bias != null) {\n result = add2(result, bias);\n }\n return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n }\n let $a = convertToTensor(a, \"a\", \"fused matMul\");\n let $b = convertToTensor(b, \"b\", \"fused matMul\");\n [$a, $b] = makeTypesMatch($a, $b);\n const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n const outerDimsA = $a.shape.slice(0, -2);\n const outerDimsB = $b.shape.slice(0, -2);\n const batchDimA = sizeFromShape(outerDimsA);\n const batchDimB = sizeFromShape(outerDimsB);\n assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));\n const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n let $bias;\n if (bias != null) {\n $bias = convertToTensor(bias, \"bias\", \"fused matMul\");\n [$bias] = makeTypesMatch($bias, $a);\n assertAndGetBroadcastShape(outShape, $bias.shape);\n }\n let $preluActivationWeights;\n if (preluActivationWeights != null) {\n $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused matMul\");\n }\n const grad2 = (dy, saved) => {\n const [a3D2, b3D2, y, $bias2] = saved;\n const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation2);\n let aDer;\n let bDer;\n if (!transposeA && !transposeB) {\n aDer = matMul(dyActivation, b3D2, false, true);\n bDer = matMul(a3D2, dyActivation, true, false);\n } else if (!transposeA && transposeB) {\n aDer = matMul(dyActivation, b3D2, false, false);\n bDer = matMul(dyActivation, a3D2, true, false);\n } else if (transposeA && !transposeB) {\n aDer = matMul(b3D2, dyActivation, false, true);\n bDer = matMul(a3D2, dyActivation, false, false);\n } else {\n aDer = matMul(b3D2, dyActivation, true, true);\n bDer = matMul(dyActivation, a3D2, true, true);\n }\n if (bias != null) {\n const biasDer = getFusedBiasGradient($bias2, dyActivation);\n return [aDer, bDer, biasDer];\n } else {\n return [aDer, bDer];\n }\n };\n const inputs = {\n a: a3D,\n b: b3D,\n bias: $bias,\n preluActivationWeights: $preluActivationWeights\n };\n const attrs = { transposeA, transposeB, activation: activation2, leakyreluAlpha };\n if (bias == null) {\n const customOp = customGrad((a3D2, b3D2, save) => {\n const res = (\n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(_FusedMatMul, inputs, attrs)\n );\n save([a3D2, b3D2, res]);\n return { value: reshape(res, outShape), gradFunc: grad2 };\n });\n return customOp(a3D, b3D);\n } else {\n const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {\n const res = (\n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(_FusedMatMul, inputs, attrs)\n );\n save([a3D2, b3D2, res, $bias2]);\n return { value: reshape(res, outShape), gradFunc: grad2 };\n });\n return customOpWithBias(a3D, b3D, $bias);\n }\n}\nvar matMul2 = op({ fusedMatMul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js\nfunction hammingWindow_(windowLength) {\n return cosineWindow(windowLength, 0.54, 0.46);\n}\nvar hammingWindow = op({ hammingWindow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js\nfunction hannWindow_(windowLength) {\n return cosineWindow(windowLength, 0.5, 0.5);\n}\nvar hannWindow = op({ hannWindow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js\nfunction frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {\n let start = 0;\n const output = [];\n while (start + frameLength <= signal2.size) {\n output.push(slice(signal2, start, frameLength));\n start += frameStep;\n }\n if (padEnd) {\n while (start < signal2.size) {\n const padLen = start + frameLength - signal2.size;\n const pad3 = concat([\n slice(signal2, start, frameLength - padLen),\n fill([padLen], padValue)\n ]);\n output.push(pad3);\n start += frameStep;\n }\n }\n if (output.length === 0) {\n return tensor2d([], [0, frameLength]);\n }\n return reshape(concat(output), [output.length, frameLength]);\n}\nvar frame = op({ frame_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js\nfunction stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n if (fftLength == null) {\n fftLength = enclosingPowerOfTwo(frameLength);\n }\n const framedSignal = frame(signal2, frameLength, frameStep);\n const windowedSignal = mul(framedSignal, windowFn(frameLength));\n return rfft(windowedSignal, fftLength);\n}\nvar stft = op({ stft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js\nfunction cropAndResize_(image2, boxes, boxInd, cropSize, method = \"bilinear\", extrapolationValue = 0) {\n const $image = convertToTensor(image2, \"image\", \"cropAndResize\");\n const $boxes = convertToTensor(boxes, \"boxes\", \"cropAndResize\", \"float32\");\n const $boxInd = convertToTensor(boxInd, \"boxInd\", \"cropAndResize\", \"int32\");\n const numBoxes = $boxes.shape[0];\n assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);\n assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);\n assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);\n assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);\n assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n assert(method === \"bilinear\" || method === \"nearest\", () => `method must be bilinear or nearest, but was ${method}`);\n const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n const attrs = { method, extrapolationValue, cropSize };\n const res = ENGINE.runKernel(CropAndResize, inputs, attrs);\n return res;\n}\nvar cropAndResize = op({ cropAndResize_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js\nfunction flipLeftRight_(image2) {\n const $image = convertToTensor(image2, \"image\", \"flipLeftRight\", \"float32\");\n assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);\n const inputs = { image: $image };\n const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n return res;\n}\nvar flipLeftRight = op({ flipLeftRight_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/grayscale_to_rgb.js\nfunction grayscaleToRGB_(image2) {\n const $image = convertToTensor(image2, \"image\", \"grayscaleToRGB\");\n const lastDimsIdx = $image.rank - 1;\n const lastDims = $image.shape[lastDimsIdx];\n assert($image.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${$image.rank}.`);\n assert(lastDims === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${lastDims}.`);\n const reps = new Array($image.rank);\n reps.fill(1, 0, lastDimsIdx);\n reps[lastDimsIdx] = 3;\n return tile($image, reps);\n}\nvar grayscaleToRGB = op({ grayscaleToRGB_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/rgb_to_grayscale.js\nfunction rgbToGrayscale_(image2) {\n const $image = convertToTensor(image2, \"image\", \"RGBToGrayscale\");\n const lastDimsIdx = $image.rank - 1;\n const lastDims = $image.shape[lastDimsIdx];\n assert($image.rank >= 2, () => `Error in RGBToGrayscale: images must be at least rank 2, but got rank ${$image.rank}.`);\n assert(lastDims === 3, () => `Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${lastDims}.`);\n const origDtype = $image.dtype;\n const fltImage = cast($image, \"float32\");\n const rgbWeights = tensor1d([0.2989, 0.587, 0.114]);\n let grayFloat;\n switch ($image.rank) {\n case 2:\n grayFloat = einsum(\"ij,j->i\", fltImage, rgbWeights);\n break;\n case 3:\n grayFloat = einsum(\"ijk,k->ij\", fltImage, rgbWeights);\n break;\n case 4:\n grayFloat = einsum(\"ijkl,l->ijk\", fltImage, rgbWeights);\n break;\n case 5:\n grayFloat = einsum(\"ijklm,m->ijkl\", fltImage, rgbWeights);\n break;\n case 6:\n grayFloat = einsum(\"ijklmn,n->ijklm\", fltImage, rgbWeights);\n break;\n default:\n throw new Error(\"Not a valid tensor rank.\");\n }\n grayFloat = expandDims(grayFloat, -1);\n return cast(grayFloat, origDtype);\n}\nvar rgbToGrayscale = op({ rgbToGrayscale_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js\nfunction rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) {\n const $image = convertToTensor(image2, \"image\", \"rotateWithOffset\", \"float32\");\n assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);\n const inputs = { image: $image };\n const attrs = { radians, fillValue, center };\n const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n return res;\n}\nvar rotateWithOffset = op({ rotateWithOffset_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n if (iouThreshold == null) {\n iouThreshold = 0.5;\n }\n if (scoreThreshold == null) {\n scoreThreshold = Number.NEGATIVE_INFINITY;\n }\n if (softNmsSigma == null) {\n softNmsSigma = 0;\n }\n const numBoxes = boxes.shape[0];\n maxOutputSize = Math.min(maxOutputSize, numBoxes);\n assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n assert(scores.rank === 1, () => \"scores must be a 1D tensor\");\n assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);\n assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\", \"float32\");\n const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\", \"float32\");\n const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n maxOutputSize = inputs.maxOutputSize;\n iouThreshold = inputs.iouThreshold;\n scoreThreshold = inputs.scoreThreshold;\n const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);\n}\nvar nonMaxSuppression = op({ nonMaxSuppression_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_util.js\nfunction binaryInsert(arr, element, comparator) {\n const index = binarySearch(arr, element, comparator);\n const insertionPoint = index < 0 ? -(index + 1) : index;\n arr.splice(insertionPoint, 0, element);\n}\nfunction binarySearch(arr, target, comparator) {\n return binarySearch_(arr, target, comparator || defaultComparator);\n}\nfunction defaultComparator(a, b) {\n return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n let left = 0;\n let right = arr.length;\n let middle = 0;\n let found = false;\n while (left < right) {\n middle = left + (right - left >>> 1);\n const compareResult = comparator(target, arr[middle]);\n if (compareResult > 0) {\n left = middle + 1;\n } else {\n right = middle;\n found = !compareResult;\n }\n }\n return found ? left : -left - 1;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js\nfunction nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n return nonMaxSuppressionImpl_(\n boxes,\n scores,\n maxOutputSize,\n iouThreshold,\n scoreThreshold,\n 0\n /* softNmsSigma */\n );\n}\nfunction nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n return nonMaxSuppressionImpl_(\n boxes,\n scores,\n maxOutputSize,\n iouThreshold,\n scoreThreshold,\n 0,\n false,\n padToMaxOutputSize,\n true\n /* returnValidOutputs */\n );\n}\nfunction nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n return nonMaxSuppressionImpl_(\n boxes,\n scores,\n maxOutputSize,\n iouThreshold,\n scoreThreshold,\n softNmsSigma,\n true\n /* returnScoresTensor */\n );\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n const candidates = [];\n for (let i = 0; i < scores.length; i++) {\n if (scores[i] > scoreThreshold) {\n candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n }\n }\n candidates.sort(ascendingComparator);\n const scale2 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;\n const selectedIndices = [];\n const selectedScores = [];\n while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n const candidate = candidates.pop();\n const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n if (originalScore < scoreThreshold) {\n break;\n }\n let ignoreCandidate = false;\n for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n if (iou >= iouThreshold) {\n ignoreCandidate = true;\n break;\n }\n candidate.score = candidate.score * suppressWeight(iouThreshold, scale2, iou);\n if (candidate.score <= scoreThreshold) {\n break;\n }\n }\n candidate.suppressBeginIndex = selectedIndices.length;\n if (!ignoreCandidate) {\n if (candidate.score === originalScore) {\n selectedIndices.push(boxIndex);\n selectedScores.push(candidate.score);\n } else if (candidate.score > scoreThreshold) {\n binaryInsert(candidates, candidate, ascendingComparator);\n }\n }\n }\n const validOutputs = selectedIndices.length;\n const elemsToPad = maxOutputSize - validOutputs;\n if (padToMaxOutputSize && elemsToPad > 0) {\n selectedIndices.push(...new Array(elemsToPad).fill(0));\n selectedScores.push(...new Array(elemsToPad).fill(0));\n }\n const result = { selectedIndices };\n if (returnScoresTensor) {\n result[\"selectedScores\"] = selectedScores;\n }\n if (returnValidOutputs) {\n result[\"validOutputs\"] = validOutputs;\n }\n return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n const yminI = Math.min(iCoord[0], iCoord[2]);\n const xminI = Math.min(iCoord[1], iCoord[3]);\n const ymaxI = Math.max(iCoord[0], iCoord[2]);\n const xmaxI = Math.max(iCoord[1], iCoord[3]);\n const yminJ = Math.min(jCoord[0], jCoord[2]);\n const xminJ = Math.min(jCoord[1], jCoord[3]);\n const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) {\n return 0;\n }\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\nfunction suppressWeight(iouThreshold, scale2, iou) {\n const weight = Math.exp(scale2 * iou * iou);\n return iou <= iouThreshold ? weight : 0;\n}\nfunction ascendingComparator(c1, c2) {\n return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n maxOutputSize = inputs.maxOutputSize;\n iouThreshold = inputs.iouThreshold;\n scoreThreshold = inputs.scoreThreshold;\n const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n const boxesVals = boxesAndScores[0];\n const scoresVals = boxesAndScores[1];\n const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n if ($boxes !== boxes) {\n $boxes.dispose();\n }\n if ($scores !== scores) {\n $scores.dispose();\n }\n return tensor1d(selectedIndices, \"int32\");\n}\nvar nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {\n const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\");\n const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\");\n const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n maxOutputSize = params.maxOutputSize;\n iouThreshold = params.iouThreshold;\n scoreThreshold = params.scoreThreshold;\n softNmsSigma = params.softNmsSigma;\n const inputs = { boxes: $boxes, scores: $scores };\n const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n return { selectedIndices: result[0], selectedScores: result[1] };\n}\nvar nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {\n const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n maxOutputSize = params.maxOutputSize;\n iouThreshold = params.iouThreshold;\n scoreThreshold = params.scoreThreshold;\n softNmsSigma = params.softNmsSigma;\n const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n const boxesVals = boxesAndScores[0];\n const scoresVals = boxesAndScores[1];\n const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n if ($boxes !== boxes) {\n $boxes.dispose();\n }\n if ($scores !== scores) {\n $scores.dispose();\n }\n return {\n selectedIndices: tensor1d(selectedIndices, \"int32\"),\n selectedScores: tensor1d(selectedScores)\n };\n}\nvar nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\");\n const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\");\n const params = nonMaxSuppSanityCheck(\n $boxes,\n $scores,\n maxOutputSize,\n iouThreshold,\n scoreThreshold,\n null\n /* softNmsSigma */\n );\n const $maxOutputSize = params.maxOutputSize;\n const $iouThreshold = params.iouThreshold;\n const $scoreThreshold = params.scoreThreshold;\n const inputs = { boxes: $boxes, scores: $scores };\n const attrs = {\n maxOutputSize: $maxOutputSize,\n iouThreshold: $iouThreshold,\n scoreThreshold: $scoreThreshold,\n padToMaxOutputSize\n };\n const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n return { selectedIndices: result[0], validOutputs: result[1] };\n}\nvar nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n const params = nonMaxSuppSanityCheck(\n $boxes,\n $scores,\n maxOutputSize,\n iouThreshold,\n scoreThreshold,\n null\n /* softNmsSigma */\n );\n const $maxOutputSize = params.maxOutputSize;\n const $iouThreshold = params.iouThreshold;\n const $scoreThreshold = params.scoreThreshold;\n const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n if ($boxes !== boxes) {\n $boxes.dispose();\n }\n if ($scores !== scores) {\n $scores.dispose();\n }\n return {\n selectedIndices: tensor1d(selectedIndices, \"int32\"),\n validOutputs: scalar(validOutputs, \"int32\")\n };\n}\nvar nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js\nfunction resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) {\n const $images = convertToTensor(images, \"images\", \"resizeBilinear\");\n assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);\n assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);\n assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.`);\n let batchImages = $images;\n let reshapedTo4D = false;\n if ($images.rank === 3) {\n reshapedTo4D = true;\n batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n }\n const [] = size;\n const inputs = { images: batchImages };\n const attrs = { alignCorners, halfPixelCenters, size };\n const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar resizeBilinear = op({ resizeBilinear_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js\nfunction resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) {\n const $images = convertToTensor(images, \"images\", \"resizeNearestNeighbor\");\n assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);\n assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);\n assert($images.dtype === \"float32\" || $images.dtype === \"int32\", () => \"`images` must have `int32` or `float32` as dtype\");\n assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.`);\n let batchImages = $images;\n let reshapedTo4D = false;\n if ($images.rank === 3) {\n reshapedTo4D = true;\n batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n }\n const [] = size;\n const inputs = { images: batchImages };\n const attrs = { alignCorners, halfPixelCenters, size };\n const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/threshold.js\nfunction threshold_(image2, method = \"binary\", inverted = false, threshValue = 0.5) {\n const $image = convertToTensor(image2, \"image\", \"threshold\");\n const RED_INTENCITY_COEF = 0.2989;\n const GREEN_INTENCITY_COEF = 0.587;\n const BLUE_INTENCITY_COEF = 0.114;\n const totalPixelsInImage = $image.shape[0] * $image.shape[1];\n let $threshold = mul(tensor1d([threshValue]), 255);\n let r, g, b, grayscale;\n assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`);\n assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`);\n assert($image.dtype === \"int32\" || $image.dtype === \"float32\", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`);\n assert(method === \"otsu\" || method === \"binary\", () => `Method must be binary or otsu, but was ${method}`);\n if ($image.shape[2] === 3) {\n [r, g, b] = split($image, [1, 1, 1], -1);\n const $r = mul(r, RED_INTENCITY_COEF);\n const $g = mul(g, GREEN_INTENCITY_COEF);\n const $b = mul(b, BLUE_INTENCITY_COEF);\n grayscale = add2(add2($r, $g), $b);\n } else {\n grayscale = image2;\n }\n if (method === \"otsu\") {\n const $histogram = bincount(cast(round2(grayscale), \"int32\"), tensor([]), 256);\n $threshold = otsu($histogram, totalPixelsInImage);\n }\n const invCondition = inverted ? lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);\n const result = cast(mul(invCondition, 255), \"int32\");\n return result;\n}\nfunction otsu(histogram, total) {\n let bestThresh = tensor1d([-1]);\n let bestInBetVar = tensor1d([0]);\n let cInBetVar = tensor1d([0]);\n let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;\n for (let index = 0; index < histogram.size - 1; index++) {\n classFirst = slice(histogram, 0, index + 1);\n classSecond = slice(histogram, index + 1);\n weightForeground = div(sum2(classFirst), total);\n weightBack = div(sum2(classSecond), total);\n const meanFirstDivA = sum2(mul(classFirst, range(0, classFirst.size)));\n meanFirst = div(meanFirstDivA, sum2(classFirst));\n const meanSecFill = fill(classSecond.shape, classFirst.size);\n const meanSecAdd = add2(range(0, classSecond.size), meanSecFill);\n const meanSecMul = mul(classSecond, meanSecAdd);\n meanSec = div(sum2(meanSecMul), sum2(classSecond));\n const cInBetVarSubA = sub(meanFirst, meanSec);\n const cInBetVarSubB = sub(meanFirst, meanSec);\n const cInBetVarMul = mul(weightForeground, weightBack);\n cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);\n const condition = greater(cInBetVar, bestInBetVar);\n bestInBetVar = where(condition, cInBetVar, bestInBetVar);\n bestThresh = where(condition, tensor1d([index]), bestThresh);\n }\n return bestThresh;\n}\nvar threshold = op({ threshold_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/transform.js\nfunction transform_(image2, transforms, interpolation = \"nearest\", fillMode = \"constant\", fillValue = 0, outputShape) {\n const $image = convertToTensor(image2, \"image\", \"transform\", \"float32\");\n const $transforms = convertToTensor(transforms, \"transforms\", \"transform\", \"float32\");\n assert($image.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${$image.rank}.`);\n assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);\n assert(outputShape == null || outputShape.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${outputShape}.`);\n const inputs = { image: $image, transforms: $transforms };\n const attrs = { interpolation, fillMode, fillValue, outputShape };\n return ENGINE.runKernel(Transform, inputs, attrs);\n}\nvar transform = op({ transform_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js\nfunction bandPart_(a, numLower, numUpper) {\n const $a = convertToTensor(a, \"a\", \"bandPart\");\n assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n const shape = $a.shape;\n const [M, N] = $a.shape.slice(-2);\n let $numLower;\n let $numUpper;\n if (typeof numLower === \"number\") {\n assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n assert(numLower <= M, () => `bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);\n $numLower = convertToTensor(numLower < 0 ? M : numLower, \"numLower\", \"bandPart\");\n } else {\n assert(numLower.dtype === \"int32\", () => `bandPart(): numLower's dtype must be an int32.`);\n $numLower = where(less(numLower, 0), M, minimum(numLower, M));\n }\n if (typeof numUpper === \"number\") {\n assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n assert(numUpper <= N, () => `bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);\n $numUpper = convertToTensor(numUpper < 0 ? N : numUpper, \"numUpper\", \"bandPart\");\n } else {\n assert(numUpper.dtype === \"int32\", () => `bandPart(): numUpper's dtype must be an int32.`);\n $numUpper = where(less(numUpper, 0), N, minimum(numUpper, N));\n }\n const i = reshape(range(0, M, 1, \"int32\"), [-1, 1]);\n const j = range(0, N, 1, \"int32\");\n const ij = sub(i, j);\n const inBand = logicalAnd(lessEqual(ij, $numLower), greaterEqual(ij, neg($numUpper)));\n const zero = zeros([M, N], $a.dtype);\n return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);\n}\nvar bandPart = op({ bandPart_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js\nfunction gramSchmidt_(xs) {\n let inputIsTensor2D;\n if (Array.isArray(xs)) {\n inputIsTensor2D = false;\n assert(xs != null && xs.length > 0, () => \"Gram-Schmidt process: input must not be null, undefined, or empty\");\n const dim = xs[0].shape[0];\n for (let i = 1; i < xs.length; ++i) {\n assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`);\n }\n } else {\n inputIsTensor2D = true;\n xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));\n }\n assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);\n const ys = [];\n const xs1d = xs;\n for (let i = 0; i < xs.length; ++i) {\n ys.push(ENGINE.tidy(() => {\n let x = xs1d[i];\n if (i > 0) {\n for (let j = 0; j < i; ++j) {\n const proj = mul(sum2(mul(ys[j], x)), ys[j]);\n x = sub(x, proj);\n }\n }\n return div(x, norm(x, \"euclidean\"));\n }));\n }\n if (inputIsTensor2D) {\n return stack(ys, 0);\n } else {\n return ys;\n }\n}\nvar gramSchmidt = op({ gramSchmidt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js\nfunction qr_(x, fullMatrices = false) {\n assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n if (x.rank === 2) {\n return qr2d(x, fullMatrices);\n } else {\n const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);\n const x2ds = unstack(reshape(x, [\n outerDimsProd,\n x.shape[x.shape.length - 2],\n x.shape[x.shape.length - 1]\n ]), 0);\n const q2ds = [];\n const r2ds = [];\n x2ds.forEach((x2d) => {\n const [q2d, r2d] = qr2d(x2d, fullMatrices);\n q2ds.push(q2d);\n r2ds.push(r2d);\n });\n const q = reshape(stack(q2ds, 0), x.shape);\n const r = reshape(stack(r2ds, 0), x.shape);\n return [q, r];\n }\n}\nfunction qr2d(x, fullMatrices = false) {\n return ENGINE.tidy(() => {\n assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n const m = x.shape[0];\n const n = x.shape[1];\n let q = eye(m);\n let r = clone(x);\n const one2D = tensor2d([[1]], [1, 1]);\n let w = clone(one2D);\n const iters = m >= n ? n : m;\n for (let j = 0; j < iters; ++j) {\n const rTemp = r;\n const wTemp = w;\n const qTemp = q;\n [w, r, q] = ENGINE.tidy(() => {\n const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n const normX = norm(rjEnd1);\n const rjj = slice(r, [j, j], [1, 1]);\n const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n const u1 = sub(rjj, mul(s, normX));\n const wPre = div(rjEnd1, u1);\n if (wPre.shape[0] === 1) {\n w = clone(one2D);\n } else {\n w = concat([\n one2D,\n slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n ], 0);\n }\n const tau = neg(div(matMul(s, u1), normX));\n const rjEndAll = slice(r, [j, 0], [m - j, n]);\n const tauTimesW = mul(tau, w);\n const wT = transpose(w);\n if (j === 0) {\n r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n } else {\n const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n }\n const tawTimesWT = transpose(tauTimesW);\n const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n if (j === 0) {\n q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n } else {\n const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n }\n return [w, r, q];\n });\n dispose([rTemp, wTemp, qTemp]);\n }\n if (!fullMatrices && m > n) {\n q = slice(q, [0, 0], [m, n]);\n r = slice(r, [0, 0], [n, n]);\n }\n return [q, r];\n });\n}\nvar qr = op({ qr_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js\nvar Reduction;\n(function(Reduction2) {\n Reduction2[Reduction2[\"NONE\"] = 0] = \"NONE\";\n Reduction2[Reduction2[\"MEAN\"] = 1] = \"MEAN\";\n Reduction2[Reduction2[\"SUM\"] = 2] = \"SUM\";\n Reduction2[Reduction2[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js\nfunction computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $losses = convertToTensor(losses2, \"losses\", \"computeWeightedLoss\");\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, \"weights\", \"computeWeightedLoss\");\n }\n const weightedLoss = $weights == null ? $losses : mul($losses, $weights);\n if (reduction === Reduction.NONE) {\n return weightedLoss;\n }\n if (reduction === Reduction.SUM) {\n return sum2(weightedLoss);\n }\n if (reduction === Reduction.MEAN) {\n if ($weights == null) {\n return mean(weightedLoss);\n } else {\n const broadcastFactor = $losses.size / $weights.size;\n const result = div(sum2(weightedLoss), sum2($weights));\n return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;\n }\n }\n if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n if ($weights == null) {\n return div(sum2(weightedLoss), scalar($losses.size));\n } else {\n const broadcastedWeights = mul($weights, ones2($losses.shape));\n const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), \"float32\");\n return div(sum2(weightedLoss), numNonZeros);\n }\n }\n throw Error(`Unknown reduction: ${reduction}`);\n}\nvar computeWeightedLoss = op({ computeWeightedLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, \"labels\", \"absoluteDifference\");\n const $predictions = convertToTensor(predictions, \"predictions\", \"absoluteDifference\");\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, \"weights\", \"absoluteDifference\");\n }\n assertShapesMatch($labels.shape, $predictions.shape, \"Error in absoluteDifference: \");\n const losses2 = abs(sub($labels, $predictions));\n return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar absoluteDifference = op({ absoluteDifference_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, \"labels\", \"cosineDistance\");\n const $predictions = convertToTensor(predictions, \"predictions\", \"cosineDistance\");\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, \"weights\", \"cosineDistance\");\n }\n assertShapesMatch($labels.shape, $predictions.shape, \"Error in cosineDistance: \");\n const one = scalar(1);\n const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true));\n return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar cosineDistance = op({ cosineDistance_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n let $labels = convertToTensor(labels, \"labels\", \"hingeLoss\");\n const $predictions = convertToTensor(predictions, \"predictions\", \"hingeLoss\");\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, \"weights\", \"hingeLoss\");\n }\n assertShapesMatch($labels.shape, $predictions.shape, \"Error in hingeLoss: \");\n const one = scalar(1);\n $labels = sub(mul(scalar(2), $labels), one);\n const losses2 = relu(sub(one, mul($labels, $predictions)));\n return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar hingeLoss = op({ hingeLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js\nfunction huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, \"labels\", \"huberLoss\");\n const $predictions = convertToTensor(predictions, \"predictions\", \"huberLoss\");\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, \"weights\", \"huberLoss\");\n }\n assertShapesMatch($labels.shape, $predictions.shape, \"Error in huberLoss: \");\n const deltaScalar = scalar(delta);\n const error = abs(sub($predictions, $labels));\n const quadratic = minimum(error, deltaScalar);\n const linear = sub(error, quadratic);\n const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar huberLoss = op({ huberLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js\nfunction logLoss_(labels, predictions, weights, epsilon3 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, \"labels\", \"logLoss\");\n const $predictions = convertToTensor(predictions, \"predictions\", \"logLoss\");\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, \"weights\", \"logLoss\");\n }\n assertShapesMatch($labels.shape, $predictions.shape, \"Error in logLoss: \");\n const one = scalar(1);\n const epsilonScalar = scalar(epsilon3);\n const l13 = neg(mul($labels, log2(add2($predictions, epsilonScalar))));\n const l23 = mul(sub(one, $labels), log2(add2(sub(one, $predictions), epsilonScalar)));\n const losses2 = sub(l13, l23);\n return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar logLoss = op({ logLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n const $labels = convertToTensor(labels, \"labels\", \"meanSquaredError\");\n const $predictions = convertToTensor(predictions, \"predictions\", \"meanSquaredError\");\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, \"weights\", \"meanSquaredError\");\n }\n assertShapesMatch($labels.shape, $predictions.shape, \"Error in meanSquaredError: \");\n const losses2 = squaredDifference($labels, $predictions);\n return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar meanSquaredError = op({ meanSquaredError_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n const $labels = convertToTensor(labels, \"labels\", \"sigmoidCrossEntropyWithLogits\");\n const $logits = convertToTensor(logits, \"logits\", \"sigmoidCrossEntropyWithLogits\");\n assertShapesMatch($labels.shape, $logits.shape, \"Error in sigmoidCrossEntropyWithLogits: \");\n const maxOutput = relu($logits);\n const outputXTarget = mul($logits, $labels);\n const sigmoidOutput = log1p(exp(neg(abs($logits))));\n return add2(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n let $multiClassLabels = convertToTensor(multiClassLabels, \"multiClassLabels\", \"sigmoidCrossEntropy\");\n const $logits = convertToTensor(logits, \"logits\", \"sigmoidCrossEntropy\");\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, \"weights\", \"sigmoidCrossEntropy\");\n }\n assertShapesMatch($multiClassLabels.shape, $logits.shape, \"Error in sigmoidCrossEntropy: \");\n if (labelSmoothing > 0) {\n const labelSmoothingScalar = scalar(labelSmoothing);\n const one = scalar(1);\n const half = scalar(0.5);\n $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n }\n const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n if (dim === -1) {\n dim = logits.rank - 1;\n }\n if (dim !== logits.rank - 1) {\n throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);\n }\n const customOp = customGrad((labels2, logits2, save) => {\n const keepDims = true;\n const lse = logSumExp(logits2, [dim], keepDims);\n const logResult = sub(cast(logits2, \"float32\"), lse);\n save([labels2, logResult]);\n const costVector = neg(mul(logResult, labels2));\n const value = sum2(costVector, [dim]);\n const gradFunc = (dy, saved) => {\n const [labels3, logResult2] = saved;\n const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n return [\n mul(reshape(dy, dyShape), sub(cast(labels3, \"float32\"), exp(logResult2))),\n mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, \"float32\")))\n ];\n };\n return { value, gradFunc };\n });\n return customOp(labels, logits);\n}\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n let $onehotLabels = convertToTensor(onehotLabels, \"onehotLabels\", \"softmaxCrossEntropy\");\n const $logits = convertToTensor(logits, \"logits\", \"softmaxCrossEntropy\");\n let $weights = null;\n if (weights != null) {\n $weights = convertToTensor(weights, \"weights\", \"softmaxCrossEntropy\");\n }\n assertShapesMatch($onehotLabels.shape, $logits.shape, \"Error in softmaxCrossEntropy: \");\n if (labelSmoothing > 0) {\n const labelSmoothingScalar = scalar(labelSmoothing);\n const one = scalar(1);\n const numClasses = scalar($onehotLabels.shape[1]);\n $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n }\n const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows.js\nfunction sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {\n const $indices = convertToTensor(indices, \"indices\", \"sparseFillEmptyRows\", \"int32\");\n const $values = convertToTensor(values, \"values\", \"sparseFillEmptyRows\");\n const $denseShape = convertToTensor(denseShape, \"denseShape\", \"sparseFillEmptyRows\", \"int32\");\n const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"sparseFillEmptyRows\", $values.dtype);\n if ($indices.rank !== 2) {\n throw new Error(`Indices should be Tensor2D but received shape\n ${$indices.shape}`);\n }\n if ($values.rank !== 1) {\n throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`);\n }\n if ($denseShape.rank !== 1) {\n throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`);\n }\n if ($defaultValue.rank !== 0) {\n throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`);\n }\n const inputs = {\n indices: $indices,\n values: $values,\n denseShape: $denseShape,\n defaultValue: $defaultValue\n };\n const result = ENGINE.runKernel(SparseFillEmptyRows, inputs);\n return {\n outputIndices: result[0],\n outputValues: result[1],\n emptyRowIndicator: result[2],\n reverseIndexMap: result[3]\n };\n}\nvar sparseFillEmptyRows = op({ sparseFillEmptyRows_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape.js\nfunction sparseReshape_(inputIndices, inputShape, newShape) {\n const $inputIndices = convertToTensor(inputIndices, \"inputIndices\", \"sparseReshape\", \"int32\");\n const $inputShape = convertToTensor(inputShape, \"inputShape\", \"sparseReshape\", \"int32\");\n const $newShape = convertToTensor(newShape, \"newShape\", \"sparseReshape\", \"int32\");\n if ($inputIndices.rank !== 2) {\n throw new Error(`Input indices should be Tensor2D but received shape\n ${$inputIndices.shape}`);\n }\n if ($inputShape.rank !== 1) {\n throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`);\n }\n if ($newShape.rank !== 1) {\n throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`);\n }\n const inputs = {\n inputIndices: $inputIndices,\n inputShape: $inputShape,\n newShape: $newShape\n };\n const result = ENGINE.runKernel(SparseReshape, inputs);\n return { outputIndices: result[0], outputShape: result[1] };\n}\nvar sparseReshape = op({ sparseReshape_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_mean.js\nfunction sparseSegmentMean_(data, indices, segmentIds) {\n const $data = convertToTensor(data, \"data\", \"sparseSegmentMean\");\n const $indices = convertToTensor(indices, \"indices\", \"sparseSegmentMean\", \"int32\");\n const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"sparseSegmentMean\", \"int32\");\n if ($data.rank < 1) {\n throw new Error(`Data should be at least 1 dimensional but received scalar`);\n }\n if ($indices.rank !== 1) {\n throw new Error(`Indices should be Tensor1D but received shape\n ${$indices.shape}`);\n }\n if ($segmentIds.rank !== 1) {\n throw new Error(`Segment ids should be Tensor1D but received shape\n ${$segmentIds.shape}`);\n }\n const inputs = {\n data: $data,\n indices: $indices,\n segmentIds: $segmentIds\n };\n return ENGINE.runKernel(SparseSegmentMean, inputs);\n}\nvar sparseSegmentMean = op({ sparseSegmentMean_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_sum.js\nfunction sparseSegmentSum_(data, indices, segmentIds) {\n const $data = convertToTensor(data, \"data\", \"sparseSegmentSum\");\n const $indices = convertToTensor(indices, \"indices\", \"sparseSegmentSum\", \"int32\");\n const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"sparseSegmentSum\", \"int32\");\n if ($data.rank < 1) {\n throw new Error(`Data should be at least 1 dimensional but received scalar`);\n }\n if ($indices.rank !== 1) {\n throw new Error(`Indices should be Tensor1D but received shape\n ${$indices.shape}`);\n }\n if ($segmentIds.rank !== 1) {\n throw new Error(`Segment ids should be Tensor1D but received shape\n ${$segmentIds.shape}`);\n }\n const inputs = {\n data: $data,\n indices: $indices,\n segmentIds: $segmentIds\n };\n return ENGINE.runKernel(SparseSegmentSum, inputs);\n}\nvar sparseSegmentSum = op({ sparseSegmentSum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_n_grams.js\nfunction stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n const $data = convertToTensor(data, \"data\", \"stringNGrams\", \"string\");\n if ($data.dtype !== \"string\") {\n throw new Error(\"Data must be of datatype string\");\n }\n if ($data.shape.length !== 1) {\n throw new Error(`Data must be a vector, saw: ${$data.shape}`);\n }\n const $dataSplits = convertToTensor(dataSplits, \"dataSplits\", \"stringNGrams\");\n if ($dataSplits.dtype !== \"int32\") {\n throw new Error(\"Data splits must be of datatype int32\");\n }\n const attrs = {\n separator,\n nGramWidths,\n leftPad,\n rightPad: rightPad2,\n padWidth,\n preserveShortSequences\n };\n const inputs = { data: $data, dataSplits: $dataSplits };\n const result = ENGINE.runKernel(StringNGrams, inputs, attrs);\n return { nGrams: result[0], nGramsSplits: result[1] };\n}\nvar stringNGrams = op({ stringNGrams_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_split.js\nfunction stringSplit_(input2, delimiter, skipEmpty = true) {\n const $input = convertToTensor(input2, \"input\", \"stringSplit\", \"string\");\n const $delimiter = convertToTensor(delimiter, \"delimiter\", \"stringSplit\", \"string\");\n if ($input.rank !== 1) {\n throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`);\n }\n if ($delimiter.rank !== 0) {\n throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`);\n }\n const attrs = { skipEmpty };\n const inputs = { input: $input, delimiter: $delimiter };\n const result = ENGINE.runKernel(StringSplit, inputs, attrs);\n return { indices: result[0], values: result[1], shape: result[2] };\n}\nvar stringSplit = op({ stringSplit_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_to_hash_bucket_fast.js\nfunction stringToHashBucketFast_(input2, numBuckets) {\n const $input = convertToTensor(input2, \"input\", \"stringToHashBucketFast\", \"string\");\n const attrs = { numBuckets };\n if (numBuckets <= 0) {\n throw new Error(`Number of buckets must be at least 1`);\n }\n const inputs = { input: $input };\n return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs);\n}\nvar stringToHashBucketFast = op({ stringToHashBucketFast_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/static_regex_replace.js\nfunction staticRegexReplace_(input2, pattern, rewrite, replaceGlobal = true) {\n const $input = convertToTensor(input2, \"input\", \"staticRegexReplace\", \"string\");\n const attrs = { pattern, rewrite, replaceGlobal };\n return ENGINE.runKernel(StaticRegexReplace, { x: $input }, attrs);\n}\nvar staticRegexReplace = op({ staticRegexReplace_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js\nvar spectral = {\n fft,\n ifft,\n rfft,\n irfft\n};\nvar signal = {\n hammingWindow,\n hannWindow,\n frame,\n stft\n};\nvar image = {\n flipLeftRight,\n grayscaleToRGB,\n resizeNearestNeighbor,\n resizeBilinear,\n rgbToGrayscale,\n rotateWithOffset,\n cropAndResize,\n nonMaxSuppression,\n nonMaxSuppressionAsync,\n nonMaxSuppressionWithScore,\n nonMaxSuppressionWithScoreAsync,\n nonMaxSuppressionPadded,\n nonMaxSuppressionPaddedAsync,\n threshold,\n transform\n};\nvar linalg = {\n bandPart,\n gramSchmidt,\n qr\n};\nvar losses = {\n absoluteDifference,\n computeWeightedLoss,\n cosineDistance,\n hingeLoss,\n huberLoss,\n logLoss,\n meanSquaredError,\n sigmoidCrossEntropy,\n softmaxCrossEntropy\n};\nvar sparse = {\n sparseFillEmptyRows,\n sparseReshape,\n sparseSegmentMean,\n sparseSegmentSum\n};\nvar string = {\n stringNGrams,\n stringSplit,\n stringToHashBucketFast,\n staticRegexReplace\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/serialization.js\nvar serialization_exports = {};\n__export(serialization_exports, {\n Serializable: () => Serializable,\n SerializationMap: () => SerializationMap,\n getRegisteredName: () => getRegisteredName,\n registerClass: () => registerClass\n});\nvar GLOBAL_CUSTOM_OBJECT = /* @__PURE__ */ new Map();\nvar GLOBAL_CUSTOM_NAMES = /* @__PURE__ */ new Map();\nvar Serializable = class {\n /**\n * Return the class name for this class to use in serialization contexts.\n *\n * Generally speaking this will be the same thing that constructor.name\n * would have returned. However, the class name needs to be robust\n * against minification for serialization/deserialization to work properly.\n *\n * There's also places such as initializers.VarianceScaling, where\n * implementation details between different languages led to different\n * class hierarchies and a non-leaf node is used for serialization purposes.\n */\n getClassName() {\n return this.constructor.className;\n }\n /**\n * Creates an instance of T from a ConfigDict.\n *\n * This works for most descendants of serializable. A few need to\n * provide special handling.\n * @param cls A Constructor for the class to instantiate.\n * @param config The Configuration for the object.\n */\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls(config);\n }\n};\nvar SerializationMap = class _SerializationMap {\n constructor() {\n this.classNameMap = {};\n }\n /**\n * Returns the singleton instance of the map.\n */\n static getMap() {\n if (_SerializationMap.instance == null) {\n _SerializationMap.instance = new _SerializationMap();\n }\n return _SerializationMap.instance;\n }\n /**\n * Registers the class as serializable.\n */\n static register(cls) {\n _SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];\n }\n};\nfunction registerClass(cls, pkg, name) {\n assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);\n assert(typeof cls.className === \"string\", () => `className is required to be a string, but got type ` + typeof cls.className);\n assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);\n if (typeof pkg === \"undefined\") {\n pkg = \"Custom\";\n }\n if (typeof name === \"undefined\") {\n name = cls.className;\n }\n const className = name;\n const registerName = pkg + \">\" + className;\n SerializationMap.register(cls);\n GLOBAL_CUSTOM_OBJECT.set(registerName, cls);\n GLOBAL_CUSTOM_NAMES.set(cls, registerName);\n return cls;\n}\nfunction getRegisteredName(cls) {\n if (GLOBAL_CUSTOM_NAMES.has(cls)) {\n return GLOBAL_CUSTOM_NAMES.get(cls);\n } else {\n return cls.className;\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js\nvar Optimizer = class extends Serializable {\n /**\n * Executes `f()` and minimizes the scalar output of `f()` by computing\n * gradients of y with respect to the list of trainable variables provided by\n * `varList`. If no list is provided, it defaults to all trainable variables.\n *\n * @param f The function to execute and whose output to minimize.\n * @param returnCost Whether to return the scalar cost value produced by\n * executing `f()`.\n * @param varList An optional list of variables to update. If specified, only\n * the trainable variables in varList will be updated by minimize. Defaults to\n * all trainable variables.\n *\n * @doc {heading: 'Training', subheading: 'Optimizers'}\n */\n minimize(f, returnCost = false, varList) {\n const { value, grads: grads2 } = this.computeGradients(f, varList);\n if (varList != null) {\n const gradArray = varList.map((v) => ({ name: v.name, tensor: grads2[v.name] }));\n this.applyGradients(gradArray);\n } else {\n this.applyGradients(grads2);\n }\n dispose(grads2);\n if (returnCost) {\n return value;\n } else {\n value.dispose();\n return null;\n }\n }\n /**\n * The number of iterations that this optimizer instance has been invoked for.\n */\n get iterations() {\n if (this.iterations_ == null) {\n this.iterations_ = 0;\n }\n return this.iterations_;\n }\n incrementIterations() {\n this.iterations_ = this.iterations + 1;\n }\n /**\n * Executes f() and computes the gradient of the scalar output of f() with\n * respect to the list of trainable variables provided by `varList`. If no\n * list is provided, it defaults to all trainable variables.\n *\n * @param f The function to execute and whose output to use for computing\n * gradients with respect to variables.\n * @param varList An optional list of variables to compute gradients with\n * respect to. If specified, only the trainable variables in varList will have\n * gradients computed with respect to. Defaults to all trainable variables.\n *\n * @doc {heading: 'Training', subheading: 'Optimizers'}\n */\n computeGradients(f, varList) {\n return variableGrads(f, varList);\n }\n /**\n * Dispose the variables (if any) owned by this optimizer instance.\n */\n dispose() {\n if (this.iterations_ != null) {\n dispose(this.iterations_);\n }\n }\n async saveIterations() {\n if (this.iterations_ == null) {\n this.iterations_ = 0;\n }\n return {\n name: \"iter\",\n // TODO(cais): Use 'int64' type when available.\n tensor: scalar(this.iterations_, \"int32\")\n };\n }\n async getWeights() {\n throw new Error(\"getWeights() is not implemented for this optimizer yet.\");\n }\n async setWeights(weightValues) {\n throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);\n }\n /**\n * Extract the first element of the weight values and set it\n * as the iterations counter variable of this instance of optimizer.\n *\n * @param weightValues\n * @returns Weight values with the first element consumed and excluded.\n */\n async extractIterations(weightValues) {\n this.iterations_ = (await weightValues[0].tensor.data())[0];\n return weightValues.slice(1);\n }\n};\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n value: (instance) => {\n return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;\n }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js\nvar AdadeltaOptimizer = class extends Optimizer {\n /** @nocollapse */\n static get className() {\n return \"Adadelta\";\n }\n constructor(learningRate, rho, epsilon3 = null) {\n super();\n this.learningRate = learningRate;\n this.rho = rho;\n this.epsilon = epsilon3;\n this.accumulatedGrads = [];\n this.accumulatedUpdates = [];\n if (epsilon3 == null) {\n this.epsilon = ENGINE.backend.epsilon();\n }\n }\n applyGradients(variableGradients) {\n const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n variableNames.forEach((name, i) => {\n const value = ENGINE.registeredVariables[name];\n const trainable = false;\n if (this.accumulatedGrads[i] == null) {\n this.accumulatedGrads[i] = {\n originalName: `${name}/accum_grad`,\n variable: tidy(() => zerosLike(value).variable(trainable))\n };\n }\n if (this.accumulatedUpdates[i] == null) {\n this.accumulatedUpdates[i] = {\n originalName: `${name}/accum_var`,\n variable: tidy(() => zerosLike(value).variable(trainable))\n };\n }\n const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n if (gradient == null) {\n return;\n }\n const accumulatedGrad = this.accumulatedGrads[i].variable;\n const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n tidy(() => {\n const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);\n const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n accumulatedGrad.assign(newAccumulatedGrad);\n accumulatedUpdate.assign(newAccumulatedUpdate);\n const newValue = add2(mul(updates, -this.learningRate), value);\n value.assign(newValue);\n });\n });\n this.incrementIterations();\n }\n dispose() {\n if (this.accumulatedUpdates != null) {\n dispose(this.accumulatedGrads.map((v) => v.variable));\n dispose(this.accumulatedUpdates.map((v) => v.variable));\n }\n }\n async getWeights() {\n const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n }\n async setWeights(weightValues) {\n weightValues = await this.extractIterations(weightValues);\n const variableCount = weightValues.length / 2;\n const trainable = false;\n this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({\n originalName: v.name,\n variable: v.tensor.variable(trainable)\n }));\n this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n originalName: v.name,\n variable: v.tensor.variable(trainable)\n }));\n }\n getConfig() {\n return {\n \"learningRate\": this.learningRate,\n \"rho\": this.rho,\n \"epsilon\": this.epsilon\n };\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls(config[\"learningRate\"], config[\"rho\"], config[\"epsilon\"]);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js\nvar AdagradOptimizer = class extends Optimizer {\n /** @nocollapse */\n static get className() {\n return \"Adagrad\";\n }\n constructor(learningRate, initialAccumulatorValue = 0.1) {\n super();\n this.learningRate = learningRate;\n this.initialAccumulatorValue = initialAccumulatorValue;\n this.accumulatedGrads = [];\n }\n applyGradients(variableGradients) {\n const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n variableNames.forEach((name, i) => {\n const value = ENGINE.registeredVariables[name];\n if (this.accumulatedGrads[i] == null) {\n const trainable = false;\n this.accumulatedGrads[i] = {\n originalName: `${name}/accumulator`,\n variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))\n };\n }\n const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n if (gradient == null) {\n return;\n }\n const accumulatedGrad = this.accumulatedGrads[i].variable;\n tidy(() => {\n const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));\n accumulatedGrad.assign(newAccumulatedGrad);\n const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n value.assign(newValue);\n });\n });\n this.incrementIterations();\n }\n dispose() {\n if (this.accumulatedGrads != null) {\n dispose(this.accumulatedGrads.map((v) => v.variable));\n }\n }\n async getWeights() {\n return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({ name: v.originalName, tensor: v.variable })));\n }\n async setWeights(weightValues) {\n weightValues = await this.extractIterations(weightValues);\n const trainable = false;\n this.accumulatedGrads = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n }\n getConfig() {\n return {\n \"learningRate\": this.learningRate,\n \"initialAccumulatorValue\": this.initialAccumulatorValue\n };\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls(config[\"learningRate\"], config[\"initialAccumulatorValue\"]);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js\nvar AdamOptimizer = class extends Optimizer {\n /** @nocollapse */\n static get className() {\n return \"Adam\";\n }\n constructor(learningRate, beta1, beta2, epsilon3 = null) {\n super();\n this.learningRate = learningRate;\n this.beta1 = beta1;\n this.beta2 = beta2;\n this.epsilon = epsilon3;\n this.accumulatedFirstMoment = [];\n this.accumulatedSecondMoment = [];\n tidy(() => {\n this.accBeta1 = scalar(beta1).variable();\n this.accBeta2 = scalar(beta2).variable();\n });\n if (epsilon3 == null) {\n this.epsilon = ENGINE.backend.epsilon();\n }\n }\n applyGradients(variableGradients) {\n const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);\n tidy(() => {\n const oneMinusAccBeta1 = sub(1, this.accBeta1);\n const oneMinusAccBeta2 = sub(1, this.accBeta2);\n varNames.forEach((name, i) => {\n const value = ENGINE.registeredVariables[name];\n const trainable = false;\n if (this.accumulatedFirstMoment[i] == null) {\n this.accumulatedFirstMoment[i] = {\n originalName: `${name}/m`,\n variable: tidy(() => zerosLike(value).variable(trainable))\n };\n }\n if (this.accumulatedSecondMoment[i] == null) {\n this.accumulatedSecondMoment[i] = {\n originalName: `${name}/v`,\n variable: tidy(() => zerosLike(value).variable(trainable))\n };\n }\n const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n if (gradient == null) {\n return;\n }\n const firstMoment = this.accumulatedFirstMoment[i].variable;\n const secondMoment = this.accumulatedSecondMoment[i].variable;\n const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n firstMoment.assign(newFirstMoment);\n secondMoment.assign(newSecondMoment);\n const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n value.assign(newValue);\n });\n this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n });\n this.incrementIterations();\n }\n dispose() {\n this.accBeta1.dispose();\n this.accBeta2.dispose();\n if (this.accumulatedFirstMoment != null) {\n dispose(this.accumulatedFirstMoment.map((v) => v.variable));\n }\n if (this.accumulatedSecondMoment != null) {\n dispose(this.accumulatedSecondMoment.map((v) => v.variable));\n }\n }\n async getWeights() {\n const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n }\n async setWeights(weightValues) {\n weightValues = await this.extractIterations(weightValues);\n tidy(() => {\n this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n });\n const variableCount = weightValues.length / 2;\n const trainable = false;\n this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({\n originalName: v.name,\n variable: v.tensor.variable(trainable)\n }));\n this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n originalName: v.name,\n variable: v.tensor.variable(trainable)\n }));\n }\n getConfig() {\n return {\n \"learningRate\": this.learningRate,\n \"beta1\": this.beta1,\n \"beta2\": this.beta2,\n \"epsilon\": this.epsilon\n };\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls(config[\"learningRate\"], config[\"beta1\"], config[\"beta2\"], config[\"epsilon\"]);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js\nvar AdamaxOptimizer = class extends Optimizer {\n /** @nocollapse */\n static get className() {\n return \"Adamax\";\n }\n constructor(learningRate, beta1, beta2, epsilon3 = null, decay = 0) {\n super();\n this.learningRate = learningRate;\n this.beta1 = beta1;\n this.beta2 = beta2;\n this.epsilon = epsilon3;\n this.decay = decay;\n this.accumulatedFirstMoment = [];\n this.accumulatedWeightedInfNorm = [];\n tidy(() => {\n this.iteration = scalar(0).variable();\n this.accBeta1 = scalar(beta1).variable();\n });\n if (epsilon3 == null) {\n this.epsilon = ENGINE.backend.epsilon();\n }\n }\n applyGradients(variableGradients) {\n const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n tidy(() => {\n const oneMinusAccBeta1 = sub(1, this.accBeta1);\n const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));\n variableNames.forEach((name, i) => {\n const value = ENGINE.registeredVariables[name];\n const trainable = false;\n if (this.accumulatedFirstMoment[i] == null) {\n this.accumulatedFirstMoment[i] = {\n originalName: `${name}/m`,\n variable: zerosLike(value).variable(trainable)\n };\n }\n if (this.accumulatedWeightedInfNorm[i] == null) {\n this.accumulatedWeightedInfNorm[i] = {\n originalName: `${name}/v`,\n variable: zerosLike(value).variable(trainable)\n };\n }\n const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n if (gradient == null) {\n return;\n }\n const firstMoment = this.accumulatedFirstMoment[i].variable;\n const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n const ut0 = mul(weightedInfNorm, this.beta2);\n const ut1 = abs(gradient);\n const newWeightedInfNorm = maximum(ut0, ut1);\n firstMoment.assign(newFirstMoment);\n weightedInfNorm.assign(newWeightedInfNorm);\n const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);\n value.assign(newValue);\n });\n this.iteration.assign(add2(this.iteration, 1));\n this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n });\n this.incrementIterations();\n }\n dispose() {\n this.accBeta1.dispose();\n this.iteration.dispose();\n if (this.accumulatedFirstMoment != null) {\n dispose(this.accumulatedFirstMoment.map((v) => v.variable));\n }\n if (this.accumulatedWeightedInfNorm != null) {\n dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));\n }\n }\n async getWeights() {\n throw new Error(\"getWeights() is not implemented for Adamax yet.\");\n }\n async setWeights(weightValues) {\n throw new Error(\"setWeights() is not implemented for Adamax yet.\");\n }\n getConfig() {\n return {\n \"learningRate\": this.learningRate,\n \"beta1\": this.beta1,\n \"beta2\": this.beta2,\n \"epsilon\": this.epsilon,\n \"decay\": this.decay\n };\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls(config[\"learningRate\"], config[\"beta1\"], config[\"beta2\"], config[\"epsilon\"], config[\"decay\"]);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js\nvar SGDOptimizer = class extends Optimizer {\n /** @nocollapse */\n static get className() {\n return \"SGD\";\n }\n constructor(learningRate) {\n super();\n this.learningRate = learningRate;\n this.setLearningRate(learningRate);\n }\n applyGradients(variableGradients) {\n const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);\n varNames.forEach((name, i) => {\n const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n if (gradient == null) {\n return;\n }\n const value = ENGINE.registeredVariables[name];\n tidy(() => {\n const newValue = add2(mul(this.c, gradient), value);\n value.assign(newValue);\n });\n });\n this.incrementIterations();\n }\n /**\n * Sets the learning rate of the optimizer.\n */\n setLearningRate(learningRate) {\n this.learningRate = learningRate;\n if (this.c != null) {\n this.c.dispose();\n }\n this.c = keep(scalar(-learningRate));\n }\n dispose() {\n this.c.dispose();\n }\n async getWeights() {\n return [await this.saveIterations()];\n }\n async setWeights(weightValues) {\n weightValues = await this.extractIterations(weightValues);\n if (weightValues.length !== 0) {\n throw new Error(\"SGD optimizer does not have settable weights.\");\n }\n }\n getConfig() {\n return { \"learningRate\": this.learningRate };\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls(config[\"learningRate\"]);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js\nvar MomentumOptimizer = class extends SGDOptimizer {\n /** @nocollapse */\n // Name matters for Python compatibility.\n static get className() {\n return \"Momentum\";\n }\n constructor(learningRate, momentum, useNesterov = false) {\n super(learningRate);\n this.learningRate = learningRate;\n this.momentum = momentum;\n this.useNesterov = useNesterov;\n this.accumulations = [];\n this.m = scalar(this.momentum);\n }\n applyGradients(variableGradients) {\n const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n variableNames.forEach((name, i) => {\n const value = ENGINE.registeredVariables[name];\n if (this.accumulations[i] == null) {\n const trainable = false;\n this.accumulations[i] = {\n originalName: `${name}/momentum`,\n variable: tidy(() => zerosLike(value).variable(trainable))\n };\n }\n const accumulation = this.accumulations[i].variable;\n const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n if (gradient == null) {\n return;\n }\n tidy(() => {\n let newValue;\n const newAccumulation = add2(mul(this.m, accumulation), gradient);\n if (this.useNesterov) {\n newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);\n } else {\n newValue = add2(mul(this.c, newAccumulation), value);\n }\n accumulation.assign(newAccumulation);\n value.assign(newValue);\n });\n });\n this.incrementIterations();\n }\n dispose() {\n this.m.dispose();\n if (this.accumulations != null) {\n dispose(this.accumulations.map((v) => v.variable));\n }\n }\n /**\n * Sets the momentum of the optimizer.\n *\n * @param momentum\n */\n setMomentum(momentum) {\n this.momentum = momentum;\n }\n async getWeights() {\n return [await this.saveIterations()].concat(this.accumulations.map((v) => ({ name: v.originalName, tensor: v.variable })));\n }\n async setWeights(weightValues) {\n weightValues = await this.extractIterations(weightValues);\n const trainable = false;\n this.accumulations = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n }\n getConfig() {\n return {\n \"learningRate\": this.learningRate,\n \"momentum\": this.momentum,\n \"useNesterov\": this.useNesterov\n };\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls(config[\"learningRate\"], config[\"momentum\"], config[\"useNesterov\"]);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js\nvar RMSPropOptimizer = class extends Optimizer {\n /** @nocollapse */\n static get className() {\n return \"RMSProp\";\n }\n constructor(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {\n super();\n this.learningRate = learningRate;\n this.decay = decay;\n this.momentum = momentum;\n this.epsilon = epsilon3;\n this.accumulatedMeanSquares = [];\n this.accumulatedMoments = [];\n this.accumulatedMeanGrads = [];\n this.centered = centered;\n if (epsilon3 == null) {\n this.epsilon = ENGINE.backend.epsilon();\n }\n if (learningRate == null) {\n throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n }\n }\n applyGradients(variableGradients) {\n const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n variableNames.forEach((name, i) => {\n const value = ENGINE.registeredVariables[name];\n const trainable = false;\n if (this.accumulatedMeanSquares[i] == null) {\n this.accumulatedMeanSquares[i] = {\n originalName: `${name}/rms`,\n variable: tidy(() => zerosLike(value).variable(trainable))\n };\n }\n if (this.accumulatedMoments[i] == null) {\n this.accumulatedMoments[i] = {\n originalName: `${name}/momentum`,\n variable: tidy(() => zerosLike(value).variable(trainable))\n };\n }\n if (this.accumulatedMeanGrads[i] == null && this.centered) {\n this.accumulatedMeanGrads[i] = {\n originalName: `${name}/mg`,\n variable: tidy(() => zerosLike(value).variable(trainable))\n };\n }\n const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n if (gradient == null) {\n return;\n }\n const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n const accumulatedMoments = this.accumulatedMoments[i].variable;\n tidy(() => {\n const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n if (this.centered) {\n const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));\n const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);\n accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n accumulatedMoments.assign(newAccumulatedMoments);\n const newValue = sub(value, newAccumulatedMoments);\n value.assign(newValue);\n } else {\n const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));\n accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);\n accumulatedMoments.assign(newAccumulatedMoments);\n const newValue = sub(value, newAccumulatedMoments);\n value.assign(newValue);\n }\n });\n });\n this.incrementIterations();\n }\n dispose() {\n if (this.accumulatedMeanSquares != null) {\n dispose(this.accumulatedMeanSquares.map((v) => v.variable));\n }\n if (this.accumulatedMeanGrads != null && this.centered) {\n dispose(this.accumulatedMeanGrads.map((v) => v.variable));\n }\n if (this.accumulatedMoments != null) {\n dispose(this.accumulatedMoments.map((v) => v.variable));\n }\n }\n async getWeights() {\n const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n if (this.centered) {\n variables.push(...this.accumulatedMeanGrads);\n }\n return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n }\n async setWeights(weightValues) {\n weightValues = await this.extractIterations(weightValues);\n const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n const trainable = false;\n this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({\n originalName: v.name,\n variable: v.tensor.variable(trainable)\n }));\n this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n originalName: v.name,\n variable: v.tensor.variable(trainable)\n }));\n if (this.centered) {\n this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({\n originalName: v.name,\n variable: v.tensor.variable(trainable)\n }));\n }\n }\n getConfig() {\n return {\n \"learningRate\": this.learningRate,\n \"decay\": this.decay,\n \"momentum\": this.momentum,\n \"epsilon\": this.epsilon,\n \"centered\": this.centered\n };\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls(config[\"learningRate\"], config[\"decay\"], config[\"momentum\"], config[\"epsilon\"], config[\"centered\"]);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/register_optimizers.js\nvar OPTIMIZERS = [\n AdadeltaOptimizer,\n AdagradOptimizer,\n AdamOptimizer,\n AdamaxOptimizer,\n MomentumOptimizer,\n RMSPropOptimizer,\n SGDOptimizer\n];\nfunction registerOptimizers() {\n for (const optimizer of OPTIMIZERS) {\n registerClass(optimizer);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/io.js\nvar io_exports = {};\n__export(io_exports, {\n CompositeArrayBuffer: () => CompositeArrayBuffer,\n browserFiles: () => browserFiles,\n browserHTTPRequest: () => browserHTTPRequest,\n concatenateArrayBuffers: () => concatenateArrayBuffers,\n copyModel: () => copyModel,\n decodeWeights: () => decodeWeights,\n decodeWeightsStream: () => decodeWeightsStream,\n encodeWeights: () => encodeWeights,\n fromMemory: () => fromMemory,\n fromMemorySync: () => fromMemorySync,\n getLoadHandlers: () => getLoadHandlers,\n getModelArtifactsForJSON: () => getModelArtifactsForJSON,\n getModelArtifactsForJSONSync: () => getModelArtifactsForJSONSync,\n getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,\n getSaveHandlers: () => getSaveHandlers,\n getWeightSpecs: () => getWeightSpecs,\n http: () => http,\n isHTTPScheme: () => isHTTPScheme,\n listModels: () => listModels,\n loadWeights: () => loadWeights,\n moveModel: () => moveModel,\n registerLoadRouter: () => registerLoadRouter,\n registerSaveRouter: () => registerSaveRouter,\n removeModel: () => removeModel,\n weightsLoaderFactory: () => weightsLoaderFactory,\n withSaveHandler: () => withSaveHandler,\n withSaveHandlerSync: () => withSaveHandlerSync\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js\nvar DEFAULT_FILE_NAME_PREFIX = \"model\";\nvar DEFAULT_JSON_EXTENSION_NAME = \".json\";\nvar DEFAULT_WEIGHT_DATA_EXTENSION_NAME = \".weights.bin\";\nfunction defer(f) {\n return new Promise((resolve) => setTimeout(resolve)).then(f);\n}\nvar BrowserDownloads = class _BrowserDownloads {\n constructor(fileNamePrefix) {\n if (!env().getBool(\"IS_BROWSER\")) {\n throw new Error(\"browserDownloads() cannot proceed because the current environment is not a browser.\");\n }\n if (fileNamePrefix.startsWith(_BrowserDownloads.URL_SCHEME)) {\n fileNamePrefix = fileNamePrefix.slice(_BrowserDownloads.URL_SCHEME.length);\n }\n if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n }\n this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n }\n async save(modelArtifacts) {\n if (typeof document === \"undefined\") {\n throw new Error(\"Browser downloads are not supported in this environment since `document` is not present\");\n }\n const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData);\n const weightsURL = window.URL.createObjectURL(new Blob([weightBuffer], { type: \"application/octet-stream\" }));\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");\n } else {\n const weightsManifest = [{\n paths: [\"./\" + this.weightDataFileName],\n weights: modelArtifacts.weightSpecs\n }];\n const modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);\n const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: \"application/json\" }));\n const jsonAnchor = this.modelJsonAnchor == null ? document.createElement(\"a\") : this.modelJsonAnchor;\n jsonAnchor.download = this.modelJsonFileName;\n jsonAnchor.href = modelJsonURL;\n await defer(() => jsonAnchor.dispatchEvent(new MouseEvent(\"click\")));\n if (modelArtifacts.weightData != null) {\n const weightDataAnchor = this.weightDataAnchor == null ? document.createElement(\"a\") : this.weightDataAnchor;\n weightDataAnchor.download = this.weightDataFileName;\n weightDataAnchor.href = weightsURL;\n await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent(\"click\")));\n }\n return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n }\n }\n};\nBrowserDownloads.URL_SCHEME = \"downloads://\";\nvar BrowserFiles = class {\n constructor(files) {\n if (files == null || files.length < 1) {\n throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);\n }\n this.jsonFile = files[0];\n this.weightsFiles = files.slice(1);\n }\n async load() {\n return new Promise((resolve, reject) => {\n const jsonReader = new FileReader();\n jsonReader.onload = (event) => {\n const modelJSON = JSON.parse(event.target.result);\n const modelTopology = modelJSON.modelTopology;\n if (modelTopology == null) {\n reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));\n return;\n }\n const weightsManifest = modelJSON.weightsManifest;\n if (weightsManifest == null) {\n reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));\n return;\n }\n if (this.weightsFiles.length === 0) {\n resolve({ modelTopology });\n return;\n }\n const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));\n resolve(modelArtifactsPromise);\n };\n jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);\n jsonReader.readAsText(this.jsonFile);\n });\n }\n loadWeights(weightsManifest) {\n const weightSpecs = [];\n const paths = [];\n for (const entry of weightsManifest) {\n weightSpecs.push(...entry.weights);\n paths.push(...entry.paths);\n }\n const pathToFile = this.checkManifestAndWeightFiles(weightsManifest);\n const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path]));\n return Promise.all(promises).then((buffers) => [weightSpecs, buffers]);\n }\n loadWeightsFile(path, file) {\n return new Promise((resolve, reject) => {\n const weightFileReader = new FileReader();\n weightFileReader.onload = (event) => {\n const weightData = event.target.result;\n resolve(weightData);\n };\n weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);\n weightFileReader.readAsArrayBuffer(file);\n });\n }\n /**\n * Check the compatibility between weights manifest and weight files.\n */\n checkManifestAndWeightFiles(manifest) {\n const basenames = [];\n const fileNames = this.weightsFiles.map((file) => basename(file.name));\n const pathToFile = {};\n for (const group of manifest) {\n group.paths.forEach((path) => {\n const pathBasename = basename(path);\n if (basenames.indexOf(pathBasename) !== -1) {\n throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);\n }\n basenames.push(pathBasename);\n if (fileNames.indexOf(pathBasename) === -1) {\n throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n } else {\n pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)];\n }\n });\n }\n if (basenames.length !== this.weightsFiles.length) {\n throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`);\n }\n return pathToFile;\n }\n};\nvar browserDownloadsRouter = (url) => {\n if (!env().getBool(\"IS_BROWSER\")) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\nfunction browserDownloads(fileNamePrefix = \"model\") {\n return new BrowserDownloads(fileNamePrefix);\n}\nfunction browserFiles(files) {\n return new BrowserFiles(files);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/progress.js\nfunction monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n checkPromises(promises);\n startFraction = startFraction == null ? 0 : startFraction;\n endFraction = endFraction == null ? 1 : endFraction;\n checkFraction(startFraction, endFraction);\n let resolvedPromise = 0;\n const registerMonitor = (promise) => {\n promise.then((value) => {\n const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);\n onProgress(fraction);\n return value;\n });\n return promise;\n };\n function checkPromises(promises2) {\n assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => \"promises must be a none empty array\");\n }\n function checkFraction(startFraction2, endFraction2) {\n assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);\n assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);\n assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);\n }\n return Promise.all(promises.map(registerMonitor));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js\nasync function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n if (loadOptions == null) {\n loadOptions = {};\n }\n const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;\n const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n const fetchStartFraction = 0;\n const fetchEndFraction = 0.5;\n const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n const bufferPromises = responses.map((response) => response.arrayBuffer());\n const bufferStartFraction = 0.5;\n const bufferEndFraction = 1;\n const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n return buffers;\n}\nfunction streamWeights(fetchURLs, loadOptions) {\n var _a;\n const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;\n let fetchIndex = 0;\n let chunkReader;\n (_a = loadOptions.onProgress) === null || _a === void 0 ? void 0 : _a.call(loadOptions, 0);\n return new ReadableStream({\n pull: async (controller) => {\n var _a2;\n while (fetchIndex < fetchURLs.length) {\n if (!chunkReader) {\n const body = (await fetchFunc(fetchURLs[fetchIndex], loadOptions.requestInit, { isBinary: true })).body;\n chunkReader = body.getReader();\n }\n const { done, value } = await chunkReader.read();\n if (done) {\n fetchIndex++;\n chunkReader = void 0;\n (_a2 = loadOptions.onProgress) === null || _a2 === void 0 ? void 0 : _a2.call(loadOptions, fetchIndex / fetchURLs.length);\n continue;\n }\n controller.enqueue(value);\n return;\n }\n controller.close();\n }\n });\n}\nasync function loadWeights(manifest, filePathPrefix = \"\", weightNames, requestInit) {\n const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n const loadWeights2 = weightsLoaderFactory(fetchWeights);\n return loadWeights2(manifest, filePathPrefix, weightNames);\n}\nfunction weightsLoaderFactory(fetchWeightsFunction) {\n return async (manifest, filePathPrefix = \"\", weightNames) => {\n const groupIndicesToFetchMap = manifest.map(() => false);\n const groupWeightsToFetch = {};\n const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n const allManifestWeightNames = [];\n manifest.forEach((manifestGroupConfig, groupIndex) => {\n let groupOffset = 0;\n manifestGroupConfig.weights.forEach((weightsEntry) => {\n const rawDtype = \"quantization\" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;\n const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);\n const enqueueWeightsForFetchingFn = () => {\n groupIndicesToFetchMap[groupIndex] = true;\n if (groupWeightsToFetch[groupIndex] == null) {\n groupWeightsToFetch[groupIndex] = [];\n }\n groupWeightsToFetch[groupIndex].push({\n manifestEntry: weightsEntry,\n groupOffset,\n sizeBytes: weightsBytes\n });\n };\n if (weightNames != null) {\n weightNames.forEach((weightName, weightIndex) => {\n if (weightName === weightsEntry.name) {\n enqueueWeightsForFetchingFn();\n weightsFound[weightIndex] = true;\n }\n });\n } else {\n enqueueWeightsForFetchingFn();\n }\n allManifestWeightNames.push(weightsEntry.name);\n groupOffset += weightsBytes;\n });\n });\n if (!weightsFound.every((found) => found)) {\n const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(\", \")}. \nManifest JSON has weights with names: ${allManifestWeightNames.join(\", \")}.`);\n }\n const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n if (shouldFetch) {\n accumulator.push(i);\n }\n return accumulator;\n }, []);\n const fetchUrls = [];\n groupIndicesToFetch.forEach((i) => {\n manifest[i].paths.forEach((filepath) => {\n const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith(\"/\") ? \"/\" : \"\") + filepath;\n fetchUrls.push(fetchUrl);\n });\n });\n const buffers = await fetchWeightsFunction(fetchUrls);\n const weightsTensorMap = {};\n let bufferIndexOffset = 0;\n groupIndicesToFetch.forEach((i) => {\n const numBuffers = manifest[i].paths.length;\n const weightsBuffer = new CompositeArrayBuffer(buffers.slice(bufferIndexOffset, bufferIndexOffset + numBuffers));\n const weightsEntries = groupWeightsToFetch[i];\n weightsEntries.forEach((weightsEntry) => {\n const byteBuffer = weightsBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n for (const name in nameToTensorMap) {\n weightsTensorMap[name] = nameToTensorMap[name];\n }\n });\n bufferIndexOffset += numBuffers;\n });\n return weightsTensorMap;\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/http.js\nvar OCTET_STREAM_MIME_TYPE = \"application/octet-stream\";\nvar JSON_TYPE = \"application/json\";\nvar HTTPRequest = class {\n constructor(path, loadOptions) {\n this.DEFAULT_METHOD = \"POST\";\n if (loadOptions == null) {\n loadOptions = {};\n }\n this.weightPathPrefix = loadOptions.weightPathPrefix;\n this.weightUrlConverter = loadOptions.weightUrlConverter;\n if (loadOptions.fetchFunc != null) {\n assert(typeof loadOptions.fetchFunc === \"function\", () => \"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)\");\n this.fetch = loadOptions.fetchFunc;\n } else {\n this.fetch = env().platform.fetch;\n }\n assert(path != null && path.length > 0, () => \"URL path for http must not be null, undefined or empty.\");\n if (Array.isArray(path)) {\n assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);\n }\n this.path = path;\n if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {\n throw new Error(\"requestInit is expected to have no pre-existing body, but has one.\");\n }\n this.requestInit = loadOptions.requestInit || {};\n this.loadOptions = loadOptions;\n }\n async save(modelArtifacts) {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\"BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.\");\n }\n const init2 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n init2.body = new FormData();\n const weightsManifest = [{\n paths: [\"./model.weights.bin\"],\n weights: modelArtifacts.weightSpecs\n }];\n const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);\n init2.body.append(\"model.json\", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), \"model.json\");\n if (modelArtifacts.weightData != null) {\n const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData);\n init2.body.append(\"model.weights.bin\", new Blob([weightBuffer], { type: OCTET_STREAM_MIME_TYPE }), \"model.weights.bin\");\n }\n const response = await this.fetch(this.path, init2);\n if (response.ok) {\n return {\n modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n responses: [response]\n };\n } else {\n throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);\n }\n }\n async loadModelJSON() {\n const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n if (!modelConfigRequest.ok) {\n throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);\n }\n let modelJSON;\n try {\n modelJSON = await modelConfigRequest.json();\n } catch (e) {\n let message = `Failed to parse model JSON of response from ${this.path}.`;\n if (this.path.endsWith(\".pb\")) {\n message += \" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.\";\n } else {\n message += \" Please make sure the server is serving valid JSON for this request.\";\n }\n throw new Error(message);\n }\n const modelTopology = modelJSON.modelTopology;\n const weightsManifest = modelJSON.weightsManifest;\n if (modelTopology == null && weightsManifest == null) {\n throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);\n }\n return modelJSON;\n }\n /**\n * Load model artifacts via HTTP request(s).\n *\n * See the documentation to `tf.io.http` for details on the saved\n * artifacts.\n *\n * @returns The loaded model artifacts (if loading succeeds).\n */\n async load() {\n if (this.loadOptions.streamWeights) {\n return this.loadStream();\n }\n const modelJSON = await this.loadModelJSON();\n return getModelArtifactsForJSON(modelJSON, (weightsManifest) => this.loadWeights(weightsManifest));\n }\n async loadStream() {\n const modelJSON = await this.loadModelJSON();\n const fetchURLs = await this.getWeightUrls(modelJSON.weightsManifest);\n const weightSpecs = getWeightSpecs(modelJSON.weightsManifest);\n const stream = () => streamWeights(fetchURLs, this.loadOptions);\n return Object.assign(Object.assign({}, modelJSON), { weightSpecs, getWeightStream: stream });\n }\n async getWeightUrls(weightsManifest) {\n const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n const [prefix, suffix] = parseUrl(weightPath);\n const pathPrefix = this.weightPathPrefix || prefix;\n const fetchURLs = [];\n const urlPromises = [];\n for (const weightsGroup of weightsManifest) {\n for (const path of weightsGroup.paths) {\n if (this.weightUrlConverter != null) {\n urlPromises.push(this.weightUrlConverter(path));\n } else {\n fetchURLs.push(pathPrefix + path + suffix);\n }\n }\n }\n if (this.weightUrlConverter) {\n fetchURLs.push(...await Promise.all(urlPromises));\n }\n return fetchURLs;\n }\n async loadWeights(weightsManifest) {\n const fetchURLs = await this.getWeightUrls(weightsManifest);\n const weightSpecs = getWeightSpecs(weightsManifest);\n const buffers = await loadWeightsAsArrayBuffer(fetchURLs, this.loadOptions);\n return [weightSpecs, buffers];\n }\n};\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\nfunction parseUrl(url) {\n const lastSlash = url.lastIndexOf(\"/\");\n const lastSearchParam = url.lastIndexOf(\"?\");\n const prefix = url.substring(0, lastSlash);\n const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : \"\";\n return [prefix + \"/\", suffix];\n}\nfunction isHTTPScheme(url) {\n return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nvar httpRouter = (url, loadOptions) => {\n if (typeof fetch === \"undefined\" && (loadOptions == null || loadOptions.fetchFunc == null)) {\n return null;\n } else {\n let isHTTP = true;\n if (Array.isArray(url)) {\n isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));\n } else {\n isHTTP = isHTTPScheme(url);\n }\n if (isHTTP) {\n return http(url, loadOptions);\n }\n }\n return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\nfunction http(path, loadOptions) {\n return new HTTPRequest(path, loadOptions);\n}\nfunction browserHTTPRequest(path, loadOptions) {\n return http(path, loadOptions);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js\nvar PassthroughLoader = class {\n constructor(modelArtifacts) {\n this.modelArtifacts = modelArtifacts;\n }\n load() {\n return this.modelArtifacts;\n }\n};\nvar PassthroughSaver = class {\n constructor(saveHandler) {\n this.saveHandler = saveHandler;\n }\n save(modelArtifacts) {\n return this.saveHandler(modelArtifacts);\n }\n};\nvar PassthroughAsync = class {\n constructor(handler) {\n if (handler.load) {\n this.load = () => Promise.resolve(handler.load());\n }\n if (handler.save) {\n this.save = (modelArtifacts) => Promise.resolve(handler.save(modelArtifacts));\n }\n }\n};\nfunction fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n const args = arguments;\n return new PassthroughAsync(fromMemorySync(...args));\n}\nfunction fromMemorySync(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n if (arguments.length === 1) {\n const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;\n if (isModelArtifacts) {\n return new PassthroughLoader(modelArtifacts);\n } else {\n console.warn(\"Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.\");\n return new PassthroughLoader({ modelTopology: modelArtifacts });\n }\n } else {\n console.warn(\"Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.\");\n return new PassthroughLoader({\n modelTopology: modelArtifacts,\n weightSpecs,\n weightData,\n trainingConfig\n });\n }\n}\nfunction withSaveHandler(saveHandler) {\n return new PassthroughSaver(saveHandler);\n}\nfunction withSaveHandlerSync(saveHandler) {\n return new PassthroughSaver(saveHandler);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/math.js\nvar math_exports = {};\n__export(math_exports, {\n confusionMatrix: () => confusionMatrix\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js\nfunction confusionMatrix_(labels, predictions, numClasses) {\n const $labels = convertToTensor(labels, \"labels\", \"confusionMatrix\");\n const $predictions = convertToTensor(predictions, \"predictions\", \"confusionMatrix\");\n assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);\n assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);\n assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);\n assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);\n const oneHotLabels = oneHot(cast($labels, \"int32\"), numClasses);\n const oneHotPredictions = oneHot(cast($predictions, \"int32\"), numClasses);\n const oneHotLabelsT = transpose(oneHotLabels);\n const product = matMul(oneHotLabelsT, oneHotPredictions);\n return cast(product, \"int32\");\n}\nvar confusionMatrix = op({ confusionMatrix_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js\nvar browser_exports = {};\n__export(browser_exports, {\n draw: () => draw,\n fromPixels: () => fromPixels,\n fromPixelsAsync: () => fromPixelsAsync,\n toPixels: () => toPixels\n});\nvar fromPixels2DContext;\nvar hasToPixelsWarned = false;\nfunction fromPixels_(pixels, numChannels = 3) {\n if (numChannels > 4) {\n throw new Error(\"Cannot construct Tensor with more than 4 channels from pixels.\");\n }\n if (pixels == null) {\n throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");\n }\n let isPixelData2 = false;\n let isImageData = false;\n let isVideo = false;\n let isImage = false;\n let isCanvasLike = false;\n let isImageBitmap = false;\n if (pixels.data instanceof Uint8Array) {\n isPixelData2 = true;\n } else if (typeof ImageData !== \"undefined\" && pixels instanceof ImageData) {\n isImageData = true;\n } else if (typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement) {\n isVideo = true;\n } else if (typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement) {\n isImage = true;\n } else if (pixels.getContext != null) {\n isCanvasLike = true;\n } else if (typeof ImageBitmap !== \"undefined\" && pixels instanceof ImageBitmap) {\n isImageBitmap = true;\n } else {\n throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);\n }\n const kernel = getKernel(FromPixels, ENGINE.backendName);\n if (kernel != null) {\n const inputs = { pixels };\n const attrs = { numChannels };\n return ENGINE.runKernel(FromPixels, inputs, attrs);\n }\n const [width, height] = isVideo ? [\n pixels.videoWidth,\n pixels.videoHeight\n ] : [pixels.width, pixels.height];\n let vals;\n if (isCanvasLike) {\n vals = // tslint:disable-next-line:no-any\n pixels.getContext(\"2d\").getImageData(0, 0, width, height).data;\n } else if (isImageData || isPixelData2) {\n vals = pixels.data;\n } else if (isImage || isVideo || isImageBitmap) {\n if (fromPixels2DContext == null) {\n if (typeof document === \"undefined\") {\n if (typeof OffscreenCanvas !== \"undefined\" && typeof OffscreenCanvasRenderingContext2D !== \"undefined\") {\n fromPixels2DContext = new OffscreenCanvas(1, 1).getContext(\"2d\");\n } else {\n throw new Error(\"Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.\");\n }\n } else {\n fromPixels2DContext = document.createElement(\"canvas\").getContext(\"2d\", { willReadFrequently: true });\n }\n }\n fromPixels2DContext.canvas.width = width;\n fromPixels2DContext.canvas.height = height;\n fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n }\n let values;\n if (numChannels === 4) {\n values = new Int32Array(vals);\n } else {\n const numPixels = width * height;\n values = new Int32Array(numPixels * numChannels);\n for (let i = 0; i < numPixels; i++) {\n for (let channel = 0; channel < numChannels; ++channel) {\n values[i * numChannels + channel] = vals[i * 4 + channel];\n }\n }\n }\n const outShape = [height, width, numChannels];\n return tensor3d(values, outShape, \"int32\");\n}\nfunction isPixelData(pixels) {\n return pixels != null && pixels.data instanceof Uint8Array;\n}\nfunction isImageBitmapFullySupported() {\n return typeof window !== \"undefined\" && typeof ImageBitmap !== \"undefined\" && window.hasOwnProperty(\"createImageBitmap\");\n}\nfunction isNonEmptyPixels(pixels) {\n return pixels != null && pixels.width !== 0 && pixels.height !== 0;\n}\nfunction canWrapPixelsToImageBitmap(pixels) {\n return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels);\n}\nasync function fromPixelsAsync(pixels, numChannels = 3) {\n let inputs = null;\n if (env().getBool(\"WRAP_TO_IMAGEBITMAP\") && canWrapPixelsToImageBitmap(pixels)) {\n let imageBitmap;\n try {\n imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: \"none\" });\n } catch (e) {\n imageBitmap = null;\n }\n if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) {\n inputs = imageBitmap;\n } else {\n inputs = pixels;\n }\n } else {\n inputs = pixels;\n }\n return fromPixels_(inputs, numChannels);\n}\nfunction validateImgTensor(img) {\n if (img.rank !== 2 && img.rank !== 3) {\n throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${img.rank}.`);\n }\n const depth = img.rank === 2 ? 1 : img.shape[2];\n if (depth > 4 || depth === 2) {\n throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);\n }\n if (img.dtype !== \"float32\" && img.dtype !== \"int32\") {\n throw new Error(`Unsupported type for toPixels: ${img.dtype}. Please use float32 or int32 tensors.`);\n }\n}\nfunction validateImageOptions(imageOptions) {\n const alpha = (imageOptions === null || imageOptions === void 0 ? void 0 : imageOptions.alpha) || 1;\n if (alpha > 1 || alpha < 0) {\n throw new Error(`Alpha value ${alpha} is suppoed to be in range [0 - 1].`);\n }\n}\nasync function toPixels(img, canvas) {\n let $img = convertToTensor(img, \"img\", \"toPixels\");\n if (!(img instanceof Tensor)) {\n const originalImgTensor = $img;\n $img = cast(originalImgTensor, \"int32\");\n originalImgTensor.dispose();\n }\n validateImgTensor($img);\n const [height, width] = $img.shape.slice(0, 2);\n const depth = $img.rank === 2 ? 1 : $img.shape[2];\n const data = await $img.data();\n const multiplier = $img.dtype === \"float32\" ? 255 : 1;\n const bytes = new Uint8ClampedArray(width * height * 4);\n for (let i = 0; i < height * width; ++i) {\n const rgba = [0, 0, 0, 255];\n for (let d = 0; d < depth; d++) {\n const value = data[i * depth + d];\n if ($img.dtype === \"float32\") {\n if (value < 0 || value > 1) {\n throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);\n }\n } else if ($img.dtype === \"int32\") {\n if (value < 0 || value > 255) {\n throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);\n }\n }\n if (depth === 1) {\n rgba[0] = value * multiplier;\n rgba[1] = value * multiplier;\n rgba[2] = value * multiplier;\n } else {\n rgba[d] = value * multiplier;\n }\n }\n const j = i * 4;\n bytes[j + 0] = Math.round(rgba[0]);\n bytes[j + 1] = Math.round(rgba[1]);\n bytes[j + 2] = Math.round(rgba[2]);\n bytes[j + 3] = Math.round(rgba[3]);\n }\n if (canvas != null) {\n if (!hasToPixelsWarned) {\n const kernel = getKernel(Draw, ENGINE.backendName);\n if (kernel != null) {\n console.warn(\"tf.browser.toPixels is not efficient to draw tensor on canvas. Please try tf.browser.draw instead.\");\n hasToPixelsWarned = true;\n }\n }\n canvas.width = width;\n canvas.height = height;\n const ctx = canvas.getContext(\"2d\");\n const imageData = new ImageData(bytes, width, height);\n ctx.putImageData(imageData, 0, 0);\n }\n if ($img !== img) {\n $img.dispose();\n }\n return bytes;\n}\nfunction draw(image2, canvas, options) {\n let $img = convertToTensor(image2, \"img\", \"draw\");\n if (!(image2 instanceof Tensor)) {\n const originalImgTensor = $img;\n $img = cast(originalImgTensor, \"int32\");\n originalImgTensor.dispose();\n }\n validateImgTensor($img);\n validateImageOptions(options === null || options === void 0 ? void 0 : options.imageOptions);\n const inputs = { image: $img };\n const attrs = { canvas, options };\n ENGINE.runKernel(Draw, inputs, attrs);\n}\nvar fromPixels = op({ fromPixels_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js\nvar gather_nd_util_exports = {};\n__export(gather_nd_util_exports, {\n prepareAndValidate: () => prepareAndValidate\n});\nfunction prepareAndValidate(tensor2, indices) {\n const tensorRank = tensor2.shape.length;\n const indicesRank = indices.shape.length;\n if (tensorRank < 1) {\n throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);\n }\n if (indicesRank < 1) {\n throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);\n }\n if (indices.dtype !== \"int32\") {\n throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);\n }\n if (indices.shape[indicesRank - 1] > tensorRank) {\n throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);\n }\n if (sizeFromShape(tensor2.shape) === 0) {\n throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);\n }\n const indicesShape = indices.shape;\n const sliceRank = indicesShape[indicesShape.length - 1];\n let nResult = 1;\n for (let i = 0; i < indicesShape.length - 1; ++i) {\n nResult *= indicesShape[i];\n }\n const inputShape = tensor2.shape;\n const resultShape = indicesShape.slice();\n resultShape.pop();\n let sliceSize = 1;\n for (let i = sliceRank; i < tensorRank; ++i) {\n sliceSize *= inputShape[i];\n resultShape.push(inputShape[i]);\n }\n const strides = [\n ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize),\n 1\n ].slice(0, sliceRank);\n return [resultShape, nResult, sliceSize, strides];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js\nvar slice_util_exports = {};\n__export(slice_util_exports, {\n assertParamsValid: () => assertParamsValid,\n computeFlatOffset: () => computeFlatOffset,\n computeOutShape: () => computeOutShape,\n getNormalizedAxes: () => getNormalizedAxes,\n isSliceContinous: () => isSliceContinous,\n maskToAxes: () => maskToAxes,\n parseSliceParams: () => parseSliceParams,\n sliceInfo: () => sliceInfo,\n startForAxis: () => startForAxis,\n startIndicesWithElidedDims: () => startIndicesWithElidedDims,\n stopForAxis: () => stopForAxis,\n stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,\n stridesForAxis: () => stridesForAxis,\n stridesWithElidedDims: () => stridesWithElidedDims\n});\nvar NEW_AXIS = -2;\nvar SHRINK_AXIS = -1;\nfunction assertParamsValid(input2, begin, size) {\n const inputRank = input2.shape.length;\n assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`);\n assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);\n for (let i = 0; i < inputRank; ++i) {\n assert(begin[i] + size[i] <= input2.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`);\n }\n}\nfunction maskToAxes(mask) {\n const axes = [];\n let axis = 0;\n while (mask > 0) {\n if (mask & 1) {\n axes.push(axis);\n }\n mask /= 2;\n axis++;\n }\n return axes;\n}\nfunction computeOutShape(begin, end, strides) {\n const size = [];\n for (let axis = 0; axis < begin.length; axis++) {\n size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n }\n return size;\n}\nfunction stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n const newStrides = [...strides];\n for (let i = newStrides.length; i < inputShape.length; i++) {\n newStrides.push(1);\n }\n for (let i = 0; i < numElidedAxes; i++) {\n if (i === 0) {\n newStrides[ellipsisInsertionIndex] = 1;\n } else {\n newStrides.splice(\n ellipsisInsertionIndex,\n 0,\n 1\n /* element to add */\n );\n newStrides.pop();\n }\n }\n return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n if (normalizedAxis <= ellipsisInsertionIndex) {\n return normalizedAxis;\n }\n return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n const elidedAxes = [];\n for (let i = 0; i < numElidedAxes; i++) {\n elidedAxes.push(ellipsisInsertionIndex + i);\n }\n return elidedAxes;\n}\nfunction getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n const inputRank = inputShape.length;\n let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n const fullIndex = ellipsisAxes[0];\n const numElidedAxes = numInterpolatedAxes + 1;\n normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n } else {\n for (let axis = 0; axis < inputRank; axis++) {\n normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n }\n }\n return {\n begin: normalizedBegin,\n end: normalizedEnd,\n strides: normalizedStrides\n };\n}\nfunction startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n const newIndices = [...inputShape];\n const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n for (let axis = 0; axis < newIndices.length; axis++) {\n if (elidedAxes.indexOf(axis) > -1) {\n newIndices[axis] = 0;\n } else {\n const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n let originalValue = originalBegin[originalAxis];\n if (beginMask & 1 << originalAxis) {\n originalValue = 0;\n }\n newIndices[axis] = originalValue;\n }\n }\n return newIndices;\n}\nfunction stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n const newIndices = [...inputShape];\n const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n for (let axis = 0; axis < newIndices.length; axis++) {\n if (elidedAxes.indexOf(axis) > -1) {\n newIndices[axis] = Number.MAX_SAFE_INTEGER;\n } else {\n const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n let originalValue = originalEnd[originalAxis];\n if (endMask & 1 << originalAxis) {\n originalValue = Number.MAX_SAFE_INTEGER;\n }\n newIndices[axis] = originalValue;\n }\n }\n for (let i = 0; i < newIndices.length; i++) {\n const axisSize = inputShape[i];\n if (newIndices[i] < 0) {\n newIndices[i] += axisSize;\n }\n newIndices[i] = clamp(0, newIndices[i], inputShape[i]);\n }\n return newIndices;\n}\nfunction stridesForAxis(strides, axis, ellipsisMask) {\n let stride = strides[axis];\n if (ellipsisMask & 1 << axis || stride == null) {\n stride = 1;\n }\n return stride;\n}\nfunction startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n let start = startIndices[axis];\n const stride = strides[axis] || 1;\n if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n if (stride > 0) {\n start = Number.MIN_SAFE_INTEGER;\n } else {\n start = Number.MAX_SAFE_INTEGER;\n }\n }\n const axisSize = inputShape[axis];\n if (start < 0) {\n start += axisSize;\n }\n start = clamp(0, start, axisSize - 1);\n return start;\n}\nfunction stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n let stop = stopIndices[axis];\n const stride = strides[axis] || 1;\n if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {\n if (stride > 0) {\n stop = Number.MAX_SAFE_INTEGER;\n } else {\n stop = Number.MIN_SAFE_INTEGER;\n }\n }\n const axisSize = inputShape[axis];\n if (stop < 0) {\n stop += axisSize;\n }\n if (stride > 0) {\n stop = clamp(0, stop, axisSize);\n } else {\n stop = clamp(-1, stop, axisSize - 1);\n }\n return stop;\n}\nfunction isSliceContinous(shape, begin, size) {\n let firstNonOneAxis = size.length;\n for (let i = 0; i < size.length; i++) {\n if (size[i] > 1) {\n firstNonOneAxis = i;\n break;\n }\n }\n for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n if (begin[i] > 0 || size[i] !== shape[i]) {\n return false;\n }\n }\n return true;\n}\nfunction computeFlatOffset(begin, strides) {\n let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n for (let i = 0; i < begin.length - 1; i++) {\n flatOffset += begin[i] * strides[i];\n }\n return flatOffset;\n}\nfunction parseSliceParams(x, begin, size) {\n let begin_;\n const xRank = x.shape.length;\n if (typeof begin === \"number\") {\n begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n } else if (begin.length < xRank) {\n begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n } else {\n begin_ = begin.slice();\n }\n begin_.forEach((d) => {\n assert(d !== -1, () => \"slice() does not support negative begin indexing.\");\n });\n let size_;\n if (size == null) {\n size_ = new Array(xRank).fill(-1);\n } else if (typeof size === \"number\") {\n size_ = [size, ...new Array(xRank - 1).fill(-1)];\n } else if (size.length < xRank) {\n size_ = size.concat(new Array(xRank - size.length).fill(-1));\n } else {\n size_ = size;\n }\n size_ = size_.map((d, i) => {\n if (d >= 0) {\n return d;\n } else {\n assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`);\n return x.shape[i] - begin_[i];\n }\n });\n return [begin_, size_];\n}\nfunction sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n let stridesNonNull;\n if (strides == null) {\n stridesNonNull = new Array(begin.length);\n stridesNonNull.fill(1);\n } else {\n stridesNonNull = strides;\n }\n if (ellipsisMask != null && (ellipsisMask & ellipsisMask - 1) !== 0) {\n throw new Error(\"Multiple ellipses in slice is not allowed.\");\n }\n let ellipsisSeen = false;\n const sparseSpec = {\n dims: stridesNonNull.length,\n numAddAxisAfterEllipsis: 0,\n begin: begin.slice(),\n end: end.slice(),\n strides: stridesNonNull.slice(),\n beginMask,\n endMask,\n ellipsisMask,\n newAxisMask,\n shrinkAxisMask\n };\n for (let i = 0; i < sparseSpec.dims; i++) {\n if (ellipsisSeen && (1 << i & newAxisMask) !== 0) {\n sparseSpec.numAddAxisAfterEllipsis++;\n }\n if (1 << i & ellipsisMask) {\n ellipsisSeen = true;\n }\n }\n if (!ellipsisSeen) {\n sparseSpec.ellipsisMask |= 1 << sparseSpec.dims;\n sparseSpec.dims++;\n }\n const denseSpec = {\n dims: xShape.length,\n beginMask: 0,\n endMask: 0,\n beginValid: false,\n endValid: false\n };\n buildDenseSpec(sparseSpec, denseSpec);\n let isIdentity = true;\n let sliceDim0 = true;\n let isSimpleSlice = true;\n const processingShape = [];\n const finalShape = [];\n for (let i = 0; i < xShape.length; ++i) {\n if (denseSpec.strides[i] === 0) {\n throw Error(`strides[${i}] must be non-zero`);\n }\n const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i);\n const dimI = xShape[i];\n if (dimI === -1) {\n processingShape.push(shrinkI ? 1 : -1);\n continue;\n }\n const masks = [denseSpec.beginMask & 1 << i, denseSpec.endMask & 1 << i];\n const validRange = [\n denseSpec.strides[i] > 0 ? 0 : -1,\n denseSpec.strides[i] > 0 ? dimI : dimI - 1\n ];\n if (shrinkI && denseSpec.strides[i] <= 0) {\n throw Error(\"only stride 1 allowed on non-range indexing.\");\n }\n isSimpleSlice = isSimpleSlice && denseSpec.strides[i] === 1;\n const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i && denseSpec.endMask & 1 << i);\n if (denseSpec.beginValid && denseSpec.endValid) {\n if (shrinkI) {\n const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] : denseSpec.begin[i];\n denseSpec.begin[i] = xFwd;\n denseSpec.end[i] = denseSpec.begin[i] + 1;\n if (xFwd < 0 || xFwd >= dimI) {\n throw Error(`slice index ${denseSpec.begin[i]} of dimension ${i} out of bounds.`);\n }\n } else {\n denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange);\n denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);\n }\n const takeAllInDimension = denseSpec.strides[i] === 1 && denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;\n isIdentity = isIdentity && takeAllInDimension;\n sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || takeAllInDimension);\n } else {\n isIdentity = isIdentity && (denseSpec.strides[i] === 1 && beginAndEndMasked);\n sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || beginAndEndMasked);\n }\n let intervalLength;\n let knownInterval = false;\n if (denseSpec.beginValid && denseSpec.endValid) {\n intervalLength = denseSpec.end[i] - denseSpec.begin[i];\n knownInterval = true;\n } else if (shrinkI) {\n intervalLength = 1;\n knownInterval = true;\n } else if (beginAndEndMasked) {\n if (dimI >= 0) {\n if (denseSpec.strides[i] < 0) {\n intervalLength = -dimI;\n } else {\n intervalLength = dimI;\n }\n knownInterval = true;\n }\n }\n if (knownInterval) {\n let sizeI;\n if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i] < 0) {\n sizeI = 0;\n } else {\n sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) + (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);\n }\n processingShape.push(sizeI);\n } else {\n processingShape.push(-1);\n }\n }\n for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) {\n const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];\n if (gatherIndex >= 0) {\n finalShape.push(processingShape[gatherIndex]);\n } else if (gatherIndex === NEW_AXIS) {\n finalShape.push(1);\n }\n }\n const finalShapeSparse = finalShape.filter((dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS);\n return {\n finalShapeSparse,\n finalShape,\n isIdentity,\n sliceDim0,\n isSimpleSlice,\n begin: denseSpec.begin,\n end: denseSpec.end,\n strides: denseSpec.strides\n };\n}\nfunction buildDenseSpec(sparse2, dense2) {\n dense2.beginMask = 0;\n dense2.endMask = 0;\n dense2.shrinkAxisMask = 0;\n let fullIndex = 0;\n dense2.beginValid = sparse2.begin != null;\n dense2.endValid = sparse2.end != null;\n dense2.begin = new Array(dense2.dims);\n dense2.end = new Array(dense2.dims);\n dense2.strides = new Array(dense2.dims);\n dense2.finalShapeGatherIndices = [];\n dense2.finalShapeGatherIndicesSparse = [];\n dense2.inputShapeGatherIndicesSparse = new Array(dense2.dims);\n for (let i = 0; i < sparse2.dims; i++) {\n if (1 << i & sparse2.ellipsisMask) {\n const nextIndex = Math.min(dense2.dims - (sparse2.dims - i) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims);\n for (; fullIndex < nextIndex; fullIndex++) {\n dense2.begin[fullIndex] = 0;\n dense2.end[fullIndex] = 0;\n dense2.strides[fullIndex] = 1;\n dense2.beginMask |= 1 << fullIndex;\n dense2.endMask |= 1 << fullIndex;\n dense2.finalShapeGatherIndices.push(fullIndex);\n dense2.finalShapeGatherIndicesSparse.push(-1);\n dense2.inputShapeGatherIndicesSparse[fullIndex] = i;\n }\n } else if (1 << i & sparse2.newAxisMask) {\n dense2.finalShapeGatherIndices.push(NEW_AXIS);\n dense2.finalShapeGatherIndicesSparse.push(-1);\n } else {\n if (fullIndex === dense2.begin.length) {\n throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense2.dims} dims, ${dense2.begin.length}.`);\n }\n if (sparse2.begin != null) {\n dense2.begin[fullIndex] = sparse2.begin[i];\n }\n if (sparse2.end != null) {\n dense2.end[fullIndex] = sparse2.end[i];\n }\n dense2.strides[fullIndex] = sparse2.strides[i];\n if (sparse2.beginMask & 1 << i) {\n dense2.beginMask |= 1 << fullIndex;\n }\n if (sparse2.endMask & 1 << i) {\n dense2.endMask |= 1 << fullIndex;\n }\n if (sparse2.shrinkAxisMask & 1 << i) {\n dense2.finalShapeGatherIndices.push(SHRINK_AXIS);\n dense2.finalShapeGatherIndicesSparse.push(-1);\n dense2.shrinkAxisMask |= 1 << fullIndex;\n } else {\n dense2.finalShapeGatherIndices.push(fullIndex);\n dense2.finalShapeGatherIndicesSparse.push(i);\n }\n dense2.inputShapeGatherIndicesSparse[fullIndex] = i;\n fullIndex++;\n }\n }\n}\nfunction canonical(x, c, strideI, dimI, masks, validRange) {\n if (masks[c]) {\n return strideI > 0 ? validRange[c] : validRange[c + 1 & 1];\n } else {\n const xFwd = x < 0 ? dimI + x : x;\n return xFwd < validRange[0] ? validRange[0] : xFwd > validRange[1] ? validRange[1] : xFwd;\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/version.js\nvar version = \"4.16.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js\nvar OptimizerConstructors = class {\n /**\n * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n *\n * ```js\n * // Fit a quadratic function by learning the coefficients a, b, c.\n * const xs = tf.tensor1d([0, 1, 2, 3]);\n * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n *\n * const a = tf.scalar(Math.random()).variable();\n * const b = tf.scalar(Math.random()).variable();\n * const c = tf.scalar(Math.random()).variable();\n *\n * // y = a * x^2 + b * x + c.\n * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n * const loss = (pred, label) => pred.sub(label).square().mean();\n *\n * const learningRate = 0.01;\n * const optimizer = tf.train.sgd(learningRate);\n *\n * // Train the model.\n * for (let i = 0; i < 10; i++) {\n * optimizer.minimize(() => loss(f(xs), ys));\n * }\n *\n * // Make predictions.\n * console.log(\n * `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n * const preds = f(xs).dataSync();\n * preds.forEach((pred, i) => {\n * console.log(`x: ${i}, pred: ${pred}`);\n * });\n * ```\n *\n * @param learningRate The learning rate to use for the SGD algorithm.\n *\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static sgd(learningRate) {\n return new SGDOptimizer(learningRate);\n }\n /**\n * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n * descent.\n *\n * See\n * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n * http://proceedings.mlr.press/v28/sutskever13.pdf)\n *\n * @param learningRate The learning rate to use for the Momentum gradient\n * descent algorithm.\n * @param momentum The momentum to use for the momentum gradient descent\n * algorithm.\n *\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static momentum(learningRate, momentum, useNesterov = false) {\n return new MomentumOptimizer(learningRate, momentum, useNesterov);\n }\n /**\n * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n * descent. This implementation uses plain momentum and is not centered\n * version of RMSProp.\n *\n * See\n * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n *\n * @param learningRate The learning rate to use for the RMSProp gradient\n * descent algorithm.\n * @param decay The discounting factor for the history/coming gradient.\n * @param momentum The momentum to use for the RMSProp gradient descent\n * algorithm.\n * @param epsilon Small value to avoid zero denominator.\n * @param centered If true, gradients are normalized by the estimated\n * variance of the gradient.\n *\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {\n return new RMSPropOptimizer(learningRate, decay, momentum, epsilon3, centered);\n }\n /**\n * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n *\n * @param learningRate The learning rate to use for the Adam gradient\n * descent algorithm.\n * @param beta1 The exponential decay rate for the 1st moment estimates.\n * @param beta2 The exponential decay rate for the 2nd moment estimates.\n * @param epsilon A small constant for numerical stability.\n *\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null) {\n return new AdamOptimizer(learningRate, beta1, beta2, epsilon3);\n }\n /**\n * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n *\n * @param learningRate The learning rate to use for the Adadelta gradient\n * descent algorithm.\n * @param rho The learning rate decay over each update.\n * @param epsilon A constant epsilon used to better condition the grad\n * update.\n *\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static adadelta(learningRate = 1e-3, rho = 0.95, epsilon3 = null) {\n return new AdadeltaOptimizer(learningRate, rho, epsilon3);\n }\n /**\n * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n *\n * @param learningRate The learning rate to use for the Adamax gradient\n * descent algorithm.\n * @param beta1 The exponential decay rate for the 1st moment estimates.\n * @param beta2 The exponential decay rate for the 2nd moment estimates.\n * @param epsilon A small constant for numerical stability.\n * @param decay The learning rate decay over each update.\n *\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null, decay = 0) {\n return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon3, decay);\n }\n /**\n * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n * See\n * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n * or\n * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n *\n * @param learningRate The learning rate to use for the Adagrad gradient\n * descent algorithm.\n * @param initialAccumulatorValue Starting value for the accumulators, must be\n * positive.\n *\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/train.js\nvar train = OptimizerConstructors;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/browser_util.js\nvar delayCallback = (() => {\n if (typeof requestAnimationFrame !== \"undefined\") {\n return requestAnimationFrame;\n } else if (typeof setImmediate !== \"undefined\") {\n return setImmediate;\n }\n return (f) => f();\n})();\nfunction nextFrame() {\n return new Promise((resolve) => delayCallback(() => resolve()));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js\nvar backend_util_exports = {};\n__export(backend_util_exports, {\n ERF_A1: () => ERF_A1,\n ERF_A2: () => ERF_A2,\n ERF_A3: () => ERF_A3,\n ERF_A4: () => ERF_A4,\n ERF_A5: () => ERF_A5,\n ERF_P: () => ERF_P,\n PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,\n RowPartitionType: () => RowPartitionType,\n SELU_SCALE: () => SELU_SCALE,\n SELU_SCALEALPHA: () => SELU_SCALEALPHA,\n applyActivation: () => applyActivation,\n assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,\n assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,\n assertParamsConsistent: () => assertParamsConsistent,\n assignToTypedArray: () => assignToTypedArray,\n axesAreInnerMostDims: () => axesAreInnerMostDims,\n calculateShapes: () => calculateShapes,\n checkEinsumDimSizes: () => checkEinsumDimSizes,\n checkPadOnDimRoundingMode: () => checkPadOnDimRoundingMode,\n combineLocations: () => combineLocations,\n combineRaggedTensorToTensorShapes: () => combineRaggedTensorToTensorShapes,\n complexWithEvenIndex: () => complexWithEvenIndex,\n complexWithOddIndex: () => complexWithOddIndex,\n computeConv2DInfo: () => computeConv2DInfo,\n computeConv3DInfo: () => computeConv3DInfo,\n computeDefaultPad: () => computeDefaultPad,\n computeDilation2DInfo: () => computeDilation2DInfo,\n computeOptimalWindowSize: () => computeOptimalWindowSize,\n computeOutAndReduceShapes: () => computeOutAndReduceShapes,\n computeOutShape: () => computeOutShape2,\n computePool2DInfo: () => computePool2DInfo,\n computePool3DInfo: () => computePool3DInfo,\n convertConv2DDataFormat: () => convertConv2DDataFormat,\n decodeEinsumEquation: () => decodeEinsumEquation,\n eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,\n expandShapeToKeepDim: () => expandShapeToKeepDim,\n exponent: () => exponent,\n exponents: () => exponents,\n fromStringArrayToUint8: () => fromStringArrayToUint8,\n fromUint8ToStringArray: () => fromUint8ToStringArray,\n getAxesPermutation: () => getAxesPermutation,\n getBroadcastDims: () => getBroadcastDims,\n getComplexWithIndex: () => getComplexWithIndex,\n getEinsumComputePath: () => getEinsumComputePath,\n getEinsumPermutation: () => getEinsumPermutation,\n getFusedBiasGradient: () => getFusedBiasGradient,\n getFusedDyActivation: () => getFusedDyActivation,\n getImageCenter: () => getImageCenter,\n getInnerMostAxes: () => getInnerMostAxes,\n getPermuted: () => getPermuted,\n getRaggedRank: () => getRaggedRank,\n getReductionAxes: () => getReductionAxes,\n getReshaped: () => getReshaped,\n getReshapedPermuted: () => getReshapedPermuted,\n getRowPartitionTypesHelper: () => getRowPartitionTypesHelper,\n getSliceBeginCoords: () => getSliceBeginCoords,\n getSliceSize: () => getSliceSize,\n getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => getSparseFillEmptyRowsIndicesDenseShapeMismatch,\n getSparseFillEmptyRowsNegativeIndexErrorMessage: () => getSparseFillEmptyRowsNegativeIndexErrorMessage,\n getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => getSparseFillEmptyRowsOutOfRangeIndexErrorMessage,\n getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => getSparseReshapeEmptyTensorZeroOutputDimErrorMessage,\n getSparseReshapeInputOutputMismatchErrorMessage: () => getSparseReshapeInputOutputMismatchErrorMessage,\n getSparseReshapeInputOutputMultipleErrorMessage: () => getSparseReshapeInputOutputMultipleErrorMessage,\n getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => getSparseReshapeMultipleNegativeOneOutputDimErrorMessage,\n getSparseReshapeNegativeOutputDimErrorMessage: () => getSparseReshapeNegativeOutputDimErrorMessage,\n getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => getSparseSegmentReductionIndicesOutOfRangeErrorMessage,\n getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => getSparseSegmentReductionNegativeSegmentIdsErrorMessage,\n getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage,\n getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage,\n getUndoAxesPermutation: () => getUndoAxesPermutation,\n isIdentityPermutation: () => isIdentityPermutation,\n log: () => log,\n mergeRealAndImagArrays: () => mergeRealAndImagArrays,\n prepareAndValidate: () => prepareAndValidate,\n prepareSplitSize: () => prepareSplitSize,\n segment_util: () => segment_util_exports,\n shouldFuse: () => shouldFuse,\n slice_util: () => slice_util_exports,\n splitRealAndImagArrays: () => splitRealAndImagArrays,\n stridesOrDilationsArePositive: () => stridesOrDilationsArePositive,\n tupleValuesAreOne: () => tupleValuesAreOne,\n upcastType: () => upcastType,\n validateDefaultValueShape: () => validateDefaultValueShape,\n validateInput: () => validateInput,\n validateUpdateShape: () => validateUpdateShape,\n warn: () => warn\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js\nfunction assertParamsConsistent(shapes, axis) {\n const rank = shapes[0].length;\n shapes.forEach((shape, i) => {\n assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`);\n });\n assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n const firstShape = shapes[0];\n shapes.forEach((shape, i) => {\n for (let r = 0; r < rank; r++) {\n assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`);\n }\n });\n}\nfunction computeOutShape2(shapes, axis) {\n const outputShape = shapes[0].slice();\n for (let i = 1; i < shapes.length; i++) {\n outputShape[axis] += shapes[i][axis];\n }\n return outputShape;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_to_dense_util.js\nvar RowPartitionType;\n(function(RowPartitionType3) {\n RowPartitionType3[RowPartitionType3[\"FIRST_DIM_SIZE\"] = 0] = \"FIRST_DIM_SIZE\";\n RowPartitionType3[RowPartitionType3[\"VALUE_ROWIDS\"] = 1] = \"VALUE_ROWIDS\";\n RowPartitionType3[RowPartitionType3[\"ROW_LENGTHS\"] = 2] = \"ROW_LENGTHS\";\n RowPartitionType3[RowPartitionType3[\"ROW_SPLITS\"] = 3] = \"ROW_SPLITS\";\n RowPartitionType3[RowPartitionType3[\"ROW_LIMITS\"] = 4] = \"ROW_LIMITS\";\n RowPartitionType3[RowPartitionType3[\"ROW_STARTS\"] = 5] = \"ROW_STARTS\";\n})(RowPartitionType || (RowPartitionType = {}));\nfunction combineRaggedTensorToTensorShapes(raggedRank, shape, valueShape) {\n let outputShape = new Array();\n if (valueShape == null && shape == null) {\n return outputShape;\n }\n if (shape == null) {\n while (outputShape.length < raggedRank + valueShape.length) {\n outputShape.push(-1);\n }\n } else {\n outputShape = shape.slice();\n }\n if (valueShape == null) {\n return outputShape;\n }\n if (raggedRank + valueShape.length !== outputShape.length) {\n throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.rank = ${raggedRank + valueShape.length}, but shape.rank = ${outputShape.length}`);\n }\n for (let i = 1; i < valueShape.length; ++i) {\n const valueDim = valueShape[i];\n const outputShapeDimIndex = outputShape[outputShape.length - valueShape.length + i];\n const outputShapeDim = outputShape[outputShapeDimIndex];\n if (valueDim >= 0) {\n if (outputShapeDim >= 0) {\n if (outputShapeDim !== valueDim) {\n throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.shape[${i + raggedRank}] = ${valueDim} but shape[${i + raggedRank}] = ${outputShapeDim}`);\n }\n } else {\n outputShape[outputShapeDimIndex] = valueDim;\n }\n }\n }\n return outputShape;\n}\nfunction getRowPartitionTypesHelper(rowPartitionTypeStrings) {\n const stringToType = {\n \"FIRST_DIM_SIZE\": RowPartitionType.FIRST_DIM_SIZE,\n \"VALUE_ROWIDS\": RowPartitionType.VALUE_ROWIDS,\n \"ROW_LENGTHS\": RowPartitionType.ROW_LENGTHS,\n \"ROW_SPLITS\": RowPartitionType.ROW_SPLITS,\n \"ROW_LIMITS\": RowPartitionType.ROW_LIMITS,\n \"ROW_STARTS\": RowPartitionType.ROW_STARTS\n };\n const result = [];\n for (const typeStr of rowPartitionTypeStrings) {\n if (typeStr in stringToType) {\n result.push(stringToType[typeStr]);\n } else {\n break;\n }\n }\n return result;\n}\nfunction getRaggedRank(rowPartitionTypes) {\n if (rowPartitionTypes.length === 0) {\n return 0;\n }\n if (rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) {\n return rowPartitionTypes.length - 1;\n }\n return rowPartitionTypes.length;\n}\nfunction validateDefaultValueShape(defaultValueShape, valueShape) {\n if (defaultValueShape == null || valueShape == null) {\n return;\n }\n const defaultNDims = defaultValueShape.length;\n const valuesNDims = valueShape.length;\n if (defaultNDims >= valuesNDims) {\n throw new Error(`defaultValue.shape=${defaultValueShape} and ragged tensor flatValues.shape=${valueShape}, are incompatible: defaultValue.rank = ${defaultNDims} must be less than ragged tensor input flatValues.rank = ${valuesNDims})`);\n }\n for (let i = 0; i < Math.min(defaultNDims, valuesNDims - 1); ++i) {\n const defaultDim = defaultValueShape[i];\n const valueDim = valueShape[i + 1];\n if (defaultDim >= 0 && valueDim >= 0 && defaultDim !== 1 && defaultDim !== valueDim) {\n throw new Error(`defaultValue.shape=${defaultValueShape}, and ragged tensor input flatValues.shape=${valueShape} are incompatible: defaultValue.shape[${i - defaultValueShape.length}] = ${defaultDim} but ragged tensor input.flatValues.shape[${i - defaultValueShape.length}] = ${valueDim}`);\n }\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js\nvar PARALLELIZE_THRESHOLD = 30;\nfunction computeOptimalWindowSize(inSize) {\n if (inSize <= PARALLELIZE_THRESHOLD) {\n return inSize;\n }\n return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js\nfunction getImageCenter(center, imageHeight, imageWidth) {\n const centerX = imageWidth * (typeof center === \"number\" ? center : center[0]);\n const centerY = imageHeight * (typeof center === \"number\" ? center : center[1]);\n return [centerX, centerY];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js\nfunction getReshaped(inputShape, blockShape, prod5, batchToSpace = true) {\n let reshaped = [];\n if (batchToSpace) {\n reshaped = reshaped.concat(blockShape.slice(0));\n reshaped.push(inputShape[0] / prod5);\n reshaped = reshaped.concat(inputShape.slice(1));\n } else {\n reshaped = reshaped.concat(inputShape[0]);\n const spatialLength = blockShape.length;\n for (let i = 0; i < spatialLength; ++i) {\n reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n }\n reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n }\n return reshaped;\n}\nfunction getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n const permuted = [];\n if (batchToSpace) {\n permuted.push(blockShapeRank);\n for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n if (i <= 2 * blockShapeRank) {\n permuted.push(i);\n permuted.push(i - (blockShapeRank + 1));\n } else {\n permuted.push(i);\n }\n }\n } else {\n const permutedBeforeBatch = [];\n const permutedAfterBatch = [];\n for (let i = 1; i < reshapedRank; ++i) {\n if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n permutedAfterBatch.push(i);\n } else {\n permutedBeforeBatch.push(i);\n }\n }\n permuted.push(...permutedBeforeBatch);\n permuted.push(0);\n permuted.push(...permutedAfterBatch);\n }\n return permuted;\n}\nfunction getReshapedPermuted(inputShape, blockShape, prod5, batchToSpace = true) {\n const reshapedPermuted = [];\n if (batchToSpace) {\n reshapedPermuted.push(inputShape[0] / prod5);\n } else {\n reshapedPermuted.push(inputShape[0] * prod5);\n }\n for (let i = 1; i < inputShape.length; ++i) {\n if (i <= blockShape.length) {\n if (batchToSpace) {\n reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n } else {\n reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n }\n } else {\n reshapedPermuted.push(inputShape[i]);\n }\n }\n return reshapedPermuted;\n}\nfunction getSliceBeginCoords(crops, blockShape) {\n const sliceBeginCoords = [0];\n for (let i = 0; i < blockShape; ++i) {\n sliceBeginCoords.push(crops[i][0]);\n }\n return sliceBeginCoords;\n}\nfunction getSliceSize(uncroppedShape, crops, blockShape) {\n const sliceSize = uncroppedShape.slice(0, 1);\n for (let i = 0; i < blockShape; ++i) {\n sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n }\n return sliceSize;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js\nvar SELU_SCALEALPHA = 1.7580993408473768;\nvar SELU_SCALE = 1.0507009873554805;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js\nvar ERF_P = 0.3275911;\nvar ERF_A1 = 0.254829592;\nvar ERF_A2 = -0.284496736;\nvar ERF_A3 = 1.421413741;\nvar ERF_A4 = -1.453152027;\nvar ERF_A5 = 1.061405429;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js\nfunction mergeRealAndImagArrays(real4, imag4) {\n if (real4.length !== imag4.length) {\n throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`);\n }\n const result = new Float32Array(real4.length * 2);\n for (let i = 0; i < result.length; i += 2) {\n result[i] = real4[i / 2];\n result[i + 1] = imag4[i / 2];\n }\n return result;\n}\nfunction splitRealAndImagArrays(complex4) {\n const real4 = new Float32Array(complex4.length / 2);\n const imag4 = new Float32Array(complex4.length / 2);\n for (let i = 0; i < complex4.length; i += 2) {\n real4[i / 2] = complex4[i];\n imag4[i / 2] = complex4[i + 1];\n }\n return { real: real4, imag: imag4 };\n}\nfunction complexWithEvenIndex(complex4) {\n const len = Math.ceil(complex4.length / 4);\n const real4 = new Float32Array(len);\n const imag4 = new Float32Array(len);\n for (let i = 0; i < complex4.length; i += 4) {\n real4[Math.floor(i / 4)] = complex4[i];\n imag4[Math.floor(i / 4)] = complex4[i + 1];\n }\n return { real: real4, imag: imag4 };\n}\nfunction complexWithOddIndex(complex4) {\n const len = Math.floor(complex4.length / 4);\n const real4 = new Float32Array(len);\n const imag4 = new Float32Array(len);\n for (let i = 2; i < complex4.length; i += 4) {\n real4[Math.floor(i / 4)] = complex4[i];\n imag4[Math.floor(i / 4)] = complex4[i + 1];\n }\n return { real: real4, imag: imag4 };\n}\nfunction getComplexWithIndex(complex4, index) {\n const real4 = complex4[index * 2];\n const imag4 = complex4[index * 2 + 1];\n return { real: real4, imag: imag4 };\n}\nfunction assignToTypedArray(data, real4, imag4, index) {\n data[index * 2] = real4;\n data[index * 2 + 1] = imag4;\n}\nfunction exponents(n, inverse) {\n const real4 = new Float32Array(n / 2);\n const imag4 = new Float32Array(n / 2);\n for (let i = 0; i < Math.ceil(n / 2); i++) {\n const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n real4[i] = Math.cos(x);\n imag4[i] = Math.sin(x);\n }\n return { real: real4, imag: imag4 };\n}\nfunction exponent(k, n, inverse) {\n const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n const real4 = Math.cos(x);\n const imag4 = Math.sin(x);\n return { real: real4, imag: imag4 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/einsum_util.js\nvar ARROW = \"->\";\nvar ARROW_REGEX = /->/g;\nvar COMMA = \",\";\nvar ELLIPSIS = \"...\";\nfunction decodeEinsumEquation(equation, numTensors) {\n equation = equation.replace(/\\s/g, \"\");\n const numArrows = (equation.length - equation.replace(ARROW_REGEX, \"\").length) / ARROW.length;\n if (numArrows < 1) {\n throw new Error(\"Equations without an arrow are not supported.\");\n } else if (numArrows > 1) {\n throw new Error(`Equation must contain exactly one arrow (\"${ARROW}\").`);\n }\n const [inputString, outputString] = equation.split(ARROW);\n assert(inputString.indexOf(ELLIPSIS) === -1, () => `The ellipsis notation (\"${ELLIPSIS}\") is not supported yet.`);\n const inputTerms = inputString.split(COMMA);\n const numInputs = inputTerms.length;\n if (numTensors !== numInputs) {\n throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`);\n }\n if (numInputs > 2) {\n throw new Error(\"Support for more than 2 input tensors is not implemented yet.\");\n }\n const allDims = [];\n for (let i = 0; i < outputString.length; ++i) {\n const dimName = outputString[i];\n if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) {\n throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`);\n }\n if (allDims.indexOf(dimName) === -1) {\n allDims.push(dimName);\n }\n }\n for (let i = 0; i < inputString.length; ++i) {\n const dimName = inputString[i];\n if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {\n allDims.push(dimName);\n }\n }\n const idDims = new Array(inputTerms.length);\n for (let i = 0; i < numInputs; ++i) {\n if (new Set(inputTerms[i].split(\"\")).size !== inputTerms[i].length) {\n throw new Error(`Found duplicate axes in input component ${inputTerms[i]}. Support for duplicate axes in input is not implemented yet.`);\n }\n idDims[i] = [];\n for (let j = 0; j < inputTerms[i].length; ++j) {\n idDims[i].push(allDims.indexOf(inputTerms[i][j]));\n }\n }\n const numDims = allDims.length;\n const numOutDims = outputString.length;\n const summedDims = [];\n for (let i = numOutDims; i < numDims; ++i) {\n summedDims.push(i);\n }\n return { allDims, summedDims, idDims };\n}\nfunction getEinsumPermutation(nDims, idDims) {\n let permutationIndices = new Array(nDims);\n permutationIndices.fill(-1);\n for (let i = 0; i < idDims.length; ++i) {\n permutationIndices[idDims[i]] = i;\n }\n const expandDims6 = [];\n for (let i = 0; i < nDims; ++i) {\n if (permutationIndices[i] === -1) {\n expandDims6.push(i);\n }\n }\n permutationIndices = permutationIndices.filter((d) => d !== -1);\n return { permutationIndices, expandDims: expandDims6 };\n}\nfunction checkEinsumDimSizes(nDims, idDims, tensors) {\n const dimSizes = new Array(nDims);\n for (let i = 0; i < tensors.length; ++i) {\n const shape = tensors[i].shape;\n for (let j = 0; j < idDims[i].length; ++j) {\n if (dimSizes[idDims[i][j]] === void 0) {\n dimSizes[idDims[i][j]] = shape[j];\n } else {\n assert(dimSizes[idDims[i][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);\n }\n }\n }\n}\nfunction getEinsumComputePath(summedDims, idDims) {\n const path = summedDims;\n const steps = [];\n let nSteps = 0;\n if (summedDims.length === 0) {\n path.push(-1);\n }\n nSteps = summedDims.length + 1;\n for (let i = 0; i < nSteps; ++i) {\n steps.push([]);\n }\n const computedTermIndices = [];\n for (let i = 0; i < path.length; ++i) {\n const summedDim = path[i];\n const termIndices = findTermsWithDim(idDims, summedDim);\n for (const termIndex of termIndices) {\n if (computedTermIndices.indexOf(termIndex) === -1) {\n steps[i].push(termIndex);\n computedTermIndices.push(termIndex);\n }\n }\n }\n return { path, steps };\n}\nfunction isIdentityPermutation(perm) {\n return perm.every((dim, index) => dim === index);\n}\nfunction findTermsWithDim(idDims, dim) {\n const termIndices = [];\n for (let i = 0; i < idDims.length; ++i) {\n if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) {\n termIndices.push(i);\n }\n }\n return termIndices;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js\nfunction prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n let splitSizes = [];\n if (typeof numOrSizeSplits === \"number\") {\n assert(x.shape[axis] % numOrSizeSplits === 0, () => \"Number of splits must evenly divide the axis.\");\n splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n } else {\n const numOfNegs = numOrSizeSplits.reduce((count2, value) => {\n if (value === -1) {\n count2 += 1;\n }\n return count2;\n }, 0);\n assert(numOfNegs <= 1, () => \"There should be only one negative value in split array.\");\n const negIndex = numOrSizeSplits.indexOf(-1);\n if (negIndex !== -1) {\n const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n numOrSizeSplits[negIndex] = x.shape[axis] - total;\n }\n assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => \"The sum of sizes must match the size of the axis dimension.\");\n splitSizes = numOrSizeSplits;\n }\n return splitSizes;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows_util.js\nfunction getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) {\n return `Received SparseTensor with denseShape[0] = 0 but\n indices.shape[0] = ${indicesLength}`;\n}\nfunction getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) {\n return `indices(${index}, 0) is invalid: ${value} < 0`;\n}\nfunction getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) {\n return `indices(${index}, 0) is invalid: ${value} >= ${limit}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape_util.js\nfunction getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) {\n return `only one output dimension may be -1, not both ${dim1} and ${dim2}`;\n}\nfunction getSparseReshapeNegativeOutputDimErrorMessage(dim, value) {\n return `size ${dim} must be non-negative, not ${value}`;\n}\nfunction getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() {\n return \"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero\";\n}\nfunction getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) {\n const inputSize = sizeFromShape(inputShape);\n const outputSize = sizeFromShape(outputShape);\n return `Input to reshape is a SparseTensor with ${inputSize}\n dense values, but the requested shape requires a multiple of ${outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`;\n}\nfunction getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) {\n const inputSize = sizeFromShape(inputShape);\n const outputSize = sizeFromShape(outputShape);\n return `Input to reshape is a tensor with ${inputSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_reduction_util.js\nfunction getSparseSegmentReductionNegativeSegmentIdsErrorMessage() {\n return `segment ids must be >= 0`;\n}\nfunction getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() {\n return `segment ids are not increasing`;\n}\nfunction getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) {\n return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`;\n}\nfunction getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) {\n return `Bad: indices[${index}] == ${indexValue} out of range [0, ${inputRows})`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js\nvar segment_util_exports = {};\n__export(segment_util_exports, {\n collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,\n computeOutShape: () => computeOutShape3,\n segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize\n});\nfunction segOpComputeOptimalWindowSize(inSize, numSegments) {\n let done = false;\n let res;\n if (inSize <= PARALLELIZE_THRESHOLD) {\n res = inSize;\n done = true;\n } else {\n res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n }\n while (!done) {\n if (res > numSegments || res === inSize) {\n done = true;\n } else {\n res = nearestDivisor(inSize, res + 1);\n }\n }\n return res;\n}\nfunction computeOutShape3(aShape, axis, numSegments) {\n const outShape = [];\n const rank = aShape.length;\n for (let dim = 0; dim < rank; dim++) {\n if (dim !== axis) {\n outShape.push(aShape[dim]);\n } else {\n outShape.push(numSegments);\n }\n }\n return outShape;\n}\nfunction collectGatherOpShapeInfo(x, indices, axis, batchDims) {\n const indicesRank = indices.shape.length;\n const xRank = x.shape.length;\n if (batchDims !== 0) {\n if (batchDims < -indicesRank || batchDims > indicesRank) {\n throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);\n }\n }\n if (batchDims < 0) {\n batchDims += indicesRank;\n }\n if (batchDims > xRank) {\n throw new Error(`batchDims (${batchDims}) must be less than rank(x) (\n ${xRank}).`);\n }\n if (axis < batchDims) {\n throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);\n }\n for (let i = 0; i < batchDims; ++i) {\n if (x.shape[i] !== indices.shape[i]) {\n throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`);\n }\n }\n const dimSize = x.shape[axis];\n const outputShape = [];\n let batchSize = 1;\n let outerSize = 1;\n let sliceSize = 1;\n for (let i = 0; i < batchDims; ++i) {\n outputShape.push(x.shape[i]);\n batchSize *= x.shape[i];\n }\n for (let i = batchDims; i < axis; i++) {\n outputShape.push(x.shape[i]);\n outerSize *= x.shape[i];\n }\n for (let i = batchDims; i < indicesRank; i++) {\n outputShape.push(indices.shape[i]);\n }\n for (let i = axis + 1; i < xRank; i++) {\n outputShape.push(x.shape[i]);\n sliceSize *= x.shape[i];\n }\n return { batchSize, sliceSize, outerSize, dimSize, outputShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js\nfunction fromUint8ToStringArray(vals) {\n try {\n return vals.map((val) => decodeString(val));\n } catch (err) {\n throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`);\n }\n}\nfunction fromStringArrayToUint8(strings) {\n return strings.map((s) => encodeString(s));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js\nvar kernel_impls_exports = {};\n__export(kernel_impls_exports, {\n nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,\n nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,\n nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,\n whereImpl: () => whereImpl\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/index.js\nregisterOptimizers();\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js\nvar absGradConfig = {\n kernelName: Abs,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(dy, step(cast(x, \"float32\"), -1)) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js\nvar acosGradConfig = {\n kernelName: Acos,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return {\n x: () => {\n const a = square(cast(x, \"float32\"));\n const b = sqrt(sub(scalar(1), a));\n return neg(div(dy, b));\n }\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js\nvar acoshGradConfig = {\n kernelName: Acosh,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return {\n x: () => {\n const a = sqrt(sub(square(cast(x, \"float32\")), 1));\n return div(dy, a);\n }\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js\nvar addGradConfig = {\n kernelName: Add,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n let res = dy;\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum2(res, reduceAxes);\n }\n return reshape(res, a.shape);\n };\n const derB = () => {\n let res = dy;\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum2(res, reduceAxes);\n }\n return reshape(res, b.shape);\n };\n return { a: derA, b: derB };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js\nvar addNGradConfig = {\n kernelName: AddN,\n saveAllInputs: true,\n gradFunc: (dy, saved) => {\n const ders = {};\n saved.forEach((_, i) => {\n ders[i] = () => dy.clone();\n });\n return ders;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js\nvar argMaxGradConfig = {\n kernelName: ArgMax,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => zerosLike(x) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js\nvar argMinGradConfig = {\n kernelName: ArgMin,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => zerosLike(x) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js\nvar asinGradConfig = {\n kernelName: Asin,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, \"float32\"))))) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js\nvar asinhGradConfig = {\n kernelName: Asinh,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return {\n x: () => {\n const a = sqrt(add2(scalar(1), square(cast(x, \"float32\"))));\n return div(dy, a);\n }\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js\nvar atan2GradConfig = {\n kernelName: Atan2,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n const d = add2(square(a), square(b));\n let res = mul(dy, div(b, d));\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum2(res, reduceAxes);\n }\n return reshape(res, a.shape);\n };\n const derB = () => {\n const d = add2(square(a), square(b));\n let res = neg(mul(dy, div(a, d)));\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum2(res, reduceAxes);\n }\n return reshape(res, b.shape);\n };\n return { a: derA, b: derB };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js\nvar atanGradConfig = {\n kernelName: Atan,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, add2(square(cast(x, \"float32\")), 1)) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js\nvar atanhGradConfig = {\n kernelName: Atanh,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, sub(scalar(1), square(cast(x, \"float32\")))) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_grad.js\nfunction avgPool3dGrad_(dy, input2, filterSize, strides, pad3, dimRoundingMode) {\n const $dy = convertToTensor(dy, \"dy\", \"avgPool3dGrad\");\n const $input = convertToTensor(input2, \"input\", \"avgPool3dGrad\");\n let dy5D = $dy;\n let input5D = $input;\n let reshapedTo5D = false;\n if ($input.rank === 4) {\n reshapedTo5D = true;\n dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n input5D = reshape($input, [\n 1,\n $input.shape[0],\n $input.shape[1],\n $input.shape[2],\n $input.shape[3]\n ]);\n }\n assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);\n assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);\n checkPadOnDimRoundingMode(\"avgPool3dGrad\", pad3, dimRoundingMode);\n const inputs = { dy: dy5D, input: input5D };\n const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nvar avgPool3dGrad = op({ avgPool3dGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js\nvar avgPool3DGradConfig = {\n kernelName: AvgPool3D,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n return {\n x: () => avgPool3dGrad(dy, x, filterSize, strides, pad3, dimRoundingMode)\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_grad.js\nfunction avgPoolGrad_(dy, input2, filterSize, strides, pad3) {\n const $dy = convertToTensor(dy, \"dy\", \"avgPoolGrad\");\n const $input = convertToTensor(input2, \"input\", \"avgPoolGrad\");\n assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n let input4D = $input;\n let dy4D = $dy;\n let reshapedTo4D = false;\n if ($input.rank === 3) {\n reshapedTo4D = true;\n input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n }\n assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`);\n assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);\n const inputs = { dy: dy4D, input: input4D };\n const attrs = { filterSize, strides, pad: pad3 };\n const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);\n if (reshapedTo4D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n }\n return res;\n}\nvar avgPoolGrad = op({ avgPoolGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js\nvar avgPoolGradConfig = {\n kernelName: AvgPool,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { filterSize, strides, pad: pad3 } = attrs;\n return { x: () => avgPoolGrad(dy, x, filterSize, strides, pad3) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js\nvar batchMatMulGradConfig = {\n kernelName: BatchMatMul,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved, attrs) => {\n const [a, b] = saved;\n const { transposeA, transposeB } = attrs;\n if (!transposeA && !transposeB) {\n return {\n a: () => matMul(dy, b, false, true),\n b: () => matMul(a, dy, true, false)\n };\n } else if (!transposeA && transposeB) {\n return {\n a: () => matMul(dy, b, false, false),\n b: () => matMul(dy, a, true, false)\n };\n } else if (transposeA && !transposeB) {\n return {\n a: () => matMul(b, dy, false, true),\n b: () => matMul(a, dy, false, false)\n };\n } else {\n return {\n a: () => matMul(b, dy, true, true),\n b: () => matMul(dy, a, true, true)\n };\n }\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js\nvar batchToSpaceNDGradConfig = {\n kernelName: BatchToSpaceND,\n gradFunc: (dy, saved, attrs) => {\n const { blockShape, crops } = attrs;\n return { x: () => spaceToBatchND(dy, blockShape, crops) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js\nvar broadcastToGradConfig = {\n kernelName: BroadcastTo,\n gradFunc: (dy, saved, attrs) => {\n const broadCastToAttrs = attrs;\n const inputShape = broadCastToAttrs.inputShape;\n const outputShape = broadCastToAttrs.shape;\n const reps = Array.from(outputShape);\n for (let i = inputShape.length - 1; i >= 0; i--) {\n if (inputShape[i] === outputShape[i]) {\n reps[i] = 1;\n } else if (inputShape[i] !== 1) {\n throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n }\n }\n const axes = [];\n for (let i = 0; i < reps.length; i++) {\n if (reps[i] > 1) {\n axes.push(i);\n }\n }\n return { x: () => sum2(\n dy,\n axes,\n true\n /* keepDims */\n ) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js\nvar castGradConfig = {\n kernelName: Cast,\n gradFunc: (dy) => {\n return { x: () => dy.clone() };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js\nvar ceilGradConfig = {\n kernelName: Ceil,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js\nvar clipByValueGradConfig = {\n kernelName: ClipByValue,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { clipValueMin, clipValueMax } = attrs;\n return {\n x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ComplexAbs_grad.js\nvar complexAbsGradConfig = {\n kernelName: ComplexAbs,\n inputsToSave: [\"x\"],\n gradFunc: absGradConfig.gradFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js\nvar concatGradConfig = {\n kernelName: Concat,\n saveAllInputs: true,\n gradFunc: (dy, saved, attrs) => {\n const shapes = saved.map((t) => t.shape);\n const { axis } = attrs;\n const $axis = parseAxisParam(axis, saved[0].shape)[0];\n const sizeSplits = shapes.map((s) => s[$axis]);\n const derTensors = split(dy, sizeSplits, $axis);\n return derTensors.map((t) => () => t);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js\nvar conv2DGradConfig = {\n kernelName: Conv2D,\n inputsToSave: [\"x\", \"filter\"],\n gradFunc: (dy, saved, attrs) => {\n const [x4D, $filter] = saved;\n const { dilations, strides, pad: pad3, dataFormat } = attrs;\n assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n return {\n x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad3, dataFormat),\n filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad3, dataFormat)\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js\nvar conv2DBackpropInputGradConfig = {\n kernelName: Conv2DBackpropInput,\n inputsToSave: [\"dy\", \"filter\"],\n gradFunc: (ddx, saved, attrs) => {\n const [dy, filter] = saved;\n const { strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n return {\n dy: () => conv2d(ddx, filter, strides, pad3, dataFormat, 1, dimRoundingMode),\n filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad3, dataFormat, dimRoundingMode)\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad3) {\n let x5D = x;\n if (x.rank === 4) {\n x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n }\n let dy5D = dy;\n if (dy5D.rank === 4) {\n dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n }\n assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);\n assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);\n assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);\n assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);\n assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);\n const inputs = { x: x5D, dy: dy5D };\n const attrs = { strides, pad: pad3, filterShape };\n return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs);\n}\nvar conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js\nvar conv3DGradConfig = {\n kernelName: Conv3D,\n inputsToSave: [\"x\", \"filter\"],\n gradFunc: (dy, saved, attrs) => {\n const { dilations, strides, pad: pad3 } = attrs;\n assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n const [x5D, $filter] = saved;\n return {\n x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad3),\n filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad3)\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js\nvar cosGradConfig = {\n kernelName: Cos,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(neg(sin(cast(x, \"float32\"))), dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js\nvar coshGradConfig = {\n kernelName: Cosh,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(sinh(cast(x, \"float32\")), dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js\nvar cumsumGradConfig = {\n kernelName: Cumsum,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { axis, exclusive, reverse: reverse5 } = attrs;\n return {\n x: () => {\n const permutation = getAxesPermutation([axis], x.rank);\n let out = cumsum(dy, axis, exclusive, !reverse5);\n if (permutation != null) {\n out = transpose(out, permutation);\n }\n return out;\n }\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js\nvar depthwiseConv2dNativeGradConfig = {\n kernelName: DepthwiseConv2dNative,\n inputsToSave: [\"x\", \"filter\"],\n gradFunc: (dy, saved, attrs) => {\n const { dilations, strides, pad: pad3, dimRoundingMode } = attrs;\n const $dilations = dilations == null ? [1, 1] : dilations;\n assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);\n const [x, filter] = saved;\n assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);\n assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);\n assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);\n assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'.`);\n checkPadOnDimRoundingMode(\"depthwiseConv2d\", pad3, dimRoundingMode);\n return {\n x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad3, $dilations, dimRoundingMode),\n filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad3, $dilations, dimRoundingMode)\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js\nvar dilation2dGradConfig = {\n kernelName: Dilation2D,\n inputsToSave: [\"x\", \"filter\"],\n gradFunc: (dy, saved, attrs) => {\n const [x, filter] = saved;\n const inputInputs = { x, filter, dy };\n const filterInputs = { x, filter, dy };\n return {\n x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js\nvar eluGradConfig = {\n kernelName: Elu,\n outputsToSave: [true],\n gradFunc: (dy, saved) => {\n const [y] = saved;\n const inputs = { dy, y };\n return { x: () => ENGINE.runKernel(EluGrad, inputs) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js\nvar erfGradConfig = {\n kernelName: Erf,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n return { x: () => mul(dy, a) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js\nvar expGradConfig = {\n kernelName: Exp,\n outputsToSave: [true],\n gradFunc: (dy, saved) => {\n const [y] = saved;\n return { x: () => mul(dy, y) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ExpandDims_grad.js\nvar expandDimsGradConfig = {\n kernelName: ExpandDims,\n inputsToSave: [\"input\"],\n gradFunc: (dy, saved) => {\n const [input2] = saved;\n return { input: () => reshape(dy, input2.shape) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js\nvar expm1GradConfig = {\n kernelName: Expm1,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(dy, exp(x)) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js\nvar floorGradConfig = {\n kernelName: Floor,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js\nvar floorDivGradConfig = {\n kernelName: FloorDiv,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n const res = div(dy, cast(b, \"float32\"));\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum2(res, reduceAxes), a.shape);\n }\n return res;\n };\n const derB = () => {\n let res = mul(dy, cast(a, \"float32\"));\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = reshape(sum2(res, reduceAxes), b.shape);\n }\n const tmp = square(b);\n return neg(div(res, cast(tmp, \"float32\")));\n };\n return { a: derA, b: derB };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js\nvar fusedBatchNormGradConfig = {\n kernelName: FusedBatchNorm,\n inputsToSave: [\"x\", \"mean\", \"variance\", \"scale\"],\n gradFunc: (dy, saved, attrs) => {\n const { varianceEpsilon } = attrs;\n const [x, mean4, variance, scale2] = saved;\n const scaleValue = scale2 == null ? scalar(1) : scale2;\n const reductionAxes = getReductionAxes(mean4.shape, x.shape);\n const tileShape = [];\n if (mean4.rank === 1) {\n for (let i = 0; i < x.shape.length - 1; ++i) {\n tileShape.push(x.shape[i]);\n }\n tileShape.push(1);\n }\n const xMinusMean = sub(x, mean4);\n const dyTimesScaleValue = mul(dy, scaleValue);\n const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));\n const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n const derX = () => {\n if (mean4.rank === 1) {\n return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean4.shape[0]]), tileShape)), scaleValue), x.shape);\n } else {\n return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n }\n };\n const derMean = () => {\n let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n if (mean4.rank === 1) {\n meanDer = sum2(meanDer, reductionAxes);\n }\n return reshape(meanDer, mean4.shape);\n };\n const derVariance = () => {\n let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n if (mean4.rank === 1) {\n varianceDer = sum2(varianceDer, reductionAxes);\n }\n return reshape(varianceDer, mean4.shape);\n };\n const derScale = () => {\n const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n if (mean4.rank === 1) {\n scaleDer = sum2(scaleDer, reductionAxes);\n }\n return reshape(scaleDer, mean4.shape);\n };\n const derOffset = () => {\n let offsetDer = dy;\n if (mean4.rank === 1) {\n offsetDer = sum2(offsetDer, reductionAxes);\n }\n return reshape(offsetDer, mean4.shape);\n };\n return {\n x: derX,\n mean: derMean,\n variance: derVariance,\n scale: derScale,\n offset: derOffset\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js\nvar gatherGradConfig = {\n kernelName: GatherV2,\n inputsToSave: [\"x\", \"indices\"],\n gradFunc: (dy, saved, attrs) => {\n const [x, indices] = saved;\n const { axis, batchDims } = attrs;\n const parsedAxis = parseAxisParam(axis, x.shape)[0];\n const derXBatch = (x2, indices2, dy2) => {\n return () => {\n const paramsShape = x2.shape;\n const indicesSize = indices2.size;\n const outerShape = paramsShape.slice(0, parsedAxis);\n const outerDims = outerShape.length;\n const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n const innerDims = innerShape.length;\n const outerAxesIndices = arrayRange(0, outerDims);\n const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n const valuesShape = arrayConcat([\n outerShape,\n [indicesSize],\n innerShape\n ]);\n const values = reshape(dy2, valuesShape);\n const reshapedIndices = reshape(indices2, [indicesSize]);\n const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n const valuesTranspose = transpose(values, transposeDims);\n let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x2.shape[parsedAxis]);\n const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n paramsGrad = transpose(paramsGrad, invertTransposeDims);\n return paramsGrad;\n };\n };\n if (batchDims === 1) {\n const batchSize = x.shape[0];\n const xBatch = x.split(batchSize, 0);\n const derXBatched = () => {\n const stacked = stack(xBatch.map((x2, i) => {\n return derXBatch(x2, indices.slice(i, 1), dy.slice(i, 1))();\n }));\n return stacked.reshape(x.shape);\n };\n return { x: derXBatched, indices: () => indices };\n } else {\n return { x: derXBatch(x, indices, dy), indices: () => indices };\n }\n }\n};\nfunction arrayRange(start, stop) {\n const result = [];\n for (let i = start; i < stop; ++i) {\n result.push(i);\n }\n return result;\n}\nfunction arrayConcat(arrays) {\n const result = [];\n for (let i = 0; i < arrays.length; ++i) {\n for (let j = 0; j < arrays[i].length; ++j) {\n result.push(arrays[i][j]);\n }\n }\n return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js\nvar greaterEqualGradConfig = {\n kernelName: GreaterEqual,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n return { a: () => zerosLike(a), b: () => zerosLike(b) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js\nvar identityGradConfig = {\n kernelName: Identity,\n gradFunc: (dy) => {\n return { x: () => cast(dy, \"float32\") };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js\nvar isFiniteGradConfig = {\n kernelName: IsFinite,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js\nvar isInfGradConfig = {\n kernelName: IsInf,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js\nvar isNanGradConfig = {\n kernelName: IsNan,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LeakyRelu_grad.js\nvar leakyReluGradConfig = {\n kernelName: LeakyRelu,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { alpha } = attrs;\n const mask = greater(x, 0);\n return { x: () => where(mask, dy, mul(dy, alpha)) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js\nvar log1pGradConfig = {\n kernelName: Log1p,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, add2(x, 1)) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js\nvar logGradConfig = {\n kernelName: Log,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, cast(x, \"float32\")) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js\nvar logSoftmaxGradConfig = {\n kernelName: LogSoftmax,\n inputsToSave: [],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const [value] = saved;\n const { axis } = attrs;\n return {\n logits: () => {\n const keepDims = true;\n const softmax6 = exp(value);\n return sub(dy, mul(sum2(dy, axis, keepDims), softmax6));\n }\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n const inputs = { x, y, dy };\n const attrs = { depthRadius, bias, alpha, beta };\n return ENGINE.runKernel(LRNGrad, inputs, attrs);\n}\nvar localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js\nvar lrnGradConfig = {\n kernelName: LRN,\n inputsToSave: [\"x\"],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const [x, y] = saved;\n const { depthRadius, bias, alpha, beta } = attrs;\n return {\n x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js\nfunction gradForMinAndMax(dy, y, xOrig, origAxes) {\n if (y.rank < xOrig.rank) {\n y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));\n }\n if (dy.rank < xOrig.rank) {\n dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));\n }\n return {\n x: () => {\n const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n return dx;\n }\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js\nvar maxGradConfig = {\n kernelName: Max,\n inputsToSave: [\"x\"],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const maxAttrs = attrs;\n const { reductionIndices } = maxAttrs;\n const x = saved[0];\n const y = saved[1];\n const origAxes = parseAxisParam(reductionIndices, x.shape);\n const maxGrad = gradForMinAndMax(dy, y, x, origAxes);\n return {\n x: () => {\n return maxGrad[\"x\"]();\n }\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js\nvar maximumGradConfig = {\n kernelName: Maximum,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const derA = () => mul(dy, cast(greaterEqual(a, b), \"float32\"));\n const derB = () => mul(dy, cast(less(a, b), \"float32\"));\n return { a: derA, b: derB };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_grad.js\nfunction maxPool3dGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {\n const $dy = convertToTensor(dy, \"dy\", \"maxPool3dGrad\");\n const $input = convertToTensor(input2, \"input\", \"maxPool3dGrad\");\n const $output = convertToTensor(output, \"output\", \"maxPool3dGrad\");\n let dy5D = $dy;\n let input5D = $input;\n let output5D = $output;\n let reshapedTo5D = false;\n if ($input.rank === 4) {\n reshapedTo5D = true;\n dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n input5D = reshape($input, [\n 1,\n $input.shape[0],\n $input.shape[1],\n $input.shape[2],\n $input.shape[3]\n ]);\n output5D = reshape($output, [\n 1,\n $output.shape[0],\n $output.shape[1],\n $output.shape[2],\n $output.shape[3]\n ]);\n }\n assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);\n assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);\n assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`);\n checkPadOnDimRoundingMode(\"maxPool3dGrad\", pad3, dimRoundingMode);\n const inputs = { dy: dy5D, input: input5D, output: output5D };\n const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs);\n if (reshapedTo5D) {\n return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n }\n return res;\n}\nvar maxPool3dGrad = op({ maxPool3dGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js\nvar maxPool3DGradConfig = {\n kernelName: MaxPool3D,\n inputsToSave: [\"x\"],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const [x, y] = saved;\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n return {\n x: () => maxPool3dGrad(dy, x, y, filterSize, strides, pad3, dimRoundingMode)\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_grad.js\nfunction maxPoolGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {\n const $dy = convertToTensor(dy, \"dy\", \"maxPoolGrad\");\n const $input = convertToTensor(input2, \"input\", \"maxPoolGrad\");\n const $output = convertToTensor(output, \"output\", \"maxPoolGrad\");\n assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`);\n assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`);\n checkPadOnDimRoundingMode(\"maxPoolGrad\", pad3, dimRoundingMode);\n const inputs = { dy: $dy, input: $input, output: $output };\n const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n return ENGINE.runKernel(MaxPoolGrad, inputs, attrs);\n}\nvar maxPoolGrad = op({ maxPoolGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js\nvar maxPoolGradConfig = {\n kernelName: MaxPool,\n inputsToSave: [\"x\"],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const [x, y] = saved;\n const { filterSize, strides, pad: pad3 } = attrs;\n return {\n x: () => maxPoolGrad(dy, x, y, filterSize, strides, pad3)\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mean_grad.js\nvar meanGradConfig = {\n kernelName: Mean,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { axis } = attrs;\n const axes = parseAxisParam(axis, x.shape);\n const shapes = computeOutAndReduceShapes(x.shape, axes);\n const reduceShape = shapes[1];\n const reduceSize = sizeFromShape(reduceShape);\n const derX = () => {\n const expandedDyShape = x.shape.slice();\n axes.forEach((axis2) => {\n expandedDyShape[axis2] = 1;\n });\n const expandedDy = reshape(dy, expandedDyShape);\n const res = div(mul(expandedDy, ones2(x.shape, \"float32\")), reduceSize);\n return res;\n };\n return { x: derX };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js\nvar minGradConfig = {\n kernelName: Min,\n inputsToSave: [\"x\"],\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const minAttrs = attrs;\n const { axis } = minAttrs;\n const [x, y] = saved;\n const origAxes = parseAxisParam(axis, x.shape);\n const minGrad = gradForMinAndMax(dy, y, x, origAxes);\n return {\n x: () => {\n return minGrad[\"x\"]();\n }\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js\nvar minimumGradConfig = {\n kernelName: Minimum,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const derA = () => mul(dy, cast(lessEqual(a, b), \"float32\"));\n const derB = () => mul(dy, cast(greater(a, b), \"float32\"));\n return { a: derA, b: derB };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MirrorPad_grad.js\nvar mirrorPadGradConfig = {\n kernelName: MirrorPad,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const x = saved[0];\n const { paddings } = attrs;\n const begin = paddings.map((p2) => p2[0]);\n return { x: () => slice(dy, begin, x.shape) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js\nvar modGradConfig = {\n kernelName: Mod,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum2(dy, reduceAxes), a.shape);\n }\n return dy;\n };\n const derB = () => {\n const res = mul(dy, neg(floor(div(a, b))));\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum2(res, reduceAxes), b.shape);\n }\n return res;\n };\n return { a: derA, b: derB };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js\nvar multiplyGradConfig = {\n kernelName: Multiply,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n const res = mul(dy, cast(b, \"float32\"));\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum2(res, reduceAxes), a.shape);\n }\n return res;\n };\n const derB = () => {\n const res = mul(dy, cast(a, \"float32\"));\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum2(res, reduceAxes), b.shape);\n }\n return res;\n };\n return { a: derA, b: derB };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Neg_grad.js\nvar negGradConfig = {\n kernelName: Neg,\n gradFunc: (dy) => {\n return { x: () => neg(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js\nvar oneHotGradConfig = {\n kernelName: OneHot,\n inputsToSave: [\"indices\"],\n gradFunc: (dy, saved) => {\n const indices = saved[0];\n return { indices: () => zeros(indices.shape, \"float32\") };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js\nvar onesLikeGradConfig = {\n kernelName: OnesLike,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pack_grad.js\nvar packGradConfig = {\n kernelName: Pack,\n saveAllInputs: true,\n gradFunc: (dy, saved, attrs) => {\n const { axis } = attrs;\n const derTensors = unstack(dy, axis);\n return derTensors.map((t) => () => t);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js\nvar padV2GradConfig = {\n kernelName: PadV2,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const x = saved[0];\n const { paddings } = attrs;\n const begin = paddings.map((p2) => p2[0]);\n return { x: () => slice(dy, begin, x.shape) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js\nvar powGradConfig = {\n kernelName: Pow,\n inputsToSave: [\"a\", \"b\"],\n outputsToSave: [true],\n gradFunc: (dy, saved) => {\n const [a, b, y] = saved;\n const base = a;\n const exp4 = b;\n const outShape = assertAndGetBroadcastShape(base.shape, exp4.shape);\n const derBase = () => {\n const expFloat = cast(exp4, \"float32\");\n let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n const reduceAxes = getReductionAxes(base.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum2(res, reduceAxes);\n }\n return reshape(res, base.shape);\n };\n const derExp = () => {\n const condition = greater(base, 0);\n const logBase = where(condition, log2(base), zerosLike(base));\n let res = mul(dy, mul(y, logBase));\n const reduceAxes = getReductionAxes(exp4.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum2(res, reduceAxes);\n }\n return reshape(res, exp4.shape);\n };\n return { a: derBase, b: derExp };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js\nvar preluGradConfig = {\n kernelName: Prelu,\n inputsToSave: [\"x\", \"alpha\"],\n gradFunc: (dy, saved) => {\n const [x, alpha] = saved;\n const mask = greater(x, 0);\n return {\n x: () => where(mask, dy, mul(dy, alpha)),\n alpha: () => {\n let res = where(mask, zerosLike(dy), mul(dy, x));\n const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n if (reduceAxes.length > 0) {\n res = sum2(res, reduceAxes);\n }\n return reshape(res, alpha.shape);\n }\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prod_grad.js\nfunction prodGradFn_(x, dy, axis) {\n const expandedYShape = x.shape.slice();\n expandedYShape[axis] = 1;\n const expandedDy = reshape(dy, expandedYShape);\n const xCumProd = cumprod(x, axis, true, false);\n const xCumRevProd = cumprod(x, axis, true, true);\n const dx = mul(xCumProd, xCumRevProd);\n return mul(expandedDy, dx);\n}\nfunction prodsGradFn_(x, dy, axis) {\n const xRank = x.shape.length;\n const finalProdAxis = xRank - axis.length;\n const xPermutation = backend_util_exports.getAxesPermutation(axis, xRank);\n let permutedX = x;\n if (xPermutation != null) {\n permutedX = transpose(x, xPermutation);\n }\n const newShape = permutedX.shape.slice();\n const removedShape = newShape.splice(xRank - axis.length, axis.length);\n const endPartShape = removedShape.reduce((p2, c) => p2 * c, 1);\n newShape.push(endPartShape);\n const reshapedPermutedX = permutedX.reshape(newShape);\n let prodGrad = prodGradFn_(reshapedPermutedX, dy, finalProdAxis);\n prodGrad = prodGrad.reshape(permutedX.shape);\n if (xPermutation != null) {\n const undoPermutation = backend_util_exports.getUndoAxesPermutation(xPermutation);\n prodGrad = transpose(prodGrad, undoPermutation);\n }\n return prodGrad;\n}\nvar prodGradConfig = {\n kernelName: Prod,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { axis } = attrs;\n let axisArr = [];\n if (axis === void 0 || axis === null) {\n axisArr = x.shape.map((_, i) => i);\n } else if (typeof axis === \"number\") {\n axisArr = [axis];\n } else {\n axisArr = axis;\n }\n return { x: () => prodsGradFn_(x, dy, axisArr) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/RealDiv_grad.js\nvar divGradConfig = {\n kernelName: RealDiv,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n const res = div(dy, cast(b, \"float32\"));\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n return reshape(sum2(res, reduceAxes), a.shape);\n }\n return res;\n };\n const derB = () => {\n let res = mul(dy, cast(a, \"float32\"));\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = reshape(sum2(res, reduceAxes), b.shape);\n }\n const tmp = square(b);\n return neg(div(res, cast(tmp, \"float32\")));\n };\n return { a: derA, b: derB };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js\nvar reciprocalGradConfig = {\n kernelName: Reciprocal,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, neg(square(x))) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js\nvar relu6GradConfig = {\n kernelName: Relu6,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n const mask = mul(lessEqual(x, 6), step(x));\n return { x: () => mul(dy, cast(mask, \"float32\")) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js\nvar reluGradConfig = {\n kernelName: Relu,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(dy, cast(step(x), \"float32\")) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js\nvar reshapeGradConfig = {\n kernelName: Reshape,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => reshape(dy, x.shape) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js\nvar resizeBilinearGradConfig = {\n kernelName: ResizeBilinear,\n inputsToSave: [\"images\"],\n gradFunc: (dy, saved, attrs) => {\n const [images] = saved;\n const inputs = { dy, images };\n const imagesDer = () => (\n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs)\n );\n return { images: imagesDer };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js\nvar resizeNearestNeighborGradConfig = {\n kernelName: ResizeNearestNeighbor,\n inputsToSave: [\"images\"],\n gradFunc: (dy, saved, attrs) => {\n const [images] = saved;\n const inputs = { dy, images };\n const imagesDer = () => (\n // tslint:disable-next-line: no-unnecessary-type-assertion\n ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs)\n );\n return { images: imagesDer };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js\nvar reverseGradConfig = {\n kernelName: Reverse,\n gradFunc: (dy, saved, attrs) => {\n const { dims } = attrs;\n const axes = parseAxisParam(dims, dy.shape);\n return { x: () => reverse(dy, axes) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js\nvar roundGradConfig = {\n kernelName: Round,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js\nvar rsqrtGradConfig = {\n kernelName: Rsqrt,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Select_grad.js\nvar selectGradConfig = {\n kernelName: Select,\n inputsToSave: [\"condition\"],\n gradFunc: (dy, saved) => {\n const [condition] = saved;\n return {\n // TODO(julianoks): Return null for condition gradient\n // when backprop supports it.\n condition: () => cast(zerosLike(condition), \"float32\"),\n t: () => mul(dy, cast(condition, dy.dtype)),\n e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js\nvar seluGradConfig = {\n kernelName: Selu,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return {\n x: () => {\n const mask = greater(x, scalar(0));\n const scaleAlpha2 = scalar(SELU_SCALEALPHA);\n const scale2 = scalar(SELU_SCALE);\n const greaterThanZeroDer = mul(dy, scale2);\n const lessEqualZeroDer = mul(mul(dy, scaleAlpha2), exp(cast(x, \"float32\")));\n return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n }\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js\nvar sigmoidGradConfig = {\n kernelName: Sigmoid,\n outputsToSave: [true],\n gradFunc: (dy, saved) => {\n const [y] = saved;\n return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js\nvar signGradConfig = {\n kernelName: Sign,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js\nvar sinGradConfig = {\n kernelName: Sin,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(cos(cast(x, \"float32\")), dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js\nvar sinhGradConfig = {\n kernelName: Sinh,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(cosh(cast(x, \"float32\")), dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js\nvar sliceGradConfig = {\n kernelName: Slice,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { begin, size } = attrs;\n const inputShape = x.shape;\n const [begin_, size_] = parseSliceParams(x, begin, size);\n const paddings = [];\n for (let i = 0; i < dy.rank; i++) {\n paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n }\n return { x: () => pad(dy, paddings) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js\nvar softmaxGradConfig = {\n kernelName: Softmax,\n outputsToSave: [true],\n gradFunc: (dy, saved, attrs) => {\n const [y] = saved;\n const { dim } = attrs;\n const keepDims = true;\n const dyTimesY = mul(dy, y);\n return {\n logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))\n };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js\nvar softplusGradConfig = {\n kernelName: Softplus,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(dy, sigmoid(x)) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js\nvar spaceToBatchNDGradConfig = {\n kernelName: SpaceToBatchND,\n gradFunc: (dy, saved, attrs) => {\n const { blockShape, paddings } = attrs;\n return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js\nvar splitVGradConfig = {\n kernelName: SplitV,\n gradFunc: (dy, saved, attrs) => {\n const { axis } = attrs;\n return { x: () => concat(dy, axis) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js\nvar sqrtGradConfig = {\n kernelName: Sqrt,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, mul(sqrt(cast(x, \"float32\")), 2)) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js\nvar squareGradConfig = {\n kernelName: Square,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => mul(dy, mul(cast(x, \"float32\"), 2)) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js\nvar squaredDifferenceGradConfig = {\n kernelName: SquaredDifference,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const two = scalar(2);\n const derA = () => mul(dy, mul(two, sub(a, b)));\n const derB = () => mul(dy, mul(two, sub(b, a)));\n return { a: derA, b: derB };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js\nvar stepGradConfig = {\n kernelName: Step,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js\nvar subGradConfig = {\n kernelName: Sub,\n inputsToSave: [\"a\", \"b\"],\n gradFunc: (dy, saved) => {\n const [a, b] = saved;\n const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n const derA = () => {\n let res = dy;\n const reduceAxes = getReductionAxes(a.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum2(res, reduceAxes);\n }\n return reshape(res, a.shape);\n };\n const derB = () => {\n let res = dy;\n const reduceAxes = getReductionAxes(b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = sum2(res, reduceAxes);\n }\n return reshape(neg(res), b.shape);\n };\n return { a: derA, b: derB };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js\nvar sumGradConfig = {\n kernelName: Sum,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const expandedDyShape = x.shape.slice();\n const { axis } = attrs;\n const axes = parseAxisParam(axis, x.shape);\n axes.forEach((axis2) => {\n expandedDyShape[axis2] = 1;\n });\n const expandedDy = reshape(dy, expandedDyShape);\n const derX = mul(expandedDy, ones2(x.shape, \"float32\"));\n return { x: () => derX };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js\nvar tanGradConfig = {\n kernelName: Tan,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved) => {\n const [x] = saved;\n return { x: () => div(dy, square(cos(x))) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js\nvar tanhGradConfig = {\n kernelName: Tanh,\n outputsToSave: [true],\n gradFunc: (dy, saved) => {\n const [y] = saved;\n return { x: () => mul(sub(scalar(1), square(y)), dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js\nvar tileGradConfig = {\n kernelName: Tile,\n inputsToSave: [\"x\"],\n gradFunc: (dy, saved, attrs) => {\n const [x] = saved;\n const { reps } = attrs;\n const derX = () => {\n let xGrad = zerosLike(x);\n if (x.rank === 1) {\n for (let i = 0; i < reps[0]; ++i) {\n xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n }\n } else if (x.rank === 2) {\n for (let i = 0; i < reps[0]; ++i) {\n for (let j = 0; j < reps[1]; ++j) {\n xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n x.shape[0],\n x.shape[1]\n ]));\n }\n }\n } else if (x.rank === 3) {\n for (let i = 0; i < reps[0]; ++i) {\n for (let j = 0; j < reps[1]; ++j) {\n for (let k = 0; k < reps[2]; ++k) {\n xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n }\n }\n }\n } else if (x.rank === 4) {\n for (let i = 0; i < reps[0]; ++i) {\n for (let j = 0; j < reps[1]; ++j) {\n for (let k = 0; k < reps[2]; ++k) {\n for (let l = 0; l < reps[3]; ++l) {\n xGrad = add2(xGrad, slice(dy, [\n i * x.shape[0],\n j * x.shape[1],\n k * x.shape[2],\n l * x.shape[3]\n ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n }\n }\n }\n }\n } else {\n throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);\n }\n return xGrad;\n };\n return { x: derX };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js\nvar transposeGradConfig = {\n kernelName: Transpose,\n gradFunc: (dy, saved, attrs) => {\n const transposeAttrs = attrs;\n const { perm } = transposeAttrs;\n const undoPerm = getUndoAxesPermutation(perm);\n return { x: () => transpose(dy, undoPerm) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js\nvar unpackGradConfig = {\n kernelName: Unpack,\n gradFunc: (dy, saved, attrs) => {\n const unpackAttrs = attrs;\n const { axis } = unpackAttrs;\n return { value: () => stack(dy, axis) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js\nvar unsortedSegmentSumGradConfig = {\n kernelName: UnsortedSegmentSum,\n inputsToSave: [\"segmentIds\"],\n gradFunc: (dy, saved) => {\n const [segmentIds] = saved;\n const derX = () => {\n return gatherDropNegatives(dy, segmentIds);\n };\n return { x: derX };\n }\n};\nfunction gatherDropNegatives(x, indices) {\n const zeroClippedIndices = maximum(indices, zerosLike(indices));\n const gathered = gather(x, zeroClippedIndices);\n let isPositive = greaterEqual(indices, scalar(0, \"int32\"));\n const numIters = gathered.rank - isPositive.rank;\n for (let i = 0; i < numIters; ++i) {\n isPositive = expandDims(isPositive, i + 1);\n }\n isPositive = logicalAnd(isPositive, ones2(gathered.shape, \"bool\"));\n const zeroSlice = zerosLike(gathered);\n return where(isPositive, gathered, zeroSlice);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js\nvar zerosLikeGradConfig = {\n kernelName: ZerosLike,\n gradFunc: (dy) => {\n return { x: () => zerosLike(dy) };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js\nvar gradConfigs = [\n absGradConfig,\n acosGradConfig,\n acoshGradConfig,\n addGradConfig,\n addNGradConfig,\n argMaxGradConfig,\n argMinGradConfig,\n asinGradConfig,\n asinhGradConfig,\n atan2GradConfig,\n atanGradConfig,\n atanhGradConfig,\n avgPool3DGradConfig,\n avgPoolGradConfig,\n batchMatMulGradConfig,\n batchToSpaceNDGradConfig,\n broadcastToGradConfig,\n castGradConfig,\n ceilGradConfig,\n clipByValueGradConfig,\n complexAbsGradConfig,\n concatGradConfig,\n conv2DBackpropInputGradConfig,\n conv2DGradConfig,\n conv3DGradConfig,\n cosGradConfig,\n coshGradConfig,\n cumsumGradConfig,\n depthwiseConv2dNativeGradConfig,\n dilation2dGradConfig,\n divGradConfig,\n eluGradConfig,\n erfGradConfig,\n expGradConfig,\n expandDimsGradConfig,\n expm1GradConfig,\n floorDivGradConfig,\n floorGradConfig,\n fusedBatchNormGradConfig,\n gatherGradConfig,\n greaterEqualGradConfig,\n identityGradConfig,\n isFiniteGradConfig,\n isInfGradConfig,\n isNanGradConfig,\n leakyReluGradConfig,\n log1pGradConfig,\n logGradConfig,\n logSoftmaxGradConfig,\n lrnGradConfig,\n maxGradConfig,\n maxGradConfig,\n maximumGradConfig,\n maxPool3DGradConfig,\n maxPoolGradConfig,\n meanGradConfig,\n minGradConfig,\n minimumGradConfig,\n mirrorPadGradConfig,\n modGradConfig,\n multiplyGradConfig,\n negGradConfig,\n oneHotGradConfig,\n onesLikeGradConfig,\n packGradConfig,\n padV2GradConfig,\n padV2GradConfig,\n powGradConfig,\n preluGradConfig,\n prodGradConfig,\n reciprocalGradConfig,\n relu6GradConfig,\n reluGradConfig,\n reshapeGradConfig,\n resizeBilinearGradConfig,\n resizeNearestNeighborGradConfig,\n reverseGradConfig,\n roundGradConfig,\n rsqrtGradConfig,\n selectGradConfig,\n seluGradConfig,\n sigmoidGradConfig,\n signGradConfig,\n sinGradConfig,\n sinhGradConfig,\n sliceGradConfig,\n softmaxGradConfig,\n softplusGradConfig,\n spaceToBatchNDGradConfig,\n spaceToBatchNDGradConfig,\n splitVGradConfig,\n splitVGradConfig,\n sqrtGradConfig,\n squaredDifferenceGradConfig,\n squareGradConfig,\n stepGradConfig,\n subGradConfig,\n sumGradConfig,\n tanGradConfig,\n tanhGradConfig,\n tileGradConfig,\n transposeGradConfig,\n unpackGradConfig,\n unsortedSegmentSumGradConfig,\n zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n registerGradient(gradientConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.js\ngetGlobalTensorClass().prototype.abs = function() {\n this.throwIfDisposed();\n return abs(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.js\ngetGlobalTensorClass().prototype.acos = function() {\n this.throwIfDisposed();\n return acos(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.js\ngetGlobalTensorClass().prototype.acosh = function() {\n this.throwIfDisposed();\n return acosh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.js\ngetGlobalTensorClass().prototype.add = function(b) {\n this.throwIfDisposed();\n return add2(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.js\ngetGlobalTensorClass().prototype.all = function(axis, keepDims) {\n this.throwIfDisposed();\n return all(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.js\ngetGlobalTensorClass().prototype.any = function(axis, keepDims) {\n this.throwIfDisposed();\n return any(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.js\ngetGlobalTensorClass().prototype.argMax = function(axis) {\n this.throwIfDisposed();\n return argMax(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.js\ngetGlobalTensorClass().prototype.argMin = function(axis) {\n this.throwIfDisposed();\n return argMin(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.js\ngetGlobalTensorClass().prototype.asScalar = function() {\n this.throwIfDisposed();\n assert(this.size === 1, () => \"The array must have only 1 element.\");\n return reshape(this, []);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.js\ngetGlobalTensorClass().prototype.asType = function(dtype) {\n this.throwIfDisposed();\n return cast(this, dtype);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.js\ngetGlobalTensorClass().prototype.as1D = function() {\n this.throwIfDisposed();\n return reshape(this, [this.size]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.js\ngetGlobalTensorClass().prototype.as2D = function(rows, columns) {\n this.throwIfDisposed();\n return reshape(this, [rows, columns]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.js\ngetGlobalTensorClass().prototype.as3D = function(rows, columns, depth) {\n this.throwIfDisposed();\n return reshape(this, [rows, columns, depth]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.js\ngetGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) {\n this.throwIfDisposed();\n return reshape(this, [rows, columns, depth, depth2]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.js\ngetGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) {\n this.throwIfDisposed();\n return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.js\ngetGlobalTensorClass().prototype.asin = function() {\n this.throwIfDisposed();\n return asin(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.js\ngetGlobalTensorClass().prototype.asinh = function() {\n this.throwIfDisposed();\n return asinh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.js\ngetGlobalTensorClass().prototype.atan = function() {\n this.throwIfDisposed();\n return atan(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.js\ngetGlobalTensorClass().prototype.atan2 = function(b) {\n this.throwIfDisposed();\n return atan2(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.js\ngetGlobalTensorClass().prototype.atanh = function() {\n this.throwIfDisposed();\n return atanh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.js\ngetGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad3, dimRoundingMode) {\n this.throwIfDisposed();\n return avgPool(this, filterSize, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.js\ngetGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) {\n this.throwIfDisposed();\n return batchToSpaceND(this, blockShape, crops);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.js\ngetGlobalTensorClass().prototype.batchNorm = function(mean4, variance, offset, scale2, varianceEpsilon) {\n this.throwIfDisposed();\n return batchNorm(this, mean4, variance, offset, scale2, varianceEpsilon);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.js\ngetGlobalTensorClass().prototype.broadcastTo = function(shape) {\n this.throwIfDisposed();\n return broadcastTo(this, shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.js\ngetGlobalTensorClass().prototype.cast = function(dtype) {\n this.throwIfDisposed();\n return cast(this, dtype);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.js\ngetGlobalTensorClass().prototype.ceil = function() {\n this.throwIfDisposed();\n return ceil(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.js\ngetGlobalTensorClass().prototype.clipByValue = function(min6, max6) {\n this.throwIfDisposed();\n return clipByValue(this, min6, max6);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.js\ngetGlobalTensorClass().prototype.concat = function(x, axis) {\n this.throwIfDisposed();\n if (x instanceof Tensor) {\n x = [x];\n }\n return concat([this, ...x], axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.js\ngetGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFormat, dilation, dimRoundingMode) {\n this.throwIfDisposed();\n return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.js\ngetGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides, pad3, dimRoundingMode) {\n this.throwIfDisposed();\n return conv2dTranspose(this, filter, outputShape, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.js\ngetGlobalTensorClass().prototype.conv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {\n this.throwIfDisposed();\n return conv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.js\ngetGlobalTensorClass().prototype.cos = function() {\n this.throwIfDisposed();\n return cos(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.js\ngetGlobalTensorClass().prototype.cosh = function() {\n this.throwIfDisposed();\n return cosh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.js\ngetGlobalTensorClass().prototype.cumprod = function(axis, exclusive, reverse5) {\n this.throwIfDisposed();\n return cumprod(this, axis, exclusive, reverse5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.js\ngetGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) {\n this.throwIfDisposed();\n return cumsum(this, axis, exclusive, reverse5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.js\ngetGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) {\n this.throwIfDisposed();\n return depthToSpace(this, blockSize, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.js\ngetGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {\n this.throwIfDisposed();\n return depthwiseConv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.js\ngetGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad3, dilations, dataFormat) {\n this.throwIfDisposed();\n return dilation2d(this, filter, strides, pad3, dilations, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.js\ngetGlobalTensorClass().prototype.divNoNan = function(b) {\n this.throwIfDisposed();\n return divNoNan(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.js\ngetGlobalTensorClass().prototype.div = function(b) {\n this.throwIfDisposed();\n return div(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.js\ngetGlobalTensorClass().prototype.dot = function(b) {\n this.throwIfDisposed();\n return dot(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.js\ngetGlobalTensorClass().prototype.elu = function() {\n this.throwIfDisposed();\n return elu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.js\ngetGlobalTensorClass().prototype.equal = function(b) {\n this.throwIfDisposed();\n return equal(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.js\ngetGlobalTensorClass().prototype.erf = function() {\n this.throwIfDisposed();\n return erf(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.js\ngetGlobalTensorClass().prototype.euclideanNorm = function(axis, keepDims) {\n this.throwIfDisposed();\n return euclideanNorm(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.js\ngetGlobalTensorClass().prototype.exp = function() {\n this.throwIfDisposed();\n return exp(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.js\ngetGlobalTensorClass().prototype.expandDims = function(axis) {\n this.throwIfDisposed();\n return expandDims(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.js\ngetGlobalTensorClass().prototype.expm1 = function() {\n this.throwIfDisposed();\n return expm1(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.js\ngetGlobalTensorClass().prototype.fft = function() {\n this.throwIfDisposed();\n return fft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.js\ngetGlobalTensorClass().prototype.flatten = function() {\n this.throwIfDisposed();\n return reshape(this, [this.size]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.js\ngetGlobalTensorClass().prototype.floor = function() {\n this.throwIfDisposed();\n return floor(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.js\ngetGlobalTensorClass().prototype.floorDiv = function(b) {\n this.throwIfDisposed();\n return floorDiv(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.js\ngetGlobalTensorClass().prototype.gather = function(indices, axis, batchDims) {\n this.throwIfDisposed();\n return gather(this, indices, axis, batchDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.js\ngetGlobalTensorClass().prototype.greaterEqual = function(b) {\n this.throwIfDisposed();\n return greaterEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.js\ngetGlobalTensorClass().prototype.greater = function(b) {\n this.throwIfDisposed();\n return greater(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.js\ngetGlobalTensorClass().prototype.ifft = function() {\n this.throwIfDisposed();\n return ifft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.js\ngetGlobalTensorClass().prototype.irfft = function() {\n this.throwIfDisposed();\n return irfft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.js\ngetGlobalTensorClass().prototype.isFinite = function() {\n this.throwIfDisposed();\n return isFinite2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.js\ngetGlobalTensorClass().prototype.isInf = function() {\n this.throwIfDisposed();\n return isInf(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.js\ngetGlobalTensorClass().prototype.isNaN = function() {\n this.throwIfDisposed();\n return isNaN2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.js\ngetGlobalTensorClass().prototype.leakyRelu = function(alpha) {\n this.throwIfDisposed();\n return leakyRelu(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.js\ngetGlobalTensorClass().prototype.lessEqual = function(b) {\n this.throwIfDisposed();\n return lessEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.js\ngetGlobalTensorClass().prototype.less = function(b) {\n this.throwIfDisposed();\n return less(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.js\ngetGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha, beta) {\n this.throwIfDisposed();\n return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.js\ngetGlobalTensorClass().prototype.logSigmoid = function() {\n this.throwIfDisposed();\n return logSigmoid(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.js\ngetGlobalTensorClass().prototype.logSoftmax = function(axis) {\n this.throwIfDisposed();\n return logSoftmax(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.js\ngetGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) {\n this.throwIfDisposed();\n return logSumExp(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.js\ngetGlobalTensorClass().prototype.log = function() {\n this.throwIfDisposed();\n return log2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.js\ngetGlobalTensorClass().prototype.log1p = function() {\n this.throwIfDisposed();\n return log1p(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.js\ngetGlobalTensorClass().prototype.logicalAnd = function(b) {\n this.throwIfDisposed();\n return logicalAnd(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.js\ngetGlobalTensorClass().prototype.logicalNot = function() {\n this.throwIfDisposed();\n return logicalNot(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.js\ngetGlobalTensorClass().prototype.logicalOr = function(b) {\n this.throwIfDisposed();\n return logicalOr(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.js\ngetGlobalTensorClass().prototype.logicalXor = function(b) {\n this.throwIfDisposed();\n return logicalXor(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.js\ngetGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) {\n this.throwIfDisposed();\n return matMul(this, b, transposeA, transposeB);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.js\ngetGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad3, dimRoundingMode) {\n this.throwIfDisposed();\n return maxPool(this, filterSize, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.js\ngetGlobalTensorClass().prototype.max = function(axis, keepDims) {\n this.throwIfDisposed();\n return max(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.js\ngetGlobalTensorClass().prototype.maximum = function(b) {\n this.throwIfDisposed();\n return maximum(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.js\ngetGlobalTensorClass().prototype.mean = function(axis, keepDims) {\n this.throwIfDisposed();\n return mean(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.js\ngetGlobalTensorClass().prototype.min = function(axis, keepDims) {\n this.throwIfDisposed();\n return min(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.js\ngetGlobalTensorClass().prototype.minimum = function(b) {\n this.throwIfDisposed();\n return minimum(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.js\ngetGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) {\n this.throwIfDisposed();\n return mirrorPad(this, paddings, mode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.js\ngetGlobalTensorClass().prototype.mod = function(b) {\n this.throwIfDisposed();\n return mod(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.js\ngetGlobalTensorClass().prototype.mul = function(b) {\n this.throwIfDisposed();\n return mul(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.js\ngetGlobalTensorClass().prototype.neg = function() {\n this.throwIfDisposed();\n return neg(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.js\ngetGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) {\n this.throwIfDisposed();\n return norm(this, ord, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.js\ngetGlobalTensorClass().prototype.notEqual = function(b) {\n this.throwIfDisposed();\n return notEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.js\ngetGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) {\n this.throwIfDisposed();\n return oneHot(this, depth, onValue, offValue);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.js\ngetGlobalTensorClass().prototype.onesLike = function() {\n this.throwIfDisposed();\n return onesLike(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.js\ngetGlobalTensorClass().prototype.pad = function(paddings, constantValue) {\n this.throwIfDisposed();\n return pad(this, paddings, constantValue);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.js\ngetGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode) {\n this.throwIfDisposed();\n return pool(this, windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.js\ngetGlobalTensorClass().prototype.pow = function(exp4) {\n this.throwIfDisposed();\n return pow(this, exp4);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.js\ngetGlobalTensorClass().prototype.prelu = function(alpha) {\n this.throwIfDisposed();\n return prelu(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.js\ngetGlobalTensorClass().prototype.prod = function(axis, keepDims) {\n this.throwIfDisposed();\n return prod(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.js\ngetGlobalTensorClass().prototype.reciprocal = function() {\n this.throwIfDisposed();\n return reciprocal(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.js\ngetGlobalTensorClass().prototype.relu = function() {\n this.throwIfDisposed();\n return relu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.js\ngetGlobalTensorClass().prototype.relu6 = function() {\n this.throwIfDisposed();\n return relu6(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.js\ngetGlobalTensorClass().prototype.reshapeAs = function(x) {\n this.throwIfDisposed();\n return reshape(this, x.shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.js\ngetGlobalTensorClass().prototype.reshape = function(shape) {\n this.throwIfDisposed();\n return reshape(this, shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.js\ngetGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) {\n this.throwIfDisposed();\n return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.js\ngetGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) {\n this.throwIfDisposed();\n return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.js\ngetGlobalTensorClass().prototype.reverse = function(axis) {\n this.throwIfDisposed();\n return reverse(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.js\ngetGlobalTensorClass().prototype.rfft = function() {\n this.throwIfDisposed();\n return rfft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.js\ngetGlobalTensorClass().prototype.round = function() {\n this.throwIfDisposed();\n return round2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.js\ngetGlobalTensorClass().prototype.rsqrt = function() {\n this.throwIfDisposed();\n return rsqrt(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.js\ngetGlobalTensorClass().prototype.selu = function() {\n this.throwIfDisposed();\n return selu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.js\ngetGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat) {\n this.throwIfDisposed();\n return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.js\ngetGlobalTensorClass().prototype.sigmoid = function() {\n this.throwIfDisposed();\n return sigmoid(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.js\ngetGlobalTensorClass().prototype.sign = function() {\n this.throwIfDisposed();\n return sign(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.js\ngetGlobalTensorClass().prototype.sin = function() {\n this.throwIfDisposed();\n return sin(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.js\ngetGlobalTensorClass().prototype.sinh = function() {\n this.throwIfDisposed();\n return sinh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.js\ngetGlobalTensorClass().prototype.slice = function(begin, size) {\n this.throwIfDisposed();\n return slice(this, begin, size);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.js\ngetGlobalTensorClass().prototype.softmax = function(dim) {\n this.throwIfDisposed();\n return softmax(this, dim);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.js\ngetGlobalTensorClass().prototype.softplus = function() {\n this.throwIfDisposed();\n return softplus(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.js\ngetGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) {\n this.throwIfDisposed();\n return spaceToBatchND(this, blockShape, paddings);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.js\ngetGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) {\n this.throwIfDisposed();\n return split(this, numOrSizeSplits, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.js\ngetGlobalTensorClass().prototype.sqrt = function() {\n this.throwIfDisposed();\n return sqrt(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.js\ngetGlobalTensorClass().prototype.square = function() {\n this.throwIfDisposed();\n return square(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.js\ngetGlobalTensorClass().prototype.squaredDifference = function(b) {\n this.throwIfDisposed();\n return squaredDifference(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.js\ngetGlobalTensorClass().prototype.squeeze = function(axis) {\n this.throwIfDisposed();\n return squeeze(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.js\ngetGlobalTensorClass().prototype.stack = function(x, axis) {\n this.throwIfDisposed();\n const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n return stack(tensorsToBeStacked, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.js\ngetGlobalTensorClass().prototype.step = function(alpha) {\n this.throwIfDisposed();\n return step(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.js\ngetGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n this.throwIfDisposed();\n return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.js\ngetGlobalTensorClass().prototype.sub = function(b) {\n this.throwIfDisposed();\n return sub(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.js\ngetGlobalTensorClass().prototype.sum = function(axis, keepDims) {\n this.throwIfDisposed();\n return sum2(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.js\ngetGlobalTensorClass().prototype.tan = function() {\n this.throwIfDisposed();\n return tan(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.js\ngetGlobalTensorClass().prototype.tanh = function() {\n this.throwIfDisposed();\n return tanh2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.js\ngetGlobalTensorClass().prototype.tile = function(reps) {\n this.throwIfDisposed();\n return tile(this, reps);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.js\ngetGlobalTensorClass().prototype.toBool = function() {\n this.throwIfDisposed();\n return cast(this, \"bool\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.js\ngetGlobalTensorClass().prototype.toFloat = function() {\n this.throwIfDisposed();\n return cast(this, \"float32\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.js\ngetGlobalTensorClass().prototype.toInt = function() {\n this.throwIfDisposed();\n return cast(this, \"int32\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.js\ngetGlobalTensorClass().prototype.topk = function(k, sorted) {\n this.throwIfDisposed();\n return topk(this, k, sorted);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.js\ngetGlobalTensorClass().prototype.transpose = function(perm) {\n this.throwIfDisposed();\n return transpose(this, perm);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.js\ngetGlobalTensorClass().prototype.unique = function(axis) {\n this.throwIfDisposed();\n return unique(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.js\ngetGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) {\n this.throwIfDisposed();\n return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.js\ngetGlobalTensorClass().prototype.unstack = function(axis) {\n this.throwIfDisposed();\n return unstack(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.js\ngetGlobalTensorClass().prototype.where = function(condition, x) {\n this.throwIfDisposed();\n return where(condition, this, x);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.js\ngetGlobalTensorClass().prototype.zerosLike = function() {\n this.throwIfDisposed();\n return zerosLike(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/errors.js\nvar AttributeError = class _AttributeError extends Error {\n constructor(message) {\n super(message);\n Object.setPrototypeOf(this, _AttributeError.prototype);\n }\n};\nvar RuntimeError = class _RuntimeError extends Error {\n constructor(message) {\n super(message);\n Object.setPrototypeOf(this, _RuntimeError.prototype);\n }\n};\nvar ValueError = class _ValueError extends Error {\n constructor(message) {\n super(message);\n Object.setPrototypeOf(this, _ValueError.prototype);\n }\n};\nvar NotImplementedError = class _NotImplementedError extends Error {\n constructor(message) {\n super(message);\n Object.setPrototypeOf(this, _NotImplementedError.prototype);\n }\n};\nvar AssertionError = class _AssertionError extends Error {\n constructor(message) {\n super(message);\n Object.setPrototypeOf(this, _AssertionError.prototype);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/executor_utils.js\nvar LruCache = class {\n constructor(maxEntries) {\n this.maxEntries = maxEntries || 100;\n this.cache = /* @__PURE__ */ new Map();\n }\n /**\n * Get the entry for the key and mark it as used recently.\n */\n get(key) {\n let entry;\n if (this.cache.has(key)) {\n entry = this.cache.get(key);\n this.cache.delete(key);\n this.cache.set(key, entry);\n }\n return entry;\n }\n /**\n * Put the entry into the cache. If the key already existed, mark the key as\n * used recently.\n */\n put(key, value) {\n if (this.cache.has(key)) {\n this.cache.delete(key);\n } else if (this.cache.size >= this.maxEntries) {\n const keyToDelete = this.cache.keys().next().value;\n this.cache.delete(keyToDelete);\n }\n this.cache.set(key, value);\n }\n /**\n * Get the MaxEntries of the cache.\n */\n getMaxEntries() {\n return this.maxEntries;\n }\n /**\n * Set the MaxEntries of the cache. If the maxEntries is decreased, reduce\n * entries in the cache.\n */\n setMaxEntries(maxEntries) {\n if (maxEntries < 0) {\n throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`);\n }\n if (this.maxEntries > maxEntries) {\n for (let i = 0; i < this.maxEntries - maxEntries; i++) {\n const keyToDelete = this.cache.keys().next().value;\n this.cache.delete(keyToDelete);\n }\n }\n this.maxEntries = maxEntries;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/generic_utils.js\nfunction pyListRepeat(value, numValues) {\n if (Array.isArray(value)) {\n let newArray = [];\n for (let i = 0; i < numValues; i++) {\n newArray = newArray.concat(value);\n }\n return newArray;\n } else {\n const newArray = new Array(numValues);\n newArray.fill(value);\n return newArray;\n }\n}\nfunction assert2(val, message) {\n if (!val) {\n throw new AssertionError(message);\n }\n}\nfunction count(array2, refernce) {\n let counter = 0;\n for (const item of array2) {\n if (item === refernce) {\n counter++;\n }\n }\n return counter;\n}\nfunction singletonOrArray(xs) {\n if (xs.length === 1) {\n return xs[0];\n }\n return xs;\n}\nfunction toList(x) {\n if (Array.isArray(x)) {\n return x;\n }\n return [x];\n}\nfunction toSnakeCase(name) {\n const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, \"$1_$2\");\n const insecure = intermediate.replace(/([a-z])([A-Z])/g, \"$1_$2\").toLowerCase();\n if (insecure[0] !== \"_\") {\n return insecure;\n }\n return \"private\" + insecure;\n}\nfunction toCamelCase(identifier) {\n if (identifier.length <= 1) {\n return identifier;\n }\n if (identifier.indexOf(\"_\") === -1) {\n return identifier;\n }\n return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\nvar _GLOBAL_CUSTOM_OBJECTS = {};\nfunction serializeKerasObject(instance) {\n if (instance === null || instance === void 0) {\n return null;\n }\n const dict = {};\n dict[\"className\"] = instance.getClassName();\n dict[\"config\"] = instance.getConfig();\n return dict;\n}\nfunction convertNDArrayScalarsInConfig(config) {\n if (config == null || typeof config !== \"object\") {\n return;\n } else if (Array.isArray(config)) {\n config.forEach((configItem) => convertNDArrayScalarsInConfig(configItem));\n } else {\n const fields = Object.keys(config);\n for (const field of fields) {\n const value = config[field];\n if (value != null && typeof value === \"object\") {\n if (!Array.isArray(value) && value[\"type\"] === \"ndarray\" && typeof value[\"value\"] === \"number\") {\n config[field] = value[\"value\"];\n } else {\n convertNDArrayScalarsInConfig(value);\n }\n }\n }\n }\n}\nfunction deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = \"object\", fastWeightInit = false) {\n if (typeof identifier === \"string\") {\n const functionName = identifier;\n let fn;\n if (functionName in customObjects) {\n fn = customObjects[functionName];\n } else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n } else {\n fn = moduleObjects[functionName];\n if (fn == null) {\n throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:\n1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.\n2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);\n }\n }\n return fn;\n } else {\n const config = identifier;\n if (config[\"className\"] == null || config[\"config\"] == null) {\n throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.\n'className' and 'config' must set.`);\n }\n const className = config[\"className\"];\n let cls, fromConfig;\n if (className in customObjects) {\n [cls, fromConfig] = customObjects[className];\n } else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS[\"className\"];\n } else if (className in moduleObjects) {\n [cls, fromConfig] = moduleObjects[className];\n }\n if (cls == null) {\n throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:\n1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.\n2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);\n }\n if (fromConfig != null) {\n const customObjectsCombined = {};\n for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n }\n for (const key of Object.keys(customObjects)) {\n customObjectsCombined[key] = customObjects[key];\n }\n const nestedConfig = config[\"config\"];\n nestedConfig[\"customObjects\"] = customObjectsCombined;\n const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n for (const key of Object.keys(customObjects)) {\n _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n }\n convertNDArrayScalarsInConfig(config[\"config\"]);\n const returnObj = fromConfig(cls, config[\"config\"], customObjects, fastWeightInit);\n _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n return returnObj;\n } else {\n const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n for (const key of Object.keys(customObjects)) {\n _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n }\n const returnObj = new cls(config[\"config\"]);\n _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n return returnObj;\n }\n }\n}\nfunction numberCompare(a, b) {\n return a < b ? -1 : a > b ? 1 : 0;\n}\nfunction reverseNumberCompare(a, b) {\n return -1 * numberCompare(a, b);\n}\nfunction unique2(xs) {\n if (xs == null) {\n return xs;\n }\n const out = [];\n for (const x of xs) {\n if (out.indexOf(x) === -1) {\n out.push(x);\n }\n }\n return out;\n}\nfunction isObjectEmpty(obj) {\n if (obj == null) {\n throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n }\n for (const key in obj) {\n if (obj.hasOwnProperty(key)) {\n return false;\n }\n }\n return true;\n}\nfunction checkStringTypeUnionValue(values, label, value) {\n if (value == null) {\n return;\n }\n if (values.indexOf(value) < 0) {\n throw new ValueError(`${value} is not a valid ${label}. Valid values are ${values} or null/undefined.`);\n }\n}\nfunction checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n assert2(minLength >= 0);\n assert2(maxLength >= minLength);\n return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e) => typeof e === expectedType);\n}\nfunction assertPositiveInteger(value, name) {\n if (Array.isArray(value)) {\n util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n } else {\n util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`);\n }\n}\nfunction formatAsFriendlyString(value) {\n if (value === null) {\n return \"null\";\n } else if (Array.isArray(value)) {\n return \"[\" + value.map((v) => formatAsFriendlyString(v)).join(\",\") + \"]\";\n } else if (typeof value === \"string\") {\n return `\"${value}\"`;\n } else {\n return `${value}`;\n }\n}\nfunction debounce(f, waitMs, nowFunc) {\n let lastTime = nowFunc != null ? nowFunc() : util_exports.now();\n let lastResult;\n const f2 = (...args) => {\n const now2 = nowFunc != null ? nowFunc() : util_exports.now();\n if (now2 - lastTime < waitMs) {\n return lastResult;\n }\n lastTime = now2;\n lastResult = f(...args);\n return lastResult;\n };\n return f2;\n}\nfunction mapActivationToFusedKernel(activationName) {\n if (activationName === \"relu\") {\n return \"relu\";\n }\n if (activationName === \"linear\") {\n return \"linear\";\n }\n if (activationName === \"elu\") {\n return \"elu\";\n }\n return null;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/backend/state.js\nvar _nextUniqueTensorId = 0;\nfunction getNextUniqueTensorId() {\n return _nextUniqueTensorId++;\n}\nvar _uidPrefixes = {};\nfunction getUid(prefix = \"\") {\n if (!(prefix in _uidPrefixes)) {\n _uidPrefixes[prefix] = 0;\n }\n _uidPrefixes[prefix] += 1;\n return prefix + _uidPrefixes[prefix].toString();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/keras_format/common.js\nvar VALID_DATA_FORMAT_VALUES = [\"channelsFirst\", \"channelsLast\"];\nvar VALID_INTERPOLATION_FORMAT_VALUES = [\"nearest\", \"bilinear\"];\nvar VALID_PADDING_MODE_VALUES = [\"valid\", \"same\", \"causal\"];\nvar VALID_POOL_MODE_VALUES = [\"max\", \"avg\"];\nvar VALID_BIDIRECTIONAL_MERGE_MODES = [\"sum\", \"mul\", \"concat\", \"ave\"];\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/common.js\nvar nameMap = /* @__PURE__ */ new Map();\nfunction checkDataFormat(value) {\n checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, \"DataFormat\", value);\n}\nfunction checkInterpolationFormat(value) {\n checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES, \"InterpolationFormat\", value);\n}\nfunction checkPaddingMode(value) {\n checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, \"PaddingMode\", value);\n}\nfunction checkPoolMode(value) {\n checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, \"PoolMode\", value);\n}\nvar _nameScopeStack = [];\nvar _nameScopeDivider = \"/\";\nfunction nameScope(name, fn) {\n _nameScopeStack.push(name);\n try {\n const val = fn();\n _nameScopeStack.pop();\n return val;\n } catch (e) {\n _nameScopeStack.pop();\n throw e;\n }\n}\nfunction currentNameScopePrefix() {\n if (_nameScopeStack.length === 0) {\n return \"\";\n } else {\n return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n }\n}\nfunction getScopedTensorName(tensorName) {\n if (!isValidTensorName(tensorName)) {\n throw new Error(\"Not a valid tensor name: '\" + tensorName + \"'\");\n }\n return currentNameScopePrefix() + tensorName;\n}\nfunction getUniqueTensorName(scopedName) {\n if (!isValidTensorName(scopedName)) {\n throw new Error(\"Not a valid tensor name: '\" + scopedName + \"'\");\n }\n if (!nameMap.has(scopedName)) {\n nameMap.set(scopedName, 0);\n }\n const index = nameMap.get(scopedName);\n nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n if (index > 0) {\n const result = `${scopedName}_${index}`;\n nameMap.set(result, 1);\n return result;\n } else {\n return scopedName;\n }\n}\nvar tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\nfunction isValidTensorName(name) {\n return !!name.match(tensorNameRegex);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/math_utils.js\nfunction isInteger(x) {\n return x === parseInt(x.toString(), 10);\n}\nfunction arrayProd(array2, begin, end) {\n if (begin == null) {\n begin = 0;\n }\n if (end == null) {\n end = array2.length;\n }\n let prod5 = 1;\n for (let i = begin; i < end; ++i) {\n prod5 *= array2[i];\n }\n return prod5;\n}\nfunction min2(array2) {\n if (array2.length === 0) {\n return Number.NaN;\n }\n let min6 = Number.POSITIVE_INFINITY;\n for (let i = 0; i < array2.length; i++) {\n const value = array2[i];\n if (value < min6) {\n min6 = value;\n }\n }\n return min6;\n}\nfunction max2(array2) {\n if (array2.length === 0) {\n return Number.NaN;\n }\n let max6 = Number.NEGATIVE_INFINITY;\n for (let i = 0; i < array2.length; i++) {\n const value = array2[i];\n if (value > max6) {\n max6 = value;\n }\n }\n return max6;\n}\nfunction range2(begin, end) {\n if (end < begin) {\n throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n }\n const out = [];\n for (let i = begin; i < end; ++i) {\n out.push(i);\n }\n return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/backend/common.js\nvar _epsilon;\nfunction epsilon() {\n if (_epsilon == null) {\n _epsilon = backend().epsilon();\n }\n return _epsilon;\n}\nfunction imageDataFormat() {\n return \"channelsLast\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js\nfunction cast2(x, dtype) {\n return cast(x, dtype);\n}\nfunction expandDims2(x, axis = -1) {\n const outShape = x.shape.slice();\n if (axis < 0) {\n axis = outShape.length + axis + 1;\n }\n outShape.splice(axis, 0, 1);\n return reshape(x, outShape);\n}\nfunction repeat(x, n) {\n return tidy(() => {\n if (x.shape.length !== 2) {\n throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);\n }\n const y = expandDims2(x, 1);\n return tile2(y, [1, n, 1]);\n });\n}\nfunction flatten2(x) {\n const newShape = [arrayProd(x.shape)];\n return reshape(x, newShape);\n}\nfunction batchFlatten(x) {\n if (x.rank <= 1) {\n throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n }\n const newShape = [x.shape[0], arrayProd(x.shape, 1)];\n return reshape(x, newShape);\n}\nfunction sliceAlongFirstAxis(array2, start, size) {\n return tidy(() => {\n switch (array2.rank) {\n case 1:\n return slice1d(array2, start, size);\n case 2:\n return slice2d(array2, [start, 0], [size, array2.shape[1]]);\n case 3:\n return slice3d(array2, [start, 0, 0], [size, array2.shape[1], array2.shape[2]]);\n case 4:\n return slice4d(array2, [start, 0, 0, 0], [size, array2.shape[1], array2.shape[2], array2.shape[3]]);\n case 5:\n return slice(array2, [start, 0, 0, 0, 0], [\n size,\n array2.shape[1],\n array2.shape[2],\n array2.shape[3],\n array2.shape[4]\n ]);\n case 6:\n return slice(array2, [start, 0, 0, 0, 0, 0], [\n size,\n array2.shape[1],\n array2.shape[2],\n array2.shape[3],\n array2.shape[4],\n array2.shape[5]\n ]);\n default:\n throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`);\n }\n });\n}\nfunction sliceAlongLastAxis(array2, start, size) {\n return tidy(() => {\n switch (array2.rank) {\n case 1:\n return slice1d(array2, start, size);\n case 2:\n return slice2d(array2, [0, start], [array2.shape[0], size]);\n case 3:\n return slice3d(array2, [0, 0, start], [array2.shape[0], array2.shape[1], size]);\n case 4:\n return slice4d(array2, [0, 0, 0, start], [array2.shape[0], array2.shape[1], array2.shape[2], size]);\n default:\n throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);\n }\n });\n}\nfunction sliceAlongAxis(array2, start, size, axis) {\n return tidy(() => {\n switch (array2.rank) {\n case 1:\n return slice1d(array2, start, size);\n case 2:\n switch (axis) {\n case 1:\n return sliceAlongFirstAxis(array2, start, size);\n case 2:\n return sliceAlongLastAxis(array2, start, size);\n default:\n throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n }\n case 3:\n switch (axis) {\n case 1:\n return sliceAlongFirstAxis(array2, start, size);\n case 2:\n return slice3d(array2, [0, start, 0], [array2.shape[0], size, array2.shape[2]]);\n case 3:\n return sliceAlongLastAxis(array2, start, size);\n default:\n throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n }\n case 4:\n switch (axis) {\n case 1:\n return sliceAlongFirstAxis(array2, start, size);\n case 2:\n return slice4d(array2, [0, start, 0, 0], [array2.shape[0], size, array2.shape[2], array2.shape[3]]);\n case 3:\n return slice4d(array2, [0, 0, start, 0], [array2.shape[0], array2.shape[1], size, array2.shape[3]]);\n case 4:\n return sliceAlongLastAxis(array2, start, size);\n default:\n throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n }\n default:\n throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);\n }\n });\n}\nfunction concatenate(tensors, axis = -1) {\n let rank;\n if (axis < 0) {\n rank = tensors[0].rank;\n if (rank !== 0) {\n axis = rank;\n } else {\n axis = 0;\n }\n }\n if (axis === tensors[0].rank) {\n axis = -1;\n }\n return concat(tensors, axis);\n}\nfunction concatAlongFirstAxis(a, b) {\n switch (a.rank) {\n case 1:\n return concat1d([a, b]);\n case 2:\n return concat2d([a, b], 0);\n case 3:\n return concat3d([a, b], 0);\n case 4:\n return concat4d([a, b], 0);\n default:\n throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`);\n }\n}\nfunction tile2(x, n) {\n if (!Array.isArray(n)) {\n n = [n];\n }\n if (x.rank !== n.length) {\n throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);\n }\n return tile(x, n);\n}\nfunction randomNormal2(shape, mean4 = 0, stddev = 1, dtype, seed) {\n return randomNormal(shape, mean4, stddev, dtype, seed);\n}\nfunction dot2(a, b, activation2, bias) {\n if (a.rank < 2 || b.rank < 2) {\n throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);\n }\n if (b.rank >= 3) {\n const xLastDim = a.shape.slice(-1)[0];\n const ySecondLastDim = b.shape.slice(-2)[0];\n if (xLastDim !== ySecondLastDim) {\n throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and y shape = ${b.shape}`);\n }\n }\n if (a.rank === 2 && b.rank === 2) {\n const transposeA = false;\n const transposeB = false;\n return fused_ops_exports.matMul({\n a,\n b,\n transposeA,\n transposeB,\n bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n activation: activation2\n });\n } else {\n const aFirstDims = a.shape.slice();\n const aLastDim = aFirstDims.pop();\n a = reshape(a, [-1, aLastDim]);\n const bShape = b.shape.slice();\n const bLastDim = bShape.pop();\n const ySecondLastDim = bShape.pop();\n const yOtherDims = [...bShape, bLastDim];\n const perm = Array.from({ length: b.rank }, (_, i) => {\n if (i === 0) {\n return b.rank - 2;\n } else if (i <= b.rank - 2) {\n return i - 1;\n }\n return i;\n });\n b = reshape(transpose(b, perm), [ySecondLastDim, -1]);\n const outputShape = [...aFirstDims, ...yOtherDims];\n const transposeA = false;\n const transposeB = false;\n return reshape(fused_ops_exports.matMul({\n a,\n b,\n transposeA,\n transposeB,\n bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n activation: activation2\n }), outputShape);\n }\n}\nfunction gather2(reference, indices, axis) {\n return tidy(() => {\n if (Array.isArray(indices)) {\n indices = tensor1d(indices, \"int32\");\n } else {\n indices = cast(indices, \"int32\");\n }\n return gather(reference, indices, axis);\n });\n}\nfunction square2(x) {\n return mul(x, x);\n}\nfunction reshapeBias(xRank, bias, dataFormat) {\n const biasShape = bias.shape;\n if (bias.rank !== 1 && bias.rank !== xRank) {\n throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);\n }\n if (xRank === 5) {\n if (dataFormat === \"channelsFirst\") {\n if (biasShape.length === 1) {\n return reshape(bias, [1, biasShape[0], 1, 1, 1]);\n } else {\n return reshape(bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n }\n } else if (dataFormat === \"channelsLast\") {\n if (biasShape.length === 1) {\n return reshape(bias, [1, 1, 1, 1, biasShape[0]]);\n } else {\n return reshape(bias, [1].concat(biasShape));\n }\n }\n } else if (xRank === 4) {\n if (dataFormat === \"channelsFirst\") {\n if (biasShape.length === 1) {\n return reshape(bias, [1, biasShape[0], 1, 1]);\n } else {\n return reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]);\n }\n } else if (dataFormat === \"channelsLast\") {\n if (biasShape.length === 1) {\n return reshape(bias, [1, 1, 1, biasShape[0]]);\n } else {\n return reshape(bias, [1].concat(biasShape));\n }\n }\n } else if (xRank === 3) {\n if (dataFormat === \"channelsFirst\") {\n if (biasShape.length === 1) {\n return reshape(bias, [1, biasShape[0], 1]);\n } else {\n return reshape(bias, [1, biasShape[1], biasShape[0]]);\n }\n } else if (dataFormat === \"channelsLast\") {\n if (biasShape.length === 1) {\n return reshape(bias, [1, 1, biasShape[0]]);\n } else {\n return reshape(bias, [1].concat(biasShape));\n }\n }\n } else if (xRank < 3) {\n return bias;\n }\n throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\nfunction biasAdd(x, bias, dataFormat) {\n return tidy(() => {\n if (dataFormat == null) {\n dataFormat = imageDataFormat();\n }\n checkDataFormat(dataFormat);\n return add2(x, reshapeBias(x.rank, bias, dataFormat));\n });\n}\nfunction elu2(x, alpha = 1) {\n if (alpha !== 1) {\n throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);\n }\n return elu(x);\n}\nfunction softsign(x) {\n return tidy(() => div(x, add2(abs(x), 1)));\n}\nfunction dropout2(x, level, noiseShape, seed) {\n return tidy(() => dropout(x, level, noiseShape, seed));\n}\nfunction hardSigmoid(x) {\n return tidy(() => {\n const y = add2(0.5, mul(0.2, x));\n return clipByValue(y, 0, 1);\n });\n}\nfunction inTrainPhase(x, alt, training = false) {\n return training ? x() : alt();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/keras_format/initializer_config.js\nvar VALID_FAN_MODE_VALUES = [\"fanIn\", \"fanOut\", \"fanAvg\"];\nvar VALID_DISTRIBUTION_VALUES = [\"normal\", \"uniform\", \"truncatedNormal\"];\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/initializers.js\nfunction checkFanMode(value) {\n checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, \"FanMode\", value);\n}\nfunction checkDistribution(value) {\n checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, \"Distribution\", value);\n}\nvar Initializer = class extends serialization_exports.Serializable {\n fromConfigUsesCustomObjects() {\n return false;\n }\n getConfig() {\n return {};\n }\n};\nvar Zeros = class extends Initializer {\n apply(shape, dtype) {\n return zeros(shape, dtype);\n }\n};\nZeros.className = \"Zeros\";\nserialization_exports.registerClass(Zeros);\nvar Ones = class extends Initializer {\n apply(shape, dtype) {\n return ones2(shape, dtype);\n }\n};\nOnes.className = \"Ones\";\nserialization_exports.registerClass(Ones);\nvar Constant = class extends Initializer {\n constructor(args) {\n super();\n if (typeof args !== \"object\") {\n throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n }\n if (args.value === void 0) {\n throw new ValueError(`config must have value set but got ${args}`);\n }\n this.value = args.value;\n }\n apply(shape, dtype) {\n return tidy(() => mul(scalar(this.value), ones2(shape, dtype)));\n }\n getConfig() {\n return {\n value: this.value\n };\n }\n};\nConstant.className = \"Constant\";\nserialization_exports.registerClass(Constant);\nvar RandomUniform = class extends Initializer {\n constructor(args) {\n super();\n this.DEFAULT_MINVAL = -0.05;\n this.DEFAULT_MAXVAL = 0.05;\n this.minval = args.minval || this.DEFAULT_MINVAL;\n this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n this.seed = args.seed;\n }\n apply(shape, dtype) {\n return randomUniform(shape, this.minval, this.maxval, dtype, this.seed);\n }\n getConfig() {\n return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n }\n};\nRandomUniform.className = \"RandomUniform\";\nserialization_exports.registerClass(RandomUniform);\nvar RandomNormal = class extends Initializer {\n constructor(args) {\n super();\n this.DEFAULT_MEAN = 0;\n this.DEFAULT_STDDEV = 0.05;\n this.mean = args.mean || this.DEFAULT_MEAN;\n this.stddev = args.stddev || this.DEFAULT_STDDEV;\n this.seed = args.seed;\n }\n apply(shape, dtype) {\n dtype = dtype || \"float32\";\n if (dtype !== \"float32\" && dtype !== \"int32\") {\n throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n }\n return randomNormal2(shape, this.mean, this.stddev, dtype, this.seed);\n }\n getConfig() {\n return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n }\n};\nRandomNormal.className = \"RandomNormal\";\nserialization_exports.registerClass(RandomNormal);\nvar TruncatedNormal = class extends Initializer {\n constructor(args) {\n super();\n this.DEFAULT_MEAN = 0;\n this.DEFAULT_STDDEV = 0.05;\n this.mean = args.mean || this.DEFAULT_MEAN;\n this.stddev = args.stddev || this.DEFAULT_STDDEV;\n this.seed = args.seed;\n }\n apply(shape, dtype) {\n dtype = dtype || \"float32\";\n if (dtype !== \"float32\" && dtype !== \"int32\") {\n throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n }\n return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n }\n getConfig() {\n return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n }\n};\nTruncatedNormal.className = \"TruncatedNormal\";\nserialization_exports.registerClass(TruncatedNormal);\nvar Identity2 = class extends Initializer {\n constructor(args) {\n super();\n this.gain = args.gain != null ? args.gain : 1;\n }\n apply(shape, dtype) {\n return tidy(() => {\n if (shape.length !== 2 || shape[0] !== shape[1]) {\n throw new ValueError(\"Identity matrix initializer can only be used for 2D square matrices.\");\n } else {\n return mul(this.gain, eye(shape[0]));\n }\n });\n }\n getConfig() {\n return { gain: this.gain };\n }\n};\nIdentity2.className = \"Identity\";\nserialization_exports.registerClass(Identity2);\nfunction computeFans(shape, dataFormat = \"channelsLast\") {\n let fanIn;\n let fanOut;\n checkDataFormat(dataFormat);\n if (shape.length === 2) {\n fanIn = shape[0];\n fanOut = shape[1];\n } else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n if (dataFormat === \"channelsFirst\") {\n const receptiveFieldSize = arrayProd(shape, 2);\n fanIn = shape[1] * receptiveFieldSize;\n fanOut = shape[0] * receptiveFieldSize;\n } else if (dataFormat === \"channelsLast\") {\n const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n fanIn = shape[shape.length - 2] * receptiveFieldSize;\n fanOut = shape[shape.length - 1] * receptiveFieldSize;\n }\n } else {\n const shapeProd = arrayProd(shape);\n fanIn = Math.sqrt(shapeProd);\n fanOut = Math.sqrt(shapeProd);\n }\n return [fanIn, fanOut];\n}\nvar VarianceScaling = class extends Initializer {\n /**\n * Constructor of VarianceScaling.\n * @throws ValueError for invalid value in scale.\n */\n constructor(args) {\n super();\n if (args.scale < 0) {\n throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n }\n this.scale = args.scale == null ? 1 : args.scale;\n this.mode = args.mode == null ? \"fanIn\" : args.mode;\n checkFanMode(this.mode);\n this.distribution = args.distribution == null ? \"normal\" : args.distribution;\n checkDistribution(this.distribution);\n this.seed = args.seed;\n }\n apply(shape, dtype) {\n const fans = computeFans(shape);\n const fanIn = fans[0];\n const fanOut = fans[1];\n let scale2 = this.scale;\n if (this.mode === \"fanIn\") {\n scale2 /= Math.max(1, fanIn);\n } else if (this.mode === \"fanOut\") {\n scale2 /= Math.max(1, fanOut);\n } else {\n scale2 /= Math.max(1, (fanIn + fanOut) / 2);\n }\n if (this.distribution === \"normal\") {\n const stddev = Math.sqrt(scale2);\n dtype = dtype || \"float32\";\n if (dtype !== \"float32\" && dtype !== \"int32\") {\n throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n }\n return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n } else {\n const limit = Math.sqrt(3 * scale2);\n return randomUniform(shape, -limit, limit, dtype, this.seed);\n }\n }\n getConfig() {\n return {\n scale: this.scale,\n mode: this.mode,\n distribution: this.distribution,\n seed: this.seed\n };\n }\n};\nVarianceScaling.className = \"VarianceScaling\";\nserialization_exports.registerClass(VarianceScaling);\nvar GlorotUniform = class extends VarianceScaling {\n /**\n * Constructor of GlorotUniform\n * @param scale\n * @param mode\n * @param distribution\n * @param seed\n */\n constructor(args) {\n super({\n scale: 1,\n mode: \"fanAvg\",\n distribution: \"uniform\",\n seed: args == null ? null : args.seed\n });\n }\n getClassName() {\n return VarianceScaling.className;\n }\n};\nGlorotUniform.className = \"GlorotUniform\";\nserialization_exports.registerClass(GlorotUniform);\nvar GlorotNormal = class extends VarianceScaling {\n /**\n * Constructor of GlorotNormal.\n * @param scale\n * @param mode\n * @param distribution\n * @param seed\n */\n constructor(args) {\n super({\n scale: 1,\n mode: \"fanAvg\",\n distribution: \"normal\",\n seed: args == null ? null : args.seed\n });\n }\n getClassName() {\n return VarianceScaling.className;\n }\n};\nGlorotNormal.className = \"GlorotNormal\";\nserialization_exports.registerClass(GlorotNormal);\nvar HeNormal = class extends VarianceScaling {\n constructor(args) {\n super({\n scale: 2,\n mode: \"fanIn\",\n distribution: \"normal\",\n seed: args == null ? null : args.seed\n });\n }\n getClassName() {\n return VarianceScaling.className;\n }\n};\nHeNormal.className = \"HeNormal\";\nserialization_exports.registerClass(HeNormal);\nvar HeUniform = class extends VarianceScaling {\n constructor(args) {\n super({\n scale: 2,\n mode: \"fanIn\",\n distribution: \"uniform\",\n seed: args == null ? null : args.seed\n });\n }\n getClassName() {\n return VarianceScaling.className;\n }\n};\nHeUniform.className = \"HeUniform\";\nserialization_exports.registerClass(HeUniform);\nvar LeCunNormal = class extends VarianceScaling {\n constructor(args) {\n super({\n scale: 1,\n mode: \"fanIn\",\n distribution: \"normal\",\n seed: args == null ? null : args.seed\n });\n }\n getClassName() {\n return VarianceScaling.className;\n }\n};\nLeCunNormal.className = \"LeCunNormal\";\nserialization_exports.registerClass(LeCunNormal);\nvar LeCunUniform = class extends VarianceScaling {\n constructor(args) {\n super({\n scale: 1,\n mode: \"fanIn\",\n distribution: \"uniform\",\n seed: args == null ? null : args.seed\n });\n }\n getClassName() {\n return VarianceScaling.className;\n }\n};\nLeCunUniform.className = \"LeCunUniform\";\nserialization_exports.registerClass(LeCunUniform);\nvar Orthogonal = class extends Initializer {\n constructor(args) {\n super();\n this.DEFAULT_GAIN = 1;\n this.ELEMENTS_WARN_SLOW = 2e3;\n this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n this.seed = args.seed;\n }\n apply(shape, dtype) {\n return tidy(() => {\n if (shape.length < 2) {\n throw new NotImplementedError(\"Shape must be at least 2D.\");\n }\n if (dtype !== \"int32\" && dtype !== \"float32\" && dtype !== void 0) {\n throw new TypeError(`Unsupported data type ${dtype}.`);\n }\n dtype = dtype;\n const numRows = util_exports.sizeFromShape(shape.slice(0, -1));\n const numCols = shape[shape.length - 1];\n const numElements = numRows * numCols;\n if (numElements > this.ELEMENTS_WARN_SLOW) {\n console.warn(`Orthogonal initializer is being called on a matrix with more than ${this.ELEMENTS_WARN_SLOW} (${numElements}) elements: Slowness may result.`);\n }\n const flatShape = [Math.max(numCols, numRows), Math.min(numCols, numRows)];\n const randNormalMat = randomNormal2(flatShape, 0, 1, dtype, this.seed);\n const qr2 = linalg.qr(randNormalMat, false);\n let qMat = qr2[0];\n const rMat = qr2[1];\n const diag5 = rMat.flatten().stridedSlice([0], [Math.min(numCols, numRows) * Math.min(numCols, numRows)], [Math.min(numCols, numRows) + 1]);\n qMat = mul(qMat, diag5.sign());\n if (numRows < numCols) {\n qMat = qMat.transpose();\n }\n return mul(scalar(this.gain), qMat.reshape(shape));\n });\n }\n getConfig() {\n return {\n gain: this.gain,\n seed: this.seed\n };\n }\n};\nOrthogonal.className = \"Orthogonal\";\nserialization_exports.registerClass(Orthogonal);\nvar INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n \"constant\": \"Constant\",\n \"glorotNormal\": \"GlorotNormal\",\n \"glorotUniform\": \"GlorotUniform\",\n \"heNormal\": \"HeNormal\",\n \"heUniform\": \"HeUniform\",\n \"identity\": \"Identity\",\n \"leCunNormal\": \"LeCunNormal\",\n \"leCunUniform\": \"LeCunUniform\",\n \"ones\": \"Ones\",\n \"orthogonal\": \"Orthogonal\",\n \"randomNormal\": \"RandomNormal\",\n \"randomUniform\": \"RandomUniform\",\n \"truncatedNormal\": \"TruncatedNormal\",\n \"varianceScaling\": \"VarianceScaling\",\n \"zeros\": \"Zeros\"\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"initializer\");\n}\nfunction serializeInitializer(initializer) {\n return serializeKerasObject(initializer);\n}\nfunction getInitializer(identifier) {\n if (typeof identifier === \"string\") {\n const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n if (className === \"GlorotNormal\") {\n return new GlorotNormal();\n } else if (className === \"GlorotUniform\") {\n return new GlorotUniform();\n } else if (className === \"HeNormal\") {\n return new HeNormal();\n } else if (className === \"HeUniform\") {\n return new HeUniform();\n } else if (className === \"LeCunNormal\") {\n return new LeCunNormal();\n } else if (className === \"LeCunUniform\") {\n return new LeCunUniform();\n } else {\n const config = {};\n config[\"className\"] = className;\n config[\"config\"] = {};\n return deserializeInitializer(config);\n }\n } else if (identifier instanceof Initializer) {\n return identifier;\n } else {\n return deserializeInitializer(identifier);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/types_utils.js\nfunction isArrayOfShapes(x) {\n return Array.isArray(x) && Array.isArray(x[0]);\n}\nfunction normalizeShapeList(x) {\n if (x.length === 0) {\n return [];\n }\n if (!Array.isArray(x[0])) {\n return [x];\n }\n return x;\n}\nfunction getExactlyOneTensor(xs) {\n let x;\n if (Array.isArray(xs)) {\n if (xs.length !== 1) {\n throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n }\n x = xs[0];\n } else {\n x = xs;\n }\n return x;\n}\nfunction getExactlyOneShape(shapes) {\n if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n if (shapes.length === 1) {\n shapes = shapes;\n return shapes[0];\n } else {\n throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n }\n } else {\n return shapes;\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/variable_utils.js\nfunction countParamsInWeights(weights) {\n let count2 = 0;\n for (const weight of weights) {\n if (weight.shape.length === 0) {\n count2 += 1;\n } else {\n count2 += weight.shape.reduce((a, b) => a * b);\n }\n }\n return count2;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/variables.js\nvar DEFAULT_VARIABLE_NAME_PREFIX = \"Variable\";\nvar LayerVariable = class {\n /**\n * Construct Variable from a `tf.Tensor`.\n *\n * If not explicitly named, the Variable will be given a name with the\n * prefix 'Variable'. Variable names are unique. In the case of name\n * collision, suffixies '_' will be added to the name.\n *\n * @param val Initial value of the Variable.\n * @param name Name of the variable. If `null` or `undefined` is provided, it\n * will default a name with the prefix 'Variable'.\n * @param constraint Optional, projection function to be applied to the\n * variable after optimize updates\n * @throws ValueError if `name` is `null` or `undefined`.\n */\n constructor(val, dtype = \"float32\", name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n this.dtype = dtype == null ? \"float32\" : dtype;\n this.shape = val.shape;\n this.id = getNextUniqueTensorId();\n name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n this.originalName = getScopedTensorName(name);\n this.name = getUniqueTensorName(this.originalName);\n this.trainable_ = trainable;\n this.constraint = constraint;\n this.val = variable(val, this.trainable_, this.name, this.dtype);\n }\n /**\n * Get a snapshot of the Variable's value.\n *\n * The returned value is a snapshot of the Variable's value at the time of\n * the invocation. Future mutations in the value of the tensor will only\n * be reflected by future calls to this method.\n */\n read() {\n this.assertNotDisposed();\n return this.val;\n }\n /**\n * Update the value of the Variable.\n *\n * @param newVal: The new value to update to. Must be consistent with the\n * dtype and shape of the Variable.\n * @return This Variable.\n */\n write(newVal) {\n this.assertNotDisposed();\n checkShapesMatch(this.val, newVal);\n if (this.val.id !== newVal.id) {\n this.val.assign(newVal);\n if (this.constraint != null) {\n this.val.assign(this.constraint.apply(this.val));\n }\n }\n return this;\n }\n /**\n * Dispose this LayersVariable instance from memory.\n */\n dispose() {\n this.assertNotDisposed();\n this.val.dispose();\n }\n assertNotDisposed() {\n if (this.val.isDisposed) {\n throw new Error(`LayersVariable ${this.name} is already disposed.`);\n }\n }\n get trainable() {\n return this.trainable_;\n }\n set trainable(trainable) {\n this.trainable_ = trainable;\n this.val.trainable = trainable;\n }\n};\nfunction checkShapesMatch(x, y) {\n if (x.shape.toString() !== y.shape.toString()) {\n throw new Error(\"Shape mismatch: \" + JSON.stringify(x.shape) + \" vs. \" + JSON.stringify(y.shape));\n }\n}\nfunction batchGetValue(xs) {\n return xs.map((x) => x.read());\n}\nfunction batchSetValue(variablesAndValues) {\n variablesAndValues.forEach((variableAndValue) => {\n const variable2 = variableAndValue[0];\n variable2.write(variableAndValue[1]);\n });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js\nvar InputSpec = class {\n constructor(args) {\n this.dtype = args.dtype;\n this.shape = args.shape;\n if (args.shape != null) {\n this.ndim = args.shape.length;\n } else {\n this.ndim = args.ndim;\n }\n this.maxNDim = args.maxNDim;\n this.minNDim = args.minNDim;\n this.axes = args.axes || {};\n }\n};\nvar SymbolicTensor = class {\n /**\n *\n * @param dtype\n * @param shape\n * @param sourceLayer The Layer that produced this symbolic tensor.\n * @param inputs The inputs passed to sourceLayer's __call__() method.\n * @param nodeIndex\n * @param tensorIndex\n * @param callArgs The keyword arguments passed to the __call__() method.\n * @param name\n * @param outputTensorIndex The index of this tensor in the list of outputs\n * returned by apply().\n */\n constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n this.dtype = dtype;\n this.shape = shape;\n this.sourceLayer = sourceLayer;\n this.inputs = inputs;\n this.callArgs = callArgs;\n this.outputTensorIndex = outputTensorIndex;\n this.id = getNextUniqueTensorId();\n if (name != null) {\n this.originalName = getScopedTensorName(name);\n this.name = getUniqueTensorName(this.originalName);\n }\n this.rank = shape.length;\n }\n};\nvar _nextNodeID = 0;\nvar Node = class {\n constructor(args, callArgs) {\n this.callArgs = callArgs;\n this.id = _nextNodeID++;\n this.outboundLayer = args.outboundLayer;\n this.inboundLayers = args.inboundLayers;\n this.nodeIndices = args.nodeIndices;\n this.tensorIndices = args.tensorIndices;\n this.inputTensors = args.inputTensors;\n this.outputTensors = args.outputTensors;\n this.inputMasks = args.inputMasks;\n this.outputMasks = args.outputMasks;\n this.inputShapes = args.inputShapes;\n this.outputShapes = args.outputShapes;\n for (const layer of args.inboundLayers) {\n if (layer != null) {\n layer.outboundNodes.push(this);\n }\n }\n args.outboundLayer.inboundNodes.push(this);\n }\n getConfig() {\n const inboundNames = [];\n for (const layer of this.inboundLayers) {\n if (layer != null) {\n inboundNames.push(layer.name);\n } else {\n inboundNames.push(null);\n }\n }\n return {\n outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n inboundLayers: inboundNames,\n nodeIndices: this.nodeIndices,\n tensorIndices: this.tensorIndices\n };\n }\n};\nvar _nextLayerID = 0;\nvar Layer = class extends serialization_exports.Serializable {\n constructor(args = {}) {\n super();\n this._callHook = null;\n this._addedWeightNames = [];\n this._stateful = false;\n this.id = _nextLayerID++;\n this.activityRegularizer = null;\n this.inputSpec = null;\n this.supportsMasking = false;\n this._trainableWeights = [];\n this._nonTrainableWeights = [];\n this._losses = [];\n this._updates = [];\n this._built = false;\n this.inboundNodes = [];\n this.outboundNodes = [];\n let name = args.name;\n if (!name) {\n const prefix = this.getClassName();\n name = toSnakeCase(prefix) + \"_\" + getUid(prefix);\n }\n this.name = name;\n this.trainable_ = args.trainable == null ? true : args.trainable;\n if (args.inputShape != null || args.batchInputShape != null) {\n let batchInputShape;\n if (args.batchInputShape != null) {\n batchInputShape = args.batchInputShape;\n } else if (args.inputShape != null) {\n let batchSize = null;\n if (args.batchSize != null) {\n batchSize = args.batchSize;\n }\n batchInputShape = [batchSize].concat(args.inputShape);\n }\n this.batchInputShape = batchInputShape;\n let dtype = args.dtype;\n if (dtype == null) {\n dtype = args.inputDType;\n }\n if (dtype == null) {\n dtype = \"float32\";\n }\n this.dtype = dtype;\n }\n if (args.weights != null) {\n this.initialWeights = args.weights;\n } else {\n this.initialWeights = null;\n }\n this._refCount = null;\n this.fastWeightInitDuringBuild = false;\n }\n /**\n * Converts a layer and its index to a unique (immutable type) name.\n * This function is used internally with `this.containerNodes`.\n * @param layer The layer.\n * @param nodeIndex The layer's position (e.g. via enumerate) in a list of\n * nodes.\n *\n * @returns The unique name.\n */\n static nodeKey(layer, nodeIndex) {\n return layer.name + \"_ib-\" + nodeIndex.toString();\n }\n /**\n * Returns this.inboundNode at index nodeIndex.\n *\n * Porting note: This is a replacement for _get_node_attribute_at_index()\n * @param nodeIndex\n * @param attrName The name of the attribute related to request for this node.\n */\n getNodeAtIndex(nodeIndex, attrName) {\n if (this.inboundNodes.length === 0) {\n throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);\n }\n if (this.inboundNodes.length <= nodeIndex) {\n throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n }\n return this.inboundNodes[nodeIndex];\n }\n /**\n * Retrieves the input tensor(s) of a layer at a given node.\n *\n * @param nodeIndex Integer, index of the node from which to retrieve the\n * attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n * was called.\n *\n * @return A tensor (or list of tensors if the layer has multiple inputs).\n */\n getInputAt(nodeIndex) {\n return singletonOrArray(this.getNodeAtIndex(nodeIndex, \"input\").inputTensors);\n }\n /**\n * Retrieves the output tensor(s) of a layer at a given node.\n *\n * @param nodeIndex Integer, index of the node from which to retrieve the\n * attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n * was called.\n *\n * @return A tensor (or list of tensors if the layer has multiple outputs).\n */\n getOutputAt(nodeIndex) {\n return singletonOrArray(this.getNodeAtIndex(nodeIndex, \"output\").outputTensors);\n }\n // Properties\n /**\n * Retrieves the input tensor(s) of a layer.\n *\n * Only applicable if the layer has exactly one inbound node,\n * i.e. if it is connected to one incoming layer.\n *\n * @return Input tensor or list of input tensors.\n *\n * @exception AttributeError if the layer is connected to more than one\n * incoming layers.\n */\n get input() {\n if (this.inboundNodes.length > 1) {\n throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer input\" is ill-defined. Use \\`getInputAt(nodeIndex)\\` instead.`);\n } else if (this.inboundNodes.length === 0) {\n throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);\n }\n return singletonOrArray(this.getNodeAtIndex(0, \"input\").inputTensors);\n }\n /**\n * Retrieves the output tensor(s) of a layer.\n *\n * Only applicable if the layer has exactly one inbound node,\n * i.e. if it is connected to one incoming layer.\n *\n * @return Output tensor or list of output tensors.\n *\n * @exception AttributeError if the layer is connected to more than one\n * incoming layers.\n */\n get output() {\n if (this.inboundNodes.length === 0) {\n throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);\n }\n if (this.inboundNodes.length > 1) {\n throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer output\" is ill-defined. Use \\`getOutputAt(nodeIndex)\\` instead.`);\n }\n return singletonOrArray(this.getNodeAtIndex(0, \"output\").outputTensors);\n }\n get losses() {\n return this._losses;\n }\n /**\n * Retrieves the Layer's current loss values.\n *\n * Used for regularizers during training.\n */\n calculateLosses() {\n return this.losses.map((lossFn) => lossFn());\n }\n get updates() {\n return this._updates;\n }\n get built() {\n return this._built;\n }\n set built(built) {\n this._built = built;\n }\n get trainable() {\n return this.trainable_;\n }\n set trainable(trainable) {\n this._trainableWeights.forEach((w) => w.trainable = trainable);\n this.trainable_ = trainable;\n }\n get trainableWeights() {\n if (this.trainable_) {\n return this._trainableWeights.filter((w) => w.trainable);\n } else {\n return [];\n }\n }\n set trainableWeights(weights) {\n this._trainableWeights = weights;\n }\n get nonTrainableWeights() {\n if (this.trainable) {\n return this._trainableWeights.filter((w) => !w.trainable).concat(this._nonTrainableWeights);\n } else {\n return this._trainableWeights.concat(this._nonTrainableWeights);\n }\n }\n set nonTrainableWeights(weights) {\n this._nonTrainableWeights = weights;\n }\n /**\n * The concatenation of the lists trainableWeights and nonTrainableWeights\n * (in this order).\n */\n get weights() {\n return this.trainableWeights.concat(this.nonTrainableWeights);\n }\n get stateful() {\n return this._stateful;\n }\n /**\n * Reset the states of the layer.\n *\n * This method of the base Layer class is essentially a no-op.\n * Subclasses that are stateful (e.g., stateful RNNs) should override this\n * method.\n */\n resetStates() {\n if (!this.stateful) {\n throw new Error(\"Cannot call the resetStates() method of a non-stateful Layer object.\");\n }\n }\n /**\n * Checks compatibility between the layer and provided inputs.\n *\n * This checks that the tensor(s) `input`\n * verify the input assumptions of the layer\n * (if any). If not, exceptions are raised.\n *\n * @param inputs Input tensor or list of input tensors.\n *\n * @exception ValueError in case of mismatch between\n * the provided inputs and the expectations of the layer.\n */\n assertInputCompatibility(inputs) {\n const inputsList = toList(inputs);\n if (this.inputSpec == null || this.inputSpec.length === 0) {\n return;\n }\n const inputSpec = toList(this.inputSpec);\n if (inputsList.length !== inputSpec.length) {\n throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputsList.length} input tensors. Input received: ${inputs}`);\n }\n for (let inputIndex = 0; inputIndex < inputsList.length; inputIndex++) {\n const x = inputsList[inputIndex];\n const spec = inputSpec[inputIndex];\n if (spec == null) {\n continue;\n }\n const ndim = x.rank;\n if (spec.ndim != null) {\n if (ndim !== spec.ndim) {\n throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);\n }\n }\n if (spec.maxNDim != null) {\n if (ndim > spec.maxNDim) {\n throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n }\n }\n if (spec.minNDim != null) {\n if (ndim < spec.minNDim) {\n throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n }\n }\n if (spec.dtype != null) {\n if (x.dtype !== spec.dtype) {\n throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n }\n }\n if (spec.axes) {\n const xShape = x.shape;\n for (const key in spec.axes) {\n const axis = Number(key);\n const value = spec.axes[key];\n const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`);\n }\n }\n }\n if (spec.shape != null) {\n for (let i = 0; i < spec.shape.length; ++i) {\n const specDim = spec.shape[i];\n const dim = x.shape[i];\n if (specDim != null && dim != null) {\n if (specDim !== dim) {\n throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`);\n }\n }\n }\n }\n }\n }\n /**\n * This is where the layer's logic lives.\n *\n * @param inputs Input tensor, or list/tuple of input tensors.\n * @param kwargs Additional keyword arguments.\n *\n * @return A tensor or list/tuple of tensors.\n */\n call(inputs, kwargs) {\n return inputs;\n }\n invokeCallHook(inputs, kwargs) {\n if (this._callHook != null) {\n this._callHook(inputs, kwargs);\n }\n }\n /**\n * Set call hook.\n * This is currently used for testing only.\n * @param callHook\n */\n setCallHook(callHook) {\n this._callHook = callHook;\n }\n /**\n * Clear call hook.\n * This is currently used for testing only.\n */\n clearCallHook() {\n this._callHook = null;\n }\n /**\n * Builds or executes a `Layer`'s logic.\n *\n * When called with `tf.Tensor`(s), execute the `Layer`'s computation and\n * return Tensor(s). For example:\n *\n * ```js\n * const denseLayer = tf.layers.dense({\n * units: 1,\n * kernelInitializer: 'zeros',\n * useBias: false\n * });\n *\n * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete\n * // numeric values).\n * const input = tf.ones([2, 2]);\n * const output = denseLayer.apply(input);\n *\n * // The output's value is expected to be [[0], [0]], due to the fact that\n * // the dense layer has a kernel initialized to all-zeros and does not have\n * // a bias.\n * output.print();\n * ```\n *\n * When called with `tf.SymbolicTensor`(s), this will prepare the layer for\n * future execution. This entails internal book-keeping on shapes of\n * expected Tensors, wiring layers together, and initializing weights.\n *\n * Calling `apply` with `tf.SymbolicTensor`s are typically used during the\n * building of non-`tf.Sequential` models. For example:\n *\n * ```js\n * const flattenLayer = tf.layers.flatten();\n * const denseLayer = tf.layers.dense({units: 1});\n *\n * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().\n * const input = tf.input({shape: [2, 2]});\n * const output1 = flattenLayer.apply(input);\n *\n * // output1.shape is [null, 4]. The first dimension is the undetermined\n * // batch size. The second dimension comes from flattening the [2, 2]\n * // shape.\n * console.log(JSON.stringify(output1.shape));\n *\n * // The output SymbolicTensor of the flatten layer can be used to call\n * // the apply() of the dense layer:\n * const output2 = denseLayer.apply(output1);\n *\n * // output2.shape is [null, 1]. The first dimension is the undetermined\n * // batch size. The second dimension matches the number of units of the\n * // dense layer.\n * console.log(JSON.stringify(output2.shape));\n *\n * // The input and output can be used to construct a model that consists\n * // of the flatten and dense layers.\n * const model = tf.model({inputs: input, outputs: output2});\n * ```\n *\n * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.\n * @param kwargs Additional keyword arguments to be passed to `call()`.\n *\n * @return Output of the layer's `call` method.\n *\n * @exception ValueError error in case the layer is missing shape information\n * for its `build` call.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n // Porting Note: This is a replacement for __call__() in Python.\n apply(inputs, kwargs) {\n kwargs = kwargs || {};\n this.assertNotDisposed();\n const inputsList = toList(inputs);\n const allAreSymbolic = checkAllSymbolic(inputs);\n const noneAreSymbolic = checkNoneSymbolic(inputs);\n if (allAreSymbolic === noneAreSymbolic) {\n throw new ValueError(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");\n }\n return nameScope(this.name, () => {\n if (!this.built) {\n this.assertInputCompatibility(inputs);\n const inputShapes = [];\n for (const xElem of toList(inputs)) {\n inputShapes.push(xElem.shape);\n }\n this.build(singletonOrArray(inputShapes));\n this.built = true;\n if (this.initialWeights) {\n this.setWeights(this.initialWeights);\n }\n if (this._refCount === null && noneAreSymbolic) {\n this._refCount = 1;\n }\n }\n this.assertInputCompatibility(inputs);\n if (noneAreSymbolic) {\n let output = this.call(inputs, kwargs);\n if (this.supportsMasking) {\n this.setMaskMetadata(inputs, output);\n }\n const outputList = toList(output);\n const outputListCopy = [];\n for (let x of outputList) {\n if (inputsList.indexOf(x) !== -1) {\n x = x.clone();\n }\n outputListCopy.push(x);\n }\n output = singletonOrArray(outputListCopy);\n if (this.activityRegularizer != null) {\n throw new NotImplementedError(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");\n }\n return output;\n } else {\n const inputShape = collectInputShape(inputs);\n const outputShape = this.computeOutputShape(inputShape);\n let output;\n const outputDType = guessOutputDType(inputs);\n this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] : inputShape);\n if (outputShape != null && outputShape.length > 0 && Array.isArray(outputShape[0])) {\n output = outputShape.map((shape, index) => new SymbolicTensor(outputDType, shape, this, toList(inputs), kwargs, this.name, index));\n } else {\n output = new SymbolicTensor(outputDType, outputShape, this, toList(inputs), kwargs, this.name);\n }\n this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n this._refCount++;\n if (this.activityRegularizer != null) {\n throw new NotImplementedError(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");\n }\n return output;\n }\n });\n }\n /**\n * Check compatibility between input shape and this layer's batchInputShape.\n *\n * Print warning if any incompatibility is found.\n *\n * @param inputShape Input shape to be checked.\n */\n warnOnIncompatibleInputShape(inputShape) {\n if (this.batchInputShape == null) {\n return;\n } else if (inputShape.length !== this.batchInputShape.length) {\n console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);\n } else {\n let dimMismatch = false;\n this.batchInputShape.forEach((dimension, i) => {\n if (dimension != null && inputShape[i] != null && inputShape[i] !== dimension) {\n dimMismatch = true;\n }\n });\n if (dimMismatch) {\n console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`);\n }\n }\n }\n /**\n * Retrieves the output shape(s) of a layer.\n *\n * Only applicable if the layer has only one inbound node, or if all inbound\n * nodes have the same output shape.\n *\n * @returns Output shape or shapes.\n * @throws AttributeError: if the layer is connected to more than one incoming\n * nodes.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n get outputShape() {\n if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);\n }\n const allOutputShapes = [];\n for (const node of this.inboundNodes) {\n const shapeString = JSON.stringify(node.outputShapes);\n if (allOutputShapes.indexOf(shapeString) === -1) {\n allOutputShapes.push(shapeString);\n }\n }\n if (allOutputShapes.length === 1) {\n const outputShapes = this.inboundNodes[0].outputShapes;\n if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) && outputShapes.length === 1) {\n return outputShapes[0];\n } else {\n return outputShapes;\n }\n } else {\n throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of \"output shape\" is ill-defined for the layer.`);\n }\n }\n /**\n * Counts the total number of numbers (e.g., float32, int32) in the\n * weights.\n *\n * @returns An integer count.\n * @throws RuntimeError: If the layer is not built yet (in which case its\n * weights are not defined yet.)\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n countParams() {\n if (!this.built) {\n throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);\n }\n return countParamsInWeights(this.weights);\n }\n /**\n * Creates the layer weights.\n *\n * Must be implemented on all layers that have weights.\n *\n * Called when apply() is called to construct the weights.\n *\n * @param inputShape A `Shape` or array of `Shape` (unused).\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n build(inputShape) {\n this.built = true;\n }\n /**\n * Returns the current values of the weights of the layer.\n *\n * @param trainableOnly Whether to get the values of only trainable weights.\n * @returns Weight values as an `Array` of `tf.Tensor`s.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n getWeights(trainableOnly = false) {\n return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n }\n /**\n * Sets the weights of the layer, from Tensors.\n *\n * @param weights a list of Tensors. The number of arrays and their shape\n * must match number of the dimensions of the weights of the layer (i.e.\n * it should match the output of `getWeights`).\n *\n * @exception ValueError If the provided weights list does not match the\n * layer's specifications.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n setWeights(weights) {\n tidy(() => {\n const params = this.weights;\n if (params.length !== weights.length) {\n throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);\n }\n if (params.length === 0) {\n return;\n }\n const weightValueTuples = [];\n const paramValues = batchGetValue(params);\n for (let i = 0; i < paramValues.length; ++i) {\n const pv = paramValues[i];\n const p2 = params[i];\n const w = weights[i];\n if (!util_exports.arraysEqual(pv.shape, w.shape)) {\n throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);\n }\n weightValueTuples.push([p2, w]);\n }\n batchSetValue(weightValueTuples);\n });\n }\n /**\n * Adds a weight variable to the layer.\n *\n * @param name Name of the new weight variable.\n * @param shape The shape of the weight.\n * @param dtype The dtype of the weight.\n * @param initializer An initializer instance.\n * @param regularizer A regularizer instance.\n * @param trainable Whether the weight should be trained via backprop or not\n * (assuming that the layer itself is also trainable).\n * @param constraint An optional trainable.\n * @return The created weight variable.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint, getInitializerFunc) {\n if (this._addedWeightNames.indexOf(name) !== -1) {\n throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n }\n this._addedWeightNames.push(name);\n if (dtype == null) {\n dtype = \"float32\";\n }\n if (this.fastWeightInitDuringBuild) {\n initializer = getInitializerFunc != null ? getInitializerFunc() : getInitializer(\"zeros\");\n }\n const initValue = initializer.apply(shape, dtype);\n const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n initValue.dispose();\n if (regularizer != null) {\n this.addLoss(() => regularizer.apply(weight.read()));\n }\n if (trainable == null) {\n trainable = true;\n }\n if (trainable) {\n this._trainableWeights.push(weight);\n } else {\n this._nonTrainableWeights.push(weight);\n }\n return weight;\n }\n /**\n * Set the fast-weight-initialization flag.\n *\n * In cases where the initialized weight values will be immediately\n * overwritten by loaded weight values during model loading, setting\n * the flag to `true` saves unnecessary calls to potentially expensive\n * initializers and speeds up the loading process.\n *\n * @param value Target value of the flag.\n */\n setFastWeightInitDuringBuild(value) {\n this.fastWeightInitDuringBuild = value;\n }\n /**\n * Add losses to the layer.\n *\n * The loss may potentially be conditional on some inputs tensors,\n * for instance activity losses are conditional on the layer's inputs.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n addLoss(losses2) {\n if (losses2 == null || Array.isArray(losses2) && losses2.length === 0) {\n return;\n }\n losses2 = toList(losses2);\n if (this._losses !== void 0 && this._losses !== null) {\n this.losses.push(...losses2);\n }\n }\n /**\n * Computes the output shape of the layer.\n *\n * Assumes that the layer will be built to match that input shape provided.\n *\n * @param inputShape A shape (tuple of integers) or a list of shape tuples\n * (one per output tensor of the layer). Shape tuples can include null for\n * free dimensions, instead of an integer.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n computeOutputShape(inputShape) {\n return inputShape;\n }\n /**\n * Computes an output mask tensor.\n *\n * @param inputs Tensor or list of tensors.\n * @param mask Tensor or list of tensors.\n *\n * @return null or a tensor (or list of tensors, one per output tensor of the\n * layer).\n */\n computeMask(inputs, mask) {\n if (!this.supportsMasking) {\n if (mask != null) {\n if (Array.isArray(mask)) {\n mask.forEach((maskElement) => {\n if (maskElement != null) {\n throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);\n }\n });\n } else {\n throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);\n }\n }\n return null;\n }\n return mask;\n }\n setMaskMetadata(inputs, outputs, previousMask) {\n if (!this.supportsMasking) {\n return;\n }\n const outputMasks = this.computeMask(inputs, previousMask);\n const outputsList = toList(outputs);\n const outputMasksList = toList(outputMasks);\n if (outputsList.length !== outputMasksList.length) {\n throw new Error(`${this.name} outputs ${outputsList.length} tensors but ${outputsList.length} masks for those tensors`);\n }\n for (let i = 0; i < outputsList.length; i++) {\n outputsList[i].kerasMask = outputMasksList[i];\n }\n }\n /**\n * Internal method to create an inbound node for the layer.\n *\n * @param inputTensors List of input tensors.\n * @param outputTensors List of output tensors.\n * @param inputMasks List of input masks (a mask can be a tensor, or null).\n * @param outputMasks List of output masks (a mask can be a tensor, or null).\n * @param inputShapes List of input shape tuples.\n * @param outputShapes List of output shape tuples.\n * @param kwargs Dictionary of keyword arguments that were passed to the\n * `call` method of the layer at the call that created the node.\n */\n addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n const inputTensorList = toList(inputTensors);\n outputTensors = toList(outputTensors);\n inputMasks = toList(inputMasks);\n outputMasks = toList(outputMasks);\n inputShapes = normalizeShapeList(inputShapes);\n outputShapes = normalizeShapeList(outputShapes);\n const inboundLayers = [];\n const nodeIndices = [];\n const tensorIndices = [];\n for (const x of inputTensorList) {\n inboundLayers.push(x.sourceLayer);\n nodeIndices.push(x.nodeIndex);\n tensorIndices.push(x.tensorIndex);\n }\n new Node({\n outboundLayer: this,\n inboundLayers,\n nodeIndices,\n tensorIndices,\n inputTensors: inputTensorList,\n outputTensors,\n inputMasks,\n outputMasks,\n inputShapes,\n outputShapes\n }, kwargs);\n for (let i = 0; i < outputTensors.length; i++) {\n outputTensors[i].sourceLayer = this;\n outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n outputTensors[i].tensorIndex = i;\n }\n }\n /**\n * Returns the config of the layer.\n *\n * A layer config is a TS dictionary (serializable)\n * containing the configuration of a layer.\n * The same layer can be reinstantiated later\n * (without its trained weights) from this configuration.\n *\n * The config of a layer does not include connectivity\n * information, nor the layer class name. These are handled\n * by 'Container' (one layer of abstraction above).\n *\n * Porting Note: The TS dictionary follows TS naming standards for\n * keys, and uses tfjs-layers type-safe Enums. Serialization methods\n * should use a helper function to convert to the pythonic storage\n * standard. (see serialization_utils.convertTsToPythonic)\n *\n * @returns TS dictionary of configuration.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n getConfig() {\n const config = { name: this.name, trainable: this.trainable };\n if (this.batchInputShape != null) {\n config[\"batchInputShape\"] = this.batchInputShape;\n }\n if (this.dtype != null) {\n config[\"dtype\"] = this.dtype;\n }\n return config;\n }\n /**\n * Dispose the weight variables that this Layer instance holds.\n *\n * @returns {number} Number of disposed variables.\n */\n disposeWeights() {\n this.weights.forEach((weight) => weight.dispose());\n return this.weights.length;\n }\n assertNotDisposed() {\n if (this._refCount === 0) {\n throw new Error(`Layer '${this.name}' is already disposed.`);\n }\n }\n /**\n * Attempt to dispose layer's weights.\n *\n * This method decreases the reference count of the Layer object by 1.\n *\n * A Layer is reference-counted. Its reference count is incremented by 1\n * the first item its `apply()` method is called and when it becomes a part\n * of a new `Node` (through calling the `apply()` method on a\n * `tf.SymbolicTensor`).\n *\n * If the reference count of a Layer becomes 0, all the weights will be\n * disposed and the underlying memory (e.g., the textures allocated in WebGL)\n * will be freed.\n *\n * Note: If the reference count is greater than 0 after the decrement, the\n * weights of the Layer will *not* be disposed.\n *\n * After a Layer is disposed, it cannot be used in calls such as `apply()`,\n * `getWeights()` or `setWeights()` anymore.\n *\n * @returns A DisposeResult Object with the following fields:\n * - refCountAfterDispose: The reference count of the Container after this\n * `dispose()` call.\n * - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n * during this `dispose()` call.\n * @throws {Error} If the layer is not built yet, or if the layer has already\n * been disposed.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\n dispose() {\n if (!this.built) {\n throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);\n }\n if (this._refCount === null) {\n throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);\n }\n this.assertNotDisposed();\n let numDisposedVariables = 0;\n if (--this._refCount === 0) {\n numDisposedVariables = this.disposeWeights();\n }\n return { refCountAfterDispose: this._refCount, numDisposedVariables };\n }\n};\nfunction collectInputShape(inputTensors) {\n inputTensors = toList(inputTensors);\n const shapes = [];\n for (const x of inputTensors) {\n shapes.push(x.shape);\n }\n return singletonOrArray(shapes);\n}\nfunction guessOutputDType(inputTensors) {\n return \"float32\";\n}\nfunction getSourceInputs(tensor2, layer, nodeIndex) {\n if (layer == null || nodeIndex != null && nodeIndex > 0) {\n layer = tensor2.sourceLayer;\n nodeIndex = tensor2.nodeIndex;\n }\n if (layer.inboundNodes.length === 0) {\n return [tensor2];\n } else {\n const node = layer.inboundNodes[nodeIndex];\n if (node.inboundLayers.length === 0) {\n return node.inputTensors;\n } else {\n const sourceTensors = [];\n for (let i = 0; i < node.inboundLayers.length; i++) {\n const x = node.inputTensors[i];\n const layer2 = node.inboundLayers[i];\n const nodeIndex2 = node.nodeIndices[i];\n const previousSources = getSourceInputs(x, layer2, nodeIndex2);\n for (const x2 of previousSources) {\n if (sourceTensors.indexOf(x2) === -1) {\n sourceTensors.push(x2);\n }\n }\n }\n return sourceTensors;\n }\n }\n}\nfunction checkAllSymbolic(tensors) {\n let allAreSymbolic = true;\n for (const tensor2 of toList(tensors)) {\n if (!(tensor2 instanceof SymbolicTensor)) {\n allAreSymbolic = false;\n break;\n }\n }\n return allAreSymbolic;\n}\nfunction checkNoneSymbolic(tensors) {\n let noneAreSymbolic = true;\n for (const tensor2 of toList(tensors)) {\n if (tensor2 instanceof SymbolicTensor) {\n noneAreSymbolic = false;\n break;\n }\n }\n return noneAreSymbolic;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js\nvar InputLayer = class extends Layer {\n constructor(args) {\n super({\n dtype: args.dtype,\n name: args.name != null ? args.name : getUid(\"input\").toString()\n });\n if (args.batchSize == null) {\n args.batchSize = null;\n }\n if (args.sparse == null) {\n args.sparse = false;\n }\n this.trainable = false;\n this.built = true;\n this.sparse = args.sparse;\n if (args.inputShape != null && args.batchInputShape != null) {\n throw new ValueError(\"Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.\");\n }\n let batchInputShape = args.batchInputShape;\n if (batchInputShape == null) {\n if (args.inputShape == null) {\n throw new ValueError(\"An InputLayer should be passed either a `batchInputShape` or an `inputShape`.\");\n } else {\n batchInputShape = [args.batchSize].concat(args.inputShape);\n }\n } else {\n if (args.batchSize != null) {\n throw new ValueError(\"Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.\");\n }\n }\n const dtype = args.dtype || \"float32\";\n this.batchInputShape = batchInputShape;\n this.dtype = dtype;\n this.inputSpec = [{ shape: batchInputShape }];\n const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n inputTensor.nodeIndex = 0;\n inputTensor.tensorIndex = 0;\n new Node({\n outboundLayer: this,\n inboundLayers: [],\n nodeIndices: [],\n tensorIndices: [],\n inputTensors: [inputTensor],\n outputTensors: [inputTensor],\n inputMasks: [null],\n outputMasks: [null],\n inputShapes: [batchInputShape],\n outputShapes: [batchInputShape]\n });\n }\n apply(inputs, kwargs) {\n throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);\n }\n dispose() {\n return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n }\n getConfig() {\n return {\n batchInputShape: this.batchInputShape,\n dtype: this.dtype,\n sparse: this.sparse,\n name: this.name\n };\n }\n};\nInputLayer.className = \"InputLayer\";\nserialization_exports.registerClass(InputLayer);\nfunction Input(config) {\n if (config.batchShape == null && config.shape == null) {\n throw new Error(\"Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.\");\n }\n if (config.batchShape != null && config.shape != null) {\n throw new ValueError(\"Please provide either a `shape` or `batchShape` argument to Input, but not both.\");\n }\n let batchShape = config.batchShape;\n if (config.shape != null && batchShape == null) {\n batchShape = [null].concat(config.shape);\n }\n let dtype = config.dtype;\n if (dtype == null) {\n dtype = \"float32\";\n }\n const inputLayer2 = new InputLayer({\n batchInputShape: batchShape,\n name: config.name,\n dtype,\n sparse: config.sparse\n });\n const outputs = inputLayer2.inboundNodes[0].outputTensors;\n return outputs[0];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js\nfunction assertFeedCompatibility(key, val) {\n if (key.dtype == null || key.dtype === val.dtype) {\n return val;\n }\n try {\n return cast(val, key.dtype);\n } catch (err) {\n throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`);\n }\n}\nvar FeedDict = class _FeedDict {\n /**\n * Constructor, optionally does copy-construction.\n * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n * copy-construction will be performed.\n */\n constructor(feeds) {\n this.id2Value = {};\n this.id2Mask = {};\n this.name2Id = {};\n if (feeds instanceof _FeedDict) {\n for (const id in feeds.id2Value) {\n this.id2Value[id] = feeds.id2Value[id];\n if (id in feeds.id2Mask) {\n this.id2Mask[id] = feeds.id2Mask[id];\n }\n }\n } else {\n if (feeds == null) {\n return;\n }\n for (const feed of feeds) {\n this.add(feed.key, feed.value);\n }\n }\n }\n /**\n * Add a key-value pair to the FeedDict.\n *\n * @param key The key of the feed.\n * @param value The value of the tensor feed.\n * @param mask The value of the mask feed (optional).\n * @returns This `FeedDict`.\n * @throws ValueError: If the key `SymbolicTensor` already exists in the\n * `FeedDict`.\n */\n add(key, value, mask) {\n if (this.id2Value[key.id] == null) {\n this.id2Value[key.id] = assertFeedCompatibility(key, value);\n this.name2Id[key.name] = key.id;\n if (mask != null) {\n this.id2Mask[key.id] = mask;\n }\n } else {\n throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n }\n return this;\n }\n /**\n * Add a Feed to the FeedDict.\n * @param feed The new `Feed` to add.\n * @returns This `FeedDict`.\n */\n addFeed(feed) {\n this.add(feed.key, feed.value);\n }\n /**\n * Probe whether a key already exists in the FeedDict.\n * @param key\n */\n hasKey(key) {\n return this.id2Value[key.id] != null;\n }\n /**\n * Get all the SymbolicTensor available in this FeedDict.\n */\n names() {\n return Object.keys(this.name2Id);\n }\n /**\n * Get the feed value for given key.\n * @param key The SymbolicTensor, or its name (as a string), of which the\n * value is sought.\n * @returns If `key` exists, the corresponding feed value.\n * @throws ValueError: If `key` does not exist in this `FeedDict`.\n */\n getValue(key) {\n if (key instanceof SymbolicTensor) {\n if (this.id2Value[key.id] == null) {\n throw new ValueError(`Nonexistent key: ${key.name}`);\n } else {\n return this.id2Value[key.id];\n }\n } else {\n const id = this.name2Id[key];\n if (id == null) {\n throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n }\n return this.id2Value[id];\n }\n }\n /**\n * Get the feed mask for given key.\n * @param key The SymbolicTensor, or its name (as a string), of which the\n * value is sought.\n * @returns If `key` exists, the corresponding feed mask.\n * @throws ValueError: If `key` does not exist in this `FeedDict`.\n */\n getMask(key) {\n if (key instanceof SymbolicTensor) {\n if (this.id2Value[key.id] == null) {\n throw new ValueError(`Nonexistent key: ${key.name}`);\n } else {\n return this.id2Mask[key.id];\n }\n } else {\n const id = this.name2Id[key];\n if (id == null) {\n throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n }\n return this.id2Mask[id];\n }\n }\n /** Dispose all mask Tensors held by this object. */\n disposeMasks() {\n if (this.id2Mask != null) {\n dispose(this.id2Mask);\n }\n }\n};\nvar cachedSorted = new LruCache();\nvar cachedRecipientCounts = new LruCache();\nfunction updateCacheMaxEntries(maxEntries) {\n if (cachedSorted != null) {\n cachedSorted.setMaxEntries(maxEntries);\n }\n if (cachedRecipientCounts != null) {\n cachedRecipientCounts.setMaxEntries(maxEntries);\n }\n}\nfunction execute(fetches, feedDict, kwargs, probe) {\n const training = kwargs == null ? false : kwargs[\"training\"];\n const arrayFetches = Array.isArray(fetches);\n const fetchArray = arrayFetches ? fetches : [fetches];\n const outputNames = fetchArray.map((t) => t.name);\n const finalOutputs = [];\n const feedNames = feedDict.names();\n for (const outputName of outputNames) {\n if (feedNames.indexOf(outputName) !== -1) {\n finalOutputs.push(feedDict.getValue(outputName));\n } else {\n finalOutputs.push(null);\n }\n }\n if (probe != null) {\n probe.maxNumTensors = -Infinity;\n probe.minNumTensors = Infinity;\n }\n const fetchAndFeedKey = outputNames.join(\",\") + \"|\" + feedDict.names().sort().join(\",\");\n let sorted = cachedSorted.get(fetchAndFeedKey);\n let recipientCounts;\n if (sorted == null) {\n const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n sorted = out.sorted;\n recipientCounts = out.recipientCounts;\n cachedSorted.put(fetchAndFeedKey, sorted);\n cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts);\n }\n recipientCounts = {};\n if (!training) {\n Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));\n }\n const internalFeedDict = new FeedDict(feedDict);\n for (let i = 0; i < sorted.length; ++i) {\n if (probe != null) {\n const numTensors = memory().numTensors;\n if (numTensors > probe.maxNumTensors) {\n probe.maxNumTensors = numTensors;\n }\n if (numTensors < probe.minNumTensors) {\n probe.minNumTensors = numTensors;\n }\n }\n const symbolic = sorted[i];\n const srcLayer = symbolic.sourceLayer;\n if (srcLayer instanceof InputLayer) {\n continue;\n }\n const inputValues = [];\n const inputMasks = [];\n const tensorsToDispose = [];\n let maskExists = false;\n for (const input2 of symbolic.inputs) {\n const value = internalFeedDict.getValue(input2);\n const mask = internalFeedDict.getMask(input2);\n inputValues.push(value);\n inputMasks.push(mask);\n if (mask != null) {\n maskExists = true;\n }\n if (!training) {\n recipientCounts[input2.name]--;\n if (recipientCounts[input2.name] === 0 && !feedDict.hasKey(input2) && outputNames.indexOf(input2.name) === -1 && !value.isDisposed && input2.sourceLayer.stateful !== true) {\n tensorsToDispose.push(value);\n }\n }\n }\n if (maskExists) {\n kwargs = kwargs || {};\n kwargs[\"mask\"] = inputMasks[0];\n }\n const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n let outputMask = null;\n if (srcLayer.supportsMasking) {\n outputMask = srcLayer.computeMask(inputValues, inputMasks);\n }\n const layerOutputs = getNodeOutputs(symbolic);\n const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n for (let i2 = 0; i2 < outputSymbolicTensors.length; ++i2) {\n if (!internalFeedDict.hasKey(outputSymbolicTensors[i2])) {\n internalFeedDict.add(outputSymbolicTensors[i2], outputTensors[i2], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n }\n const index = outputNames.indexOf(outputSymbolicTensors[i2].name);\n if (index !== -1) {\n finalOutputs[index] = outputTensors[i2];\n }\n }\n if (!training) {\n dispose(tensorsToDispose);\n }\n }\n internalFeedDict.disposeMasks();\n return arrayFetches ? finalOutputs : finalOutputs[0];\n}\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n util_exports.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n let finalSorted = [];\n let finalRecipientMap = {};\n if (fetches.length === 1) {\n const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n finalSorted = out.sorted;\n finalRecipientMap = out.recipientMap;\n } else {\n const visited = /* @__PURE__ */ new Set();\n for (const fetch4 of fetches) {\n const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict);\n for (const symbolicTensor of sorted) {\n if (!visited.has(symbolicTensor.name)) {\n finalSorted.push(symbolicTensor);\n visited.add(symbolicTensor.name);\n }\n }\n for (const name in recipientMap) {\n if (finalRecipientMap[name] == null) {\n finalRecipientMap[name] = /* @__PURE__ */ new Set();\n }\n recipientMap[name].forEach((recipient) => finalRecipientMap[name].add(recipient));\n }\n }\n }\n return {\n sorted: finalSorted,\n recipientCounts: recipientMap2Counts(finalRecipientMap)\n };\n}\nfunction recipientMap2Counts(recipientMap) {\n const recipientCounts = {};\n for (const name in recipientMap) {\n recipientCounts[name] = recipientMap[name].size;\n }\n return recipientCounts;\n}\nfunction getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict) {\n const visited = /* @__PURE__ */ new Set();\n const sorted = [];\n const recipientMap = {};\n for (const key of feedDict.names()) {\n visited.add(key);\n }\n const stack2 = [];\n const marks = [];\n stack2.push(fetch4);\n while (stack2.length > 0) {\n const top = stack2[stack2.length - 1];\n if (visited.has(top.name)) {\n stack2.pop();\n continue;\n }\n const topIsMarked = marks[marks.length - 1] === stack2.length - 1;\n if (top.inputs.length === 0 || topIsMarked) {\n stack2.pop();\n sorted.push(top);\n visited.add(top.name);\n if (topIsMarked) {\n marks.pop();\n }\n } else {\n marks.push(stack2.length - 1);\n for (const input2 of top.inputs) {\n if (recipientMap[input2.name] == null) {\n recipientMap[input2.name] = /* @__PURE__ */ new Set();\n }\n recipientMap[input2.name].add(top.name);\n if (visited.has(input2.name)) {\n continue;\n }\n stack2.push(input2);\n }\n }\n }\n return { sorted, recipientMap };\n}\nfunction getNodeOutputs(fetch4) {\n let layerOutputs;\n if (fetch4.sourceLayer.inboundNodes.length === 1) {\n layerOutputs = fetch4.sourceLayer.output;\n } else {\n let nodeIndex = null;\n for (let i = 0; i < fetch4.sourceLayer.inboundNodes.length; ++i) {\n for (const outputTensor of fetch4.sourceLayer.inboundNodes[i].outputTensors) {\n if (outputTensor.id === fetch4.id) {\n nodeIndex = i;\n break;\n }\n }\n }\n layerOutputs = fetch4.sourceLayer.getOutputAt(nodeIndex);\n }\n return layerOutputs;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/flags_layers.js\nvar ENV3 = env();\nENV3.registerFlag(\"TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES\", () => 100, updateCacheMaxEntries);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js\nvar exports_constraints_exports = {};\n__export(exports_constraints_exports, {\n maxNorm: () => maxNorm,\n minMaxNorm: () => minMaxNorm,\n nonNeg: () => nonNeg,\n unitNorm: () => unitNorm\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/constraints.js\nfunction calcL2Norms(w, axis) {\n return tidy(() => sqrt(sum2(mul(w, w), axis, true)));\n}\nvar Constraint = class extends serialization_exports.Serializable {\n getConfig() {\n return {};\n }\n};\nvar MaxNorm = class extends Constraint {\n constructor(args) {\n super();\n this.defaultMaxValue = 2;\n this.defaultAxis = 0;\n this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n this.axis = args.axis != null ? args.axis : this.defaultAxis;\n }\n apply(w) {\n return tidy(() => {\n const norms = calcL2Norms(w, this.axis);\n const desired = clipByValue(norms, 0, this.maxValue);\n return mul(w, div(desired, add2(epsilon(), norms)));\n });\n }\n getConfig() {\n return { maxValue: this.maxValue, axis: this.axis };\n }\n};\nMaxNorm.className = \"MaxNorm\";\nserialization_exports.registerClass(MaxNorm);\nvar UnitNorm = class extends Constraint {\n constructor(args) {\n super();\n this.defaultAxis = 0;\n this.axis = args.axis != null ? args.axis : this.defaultAxis;\n }\n apply(w) {\n return tidy(() => div(w, add2(epsilon(), calcL2Norms(w, this.axis))));\n }\n getConfig() {\n return { axis: this.axis };\n }\n};\nUnitNorm.className = \"UnitNorm\";\nserialization_exports.registerClass(UnitNorm);\nvar NonNeg = class extends Constraint {\n apply(w) {\n return relu(w);\n }\n};\nNonNeg.className = \"NonNeg\";\nserialization_exports.registerClass(NonNeg);\nvar MinMaxNorm = class extends Constraint {\n constructor(args) {\n super();\n this.defaultMinValue = 0;\n this.defaultMaxValue = 1;\n this.defaultRate = 1;\n this.defaultAxis = 0;\n this.minValue = args.minValue != null ? args.minValue : this.defaultMinValue;\n this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n this.rate = args.rate != null ? args.rate : this.defaultRate;\n this.axis = args.axis != null ? args.axis : this.defaultAxis;\n }\n apply(w) {\n return tidy(() => {\n const norms = calcL2Norms(w, this.axis);\n const desired = add2(mul(this.rate, clipByValue(norms, this.minValue, this.maxValue)), mul(1 - this.rate, norms));\n return mul(w, div(desired, add2(epsilon(), norms)));\n });\n }\n getConfig() {\n return {\n minValue: this.minValue,\n maxValue: this.maxValue,\n rate: this.rate,\n axis: this.axis\n };\n }\n};\nMinMaxNorm.className = \"MinMaxNorm\";\nserialization_exports.registerClass(MinMaxNorm);\nvar CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n \"maxNorm\": \"MaxNorm\",\n \"minMaxNorm\": \"MinMaxNorm\",\n \"nonNeg\": \"NonNeg\",\n \"unitNorm\": \"UnitNorm\"\n};\nfunction serializeConstraint(constraint) {\n return serializeKerasObject(constraint);\n}\nfunction deserializeConstraint(config, customObjects = {}) {\n return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"constraint\");\n}\nfunction getConstraint(identifier) {\n if (identifier == null) {\n return null;\n }\n if (typeof identifier === \"string\") {\n const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ? CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n const config = { className, config: {} };\n return deserializeConstraint(config);\n } else if (identifier instanceof Constraint) {\n return identifier;\n } else {\n return deserializeConstraint(identifier);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js\nfunction maxNorm(args) {\n return new MaxNorm(args);\n}\nfunction unitNorm(args) {\n return new UnitNorm(args);\n}\nfunction nonNeg() {\n return new NonNeg();\n}\nfunction minMaxNorm(config) {\n return new MinMaxNorm(config);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js\nvar exports_initializers_exports = {};\n__export(exports_initializers_exports, {\n constant: () => constant,\n glorotNormal: () => glorotNormal,\n glorotUniform: () => glorotUniform,\n heNormal: () => heNormal,\n heUniform: () => heUniform,\n identity: () => identity,\n leCunNormal: () => leCunNormal,\n leCunUniform: () => leCunUniform,\n ones: () => ones3,\n orthogonal: () => orthogonal,\n randomNormal: () => randomNormal3,\n randomUniform: () => randomUniform2,\n truncatedNormal: () => truncatedNormal2,\n varianceScaling: () => varianceScaling,\n zeros: () => zeros2\n});\nfunction zeros2() {\n return new Zeros();\n}\nfunction ones3() {\n return new Ones();\n}\nfunction constant(args) {\n return new Constant(args);\n}\nfunction randomUniform2(args) {\n return new RandomUniform(args);\n}\nfunction randomNormal3(args) {\n return new RandomNormal(args);\n}\nfunction truncatedNormal2(args) {\n return new TruncatedNormal(args);\n}\nfunction identity(args) {\n return new Identity2(args);\n}\nfunction varianceScaling(config) {\n return new VarianceScaling(config);\n}\nfunction glorotUniform(args) {\n return new GlorotUniform(args);\n}\nfunction glorotNormal(args) {\n return new GlorotNormal(args);\n}\nfunction heNormal(args) {\n return new HeNormal(args);\n}\nfunction heUniform(args) {\n return new HeUniform(args);\n}\nfunction leCunNormal(args) {\n return new LeCunNormal(args);\n}\nfunction leCunUniform(args) {\n return new LeCunUniform(args);\n}\nfunction orthogonal(args) {\n return new Orthogonal(args);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js\nvar exports_layers_exports = {};\n__export(exports_layers_exports, {\n Layer: () => Layer,\n RNN: () => RNN,\n RNNCell: () => RNNCell,\n activation: () => activation,\n add: () => add3,\n alphaDropout: () => alphaDropout,\n average: () => average,\n averagePooling1d: () => averagePooling1d,\n averagePooling2d: () => averagePooling2d,\n averagePooling3d: () => averagePooling3d,\n avgPool1d: () => avgPool1d,\n avgPool2d: () => avgPool2d,\n avgPool3d: () => avgPool3d2,\n avgPooling1d: () => avgPooling1d,\n avgPooling2d: () => avgPooling2d,\n avgPooling3d: () => avgPooling3d,\n batchNormalization: () => batchNormalization2,\n bidirectional: () => bidirectional,\n categoryEncoding: () => categoryEncoding,\n centerCrop: () => centerCrop,\n concatenate: () => concatenate2,\n conv1d: () => conv1d2,\n conv2d: () => conv2d3,\n conv2dTranspose: () => conv2dTranspose2,\n conv3d: () => conv3d2,\n conv3dTranspose: () => conv3dTranspose2,\n convLstm2d: () => convLstm2d,\n convLstm2dCell: () => convLstm2dCell,\n cropping2D: () => cropping2D,\n dense: () => dense,\n depthwiseConv2d: () => depthwiseConv2d4,\n dot: () => dot3,\n dropout: () => dropout3,\n elu: () => elu3,\n embedding: () => embedding,\n flatten: () => flatten3,\n gaussianDropout: () => gaussianDropout,\n gaussianNoise: () => gaussianNoise,\n globalAveragePooling1d: () => globalAveragePooling1d,\n globalAveragePooling2d: () => globalAveragePooling2d,\n globalMaxPool1d: () => globalMaxPool1d,\n globalMaxPool2d: () => globalMaxPool2d,\n globalMaxPooling1d: () => globalMaxPooling1d,\n globalMaxPooling2d: () => globalMaxPooling2d,\n gru: () => gru,\n gruCell: () => gruCell,\n input: () => input,\n inputLayer: () => inputLayer,\n layerNormalization: () => layerNormalization,\n leakyReLU: () => leakyReLU,\n lstm: () => lstm,\n lstmCell: () => lstmCell,\n masking: () => masking,\n maxPool1d: () => maxPool1d,\n maxPool2d: () => maxPool2d,\n maxPooling1d: () => maxPooling1d,\n maxPooling2d: () => maxPooling2d,\n maxPooling3d: () => maxPooling3d,\n maximum: () => maximum2,\n minimum: () => minimum2,\n multiply: () => multiply,\n permute: () => permute,\n prelu: () => prelu2,\n randomWidth: () => randomWidth,\n reLU: () => reLU,\n repeatVector: () => repeatVector,\n rescaling: () => rescaling,\n reshape: () => reshape2,\n resizing: () => resizing,\n rnn: () => rnn2,\n separableConv2d: () => separableConv2d2,\n simpleRNN: () => simpleRNN,\n simpleRNNCell: () => simpleRNNCell,\n softmax: () => softmax2,\n spatialDropout1d: () => spatialDropout1d,\n stackedRNNCells: () => stackedRNNCells,\n thresholdedReLU: () => thresholdedReLU,\n timeDistributed: () => timeDistributed,\n upSampling2d: () => upSampling2d,\n zeroPadding2d: () => zeroPadding2d\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/logs.js\nasync function resolveScalarsInLogs(logs) {\n if (logs == null) {\n return;\n }\n const promises = [];\n const keys = [];\n const scalarsToDispose = [];\n for (const key in logs) {\n const value = logs[key];\n if (typeof value !== \"number\") {\n const valueScalar = value;\n promises.push(valueScalar.data());\n keys.push(key);\n scalarsToDispose.push(valueScalar);\n }\n }\n if (promises.length > 0) {\n const values = await Promise.all(promises);\n for (let i = 0; i < values.length; ++i) {\n logs[keys[i]] = values[i][0];\n }\n dispose(scalarsToDispose);\n }\n}\nfunction disposeTensorsInLogs(logs) {\n if (logs == null) {\n return;\n }\n for (const key in logs) {\n const value = logs[key];\n if (typeof value !== \"number\") {\n value.dispose();\n }\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js\nvar ModelLoggingVerbosity;\n(function(ModelLoggingVerbosity2) {\n ModelLoggingVerbosity2[ModelLoggingVerbosity2[\"SILENT\"] = 0] = \"SILENT\";\n ModelLoggingVerbosity2[ModelLoggingVerbosity2[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\nvar DEFAULT_YIELD_EVERY_MS = 125;\nvar BaseCallback = class {\n constructor() {\n this.validationData = null;\n }\n setParams(params) {\n this.params = params;\n }\n async onEpochBegin(epoch, logs) {\n }\n async onEpochEnd(epoch, logs) {\n }\n async onBatchBegin(batch, logs) {\n }\n async onBatchEnd(batch, logs) {\n }\n async onTrainBegin(logs) {\n }\n async onTrainEnd(logs) {\n }\n // LayersModel needs to call Callback.setModel(), but cannot actually depend\n // on Callback because that creates a cyclic dependency. Providing this no-op\n // method on BaseCallback breaks the cycle: this way LayersModel can depend on\n // BaseCallback but not on Callback. The argument is typed as `Container`\n // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback\n // overrides this method and enforces that the argument is really a\n // LayersModel.\n setModel(model2) {\n }\n};\nvar CallbackList = class {\n // TODO(cais): When the need arises, uncomment the following lines and\n // implement the queue for time values.\n // private deltaTBatch: number;\n // private deltaTsBatchBegin: Array;\n // private deltaTsBatchEnd: Array;\n /**\n * Constructor of CallbackList.\n * @param callbacks Array of `Callback` instances.\n * @param queueLength Queue length for keeping running statistics over\n * callback execution time.\n */\n constructor(callbacks2, queueLength = 10) {\n if (callbacks2 == null) {\n callbacks2 = [];\n }\n this.callbacks = callbacks2;\n this.queueLength = queueLength;\n }\n append(callback) {\n this.callbacks.push(callback);\n }\n setParams(params) {\n for (const callback of this.callbacks) {\n callback.setParams(params);\n }\n }\n setModel(model2) {\n for (const callback of this.callbacks) {\n callback.setModel(model2);\n }\n }\n /**\n * Called at the start of an epoch.\n * @param epoch Index of epoch.\n * @param logs Dictionary of logs.\n */\n async onEpochBegin(epoch, logs) {\n if (logs == null) {\n logs = {};\n }\n for (const callback of this.callbacks) {\n await callback.onEpochBegin(epoch, logs);\n }\n }\n /**\n * Called at the end of an epoch.\n * @param epoch Index of epoch.\n * @param logs Dictionary of logs.\n */\n async onEpochEnd(epoch, logs) {\n if (logs == null) {\n logs = {};\n }\n for (const callback of this.callbacks) {\n await callback.onEpochEnd(epoch, logs);\n }\n }\n /**\n * Called right before processing a batch.\n * @param batch Index of batch within the current epoch.\n * @param logs Dictionary of logs.\n */\n async onBatchBegin(batch, logs) {\n if (logs == null) {\n logs = {};\n }\n for (const callback of this.callbacks) {\n await callback.onBatchBegin(batch, logs);\n }\n }\n /**\n * Called at the end of a batch.\n * @param batch Index of batch within the current epoch.\n * @param logs Dictionary of logs.\n */\n async onBatchEnd(batch, logs) {\n if (logs == null) {\n logs = {};\n }\n for (const callback of this.callbacks) {\n await callback.onBatchEnd(batch, logs);\n }\n }\n /**\n * Called at the beginning of training.\n * @param logs Dictionary of logs.\n */\n async onTrainBegin(logs) {\n if (logs == null) {\n logs = {};\n }\n for (const callback of this.callbacks) {\n await callback.onTrainBegin(logs);\n }\n }\n /**\n * Called at the end of training.\n * @param logs Dictionary of logs.\n */\n async onTrainEnd(logs) {\n if (logs == null) {\n logs = {};\n }\n for (const callback of this.callbacks) {\n await callback.onTrainEnd(logs);\n }\n }\n};\nvar BaseLogger = class extends BaseCallback {\n constructor() {\n super();\n }\n async onEpochBegin(epoch) {\n this.seen = 0;\n this.totals = {};\n }\n async onBatchEnd(batch, logs) {\n if (logs == null) {\n logs = {};\n }\n const batchSize = logs[\"size\"] == null ? 0 : logs[\"size\"];\n this.seen += batchSize;\n for (const key in logs) {\n const value = logs[key];\n if (typeof value === \"number\") {\n if (!this.totals.hasOwnProperty(key)) {\n this.totals[key] = 0;\n }\n this.totals[key] = this.totals[key] + value * batchSize;\n } else {\n let oldTotalsToDispose;\n if (key in this.totals) {\n oldTotalsToDispose = this.totals[key];\n } else {\n this.totals[key] = 0;\n }\n const total = tidy(() => add2(this.totals[key], mul(value, batchSize)));\n this.totals[key] = total;\n if (oldTotalsToDispose != null) {\n oldTotalsToDispose.dispose();\n }\n }\n }\n }\n async onEpochEnd(epoch, logs) {\n if (logs != null) {\n for (const key of this.params[\"metrics\"]) {\n if (this.totals[key] == null) {\n continue;\n }\n if (typeof this.totals[key] === \"number\") {\n logs[key] = this.totals[key] / this.seen;\n } else {\n tidy(() => {\n const log5 = mul(div(1, this.seen), this.totals[key]);\n logs[key] = log5;\n this.totals[key].dispose();\n keep(logs[key]);\n });\n }\n }\n }\n }\n};\nvar History = class extends BaseCallback {\n async onTrainBegin(logs) {\n this.epoch = [];\n this.history = {};\n }\n async onEpochEnd(epoch, logs) {\n if (logs == null) {\n logs = {};\n }\n this.epoch.push(epoch);\n for (const key in logs) {\n if (this.history[key] == null) {\n this.history[key] = [];\n }\n this.history[key].push(logs[key]);\n }\n }\n /**\n * Await the values of all losses and metrics.\n */\n async syncData() {\n const promises = [];\n const keys = [];\n const indices = [];\n for (const key in this.history) {\n const valueArray = this.history[key];\n for (let i = 0; i < valueArray.length; ++i) {\n if (typeof valueArray[i] !== \"number\") {\n const valueScalar = valueArray[i];\n promises.push(valueScalar.data());\n keys.push(key);\n indices.push(i);\n }\n }\n }\n const values = await Promise.all(promises);\n for (let n = 0; n < values.length; ++n) {\n const tensorToDispose = this.history[keys[n]][indices[n]];\n tensorToDispose.dispose();\n this.history[keys[n]][indices[n]] = values[n][0];\n }\n }\n};\nvar CustomCallback = class extends BaseCallback {\n constructor(args, yieldEvery) {\n super();\n this.currentEpoch = 0;\n this.nowFunc = args.nowFunc;\n this.nextFrameFunc = args.nextFrameFunc || nextFrame;\n this.yieldEvery = yieldEvery || \"auto\";\n if (this.yieldEvery === \"auto\") {\n this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n }\n if (this.yieldEvery === \"never\" && args.onYield != null) {\n throw new Error(\"yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback\");\n }\n if (util_exports.isNumber(this.yieldEvery)) {\n this.maybeWait = debounce(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc);\n }\n this.trainBegin = args.onTrainBegin;\n this.trainEnd = args.onTrainEnd;\n this.epochBegin = args.onEpochBegin;\n this.epochEnd = args.onEpochEnd;\n this.batchBegin = args.onBatchBegin;\n this.batchEnd = args.onBatchEnd;\n this.yield = args.onYield;\n }\n async maybeWait(epoch, batch, logs) {\n const ps = [];\n if (this.yield != null) {\n await resolveScalarsInLogs(logs);\n ps.push(this.yield(epoch, batch, logs));\n }\n ps.push(this.nextFrameFunc());\n await Promise.all(ps);\n }\n async onEpochBegin(epoch, logs) {\n this.currentEpoch = epoch;\n if (this.epochBegin != null) {\n await resolveScalarsInLogs(logs);\n await this.epochBegin(epoch, logs);\n }\n }\n async onEpochEnd(epoch, logs) {\n const ps = [];\n if (this.epochEnd != null) {\n await resolveScalarsInLogs(logs);\n ps.push(this.epochEnd(epoch, logs));\n }\n if (this.yieldEvery === \"epoch\") {\n ps.push(this.nextFrameFunc());\n }\n await Promise.all(ps);\n }\n async onBatchBegin(batch, logs) {\n if (this.batchBegin != null) {\n await resolveScalarsInLogs(logs);\n await this.batchBegin(batch, logs);\n }\n }\n async onBatchEnd(batch, logs) {\n const ps = [];\n if (this.batchEnd != null) {\n await resolveScalarsInLogs(logs);\n ps.push(this.batchEnd(batch, logs));\n }\n if (this.yieldEvery === \"batch\") {\n ps.push(this.nextFrameFunc());\n } else if (util_exports.isNumber(this.yieldEvery)) {\n ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n }\n await Promise.all(ps);\n }\n async onTrainBegin(logs) {\n if (this.trainBegin != null) {\n await resolveScalarsInLogs(logs);\n await this.trainBegin(logs);\n }\n }\n async onTrainEnd(logs) {\n if (this.trainEnd != null) {\n await resolveScalarsInLogs(logs);\n await this.trainEnd(logs);\n }\n }\n};\nfunction standardizeCallbacks(callbacks2, yieldEvery) {\n if (callbacks2 == null) {\n callbacks2 = {};\n }\n if (callbacks2 instanceof BaseCallback) {\n return [callbacks2];\n }\n if (Array.isArray(callbacks2) && callbacks2[0] instanceof BaseCallback) {\n return callbacks2;\n }\n const callbackConfigs = toList(callbacks2);\n return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery));\n}\nvar CallbackConstructorRegistry = class _CallbackConstructorRegistry {\n /**\n * Blocks public access to constructor.\n */\n constructor() {\n }\n /**\n * Register a tf.LayersModel.fit() callback constructor.\n *\n * The registered callback constructor will be used to instantiate\n * callbacks for every tf.LayersModel.fit() call afterwards.\n *\n * @param verbosityLevel Level of verbosity at which the `callbackConstructor`\n * is to be reigstered.\n * @param callbackConstructor A no-arg constructor for `tf.Callback`.\n * @throws Error, if the same callbackConstructor has been registered before,\n * either at the same or a different `verbosityLevel`.\n */\n static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`);\n _CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n if (_CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n _CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n }\n _CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n }\n static checkForDuplicate(callbackConstructor) {\n for (const levelName in _CallbackConstructorRegistry.constructors) {\n const constructors = _CallbackConstructorRegistry.constructors[+levelName];\n constructors.forEach((ctor) => {\n if (ctor === callbackConstructor) {\n throw new ValueError(\"Duplicate callback constructor.\");\n }\n });\n }\n }\n /**\n * Clear all registered callback constructors.\n */\n static clear() {\n _CallbackConstructorRegistry.constructors = {};\n }\n /**\n * Create callbacks using the registered callback constructors.\n *\n * Given `verbosityLevel`, all constructors registered at that level or above\n * will be called and the instantiated callbacks will be used.\n *\n * @param verbosityLevel: Level of verbosity.\n */\n static createCallbacks(verbosityLevel) {\n const constructors = [];\n for (const levelName in _CallbackConstructorRegistry.constructors) {\n const level = +levelName;\n if (verbosityLevel >= level) {\n constructors.push(..._CallbackConstructorRegistry.constructors[level]);\n }\n }\n return constructors.map((ctor) => new ctor());\n }\n};\nCallbackConstructorRegistry.constructors = {};\nfunction configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n const history = new History();\n const actualCallbacks = [\n new BaseLogger(),\n ...CallbackConstructorRegistry.createCallbacks(verbose)\n ];\n if (callbacks2 != null) {\n actualCallbacks.push(...callbacks2);\n }\n actualCallbacks.push(history);\n const callbackList = new CallbackList(actualCallbacks);\n callbackList.setParams({\n epochs,\n initialEpoch,\n samples: numTrainSamples,\n steps: stepsPerEpoch,\n batchSize,\n verbose,\n doValidation,\n metrics: callbackMetrics\n });\n return { callbackList, history };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/serialization.js\nfunction deserialize(config, customObjects = {}, fastWeightInit = false) {\n return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"layer\", fastWeightInit);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/losses.js\nfunction l2Normalize(x, axis) {\n return tidy(() => {\n if (x.dtype !== \"float32\") {\n x = cast(x, \"float32\");\n }\n const squareSum = sum2(square2(x), axis, true);\n const epsilonTensor = fill(squareSum.shape, epsilon());\n const norm2 = sqrt(maximum(squareSum, epsilonTensor));\n return div(x, norm2);\n });\n}\nfunction meanSquaredError2(yTrue, yPred) {\n return tidy(() => mean(square2(sub(yPred, yTrue)), -1));\n}\nfunction meanAbsoluteError(yTrue, yPred) {\n return tidy(() => mean(abs(sub(yPred, yTrue)), -1));\n}\nfunction meanAbsolutePercentageError(yTrue, yPred) {\n return tidy(() => {\n const diff = sub(yTrue, yPred);\n const clippedTrue = clipByValue(abs(yTrue), epsilon(), Number.MAX_VALUE);\n const absResult = abs(div(diff, clippedTrue));\n return mul(100, mean(absResult, -1));\n });\n}\nfunction meanSquaredLogarithmicError(yTrue, yPred) {\n return tidy(() => {\n const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n const firstLog = log2(add2(1, clippedPred));\n const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n const secondLog = log2(add2(1, clippedTrue));\n return mean(square2(sub(firstLog, secondLog)), -1);\n });\n}\nfunction squaredHinge(yTrue, yPred) {\n return tidy(() => {\n const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));\n return mean(square2(maxResult), -1);\n });\n}\nfunction hinge(yTrue, yPred) {\n return tidy(() => {\n const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));\n return mean(maxResult, -1);\n });\n}\nfunction categoricalHinge(yTrue, yPred) {\n return tidy(() => {\n const pos = sum2(mul(yTrue, yPred), -1);\n const neg4 = max(mul(sub(1, yTrue), yPred), -1);\n return maximum(0, add2(1, sub(neg4, pos)));\n });\n}\nfunction logcosh(yTrue, yPred) {\n return tidy(() => {\n const log22 = Math.log(2);\n const predictionDiff = sub(yPred, yTrue);\n const logcoshResult = sub(add2(predictionDiff, softplus(mul(-2, predictionDiff))), log22);\n return mean(logcoshResult, -1);\n });\n}\nfunction categoricalCrossentropy(target, output, fromLogits = false) {\n return tidy(() => {\n if (fromLogits) {\n output = softmax(output);\n } else {\n const outputSum = sum2(output, output.shape.length - 1, true);\n output = div(output, outputSum);\n }\n output = clipByValue(output, epsilon(), 1 - epsilon());\n return neg(sum2(mul(cast(target, \"float32\"), log2(output)), output.shape.length - 1));\n });\n}\nfunction sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n return tidy(() => {\n const flatTarget = cast(floor(flatten2(target)), \"int32\");\n output = clipByValue(output, epsilon(), 1 - epsilon());\n const outputShape = output.shape;\n const oneHotTarget = reshape(oneHot(flatTarget, outputShape[outputShape.length - 1]), outputShape);\n return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n });\n}\nfunction sigmoidCrossEntropyWithLogits(labels, logits) {\n if (!util_exports.arraysEqual(labels.shape, logits.shape)) {\n throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n }\n return tidy(() => {\n const reluLogits = relu(logits);\n const negAbsLogits = neg(abs(logits));\n return add2(sub(reluLogits, mul(logits, labels)), log1p(exp(negAbsLogits)));\n });\n}\nfunction binaryCrossentropy(yTrue, yPred) {\n return tidy(() => {\n let y;\n y = clipByValue(yPred, epsilon(), 1 - epsilon());\n y = log2(div(y, sub(1, y)));\n return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n });\n}\nfunction kullbackLeiblerDivergence(yTrue, yPred) {\n return tidy(() => {\n const clippedTrue = clipByValue(yTrue, epsilon(), 1);\n const clippedPred = clipByValue(yPred, epsilon(), 1);\n return sum2(mul(yTrue, log2(div(clippedTrue, clippedPred))), -1);\n });\n}\nfunction poisson(yTrue, yPred) {\n return tidy(() => {\n const logPred = log2(add2(epsilon(), yPred));\n return mean(sub(yPred, mul(yTrue, logPred)), -1);\n });\n}\nfunction cosineProximity(yTrue, yPred) {\n return tidy(() => {\n const trueNormalized = l2Normalize(yTrue, -1);\n const predNormalized = l2Normalize(yPred, -1);\n const trueXPred = mul(trueNormalized, predNormalized);\n return neg(sum2(trueXPred, -1));\n });\n}\nvar lossesMap = {\n meanSquaredError: meanSquaredError2,\n meanAbsoluteError,\n meanAbsolutePercentageError,\n meanSquaredLogarithmicError,\n squaredHinge,\n hinge,\n categoricalHinge,\n logcosh,\n categoricalCrossentropy,\n sparseCategoricalCrossentropy,\n binaryCrossentropy,\n kullbackLeiblerDivergence,\n poisson,\n cosineProximity\n};\nfunction get(identifierOrFn) {\n if (typeof identifierOrFn === \"string\") {\n if (identifierOrFn in lossesMap) {\n return lossesMap[identifierOrFn];\n }\n let errMsg = `Unknown loss ${identifierOrFn}`;\n if (identifierOrFn.toLowerCase().includes(\"softmaxcrossentropy\")) {\n errMsg = `Unknown loss ${identifierOrFn}. Use \"categoricalCrossentropy\" as the string name for tf.losses.softmaxCrossEntropy`;\n }\n throw new ValueError(errMsg);\n } else {\n return identifierOrFn;\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/metrics.js\nfunction binaryAccuracy(yTrue, yPred) {\n return tidy(() => {\n const threshold3 = mul(0.5, onesLike(yPred));\n const yPredThresholded = cast2(greater(yPred, threshold3), yTrue.dtype);\n return mean(equal(yTrue, yPredThresholded), -1);\n });\n}\nfunction categoricalAccuracy(yTrue, yPred) {\n return tidy(() => cast2(equal(argMax(yTrue, -1), argMax(yPred, -1)), \"float32\"));\n}\nfunction truePositives(yTrue, yPred) {\n return tidy(() => {\n return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 1))), \"float32\");\n });\n}\nfunction falseNegatives(yTrue, yPred) {\n return tidy(() => {\n return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 0))), \"float32\");\n });\n}\nfunction falsePositives(yTrue, yPred) {\n return tidy(() => {\n return cast(sum2(logicalAnd(equal(yTrue, 0), equal(yPred, 1))), \"float32\");\n });\n}\nfunction precision(yTrue, yPred) {\n return tidy(() => {\n const tp = truePositives(yTrue, yPred);\n const fp = falsePositives(yTrue, yPred);\n const denominator = add2(tp, fp);\n return cast(where(greater(denominator, 0), div(tp, denominator), 0), \"float32\");\n });\n}\nfunction recall(yTrue, yPred) {\n return tidy(() => {\n const tp = truePositives(yTrue, yPred);\n const fn = falseNegatives(yTrue, yPred);\n const denominator = add2(tp, fn);\n return cast(where(greater(denominator, 0), div(tp, denominator), 0), \"float32\");\n });\n}\nfunction binaryCrossentropy2(yTrue, yPred) {\n return binaryCrossentropy(yTrue, yPred);\n}\nfunction sparseCategoricalAccuracy(yTrue, yPred) {\n if (yTrue.rank === yPred.rank) {\n yTrue = squeeze(yTrue, [yTrue.rank - 1]);\n }\n yPred = argMax(yPred, -1);\n if (yPred.dtype !== yTrue.dtype) {\n yPred = cast(yPred, yTrue.dtype);\n }\n return cast(equal(yTrue, yPred), \"float32\");\n}\nvar mse = meanSquaredError2;\nvar MSE = meanSquaredError2;\nvar mae = meanAbsoluteError;\nvar MAE = meanAbsoluteError;\nvar mape = meanAbsolutePercentageError;\nvar MAPE = meanAbsolutePercentageError;\nvar categoricalCrossentropy2 = categoricalCrossentropy;\nvar cosine = cosineProximity;\nvar sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy;\nvar metricsMap = {\n binaryAccuracy,\n categoricalAccuracy,\n precision,\n categoricalCrossentropy: categoricalCrossentropy2,\n sparseCategoricalCrossentropy: sparseCategoricalCrossentropy2,\n mse,\n MSE,\n mae,\n MAE,\n mape,\n MAPE,\n cosine\n};\nfunction get2(identifier) {\n if (typeof identifier === \"string\" && identifier in metricsMap) {\n return metricsMap[identifier];\n } else if (typeof identifier !== \"string\" && identifier != null) {\n return identifier;\n } else {\n throw new ValueError(`Unknown metric ${identifier}`);\n }\n}\nfunction getLossOrMetricName(fn) {\n assert2(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n if (typeof fn === \"string\") {\n return fn;\n } else {\n let fnName;\n for (const key of Object.keys(lossesMap)) {\n if (lossesMap[key] === fn) {\n fnName = key;\n break;\n }\n }\n if (fnName !== void 0) {\n return fnName;\n }\n for (const key of Object.keys(metricsMap)) {\n if (metricsMap[key] === fn) {\n fnName = key;\n break;\n }\n }\n if (fnName !== void 0) {\n return fnName;\n }\n return fn.name;\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/optimizers.js\nfunction getOptimizer(identifier) {\n const optimizerMap = {\n \"Adagrad\": () => train.adagrad(0.01),\n \"Adadelta\": () => train.adadelta(1, 0.95, epsilon()),\n \"Adam\": () => train.adam(1e-3, 0.9, 0.999, epsilon()),\n \"Adamax\": () => train.adamax(2e-3, 0.9, 0.999, epsilon(), 0),\n \"RMSProp\": () => train.rmsprop(1e-3, 0.9, 0, epsilon()),\n \"SGD\": () => train.sgd(0.01)\n };\n optimizerMap[\"adagrad\"] = optimizerMap[\"Adagrad\"];\n optimizerMap[\"adadelta\"] = optimizerMap[\"Adadelta\"];\n optimizerMap[\"adam\"] = optimizerMap[\"Adam\"];\n optimizerMap[\"adamax\"] = optimizerMap[\"Adamax\"];\n optimizerMap[\"rmsprop\"] = optimizerMap[\"RMSProp\"];\n optimizerMap[\"sgd\"] = optimizerMap[\"SGD\"];\n if (identifier in optimizerMap) {\n return optimizerMap[identifier]();\n }\n throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/user_defined_metadata.js\nvar MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\nfunction checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n if (userDefinedMetadata == null || typeof userDefinedMetadata !== \"object\" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) {\n throw new Error(\"User-defined metadata is expected to be a JSON object, but is not.\");\n }\n if (checkSize) {\n const out = JSON.stringify(userDefinedMetadata);\n if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n console.warn(`User-defined metadata of model \"${modelName}\" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n }\n }\n}\nfunction plainObjectCheck(x) {\n if (x === null) {\n return true;\n } else if (typeof x === \"object\") {\n if (Object.getPrototypeOf(x) === Object.prototype) {\n const keys = Object.keys(x);\n for (const key of keys) {\n if (typeof key !== \"string\") {\n return false;\n }\n if (!plainObjectCheck(x[key])) {\n return false;\n }\n }\n return true;\n } else {\n if (Array.isArray(x)) {\n for (const item of x) {\n if (!plainObjectCheck(item)) {\n return false;\n }\n }\n return true;\n } else {\n return false;\n }\n }\n } else {\n const xType = typeof x;\n return xType === \"string\" || xType === \"number\" || xType === \"boolean\";\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/layer_utils.js\nfunction printSummary(model2, lineLength, positions, printFn = console.log) {\n const sequentialLike = isModelSequentialLike(model2);\n const toDisplay = [\"Layer (type)\", \"Input Shape\", \"Output shape\", \"Param #\"];\n if (sequentialLike) {\n lineLength = lineLength || 90;\n positions = positions || [0.32, 0.61, 0.89, 1];\n } else {\n lineLength = lineLength || 115;\n positions = positions || [0.24, 0.48, 0.7, 0.8, 1];\n }\n if (positions[positions.length - 1] <= 1) {\n positions = positions.map((p2) => Math.floor(lineLength * p2));\n }\n let relevantNodes;\n if (!sequentialLike) {\n toDisplay.push(\"Receives inputs\");\n relevantNodes = [];\n for (const depth in model2.nodesByDepth) {\n relevantNodes.push(...model2.nodesByDepth[depth]);\n }\n }\n printFn(\"_\".repeat(lineLength));\n printRow(toDisplay, positions, printFn);\n printFn(\"=\".repeat(lineLength));\n const layers = model2.layers;\n for (let i = 0; i < layers.length; ++i) {\n if (sequentialLike) {\n printLayerSummary(layers[i], positions, printFn);\n } else {\n printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n }\n printFn((i === layers.length - 1 ? \"=\" : \"_\").repeat(lineLength));\n }\n model2.checkTrainableWeightsConsistency();\n const trainableCount = countTrainableParams(model2);\n const nonTrainableCount = countParamsInWeights(model2.nonTrainableWeights);\n printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n printFn(`Trainable params: ${trainableCount}`);\n printFn(`Non-trainable params: ${nonTrainableCount}`);\n printFn(\"_\".repeat(lineLength));\n}\nfunction countTrainableParams(model2) {\n let trainableCount;\n if (model2.collectedTrainableWeights != null) {\n trainableCount = countParamsInWeights(model2.collectedTrainableWeights);\n } else {\n trainableCount = countParamsInWeights(model2.trainableWeights);\n }\n return trainableCount;\n}\nfunction isModelSequentialLike(model2) {\n let sequentialLike = true;\n const nodesByDepth = [];\n const nodes = [];\n for (const depth in model2.nodesByDepth) {\n nodesByDepth.push(model2.nodesByDepth[depth]);\n }\n for (const depthNodes of nodesByDepth) {\n if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n sequentialLike = false;\n break;\n }\n nodes.push(...depthNodes);\n }\n if (sequentialLike) {\n for (const layer of model2.layers) {\n let flag = false;\n for (const node of layer.inboundNodes) {\n if (nodes.indexOf(node) !== -1) {\n if (flag) {\n sequentialLike = false;\n break;\n } else {\n flag = true;\n }\n }\n }\n if (!sequentialLike) {\n break;\n }\n }\n }\n return sequentialLike;\n}\nfunction printRow(fields, positions, printFn = console.log) {\n let line = \"\";\n for (let i = 0; i < fields.length; ++i) {\n if (i > 0) {\n line = line.slice(0, line.length - 1) + \" \";\n }\n line += fields[i];\n line = line.slice(0, positions[i]);\n line += \" \".repeat(positions[i] - line.length);\n }\n printFn(line);\n}\nfunction printLayerSummary(layer, positions, printFn) {\n let outputShape;\n let inputShape;\n try {\n inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(\",\");\n } catch (err) {\n inputShape = \"multiple\";\n }\n try {\n outputShape = JSON.stringify(layer.outputShape);\n } catch (err) {\n outputShape = \"multiple\";\n }\n const name = layer.name;\n const className = layer.getClassName();\n const fields = [\n `${name} (${className})`,\n inputShape,\n outputShape,\n layer.countParams().toString()\n ];\n printRow(fields, positions, printFn);\n}\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) {\n let outputShape;\n let inputShape;\n try {\n inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(\",\");\n } catch (err) {\n inputShape = \"multiple\";\n }\n try {\n outputShape = JSON.stringify(layer.outputShape);\n } catch (err) {\n outputShape = \"multiple\";\n }\n const connections = [];\n for (const node of layer.inboundNodes) {\n if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node) === -1) {\n continue;\n }\n for (let i = 0; i < node.inboundLayers.length; ++i) {\n const inboundLayer = node.inboundLayers[i].name;\n const inboundLayerIndex = node.nodeIndices[i];\n const inboundTensorIndex = node.tensorIndices[i];\n connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n }\n }\n const name = layer.name;\n const className = layer.getClassName();\n const firstConnection = connections.length === 0 ? \"\" : connections[0];\n const fields = [\n `${name} (${className})`,\n inputShape,\n outputShape,\n layer.countParams().toString(),\n firstConnection\n ];\n printRow(fields, positions, printFn);\n for (let i = 1; i < connections.length; ++i) {\n printRow([\"\", \"\", \"\", \"\", connections[i]], positions, printFn);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/serialization_utils.js\nfunction isArrayItemInputOrOutputName(key, index, value) {\n return (key === \"inboundNodes\" || key === \"outputLayers\" || key === \"inputLayers\") && index === 0 && typeof value === \"string\";\n}\nfunction convertPythonicToTs(pythonicConfig, key) {\n if (pythonicConfig === null) {\n return null;\n } else if (typeof pythonicConfig === \"string\") {\n return toCamelCase(pythonicConfig);\n } else if (typeof pythonicConfig === \"number\" || typeof pythonicConfig === \"boolean\") {\n return pythonicConfig;\n } else if (pythonicConfig instanceof Array) {\n const tsArray = [];\n const arrayLength = pythonicConfig.length;\n for (let i = 0; i < arrayLength; ++i) {\n const item = pythonicConfig[i];\n if (isArrayItemInputOrOutputName(key, i, item)) {\n tsArray.push(item);\n } else {\n tsArray.push(convertPythonicToTs(item, key));\n }\n }\n return tsArray;\n } else {\n const tsDict = {};\n for (const pythonicKey of Object.keys(pythonicConfig)) {\n const pythonicValue = pythonicConfig[pythonicKey];\n if (pythonicKey === \"name\" && typeof pythonicValue === \"string\") {\n tsDict[pythonicKey] = pythonicValue;\n } else {\n const tsKey = toCamelCase(pythonicKey);\n tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n }\n }\n return tsDict;\n }\n}\nfunction convertTsToPythonic(tsConfig, key) {\n if (tsConfig === null || tsConfig === void 0) {\n return null;\n } else if (typeof tsConfig === \"string\") {\n return toSnakeCase(tsConfig);\n } else if (typeof tsConfig === \"number\" || typeof tsConfig === \"boolean\") {\n return tsConfig;\n } else if (tsConfig instanceof Array) {\n const pyArray = [];\n const arrayLength = tsConfig.length;\n for (let i = 0; i < arrayLength; ++i) {\n const item = tsConfig[i];\n if (isArrayItemInputOrOutputName(key, i, item)) {\n pyArray.push(item);\n } else {\n pyArray.push(convertTsToPythonic(item, key));\n }\n }\n return pyArray;\n } else {\n const pyDict = {};\n for (const tsKey of Object.keys(tsConfig)) {\n const tsValue = tsConfig[tsKey];\n const pyKey = toSnakeCase(tsKey);\n if ((tsKey === \"name\" || tsKey === \"className\") && typeof tsValue === \"string\") {\n pyDict[pyKey] = tsValue;\n } else {\n pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n }\n }\n return pyDict;\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/version.js\nvar version2 = \"4.16.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/container.js\nvar isKerasSavedModelFormat = (weights) => {\n const keys = Object.keys(weights);\n if (keys.length === 0) {\n return false;\n }\n const key = keys[0].split(\"/\");\n return !isNaN(parseInt(key[key.length - 1], 10));\n};\nvar Container = class _Container extends Layer {\n constructor(args) {\n super({});\n this.containerNodes = /* @__PURE__ */ new Set();\n this.name = args.name;\n if (this.name == null) {\n const prefix = this.getClassName().toLowerCase();\n this.name = getUid(prefix);\n }\n this.supportsMasking = false;\n this.trainable_ = true;\n if (Array.isArray(args.inputs)) {\n this.inputs = args.inputs.slice();\n } else {\n this.inputs = [args.inputs];\n }\n if (Array.isArray(args.outputs)) {\n this.outputs = args.outputs.slice();\n } else {\n this.outputs = [args.outputs];\n }\n if (unique2(this.inputs).length !== this.inputs.length) {\n throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((x) => x.name)}`);\n }\n if (unique2(this.outputs).length !== this.outputs.length) {\n console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((x) => x.name)}`);\n }\n this.inputLayers = [];\n this.inputLayersNodeIndices = [];\n this.inputLayersTensorIndices = [];\n this.outputLayers = [];\n this.outputLayersNodeIndices = [];\n this.outputLayersTensorIndices = [];\n this.layers = [];\n this.internalContainerRefs = [];\n for (const x of this.outputs) {\n const layer = x.sourceLayer;\n const nodeIndex = x.nodeIndex;\n const tensorIndex = x.tensorIndex;\n this.outputLayers.push(layer);\n this.outputLayersNodeIndices.push(nodeIndex);\n this.outputLayersTensorIndices.push(tensorIndex);\n }\n for (const x of this.inputs) {\n const layer = x.sourceLayer;\n const nodeIndex = x.nodeIndex;\n const tensorIndex = x.tensorIndex;\n assert2(nodeIndex === 0, \"input layer has >1 nodes\");\n assert2(tensorIndex === 0, \"input layer has >1 tensors\");\n this.inputLayers.push(layer);\n this.inputLayersNodeIndices.push(nodeIndex);\n this.inputLayersTensorIndices.push(tensorIndex);\n }\n this.inputNames = [];\n this.outputNames = [];\n this.feedInputShapes = [];\n this.feedInputNames = [];\n this.feedOutputNames = [];\n for (let i = 0; i < this.inputLayers.length; i++) {\n const layer = this.inputLayers[i];\n if (!(layer instanceof InputLayer)) {\n throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);\n }\n this.inputNames.push(layer.name);\n this.feedInputShapes.push(layer.batchInputShape);\n this.feedInputNames.push(layer.name);\n }\n for (const layer of this.outputLayers) {\n this.outputNames.push(layer.name);\n }\n this.internalInputShapes = this.inputs.map((x) => x.shape);\n this.internalOutputShapes = this.outputs.map((x) => x.shape);\n const nodesDepths = {};\n const nodeIDToNode = {};\n const layersDepths = {};\n const layerIDToLayer = {};\n const layerIndices = {};\n const nodesInDecreasingDepth = [];\n const buildMapOfGraph = (tensor2, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => {\n if (layer == null || nodeIndex == null || tensorIndex == null) {\n layer = tensor2.sourceLayer;\n nodeIndex = tensor2.nodeIndex;\n tensorIndex = tensor2.tensorIndex;\n }\n const node = layer.inboundNodes[nodeIndex];\n if (nodesInProgress2.indexOf(node) !== -1) {\n throw new RuntimeError(`The tensor ${tensor2.name} at layer \"${layer.name}\" is part of a cycle.`);\n }\n if (finishedNodes2.indexOf(node) !== -1) {\n return;\n }\n this.containerNodes.add(_Container.nodeKey(layer, nodeIndex));\n if (!(layer.id in layerIndices)) {\n layerIndices[layer.id] = Object.keys(layerIndices).length;\n }\n if (nodesInProgress2.indexOf(node) === -1) {\n nodesInProgress2.push(node);\n }\n const numInboundLayers = node.inboundLayers.length;\n for (let i = 0; i < numInboundLayers; i++) {\n const x = node.inputTensors[i];\n const layer2 = node.inboundLayers[i];\n const nodeIndex2 = node.nodeIndices[i];\n const tensorIndex2 = node.tensorIndices[i];\n buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2);\n }\n finishedNodes2.push(node);\n while (nodesInProgress2.indexOf(node) >= 0) {\n nodesInProgress2.splice(nodesInProgress2.indexOf(node), 1);\n }\n nodesInDecreasingDepth.push(node);\n };\n const finishedNodes = [];\n const nodesInProgress = [];\n for (const x of this.outputs) {\n buildMapOfGraph(x, finishedNodes, nodesInProgress);\n }\n const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n for (const node of reversedNodesInDecreasingDepth) {\n nodeIDToNode[node.id] = node;\n if (!(node.id in nodesDepths)) {\n nodesDepths[node.id] = 0;\n }\n let depth = nodesDepths[node.id];\n const previousDepth = layersDepths[node.outboundLayer.id] == null ? 0 : layersDepths[node.outboundLayer.id];\n depth = Math.max(depth, previousDepth);\n layersDepths[node.outboundLayer.id] = depth;\n layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n nodesDepths[node.id] = depth;\n for (let i = 0; i < node.inboundLayers.length; i++) {\n const inboundLayer = node.inboundLayers[i];\n const nodeIndex = node.nodeIndices[i];\n const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id];\n nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2);\n nodeIDToNode[inboundNode.id] = inboundNode;\n }\n }\n const nodesByDepth = {};\n for (const nodeID in nodesDepths) {\n const depth = nodesDepths[nodeID];\n if (!(depth in nodesByDepth)) {\n nodesByDepth[depth] = [];\n }\n nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n }\n const layersByDepth = {};\n for (const layerID in layersDepths) {\n const depth = layersDepths[layerID];\n if (!(depth in layersByDepth)) {\n layersByDepth[depth] = [];\n }\n layersByDepth[depth].push(layerIDToLayer[layerID]);\n }\n let depthKeys = Object.keys(layersByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n this.layers = [];\n for (const depth of depthKeys) {\n const layersForDepth = layersByDepth[depth];\n layersForDepth.sort((a, b) => {\n const aIndex = layerIndices[a.id];\n const bIndex = layerIndices[b.id];\n if (aIndex < bIndex) {\n return -1;\n }\n if (aIndex > bIndex) {\n return 1;\n }\n return 0;\n });\n for (const layer of layersForDepth) {\n if (layer instanceof _Container) {\n this.internalContainerRefs.push(layer);\n }\n this.layers.push(layer);\n }\n }\n this.layersByDepth = layersByDepth;\n depthKeys = Object.keys(nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n const computableTensors = this.inputs.slice();\n const layersWithCompleteInput = [];\n for (const depth of depthKeys) {\n for (const node of nodesByDepth[depth]) {\n const layer = node.outboundLayer;\n if (layer != null) {\n for (const x of node.inputTensors) {\n if (computableTensors.indexOf(x) === -1) {\n throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer \"${layer.name}\". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);\n }\n }\n for (const x of node.outputTensors) {\n computableTensors.push(x);\n }\n layersWithCompleteInput.push(layer.name);\n }\n }\n }\n this.nodesByDepth = nodesByDepth;\n const allNames = this.layers.map((x) => x.name);\n for (const name of allNames) {\n const numOccurrences = allNames.filter((x) => x === name).length;\n if (numOccurrences !== 1) {\n throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(allNames));\n }\n }\n this.outboundNodes = [];\n this.inboundNodes = [];\n new Node({\n outboundLayer: this,\n inboundLayers: [],\n nodeIndices: [],\n tensorIndices: [],\n inputTensors: this.inputs,\n outputTensors: this.outputs,\n inputMasks: this.inputs.map((x) => null),\n outputMasks: this.outputs.map((x) => null),\n inputShapes: this.inputs.map((x) => x.shape),\n outputShapes: this.outputs.map((x) => x.shape)\n });\n this.built = true;\n this._refCount = 1;\n }\n assertNotDisposed() {\n if (this._refCount === 0) {\n throw new Error(`Container '${this.name}' is already disposed.`);\n }\n }\n /**\n * Attempt to dispose a LayersModel's weights.\n *\n * This method decrease the reference count of the LayersModel object by 1.\n *\n * A LayersModel is reference-counted. Its reference count is incremented by 1\n * when it is first constructed and when it is used as a Layer of another\n * LayersModel.\n *\n * If the reference count of a LayersModel becomes 0, the `dispose` method of\n * all its constituent `Layer`s will be called.\n *\n * Note: If the reference count is greater than 0 after the decrement, the\n * `dispose` method of its constituent `Layer`s will *not* be called.\n *\n * After a LayersModel is disposed, it cannot be used in calls such as\n * 'predict`, `evaluate` or `fit` anymore.\n *\n * @returns A DisposeResult Object with the following fields:\n * - refCountAfterDispose: The reference count of the LayersModel after this\n * `dispose()` call.\n * - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n * during this `dispose()` call.\n * @throws {Error} If the layer is not built yet, or if the LayersModel has\n * already been disposed.\n */\n dispose() {\n this.assertNotDisposed();\n const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n if (--this._refCount === 0) {\n for (const layer of this.layers) {\n result.numDisposedVariables += layer.dispose().numDisposedVariables;\n }\n for (const container of this.internalContainerRefs) {\n result.numDisposedVariables += container.dispose().numDisposedVariables;\n }\n }\n result.refCountAfterDispose = this._refCount;\n return result;\n }\n get trainable() {\n return this.trainable_;\n }\n set trainable(trainable) {\n this.layers.forEach((layer) => {\n layer._trainableWeights.forEach((w) => w.trainable = trainable);\n });\n this.trainable_ = trainable;\n }\n get trainableWeights() {\n if (this._trainableWeights.length > 0) {\n throw new ValueError(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");\n }\n if (!this.trainable) {\n return [];\n }\n let weights = [];\n for (const layer of this.layers) {\n weights = weights.concat(layer.trainableWeights);\n }\n return weights;\n }\n get nonTrainableWeights() {\n const weights = [];\n for (const layer of this.layers) {\n weights.push(...layer.nonTrainableWeights);\n }\n if (!this.trainable) {\n const trainableWeights = [];\n for (const layer of this.layers) {\n trainableWeights.push(...layer.trainableWeights);\n }\n return trainableWeights.concat(weights);\n }\n return weights;\n }\n get weights() {\n return this.trainableWeights.concat(this.nonTrainableWeights);\n }\n /**\n * Loads all layer weights from a JSON object.\n *\n * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /\n * TypeScript. The utility script at `scripts/pykeras.py` offers means\n * to convert them into JSON strings compatible with this method.\n * Porting Note: TensorFlow.js Layers supports only loading by name currently.\n *\n * @param weights A JSON mapping weight names to weight values as nested\n * arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight\n * names to `tf.Tensor` objects.\n * @param strict Require that the provided weights exactly match those\n * required by the container. Default: `true`. Passing `false` means that\n * extra weights and missing weights will be silently ignored.\n */\n loadWeights(weights, strict = true) {\n const nameToWeight = {};\n let totalWeightsCount = 0;\n const modelIsKerasSavedModelFormat = isKerasSavedModelFormat(weights);\n if (modelIsKerasSavedModelFormat) {\n this.parseWeights(weights);\n }\n for (const layer of this.layers) {\n for (const [index, weight] of layer.weights.entries()) {\n const parsedName = modelIsKerasSavedModelFormat ? `${weight.name.split(\"/\").slice(0, -1).join(\"/\") + \"/\"}${index}` : weight.originalName;\n if (nameToWeight[parsedName] != null) {\n throw new ValueError(`Duplicate weight name: ${parsedName}`);\n }\n nameToWeight[parsedName] = weight;\n totalWeightsCount++;\n }\n }\n const weightValueTuples = [];\n for (const name in weights) {\n let validatedName = name;\n if (nameToWeight[name] == null) {\n const tokens = name.split(\"/\");\n const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n validatedName = shortenNameArray.join(\"/\");\n }\n if (nameToWeight[validatedName] != null) {\n weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n } else if (strict) {\n throw new ValueError(`Provided weight data has no target variable: ${name}`);\n }\n delete nameToWeight[validatedName];\n }\n if (strict) {\n const unsetNames = [];\n for (const name in nameToWeight) {\n unsetNames.push(name);\n }\n if (unsetNames.length > 0) {\n throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`);\n }\n }\n batchSetValue(weightValueTuples);\n }\n parseWeights(weights) {\n for (const key in Object.keys(weights)) {\n const listParts = key.split(\"/\");\n const list = [\"vars\", \"layer_checkpoint_dependencies\"];\n const newKey = listParts.map((str) => {\n if (str.startsWith(\"_\")) {\n return str.slice(1);\n }\n return str;\n }).filter((str) => !list.includes(str)).join(\"/\");\n if (newKey !== key) {\n weights[newKey] = weights[key];\n delete weights[key];\n }\n }\n }\n /**\n * Util shared between different serialization methods.\n * @returns LayersModel config with Keras version information added.\n */\n updatedConfig() {\n const theConfig = this.getConfig();\n const modelConfig = {};\n modelConfig[\"className\"] = this.getClassName();\n modelConfig[\"config\"] = theConfig;\n modelConfig[\"kerasVersion\"] = `tfjs-layers ${version2}`;\n modelConfig[\"backend\"] = \"TensorFlow.js\";\n return modelConfig;\n }\n /**\n * Returns a JSON string containing the network configuration.\n *\n * To load a network from a JSON save file, use\n * models.modelFromJSON(jsonString);\n * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras\n * @param returnString Whether the return value should be stringified\n * (default: `true`).\n * @returns a JSON string if `returnString` (default), or a JSON object if\n * `!returnString`.\n */\n // tslint:disable-next-line:no-any\n toJSON(unused, returnString = true) {\n const modelConfig = convertTsToPythonic(this.updatedConfig());\n return returnString ? JSON.stringify(modelConfig) : modelConfig;\n }\n /**\n * Call the model on new inputs.\n *\n * In this case `call` just reapplies all ops in the graph to the new inputs\n * (e.g. build a new computational graph from the provided inputs).\n *\n * @param inputs A tensor or list of tensors.\n * @param mask A mask or list of masks. A mask can be either a tensor or null\n * (no mask).\n *\n * @return A tensor if there is a single output, or a list of tensors if there\n * are more than one outputs.\n */\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = toList(inputs);\n const feedDict = new FeedDict();\n for (let i = 0; i < this.inputs.length; ++i) {\n feedDict.add(this.inputs[i], inputs[i]);\n }\n return execute(this.outputs, feedDict, kwargs);\n });\n }\n /**\n * Computes an output mask tensor.\n *\n * @param inputs Tensor or list of tensors.\n * @param mask Tensor or list of tensors.\n *\n * @return null or a tensor (or list of tensors, one per output tensor of the\n * layer).\n */\n computeMask(inputs, mask) {\n return tidy(() => {\n inputs = toList(inputs);\n let masks;\n if (mask == null) {\n masks = pyListRepeat(null, inputs.length);\n } else {\n masks = toList(mask);\n }\n return this.runInternalGraph(inputs, masks)[1];\n });\n }\n /**\n * Computes the output shape of the layer.\n *\n * Assumes that the layer will be built to match that input shape provided.\n *\n * @param inputShape A shape (tuple of integers) or a list of shape tuples\n * (one per output tensor of the layer). Shape tuples can include null for\n * free dimensions, instead of an integer.\n */\n computeOutputShape(inputShape) {\n const inputShapes = normalizeShapeList(inputShape);\n if (inputShapes.length !== this.inputLayers.length) {\n throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);\n }\n const layersToOutputShapes = {};\n for (let i = 0; i < inputShapes.length; i++) {\n const layer = this.inputLayers[i];\n const inputShape2 = inputShapes[i];\n const shapeKey = layer.name + \"_0_0\";\n layersToOutputShapes[shapeKey] = inputShape2;\n }\n const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n if (depthKeys.length > 1) {\n for (const depth of depthKeys) {\n const nodes = this.nodesByDepth[depth];\n for (const node of nodes) {\n const layer = node.outboundLayer;\n if (this.inputLayers.map((x) => x.id).indexOf(layer.id) !== -1) {\n continue;\n }\n const inputShapes2 = [];\n for (let j = 0; j < node.inboundLayers.length; j++) {\n const inboundLayer = node.inboundLayers[j];\n const nodeIndex2 = node.nodeIndices[j];\n const tensorIndex = node.tensorIndices[j];\n const shapeKey = `${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`;\n const inputShape2 = layersToOutputShapes[shapeKey];\n inputShapes2.push(inputShape2);\n }\n const outputShape = layer.computeOutputShape(singletonOrArray(inputShapes2));\n const outputShapes2 = normalizeShapeList(outputShape);\n const nodeIndex = layer.inboundNodes.indexOf(node);\n for (let j = 0; j < outputShapes2.length; j++) {\n const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n layersToOutputShapes[shapeKey] = outputShapes2[j];\n }\n }\n }\n }\n const outputShapes = [];\n const outputShapeKeys = [];\n for (let i = 0; i < this.outputLayers.length; i++) {\n const layer = this.outputLayers[i];\n const nodeIndex = this.outputLayersNodeIndices[i];\n const tensorIndex = this.outputLayersTensorIndices[i];\n const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n outputShapeKeys.push(shapeKey);\n }\n for (let i = 0; i < outputShapeKeys.length; i++) {\n const key = outputShapeKeys[i];\n assert2(key in layersToOutputShapes);\n outputShapes.push(layersToOutputShapes[key]);\n }\n return singletonOrArray(outputShapes);\n }\n /**\n * Computes output tensors for new inputs.\n *\n * Note:\n * - Expects `inputs` to be a list (potentially with 1 element).\n *\n * @param inputs List of tensors\n * @param masks List of masks (tensors or null).\n * @return Three lists: outputTensors, outputMasks, outputShapes\n */\n runInternalGraph(inputs, masks) {\n if (masks == null) {\n masks = pyListRepeat(null, inputs.length);\n }\n const tensorMap = {};\n for (let i = 0; i < this.inputs.length; ++i) {\n const x = this.inputs[i];\n const y = inputs[i];\n const mask = masks[i];\n tensorMap[x.id] = [y, mask];\n }\n const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n for (const depth of depthKeys) {\n const nodes = this.nodesByDepth[depth];\n for (const node of nodes) {\n const layer = node.outboundLayer;\n const referenceInputTensors = node.inputTensors;\n const referenceOutputTensors = node.outputTensors;\n const computedData = new Array();\n for (const x of referenceInputTensors) {\n if (x.id in tensorMap) {\n computedData.push(tensorMap[x.id]);\n }\n }\n if (computedData.length === referenceInputTensors.length) {\n let kwargs = {};\n let computedTensors;\n let computedMasks;\n let outputTensors2;\n let outputMasks2;\n if (node.callArgs != null) {\n kwargs = node.callArgs;\n }\n if (computedData.length === 1) {\n const [computedTensor, computedMask] = computedData[0];\n if (kwargs[\"mask\"] == null) {\n kwargs[\"mask\"] = computedMask;\n }\n outputTensors2 = toList(layer.call(computedTensor, kwargs));\n outputMasks2 = toList(layer.computeMask(computedTensor, computedMask));\n computedTensors = [computedTensor];\n computedMasks = [computedMask];\n } else {\n computedTensors = computedData.map((x) => x[0]);\n computedMasks = computedData.map((x) => x[1]);\n if (kwargs[\"mask\"] == null) {\n kwargs[\"mask\"] = computedMasks;\n }\n outputTensors2 = toList(layer.call(computedTensors, kwargs));\n outputMasks2 = toList(layer.computeMask(computedTensors, computedMasks));\n }\n if (layer.activityRegularizer) {\n throw new NotImplementedError(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");\n }\n for (let i = 0; i < referenceOutputTensors.length; ++i) {\n const x = referenceOutputTensors[i];\n const y = outputTensors2[i];\n const mask = outputMasks2[i];\n tensorMap[x.id] = [y, mask];\n }\n }\n }\n }\n const outputTensors = [];\n const outputMasks = [];\n const outputShapes = [];\n for (const x of this.outputs) {\n assert2(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n const [tensor2, mask] = tensorMap[x.id];\n outputShapes.push(tensor2.shape);\n outputTensors.push(tensor2);\n outputMasks.push(mask);\n }\n return [outputTensors, outputMasks, outputShapes];\n }\n /**\n * Builds a map of internal node keys to node ordering.\n * Used in serializaion a node orderings may change as unused nodes are\n * dropped. Porting Note: This helper method was pulled out of getConfig to\n * improve readability.\n * @param layers An array of Layers in the model.\n * @returns Map of Node Keys to index order within the layer.\n */\n buildNodeConversionMap(layers) {\n const nodeConversionMap = {};\n let keptNodes;\n for (const layer of this.layers) {\n keptNodes = layer instanceof _Container ? 1 : 0;\n for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n const nodeKey = _Container.nodeKey(layer, originalNodeIndex);\n if (this.containerNodes.has(nodeKey)) {\n nodeConversionMap[nodeKey] = keptNodes;\n keptNodes += 1;\n }\n }\n }\n return nodeConversionMap;\n }\n getLayer(nameOrIndex, index) {\n if (index != null) {\n return this.findLayer(index);\n } else {\n if (nameOrIndex == null) {\n throw new ValueError(\"Provide either a layer name or layer index\");\n }\n if (typeof nameOrIndex === \"number\") {\n return this.findLayer(nameOrIndex);\n }\n }\n for (const layer of this.layers) {\n if (layer.name === nameOrIndex) {\n return layer;\n }\n }\n throw new ValueError(`No such layer: ${nameOrIndex}`);\n }\n findLayer(index) {\n if (this.layers.length <= index) {\n throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);\n } else {\n return this.layers[index];\n }\n }\n /**\n * Retrieves the Container's current loss values.\n *\n * Used for regularizers during training.\n */\n calculateLosses() {\n return tidy(() => {\n const losses2 = [];\n for (const layer of this.layers) {\n for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n const nodeKey = _Container.nodeKey(layer, nodeIndex);\n if (this.containerNodes.has(nodeKey)) {\n losses2.push(...layer.calculateLosses());\n }\n }\n }\n return losses2;\n });\n }\n getConfig() {\n const config = { name: this.name };\n const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n const layerConfigs = [];\n for (const layer of this.layers) {\n const layerClassName = layer.getClassName();\n const layerConfig = layer.getConfig();\n const filteredInboundNodes = [];\n for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n const node = layer.inboundNodes[originalNodeIndex];\n const nodeKey = _Container.nodeKey(layer, originalNodeIndex);\n let kwargs = {};\n if (this.containerNodes.has(nodeKey)) {\n if (node.callArgs) {\n try {\n JSON.stringify(node.callArgs);\n kwargs = node.callArgs;\n } catch (err) {\n console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`);\n kwargs = {};\n }\n }\n if (node.inboundLayers.length > 0) {\n const nodeData = [];\n for (let i = 0; i < node.inboundLayers.length; i++) {\n const inboundLayer = node.inboundLayers[i];\n const nodeIndex = node.nodeIndices[i];\n const tensorIndex = node.tensorIndices[i];\n const nodeKey2 = _Container.nodeKey(inboundLayer, nodeIndex);\n let newNodeIndex = nodeConversionMap[nodeKey2];\n if (newNodeIndex == null) {\n newNodeIndex = 0;\n }\n nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n }\n filteredInboundNodes.push(nodeData);\n }\n }\n }\n const dict = {};\n dict[\"name\"] = layer.name;\n dict[\"className\"] = layerClassName;\n dict[\"config\"] = layerConfig;\n dict[\"inboundNodes\"] = filteredInboundNodes;\n layerConfigs.push(dict);\n }\n config[\"layers\"] = layerConfigs;\n const modelInputs = [];\n for (let i = 0; i < this.inputLayers.length; i++) {\n const layer = this.inputLayers[i];\n const nodeIndex = this.inputLayersNodeIndices[i];\n const nodeKey = _Container.nodeKey(layer, nodeIndex);\n if (!this.containerNodes.has(nodeKey)) {\n continue;\n }\n let newNodeIndex = nodeConversionMap[nodeKey];\n if (newNodeIndex === null || newNodeIndex === void 0) {\n newNodeIndex = 0;\n }\n const tensorIndex = this.inputLayersTensorIndices[i];\n modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n }\n config[\"inputLayers\"] = modelInputs;\n const modelOutputs = [];\n for (let i = 0; i < this.outputLayers.length; i++) {\n const layer = this.outputLayers[i];\n const nodeIndex = this.outputLayersNodeIndices[i];\n const nodeKey = _Container.nodeKey(layer, nodeIndex);\n if (!this.containerNodes.has(nodeKey)) {\n continue;\n }\n let newNodeIndex = nodeConversionMap[nodeKey];\n if (newNodeIndex === null || newNodeIndex === void 0) {\n newNodeIndex = 0;\n }\n const tensorIndex = this.outputLayersTensorIndices[i];\n modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n }\n config[\"outputLayers\"] = modelOutputs;\n return config;\n }\n /**\n * Instantiates a LayersModel from its config (output of `get_config()`).\n * @param cls the class to create\n * @param config LayersModel config dictionary.\n * @param customObjects An optional dictionary of custom objects.\n * @param fastWeightInit Optional flag to use fast weight initialization\n * during deserialization. This is applicable to cases in which\n * the initialization will be immediately overwritten by loaded weight\n * values. Default: `false`.\n * @returns A LayersModel instance.\n * @throws ValueError: In case of improperly formatted config dict.\n */\n /** @nocollapse */\n static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n const createdLayers = {};\n const unprocessedNodes = {};\n function addUnprocessedNode(layer, nodeData) {\n if (!(layer.name in unprocessedNodes)) {\n unprocessedNodes[layer.name] = [nodeData];\n } else {\n unprocessedNodes[layer.name].push(nodeData);\n }\n }\n function processNode(layer, nodeData) {\n const inputTensors2 = [];\n let kwargs;\n for (const inputData of nodeData) {\n const inboundLayerName = inputData[0];\n const inboundNodeIndex = inputData[1];\n const inboundTensorIndex = inputData[2];\n kwargs = inputData[3] == null ? {} : inputData[3];\n if (!(inboundLayerName in createdLayers)) {\n addUnprocessedNode(layer, nodeData);\n return;\n }\n const inboundLayer = createdLayers[inboundLayerName];\n if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n addUnprocessedNode(layer, nodeData);\n return;\n }\n const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex]);\n }\n if (inputTensors2.length > 0) {\n layer.apply(singletonOrArray(inputTensors2), kwargs);\n }\n }\n function processLayer(layerData) {\n const layerName = layerData[\"name\"];\n const layer = deserialize(layerData, config[\"customObjects\"] != null ? config[\"customObjects\"] : {});\n layer.setFastWeightInitDuringBuild(fastWeightInit);\n createdLayers[layerName] = layer;\n const inboundNodesData = layerData[\"inboundNodes\"];\n inboundNodesData.forEach((nodeData) => {\n if (!(nodeData instanceof Array)) {\n throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n }\n addUnprocessedNode(layer, nodeData);\n });\n }\n const name = config[\"name\"];\n const layersFromConfig = config[\"layers\"];\n for (const layerData of layersFromConfig) {\n processLayer(layerData);\n }\n while (!isObjectEmpty(unprocessedNodes)) {\n for (const layerData of layersFromConfig) {\n const layer = createdLayers[layerData[\"name\"]];\n if (layer.name in unprocessedNodes) {\n const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n delete unprocessedNodes[layer.name];\n for (const nodeData of currentUnprocessedNodesForLayer) {\n processNode(layer, nodeData);\n }\n }\n }\n }\n const inputTensors = [];\n const outputTensors = [];\n const inputLayersFromConfig = config[\"inputLayers\"];\n for (const layerData of inputLayersFromConfig) {\n const layerName = layerData[0];\n const nodeIndex = layerData[1];\n const tensorIndex = layerData[2];\n assert2(layerName in createdLayers);\n const layer = createdLayers[layerName];\n const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n inputTensors.push(layerOutputTensors[tensorIndex]);\n }\n const outputLayersFromConfig = config[\"outputLayers\"];\n for (const layerData of outputLayersFromConfig) {\n const layerName = layerData[0];\n const nodeIndex = layerData[1];\n const tensorIndex = layerData[2];\n assert2(layerName in createdLayers);\n const layer = createdLayers[layerName];\n const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n outputTensors.push(layerOutputTensors[tensorIndex]);\n }\n return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n }\n /**\n * Determine whether the container is stateful.\n *\n * Porting Note: this is the equivalent of the stateful @property of\n * the Container class in PyKeras.\n */\n get stateful() {\n if (this._stateful) {\n throw new ValueError(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");\n }\n for (const layer of this.layers) {\n if (layer.stateful) {\n return true;\n }\n }\n return false;\n }\n /**\n * Reset the state of all stateful constituent layers (if any).\n *\n * Examples of stateful layers include RNN layers whose `stateful` property\n * is set as `true`.\n */\n resetStates() {\n tidy(() => {\n this.layers.forEach((layer) => {\n if (layer.stateful) {\n layer.resetStates();\n }\n });\n });\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_utils.js\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n const numOutputs = outputNames.length;\n if (xWeight == null || Array.isArray(xWeight) && xWeight.length === 0) {\n return outputNames.map((name) => null);\n }\n if (numOutputs === 1) {\n if (Array.isArray(xWeight) && xWeight.length === 1) {\n return xWeight;\n } else if (typeof xWeight === \"object\" && outputNames[0] in xWeight) {\n return [xWeight[outputNames[0]]];\n } else {\n return [xWeight];\n }\n }\n if (Array.isArray(xWeight)) {\n if (xWeight.length !== numOutputs) {\n throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);\n }\n return xWeight;\n } else if (typeof xWeight === \"object\" && Object.keys(xWeight).length > 0 && typeof xWeight[Object.keys(xWeight)[0]] === \"object\") {\n const output = [];\n outputNames.forEach((outputName) => {\n if (outputName in xWeight) {\n output.push(xWeight[outputName]);\n } else {\n output.push(null);\n }\n });\n return output;\n } else {\n throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n }\n}\nfunction standardizeClassWeights(classWeight, outputNames) {\n return standardizeSampleOrClassWeights(classWeight, outputNames, \"classWeight\");\n}\nasync function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n if (sampleWeight != null || sampleWeightMode != null) {\n throw new Error(\"Support sampleWeight is not implemented yet\");\n }\n if (classWeight != null) {\n const yClasses = tidy(() => {\n if (y.shape.length === 1) {\n return clone(y);\n } else if (y.shape.length === 2) {\n if (y.shape[1] > 1) {\n const axis = 1;\n return argMax(y, axis);\n } else if (y.shape[1] === 1) {\n return reshape(y, [y.shape[0]]);\n } else {\n throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);\n }\n } else {\n throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);\n }\n });\n const yClassIndices = Array.from(await yClasses.data());\n dispose(yClasses);\n const classSampleWeight = [];\n yClassIndices.forEach((classIndex) => {\n if (classWeight[classIndex] == null) {\n throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);\n } else {\n classSampleWeight.push(classWeight[classIndex]);\n }\n });\n return tensor1d(classSampleWeight, \"float32\");\n } else {\n return null;\n }\n}\nfunction computeWeightedLoss2(losses2, sampleWeights) {\n return mul(losses2, sampleWeights);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js\nvar DEFAULT_VALIDATION_BATCH_SIZE = 32;\nfunction standardizeDataIteratorOutput(model2, iteratorOut) {\n let xs;\n let ys;\n const iteratorOutObj = iteratorOut;\n xs = iteratorOutObj[\"xs\"];\n ys = iteratorOutObj[\"ys\"];\n util_exports.assert(xs != null && ys != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \\`{xs: xVal, ys: yVal}\\`, where the two values may be \\`tf.Tensor\\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${iteratorOut}`);\n const flattenedXs = flattenTensorOrArrayOrMap(\"input\", model2.inputNames, xs);\n const flattenedYs = flattenTensorOrArrayOrMap(\"output\", model2.outputNames, ys);\n const batchSize = flattenedXs[0].shape[0];\n util_exports.assert(flattenedXs.length === model2.inputs.length, () => `LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model2.inputNames)})`);\n util_exports.assert(flattenedYs.length === model2.outputs.length, () => `LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model2.outputNames)})`);\n for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);\n }\n for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);\n }\n return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n if (values instanceof Tensor) {\n return [values];\n } else if (Array.isArray(values)) {\n util_exports.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n return values;\n } else {\n const result = [];\n for (const name of names) {\n if (values[name] == null) {\n throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);\n }\n result.push(values[name]);\n }\n return result;\n }\n}\nfunction standardizeTensorValidationData(data) {\n if (data.length === 3) {\n throw new NotImplementedError(\"Validation with sample weights is not implemented yet.\");\n }\n return { xs: data[0], ys: data[1] };\n}\nasync function fitDataset(model2, dataset, args) {\n const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n util_exports.assert(model2.optimizer != null, () => \"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).\");\n util_exports.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.`);\n util_exports.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`);\n util_exports.assert(!hasBatchesPerEpoch || args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`);\n util_exports.assert(\n // tslint:disable-next-line:no-any\n args[\"validationSplit\"] == null,\n () => \"`validationSplit` is not supported by `fitDataset()`. Use validationData instead.\"\n );\n if (model2.isTraining) {\n throw new Error(\"Cannot start training because another fit() call is ongoing.\");\n }\n model2.isTraining = true;\n try {\n const doValidation = args.validationData != null;\n let valXs;\n let valYs;\n if (doValidation) {\n if (isDatasetObject(args.validationData)) {\n util_exports.assert(args.validationBatches == null || args.validationBatches > 0 && Number.isInteger(args.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);\n } else {\n const validationData = standardizeTensorValidationData(args.validationData);\n valXs = validationData.xs;\n valYs = validationData.ys;\n }\n }\n const trainFunction = model2.makeTrainFunction();\n const outLabels = model2.getDedupedMetricsNames();\n let callbackMetrics;\n if (doValidation) {\n callbackMetrics = outLabels.slice().concat(outLabels.map((n) => \"val_\" + n));\n } else {\n callbackMetrics = outLabels.slice();\n }\n const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);\n const verbose = args.verbose == null ? 1 : args.verbose;\n const { callbackList, history } = configureCallbacks(\n callbacks2,\n verbose,\n args.epochs,\n null,\n null,\n getStepsPerEpoch(dataset, args),\n null,\n // Batch size determined by the dataset itself.\n doValidation,\n callbackMetrics\n );\n callbackList.setModel(model2);\n model2.history = history;\n await callbackList.onTrainBegin();\n model2.stopTraining_ = false;\n let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n let dataIterator = await dataset.iterator();\n while (epoch < args.epochs) {\n const epochLogs = {};\n await callbackList.onEpochBegin(epoch);\n let stepsDone = 0;\n let batchIndex = 0;\n if (!hasBatchesPerEpoch) {\n dataIterator = await dataset.iterator();\n }\n while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n const iteratorOut = await dataIterator.next();\n if (hasBatchesPerEpoch && iteratorOut.done) {\n console.warn(`You provided \\`batchesPerEpoch\\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \\`batchesPerEpoch * epochs\\` batches (in this case, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`);\n break;\n }\n if (iteratorOut.value != null) {\n const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value);\n const batchLogs = {};\n batchLogs[\"batch\"] = batchIndex;\n batchLogs[\"size\"] = xs[0].shape[0];\n await callbackList.onBatchBegin(batchIndex, batchLogs);\n const sampleWeights = [];\n if (args.classWeight != null) {\n const standardClassWeights = standardizeClassWeights(args.classWeight, model2.outputNames);\n for (let i = 0; i < standardClassWeights.length; ++i) {\n sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n }\n }\n const ins = xs.concat(ys).concat(sampleWeights);\n const outs = trainFunction(ins);\n dispose(ins);\n for (let i = 0; i < outLabels.length; ++i) {\n const label = outLabels[i];\n const out = outs[i];\n batchLogs[label] = out;\n keep(out);\n }\n await callbackList.onBatchEnd(batchIndex, batchLogs);\n disposeTensorsInLogs(batchLogs);\n batchIndex++;\n stepsDone++;\n }\n if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) {\n if (doValidation) {\n let valOuts;\n if (isDatasetObject(args.validationData)) {\n valOuts = toList(await model2.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n } else {\n valOuts = toList(model2.evaluate(valXs, valYs, {\n batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize,\n verbose: 0\n }));\n }\n for (let i = 0; i < model2.metricsNames.length; ++i) {\n epochLogs[`val_${model2.metricsNames[i]}`] = valOuts[i];\n }\n }\n break;\n }\n if (model2.stopTraining_) {\n break;\n }\n }\n await callbackList.onEpochEnd(epoch, epochLogs);\n epoch++;\n if (model2.stopTraining_) {\n break;\n }\n }\n await callbackList.onTrainEnd();\n await model2.history.syncData();\n return model2.history;\n } finally {\n model2.isTraining = false;\n }\n}\nfunction getStepsPerEpoch(dataset, args) {\n let stepsPerEpoch = null;\n if (args.batchesPerEpoch != null) {\n stepsPerEpoch = args.batchesPerEpoch;\n } else if (Number.isFinite(dataset.size)) {\n stepsPerEpoch = dataset.size;\n }\n return stepsPerEpoch;\n}\nfunction isDatasetObject(dataset) {\n return typeof dataset.iterator === \"function\";\n}\nfunction isLazyIteratorObject(iterator) {\n return typeof iterator.next === \"function\";\n}\nasync function evaluateDataset(model2, dataset, args) {\n args = args || {};\n const hasBatches = args.batches != null;\n const f = model2.testFunction;\n let outs = [];\n if (args.verbose > 0) {\n throw new NotImplementedError(\"Verbose mode is not implemented yet.\");\n }\n util_exports.assert(!hasBatches || args.batches > 0 && Number.isInteger(args.batches), () => `Test loop expects \\`batches\\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);\n const dataIterator = isLazyIteratorObject(dataset) ? dataset : await dataset.iterator();\n let numExamples = 0;\n let batch = 0;\n while (hasBatches ? batch < args.batches : true) {\n const iteratorOut = await dataIterator.next();\n outs = tidy(() => {\n if (iteratorOut.value) {\n const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value);\n const xsAndYs = xs.concat(ys);\n const batchOuts = tidy(() => f(xsAndYs));\n dispose(xsAndYs);\n if (batch === 0) {\n for (let i = 0; i < batchOuts.length; ++i) {\n outs.push(scalar(0));\n }\n }\n const batchSize = xsAndYs[0].shape[0];\n for (let i = 0; i < batchOuts.length; ++i) {\n const batchOut = batchOuts[i];\n const oldScalar = outs[i];\n outs[i] = tidy(() => add2(outs[i], mul(batchSize, batchOut)));\n if (batch > 0) {\n dispose(oldScalar);\n }\n }\n dispose(batchOuts);\n numExamples += batchSize;\n ++batch;\n }\n return outs;\n });\n if (iteratorOut.done) {\n if (hasBatches) {\n console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \\`batches\\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);\n }\n break;\n }\n }\n for (let i = 0; i < outs.length; ++i) {\n const oldScalar = outs[i];\n outs[i] = div(outs[i], numExamples);\n dispose(oldScalar);\n }\n return singletonOrArray(outs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_tensors.js\nfunction checkBatchSize(batchSize) {\n util_exports.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\nfunction sliceArrays(arrays, start, stop) {\n if (arrays == null) {\n return [null];\n } else if (Array.isArray(arrays)) {\n return arrays.map((array2) => sliceAlongFirstAxis(array2, start, stop - start));\n } else {\n return sliceAlongFirstAxis(arrays, start, stop - start);\n }\n}\nfunction sliceArraysByIndices(arrays, indices) {\n return tidy(() => {\n if (arrays == null) {\n return null;\n } else if (Array.isArray(arrays)) {\n return arrays.map((array2) => sliceArraysByIndices(array2, indices));\n } else {\n return gather2(arrays, indices.dtype === \"int32\" ? indices : cast(indices, \"int32\"));\n }\n });\n}\nfunction makeBatches(size, batchSize) {\n const output = [];\n let batchStart = 0;\n let batchEnd = null;\n while (batchStart < size) {\n batchEnd = batchStart + batchSize;\n if (batchEnd >= size) {\n batchEnd = size;\n }\n output.push([batchStart, batchEnd]);\n batchStart = batchEnd;\n }\n return output;\n}\nfunction ensureTensorsRank2OrHigher(tensors) {\n const outs = [];\n if (tensors instanceof Tensor) {\n tensors = [tensors];\n }\n for (let i = 0; i < tensors.length; ++i) {\n const tensor2 = tensors[i];\n if (tensor2.rank === 1) {\n outs.push(expandDims2(tensor2, 1));\n } else if (tensor2.rank === 0) {\n throw new Error(\"Expected tensor to be at least 1D, but received a 0D tensor (scalar).\");\n } else {\n outs.push(tensor2);\n }\n }\n return outs;\n}\nfunction disposeNewTensors(tensors, refTensors) {\n if (tensors == null) {\n return;\n }\n const oldTensorIds = [];\n if (refTensors instanceof Tensor) {\n oldTensorIds.push(refTensors.id);\n } else if (Array.isArray(refTensors)) {\n refTensors.forEach((t) => oldTensorIds.push(t.id));\n } else if (refTensors != null) {\n for (const name in refTensors) {\n const oldTensor = refTensors[name];\n oldTensorIds.push(oldTensor.id);\n }\n }\n const tensorsToDispose = [];\n if (tensors instanceof Tensor) {\n if (oldTensorIds.indexOf(tensors.id) === -1) {\n tensorsToDispose.push(tensors);\n }\n } else if (Array.isArray(tensors)) {\n tensors.forEach((t) => {\n if (oldTensorIds.indexOf(t.id) === -1) {\n tensorsToDispose.push(t);\n }\n });\n } else if (tensors != null) {\n for (const name in tensors) {\n const tensor2 = tensors[name];\n if (oldTensorIds.indexOf(tensor2.id) === -1) {\n tensorsToDispose.push(tensor2);\n }\n }\n }\n tensorsToDispose.forEach((t) => {\n if (!t.isDisposed) {\n t.dispose();\n }\n });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training.js\nfunction isDataTensor(x) {\n return x instanceof Tensor;\n}\nfunction isDataArray(x) {\n return Array.isArray(x);\n}\nfunction isDataDict(x) {\n return !isDataTensor(x) && !isDataArray(x);\n}\nfunction standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = \"\") {\n if (names == null || names.length === 0) {\n if (data != null) {\n let gotUnexpectedData = false;\n if (isDataArray(data) && data.length > 0) {\n gotUnexpectedData = true;\n } else if (isDataDict(data)) {\n for (const key in data) {\n if (data.hasOwnProperty(key)) {\n gotUnexpectedData = true;\n break;\n }\n }\n } else {\n gotUnexpectedData = true;\n }\n if (gotUnexpectedData) {\n throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`);\n }\n }\n return [];\n }\n if (data == null) {\n return names.map((name) => null);\n }\n let arrays;\n if (isDataDict(data)) {\n data = data;\n arrays = [];\n for (const name of names) {\n if (data[name] == null) {\n throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ${names}`);\n }\n arrays.push(data[name]);\n }\n } else if (isDataArray(data)) {\n data = data;\n if (data.length !== names.length) {\n throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);\n }\n arrays = data;\n } else {\n data = data;\n if (names.length > 1) {\n throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n }\n arrays = [data];\n }\n arrays = ensureTensorsRank2OrHigher(arrays);\n if (shapes != null) {\n for (let i = 0; i < names.length; ++i) {\n if (shapes[i] == null) {\n continue;\n }\n const array2 = arrays[i];\n if (array2.shape.length !== shapes[i].length) {\n throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);\n }\n for (let j = 0; j < shapes[i].length; ++j) {\n if (j === 0 && !checkBatchAxis) {\n continue;\n }\n const dim = array2.shape[j];\n const refDim = shapes[i][j];\n if (refDim != null && refDim >= 0 && dim !== refDim) {\n throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i].slice(1, shapes[i].length)}] (i.e.,tensor shape [*,${shapes[i].slice(1, shapes[i].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);\n }\n }\n }\n }\n return arrays;\n}\nfunction checkArrayLengths(inputs, targets, weights) {\n const setX = unique2(inputs.map((input2) => input2.shape[0]));\n setX.sort();\n const setY = unique2(targets.map((target) => target.shape[0]));\n setY.sort();\n if (setX.length > 1) {\n throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map((input2) => input2.shape))}`);\n }\n if (setY.length > 1) {\n throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map((target) => target.shape))}`);\n }\n if (setX.length > 0 && setY.length > 0 && !util_exports.arraysEqual(setX, setY)) {\n throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`);\n }\n}\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n const keyLosses = [\n meanSquaredError2,\n binaryCrossentropy,\n categoricalCrossentropy\n ];\n for (let i = 0; i < targets.length; ++i) {\n const y = targets[i];\n const loss = lossFns[i];\n const shape = outputShapes[i];\n if (loss == null) {\n continue;\n }\n if (loss === categoricalCrossentropy) {\n if (y.shape[y.shape.length - 1] === 1) {\n throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);\n }\n }\n if (keyLosses.indexOf(loss) !== -1) {\n const slicedYShape = y.shape.slice(1);\n const slicedShape = shape.slice(1);\n for (let j = 0; j < slicedYShape.length; ++j) {\n const targetDim = slicedYShape[j];\n const outDim = slicedShape[j];\n if (outDim != null && targetDim !== outDim) {\n throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`);\n }\n }\n }\n }\n}\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = \"\") {\n let arrays;\n if (Array.isArray(data)) {\n if (data.length !== names.length) {\n throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);\n }\n arrays = data;\n } else {\n if (names.length > 1) {\n throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);\n }\n arrays = [data];\n }\n if (shapes != null) {\n for (let i = 0; i < names.length; ++i) {\n if (shapes[i] == null) {\n continue;\n }\n const array2 = arrays[i];\n if (array2.shape.length !== shapes[i].length) {\n throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);\n }\n for (let j = 0; j < shapes[i].length; ++j) {\n if (j === 0 && !checkBatchAxis) {\n continue;\n }\n const dim = array2.shape[j];\n const refDim = shapes[i][j];\n if (refDim != null) {\n if (refDim !== dim) {\n throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`);\n }\n }\n }\n }\n }\n}\nfunction collectMetrics(metrics, outputNames) {\n if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n return outputNames.map((name) => []);\n }\n let wrappedMetrics;\n if (typeof metrics === \"string\" || typeof metrics === \"function\") {\n wrappedMetrics = [metrics];\n } else if (Array.isArray(metrics) || typeof metrics === \"object\") {\n wrappedMetrics = metrics;\n } else {\n throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics}`);\n }\n if (Array.isArray(wrappedMetrics)) {\n return outputNames.map((name) => wrappedMetrics);\n } else {\n const nestedMetrics = [];\n for (const name of outputNames) {\n let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n if (!Array.isArray(outputMetrics)) {\n outputMetrics = [outputMetrics];\n }\n nestedMetrics.push(outputMetrics);\n }\n return nestedMetrics;\n }\n}\nvar LAYERS_MODEL_FORMAT_NAME = \"layers-model\";\nvar LayersModel = class extends Container {\n constructor(args) {\n super(args);\n this.isTraining = false;\n }\n /**\n * Print a text summary of the model's layers.\n *\n * The summary includes\n * - Name and type of all layers that comprise the model.\n * - Output shape(s) of the layers\n * - Number of weight parameters of each layer\n * - If the model has non-sequential-like topology, the inputs each layer\n * receives\n * - The total number of trainable and non-trainable parameters of the model.\n *\n * ```js\n * const input1 = tf.input({shape: [10]});\n * const input2 = tf.input({shape: [20]});\n * const dense1 = tf.layers.dense({units: 4}).apply(input1);\n * const dense2 = tf.layers.dense({units: 8}).apply(input2);\n * const concat = tf.layers.concatenate().apply([dense1, dense2]);\n * const output =\n * tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);\n *\n * const model = tf.model({inputs: [input1, input2], outputs: output});\n * model.summary();\n * ```\n *\n * @param lineLength Custom line length, in number of characters.\n * @param positions Custom widths of each of the columns, as either\n * fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n * of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n * right-most (i.e., ending) position of a column.\n * @param printFn Custom print function. Can be used to replace the default\n * `console.log`. For example, you can use `x => {}` to mute the printed\n * messages in the console.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n summary(lineLength, positions, printFn = console.log) {\n if (!this.built) {\n throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`);\n }\n printSummary(this, lineLength, positions, printFn);\n }\n /**\n * Configures and prepares the model for training and evaluation. Compiling\n * outfits the model with an optimizer, loss, and/or metrics. Calling `fit`\n * or `evaluate` on an un-compiled model will throw an error.\n *\n * @param args a `ModelCompileArgs` specifying the loss, optimizer, and\n * metrics to be used for fitting and evaluating this model.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n compile(args) {\n if (args.loss == null) {\n args.loss = [];\n }\n this.loss = args.loss;\n if (typeof args.optimizer === \"string\") {\n this.optimizer_ = getOptimizer(args.optimizer);\n this.isOptimizerOwned = true;\n } else {\n if (!(args.optimizer instanceof Optimizer)) {\n throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n }\n this.optimizer_ = args.optimizer;\n this.isOptimizerOwned = false;\n }\n let lossFunctions = [];\n if (!Array.isArray(args.loss) && typeof args.loss !== \"string\" && typeof args.loss !== \"function\") {\n args.loss = args.loss;\n for (const name in args.loss) {\n if (this.outputNames.indexOf(name) === -1) {\n throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". Only expected the following keys: ${this.outputNames}`);\n }\n }\n for (const name of this.outputNames) {\n if (args.loss[name] == null) {\n console.warn(`Output \"${name}\" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`);\n }\n lossFunctions.push(get(args.loss[name]));\n }\n } else if (Array.isArray(args.loss)) {\n if (args.loss.length !== this.outputs.length) {\n throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);\n }\n const theLosses = args.loss;\n lossFunctions = theLosses.map((l) => get(l));\n } else {\n const lossFunction = get(args.loss);\n this.outputs.forEach((_) => {\n lossFunctions.push(lossFunction);\n });\n }\n this.lossFunctions = lossFunctions;\n this.feedOutputNames = [];\n this.feedOutputShapes = [];\n this.feedLossFns = [];\n for (let i = 0; i < this.outputs.length; ++i) {\n const shape = this.internalOutputShapes[i];\n const name = this.outputNames[i];\n this.feedOutputNames.push(name);\n this.feedOutputShapes.push(shape);\n this.feedLossFns.push(this.lossFunctions[i]);\n }\n const skipTargetIndices = [];\n this.metrics = args.metrics;\n this.metricsNames = [\"loss\"];\n this.metricsTensors = [];\n nameScope(\"loss\", () => {\n for (let i = 0; i < this.outputs.length; ++i) {\n if (skipTargetIndices.indexOf(i) !== -1) {\n continue;\n }\n const weightedLoss = this.lossFunctions[i];\n if (this.outputs.length > 1) {\n this.metricsTensors.push([weightedLoss, i]);\n this.metricsNames.push(this.outputNames[i] + \"_loss\");\n }\n }\n });\n const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n const appendMetric = (outputIndex, metricName, metricTensor) => {\n if (this.outputNames.length > 1) {\n metricName = this.outputNames[outputIndex] + \"_\" + metricName;\n }\n this.metricsNames.push(metricName);\n this.metricsTensors.push([metricTensor, outputIndex]);\n };\n nameScope(\"metric\", () => {\n for (let i = 0; i < this.outputs.length; ++i) {\n if (skipTargetIndices.indexOf(i) !== -1) {\n continue;\n }\n const outputMetrics = nestedMetrics[i];\n const handleMetrics = (metrics) => {\n const metricNamePrefix = \"\";\n let metricName;\n let accFn;\n let weightedMetricFn;\n for (const metric of metrics) {\n if (typeof metric === \"string\" && [\"accuracy\", \"acc\", \"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n const outputShape = this.internalOutputShapes[i];\n if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i] === binaryCrossentropy) {\n if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n accFn = binaryAccuracy;\n } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n accFn = binaryCrossentropy2;\n }\n } else if (this.lossFunctions[i] === sparseCategoricalCrossentropy) {\n if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n accFn = sparseCategoricalAccuracy;\n } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n accFn = sparseCategoricalCrossentropy2;\n }\n } else {\n if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n accFn = categoricalAccuracy;\n } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n accFn = categoricalCrossentropy2;\n }\n }\n let suffix;\n if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n suffix = \"acc\";\n } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n suffix = \"ce\";\n }\n weightedMetricFn = accFn;\n metricName = metricNamePrefix + suffix;\n } else {\n const metricFn = get2(metric);\n weightedMetricFn = metricFn;\n metricName = metricNamePrefix + getLossOrMetricName(metric);\n }\n let metricResult;\n nameScope(metricName, () => {\n metricResult = weightedMetricFn;\n });\n appendMetric(i, metricName, metricResult);\n }\n };\n handleMetrics(outputMetrics);\n }\n });\n this.collectedTrainableWeights = this.trainableWeights;\n }\n /**\n * Check trainable weights count consistency.\n *\n * This will raise a warning if `this.trainableWeights` and\n * `this.collectedTrainableWeights` are inconsistent (i.e., have different\n * numbers of parameters).\n * Inconsistency will typically arise when one modifies `model.trainable`\n * without calling `model.compile()` again.\n */\n checkTrainableWeightsConsistency() {\n if (this.collectedTrainableWeights == null) {\n return;\n }\n if (this.trainableWeights.length !== this.collectedTrainableWeights.length) {\n console.warn(\"Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?\");\n }\n }\n /**\n * Returns the loss value & metrics values for the model in test mode.\n *\n * Loss and metrics are specified during `compile()`, which needs to happen\n * before calls to `evaluate()`.\n *\n * Computation is done in batches.\n *\n * ```js\n * const model = tf.sequential({\n * layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n * });\n * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n * const result = model.evaluate(\n * tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});\n * result.print();\n * ```\n *\n * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n * model has multiple inputs.\n * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n * model has multiple outputs.\n * @param args A `ModelEvaluateArgs`, containing optional fields.\n *\n * @return `Scalar` test loss (if the model has a single output and no\n * metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n * and/or metrics). The attribute `model.metricsNames`\n * will give you the display labels for the scalar outputs.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n evaluate(x, y, args = {}) {\n const batchSize = args.batchSize == null ? 32 : args.batchSize;\n checkBatchSize(batchSize);\n const checkBatchAxis = true;\n const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n try {\n const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n this.makeTestFunction();\n const f = this.testFunction;\n const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n return singletonOrArray(testOuts);\n } finally {\n disposeNewTensors(standardizedOuts[0], x);\n disposeNewTensors(standardizedOuts[1], y);\n }\n }\n // TODO(cais): Add code snippet below once real dataset objects are\n // available.\n /**\n * Evaluate model using a dataset object.\n *\n * Note: Unlike `evaluate()`, this method is asynchronous (`async`).\n *\n * @param dataset A dataset object. Its `iterator()` method is expected\n * to generate a dataset iterator object, the `next()` method of which\n * is expected to produce data batches for evaluation. The return value\n * of the `next()` call ought to contain a boolean `done` field and a\n * `value` field. The `value` field is expected to be an array of two\n * `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n * case is for models with exactly one input and one output (e.g.\n * a sequential model). The latter case is for models with multiple\n * inputs and/or multiple outputs. Of the two items in the array, the\n * first is the input feature(s) and the second is the output target(s).\n * @param args A configuration object for the dataset-based evaluation.\n * @returns Loss and metric values as an Array of `Scalar` objects.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n async evaluateDataset(dataset, args) {\n this.makeTestFunction();\n return evaluateDataset(this, dataset, args);\n }\n /**\n * Get number of samples provided for training, evaluation or prediction.\n *\n * @param ins Input `tf.Tensor`.\n * @param batchSize Integer batch size, optional.\n * @param steps Total number of steps (batches of samples) before\n * declaring loop finished. Optional.\n * @param stepsName The public API's parameter name for `steps`.\n * @returns Number of samples provided.\n */\n checkNumSamples(ins, batchSize, steps, stepsName = \"steps\") {\n let numSamples;\n if (steps != null) {\n numSamples = null;\n if (batchSize != null) {\n throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`);\n }\n } else if (ins != null) {\n if (Array.isArray(ins)) {\n numSamples = ins[0].shape[0];\n } else {\n numSamples = ins.shape[0];\n }\n } else {\n throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);\n }\n return numSamples;\n }\n /**\n * Execute internal tensors of the model with input data feed.\n * @param inputs Input data feed. Must match the inputs of the model.\n * @param outputs Names of the output tensors to be fetched. Must match\n * names of the SymbolicTensors that belong to the graph.\n * @returns Fetched values for `outputs`.\n */\n execute(inputs, outputs) {\n if (Array.isArray(outputs) && outputs.length === 0) {\n throw new ValueError(\"`outputs` is an empty Array, which is not allowed.\");\n }\n const outputsIsArray = Array.isArray(outputs);\n const outputNames = outputsIsArray ? outputs : [outputs];\n const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n const feedDict = new FeedDict();\n if (inputs instanceof Tensor) {\n inputs = [inputs];\n }\n if (Array.isArray(inputs)) {\n if (inputs.length !== this.inputs.length) {\n throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);\n }\n for (let i = 0; i < this.inputs.length; ++i) {\n feedDict.add(this.inputs[i], inputs[i]);\n }\n } else {\n for (const input2 of this.inputs) {\n const tensorValue = inputs[input2.name];\n if (tensorValue == null) {\n throw new ValueError(`No value is provided for the model's input ${input2.name}`);\n }\n feedDict.add(input2, tensorValue);\n }\n }\n const executeOutputs = execute(outputSymbolicTensors, feedDict);\n return outputsIsArray ? executeOutputs : executeOutputs[0];\n }\n /**\n * Retrieve the model's internal symbolic tensors from symbolic-tensor names.\n */\n retrieveSymbolicTensors(symbolicTensorNames) {\n const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n let outputsRemaining = symbolicTensorNames.length;\n for (const layer of this.layers) {\n const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n const layerOutputNames = layerOutputs.map((output) => output.name);\n for (let i = 0; i < symbolicTensorNames.length; ++i) {\n const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n if (index !== -1) {\n outputSymbolicTensors[i] = layerOutputs[index];\n outputsRemaining--;\n }\n if (outputsRemaining === 0) {\n break;\n }\n }\n if (outputsRemaining === 0) {\n break;\n }\n }\n if (outputsRemaining > 0) {\n const remainingNames = [];\n outputSymbolicTensors.forEach((tensor2, i) => {\n if (tensor2 == null) {\n remainingNames.push(symbolicTensorNames[i]);\n }\n });\n throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`);\n }\n return outputSymbolicTensors;\n }\n /**\n * Helper method to loop over some data in batches.\n *\n * Porting Note: Not using the functional approach in the Python equivalent\n * due to the imperative backend.\n * Porting Note: Does not support step mode currently.\n *\n * @param ins: input data\n * @param batchSize: integer batch size.\n * @param verbose: verbosity model\n * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of\n * `tf.Tensor` (if multipe outputs).\n */\n predictLoop(ins, batchSize = 32, verbose = false) {\n return tidy(() => {\n const numSamples = this.checkNumSamples(ins);\n if (verbose) {\n throw new NotImplementedError(\"Verbose predictLoop() is not implemented yet.\");\n }\n const batches = makeBatches(numSamples, batchSize);\n const outsBatches = this.outputs.map((output) => []);\n for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n const batchOuts = tidy(() => {\n const batchStart = batches[batchIndex][0];\n const batchEnd = batches[batchIndex][1];\n const insBatch = sliceArrays(ins, batchStart, batchEnd);\n const feeds = [];\n if (Array.isArray(insBatch)) {\n for (let i = 0; i < insBatch.length; ++i) {\n feeds.push({ key: this.inputs[i], value: insBatch[i] });\n }\n } else {\n feeds.push({ key: this.inputs[0], value: insBatch });\n }\n const feedDict = new FeedDict(feeds);\n return execute(this.outputs, feedDict);\n });\n batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n }\n return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0)));\n });\n }\n /**\n * Generates output predictions for the input samples.\n *\n * Computation is done in batches.\n *\n * Note: the \"step\" mode of predict() is currently not supported.\n * This is because the TensorFlow.js core backend is imperative only.\n *\n * ```js\n * const model = tf.sequential({\n * layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n * });\n * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();\n * ```\n *\n * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n * the model has multiple inputs.\n * @param args A `ModelPredictArgs` object containing optional fields.\n *\n * @return Prediction results as a `tf.Tensor`(s).\n *\n * @exception ValueError In case of mismatch between the provided input data\n * and the model's expectations, or in case a stateful model receives a\n * number of samples that is not a multiple of the batch size.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n predict(x, args = {}) {\n const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n try {\n const batchSize = args.batchSize == null ? 32 : args.batchSize;\n checkBatchSize(batchSize);\n return this.predictLoop(xsRank2OrHigher, batchSize);\n } finally {\n disposeNewTensors(xsRank2OrHigher, x);\n }\n }\n /**\n * Returns predictions for a single batch of samples.\n *\n * ```js\n * const model = tf.sequential({\n * layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n * });\n * model.predictOnBatch(tf.ones([8, 10])).print();\n * ```\n * @param x: Input samples, as a Tensor (for models with exactly one\n * input) or an array of Tensors (for models with more than one input).\n * @return Tensor(s) of predictions\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n predictOnBatch(x) {\n checkInputData(x, this.inputNames, this.feedInputShapes, true);\n const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n return this.predictLoop(x, batchSize);\n }\n standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n if (this.optimizer_ == null) {\n throw new RuntimeError(\"You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).\");\n }\n const outputShapes = [];\n for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n const outputShape = this.feedOutputShapes[i];\n const lossFn = this.feedLossFns[i];\n if (lossFn === sparseCategoricalCrossentropy) {\n outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n } else {\n outputShapes.push(outputShape);\n }\n }\n x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, \"input\");\n y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, \"target\");\n checkArrayLengths(x, y, null);\n checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n if (this.stateful && batchSize != null && batchSize > 0) {\n if (x[0].shape[0] % batchSize !== 0) {\n throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n }\n }\n return [x, y];\n }\n async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n if (sampleWeight != null) {\n throw new Error(\"sample weight is not supported yet.\");\n }\n let standardSampleWeights = null;\n if (classWeight != null) {\n const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n standardSampleWeights = [];\n for (let i = 0; i < classWeights.length; ++i) {\n standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n }\n }\n return [standardXs, standardYs, standardSampleWeights];\n }\n /**\n * Loop over some test data in batches.\n * @param f A Function returning a list of tensors.\n * @param ins Array of tensors to be fed to `f`.\n * @param batchSize Integer batch size or `null` / `undefined`.\n * @param verbose verbosity mode.\n * @param steps Total number of steps (batches of samples) before\n * declaring test finished. Ignored with the default value of `null` /\n * `undefined`.\n * @returns Array of Scalars.\n */\n testLoop(f, ins, batchSize, verbose = 0, steps) {\n return tidy(() => {\n const numSamples = this.checkNumSamples(ins, batchSize, steps, \"steps\");\n const outs = [];\n if (verbose > 0) {\n throw new NotImplementedError(\"Verbose mode is not implemented yet.\");\n }\n if (steps != null) {\n throw new NotImplementedError(\"steps mode in testLoop() is not implemented yet\");\n } else {\n const batches = makeBatches(numSamples, batchSize);\n const indexArray = tensor1d(range2(0, numSamples));\n for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n const batchStart = batches[batchIndex][0];\n const batchEnd = batches[batchIndex][1];\n const batchIds = sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n const insBatch = sliceArraysByIndices(ins, batchIds);\n const batchOuts = f(insBatch);\n if (batchIndex === 0) {\n for (let i = 0; i < batchOuts.length; ++i) {\n outs.push(scalar(0));\n }\n }\n for (let i = 0; i < batchOuts.length; ++i) {\n const batchOut = batchOuts[i];\n outs[i] = add2(outs[i], mul(batchEnd - batchStart, batchOut));\n }\n }\n for (let i = 0; i < outs.length; ++i) {\n outs[i] = div(outs[i], numSamples);\n }\n }\n return outs;\n });\n }\n getDedupedMetricsNames() {\n const outLabels = this.metricsNames;\n const dedupedOutLabels = [];\n for (let i = 0; i < outLabels.length; ++i) {\n const label = outLabels[i];\n let newLabel = label;\n if (count(outLabels, label) > 1) {\n const dupIndex = count(outLabels.slice(0, i), label);\n newLabel += `_${dupIndex}`;\n }\n dedupedOutLabels.push(newLabel);\n }\n return dedupedOutLabels;\n }\n /**\n * Creates a function that performs the following actions:\n *\n * 1. computes the losses\n * 2. sums them to get the total loss\n * 3. call the optimizer computes the gradients of the LayersModel's\n * trainable weights w.r.t. the total loss and update the variables\n * 4. calculates the metrics\n * 5. returns the values of the losses and metrics.\n */\n makeTrainFunction() {\n return (data) => {\n const lossValues = [];\n const inputs = data.slice(0, this.inputs.length);\n const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n const metricsValues = [];\n const totalLossFunction = () => {\n const feeds = [];\n for (let i = 0; i < this.inputs.length; ++i) {\n feeds.push({ key: this.inputs[i], value: inputs[i] });\n }\n const feedDict = new FeedDict(feeds);\n const outputs = execute(this.outputs, feedDict, { \"training\": true });\n let totalLoss;\n for (let i = 0; i < this.lossFunctions.length; ++i) {\n const lossFunction = this.lossFunctions[i];\n let loss = lossFunction(targets[i], outputs[i]);\n if (sampleWeights[i] != null) {\n loss = computeWeightedLoss2(loss, sampleWeights[i]);\n }\n const meanLoss = mean(loss);\n lossValues.push(meanLoss);\n if (i === 0) {\n totalLoss = loss;\n } else {\n totalLoss = add2(totalLoss, loss);\n }\n }\n for (let i = 0; i < this.metricsTensors.length; ++i) {\n let weightedMetric;\n if (this.outputs.length > 1 && i < this.outputs.length) {\n weightedMetric = lossValues[i];\n } else {\n const metric = this.metricsTensors[i][0];\n const outputIndex = this.metricsTensors[i][1];\n weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));\n }\n keep(weightedMetric);\n metricsValues.push(weightedMetric);\n }\n totalLoss = mean(totalLoss);\n this.calculateLosses().forEach((regularizerLoss) => {\n totalLoss = add2(totalLoss, regularizerLoss);\n });\n return totalLoss;\n };\n const variables = this.collectedTrainableWeights.map((param) => param.read());\n const returnCost = true;\n const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n return [totalLossValue].concat(metricsValues);\n };\n }\n /**\n * Create a function which, when invoked with an array of `tf.Tensor`s as a\n * batch of inputs, returns the prespecified loss and metrics of the model\n * under the batch of input data.\n */\n makeTestFunction() {\n this.testFunction = (data) => {\n return tidy(() => {\n const valOutputs = [];\n let totalLoss;\n const inputs = data.slice(0, this.inputs.length);\n const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n const feeds = [];\n for (let i = 0; i < this.inputs.length; ++i) {\n feeds.push({ key: this.inputs[i], value: inputs[i] });\n }\n const feedDict = new FeedDict(feeds);\n const outputs = execute(this.outputs, feedDict);\n for (let i = 0; i < this.lossFunctions.length; ++i) {\n const lossFunction = this.lossFunctions[i];\n const loss = mean(lossFunction(targets[i], outputs[i]));\n if (i === 0) {\n totalLoss = loss;\n } else {\n totalLoss = add2(totalLoss, loss);\n }\n valOutputs.push(totalLoss);\n }\n for (let i = 0; i < this.metricsTensors.length; ++i) {\n const metric = this.metricsTensors[i][0];\n const outputIndex = this.metricsTensors[i][1];\n const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));\n valOutputs.push(meanMetric);\n }\n return valOutputs;\n });\n };\n }\n /**\n * Trains the model for a fixed number of epochs (iterations on a\n * dataset).\n *\n * ```js\n * const model = tf.sequential({\n * layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n * });\n * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n * for (let i = 1; i < 5 ; ++i) {\n * const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n * batchSize: 4,\n * epochs: 3\n * });\n * console.log(\"Loss after Epoch \" + i + \" : \" + h.history.loss[0]);\n * }\n * ```\n *\n * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n * model has multiple inputs. If all inputs in the model are named, you\n * can also pass a dictionary mapping input names to `tf.Tensor`s.\n * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n * the model has multiple outputs. If all outputs in the model are named,\n * you can also pass a dictionary mapping output names to `tf.Tensor`s.\n * @param args A `ModelFitArgs`, containing optional fields.\n *\n * @return A `History` instance. Its `history` attribute contains all\n * information collected during training.\n *\n * @exception ValueError In case of mismatch between the provided input\n * data and what the model expects.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n async fit(x, y, args = {}) {\n if (this.isTraining) {\n throw new Error(\"Cannot start training because another fit() call is ongoing.\");\n }\n this.isTraining = true;\n let inputs;\n let targets;\n let originalInputs;\n let originalTargets;\n let inputValX;\n let inputValY;\n let valX;\n let valY;\n let sampleWeights;\n try {\n const batchSize = args.batchSize == null ? 32 : args.batchSize;\n checkBatchSize(batchSize);\n const checkBatchAxis = false;\n const standardizedOuts = await this.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n inputs = standardizedOuts[0];\n targets = standardizedOuts[1];\n sampleWeights = standardizedOuts[2];\n let doValidation = false;\n let valIns;\n if (args.validationData != null && args.validationData.length > 0) {\n doValidation = true;\n if (args.validationData.length === 2) {\n inputValX = args.validationData[0];\n inputValY = args.validationData[1];\n } else if (args.validationData.length === 3) {\n throw new NotImplementedError(\"validationData including sample weights is not supported yet.\");\n } else {\n throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);\n }\n const checkBatchAxis2 = true;\n const valStandardized = await this.standardizeUserData(\n inputValX,\n inputValY,\n null,\n /** Unused sample weights. */\n null,\n /** Unused class weights. */\n checkBatchAxis2,\n batchSize\n );\n valX = valStandardized[0];\n valY = valStandardized[1];\n valIns = valX.concat(valY);\n } else if (args.validationSplit != null && args.validationSplit > 0 && args.validationSplit < 1) {\n doValidation = true;\n const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n const originalBatchSize = inputs[0].shape[0];\n valX = sliceArrays(inputs, splitAt, originalBatchSize);\n originalInputs = inputs;\n inputs = sliceArrays(inputs, 0, splitAt);\n valY = sliceArrays(targets, splitAt, originalBatchSize);\n originalTargets = targets;\n targets = sliceArrays(targets, 0, splitAt);\n valIns = valX.concat(valY);\n } else if (args.validationSteps != null) {\n doValidation = true;\n }\n const ins = inputs.concat(targets).concat(sampleWeights);\n this.checkTrainableWeightsConsistency();\n const trainFunction = this.makeTrainFunction();\n const outLabels = this.getDedupedMetricsNames();\n let valFunction;\n let callbackMetrics;\n if (doValidation) {\n this.makeTestFunction();\n valFunction = this.testFunction;\n callbackMetrics = outLabels.slice().concat(outLabels.map((n) => \"val_\" + n));\n } else {\n valFunction = null;\n valIns = [];\n callbackMetrics = outLabels.slice();\n }\n const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);\n const out = await this.fitLoop(trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks2, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n return out;\n } finally {\n this.isTraining = false;\n disposeNewTensors(inputs, x);\n disposeNewTensors(targets, y);\n disposeNewTensors(originalInputs, x);\n disposeNewTensors(originalTargets, y);\n disposeNewTensors(valX, inputValX);\n disposeNewTensors(valY, inputValY);\n if (sampleWeights != null) {\n dispose(sampleWeights);\n }\n }\n }\n /**\n * Abstract fit function for `f(ins)`.\n * @param f A Function returning a list of tensors. For training, this\n * function is expected to perform the updates to the variables.\n * @param ins List of tensors to be fed to `f`.\n * @param outLabels List of strings, display names of the outputs of `f`.\n * @param batchSize Integer batch size or `== null` if unknown. Default : 32.\n * @param epochs Number of times to iterate over the data. Default : 1.\n * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.\n * @param callbacks List of callbacks to be called during training.\n * @param valF Function to call for validation.\n * @param valIns List of tensors to be fed to `valF`.\n * @param shuffle Whether to shuffle the data at the beginning of every\n * epoch. Default : true.\n * @param callbackMetrics List of strings, the display names of the metrics\n * passed to the callbacks. They should be the concatenation of the\n * display names of the outputs of `f` and the list of display names\n * of the outputs of `valF`.\n * @param initialEpoch Epoch at which to start training (useful for\n * resuming a previous training run). Default : 0.\n * @param stepsPerEpoch Total number of steps (batches on samples) before\n * declaring one epoch finished and starting the next epoch. Ignored with\n * the default value of `undefined` or `null`.\n * @param validationSteps Number of steps to run validation for (only if\n * doing validation from data tensors). Not applicable for tfjs-layers.\n * @returns A `History` object.\n */\n async fitLoop(f, ins, outLabels, batchSize, epochs, verbose, callbacks2, valF, valIns, shuffle2, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n if (batchSize == null) {\n batchSize = 32;\n }\n if (epochs == null) {\n epochs = 1;\n }\n if (shuffle2 == null) {\n shuffle2 = true;\n }\n if (initialEpoch == null) {\n initialEpoch = 0;\n }\n let doValidation = false;\n if (valF != null && valIns != null) {\n doValidation = true;\n }\n if (validationSteps != null) {\n doValidation = true;\n if (stepsPerEpoch == null) {\n throw new ValueError(\"Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.\");\n }\n }\n const numTrainSamples = this.checkNumSamples(ins, batchSize, stepsPerEpoch, \"steps_per_epoch\");\n let indexArray;\n if (numTrainSamples != null) {\n indexArray = range2(0, numTrainSamples);\n }\n if (verbose == null) {\n verbose = 1;\n }\n const { callbackList, history } = configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n callbackList.setModel(this);\n this.history = history;\n await callbackList.onTrainBegin();\n this.stopTraining_ = false;\n for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n await callbackList.onEpochBegin(epoch);\n const epochLogs = {};\n if (stepsPerEpoch != null) {\n throw new NotImplementedError(\"stepsPerEpoch mode is not implemented yet.\");\n } else {\n if (shuffle2 === \"batch\") {\n throw new NotImplementedError(\"batch shuffling is not implemneted yet\");\n } else if (shuffle2) {\n util_exports.shuffle(indexArray);\n }\n const epochIndexArray1D = tensor1d(indexArray);\n const batches = makeBatches(numTrainSamples, batchSize);\n for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n const batchLogs = {};\n await callbackList.onBatchBegin(batchIndex, batchLogs);\n tidy(() => {\n const batchStart = batches[batchIndex][0];\n const batchEnd = batches[batchIndex][1];\n const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n batchLogs[\"batch\"] = batchIndex;\n batchLogs[\"size\"] = batchEnd - batchStart;\n const insBatch = sliceArraysByIndices(ins, batchIds);\n const outs = f(insBatch);\n for (let i = 0; i < outLabels.length; ++i) {\n const label = outLabels[i];\n const out = outs[i];\n batchLogs[label] = out;\n keep(out);\n }\n if (batchIndex === batches.length - 1) {\n if (doValidation) {\n const valOuts = this.testLoop(valF, valIns, batchSize);\n for (let i = 0; i < outLabels.length; ++i) {\n const label = outLabels[i];\n const out = valOuts[i];\n keep(out);\n epochLogs[\"val_\" + label] = out;\n }\n }\n }\n });\n await callbackList.onBatchEnd(batchIndex, batchLogs);\n disposeTensorsInLogs(batchLogs);\n if (this.stopTraining_) {\n break;\n }\n }\n epochIndexArray1D.dispose();\n }\n await callbackList.onEpochEnd(epoch, epochLogs);\n if (this.stopTraining_) {\n break;\n }\n }\n await callbackList.onTrainEnd();\n await this.history.syncData();\n return this.history;\n }\n // TODO(cais): Add code snippet below when it's possible to instantiate\n // actual dataset objects.\n /**\n * Trains the model using a dataset object.\n *\n * @param dataset A dataset object. Its `iterator()` method is expected\n * to generate a dataset iterator object, the `next()` method of which\n * is expected to produce data batches for training. The return value\n * of the `next()` call ought to contain a boolean `done` field and a\n * `value` field. The `value` field is expected to be an array of two\n * `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n * case is for models with exactly one input and one output (e.g.\n * a sequential model). The latter case is for models with multiple\n * inputs and/or multiple outputs.\n * Of the two items in the array, the first is the input feature(s) and\n * the second is the output target(s).\n * @param args A `ModelFitDatasetArgs`, containing optional fields.\n *\n * @return A `History` instance. Its `history` attribute contains all\n * information collected during training.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n async fitDataset(dataset, args) {\n return fitDataset(this, dataset, args);\n }\n /**\n * Runs a single gradient update on a single batch of data.\n *\n * This method differs from `fit()` and `fitDataset()` in the following\n * regards:\n * - It operates on exactly one batch of data.\n * - It returns only the loss and metric values, instead of\n * returning the batch-by-batch loss and metric values.\n * - It doesn't support fine-grained options such as verbosity and\n * callbacks.\n *\n * @param x Input data. It could be one of the following:\n * - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n * multiple inputs).\n * - An Object mapping input names to corresponding `tf.Tensor` (if the\n * model has named inputs).\n * @param y Target data. It could be either a `tf.Tensor` or multiple\n * `tf.Tensor`s. It should be consistent with `x`.\n * @returns Training loss or losses (in case the model has\n * multiple outputs), along with metrics (if any), as numbers.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n async trainOnBatch(x, y) {\n const standardizeOut = await this.standardizeUserData(x, y);\n const inputs = standardizeOut[0];\n const targets = standardizeOut[1];\n const trainFunction = this.makeTrainFunction();\n const losses2 = trainFunction(inputs.concat(targets));\n const lossValues = [];\n for (const loss of losses2) {\n const v = await loss.data();\n lossValues.push(v[0]);\n }\n dispose(losses2);\n disposeNewTensors(standardizeOut[0], x);\n disposeNewTensors(standardizeOut[1], y);\n return singletonOrArray(lossValues);\n }\n /**\n * Extract weight values of the model.\n *\n * @param config: An instance of `io.SaveConfig`, which specifies\n * model-saving options such as whether only trainable weights are to be\n * saved.\n * @returns A `NamedTensorMap` mapping original weight names (i.e.,\n * non-uniqueified weight names) to their values.\n */\n getNamedWeights(config) {\n const namedWeights = [];\n const trainableOnly = config != null && config.trainableOnly;\n const weights = trainableOnly ? this.trainableWeights : this.weights;\n const weightValues = this.getWeights(trainableOnly);\n for (let i = 0; i < weights.length; ++i) {\n if (trainableOnly && !weights[i].trainable) {\n continue;\n }\n namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n }\n return namedWeights;\n }\n /**\n * Setter used for force stopping of LayersModel.fit() (i.e., training).\n *\n * Example:\n *\n * ```js\n * const input = tf.input({shape: [10]});\n * const output = tf.layers.dense({units: 1}).apply(input);\n * const model = tf.model({inputs: [input], outputs: [output]});\n * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n * const xs = tf.ones([8, 10]);\n * const ys = tf.zeros([8, 1]);\n *\n * const history = await model.fit(xs, ys, {\n * epochs: 10,\n * callbacks: {\n * onEpochEnd: async (epoch, logs) => {\n * if (epoch === 2) {\n * model.stopTraining = true;\n * }\n * }\n * }\n * });\n *\n * // There should be only 3 values in the loss array, instead of 10\n * values,\n * // due to the stopping after 3 epochs.\n * console.log(history.history.loss);\n * ```\n */\n set stopTraining(stop) {\n this.stopTraining_ = stop;\n }\n get stopTraining() {\n return this.stopTraining_;\n }\n get optimizer() {\n return this.optimizer_;\n }\n set optimizer(optimizer) {\n if (this.optimizer_ !== optimizer) {\n this.optimizer_ = optimizer;\n this.isOptimizerOwned = false;\n }\n }\n dispose() {\n const result = super.dispose();\n if (result.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {\n const numTensorsBeforeOptmizerDisposal = memory().numTensors;\n this.optimizer_.dispose();\n result.numDisposedVariables += numTensorsBeforeOptmizerDisposal - memory().numTensors;\n }\n return result;\n }\n getLossIdentifiers() {\n let lossNames;\n if (typeof this.loss === \"string\") {\n lossNames = toSnakeCase(this.loss);\n } else if (Array.isArray(this.loss)) {\n for (const loss of this.loss) {\n if (typeof loss !== \"string\") {\n throw new Error(\"Serialization of non-string loss is not supported.\");\n }\n }\n lossNames = this.loss.map((name) => toSnakeCase(name));\n } else {\n const outputNames = Object.keys(this.loss);\n lossNames = {};\n const losses2 = this.loss;\n for (const outputName of outputNames) {\n if (typeof losses2[outputName] === \"string\") {\n lossNames[outputName] = toSnakeCase(losses2[outputName]);\n } else {\n throw new Error(\"Serialization of non-string loss is not supported.\");\n }\n }\n }\n return lossNames;\n }\n getMetricIdentifiers() {\n if (typeof this.metrics === \"string\" || typeof this.metrics === \"function\") {\n return [toSnakeCase(getLossOrMetricName(this.metrics))];\n } else if (Array.isArray(this.metrics)) {\n return this.metrics.map((metric) => toSnakeCase(getLossOrMetricName(metric)));\n } else {\n const metricsIdentifiers = {};\n for (const key in this.metrics) {\n metricsIdentifiers[key] = toSnakeCase(getLossOrMetricName(this.metrics[key]));\n }\n return metricsIdentifiers;\n }\n }\n getTrainingConfig() {\n return {\n loss: this.getLossIdentifiers(),\n metrics: this.getMetricIdentifiers(),\n optimizer_config: {\n class_name: this.optimizer.getClassName(),\n config: this.optimizer.getConfig()\n }\n };\n }\n loadTrainingConfig(trainingConfig) {\n if (trainingConfig.weighted_metrics != null) {\n throw new Error(\"Loading weight_metrics is not supported yet.\");\n }\n if (trainingConfig.loss_weights != null) {\n throw new Error(\"Loading loss_weights is not supported yet.\");\n }\n if (trainingConfig.sample_weight_mode != null) {\n throw new Error(\"Loading sample_weight_mode is not supported yet.\");\n }\n const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n const optimizer = deserialize(tsConfig);\n let loss;\n if (typeof trainingConfig.loss === \"string\") {\n loss = toCamelCase(trainingConfig.loss);\n } else if (Array.isArray(trainingConfig.loss)) {\n loss = trainingConfig.loss.map((lossEntry) => toCamelCase(lossEntry));\n } else if (trainingConfig.loss != null) {\n loss = {};\n for (const key in trainingConfig.loss) {\n loss[key] = toCamelCase(trainingConfig.loss[key]);\n }\n }\n let metrics;\n if (Array.isArray(trainingConfig.metrics)) {\n metrics = trainingConfig.metrics.map((metric) => toCamelCase(metric));\n } else if (trainingConfig.metrics != null) {\n metrics = {};\n for (const key in trainingConfig.metrics) {\n metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n }\n }\n this.compile({ loss, metrics, optimizer });\n }\n /**\n * Save the configuration and/or weights of the LayersModel.\n *\n * An `IOHandler` is an object that has a `save` method of the proper\n * signature defined. The `save` method manages the storing or\n * transmission of serialized data (\"artifacts\") that represent the\n * model's topology and weights onto or via a specific medium, such as\n * file downloads, local storage, IndexedDB in the web browser and HTTP\n * requests to a server. TensorFlow.js provides `IOHandler`\n * implementations for a number of frequently used saving mediums, such as\n * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n * for more details.\n *\n * This method also allows you to refer to certain types of `IOHandler`s\n * as URL-like string shortcuts, such as 'localstorage://' and\n * 'indexeddb://'.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 2. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights as two files\n * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from\n * browser.\n *\n * ```js\n * const model = tf.sequential(\n * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * const saveResults = await model.save('downloads://my-model-1');\n * ```\n *\n * Example 4. Send `model`'s topology and weights to an HTTP server.\n * See the documentation of `tf.io.http` for more details\n * including specifying request parameters and implementation of the\n * server.\n *\n * ```js\n * const model = tf.sequential(\n * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * const saveResults = await model.save('http://my-server/model/upload');\n * ```\n *\n * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n * scheme-based string shortcut for `IOHandler`.\n * @param config Options for saving the model.\n * @returns A `Promise` of `SaveResult`, which summarizes the result of\n * the saving, such as byte sizes of the saved artifacts for the model's\n * topology and weight values.\n *\n * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n */\n async save(handlerOrURL, config) {\n if (typeof handlerOrURL === \"string\") {\n const handlers = io_exports.getSaveHandlers(handlerOrURL);\n if (handlers.length === 0) {\n throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n } else if (handlers.length > 1) {\n throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);\n }\n handlerOrURL = handlers[0];\n }\n if (handlerOrURL.save == null) {\n throw new ValueError(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");\n }\n const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config));\n const returnString = false;\n const unusedArg = null;\n const modelConfig = this.toJSON(unusedArg, returnString);\n const modelArtifacts = {\n modelTopology: modelConfig,\n format: LAYERS_MODEL_FORMAT_NAME,\n generatedBy: `TensorFlow.js tfjs-layers v${version2}`,\n convertedBy: null\n };\n const includeOptimizer = config == null ? false : config.includeOptimizer;\n if (includeOptimizer && this.optimizer != null) {\n modelArtifacts.trainingConfig = this.getTrainingConfig();\n const weightType = \"optimizer\";\n const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io_exports.encodeWeights(await this.optimizer.getWeights(), weightType);\n weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n weightDataAndSpecs.data = io_exports.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n }\n if (this.userDefinedMetadata != null) {\n const checkSize = true;\n checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n }\n modelArtifacts.weightData = weightDataAndSpecs.data;\n modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n return handlerOrURL.save(modelArtifacts);\n }\n /**\n * Set user-defined metadata.\n *\n * The set metadata will be serialized together with the topology\n * and weights of the model during `save()` calls.\n *\n * @param setUserDefinedMetadata\n */\n setUserDefinedMetadata(userDefinedMetadata) {\n checkUserDefinedMetadata(userDefinedMetadata, this.name);\n this.userDefinedMetadata = userDefinedMetadata;\n }\n /**\n * Get user-defined metadata.\n *\n * The metadata is supplied via one of the two routes:\n * 1. By calling `setUserDefinedMetadata()`.\n * 2. Loaded during model loading (if the model is constructed\n * via `tf.loadLayersModel()`.)\n *\n * If no user-defined metadata is available from either of the\n * two routes, this function will return `undefined`.\n */\n getUserDefinedMetadata() {\n return this.userDefinedMetadata;\n }\n};\nLayersModel.className = \"Model\";\nserialization_exports.registerClass(LayersModel);\nvar Functional = class extends LayersModel {\n};\nFunctional.className = \"Functional\";\nserialization_exports.registerClass(Functional);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/models.js\nasync function modelFromJSON(modelAndWeightsConfig, customObjects) {\n if (!(\"modelTopology\" in modelAndWeightsConfig)) {\n modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n }\n modelAndWeightsConfig = modelAndWeightsConfig;\n let modelTopology = modelAndWeightsConfig.modelTopology;\n if (modelTopology[\"model_config\"] != null) {\n modelTopology = modelTopology[\"model_config\"];\n }\n const tsConfig = convertPythonicToTs(modelTopology);\n const model2 = deserialize(tsConfig, customObjects);\n if (modelAndWeightsConfig.weightsManifest != null) {\n const weightValues = await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model2.weights.map((weight) => weight.originalName));\n const uniqueWeightValues = {};\n for (const weight of model2.weights) {\n uniqueWeightValues[weight.originalName] = weightValues[weight.originalName];\n }\n model2.loadWeights(uniqueWeightValues);\n dispose(weightValues);\n }\n return model2;\n}\nasync function loadLayersModel(pathOrIOHandler, options) {\n if (options == null) {\n options = {};\n }\n if (typeof pathOrIOHandler === \"string\") {\n const handlers = io_exports.getLoadHandlers(pathOrIOHandler, options);\n if (handlers.length === 0) {\n handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler, options));\n } else if (handlers.length > 1) {\n throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);\n }\n pathOrIOHandler = handlers[0];\n }\n return loadLayersModelFromIOHandler(pathOrIOHandler, void 0, options);\n}\nasync function loadLayersModelFromIOHandler(handler, customObjects, options) {\n if (options == null) {\n options = {};\n }\n if (handler.load == null) {\n throw new ValueError(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");\n }\n const artifacts = await handler.load();\n let modelTopology = artifacts.modelTopology;\n if (modelTopology[\"model_config\"] != null) {\n modelTopology = modelTopology[\"model_config\"];\n }\n const strict = options.strict == null ? true : options.strict;\n const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n const model2 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n const trainingConfig = artifacts.trainingConfig;\n if (trainingConfig != null) {\n model2.loadTrainingConfig(trainingConfig);\n }\n if (artifacts.userDefinedMetadata != null) {\n model2.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n }\n if (artifacts.weightData != null) {\n if (artifacts.weightSpecs == null) {\n throw new ValueError(\"LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.\");\n }\n const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n model2.loadWeights(modelWeights, strict);\n if (model2.optimizer != null && optimizerWeights.length > 0) {\n await model2.optimizer.setWeights(optimizerWeights);\n }\n dispose(modelWeights);\n dispose(optimizerWeights.map((w) => w.tensor));\n }\n return model2;\n}\nfunction decodeModelAndOptimizerWeights(weightData, specs) {\n const name2Tensor = io_exports.decodeWeights(weightData, specs);\n const modelWeights = {};\n const optimizerWeights = [];\n specs.forEach((spec) => {\n if (spec.group === \"optimizer\") {\n optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n } else {\n modelWeights[spec.name] = name2Tensor[spec.name];\n }\n });\n return { modelWeights, optimizerWeights };\n}\nvar Sequential = class _Sequential extends LayersModel {\n constructor(args) {\n super({ inputs: [], outputs: [] });\n args = args || {};\n this.trainable = true;\n this.built = false;\n this.name = args.name != null ? args.name : getUid(\"sequential_\");\n if (args.layers != null) {\n for (const layer of args.layers) {\n this.add(layer);\n }\n }\n }\n // Helper function to Sequential.add Throws if the new output shape will be\n // invalid.\n checkShape(layer) {\n const shape = layer.inboundNodes[0].outputTensors[0].shape;\n if (shape.some((x) => x < 0)) {\n throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`);\n }\n }\n /**\n * Adds a layer instance on top of the layer stack.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({units: 8, inputShape: [1]}));\n * model.add(tf.layers.dense({units: 4, activation: 'relu6'}));\n * model.add(tf.layers.dense({units: 1, activation: 'relu6'}));\n * // Note that the untrained model is random at this point.\n * model.predict(tf.randomNormal([10, 1])).print();\n * ```\n * @param layer Layer instance.\n *\n * @exception ValueError In case the `layer` argument does not know its\n * input shape.\n * @exception ValueError In case the `layer` argument has multiple output\n * tensors, or is already connected somewhere else (forbidden in\n * `Sequential` models).\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n add(layer) {\n const isLayerModelInstance = layer instanceof _Sequential || layer instanceof LayersModel;\n let modelLayer;\n if (isLayerModelInstance) {\n modelLayer = layer;\n if (modelLayer.outputs.length !== 1) {\n throw new ValueError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n }\n if (modelLayer.inputs.length !== 1) {\n throw new ValueError(\"All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.\");\n }\n }\n if (this.outputs.length === 0) {\n if (layer.inboundNodes.length === 0) {\n if (layer.batchInputShape == null) {\n throw new ValueError(\"The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.\");\n }\n const x = Input({\n batchShape: layer.batchInputShape,\n dtype: layer.dtype,\n name: layer.name + \"_input\"\n });\n layer.apply(x);\n }\n if (isLayerModelInstance) {\n this.outputs = modelLayer.outputs;\n this.inputs = modelLayer.inputs;\n } else {\n if (layer.inboundNodes.length !== 1) {\n throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);\n }\n if (layer.inboundNodes[0].outputTensors.length !== 1) {\n throw new ValueError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n }\n this.checkShape(layer);\n this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n this.inputs = getSourceInputs(this.outputs[0]);\n }\n this.inboundNodes = [];\n new Node({\n outboundLayer: this,\n inboundLayers: [],\n nodeIndices: [],\n tensorIndices: [],\n inputTensors: this.inputs,\n outputTensors: this.outputs,\n // no model-level masking for now\n inputMasks: pyListRepeat(null, this.inputs.length),\n outputMasks: [null],\n inputShapes: this.inputs.map((x) => x.shape),\n outputShapes: this.outputs[0].shape\n });\n } else {\n const outputTensor = layer.apply(this.outputs[0]);\n if (Array.isArray(outputTensor)) {\n throw new TypeError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n }\n this.checkShape(layer);\n this.outputs = [outputTensor];\n this.inboundNodes[0].outputTensors = this.outputs;\n this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n }\n this.layers.push(layer);\n this.built = false;\n }\n /**\n * Removes the last layer in the model.\n *\n * @exception TypeError if there are no layers in the model.\n */\n pop() {\n if (this.layers.length === 0) {\n throw new TypeError(\"There are no layers in the model.\");\n }\n this.layers.pop();\n if (this.layers.length === 0) {\n this.outputs = [];\n this.inboundNodes = [];\n this.outboundNodes = [];\n } else {\n const lastLayerIndex = this.layers.length - 1;\n this.layers[lastLayerIndex].outboundNodes = [];\n this.outputs = [this.layers[lastLayerIndex].output];\n this.inboundNodes[0].outputTensors = this.outputs;\n this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n }\n }\n call(inputs, kwargs) {\n if (this.model == null) {\n this.build();\n }\n return this.model.call(inputs, kwargs);\n }\n build(inputShape) {\n getExactlyOneShape(inputShape);\n if (this.inputs.length === 0 || this.outputs.length === 0) {\n throw new TypeError(\"Sequential model cannot be built: model is empty. Add some layers first.\");\n }\n this.model = new LayersModel({\n inputs: this.inputs,\n outputs: this.outputs[0],\n name: this.name + \"_model\"\n });\n this.model.trainable = this.trainable;\n this.supportsMasking = this.model.supportsMasking;\n this.inputLayers = this.model.inputLayers;\n this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n this.outputLayers = this.model.outputLayers;\n this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n this.nodesByDepth = this.model.nodesByDepth;\n this.containerNodes = this.model.containerNodes;\n this.outputNames = this.model.outputNames;\n this.inputNames = this.model.inputNames;\n this.built = true;\n }\n countParams() {\n if (!this.built) {\n this.build();\n }\n return super.countParams();\n }\n /**\n * Print a text summary of the Sequential model's layers.\n *\n * The summary includes\n * - Name and type of all layers that comprise the model.\n * - Output shape(s) of the layers\n * - Number of weight parameters of each layer\n * - The total number of trainable and non-trainable parameters of the\n * model.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n * tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));\n * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n *\n * model.summary();\n * ```\n *\n * @param lineLength Custom line length, in number of characters.\n * @param positions Custom widths of each of the columns, as either\n * fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n * of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n * right-most (i.e., ending) position of a column.\n * @param printFn Custom print function. Can be used to replace the default\n * `console.log`. For example, you can use `x => {}` to mute the printed\n * messages in the console.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n summary(lineLength, positions, printFn = console.log) {\n if (!this.built) {\n this.build();\n }\n super.summary(lineLength, positions, printFn);\n }\n /**\n * Sets the weights of the model.\n *\n * @param weights Should be a list of Tensors with shapes and types matching\n * the output of `model.getWeights()`.\n */\n setWeights(weights) {\n if (this.model == null) {\n this.build();\n }\n this.model.setWeights(weights);\n }\n /**\n * Returns the loss value & metrics values for the model in test mode.\n *\n * Loss and metrics are specified during `compile()`, which needs to happen\n * before calls to `evaluate()`.\n *\n * Computation is done in batches.\n *\n * ```js\n * const model = tf.sequential({\n * layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n * });\n * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {\n * batchSize: 4,\n * });\n * result.print();\n * ```\n *\n * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n * model has multiple inputs.\n * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n * model has multiple outputs.\n * @param args A `ModelEvaluateConfig`, containing optional fields.\n *\n * @return `Scalar` test loss (if the model has a single output and no\n * metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n * and/or metrics). The attribute `model.metricsNames`\n * will give you the display labels for the scalar outputs.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n evaluate(x, y, args = {}) {\n if (!this.built) {\n throw new RuntimeError(\"The model needs to be compiled before being used.\");\n }\n return this.model.evaluate(x, y, args);\n }\n // TODO(cais): Add code snippet below once real dataset objects are\n // available.\n /**\n * Evaluate model using a dataset object.\n *\n * Note: Unlike `evaluate()`, this method is asynchronous (`async`).\n *\n * @param dataset A dataset object. Its `iterator()` method is expected\n * to generate a dataset iterator object, the `next()` method of which\n * is expected to produce data batches for evaluation. The return value\n * of the `next()` call ought to contain a boolean `done` field and a\n * `value` field. The `value` field is expected to be an array of two\n * `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n * case is for models with exactly one input and one output (e.g.\n * a sequential model). The latter case is for models with multiple\n * inputs and/or multiple outputs. Of the two items in the array, the\n * first is the input feature(s) and the second is the output target(s).\n * @param args A configuration object for the dataset-based evaluation.\n * @returns Loss and metric values as an Array of `Scalar` objects.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n async evaluateDataset(dataset, args) {\n if (!this.built) {\n throw new RuntimeError(\"The model needs to be compiled before being used.\");\n }\n return this.model.evaluateDataset(dataset, args);\n }\n /**\n * Generates output predictions for the input samples.\n *\n * Computation is done in batches.\n *\n * Note: the \"step\" mode of predict() is currently not supported.\n * This is because the TensorFlow.js core backend is imperative only.\n *\n * ```js\n * const model = tf.sequential({\n * layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n * });\n * model.predict(tf.ones([2, 10])).print();\n * ```\n *\n * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n * the model has multiple inputs.\n * @param conifg A `ModelPredictConfig` object containing optional fields.\n *\n * @return `tf.Tensor`(s) of predictions.\n *\n * @exception ValueError In case of mismatch between the provided input data\n * and the model's expectations, or in case a stateful model receives a\n * number of samples that is not a multiple of the batch size.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n predict(x, args = {}) {\n if (this.model == null) {\n this.build();\n }\n return this.model.predict(x, args);\n }\n /**\n * Returns predictions for a single batch of samples.\n *\n * @param x: Input samples, as a Tensor, or list of Tensors (if the model\n * has multiple inputs).\n * @return Tensor(s) of predictions\n */\n predictOnBatch(x) {\n if (this.model == null) {\n this.build();\n }\n return this.model.predictOnBatch(x);\n }\n /**\n * See `LayersModel.compile`.\n *\n * @param args\n */\n compile(args) {\n this.build();\n this.model.compile(args);\n this.optimizer_ = this.model.optimizer;\n this.isOptimizerOwned = this.model.isOptimizerOwned;\n this.loss = this.model.loss;\n this.metrics = this.model.metrics;\n this.metricsTensors = this.model.metricsTensors;\n this.metricsNames = this.model.metricsNames;\n }\n get optimizer() {\n return this.model == null ? void 0 : this.model.optimizer;\n }\n set optimizer(optimizer) {\n this.model.optimizer = optimizer;\n }\n /**\n * Trains the model for a fixed number of epochs (iterations on a dataset).\n *\n * ```js\n * const model = tf.sequential({\n * layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n * });\n * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n * batchSize: 4,\n * epochs: 3\n * });\n * console.log(history.history.loss[0]);\n * ```\n *\n * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n * model has multiple inputs. If all inputs in the model are named, you can\n * also pass a dictionary mapping input names to `tf.Tensor`s.\n * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n * the model has multiple outputs. If all outputs in the model are named, you\n * can also pass a dictionary mapping output names to `tf.Tensor`s.\n * @param args A `ModelFitConfig`, containing optional fields.\n *\n * @return A `History` instance. Its `history` attribute contains all\n * information collected during training.\n *\n * @exception ValueError In case of mismatch between the provided input data\n * and what the model expects.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n async fit(x, y, args = {}) {\n if (!this.built) {\n throw new RuntimeError(\"The model needs to be compiled before being used.\");\n }\n return this.model.fit(x, y, args);\n }\n /**\n * Trains the model using a dataset object.\n *\n * ```js\n * const xArray = [\n * [1, 1, 1, 1, 1, 1, 1, 1, 1],\n * [1, 1, 1, 1, 1, 1, 1, 1, 1],\n * [1, 1, 1, 1, 1, 1, 1, 1, 1],\n * [1, 1, 1, 1, 1, 1, 1, 1, 1],\n * ];\n * const yArray = [1, 1, 1, 1];\n * // Create a dataset from the JavaScript array.\n * const xDataset = tf.data.array(xArray);\n * const yDataset = tf.data.array(yArray);\n * // Zip combines the `x` and `y` Datasets into a single Dataset, the\n * // iterator of which will return an object containing of two tensors,\n * // corresponding to `x` and `y`. The call to `batch(4)` will bundle\n * // four such samples into a single object, with the same keys now pointing\n * // to tensors that hold 4 examples, organized along the batch dimension.\n * // The call to `shuffle(4)` causes each iteration through the dataset to\n * // happen in a different order. The size of the shuffle window is 4.\n * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})\n * .batch(4)\n * .shuffle(4);\n * const model = tf.sequential({\n * layers: [tf.layers.dense({units: 1, inputShape: [9]})]\n * });\n * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n * const history = await model.fitDataset(xyDataset, {\n * epochs: 4,\n * callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}\n * });\n * ```\n *\n * @param dataset A dataset object. Its `iterator()` method is expected to\n * generate a dataset iterator object, the `next()` method of which is\n * expected to produce data batches for evaluation. The return value of the\n * `next()` call ought to contain a boolean `done` field and a `value`\n * field.\n *\n * The `value` field is expected to be an object of with fields\n * `xs` and `ys`, which point to the feature tensor and the target tensor,\n * respectively. This case is for models with exactly one input and one\n * output (e.g. a sequential model). For example:\n * ```js\n * {value: {xs: xsTensor, ys: ysTensor}, done: false}\n * ```\n *\n * If the model has multiple inputs, the `xs` field of `value` should\n * be an object mapping input names to their respective feature tensors.\n * For example:\n * ```js\n * {\n * value: {\n * xs: {\n * input_1: xsTensor1,\n * input_2: xsTensor2\n * },\n * ys: ysTensor\n * },\n * done: false\n * }\n * ```\n * If the model has multiple outputs, the `ys` field of `value` should\n * be an object mapping output names to their respective target tensors.\n * For example:\n * ```js\n * {\n * value: {\n * xs: xsTensor,\n * ys: {\n * output_1: ysTensor1,\n * output_2: ysTensor2\n * },\n * },\n * done: false\n * }\n * ```\n * @param args A `ModelFitDatasetArgs`, containing optional fields.\n *\n * @return A `History` instance. Its `history` attribute contains all\n * information collected during training.\n *\n * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n */\n async fitDataset(dataset, args) {\n if (!this.built) {\n throw new RuntimeError(\"The model needs to be compiled before being used.\");\n }\n return this.model.fitDataset(dataset, args);\n }\n /**\n * Runs a single gradient update on a single batch of data.\n *\n * This method differs from `fit()` and `fitDataset()` in the following\n * regards:\n * - It operates on exactly one batch of data.\n * - It returns only the loss and metric values, instead of\n * returning the batch-by-batch loss and metric values.\n * - It doesn't support fine-grained options such as verbosity and\n * callbacks.\n *\n * @param x Input data. It could be one of the following:\n * - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n * multiple inputs).\n * - An Object mapping input names to corresponding `tf.Tensor` (if the\n * model has named inputs).\n * @param y Target data. It could be either a `tf.Tensor` or multiple\n * `tf.Tensor`s. It should be consistent with `x`.\n * @returns Training loss or losses (in case the model has\n * multiple outputs), along with metrics (if any), as numbers.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n async trainOnBatch(x, y) {\n return this.model.trainOnBatch(x, y);\n }\n /* See parent class for JsDoc */\n /** @nocollapse */\n static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n let configArray;\n let extraModelConfig = {};\n if (config instanceof Array) {\n if (!(config[0].className != null) || config[0][\"className\"] === \"Merge\") {\n throw new ValueError(\"Legacy serialization format not supported yet.\");\n }\n configArray = config;\n } else {\n util_exports.assert(config[\"layers\"] != null, () => `When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.`);\n configArray = config[\"layers\"];\n delete config[\"layers\"];\n extraModelConfig = config;\n }\n const model2 = new cls(extraModelConfig);\n if (!(model2 instanceof _Sequential)) {\n throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);\n }\n for (const conf of configArray) {\n const customObjects2 = void 0;\n const layer = deserialize(conf, customObjects2, fastWeightInit);\n if (fastWeightInit) {\n layer.setFastWeightInitDuringBuild(true);\n }\n model2.add(layer);\n }\n return model2;\n }\n /**\n * Setter used for force stopping of LayersModel.fit() (i.e., training).\n *\n * Example:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({units: 1, inputShape: [10]}));\n * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n * const xs = tf.ones([8, 10]);\n * const ys = tf.zeros([8, 1]);\n *\n * const history = await model.fit(xs, ys, {\n * epochs: 10,\n * callbacks: {\n * onEpochEnd: async (epoch, logs) => {\n * if (epoch === 2) {\n * model.stopTraining = true;\n * }\n * }\n * }\n * });\n *\n * // There should be only 3 values in the loss array, instead of 10 values,\n * // due to the stopping after 3 epochs.\n * console.log(history.history.loss);\n * ```\n */\n set stopTraining(stop) {\n if (this.model == null) {\n throw new ValueError(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");\n }\n this.model.stopTraining = stop;\n }\n get stopTraining() {\n if (this.model == null) {\n throw new ValueError(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");\n }\n return this.model.stopTraining;\n }\n // TODO(cais): Override get trainableWeights() here\n // tslint:disable-next-line:no-any\n getConfig() {\n const layers = [];\n for (const layer of this.layers) {\n const dict = {};\n dict[\"className\"] = layer.getClassName();\n dict[\"config\"] = layer.getConfig();\n layers.push(dict);\n }\n return { name: this.name, layers };\n }\n};\nSequential.className = \"Sequential\";\nserialization_exports.registerClass(Sequential);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports.js\nfunction model(args) {\n return new LayersModel(args);\n}\nfunction sequential(config) {\n return new Sequential(config);\n}\nfunction input(config) {\n return Input(config);\n}\nfunction registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/activations.js\nvar Activation = class extends serialization_exports.Serializable {\n getConfig() {\n return {};\n }\n};\nvar Elu2 = class extends Activation {\n /**\n * Calculate the activation function.\n *\n * @param x: Input.\n * @param alpha: Scaling factor the negative section.\n * @return Output of the ELU activation.\n */\n apply(x, alpha = 1) {\n return elu2(x, alpha);\n }\n};\nElu2.className = \"elu\";\nserialization_exports.registerClass(Elu2);\nvar Selu2 = class extends Activation {\n apply(x) {\n return selu(x);\n }\n};\nSelu2.className = \"selu\";\nserialization_exports.registerClass(Selu2);\nvar Relu2 = class extends Activation {\n apply(x) {\n return relu(x);\n }\n};\nRelu2.className = \"relu\";\nserialization_exports.registerClass(Relu2);\nvar Relu62 = class extends Activation {\n apply(x) {\n return tidy(() => minimum(6, relu(x)));\n }\n};\nRelu62.className = \"relu6\";\nserialization_exports.registerClass(Relu62);\nvar Linear = class extends Activation {\n apply(x) {\n return x;\n }\n};\nLinear.className = \"linear\";\nserialization_exports.registerClass(Linear);\nvar Sigmoid2 = class extends Activation {\n apply(x) {\n return sigmoid(x);\n }\n};\nSigmoid2.className = \"sigmoid\";\nserialization_exports.registerClass(Sigmoid2);\nvar HardSigmoid = class extends Activation {\n apply(x) {\n return hardSigmoid(x);\n }\n};\nHardSigmoid.className = \"hardSigmoid\";\nserialization_exports.registerClass(HardSigmoid);\nvar Softplus2 = class extends Activation {\n apply(x) {\n return softplus(x);\n }\n};\nSoftplus2.className = \"softplus\";\nserialization_exports.registerClass(Softplus2);\nvar Softsign = class extends Activation {\n apply(x) {\n return softsign(x);\n }\n};\nSoftsign.className = \"softsign\";\nserialization_exports.registerClass(Softsign);\nvar Tanh2 = class extends Activation {\n apply(x) {\n return tanh2(x);\n }\n};\nTanh2.className = \"tanh\";\nserialization_exports.registerClass(Tanh2);\nvar Softmax2 = class extends Activation {\n /**\n * Calculate the activation function.\n *\n * @param x Tensor.\n * @param axis Integer, axis along which the softmax normalization is applied.\n * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n * an error.\n *\n * @returns a Tensor of the same shape as x\n *\n * @throws ValueError: In case `dim(x) < 2`.\n */\n apply(x, axis = -1) {\n return softmax(x, axis);\n }\n};\nSoftmax2.className = \"softmax\";\nserialization_exports.registerClass(Softmax2);\nvar LogSoftmax2 = class extends Activation {\n /**\n * Calculate the activation function of log softmax:\n * log( exp(x_i) / sum(exp(x)) )\n *\n * @param x Tensor.\n * @param axis Integer, axis along which the softmax normalization is applied.\n * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n * an error.\n *\n * @returns a Tensor of the same shape as x\n *\n * @throws ValueError: In case `dim(x) < 2`.\n */\n apply(x, axis = -1) {\n return logSoftmax(x, axis);\n }\n};\nLogSoftmax2.className = \"logSoftmax\";\nserialization_exports.registerClass(LogSoftmax2);\nvar Swish = class extends Activation {\n /**\n * Calculate the activation function.\n *\n * @param x Tensor.\n * @param alpha Scaling factor for the sigmoid function.\n * @returns a Tensor of the same shape as x\n */\n apply(x, alpha = 1) {\n return tidy(() => mul(sigmoid(mul(x, alpha)), x));\n }\n};\nSwish.className = \"swish\";\nserialization_exports.registerClass(Swish);\nvar Mish = class extends Activation {\n /**\n * Calculate the activation function.\n *\n * @param x Tensor.\n * @returns a Tensor of the same shape as x\n */\n apply(x) {\n return tidy(() => mul(x, tanh2(softplus(x))));\n }\n};\nMish.className = \"mish\";\nserialization_exports.registerClass(Mish);\nfunction serializeActivation(activation2) {\n return activation2.getClassName();\n}\nfunction deserializeActivation(config, customObjects = {}) {\n return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"activation\");\n}\nfunction getActivation(identifier) {\n if (identifier == null) {\n const config = {};\n config[\"className\"] = \"linear\";\n config[\"config\"] = {};\n return deserializeActivation(config);\n }\n if (typeof identifier === \"string\") {\n const config = {};\n config[\"className\"] = identifier;\n config[\"config\"] = {};\n return deserializeActivation(config);\n } else if (identifier instanceof Activation) {\n return identifier;\n } else {\n return deserializeActivation(identifier);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/regularizers.js\nfunction assertObjectArgs(args) {\n if (args != null && typeof args !== \"object\") {\n throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`);\n }\n}\nvar Regularizer = class extends serialization_exports.Serializable {\n};\nvar L1L2 = class extends Regularizer {\n constructor(args) {\n super();\n assertObjectArgs(args);\n this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n this.hasL1 = this.l1 !== 0;\n this.hasL2 = this.l2 !== 0;\n }\n /**\n * Porting note: Renamed from __call__.\n * @param x Variable of which to calculate the regularization score.\n */\n apply(x) {\n return tidy(() => {\n let regularization = zeros([1]);\n if (this.hasL1) {\n regularization = add2(regularization, sum2(mul(this.l1, abs(x))));\n }\n if (this.hasL2) {\n regularization = add2(regularization, sum2(mul(this.l2, square2(x))));\n }\n return reshape(regularization, []);\n });\n }\n getConfig() {\n return { \"l1\": this.l1, \"l2\": this.l2 };\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls({ l1: config[\"l1\"], l2: config[\"l2\"] });\n }\n};\nL1L2.className = \"L1L2\";\nserialization_exports.registerClass(L1L2);\nfunction l1(args) {\n assertObjectArgs(args);\n return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nfunction l2(args) {\n assertObjectArgs(args);\n return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\nvar REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n \"l1l2\": \"L1L2\"\n};\nfunction serializeRegularizer(constraint) {\n return serializeKerasObject(constraint);\n}\nfunction deserializeRegularizer(config, customObjects = {}) {\n return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"regularizer\");\n}\nfunction getRegularizer(identifier) {\n if (identifier == null) {\n return null;\n }\n if (typeof identifier === \"string\") {\n const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n const config = { className, config: {} };\n return deserializeRegularizer(config);\n } else if (identifier instanceof Regularizer) {\n return identifier;\n } else {\n return deserializeRegularizer(identifier);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js\nvar ReLU = class extends Layer {\n constructor(args) {\n super(args == null ? {} : args);\n this.supportsMasking = true;\n if (args != null) {\n this.maxValue = args.maxValue;\n }\n }\n call(inputs, kwargs) {\n inputs = getExactlyOneTensor(inputs);\n let output = relu(inputs);\n if (this.maxValue != null) {\n output = clipByValue(output, 0, this.maxValue);\n }\n return output;\n }\n computeOutputShape(inputShape) {\n return inputShape;\n }\n getConfig() {\n const config = { maxValue: this.maxValue };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nReLU.className = \"ReLU\";\nserialization_exports.registerClass(ReLU);\nvar LeakyReLU = class extends Layer {\n constructor(args) {\n super(args == null ? {} : args);\n this.DEFAULT_ALPHA = 0.3;\n if (args == null) {\n args = {};\n }\n this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n }\n call(inputs, kwargs) {\n const x = getExactlyOneTensor(inputs);\n return leakyRelu(x, this.alpha);\n }\n computeOutputShape(inputShape) {\n return inputShape;\n }\n getConfig() {\n const config = { alpha: this.alpha };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nLeakyReLU.className = \"LeakyReLU\";\nserialization_exports.registerClass(LeakyReLU);\nvar PReLU = class extends Layer {\n constructor(args) {\n super(args == null ? {} : args);\n this.DEFAULT_ALPHA_INITIALIZER = \"zeros\";\n if (args == null) {\n args = {};\n }\n this.supportsMasking = true;\n this.alphaInitializer = getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n this.alphaConstraint = getConstraint(args.alphaConstraint);\n if (args.sharedAxes == null) {\n this.sharedAxes = null;\n } else if (Array.isArray(args.sharedAxes)) {\n this.sharedAxes = args.sharedAxes;\n } else if (typeof args.sharedAxes === \"number\") {\n this.sharedAxes = [args.sharedAxes];\n } else {\n throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`);\n }\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const paramShape = inputShape.slice(1);\n if (this.sharedAxes != null) {\n for (const i of this.sharedAxes) {\n paramShape[i - 1] = 1;\n }\n }\n this.alpha = this.addWeight(\"alpha\", paramShape, \"float32\", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n const axes = {};\n if (this.sharedAxes != null) {\n for (let i = 1; i < inputShape.length; ++i) {\n axes[i] = inputShape[i];\n }\n }\n this.inputSpec = [new InputSpec({\n ndim: inputShape.length,\n axes\n })];\n this.built = true;\n }\n call(inputs, kwargs) {\n inputs = getExactlyOneTensor(inputs);\n return prelu(inputs, this.alpha.read());\n }\n getConfig() {\n const config = {\n alphaInitializer: serializeInitializer(this.alphaInitializer),\n alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n alphaConstraint: serializeConstraint(this.alphaConstraint),\n sharedAxes: this.sharedAxes\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nPReLU.className = \"PReLU\";\nserialization_exports.registerClass(PReLU);\nvar ELU = class extends Layer {\n constructor(args) {\n super(args == null ? {} : args);\n this.DEFAULT_ALPHA = 1;\n if (args == null) {\n args = {};\n }\n if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);\n }\n this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n }\n call(inputs, kwargs) {\n const x = getExactlyOneTensor(inputs);\n return elu(x);\n }\n computeOutputShape(inputShape) {\n return inputShape;\n }\n getConfig() {\n const config = { alpha: this.alpha };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nELU.className = \"ELU\";\nserialization_exports.registerClass(ELU);\nvar ThresholdedReLU = class extends Layer {\n constructor(args) {\n super(args == null ? {} : args);\n this.DEFAULT_THETA = 1;\n if (args == null) {\n args = {};\n }\n this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n }\n call(inputs, kwargs) {\n const x = getExactlyOneTensor(inputs);\n return mul(x, cast(greater(x, this.theta), \"float32\"));\n }\n computeOutputShape(inputShape) {\n return inputShape;\n }\n getConfig() {\n const config = { theta: this.theta };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nThresholdedReLU.className = \"ThresholdedReLU\";\nserialization_exports.registerClass(ThresholdedReLU);\nvar Softmax3 = class extends Layer {\n constructor(args) {\n super(args == null ? {} : args);\n this.DEFAULT_AXIS = 1;\n if (args == null) {\n args = {};\n }\n this.softmax = new Softmax2().apply;\n this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n let x = getExactlyOneTensor(inputs);\n const mask = kwargs[\"mask\"];\n if (mask != null) {\n const adder = mul(sub(ones2(x.shape), cast(mask, x.dtype)), scalar(-1e9));\n x = add2(x, adder);\n }\n if (this.axis instanceof Array) {\n if (this.axis.length > 1) {\n return exp(sub(x, logSumExp(x, this.axis, true)));\n } else {\n return this.softmax(x, this.axis[0]);\n }\n }\n return this.softmax(x, this.axis);\n });\n }\n computeOutputShape(inputShape) {\n return inputShape;\n }\n getConfig() {\n const config = { axis: this.axis };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nSoftmax3.className = \"Softmax\";\nserialization_exports.registerClass(Softmax3);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js\nfunction normalizeArray(value, n, name) {\n if (typeof value === \"number\") {\n return pyListRepeat(value, n);\n } else {\n if (value.length !== n) {\n throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);\n }\n for (let i = 0; i < n; ++i) {\n const singleValue = value[i];\n if (!isInteger(singleValue)) {\n throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);\n }\n }\n return value;\n }\n}\nfunction convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n if (inputLength == null) {\n return inputLength;\n }\n const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n let outputLength;\n if (padding === \"same\") {\n outputLength = inputLength;\n } else {\n outputLength = inputLength - dilatedFilterSize + 1;\n }\n return Math.floor((outputLength + stride - 1) / stride);\n}\nfunction deconvLength(dimSize, strideSize, kernelSize, padding) {\n if (dimSize == null) {\n return null;\n }\n if (padding === \"valid\") {\n dimSize = dimSize * strideSize + max2([kernelSize - strideSize, 0]);\n } else if (padding === \"same\") {\n dimSize = dimSize * strideSize;\n } else {\n throw new ValueError(`Unsupport padding mode: ${padding}.`);\n }\n return dimSize;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js\nfunction preprocessConv2DInput(x, dataFormat) {\n return tidy(() => {\n checkDataFormat(dataFormat);\n if (dataFormat === \"channelsFirst\") {\n return transpose(x, [0, 2, 3, 1]);\n } else {\n return x;\n }\n });\n}\nfunction preprocessConv3DInput(x, dataFormat) {\n return tidy(() => {\n checkDataFormat(dataFormat);\n if (dataFormat === \"channelsFirst\") {\n return transpose(x, [0, 2, 3, 4, 1]);\n } else {\n return x;\n }\n });\n}\nfunction conv1dWithBias(x, kernel, bias, strides = 1, padding = \"valid\", dataFormat, dilationRate = 1) {\n return tidy(() => {\n if (dataFormat == null) {\n dataFormat = imageDataFormat();\n }\n checkDataFormat(dataFormat);\n if (x.shape.length !== 3) {\n throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);\n }\n if (kernel.shape.length !== 3) {\n throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);\n }\n if (bias != null && bias.shape.length !== 1) {\n throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);\n }\n if (dataFormat === \"channelsFirst\") {\n x = transpose(x, [0, 2, 1]);\n }\n if (padding === \"causal\") {\n throw new NotImplementedError(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");\n }\n let y = conv1d(x, kernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NWC\", dilationRate);\n if (bias != null) {\n y = biasAdd(y, bias);\n }\n return y;\n });\n}\nfunction conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = \"valid\", dataFormat, dilationRate, activation2 = null) {\n return tidy(() => {\n if (dataFormat == null) {\n dataFormat = imageDataFormat();\n }\n checkDataFormat(dataFormat);\n if (x.rank !== 3 && x.rank !== 4) {\n throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);\n }\n if (kernel.rank !== 3 && kernel.rank !== 4) {\n throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);\n }\n let y = preprocessConv2DInput(x, dataFormat);\n if (padding === \"causal\") {\n throw new NotImplementedError(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");\n }\n y = fused_ops_exports.conv2d({\n x: y,\n filter: kernel,\n strides,\n pad: padding === \"same\" ? \"same\" : \"valid\",\n dilations: dilationRate,\n dataFormat: \"NHWC\",\n bias,\n activation: activation2\n });\n if (dataFormat === \"channelsFirst\") {\n y = transpose(y, [0, 3, 1, 2]);\n }\n return y;\n });\n}\nfunction conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = \"valid\", dataFormat, dilationRate) {\n return tidy(() => {\n if (dataFormat == null) {\n dataFormat = imageDataFormat();\n }\n checkDataFormat(dataFormat);\n if (x.rank !== 4 && x.rank !== 5) {\n throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);\n }\n if (kernel.rank !== 4 && kernel.rank !== 5) {\n throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);\n }\n let y = preprocessConv3DInput(x, dataFormat);\n if (padding === \"causal\") {\n throw new NotImplementedError(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");\n }\n y = conv3d(y, kernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NDHWC\", dilationRate);\n if (bias != null) {\n y = biasAdd(y, bias);\n }\n if (dataFormat === \"channelsFirst\") {\n y = transpose(y, [0, 4, 1, 2, 3]);\n }\n return y;\n });\n}\nvar BaseConv = class _BaseConv extends Layer {\n constructor(rank, args) {\n super(args);\n this.bias = null;\n this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n _BaseConv.verifyArgs(args);\n this.rank = rank;\n assertPositiveInteger(this.rank, \"rank\");\n if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);\n }\n this.kernelSize = normalizeArray(args.kernelSize, rank, \"kernelSize\");\n this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, \"strides\");\n this.padding = args.padding == null ? \"valid\" : args.padding;\n checkPaddingMode(this.padding);\n this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n checkDataFormat(this.dataFormat);\n this.activation = getActivation(args.activation);\n this.useBias = args.useBias == null ? true : args.useBias;\n this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n this.biasConstraint = getConstraint(args.biasConstraint);\n this.biasRegularizer = getRegularizer(args.biasRegularizer);\n this.activityRegularizer = getRegularizer(args.activityRegularizer);\n this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, \"dilationRate\");\n if (this.rank === 1 && (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n } else if (this.rank === 2) {\n if (typeof this.dilationRate === \"number\") {\n this.dilationRate = [this.dilationRate, this.dilationRate];\n } else if (this.dilationRate.length !== 2) {\n throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n }\n } else if (this.rank === 3) {\n if (typeof this.dilationRate === \"number\") {\n this.dilationRate = [this.dilationRate, this.dilationRate, this.dilationRate];\n } else if (this.dilationRate.length !== 3) {\n throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n }\n }\n }\n static verifyArgs(args) {\n assert2(\"kernelSize\" in args, `required key 'kernelSize' not in config`);\n if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 3)) {\n throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n }\n }\n getConfig() {\n const config = {\n kernelSize: this.kernelSize,\n strides: this.strides,\n padding: this.padding,\n dataFormat: this.dataFormat,\n dilationRate: this.dilationRate,\n activation: serializeActivation(this.activation),\n useBias: this.useBias,\n biasInitializer: serializeInitializer(this.biasInitializer),\n biasRegularizer: serializeRegularizer(this.biasRegularizer),\n activityRegularizer: serializeRegularizer(this.activityRegularizer),\n biasConstraint: serializeConstraint(this.biasConstraint)\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nvar Conv = class _Conv extends BaseConv {\n constructor(rank, args) {\n super(rank, args);\n this.kernel = null;\n _Conv.verifyArgs(args);\n this.filters = args.filters;\n assertPositiveInteger(this.filters, \"filters\");\n this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n this.kernelConstraint = getConstraint(args.kernelConstraint);\n this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n if (inputShape[channelAxis] == null) {\n throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);\n }\n const inputDim = inputShape[channelAxis];\n const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n this.kernel = this.addWeight(\"kernel\", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n if (this.useBias) {\n this.bias = this.addWeight(\"bias\", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n }\n this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n this.built = true;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = getExactlyOneTensor(inputs);\n let outputs;\n const biasValue = this.bias == null ? null : this.bias.read();\n const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n if (fusedActivationName != null && this.rank === 2) {\n outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n } else {\n if (this.rank === 1) {\n outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n } else if (this.rank === 2) {\n outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n } else if (this.rank === 3) {\n outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n } else {\n throw new NotImplementedError(\"convolutions greater than 3D are not implemented yet.\");\n }\n if (this.activation != null) {\n outputs = this.activation.apply(outputs);\n }\n }\n return outputs;\n });\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const newSpace = [];\n const space = this.dataFormat === \"channelsLast\" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2);\n for (let i = 0; i < space.length; ++i) {\n const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === \"number\" ? this.dilationRate : this.dilationRate[i]);\n newSpace.push(newDim);\n }\n let outputShape = [inputShape[0]];\n if (this.dataFormat === \"channelsLast\") {\n outputShape = outputShape.concat(newSpace);\n outputShape.push(this.filters);\n } else {\n outputShape.push(this.filters);\n outputShape = outputShape.concat(newSpace);\n }\n return outputShape;\n }\n getConfig() {\n const config = {\n filters: this.filters,\n kernelInitializer: serializeInitializer(this.kernelInitializer),\n kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n kernelConstraint: serializeConstraint(this.kernelConstraint)\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n static verifyArgs(args) {\n if (!(\"filters\" in args) || typeof args.filters !== \"number\" || args.filters < 1) {\n throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`);\n }\n }\n};\nvar Conv2D2 = class _Conv2D extends Conv {\n constructor(args) {\n super(2, args);\n _Conv2D.verifyArgs(args);\n }\n getConfig() {\n const config = super.getConfig();\n delete config[\"rank\"];\n return config;\n }\n static verifyArgs(args) {\n if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 2)) {\n throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n }\n }\n};\nConv2D2.className = \"Conv2D\";\nserialization_exports.registerClass(Conv2D2);\nvar Conv3D2 = class _Conv3D extends Conv {\n constructor(args) {\n super(3, args);\n _Conv3D.verifyArgs(args);\n }\n getConfig() {\n const config = super.getConfig();\n delete config[\"rank\"];\n return config;\n }\n static verifyArgs(args) {\n if (typeof args.kernelSize !== \"number\") {\n if (!(Array.isArray(args.kernelSize) && (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n }\n }\n }\n};\nConv3D2.className = \"Conv3D\";\nserialization_exports.registerClass(Conv3D2);\nvar Conv2DTranspose = class extends Conv2D2 {\n constructor(args) {\n super(args);\n this.inputSpec = [new InputSpec({ ndim: 4 })];\n if (this.padding !== \"same\" && this.padding !== \"valid\") {\n throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);\n }\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n if (inputShape.length !== 4) {\n throw new ValueError(\"Input should have rank 4; Received input shape: \" + JSON.stringify(inputShape));\n }\n const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n if (inputShape[channelAxis] == null) {\n throw new ValueError(\"The channel dimension of the inputs should be defined. Found `None`.\");\n }\n const inputDim = inputShape[channelAxis];\n const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n this.kernel = this.addWeight(\"kernel\", kernelShape, \"float32\", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n if (this.useBias) {\n this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n }\n this.inputSpec = [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n this.built = true;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n let input2 = getExactlyOneTensor(inputs);\n if (input2.shape.length !== 4) {\n throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);\n }\n const inputShape = input2.shape;\n const batchSize = inputShape[0];\n let hAxis;\n let wAxis;\n if (this.dataFormat === \"channelsFirst\") {\n hAxis = 2;\n wAxis = 3;\n } else {\n hAxis = 1;\n wAxis = 2;\n }\n const height = inputShape[hAxis];\n const width = inputShape[wAxis];\n const kernelH = this.kernelSize[0];\n const kernelW = this.kernelSize[1];\n const strideH = this.strides[0];\n const strideW = this.strides[1];\n const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n const outputShape = [batchSize, outHeight, outWidth, this.filters];\n if (this.dataFormat !== \"channelsLast\") {\n input2 = transpose(input2, [0, 2, 3, 1]);\n }\n let outputs = conv2dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);\n if (this.dataFormat !== \"channelsLast\") {\n outputs = transpose(outputs, [0, 3, 1, 2]);\n }\n if (this.bias != null) {\n outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n }\n if (this.activation != null) {\n outputs = this.activation.apply(outputs);\n }\n return outputs;\n });\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const outputShape = inputShape.slice();\n let channelAxis;\n let heightAxis;\n let widthAxis;\n if (this.dataFormat === \"channelsFirst\") {\n channelAxis = 1;\n heightAxis = 2;\n widthAxis = 3;\n } else {\n channelAxis = 3;\n heightAxis = 1;\n widthAxis = 2;\n }\n const kernelH = this.kernelSize[0];\n const kernelW = this.kernelSize[1];\n const strideH = this.strides[0];\n const strideW = this.strides[1];\n outputShape[channelAxis] = this.filters;\n outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n return outputShape;\n }\n getConfig() {\n const config = super.getConfig();\n delete config[\"dilationRate\"];\n return config;\n }\n};\nConv2DTranspose.className = \"Conv2DTranspose\";\nserialization_exports.registerClass(Conv2DTranspose);\nvar Conv3DTranspose = class extends Conv3D2 {\n constructor(args) {\n super(args);\n this.inputSpec = [new InputSpec({ ndim: 5 })];\n if (this.padding !== \"same\" && this.padding !== \"valid\") {\n throw new ValueError(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);\n }\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n if (inputShape.length !== 5) {\n throw new ValueError(\"Input should have rank 5; Received input shape: \" + JSON.stringify(inputShape));\n }\n const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n if (inputShape[channelAxis] == null) {\n throw new ValueError(\"The channel dimension of the inputs should be defined. Found `None`.\");\n }\n const inputDim = inputShape[channelAxis];\n const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n this.kernel = this.addWeight(\"kernel\", kernelShape, \"float32\", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n if (this.useBias) {\n this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n }\n this.inputSpec = [new InputSpec({ ndim: 5, axes: { [channelAxis]: inputDim } })];\n this.built = true;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n let input2 = getExactlyOneTensor(inputs);\n if (input2.shape.length !== 5) {\n throw new ValueError(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);\n }\n const inputShape = input2.shape;\n const batchSize = inputShape[0];\n let hAxis;\n let wAxis;\n let dAxis;\n if (this.dataFormat === \"channelsFirst\") {\n dAxis = 2;\n hAxis = 3;\n wAxis = 4;\n } else {\n dAxis = 1;\n hAxis = 2;\n wAxis = 3;\n }\n const depth = inputShape[dAxis];\n const height = inputShape[hAxis];\n const width = inputShape[wAxis];\n const kernelD = this.kernelSize[0];\n const kernelH = this.kernelSize[1];\n const kernelW = this.kernelSize[2];\n const strideD = this.strides[0];\n const strideH = this.strides[1];\n const strideW = this.strides[2];\n const outDepth = deconvLength(depth, strideD, kernelD, this.padding);\n const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n const outputShape = [batchSize, outDepth, outHeight, outWidth, this.filters];\n if (this.dataFormat !== \"channelsLast\") {\n input2 = transpose(input2, [0, 2, 3, 4, 1]);\n }\n let outputs = conv3dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);\n if (this.dataFormat !== \"channelsLast\") {\n outputs = transpose(outputs, [0, 4, 1, 2, 3]);\n }\n if (this.bias !== null) {\n outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n }\n if (this.activation !== null) {\n outputs = this.activation.apply(outputs);\n }\n return outputs;\n });\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const outputShape = inputShape.slice();\n let channelAxis;\n let depthAxis;\n let heightAxis;\n let widthAxis;\n if (this.dataFormat === \"channelsFirst\") {\n channelAxis = 1;\n depthAxis = 2;\n heightAxis = 3;\n widthAxis = 4;\n } else {\n channelAxis = 4;\n depthAxis = 1;\n heightAxis = 2;\n widthAxis = 3;\n }\n const kernelD = this.kernelSize[0];\n const kernelH = this.kernelSize[1];\n const kernelW = this.kernelSize[2];\n const strideD = this.strides[0];\n const strideH = this.strides[1];\n const strideW = this.strides[2];\n outputShape[channelAxis] = this.filters;\n outputShape[depthAxis] = deconvLength(outputShape[depthAxis], strideD, kernelD, this.padding);\n outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n return outputShape;\n }\n getConfig() {\n const config = super.getConfig();\n delete config[\"dilationRate\"];\n return config;\n }\n};\nConv3DTranspose.className = \"Conv3DTranspose\";\nserialization_exports.registerClass(Conv3DTranspose);\nvar SeparableConv = class extends Conv {\n constructor(rank, config) {\n super(rank, config);\n this.DEFAULT_DEPTHWISE_INITIALIZER = \"glorotUniform\";\n this.DEFAULT_POINTWISE_INITIALIZER = \"glorotUniform\";\n this.depthwiseKernel = null;\n this.pointwiseKernel = null;\n if (config.filters == null) {\n throw new ValueError(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");\n }\n if (config.kernelInitializer != null || config.kernelRegularizer != null || config.kernelConstraint != null) {\n throw new ValueError(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");\n }\n if (config.padding != null && config.padding !== \"same\" && config.padding !== \"valid\") {\n throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n }\n this.depthMultiplier = config.depthMultiplier == null ? 1 : config.depthMultiplier;\n this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n if (inputShape.length < this.rank + 2) {\n throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(inputShape)}`);\n }\n const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);\n }\n const inputDim = inputShape[channelAxis];\n const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n const pointwiseKernelShape = [];\n for (let i = 0; i < this.rank; ++i) {\n pointwiseKernelShape.push(1);\n }\n pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n const trainable = true;\n this.depthwiseKernel = this.addWeight(\"depthwise_kernel\", depthwiseKernelShape, \"float32\", this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n this.pointwiseKernel = this.addWeight(\"pointwise_kernel\", pointwiseKernelShape, \"float32\", this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n if (this.useBias) {\n this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n } else {\n this.bias = null;\n }\n this.inputSpec = [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n this.built = true;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = getExactlyOneTensor(inputs);\n let output;\n if (this.rank === 1) {\n throw new NotImplementedError(\"1D separable convolution is not implemented yet.\");\n } else if (this.rank === 2) {\n if (this.dataFormat === \"channelsFirst\") {\n inputs = transpose(inputs, [0, 2, 3, 1]);\n }\n output = separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, \"NHWC\");\n }\n if (this.useBias) {\n output = biasAdd(output, this.bias.read(), this.dataFormat);\n }\n if (this.activation != null) {\n output = this.activation.apply(output);\n }\n if (this.dataFormat === \"channelsFirst\") {\n output = transpose(output, [0, 3, 1, 2]);\n }\n return output;\n });\n }\n getConfig() {\n const config = super.getConfig();\n delete config[\"rank\"];\n delete config[\"kernelInitializer\"];\n delete config[\"kernelRegularizer\"];\n delete config[\"kernelConstraint\"];\n config[\"depthwiseInitializer\"] = serializeInitializer(this.depthwiseInitializer);\n config[\"pointwiseInitializer\"] = serializeInitializer(this.pointwiseInitializer);\n config[\"depthwiseRegularizer\"] = serializeRegularizer(this.depthwiseRegularizer);\n config[\"pointwiseRegularizer\"] = serializeRegularizer(this.pointwiseRegularizer);\n config[\"depthwiseConstraint\"] = serializeConstraint(this.depthwiseConstraint);\n config[\"pointwiseConstraint\"] = serializeConstraint(this.pointwiseConstraint);\n return config;\n }\n};\nSeparableConv.className = \"SeparableConv\";\nvar SeparableConv2D = class extends SeparableConv {\n constructor(args) {\n super(2, args);\n }\n};\nSeparableConv2D.className = \"SeparableConv2D\";\nserialization_exports.registerClass(SeparableConv2D);\nvar Conv1D = class _Conv1D extends Conv {\n constructor(args) {\n super(1, args);\n _Conv1D.verifyArgs(args);\n this.inputSpec = [{ ndim: 3 }];\n }\n getConfig() {\n const config = super.getConfig();\n delete config[\"rank\"];\n delete config[\"dataFormat\"];\n return config;\n }\n static verifyArgs(args) {\n if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 1)) {\n throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n }\n }\n};\nConv1D.className = \"Conv1D\";\nserialization_exports.registerClass(Conv1D);\nvar Cropping2D = class extends Layer {\n constructor(args) {\n super(args);\n if (typeof args.cropping === \"number\") {\n this.cropping = [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n } else if (typeof args.cropping[0] === \"number\") {\n this.cropping = [\n [args.cropping[0], args.cropping[0]],\n [args.cropping[1], args.cropping[1]]\n ];\n } else {\n this.cropping = args.cropping;\n }\n this.dataFormat = args.dataFormat === void 0 ? \"channelsLast\" : args.dataFormat;\n this.inputSpec = [{ ndim: 4 }];\n }\n computeOutputShape(inputShape) {\n if (this.dataFormat === \"channelsFirst\") {\n return [\n inputShape[0],\n inputShape[1],\n inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n ];\n } else {\n return [\n inputShape[0],\n inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n inputShape[2] - this.cropping[1][0] - this.cropping[1][1],\n inputShape[3]\n ];\n }\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = getExactlyOneTensor(inputs);\n if (this.dataFormat === \"channelsLast\") {\n const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n } else {\n const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n }\n });\n }\n getConfig() {\n const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nCropping2D.className = \"Cropping2D\";\nserialization_exports.registerClass(Cropping2D);\nvar UpSampling2D = class extends Layer {\n constructor(args) {\n super(args);\n this.DEFAULT_SIZE = [2, 2];\n this.inputSpec = [{ ndim: 4 }];\n this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n checkDataFormat(this.dataFormat);\n this.interpolation = args.interpolation == null ? \"nearest\" : args.interpolation;\n checkInterpolationFormat(this.interpolation);\n }\n computeOutputShape(inputShape) {\n if (this.dataFormat === \"channelsFirst\") {\n const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n return [inputShape[0], inputShape[1], height, width];\n } else {\n const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n return [inputShape[0], height, width, inputShape[3]];\n }\n }\n call(inputs, kwargs) {\n return tidy(() => {\n let input2 = getExactlyOneTensor(inputs);\n const inputShape = input2.shape;\n if (this.dataFormat === \"channelsFirst\") {\n input2 = transpose(input2, [0, 2, 3, 1]);\n const height = this.size[0] * inputShape[2];\n const width = this.size[1] * inputShape[3];\n const resized = this.interpolation === \"nearest\" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);\n return transpose(resized, [0, 3, 1, 2]);\n } else {\n const height = this.size[0] * inputShape[1];\n const width = this.size[1] * inputShape[2];\n return this.interpolation === \"nearest\" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);\n }\n });\n }\n getConfig() {\n const config = {\n size: this.size,\n dataFormat: this.dataFormat,\n interpolation: this.interpolation\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nUpSampling2D.className = \"UpSampling2D\";\nserialization_exports.registerClass(UpSampling2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_depthwise.js\nfunction depthwiseConv2d3(x, depthwiseKernel, strides = [1, 1], padding = \"valid\", dataFormat, dilationRate) {\n return tidy(() => {\n if (dataFormat == null) {\n dataFormat = imageDataFormat();\n }\n checkDataFormat(dataFormat);\n let y = preprocessConv2DInput(x, dataFormat);\n if (x.rank !== 4) {\n throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);\n }\n if (depthwiseKernel.rank !== 4) {\n throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);\n }\n y = depthwiseConv2d(y, depthwiseKernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NHWC\", dilationRate);\n if (dataFormat === \"channelsFirst\") {\n y = transpose(y, [0, 3, 1, 2]);\n }\n return y;\n });\n}\nvar DepthwiseConv2D = class extends BaseConv {\n constructor(args) {\n super(2, args);\n this.depthwiseKernel = null;\n this.depthMultiplier = args.depthMultiplier == null ? 1 : args.depthMultiplier;\n this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n if (inputShape.length < 4) {\n throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);\n }\n const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : 3;\n if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);\n }\n const inputDim = inputShape[channelAxis];\n const depthwiseKernelShape = [\n this.kernelSize[0],\n this.kernelSize[1],\n inputDim,\n this.depthMultiplier\n ];\n this.depthwiseKernel = this.addWeight(\"depthwise_kernel\", depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n if (this.useBias) {\n this.bias = this.addWeight(\"bias\", [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n } else {\n this.bias = null;\n }\n this.built = true;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = getExactlyOneTensor(inputs);\n let outputs = depthwiseConv2d3(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n if (this.useBias) {\n outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n }\n if (this.activation != null) {\n outputs = this.activation.apply(outputs);\n }\n return outputs;\n });\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const rows = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n const cols = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n const outFilters = this.dataFormat === \"channelsFirst\" ? inputShape[1] * this.depthMultiplier : inputShape[3] * this.depthMultiplier;\n const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n if (this.dataFormat === \"channelsFirst\") {\n return [inputShape[0], outFilters, outRows, outCols];\n } else {\n return [inputShape[0], outRows, outCols, outFilters];\n }\n }\n getConfig() {\n const config = super.getConfig();\n config[\"depthMultiplier\"] = this.depthMultiplier;\n config[\"depthwiseInitializer\"] = serializeInitializer(this.depthwiseInitializer);\n config[\"depthwiseRegularizer\"] = serializeRegularizer(this.depthwiseRegularizer);\n config[\"depthwiseConstraint\"] = serializeConstraint(this.depthwiseRegularizer);\n return config;\n }\n};\nDepthwiseConv2D.className = \"DepthwiseConv2D\";\nserialization_exports.registerClass(DepthwiseConv2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/recurrent.js\nfunction standardizeArgs(inputs, initialState, constants, numConstants) {\n if (Array.isArray(inputs)) {\n if (initialState != null || constants != null) {\n throw new ValueError(\"When inputs is an array, neither initialState or constants should be provided\");\n }\n if (numConstants != null) {\n constants = inputs.slice(inputs.length - numConstants, inputs.length);\n inputs = inputs.slice(0, inputs.length - numConstants);\n }\n if (inputs.length > 1) {\n initialState = inputs.slice(1, inputs.length);\n }\n inputs = inputs[0];\n }\n function toListOrNull(x) {\n if (x == null || Array.isArray(x)) {\n return x;\n } else {\n return [x];\n }\n }\n initialState = toListOrNull(initialState);\n constants = toListOrNull(constants);\n return { inputs, initialState, constants };\n}\nfunction rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n return tidy(() => {\n const ndim = inputs.shape.length;\n if (ndim < 3) {\n throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n }\n const axes = [1, 0].concat(range2(2, ndim));\n inputs = transpose(inputs, axes);\n if (constants != null) {\n throw new NotImplementedError(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");\n }\n if (unroll) {\n console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\");\n }\n if (mask != null) {\n mask = cast(cast(mask, \"bool\"), \"float32\");\n if (mask.rank === ndim - 1) {\n mask = expandDims(mask, -1);\n }\n mask = transpose(mask, axes);\n }\n if (goBackwards) {\n inputs = reverse(inputs, 0);\n if (mask != null) {\n mask = reverse(mask, 0);\n }\n }\n const perStepOutputs = [];\n let lastOutput;\n let states = initialStates;\n const timeSteps = inputs.shape[0];\n const perStepInputs = unstack(inputs);\n let perStepMasks;\n if (mask != null) {\n perStepMasks = unstack(mask);\n }\n for (let t = 0; t < timeSteps; ++t) {\n const currentInput = perStepInputs[t];\n const stepOutputs = tidy(() => stepFunction(currentInput, states));\n if (mask == null) {\n lastOutput = stepOutputs[0];\n states = stepOutputs[1];\n } else {\n const maskedOutputs = tidy(() => {\n const stepMask = perStepMasks[t];\n const negStepMask = sub(onesLike(stepMask), stepMask);\n const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask));\n const newStates = states.map((state, i) => {\n return add2(mul(stepOutputs[1][i], stepMask), mul(state, negStepMask));\n });\n return { output, newStates };\n });\n lastOutput = maskedOutputs.output;\n states = maskedOutputs.newStates;\n }\n if (needPerStepOutputs) {\n perStepOutputs.push(lastOutput);\n }\n }\n let outputs;\n if (needPerStepOutputs) {\n const axis = 1;\n outputs = stack(perStepOutputs, axis);\n }\n return [lastOutput, outputs, states];\n });\n}\nvar RNN = class _RNN extends Layer {\n constructor(args) {\n super(args);\n let cell;\n if (args.cell == null) {\n throw new ValueError(\"cell property is missing for the constructor of RNN.\");\n } else if (Array.isArray(args.cell)) {\n cell = new StackedRNNCells({ cells: args.cell });\n } else {\n cell = args.cell;\n }\n if (cell.stateSize == null) {\n throw new ValueError(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");\n }\n this.cell = cell;\n this.returnSequences = args.returnSequences == null ? false : args.returnSequences;\n this.returnState = args.returnState == null ? false : args.returnState;\n this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n this._stateful = args.stateful == null ? false : args.stateful;\n this.unroll = args.unroll == null ? false : args.unroll;\n this.supportsMasking = true;\n this.inputSpec = [new InputSpec({ ndim: 3 })];\n this.stateSpec = null;\n this.states_ = null;\n this.numConstants = null;\n this.keptStates = [];\n }\n // Porting Note: This is the equivalent of `RNN.states` property getter in\n // PyKeras.\n getStates() {\n if (this.states_ == null) {\n const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n return range2(0, numStates).map((x) => null);\n } else {\n return this.states_;\n }\n }\n // Porting Note: This is the equivalent of the `RNN.states` property setter in\n // PyKeras.\n setStates(states) {\n this.states_ = states;\n }\n computeOutputShape(inputShape) {\n if (isArrayOfShapes(inputShape)) {\n inputShape = inputShape[0];\n }\n inputShape = inputShape;\n let stateSize = this.cell.stateSize;\n if (!Array.isArray(stateSize)) {\n stateSize = [stateSize];\n }\n const outputDim = stateSize[0];\n let outputShape;\n if (this.returnSequences) {\n outputShape = [inputShape[0], inputShape[1], outputDim];\n } else {\n outputShape = [inputShape[0], outputDim];\n }\n if (this.returnState) {\n const stateShape = [];\n for (const dim of stateSize) {\n stateShape.push([inputShape[0], dim]);\n }\n return [outputShape].concat(stateShape);\n } else {\n return outputShape;\n }\n }\n computeMask(inputs, mask) {\n return tidy(() => {\n if (Array.isArray(mask)) {\n mask = mask[0];\n }\n const outputMask = this.returnSequences ? mask : null;\n if (this.returnState) {\n const stateMask = this.states.map((s) => null);\n return [outputMask].concat(stateMask);\n } else {\n return outputMask;\n }\n });\n }\n /**\n * Get the current state tensors of the RNN.\n *\n * If the state hasn't been set, return an array of `null`s of the correct\n * length.\n */\n get states() {\n if (this.states_ == null) {\n const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n const output = [];\n for (let i = 0; i < numStates; ++i) {\n output.push(null);\n }\n return output;\n } else {\n return this.states_;\n }\n }\n set states(s) {\n this.states_ = s;\n }\n build(inputShape) {\n const constantShape = null;\n if (this.numConstants != null) {\n throw new NotImplementedError(\"Constants support is not implemented in RNN yet.\");\n }\n if (isArrayOfShapes(inputShape)) {\n inputShape = inputShape[0];\n }\n inputShape = inputShape;\n const batchSize = this.stateful ? inputShape[0] : null;\n const inputDim = inputShape.slice(2);\n this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n if (constantShape != null) {\n throw new NotImplementedError(\"Constants support is not implemented in RNN yet.\");\n } else {\n this.cell.build(stepInputShape);\n }\n let stateSize;\n if (Array.isArray(this.cell.stateSize)) {\n stateSize = this.cell.stateSize;\n } else {\n stateSize = [this.cell.stateSize];\n }\n if (this.stateSpec != null) {\n if (!util_exports.arraysEqual(this.stateSpec.map((spec) => spec.shape[spec.shape.length - 1]), stateSize)) {\n throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);\n }\n } else {\n this.stateSpec = stateSize.map((dim) => new InputSpec({ shape: [null, dim] }));\n }\n if (this.stateful) {\n this.resetStates();\n }\n }\n /**\n * Reset the state tensors of the RNN.\n *\n * If the `states` argument is `undefined` or `null`, will set the\n * state tensor(s) of the RNN to all-zero tensors of the appropriate\n * shape(s).\n *\n * If `states` is provided, will set the state tensors of the RNN to its\n * value.\n *\n * @param states Optional externally-provided initial states.\n * @param training Whether this call is done during training. For stateful\n * RNNs, this affects whether the old states are kept or discarded. In\n * particular, if `training` is `true`, the old states will be kept so\n * that subsequent backpropgataion through time (BPTT) may work properly.\n * Else, the old states will be discarded.\n */\n resetStates(states, training = false) {\n tidy(() => {\n if (!this.stateful) {\n throw new AttributeError(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");\n }\n const batchSize = this.inputSpec[0].shape[0];\n if (batchSize == null) {\n throw new ValueError(\"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.\");\n }\n if (this.states_ == null) {\n if (Array.isArray(this.cell.stateSize)) {\n this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));\n } else {\n this.states_ = [zeros([batchSize, this.cell.stateSize])];\n }\n } else if (states == null) {\n dispose(this.states_);\n if (this.keptStates != null) {\n dispose(this.keptStates);\n this.keptStates = [];\n }\n if (Array.isArray(this.cell.stateSize)) {\n this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));\n } else {\n this.states_[0] = zeros([batchSize, this.cell.stateSize]);\n }\n } else {\n if (!Array.isArray(states)) {\n states = [states];\n }\n if (states.length !== this.states_.length) {\n throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);\n }\n if (training === true) {\n this.keptStates.push(this.states_.slice());\n } else {\n dispose(this.states_);\n }\n for (let index = 0; index < this.states_.length; ++index) {\n const value = states[index];\n const dim = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[index] : this.cell.stateSize;\n const expectedShape = [batchSize, dim];\n if (!util_exports.arraysEqual(value.shape, expectedShape)) {\n throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);\n }\n this.states_[index] = value;\n }\n }\n this.states_ = this.states_.map((state) => keep(state.clone()));\n });\n }\n apply(inputs, kwargs) {\n let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n let constants = kwargs == null ? null : kwargs[\"constants\"];\n if (kwargs == null) {\n kwargs = {};\n }\n const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n inputs = standardized.inputs;\n initialState = standardized.initialState;\n constants = standardized.constants;\n let additionalInputs = [];\n let additionalSpecs = [];\n if (initialState != null) {\n kwargs[\"initialState\"] = initialState;\n additionalInputs = additionalInputs.concat(initialState);\n this.stateSpec = [];\n for (const state of initialState) {\n this.stateSpec.push(new InputSpec({ shape: state.shape }));\n }\n additionalSpecs = additionalSpecs.concat(this.stateSpec);\n }\n if (constants != null) {\n kwargs[\"constants\"] = constants;\n additionalInputs = additionalInputs.concat(constants);\n this.numConstants = constants.length;\n }\n const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n if (isTensor) {\n const fullInput = [inputs].concat(additionalInputs);\n const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n const originalInputSpec = this.inputSpec;\n this.inputSpec = fullInputSpec;\n const output = super.apply(fullInput, kwargs);\n this.inputSpec = originalInputSpec;\n return output;\n } else {\n return super.apply(inputs, kwargs);\n }\n }\n // tslint:disable-next-line:no-any\n call(inputs, kwargs) {\n return tidy(() => {\n const mask = kwargs == null ? null : kwargs[\"mask\"];\n const training = kwargs == null ? null : kwargs[\"training\"];\n let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n inputs = getExactlyOneTensor(inputs);\n if (initialState == null) {\n if (this.stateful) {\n initialState = this.states_;\n } else {\n initialState = this.getInitialState(inputs);\n }\n }\n const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n if (initialState.length !== numStates) {\n throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);\n }\n if (this.unroll) {\n console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");\n }\n const cellCallKwargs = { training };\n const step5 = (inputs2, states2) => {\n const outputs2 = this.cell.call([inputs2].concat(states2), cellCallKwargs);\n return [outputs2[0], outputs2.slice(1)];\n };\n const rnnOutputs = rnn(step5, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n const lastOutput = rnnOutputs[0];\n const outputs = rnnOutputs[1];\n const states = rnnOutputs[2];\n if (this.stateful) {\n this.resetStates(states, training);\n }\n const output = this.returnSequences ? outputs : lastOutput;\n if (this.returnState) {\n return [output].concat(states);\n } else {\n return output;\n }\n });\n }\n getInitialState(inputs) {\n return tidy(() => {\n let initialState = zeros(inputs.shape);\n initialState = sum2(initialState, [1, 2]);\n initialState = expandDims2(initialState);\n if (Array.isArray(this.cell.stateSize)) {\n return this.cell.stateSize.map((dim) => dim > 1 ? tile2(initialState, [1, dim]) : initialState);\n } else {\n return this.cell.stateSize > 1 ? [tile2(initialState, [1, this.cell.stateSize])] : [initialState];\n }\n });\n }\n get trainableWeights() {\n if (!this.trainable) {\n return [];\n }\n return this.cell.trainableWeights;\n }\n get nonTrainableWeights() {\n if (!this.trainable) {\n return this.cell.weights;\n }\n return this.cell.nonTrainableWeights;\n }\n setFastWeightInitDuringBuild(value) {\n super.setFastWeightInitDuringBuild(value);\n if (this.cell != null) {\n this.cell.setFastWeightInitDuringBuild(value);\n }\n }\n getConfig() {\n const baseConfig = super.getConfig();\n const config = {\n returnSequences: this.returnSequences,\n returnState: this.returnState,\n goBackwards: this.goBackwards,\n stateful: this.stateful,\n unroll: this.unroll\n };\n if (this.numConstants != null) {\n config[\"numConstants\"] = this.numConstants;\n }\n const cellConfig = this.cell.getConfig();\n if (this.getClassName() === _RNN.className) {\n config[\"cell\"] = {\n \"className\": this.cell.getClassName(),\n \"config\": cellConfig\n };\n }\n return Object.assign(Object.assign(Object.assign({}, cellConfig), baseConfig), config);\n }\n /** @nocollapse */\n static fromConfig(cls, config, customObjects = {}) {\n const cellConfig = config[\"cell\"];\n const cell = deserialize(cellConfig, customObjects);\n return new cls(Object.assign(config, { cell }));\n }\n};\nRNN.className = \"RNN\";\nserialization_exports.registerClass(RNN);\nvar RNNCell = class extends Layer {\n};\nvar SimpleRNNCell = class extends RNNCell {\n constructor(args) {\n super(args);\n this.DEFAULT_ACTIVATION = \"tanh\";\n this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n this.units = args.units;\n assertPositiveInteger(this.units, `units`);\n this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n this.useBias = args.useBias == null ? true : args.useBias;\n this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n this.biasRegularizer = getRegularizer(args.biasRegularizer);\n this.kernelConstraint = getConstraint(args.kernelConstraint);\n this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n this.biasConstraint = getConstraint(args.biasConstraint);\n this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n this.recurrentDropout = min2([\n 1,\n max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n ]);\n this.dropoutFunc = args.dropoutFunc;\n this.stateSize = this.units;\n this.dropoutMask = null;\n this.recurrentDropoutMask = null;\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n this.kernel = this.addWeight(\"kernel\", [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n if (this.useBias) {\n this.bias = this.addWeight(\"bias\", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n } else {\n this.bias = null;\n }\n this.built = true;\n }\n // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n // `inputs` and `states`. Here, the two tensors are combined into an\n // `Tensor[]` Array as the first input argument.\n // Similarly, PyKeras' equivalent of this method returns two values:\n // `output` and `[output]`. Here the two are combined into one length-2\n // `Tensor[]`, consisting of `output` repeated.\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = inputs;\n if (inputs.length !== 2) {\n throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n }\n let prevOutput = inputs[1];\n inputs = inputs[0];\n const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n this.dropoutMask = generateDropoutMask({\n ones: () => onesLike(inputs),\n rate: this.dropout,\n training,\n dropoutFunc: this.dropoutFunc\n });\n }\n if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n this.recurrentDropoutMask = generateDropoutMask({\n ones: () => onesLike(prevOutput),\n rate: this.recurrentDropout,\n training,\n dropoutFunc: this.dropoutFunc\n });\n }\n let h;\n const dpMask = this.dropoutMask;\n const recDpMask = this.recurrentDropoutMask;\n if (dpMask != null) {\n h = dot2(mul(inputs, dpMask), this.kernel.read());\n } else {\n h = dot2(inputs, this.kernel.read());\n }\n if (this.bias != null) {\n h = biasAdd(h, this.bias.read());\n }\n if (recDpMask != null) {\n prevOutput = mul(prevOutput, recDpMask);\n }\n let output = add2(h, dot2(prevOutput, this.recurrentKernel.read()));\n if (this.activation != null) {\n output = this.activation.apply(output);\n }\n return [output, output];\n });\n }\n getConfig() {\n const baseConfig = super.getConfig();\n const config = {\n units: this.units,\n activation: serializeActivation(this.activation),\n useBias: this.useBias,\n kernelInitializer: serializeInitializer(this.kernelInitializer),\n recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n biasInitializer: serializeInitializer(this.biasInitializer),\n kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n biasRegularizer: serializeRegularizer(this.biasRegularizer),\n activityRegularizer: serializeRegularizer(this.activityRegularizer),\n kernelConstraint: serializeConstraint(this.kernelConstraint),\n recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n biasConstraint: serializeConstraint(this.biasConstraint),\n dropout: this.dropout,\n recurrentDropout: this.recurrentDropout\n };\n return Object.assign(Object.assign({}, baseConfig), config);\n }\n};\nSimpleRNNCell.className = \"SimpleRNNCell\";\nserialization_exports.registerClass(SimpleRNNCell);\nvar SimpleRNN = class extends RNN {\n constructor(args) {\n args.cell = new SimpleRNNCell(args);\n super(args);\n }\n call(inputs, kwargs) {\n return tidy(() => {\n if (this.cell.dropoutMask != null) {\n dispose(this.cell.dropoutMask);\n this.cell.dropoutMask = null;\n }\n if (this.cell.recurrentDropoutMask != null) {\n dispose(this.cell.recurrentDropoutMask);\n this.cell.recurrentDropoutMask = null;\n }\n const mask = kwargs == null ? null : kwargs[\"mask\"];\n const training = kwargs == null ? null : kwargs[\"training\"];\n const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n return super.call(inputs, { mask, training, initialState });\n });\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls(config);\n }\n};\nSimpleRNN.className = \"SimpleRNN\";\nserialization_exports.registerClass(SimpleRNN);\nvar GRUCell = class extends RNNCell {\n constructor(args) {\n super(args);\n this.DEFAULT_ACTIVATION = \"tanh\";\n this.DEFAULT_RECURRENT_ACTIVATION = \"hardSigmoid\";\n this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n if (args.resetAfter) {\n throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n }\n this.units = args.units;\n assertPositiveInteger(this.units, \"units\");\n this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);\n this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);\n this.useBias = args.useBias == null ? true : args.useBias;\n this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n this.biasRegularizer = getRegularizer(args.biasRegularizer);\n this.kernelConstraint = getConstraint(args.kernelConstraint);\n this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n this.biasConstraint = getConstraint(args.biasConstraint);\n this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n this.recurrentDropout = min2([\n 1,\n max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n ]);\n this.dropoutFunc = args.dropoutFunc;\n this.implementation = args.implementation;\n this.stateSize = this.units;\n this.dropoutMask = null;\n this.recurrentDropoutMask = null;\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const inputDim = inputShape[inputShape.length - 1];\n this.kernel = this.addWeight(\"kernel\", [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n if (this.useBias) {\n this.bias = this.addWeight(\"bias\", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n } else {\n this.bias = null;\n }\n this.built = true;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = inputs;\n if (inputs.length !== 2) {\n throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);\n }\n const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n let hTMinus1 = inputs[1];\n inputs = inputs[0];\n if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n this.dropoutMask = generateDropoutMask({\n ones: () => onesLike(inputs),\n rate: this.dropout,\n training,\n count: 3,\n dropoutFunc: this.dropoutFunc\n });\n }\n if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n this.recurrentDropoutMask = generateDropoutMask({\n ones: () => onesLike(hTMinus1),\n rate: this.recurrentDropout,\n training,\n count: 3,\n dropoutFunc: this.dropoutFunc\n });\n }\n const dpMask = this.dropoutMask;\n const recDpMask = this.recurrentDropoutMask;\n let z;\n let r;\n let hh;\n if (0 < this.dropout && this.dropout < 1) {\n inputs = mul(inputs, dpMask[0]);\n }\n let matrixX = dot2(inputs, this.kernel.read());\n if (this.useBias) {\n matrixX = biasAdd(matrixX, this.bias.read());\n }\n if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n hTMinus1 = mul(hTMinus1, recDpMask[0]);\n }\n const recurrentKernelValue = this.recurrentKernel.read();\n const [rk1, rk2] = split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n const matrixInner = dot2(hTMinus1, rk1);\n const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1);\n const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1);\n z = this.recurrentActivation.apply(add2(xZ, recurrentZ));\n r = this.recurrentActivation.apply(add2(xR, recurrentR));\n const recurrentH = dot2(mul(r, hTMinus1), rk2);\n hh = this.activation.apply(add2(xH, recurrentH));\n const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh));\n return [h, h];\n });\n }\n getConfig() {\n const baseConfig = super.getConfig();\n const config = {\n units: this.units,\n activation: serializeActivation(this.activation),\n recurrentActivation: serializeActivation(this.recurrentActivation),\n useBias: this.useBias,\n kernelInitializer: serializeInitializer(this.kernelInitializer),\n recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n biasInitializer: serializeInitializer(this.biasInitializer),\n kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n biasRegularizer: serializeRegularizer(this.biasRegularizer),\n activityRegularizer: serializeRegularizer(this.activityRegularizer),\n kernelConstraint: serializeConstraint(this.kernelConstraint),\n recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n biasConstraint: serializeConstraint(this.biasConstraint),\n dropout: this.dropout,\n recurrentDropout: this.recurrentDropout,\n implementation: this.implementation,\n resetAfter: false\n };\n return Object.assign(Object.assign({}, baseConfig), config);\n }\n};\nGRUCell.className = \"GRUCell\";\nserialization_exports.registerClass(GRUCell);\nvar GRU = class extends RNN {\n constructor(args) {\n if (args.implementation === 0) {\n console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\");\n }\n args.cell = new GRUCell(args);\n super(args);\n }\n call(inputs, kwargs) {\n return tidy(() => {\n if (this.cell.dropoutMask != null) {\n dispose(this.cell.dropoutMask);\n this.cell.dropoutMask = null;\n }\n if (this.cell.recurrentDropoutMask != null) {\n dispose(this.cell.recurrentDropoutMask);\n this.cell.recurrentDropoutMask = null;\n }\n const mask = kwargs == null ? null : kwargs[\"mask\"];\n const training = kwargs == null ? null : kwargs[\"training\"];\n const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n return super.call(inputs, { mask, training, initialState });\n });\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n if (config[\"implmentation\"] === 0) {\n config[\"implementation\"] = 1;\n }\n return new cls(config);\n }\n};\nGRU.className = \"GRU\";\nserialization_exports.registerClass(GRU);\nvar LSTMCell = class extends RNNCell {\n constructor(args) {\n super(args);\n this.DEFAULT_ACTIVATION = \"tanh\";\n this.DEFAULT_RECURRENT_ACTIVATION = \"hardSigmoid\";\n this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n this.units = args.units;\n assertPositiveInteger(this.units, \"units\");\n this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);\n this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);\n this.useBias = args.useBias == null ? true : args.useBias;\n this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n this.unitForgetBias = args.unitForgetBias;\n this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n this.biasRegularizer = getRegularizer(args.biasRegularizer);\n this.kernelConstraint = getConstraint(args.kernelConstraint);\n this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n this.biasConstraint = getConstraint(args.biasConstraint);\n this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n this.recurrentDropout = min2([\n 1,\n max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n ]);\n this.dropoutFunc = args.dropoutFunc;\n this.implementation = args.implementation;\n this.stateSize = [this.units, this.units];\n this.dropoutMask = null;\n this.recurrentDropoutMask = null;\n }\n build(inputShape) {\n var _a;\n inputShape = getExactlyOneShape(inputShape);\n const inputDim = inputShape[inputShape.length - 1];\n this.kernel = this.addWeight(\"kernel\", [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n let biasInitializer;\n if (this.useBias) {\n if (this.unitForgetBias) {\n const capturedBiasInit = this.biasInitializer;\n const capturedUnits = this.units;\n biasInitializer = new (_a = class CustomInit extends Initializer {\n apply(shape, dtype) {\n const bI = capturedBiasInit.apply([capturedUnits]);\n const bF = new Ones().apply([capturedUnits]);\n const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n return concatAlongFirstAxis(concatAlongFirstAxis(bI, bF), bCAndH);\n }\n }, /** @nocollapse */\n _a.className = \"CustomInit\", _a)();\n } else {\n biasInitializer = this.biasInitializer;\n }\n this.bias = this.addWeight(\"bias\", [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n } else {\n this.bias = null;\n }\n this.built = true;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n inputs = inputs;\n if (inputs.length !== 3) {\n throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);\n }\n let hTMinus1 = inputs[1];\n const cTMinus1 = inputs[2];\n inputs = inputs[0];\n if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n this.dropoutMask = generateDropoutMask({\n ones: () => onesLike(inputs),\n rate: this.dropout,\n training,\n count: 4,\n dropoutFunc: this.dropoutFunc\n });\n }\n if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n this.recurrentDropoutMask = generateDropoutMask({\n ones: () => onesLike(hTMinus1),\n rate: this.recurrentDropout,\n training,\n count: 4,\n dropoutFunc: this.dropoutFunc\n });\n }\n const dpMask = this.dropoutMask;\n const recDpMask = this.recurrentDropoutMask;\n let i;\n let f;\n let c;\n let o;\n if (0 < this.dropout && this.dropout < 1) {\n inputs = mul(inputs, dpMask[0]);\n }\n let z = dot2(inputs, this.kernel.read());\n if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n hTMinus1 = mul(hTMinus1, recDpMask[0]);\n }\n z = add2(z, dot2(hTMinus1, this.recurrentKernel.read()));\n if (this.useBias) {\n z = biasAdd(z, this.bias.read());\n }\n const [z0, z1, z2, z3] = split(z, 4, z.rank - 1);\n i = this.recurrentActivation.apply(z0);\n f = this.recurrentActivation.apply(z1);\n c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(z2)));\n o = this.recurrentActivation.apply(z3);\n const h = mul(o, this.activation.apply(c));\n return [h, h, c];\n });\n }\n getConfig() {\n const baseConfig = super.getConfig();\n const config = {\n units: this.units,\n activation: serializeActivation(this.activation),\n recurrentActivation: serializeActivation(this.recurrentActivation),\n useBias: this.useBias,\n kernelInitializer: serializeInitializer(this.kernelInitializer),\n recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n biasInitializer: serializeInitializer(this.biasInitializer),\n unitForgetBias: this.unitForgetBias,\n kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n biasRegularizer: serializeRegularizer(this.biasRegularizer),\n activityRegularizer: serializeRegularizer(this.activityRegularizer),\n kernelConstraint: serializeConstraint(this.kernelConstraint),\n recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n biasConstraint: serializeConstraint(this.biasConstraint),\n dropout: this.dropout,\n recurrentDropout: this.recurrentDropout,\n implementation: this.implementation\n };\n return Object.assign(Object.assign({}, baseConfig), config);\n }\n};\nLSTMCell.className = \"LSTMCell\";\nserialization_exports.registerClass(LSTMCell);\nvar LSTM = class extends RNN {\n constructor(args) {\n if (args.implementation === 0) {\n console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\");\n }\n args.cell = new LSTMCell(args);\n super(args);\n }\n call(inputs, kwargs) {\n return tidy(() => {\n if (this.cell.dropoutMask != null) {\n dispose(this.cell.dropoutMask);\n this.cell.dropoutMask = null;\n }\n if (this.cell.recurrentDropoutMask != null) {\n dispose(this.cell.recurrentDropoutMask);\n this.cell.recurrentDropoutMask = null;\n }\n const mask = kwargs == null ? null : kwargs[\"mask\"];\n const training = kwargs == null ? null : kwargs[\"training\"];\n const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n return super.call(inputs, { mask, training, initialState });\n });\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n if (config[\"implmentation\"] === 0) {\n config[\"implementation\"] = 1;\n }\n return new cls(config);\n }\n};\nLSTM.className = \"LSTM\";\nserialization_exports.registerClass(LSTM);\nvar StackedRNNCells = class extends RNNCell {\n constructor(args) {\n super(args);\n this.cells = args.cells;\n }\n get stateSize() {\n const stateSize = [];\n for (const cell of this.cells.slice().reverse()) {\n if (Array.isArray(cell.stateSize)) {\n stateSize.push(...cell.stateSize);\n } else {\n stateSize.push(cell.stateSize);\n }\n }\n return stateSize;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = inputs;\n let states = inputs.slice(1);\n const nestedStates = [];\n for (const cell of this.cells.slice().reverse()) {\n if (Array.isArray(cell.stateSize)) {\n nestedStates.push(states.splice(0, cell.stateSize.length));\n } else {\n nestedStates.push(states.splice(0, 1));\n }\n }\n nestedStates.reverse();\n const newNestedStates = [];\n let callInputs;\n for (let i = 0; i < this.cells.length; ++i) {\n const cell = this.cells[i];\n states = nestedStates[i];\n if (i === 0) {\n callInputs = [inputs[0]].concat(states);\n } else {\n callInputs = [callInputs[0]].concat(states);\n }\n callInputs = cell.call(callInputs, kwargs);\n newNestedStates.push(callInputs.slice(1));\n }\n states = [];\n for (const cellStates of newNestedStates.slice().reverse()) {\n states.push(...cellStates);\n }\n return [callInputs[0]].concat(states);\n });\n }\n build(inputShape) {\n if (isArrayOfShapes(inputShape)) {\n inputShape = inputShape[0];\n }\n inputShape = inputShape;\n let outputDim;\n this.cells.forEach((cell, i) => {\n nameScope(`RNNCell_${i}`, () => {\n cell.build(inputShape);\n if (Array.isArray(cell.stateSize)) {\n outputDim = cell.stateSize[0];\n } else {\n outputDim = cell.stateSize;\n }\n inputShape = [inputShape[0], outputDim];\n });\n });\n this.built = true;\n }\n getConfig() {\n const baseConfig = super.getConfig();\n const getCellConfig = (cell) => {\n return {\n \"className\": cell.getClassName(),\n \"config\": cell.getConfig()\n };\n };\n const cellConfigs = this.cells.map(getCellConfig);\n const config = { \"cells\": cellConfigs };\n return Object.assign(Object.assign({}, baseConfig), config);\n }\n /** @nocollapse */\n static fromConfig(cls, config, customObjects = {}) {\n const cells = [];\n for (const cellConfig of config[\"cells\"]) {\n cells.push(deserialize(cellConfig, customObjects));\n }\n return new cls({ cells });\n }\n get trainableWeights() {\n if (!this.trainable) {\n return [];\n }\n const weights = [];\n for (const cell of this.cells) {\n weights.push(...cell.trainableWeights);\n }\n return weights;\n }\n get nonTrainableWeights() {\n const weights = [];\n for (const cell of this.cells) {\n weights.push(...cell.nonTrainableWeights);\n }\n if (!this.trainable) {\n const trainableWeights = [];\n for (const cell of this.cells) {\n trainableWeights.push(...cell.trainableWeights);\n }\n return trainableWeights.concat(weights);\n }\n return weights;\n }\n /**\n * Retrieve the weights of a the model.\n *\n * @returns A flat `Array` of `tf.Tensor`s.\n */\n getWeights() {\n const weights = [];\n for (const cell of this.cells) {\n weights.push(...cell.weights);\n }\n return batchGetValue(weights);\n }\n /**\n * Set the weights of the model.\n *\n * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n * the output of `getWeights()`.\n */\n setWeights(weights) {\n const tuples = [];\n for (const cell of this.cells) {\n const numParams = cell.weights.length;\n const inputWeights = weights.splice(numParams);\n for (let i = 0; i < cell.weights.length; ++i) {\n tuples.push([cell.weights[i], inputWeights[i]]);\n }\n }\n batchSetValue(tuples);\n }\n};\nStackedRNNCells.className = \"StackedRNNCells\";\nserialization_exports.registerClass(StackedRNNCells);\nfunction generateDropoutMask(args) {\n const { ones: ones4, rate, training = false, count: count2 = 1, dropoutFunc } = args;\n const droppedInputs = () => dropoutFunc != null ? dropoutFunc(ones4(), rate) : dropout2(ones4(), rate);\n const createMask = () => inTrainPhase(droppedInputs, ones4, training);\n if (!count2 || count2 <= 1) {\n return keep(createMask().clone());\n }\n const masks = Array(count2).fill(void 0).map(createMask);\n return masks.map((m) => keep(m.clone()));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js\nvar __rest = function(s, e) {\n var t = {};\n for (var p2 in s)\n if (Object.prototype.hasOwnProperty.call(s, p2) && e.indexOf(p2) < 0)\n t[p2] = s[p2];\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n for (var i = 0, p2 = Object.getOwnPropertySymbols(s); i < p2.length; i++) {\n if (e.indexOf(p2[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p2[i]))\n t[p2[i]] = s[p2[i]];\n }\n return t;\n};\nvar ConvRNN2D = class extends RNN {\n constructor(args) {\n if (args.unroll) {\n throw new NotImplementedError(\"Unrolling is not possible with convolutional RNNs.\");\n }\n if (Array.isArray(args.cell)) {\n throw new NotImplementedError(\"It is not possible at the moment to stack convolutional cells.\");\n }\n super(args);\n this.inputSpec = [new InputSpec({ ndim: 5 })];\n }\n call(inputs, kwargs) {\n return tidy(() => {\n if (this.cell.dropoutMask != null) {\n dispose(this.cell.dropoutMask);\n this.cell.dropoutMask = null;\n }\n if (this.cell.recurrentDropoutMask != null) {\n dispose(this.cell.recurrentDropoutMask);\n this.cell.recurrentDropoutMask = null;\n }\n if (kwargs && kwargs[\"constants\"]) {\n throw new ValueError(\"ConvRNN2D cell does not support constants\");\n }\n const mask = kwargs == null ? null : kwargs[\"mask\"];\n const training = kwargs == null ? null : kwargs[\"training\"];\n const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n return super.call(inputs, { mask, training, initialState });\n });\n }\n computeOutputShape(inputShape) {\n let outShape = this.computeSingleOutputShape(inputShape);\n if (!this.returnSequences) {\n outShape = [outShape[0], ...outShape.slice(2)];\n }\n if (this.returnState) {\n outShape = [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n }\n return outShape;\n }\n getInitialState(inputs) {\n return tidy(() => {\n const { stateSize } = this.cell;\n const inputShape = inputs.shape;\n const outputShape = this.computeSingleOutputShape(inputShape);\n const stateShape = [outputShape[0], ...outputShape.slice(2)];\n const initialState = zeros(stateShape);\n if (Array.isArray(stateSize)) {\n return Array(stateSize.length).fill(initialState);\n }\n return [initialState];\n });\n }\n resetStates(states, training = false) {\n tidy(() => {\n if (!this.stateful) {\n throw new AttributeError(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");\n }\n const inputShape = this.inputSpec[0].shape;\n const outputShape = this.computeSingleOutputShape(inputShape);\n const stateShape = [outputShape[0], ...outputShape.slice(2)];\n const batchSize = inputShape[0];\n if (batchSize == null) {\n throw new ValueError(\"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.\");\n }\n if (this.getStates() == null) {\n if (Array.isArray(this.cell.stateSize)) {\n this.states_ = this.cell.stateSize.map(() => zeros(stateShape));\n } else {\n this.states_ = [zeros(stateShape)];\n }\n } else if (states == null) {\n dispose(this.states_);\n if (this.keptStates != null) {\n dispose(this.keptStates);\n this.keptStates = [];\n }\n if (Array.isArray(this.cell.stateSize)) {\n this.states_ = this.cell.stateSize.map(() => zeros(stateShape));\n } else {\n this.states_[0] = zeros(stateShape);\n }\n } else {\n if (!Array.isArray(states)) {\n states = [states];\n }\n if (states.length !== this.states_.length) {\n throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);\n }\n if (training) {\n this.keptStates.push(this.states_.slice());\n } else {\n dispose(this.states_);\n }\n for (let index = 0; index < this.states_.length; ++index) {\n const value = states[index];\n const expectedShape = stateShape;\n if (!util_exports.arraysEqual(value.shape, expectedShape)) {\n throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);\n }\n this.states_[index] = value;\n }\n }\n this.states_ = this.states_.map((state) => keep(state.clone()));\n });\n }\n computeSingleOutputShape(inputShape) {\n const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n const isChannelsFirst = dataFormat === \"channelsFirst\";\n const h = inputShape[isChannelsFirst ? 3 : 2];\n const w = inputShape[isChannelsFirst ? 4 : 3];\n const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n const outShape = [\n ...inputShape.slice(0, 2),\n ...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters]\n ];\n return outShape;\n }\n};\nConvRNN2D.className = \"ConvRNN2D\";\nvar ConvLSTM2DCell = class extends LSTMCell {\n constructor(args) {\n const { filters, kernelSize, strides, padding, dataFormat, dilationRate } = args;\n super(Object.assign(Object.assign({}, args), { units: filters }));\n this.filters = filters;\n assertPositiveInteger(this.filters, \"filters\");\n this.kernelSize = normalizeArray(kernelSize, 2, \"kernelSize\");\n this.kernelSize.forEach((size) => assertPositiveInteger(size, \"kernelSize\"));\n this.strides = normalizeArray(strides || 1, 2, \"strides\");\n this.strides.forEach((stride) => assertPositiveInteger(stride, \"strides\"));\n this.padding = padding || \"valid\";\n checkPaddingMode(this.padding);\n this.dataFormat = dataFormat || \"channelsLast\";\n checkDataFormat(this.dataFormat);\n this.dilationRate = normalizeArray(dilationRate || 1, 2, \"dilationRate\");\n this.dilationRate.forEach((rate) => assertPositiveInteger(rate, \"dilationRate\"));\n }\n build(inputShape) {\n var _a;\n inputShape = getExactlyOneShape(inputShape);\n const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n if (inputShape[channelAxis] == null) {\n throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);\n }\n const inputDim = inputShape[channelAxis];\n const numOfKernels = 4;\n const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n this.kernel = this.addWeight(\"kernel\", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n this.recurrentKernel = this.addWeight(\"recurrent_kernel\", recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n if (this.useBias) {\n let biasInitializer;\n if (this.unitForgetBias) {\n const init2 = this.biasInitializer;\n const filters = this.filters;\n biasInitializer = new (_a = class CustomInit extends Initializer {\n apply(shape, dtype) {\n const biasI = init2.apply([filters]);\n const biasF = ones2([filters]);\n const biasCAndO = init2.apply([filters * 2]);\n return concatenate([biasI, biasF, biasCAndO]);\n }\n }, /** @nocollapse */\n _a.className = \"CustomInit\", _a)();\n } else {\n biasInitializer = this.biasInitializer;\n }\n this.bias = this.addWeight(\"bias\", [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n }\n this.built = true;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n if (inputs.length !== 3) {\n throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);\n }\n const training = kwargs[\"training\"] || false;\n const x = inputs[0];\n const hTMinus1 = inputs[1];\n const cTMinus1 = inputs[2];\n const numOfKernels = 4;\n if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n this.dropoutMask = generateDropoutMask({\n ones: () => onesLike(x),\n rate: this.dropout,\n training,\n count: numOfKernels,\n dropoutFunc: this.dropoutFunc\n });\n }\n const dropoutMask = this.dropoutMask;\n const applyDropout = (x2, mask, index) => {\n if (!mask || !mask[index]) {\n return x2;\n }\n return mul(mask[index], x2);\n };\n let xI = applyDropout(x, dropoutMask, 0);\n let xF = applyDropout(x, dropoutMask, 1);\n let xC = applyDropout(x, dropoutMask, 2);\n let xO = applyDropout(x, dropoutMask, 3);\n if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n this.recurrentDropoutMask = generateDropoutMask({\n ones: () => onesLike(hTMinus1),\n rate: this.recurrentDropout,\n training,\n count: numOfKernels,\n dropoutFunc: this.dropoutFunc\n });\n }\n const recDropoutMask = this.recurrentDropoutMask;\n let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n const kernelChannelAxis = 3;\n const [kernelI, kernelF, kernelC, kernelO] = split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n const [biasI, biasF, biasC, biasO] = this.useBias ? split(this.bias.read(), numOfKernels) : [null, null, null, null];\n xI = this.inputConv(xI, kernelI, biasI, this.padding);\n xF = this.inputConv(xF, kernelF, biasF, this.padding);\n xC = this.inputConv(xC, kernelC, biasC, this.padding);\n xO = this.inputConv(xO, kernelO, biasO, this.padding);\n const [recKernelI, recKernelF, recKernelC, recKernelO] = split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n hI = this.recurrentConv(hI, recKernelI);\n hF = this.recurrentConv(hF, recKernelF);\n hC = this.recurrentConv(hC, recKernelC);\n hO = this.recurrentConv(hO, recKernelO);\n const i = this.recurrentActivation.apply(add2(xI, hI));\n const f = this.recurrentActivation.apply(add2(xF, hF));\n const c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(add2(xC, hC))));\n const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c));\n return [h, h, c];\n });\n }\n getConfig() {\n const _a = super.getConfig(), { \"units\": _ } = _a, baseConfig = __rest(_a, [\"units\"]);\n const config = {\n filters: this.filters,\n kernelSize: this.kernelSize,\n padding: this.padding,\n dataFormat: this.dataFormat,\n dilationRate: this.dilationRate,\n strides: this.strides\n };\n return Object.assign(Object.assign({}, baseConfig), config);\n }\n inputConv(x, w, b, padding) {\n const out = conv2d(x, w, this.strides, padding || \"valid\", this.dataFormat === \"channelsFirst\" ? \"NCHW\" : \"NHWC\", this.dilationRate);\n if (b) {\n return biasAdd(out, b, this.dataFormat);\n }\n return out;\n }\n recurrentConv(x, w) {\n const strides = 1;\n return conv2d(x, w, strides, \"same\", this.dataFormat === \"channelsFirst\" ? \"NCHW\" : \"NHWC\");\n }\n};\nConvLSTM2DCell.className = \"ConvLSTM2DCell\";\nserialization_exports.registerClass(ConvLSTM2DCell);\nvar ConvLSTM2D = class extends ConvRNN2D {\n constructor(args) {\n const cell = new ConvLSTM2DCell(args);\n super(Object.assign(Object.assign({}, args), { cell }));\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n return new cls(config);\n }\n};\nConvLSTM2D.className = \"ConvLSTM2D\";\nserialization_exports.registerClass(ConvLSTM2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/core.js\nvar Dropout = class extends Layer {\n constructor(args) {\n super(args);\n this.rate = Math.max(Math.min(args.rate, 1), 0);\n this.noiseShape = args.noiseShape;\n this.seed = args.seed;\n this.supportsMasking = true;\n }\n getNoiseShape(input2) {\n if (this.noiseShape == null) {\n return this.noiseShape;\n }\n const inputShape = input2.shape;\n const noiseShape = [];\n for (let i = 0; i < this.noiseShape.length; ++i) {\n noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n }\n return noiseShape;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n const input2 = getExactlyOneTensor(inputs);\n if (0 < this.rate && this.rate < 1) {\n const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n const noiseShape = this.getNoiseShape(input2);\n const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training);\n return output;\n }\n return inputs;\n });\n }\n getConfig() {\n const config = {\n rate: this.rate,\n noiseShape: this.noiseShape,\n seed: this.seed\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n dispose() {\n return super.dispose();\n }\n};\nDropout.className = \"Dropout\";\nserialization_exports.registerClass(Dropout);\nvar SpatialDropout1D = class extends Dropout {\n constructor(args) {\n super(args);\n this.inputSpec = [{ ndim: 3 }];\n }\n getNoiseShape(input2) {\n const inputShape = input2.shape;\n return [inputShape[0], 1, inputShape[2]];\n }\n};\nSpatialDropout1D.className = \"SpatialDropout1D\";\nserialization_exports.registerClass(SpatialDropout1D);\nvar Dense = class extends Layer {\n constructor(args) {\n super(args);\n this.activation = null;\n this.useBias = true;\n this.kernel = null;\n this.bias = null;\n this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n if (args.batchInputShape == null && args.inputShape == null && args.inputDim != null) {\n let batchSize = null;\n if (args.batchSize != null) {\n batchSize = args.batchSize;\n }\n this.batchInputShape = [batchSize, args.inputDim];\n }\n this.units = args.units;\n assertPositiveInteger(this.units, \"units\");\n this.activation = getActivation(args.activation);\n if (args.useBias != null) {\n this.useBias = args.useBias;\n }\n this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n this.kernelConstraint = getConstraint(args.kernelConstraint);\n this.biasConstraint = getConstraint(args.biasConstraint);\n this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n this.biasRegularizer = getRegularizer(args.biasRegularizer);\n this.activityRegularizer = getRegularizer(args.activityRegularizer);\n this.supportsMasking = true;\n this.inputSpec = [{ minNDim: 2 }];\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const inputLastDim = inputShape[inputShape.length - 1];\n if (this.kernel == null) {\n this.kernel = this.addWeight(\"kernel\", [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n if (this.useBias) {\n this.bias = this.addWeight(\"bias\", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n }\n }\n this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n this.built = true;\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const outputShape = inputShape.slice();\n outputShape[outputShape.length - 1] = this.units;\n return outputShape;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n const input2 = getExactlyOneTensor(inputs);\n const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n let output;\n if (fusedActivationName != null) {\n output = dot2(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n } else {\n output = dot2(input2, this.kernel.read());\n if (this.bias != null) {\n output = biasAdd(output, this.bias.read());\n }\n if (this.activation != null) {\n output = this.activation.apply(output);\n }\n }\n return output;\n });\n }\n getConfig() {\n const config = {\n units: this.units,\n activation: serializeActivation(this.activation),\n useBias: this.useBias,\n kernelInitializer: serializeInitializer(this.kernelInitializer),\n biasInitializer: serializeInitializer(this.biasInitializer),\n kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n biasRegularizer: serializeRegularizer(this.biasRegularizer),\n activityRegularizer: serializeRegularizer(this.activityRegularizer),\n kernelConstraint: serializeConstraint(this.kernelConstraint),\n biasConstraint: serializeConstraint(this.biasConstraint)\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nDense.className = \"Dense\";\nserialization_exports.registerClass(Dense);\nvar Flatten = class extends Layer {\n constructor(args) {\n args = args || {};\n super(args);\n this.inputSpec = [{ minNDim: 3 }];\n this.dataFormat = args.dataFormat;\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n for (const dim of inputShape.slice(1)) {\n if (dim == null) {\n throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);\n }\n }\n return [inputShape[0], arrayProd(inputShape, 1)];\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n let input2 = getExactlyOneTensor(inputs);\n if (this.dataFormat === \"channelsFirst\" && input2.rank > 1) {\n const permutation = [0];\n for (let i = 2; i < input2.rank; ++i) {\n permutation.push(i);\n }\n permutation.push(1);\n input2 = transpose(input2, permutation);\n }\n return batchFlatten(input2);\n });\n }\n getConfig() {\n const config = {};\n if (this.dataFormat != null) {\n config[\"dataFormat\"] = this.dataFormat;\n }\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nFlatten.className = \"Flatten\";\nserialization_exports.registerClass(Flatten);\nvar Activation2 = class extends Layer {\n constructor(args) {\n super(args);\n this.supportsMasking = true;\n this.activation = getActivation(args.activation);\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n const input2 = getExactlyOneTensor(inputs);\n return this.activation.apply(input2);\n });\n }\n getConfig() {\n const config = { activation: serializeActivation(this.activation) };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nActivation2.className = \"Activation\";\nserialization_exports.registerClass(Activation2);\nvar RepeatVector = class extends Layer {\n constructor(args) {\n super(args);\n this.n = args.n;\n this.inputSpec = [{ ndim: 2 }];\n }\n computeOutputShape(inputShape) {\n return [inputShape[0], this.n, inputShape[1]];\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = getExactlyOneTensor(inputs);\n return repeat(inputs, this.n);\n });\n }\n getConfig() {\n const config = {\n n: this.n\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nRepeatVector.className = \"RepeatVector\";\nserialization_exports.registerClass(RepeatVector);\nvar Reshape2 = class extends Layer {\n constructor(args) {\n super(args);\n this.targetShape = args.targetShape;\n for (let i = 0; i < this.targetShape.length; ++i) {\n if (this.isUnknown(this.targetShape[i])) {\n this.targetShape[i] = null;\n }\n }\n }\n isUnknown(dim) {\n return dim < 0 || dim == null;\n }\n /**\n * Finds and replaces a missing dimension in output shape.\n *\n * This is a near direct port of the internal Numpy function\n * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.\n *\n * @param inputShape: Original shape of array begin reshape.\n * @param outputShape: Target shape of the array, with at most a single\n * `null` or negative number, which indicates an underdetermined dimension\n * that should be derived from `inputShape` and the known dimensions of\n * `outputShape`.\n * @returns: The output shape with `null` replaced with its computed value.\n * @throws: ValueError: If `inputShape` and `outputShape` do not match.\n */\n fixUnknownDimension(inputShape, outputShape) {\n const errorMsg = \"Total size of new array must be unchanged.\";\n const finalShape = outputShape.slice();\n let known = 1;\n let unknown = null;\n for (let i = 0; i < finalShape.length; ++i) {\n const dim = finalShape[i];\n if (this.isUnknown(dim)) {\n if (unknown === null) {\n unknown = i;\n } else {\n throw new ValueError(\"Can only specifiy one unknown dimension.\");\n }\n } else {\n known *= dim;\n }\n }\n const originalSize = arrayProd(inputShape);\n if (unknown !== null) {\n if (known === 0 || originalSize % known !== 0) {\n throw new ValueError(errorMsg);\n }\n finalShape[unknown] = originalSize / known;\n } else if (originalSize !== known) {\n throw new ValueError(errorMsg);\n }\n return finalShape;\n }\n computeOutputShape(inputShape) {\n let anyUnknownDims = false;\n for (let i = 0; i < inputShape.length; ++i) {\n if (this.isUnknown(inputShape[i])) {\n anyUnknownDims = true;\n break;\n }\n }\n if (anyUnknownDims) {\n return inputShape.slice(0, 1).concat(this.targetShape);\n } else {\n return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n }\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n const input2 = getExactlyOneTensor(inputs);\n const inputShape = input2.shape;\n const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n return reshape(input2, outputShape);\n });\n }\n getConfig() {\n const config = {\n targetShape: this.targetShape\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nReshape2.className = \"Reshape\";\nserialization_exports.registerClass(Reshape2);\nvar Permute = class extends Layer {\n constructor(args) {\n super(args);\n if (args.dims == null) {\n throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");\n }\n if (!Array.isArray(args.dims)) {\n throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${args.dims} instead.`);\n }\n const expectedSortedIndices = range2(1, args.dims.length + 1);\n if (!util_exports.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n throw new Error(\"Invalid permutation `dims`: \" + JSON.stringify(args.dims) + \" `dims` must contain consecutive integers starting from 1.\");\n }\n this.dims = args.dims;\n this.dimsIncludingBatch = [0].concat(this.dims);\n this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const outputShape = inputShape.slice();\n this.dims.forEach((dim, i) => {\n outputShape[i + 1] = inputShape[dim];\n });\n return outputShape;\n }\n call(inputs, kwargs) {\n return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n }\n getConfig() {\n const config = {\n dims: this.dims\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nPermute.className = \"Permute\";\nserialization_exports.registerClass(Permute);\nvar Masking = class extends Layer {\n constructor(args) {\n super(args == null ? {} : args);\n this.supportsMasking = true;\n if (args != null) {\n this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n } else {\n this.maskValue = 0;\n }\n }\n computeOutputShape(inputShape) {\n return inputShape;\n }\n getConfig() {\n const baseConfig = super.getConfig();\n const config = { maskValue: this.maskValue };\n Object.assign(config, baseConfig);\n return config;\n }\n computeMask(inputs, mask) {\n const input2 = getExactlyOneTensor(inputs);\n const axis = -1;\n return any(notEqual(input2, this.maskValue), axis);\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n const input2 = getExactlyOneTensor(inputs);\n const axis = -1;\n const keepDims = true;\n const booleanMask = any(notEqual(input2, this.maskValue), axis, keepDims);\n const output = mul(input2, cast(booleanMask, input2.dtype));\n return output;\n });\n }\n};\nMasking.className = \"Masking\";\nserialization_exports.registerClass(Masking);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/embeddings.js\nvar Embedding = class extends Layer {\n constructor(args) {\n super(args);\n this.embeddings = null;\n this.DEFAULT_EMBEDDINGS_INITIALIZER = \"randomUniform\";\n if (args.batchInputShape == null && args.inputShape == null) {\n let batchSize = null;\n if (args.batchSize != null) {\n batchSize = args.batchSize;\n }\n if (args.inputLength == null) {\n this.batchInputShape = [batchSize, null];\n } else {\n this.batchInputShape = [batchSize].concat(toList(args.inputLength));\n }\n }\n this.inputDim = args.inputDim;\n assertPositiveInteger(this.inputDim, \"inputDim\");\n this.outputDim = args.outputDim;\n assertPositiveInteger(this.outputDim, \"outputDim\");\n this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n this.activityRegularizer = getRegularizer(args.activityRegularizer);\n this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n this.maskZero = args.maskZero;\n this.supportsMasking = args.maskZero;\n this.inputLength = args.inputLength;\n }\n build(inputShape) {\n this.embeddings = this.addWeight(\"embeddings\", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n this.built = true;\n }\n // Override warnOnIncompatibleInputShape because an embedding layer allows\n // the input to have varying ranks.\n warnOnIncompatibleInputShape(inputShape) {\n }\n computeMask(inputs, mask) {\n return tidy(() => {\n if (!this.maskZero) {\n return null;\n } else {\n inputs = getExactlyOneTensor(inputs);\n return notEqual(inputs, zerosLike(inputs));\n }\n });\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n if (this.inputLength == null) {\n return [...inputShape, this.outputDim];\n }\n const inLens = toList(this.inputLength);\n if (inLens.length !== inputShape.length - 1) {\n throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${inputShape}`);\n } else {\n let i = 0;\n for (let k = 0; k < inLens.length; ++k) {\n const s1 = inLens[k];\n const s2 = inputShape[k + 1];\n if (s1 != null && s2 != null && s1 !== s2) {\n throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${inputShape}`);\n } else if (s1 == null) {\n inLens[i] = s2;\n }\n i++;\n }\n }\n return [inputShape[0], ...inLens, this.outputDim];\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n let input2 = getExactlyOneTensor(inputs);\n if (input2.dtype !== \"int32\") {\n input2 = cast2(input2, \"int32\");\n }\n const output = gather2(this.embeddings.read(), reshape(input2, [input2.size]));\n return reshape(output, getExactlyOneShape(this.computeOutputShape(input2.shape)));\n });\n }\n getConfig() {\n const config = {\n inputDim: this.inputDim,\n outputDim: this.outputDim,\n embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n activityRegularizer: serializeRegularizer(this.activityRegularizer),\n embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n maskZero: this.maskZero,\n inputLength: this.inputLength\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nEmbedding.className = \"Embedding\";\nserialization_exports.registerClass(Embedding);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/merge.js\nvar Merge = class extends Layer {\n constructor(args) {\n super(args || {});\n this.supportsMasking = true;\n }\n /**\n * Logic for merging multiple tensors, to be overridden by subclasses.\n * @param inputs\n */\n mergeFunction(inputs) {\n throw new NotImplementedError();\n }\n /**\n * Computes the shape of the result of an elementwise operation.\n *\n * @param shape1: Shape of the first tensor.\n * @param shape2: Shape of the second tensor.\n * @returns Expected output shape when an elementwise operation is carried\n * out on 2 tensors with shapes `shape1` and `shape2`.\n * @throws ValueError: If `shape1` and `shape2` are not compatible for\n * element-wise operations.\n */\n computeElementwiseOpOutputShape(shape1, shape2) {\n if (shape1 == null || shape2 == null) {\n return null;\n } else if (shape1.length < shape2.length) {\n return this.computeElementwiseOpOutputShape(shape2, shape1);\n } else if (shape2.length === 0) {\n return shape1;\n }\n const outputShape = shape1.slice(0, shape1.length - shape2.length);\n for (let k = 0; k < shape2.length; ++k) {\n const i = shape1[shape1.length - shape2.length + k];\n const j = shape2[k];\n if (i == null || j == null || i < 0 || j < 0) {\n outputShape.push(null);\n } else if (i === 1) {\n outputShape.push(j);\n } else if (j === 1) {\n outputShape.push(i);\n } else {\n if (i !== j) {\n throw new ValueError(\"Operands could not be broadcast together with shapes \" + JSON.stringify(shape1) + \" \" + JSON.stringify(shape2));\n }\n outputShape.push(i);\n }\n }\n return outputShape;\n }\n build(inputShape) {\n if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n inputShape = [getExactlyOneShape(inputShape)];\n }\n inputShape = inputShape;\n if (inputShape.length < 2) {\n throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);\n }\n let batchSizes = [];\n for (const shape of inputShape) {\n if (shape != null && shape[0] !== null) {\n batchSizes.push(shape[0]);\n }\n }\n batchSizes = unique2(batchSizes);\n if (batchSizes.length > 1) {\n throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n }\n let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n for (let i = 1; i < inputShape.length; ++i) {\n const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n }\n const allRanks = inputShape.map((shape) => shape.length);\n if (inputShape.indexOf(null) === -1 && unique2(allRanks).length === 1) {\n this.reshapeRequired = false;\n } else {\n this.reshapeRequired = true;\n }\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = inputs;\n if (this.reshapeRequired) {\n const reshapedInputs = [];\n const inputDims = inputs.map((input2) => input2.rank);\n if (inputDims.indexOf(null) === -1) {\n const maxNDim = max2(inputDims);\n for (let x of inputs) {\n const xNDim = x.rank;\n for (let k = 0; k < maxNDim - xNDim; ++k) {\n x = expandDims2(x, 1);\n }\n reshapedInputs.push(x);\n }\n return this.mergeFunction(reshapedInputs);\n } else {\n let transposed = false;\n for (const x of inputs) {\n const xNDim = x.rank;\n if (xNDim == null) {\n const xShape = x.shape;\n const batchSize = xShape[0];\n const newShape = xShape.slice(1).concat([batchSize]);\n let xTransposed = reshape(x, [batchSize].concat(arrayProd(xShape.slice(1))));\n xTransposed = transpose(xTransposed, [1, 0]);\n xTransposed = reshape(xTransposed, newShape);\n reshapedInputs.push(xTransposed);\n transposed = true;\n } else if (xNDim > 1) {\n const dims = range2(1, xNDim).concat([0]);\n reshapedInputs.push(transpose(x, dims));\n transposed = true;\n } else {\n reshapedInputs.push(x);\n }\n }\n let y = this.mergeFunction(reshapedInputs);\n const yNDim = y.rank;\n if (transposed) {\n if (yNDim == null) {\n const yShape = y.shape;\n const yNDim2 = yShape.length;\n const batchSize = yShape[yNDim2 - 1];\n const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n y = reshape(transpose(reshape(y, [-1, batchSize]), [1, 0]), newShape);\n } else if (yNDim > 1) {\n const dims = [yNDim - 1].concat(range2(0, yNDim - 1));\n y = transpose(y, dims);\n }\n }\n return y;\n }\n } else {\n return this.mergeFunction(inputs);\n }\n });\n }\n computeOutputShape(inputShape) {\n inputShape = inputShape;\n let outputShape;\n if (inputShape[0] == null) {\n outputShape = null;\n } else {\n outputShape = inputShape[0].slice(1);\n }\n for (let i = 1; i < inputShape.length; ++i) {\n const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n }\n let batchSizes = [];\n for (const shape of inputShape) {\n if (shape != null && shape[0] !== null) {\n batchSizes.push(shape[0]);\n }\n }\n batchSizes = unique2(batchSizes);\n if (batchSizes.length === 1) {\n outputShape = batchSizes.concat(outputShape);\n } else {\n outputShape = [null].concat(outputShape);\n }\n return outputShape;\n }\n computeMask(inputs, mask) {\n return tidy(() => {\n if (mask == null) {\n return null;\n }\n if (!Array.isArray(mask)) {\n throw new ValueError(\"`mask` should be an Array\");\n }\n if (!Array.isArray(inputs)) {\n throw new ValueError(\"`inputs` should be an Array\");\n }\n if (mask.length !== inputs.length) {\n throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);\n }\n if (mask.every((m) => m == null)) {\n return null;\n }\n mask = mask.map((m) => m == null ? m : expandDims(m, 0));\n let output = mask[0];\n for (let i = 1; i < mask.length - 1; ++i) {\n output = logicalAnd(output, mask[i]);\n }\n return output;\n });\n }\n};\nvar Add2 = class extends Merge {\n constructor(args) {\n super(args);\n }\n mergeFunction(inputs) {\n return tidy(() => {\n let output = inputs[0].clone();\n for (let i = 1; i < inputs.length; ++i) {\n output = add2(output, inputs[i]);\n }\n return output;\n });\n }\n};\nAdd2.className = \"Add\";\nserialization_exports.registerClass(Add2);\nvar Multiply2 = class extends Merge {\n constructor(args) {\n super(args);\n }\n mergeFunction(inputs) {\n return tidy(() => {\n let output = inputs[0].clone();\n for (let i = 1; i < inputs.length; ++i) {\n output = mul(output, inputs[i]);\n }\n return output;\n });\n }\n};\nMultiply2.className = \"Multiply\";\nserialization_exports.registerClass(Multiply2);\nvar Average = class extends Merge {\n constructor(args) {\n super(args);\n }\n mergeFunction(inputs) {\n return tidy(() => {\n let output = inputs[0].clone();\n for (let i = 1; i < inputs.length; ++i) {\n output = add2(output, inputs[i]);\n }\n return mul(1 / inputs.length, output);\n });\n }\n};\nAverage.className = \"Average\";\nserialization_exports.registerClass(Average);\nvar Maximum2 = class extends Merge {\n constructor(args) {\n super(args);\n }\n mergeFunction(inputs) {\n return tidy(() => {\n let output = inputs[0];\n for (let i = 1; i < inputs.length; ++i) {\n output = maximum(output, inputs[i]);\n }\n return output;\n });\n }\n};\nMaximum2.className = \"Maximum\";\nserialization_exports.registerClass(Maximum2);\nvar Minimum2 = class extends Merge {\n constructor(args) {\n super(args);\n }\n mergeFunction(inputs) {\n return tidy(() => {\n let output = inputs[0];\n for (let i = 1; i < inputs.length; ++i) {\n output = minimum(output, inputs[i]);\n }\n return output;\n });\n }\n};\nMinimum2.className = \"Minimum\";\nserialization_exports.registerClass(Minimum2);\nvar Concatenate = class extends Merge {\n constructor(args) {\n super(args);\n this.DEFAULT_AXIS = -1;\n if (args == null) {\n args = {};\n }\n this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n this.supportsMasking = true;\n this.reshapeRequired = false;\n }\n build(inputShape) {\n if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) || inputShape.length === 1) {\n throw new ValueError(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");\n }\n inputShape = inputShape;\n let allNoneShape = true;\n for (const shape of inputShape) {\n if (shape != null) {\n allNoneShape = false;\n break;\n }\n }\n if (allNoneShape) {\n return;\n }\n const shapeSet = [];\n for (let i = 0; i < inputShape.length; ++i) {\n const shapeWithoutConcatAxis = inputShape[i].slice();\n shapeWithoutConcatAxis.splice(this.axis, 1);\n let exists = false;\n for (const shape of shapeSet) {\n if (util_exports.arraysEqual(shape, shapeWithoutConcatAxis)) {\n exists = true;\n break;\n }\n }\n if (!exists) {\n shapeSet.push(shapeWithoutConcatAxis);\n }\n }\n if (shapeSet.length > 1) {\n throw new ValueError(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \" + JSON.stringify(inputShape));\n }\n }\n mergeFunction(inputs) {\n return tidy(() => {\n return concatenate(inputs, this.axis);\n });\n }\n computeOutputShape(inputShape) {\n if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n throw new ValueError(\"A `Concatenate` layer should be called on a list of inputs.\");\n }\n const inputShapes = inputShape;\n const outputShape = inputShapes[0].slice();\n const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n for (const shape of inputShapes.slice(1)) {\n if (outputShape[axis] == null || shape[axis] == null) {\n outputShape[axis] = null;\n break;\n }\n outputShape[axis] += shape[axis];\n }\n return outputShape;\n }\n computeMask(inputs, mask) {\n if (mask == null) {\n return null;\n }\n if (!Array.isArray(mask)) {\n throw new ValueError(\"`mask` should be an array for Concatenate\");\n }\n if (!Array.isArray(inputs)) {\n throw new ValueError(\"`inputs` should be an array for Concatenate\");\n }\n if (mask.length !== inputs.length) {\n throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);\n }\n return tidy(() => {\n let allNullMasks = true;\n mask.forEach((m) => {\n if (m != null) {\n allNullMasks = false;\n return;\n }\n });\n if (allNullMasks) {\n return null;\n }\n const outputMasks = [];\n for (let i = 0; i < inputs.length; ++i) {\n if (mask[i] == null) {\n outputMasks.push(cast(onesLike(inputs[i]), \"bool\"));\n } else if (mask[i].rank < inputs[i].rank) {\n outputMasks.push(expandDims(mask[i], -1));\n } else {\n outputMasks.push(mask[i]);\n }\n }\n const concatenatedMasks = concat(outputMasks, this.axis);\n return all(concatenatedMasks, -1, false);\n });\n }\n getConfig() {\n const config = {\n \"axis\": this.axis\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nConcatenate.className = \"Concatenate\";\nserialization_exports.registerClass(Concatenate);\nfunction interpretAxis(axis, dim) {\n while (axis < 0) {\n axis += dim;\n }\n return axis;\n}\nfunction batchDot(x, y, axes) {\n if (x.shape.length > 3 || y.shape.length > 3) {\n throw new NotImplementedError(\"batchDot is not implemented for tensors of 4D or higher rank yet\");\n }\n util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`);\n util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`);\n if (typeof axes === \"number\") {\n axes = [axes, axes];\n }\n if (x.dtype === \"complex64\" || y.dtype === \"complex64\") {\n throw new NotImplementedError(\"batchDot is not implemented for complex64-type Tensors yet.\");\n }\n const xNDim = x.shape.length;\n const yNDim = y.shape.length;\n if (axes == null) {\n axes = [xNDim - 1, yNDim - 2];\n }\n const axesArray = axes;\n return tidy(() => {\n let diff;\n if (xNDim > yNDim) {\n diff = xNDim - yNDim;\n const diffShape = [];\n for (let i = 0; i < diff; ++i) {\n diffShape.push(1);\n }\n y = reshape(y, y.shape.concat(diffShape));\n } else if (yNDim > xNDim) {\n diff = yNDim - xNDim;\n const diffShape = [];\n for (let i = 0; i < diff; ++i) {\n diffShape.push(1);\n }\n x = reshape(x, x.shape.concat(diffShape));\n } else {\n diff = 0;\n }\n let out;\n if (x.shape.length === 2 && y.shape.length === 2) {\n if (axesArray[0] === axesArray[1]) {\n out = sum2(mul(x, y), axesArray[0]);\n } else {\n out = sum2(mul(transpose(x, [1, 0]), y), axesArray[1]);\n }\n } else {\n const adjX = axesArray[0] !== x.shape.length - 1;\n const adjY = axesArray[1] === y.shape.length - 1;\n out = matMul(x, y, adjX, adjY);\n }\n if (diff > 0) {\n let idx;\n if (xNDim > yNDim) {\n idx = xNDim + yNDim - 3;\n } else {\n idx = xNDim - 1;\n }\n const squeezeAxes = [];\n for (let i = idx; i < idx + diff; ++i) {\n squeezeAxes.push(i);\n }\n out = squeeze(out, squeezeAxes);\n }\n if (out.shape.length === 1) {\n out = expandDims(out, 1);\n }\n return out;\n });\n}\nvar Dot = class extends Merge {\n constructor(args) {\n super(args);\n this.axes = args.axes;\n this.normalize = args.normalize == null ? false : args.normalize;\n this.supportsMasking = true;\n this.reshapeRequired = false;\n }\n build(inputShape) {\n util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => \"A `Dot` layer should be called on a list of exactly 2 inputs.\");\n const shape1 = inputShape[0];\n const shape2 = inputShape[1];\n if (shape1.length > 3 || shape2.length > 3) {\n throw new NotImplementedError(\"Dot layer does not support tensors of 4D or higher rank yet.\");\n }\n const axes = this.interpretAxes(shape1, shape2);\n if (shape1[axes[0]] !== shape2[axes[1]]) {\n throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n }\n }\n mergeFunction(inputs) {\n if (inputs.length !== 2) {\n throw new ValueError(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);\n }\n let x1 = inputs[0];\n let x2 = inputs[1];\n let axes;\n if (!Array.isArray(this.axes)) {\n axes = [\n interpretAxis(this.axes, x1.shape.length),\n interpretAxis(this.axes, x2.shape.length)\n ];\n } else {\n axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n }\n if (this.normalize) {\n x1 = l2Normalize(x1, axes[0]);\n x2 = l2Normalize(x2, axes[1]);\n }\n return batchDot(x1, x2, axes);\n }\n interpretAxes(shape1, shape2) {\n let axes;\n if (!Array.isArray(this.axes)) {\n axes = [\n interpretAxis(this.axes, shape1.length),\n interpretAxis(this.axes, shape2.length)\n ];\n } else {\n axes = this.axes;\n }\n return axes;\n }\n computeOutputShape(inputShape) {\n util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => \"A `Dot` layer should be called on a list of exactly 2 inputs.\");\n const shape1 = inputShape[0].slice();\n const shape2 = inputShape[1].slice();\n if (shape1.length > 3 || shape2.length > 3) {\n throw new NotImplementedError(\"Dot layer does not support tensors of 4D or higher rank yet.\");\n }\n const axes = this.interpretAxes(shape1, shape2);\n shape1.splice(axes[0], 1);\n shape2.splice(axes[1], 1);\n shape2.splice(0, 1);\n const outputShape = shape1.concat(shape2);\n if (outputShape.length === 1) {\n outputShape.push(1);\n }\n return outputShape;\n }\n computeMask(inputs, mask) {\n return null;\n }\n getConfig() {\n const config = {\n \"axes\": this.axes,\n \"normalize\": this.normalize\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nDot.className = \"Dot\";\nserialization_exports.registerClass(Dot);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/noise.js\nvar GaussianNoise = class extends Layer {\n constructor(args) {\n super(args);\n this.supportsMasking = true;\n this.stddev = args.stddev;\n }\n computeOutputShape(inputShape) {\n return inputShape;\n }\n getConfig() {\n const baseConfig = super.getConfig();\n const config = { stddev: this.stddev };\n Object.assign(config, baseConfig);\n return config;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n const input2 = getExactlyOneTensor(inputs);\n const noised = () => add2(randomNormal2(input2.shape, 0, this.stddev), input2);\n const output = inTrainPhase(noised, () => input2, kwargs[\"training\"] || false);\n return output;\n });\n }\n};\nGaussianNoise.className = \"GaussianNoise\";\nserialization_exports.registerClass(GaussianNoise);\nvar GaussianDropout = class extends Layer {\n constructor(args) {\n super(args);\n this.supportsMasking = true;\n this.rate = args.rate;\n }\n computeOutputShape(inputShape) {\n return inputShape;\n }\n getConfig() {\n const baseConfig = super.getConfig();\n const config = { rate: this.rate };\n Object.assign(config, baseConfig);\n return config;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n const input2 = getExactlyOneTensor(inputs);\n if (this.rate > 0 && this.rate < 1) {\n const noised = () => {\n const stddev = Math.sqrt(this.rate / (1 - this.rate));\n return mul(input2, randomNormal2(input2.shape, 1, stddev));\n };\n return inTrainPhase(noised, () => input2, kwargs[\"training\"] || false);\n }\n return input2;\n });\n }\n};\nGaussianDropout.className = \"GaussianDropout\";\nserialization_exports.registerClass(GaussianDropout);\nvar AlphaDropout = class extends Layer {\n constructor(args) {\n super(args);\n this.supportsMasking = true;\n this.rate = args.rate;\n this.noiseShape = args.noiseShape;\n }\n _getNoiseShape(inputs) {\n return this.noiseShape || getExactlyOneTensor(inputs).shape;\n }\n computeOutputShape(inputShape) {\n return inputShape;\n }\n getConfig() {\n const baseConfig = super.getConfig();\n const config = { rate: this.rate };\n Object.assign(config, baseConfig);\n return config;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n if (this.rate < 1 && this.rate > 0) {\n const noiseShape = this._getNoiseShape(inputs);\n const droppedInputs = () => {\n const input2 = getExactlyOneTensor(inputs);\n const alpha = 1.6732632423543772;\n const scale2 = 1.0507009873554805;\n const alphaP = -alpha * scale2;\n let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n keptIdx = cast2(keptIdx, \"float32\");\n const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n const b = -a * alphaP * this.rate;\n const x = add2(mul(input2, keptIdx), mul(add2(keptIdx, -1), alphaP));\n return add2(mul(x, a), b);\n };\n return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs[\"training\"] || false);\n }\n return inputs;\n });\n }\n};\nAlphaDropout.className = \"AlphaDropout\";\nserialization_exports.registerClass(AlphaDropout);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/normalization.js\nfunction batchNormalization(x, mean4, variance, beta, gamma, epsilon3 = 1e-3) {\n let out;\n if (x.rank === 2) {\n out = batchNorm2d(x, mean4, variance, beta, gamma, epsilon3);\n } else if (x.rank === 3) {\n out = batchNorm3d(x, mean4, variance, beta, gamma, epsilon3);\n } else if (x.rank === 4) {\n out = batchNorm4d(x, mean4, variance, beta, gamma, epsilon3);\n } else {\n throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);\n }\n return out;\n}\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n return tidy(() => {\n const meanAndVariance = moments(x, reductionAxes);\n const mean4 = meanAndVariance.mean;\n const variance = meanAndVariance.variance;\n const normed = batchNormalization(x, mean4, variance, beta, gamma, epsilon3);\n return [normed, mean4, variance];\n });\n}\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n return tidy(() => {\n const meanAndVariance = moments(x, reductionAxes);\n const mean4 = meanAndVariance.mean;\n const variance = meanAndVariance.variance;\n const targetShape = [];\n for (const axis of range2(0, x.rank)) {\n if (reductionAxes.indexOf(axis) !== -1) {\n targetShape.push(1);\n } else {\n targetShape.push(x.shape[axis]);\n }\n }\n const broadcastMean = reshape(mean4, targetShape);\n const broadcastVariance = reshape(variance, targetShape);\n const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape);\n const broadcastBeta = beta == null ? null : reshape(beta, targetShape);\n const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon3);\n return [normed, mean4, variance];\n });\n}\nfunction normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n if (util_exports.arraysEqual(reductionAxes.slice().sort(), range2(0, x.rank - 1))) {\n return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);\n } else {\n return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);\n }\n}\nvar BatchNormalization = class extends Layer {\n constructor(args) {\n if (args == null) {\n args = {};\n }\n super(args);\n this.supportsMasking = true;\n this.axis = args.axis == null ? -1 : args.axis;\n this.momentum = args.momentum == null ? 0.99 : args.momentum;\n this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n this.center = args.center == null ? true : args.center;\n this.scale = args.scale == null ? true : args.scale;\n this.betaInitializer = getInitializer(args.betaInitializer || \"zeros\");\n this.gammaInitializer = getInitializer(args.gammaInitializer || \"ones\");\n this.movingMeanInitializer = getInitializer(args.movingMeanInitializer || \"zeros\");\n this.movingVarianceInitializer = getInitializer(args.movingVarianceInitializer || \"ones\");\n this.betaConstraint = getConstraint(args.betaConstraint);\n this.gammaConstraint = getConstraint(args.gammaConstraint);\n this.betaRegularizer = getRegularizer(args.betaRegularizer);\n this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const axis = this.axis >= 0 ? this.axis : this.axis + inputShape.length;\n const dim = inputShape[axis];\n if (dim == null) {\n throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);\n }\n this.inputSpec = [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n const shape = [dim];\n if (this.scale) {\n this.gamma = this.addWeight(\"gamma\", shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n }\n if (this.center) {\n this.beta = this.addWeight(\"beta\", shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n }\n this.movingMean = this.addWeight(\"moving_mean\", shape, null, this.movingMeanInitializer, null, false);\n this.movingVariance = this.addWeight(\"moving_variance\", shape, null, this.movingVarianceInitializer, null, false);\n this.built = true;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n const input2 = getExactlyOneTensor(inputs);\n const inputShape = input2.shape;\n const ndim = inputShape.length;\n const reductionAxes = range2(0, ndim);\n const axis = this.axis >= 0 ? this.axis : this.axis + ndim;\n reductionAxes.splice(axis, 1);\n const broadcastShape = pyListRepeat(1, ndim);\n broadcastShape[axis] = inputShape[axis];\n const sortedReductionAxes = reductionAxes.slice();\n sortedReductionAxes.sort();\n const needsBroadcasting = !util_exports.arraysEqual(sortedReductionAxes, range2(0, ndim).slice(0, ndim - 1));\n const normalizeInference = () => {\n if (needsBroadcasting) {\n const broadcastMovingMean = reshape(this.movingMean.read(), broadcastShape);\n const broadcastMovingVariance = reshape(this.movingVariance.read(), broadcastShape);\n const broadcastBeta = this.center ? reshape(this.beta.read(), broadcastShape) : null;\n const broadcastGamma = this.scale ? reshape(this.gamma.read(), broadcastShape) : null;\n return batchNormalization(input2, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n } else {\n return batchNormalization(input2, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n }\n };\n if (!training) {\n return normalizeInference();\n }\n const [normedTraining, mean4, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n const doMovingAverage = (variable2, value, momentum) => {\n tidy(() => {\n const decay = 1 - momentum;\n const origValue = variable2.read();\n const updateDelta = mul(sub(origValue, value), decay);\n variable2.write(sub(origValue, updateDelta));\n });\n };\n const updateMovingMeanAndVariance = () => {\n doMovingAverage(this.movingMean, mean4, this.momentum);\n doMovingAverage(this.movingVariance, variance, this.momentum);\n };\n updateMovingMeanAndVariance();\n return normedTraining;\n });\n }\n getConfig() {\n const config = {\n axis: this.axis,\n momentum: this.momentum,\n epsilon: this.epsilon,\n center: this.center,\n scale: this.scale,\n betaInitializer: serializeInitializer(this.betaInitializer),\n gammaInitializer: serializeInitializer(this.gammaInitializer),\n movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n betaRegularizer: serializeRegularizer(this.betaRegularizer),\n gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n betaConstraint: serializeConstraint(this.betaConstraint),\n gammaConstraint: serializeConstraint(this.gammaConstraint)\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nBatchNormalization.className = \"BatchNormalization\";\nserialization_exports.registerClass(BatchNormalization);\nvar LayerNormalization = class extends Layer {\n constructor(args) {\n if (args == null) {\n args = {};\n }\n super(args);\n this.axis = args.axis == null ? -1 : args.axis;\n if (typeof this.axis === \"number\") {\n if (!Number.isInteger(this.axis)) {\n throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n }\n } else if (Array.isArray(this.axis)) {\n for (const axis of this.axis) {\n if (!Number.isInteger(axis)) {\n throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`);\n }\n }\n } else {\n throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);\n }\n this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n this.center = args.center == null ? true : args.center;\n this.scale = args.scale == null ? true : args.scale;\n this.betaInitializer = getInitializer(args.betaInitializer || \"zeros\");\n this.gammaInitializer = getInitializer(args.gammaInitializer || \"ones\");\n this.betaRegularizer = getRegularizer(args.betaRegularizer);\n this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n this.supportsMasking = true;\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const nDims = inputShape.length;\n if (typeof this.axis === \"number\") {\n this.axis = [this.axis];\n }\n for (let i = 0; i < this.axis.length; ++i) {\n if (this.axis[i] < 0) {\n this.axis[i] += nDims;\n }\n }\n for (const axis of this.axis) {\n if (axis < 0 || axis >= nDims) {\n throw new Error(`Invalid axis: ${axis}`);\n }\n }\n if (this.axis.length !== unique2(this.axis).length) {\n throw new Error(`Found duplicate axes in: ${this.axis}`);\n }\n const paramShape = this.axis.map((axis) => inputShape[axis]);\n const trainable = true;\n if (this.scale) {\n this.gamma = this.addWeight(\"gamma\", paramShape, \"float32\", this.gammaInitializer, this.gammaRegularizer, trainable);\n } else {\n this.gamma = null;\n }\n if (this.center) {\n this.beta = this.addWeight(\"beta\", paramShape, \"float32\", this.betaInitializer, this.betaRegularizer, trainable);\n } else {\n this.beta = null;\n }\n this.built = true;\n }\n call(inputs, kwargs) {\n const input2 = getExactlyOneTensor(inputs);\n const inputShape = input2.shape;\n const nDims = inputShape.length;\n return tidy(() => {\n const keepDims = true;\n let { mean: mean4, variance } = moments(input2, this.axis, keepDims);\n const broadcastShape = pyListRepeat(1, nDims);\n for (const dim of this.axis) {\n broadcastShape[dim] = inputShape[dim];\n }\n const broadcast = (v) => {\n if (v != null && v.shape.length !== nDims) {\n return reshape(v, broadcastShape);\n } else {\n return v;\n }\n };\n let scale2 = this.scale ? broadcast(this.gamma.read()) : null;\n let offset = this.center ? broadcast(this.beta.read()) : null;\n const momentsTiling = [];\n const scaleOffsetTiling = [];\n for (let i = 0; i < nDims; ++i) {\n if (this.axis.indexOf(i) !== -1) {\n momentsTiling.push(inputShape[i]);\n scaleOffsetTiling.push(1);\n } else {\n momentsTiling.push(1);\n scaleOffsetTiling.push(inputShape[i]);\n }\n }\n mean4 = tile(mean4, momentsTiling);\n variance = tile(variance, momentsTiling);\n if (scale2 != null) {\n scale2 = tile(scale2, scaleOffsetTiling);\n }\n if (offset != null) {\n offset = tile(offset, scaleOffsetTiling);\n }\n return batchNormalization(input2, mean4, variance, offset, scale2, this.epsilon);\n });\n }\n getConfig() {\n const config = {\n axis: this.axis,\n epsilon: this.epsilon,\n center: this.center,\n scale: this.scale,\n betaInitializer: serializeInitializer(this.betaInitializer),\n gammaInitializer: serializeInitializer(this.gammaInitializer),\n betaRegularizer: serializeRegularizer(this.betaRegularizer),\n gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nLayerNormalization.className = \"LayerNormalization\";\nserialization_exports.registerClass(LayerNormalization);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/padding.js\nfunction spatial2dPadding(x, padding, dataFormat) {\n return tidy(() => {\n if (x.rank !== 4) {\n throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);\n }\n if (padding == null) {\n padding = [[1, 1], [1, 1]];\n }\n if (padding.length !== 2 || padding[0].length !== 2 || padding[1].length !== 2) {\n throw new ValueError(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");\n }\n if (dataFormat == null) {\n dataFormat = imageDataFormat();\n }\n if (dataFormat !== \"channelsLast\" && dataFormat !== \"channelsFirst\") {\n throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);\n }\n let pattern;\n if (dataFormat === \"channelsFirst\") {\n pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n } else {\n pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n }\n return pad(x, pattern);\n });\n}\nvar ZeroPadding2D = class extends Layer {\n constructor(args) {\n if (args == null) {\n args = {};\n }\n super(args);\n this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n if (args.padding == null) {\n this.padding = [[1, 1], [1, 1]];\n } else if (typeof args.padding === \"number\") {\n this.padding = [[args.padding, args.padding], [args.padding, args.padding]];\n } else {\n args.padding = args.padding;\n if (args.padding.length !== 2) {\n throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);\n }\n let heightPadding;\n let widthPadding;\n if (typeof args.padding[0] === \"number\") {\n heightPadding = [args.padding[0], args.padding[0]];\n widthPadding = [args.padding[1], args.padding[1]];\n } else {\n args.padding = args.padding;\n if (args.padding[0].length !== 2) {\n throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);\n }\n heightPadding = args.padding[0];\n if (args.padding[1].length !== 2) {\n throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);\n }\n widthPadding = args.padding[1];\n }\n this.padding = [heightPadding, widthPadding];\n }\n this.inputSpec = [new InputSpec({ ndim: 4 })];\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n let rows;\n let cols;\n if (this.dataFormat === \"channelsFirst\") {\n if (inputShape[2] != null && inputShape[2] >= 0) {\n rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n } else {\n rows = null;\n }\n if (inputShape[3] != null && inputShape[3] >= 0) {\n cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n } else {\n cols = null;\n }\n return [inputShape[0], inputShape[1], rows, cols];\n } else {\n if (inputShape[1] != null && inputShape[1] >= 0) {\n rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n } else {\n rows = null;\n }\n if (inputShape[2] != null && inputShape[2] >= 0) {\n cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n } else {\n cols = null;\n }\n return [inputShape[0], rows, cols, inputShape[3]];\n }\n }\n call(inputs, kwargs) {\n return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n }\n getConfig() {\n const config = {\n padding: this.padding,\n dataFormat: this.dataFormat\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nZeroPadding2D.className = \"ZeroPadding2D\";\nserialization_exports.registerClass(ZeroPadding2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/pooling.js\nfunction pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n return tidy(() => {\n checkDataFormat(dataFormat);\n checkPoolMode(poolMode);\n checkPaddingMode(padding);\n if (strides == null) {\n strides = [1, 1];\n }\n if (padding == null) {\n padding = \"valid\";\n }\n if (dataFormat == null) {\n dataFormat = imageDataFormat();\n }\n if (poolMode == null) {\n poolMode = \"max\";\n }\n x = preprocessConv2DInput(x, dataFormat);\n let y;\n const paddingString = padding === \"same\" ? \"same\" : \"valid\";\n if (poolMode === \"max\") {\n y = maxPool(x, poolSize, strides, paddingString);\n } else {\n y = avgPool(\n // TODO(cais): Rank check?\n x,\n poolSize,\n strides,\n paddingString\n );\n }\n if (dataFormat === \"channelsFirst\") {\n y = transpose(y, [0, 3, 1, 2]);\n }\n return y;\n });\n}\nfunction pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n return tidy(() => {\n checkDataFormat(dataFormat);\n checkPoolMode(poolMode);\n checkPaddingMode(padding);\n if (strides == null) {\n strides = [1, 1, 1];\n }\n if (padding == null) {\n padding = \"valid\";\n }\n if (dataFormat == null) {\n dataFormat = imageDataFormat();\n }\n if (poolMode == null) {\n poolMode = \"max\";\n }\n x = preprocessConv3DInput(x, dataFormat);\n let y;\n const paddingString = padding === \"same\" ? \"same\" : \"valid\";\n if (poolMode === \"max\") {\n y = maxPool3d(x, poolSize, strides, paddingString);\n } else {\n y = avgPool3d(x, poolSize, strides, paddingString);\n }\n if (dataFormat === \"channelsFirst\") {\n y = transpose(y, [0, 4, 1, 2, 3]);\n }\n return y;\n });\n}\nvar Pooling1D = class extends Layer {\n /**\n *\n * @param args Parameters for the Pooling layer.\n *\n * config.poolSize defaults to 2.\n */\n constructor(args) {\n if (args.poolSize == null) {\n args.poolSize = 2;\n }\n super(args);\n if (typeof args.poolSize === \"number\") {\n this.poolSize = [args.poolSize];\n } else if (Array.isArray(args.poolSize) && args.poolSize.length === 1 && typeof args.poolSize[0] === \"number\") {\n this.poolSize = args.poolSize;\n } else {\n throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);\n }\n assertPositiveInteger(this.poolSize, \"poolSize\");\n if (args.strides == null) {\n this.strides = this.poolSize;\n } else {\n if (typeof args.strides === \"number\") {\n this.strides = [args.strides];\n } else if (Array.isArray(args.strides) && args.strides.length === 1 && typeof args.strides[0] === \"number\") {\n this.strides = args.strides;\n } else {\n throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);\n }\n }\n assertPositiveInteger(this.strides, \"strides\");\n this.padding = args.padding == null ? \"valid\" : args.padding;\n checkPaddingMode(this.padding);\n this.inputSpec = [new InputSpec({ ndim: 3 })];\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n return [inputShape[0], length, inputShape[2]];\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n inputs = expandDims2(getExactlyOneTensor(inputs), 2);\n const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, \"channelsLast\");\n return squeeze(output, [2]);\n });\n }\n getConfig() {\n const config = {\n poolSize: this.poolSize,\n padding: this.padding,\n strides: this.strides\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nvar MaxPooling1D = class extends Pooling1D {\n constructor(args) {\n super(args);\n }\n poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n checkDataFormat(dataFormat);\n checkPaddingMode(padding);\n return pool2d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n }\n};\nMaxPooling1D.className = \"MaxPooling1D\";\nserialization_exports.registerClass(MaxPooling1D);\nvar AveragePooling1D = class extends Pooling1D {\n constructor(args) {\n super(args);\n }\n poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n checkDataFormat(dataFormat);\n checkPaddingMode(padding);\n return pool2d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n }\n};\nAveragePooling1D.className = \"AveragePooling1D\";\nserialization_exports.registerClass(AveragePooling1D);\nvar Pooling2D = class extends Layer {\n constructor(args) {\n if (args.poolSize == null) {\n args.poolSize = [2, 2];\n }\n super(args);\n this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize];\n if (args.strides == null) {\n this.strides = this.poolSize;\n } else if (Array.isArray(args.strides)) {\n if (args.strides.length !== 2) {\n throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);\n }\n this.strides = args.strides;\n } else {\n this.strides = [args.strides, args.strides];\n }\n assertPositiveInteger(this.poolSize, \"poolSize\");\n assertPositiveInteger(this.strides, \"strides\");\n this.padding = args.padding == null ? \"valid\" : args.padding;\n this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n checkDataFormat(this.dataFormat);\n checkPaddingMode(this.padding);\n this.inputSpec = [new InputSpec({ ndim: 4 })];\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n let rows = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n let cols = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n rows = convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n cols = convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n if (this.dataFormat === \"channelsFirst\") {\n return [inputShape[0], inputShape[1], rows, cols];\n } else {\n return [inputShape[0], rows, cols, inputShape[3]];\n }\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n });\n }\n getConfig() {\n const config = {\n poolSize: this.poolSize,\n padding: this.padding,\n strides: this.strides,\n dataFormat: this.dataFormat\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nvar MaxPooling2D = class extends Pooling2D {\n constructor(args) {\n super(args);\n }\n poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n checkDataFormat(dataFormat);\n checkPaddingMode(padding);\n return pool2d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n }\n};\nMaxPooling2D.className = \"MaxPooling2D\";\nserialization_exports.registerClass(MaxPooling2D);\nvar AveragePooling2D = class extends Pooling2D {\n constructor(args) {\n super(args);\n }\n poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n checkDataFormat(dataFormat);\n checkPaddingMode(padding);\n return pool2d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n }\n};\nAveragePooling2D.className = \"AveragePooling2D\";\nserialization_exports.registerClass(AveragePooling2D);\nvar Pooling3D = class extends Layer {\n constructor(args) {\n if (args.poolSize == null) {\n args.poolSize = [2, 2, 2];\n }\n super(args);\n this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize, args.poolSize];\n if (args.strides == null) {\n this.strides = this.poolSize;\n } else if (Array.isArray(args.strides)) {\n if (args.strides.length !== 3) {\n throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);\n }\n this.strides = args.strides;\n } else {\n this.strides = [args.strides, args.strides, args.strides];\n }\n assertPositiveInteger(this.poolSize, \"poolSize\");\n assertPositiveInteger(this.strides, \"strides\");\n this.padding = args.padding == null ? \"valid\" : args.padding;\n this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n checkDataFormat(this.dataFormat);\n checkPaddingMode(this.padding);\n this.inputSpec = [new InputSpec({ ndim: 5 })];\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n let depths = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n let rows = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n let cols = this.dataFormat === \"channelsFirst\" ? inputShape[4] : inputShape[3];\n depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n rows = convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n cols = convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n if (this.dataFormat === \"channelsFirst\") {\n return [inputShape[0], inputShape[1], depths, rows, cols];\n } else {\n return [inputShape[0], depths, rows, cols, inputShape[4]];\n }\n }\n call(inputs, kwargs) {\n return tidy(() => {\n this.invokeCallHook(inputs, kwargs);\n return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n });\n }\n getConfig() {\n const config = {\n poolSize: this.poolSize,\n padding: this.padding,\n strides: this.strides,\n dataFormat: this.dataFormat\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nvar MaxPooling3D = class extends Pooling3D {\n constructor(args) {\n super(args);\n }\n poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n checkDataFormat(dataFormat);\n checkPaddingMode(padding);\n return pool3d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n }\n};\nMaxPooling3D.className = \"MaxPooling3D\";\nserialization_exports.registerClass(MaxPooling3D);\nvar AveragePooling3D = class extends Pooling3D {\n constructor(args) {\n super(args);\n }\n poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n checkDataFormat(dataFormat);\n checkPaddingMode(padding);\n return pool3d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n }\n};\nAveragePooling3D.className = \"AveragePooling3D\";\nserialization_exports.registerClass(AveragePooling3D);\nvar GlobalPooling1D = class extends Layer {\n constructor(args) {\n super(args);\n this.inputSpec = [new InputSpec({ ndim: 3 })];\n }\n computeOutputShape(inputShape) {\n return [inputShape[0], inputShape[2]];\n }\n call(inputs, kwargs) {\n throw new NotImplementedError();\n }\n};\nvar GlobalAveragePooling1D = class extends GlobalPooling1D {\n constructor(args) {\n super(args || {});\n }\n call(inputs, kwargs) {\n return tidy(() => {\n const input2 = getExactlyOneTensor(inputs);\n return mean(input2, 1);\n });\n }\n};\nGlobalAveragePooling1D.className = \"GlobalAveragePooling1D\";\nserialization_exports.registerClass(GlobalAveragePooling1D);\nvar GlobalMaxPooling1D = class extends GlobalPooling1D {\n constructor(args) {\n super(args || {});\n }\n call(inputs, kwargs) {\n return tidy(() => {\n const input2 = getExactlyOneTensor(inputs);\n return max(input2, 1);\n });\n }\n};\nGlobalMaxPooling1D.className = \"GlobalMaxPooling1D\";\nserialization_exports.registerClass(GlobalMaxPooling1D);\nvar GlobalPooling2D = class extends Layer {\n constructor(args) {\n super(args);\n this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n checkDataFormat(this.dataFormat);\n this.inputSpec = [new InputSpec({ ndim: 4 })];\n }\n computeOutputShape(inputShape) {\n inputShape = inputShape;\n if (this.dataFormat === \"channelsLast\") {\n return [inputShape[0], inputShape[3]];\n } else {\n return [inputShape[0], inputShape[1]];\n }\n }\n call(inputs, kwargs) {\n throw new NotImplementedError();\n }\n getConfig() {\n const config = { dataFormat: this.dataFormat };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nvar GlobalAveragePooling2D = class extends GlobalPooling2D {\n call(inputs, kwargs) {\n return tidy(() => {\n const input2 = getExactlyOneTensor(inputs);\n if (this.dataFormat === \"channelsLast\") {\n return mean(input2, [1, 2]);\n } else {\n return mean(input2, [2, 3]);\n }\n });\n }\n};\nGlobalAveragePooling2D.className = \"GlobalAveragePooling2D\";\nserialization_exports.registerClass(GlobalAveragePooling2D);\nvar GlobalMaxPooling2D = class extends GlobalPooling2D {\n call(inputs, kwargs) {\n return tidy(() => {\n const input2 = getExactlyOneTensor(inputs);\n if (this.dataFormat === \"channelsLast\") {\n return max(input2, [1, 2]);\n } else {\n return max(input2, [2, 3]);\n }\n });\n }\n};\nGlobalMaxPooling2D.className = \"GlobalMaxPooling2D\";\nserialization_exports.registerClass(GlobalMaxPooling2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/wrappers.js\nvar Wrapper = class extends Layer {\n constructor(args) {\n super(args);\n this.layer = args.layer;\n }\n build(inputShape) {\n this.built = true;\n }\n // TODO(cais): Implement activityRegularizer getter.\n get trainable() {\n if (this.layer != null) {\n return this.layer.trainable;\n } else {\n return false;\n }\n }\n set trainable(value) {\n if (this.layer != null) {\n this.layer.trainable = value;\n }\n }\n get trainableWeights() {\n return this.layer.trainableWeights;\n }\n // TODO(cais): Implement setter for trainableWeights.\n get nonTrainableWeights() {\n return this.layer.nonTrainableWeights;\n }\n // TODO(cais): Implement setter for nonTrainableWeights.\n get updates() {\n return this.layer._updates;\n }\n // TODO(cais): Implement getUpdatesFor().\n get losses() {\n return this.layer.losses;\n }\n // TODO(cais): Implement getLossesFor().\n getWeights() {\n return this.layer.getWeights();\n }\n setWeights(weights) {\n this.layer.setWeights(weights);\n }\n getConfig() {\n const config = {\n \"layer\": {\n \"className\": this.layer.getClassName(),\n \"config\": this.layer.getConfig()\n }\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n setFastWeightInitDuringBuild(value) {\n super.setFastWeightInitDuringBuild(value);\n if (this.layer != null) {\n this.layer.setFastWeightInitDuringBuild(value);\n }\n }\n /** @nocollapse */\n static fromConfig(cls, config, customObjects = {}) {\n const layerConfig = config[\"layer\"];\n const layer = deserialize(layerConfig, customObjects);\n delete config[\"layer\"];\n const newConfig = { layer };\n Object.assign(newConfig, config);\n return new cls(newConfig);\n }\n};\nvar TimeDistributed = class extends Wrapper {\n constructor(args) {\n super(args);\n this.supportsMasking = true;\n }\n build(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n if (inputShape.length < 3) {\n throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);\n }\n this.inputSpec = [{ shape: inputShape }];\n const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n if (!this.layer.built) {\n this.layer.build(childInputShape);\n this.layer.built = true;\n }\n super.build(inputShape);\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n const childOutputShape = this.layer.computeOutputShape(childInputShape);\n const timesteps = inputShape[1];\n return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = getExactlyOneTensor(inputs);\n const step5 = (inputs2, states) => {\n const output = getExactlyOneTensor(this.layer.call(inputs2, kwargs));\n return [output, []];\n };\n const rnnOutputs = rnn(\n step5,\n inputs,\n [],\n false,\n null,\n null,\n false,\n true\n /* needPerStepOutputs */\n );\n const y = rnnOutputs[1];\n return y;\n });\n }\n};\nTimeDistributed.className = \"TimeDistributed\";\nserialization_exports.registerClass(TimeDistributed);\nfunction checkBidirectionalMergeMode(value) {\n checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, \"BidirectionalMergeMode\", value);\n}\nvar DEFAULT_BIDIRECTIONAL_MERGE_MODE = \"concat\";\nvar Bidirectional = class extends Wrapper {\n constructor(args) {\n super(args);\n const layerConfig = args.layer.getConfig();\n const forwDict = {};\n forwDict[\"className\"] = args.layer.getClassName();\n forwDict[\"config\"] = layerConfig;\n this.forwardLayer = deserialize(forwDict);\n layerConfig[\"goBackwards\"] = layerConfig[\"goBackwards\"] === true ? false : true;\n const backDict = {};\n backDict[\"className\"] = args.layer.getClassName();\n backDict[\"config\"] = layerConfig;\n this.backwardLayer = deserialize(backDict);\n this.forwardLayer.name = \"forward_\" + this.forwardLayer.name;\n this.backwardLayer.name = \"backward_\" + this.backwardLayer.name;\n this.mergeMode = args.mergeMode === void 0 ? DEFAULT_BIDIRECTIONAL_MERGE_MODE : args.mergeMode;\n checkBidirectionalMergeMode(this.mergeMode);\n if (args.weights) {\n throw new NotImplementedError(\"weights support is not implemented for Bidirectional layer yet.\");\n }\n this._stateful = args.layer.stateful;\n this.returnSequences = args.layer.returnSequences;\n this.returnState = args.layer.returnState;\n this.supportsMasking = true;\n this._trainable = true;\n this.inputSpec = args.layer.inputSpec;\n this.numConstants = null;\n }\n get trainable() {\n return this._trainable;\n }\n set trainable(value) {\n this._trainable = value;\n if (this.forwardLayer != null) {\n this.forwardLayer.trainable = value;\n }\n if (this.backwardLayer != null) {\n this.backwardLayer.trainable = value;\n }\n }\n getWeights() {\n return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n }\n setWeights(weights) {\n const numWeights = weights.length;\n const numeightsOver2 = Math.floor(numWeights / 2);\n this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n }\n computeOutputShape(inputShape) {\n let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n layerShapes = [layerShapes];\n }\n layerShapes = layerShapes;\n let outputShape;\n let outputShapes;\n let stateShape;\n if (this.returnState) {\n stateShape = layerShapes.slice(1);\n outputShape = layerShapes[0];\n } else {\n outputShape = layerShapes[0];\n }\n outputShape = outputShape;\n if (this.mergeMode === \"concat\") {\n outputShape[outputShape.length - 1] *= 2;\n outputShapes = [outputShape];\n } else if (this.mergeMode == null) {\n outputShapes = [outputShape, outputShape.slice()];\n } else {\n outputShapes = [outputShape];\n }\n if (this.returnState) {\n if (this.mergeMode == null) {\n return outputShapes.concat(stateShape).concat(stateShape.slice());\n }\n return [outputShape].concat(stateShape).concat(stateShape.slice());\n }\n return singletonOrArray(outputShapes);\n }\n apply(inputs, kwargs) {\n let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n let constants = kwargs == null ? null : kwargs[\"constants\"];\n if (kwargs == null) {\n kwargs = {};\n }\n const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n inputs = standardized.inputs;\n initialState = standardized.initialState;\n constants = standardized.constants;\n if (Array.isArray(inputs)) {\n initialState = inputs.slice(1);\n inputs = inputs[0];\n }\n if ((initialState == null || initialState.length === 0) && constants == null) {\n return super.apply(inputs, kwargs);\n }\n const additionalInputs = [];\n const additionalSpecs = [];\n if (initialState != null) {\n const numStates = initialState.length;\n if (numStates % 2 > 0) {\n throw new ValueError(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");\n }\n kwargs[\"initialState\"] = initialState;\n additionalInputs.push(...initialState);\n const stateSpecs = initialState.map((state) => new InputSpec({ shape: state.shape }));\n this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n additionalSpecs.push(...stateSpecs);\n }\n if (constants != null) {\n throw new NotImplementedError(\"Support for constants in Bidirectional layers is not implemented yet.\");\n }\n const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n for (const tensor2 of additionalInputs) {\n if (tensor2 instanceof SymbolicTensor !== isSymbolicTensor) {\n throw new ValueError(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");\n }\n }\n if (isSymbolicTensor) {\n const fullInput = [inputs].concat(additionalInputs);\n const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n const originalInputSpec = this.inputSpec;\n this.inputSpec = fullInputSpec;\n const output = super.apply(fullInput, kwargs);\n this.inputSpec = originalInputSpec;\n return output;\n } else {\n return super.apply(inputs, kwargs);\n }\n }\n call(inputs, kwargs) {\n return tidy(() => {\n const initialState = kwargs[\"initialState\"];\n let y;\n let yRev;\n if (initialState == null) {\n y = this.forwardLayer.call(inputs, kwargs);\n yRev = this.backwardLayer.call(inputs, kwargs);\n } else {\n const forwardState = initialState.slice(0, initialState.length / 2);\n const backwardState = initialState.slice(initialState.length / 2);\n y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n }\n let states;\n if (this.returnState) {\n if (Array.isArray(y)) {\n states = y.slice(1).concat(yRev.slice(1));\n } else {\n }\n y = y[0];\n yRev = yRev[0];\n }\n if (this.returnSequences) {\n yRev = reverse(yRev, 1);\n }\n let output;\n if (this.mergeMode === \"concat\") {\n output = concatenate([y, yRev]);\n } else if (this.mergeMode === \"sum\") {\n output = add2(y, yRev);\n } else if (this.mergeMode === \"ave\") {\n output = mul(0.5, add2(y, yRev));\n } else if (this.mergeMode === \"mul\") {\n output = mul(y, yRev);\n } else if (this.mergeMode == null) {\n output = [y, yRev];\n }\n if (this.returnState) {\n if (this.mergeMode == null) {\n return output.concat(states);\n }\n return [output].concat(states);\n }\n return output;\n });\n }\n resetStates(states) {\n this.forwardLayer.resetStates();\n this.backwardLayer.resetStates();\n }\n build(inputShape) {\n nameScope(this.forwardLayer.name, () => {\n this.forwardLayer.build(inputShape);\n });\n nameScope(this.backwardLayer.name, () => {\n this.backwardLayer.build(inputShape);\n });\n this.built = true;\n }\n computeMask(inputs, mask) {\n if (Array.isArray(mask)) {\n mask = mask[0];\n }\n let outputMask;\n if (this.returnSequences) {\n if (this.mergeMode == null) {\n outputMask = [mask, mask];\n } else {\n outputMask = mask;\n }\n } else {\n if (this.mergeMode == null) {\n outputMask = [null, null];\n } else {\n outputMask = null;\n }\n }\n if (this.returnState) {\n const states = this.forwardLayer.states;\n const stateMask = states.map((state) => null);\n if (Array.isArray(outputMask)) {\n return outputMask.concat(stateMask).concat(stateMask);\n } else {\n return [outputMask].concat(stateMask).concat(stateMask);\n }\n } else {\n return outputMask;\n }\n }\n get trainableWeights() {\n return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n }\n get nonTrainableWeights() {\n return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n }\n // TODO(cais): Implement constraints().\n setFastWeightInitDuringBuild(value) {\n super.setFastWeightInitDuringBuild(value);\n if (this.forwardLayer != null) {\n this.forwardLayer.setFastWeightInitDuringBuild(value);\n }\n if (this.backwardLayer != null) {\n this.backwardLayer.setFastWeightInitDuringBuild(value);\n }\n }\n getConfig() {\n const config = {\n \"mergeMode\": this.mergeMode\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n /** @nocollapse */\n static fromConfig(cls, config) {\n const rnnLayer = deserialize(config[\"layer\"]);\n delete config[\"layer\"];\n if (config[\"numConstants\"] != null) {\n throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`);\n }\n const newConfig = config;\n newConfig[\"layer\"] = rnnLayer;\n return new cls(newConfig);\n }\n};\nBidirectional.className = \"Bidirectional\";\nserialization_exports.registerClass(Bidirectional);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/image_preprocessing.js\nvar Rescaling = class extends Layer {\n constructor(args) {\n super(args);\n this.scale = args.scale;\n if (args.offset) {\n this.offset = args.offset;\n } else {\n this.offset = 0;\n }\n }\n getConfig() {\n const config = {\n \"scale\": this.scale,\n \"offset\": this.offset\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = getExactlyOneTensor(inputs);\n if (inputs.dtype !== \"float32\") {\n inputs = cast2(inputs, \"float32\");\n }\n return add2(mul(inputs, this.scale), this.offset);\n });\n }\n};\nRescaling.className = \"Rescaling\";\nserialization_exports.registerClass(Rescaling);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/center_crop.js\nvar { resizeBilinear: resizeBilinear2, cropAndResize: cropAndResize2 } = image;\nvar CenterCrop = class extends Layer {\n constructor(args) {\n super(args);\n this.height = args.height;\n this.width = args.width;\n }\n centerCrop(inputs, hBuffer, wBuffer, height, width, inputHeight, inputWidth, dtype) {\n return tidy(() => {\n let input2;\n let isRank3 = false;\n const top = hBuffer / inputHeight;\n const left = wBuffer / inputWidth;\n const bottom = (height + hBuffer) / inputHeight;\n const right = (width + wBuffer) / inputWidth;\n const bound = [top, left, bottom, right];\n const boxesArr = [];\n if (inputs.rank === 3) {\n isRank3 = true;\n input2 = stack([inputs]);\n } else {\n input2 = inputs;\n }\n for (let i = 0; i < input2.shape[0]; i++) {\n boxesArr.push(bound);\n }\n const boxes = tensor(boxesArr, [boxesArr.length, 4]);\n const boxInd = range(0, boxesArr.length, 1, \"int32\");\n const cropSize = [height, width];\n const cropped = cropAndResize2(input2, boxes, boxInd, cropSize, \"nearest\");\n if (isRank3) {\n return cast2(getExactlyOneTensor(unstack(cropped)), dtype);\n }\n return cast2(cropped, dtype);\n });\n }\n upsize(inputs, height, width, dtype) {\n return tidy(() => {\n const outputs = resizeBilinear2(inputs, [height, width]);\n return cast2(outputs, dtype);\n });\n }\n call(inputs, kwargs) {\n return tidy(() => {\n const rankedInputs = getExactlyOneTensor(inputs);\n const dtype = rankedInputs.dtype;\n const inputShape = rankedInputs.shape;\n const inputHeight = inputShape[inputShape.length - 3];\n const inputWidth = inputShape[inputShape.length - 2];\n let hBuffer = 0;\n if (inputHeight !== this.height) {\n hBuffer = Math.floor((inputHeight - this.height) / 2);\n }\n let wBuffer = 0;\n if (inputWidth !== this.width) {\n wBuffer = Math.floor((inputWidth - this.width) / 2);\n if (wBuffer === 0) {\n wBuffer = 1;\n }\n }\n if (hBuffer >= 0 && wBuffer >= 0) {\n return this.centerCrop(rankedInputs, hBuffer, wBuffer, this.height, this.width, inputHeight, inputWidth, dtype);\n } else {\n return this.upsize(inputs, this.height, this.width, dtype);\n }\n });\n }\n getConfig() {\n const config = {\n \"height\": this.height,\n \"width\": this.width\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const hAxis = inputShape.length - 3;\n const wAxis = inputShape.length - 2;\n inputShape[hAxis] = this.height;\n inputShape[wAxis] = this.width;\n return inputShape;\n }\n};\nCenterCrop.className = \"CenterCrop\";\nserialization_exports.registerClass(CenterCrop);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/preprocessing_utils.js\nfunction encodeCategoricalInputs(inputs, outputMode, depth, weights) {\n let input2 = getExactlyOneTensor(inputs);\n if (input2.dtype !== \"int32\") {\n input2 = cast2(input2, \"int32\");\n }\n if (outputMode === \"int\") {\n return input2;\n }\n const originalShape = input2.shape;\n if (input2.rank === 0) {\n input2 = expandDims(input2, -1);\n }\n if (outputMode === \"oneHot\") {\n if (input2.shape[input2.shape.length - 1] !== 1) {\n input2 = expandDims(input2, -1);\n }\n }\n if (input2.rank > 2) {\n throw new ValueError(`When outputMode is not int, maximum output rank is 2 Received outputMode ${outputMode} and input shape ${originalShape} which would result in output rank ${input2.rank}.`);\n }\n const binaryOutput = [\"multiHot\", \"oneHot\"].includes(outputMode);\n const denseBincountInput = input2;\n let binCounts;\n if (typeof weights !== \"undefined\" && outputMode === \"count\") {\n binCounts = denseBincount(denseBincountInput, weights, depth, binaryOutput);\n } else {\n binCounts = denseBincount(denseBincountInput, [], depth, binaryOutput);\n }\n if (outputMode !== \"tfIdf\") {\n return binCounts;\n }\n if (weights) {\n return mul(binCounts, weights);\n } else {\n throw new ValueError(`When outputMode is 'tfIdf', weights must be provided.`);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/category_encoding.js\nvar CategoryEncoding = class extends Layer {\n constructor(args) {\n super(args);\n this.numTokens = args.numTokens;\n if (args.outputMode) {\n this.outputMode = args.outputMode;\n } else {\n this.outputMode = \"multiHot\";\n }\n }\n getConfig() {\n const config = {\n \"numTokens\": this.numTokens,\n \"outputMode\": this.outputMode\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n if (inputShape == null) {\n return [this.numTokens];\n }\n if (this.outputMode === \"oneHot\" && inputShape[inputShape.length - 1] !== 1) {\n inputShape.push(this.numTokens);\n return inputShape;\n }\n inputShape[inputShape.length - 1] = this.numTokens;\n return inputShape;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n inputs = getExactlyOneTensor(inputs);\n if (inputs.dtype !== \"int32\") {\n inputs = cast2(inputs, \"int32\");\n }\n let countWeights;\n if (typeof kwargs[\"countWeights\"] !== \"undefined\") {\n if (this.outputMode !== \"count\") {\n throw new ValueError(`countWeights is not used when outputMode !== count.\n Received countWeights=${kwargs[\"countWeights\"]}`);\n }\n countWeights = getExactlyOneTensor(kwargs[\"countWeights\"]);\n }\n const maxValue = max(inputs);\n const minValue = min(inputs);\n const greaterEqualMax = greater(this.numTokens, maxValue).bufferSync().get(0);\n const greaterMin = greaterEqual(minValue, 0).bufferSync().get(0);\n if (!(greaterEqualMax && greaterMin)) {\n throw new ValueError(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);\n }\n return encodeCategoricalInputs(inputs, this.outputMode, this.numTokens, countWeights);\n });\n }\n};\nCategoryEncoding.className = \"CategoryEncoding\";\nserialization_exports.registerClass(CategoryEncoding);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/image_resizing.js\nvar INTERPOLATION_KEYS = [\"bilinear\", \"nearest\"];\nvar INTERPOLATION_METHODS = new Set(INTERPOLATION_KEYS);\nvar Resizing = class extends Layer {\n constructor(args) {\n super(args);\n this.height = args.height;\n this.width = args.width;\n if (args.interpolation) {\n if (INTERPOLATION_METHODS.has(args.interpolation)) {\n this.interpolation = args.interpolation;\n } else {\n throw new ValueError(`Invalid interpolation parameter: ${args.interpolation} is not implemented`);\n }\n } else {\n this.interpolation = \"bilinear\";\n }\n this.cropToAspectRatio = Boolean(args.cropToAspectRatio);\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const numChannels = inputShape[2];\n return [this.height, this.width, numChannels];\n }\n getConfig() {\n const config = {\n \"height\": this.height,\n \"width\": this.width,\n \"interpolation\": this.interpolation,\n \"cropToAspectRatio\": this.cropToAspectRatio\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n call(inputs, kwargs) {\n return tidy(() => {\n const size = [this.height, this.width];\n if (this.interpolation === \"bilinear\") {\n return image.resizeBilinear(inputs, size, !this.cropToAspectRatio);\n } else if (this.interpolation === \"nearest\") {\n return image.resizeNearestNeighbor(inputs, size, !this.cropToAspectRatio);\n } else {\n throw new Error(`Interpolation is ${this.interpolation} but only ${[...INTERPOLATION_METHODS]} are supported`);\n }\n });\n }\n};\nResizing.className = \"Resizing\";\nserialization_exports.registerClass(Resizing);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/backend/random_seed.js\nvar RandomSeed = class {\n constructor(seed) {\n this.seed = seed;\n }\n next() {\n if (this.seed === void 0) {\n return void 0;\n }\n return this.seed++;\n }\n};\nRandomSeed.className = \"RandomSeed\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/base_random_layer.js\nvar BaseRandomLayer = class extends Layer {\n constructor(args) {\n super(args);\n this.randomGenerator = new RandomSeed(args.seed);\n }\n getConfig() {\n const config = {\n \"seed\": this.randomGenerator.seed\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n};\nBaseRandomLayer.className = \"BaseRandomLayer\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/random_width.js\nvar INTERPOLATION_KEYS2 = [\"bilinear\", \"nearest\"];\nvar INTERPOLATION_METHODS2 = new Set(INTERPOLATION_KEYS2);\nvar RandomWidth = class extends BaseRandomLayer {\n constructor(args) {\n super(args);\n const { factor, interpolation = \"bilinear\" } = args;\n this.factor = factor;\n if (Array.isArray(this.factor) && this.factor.length === 2) {\n this.widthLower = this.factor[0];\n this.widthUpper = this.factor[1];\n } else if (!Array.isArray(this.factor) && this.factor > 0) {\n this.widthLower = -this.factor;\n this.widthUpper = this.factor;\n } else {\n throw new ValueError(`Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`);\n }\n if (this.widthLower < -1 || this.widthUpper < -1) {\n throw new ValueError(`factor must have values larger than -1. Got: ${this.factor}`);\n }\n if (this.widthUpper < this.widthLower) {\n throw new ValueError(`factor cannot have upper bound less than lower bound.\n Got upper bound: ${this.widthUpper}.\n Got lower bound: ${this.widthLower}\n `);\n }\n if (interpolation) {\n if (INTERPOLATION_METHODS2.has(interpolation)) {\n this.interpolation = interpolation;\n } else {\n throw new ValueError(`Invalid interpolation parameter: ${interpolation} is not implemented`);\n }\n }\n }\n getConfig() {\n const config = {\n \"factor\": this.factor,\n \"interpolation\": this.interpolation\n };\n const baseConfig = super.getConfig();\n Object.assign(config, baseConfig);\n return config;\n }\n computeOutputShape(inputShape) {\n inputShape = getExactlyOneShape(inputShape);\n const numChannels = inputShape[2];\n return [this.imgHeight, -1, numChannels];\n }\n call(inputs, kwargs) {\n return tidy(() => {\n const input2 = getExactlyOneTensor(inputs);\n this.imgHeight = input2.shape[input2.shape.length - 3];\n const imgWidth = input2.shape[input2.shape.length - 2];\n this.widthFactor = randomUniform([1], 1 + this.widthLower, 1 + this.widthUpper, \"float32\", this.randomGenerator.next());\n let adjustedWidth = this.widthFactor.dataSync()[0] * imgWidth;\n adjustedWidth = Math.round(adjustedWidth);\n const size = [this.imgHeight, adjustedWidth];\n switch (this.interpolation) {\n case \"bilinear\":\n return image.resizeBilinear(inputs, size);\n case \"nearest\":\n return image.resizeNearestNeighbor(inputs, size);\n default:\n throw new Error(`Interpolation is ${this.interpolation}\n but only ${[...INTERPOLATION_METHODS2]} are supported`);\n }\n });\n }\n};\nRandomWidth.className = \"RandomWidth\";\nserialization_exports.registerClass(RandomWidth);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js\nfunction inputLayer(args) {\n return new InputLayer(args);\n}\nfunction elu3(args) {\n return new ELU(args);\n}\nfunction reLU(args) {\n return new ReLU(args);\n}\nfunction leakyReLU(args) {\n return new LeakyReLU(args);\n}\nfunction prelu2(args) {\n return new PReLU(args);\n}\nfunction softmax2(args) {\n return new Softmax3(args);\n}\nfunction thresholdedReLU(args) {\n return new ThresholdedReLU(args);\n}\nfunction conv1d2(args) {\n return new Conv1D(args);\n}\nfunction conv2d3(args) {\n return new Conv2D2(args);\n}\nfunction conv2dTranspose2(args) {\n return new Conv2DTranspose(args);\n}\nfunction conv3d2(args) {\n return new Conv3D2(args);\n}\nfunction conv3dTranspose2(args) {\n return new Conv3DTranspose(args);\n}\nfunction separableConv2d2(args) {\n return new SeparableConv2D(args);\n}\nfunction cropping2D(args) {\n return new Cropping2D(args);\n}\nfunction upSampling2d(args) {\n return new UpSampling2D(args);\n}\nfunction depthwiseConv2d4(args) {\n return new DepthwiseConv2D(args);\n}\nfunction activation(args) {\n return new Activation2(args);\n}\nfunction dense(args) {\n return new Dense(args);\n}\nfunction dropout3(args) {\n return new Dropout(args);\n}\nfunction spatialDropout1d(args) {\n return new SpatialDropout1D(args);\n}\nfunction flatten3(args) {\n return new Flatten(args);\n}\nfunction repeatVector(args) {\n return new RepeatVector(args);\n}\nfunction reshape2(args) {\n return new Reshape2(args);\n}\nfunction permute(args) {\n return new Permute(args);\n}\nfunction embedding(args) {\n return new Embedding(args);\n}\nfunction add3(args) {\n return new Add2(args);\n}\nfunction average(args) {\n return new Average(args);\n}\nfunction concatenate2(args) {\n return new Concatenate(args);\n}\nfunction maximum2(args) {\n return new Maximum2(args);\n}\nfunction minimum2(args) {\n return new Minimum2(args);\n}\nfunction multiply(args) {\n return new Multiply2(args);\n}\nfunction dot3(args) {\n return new Dot(args);\n}\nfunction batchNormalization2(args) {\n return new BatchNormalization(args);\n}\nfunction layerNormalization(args) {\n return new LayerNormalization(args);\n}\nfunction zeroPadding2d(args) {\n return new ZeroPadding2D(args);\n}\nfunction averagePooling1d(args) {\n return new AveragePooling1D(args);\n}\nfunction avgPool1d(args) {\n return averagePooling1d(args);\n}\nfunction avgPooling1d(args) {\n return averagePooling1d(args);\n}\nfunction averagePooling2d(args) {\n return new AveragePooling2D(args);\n}\nfunction avgPool2d(args) {\n return averagePooling2d(args);\n}\nfunction avgPooling2d(args) {\n return averagePooling2d(args);\n}\nfunction averagePooling3d(args) {\n return new AveragePooling3D(args);\n}\nfunction avgPool3d2(args) {\n return averagePooling3d(args);\n}\nfunction avgPooling3d(args) {\n return averagePooling3d(args);\n}\nfunction globalAveragePooling1d(args) {\n return new GlobalAveragePooling1D(args);\n}\nfunction globalAveragePooling2d(args) {\n return new GlobalAveragePooling2D(args);\n}\nfunction globalMaxPooling1d(args) {\n return new GlobalMaxPooling1D(args);\n}\nfunction globalMaxPooling2d(args) {\n return new GlobalMaxPooling2D(args);\n}\nfunction maxPooling1d(args) {\n return new MaxPooling1D(args);\n}\nfunction maxPooling2d(args) {\n return new MaxPooling2D(args);\n}\nfunction maxPooling3d(args) {\n return new MaxPooling3D(args);\n}\nfunction gru(args) {\n return new GRU(args);\n}\nfunction gruCell(args) {\n return new GRUCell(args);\n}\nfunction lstm(args) {\n return new LSTM(args);\n}\nfunction lstmCell(args) {\n return new LSTMCell(args);\n}\nfunction simpleRNN(args) {\n return new SimpleRNN(args);\n}\nfunction simpleRNNCell(args) {\n return new SimpleRNNCell(args);\n}\nfunction convLstm2d(args) {\n return new ConvLSTM2D(args);\n}\nfunction convLstm2dCell(args) {\n return new ConvLSTM2DCell(args);\n}\nfunction rnn2(args) {\n return new RNN(args);\n}\nfunction stackedRNNCells(args) {\n return new StackedRNNCells(args);\n}\nfunction bidirectional(args) {\n return new Bidirectional(args);\n}\nfunction timeDistributed(args) {\n return new TimeDistributed(args);\n}\nvar globalMaxPool1d = globalMaxPooling1d;\nvar globalMaxPool2d = globalMaxPooling2d;\nvar maxPool1d = maxPooling1d;\nvar maxPool2d = maxPooling2d;\nfunction gaussianNoise(args) {\n return new GaussianNoise(args);\n}\nfunction gaussianDropout(args) {\n return new GaussianDropout(args);\n}\nfunction alphaDropout(args) {\n return new AlphaDropout(args);\n}\nfunction masking(args) {\n return new Masking(args);\n}\nfunction rescaling(args) {\n return new Rescaling(args);\n}\nfunction centerCrop(args) {\n return new CenterCrop(args);\n}\nfunction resizing(args) {\n return new Resizing(args);\n}\nfunction categoryEncoding(args) {\n return new CategoryEncoding(args);\n}\nfunction randomWidth(args) {\n return new RandomWidth(args);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_metrics.js\nvar exports_metrics_exports = {};\n__export(exports_metrics_exports, {\n MAPE: () => MAPE2,\n MSE: () => MSE2,\n binaryAccuracy: () => binaryAccuracy2,\n binaryCrossentropy: () => binaryCrossentropy3,\n categoricalAccuracy: () => categoricalAccuracy2,\n categoricalCrossentropy: () => categoricalCrossentropy3,\n cosineProximity: () => cosineProximity2,\n mape: () => mape2,\n meanAbsoluteError: () => meanAbsoluteError2,\n meanAbsolutePercentageError: () => meanAbsolutePercentageError2,\n meanSquaredError: () => meanSquaredError3,\n mse: () => mse2,\n precision: () => precision2,\n recall: () => recall2,\n sparseCategoricalAccuracy: () => sparseCategoricalAccuracy2\n});\nfunction binaryAccuracy2(yTrue, yPred) {\n return binaryAccuracy(yTrue, yPred);\n}\nfunction binaryCrossentropy3(yTrue, yPred) {\n return binaryCrossentropy2(yTrue, yPred);\n}\nfunction sparseCategoricalAccuracy2(yTrue, yPred) {\n return sparseCategoricalAccuracy(yTrue, yPred);\n}\nfunction categoricalAccuracy2(yTrue, yPred) {\n return categoricalAccuracy(yTrue, yPred);\n}\nfunction categoricalCrossentropy3(yTrue, yPred) {\n return categoricalCrossentropy2(yTrue, yPred);\n}\nfunction precision2(yTrue, yPred) {\n return precision(yTrue, yPred);\n}\nfunction recall2(yTrue, yPred) {\n return recall(yTrue, yPred);\n}\nfunction cosineProximity2(yTrue, yPred) {\n return cosineProximity(yTrue, yPred);\n}\nfunction meanAbsoluteError2(yTrue, yPred) {\n return meanAbsoluteError(yTrue, yPred);\n}\nfunction meanAbsolutePercentageError2(yTrue, yPred) {\n return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction MAPE2(yTrue, yPred) {\n return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction mape2(yTrue, yPred) {\n return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction meanSquaredError3(yTrue, yPred) {\n return meanSquaredError2(yTrue, yPred);\n}\nfunction MSE2(yTrue, yPred) {\n return meanSquaredError2(yTrue, yPred);\n}\nfunction mse2(yTrue, yPred) {\n return meanSquaredError2(yTrue, yPred);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_models.js\nvar exports_models_exports = {};\n__export(exports_models_exports, {\n modelFromJSON: () => modelFromJSON\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_regularizers.js\nvar exports_regularizers_exports = {};\n__export(exports_regularizers_exports, {\n l1: () => l12,\n l1l2: () => l1l2,\n l2: () => l22\n});\nfunction l1l2(config) {\n return new L1L2(config);\n}\nfunction l12(config) {\n return l1(config);\n}\nfunction l22(config) {\n return l2(config);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/callbacks.js\nvar Callback = class extends BaseCallback {\n constructor() {\n super(...arguments);\n this.model = null;\n }\n setModel(model2) {\n if (!(model2 instanceof LayersModel)) {\n throw new Error(\"model must be a LayersModel, not some other Container\");\n }\n this.model = model2;\n }\n};\nfunction less2(currVal, prevVal) {\n return currVal < prevVal;\n}\nfunction greater2(currVal, prevVal) {\n return currVal > prevVal;\n}\nvar EarlyStopping = class extends Callback {\n constructor(args) {\n super();\n if (args == null) {\n args = {};\n }\n if (args.restoreBestWeights) {\n throw new NotImplementedError(\"restoreBestWeights = True is not implemented in EarlyStopping yet.\");\n }\n this.monitor = args.monitor || \"val_loss\";\n this.minDelta = Math.abs(args.minDelta || 0);\n this.patience = args.patience || 0;\n this.verbose = args.verbose || 0;\n this.mode = args.mode || \"auto\";\n this.baseline = args.baseline;\n if ([\"auto\", \"min\", \"max\"].indexOf(this.mode) === -1) {\n console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`);\n this.mode = \"auto\";\n }\n if (this.mode === \"min\") {\n this.monitorFunc = less2;\n } else if (this.mode === \"max\") {\n this.monitorFunc = greater2;\n } else {\n if (this.monitor.indexOf(\"acc\") !== -1) {\n this.monitorFunc = greater2;\n } else {\n this.monitorFunc = less2;\n }\n }\n if (this.monitorFunc === less2) {\n this.minDelta *= -1;\n }\n }\n async onTrainBegin(logs) {\n this.wait = 0;\n this.stoppedEpoch = 0;\n if (this.baseline != null) {\n this.best = this.baseline;\n } else {\n this.best = this.monitorFunc === less2 ? Infinity : -Infinity;\n }\n }\n async onEpochEnd(epoch, logs) {\n await resolveScalarsInLogs(logs);\n const current = this.getMonitorValue(logs);\n if (current == null) {\n return;\n }\n if (this.monitorFunc(current - this.minDelta, this.best)) {\n this.best = current;\n this.wait = 0;\n } else {\n this.wait++;\n if (this.wait >= this.patience) {\n this.stoppedEpoch = epoch;\n this.model.stopTraining = true;\n }\n }\n }\n async onTrainEnd(logs) {\n if (this.stoppedEpoch > 0 && this.verbose) {\n console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n }\n }\n getMonitorValue(logs) {\n if (logs == null) {\n logs = {};\n }\n const monitorValue = logs[this.monitor];\n if (monitorValue == null) {\n console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`);\n }\n return monitorValue;\n }\n};\nfunction earlyStopping(args) {\n return new EarlyStopping(args);\n}\nvar callbacks = { earlyStopping };\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/flags.js\nvar ENV4 = env();\nENV4.registerFlag(\"KEEP_INTERMEDIATE_TENSORS\", () => false, (debugValue) => {\n if (debugValue) {\n console.warn(\"Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.\");\n }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/data/compiled_api.js\nvar DataType;\n(function(DataType2) {\n DataType2[DataType2[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n DataType2[DataType2[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n DataType2[DataType2[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n DataType2[DataType2[\"DT_INT32\"] = 3] = \"DT_INT32\";\n DataType2[DataType2[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n DataType2[DataType2[\"DT_INT16\"] = 5] = \"DT_INT16\";\n DataType2[DataType2[\"DT_INT8\"] = 6] = \"DT_INT8\";\n DataType2[DataType2[\"DT_STRING\"] = 7] = \"DT_STRING\";\n DataType2[DataType2[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n DataType2[DataType2[\"DT_INT64\"] = 9] = \"DT_INT64\";\n DataType2[DataType2[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n DataType2[DataType2[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n DataType2[DataType2[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n DataType2[DataType2[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n DataType2[DataType2[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n DataType2[DataType2[\"DT_QINT16\"] = 15] = \"DT_QINT16\";\n DataType2[DataType2[\"DT_QUINT16\"] = 16] = \"DT_QUINT16\";\n DataType2[DataType2[\"DT_UINT16\"] = 17] = \"DT_UINT16\";\n DataType2[DataType2[\"DT_COMPLEX128\"] = 18] = \"DT_COMPLEX128\";\n DataType2[DataType2[\"DT_HALF\"] = 19] = \"DT_HALF\";\n DataType2[DataType2[\"DT_RESOURCE\"] = 20] = \"DT_RESOURCE\";\n DataType2[DataType2[\"DT_VARIANT\"] = 21] = \"DT_VARIANT\";\n DataType2[DataType2[\"DT_UINT32\"] = 22] = \"DT_UINT32\";\n DataType2[DataType2[\"DT_UINT64\"] = 23] = \"DT_UINT64\";\n DataType2[DataType2[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n DataType2[DataType2[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n DataType2[DataType2[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n DataType2[DataType2[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n DataType2[DataType2[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n DataType2[DataType2[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n DataType2[DataType2[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n DataType2[DataType2[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n DataType2[DataType2[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n DataType2[DataType2[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n DataType2[DataType2[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n DataType2[DataType2[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n DataType2[DataType2[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n DataType2[DataType2[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n DataType2[DataType2[\"DT_QINT16_REF\"] = 115] = \"DT_QINT16_REF\";\n DataType2[DataType2[\"DT_QUINT16_REF\"] = 116] = \"DT_QUINT16_REF\";\n DataType2[DataType2[\"DT_UINT16_REF\"] = 117] = \"DT_UINT16_REF\";\n DataType2[DataType2[\"DT_COMPLEX128_REF\"] = 118] = \"DT_COMPLEX128_REF\";\n DataType2[DataType2[\"DT_HALF_REF\"] = 119] = \"DT_HALF_REF\";\n DataType2[DataType2[\"DT_RESOURCE_REF\"] = 120] = \"DT_RESOURCE_REF\";\n DataType2[DataType2[\"DT_VARIANT_REF\"] = 121] = \"DT_VARIANT_REF\";\n DataType2[DataType2[\"DT_UINT32_REF\"] = 122] = \"DT_UINT32_REF\";\n DataType2[DataType2[\"DT_UINT64_REF\"] = 123] = \"DT_UINT64_REF\";\n})(DataType || (DataType = {}));\nvar SaverDef;\n(function(SaverDef2) {\n let CheckpointFormatVersion;\n (function(CheckpointFormatVersion2) {\n CheckpointFormatVersion2[CheckpointFormatVersion2[\"LEGACY\"] = 0] = \"LEGACY\";\n CheckpointFormatVersion2[CheckpointFormatVersion2[\"V1\"] = 1] = \"V1\";\n CheckpointFormatVersion2[CheckpointFormatVersion2[\"V2\"] = 2] = \"V2\";\n })(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/register.js\nvar CUSTOM_OPS = {};\nfunction registerOp(name, opFunc) {\n const opMapper = {\n tfOpName: name,\n category: \"custom\",\n inputs: [],\n attrs: [],\n customExecutor: opFunc\n };\n CUSTOM_OPS[name] = opMapper;\n}\nfunction getRegisteredOp(name) {\n return CUSTOM_OPS[name];\n}\nfunction deregisterOp(name) {\n delete CUSTOM_OPS[name];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/utils.js\nfunction getParamValue(paramName, node, tensorMap, context, resourceManager) {\n const inputParam = node.inputParams[paramName];\n if (inputParam && inputParam.inputIndexStart !== void 0) {\n const start = inputParam.inputIndexStart;\n const end = inputParam.inputIndexEnd === 0 ? void 0 : inputParam.inputIndexEnd === void 0 ? start + 1 : inputParam.inputIndexEnd;\n const shiftedStart = start < 0 ? node.inputNames.length + start : start;\n if (inputParam.type === \"tensor\") {\n return getTensor(node.inputNames[shiftedStart], tensorMap, context, resourceManager);\n }\n if (inputParam.type === \"tensors\") {\n const inputs = node.inputs.slice(start, end);\n const inputNames = node.inputNames.slice(start, end).filter((_name, index) => {\n var _a;\n return ((_a = inputs[index]) === null || _a === void 0 ? void 0 : _a.op) !== \"NoOp\";\n });\n return inputNames.map((name) => getTensor(name, tensorMap, context, resourceManager));\n }\n const tensor2 = getTensor(node.inputNames[shiftedStart], tensorMap, context, resourceManager);\n const data = tensor2.dataSync();\n return inputParam.type === \"number\" ? data[0] : util_exports.toNestedArray(tensor2.shape, data);\n }\n const attrParam = node.attrParams[paramName];\n return attrParam && attrParam.value;\n}\nfunction getTensor(name, tensorsMap, context, resourceManager) {\n const [nodeName, index] = parseNodeName(name, context);\n if (resourceManager != null) {\n const tensor2 = resourceManager.getHashTableHandleByName(nodeName);\n if (tensor2 != null) {\n return tensor2;\n }\n }\n const contextId = context.currentContextIds.find((contextId2) => {\n return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId2)];\n });\n return contextId !== void 0 ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : void 0;\n}\nfunction getTensorsForCurrentContext(name, tensorsMap, context) {\n return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\nfunction getNodeNameAndIndex(inputName, context) {\n const [nodeName, index, outputName] = parseNodeName(inputName, context);\n return [\n getNodeNameWithContextId(nodeName, context && context.currentContextId),\n index,\n outputName\n ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n return !!contextId ? `${name}-${contextId}` : name;\n}\nfunction parseNodeName(name, context) {\n if (name === \"\") {\n return [\"\", 0, void 0];\n }\n const isCacheEnabled = context != null && context.parseNodeNameCache != null;\n if (isCacheEnabled) {\n const cachedResult = context.parseNodeNameCache.get(name);\n if (cachedResult != null) {\n return cachedResult;\n }\n }\n const parts = name.split(\":\");\n let result;\n if (parts.length === 1) {\n result = [name, 0, void 0];\n } else {\n const nodeName = parts[0];\n const outputName = parts.length === 3 ? parts[1] : void 0;\n const index = Number(parts[parts.length - 1]);\n result = [nodeName, index, outputName];\n }\n if (isCacheEnabled) {\n context.parseNodeNameCache.set(name, result);\n }\n return result;\n}\nfunction getPadding(node, tensorMap, context) {\n let pad3 = getParamValue(\"pad\", node, tensorMap, context);\n if (pad3 === \"explicit\") {\n pad3 = getParamValue(\"explicitPaddings\", node, tensorMap, context);\n const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n for (let i = 0; i < 4; i++) {\n explicitPadding[i][0] = pad3[i * 2];\n explicitPadding[i][1] = pad3[i * 2 + 1];\n }\n return explicitPadding;\n }\n return pad3;\n}\nfunction cloneTensor(tensor2) {\n return tensor2.kept ? tensor2 : clone(tensor2);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/arithmetic.js\nvar arithmetic_exports = {};\n__export(arithmetic_exports, {\n json: () => json\n});\nvar json = [\n {\n \"tfOpName\": \"Add\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"AddV2\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"AddN\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"end\": 0,\n \"name\": \"tensors\",\n \"type\": \"tensors\"\n }\n ]\n },\n {\n \"tfOpName\": \"BiasAdd\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Sub\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"RealDiv\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Div\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"DivNoNan\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"FloorDiv\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Mul\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Maximum\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Minimum\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Pow\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"SquaredDifference\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Mod\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"FloorMod\",\n \"category\": \"arithmetic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/basic_math.js\nvar basic_math_exports = {};\n__export(basic_math_exports, {\n json: () => json2\n});\nvar json2 = [\n {\n \"tfOpName\": \"Abs\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Acos\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Asin\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Atan\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Atan2\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"y\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Ceil\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"ClipByValue\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"clipValueMin\",\n \"type\": \"number\"\n },\n {\n \"start\": 2,\n \"name\": \"clipValueMax\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Complex\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"real\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"imag\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"ComplexAbs\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Cos\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Cosh\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Elu\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Exp\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Floor\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Log\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Imag\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"Tout\",\n \"name\": \"outputType\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Neg\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Real\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"Tout\",\n \"name\": \"outputType\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Prelu\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"alpha\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Relu\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Relu6\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Selu\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Sigmoid\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Sin\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Sinh\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Sqrt\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Rsqrt\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Square\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Tan\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Tanh\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Sign\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Round\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Expm1\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Log1p\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Reciprocal\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Softplus\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Asinh\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Acosh\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Atanh\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Erf\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"LeakyRelu\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"alpha\",\n \"name\": \"alpha\",\n \"type\": \"number\",\n \"defaultValue\": 0.2\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"IsNan\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"IsFinite\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"IsInf\",\n \"category\": \"basic_math\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/control.js\nvar control_exports = {};\n__export(control_exports, {\n json: () => json3\n});\nvar json3 = [\n {\n \"tfOpName\": \"EmptyTensorList\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n },\n {\n \"start\": 1,\n \"name\": \"maxNumElements\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"LoopCond\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"pred\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"Switch\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"data\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"pred\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"Merge\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"end\": 0,\n \"name\": \"tensors\",\n \"type\": \"tensors\"\n }\n ]\n },\n {\n \"tfOpName\": \"Enter\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"frame_name\",\n \"name\": \"frameName\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"is_constant\",\n \"name\": \"isConstant\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"Exit\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"NextIteration\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"TensorArrayV3\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"size\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"dtype\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n },\n {\n \"tfName\": \"element_shape\",\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n },\n {\n \"tfName\": \"dynamic_size\",\n \"name\": \"dynamicSize\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"clear_after_read\",\n \"name\": \"clearAfterRead\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"identical_element_shapes\",\n \"name\": \"identicalElementShapes\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"tensor_array_name\",\n \"name\": \"name\",\n \"type\": \"string\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorArrayWriteV3\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorArrayId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"index\",\n \"type\": \"number\"\n },\n {\n \"start\": 2,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 3,\n \"name\": \"flowIn\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"TensorArrayReadV3\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorArrayId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"index\",\n \"type\": \"number\"\n },\n {\n \"start\": 2,\n \"name\": \"flowIn\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"dtype\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"TensorArrayGatherV3\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorArrayId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"flowIn\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"dtype\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n },\n {\n \"tfName\": \"element_shape\",\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorArrayScatterV3\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorArrayId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 3,\n \"name\": \"flowIn\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorArrayConcatV3\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorArrayId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"flowIn\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"dtype\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n },\n {\n \"tfName\": \"element_shape_except0\",\n \"name\": \"elementShapeExcept0\",\n \"type\": \"shape\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"TensorArraySplitV3\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorArrayId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"lengths\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 3,\n \"name\": \"flowIn\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorArraySizeV3\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorArrayId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"flowIn\",\n \"type\": \"number\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorArrayCloseV3\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorArrayId\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"StatelessIf\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"cond\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"end\": 0,\n \"name\": \"args\",\n \"type\": \"tensors\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"then_branch\",\n \"name\": \"thenBranch\",\n \"type\": \"func\"\n },\n {\n \"tfName\": \"else_branch\",\n \"name\": \"elseBranch\",\n \"type\": \"func\"\n }\n ]\n },\n {\n \"tfOpName\": \"If\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"cond\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"end\": 0,\n \"name\": \"args\",\n \"type\": \"tensors\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"then_branch\",\n \"name\": \"thenBranch\",\n \"type\": \"func\"\n },\n {\n \"tfName\": \"else_branch\",\n \"name\": \"elseBranch\",\n \"type\": \"func\"\n }\n ]\n },\n {\n \"tfOpName\": \"StatelessWhile\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"end\": 0,\n \"name\": \"args\",\n \"type\": \"tensors\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"cond\",\n \"name\": \"cond\",\n \"type\": \"func\"\n },\n {\n \"tfName\": \"body\",\n \"name\": \"body\",\n \"type\": \"func\"\n }\n ]\n },\n {\n \"tfOpName\": \"While\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"end\": 0,\n \"name\": \"args\",\n \"type\": \"tensors\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"cond\",\n \"name\": \"cond\",\n \"type\": \"func\"\n },\n {\n \"tfName\": \"body\",\n \"name\": \"body\",\n \"type\": \"func\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListScatter\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListScatterV2\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n },\n {\n \"start\": 3,\n \"name\": \"numElements\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListGather\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorListId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListGetItem\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorListId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"index\",\n \"type\": \"number\"\n },\n {\n \"start\": 2,\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListSetItem\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorListId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"index\",\n \"type\": \"number\"\n },\n {\n \"start\": 2,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListReserve\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n },\n {\n \"start\": 1,\n \"name\": \"numElements\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListFromTensor\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListStack\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorListId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n },\n {\n \"tfName\": \"num_elements\",\n \"name\": \"numElements\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListSplit\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n },\n {\n \"start\": 2,\n \"name\": \"lengths\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListConcat\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorListId\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_shape\",\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n },\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListConcatV2\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorListId\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_shape\",\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n },\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListPopBack\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorListId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"elementShape\",\n \"type\": \"shape\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListPushBack\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorListId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"element_dtype\",\n \"name\": \"elementDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListLength\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorListId\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"TensorListResize\",\n \"category\": \"control\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensorListId\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"size\",\n \"type\": \"number\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/convolution.js\nvar convolution_exports = {};\n__export(convolution_exports, {\n json: () => json4\n});\nvar json4 = [\n {\n \"tfOpName\": \"AvgPool\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"ksize\",\n \"name\": \"kernelSize\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"MaxPool\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"ksize\",\n \"name\": \"kernelSize\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"explicit_paddings\",\n \"name\": \"explicitPaddings\",\n \"type\": \"number[]\",\n \"defaultValue\": [],\n \"notSupported\": true\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"MaxPoolWithArgmax\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"ksize\",\n \"name\": \"kernelSize\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"include_batch_in_index\",\n \"name\": \"includeBatchInIndex\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"AvgPool3D\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"ksize\",\n \"name\": \"kernelSize\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"MaxPool3D\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"ksize\",\n \"name\": \"kernelSize\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Conv1D\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"filter\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"stride\",\n \"name\": \"stride\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"defaultValue\": \"NWC\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"dilation\",\n \"name\": \"dilation\",\n \"type\": \"number\",\n \"defaultValue\": 1\n }\n ]\n },\n {\n \"tfOpName\": \"Conv2D\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"filter\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"useCudnnOnGpu\",\n \"name\": \"useCudnnOnGpu\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"defaultValue\": \"NHWC\"\n },\n {\n \"tfName\": \"explicit_paddings\",\n \"name\": \"explicitPaddings\",\n \"type\": \"number[]\",\n \"defaultValue\": []\n },\n {\n \"tfName\": \"dilations\",\n \"name\": \"dilations\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"_FusedConv2D\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"filter\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"end\": 0,\n \"name\": \"args\",\n \"type\": \"tensors\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"num_args\",\n \"name\": \"numArgs\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"explicit_paddings\",\n \"name\": \"explicitPaddings\",\n \"type\": \"number[]\",\n \"defaultValue\": []\n },\n {\n \"tfName\": \"use_cudnn_on_gpu\",\n \"name\": \"useCudnnOnGpu\",\n \"type\": \"bool\",\n \"defaultValue\": true\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"defaultValue\": \"NHWC\"\n },\n {\n \"tfName\": \"dilations\",\n \"name\": \"dilations\",\n \"type\": \"number[]\",\n \"defaultValue\": [\n 1,\n 1,\n 1,\n 1\n ]\n },\n {\n \"tfName\": \"fused_ops\",\n \"name\": \"fusedOps\",\n \"type\": \"string[]\",\n \"defaultValue\": []\n },\n {\n \"tfName\": \"epsilon\",\n \"name\": \"epsilon\",\n \"type\": \"number\",\n \"defaultValue\": 1e-4\n },\n {\n \"tfName\": \"leakyrelu_alpha\",\n \"name\": \"leakyreluAlpha\",\n \"type\": \"number\",\n \"defaultValue\": 0.2\n }\n ]\n },\n {\n \"tfOpName\": \"Conv2DBackpropInput\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 2,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"filter\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 0,\n \"name\": \"outputShape\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"explicit_paddings\",\n \"name\": \"explicitPaddings\",\n \"type\": \"number[]\",\n \"defaultValue\": []\n },\n {\n \"tfName\": \"dilations\",\n \"name\": \"dilations\",\n \"type\": \"number[]\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"DepthwiseConv2d\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"input\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"filter\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"defaultValue\": \"NHWC\"\n },\n {\n \"tfName\": \"explicit_paddings\",\n \"name\": \"explicitPaddings\",\n \"type\": \"number[]\",\n \"defaultValue\": []\n },\n {\n \"tfName\": \"dilations\",\n \"name\": \"dilations\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"DepthwiseConv2dNative\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"input\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"filter\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"defaultValue\": \"NHWC\"\n },\n {\n \"tfName\": \"explicit_paddings\",\n \"name\": \"explicitPaddings\",\n \"type\": \"number[]\",\n \"defaultValue\": []\n },\n {\n \"tfName\": \"dilations\",\n \"name\": \"dilations\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"FusedDepthwiseConv2dNative\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"filter\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"end\": 0,\n \"name\": \"args\",\n \"type\": \"tensors\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"num_args\",\n \"name\": \"numArgs\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"defaultValue\": \"NHWC\"\n },\n {\n \"tfName\": \"dilations\",\n \"name\": \"dilations\",\n \"type\": \"number[]\",\n \"defaultValue\": [\n 1,\n 1,\n 1,\n 1\n ]\n },\n {\n \"tfName\": \"fused_ops\",\n \"name\": \"fusedOps\",\n \"type\": \"string[]\",\n \"defaultValue\": []\n },\n {\n \"tfName\": \"explicit_paddings\",\n \"name\": \"explicitPaddings\",\n \"type\": \"number[]\",\n \"defaultValue\": []\n }\n ]\n },\n {\n \"tfOpName\": \"Conv3D\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"filter\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"defaultValue\": \"NHWC\"\n },\n {\n \"tfName\": \"dilations\",\n \"name\": \"dilations\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"Dilation2D\",\n \"category\": \"convolution\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"filter\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"strides\",\n \"name\": \"strides\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"rates\",\n \"name\": \"dilations\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"padding\",\n \"name\": \"pad\",\n \"type\": \"string\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/creation.js\nvar creation_exports = {};\n__export(creation_exports, {\n json: () => json5\n});\nvar json5 = [\n {\n \"tfOpName\": \"Fill\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 1,\n \"name\": \"value\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"LinSpace\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"start\",\n \"type\": \"number\"\n },\n {\n \"start\": 1,\n \"name\": \"stop\",\n \"type\": \"number\"\n },\n {\n \"start\": 2,\n \"name\": \"num\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"OneHot\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"indices\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"depth\",\n \"type\": \"number\"\n },\n {\n \"start\": 2,\n \"name\": \"onValue\",\n \"type\": \"number\",\n \"defaultValue\": 1\n },\n {\n \"start\": 3,\n \"name\": \"offValue\",\n \"type\": \"number\",\n \"defaultValue\": 0\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"axis\",\n \"name\": \"axis\",\n \"type\": \"number\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"Ones\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"OnesLike\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"dtype\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"RandomStandardNormal\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"seed\",\n \"name\": \"seed\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"tfName\": \"seed2\",\n \"name\": \"seed2\",\n \"type\": \"number\",\n \"defaultValue\": 0,\n \"notSupported\": true\n },\n {\n \"tfName\": \"dtype\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"T\",\n \"type\": \"number\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"RandomUniform\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"minval\",\n \"name\": \"minval\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"tfName\": \"maxval\",\n \"name\": \"maxval\",\n \"type\": \"number\",\n \"defaultValue\": 1\n },\n {\n \"tfName\": \"dtype\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n },\n {\n \"tfName\": \"seed\",\n \"name\": \"seed\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"tfName\": \"seed2\",\n \"name\": \"seed2\",\n \"type\": \"number\",\n \"defaultValue\": 0,\n \"notSupported\": true\n },\n {\n \"tfName\": \"T\",\n \"name\": \"T\",\n \"type\": \"number\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"RandomUniformInt\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"minval\",\n \"name\": \"minval\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"maxval\",\n \"name\": \"maxval\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"seed\",\n \"name\": \"seed\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"tfName\": \"seed2\",\n \"name\": \"seed2\",\n \"type\": \"number\",\n \"defaultValue\": 0,\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Range\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"start\",\n \"type\": \"number\"\n },\n {\n \"start\": 1,\n \"name\": \"stop\",\n \"type\": \"number\"\n },\n {\n \"start\": 2,\n \"name\": \"step\",\n \"type\": \"number\",\n \"defaultValue\": 0\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"Tidx\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"TruncatedNormal\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"means\",\n \"name\": \"mean\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"tfName\": \"stddev\",\n \"name\": \"stdDev\",\n \"type\": \"number\",\n \"defaultValue\": 1\n },\n {\n \"tfName\": \"seed\",\n \"name\": \"seed\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"seed2\",\n \"name\": \"seed2\",\n \"type\": \"number\",\n \"defaultValue\": 0,\n \"notSupported\": true\n },\n {\n \"tfName\": \"dtype\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"T\",\n \"type\": \"number\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Zeros\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"ZerosLike\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"Multinomial\",\n \"category\": \"creation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"logits\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"numSamples\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"seed\",\n \"name\": \"seed\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"seed2\",\n \"name\": \"seed2\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n },\n {\n \"tfName\": \"output_dtype\",\n \"name\": \"output_dtype\",\n \"type\": \"dtype\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/dynamic.js\nvar dynamic_exports = {};\n__export(dynamic_exports, {\n json: () => json6\n});\nvar json6 = [\n {\n \"tfOpName\": \"NonMaxSuppressionV2\",\n \"category\": \"dynamic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"boxes\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"scores\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"maxOutputSize\",\n \"type\": \"number\"\n },\n {\n \"start\": 3,\n \"name\": \"iouThreshold\",\n \"type\": \"number\"\n }\n ]\n },\n {\n \"tfOpName\": \"NonMaxSuppressionV3\",\n \"category\": \"dynamic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"boxes\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"scores\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"maxOutputSize\",\n \"type\": \"number\"\n },\n {\n \"start\": 3,\n \"name\": \"iouThreshold\",\n \"type\": \"number\"\n },\n {\n \"start\": 4,\n \"name\": \"scoreThreshold\",\n \"type\": \"number\"\n }\n ]\n },\n {\n \"tfOpName\": \"NonMaxSuppressionV4\",\n \"category\": \"dynamic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"boxes\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"scores\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"maxOutputSize\",\n \"type\": \"number\"\n },\n {\n \"start\": 3,\n \"name\": \"iouThreshold\",\n \"type\": \"number\"\n },\n {\n \"start\": 4,\n \"name\": \"scoreThreshold\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"T_threshold\",\n \"name\": \"threshold\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"pad_to_max_output_size\",\n \"name\": \"padToMaxOutputSize\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"NonMaxSuppressionV5\",\n \"category\": \"dynamic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"boxes\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"scores\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"maxOutputSize\",\n \"type\": \"number\"\n },\n {\n \"start\": 3,\n \"name\": \"iouThreshold\",\n \"type\": \"number\"\n },\n {\n \"start\": 4,\n \"name\": \"scoreThreshold\",\n \"type\": \"number\"\n },\n {\n \"start\": 5,\n \"name\": \"softNmsSigma\",\n \"type\": \"number\"\n }\n ]\n },\n {\n \"tfOpName\": \"Where\",\n \"category\": \"dynamic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"condition\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"ListDiff\",\n \"category\": \"dynamic\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"y\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/evaluation.js\nvar evaluation_exports = {};\n__export(evaluation_exports, {\n json: () => json7\n});\nvar json7 = [\n {\n \"tfOpName\": \"LowerBound\",\n \"category\": \"evaluation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"sortedSequence\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"values\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"TopKV2\",\n \"category\": \"evaluation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"k\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"sorted\",\n \"name\": \"sorted\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"UpperBound\",\n \"category\": \"evaluation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"sortedSequence\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"values\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"Unique\",\n \"category\": \"evaluation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"UniqueV2\",\n \"category\": \"evaluation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/graph.js\nvar graph_exports = {};\n__export(graph_exports, {\n json: () => json8\n});\nvar json8 = [\n {\n \"tfOpName\": \"PlaceholderWithDefault\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"default\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"shape\",\n \"name\": \"shape\",\n \"type\": \"shape\"\n },\n {\n \"tfName\": \"dtype\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"Placeholder\",\n \"category\": \"graph\",\n \"attrs\": [\n {\n \"tfName\": \"shape\",\n \"name\": \"shape\",\n \"type\": \"shape\"\n },\n {\n \"tfName\": \"dtype\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"Const\",\n \"category\": \"graph\"\n },\n {\n \"tfOpName\": \"Identity\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"IdentityN\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"end\": 0,\n \"name\": \"x\",\n \"type\": \"tensors\"\n }\n ]\n },\n {\n \"tfOpName\": \"Snapshot\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"Rank\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"Size\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"Shape\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"ShapeN\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"end\": 0,\n \"name\": \"x\",\n \"type\": \"tensors\"\n }\n ]\n },\n {\n \"tfOpName\": \"Print\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"data\",\n \"type\": \"tensors\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"message\",\n \"name\": \"message\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"first_n\",\n \"name\": \"firstN\",\n \"type\": \"number\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"summarize\",\n \"name\": \"summarize\",\n \"type\": \"number\",\n \"defaultValue\": 3\n }\n ]\n },\n {\n \"tfOpName\": \"NoOp\",\n \"category\": \"graph\",\n \"inputs\": []\n },\n {\n \"tfOpName\": \"StopGradient\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"FakeQuantWithMinMaxVars\",\n \"category\": \"graph\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"min\",\n \"name\": \"min\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"max\",\n \"name\": \"max\",\n \"type\": \"number\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/hash_table.js\nvar hash_table_exports = {};\n__export(hash_table_exports, {\n json: () => json9\n});\nvar json9 = [\n {\n \"tfOpName\": \"HashTable\",\n \"category\": \"hash_table\",\n \"inputs\": [],\n \"attrs\": [\n {\n \"tfName\": \"shared_name\",\n \"name\": \"sharedName\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"use_node_name_sharing\",\n \"name\": \"useNodeNameSharing\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"key_dtype\",\n \"name\": \"keyDType\",\n \"type\": \"dtype\"\n },\n {\n \"tfName\": \"value_dtype\",\n \"name\": \"valueDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"HashTableV2\",\n \"category\": \"hash_table\",\n \"inputs\": [],\n \"attrs\": [\n {\n \"tfName\": \"shared_name\",\n \"name\": \"sharedName\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"use_node_name_sharing\",\n \"name\": \"useNodeNameSharing\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"key_dtype\",\n \"name\": \"keyDType\",\n \"type\": \"dtype\"\n },\n {\n \"tfName\": \"value_dtype\",\n \"name\": \"valueDType\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"LookupTableImport\",\n \"category\": \"hash_table\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tableHandle\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"keys\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"values\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"Tin\",\n \"name\": \"tIn\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"Tout\",\n \"name\": \"tOut\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"LookupTableImportV2\",\n \"category\": \"hash_table\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tableHandle\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"keys\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"values\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"Tin\",\n \"name\": \"tIn\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"Tout\",\n \"name\": \"tOut\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"LookupTableFind\",\n \"category\": \"hash_table\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tableHandle\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"keys\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"defaultValue\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"Tin\",\n \"name\": \"tIn\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"Tout\",\n \"name\": \"tOut\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"LookupTableFindV2\",\n \"category\": \"hash_table\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tableHandle\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"keys\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"defaultValue\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"Tin\",\n \"name\": \"tIn\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"Tout\",\n \"name\": \"tOut\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"LookupTableSize\",\n \"category\": \"hash_table\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tableHandle\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"LookupTableSizeV2\",\n \"category\": \"hash_table\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tableHandle\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"InitializeTable\",\n \"category\": \"hash_table\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tableHandle\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"keys\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"values\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"InitializeTableV2\",\n \"category\": \"hash_table\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tableHandle\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"keys\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"values\",\n \"type\": \"tensor\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/image.js\nvar image_exports = {};\n__export(image_exports, {\n json: () => json10\n});\nvar json10 = [\n {\n \"tfOpName\": \"ResizeBilinear\",\n \"category\": \"image\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"images\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"size\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"align_corners\",\n \"name\": \"alignCorners\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"half_pixel_centers\",\n \"name\": \"halfPixelCenters\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"ResizeNearestNeighbor\",\n \"category\": \"image\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"images\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"size\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"align_corners\",\n \"name\": \"alignCorners\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"half_pixel_centers\",\n \"name\": \"halfPixelCenters\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"CropAndResize\",\n \"category\": \"image\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"image\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"boxes\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"boxInd\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 3,\n \"name\": \"cropSize\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"method\",\n \"name\": \"method\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"extrapolation_value\",\n \"name\": \"extrapolationValue\",\n \"type\": \"number\"\n }\n ]\n },\n {\n \"tfOpName\": \"ImageProjectiveTransformV3\",\n \"category\": \"image\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"images\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"transforms\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"outputShape\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 3,\n \"name\": \"fillValue\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"interpolation\",\n \"name\": \"interpolation\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"fill_mode\",\n \"name\": \"fillMode\",\n \"type\": \"string\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/logical.js\nvar logical_exports = {};\n__export(logical_exports, {\n json: () => json11\n});\nvar json11 = [\n {\n \"tfOpName\": \"Equal\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"NotEqual\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Greater\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"GreaterEqual\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Less\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"LessEqual\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"LogicalAnd\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"LogicalNot\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"LogicalOr\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Select\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"condition\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"SelectV2\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"condition\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"BitwiseAnd\",\n \"category\": \"logical\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"y\",\n \"type\": \"tensor\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/matrices.js\nvar matrices_exports = {};\n__export(matrices_exports, {\n json: () => json12\n});\nvar json12 = [\n {\n \"tfOpName\": \"_FusedMatMul\",\n \"category\": \"matrices\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"end\": 0,\n \"name\": \"args\",\n \"type\": \"tensors\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"num_args\",\n \"name\": \"numArgs\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"fused_ops\",\n \"name\": \"fusedOps\",\n \"type\": \"string[]\",\n \"defaultValue\": []\n },\n {\n \"tfName\": \"epsilon\",\n \"name\": \"epsilon\",\n \"type\": \"number\",\n \"defaultValue\": 1e-4\n },\n {\n \"tfName\": \"transpose_a\",\n \"name\": \"transposeA\",\n \"type\": \"bool\",\n \"defaultValue\": false\n },\n {\n \"tfName\": \"transpose_b\",\n \"name\": \"transposeB\",\n \"type\": \"bool\",\n \"defaultValue\": false\n },\n {\n \"tfName\": \"leakyrelu_alpha\",\n \"name\": \"leakyreluAlpha\",\n \"type\": \"number\",\n \"defaultValue\": 0.2\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"MatMul\",\n \"category\": \"matrices\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"transpose_a\",\n \"name\": \"transposeA\",\n \"type\": \"bool\",\n \"defaultValue\": false\n },\n {\n \"tfName\": \"transpose_b\",\n \"name\": \"transposeB\",\n \"type\": \"bool\",\n \"defaultValue\": false\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"BatchMatMul\",\n \"category\": \"matrices\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"adj_x\",\n \"name\": \"transposeA\",\n \"type\": \"bool\",\n \"defaultValue\": false\n },\n {\n \"tfName\": \"adj_y\",\n \"name\": \"transposeB\",\n \"type\": \"bool\",\n \"defaultValue\": false\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"BatchMatMulV2\",\n \"category\": \"matrices\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"b\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"adj_x\",\n \"name\": \"transposeA\",\n \"type\": \"bool\",\n \"defaultValue\": false\n },\n {\n \"tfName\": \"adj_y\",\n \"name\": \"transposeB\",\n \"type\": \"bool\",\n \"defaultValue\": false\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Transpose\",\n \"category\": \"matrices\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"perm\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Einsum\",\n \"category\": \"matrices\",\n \"inputs\": [\n {\n \"start\": 0,\n \"end\": 0,\n \"name\": \"tensors\",\n \"type\": \"tensors\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"equation\",\n \"name\": \"equation\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"N\",\n \"name\": \"n\",\n \"type\": \"number\",\n \"defaultValue\": 2\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"MatrixBandPart\",\n \"category\": \"matrices\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"a\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"numLower\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"numUpper\",\n \"type\": \"tensor\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/normalization.js\nvar normalization_exports = {};\n__export(normalization_exports, {\n json: () => json13\n});\nvar json13 = [\n {\n \"tfOpName\": \"EuclideanNorm\",\n \"category\": \"normalization\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"keep_dims\",\n \"name\": \"keepDims\",\n \"type\": \"bool\",\n \"defaultValue\": false\n }\n ]\n },\n {\n \"tfOpName\": \"FusedBatchNorm\",\n \"category\": \"normalization\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"scale\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"offset\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 3,\n \"name\": \"mean\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 4,\n \"name\": \"variance\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"epsilon\",\n \"name\": \"epsilon\",\n \"type\": \"number\",\n \"defaultValue\": 1e-3\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"FusedBatchNormV2\",\n \"category\": \"normalization\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"scale\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"offset\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 3,\n \"name\": \"mean\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 4,\n \"name\": \"variance\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"epsilon\",\n \"name\": \"epsilon\",\n \"type\": \"number\",\n \"defaultValue\": 1e-3\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"FusedBatchNormV3\",\n \"category\": \"normalization\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"scale\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"offset\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 3,\n \"name\": \"mean\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 4,\n \"name\": \"variance\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"epsilon\",\n \"name\": \"epsilon\",\n \"type\": \"number\",\n \"defaultValue\": 1e-3\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"LRN\",\n \"category\": \"normalization\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"depth_radius\",\n \"name\": \"radius\",\n \"type\": \"number\",\n \"defaultValue\": 5\n },\n {\n \"tfName\": \"bias\",\n \"name\": \"bias\",\n \"type\": \"number\",\n \"defaultValue\": 1\n },\n {\n \"tfName\": \"alpha\",\n \"name\": \"alpha\",\n \"type\": \"number\",\n \"defaultValue\": 1\n },\n {\n \"tfName\": \"beta\",\n \"name\": \"beta\",\n \"type\": \"number\",\n \"defaultValue\": 0.5\n }\n ]\n },\n {\n \"tfOpName\": \"Softmax\",\n \"category\": \"normalization\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"LogSoftmax\",\n \"category\": \"normalization\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/reduction.js\nvar reduction_exports = {};\n__export(reduction_exports, {\n json: () => json14\n});\nvar json14 = [\n {\n \"tfOpName\": \"Bincount\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"size\",\n \"type\": \"number\"\n },\n {\n \"start\": 2,\n \"name\": \"weights\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"DenseBincount\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"size\",\n \"type\": \"number\"\n },\n {\n \"start\": 2,\n \"name\": \"weights\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"binary_output\",\n \"name\": \"binaryOutput\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"Max\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"keep_dims\",\n \"name\": \"keepDims\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"Mean\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"keep_dims\",\n \"name\": \"keepDims\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"Min\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"keep_dims\",\n \"name\": \"keepDims\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"Sum\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"keep_dims\",\n \"name\": \"keepDims\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"All\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"keep_dims\",\n \"name\": \"keepDims\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"Any\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"keep_dims\",\n \"name\": \"keepDims\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"ArgMax\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number\"\n }\n ]\n },\n {\n \"tfOpName\": \"ArgMin\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number\"\n }\n ]\n },\n {\n \"tfOpName\": \"Prod\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"keep_dims\",\n \"name\": \"keepDims\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Cumprod\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"exclusive\",\n \"name\": \"exclusive\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"reverse\",\n \"name\": \"reverse\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"Cumsum\",\n \"category\": \"reduction\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"exclusive\",\n \"name\": \"exclusive\",\n \"type\": \"bool\"\n },\n {\n \"tfName\": \"reverse\",\n \"name\": \"reverse\",\n \"type\": \"bool\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/slice_join.js\nvar slice_join_exports = {};\n__export(slice_join_exports, {\n json: () => json15\n});\nvar json15 = [\n {\n \"tfOpName\": \"ConcatV2\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"end\": -1,\n \"name\": \"tensors\",\n \"type\": \"tensors\"\n },\n {\n \"start\": -1,\n \"name\": \"axis\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"N\",\n \"name\": \"n\",\n \"type\": \"number\",\n \"defaultValue\": 2\n }\n ]\n },\n {\n \"tfOpName\": \"Concat\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 1,\n \"end\": 0,\n \"name\": \"tensors\",\n \"type\": \"tensors\"\n },\n {\n \"start\": 0,\n \"name\": \"axis\",\n \"type\": \"number\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"N\",\n \"name\": \"n\",\n \"type\": \"number\",\n \"defaultValue\": 2\n }\n ]\n },\n {\n \"tfOpName\": \"GatherV2\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"axis\",\n \"type\": \"number\",\n \"defaultValue\": 0\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"batch_dims\",\n \"name\": \"batchDims\",\n \"type\": \"number\",\n \"defaultValue\": 0\n }\n ]\n },\n {\n \"tfOpName\": \"Gather\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"validate_indices\",\n \"name\": \"validateIndices\",\n \"type\": \"bool\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Reverse\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"dims\",\n \"type\": \"bool[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"ReverseV2\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"Slice\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"begin\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"size\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"StridedSlice\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"begin\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"end\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 3,\n \"name\": \"strides\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"begin_mask\",\n \"name\": \"beginMask\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"tfName\": \"end_mask\",\n \"name\": \"endMask\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"tfName\": \"new_axis_mask\",\n \"name\": \"newAxisMask\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"tfName\": \"ellipsis_mask\",\n \"name\": \"ellipsisMask\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"tfName\": \"shrink_axis_mask\",\n \"name\": \"shrinkAxisMask\",\n \"type\": \"number\",\n \"defaultValue\": 0\n }\n ]\n },\n {\n \"tfOpName\": \"Pack\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"end\": 0,\n \"name\": \"tensors\",\n \"type\": \"tensors\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"axis\",\n \"name\": \"axis\",\n \"type\": \"number\",\n \"defaultValue\": 0\n }\n ]\n },\n {\n \"tfOpName\": \"Unpack\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"axis\",\n \"name\": \"axis\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"tfName\": \"num\",\n \"name\": \"num\",\n \"type\": \"number\",\n \"defaultValue\": 0,\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"Tile\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"reps\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"Split\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"axis\",\n \"type\": \"number\",\n \"defaultValue\": 0\n },\n {\n \"start\": 1,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"num_split\",\n \"name\": \"numOrSizeSplits\",\n \"type\": \"number\",\n \"defaultValue\": 1\n }\n ]\n },\n {\n \"tfOpName\": \"SplitV\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"numOrSizeSplits\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"axis\",\n \"type\": \"number\",\n \"defaultValue\": 0\n }\n ]\n },\n {\n \"tfOpName\": \"ScatterNd\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"indices\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"values\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"GatherNd\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"SparseToDense\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"sparseIndices\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"outputShape\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"sparseValues\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 3,\n \"name\": \"defaultValue\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"validate_indices\",\n \"name\": \"validateIndices\",\n \"type\": \"bool\",\n \"defaultValue\": false,\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"TensorScatterUpdate\",\n \"category\": \"slice_join\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"tensor\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"values\",\n \"type\": \"tensor\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/sparse.js\nvar sparse_exports = {};\n__export(sparse_exports, {\n json: () => json16\n});\nvar json16 = [\n {\n \"tfOpName\": \"SparseFillEmptyRows\",\n \"category\": \"sparse\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"indices\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"values\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"denseShape\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 3,\n \"name\": \"defaultValue\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"SparseReshape\",\n \"category\": \"sparse\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"inputIndices\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"inputShape\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"newShape\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"T\",\n \"name\": \"dtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"SparseSegmentMean\",\n \"category\": \"sparse\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"data\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"segmentIds\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"SparseSegmentSum\",\n \"category\": \"sparse\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"data\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"indices\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 2,\n \"name\": \"segmentIds\",\n \"type\": \"tensor\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/spectral.js\nvar spectral_exports = {};\n__export(spectral_exports, {\n json: () => json17\n});\nvar json17 = [\n {\n \"tfOpName\": \"FFT\",\n \"category\": \"spectral\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"IFFT\",\n \"category\": \"spectral\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ]\n },\n {\n \"tfOpName\": \"RFFT\",\n \"category\": \"spectral\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"fft_length\",\n \"type\": \"number\",\n \"notSupported\": true\n }\n ]\n },\n {\n \"tfOpName\": \"IRFFT\",\n \"category\": \"spectral\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"fft_length\",\n \"type\": \"number\",\n \"notSupported\": true\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/string.js\nvar string_exports = {};\n__export(string_exports, {\n json: () => json18\n});\nvar json18 = [\n {\n \"tfOpName\": \"StaticRegexReplace\",\n \"category\": \"string\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"input\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"pattern\",\n \"name\": \"pattern\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"rewrite\",\n \"name\": \"rewrite\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"replace_global\",\n \"name\": \"replaceGlobal\",\n \"type\": \"bool\"\n }\n ]\n },\n {\n \"tfOpName\": \"StringNGrams\",\n \"category\": \"string\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"data\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"dataSplits\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"separator\",\n \"name\": \"separator\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"ngram_widths\",\n \"name\": \"nGramWidths\",\n \"type\": \"number[]\"\n },\n {\n \"tfName\": \"left_pad\",\n \"name\": \"leftPad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"right_pad\",\n \"name\": \"rightPad\",\n \"type\": \"string\"\n },\n {\n \"tfName\": \"pad_width\",\n \"name\": \"padWidth\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"preserve_short_sequences\",\n \"name\": \"preserveShortSequences\",\n \"type\": \"bool\"\n }\n ],\n \"outputs\": [\n \"ngrams\",\n \"ngrams_splits\"\n ]\n },\n {\n \"tfOpName\": \"StringSplit\",\n \"category\": \"string\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"input\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"delimiter\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"skip_empty\",\n \"name\": \"skipEmpty\",\n \"type\": \"bool\"\n }\n ],\n \"outputs\": [\n \"indices\",\n \"values\",\n \"shape\"\n ]\n },\n {\n \"tfOpName\": \"StringToHashBucketFast\",\n \"category\": \"string\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"input\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"num_buckets\",\n \"name\": \"numBuckets\",\n \"type\": \"number\"\n }\n ]\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/transformation.js\nvar transformation_exports = {};\n__export(transformation_exports, {\n json: () => json19\n});\nvar json19 = [\n {\n \"tfOpName\": \"Cast\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"SrcT\",\n \"name\": \"sdtype\",\n \"type\": \"dtype\",\n \"notSupported\": true\n },\n {\n \"tfName\": \"DstT\",\n \"name\": \"dtype\",\n \"type\": \"dtype\"\n }\n ]\n },\n {\n \"tfOpName\": \"ExpandDims\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"axis\",\n \"type\": \"number\"\n }\n ]\n },\n {\n \"tfOpName\": \"MirrorPad\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"padding\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"mode\",\n \"name\": \"mode\",\n \"type\": \"string\"\n }\n ]\n },\n {\n \"tfOpName\": \"Pad\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"padding\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"constant_value\",\n \"name\": \"constantValue\",\n \"type\": \"number\",\n \"defaultValue\": 0\n }\n ]\n },\n {\n \"tfOpName\": \"PadV2\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"padding\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"constantValue\",\n \"type\": \"number\",\n \"defaultValue\": 0\n }\n ]\n },\n {\n \"tfOpName\": \"Reshape\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"EnsureShape\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"Squeeze\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"axis\",\n \"tfDeprecatedName\": \"squeeze_dims\",\n \"name\": \"axis\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"SpaceToBatchND\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"blockShape\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"paddings\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"BatchToSpaceND\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"blockShape\",\n \"type\": \"number[]\"\n },\n {\n \"start\": 2,\n \"name\": \"crops\",\n \"type\": \"number[]\"\n }\n ]\n },\n {\n \"tfOpName\": \"DepthToSpace\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": [\n {\n \"tfName\": \"block_size\",\n \"name\": \"blockSize\",\n \"type\": \"number\"\n },\n {\n \"tfName\": \"data_format\",\n \"name\": \"dataFormat\",\n \"type\": \"string\"\n }\n ]\n },\n {\n \"tfOpName\": \"BroadcastTo\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"x\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"shape\",\n \"type\": \"number[]\"\n }\n ],\n \"attrs\": []\n },\n {\n \"tfOpName\": \"BroadcastArgs\",\n \"category\": \"transformation\",\n \"inputs\": [\n {\n \"start\": 0,\n \"name\": \"s0\",\n \"type\": \"tensor\"\n },\n {\n \"start\": 1,\n \"name\": \"s1\",\n \"type\": \"tensor\"\n }\n ],\n \"attrs\": []\n }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_mapper.js\nvar OperationMapper = class {\n // Singleton instance for the mapper\n static get Instance() {\n return this._instance || (this._instance = new this());\n }\n // Loads the op mapping from the JSON file.\n constructor() {\n const ops = [\n arithmetic_exports,\n basic_math_exports,\n control_exports,\n convolution_exports,\n creation_exports,\n dynamic_exports,\n evaluation_exports,\n graph_exports,\n hash_table_exports,\n image_exports,\n logical_exports,\n matrices_exports,\n normalization_exports,\n reduction_exports,\n slice_join_exports,\n sparse_exports,\n spectral_exports,\n string_exports,\n transformation_exports\n ];\n const mappersJson = [].concat(...ops.map((op2) => op2.json));\n this.opMappers = mappersJson.reduce((map, mapper) => {\n map[mapper.tfOpName] = mapper;\n return map;\n }, {});\n }\n // Converts the model inference graph from Tensorflow GraphDef to local\n // representation for TensorFlow.js API\n transformGraph(graph, signature = {}) {\n const tfNodes = graph.node;\n const placeholders = [];\n const weights = [];\n const initNodes = [];\n const nodes = tfNodes.reduce((map, node) => {\n map[node.name] = this.mapNode(node);\n if (node.op.startsWith(\"Placeholder\")) {\n placeholders.push(map[node.name]);\n } else if (node.op === \"Const\") {\n weights.push(map[node.name]);\n } else if (node.input == null || node.input.length === 0) {\n initNodes.push(map[node.name]);\n }\n return map;\n }, {});\n let inputs = [];\n const outputs = [];\n let inputNodeNameToKey = {};\n let outputNodeNameToKey = {};\n if (signature != null) {\n inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n }\n const allNodes = Object.keys(nodes);\n allNodes.forEach((key) => {\n const node = nodes[key];\n node.inputNames.forEach((name, index) => {\n const [nodeName, , outputName] = getNodeNameAndIndex(name);\n const inputNode = nodes[nodeName];\n if (inputNode.outputs != null) {\n const outputIndex = inputNode.outputs.indexOf(outputName);\n if (outputIndex !== -1) {\n const inputName = `${nodeName}:${outputIndex}`;\n node.inputNames[index] = inputName;\n }\n }\n node.inputs.push(inputNode);\n inputNode.children.push(node);\n });\n });\n if (Object.keys(outputNodeNameToKey).length === 0) {\n allNodes.forEach((key) => {\n const node = nodes[key];\n if (node.children.length === 0) {\n outputs.push(node);\n }\n });\n } else {\n Object.keys(outputNodeNameToKey).forEach((name) => {\n const [nodeName] = getNodeNameAndIndex(name);\n const node = nodes[nodeName];\n if (node != null) {\n node.signatureKey = outputNodeNameToKey[name];\n outputs.push(node);\n }\n });\n }\n if (Object.keys(inputNodeNameToKey).length > 0) {\n Object.keys(inputNodeNameToKey).forEach((name) => {\n const [nodeName] = getNodeNameAndIndex(name);\n const node = nodes[nodeName];\n if (node) {\n node.signatureKey = inputNodeNameToKey[name];\n inputs.push(node);\n }\n });\n } else {\n inputs = placeholders;\n }\n let functions = {};\n if (graph.library != null && graph.library.function != null) {\n functions = graph.library.function.reduce((functions2, func2) => {\n functions2[func2.signature.name] = this.mapFunction(func2);\n return functions2;\n }, {});\n }\n const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n if (initNodes.length > 0) {\n result.initNodes = initNodes;\n }\n return result;\n }\n mapSignatureEntries(entries) {\n return Object.keys(entries || {}).reduce((prev, curr) => {\n prev[entries[curr].name] = curr;\n return prev;\n }, {});\n }\n mapNode(node) {\n const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n if (node.attr == null) {\n node.attr = {};\n }\n const newNode = {\n name: node.name,\n op: node.op,\n category: mapper.category,\n inputNames: (node.input || []).map((input2) => input2.startsWith(\"^\") ? input2.slice(1) : input2),\n inputs: [],\n children: [],\n inputParams: {},\n attrParams: {},\n rawAttrs: node.attr,\n outputs: mapper.outputs\n };\n if (mapper.inputs != null) {\n newNode.inputParams = mapper.inputs.reduce((map, param) => {\n map[param.name] = {\n type: param.type,\n inputIndexStart: param.start,\n inputIndexEnd: param.end\n };\n return map;\n }, {});\n }\n if (mapper.attrs != null) {\n newNode.attrParams = mapper.attrs.reduce((map, param) => {\n const type = param.type;\n let value = void 0;\n switch (param.type) {\n case \"string\":\n value = getStringParam(node.attr, param.tfName, param.defaultValue);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"string[]\":\n value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"number\":\n value = getNumberParam(node.attr, param.tfName, param.defaultValue || 0);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"number[]\":\n value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"bool\":\n value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"bool[]\":\n value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"shape\":\n value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"shape[]\":\n value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"dtype\":\n value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"dtype[]\":\n value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"func\":\n value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n if (value === void 0 && !!param.tfDeprecatedName) {\n value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n }\n break;\n case \"tensor\":\n case \"tensors\":\n break;\n default:\n throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n }\n map[param.name] = { value, type };\n return map;\n }, {});\n }\n return newNode;\n }\n // map the TFunctionDef to TFJS graph object\n mapFunction(functionDef) {\n const tfNodes = functionDef.nodeDef;\n const placeholders = [];\n const weights = [];\n let nodes = {};\n if (tfNodes != null) {\n nodes = tfNodes.reduce((map, node) => {\n map[node.name] = this.mapNode(node);\n if (node.op === \"Const\") {\n weights.push(map[node.name]);\n }\n return map;\n }, {});\n }\n const inputs = [];\n const outputs = [];\n functionDef.signature.inputArg.forEach((arg) => {\n const [nodeName] = getNodeNameAndIndex(arg.name);\n const node = {\n name: nodeName,\n op: \"Placeholder\",\n inputs: [],\n inputNames: [],\n category: \"graph\",\n inputParams: {},\n attrParams: { dtype: { value: parseDtypeParam(arg.type), type: \"dtype\" } },\n children: []\n };\n node.signatureKey = arg.name;\n inputs.push(node);\n nodes[nodeName] = node;\n });\n const allNodes = Object.keys(nodes);\n allNodes.forEach((key) => {\n const node = nodes[key];\n node.inputNames.forEach((name, index) => {\n const [nodeName, , outputName] = getNodeNameAndIndex(name);\n const inputNode = nodes[nodeName];\n if (inputNode.outputs != null) {\n const outputIndex = inputNode.outputs.indexOf(outputName);\n if (outputIndex !== -1) {\n const inputName = `${nodeName}:${outputIndex}`;\n node.inputNames[index] = inputName;\n }\n }\n node.inputs.push(inputNode);\n inputNode.children.push(node);\n });\n });\n const returnNodeMap = functionDef.ret;\n functionDef.signature.outputArg.forEach((output) => {\n const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n const node = nodes[nodeName];\n if (node != null) {\n node.defaultOutput = index;\n outputs.push(node);\n }\n });\n const signature = this.mapArgsToSignature(functionDef);\n return { nodes, inputs, outputs, weights, placeholders, signature };\n }\n mapArgsToSignature(functionDef) {\n return {\n methodName: functionDef.signature.name,\n inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n map[arg.name] = this.mapArgToTensorInfo(arg);\n return map;\n }, {}),\n outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n return map;\n }, {})\n };\n }\n mapArgToTensorInfo(arg, nameMap2) {\n let name = arg.name;\n if (nameMap2 != null) {\n name = nameMap2[name];\n }\n return { name, dtype: arg.type };\n }\n};\nfunction decodeBase64(text) {\n const global2 = env().global;\n if (typeof global2.atob !== \"undefined\") {\n return global2.atob(text);\n } else if (typeof Buffer !== \"undefined\") {\n return new Buffer(text, \"base64\").toString();\n } else {\n throw new Error(\"Unable to decode base64 in this environment. Missing built-in atob() or Buffer()\");\n }\n}\nfunction parseStringParam(s, keepCase) {\n const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n return keepCase ? value : value.toLowerCase();\n}\nfunction getStringParam(attrs, name, def, keepCase = false) {\n const param = attrs[name];\n if (param != null) {\n return parseStringParam(param.s, keepCase);\n }\n return def;\n}\nfunction getBoolParam(attrs, name, def) {\n const param = attrs[name];\n return param ? param.b : def;\n}\nfunction getNumberParam(attrs, name, def) {\n const param = attrs[name] || {};\n const value = param[\"i\"] != null ? param[\"i\"] : param[\"f\"] != null ? param[\"f\"] : def;\n return typeof value === \"number\" ? value : parseInt(value, 10);\n}\nfunction parseDtypeParam(value) {\n if (typeof value === \"string\") {\n value = DataType[value];\n }\n switch (value) {\n case DataType.DT_FLOAT:\n case DataType.DT_HALF:\n return \"float32\";\n case DataType.DT_INT32:\n case DataType.DT_INT64:\n case DataType.DT_INT8:\n case DataType.DT_UINT8:\n return \"int32\";\n case DataType.DT_BOOL:\n return \"bool\";\n case DataType.DT_DOUBLE:\n return \"float32\";\n case DataType.DT_STRING:\n return \"string\";\n case DataType.DT_COMPLEX64:\n case DataType.DT_COMPLEX128:\n return \"complex64\";\n default:\n return null;\n }\n}\nfunction getFuncParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.func) {\n return param.func.name;\n }\n return def;\n}\nfunction getDtypeParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.type) {\n return parseDtypeParam(param.type);\n }\n return def;\n}\nfunction getDtypeArrayParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.list && param.list.type) {\n return param.list.type.map((v) => parseDtypeParam(v));\n }\n return def;\n}\nfunction parseTensorShapeParam(shape) {\n if (shape.unknownRank) {\n return void 0;\n }\n if (shape.dim != null) {\n return shape.dim.map((dim) => typeof dim.size === \"number\" ? dim.size : parseInt(dim.size, 10));\n }\n return [];\n}\nfunction getTensorShapeParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.shape) {\n return parseTensorShapeParam(param.shape);\n }\n return def;\n}\nfunction getNumericArrayParam(attrs, name, def) {\n const param = attrs[name];\n if (param) {\n return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v) => typeof v === \"number\" ? v : parseInt(v, 10));\n }\n return def;\n}\nfunction getStringArrayParam(attrs, name, def, keepCase = false) {\n const param = attrs[name];\n if (param && param.list && param.list.s) {\n return param.list.s.map((v) => {\n return parseStringParam(v, keepCase);\n });\n }\n return def;\n}\nfunction getTensorShapeArrayParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.list && param.list.shape) {\n return param.list.shape.map((v) => {\n return parseTensorShapeParam(v);\n });\n }\n return def;\n}\nfunction getBoolArrayParam(attrs, name, def) {\n const param = attrs[name];\n if (param && param.list && param.list.b) {\n return param.list.b;\n }\n return def;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/node_value_impl.js\nvar NodeValueImpl = class {\n constructor(node, tensorMap, context) {\n this.node = node;\n this.tensorMap = tensorMap;\n this.context = context;\n this.inputs = [];\n this.attrs = {};\n this.inputs = node.inputNames.map((name) => this.getInput(name));\n if (node.rawAttrs != null) {\n this.attrs = Object.keys(node.rawAttrs).reduce((attrs, key) => {\n attrs[key] = this.getAttr(key);\n return attrs;\n }, {});\n }\n }\n /**\n * Return the value of the attribute or input param.\n * @param name String: name of attribute or input param.\n */\n getInput(name) {\n return getTensor(name, this.tensorMap, this.context);\n }\n /**\n * Return the value of the attribute or input param.\n * @param name String: name of attribute or input param.\n */\n getAttr(name, defaultValue) {\n const value = this.node.rawAttrs[name];\n if (value.tensor != null) {\n return getTensor(name, this.tensorMap, this.context);\n }\n if (value.i != null || value.f != null) {\n return getNumberParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.s != null) {\n return getStringParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.b != null) {\n return getBoolParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.shape != null) {\n return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.type != null) {\n return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.list != null) {\n if (value.list.i != null || value.list.f != null) {\n return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.list.s != null) {\n return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.list.shape != null) {\n return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.list.b != null) {\n return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n }\n if (value.list.type != null) {\n return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n }\n }\n return defaultValue;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops_for_converter.js\nvar ops_for_converter_exports = {};\n__export(ops_for_converter_exports, {\n OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX,\n abs: () => abs,\n acos: () => acos,\n acosh: () => acosh,\n add: () => add2,\n addN: () => addN,\n all: () => all,\n any: () => any,\n argMax: () => argMax,\n argMin: () => argMin,\n asin: () => asin,\n asinh: () => asinh,\n atan: () => atan,\n atan2: () => atan2,\n atanh: () => atanh,\n avgPool: () => avgPool,\n avgPool3d: () => avgPool3d,\n basicLSTMCell: () => basicLSTMCell,\n batchNorm: () => batchNorm,\n batchNorm2d: () => batchNorm2d,\n batchNorm3d: () => batchNorm3d,\n batchNorm4d: () => batchNorm4d,\n batchToSpaceND: () => batchToSpaceND,\n bincount: () => bincount,\n bitwiseAnd: () => bitwiseAnd,\n booleanMaskAsync: () => booleanMaskAsync,\n broadcastArgs: () => broadcastArgs,\n broadcastTo: () => broadcastTo,\n buffer: () => buffer,\n cast: () => cast,\n ceil: () => ceil,\n clipByValue: () => clipByValue,\n clone: () => clone,\n complex: () => complex,\n concat: () => concat,\n concat1d: () => concat1d,\n concat2d: () => concat2d,\n concat3d: () => concat3d,\n concat4d: () => concat4d,\n conv1d: () => conv1d,\n conv2d: () => conv2d,\n conv2dTranspose: () => conv2dTranspose,\n conv3d: () => conv3d,\n conv3dTranspose: () => conv3dTranspose,\n cos: () => cos,\n cosh: () => cosh,\n cosineWindow: () => cosineWindow,\n cumprod: () => cumprod,\n cumsum: () => cumsum,\n denseBincount: () => denseBincount,\n depthToSpace: () => depthToSpace,\n depthwiseConv2d: () => depthwiseConv2d,\n diag: () => diag,\n dilation2d: () => dilation2d,\n div: () => div,\n divNoNan: () => divNoNan,\n dot: () => dot,\n dropout: () => dropout,\n einsum: () => einsum,\n elu: () => elu,\n enclosingPowerOfTwo: () => enclosingPowerOfTwo,\n ensureShape: () => ensureShape,\n equal: () => equal,\n erf: () => erf,\n euclideanNorm: () => euclideanNorm,\n exp: () => exp,\n expandDims: () => expandDims,\n expm1: () => expm1,\n eye: () => eye,\n fft: () => fft,\n fill: () => fill,\n floor: () => floor,\n floorDiv: () => floorDiv,\n fused: () => fused_ops_exports,\n gather: () => gather,\n gatherND: () => gatherND,\n greater: () => greater,\n greaterEqual: () => greaterEqual,\n ifft: () => ifft,\n imag: () => imag,\n image: () => image,\n inTopKAsync: () => inTopKAsync,\n irfft: () => irfft,\n isFinite: () => isFinite2,\n isInf: () => isInf,\n isNaN: () => isNaN2,\n leakyRelu: () => leakyRelu,\n less: () => less,\n lessEqual: () => lessEqual,\n linalg: () => linalg,\n linspace: () => linspace,\n localResponseNormalization: () => localResponseNormalization,\n log: () => log2,\n log1p: () => log1p,\n logSigmoid: () => logSigmoid,\n logSoftmax: () => logSoftmax,\n logSumExp: () => logSumExp,\n logicalAnd: () => logicalAnd,\n logicalNot: () => logicalNot,\n logicalOr: () => logicalOr,\n logicalXor: () => logicalXor,\n losses: () => losses,\n lowerBound: () => lowerBound,\n matMul: () => matMul,\n max: () => max,\n maxPool: () => maxPool,\n maxPool3d: () => maxPool3d,\n maxPoolWithArgmax: () => maxPoolWithArgmax,\n maximum: () => maximum,\n mean: () => mean,\n meshgrid: () => meshgrid,\n min: () => min,\n minimum: () => minimum,\n mirrorPad: () => mirrorPad,\n mod: () => mod,\n moments: () => moments,\n movingAverage: () => movingAverage,\n mul: () => mul,\n multiRNNCell: () => multiRNNCell,\n multinomial: () => multinomial,\n neg: () => neg,\n norm: () => norm,\n notEqual: () => notEqual,\n oneHot: () => oneHot,\n ones: () => ones2,\n onesLike: () => onesLike,\n op: () => op,\n outerProduct: () => outerProduct,\n pad: () => pad,\n pad1d: () => pad1d,\n pad2d: () => pad2d,\n pad3d: () => pad3d,\n pad4d: () => pad4d,\n pool: () => pool,\n pow: () => pow,\n prelu: () => prelu,\n print: () => print,\n prod: () => prod,\n raggedGather: () => raggedGather,\n raggedRange: () => raggedRange,\n raggedTensorToTensor: () => raggedTensorToTensor,\n rand: () => rand,\n randomGamma: () => randomGamma,\n randomNormal: () => randomNormal,\n randomStandardNormal: () => randomStandardNormal,\n randomUniform: () => randomUniform,\n randomUniformInt: () => randomUniformInt,\n range: () => range,\n real: () => real,\n reciprocal: () => reciprocal,\n relu: () => relu,\n relu6: () => relu6,\n reshape: () => reshape,\n reverse: () => reverse,\n reverse1d: () => reverse1d,\n reverse2d: () => reverse2d,\n reverse3d: () => reverse3d,\n reverse4d: () => reverse4d,\n rfft: () => rfft,\n round: () => round2,\n rsqrt: () => rsqrt,\n scalar: () => scalar,\n scatterND: () => scatterND,\n searchSorted: () => searchSorted,\n selu: () => selu,\n separableConv2d: () => separableConv2d,\n setdiff1dAsync: () => setdiff1dAsync,\n sigmoid: () => sigmoid,\n sign: () => sign,\n signal: () => signal,\n sin: () => sin,\n sinh: () => sinh,\n slice: () => slice,\n slice1d: () => slice1d,\n slice2d: () => slice2d,\n slice3d: () => slice3d,\n slice4d: () => slice4d,\n softmax: () => softmax,\n softplus: () => softplus,\n spaceToBatchND: () => spaceToBatchND,\n sparse: () => sparse,\n sparseToDense: () => sparseToDense,\n spectral: () => spectral,\n split: () => split,\n sqrt: () => sqrt,\n square: () => square,\n squaredDifference: () => squaredDifference,\n squeeze: () => squeeze,\n stack: () => stack,\n step: () => step,\n stridedSlice: () => stridedSlice,\n string: () => string,\n sub: () => sub,\n sum: () => sum2,\n tan: () => tan,\n tanh: () => tanh2,\n tensor: () => tensor,\n tensor1d: () => tensor1d,\n tensor2d: () => tensor2d,\n tensor3d: () => tensor3d,\n tensor4d: () => tensor4d,\n tensor5d: () => tensor5d,\n tensor6d: () => tensor6d,\n tensorScatterUpdate: () => tensorScatterUpdate,\n tile: () => tile,\n topk: () => topk,\n transpose: () => transpose,\n truncatedNormal: () => truncatedNormal,\n unique: () => unique,\n unsortedSegmentSum: () => unsortedSegmentSum,\n unstack: () => unstack,\n upperBound: () => upperBound,\n variable: () => variable,\n where: () => where,\n whereAsync: () => whereAsync,\n zeros: () => zeros,\n zerosLike: () => zerosLike\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/arithmetic_executor.js\nvar executeOp = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"BiasAdd\":\n case \"AddV2\":\n case \"Add\": {\n return [ops.add(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"AddN\": {\n return [ops.addN(getParamValue(\"tensors\", node, tensorMap, context))];\n }\n case \"FloorMod\":\n case \"Mod\":\n return [ops.mod(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n case \"Mul\":\n return [ops.mul(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n case \"RealDiv\":\n case \"Div\": {\n return [ops.div(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"DivNoNan\": {\n return [ops.divNoNan(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"FloorDiv\": {\n return [ops.floorDiv(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"Sub\": {\n return [ops.sub(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"Minimum\": {\n return [ops.minimum(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"Maximum\": {\n return [ops.maximum(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"Pow\": {\n return [ops.pow(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"SquaredDifference\": {\n return [ops.squaredDifference(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/basic_math_executor.js\nvar executeOp2 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"Abs\":\n case \"ComplexAbs\":\n return [ops.abs(getParamValue(\"x\", node, tensorMap, context))];\n case \"Acos\":\n return [ops.acos(getParamValue(\"x\", node, tensorMap, context))];\n case \"Acosh\":\n return [ops.acosh(getParamValue(\"x\", node, tensorMap, context))];\n case \"Asin\":\n return [ops.asin(getParamValue(\"x\", node, tensorMap, context))];\n case \"Asinh\":\n return [ops.asinh(getParamValue(\"x\", node, tensorMap, context))];\n case \"Atan\":\n return [ops.atan(getParamValue(\"x\", node, tensorMap, context))];\n case \"Atan2\":\n return [ops.atan2(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"y\", node, tensorMap, context))];\n case \"Atanh\":\n return [ops.atanh(getParamValue(\"x\", node, tensorMap, context))];\n case \"Ceil\":\n return [ops.ceil(getParamValue(\"x\", node, tensorMap, context))];\n case \"Complex\":\n return [ops.complex(getParamValue(\"real\", node, tensorMap, context), getParamValue(\"imag\", node, tensorMap, context))];\n case \"Cos\":\n return [ops.cos(getParamValue(\"x\", node, tensorMap, context))];\n case \"Cosh\":\n return [ops.cosh(getParamValue(\"x\", node, tensorMap, context))];\n case \"Elu\":\n return [ops.elu(getParamValue(\"x\", node, tensorMap, context))];\n case \"Erf\":\n return [ops.erf(getParamValue(\"x\", node, tensorMap, context))];\n case \"Exp\":\n return [ops.exp(getParamValue(\"x\", node, tensorMap, context))];\n case \"Expm1\": {\n return [ops.expm1(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"Floor\":\n return [ops.floor(getParamValue(\"x\", node, tensorMap, context))];\n case \"Log\":\n return [ops.log(getParamValue(\"x\", node, tensorMap, context))];\n case \"Log1p\": {\n return [ops.log1p(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"Imag\":\n return [ops.imag(getParamValue(\"x\", node, tensorMap, context))];\n case \"Neg\":\n return [ops.neg(getParamValue(\"x\", node, tensorMap, context))];\n case \"Reciprocal\": {\n return [ops.reciprocal(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"Real\":\n return [ops.real(getParamValue(\"x\", node, tensorMap, context))];\n case \"Relu\":\n return [ops.relu(getParamValue(\"x\", node, tensorMap, context))];\n case \"Round\": {\n return [ops.round(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"Selu\":\n return [ops.selu(getParamValue(\"x\", node, tensorMap, context))];\n case \"Sigmoid\":\n return [ops.sigmoid(getParamValue(\"x\", node, tensorMap, context))];\n case \"Sin\":\n return [ops.sin(getParamValue(\"x\", node, tensorMap, context))];\n case \"Sign\": {\n return [ops.sign(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"Sinh\": {\n return [ops.sinh(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"Softplus\": {\n return [ops.softplus(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"Sqrt\": {\n return [ops.sqrt(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"Square\": {\n return [ops.square(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"Tanh\": {\n return [ops.tanh(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"Tan\":\n return [ops.tan(getParamValue(\"x\", node, tensorMap, context))];\n case \"ClipByValue\":\n return [ops.clipByValue(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"clipValueMin\", node, tensorMap, context), getParamValue(\"clipValueMax\", node, tensorMap, context))];\n case \"Relu6\":\n return [ops.relu6(getParamValue(\"x\", node, tensorMap, context))];\n case \"Rsqrt\":\n return [ops.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n case \"LeakyRelu\":\n return [ops.leakyRelu(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context))];\n case \"Prelu\":\n return [ops.prelu(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context))];\n case \"IsNan\":\n return [ops.isNaN(getTensor(node.inputNames[0], tensorMap, context))];\n case \"IsInf\":\n return [ops.isInf(getTensor(node.inputNames[0], tensorMap, context))];\n case \"IsFinite\":\n return [ops.isFinite(getTensor(node.inputNames[0], tensorMap, context))];\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_utils.js\nfunction assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = \"\") {\n if (typeof shapeA === \"number\" || typeof shapeB === \"number\") {\n return;\n }\n util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n for (let i = 0; i < shapeA.length; i++) {\n const dim0 = shapeA[i];\n const dim1 = shapeB[i];\n util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n }\n}\nfunction fullDefinedShape(elementShape) {\n if (typeof elementShape === \"number\" || elementShape.some((dim) => dim < 0)) {\n return false;\n }\n return true;\n}\nfunction inferElementShape(listElementShape, tensors, elementShape) {\n let partialShape = mergeElementShape(listElementShape, elementShape);\n const notfullDefinedShape = !fullDefinedShape(partialShape);\n if (notfullDefinedShape && tensors.length === 0) {\n throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${partialShape}`);\n }\n if (notfullDefinedShape) {\n tensors.forEach((tensor2) => {\n partialShape = mergeElementShape(tensor2.shape, partialShape);\n });\n }\n if (!fullDefinedShape(partialShape)) {\n throw new Error(`Non-fully-defined elementShape: ${partialShape}`);\n }\n return partialShape;\n}\nfunction mergeElementShape(elementShapeA, elementShapeB) {\n if (typeof elementShapeA === \"number\") {\n return elementShapeB;\n }\n if (typeof elementShapeB === \"number\") {\n return elementShapeA;\n }\n if (elementShapeA.length !== elementShapeB.length) {\n throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);\n }\n const result = [];\n for (let i = 0; i < elementShapeA.length; ++i) {\n const dim0 = elementShapeA[i];\n const dim1 = elementShapeB[i];\n if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {\n throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);\n }\n result[i] = dim0 >= 0 ? dim0 : dim1;\n }\n return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_array.js\nvar TensorArray = class {\n constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n this.name = name;\n this.dtype = dtype;\n this.maxSize = maxSize;\n this.elementShape = elementShape;\n this.identicalElementShapes = identicalElementShapes;\n this.dynamicSize = dynamicSize;\n this.clearAfterRead = clearAfterRead;\n this.tensors = [];\n this.closed_ = false;\n this.idTensor = scalar(0);\n keep(this.idTensor);\n }\n get id() {\n return this.idTensor.id;\n }\n get closed() {\n return this.closed_;\n }\n /**\n * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n */\n clearAndClose(keepIds) {\n this.tensors.forEach((tensor2) => {\n if (keepIds == null || !keepIds.has(tensor2.tensor.id)) {\n tensor2.tensor.dispose();\n }\n });\n this.tensors = [];\n this.closed_ = true;\n this.idTensor.dispose();\n }\n size() {\n return this.tensors.length;\n }\n /**\n * Read the value at location index in the TensorArray.\n * @param index Number the index to read from.\n */\n read(index) {\n if (this.closed_) {\n throw new Error(`TensorArray ${this.name} has already been closed.`);\n }\n if (index < 0 || index >= this.size()) {\n throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n }\n const tensorWithState = this.tensors[index];\n if (tensorWithState.cleared) {\n throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);\n }\n if (this.clearAfterRead) {\n tensorWithState.cleared = true;\n }\n tensorWithState.read = true;\n return tensorWithState.tensor;\n }\n /**\n * Helper method to read multiple tensors from the specified indices.\n */\n readMany(indices) {\n return indices.map((index) => this.read(index));\n }\n /**\n * Write value into the index of the TensorArray.\n * @param index number the index to write to.\n * @param tensor\n */\n write(index, tensor2) {\n if (this.closed_) {\n throw new Error(`TensorArray ${this.name} has already been closed.`);\n }\n if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n }\n const t = this.tensors[index] || {};\n if (tensor2.dtype !== this.dtype) {\n throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);\n }\n if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) {\n this.elementShape = tensor2.shape;\n }\n assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n if (t.read) {\n throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n }\n if (t.written) {\n throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n }\n t.tensor = tensor2;\n keep(tensor2);\n t.written = true;\n this.tensors[index] = t;\n }\n /**\n * Helper method to write multiple tensors to the specified indices.\n */\n writeMany(indices, tensors) {\n if (indices.length !== tensors.length) {\n throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n }\n indices.forEach((i, index) => this.write(i, tensors[index]));\n }\n /**\n * Return selected values in the TensorArray as a packed Tensor. All of\n * selected values must have been written and their shapes must all match.\n * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n * TensorArray is not dynamic, max_value=size(). If not specified returns\n * all tensors in the original order.\n * @param [dtype]\n */\n gather(indices, dtype) {\n if (!!dtype && dtype !== this.dtype) {\n throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n }\n if (!indices) {\n indices = [];\n for (let i = 0; i < this.size(); i++) {\n indices.push(i);\n }\n } else {\n indices = indices.slice(0, this.size());\n }\n if (indices.length === 0) {\n return tensor([], [0].concat(this.elementShape));\n }\n const tensors = this.readMany(indices);\n assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, \"TensorArray shape mismatch: \");\n return stack(tensors, 0);\n }\n /**\n * Return the values in the TensorArray as a concatenated Tensor.\n */\n concat(dtype) {\n if (!!dtype && dtype !== this.dtype) {\n throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n }\n if (this.size() === 0) {\n return tensor([], [0].concat(this.elementShape));\n }\n const indices = [];\n for (let i = 0; i < this.size(); i++) {\n indices.push(i);\n }\n const tensors = this.readMany(indices);\n assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n return concat(tensors, 0);\n }\n /**\n * Scatter the values of a Tensor in specific indices of a TensorArray.\n * @param indices nummber[] values in [0, max_value). If the\n * TensorArray is not dynamic, max_value=size().\n * @param tensor Tensor input tensor.\n */\n scatter(indices, tensor2) {\n if (tensor2.dtype !== this.dtype) {\n throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);\n }\n if (indices.length !== tensor2.shape[0]) {\n throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);\n }\n const maxIndex = Math.max(...indices);\n if (!this.dynamicSize && maxIndex >= this.maxSize) {\n throw new Error(`Max index must be < array size (${maxIndex} vs. ${this.maxSize})`);\n }\n this.writeMany(indices, unstack(tensor2, 0));\n }\n /**\n * Split the values of a Tensor into the TensorArray.\n * @param length number[] with the lengths to use when splitting value along\n * its first dimension.\n * @param tensor Tensor, the tensor to split.\n */\n split(length, tensor2) {\n if (tensor2.dtype !== this.dtype) {\n throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);\n }\n let totalLength = 0;\n const cumulativeLengths = length.map((len) => {\n totalLength += len;\n return totalLength;\n });\n if (totalLength !== tensor2.shape[0]) {\n throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${totalLength}, and tensor's shape is: ${tensor2.shape}`);\n }\n if (!this.dynamicSize && length.length !== this.maxSize) {\n throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);\n }\n const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;\n const tensors = [];\n tidy(() => {\n tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);\n for (let i = 0; i < length.length; ++i) {\n const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];\n const indices2 = [0, previousLength, 0];\n const sizes = [1, length[i], elementPerRow];\n tensors[i] = reshape(slice(tensor2, indices2, sizes), this.elementShape);\n }\n return tensors;\n });\n const indices = [];\n for (let i = 0; i < length.length; i++) {\n indices[i] = i;\n }\n this.writeMany(indices, tensors);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_list.js\nvar TensorList = class _TensorList {\n get id() {\n return this.idTensor.id;\n }\n /**\n *\n * @param tensors list of tensors\n * @param elementShape shape of each tensor, this can be a single number (any\n * shape is allowed) or partial shape (dim = -1).\n * @param elementDtype data type of each tensor\n * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n * meaning that the size of `tensors` is unbounded.\n */\n constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n this.tensors = tensors;\n this.elementShape = elementShape;\n this.elementDtype = elementDtype;\n if (tensors != null) {\n tensors.forEach((tensor2) => {\n if (elementDtype !== tensor2.dtype) {\n throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);\n }\n assertShapesMatchAllowUndefinedSize(elementShape, tensor2.shape, \"TensorList shape mismatch: \");\n keep(tensor2);\n });\n }\n this.idTensor = scalar(0);\n this.maxNumElements = maxNumElements;\n keep(this.idTensor);\n }\n /**\n * Get a new TensorList containing a copy of the underlying tensor container.\n */\n copy() {\n return new _TensorList([...this.tensors], this.elementShape, this.elementDtype);\n }\n /**\n * Dispose the tensors and idTensor and clear the tensor list.\n */\n clearAndClose(keepIds) {\n this.tensors.forEach((tensor2) => {\n if (keepIds == null || !keepIds.has(tensor2.id)) {\n tensor2.dispose();\n }\n });\n this.tensors.length = 0;\n this.idTensor.dispose();\n }\n /**\n * The size of the tensors in the tensor list.\n */\n size() {\n return this.tensors.length;\n }\n /**\n * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n * tf.Tensor.\n * @param elementShape shape of each tensor\n * @param elementDtype data type of each tensor\n * @param numElements the number of elements to stack\n */\n stack(elementShape, elementDtype, numElements = -1) {\n if (elementDtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n }\n if (numElements !== -1 && this.tensors.length !== numElements) {\n throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n }\n assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, \"TensorList shape mismatch: \");\n const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n return tidy(() => {\n const reshapedTensors = this.tensors.map((tensor2) => reshape(tensor2, outputElementShape));\n return stack(reshapedTensors, 0);\n });\n }\n /**\n * Pop a tensor from the end of the list.\n * @param elementShape shape of the tensor\n * @param elementDtype data type of the tensor\n */\n popBack(elementShape, elementDtype) {\n if (elementDtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n }\n if (this.size() === 0) {\n throw new Error(\"Trying to pop from an empty list.\");\n }\n const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n const tensor2 = this.tensors.pop();\n tensor2.kept = false;\n assertShapesMatchAllowUndefinedSize(tensor2.shape, elementShape, \"TensorList shape mismatch: \");\n return reshape(tensor2, outputElementShape);\n }\n /**\n * Push a tensor to the end of the list.\n * @param tensor Tensor to be pushed.\n */\n pushBack(tensor2) {\n if (tensor2.dtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);\n }\n assertShapesMatchAllowUndefinedSize(tensor2.shape, this.elementShape, \"TensorList shape mismatch: \");\n if (this.maxNumElements === this.size()) {\n throw new Error(`Trying to push element into a full list.`);\n }\n keep(tensor2);\n this.tensors.push(tensor2);\n }\n /**\n * Update the size of the list.\n * @param size the new size of the list.\n */\n resize(size) {\n if (size < 0) {\n throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n }\n if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n }\n const destTensorList = new _TensorList([], this.elementShape, this.elementDtype, this.maxNumElements);\n destTensorList.tensors.length = size;\n for (let i = 0; i < Math.min(this.tensors.length, size); ++i) {\n destTensorList.tensors[i] = this.tensors[i];\n }\n return destTensorList;\n }\n /**\n * Retrieve the element at the provided index\n * @param elementShape shape of the tensor\n * @param elementDtype dtype of the tensor\n * @param elementIndex index of the tensor\n */\n getItem(elementIndex, elementShape, elementDtype) {\n if (elementDtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n }\n if (elementIndex < 0 || elementIndex > this.tensors.length) {\n throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n }\n if (this.tensors[elementIndex] == null) {\n throw new Error(`element at index ${elementIndex} is null.`);\n }\n assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, \"TensorList shape mismatch: \");\n const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n return reshape(this.tensors[elementIndex], outputElementShape);\n }\n /**\n * Set the tensor at the index\n * @param elementIndex index of the tensor\n * @param tensor the tensor to be inserted into the list\n */\n setItem(elementIndex, tensor2) {\n if (tensor2.dtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);\n }\n if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n }\n assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, \"TensorList shape mismatch: \");\n keep(tensor2);\n if (this.tensors[elementIndex] != null) {\n this.tensors[elementIndex].kept = false;\n }\n this.tensors[elementIndex] = tensor2;\n }\n /**\n * Return selected values in the TensorList as a stacked Tensor. All of\n * selected values must have been written and their shapes must all match.\n * @param indices indices of tensors to gather\n * @param elementDtype output tensor dtype\n * @param elementShape output tensor element shape\n */\n gather(indices, elementDtype, elementShape) {\n if (elementDtype !== this.elementDtype) {\n throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n }\n assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, \"TensorList shape mismatch: \");\n indices = indices.slice(0, this.size());\n const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n if (indices.length === 0) {\n return tensor([], [0].concat(outputElementShape));\n }\n return tidy(() => {\n const tensors = indices.map((i) => reshape(this.tensors[i], outputElementShape));\n return stack(tensors, 0);\n });\n }\n /**\n * Return the values in the TensorList as a concatenated Tensor.\n * @param elementDtype output tensor dtype\n * @param elementShape output tensor element shape\n */\n concat(elementDtype, elementShape) {\n if (!!elementDtype && elementDtype !== this.elementDtype) {\n throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n }\n assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, \"TensorList shape mismatch: \");\n const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n if (this.size() === 0) {\n return tensor([], [0].concat(outputElementShape));\n }\n return tidy(() => {\n const tensors = this.tensors.map((t) => reshape(t, outputElementShape));\n return concat(tensors, 0);\n });\n }\n};\nfunction fromTensor(tensor2, elementShape, elementDtype) {\n const dtype = tensor2.dtype;\n if (tensor2.shape.length < 1) {\n throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);\n }\n if (tensor2.dtype !== elementDtype) {\n throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);\n }\n const tensorElementShape = tensor2.shape.slice(1);\n assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, \"TensorList shape mismatch: \");\n const tensorList = unstack(tensor2);\n return new TensorList(tensorList, elementShape, dtype);\n}\nfunction reserve(elementShape, elementDtype, numElements, maxNumElements) {\n return new TensorList([], elementShape, elementDtype, maxNumElements);\n}\nfunction scatter(tensor2, indices, elementShape, numElements) {\n if (indices.length !== tensor2.shape[0]) {\n throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);\n }\n const maxIndex = Math.max(...indices);\n if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n throw new Error(`Max index must be < array size (${maxIndex} vs. ${numElements})`);\n }\n const list = new TensorList([], elementShape, tensor2.dtype, numElements);\n const tensors = unstack(tensor2, 0);\n indices.forEach((value, index) => {\n list.setItem(value, tensors[index]);\n });\n return list;\n}\nfunction split2(tensor2, length, elementShape) {\n let totalLength = 0;\n const cumulativeLengths = length.map((len) => {\n totalLength += len;\n return totalLength;\n });\n if (totalLength !== tensor2.shape[0]) {\n throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${totalLength}, and tensor's shape is: ${tensor2.shape}`);\n }\n const shapeWithoutFirstDim = tensor2.shape.slice(1);\n const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape);\n const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;\n const tensors = tidy(() => {\n const tensors2 = [];\n tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);\n for (let i = 0; i < length.length; ++i) {\n const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];\n const indices = [0, previousLength, 0];\n const sizes = [1, length[i], elementPerRow];\n tensors2[i] = reshape(slice(tensor2, indices, sizes), outputElementShape);\n }\n tensor2.dispose();\n return tensors2;\n });\n const list = new TensorList([], elementShape, tensor2.dtype, length.length);\n for (let i = 0; i < tensors.length; i++) {\n list.setItem(i, tensors[i]);\n }\n return list;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/control_executor.js\nvar executeOp3 = async (node, tensorMap, context) => {\n switch (node.op) {\n case \"If\":\n case \"StatelessIf\": {\n const thenFunc = getParamValue(\"thenBranch\", node, tensorMap, context);\n const elseFunc = getParamValue(\"elseBranch\", node, tensorMap, context);\n const cond = getParamValue(\"cond\", node, tensorMap, context);\n const args = getParamValue(\"args\", node, tensorMap, context);\n const condValue = await cond.data();\n if (condValue[0]) {\n return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n } else {\n return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n }\n }\n case \"While\":\n case \"StatelessWhile\": {\n const bodyFunc = getParamValue(\"body\", node, tensorMap, context);\n const condFunc = getParamValue(\"cond\", node, tensorMap, context);\n const args = getParamValue(\"args\", node, tensorMap, context);\n const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n const argIds = args.map((tensor2) => tensor2.id);\n let condValue = await condResult[0].data();\n condResult.forEach((tensor2) => {\n if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1) {\n tensor2.dispose();\n }\n });\n let result = args;\n while (condValue[0]) {\n const origResult = result;\n result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n const resultIds = result.map((tensor2) => tensor2.id);\n origResult.forEach((tensor2) => {\n if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {\n tensor2.dispose();\n }\n });\n const condResult2 = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n condValue = await condResult2[0].data();\n condResult2.forEach((tensor2) => {\n if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {\n tensor2.dispose();\n }\n });\n }\n return result;\n }\n case \"LoopCond\": {\n const pred = getParamValue(\"pred\", node, tensorMap, context);\n return [cloneTensor(pred)];\n }\n case \"Switch\": {\n const pred = getParamValue(\"pred\", node, tensorMap, context);\n let data = getParamValue(\"data\", node, tensorMap, context);\n if (!data.kept) {\n data = cloneTensor(data);\n }\n return (await pred.data())[0] ? [void 0, data] : [data, void 0];\n }\n case \"Merge\": {\n const inputName = node.inputNames.find((name) => getTensor(name, tensorMap, context) !== void 0);\n if (inputName) {\n const data = getTensor(inputName, tensorMap, context);\n return [cloneTensor(data)];\n }\n return void 0;\n }\n case \"Enter\": {\n const frameId = getParamValue(\"frameName\", node, tensorMap, context);\n const data = getParamValue(\"tensor\", node, tensorMap, context);\n context.enterFrame(frameId);\n return [cloneTensor(data)];\n }\n case \"Exit\": {\n const data = getParamValue(\"tensor\", node, tensorMap, context);\n context.exitFrame();\n return [cloneTensor(data)];\n }\n case \"NextIteration\": {\n const data = getParamValue(\"tensor\", node, tensorMap, context);\n context.nextIteration();\n return [cloneTensor(data)];\n }\n case \"TensorArrayV3\": {\n const size = getParamValue(\"size\", node, tensorMap, context);\n const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n const dynamicSize = getParamValue(\"dynamicSize\", node, tensorMap, context);\n const clearAfterRead = getParamValue(\"clearAfterRead\", node, tensorMap, context);\n const identicalElementShapes = getParamValue(\"identicalElementShapes\", node, tensorMap, context);\n const name = getParamValue(\"name\", node, tensorMap, context);\n const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n context.addTensorArray(tensorArray);\n return [tensorArray.idTensor, scalar(1)];\n }\n case \"TensorArrayWriteV3\": {\n const id = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n const index = getParamValue(\"index\", node, tensorMap, context);\n const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n const writeTensorArray = context.getTensorArray(id.id);\n writeTensorArray.write(index, writeTensor);\n return [writeTensorArray.idTensor];\n }\n case \"TensorArrayReadV3\": {\n const readId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n const readIndex = getParamValue(\"index\", node, tensorMap, context);\n const readTensorArray = context.getTensorArray(readId.id);\n return [readTensorArray.read(readIndex)];\n }\n case \"TensorArrayGatherV3\": {\n const gatherId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n const gatherIndices = getParamValue(\"indices\", node, tensorMap, context);\n const gatherDtype = getParamValue(\"dtype\", node, tensorMap, context);\n const gatherTensorArray = context.getTensorArray(gatherId.id);\n return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n }\n case \"TensorArrayScatterV3\": {\n const scatterId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n const scatterIndices = getParamValue(\"indices\", node, tensorMap, context);\n const scatterTensor = getParamValue(\"tensor\", node, tensorMap, context);\n const scatterTensorArray = context.getTensorArray(scatterId.id);\n scatterTensorArray.scatter(scatterIndices, scatterTensor);\n return [scatterTensorArray.idTensor];\n }\n case \"TensorArrayConcatV3\": {\n const concatId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n const concatTensorArray = context.getTensorArray(concatId.id);\n const concatDtype = getParamValue(\"dtype\", node, tensorMap, context);\n return [concatTensorArray.concat(concatDtype)];\n }\n case \"TensorArraySplitV3\": {\n const splitId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n const splitTensor = getParamValue(\"tensor\", node, tensorMap, context);\n const lengths = getParamValue(\"lengths\", node, tensorMap, context);\n const splitTensorArray = context.getTensorArray(splitId.id);\n splitTensorArray.split(lengths, splitTensor);\n return [splitTensorArray.idTensor];\n }\n case \"TensorArraySizeV3\": {\n const sizeId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n const sizeTensorArray = context.getTensorArray(sizeId.id);\n return [scalar(sizeTensorArray.size(), \"int32\")];\n }\n case \"TensorArrayCloseV3\": {\n const closeId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n const closeTensorArray = context.getTensorArray(closeId.id);\n closeTensorArray.clearAndClose();\n return [closeTensorArray.idTensor];\n }\n case \"TensorListSetItem\": {\n const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n const index = getParamValue(\"index\", node, tensorMap, context);\n const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n tensorList.setItem(index, writeTensor);\n return [tensorList.idTensor];\n }\n case \"TensorListGetItem\": {\n const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n const readIndex = getParamValue(\"index\", node, tensorMap, context);\n const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n const elementDType = getParamValue(\"elementDType\", node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n return [tensorList.getItem(readIndex, elementShape, elementDType)];\n }\n case \"TensorListScatterV2\":\n case \"TensorListScatter\": {\n const scatterIndices = getParamValue(\"indices\", node, tensorMap, context);\n const scatterTensor = getParamValue(\"tensor\", node, tensorMap, context);\n const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n const numElements = getParamValue(\"numElements\", node, tensorMap, context);\n const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n context.addTensorList(tensorList);\n return [tensorList.idTensor];\n }\n case \"TensorListReserve\":\n case \"EmptyTensorList\": {\n const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n let numElementsParam;\n if (node.op === \"TensorListReserve\") {\n numElementsParam = \"numElements\";\n } else {\n numElementsParam = \"maxNumElements\";\n }\n const numElements = getParamValue(numElementsParam, node, tensorMap, context);\n const maxNumElements = node.op === \"TensorListReserve\" ? -1 : numElements;\n const tensorList = reserve(elementShape, elementDtype, numElements, maxNumElements);\n context.addTensorList(tensorList);\n return [tensorList.idTensor];\n }\n case \"TensorListGather\": {\n const gatherId = getParamValue(\"tensorListId\", node, tensorMap, context);\n const gatherIndices = getParamValue(\"indices\", node, tensorMap, context);\n const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n const tensorList = context.getTensorList(gatherId.id);\n return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n }\n case \"TensorListStack\": {\n const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n const numElements = getParamValue(\"numElements\", node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n return [tensorList.stack(elementShape, elementDtype, numElements)];\n }\n case \"TensorListFromTensor\": {\n const tensor2 = getParamValue(\"tensor\", node, tensorMap, context);\n const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n const tensorList = fromTensor(tensor2, elementShape, elementDtype);\n context.addTensorList(tensorList);\n return [tensorList.idTensor];\n }\n case \"TensorListConcat\":\n case \"TensorListConcatV2\": {\n const concatId = getParamValue(\"tensorListId\", node, tensorMap, context);\n const tensorList = context.getTensorList(concatId.id);\n const concatDtype = getParamValue(\"dtype\", node, tensorMap, context);\n const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n return [tensorList.concat(concatDtype, elementShape)];\n }\n case \"TensorListPushBack\": {\n const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n tensorList.pushBack(writeTensor);\n return [tensorList.idTensor];\n }\n case \"TensorListPopBack\": {\n const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n const elementDType = getParamValue(\"elementDType\", node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n return [tensorList.popBack(elementShape, elementDType)];\n }\n case \"TensorListSplit\": {\n const splitTensor = getParamValue(\"tensor\", node, tensorMap, context);\n const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n const lengths = getParamValue(\"lengths\", node, tensorMap, context);\n const tensorList = split2(splitTensor, lengths, elementShape);\n context.addTensorList(tensorList);\n return [tensorList.idTensor];\n }\n case \"TensorListLength\": {\n const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n const tensorList = context.getTensorList(idTensor.id);\n return [scalar(tensorList.size(), \"int32\")];\n }\n case \"TensorListResize\": {\n const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n const size = getParamValue(\"size\", node, tensorMap, context);\n const srcTensorList = context.getTensorList(idTensor.id);\n const destTensorList = srcTensorList.resize(size);\n context.addTensorList(destTensorList);\n return [destTensorList.idTensor];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n const [extraOp, activationFunc] = getParamValue(\"fusedOps\", node, tensorMap, context);\n const isBiasAdd = extraOp === \"biasadd\";\n const noBiasAdd = !isBiasAdd;\n const isPrelu = activationFunc === \"prelu\";\n const isBatchNorm = extraOp === \"fusedbatchnorm\";\n const numArgs = getParamValue(\"numArgs\", node, tensorMap, context);\n if (isBiasAdd) {\n if (isPrelu && numArgs !== 2) {\n throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");\n }\n if (!isPrelu && isBiasAdd && numArgs !== 1) {\n throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\");\n }\n }\n if (isBatchNorm) {\n throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");\n }\n const stride = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getPadding(node, tensorMap, context);\n const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n let [biasArg, preluArg] = getParamValue(\"args\", node, tensorMap, context);\n if (noBiasAdd) {\n preluArg = biasArg;\n biasArg = void 0;\n }\n const leakyreluAlpha = getParamValue(\"leakyreluAlpha\", node, tensorMap, context);\n return {\n stride,\n pad: pad3,\n dataFormat,\n dilations,\n biasArg,\n preluArg,\n activationFunc,\n leakyreluAlpha\n };\n}\nvar executeOp4 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"Conv1D\": {\n const stride = getParamValue(\"stride\", node, tensorMap, context);\n const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n const dilation = getParamValue(\"dilation\", node, tensorMap, context);\n return [ops.conv1d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), stride, pad3, dataFormat, dilation)];\n }\n case \"Conv2D\": {\n const stride = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getPadding(node, tensorMap, context);\n const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n return [ops.conv2d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];\n }\n case \"_FusedConv2D\": {\n const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n return [ops.fused.conv2d({\n x: getParamValue(\"x\", node, tensorMap, context),\n filter: getParamValue(\"filter\", node, tensorMap, context),\n strides: [stride[1], stride[2]],\n pad: pad3,\n dataFormat,\n dilations: [dilations[1], dilations[2]],\n bias: biasArg,\n activation: activationFunc,\n preluActivationWeights: preluArg,\n leakyreluAlpha\n })];\n }\n case \"FusedDepthwiseConv2dNative\": {\n const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n return [ops.fused.depthwiseConv2d({\n x: getParamValue(\"x\", node, tensorMap, context),\n filter: getParamValue(\"filter\", node, tensorMap, context),\n strides: [stride[1], stride[2]],\n pad: pad3,\n dataFormat,\n dilations: [dilations[1], dilations[2]],\n bias: biasArg,\n activation: activationFunc,\n preluActivationWeights: preluArg,\n leakyreluAlpha\n })];\n }\n case \"Conv2DBackpropInput\":\n case \"Conv2dTranspose\": {\n const shape = getParamValue(\"outputShape\", node, tensorMap, context);\n const stride = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getPadding(node, tensorMap, context);\n return [ops.conv2dTranspose(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), shape, [stride[1], stride[2]], pad3)];\n }\n case \"DepthwiseConv2dNative\":\n case \"DepthwiseConv2d\": {\n const stride = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getPadding(node, tensorMap, context);\n const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n return [ops.depthwiseConv2d(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];\n }\n case \"Conv3D\": {\n const stride = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n return [ops.conv3d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2], stride[3]], pad3, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n }\n case \"AvgPool\": {\n const stride = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n return [ops.avgPool(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];\n }\n case \"MaxPool\": {\n const stride = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n return [ops.maxPool(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];\n }\n case \"MaxPoolWithArgmax\": {\n const stride = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n const includeBatchInIndex = getParamValue(\"includeBatchInIndex\", node, tensorMap, context);\n const { result, indexes } = ops.maxPoolWithArgmax(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3, includeBatchInIndex);\n return [result, indexes];\n }\n case \"AvgPool3D\": {\n const stride = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n return [ops.avgPool3d(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];\n }\n case \"MaxPool3D\": {\n const stride = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n return [ops.maxPool3d(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];\n }\n case \"Dilation2D\": {\n const strides = getParamValue(\"strides\", node, tensorMap, context);\n const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n const strideHeight = strides[1];\n const strideWidth = strides[2];\n const dilationHeight = dilations[1];\n const dilationWidth = dilations[2];\n return [ops.dilation2d(\n getParamValue(\"x\", node, tensorMap, context),\n getParamValue(\"filter\", node, tensorMap, context),\n [strideHeight, strideWidth],\n pad3,\n [dilationHeight, dilationWidth],\n \"NHWC\"\n /* dataFormat */\n )];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/creation_executor.js\nvar executeOp5 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"Fill\": {\n const shape = getParamValue(\"shape\", node, tensorMap, context);\n const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n const value = getParamValue(\"value\", node, tensorMap, context);\n return [ops.fill(shape, value, dtype)];\n }\n case \"LinSpace\": {\n const start = getParamValue(\"start\", node, tensorMap, context);\n const stop = getParamValue(\"stop\", node, tensorMap, context);\n const num = getParamValue(\"num\", node, tensorMap, context);\n return [ops.linspace(start, stop, num)];\n }\n case \"Multinomial\": {\n const logits = getParamValue(\"logits\", node, tensorMap, context);\n const numSamples = getParamValue(\"numSamples\", node, tensorMap, context);\n const seed = getParamValue(\"seed\", node, tensorMap, context);\n return [ops.multinomial(logits, numSamples, seed)];\n }\n case \"OneHot\": {\n const indices = getParamValue(\"indices\", node, tensorMap, context);\n const depth = getParamValue(\"depth\", node, tensorMap, context);\n const onValue = getParamValue(\"onValue\", node, tensorMap, context);\n const offValue = getParamValue(\"offValue\", node, tensorMap, context);\n const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n return [ops.oneHot(indices, depth, onValue, offValue, dtype)];\n }\n case \"Ones\": {\n return [ops.ones(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n }\n case \"OnesLike\": {\n return [ops.onesLike(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"RandomStandardNormal\": {\n return [ops.randomStandardNormal(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context), getParamValue(\"seed\", node, tensorMap, context))];\n }\n case \"RandomUniform\": {\n return [ops.randomUniform(\n // tslint:disable-next-line:no-any\n getParamValue(\"shape\", node, tensorMap, context),\n getParamValue(\"minval\", node, tensorMap, context),\n getParamValue(\"maxval\", node, tensorMap, context),\n getParamValue(\"dtype\", node, tensorMap, context)\n )];\n }\n case \"RandomUniformInt\": {\n return [ops.randomUniformInt(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"minval\", node, tensorMap, context), getParamValue(\"maxval\", node, tensorMap, context), getParamValue(\"seed\", node, tensorMap, context))];\n }\n case \"Range\": {\n const start = getParamValue(\"start\", node, tensorMap, context);\n const stop = getParamValue(\"stop\", node, tensorMap, context);\n const step5 = getParamValue(\"step\", node, tensorMap, context);\n return [ops.range(start, stop, step5, getParamValue(\"dtype\", node, tensorMap, context))];\n }\n case \"TruncatedNormal\": {\n const shape = getParamValue(\"shape\", node, tensorMap, context);\n const mean4 = getParamValue(\"mean\", node, tensorMap, context);\n const stdDev = getParamValue(\"stdDev\", node, tensorMap, context);\n const seed = getParamValue(\"seed\", node, tensorMap, context);\n return [ops.truncatedNormal(shape, mean4, stdDev, getParamValue(\"dtype\", node, tensorMap, context), seed)];\n }\n case \"Zeros\": {\n return [ops.zeros(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n }\n case \"ZerosLike\": {\n return [ops.zerosLike(getParamValue(\"x\", node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/dynamic_executor.js\nfunction nmsParams(node, tensorMap, context) {\n const boxes = getParamValue(\"boxes\", node, tensorMap, context);\n const scores = getParamValue(\"scores\", node, tensorMap, context);\n const maxOutputSize = getParamValue(\"maxOutputSize\", node, tensorMap, context);\n const iouThreshold = getParamValue(\"iouThreshold\", node, tensorMap, context);\n const scoreThreshold = getParamValue(\"scoreThreshold\", node, tensorMap, context);\n const softNmsSigma = getParamValue(\"softNmsSigma\", node, tensorMap, context);\n return {\n boxes,\n scores,\n maxOutputSize,\n iouThreshold,\n scoreThreshold,\n softNmsSigma\n };\n}\nvar executeOp6 = async (node, tensorMap, context, resourceManager, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"NonMaxSuppressionV5\": {\n const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n const result = await ops.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n return [result.selectedIndices, result.selectedScores];\n }\n case \"NonMaxSuppressionV4\": {\n const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n const padToMaxOutputSize = getParamValue(\"padToMaxOutputSize\", node, tensorMap, context);\n const result = await ops.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n return [result.selectedIndices, result.validOutputs];\n }\n case \"NonMaxSuppressionV3\":\n case \"NonMaxSuppressionV2\": {\n const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n return [await ops.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n }\n case \"Where\": {\n const condition = ops.cast(getParamValue(\"condition\", node, tensorMap, context), \"bool\");\n const result = [await ops.whereAsync(condition)];\n condition.dispose();\n return result;\n }\n case \"ListDiff\": {\n return ops.setdiff1dAsync(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"y\", node, tensorMap, context));\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/evaluation_executor.js\nvar executeOp7 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"LowerBound\": {\n const sortedSequence = getParamValue(\"sortedSequence\", node, tensorMap, context);\n const values = getParamValue(\"values\", node, tensorMap, context);\n return [ops.lowerBound(sortedSequence, values)];\n }\n case \"TopKV2\": {\n const x = getParamValue(\"x\", node, tensorMap, context);\n const k = getParamValue(\"k\", node, tensorMap, context);\n const sorted = getParamValue(\"sorted\", node, tensorMap, context);\n const result = ops.topk(x, k, sorted);\n return [result.values, result.indices];\n }\n case \"UpperBound\": {\n const sortedSequence = getParamValue(\"sortedSequence\", node, tensorMap, context);\n const values = getParamValue(\"values\", node, tensorMap, context);\n return [ops.upperBound(sortedSequence, values)];\n }\n case \"Unique\": {\n const x = getParamValue(\"x\", node, tensorMap, context);\n const result = ops.unique(x);\n return [result.values, result.indices];\n }\n case \"UniqueV2\": {\n const x = getParamValue(\"x\", node, tensorMap, context);\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const result = ops.unique(x, axis);\n return [result.values, result.indices];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/graph_executor.js\nvar executeOp8 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"Const\": {\n return tensorMap[node.name];\n }\n case \"PlaceholderWithDefault\":\n const def = getParamValue(\"default\", node, tensorMap, context);\n return [getTensor(node.name, tensorMap, context) || def];\n case \"Placeholder\":\n return [getTensor(node.name, tensorMap, context)];\n case \"Identity\":\n case \"StopGradient\":\n case \"FakeQuantWithMinMaxVars\": {\n const data2 = getParamValue(\"x\", node, tensorMap, context);\n return [cloneTensor(data2)];\n }\n case \"IdentityN\":\n return getParamValue(\"x\", node, tensorMap, context).map((t) => cloneTensor(t));\n case \"Snapshot\":\n const snapshot = getParamValue(\"x\", node, tensorMap, context);\n return [cloneTensor(snapshot)];\n case \"Shape\":\n return [ops.tensor1d(getParamValue(\"x\", node, tensorMap, context).shape, \"int32\")];\n case \"ShapeN\":\n return getParamValue(\"x\", node, tensorMap, context).map((t) => ops.tensor1d(t.shape));\n case \"Size\":\n return [ops.scalar(getParamValue(\"x\", node, tensorMap, context).size, \"int32\")];\n case \"Rank\":\n return [ops.scalar(getParamValue(\"x\", node, tensorMap, context).rank, \"int32\")];\n case \"NoOp\":\n return [ops.scalar(1)];\n case \"Print\":\n const input2 = getParamValue(\"x\", node, tensorMap, context);\n const data = getParamValue(\"data\", node, tensorMap, context);\n const message = getParamValue(\"message\", node, tensorMap, context);\n const summarize = getParamValue(\"summarize\", node, tensorMap, context);\n console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\");\n console.log(message);\n for (let i = 0; i < data.length; i++) {\n console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0, summarize));\n }\n return [input2];\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/hash_table.js\nvar HashTable = class {\n get id() {\n return this.handle.id;\n }\n /**\n * Constructor of HashTable. Creates a hash table.\n *\n * @param keyDType `dtype` of the table keys.\n * @param valueDType `dtype` of the table values.\n */\n constructor(keyDType, valueDType) {\n this.keyDType = keyDType;\n this.valueDType = valueDType;\n this.handle = scalar(0);\n this.tensorMap = /* @__PURE__ */ new Map();\n keep(this.handle);\n }\n /**\n * Dispose the tensors and handle and clear the hashtable.\n */\n clearAndClose() {\n this.tensorMap.forEach((value) => value.dispose());\n this.tensorMap.clear();\n this.handle.dispose();\n }\n /**\n * The number of items in the hash table.\n */\n size() {\n return this.tensorMap.size;\n }\n /**\n * The number of items in the hash table as a rank-0 tensor.\n */\n tensorSize() {\n return scalar(this.size(), \"int32\");\n }\n /**\n * Replaces the contents of the table with the specified keys and values.\n * @param keys Keys to store in the hashtable.\n * @param values Values to store in the hashtable.\n */\n async import(keys, values) {\n this.checkKeyAndValueTensor(keys, values);\n const $keys = await keys.data();\n this.tensorMap.forEach((value) => value.dispose());\n this.tensorMap.clear();\n return tidy(() => {\n const $values = unstack(values);\n const keysLength = $keys.length;\n const valuesLength = $values.length;\n util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);\n for (let i = 0; i < keysLength; i++) {\n const key = $keys[i];\n const value = $values[i];\n keep(value);\n this.tensorMap.set(key, value);\n }\n return this.handle;\n });\n }\n /**\n * Looks up keys in a hash table, outputs the corresponding values.\n *\n * Performs batch lookups, for every element in the key tensor, `find`\n * stacks the corresponding value into the return tensor.\n *\n * If an element is not present in the table, the given `defaultValue` is\n * used.\n *\n * @param keys Keys to look up. Must have the same type as the keys of the\n * table.\n * @param defaultValue The scalar `defaultValue` is the value output for keys\n * not present in the table. It must also be of the same type as the\n * table values.\n */\n async find(keys, defaultValue) {\n this.checkKeyAndValueTensor(keys, defaultValue);\n const $keys = await keys.data();\n return tidy(() => {\n const result = [];\n for (let i = 0; i < $keys.length; i++) {\n const key = $keys[i];\n const value = this.findWithDefault(key, defaultValue);\n result.push(value);\n }\n return stack(result);\n });\n }\n // tslint:disable-next-line: no-any\n findWithDefault(key, defaultValue) {\n const result = this.tensorMap.get(key);\n return result != null ? result : defaultValue;\n }\n checkKeyAndValueTensor(key, value) {\n if (key.dtype !== this.keyDType) {\n throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);\n }\n if (value.dtype !== this.valueDType) {\n throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`);\n }\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/hash_table_executor.js\nvar executeOp9 = async (node, tensorMap, context, resourceManager) => {\n switch (node.op) {\n case \"HashTable\":\n case \"HashTableV2\": {\n const existingTableHandle = resourceManager.getHashTableHandleByName(node.name);\n if (existingTableHandle != null) {\n return [existingTableHandle];\n } else {\n const keyDType = getParamValue(\"keyDType\", node, tensorMap, context);\n const valueDType = getParamValue(\"valueDType\", node, tensorMap, context);\n const hashTable = new HashTable(keyDType, valueDType);\n resourceManager.addHashTable(node.name, hashTable);\n return [hashTable.handle];\n }\n }\n case \"InitializeTable\":\n case \"InitializeTableV2\":\n case \"LookupTableImport\":\n case \"LookupTableImportV2\": {\n const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n const keys = getParamValue(\"keys\", node, tensorMap, context);\n const values = getParamValue(\"values\", node, tensorMap, context);\n const hashTable = resourceManager.getHashTableById(handle.id);\n return [await hashTable.import(keys, values)];\n }\n case \"LookupTableFind\":\n case \"LookupTableFindV2\": {\n const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n const keys = getParamValue(\"keys\", node, tensorMap, context);\n const defaultValue = getParamValue(\"defaultValue\", node, tensorMap, context);\n const hashTable = resourceManager.getHashTableById(handle.id);\n return [await hashTable.find(keys, defaultValue)];\n }\n case \"LookupTableSize\":\n case \"LookupTableSizeV2\": {\n const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n const hashTable = resourceManager.getHashTableById(handle.id);\n return [hashTable.tensorSize()];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/image_executor.js\nvar executeOp10 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"ResizeBilinear\": {\n const images = getParamValue(\"images\", node, tensorMap, context);\n const size = getParamValue(\"size\", node, tensorMap, context);\n const alignCorners = getParamValue(\"alignCorners\", node, tensorMap, context);\n const halfPixelCenters = getParamValue(\"halfPixelCenters\", node, tensorMap, context);\n return [ops.image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)];\n }\n case \"ResizeNearestNeighbor\": {\n const images = getParamValue(\"images\", node, tensorMap, context);\n const size = getParamValue(\"size\", node, tensorMap, context);\n const alignCorners = getParamValue(\"alignCorners\", node, tensorMap, context);\n const halfPixelCenters = getParamValue(\"halfPixelCenters\", node, tensorMap, context);\n return [ops.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)];\n }\n case \"CropAndResize\": {\n const image2 = getParamValue(\"image\", node, tensorMap, context);\n const boxes = getParamValue(\"boxes\", node, tensorMap, context);\n const boxInd = getParamValue(\"boxInd\", node, tensorMap, context);\n const cropSize = getParamValue(\"cropSize\", node, tensorMap, context);\n const method = getParamValue(\"method\", node, tensorMap, context);\n const extrapolationValue = getParamValue(\"extrapolationValue\", node, tensorMap, context);\n return [ops.image.cropAndResize(image2, boxes, boxInd, cropSize, method, extrapolationValue)];\n }\n case \"ImageProjectiveTransformV3\": {\n const images = getParamValue(\"images\", node, tensorMap, context);\n const transforms = getParamValue(\"transforms\", node, tensorMap, context);\n const outputShape = getParamValue(\"outputShape\", node, tensorMap, context);\n const fillValue = getParamValue(\"fillValue\", node, tensorMap, context);\n const interpolation = getParamValue(\"interpolation\", node, tensorMap, context);\n const fillMode = getParamValue(\"fillMode\", node, tensorMap, context);\n return [ops.image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/logical_executor.js\nvar executeOp11 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"Equal\": {\n return [ops.equal(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"NotEqual\": {\n return [ops.notEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"Greater\": {\n return [ops.greater(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"GreaterEqual\": {\n return [ops.greaterEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"Less\": {\n return [ops.less(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"LessEqual\": {\n return [ops.lessEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"LogicalAnd\": {\n return [ops.logicalAnd(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"LogicalNot\": {\n return [ops.logicalNot(getParamValue(\"a\", node, tensorMap, context))];\n }\n case \"LogicalOr\": {\n return [ops.logicalOr(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"Select\":\n case \"SelectV2\": {\n return [ops.where(getParamValue(\"condition\", node, tensorMap, context), getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n case \"BitwiseAnd\": {\n return [ops.bitwiseAnd(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/matrices_executor.js\nvar executeOp12 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"BatchMatMul\":\n case \"BatchMatMulV2\":\n case \"MatMul\":\n return [ops.matMul(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context), getParamValue(\"transposeA\", node, tensorMap, context), getParamValue(\"transposeB\", node, tensorMap, context))];\n case \"Einsum\":\n return [ops.einsum(getParamValue(\"equation\", node, tensorMap, context), ...getParamValue(\"tensors\", node, tensorMap, context))];\n case \"Transpose\":\n return [ops.transpose(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"perm\", node, tensorMap, context))];\n case \"_FusedMatMul\":\n const [extraOp, activationFunc] = getParamValue(\"fusedOps\", node, tensorMap, context);\n const isBiasAdd = extraOp === \"biasadd\";\n const isPrelu = activationFunc === \"prelu\";\n const numArgs = getParamValue(\"numArgs\", node, tensorMap, context);\n const leakyreluAlpha = getParamValue(\"leakyreluAlpha\", node, tensorMap, context);\n if (isBiasAdd) {\n if (isPrelu && numArgs !== 2) {\n throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");\n }\n if (!isPrelu && numArgs !== 1) {\n throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\");\n }\n }\n const [biasArg, preluArg] = getParamValue(\"args\", node, tensorMap, context);\n return [ops.fused.matMul({\n a: getParamValue(\"a\", node, tensorMap, context),\n b: getParamValue(\"b\", node, tensorMap, context),\n transposeA: getParamValue(\"transposeA\", node, tensorMap, context),\n transposeB: getParamValue(\"transposeB\", node, tensorMap, context),\n bias: biasArg,\n activation: activationFunc,\n preluActivationWeights: preluArg,\n leakyreluAlpha\n })];\n case \"MatrixBandPart\":\n return [ops.linalg.bandPart(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"numLower\", node, tensorMap, context), getParamValue(\"numUpper\", node, tensorMap, context))];\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/normalization_executor.js\nvar executeOp13 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"EuclideanNorm\":\n return [ops.euclideanNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"axis\", node, tensorMap, context), getParamValue(\"keepDims\", node, tensorMap, context))];\n case \"FusedBatchNorm\":\n case \"FusedBatchNormV2\": {\n return [ops.batchNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"mean\", node, tensorMap, context), getParamValue(\"variance\", node, tensorMap, context), getParamValue(\"offset\", node, tensorMap, context), getParamValue(\"scale\", node, tensorMap, context), getParamValue(\"epsilon\", node, tensorMap, context))];\n }\n case \"FusedBatchNormV3\": {\n return [ops.batchNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"mean\", node, tensorMap, context), getParamValue(\"variance\", node, tensorMap, context), getParamValue(\"offset\", node, tensorMap, context), getParamValue(\"scale\", node, tensorMap, context), getParamValue(\"epsilon\", node, tensorMap, context))];\n }\n case \"LRN\": {\n return [ops.localResponseNormalization(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"radius\", node, tensorMap, context), getParamValue(\"bias\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context), getParamValue(\"beta\", node, tensorMap, context))];\n }\n case \"Softmax\": {\n return [ops.softmax(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"LogSoftmax\": {\n return [ops.logSoftmax(getParamValue(\"x\", node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/ragged_executor.js\nvar executeOp14 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"RaggedGather\": {\n const { outputNestedSplits, outputDenseValues } = ops.raggedGather(getParamValue(\"paramsNestedSplits\", node, tensorMap, context), getParamValue(\"paramsDenseValues\", node, tensorMap, context), getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"outputRaggedRank\", node, tensorMap, context));\n return outputNestedSplits.concat(outputDenseValues);\n }\n case \"RaggedRange\": {\n const { rtNestedSplits, rtDenseValues } = ops.raggedRange(getParamValue(\"starts\", node, tensorMap, context), getParamValue(\"limits\", node, tensorMap, context), getParamValue(\"splits\", node, tensorMap, context));\n return [rtNestedSplits, rtDenseValues];\n }\n case \"RaggedTensorToTensor\": {\n return [ops.raggedTensorToTensor(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"values\", node, tensorMap, context), getParamValue(\"defaultValue\", node, tensorMap, context), getParamValue(\"rowPartitionTensors\", node, tensorMap, context), getParamValue(\"rowPartitionTypes\", node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/reduction_executor.js\nvar executeOp15 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"Max\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n return [ops.max(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n }\n case \"Mean\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n return [ops.mean(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n }\n case \"Min\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n return [ops.min(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n }\n case \"Sum\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n return [ops.sum(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n }\n case \"All\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n return [ops.all(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n }\n case \"Any\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n return [ops.any(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n }\n case \"ArgMax\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n return [ops.argMax(getParamValue(\"x\", node, tensorMap, context), axis)];\n }\n case \"ArgMin\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n return [ops.argMin(getParamValue(\"x\", node, tensorMap, context), axis)];\n }\n case \"Prod\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n return [ops.prod(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n }\n case \"Cumprod\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const exclusive = getParamValue(\"exclusive\", node, tensorMap, context);\n const reverse5 = getParamValue(\"reverse\", node, tensorMap, context);\n return [ops.cumprod(getParamValue(\"x\", node, tensorMap, context), axis, exclusive, reverse5)];\n }\n case \"Cumsum\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const exclusive = getParamValue(\"exclusive\", node, tensorMap, context);\n const reverse5 = getParamValue(\"reverse\", node, tensorMap, context);\n return [ops.cumsum(getParamValue(\"x\", node, tensorMap, context), axis, exclusive, reverse5)];\n }\n case \"Bincount\":\n const x = getParamValue(\"x\", node, tensorMap, context);\n const weights = getParamValue(\"weights\", node, tensorMap, context);\n const size = getParamValue(\"size\", node, tensorMap, context);\n return [ops.bincount(x, weights, size)];\n case \"DenseBincount\": {\n const x2 = getParamValue(\"x\", node, tensorMap, context);\n const weights2 = getParamValue(\"weights\", node, tensorMap, context);\n const size2 = getParamValue(\"size\", node, tensorMap, context);\n const binaryOutput = getParamValue(\"binaryOutput\", node, tensorMap, context);\n return [ops.denseBincount(x2, weights2, size2, binaryOutput)];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/slice_join_executor.js\nvar executeOp16 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"ConcatV2\":\n case \"Concat\": {\n const n = getParamValue(\"n\", node, tensorMap, context);\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n let inputs = getParamValue(\"tensors\", node, tensorMap, context);\n inputs = inputs.slice(0, n);\n return [ops.concat(inputs, axis)];\n }\n case \"Gather\": {\n const input2 = getParamValue(\"x\", node, tensorMap, context);\n const indices = getParamValue(\"indices\", node, tensorMap, context);\n return [ops.gather(input2, ops.cast(indices, \"int32\"), 0)];\n }\n case \"GatherV2\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const batchDims = getParamValue(\"batchDims\", node, tensorMap, context);\n const input2 = getParamValue(\"x\", node, tensorMap, context);\n const indices = getParamValue(\"indices\", node, tensorMap, context);\n return [ops.gather(input2, ops.cast(indices, \"int32\"), axis, batchDims)];\n }\n case \"Reverse\": {\n const dims = getParamValue(\"dims\", node, tensorMap, context);\n const axis = [];\n for (let i = 0; i < dims.length; i++) {\n if (dims[i]) {\n axis.push(i);\n }\n }\n const input2 = getParamValue(\"x\", node, tensorMap, context);\n return [ops.reverse(input2, axis)];\n }\n case \"ReverseV2\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const input2 = getParamValue(\"x\", node, tensorMap, context);\n return [ops.reverse(input2, axis)];\n }\n case \"Slice\": {\n const begin = getParamValue(\"begin\", node, tensorMap, context);\n const size = getParamValue(\"size\", node, tensorMap, context);\n return [ops.slice(getParamValue(\"x\", node, tensorMap, context), begin, size)];\n }\n case \"StridedSlice\": {\n const begin = getParamValue(\"begin\", node, tensorMap, context);\n const end = getParamValue(\"end\", node, tensorMap, context);\n const strides = getParamValue(\"strides\", node, tensorMap, context);\n const beginMask = getParamValue(\"beginMask\", node, tensorMap, context);\n const endMask = getParamValue(\"endMask\", node, tensorMap, context);\n const ellipsisMask = getParamValue(\"ellipsisMask\", node, tensorMap, context);\n const newAxisMask = getParamValue(\"newAxisMask\", node, tensorMap, context);\n const shrinkAxisMask = getParamValue(\"shrinkAxisMask\", node, tensorMap, context);\n const tensor2 = getParamValue(\"x\", node, tensorMap, context);\n return [ops.stridedSlice(tensor2, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n }\n case \"Pack\": {\n return tidy(() => {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const tensors = getParamValue(\"tensors\", node, tensorMap, context);\n const shape = tensors[0].shape;\n const squeezedShape = ops.squeeze(tensors[0]).shape;\n const mapped = tensors.map((tensor2) => {\n const sameShape = util_exports.arraysEqual(tensor2.shape, shape);\n if (!sameShape && !util_exports.arraysEqual(ops.squeeze(tensor2).shape, squeezedShape)) {\n throw new Error(\"the input tensors shape does not match\");\n }\n return sameShape ? tensor2 : ops.reshape(tensor2, shape);\n });\n return [ops.stack(mapped, axis)];\n });\n }\n case \"Unpack\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const tensor2 = getParamValue(\"tensor\", node, tensorMap, context);\n return ops.unstack(tensor2, axis);\n }\n case \"Tile\": {\n const reps = getParamValue(\"reps\", node, tensorMap, context);\n return [ops.tile(getParamValue(\"x\", node, tensorMap, context), reps)];\n }\n case \"Split\":\n case \"SplitV\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n const numOrSizeSplits = getParamValue(\"numOrSizeSplits\", node, tensorMap, context);\n const tensor2 = getParamValue(\"x\", node, tensorMap, context);\n return ops.split(tensor2, numOrSizeSplits, axis);\n }\n case \"ScatterNd\": {\n const indices = getParamValue(\"indices\", node, tensorMap, context);\n const values = getParamValue(\"values\", node, tensorMap, context);\n const shape = getParamValue(\"shape\", node, tensorMap, context);\n return [ops.scatterND(indices, values, shape)];\n }\n case \"GatherNd\": {\n const x = getParamValue(\"x\", node, tensorMap, context);\n const indices = getParamValue(\"indices\", node, tensorMap, context);\n return [ops.gatherND(x, indices)];\n }\n case \"SparseToDense\": {\n const indices = getParamValue(\"sparseIndices\", node, tensorMap, context);\n const shape = getParamValue(\"outputShape\", node, tensorMap, context);\n const sparseValues = getParamValue(\"sparseValues\", node, tensorMap, context);\n const defaultValue = getParamValue(\"defaultValue\", node, tensorMap, context);\n return [ops.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : ops.cast(defaultValue, sparseValues.dtype))];\n }\n case \"TensorScatterUpdate\": {\n const indices = getParamValue(\"indices\", node, tensorMap, context);\n const values = getParamValue(\"values\", node, tensorMap, context);\n const tensor2 = getParamValue(\"tensor\", node, tensorMap, context);\n return [ops.tensorScatterUpdate(tensor2, indices, values)];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/sparse_executor.js\nvar executeOp17 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"SparseFillEmptyRows\": {\n const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = ops.sparse.sparseFillEmptyRows(getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"values\", node, tensorMap, context), getParamValue(\"denseShape\", node, tensorMap, context), getParamValue(\"defaultValue\", node, tensorMap, context));\n return [\n outputIndices,\n outputValues,\n emptyRowIndicator,\n reverseIndexMap\n ];\n }\n case \"SparseReshape\": {\n const { outputIndices, outputShape } = ops.sparse.sparseReshape(getParamValue(\"inputIndices\", node, tensorMap, context), getParamValue(\"inputShape\", node, tensorMap, context), getParamValue(\"newShape\", node, tensorMap, context));\n return [outputIndices, outputShape];\n }\n case \"SparseSegmentMean\": {\n const outputData = ops.sparse.sparseSegmentMean(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"segmentIds\", node, tensorMap, context));\n return [outputData];\n }\n case \"SparseSegmentSum\": {\n const outputData = ops.sparse.sparseSegmentSum(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"segmentIds\", node, tensorMap, context));\n return [outputData];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/spectral_executor.js\nvar executeOp18 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"FFT\": {\n return [ops.fft(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"IFFT\": {\n return [ops.ifft(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"RFFT\": {\n return [ops.rfft(getParamValue(\"x\", node, tensorMap, context))];\n }\n case \"IRFFT\": {\n return [ops.irfft(getParamValue(\"x\", node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/string_executor.js\nvar executeOp19 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"StaticRegexReplace\": {\n return [ops.string.staticRegexReplace(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"pattern\", node, tensorMap, context), getParamValue(\"rewrite\", node, tensorMap, context), getParamValue(\"replaceGlobal\", node, tensorMap, context))];\n }\n case \"StringNGrams\": {\n const { nGrams, nGramsSplits } = ops.string.stringNGrams(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"dataSplits\", node, tensorMap, context), getParamValue(\"separator\", node, tensorMap, context), getParamValue(\"nGramWidths\", node, tensorMap, context), getParamValue(\"leftPad\", node, tensorMap, context), getParamValue(\"rightPad\", node, tensorMap, context), getParamValue(\"padWidth\", node, tensorMap, context), getParamValue(\"preserveShortSequences\", node, tensorMap, context));\n return [nGrams, nGramsSplits];\n }\n case \"StringSplit\": {\n const { indices, values, shape } = ops.string.stringSplit(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"delimiter\", node, tensorMap, context), getParamValue(\"skipEmpty\", node, tensorMap, context));\n return [indices, values, shape];\n }\n case \"StringToHashBucketFast\": {\n const output = ops.string.stringToHashBucketFast(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"numBuckets\", node, tensorMap, context));\n return [output];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/transformation_executor.js\nvar executeOp20 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n switch (node.op) {\n case \"Cast\": {\n return [ops.cast(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n }\n case \"ExpandDims\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n return [ops.expandDims(getParamValue(\"x\", node, tensorMap, context), axis)];\n }\n case \"Squeeze\": {\n const axis = getParamValue(\"axis\", node, tensorMap, context);\n return [ops.squeeze(getParamValue(\"x\", node, tensorMap, context), axis)];\n }\n case \"Reshape\": {\n return [ops.reshape(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"shape\", node, tensorMap, context))];\n }\n case \"EnsureShape\": {\n return [ops.ensureShape(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"shape\", node, tensorMap, context))];\n }\n case \"MirrorPad\": {\n return [ops.mirrorPad(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"padding\", node, tensorMap, context), getParamValue(\"mode\", node, tensorMap, context))];\n }\n case \"PadV2\":\n case \"Pad\": {\n return [ops.pad(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"padding\", node, tensorMap, context), getParamValue(\"constantValue\", node, tensorMap, context))];\n }\n case \"SpaceToBatchND\": {\n const blockShape = getParamValue(\"blockShape\", node, tensorMap, context);\n const paddings = getParamValue(\"paddings\", node, tensorMap, context);\n return [ops.spaceToBatchND(getParamValue(\"x\", node, tensorMap, context), blockShape, paddings)];\n }\n case \"BatchToSpaceND\": {\n const blockShape = getParamValue(\"blockShape\", node, tensorMap, context);\n const crops = getParamValue(\"crops\", node, tensorMap, context);\n return [ops.batchToSpaceND(getParamValue(\"x\", node, tensorMap, context), blockShape, crops)];\n }\n case \"DepthToSpace\": {\n const blockSize = getParamValue(\"blockSize\", node, tensorMap, context);\n const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n return [ops.depthToSpace(getParamValue(\"x\", node, tensorMap, context), blockSize, dataFormat)];\n }\n case \"BroadcastTo\": {\n return [ops.broadcastTo(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"shape\", node, tensorMap, context))];\n }\n case \"BroadcastArgs\": {\n return [ops.broadcastArgs(getParamValue(\"s0\", node, tensorMap, context), getParamValue(\"s1\", node, tensorMap, context))];\n }\n default:\n throw TypeError(`Node type ${node.op} is not implemented`);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_executor.js\nfunction executeOp21(node, tensorMap, context, resourceManager, tidy2 = tidy) {\n const value = ((node2, tensorMap2, context2) => {\n switch (node2.category) {\n case \"arithmetic\":\n return tidy2(() => executeOp(node2, tensorMap2, context2));\n case \"basic_math\":\n return tidy2(() => executeOp2(node2, tensorMap2, context2));\n case \"control\":\n return executeOp3(node2, tensorMap2, context2);\n case \"convolution\":\n return tidy2(() => executeOp4(node2, tensorMap2, context2));\n case \"creation\":\n return tidy2(() => executeOp5(node2, tensorMap2, context2));\n case \"dynamic\":\n return executeOp6(node2, tensorMap2, context2);\n case \"evaluation\":\n return tidy2(() => executeOp7(node2, tensorMap2, context2));\n case \"image\":\n return tidy2(() => executeOp10(node2, tensorMap2, context2));\n case \"graph\":\n return tidy2(() => executeOp8(node2, tensorMap2, context2));\n case \"logical\":\n return tidy2(() => executeOp11(node2, tensorMap2, context2));\n case \"matrices\":\n return tidy2(() => executeOp12(node2, tensorMap2, context2));\n case \"normalization\":\n return tidy2(() => executeOp13(node2, tensorMap2, context2));\n case \"ragged\":\n return tidy2(() => executeOp14(node2, tensorMap2, context2));\n case \"reduction\":\n return tidy2(() => executeOp15(node2, tensorMap2, context2));\n case \"slice_join\":\n return tidy2(() => executeOp16(node2, tensorMap2, context2));\n case \"sparse\":\n return tidy2(() => executeOp17(node2, tensorMap2, context2));\n case \"spectral\":\n return tidy2(() => executeOp18(node2, tensorMap2, context2));\n case \"string\":\n return tidy2(() => executeOp19(node2, tensorMap2, context2));\n case \"transformation\":\n return tidy2(() => executeOp20(node2, tensorMap2, context2));\n case \"hash_table\":\n return executeOp9(node2, tensorMap2, context2, resourceManager);\n case \"custom\":\n const opMapper = getRegisteredOp(node2.op);\n if (opMapper && opMapper.customExecutor) {\n return opMapper.customExecutor(new NodeValueImpl(node2, tensorMap2, context2));\n } else {\n throw TypeError(`Custom op ${node2.op} is not registered.`);\n }\n default:\n throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);\n }\n })(node, tensorMap, context);\n if (util_exports.isPromise(value)) {\n return value.then((data) => [].concat(data));\n }\n return [].concat(value);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/execution_context.js\nvar ExecutionContext = class {\n constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}, parseNodeNameCache) {\n this.weightMap = weightMap;\n this.tensorArrayMap = tensorArrayMap;\n this.tensorListMap = tensorListMap;\n this.functionMap = functionMap;\n this.parseNodeNameCache = parseNodeNameCache;\n this.rootContext = { id: 0, frameName: \"\", iterationId: 0 };\n this.contexts = [this.rootContext];\n this.lastId = 0;\n this.generateCurrentContextIds();\n }\n newFrame(id, frameName) {\n return { id, frameName, iterationId: 0 };\n }\n /**\n * Set the current context\n * @param contexts: ExecutionContextInfo[] the current path of execution\n * frames\n */\n set currentContext(contexts2) {\n if (this.contexts !== contexts2) {\n this.contexts = contexts2;\n this.generateCurrentContextIds();\n }\n }\n get currentContext() {\n return this.contexts;\n }\n /**\n * Returns the current context in string format.\n */\n get currentContextId() {\n return this._currentContextIds[0];\n }\n /**\n * Returns the current context and all parent contexts in string format.\n * This allow access to the nodes in the current and parent frames.\n */\n get currentContextIds() {\n return this._currentContextIds;\n }\n generateCurrentContextIds() {\n const names = [];\n for (let i = 0; i < this.contexts.length - 1; i++) {\n const contexts2 = this.contexts.slice(0, this.contexts.length - i);\n names.push(this.contextIdforContexts(contexts2));\n }\n names.push(\"\");\n this._currentContextIds = names;\n }\n contextIdforContexts(contexts2) {\n return contexts2 ? contexts2.map((context) => context.id === 0 && context.iterationId === 0 ? \"\" : `${context.frameName}-${context.iterationId}`).join(\"/\") : \"\";\n }\n /**\n * Enter a new frame, a new context is pushed on the current context list.\n * @param frameId new frame id\n */\n enterFrame(frameId) {\n if (this.contexts) {\n this.lastId++;\n this.contexts = this.contexts.slice();\n this.contexts.push(this.newFrame(this.lastId, frameId));\n this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n }\n }\n /**\n * Exit the current frame, the last context is removed from the current\n * context list.\n */\n exitFrame() {\n if (this.contexts && this.contexts.length > 1) {\n this.contexts = this.contexts.slice();\n this.contexts.splice(-1);\n this.currentContextIds.shift();\n } else {\n throw new Error(\"Cannot exit frame, the context is empty\");\n }\n }\n /**\n * Enter the next iteration of a loop, the iteration id of last context is\n * increased.\n */\n nextIteration() {\n if (this.contexts && this.contexts.length > 0) {\n this.contexts = this.contexts.slice();\n this.lastId++;\n const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n context.iterationId += 1;\n context.id = this.lastId;\n this.contexts.splice(-1, 1, context);\n this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n } else {\n throw new Error(\"Cannot increase frame iteration, the context is empty\");\n }\n }\n getWeight(name) {\n return this.weightMap[name];\n }\n addTensorArray(tensorArray) {\n this.tensorArrayMap[tensorArray.id] = tensorArray;\n }\n getTensorArray(id) {\n return this.tensorArrayMap[id];\n }\n addTensorList(tensorList) {\n this.tensorListMap[tensorList.id] = tensorList;\n }\n getTensorList(id) {\n return this.tensorListMap[id];\n }\n dispose(keepIds) {\n for (const key in this.tensorArrayMap) {\n this.tensorArrayMap[key].clearAndClose(keepIds);\n }\n for (const key in this.tensorListMap) {\n this.tensorListMap[key].clearAndClose(keepIds);\n }\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/model_analysis.js\nfunction getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n const usedNodes = /* @__PURE__ */ new Set();\n const missingInputs = [];\n let dynamicNode = null;\n let syncInputs = null;\n const seen = /* @__PURE__ */ new Set();\n const inputNodeNames = new Set(Object.keys(inputs).map((name) => parseNodeName(name)[0]));\n initNodes = initNodes || [];\n const initNodeNames = new Set(initNodes.map((node) => parseNodeName(node.name)[0]));\n const frontier = [...outputs];\n while (frontier.length > 0) {\n const node = frontier.pop();\n if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {\n if (dynamicNode == null) {\n dynamicNode = node;\n syncInputs = dynamicNode.children.map((child) => child.name).filter((name) => usedNodes.has(name));\n }\n }\n usedNodes.add(node.name);\n if (weightMap[node.name] != null) {\n continue;\n }\n if (inputNodeNames.has(node.name)) {\n continue;\n }\n if (initNodeNames.has(node.name)) {\n continue;\n }\n if (node.inputs.length === 0) {\n missingInputs.push(node.name);\n continue;\n }\n node.inputs.forEach((input2) => {\n if (seen.has(input2.name)) {\n return;\n }\n seen.add(input2.name);\n frontier.push(input2);\n });\n }\n return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\nfunction getNodesInTopologicalOrder(graph, executionInfo) {\n const { usedNodes, inputs } = executionInfo;\n const inputNodes = Object.keys(inputs).map((name) => parseNodeName(name)[0]).map((name) => graph.nodes[name]);\n const initNodes = graph.initNodes || [];\n const isUsed = (node) => usedNodes.has(typeof node === \"string\" ? node : node.name);\n function unique6(nodes) {\n return [...new Map(nodes.map((node) => [node.name, node])).values()];\n }\n const predefinedNodes = unique6([\n ...inputNodes,\n ...graph.weights,\n ...initNodes\n ]).filter(isUsed);\n const allNodes = unique6([\n ...predefinedNodes,\n ...Object.values(graph.nodes)\n ]).filter(isUsed);\n const nameToNode = new Map(allNodes.map((node) => [node.name, node]));\n const inCounts = {};\n for (const node of allNodes) {\n inCounts[node.name] = inCounts[node.name] || 0;\n for (const child of node.children) {\n if (!isUsed(child)) {\n inCounts[child.name] = Number.POSITIVE_INFINITY;\n }\n inCounts[child.name] = (inCounts[child.name] || 0) + 1;\n }\n }\n const frontier = Object.entries(inCounts).filter(([, inCount]) => inCount === 0).map(([name]) => name);\n const orderedNodeNames = [...frontier];\n while (frontier.length > 0) {\n const nodeName = frontier.pop();\n const node = nameToNode.get(nodeName);\n for (const child of node.children.filter(isUsed)) {\n if (--inCounts[child.name] === 0) {\n orderedNodeNames.push(child.name);\n frontier.push(child.name);\n }\n }\n }\n const orderedNodes = orderedNodeNames.map((name) => nameToNode.get(name));\n const filteredOrderedNodes = filterPredefinedReachableNodes(orderedNodes, predefinedNodes);\n validateNodesExecutionOrder(filteredOrderedNodes, predefinedNodes);\n return filteredOrderedNodes;\n}\nfunction filterPredefinedReachableNodes(orderedNodes, predefinedNodes) {\n const nameToNode = new Map(orderedNodes.map((node) => [node.name, node]));\n const stack2 = predefinedNodes.map((node) => node.name);\n const predefinedReachableNodeNames = new Set(stack2);\n while (stack2.length > 0) {\n const nodeName = stack2.pop();\n const node = nameToNode.get(nodeName);\n for (const child of node.children) {\n if (!nameToNode.has(child.name) || predefinedReachableNodeNames.has(child.name)) {\n continue;\n }\n predefinedReachableNodeNames.add(child.name);\n stack2.push(child.name);\n }\n }\n const filteredOrderedNodes = orderedNodes.filter((node) => predefinedReachableNodeNames.has(node.name));\n return filteredOrderedNodes;\n}\nvar NodesExecutionOrderError = class extends Error {\n constructor(message) {\n super(`NodesExecutionOrderError: ${message}`);\n }\n};\nfunction validateNodesExecutionOrder(orderedNodes, predefinedNodes) {\n const nodeNameToOrder = new Map(orderedNodes.map((node, order) => [node.name, order]));\n const predefinedNodeNames = new Set(predefinedNodes.map((node) => node.name));\n const isPredefined = (node) => predefinedNodeNames.has(typeof node === \"string\" ? node : node.name);\n const willBeExecutedNodeNames = new Set(orderedNodes.map((node) => node.name));\n const willBeExecuted = (node) => willBeExecutedNodeNames.has(typeof node === \"string\" ? node : node.name);\n for (const node of orderedNodes) {\n for (const child of node.children.filter(willBeExecuted)) {\n if (!nodeNameToOrder.has(child.name)) {\n throw new NodesExecutionOrderError(`Child ${child.name} of node ${node.name} is unreachable.`);\n }\n if (nodeNameToOrder.get(node.name) > nodeNameToOrder.get(child.name)) {\n throw new NodesExecutionOrderError(`Node ${node.name} is scheduled to run after its child ${child.name}.`);\n }\n }\n if (!isPredefined(node)) {\n for (const input2 of node.inputs) {\n if (!nodeNameToOrder.has(input2.name)) {\n throw new NodesExecutionOrderError(`Input ${input2.name} of node ${node.name} is unreachable.`);\n }\n if (nodeNameToOrder.get(input2.name) > nodeNameToOrder.get(node.name)) {\n throw new NodesExecutionOrderError(`Node ${node.name} is scheduled to run before its input ${input2.name}.`);\n }\n }\n }\n }\n}\nfunction getNodeLiveUntilMap(orderedNodes) {\n const nodeNameToOrder = new Map(orderedNodes.map((node, order) => [node.name, order]));\n const INF_LIFE = Number.MAX_SAFE_INTEGER;\n const selfLifespans = orderedNodes.map((node, nodeOrder) => isControlFlow(node) ? INF_LIFE : nodeOrder);\n const getSelfLifeSpan = (node) => {\n const selfLife = selfLifespans[nodeNameToOrder.get(node.name)];\n if (selfLife == null) {\n return -1;\n }\n return selfLife;\n };\n const liveUntilOrders = orderedNodes.map((node, nodeOrder) => {\n return node.children.map(getSelfLifeSpan).reduce((a, b) => Math.max(a, b), selfLifespans[nodeOrder]);\n });\n const liveUntilMap = /* @__PURE__ */ new Map();\n for (let nodeOrder = 0; nodeOrder < orderedNodes.length; ++nodeOrder) {\n const liveUntilOrder = liveUntilOrders[nodeOrder];\n if (liveUntilOrder === INF_LIFE) {\n continue;\n }\n const node = orderedNodes[nodeOrder];\n const liveUntilNode = orderedNodes[liveUntilOrder];\n if (!liveUntilMap.has(liveUntilNode.name)) {\n liveUntilMap.set(liveUntilNode.name, []);\n }\n liveUntilMap.get(liveUntilNode.name).push(node);\n }\n return liveUntilMap;\n}\nvar CONTROL_FLOW_OPS = /* @__PURE__ */ new Set([\n \"Switch\",\n \"Merge\",\n \"Enter\",\n \"Exit\",\n \"NextIteration\",\n \"StatelessIf\",\n \"StatelessWhile\",\n \"if\",\n \"While\"\n]);\nvar DYNAMIC_SHAPE_OPS = /* @__PURE__ */ new Set([\n \"NonMaxSuppressionV2\",\n \"NonMaxSuppressionV3\",\n \"NonMaxSuppressionV5\",\n \"Where\"\n]);\nvar HASH_TABLE_OPS = /* @__PURE__ */ new Set([\n \"HashTable\",\n \"HashTableV2\",\n \"LookupTableImport\",\n \"LookupTableImportV2\",\n \"LookupTableFind\",\n \"LookupTableFindV2\",\n \"LookupTableSize\",\n \"LookupTableSizeV2\"\n]);\nfunction isControlFlow(node) {\n return CONTROL_FLOW_OPS.has(node.op);\n}\nfunction isDynamicShape(node) {\n return DYNAMIC_SHAPE_OPS.has(node.op);\n}\nfunction isHashTable(node) {\n return HASH_TABLE_OPS.has(node.op);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_executor.js\nvar GraphExecutor = class _GraphExecutor {\n get weightIds() {\n return this.parent ? this.parent.weightIds : this._weightIds;\n }\n get functionExecutorMap() {\n return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap;\n }\n get weightMap() {\n return this.parent ? this.parent.weightMap : this._weightMap;\n }\n set weightMap(weightMap) {\n const weightIds = Object.keys(weightMap).map((key) => weightMap[key].map((tensor2) => tensor2.id));\n this._weightIds = [].concat(...weightIds);\n this._weightMap = weightMap;\n }\n /**\n * Set `ResourceManager` shared by executors of a model.\n * @param resourceManager: `ResourceManager` of the `GraphModel`.\n */\n set resourceManager(resourceManager) {\n this._resourceManager = resourceManager;\n }\n get inputs() {\n return this._inputs.map((node) => {\n return {\n name: node.name,\n shape: node.attrParams[\"shape\"] ? node.attrParams[\"shape\"].value : void 0,\n dtype: node.attrParams[\"dtype\"] ? node.attrParams[\"dtype\"].value : void 0\n };\n });\n }\n get outputs() {\n return this._outputs.map((node) => {\n return {\n name: node.name,\n shape: node.attrParams[\"shape\"] ? node.attrParams[\"shape\"].value : void 0,\n dtype: node.attrParams[\"dtype\"] ? node.attrParams[\"dtype\"].value : void 0\n };\n });\n }\n get inputNodes() {\n return this._inputs.map((node) => node.signatureKey || node.name);\n }\n get outputNodes() {\n return this._outputs.map((node) => {\n const name = node.signatureKey || node.name;\n return node.defaultOutput ? `${name}:${node.defaultOutput}` : name;\n });\n }\n get functions() {\n return Object.keys(this._functions).reduce((map, key) => {\n map[key] = this._functions[key].signature;\n return map;\n }, {});\n }\n /**\n *\n * @param graph Graph the model or function graph to be executed.\n * @param parent When building function exector you need to set the parent\n * executor. Since the weights and function executor maps are set at parant\n * level, that function executor can access the function maps and weight maps\n * through the parent.\n */\n constructor(graph, parent) {\n this.graph = graph;\n this.parent = parent;\n this.compiledMap = /* @__PURE__ */ new Map();\n this.parseNodeNameCache = /* @__PURE__ */ new Map();\n this._weightMap = {};\n this.SEPARATOR = \",\";\n this._functions = {};\n this._functionExecutorMap = {};\n this.keepIntermediateTensors = false;\n this._outputs = graph.outputs;\n this._inputs = graph.inputs;\n this._initNodes = graph.initNodes;\n this._signature = graph.signature;\n this._functions = graph.functions;\n if (graph.functions != null) {\n Object.keys(graph.functions).forEach((name) => {\n this._functionExecutorMap[name] = new _GraphExecutor(graph.functions[name], this);\n });\n }\n }\n getCompilationKey(inputs, outputs) {\n const sortedInputs = inputs.map((node) => node.name).sort();\n const sortedOutputs = outputs.map((node) => node.name).sort();\n return sortedInputs.join(this.SEPARATOR) + \"--\" + sortedOutputs.join(this.SEPARATOR);\n }\n /**\n * Compiles the inference graph and returns the minimal set of nodes that are\n * required for execution, in the correct execution order.\n * @returns {Object} compilation The compile result.\n * @returns {Node[]} compilation.orderedNodes Nodes in the correct execution\n * order.\n * @returns {Map} compilation.nodeLiveUntilMap A map from node\n * to disposable nodes after its execution. That is, for a node `x`,\n * `nodeLiveUntilMap[x]` indicates all nodes whose intermediate\n * tensors should be disposed after `x` is executed.\n */\n compile(inputs, outputs) {\n const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n if (dynamicNode != null) {\n throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);\n }\n if (missingInputs.length > 0) {\n const outNames = outputs.map((n) => n.name);\n const inNames = Object.keys(inputs);\n throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`);\n }\n const orderedNodes = getNodesInTopologicalOrder(this.graph, executionInfo);\n const nodeLiveUntilMap = getNodeLiveUntilMap(orderedNodes);\n return { orderedNodes, nodeLiveUntilMap };\n }\n cloneAndKeepTensor(tensor2) {\n if (tensor2 == null) {\n return null;\n }\n const clone2 = tensor2.clone();\n keep(clone2);\n return clone2;\n }\n cloneTensorList(tensors) {\n if (!tensors) {\n return null;\n }\n const clonedTensor = tensors.map((tensor2) => {\n return this.cloneAndKeepTensor(tensor2);\n });\n return clonedTensor;\n }\n cloneTensorMap(tensorsMap) {\n return Object.fromEntries(Object.entries(tensorsMap).map(([name, tensorsList]) => {\n return [name, this.cloneTensorList(tensorsList)];\n }));\n }\n /**\n * Executes the inference for given input tensors.\n * @param inputs Tensor map for the model inputs, keyed by the input node\n * names.\n * @param outputs Optional. output node name from the Tensorflow model, if\n * no outputs are specified, the default outputs of the model would be used.\n * You can inspect intermediate nodes of the model by adding them to the\n * outputs array.\n */\n execute(inputs, outputs) {\n this.disposeIntermediateTensors();\n inputs = this.mapInputs(inputs);\n const names = Object.keys(inputs).sort();\n this.checkInputs(inputs);\n this.checkInputShapeAndType(inputs);\n outputs = this.mapOutputs(outputs);\n this.checkOutputs(outputs);\n const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);\n const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]);\n const outputNodeNameSet = new Set(outputNodeNames);\n let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);\n if (outputNodes.length === 0) {\n outputNodes = this._outputs;\n }\n const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n let compilation = this.compiledMap.get(compilationKey);\n if (compilation == null) {\n compilation = this.compile(inputs, outputNodes);\n this.compiledMap.set(compilationKey, compilation);\n }\n try {\n this.keepIntermediateTensors = env().getBool(\"KEEP_INTERMEDIATE_TENSORS\");\n } catch (e) {\n this.keepIntermediateTensors = false;\n console.warn(e.message);\n }\n const tensorArrayMap = {};\n const tensorListMap = {};\n return tidy(() => {\n const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache);\n const tensorsMap = Object.assign({}, this.weightMap);\n if (this.keepIntermediateTensors) {\n this.clonedTensorsMap = this.cloneTensorMap(this.weightMap);\n }\n Object.keys(inputs).forEach((name) => {\n const [nodeName, index] = parseNodeName(name, context);\n const tensors = [];\n tensors[index] = inputs[name];\n tensorsMap[nodeName] = tensors;\n if (this.keepIntermediateTensors) {\n this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors);\n }\n });\n const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n const { orderedNodes, nodeLiveUntilMap } = compilation;\n for (const node of orderedNodes) {\n if (tensorsMap[node.name]) {\n continue;\n }\n const tensors = executeOp21(node, tensorsMap, context, this._resourceManager);\n if (util_exports.isPromise(tensors)) {\n throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`);\n }\n tensorsMap[node.name] = tensors;\n if (this.keepIntermediateTensors) {\n this.clonedTensorsMap[node.name] = this.cloneTensorList(tensors);\n }\n this.checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorsMap, context, tensorsToKeep, outputNodeNameSet, nodeLiveUntilMap.get(node.name));\n }\n if (this.parent == null) {\n context.dispose(tensorsToKeep);\n }\n return outputs.map((name) => getTensor(name, tensorsMap, context));\n });\n }\n getFrozenTensorIds(tensorMap) {\n const ids = [].concat.apply([], Object.keys(tensorMap).map((key) => tensorMap[key]).map((tensors) => tensors.map((tensor2) => tensor2.id)));\n return new Set(ids);\n }\n checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount) {\n if (isControlFlow(node) || outputNodeNameSet.has(nodeName)) {\n return;\n }\n for (const tensor2 of tensorMap[nodeName]) {\n if (tensor2 == null) {\n continue;\n }\n intermediateTensorConsumerCount[tensor2.id] = (intermediateTensorConsumerCount[tensor2.id] || 0) + node.children.length;\n }\n for (const input2 of node.inputs) {\n if (isControlFlow(input2)) {\n continue;\n }\n const tensors = getTensorsForCurrentContext(input2.name, tensorMap, context);\n if (tensors == null) {\n continue;\n }\n for (const tensor2 of tensors) {\n if (!tensor2 || tensor2.kept || tensorsToKeep.has(tensor2.id)) {\n continue;\n }\n const count2 = intermediateTensorConsumerCount[tensor2.id];\n if (count2 === 1) {\n tensor2.dispose();\n delete intermediateTensorConsumerCount[tensor2.id];\n } else if (count2 != null) {\n intermediateTensorConsumerCount[tensor2.id]--;\n }\n }\n }\n }\n checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorMap, context, tensorsToKeep, outputNodeNameSet, liveUntilNodes) {\n function isNonDisposableNode(node2) {\n return isControlFlow(node2) || outputNodeNameSet.has(node2.name);\n }\n if (isControlFlow(node) || liveUntilNodes == null) {\n return;\n }\n for (const nodeToDispose of liveUntilNodes) {\n if (isNonDisposableNode(nodeToDispose)) {\n continue;\n }\n const tensors = getTensorsForCurrentContext(nodeToDispose.name, tensorMap, context);\n for (const tensor2 of tensors) {\n if (!tensor2 || tensor2.kept || tensorsToKeep.has(tensor2.id)) {\n continue;\n }\n tensor2.dispose();\n }\n }\n }\n /**\n * Executes the inference for given input tensors in Async fashion.\n * @param inputs Tensor map for the model inputs, keyed by the input node\n * names.\n * @param outputs output node name from the Tensorflow model, if no outputs\n * are specified, the default outputs of the model would be used. You can\n * inspect intermediate nodes of the model by adding them to the outputs\n * array.\n */\n async executeAsync(inputs, outputs) {\n return this._executeAsync(inputs, outputs);\n }\n disposeIntermediateTensors() {\n if (!this.clonedTensorsMap) {\n return;\n }\n Object.values(this.clonedTensorsMap).forEach((tensorsList) => {\n for (const tensor2 of tensorsList) {\n if (tensor2 && !tensor2.isDisposed) {\n tensor2.dispose();\n }\n }\n });\n this.clonedTensorsMap = null;\n }\n getIntermediateTensors() {\n return this.clonedTensorsMap;\n }\n /**\n * Executes the inference for given input tensors in Async fashion.\n * @param inputs Tensor map for the model inputs, keyed by the input node\n * names.\n * @param outputs Optional. output node name from the Tensorflow model,\n * if no outputs are specified, the default outputs of the model would be\n * used. You can inspect intermediate nodes of the model by adding them to\n * the outputs array.\n * @param isFunctionExecution Optional. Flag for executing a function.\n * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n * function execution.\n * @param tensorArrayMap Optinal global TensorList map by id. Used for\n * function execution.\n */\n async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n this.disposeIntermediateTensors();\n if (!isFunctionExecution) {\n inputs = this.mapInputs(inputs);\n this.checkInputs(inputs);\n this.checkInputShapeAndType(inputs);\n outputs = this.mapOutputs(outputs);\n this.checkOutputs(outputs);\n }\n try {\n this.keepIntermediateTensors = env().getBool(\"KEEP_INTERMEDIATE_TENSORS\");\n } catch (e) {\n this.keepIntermediateTensors = false;\n console.warn(e.message);\n }\n const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache);\n if (this.keepIntermediateTensors) {\n this.clonedTensorsMap = this.cloneTensorMap(this.weightMap);\n }\n const tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n const results = outputs.map((name) => getTensor(name, tensorsMap, context));\n const outputIds = results.map((t) => t.id);\n const inputIds = Object.keys(inputs).map((name) => inputs[name].id);\n const keepIds = /* @__PURE__ */ new Set([...outputIds, ...inputIds, ...this.weightIds]);\n Object.values(tensorsMap).forEach((tensorsList) => {\n tensorsList.forEach((tensor2) => {\n if (tensor2 && !tensor2.isDisposed && !keepIds.has(tensor2.id)) {\n tensor2.dispose();\n }\n });\n });\n if (this.parent == null) {\n context.dispose(keepIds);\n }\n return results;\n }\n async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n const mappedInputs = inputs.reduce((map, tensor2, index) => {\n map[this.inputs[index].name] = tensor2;\n return map;\n }, {});\n return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n }\n /**\n * When there are control flow nodes in the graph, the graph execution use\n * ExecutionContext to keep track of the frames and loop iterators.\n * @param inputs placeholder tensors for the graph.\n * @param context the execution context object for current execution.\n * @param outputNames Optional. output node name from the Tensorflow model,\n * if no outputs are specified, the default outputs of the model would be\n * used. You can inspect intermediate nodes of the model by adding them to\n * the outputs array.\n * @param isFunctionExecution Flag for executing a function.\n */\n async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n const names = Object.keys(inputs);\n const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);\n const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]);\n const outputNodeNameSet = new Set(outputNodeNames);\n let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);\n if (outputNodes.length === 0) {\n outputNodes = this._outputs;\n }\n const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);\n const stack2 = [\n ...inputNodes,\n ...this.graph.weights,\n ...this._initNodes || []\n ].map((node) => {\n return { node, contexts: context.currentContext };\n });\n const tensorsMap = Object.assign({}, this.weightMap);\n Object.keys(inputs).forEach((name) => {\n const [nodeName, index] = parseNodeName(name);\n const tensors = [];\n tensors[index] = inputs[name];\n tensorsMap[nodeName] = tensors;\n });\n const intermediateTensorConsumerCount = {};\n const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n const added = {};\n while (stack2.length > 0) {\n const promises = this.processStack(inputNodes, stack2, context, tensorsMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes);\n await Promise.all(promises);\n }\n if (dynamicNode == null && !isFunctionExecution) {\n console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`);\n }\n const missingOutputs = outputNodes.filter((node) => !isControlFlow(node) && !getTensor(node.name, tensorsMap, context)).map((node) => node.name);\n if (missingOutputs.length > 0) {\n let alternativeMsg = \"\";\n if (dynamicNode != null) {\n alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`;\n }\n throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`);\n }\n return tensorsMap;\n }\n processStack(inputNodes, stack2, context, tensorMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes) {\n const promises = [];\n while (stack2.length > 0) {\n const item = stack2.pop();\n context.currentContext = item.contexts;\n let nodeName = \"\";\n if (item.node.op === \"Enter\" && getParamValue(\"isConstant\", item.node, tensorMap, context)) {\n [nodeName] = getNodeNameAndIndex(item.node.name, context);\n }\n if (tensorMap[item.node.name] == null) {\n const tensors = executeOp21(item.node, tensorMap, context, this._resourceManager);\n if (!nodeName) {\n [nodeName] = getNodeNameAndIndex(item.node.name, context);\n }\n const currentContext = context.currentContext;\n if (util_exports.isPromise(tensors)) {\n promises.push(tensors.then((t) => {\n tensorMap[nodeName] = t;\n if (this.keepIntermediateTensors) {\n this.clonedTensorsMap[nodeName] = this.cloneTensorList(t);\n }\n context.currentContext = currentContext;\n this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount);\n this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n return t;\n }));\n } else {\n tensorMap[nodeName] = tensors;\n if (this.keepIntermediateTensors) {\n this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors);\n }\n this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount);\n this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n }\n } else {\n this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n }\n }\n return promises;\n }\n processChildNodes(node, stack2, context, tensorMap, added, usedNodes) {\n node.children.forEach((childNode) => {\n const [nodeName] = getNodeNameAndIndex(childNode.name, context);\n if (added[nodeName] || !usedNodes.has(childNode.name)) {\n return;\n }\n if (childNode.op === \"Merge\") {\n if (childNode.inputNames.some((name) => {\n return !!getTensor(name, tensorMap, context);\n })) {\n added[nodeName] = true;\n stack2.push({ contexts: context.currentContext, node: childNode });\n }\n } else if (childNode.inputNames.every((name) => {\n return !!getTensor(name, tensorMap, context);\n })) {\n added[nodeName] = true;\n stack2.push({ contexts: context.currentContext, node: childNode });\n }\n });\n }\n /**\n * Releases the memory used by the weight tensors.\n */\n dispose() {\n Object.keys(this.weightMap).forEach((key) => this.weightMap[key].forEach((tensor2) => tensor2.dispose()));\n }\n checkInputShapeAndType(inputs) {\n Object.keys(inputs).forEach((name) => {\n const input2 = inputs[name];\n const [nodeName] = parseNodeName(name);\n const node = this.graph.nodes[nodeName];\n if (node.attrParams[\"shape\"] && node.attrParams[\"shape\"].value) {\n const shape = node.attrParams[\"shape\"].value;\n const match = shape.length === input2.shape.length && input2.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n util_exports.assert(match, () => `The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`);\n }\n if (node.attrParams[\"dtype\"] && node.attrParams[\"dtype\"].value) {\n util_exports.assert(input2.dtype === node.attrParams[\"dtype\"].value, () => `The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams[\"dtype\"].value}, but was ${input2.dtype}`);\n }\n });\n }\n mapInputs(inputs) {\n var _a, _b;\n const result = {};\n for (const inputName in inputs) {\n const tensor2 = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.inputs) === null || _b === void 0 ? void 0 : _b[inputName];\n if (tensor2 != null) {\n result[tensor2.name] = inputs[inputName];\n } else {\n result[inputName] = inputs[inputName];\n }\n }\n return result;\n }\n checkInputs(inputs) {\n const notInGraph = Object.keys(inputs).filter((name) => {\n const [nodeName] = parseNodeName(name);\n return this.graph.nodes[nodeName] == null;\n });\n if (notInGraph.length > 0) {\n throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`);\n }\n }\n mapOutputs(outputs) {\n return outputs.map((name) => {\n var _a, _b;\n const tensor2 = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.outputs) === null || _b === void 0 ? void 0 : _b[name];\n if (tensor2 != null) {\n return tensor2.name;\n }\n return name;\n }, {});\n }\n checkOutputs(outputs) {\n outputs.forEach((name) => {\n const [normalizedName] = parseNodeName(name);\n if (!this.graph.nodes[normalizedName]) {\n throw new Error(`The output '${name}' is not found in the graph`);\n }\n });\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/resource_manager.js\nvar ResourceManager = class {\n constructor(hashTableNameToHandle = {}, hashTableMap = {}) {\n this.hashTableNameToHandle = hashTableNameToHandle;\n this.hashTableMap = hashTableMap;\n }\n /**\n * Register a `HashTable` in the resource manager.\n *\n * The `HashTable` can be retrieved by `resourceManager.getHashTableById`,\n * where id is the table handle tensor's id.\n *\n * @param name Op node name that creates the `HashTable`.\n * @param hashTable The `HashTable` to be added to resource manager.\n */\n addHashTable(name, hashTable) {\n this.hashTableNameToHandle[name] = hashTable.handle;\n this.hashTableMap[hashTable.id] = hashTable;\n }\n /**\n * Get the table handle by node name.\n * @param name Op node name that creates the `HashTable`. This name is also\n * used in the inputs list of lookup and import `HashTable` ops.\n */\n getHashTableHandleByName(name) {\n return this.hashTableNameToHandle[name];\n }\n /**\n * Get the actual `HashTable` by its handle tensor's id.\n * @param id The id of the handle tensor.\n */\n getHashTableById(id) {\n return this.hashTableMap[id];\n }\n /**\n * Dispose `ResourceManager`, including its hashTables and tensors in them.\n */\n dispose() {\n for (const key in this.hashTableMap) {\n this.hashTableMap[key].clearAndClose();\n delete this.hashTableMap[key];\n }\n for (const name in this.hashTableNameToHandle) {\n this.hashTableNameToHandle[name].dispose();\n delete this.hashTableNameToHandle[name];\n }\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.js\nvar TFHUB_SEARCH_PARAM = \"?tfjs-format=file\";\nvar DEFAULT_MODEL_NAME = \"model.json\";\nvar GraphModel = class {\n // Returns the version information for the tensorflow model GraphDef.\n get modelVersion() {\n return this.version;\n }\n get inputNodes() {\n return this.executor.inputNodes;\n }\n get outputNodes() {\n return this.executor.outputNodes;\n }\n get inputs() {\n return this.executor.inputs;\n }\n get outputs() {\n return this.executor.outputs;\n }\n get weights() {\n return this.executor.weightMap;\n }\n get metadata() {\n return this.artifacts.userDefinedMetadata;\n }\n get modelSignature() {\n return this.signature;\n }\n get modelStructuredOutputKeys() {\n return this.structuredOutputKeys;\n }\n /**\n * @param modelUrl url for the model, or an `io.IOHandler`.\n * @param weightManifestUrl url for the weight file generated by\n * scripts/convert.py script.\n * @param requestOption options for Request, which allows to send credentials\n * and custom headers.\n * @param onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n */\n constructor(modelUrl, loadOptions = {}, tfio = io_exports) {\n this.modelUrl = modelUrl;\n this.loadOptions = loadOptions;\n this.version = \"n/a\";\n this.io = tfio;\n if (loadOptions == null) {\n this.loadOptions = {};\n }\n this.resourceManager = new ResourceManager();\n }\n findIOHandler() {\n const path = this.modelUrl;\n if (path.load != null) {\n this.handler = path;\n } else if (this.loadOptions.requestInit != null) {\n this.handler = this.io.browserHTTPRequest(path, this.loadOptions);\n } else {\n const handlers = this.io.getLoadHandlers(path, this.loadOptions);\n if (handlers.length === 0) {\n handlers.push(this.io.browserHTTPRequest(path, this.loadOptions));\n } else if (handlers.length > 1) {\n throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);\n }\n this.handler = handlers[0];\n }\n }\n /**\n * Loads the model and weight files, construct the in memory weight map and\n * compile the inference graph.\n */\n load() {\n this.findIOHandler();\n if (this.handler.load == null) {\n throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");\n }\n const loadResult = this.handler.load();\n if (util_exports.isPromise(loadResult)) {\n return loadResult.then((artifacts) => {\n if (artifacts.getWeightStream == null) {\n return this.loadSync(artifacts);\n }\n return this.loadStreaming(artifacts);\n });\n }\n return this.loadSync(loadResult);\n }\n /**\n * Synchronously construct the in memory weight map and\n * compile the inference graph.\n *\n * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n */\n loadSync(artifacts) {\n const weightMap = this.io.decodeWeights(artifacts.weightData, artifacts.weightSpecs);\n return this.loadWithWeightMap(artifacts, weightMap);\n }\n async loadStreaming(artifacts) {\n if (artifacts.getWeightStream == null) {\n throw new Error(\"Model artifacts missing streamWeights function\");\n }\n const weightMap = await decodeWeightsStream(artifacts.getWeightStream(), artifacts.weightSpecs);\n return this.loadWithWeightMap(artifacts, weightMap);\n }\n loadWithWeightMap(artifacts, weightMap) {\n this.artifacts = artifacts;\n const graph = this.artifacts.modelTopology;\n let signature = this.artifacts.signature;\n if (this.artifacts.userDefinedMetadata != null) {\n const metadata = this.artifacts.userDefinedMetadata;\n if (metadata.signature != null) {\n signature = metadata.signature;\n }\n if (metadata.structuredOutputKeys != null) {\n this.structuredOutputKeys = metadata.structuredOutputKeys;\n }\n }\n this.signature = signature;\n this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature));\n this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n this.executor.resourceManager = this.resourceManager;\n if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) {\n const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n this.initializer = new GraphExecutor(initializer);\n this.initializer.weightMap = this.executor.weightMap;\n this.initializer.resourceManager = this.resourceManager;\n this.initializerSignature = artifacts.initializerSignature;\n }\n return true;\n }\n /**\n * Save the configuration and/or weights of the GraphModel.\n *\n * An `IOHandler` is an object that has a `save` method of the proper\n * signature defined. The `save` method manages the storing or\n * transmission of serialized data (\"artifacts\") that represent the\n * model's topology and weights onto or via a specific medium, such as\n * file downloads, local storage, IndexedDB in the web browser and HTTP\n * requests to a server. TensorFlow.js provides `IOHandler`\n * implementations for a number of frequently used saving mediums, such as\n * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n * for more details.\n *\n * This method also allows you to refer to certain types of `IOHandler`s\n * as URL-like string shortcuts, such as 'localstorage://' and\n * 'indexeddb://'.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const modelUrl =\n * 'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * model.predict(zeros).print();\n * ```\n *\n * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n * scheme-based string shortcut for `IOHandler`.\n * @param config Options for saving the model.\n * @returns A `Promise` of `SaveResult`, which summarizes the result of\n * the saving, such as byte sizes of the saved artifacts for the model's\n * topology and weight values.\n *\n * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n */\n async save(handlerOrURL, config) {\n if (typeof handlerOrURL === \"string\") {\n const handlers = this.io.getSaveHandlers(handlerOrURL);\n if (handlers.length === 0) {\n throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n } else if (handlers.length > 1) {\n throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);\n }\n handlerOrURL = handlers[0];\n }\n if (handlerOrURL.save == null) {\n throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");\n }\n return handlerOrURL.save(this.artifacts);\n }\n addStructuredOutputNames(outputTensors) {\n if (this.structuredOutputKeys) {\n const outputTensorsArray = outputTensors instanceof Tensor ? [outputTensors] : outputTensors;\n const outputTensorMap = {};\n outputTensorsArray.forEach((outputTensor, i) => outputTensorMap[this.structuredOutputKeys[i]] = outputTensor);\n return outputTensorMap;\n }\n return outputTensors;\n }\n /**\n * Execute the inference for the input tensors.\n *\n * @param input The input tensors, when there is single input for the model,\n * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n * inputs params should be in either `tf.Tensor`[] if the input order is\n * fixed, or otherwise NamedTensorMap format.\n *\n * For model with multiple inputs, we recommend you use NamedTensorMap as the\n * input type, if you use `tf.Tensor`[], the order of the array needs to\n * follow the\n * order of inputNodes array. @see {@link GraphModel.inputNodes}\n *\n * You can also feed any intermediate nodes using the NamedTensorMap as the\n * input type. For example, given the graph\n * InputNode => Intermediate => OutputNode,\n * you can execute the subgraph Intermediate => OutputNode by calling\n * model.execute('IntermediateNode' : tf.tensor(...));\n *\n * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n * state needs to be fed manually.\n *\n * For batch inference execution, the tensors for each input need to be\n * concatenated together. For example with mobilenet, the required input shape\n * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n * If we are provide a batched data of 100 images, the input tensor should be\n * in the shape of [100, 244, 244, 3].\n *\n * @param config Prediction configuration for specifying the batch size.\n * Currently the batch size option is ignored for graph model.\n *\n * @returns Inference result tensors. If the model is converted and it\n * originally had structured_outputs in tensorflow, then a NamedTensorMap\n * will be returned matching the structured_outputs. If no structured_outputs\n * are present, the output will be single `tf.Tensor` if the model has single\n * output node, otherwise Tensor[].\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n predict(inputs, config) {\n const outputTensors = this.execute(inputs, this.outputNodes);\n return this.addStructuredOutputNames(outputTensors);\n }\n /**\n * Execute the inference for the input tensors in async fashion, use this\n * method when your model contains control flow ops.\n *\n * @param input The input tensors, when there is single input for the model,\n * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n * inputs params should be in either `tf.Tensor`[] if the input order is\n * fixed, or otherwise NamedTensorMap format.\n *\n * For model with multiple inputs, we recommend you use NamedTensorMap as the\n * input type, if you use `tf.Tensor`[], the order of the array needs to\n * follow the\n * order of inputNodes array. @see {@link GraphModel.inputNodes}\n *\n * You can also feed any intermediate nodes using the NamedTensorMap as the\n * input type. For example, given the graph\n * InputNode => Intermediate => OutputNode,\n * you can execute the subgraph Intermediate => OutputNode by calling\n * model.execute('IntermediateNode' : tf.tensor(...));\n *\n * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n * state needs to be fed manually.\n *\n * For batch inference execution, the tensors for each input need to be\n * concatenated together. For example with mobilenet, the required input shape\n * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n * If we are provide a batched data of 100 images, the input tensor should be\n * in the shape of [100, 244, 244, 3].\n *\n * @param config Prediction configuration for specifying the batch size.\n * Currently the batch size option is ignored for graph model.\n *\n * @returns A Promise of inference result tensors. If the model is converted\n * and it originally had structured_outputs in tensorflow, then a\n * NamedTensorMap will be returned matching the structured_outputs. If no\n * structured_outputs are present, the output will be single `tf.Tensor` if\n * the model has single output node, otherwise Tensor[].\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n async predictAsync(inputs, config) {\n const outputTensors = await this.executeAsync(inputs, this.outputNodes);\n return this.addStructuredOutputNames(outputTensors);\n }\n normalizeInputs(inputs) {\n var _a;\n if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n const signatureInputs = (_a = this.signature) === null || _a === void 0 ? void 0 : _a.inputs;\n if (signatureInputs != null) {\n for (const input2 in signatureInputs) {\n const tensor2 = signatureInputs[input2];\n if (tensor2.resourceId != null) {\n inputs[input2] = this.resourceIdToCapturedInput[tensor2.resourceId];\n }\n }\n }\n return inputs;\n }\n inputs = Array.isArray(inputs) ? inputs : [inputs];\n const numCapturedInputs = Object.keys(this.resourceIdToCapturedInput).length;\n if (inputs.length + numCapturedInputs !== this.inputNodes.length) {\n throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length - numCapturedInputs} non-resource placeholders, while there are ${inputs.length} input tensors provided.`);\n }\n let inputIndex = 0;\n return this.inputNodes.reduce((map, inputName) => {\n var _a2, _b, _c;\n const resourceId = (_c = (_b = (_a2 = this.signature) === null || _a2 === void 0 ? void 0 : _a2.inputs) === null || _b === void 0 ? void 0 : _b[inputName]) === null || _c === void 0 ? void 0 : _c.resourceId;\n if (resourceId != null) {\n map[inputName] = this.resourceIdToCapturedInput[resourceId];\n } else {\n map[inputName] = inputs[inputIndex++];\n }\n return map;\n }, {});\n }\n normalizeOutputs(outputs) {\n outputs = outputs || this.outputNodes;\n return !Array.isArray(outputs) ? [outputs] : outputs;\n }\n executeInitializerGraph() {\n if (this.initializer == null) {\n return [];\n }\n if (this.initializerSignature == null) {\n return this.initializer.execute({}, []);\n } else {\n return this.initializer.execute({}, Object.keys(this.initializerSignature.outputs));\n }\n }\n async executeInitializerGraphAsync() {\n if (this.initializer == null) {\n return [];\n }\n if (this.initializerSignature == null) {\n return this.initializer.executeAsync({}, []);\n } else {\n return this.initializer.executeAsync({}, Object.keys(this.initializerSignature.outputs));\n }\n }\n setResourceIdToCapturedInput(outputs) {\n this.resourceIdToCapturedInput = {};\n if (this.initializerSignature) {\n const signatureOutputs = this.initializerSignature.outputs;\n const outputNames = Object.keys(signatureOutputs);\n for (let i = 0; i < outputNames.length; i++) {\n const outputName = outputNames[i];\n const tensorInfo = signatureOutputs[outputName];\n this.resourceIdToCapturedInput[tensorInfo.resourceId] = outputs[i];\n }\n }\n }\n /**\n * Executes inference for the model for given input tensors.\n * @param inputs tensor, tensor array or tensor map of the inputs for the\n * model, keyed by the input node names.\n * @param outputs output node name from the TensorFlow model, if no\n * outputs are specified, the default outputs of the model would be used.\n * You can inspect intermediate nodes of the model by adding them to the\n * outputs array.\n *\n * @returns A single tensor if provided with a single output or no outputs\n * are provided and there is only one default output, otherwise return a\n * tensor array. The order of the tensor array is the same as the outputs\n * if provided, otherwise the order of outputNodes attribute of the model.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n execute(inputs, outputs) {\n if (this.resourceIdToCapturedInput == null) {\n this.setResourceIdToCapturedInput(this.executeInitializerGraph());\n }\n inputs = this.normalizeInputs(inputs);\n outputs = this.normalizeOutputs(outputs);\n const result = this.executor.execute(inputs, outputs);\n return result.length > 1 ? result : result[0];\n }\n /**\n * Executes inference for the model for given input tensors in async\n * fashion, use this method when your model contains control flow ops.\n * @param inputs tensor, tensor array or tensor map of the inputs for the\n * model, keyed by the input node names.\n * @param outputs output node name from the TensorFlow model, if no outputs\n * are specified, the default outputs of the model would be used. You can\n * inspect intermediate nodes of the model by adding them to the outputs\n * array.\n *\n * @returns A Promise of single tensor if provided with a single output or\n * no outputs are provided and there is only one default output, otherwise\n * return a tensor map.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n async executeAsync(inputs, outputs) {\n if (this.resourceIdToCapturedInput == null) {\n this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync());\n }\n inputs = this.normalizeInputs(inputs);\n outputs = this.normalizeOutputs(outputs);\n const result = await this.executor.executeAsync(inputs, outputs);\n return result.length > 1 ? result : result[0];\n }\n /**\n * Get intermediate tensors for model debugging mode (flag\n * KEEP_INTERMEDIATE_TENSORS is true).\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n getIntermediateTensors() {\n return this.executor.getIntermediateTensors();\n }\n /**\n * Dispose intermediate tensors for model debugging mode (flag\n * KEEP_INTERMEDIATE_TENSORS is true).\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n disposeIntermediateTensors() {\n this.executor.disposeIntermediateTensors();\n }\n convertTensorMapToTensorsMap(map) {\n return Object.keys(map).reduce((newMap, key) => {\n newMap[key] = [map[key]];\n return newMap;\n }, {});\n }\n /**\n * Releases the memory used by the weight tensors and resourceManager.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\n dispose() {\n this.executor.dispose();\n if (this.initializer) {\n this.initializer.dispose();\n if (this.resourceIdToCapturedInput) {\n dispose(this.resourceIdToCapturedInput);\n }\n }\n this.resourceManager.dispose();\n }\n};\nasync function loadGraphModel(modelUrl, options = {}, tfio = io_exports) {\n if (modelUrl == null) {\n throw new Error(\"modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model\");\n }\n if (options == null) {\n options = {};\n }\n if (options.fromTFHub && typeof modelUrl === \"string\") {\n modelUrl = getTFHubUrl(modelUrl);\n }\n const model2 = new GraphModel(modelUrl, options, tfio);\n await model2.load();\n return model2;\n}\nfunction loadGraphModelSync(modelSource) {\n if (modelSource == null) {\n throw new Error(\"modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model\");\n }\n let ioHandler;\n if (modelSource instanceof Array) {\n const [modelJSON, weights] = modelSource;\n if (!modelJSON) {\n throw new Error(\"modelJSON must be the first element of the array\");\n }\n if (!weights || !(weights instanceof ArrayBuffer)) {\n throw new Error(\"An ArrayBuffer of weights must be the second element of the array\");\n }\n if (!(\"modelTopology\" in modelJSON)) {\n throw new Error(\"Model JSON is missing 'modelTopology'\");\n }\n if (!(\"weightsManifest\" in modelJSON)) {\n throw new Error(\"Model JSON is missing 'weightsManifest'\");\n }\n const weightSpecs = io_exports.getWeightSpecs(modelJSON.weightsManifest);\n const modelArtifacts = io_exports.getModelArtifactsForJSONSync(modelJSON, weightSpecs, weights);\n ioHandler = io_exports.fromMemorySync(modelArtifacts);\n } else if (\"load\" in modelSource) {\n ioHandler = modelSource;\n } else if (\"modelTopology\" in modelSource && \"weightSpecs\" in modelSource && \"weightData\" in modelSource) {\n ioHandler = io_exports.fromMemorySync(modelSource);\n } else {\n throw new Error(\"Unknown model format\");\n }\n const model2 = new GraphModel(ioHandler);\n model2.load();\n return model2;\n}\nfunction getTFHubUrl(modelUrl) {\n if (!modelUrl.endsWith(\"/\")) {\n modelUrl = modelUrl + \"/\";\n }\n return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/version.js\nvar version3 = \"4.16.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/index.js\nvar dist_exports2 = {};\n__export(dist_exports2, {\n CSVDataset: () => CSVDataset,\n Dataset: () => Dataset,\n FileDataSource: () => FileDataSource,\n TextLineDataset: () => TextLineDataset,\n URLDataSource: () => URLDataSource,\n array: () => array,\n csv: () => csv,\n func: () => func,\n generator: () => generator,\n microphone: () => microphone,\n version_data: () => version4,\n webcam: () => webcam,\n zip: () => zip\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/dataset.js\nvar seedrandom3 = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js\nvar seedrandom2 = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/util/deep_map.js\nfunction deepMap(input2, mapFn) {\n return deepMapInternal(input2, mapFn);\n}\nfunction deepMapInternal(input2, mapFn, seen = /* @__PURE__ */ new Map(), containedIn = /* @__PURE__ */ new Set()) {\n if (input2 == null) {\n return null;\n }\n if (typeof Blob === \"function\" && input2 instanceof Blob) {\n return input2.slice();\n }\n if (containedIn.has(input2)) {\n throw new Error(\"Circular references are not supported.\");\n }\n if (seen.has(input2)) {\n return seen.get(input2);\n }\n const result = mapFn(input2);\n if (result.recurse && result.value !== null) {\n throw new Error(\"A deep map function may not return both a value and recurse=true.\");\n }\n if (!result.recurse) {\n seen.set(input2, result.value);\n return result.value;\n } else if (isIterable2(input2)) {\n const mappedIterable = Array.isArray(input2) ? [] : {};\n containedIn.add(input2);\n for (const k in input2) {\n const child = input2[k];\n const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n mappedIterable[k] = childResult;\n }\n containedIn.delete(input2);\n if (input2.__proto__) {\n mappedIterable.__proto__ = input2.__proto__;\n }\n return mappedIterable;\n } else {\n throw new Error(`Can't recurse into non-iterable type: ${input2}`);\n }\n}\nfunction deepZip(inputs, zipFn = zipToList) {\n return deepZipInternal(inputs, zipFn);\n}\nfunction deepZipInternal(inputs, zipFn, containedIn = /* @__PURE__ */ new Set()) {\n const input2 = inputs[0];\n if (containedIn.has(input2)) {\n throw new Error(\"Circular references are not supported.\");\n }\n const result = zipFn(inputs);\n if (result.recurse && result.value !== null) {\n throw new Error(\"A deep zip function may not return both a value and recurse=true.\");\n }\n if (!result.recurse) {\n return result.value;\n } else if (isIterable2(input2)) {\n const mappedIterable = Array.isArray(input2) ? [] : {};\n containedIn.add(input2);\n for (const k in input2) {\n const children = inputs.map((x) => x[k]);\n const childResult = deepZipInternal(children, zipFn, containedIn);\n mappedIterable[k] = childResult;\n }\n containedIn.delete(input2);\n return mappedIterable;\n } else {\n throw new Error(`Can't recurse into non-iterable type: ${input2}`);\n }\n}\nfunction zipToList(x) {\n if (x === null) {\n return null;\n }\n if (isIterable2(x[0])) {\n return { value: null, recurse: true };\n } else {\n return { value: x, recurse: false };\n }\n}\nasync function deepMapAndAwaitAll(input2, mapFn) {\n const seen = /* @__PURE__ */ new Map();\n deepMapInternal(input2, mapFn, seen);\n for (const key of Array.from(seen.keys())) {\n const value = seen.get(key);\n if (util_exports.isPromise(value)) {\n const mappedValue = await value;\n seen.set(key, mappedValue);\n }\n }\n const result = deepMapInternal(input2, mapFn, seen);\n return result;\n}\nfunction isIterable2(obj) {\n let isTextDecoder = false;\n if (env().get(\"IS_BROWSER\")) {\n isTextDecoder = obj instanceof TextDecoder;\n } else {\n const { StringDecoder } = require_string_decoder();\n isTextDecoder = obj instanceof StringDecoder;\n }\n return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === \"object\" && !(obj instanceof Tensor) && !(obj instanceof Promise) && !isTextDecoder);\n}\nfunction canTensorify(obj) {\n return obj == null || isPrimitive(obj) || Array.isArray(obj) || typeof obj === \"object\" && obj instanceof Tensor || util_exports.isTypedArray(obj);\n}\nfunction isPrimitive(value) {\n return value === null || typeof value !== \"object\" && typeof value !== \"function\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js\nfunction deepClone(container) {\n return deepMap(container, cloneIfTensor);\n}\nfunction cloneIfTensor(item) {\n if (item instanceof Tensor) {\n return { value: item.clone(), recurse: false };\n } else if (isIterable2(item)) {\n return { value: null, recurse: true };\n } else {\n return { value: item, recurse: false };\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/util/ring_buffer.js\nvar RingBuffer = class {\n /**\n * Constructs a `RingBuffer`.\n * @param capacity The number of items that the buffer can accomodate.\n */\n constructor(capacity) {\n this.capacity = capacity;\n this.begin = 0;\n this.end = 0;\n if (capacity == null) {\n throw new RangeError(\"Can't create a ring buffer of unknown capacity.\");\n }\n if (capacity < 1) {\n throw new RangeError(\"Can't create ring buffer of capacity < 1.\");\n }\n this.data = new Array(capacity);\n this.doubledCapacity = 2 * capacity;\n }\n /**\n * Map any index into the range 0 <= index < 2*capacity.\n */\n wrap(index) {\n while (index < 0) {\n index += this.doubledCapacity;\n }\n return index % this.doubledCapacity;\n }\n get(index) {\n if (index < 0) {\n throw new RangeError(\"Can't get item at a negative index.\");\n }\n return this.data[index % this.capacity];\n }\n set(index, value) {\n if (index < 0) {\n throw new RangeError(\"Can't set item at a negative index.\");\n }\n this.data[index % this.capacity] = value;\n }\n /**\n * Returns the current number of items in the buffer.\n */\n length() {\n let length = this.end - this.begin;\n if (length < 0) {\n length = this.doubledCapacity + length;\n }\n return length;\n }\n /**\n * Reports whether the buffer is full.\n * @returns true if the number of items in the buffer equals its capacity, and\n * false otherwise.\n */\n isFull() {\n return this.length() === this.capacity;\n }\n /**\n * Reports whether the buffer is empty.\n * @returns true if the number of items in the buffer equals zero, and\n * false otherwise.\n */\n isEmpty() {\n return this.length() === 0;\n }\n /**\n * Adds an item to the end of the buffer.\n */\n push(value) {\n if (this.isFull()) {\n throw new RangeError(\"Ring buffer is full.\");\n }\n this.set(this.end, value);\n this.end = this.wrap(this.end + 1);\n }\n /**\n * Adds many items to the end of the buffer, in order.\n */\n pushAll(values) {\n for (const value of values) {\n this.push(value);\n }\n }\n /**\n * Removes and returns the last item in the buffer.\n */\n pop() {\n if (this.isEmpty()) {\n throw new RangeError(\"Ring buffer is empty.\");\n }\n this.end = this.wrap(this.end - 1);\n const result = this.get(this.end);\n this.set(this.end, void 0);\n return result;\n }\n /**\n * Adds an item to the beginning of the buffer.\n */\n unshift(value) {\n if (this.isFull()) {\n throw new RangeError(\"Ring buffer is full.\");\n }\n this.begin = this.wrap(this.begin - 1);\n this.set(this.begin, value);\n }\n /**\n * Removes and returns the first item in the buffer.\n */\n shift() {\n if (this.isEmpty()) {\n throw new RangeError(\"Ring buffer is empty.\");\n }\n const result = this.get(this.begin);\n this.set(this.begin, void 0);\n this.begin = this.wrap(this.begin + 1);\n return result;\n }\n /**\n * Removes and returns a specific item in the buffer, and moves the last item\n * to the vacated slot. This is useful for implementing a shuffling stream.\n * Note that this operation necessarily scrambles the original order.\n *\n * @param relativeIndex: the index of the item to remove, relative to the\n * first item in the buffer (e.g., hiding the ring nature of the underlying\n * storage).\n */\n shuffleExcise(relativeIndex) {\n if (this.isEmpty()) {\n throw new RangeError(\"Ring buffer is empty.\");\n }\n const index = this.wrap(this.begin + relativeIndex);\n const result = this.get(index);\n this.set(index, this.pop());\n return result;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js\nvar GrowingRingBuffer = class _GrowingRingBuffer extends RingBuffer {\n /**\n * Constructs a `GrowingRingBuffer`.\n */\n constructor() {\n super(_GrowingRingBuffer.INITIAL_CAPACITY);\n }\n isFull() {\n return false;\n }\n push(value) {\n if (super.isFull()) {\n this.expand();\n }\n super.push(value);\n }\n unshift(value) {\n if (super.isFull()) {\n this.expand();\n }\n super.unshift(value);\n }\n /**\n * Doubles the capacity of the buffer.\n */\n expand() {\n const newCapacity = this.capacity * 2;\n const newData = new Array(newCapacity);\n const len = this.length();\n for (let i = 0; i < len; i++) {\n newData[i] = this.get(this.wrap(this.begin + i));\n }\n this.data = newData;\n this.capacity = newCapacity;\n this.doubledCapacity = 2 * this.capacity;\n this.begin = 0;\n this.end = len;\n }\n};\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js\nfunction iteratorFromItems(items) {\n return new ArrayIterator(items);\n}\nfunction iteratorFromFunction(func2) {\n return new FunctionCallIterator(func2);\n}\nfunction iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n return new ChainedIterator(baseIterators, baseErrorHandler);\n}\nfunction iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n return new ZipIterator(iterators, mismatchMode);\n}\nvar LazyIterator = class {\n /**\n * Collect all remaining elements of a bounded stream into an array.\n * Obviously this will succeed only for small streams that fit in memory.\n * Useful for testing.\n *\n * @returns A Promise for an array of stream elements, which will resolve\n * when the stream is exhausted.\n */\n async toArray() {\n const result = [];\n let x = await this.next();\n while (!x.done) {\n result.push(x.value);\n x = await this.next();\n }\n return result;\n }\n /**\n * Collect all elements of this dataset into an array with prefetching 100\n * elements. This is useful for testing, because the prefetch changes the\n * order in which the Promises are resolved along the processing pipeline.\n * This may help expose bugs where results are dependent on the order of\n * Promise resolution rather than on the logical order of the stream (i.e.,\n * due to hidden mutable state).\n *\n * @returns A Promise for an array of stream elements, which will resolve\n * when the stream is exhausted.\n */\n async toArrayForTest() {\n const stream = this.prefetch(100);\n const result = [];\n let x = await stream.next();\n while (!x.done) {\n result.push(x.value);\n x = await stream.next();\n }\n return result;\n }\n /**\n * Draw items from the stream until it is exhausted.\n *\n * This can be useful when the stream has side effects but no output. In\n * that case, calling this function guarantees that the stream will be\n * fully processed.\n */\n async resolveFully() {\n let x = await this.next();\n while (!x.done) {\n x = await this.next();\n }\n }\n /**\n * Draw items from the stream until it is exhausted, or a predicate fails.\n *\n * This can be useful when the stream has side effects but no output. In\n * that case, calling this function guarantees that the stream will be\n * fully processed.\n */\n async resolveWhile(predicate) {\n let x = await this.next();\n let shouldContinue = predicate(x.value);\n while (!x.done && shouldContinue) {\n x = await this.next();\n shouldContinue = predicate(x.value);\n }\n }\n /**\n * Handles errors thrown on this stream using a provided handler function.\n *\n * @param handler A function that handles any `Error` thrown during a `next()`\n * call and returns true if the stream should continue (dropping the failed\n * call) or false if the stream should quietly terminate. If the handler\n * itself throws (or rethrows) an `Error`, that will be propagated.\n *\n * @returns A `LazyIterator` of elements passed through from upstream,\n * possibly filtering or terminating on upstream `next()` calls that\n * throw an `Error`.\n */\n handleErrors(handler) {\n return new ErrorHandlingLazyIterator(this, handler);\n }\n // TODO(soergel): Implement reduce() etc.\n /**\n * Filters this stream according to `predicate`.\n *\n * @param predicate A function mapping a stream element to a boolean or a\n * `Promise` for one.\n *\n * @returns A `LazyIterator` of elements for which the predicate was true.\n */\n filter(predicate) {\n return new FilterIterator(this, predicate);\n }\n /**\n * Maps this stream through a 1-to-1 transform.\n *\n * @param transform A function mapping a stream element to a transformed\n * element.\n *\n * @returns A `LazyIterator` of transformed elements.\n */\n map(transform5) {\n return new MapIterator(this, transform5);\n }\n /**\n * Maps this stream through an async 1-to-1 transform.\n *\n * @param transform A function mapping a stream element to a `Promise` for a\n * transformed stream element.\n *\n * @returns A `LazyIterator` of transformed elements.\n */\n mapAsync(transform5) {\n return new AsyncMapIterator(this, transform5);\n }\n /**\n * Maps this stream through a 1-to-1 transform, forcing serial execution.\n *\n * @param transform A function mapping a stream element to a transformed\n * element.\n *\n * @returns A `LazyIterator` of transformed elements.\n */\n serialMapAsync(transform5) {\n return new AsyncMapIterator(this, transform5).serial();\n }\n /**\n * Maps this stream through a 1-to-many transform.\n *\n * @param transform A function mapping a stream element to an array of\n * transformed elements.\n *\n * @returns A `DataStream` of transformed elements.\n */\n flatmap(transform5) {\n return new FlatmapIterator(this, transform5);\n }\n /**\n * Apply a function to every element of the stream.\n *\n * @param f A function to apply to each stream element.\n */\n async forEachAsync(f) {\n return this.map(f).resolveFully();\n }\n /**\n * Apply a function to every element of the stream, forcing serial execution.\n *\n * @param f A function to apply to each stream element. Should return 'true'\n * to indicate that the stream should continue, or 'false' to cause it to\n * terminate.\n */\n async serialForEach(f) {\n return this.serialMapAsync(f).resolveWhile((x) => x === true);\n }\n /**\n * Groups elements into batches, represented as arrays of elements.\n *\n * We can think of the elements of this iterator as 'rows' (even if they are\n * nested structures). By the same token, consecutive values for a given\n * key within the elements form a 'column'. This matches the usual sense of\n * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n *\n * Thus, \"Row-major\" means that the resulting batch is simply a collection of\n * rows: `[row1, row2, row3, ...]`. This is contrast to the column-major\n * form, which is needed for vectorized computation.\n *\n * @param batchSize The number of elements desired per batch.\n * @param smallLastBatch Whether to emit the final batch when it has fewer\n * than batchSize elements. Default true.\n * @returns A `LazyIterator` of batches of elements, represented as arrays\n * of the original element type.\n */\n rowMajorBatch(batchSize, smallLastBatch = true) {\n return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n }\n /**\n * Groups elements into batches, represented in column-major form.\n *\n * We can think of the elements of this iterator as 'rows' (even if they are\n * nested structures). By the same token, consecutive values for a given\n * key within the elements form a 'column'. This matches the usual sense of\n * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n *\n * Thus, \"column-major\" means that the resulting batch is a (potentially\n * nested) structure representing the columns. Each column entry, then,\n * contains a collection of the values found in that column for a range of\n * input elements. This representation allows for vectorized computation, in\n * contrast to the row-major form.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts). The result is a single object with the same nested structure,\n * where the leaves are arrays collecting the values of the inputs at that\n * location (or, optionally, the result of a custom function applied to those\n * arrays).\n *\n * @param batchSize The number of elements desired per batch.\n * @param smallLastBatch Whether to emit the final batch when it has fewer\n * than batchSize elements. Default true.\n * @param zipFn: (optional) A function that expects an array of elements at a\n * single node of the object tree, and returns a `DeepMapResult`. The\n * `DeepMapResult` either provides a result value for that node (i.e.,\n * representing the subtree), or indicates that the node should be processed\n * recursively. The default zipFn recurses as far as possible and places\n * arrays at the leaves.\n * @returns A `LazyIterator` of batches of elements, represented as an object\n * with collections at the leaves.\n */\n columnMajorBatch(batchSize, smallLastBatch = true, zipFn = zipToList) {\n const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n return rowBatches.map((x) => deepZip(x, zipFn));\n }\n /**\n * Concatenate this `LazyIterator` with another.\n *\n * @param iterator A `LazyIterator` to be concatenated onto this one.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n * raised during a `next()` call on the base stream. This function can\n * decide whether the error should be propagated, whether the error should\n * be ignored, or whether the base stream should be terminated.\n * @returns A `LazyIterator`.\n */\n concatenate(iterator, baseErrorHandler) {\n return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n }\n /**\n * Limits this stream to return at most `count` items.\n *\n * @param count The maximum number of items to provide from the stream. If\n * a negative or undefined value is given, the entire stream is returned\n * unaltered.\n */\n take(count2) {\n if (count2 < 0 || count2 == null) {\n return this;\n }\n return new TakeIterator(this, count2);\n }\n /**\n * Skips the first `count` items in this stream.\n *\n * @param count The number of items to skip. If a negative or undefined\n * value is given, the entire stream is returned unaltered.\n */\n skip(count2) {\n if (count2 < 0 || count2 == null) {\n return this;\n }\n return new SkipIterator(this, count2);\n }\n /**\n * Prefetch the first `bufferSize` items in this stream.\n *\n * Note this prefetches Promises, but makes no guarantees about when those\n * Promises resolve.\n *\n * @param bufferSize: An integer specifying the number of elements to be\n * prefetched.\n */\n prefetch(bufferSize) {\n return new PrefetchIterator(this, bufferSize);\n }\n // TODO(soergel): deep sharded shuffle, where supported\n /**\n * Randomly shuffles the elements of this stream.\n *\n * @param bufferSize: An integer specifying the number of elements from\n * this stream from which the new stream will sample.\n * @param seed: (Optional.) An integer specifying the random seed that\n * will be used to create the distribution.\n */\n shuffle(windowSize, seed) {\n return new ShuffleIterator(this, windowSize, seed);\n }\n /**\n * Force an iterator to execute serially: each next() call will await the\n * prior one, so that they cannot execute concurrently.\n */\n serial() {\n return new SerialIterator(this);\n }\n};\nvar ArrayIterator = class extends LazyIterator {\n constructor(items) {\n super();\n this.items = items;\n this.trav = 0;\n }\n summary() {\n return `Array of ${this.items.length} items`;\n }\n async next() {\n if (this.trav >= this.items.length) {\n return { value: null, done: true };\n }\n const item = this.items[this.trav];\n this.trav++;\n return { value: deepClone(item), done: false };\n }\n};\nvar FunctionCallIterator = class extends LazyIterator {\n constructor(nextFn) {\n super();\n this.nextFn = nextFn;\n }\n summary() {\n return `Function call`;\n }\n async next() {\n try {\n return this.nextFn();\n } catch (e) {\n e.message = `Error thrown while iterating through a dataset: ${e.message}`;\n throw e;\n }\n }\n};\nvar SerialIterator = class extends LazyIterator {\n constructor(upstream) {\n super();\n this.upstream = upstream;\n this.lastRead = Promise.resolve({ value: null, done: false });\n }\n summary() {\n return `${this.upstream.summary()} -> Serial`;\n }\n async next() {\n this.lastRead = this.lastRead.then(() => this.serialNext());\n return this.lastRead;\n }\n async serialNext() {\n return this.upstream.next();\n }\n};\nvar SkipIterator = class extends LazyIterator {\n constructor(upstream, maxCount) {\n super();\n this.upstream = upstream;\n this.maxCount = maxCount;\n this.count = 0;\n this.lastRead = Promise.resolve({ value: null, done: false });\n }\n summary() {\n return `${this.upstream.summary()} -> Skip`;\n }\n async next() {\n this.lastRead = this.lastRead.then(() => this.serialNext());\n return this.lastRead;\n }\n async serialNext() {\n while (this.count++ < this.maxCount) {\n const skipped = await this.upstream.next();\n if (skipped.done) {\n return skipped;\n }\n dispose(skipped.value);\n }\n return this.upstream.next();\n }\n};\nvar TakeIterator = class extends LazyIterator {\n constructor(upstream, maxCount) {\n super();\n this.upstream = upstream;\n this.maxCount = maxCount;\n this.count = 0;\n }\n summary() {\n return `${this.upstream.summary()} -> Take`;\n }\n async next() {\n if (this.count++ >= this.maxCount) {\n return { value: null, done: true };\n }\n return this.upstream.next();\n }\n};\nvar RowMajorBatchIterator = class extends LazyIterator {\n constructor(upstream, batchSize, enableSmallLastBatch = true) {\n super();\n this.upstream = upstream;\n this.batchSize = batchSize;\n this.enableSmallLastBatch = enableSmallLastBatch;\n this.lastRead = Promise.resolve({ value: null, done: false });\n }\n summary() {\n return `${this.upstream.summary()} -> RowMajorBatch`;\n }\n async next() {\n this.lastRead = this.lastRead.then(() => this.serialNext());\n return this.lastRead;\n }\n async serialNext() {\n const batch = [];\n while (batch.length < this.batchSize) {\n const item = await this.upstream.next();\n if (item.done) {\n if (this.enableSmallLastBatch && batch.length > 0) {\n return { value: batch, done: false };\n }\n return { value: null, done: true };\n }\n batch.push(item.value);\n }\n return { value: batch, done: false };\n }\n};\nvar FilterIterator = class extends LazyIterator {\n constructor(upstream, predicate) {\n super();\n this.upstream = upstream;\n this.predicate = predicate;\n this.lastRead = Promise.resolve({ value: null, done: false });\n }\n summary() {\n return `${this.upstream.summary()} -> Filter`;\n }\n async next() {\n this.lastRead = this.lastRead.then(() => this.serialNext());\n return this.lastRead;\n }\n async serialNext() {\n while (true) {\n const item = await this.upstream.next();\n if (item.done || this.predicate(item.value)) {\n return item;\n }\n dispose(item.value);\n }\n }\n};\nvar MapIterator = class extends LazyIterator {\n constructor(upstream, transform5) {\n super();\n this.upstream = upstream;\n this.transform = transform5;\n }\n summary() {\n return `${this.upstream.summary()} -> Map`;\n }\n async next() {\n const item = await this.upstream.next();\n if (item.done) {\n return { value: null, done: true };\n }\n const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n const mapped = this.transform(item.value);\n const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);\n for (const t of inputTensors) {\n if (!tensor_util_exports.isTensorInList(t, outputTensors)) {\n t.dispose();\n }\n }\n return { value: mapped, done: false };\n }\n};\nvar ErrorHandlingLazyIterator = class extends LazyIterator {\n constructor(upstream, handler) {\n super();\n this.upstream = upstream;\n this.handler = handler;\n this.count = 0;\n this.lastRead = Promise.resolve({ value: null, done: false });\n }\n summary() {\n return `${this.upstream.summary()} -> handleErrors`;\n }\n async next() {\n this.lastRead = this.lastRead.then(() => this.serialNext());\n return this.lastRead;\n }\n async serialNext() {\n while (true) {\n try {\n return await this.upstream.next();\n } catch (e) {\n if (!this.handler(e)) {\n return { value: null, done: true };\n }\n }\n }\n }\n};\nvar AsyncMapIterator = class extends LazyIterator {\n constructor(upstream, transform5) {\n super();\n this.upstream = upstream;\n this.transform = transform5;\n }\n summary() {\n return `${this.upstream.summary()} -> AsyncMap`;\n }\n async next() {\n const item = await this.upstream.next();\n if (item.done) {\n return { value: null, done: true };\n }\n const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n const mapped = await this.transform(item.value);\n const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);\n for (const t of inputTensors) {\n if (!tensor_util_exports.isTensorInList(t, outputTensors)) {\n t.dispose();\n }\n }\n return { value: mapped, done: false };\n }\n};\nvar OneToManyIterator = class extends LazyIterator {\n constructor() {\n super();\n this.outputQueue = new GrowingRingBuffer();\n this.lastRead = Promise.resolve({ value: null, done: false });\n }\n async next() {\n this.lastRead = this.lastRead.then(() => this.serialNext());\n return this.lastRead;\n }\n async serialNext() {\n while (this.outputQueue.length() === 0) {\n if (!await this.pump()) {\n return { value: null, done: true };\n }\n }\n return { value: this.outputQueue.shift(), done: false };\n }\n};\nvar FlatmapIterator = class extends OneToManyIterator {\n constructor(upstream, transform5) {\n super();\n this.upstream = upstream;\n this.transform = transform5;\n }\n summary() {\n return `${this.upstream.summary()} -> Flatmap`;\n }\n async pump() {\n const item = await this.upstream.next();\n if (item.done) {\n return false;\n }\n const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n const mappedArray = this.transform(item.value);\n const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray);\n this.outputQueue.pushAll(mappedArray);\n for (const t of inputTensors) {\n if (!tensor_util_exports.isTensorInList(t, outputTensors)) {\n t.dispose();\n }\n }\n return true;\n }\n};\nvar ChainedIterator = class extends LazyIterator {\n constructor(iterators, baseErrorHandler) {\n super();\n this.baseErrorHandler = baseErrorHandler;\n this.lastRead = null;\n this.iterator = null;\n this.moreIterators = iterators;\n }\n summary() {\n const upstreamSummaries = \"TODO: fill in upstream of chained summaries\";\n return `${upstreamSummaries} -> Chained`;\n }\n async next() {\n this.lastRead = this.readFromChain(this.lastRead);\n return this.lastRead;\n }\n async readFromChain(lastRead) {\n await lastRead;\n if (this.iterator == null) {\n const iteratorResult = await this.moreIterators.next();\n if (iteratorResult.done) {\n return { value: null, done: true };\n }\n this.iterator = iteratorResult.value;\n if (this.baseErrorHandler != null) {\n this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n }\n }\n const itemResult = await this.iterator.next();\n if (itemResult.done) {\n this.iterator = null;\n return this.readFromChain(lastRead);\n }\n return itemResult;\n }\n};\nvar ZipMismatchMode;\n(function(ZipMismatchMode2) {\n ZipMismatchMode2[ZipMismatchMode2[\"FAIL\"] = 0] = \"FAIL\";\n ZipMismatchMode2[ZipMismatchMode2[\"SHORTEST\"] = 1] = \"SHORTEST\";\n ZipMismatchMode2[ZipMismatchMode2[\"LONGEST\"] = 2] = \"LONGEST\";\n})(ZipMismatchMode || (ZipMismatchMode = {}));\nvar ZipIterator = class extends LazyIterator {\n constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n super();\n this.iterators = iterators;\n this.mismatchMode = mismatchMode;\n this.count = 0;\n this.currentPromise = null;\n }\n summary() {\n const upstreamSummaries = \"TODO: fill in upstream of zip summaries\";\n return `{${upstreamSummaries}} -> Zip`;\n }\n async nextState(afterState) {\n await afterState;\n let numIterators = 0;\n let iteratorsDone = 0;\n function getNext(container) {\n if (container instanceof LazyIterator) {\n const result = container.next();\n return {\n value: result.then((x) => {\n numIterators++;\n if (x.done) {\n iteratorsDone++;\n }\n return x.value;\n }),\n recurse: false\n };\n } else {\n return { value: null, recurse: true };\n }\n }\n const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n if (numIterators === iteratorsDone) {\n return { value: null, done: true };\n }\n if (iteratorsDone > 0) {\n switch (this.mismatchMode) {\n case ZipMismatchMode.FAIL:\n throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);\n case ZipMismatchMode.SHORTEST:\n return { value: null, done: true };\n case ZipMismatchMode.LONGEST:\n default:\n }\n }\n this.count++;\n return { value: mapped, done: false };\n }\n async next() {\n this.currentPromise = this.nextState(this.currentPromise);\n return this.currentPromise;\n }\n};\nvar PrefetchIterator = class extends LazyIterator {\n constructor(upstream, bufferSize) {\n super();\n this.upstream = upstream;\n this.bufferSize = bufferSize;\n this.buffer = new RingBuffer(bufferSize);\n }\n summary() {\n return `${this.upstream.summary()} -> Prefetch`;\n }\n /**\n * Refill the prefetch buffer. Returns only after the buffer is full, or\n * the upstream source is exhausted.\n */\n refill() {\n while (!this.buffer.isFull()) {\n const v = this.upstream.next();\n this.buffer.push(v);\n }\n }\n next() {\n this.refill();\n return this.buffer.shift();\n }\n};\nvar ShuffleIterator = class extends PrefetchIterator {\n constructor(upstream, windowSize, seed) {\n super(upstream, windowSize);\n this.upstream = upstream;\n this.windowSize = windowSize;\n this.upstreamExhausted = false;\n this.random = seedrandom2.alea(seed || util_exports.now().toString());\n this.lastRead = Promise.resolve({ value: null, done: false });\n }\n async next() {\n this.lastRead = this.lastRead.then(() => this.serialNext());\n return this.lastRead;\n }\n randomInt(max6) {\n return Math.floor(this.random() * max6);\n }\n chooseIndex() {\n return this.randomInt(this.buffer.length());\n }\n async serialNext() {\n if (!this.upstreamExhausted) {\n this.refill();\n }\n while (!this.buffer.isEmpty()) {\n const chosenIndex = this.chooseIndex();\n const result = await this.buffer.shuffleExcise(chosenIndex);\n if (result.done) {\n this.upstreamExhausted = true;\n } else {\n this.refill();\n return result;\n }\n }\n return { value: null, done: true };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/dataset.js\nvar Dataset = class {\n constructor() {\n this.size = null;\n }\n // TODO(soergel): Make Datasets report whether repeated iterator() calls\n // produce the same result (e.g., reading from a file) or different results\n // (e.g., from the webcam). Currently we don't make this distinction but it\n // could be important for the user to know.\n // abstract isDeterministic(): boolean;\n /**\n * Groups elements into batches.\n *\n * It is assumed that each of the incoming dataset elements has the same\n * structure -- i.e. the same set of keys at each location in an object\n * hierarchy. For each key, the resulting `Dataset` provides a batched\n * element collecting all of the incoming values for that key.\n *\n * * Incoming primitives are grouped into a 1-D Tensor.\n * * Incoming Tensors are grouped into a new Tensor where the 0th axis is\n * the batch dimension.\n * * Incoming arrays are converted to Tensor and then batched.\n * * A nested array is interpreted as an n-D Tensor, so the batched result\n * has n+1 dimensions.\n * * An array that cannot be converted to Tensor produces an error.\n *\n * If an array should not be batched as a unit, it should first be converted\n * to an object with integer keys.\n *\n * Here are a few examples:\n *\n * Batch a dataset of numbers:\n * ```js\n * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);\n * await a.forEachAsync(e => e.print());\n * ```\n *\n * Batch a dataset of arrays:\n * ```js\n * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);\n * await b.forEachAsync(e => e.print());\n * ```\n *\n * Batch a dataset of objects:\n * ```js\n * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},\n * {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},\n * {a: 8, b: 18}]).batch(4);\n * await c.forEachAsync(e => {\n * console.log('{');\n * for(var key in e) {\n * console.log(key+':');\n * e[key].print();\n * }\n * console.log('}');\n * })\n * ```\n *\n * @param batchSize The number of elements desired per batch.\n * @param smallLastBatch Whether to emit the final batch when it has fewer\n * than batchSize elements. Default true.\n * @returns A `Dataset`, from which a stream of batches can be obtained.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n batch(batchSize, smallLastBatch = true) {\n const base = this;\n util_exports.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n ${batchSize}`);\n let size;\n if (this.size === Infinity || this.size == null) {\n size = this.size;\n } else if (smallLastBatch) {\n size = Math.ceil(this.size / batchSize);\n } else {\n size = Math.floor(this.size / batchSize);\n }\n return datasetFromIteratorFn(async () => {\n return (await base.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n }, size);\n }\n /**\n * Concatenates this `Dataset` with another.\n *\n * ```js\n * const a = tf.data.array([1, 2, 3]);\n * const b = tf.data.array([4, 5, 6]);\n * const c = a.concatenate(b);\n * await c.forEachAsync(e => console.log(e));\n * ```\n *\n * @param dataset A `Dataset` to be concatenated onto this one.\n * @returns A `Dataset`.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n concatenate(dataset) {\n const base = this;\n let size;\n if (this.size === Infinity || dataset.size === Infinity) {\n size = Infinity;\n } else if (this.size != null && dataset.size != null) {\n size = this.size + dataset.size;\n } else {\n size = null;\n }\n return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n }\n /**\n * Filters this dataset according to `predicate`.\n *\n * ```js\n * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n * .filter(x => x%2 === 0);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * @param predicate A function mapping a dataset element to a boolean or a\n * `Promise` for one.\n *\n * @returns A `Dataset` of elements for which the predicate was true.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n filter(predicate) {\n const base = this;\n let size;\n if (this.size === Infinity) {\n size = Infinity;\n } else {\n size = null;\n }\n return datasetFromIteratorFn(async () => {\n return (await base.iterator()).filter((x) => tidy(() => predicate(x)));\n }, size);\n }\n /**\n * Apply a function to every element of the dataset.\n *\n * After the function is applied to a dataset element, any Tensors contained\n * within that element are disposed.\n *\n * ```js\n * const a = tf.data.array([1, 2, 3]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * @param f A function to apply to each dataset element.\n * @returns A `Promise` that resolves after all elements have been processed.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n async forEachAsync(f) {\n return (await this.iterator()).forEachAsync(f);\n }\n /**\n * Maps this dataset through a 1-to-1 transform.\n *\n * ```js\n * const a = tf.data.array([1, 2, 3]).map(x => x*x);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * @param transform A function mapping a dataset element to a transformed\n * dataset element.\n *\n * @returns A `Dataset` of transformed elements.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n map(transform5) {\n const base = this;\n return datasetFromIteratorFn(async () => {\n return (await base.iterator()).map((x) => tidy(() => transform5(x)));\n }, this.size);\n }\n /**\n * Maps this dataset through an async 1-to-1 transform.\n *\n * ```js\n * const a =\n * tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){\n * setTimeout(() => {\n * resolve(x * x);\n * }, Math.random()*1000 + 500);\n * }));\n * console.log(await a.toArray());\n * ```\n *\n * @param transform A function mapping a dataset element to a `Promise` for a\n * transformed dataset element. This transform is responsible for disposing\n * any intermediate `Tensor`s, i.e. by wrapping its computation in\n * `tf.tidy()`; that cannot be automated here (as it is in the synchronous\n * `map()` case).\n *\n * @returns A `Dataset` of transformed elements.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n mapAsync(transform5) {\n const base = this;\n return datasetFromIteratorFn(async () => {\n return (await base.iterator()).mapAsync(transform5);\n }, this.size);\n }\n /**\n * Creates a `Dataset` that prefetches elements from this dataset.\n *\n * @param bufferSize: An integer specifying the number of elements to be\n * prefetched.\n * @returns A `Dataset`.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n prefetch(bufferSize) {\n if (bufferSize == null) {\n throw new RangeError(\"`Dataset.prefetch()` requires bufferSize to be specified.\");\n }\n const base = this;\n return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n }\n /**\n * Repeats this dataset `count` times.\n *\n * NOTE: If this dataset is a function of global state (e.g. a random number\n * generator), then different repetitions may produce different elements.\n *\n * ```js\n * const a = tf.data.array([1, 2, 3]).repeat(3);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * @param count: (Optional) An integer, representing the number of times\n * the dataset should be repeated. The default behavior (if `count` is\n * `undefined` or negative) is for the dataset be repeated indefinitely.\n * @returns A `Dataset`.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n repeat(count2) {\n const base = this;\n let size;\n if (this.size != null && count2 > 0) {\n size = this.size * count2;\n } else if (count2 === 0) {\n size = 0;\n } else if (this.size != null && (count2 === void 0 || count2 < 0)) {\n size = Infinity;\n } else {\n size = null;\n }\n return datasetFromIteratorFn(async () => {\n const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n return iteratorFromConcatenated(iteratorIterator.take(count2));\n }, size);\n }\n /**\n * Creates a `Dataset` that skips `count` initial elements from this dataset.\n *\n * ```js\n * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * @param count: The number of elements of this dataset that should be skipped\n * to form the new dataset. If `count` is greater than the size of this\n * dataset, the new dataset will contain no elements. If `count`\n * is `undefined` or negative, skips the entire dataset.\n *\n * @returns A `Dataset`.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n skip(count2) {\n const base = this;\n let size;\n if (this.size != null && count2 >= 0 && this.size >= count2) {\n size = this.size - count2;\n } else if (this.size != null && (this.size < count2 || count2 === void 0 || count2 < 0)) {\n size = 0;\n } else {\n size = null;\n }\n return datasetFromIteratorFn(async () => (await base.iterator()).skip(count2), size);\n }\n /**\n * Pseudorandomly shuffles the elements of this dataset. This is done in a\n * streaming manner, by sampling from a given number of prefetched elements.\n *\n * ```js\n * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * @param bufferSize: An integer specifying the number of elements from this\n * dataset from which the new dataset will sample.\n * @param seed: (Optional) An integer specifying the random seed that will\n * be used to create the distribution.\n * @param reshuffleEachIteration: (Optional) A boolean, which if true\n * indicates that the dataset should be pseudorandomly reshuffled each time\n * it is iterated over. If false, elements will be returned in the same\n * shuffled order on each iteration. (Defaults to `true`.)\n * @returns A `Dataset`.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n if (bufferSize == null || bufferSize < 0) {\n if (this.size == null) {\n throw new RangeError(\"`Dataset.shuffle()` requires bufferSize to be specified.\");\n } else {\n throw 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)`);\n }\n }\n const base = this;\n const random = seedrandom3.alea(seed || util_exports.now().toString());\n return datasetFromIteratorFn(async () => {\n let seed2 = random.int32();\n if (reshuffleEachIteration) {\n seed2 += random.int32();\n }\n return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n }, this.size);\n }\n /**\n * Creates a `Dataset` with at most `count` initial elements from this\n * dataset.\n *\n * ```js\n * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * @param count: The number of elements of this dataset that should be taken\n * to form the new dataset. If `count` is `undefined` or negative, or if\n * `count` is greater than the size of this dataset, the new dataset will\n * contain all elements of this dataset.\n * @returns A `Dataset`.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n take(count2) {\n const base = this;\n let size;\n if (this.size != null && this.size > count2) {\n size = count2;\n } else if (this.size != null && this.size <= count2) {\n size = this.size;\n } else {\n size = null;\n }\n return datasetFromIteratorFn(async () => (await base.iterator()).take(count2), size);\n }\n /**\n * Collect all elements of this dataset into an array.\n *\n * Obviously this will succeed only for small datasets that fit in memory.\n * Useful for testing and generally should be avoided if possible.\n *\n * ```js\n * const a = tf.data.array([1, 2, 3, 4, 5, 6]);\n * console.log(await a.toArray());\n * ```\n *\n * @returns A Promise for an array of elements, which will resolve\n * when a new stream has been obtained and fully consumed.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n async toArray() {\n if (this.size === Infinity) {\n throw new Error(\"Can not convert infinite data stream to array.\");\n }\n return (await this.iterator()).toArray();\n }\n /**\n * Collect all elements of this dataset into an array with prefetching 100\n * elements. This is useful for testing, because the prefetch changes the\n * order in which the Promises are resolved along the processing pipeline.\n * This may help expose bugs where results are dependent on the order of\n * Promise resolution rather than on the logical order of the stream (i.e.,\n * due to hidden mutable state).\n *\n * @returns A Promise for an array of elements, which will resolve\n * when a new stream has been obtained and fully consumed.\n */\n async toArrayForTest() {\n if (this.size === Infinity) {\n throw new Error(\"Can not convert infinite data stream to array.\");\n }\n return (await this.iterator()).toArrayForTest();\n }\n};\nDataset.MAX_BUFFER_SIZE = 1e4;\nfunction datasetFromIteratorFn(iteratorFn, size = null) {\n return new class extends Dataset {\n constructor() {\n super(...arguments);\n this.size = size;\n }\n /*\n * Provide a new stream of elements. Note this will also start new streams\n * from any underlying `Dataset`s.\n */\n async iterator() {\n return iteratorFn();\n }\n }();\n}\nfunction array(items) {\n return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\nfunction zip(datasets) {\n if (!isIterable2(datasets)) {\n throw new Error(\"The argument to zip() must be an object or array.\");\n }\n let size;\n if (Array.isArray(datasets)) {\n for (let i = 0; i < datasets.length; i++) {\n size = size == null ? datasets[i].size : Math.min(size, datasets[i].size);\n }\n } else if (datasets instanceof Object) {\n for (const ds in datasets) {\n size = size == null ? datasets[ds].size : Math.min(size, datasets[ds].size);\n }\n }\n return datasetFromIteratorFn(async () => {\n const streams = await deepMapAndAwaitAll(datasets, (d) => {\n if (d instanceof Dataset) {\n return { value: d.iterator(), recurse: false };\n } else if (isIterable2(d)) {\n return { value: null, recurse: true };\n } else {\n throw new Error(\"Leaves of the structure passed to zip() must be Datasets, not primitives.\");\n }\n });\n return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n }, size);\n}\nfunction deepBatchConcat(rows) {\n if (rows === null) {\n return null;\n }\n const exampleRow = rows[0];\n if (canTensorify(exampleRow)) {\n const value = batchConcat(rows);\n return { value, recurse: false };\n }\n return { value: null, recurse: true };\n}\nfunction batchConcat(arrays) {\n if (arrays.length === 0) {\n throw new Error(\"Can't make a batch of zero elements.\");\n }\n if (arrays[0] instanceof Tensor) {\n return stack(arrays);\n } else {\n return tensor(arrays);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/datasets/text_line_dataset.js\nvar TextLineDataset = class extends Dataset {\n /**\n * Create a `TextLineDataset`.\n *\n * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n */\n constructor(input2) {\n super();\n this.input = input2;\n }\n async iterator() {\n const inputIterator = await this.input.iterator();\n const utf8Iterator = inputIterator.decodeUTF8();\n const lineIterator = utf8Iterator.split(\"\\n\").map((line) => {\n if (line.endsWith(\"\\r\")) {\n line = line.slice(0, -1);\n }\n return line;\n });\n return lineIterator;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js\nvar CODE_QUOTE = '\"';\nvar STATE_OUT = Symbol(\"out\");\nvar STATE_FIELD = Symbol(\"field\");\nvar STATE_QUOTE = Symbol(\"quote\");\nvar STATE_QUOTE_AFTER_QUOTE = Symbol(\"quoteafterquote\");\nvar STATE_WITHIN_QUOTE_IN_QUOTE = Symbol(\"quoteinquote\");\nvar CSVDataset = class extends Dataset {\n /**\n * Returns column names of the csv dataset. If `configuredColumnsOnly` is\n * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is\n * false and `columnNames` is provided, `columnNames`. If\n * `configuredColumnsOnly` is false and `columnNames` is not provided, return\n * all column names parsed from the csv file. For example usage please go to\n * `tf.data.csv`.\n *\n * @doc {heading: 'Data', subheading: 'Classes'}\n */\n async columnNames() {\n if (!this.columnNamesValidated) {\n await this.setColumnNames();\n }\n return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;\n }\n /* 1) If `columnNames` is provided as string[], use this string[] as output\n * keys in corresponding order. The length must match the number of inferred\n * columns if `hasHeader` is true .\n * 2) If `columnNames` is not provided, parse header line as `columnNames` if\n * hasHeader is true. If `hasHeader` is false, throw an error.\n * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must\n * exist in parsed `columnNames`.\n */\n async setColumnNames() {\n const columnNamesFromFile = await this.maybeReadHeaderLine();\n if (!this.fullColumnNames && !columnNamesFromFile) {\n throw new Error(\"Column names must be provided if there is no header line.\");\n } else if (this.fullColumnNames && columnNamesFromFile) {\n util_exports.assert(columnNamesFromFile.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 (\" + columnNamesFromFile.length.toString() + \").\");\n }\n if (!this.fullColumnNames) {\n this.fullColumnNames = columnNamesFromFile;\n }\n const counts = this.fullColumnNames.reduce((countAcc, name) => {\n countAcc[name] = countAcc[name] + 1 || 1;\n return countAcc;\n }, {});\n const duplicateNames = Object.keys(counts).filter((name) => counts[name] > 1);\n util_exports.assert(duplicateNames.length === 0, () => \"Duplicate column names found: \" + duplicateNames.toString());\n if (this.columnConfigs) {\n for (const key of Object.keys(this.columnConfigs)) {\n const index = this.fullColumnNames.indexOf(key);\n if (index === -1) {\n throw new Error('The key \"' + key + '\" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + \").\");\n }\n }\n }\n this.columnNamesValidated = true;\n }\n async maybeReadHeaderLine() {\n if (this.hasHeader) {\n const iter = await this.base.iterator();\n const firstElement = await iter.next();\n if (firstElement.done) {\n throw new Error(\"No data was found for CSV parsing.\");\n }\n const firstLine = firstElement.value;\n const headers = this.parseRow(firstLine, false);\n return headers;\n } else {\n return null;\n }\n }\n /**\n * Create a `CSVDataset`.\n *\n * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n * @param csvConfig (Optional) A CSVConfig object that contains configurations\n * of reading and decoding from CSV file(s).\n *\n * hasHeader: (Optional) A boolean value that indicates whether the first\n * row of provided CSV file is a header line with column names, and should\n * not be included in the data. Defaults to `true`.\n *\n * columnNames: (Optional) A list of strings that corresponds to\n * the CSV column names, in order. If provided, it ignores the column\n * names inferred from the header row. If not provided, infers the column\n * names from the first row of the records. If hasHeader is false and\n * columnNames is not provided, this method throws an error.\n *\n * columnConfigs: (Optional) A dictionary whose key is column names, value\n * is an object stating if this column is required, column's data type,\n * default value, and if this column is label. If provided, keys must\n * correspond to names provided in columnNames or inferred from the file\n * header lines. If isLabel is true any column, returns an array of two\n * items: the first item is a dict of features key/value pairs, the second\n * item is a dict of labels key/value pairs. If no feature is marked as\n * label, returns a dict of features only.\n *\n * configuredColumnsOnly (Optional) If true, only columns provided in\n * columnConfigs will be parsed and provided during iteration.\n *\n * delimiter (Optional) The string used to parse each line of the input\n * file. Defaults to `,`.\n */\n constructor(input2, csvConfig) {\n super();\n this.input = input2;\n this.hasHeader = true;\n this.fullColumnNames = null;\n this.columnNamesValidated = false;\n this.columnConfigs = null;\n this.configuredColumnsOnly = false;\n this.delimiter = \",\";\n this.delimWhitespace = false;\n this.base = new TextLineDataset(input2);\n if (!csvConfig) {\n csvConfig = {};\n }\n this.hasHeader = csvConfig.hasHeader === false ? false : true;\n this.fullColumnNames = csvConfig.columnNames;\n this.columnConfigs = csvConfig.columnConfigs;\n this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n if (csvConfig.delimWhitespace) {\n util_exports.assert(csvConfig.delimiter == null, () => \"Delimiter should not be provided when delimWhitespace is true.\");\n this.delimWhitespace = true;\n this.delimiter = \" \";\n } else {\n this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : \",\";\n }\n }\n async iterator() {\n if (!this.columnNamesValidated) {\n await this.setColumnNames();\n }\n let lines = await this.base.iterator();\n if (this.hasHeader) {\n lines = lines.skip(1);\n }\n return lines.map((x) => this.makeDataElement(x));\n }\n makeDataElement(line) {\n const values = this.parseRow(line);\n const features = {};\n const labels = {};\n for (let i = 0; i < this.fullColumnNames.length; i++) {\n const key = this.fullColumnNames[i];\n const config = this.columnConfigs ? this.columnConfigs[key] : null;\n if (this.configuredColumnsOnly && !config) {\n continue;\n } else {\n const value = values[i];\n let parsedValue = null;\n if (value === \"\") {\n if (config && config.default !== void 0) {\n parsedValue = config.default;\n } else if (config && (config.required || config.isLabel)) {\n throw new Error(`Required column ${key} is empty in this line: ${line}`);\n } else {\n parsedValue = void 0;\n }\n } else {\n const valueAsNum = Number(value);\n if (isNaN(valueAsNum)) {\n if (config && config.dtype === \"bool\") {\n parsedValue = this.getBoolean(value);\n } else {\n parsedValue = value;\n }\n } else if (!config || !config.dtype) {\n parsedValue = valueAsNum;\n } else {\n switch (config.dtype) {\n case \"float32\":\n parsedValue = valueAsNum;\n break;\n case \"int32\":\n parsedValue = Math.floor(valueAsNum);\n break;\n case \"bool\":\n parsedValue = this.getBoolean(value);\n break;\n default:\n parsedValue = valueAsNum;\n }\n }\n }\n config && config.isLabel ? labels[key] = parsedValue : features[key] = parsedValue;\n }\n }\n if (Object.keys(labels).length === 0) {\n return features;\n } else {\n return { xs: features, ys: labels };\n }\n }\n getBoolean(value) {\n if (value === \"1\" || value.toLowerCase() === \"true\") {\n return 1;\n } else {\n return 0;\n }\n }\n // adapted from https://beta.observablehq.com/@mbostock/streaming-csv\n parseRow(line, validateElementCount = true) {\n const result = [];\n let readOffset = 0;\n const readLength = line.length;\n let currentState = STATE_OUT;\n for (let i = 0; i < readLength; i++) {\n switch (currentState) {\n case STATE_OUT:\n switch (line.charAt(i)) {\n case CODE_QUOTE:\n readOffset = i + 1;\n currentState = STATE_QUOTE;\n break;\n case this.delimiter:\n readOffset = i + 1;\n if (this.delimiter === \" \" && this.delimWhitespace) {\n break;\n }\n result.push(\"\");\n currentState = STATE_OUT;\n break;\n default:\n currentState = STATE_FIELD;\n readOffset = i;\n break;\n }\n break;\n case STATE_FIELD:\n switch (line.charAt(i)) {\n case this.delimiter:\n result.push(line.substring(readOffset, i));\n currentState = STATE_OUT;\n readOffset = i + 1;\n break;\n default:\n }\n break;\n case STATE_QUOTE:\n switch (line.charAt(i)) {\n case CODE_QUOTE:\n currentState = STATE_QUOTE_AFTER_QUOTE;\n break;\n default:\n }\n break;\n case STATE_QUOTE_AFTER_QUOTE:\n switch (line.charAt(i)) {\n case this.delimiter:\n result.push(line.substring(readOffset, i - 1));\n currentState = STATE_OUT;\n readOffset = i + 1;\n break;\n case CODE_QUOTE:\n currentState = STATE_QUOTE;\n break;\n default:\n currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n break;\n }\n break;\n case STATE_WITHIN_QUOTE_IN_QUOTE:\n switch (line.charAt(i)) {\n case CODE_QUOTE:\n currentState = STATE_QUOTE;\n break;\n default:\n }\n break;\n default:\n }\n }\n if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n result.push(line.substring(readOffset, readLength - 1));\n } else {\n result.push(line.substring(readOffset));\n }\n if (validateElementCount && result.length !== this.fullColumnNames.length) {\n throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n }\n return result;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/microphone_iterator.js\nvar MicrophoneIterator = class _MicrophoneIterator extends LazyIterator {\n constructor(microphoneConfig) {\n super();\n this.microphoneConfig = microphoneConfig;\n this.isClosed = false;\n this.fftSize = microphoneConfig.fftSize || 1024;\n const fftSizeLog2 = Math.log2(this.fftSize);\n if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 || !Number.isInteger(fftSizeLog2)) {\n throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);\n }\n this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n this.sampleRateHz = microphoneConfig.sampleRateHz;\n this.columnTruncateLength = microphoneConfig.columnTruncateLength || this.fftSize;\n this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n this.includeSpectrogram = microphoneConfig.includeSpectrogram === false ? false : true;\n this.includeWaveform = microphoneConfig.includeWaveform === true ? true : false;\n if (!this.includeSpectrogram && !this.includeWaveform) {\n throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\");\n }\n }\n summary() {\n return `microphone`;\n }\n // Construct a MicrophoneIterator and start the audio stream.\n static async create(microphoneConfig = {}) {\n if (!env().get(\"IS_BROWSER\")) {\n throw new Error(\"microphone API is only supported in browser environment.\");\n }\n const microphoneIterator = new _MicrophoneIterator(microphoneConfig);\n await microphoneIterator.start();\n return microphoneIterator;\n }\n // Start the audio stream and FFT.\n async start() {\n try {\n this.stream = await navigator.mediaDevices.getUserMedia({\n audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints,\n video: false\n });\n } catch (e) {\n throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n }\n if (!this.stream) {\n throw new Error(\"Could not obtain audio from microphone.\");\n }\n const ctxConstructor = (\n // tslint:disable-next-line:no-any\n window.AudioContext || window.webkitAudioContext\n );\n this.audioContext = new ctxConstructor();\n if (!this.sampleRateHz) {\n this.sampleRateHz = this.audioContext.sampleRate;\n } else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);\n }\n const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n this.analyser = this.audioContext.createAnalyser();\n this.analyser.fftSize = this.fftSize * 2;\n this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n streamSource.connect(this.analyser);\n this.freqData = new Float32Array(this.fftSize);\n this.timeData = new Float32Array(this.fftSize);\n return;\n }\n async next() {\n if (this.isClosed) {\n return { value: null, done: true };\n }\n let spectrogramTensor;\n let waveformTensor;\n const audioDataQueue = await this.getAudioData();\n if (this.includeSpectrogram) {\n const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n }\n if (this.includeWaveform) {\n const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n }\n return {\n value: { \"spectrogram\": spectrogramTensor, \"waveform\": waveformTensor },\n done: false\n };\n }\n // Capture one result from the audio stream, and extract the value from\n // iterator.next() result.\n async capture() {\n return (await this.next()).value;\n }\n async getAudioData() {\n const freqDataQueue = [];\n const timeDataQueue = [];\n let currentFrames = 0;\n return new Promise((resolve) => {\n const intervalID = setInterval(() => {\n if (this.includeSpectrogram) {\n this.analyser.getFloatFrequencyData(this.freqData);\n if (this.freqData[0] === -Infinity) {\n resolve({ freqDataQueue, timeDataQueue });\n }\n freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n }\n if (this.includeWaveform) {\n this.analyser.getFloatTimeDomainData(this.timeData);\n timeDataQueue.push(this.timeData.slice());\n }\n if (++currentFrames === this.numFrames) {\n clearInterval(intervalID);\n resolve({ freqDataQueue, timeDataQueue });\n }\n }, this.fftSize / this.sampleRateHz * 1e3);\n });\n }\n // Stop the audio stream and pause the iterator.\n stop() {\n if (!this.isClosed) {\n this.isClosed = true;\n this.analyser.disconnect();\n this.audioContext.close();\n if (this.stream != null && this.stream.getTracks().length > 0) {\n this.stream.getTracks()[0].stop();\n }\n }\n }\n // Override toArray() function to prevent collecting.\n toArray() {\n throw new Error(\"Can not convert infinite audio stream to array.\");\n }\n // Return audio sampling rate in Hz\n getSampleRate() {\n return this.sampleRateHz;\n }\n flattenQueue(queue) {\n const frameSize = queue[0].length;\n const freqData = new Float32Array(queue.length * frameSize);\n queue.forEach((data, i) => freqData.set(data, i * frameSize));\n return freqData;\n }\n getTensorFromAudioDataArray(freqData, shape) {\n const vals = new Float32Array(util_exports.sizeFromShape(shape));\n vals.set(freqData, vals.length - freqData.length);\n return tensor(vals, shape);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/webcam_iterator.js\nvar WebcamIterator = class _WebcamIterator extends LazyIterator {\n constructor(webcamVideoElement, webcamConfig) {\n super();\n this.webcamVideoElement = webcamVideoElement;\n this.webcamConfig = webcamConfig;\n this.isClosed = true;\n this.resize = false;\n if (this.needToResize()) {\n this.resize = true;\n this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n this.cropBoxInd = tensor1d([0], \"int32\");\n if (this.webcamConfig.centerCrop) {\n const widthCroppingRatio = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width;\n const heightCroppingRatio = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height;\n const widthCropStart = (1 - widthCroppingRatio) / 2;\n const heightCropStart = (1 - heightCroppingRatio) / 2;\n const widthCropEnd = widthCropStart + widthCroppingRatio;\n const heightCropEnd = heightCroppingRatio + heightCropStart;\n this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n } else {\n this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n }\n }\n }\n summary() {\n return `webcam`;\n }\n // Construct a WebcamIterator and start it's video stream.\n static async create(webcamVideoElement, webcamConfig = {}) {\n if (!env().get(\"IS_BROWSER\")) {\n throw new Error(\"tf.data.webcam is only supported in browser environment.\");\n }\n if (!webcamVideoElement) {\n webcamVideoElement = document.createElement(\"video\");\n if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");\n }\n webcamVideoElement.width = webcamConfig.resizeWidth;\n webcamVideoElement.height = webcamConfig.resizeHeight;\n }\n const webcamIterator = new _WebcamIterator(webcamVideoElement, webcamConfig);\n await webcamIterator.start();\n return webcamIterator;\n }\n // Async function to start video stream.\n async start() {\n if (this.webcamConfig.facingMode) {\n util_exports.assert(this.webcamConfig.facingMode === \"user\" || this.webcamConfig.facingMode === \"environment\", () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia({\n video: {\n deviceId: this.webcamConfig.deviceId,\n facingMode: this.webcamConfig.facingMode ? this.webcamConfig.facingMode : \"user\",\n width: this.webcamVideoElement.width,\n height: this.webcamVideoElement.height\n }\n });\n } catch (e) {\n e.message = `Error thrown while initializing video stream: ${e.message}`;\n throw e;\n }\n if (!this.stream) {\n throw new Error(\"Could not obtain video from webcam.\");\n }\n try {\n this.webcamVideoElement.srcObject = this.stream;\n } catch (error) {\n console.log(error);\n this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n }\n this.webcamVideoElement.play();\n this.isClosed = false;\n return new Promise((resolve) => {\n this.webcamVideoElement.onloadedmetadata = () => {\n resolve();\n };\n });\n }\n async next() {\n if (this.isClosed) {\n return { value: null, done: true };\n }\n let img;\n try {\n img = browser_exports.fromPixels(this.webcamVideoElement);\n } catch (e) {\n throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n }\n if (this.resize) {\n try {\n return { value: this.cropAndResizeFrame(img), done: false };\n } catch (e) {\n throw new Error(`Error thrown cropping the video: ${e.message}`);\n } finally {\n img.dispose();\n }\n } else {\n return { value: img, done: false };\n }\n }\n needToResize() {\n if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n return true;\n }\n return false;\n }\n // Cropping and resizing each frame based on config\n cropAndResizeFrame(img) {\n return tidy(() => {\n const expandedImage = expandDims(cast(img, \"float32\"), 0);\n let resizedImage;\n resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, \"bilinear\");\n const shape = resizedImage.shape;\n return reshape(resizedImage, shape.slice(1));\n });\n }\n // Capture one frame from the video stream, and extract the value from\n // iterator.next() result.\n async capture() {\n return (await this.next()).value;\n }\n // Stop the video stream and pause webcam iterator.\n stop() {\n const tracks = this.stream.getTracks();\n tracks.forEach((track) => track.stop());\n try {\n this.webcamVideoElement.srcObject = null;\n } catch (error) {\n console.log(error);\n this.webcamVideoElement.src = null;\n }\n this.isClosed = true;\n }\n // Override toArray() function to prevent collecting.\n toArray() {\n throw new Error(\"Can not convert infinite video stream to array.\");\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/datasource.js\nvar DataSource = class {\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/string_iterator.js\nvar StringIterator = class extends LazyIterator {\n /**\n * Splits a string stream on a given separator.\n *\n * It is assumed that the incoming chunk boundaries have no semantic meaning,\n * so conceptually the incoming stream is treated simply as the concatenation\n * of its elements.\n *\n * The outgoing stream provides chunks corresponding to the results of the\n * standard string split() operation (even if such a chunk spanned incoming\n * chunks). The separators are not included.\n *\n * A typical usage is to split a text file (represented as a stream with\n * arbitrary chunk boundaries) into lines.\n *\n * @param upstream A readable stream of strings that can be treated as\n * concatenated.\n * @param separator A character to split on.\n */\n split(separator) {\n return new SplitIterator(this, separator);\n }\n};\nvar SplitIterator = class extends StringIterator {\n constructor(upstream, separator) {\n super();\n this.upstream = upstream;\n this.impl = new SplitIteratorImpl(upstream, separator);\n }\n summary() {\n return this.impl.summary();\n }\n async next() {\n return this.impl.next();\n }\n};\nvar SplitIteratorImpl = class extends OneToManyIterator {\n constructor(upstream, separator) {\n super();\n this.upstream = upstream;\n this.separator = separator;\n this.carryover = \"\";\n }\n summary() {\n return `${this.upstream.summary()} -> Split('${this.separator}')`;\n }\n async pump() {\n const chunkResult = await this.upstream.next();\n if (chunkResult.done) {\n if (this.carryover === \"\") {\n return false;\n }\n this.outputQueue.push(this.carryover);\n this.carryover = \"\";\n return true;\n }\n const lines = chunkResult.value.split(this.separator);\n lines[0] = this.carryover + lines[0];\n for (const line of lines.slice(0, -1)) {\n this.outputQueue.push(line);\n }\n this.carryover = lines[lines.length - 1];\n return true;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/byte_chunk_iterator.js\nvar ByteChunkIterator = class extends LazyIterator {\n /**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * The byte arrays producetd from the ByteChunkIterator on which this is\n * called will be interpreted as concatenated. No assumptions are made about\n * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n * character may span the boundary between chunks. This naturally happens,\n * for instance, when reading fixed-size byte arrays from a file.\n */\n decodeUTF8() {\n return new Utf8Iterator(this);\n }\n};\nvar Utf8Iterator = class extends StringIterator {\n constructor(upstream) {\n super();\n this.upstream = upstream;\n this.impl = new Utf8IteratorImpl(upstream);\n }\n summary() {\n return this.impl.summary();\n }\n async next() {\n return this.impl.next();\n }\n};\nvar Utf8IteratorImpl = class extends OneToManyIterator {\n constructor(upstream) {\n super();\n this.upstream = upstream;\n if (env().get(\"IS_BROWSER\")) {\n this.decoder = new TextDecoder(\"utf-8\");\n } else {\n const { StringDecoder } = require_string_decoder();\n this.decoder = new StringDecoder(\"utf8\");\n }\n }\n summary() {\n return `${this.upstream.summary()} -> Utf8`;\n }\n async pump() {\n const chunkResult = await this.upstream.next();\n let chunk;\n if (chunkResult.done) {\n return false;\n } else {\n chunk = chunkResult.value;\n }\n let text;\n if (env().get(\"IS_BROWSER\")) {\n text = this.decoder.decode(chunk, { stream: true });\n } else {\n text = this.decoder.write(Buffer.from(chunk.buffer));\n }\n this.outputQueue.push(text);\n return true;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/file_chunk_iterator.js\nvar FileChunkIterator = class extends ByteChunkIterator {\n constructor(file, options = {}) {\n super();\n this.file = file;\n this.options = options;\n util_exports.assert(file instanceof Uint8Array || (env().get(\"IS_BROWSER\") ? file instanceof File || file instanceof Blob : false), () => \"FileChunkIterator only supports File, Blob and Uint8Array right now.\");\n this.offset = options.offset || 0;\n this.chunkSize = options.chunkSize || 1024 * 1024;\n }\n summary() {\n return `FileChunks ${this.file}`;\n }\n async next() {\n if (this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size)) {\n return { value: null, done: true };\n }\n const chunk = new Promise((resolve, reject) => {\n const end = this.offset + this.chunkSize;\n if (this.file instanceof Uint8Array) {\n resolve(new Uint8Array(this.file.slice(this.offset, end)));\n } else {\n const fileReader = new FileReader();\n fileReader.onload = (event) => {\n let data = fileReader.result;\n if (data instanceof ArrayBuffer) {\n data = new Uint8Array(data);\n }\n if (!(data instanceof Uint8Array)) {\n return reject(new TypeError(\"FileReader returned unknown type.\"));\n }\n resolve(data);\n };\n fileReader.onabort = (event) => {\n return reject(new Error(\"Aborted\"));\n };\n fileReader.onerror = (event) => {\n return reject(new Error(event.type));\n };\n const slice5 = this.file.slice(this.offset, end);\n fileReader.readAsArrayBuffer(slice5);\n }\n this.offset = end;\n });\n return { value: await chunk, done: false };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/url_chunk_iterator.js\nasync function urlChunkIterator(url, options = {}, fetchFunc) {\n let urlString;\n let requestInit;\n if (typeof url === \"string\") {\n urlString = url;\n } else {\n urlString = url.url;\n requestInit = getRequestInitFromRequest(url);\n }\n const response = await (fetchFunc || util_exports.fetch)(urlString, requestInit);\n if (response.ok) {\n const uint8Array = new Uint8Array(await response.arrayBuffer());\n return new FileChunkIterator(uint8Array, options);\n } else {\n throw new Error(response.statusText);\n }\n}\nvar getRequestInitFromRequest = (request) => {\n const init2 = {\n method: request.method,\n headers: request.headers,\n body: request.body,\n mode: request.mode,\n credentials: request.credentials,\n cache: request.cache,\n redirect: request.redirect,\n referrer: request.referrer,\n integrity: request.integrity\n };\n return init2;\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/util/source_util.js\nfunction isLocalPath(source) {\n return typeof source === \"string\" && source.slice(0, 7) === \"file://\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/sources/file_data_source.js\nvar FileDataSource = class extends DataSource {\n /**\n * Create a `FileDataSource`.\n *\n * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to\n * read. Local file only works in node environment.\n * @param options Options passed to the underlying `FileChunkIterator`s,\n * such as {chunksize: 1024}.\n */\n constructor(input2, options = {}) {\n super();\n this.input = input2;\n this.options = options;\n }\n async iterator() {\n if (isLocalPath(this.input) && env().get(\"IS_NODE\")) {\n const fs = require_fs();\n this.input = fs.readFileSync(this.input.slice(7));\n }\n return new FileChunkIterator(this.input, this.options);\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/sources/url_data_source.js\nvar URLDataSource = class extends DataSource {\n /**\n * Create a `URLDataSource`.\n *\n * @param url A source URL string, or a `Request` object.\n * @param options Options passed to the underlying `FileChunkIterator`s,\n * such as {chunksize: 1024}.\n */\n constructor(url, fileOptions = {}) {\n super();\n this.url = url;\n this.fileOptions = fileOptions;\n }\n // TODO(soergel): provide appropriate caching options. Currently this\n // will download the URL anew for each call to iterator(). Since we have\n // to treat the downloaded file as a blob/buffer anyway, we may as well retain\n // it-- but that raises GC issues. Also we may want a persistent disk cache.\n async iterator() {\n if (isLocalPath(this.url)) {\n return new FileDataSource(this.url, this.fileOptions).iterator();\n } else {\n return urlChunkIterator(this.url, this.fileOptions);\n }\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/readers.js\nfunction csv(source, csvConfig = {}) {\n return new CSVDataset(new URLDataSource(source), csvConfig);\n}\nfunction func(f) {\n const iter = iteratorFromFunction(f);\n return datasetFromIteratorFn(async () => iter);\n}\nfunction generator(generator2) {\n return datasetFromIteratorFn(async () => {\n const gen = await generator2();\n return iteratorFromFunction(() => gen.next());\n });\n}\nasync function webcam(webcamVideoElement, webcamConfig) {\n return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\nasync function microphone(microphoneConfig) {\n return MicrophoneIterator.create(microphoneConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/version.js\nvar version4 = \"4.16.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/cpu_util.js\nfunction assertNotComplex(tensor2, opName) {\n if (!Array.isArray(tensor2)) {\n tensor2 = [tensor2];\n }\n tensor2.forEach((t) => {\n if (t != null) {\n util_exports.assert(t.dtype !== \"complex64\", () => `${opName} does not support complex64 tensors in the CPU backend.`);\n }\n });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js\nvar whereImpl2 = kernel_impls_exports.whereImpl;\nvar MathBackendCPU = class _MathBackendCPU extends KernelBackend {\n nextDataId() {\n return _MathBackendCPU.nextDataId++;\n }\n constructor() {\n super();\n this.blockSize = 48;\n this.firstUse = true;\n this.data = new DataStorage(this, engine());\n }\n write(values, shape, dtype) {\n if (this.firstUse) {\n this.firstUse = false;\n if (env().get(\"IS_NODE\")) {\n backend_util_exports.warn(\"\\n============================\\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \\n============================\");\n }\n }\n const dataId = { id: this.nextDataId() };\n this.data.set(dataId, { values, dtype, refCount: 1 });\n return dataId;\n }\n /**\n * Create a data bucket in cpu backend.\n * @param shape Shape of the `TensorInfo`.\n * @param dtype DType of the `TensorInfo`.\n * @param values The value of the `TensorInfo` stored as a flattened array.\n */\n makeTensorInfo(shape, dtype, values) {\n let outId;\n if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n const encodedValues = values.map((d) => util_exports.encodeString(d));\n outId = this.write(encodedValues, shape, dtype);\n } else {\n outId = this.write(values, shape, dtype);\n }\n return { dataId: outId, shape, dtype };\n }\n /** Return refCount of a `TensorData`. */\n refCount(dataId) {\n if (this.data.has(dataId)) {\n const tensorData = this.data.get(dataId);\n return tensorData.refCount;\n }\n return 0;\n }\n /** Increase refCount of a `TensorData`. */\n incRef(dataId) {\n const tensorData = this.data.get(dataId);\n tensorData.refCount++;\n }\n /** Decrease refCount of a `TensorData`. */\n decRef(dataId) {\n if (this.data.has(dataId)) {\n const tensorData = this.data.get(dataId);\n tensorData.refCount--;\n }\n }\n move(dataId, values, shape, dtype, refCount) {\n this.data.set(dataId, { values, dtype, refCount });\n }\n numDataIds() {\n return this.data.numDataIds();\n }\n async read(dataId) {\n return this.readSync(dataId);\n }\n readSync(dataId) {\n const { dtype, complexTensorInfos } = this.data.get(dataId);\n if (dtype === \"complex64\") {\n const realValues = this.readSync(complexTensorInfos.real.dataId);\n const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n return backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n }\n return util_exports.convertBackendValuesAndArrayBuffer(this.data.get(dataId).values, dtype);\n }\n bufferSync(t) {\n const data = this.readSync(t.dataId);\n if (t.dtype === \"string\") {\n try {\n const strings = data.map((d) => util_exports.decodeString(d));\n return buffer(t.shape, t.dtype, strings);\n } catch (_a) {\n throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n }\n }\n return buffer(t.shape, t.dtype, data);\n }\n makeOutput(values, shape, dtype) {\n return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);\n }\n /**\n * Dispose the memory if the dataId has 0 refCount. Return true if the memory\n * is released or memory is not managed in this backend, false if memory is\n * not cleared.\n * @param dataId\n * @oaram force Optional, remove the data regardless of refCount\n */\n disposeData(dataId, force = false) {\n if (this.data.has(dataId)) {\n this.data.get(dataId).refCount--;\n if (!force && this.data.get(dataId).refCount > 0) {\n return false;\n }\n const { complexTensorInfos } = this.data.get(dataId);\n if (complexTensorInfos != null) {\n this.disposeData(complexTensorInfos.real.dataId, true);\n this.disposeData(complexTensorInfos.imag.dataId, true);\n }\n this.data.delete(dataId);\n }\n return true;\n }\n disposeIntermediateTensorInfo(tensorInfo) {\n this.disposeData(tensorInfo.dataId);\n }\n async time(f) {\n const start = util_exports.now();\n f();\n const kernelMs = util_exports.now() - start;\n return { kernelMs };\n }\n memory() {\n return {\n // Unreliable due to automatic gc. The numbers above are cumulative.\n unreliable: true,\n reasons: [\"The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less.\"]\n };\n }\n where(condition) {\n assertNotComplex([condition], \"where\");\n const condVals = this.readSync(condition.dataId);\n return whereImpl2(condition.shape, condVals);\n }\n dispose() {\n }\n floatPrecision() {\n return 32;\n }\n /** Returns the smallest representable number. */\n epsilon() {\n return super.epsilon();\n }\n};\nMathBackendCPU.nextDataId = 0;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js\nvar shared_exports = {};\n__export(shared_exports, {\n addImpl: () => addImpl,\n bincountImpl: () => bincountImpl,\n bincountReduceImpl: () => bincountReduceImpl,\n bitwiseAndImpl: () => bitwiseAndImpl,\n castImpl: () => castImpl,\n ceilImpl: () => ceilImpl,\n concatImpl: () => concatImpl,\n equalImpl: () => equalImpl,\n expImpl: () => expImpl,\n expm1Impl: () => expm1Impl,\n floorDivImpl: () => floorDivImpl,\n floorImpl: () => floorImpl,\n gatherNdImpl: () => gatherNdImpl,\n gatherV2Impl: () => gatherV2Impl,\n greaterEqualImpl: () => greaterEqualImpl,\n greaterImpl: () => greaterImpl,\n lessEqualImpl: () => lessEqualImpl,\n lessImpl: () => lessImpl,\n linSpaceImpl: () => linSpaceImpl,\n logImpl: () => logImpl,\n maxImpl: () => maxImpl,\n maximumImpl: () => maximumImpl,\n minimumImpl: () => minimumImpl,\n multiplyImpl: () => multiplyImpl,\n negImpl: () => negImpl,\n notEqualImpl: () => notEqualImpl,\n prodImpl: () => prodImpl,\n raggedGatherImpl: () => raggedGatherImpl,\n raggedRangeImpl: () => raggedRangeImpl,\n raggedTensorToTensorImpl: () => raggedTensorToTensorImpl,\n rangeImpl: () => rangeImpl,\n rsqrtImpl: () => rsqrtImpl,\n scatterImpl: () => scatterImpl,\n sigmoidImpl: () => sigmoidImpl,\n simpleAbsImpl: () => simpleAbsImpl,\n sliceImpl: () => sliceImpl,\n sparseFillEmptyRowsImpl: () => sparseFillEmptyRowsImpl,\n sparseReshapeImpl: () => sparseReshapeImpl,\n sparseSegmentReductionImpl: () => sparseSegmentReductionImpl,\n sqrtImpl: () => sqrtImpl,\n squaredDifferenceImpl: () => squaredDifferenceImpl,\n staticRegexReplaceImpl: () => staticRegexReplaceImpl,\n stridedSliceImpl: () => stridedSliceImpl,\n stringNGramsImpl: () => stringNGramsImpl,\n stringSplitImpl: () => stringSplitImpl,\n stringToHashBucketFastImpl: () => stringToHashBucketFastImpl,\n subImpl: () => subImpl,\n tileImpl: () => tileImpl,\n topKImpl: () => topKImpl,\n transposeImpl: () => transposeImpl,\n uniqueImpl: () => uniqueImpl\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js\nfunction simpleAbsImpl(vals) {\n const resultValues = new Float32Array(vals.length);\n for (let i = 0; i < vals.length; ++i) {\n resultValues[i] = Math.abs(vals[i]);\n }\n return resultValues;\n}\nvar abs2 = (args) => {\n const { x } = args.inputs;\n const cpuBackend = args.backend;\n assertNotComplex(x, \"abs\");\n let resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));\n const values = cpuBackend.data.get(x.dataId).values;\n resultValues = simpleAbsImpl(values);\n return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);\n};\nvar absConfig = {\n kernelName: Abs,\n backendName: \"cpu\",\n kernelFunc: abs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_impl.js\nfunction createSimpleBinaryKernelImpl(op2) {\n return (aShape, bShape, aVals, bVals, dtype) => {\n const newShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n const resultRank = newShape.length;\n const resultStrides = util_exports.computeStrides(newShape);\n const resultSize = util_exports.sizeFromShape(newShape);\n const result = util_exports.getTypedArrayFromDType(dtype, resultSize);\n const aRank = aShape.length;\n const bRank = bShape.length;\n const aStrides = util_exports.computeStrides(aShape);\n const bStrides = util_exports.computeStrides(bShape);\n const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape);\n const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape);\n if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n for (let i = 0; i < result.length; ++i) {\n result[i] = op2(aVals[i % aVals.length], bVals[i % bVals.length]);\n }\n } else {\n for (let i = 0; i < result.length; ++i) {\n const loc = util_exports.indexToLoc(i, resultRank, resultStrides);\n const aLoc = loc.slice(-aRank);\n aBroadcastDims.forEach((d) => aLoc[d] = 0);\n const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);\n const bLoc = loc.slice(-bRank);\n bBroadcastDims.forEach((d) => bLoc[d] = 0);\n const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);\n result[i] = op2(aVals[aIndex], bVals[bIndex]);\n }\n }\n return [result, newShape];\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Complex.js\nfunction complex2(args) {\n const { inputs, backend: backend2 } = args;\n const { real: real4, imag: imag4 } = inputs;\n const realVals = backend2.data.get(real4.dataId).values;\n const imagVals = backend2.data.get(imag4.dataId).values;\n const complexInfo = backend2.makeTensorInfo(real4.shape, \"complex64\");\n const complex4 = backend2.data.get(complexInfo.dataId);\n complex4.complexTensorInfos = {\n real: backend2.makeTensorInfo(real4.shape, \"float32\", realVals),\n imag: backend2.makeTensorInfo(imag4.shape, \"float32\", imagVals)\n };\n return complexInfo;\n}\nvar complexConfig = {\n kernelName: Complex,\n backendName: \"cpu\",\n kernelFunc: complex2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/zeros_impl.js\nfunction zeros3(backend2, shape, dtype = \"float32\") {\n if (dtype === \"complex64\") {\n const real4 = zeros3(backend2, shape, \"float32\");\n const imag4 = zeros3(backend2, shape, \"float32\");\n return complex2({ inputs: { real: real4, imag: imag4 }, backend: backend2 });\n }\n const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype);\n return backend2.makeTensorInfo(shape, dtype, values);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Identity.js\nfunction identity2(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n backend2.incRef(x.dataId);\n return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig = {\n kernelName: Identity,\n backendName: \"cpu\",\n kernelFunc: identity2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Real.js\nfunction real2(args) {\n const { inputs, backend: backend2 } = args;\n const { input: input2 } = inputs;\n const real4 = backend2.data.get(input2.dataId).complexTensorInfos.real;\n const realVal = backend2.data.get(real4.dataId).values;\n return backend2.makeTensorInfo(real4.shape, real4.dtype, realVal);\n}\nvar realConfig = {\n kernelName: Real,\n backendName: \"cpu\",\n kernelFunc: real2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cast.js\nfunction castImpl(values, shape, inputType, dtype) {\n if (dtype === \"int32\") {\n const resultValues = Int32Array.from(values);\n return [shape, \"int32\", resultValues];\n }\n if (dtype === \"bool\") {\n const zero = util_exports.toTypedArray([0], inputType);\n const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0)(shape, [], values, zero, \"bool\");\n return [resultShape, \"bool\", resultData];\n }\n throw new Error(`Error in Cast: failed to cast ${inputType} to ${dtype}`);\n}\nfunction cast3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { dtype } = attrs;\n if (dtype === \"complex64\") {\n if (x.dtype === \"complex64\") {\n return identity2({ inputs: { x }, backend: backend2 });\n }\n const zerosTensorInfo = zeros3(backend2, x.shape, x.dtype);\n const floatX = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n const result = complex2({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(zerosTensorInfo);\n backend2.disposeIntermediateTensorInfo(floatX);\n return result;\n }\n if (x.dtype === \"complex64\") {\n const realPart = real2({ inputs: { input: x }, backend: backend2 });\n const result = cast3({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n backend2.disposeIntermediateTensorInfo(realPart);\n return result;\n }\n if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n const result = identity2({ inputs: { x }, backend: backend2 });\n return { dataId: result.dataId, shape: result.shape, dtype };\n }\n const values = backend2.data.get(x.dataId).values;\n const [resultShape, resultType, resultData] = castImpl(values, x.shape, x.dtype, dtype);\n return backend2.makeTensorInfo(resultShape, resultType, resultData);\n}\nvar castConfig = {\n kernelName: Cast,\n backendName: \"cpu\",\n kernelFunc: cast3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_utils.js\nfunction binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n if (complexImpl == null) {\n return ({ inputs, backend: backend2 }) => {\n const { a, b } = inputs;\n const cpuBackend = backend2;\n assertNotComplex([a, b], name);\n const aVals = cpuBackend.data.get(a.dataId).values;\n const bVals = cpuBackend.data.get(b.dataId).values;\n const decodedAVals = a.dtype === \"string\" ? (\n // tslint:disable-next-line: no-any\n backend_util_exports.fromUint8ToStringArray(aVals)\n ) : aVals;\n const decodedBVals = a.dtype === \"string\" ? (\n // tslint:disable-next-line: no-any\n backend_util_exports.fromUint8ToStringArray(bVals)\n ) : bVals;\n const $dtype = dtype || a.dtype;\n const [resultData, resultShape] = simpleImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n };\n }\n return ({ inputs, backend: backend2 }) => {\n const { a, b } = inputs;\n const cpuBackend = backend2;\n if (a.dtype === \"complex64\" || b.dtype === \"complex64\") {\n const $aComplex = cast3({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: \"complex64\" } });\n const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n const aReal = $aComplexVals.complexTensorInfos.real;\n const aImag = $aComplexVals.complexTensorInfos.imag;\n const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n const $bComplex = cast3({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: \"complex64\" } });\n const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n const bReal = $bComplexVals.complexTensorInfos.real;\n const bImag = $bComplexVals.complexTensorInfos.imag;\n const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n const resultReal = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultRealData);\n const resultImag = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultImagData);\n const result = complex2({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n cpuBackend.disposeIntermediateTensorInfo($aComplex);\n cpuBackend.disposeIntermediateTensorInfo($bComplex);\n cpuBackend.disposeIntermediateTensorInfo(resultReal);\n cpuBackend.disposeIntermediateTensorInfo(resultImag);\n return result;\n } else {\n const aVals = cpuBackend.data.get(a.dataId).values;\n const bVals = cpuBackend.data.get(b.dataId).values;\n const $dtype = dtype || a.dtype;\n const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n }\n };\n}\nfunction createComplexBinaryKernelImpl(op2) {\n return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n const resultShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n const resultSize = util_exports.sizeFromShape(resultShape);\n const resultRank = resultShape.length;\n const resultStrides = util_exports.computeStrides(resultShape);\n const resultRealVals = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n const resultImagVals = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, resultShape);\n const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, resultShape);\n const aVals = backend_util_exports.mergeRealAndImagArrays(aRealVals, aImagVals);\n const bVals = backend_util_exports.mergeRealAndImagArrays(bRealVals, bImagVals);\n const aRank = aShape.length;\n const aStrides = util_exports.computeStrides(aShape);\n const bRank = bShape.length;\n const bStrides = util_exports.computeStrides(bShape);\n if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n for (let i = 0; i < resultRealVals.length; i++) {\n const aIdx = i % aVals.length;\n const bIdx = i % bVals.length;\n const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n resultRealVals[i] = result.real;\n resultImagVals[i] = result.imag;\n }\n } else {\n for (let i = 0; i < resultRealVals.length; i++) {\n const loc = util_exports.indexToLoc(i, resultRank, resultStrides);\n const aLoc = loc.slice(-aRank);\n aBroadcastDims.forEach((d) => aLoc[d] = 0);\n const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);\n const bLoc = loc.slice(-bRank);\n bBroadcastDims.forEach((d) => bLoc[d] = 0);\n const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);\n const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n resultRealVals[i] = opResult.real;\n resultImagVals[i] = opResult.imag;\n }\n }\n return [resultRealVals, resultImagVals, resultShape];\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js\nvar addImpl = createSimpleBinaryKernelImpl((a, b) => a + b);\nvar addComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n return { real: aReal + bReal, imag: aImag + bImag };\n});\nvar add4 = binaryKernelFunc(Add, addImpl, addComplexImpl);\nvar addConfig = {\n kernelName: Add,\n backendName: \"cpu\",\n kernelFunc: add4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount_impl.js\nfunction bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {\n const weightsSize = util_exports.sizeFromShape(weightsShape);\n const outVals = util_exports.makeZerosTypedArray(size, weightsDtype);\n for (let i = 0; i < xVals.length; i++) {\n const value = xVals[i];\n if (value < 0) {\n throw new Error(\"Input x must be non-negative!\");\n }\n if (value >= size) {\n continue;\n }\n if (weightsSize > 0) {\n outVals[value] += weightsVals[i];\n } else {\n outVals[value] += 1;\n }\n }\n return outVals;\n}\nfunction bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {\n const numRows = xBuf.shape[0];\n const numCols = xBuf.shape[1];\n const outBuf = buffer([numRows, size], weightsBuf.dtype);\n for (let i = 0; i < numRows; i++) {\n for (let j = 0; j < numCols; j++) {\n const value = xBuf.get(i, j);\n if (value < 0) {\n throw new Error(\"Input x must be non-negative!\");\n }\n if (value >= size) {\n continue;\n }\n if (binaryOutput) {\n outBuf.set(1, i, value);\n } else {\n if (weightsBuf.size > 0) {\n outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);\n } else {\n outBuf.set(outBuf.get(i, value) + 1, i, value);\n }\n }\n }\n }\n return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BitwiseAnd.js\nvar bitwiseAndImpl = createSimpleBinaryKernelImpl((a, b) => a & b);\nvar bitwiseAnd2 = binaryKernelFunc(BitwiseAnd, bitwiseAndImpl);\nvar bitwiseAndConfig = {\n kernelName: BitwiseAnd,\n backendName: \"cpu\",\n kernelFunc: bitwiseAnd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_impl.js\nfunction createSimpleUnaryImpl(op2) {\n return (values, dtype, attrs) => {\n const newValues = util_exports.getArrayFromDType(dtype, values.length);\n for (let i = 0; i < values.length; ++i) {\n newValues[i] = op2(values[i], attrs);\n }\n return newValues;\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_utils.js\nfunction unaryKernelFunc(name, op2, dtype) {\n const impl = createSimpleUnaryImpl(op2);\n return unaryKernelFuncFromImpl(name, impl, dtype);\n}\nfunction unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n return ({ inputs, attrs, backend: backend2 }) => {\n const { x } = inputs;\n assertNotComplex(x, name);\n const cpuBackend = backend2;\n const values = cpuBackend.data.get(x.dataId).values;\n let decoded;\n if (x.dtype === \"string\") {\n if (!Array.isArray(values)) {\n throw new Error(\"String tensor's value was not an instance of Array\");\n }\n decoded = backend_util_exports.fromUint8ToStringArray(values);\n } else {\n decoded = values;\n }\n const $dtype = dtype || x.dtype;\n const newValues = unaryImpl(decoded, $dtype, attrs);\n return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js\nvar ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nvar ceil2 = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nvar ceilConfig = {\n kernelName: Ceil,\n backendName: \"cpu\",\n kernelFunc: ceil2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js\nfunction concatImpl(inputs, outShape, dtype, simplyConcat) {\n const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape));\n if (simplyConcat && dtype !== \"string\") {\n let offset = 0;\n inputs.forEach((input2) => {\n const size = util_exports.sizeFromShape(input2.shape);\n outVals.set(input2.vals, offset);\n offset += size;\n });\n } else {\n let colOffset = 0;\n inputs.forEach((input2) => {\n const decodedData = dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals;\n let tIdx = 0;\n for (let row = 0; row < input2.shape[0]; ++row) {\n const resIdx = row * outShape[1] + colOffset;\n for (let col = 0; col < input2.shape[1]; ++col) {\n outVals[resIdx + col] = decodedData[tIdx++];\n }\n }\n colOffset += input2.shape[1];\n });\n }\n return outVals;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Equal.js\nvar equalImpl = createSimpleBinaryKernelImpl((a, b) => a === b ? 1 : 0);\nvar equal2 = binaryKernelFunc(Equal, equalImpl, null, \"bool\");\nvar equalConfig = {\n kernelName: Equal,\n backendName: \"cpu\",\n kernelFunc: equal2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Exp.js\nvar expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nvar exp2 = unaryKernelFuncFromImpl(Exp, expImpl, \"float32\");\nvar expConfig = {\n kernelName: Exp,\n backendName: \"cpu\",\n kernelFunc: exp2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Expm1.js\nvar expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nvar expm12 = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nvar expm1Config = {\n kernelName: Expm1,\n backendName: \"cpu\",\n kernelFunc: expm12\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Floor.js\nvar floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nvar floor2 = unaryKernelFuncFromImpl(Floor, floorImpl);\nvar floorConfig = {\n kernelName: Floor,\n backendName: \"cpu\",\n kernelFunc: floor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FloorDiv.js\nvar floorDivImpl = createSimpleBinaryKernelImpl((a, b) => Math.floor(a / b));\nvar floorDiv2 = binaryKernelFunc(FloorDiv, floorDivImpl, null, \"int32\");\nvar floorDivConfig = {\n kernelName: FloorDiv,\n backendName: \"cpu\",\n kernelFunc: floorDiv2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd_Impl.js\nfunction gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {\n const outBuf = buffer([numSlices, sliceSize], dtype);\n for (let i = 0; i < numSlices; i++) {\n const index = [];\n let flattenIndex = 0;\n for (let j = 0; j < sliceRank; j++) {\n const dim = indicesData[i * sliceRank + j];\n flattenIndex += dim * strides[j];\n index.push(dim);\n }\n if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {\n throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);\n }\n for (let k = 0; k < sliceSize; k++) {\n outBuf.values[i * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));\n }\n }\n return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js\nfunction gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {\n const outBuf = buffer(flattenOutputShape, xBuf.dtype);\n for (let i = 0; i < outBuf.size; ++i) {\n const newLoc = outBuf.indexToLoc(i);\n const originalLoc = newLoc.slice();\n const batchIdx = originalLoc[0];\n const indicesIdx = originalLoc[2];\n const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);\n originalLoc[2] = indicesBuf.values[indicesIndex];\n const originalIndex = xBuf.locToIndex(originalLoc);\n if (0 <= originalIndex && originalIndex < xBuf.values.length) {\n outBuf.values[i] = xBuf.values[originalIndex];\n }\n }\n return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Greater.js\nvar greaterImpl = createSimpleBinaryKernelImpl((a, b) => a > b ? 1 : 0);\nvar greater3 = binaryKernelFunc(Greater, greaterImpl, null, \"bool\");\nvar greaterConfig = {\n kernelName: Greater,\n backendName: \"cpu\",\n kernelFunc: greater3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GreaterEqual.js\nvar greaterEqualImpl = createSimpleBinaryKernelImpl((a, b) => a >= b ? 1 : 0);\nvar greaterEqual2 = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null, \"bool\");\nvar greaterEqualConfig = {\n kernelName: GreaterEqual,\n backendName: \"cpu\",\n kernelFunc: greaterEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Less.js\nvar lessImpl = createSimpleBinaryKernelImpl((a, b) => a < b ? 1 : 0);\nvar less3 = binaryKernelFunc(Less, lessImpl, null, \"bool\");\nvar lessConfig = {\n kernelName: Less,\n backendName: \"cpu\",\n kernelFunc: less3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LessEqual.js\nvar lessEqualImpl = createSimpleBinaryKernelImpl((a, b) => a <= b ? 1 : 0);\nvar lessEqual2 = binaryKernelFunc(LessEqual, lessEqualImpl, null, \"bool\");\nvar lessEqualConfig = {\n kernelName: LessEqual,\n backendName: \"cpu\",\n kernelFunc: lessEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace_impl.js\nfunction linSpaceImpl(start, stop, num) {\n const step5 = (stop - start) / (num - 1);\n const values = util_exports.makeZerosTypedArray(num, \"float32\");\n values[0] = start;\n for (let i = 1; i < values.length; i++) {\n values[i] = values[i - 1] + step5;\n }\n return values;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log.js\nvar logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nvar log3 = unaryKernelFuncFromImpl(Log, logImpl);\nvar logConfig = {\n kernelName: Log,\n backendName: \"cpu\",\n kernelFunc: log3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js\nfunction maxImpl(aVals, reduceSize, outShape, dtype) {\n const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape));\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let max6 = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (Number.isNaN(value) || value > max6) {\n max6 = value;\n }\n }\n vals[i] = max6;\n }\n return vals;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Maximum.js\nvar maximumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.max(aValue, bValue));\nvar maximum3 = binaryKernelFunc(Maximum, maximumImpl);\nvar maximumConfig = {\n kernelName: Maximum,\n backendName: \"cpu\",\n kernelFunc: maximum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Minimum.js\nvar minimumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.min(aValue, bValue));\nvar minimum3 = binaryKernelFunc(Minimum, minimumImpl);\nvar minimumConfig = {\n kernelName: Minimum,\n backendName: \"cpu\",\n kernelFunc: minimum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multiply.js\nvar multiplyImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue * bValue);\nvar multiplyComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n return {\n real: aReal * bReal - aImag * bImag,\n imag: aReal * bImag + aImag * bReal\n };\n});\nvar multiply2 = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nvar multiplyConfig = {\n kernelName: Multiply,\n backendName: \"cpu\",\n kernelFunc: multiply2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Neg.js\nfunction negImpl(xVals, xShape, xDtype) {\n const minusOne = util_exports.createScalarValue(-1, xDtype);\n return multiplyImpl([], xShape, minusOne, xVals, xDtype);\n}\nfunction neg2(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n assertNotComplex(x, \"neg\");\n const xVals = backend2.data.get(x.dataId).values;\n const [res, newShape] = negImpl(xVals, x.shape, x.dtype);\n return backend2.makeTensorInfo(newShape, x.dtype, res);\n}\nvar negConfig = {\n kernelName: Neg,\n backendName: \"cpu\",\n kernelFunc: neg2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NotEqual.js\nvar notEqualImpl = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0);\nvar notEqual2 = binaryKernelFunc(NotEqual, notEqualImpl, null, \"bool\");\nvar notEqualConfig = {\n kernelName: NotEqual,\n backendName: \"cpu\",\n kernelFunc: notEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose_impl.js\nfunction transposeImpl(xVals, xShape, dtype, perm, newShape) {\n const xRank = xShape.length;\n const xSize = util_exports.sizeFromShape(xShape);\n const xStrides = util_exports.computeStrides(xShape);\n const newStrides = util_exports.computeStrides(newShape);\n const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape));\n for (let i = 0; i < xSize; ++i) {\n const loc = util_exports.indexToLoc(i, xRank, xStrides);\n const newLoc = new Array(loc.length);\n for (let i2 = 0; i2 < newLoc.length; i2++) {\n newLoc[i2] = loc[perm[i2]];\n }\n const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides);\n result[newIndex] = xVals[i];\n }\n return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose.js\nfunction transpose2(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { x } = inputs;\n const { perm } = attrs;\n assertNotComplex(x, \"transpose\");\n const xRank = x.shape.length;\n const newShape = new Array(xRank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = x.shape[perm[i]];\n }\n const values = backend2.data.get(x.dataId).values;\n const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n const dataId = backend2.write(result, newShape, x.dtype);\n return { dataId, shape: newShape, dtype: x.dtype };\n}\nvar transposeConfig = {\n kernelName: Transpose,\n backendName: \"cpu\",\n kernelFunc: transpose2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js\nfunction prodImpl(xShape, xDtype, xVals, reductionAxes) {\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, reductionAxes);\n const outDtype = upcastType(xDtype, \"int32\");\n const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n for (let i = 0; i < outVals.length; ++i) {\n const offset = i * reduceSize;\n let prod5 = 1;\n for (let j = 0; j < reduceSize; ++j) {\n prod5 *= xVals[offset + j];\n }\n outVals[i] = prod5;\n }\n return { outVals, outShape, outDtype };\n}\nfunction prod2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n assertNotComplex(x, \"prod\");\n const xRank = x.shape.length;\n const axes = util_exports.parseAxisParam(axis, x.shape);\n const permutation = backend_util_exports.getAxesPermutation(axes, xRank);\n let reductionAxes = axes;\n let permutedX = x;\n const intermediateTensorInfos = [];\n if (permutation != null) {\n permutedX = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n intermediateTensorInfos.push(permutedX);\n reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);\n }\n const xVals = backend2.data.get(permutedX.dataId).values;\n const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);\n let resultShape = outShape;\n if (keepDims) {\n resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes);\n }\n intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return backend2.makeTensorInfo(resultShape, outDtype, outVals);\n}\nvar prodConfig = {\n kernelName: Prod,\n backendName: \"cpu\",\n kernelFunc: prod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedGather_impl.js\nfunction validateIndices(indices, indicesShape, numParams) {\n indices.forEach((index, i) => {\n if (index < 0 || index >= numParams) {\n const locString = util_exports.indexToLoc(i, indicesShape.length, util_exports.computeStrides(indicesShape)).join(\",\");\n throw new Error(`indices[${locString}] = ${index} is not in [0, ${numParams})`);\n }\n });\n}\nfunction validateSplits(paramsNestedSplits, numParamsDenseValues) {\n for (let dim = 0; dim < paramsNestedSplits.length; ++dim) {\n const splits = paramsNestedSplits[dim];\n const lastSplit = dim === paramsNestedSplits.length - 1 ? numParamsDenseValues : paramsNestedSplits[dim + 1].length;\n if (splits.length === 0) {\n throw new Error(\"Ragged splits may not be empty\");\n }\n if (splits[0] < 0) {\n throw new Error(\"Ragged splits must be non-negative\");\n }\n if (splits[splits.length - 1] > lastSplit) {\n throw new Error(\"Ragged splits must not point past values\");\n }\n for (let i = 1; i < splits.length; ++i) {\n if (splits[i - 1] > splits[i]) {\n throw new Error(\"Ragged splits must be sorted in ascending order\");\n }\n }\n }\n}\nfunction makeSplits(indices, indicesShape, paramsNestedSplits, numParamsDenseValues) {\n const valueSlices = [];\n let numValues = 0;\n const numSplits = indicesShape.length - 1 + paramsNestedSplits.length;\n const outSplits = new Array(numSplits).fill(null).map(() => [0]);\n validateSplits(paramsNestedSplits, numParamsDenseValues);\n let nrows = 1;\n for (let dim = 0; dim < indicesShape.length - 1; ++dim) {\n nrows *= indicesShape[dim];\n const rowLength = indicesShape[dim + 1];\n for (let i = 1; i < nrows + 1; ++i) {\n outSplits[dim].push(i * rowLength);\n }\n }\n for (let i = 0; i < indices.length; ++i) {\n let start = indices[i];\n let limit = indices[i] + 1;\n for (let dim = 0; dim < paramsNestedSplits.length; ++dim) {\n const splits = paramsNestedSplits[dim];\n const outDim = dim + indicesShape.length - 1;\n if (outDim >= 0) {\n const outSplitsOutDim = outSplits[outDim];\n const delta = outSplitsOutDim[outSplitsOutDim.length - 1] - splits[start];\n for (let j = start; j < limit; ++j) {\n outSplits[outDim].push(splits[j + 1] + delta);\n }\n }\n start = splits[start];\n limit = splits[limit];\n }\n if (limit !== start) {\n valueSlices.push([start, limit]);\n numValues += limit - start;\n }\n }\n return { outSplits, valueSlices, numValues };\n}\nfunction getSplits(outSplits) {\n const splitsOut = [];\n for (let i = 0; i < outSplits.length; ++i) {\n const numSplits = outSplits[i].length;\n const splits = util_exports.getArrayFromDType(\"int32\", numSplits);\n splitsOut.push(splits);\n outSplits[i].forEach((value, j) => splits[j] = value);\n }\n return splitsOut;\n}\nfunction computeFlatOuterDims(orig, numOutDims) {\n const outDims = orig.slice(0, numOutDims);\n while (outDims.length < numOutDims) {\n outDims.push(1);\n }\n for (let inDim = numOutDims; inDim < orig.length; inDim++) {\n outDims[numOutDims - 1] *= orig[inDim];\n }\n return outDims;\n}\nfunction writeValueSlices(paramsDenseValues, paramsDenseValuesShape, valueSlices, valueSize, values, valuesShape) {\n const denseM = computeFlatOuterDims(paramsDenseValuesShape, 2)[1];\n const valuesM = computeFlatOuterDims(valuesShape, 2)[1];\n let outPos = 0;\n for (const slice5 of valueSlices) {\n for (let i = slice5[0]; i < slice5[1]; ++i) {\n for (let j = 0; j < valueSize; ++j) {\n values[outPos * valuesM + j] = paramsDenseValues[i * denseM + j];\n }\n ++outPos;\n }\n }\n}\nfunction getValues(paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, valueSlices, numValues) {\n const valuesShape = paramsDenseValuesShape.slice();\n valuesShape[0] = numValues;\n const valuesOut = util_exports.getArrayFromDType(paramsDenseValuesDType, util_exports.sizeFromShape(valuesShape));\n const numElements = paramsDenseValues.length;\n const valueSize = numElements === 0 ? 0 : numElements / paramsDenseValuesShape[0];\n writeValueSlices(paramsDenseValues, paramsDenseValuesShape, valueSlices, valueSize, valuesOut, valuesShape);\n return [valuesOut, valuesShape];\n}\nfunction raggedGatherImpl(paramsNestedSplits, paramsNestedSplitsShapes, paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, indices, indicesShape, outputRaggedRank) {\n if (paramsNestedSplits.length === 0) {\n throw new Error(\"paramsNestedSplits must be non empty\");\n }\n if (paramsNestedSplitsShapes[0].length === 0) {\n throw new Error(\"Split tensors must not be scalars\");\n }\n const numParams = paramsNestedSplitsShapes[0][0] - 1;\n validateIndices(indices, indicesShape, numParams);\n if (paramsDenseValuesShape.length === 0) {\n throw new Error(\"params.rank must be nonzero\");\n }\n const numParamsDenseValues = paramsDenseValuesShape[0];\n const { outSplits, valueSlices, numValues } = makeSplits(indices, indicesShape, paramsNestedSplits, numParamsDenseValues);\n const outputNestedSplits = getSplits(outSplits);\n const outputDenseValues = getValues(paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, valueSlices, numValues);\n return [outputNestedSplits, outputDenseValues[0], outputDenseValues[1]];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedRange_impl.js\nvar INT32_MAX2 = 2147483647;\nfunction raggedRangeImpl(starts, startsShape, startsDType, limits, limitsShape, deltas, deltasShape) {\n if (startsShape.length > 1) {\n throw new Error(\"starts must be a scalar or vector\");\n }\n if (limitsShape.length > 1) {\n throw new Error(\"limits must be a scalar or vector\");\n }\n if (deltasShape.length > 1) {\n throw new Error(\"deltas must be a scalar or vector\");\n }\n const broadcastStarts = startsShape.length === 0;\n const broadcastLimits = limitsShape.length === 0;\n const broadcastDeltas = deltasShape.length === 0;\n const inSizes = [];\n if (!broadcastStarts) {\n inSizes.push(startsShape[0]);\n }\n if (!broadcastLimits) {\n inSizes.push(limitsShape[0]);\n }\n if (!broadcastDeltas) {\n inSizes.push(deltasShape[0]);\n }\n for (let i = 1; i < inSizes.length; ++i) {\n if (inSizes[i] !== inSizes[i - 1]) {\n throw new Error(\"starts, limits, and deltas must have the same shape\");\n }\n }\n const nRows = inSizes.length === 0 ? 1 : inSizes[0];\n const rtNestedSplits = util_exports.getArrayFromDType(\"int32\", nRows + 1);\n rtNestedSplits[0] = 0;\n for (let row = 0; row < nRows; ++row) {\n const start = broadcastStarts ? starts[0] : starts[row];\n const limit = broadcastLimits ? limits[0] : limits[row];\n const delta = broadcastDeltas ? deltas[0] : deltas[row];\n if (delta === 0) {\n throw new Error(\"Requires delta != 0\");\n }\n let size;\n if (delta > 0 && limit < start || delta < 0 && limit > start) {\n size = 0;\n } else {\n size = Math.ceil(Math.abs((limit - start) / delta));\n if (size > INT32_MAX2) {\n throw new Error(`Requires ((limit - start) / delta) <= ${INT32_MAX2}`);\n }\n }\n rtNestedSplits[row + 1] = rtNestedSplits[row] + size;\n }\n const nVals = rtNestedSplits[nRows];\n const rtDenseValues = util_exports.getArrayFromDType(startsDType, nVals);\n let valueIndex = 0;\n for (let row = 0; row < nRows; ++row) {\n const rowSize = rtNestedSplits[row + 1] - rtNestedSplits[row];\n let value = broadcastStarts ? starts[0] : starts[row];\n const delta = broadcastDeltas ? deltas[0] : deltas[row];\n for (let i = 0; i < rowSize; ++i) {\n rtDenseValues[valueIndex++] = value;\n value += delta;\n }\n }\n return [rtNestedSplits, rtDenseValues];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedTensorToTensor_impl.js\nvar RowPartitionType2 = backend_util_exports.RowPartitionType;\nvar RaggedTensorToTensorOp = class _RaggedTensorToTensorOp {\n constructor(shape, shapeShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypeStrings) {\n this.shape = shape;\n this.shapeShape = shapeShape;\n this.values = values;\n this.valuesShape = valuesShape;\n this.valuesDType = valuesDType;\n this.defaultValue = defaultValue;\n this.defaultValueShape = defaultValueShape;\n this.rowPartitionValues = rowPartitionValues;\n this.rowPartitionValuesShapes = rowPartitionValuesShapes;\n this.rowPartitionTypes = backend_util_exports.getRowPartitionTypesHelper(rowPartitionTypeStrings);\n this.raggedRank = backend_util_exports.getRaggedRank(this.rowPartitionTypes);\n }\n getRowPartitionTypeByDimension(dimension) {\n if (this.rowPartitionTypes[0] === RowPartitionType2.FIRST_DIM_SIZE) {\n return this.rowPartitionTypes[dimension + 1];\n } else {\n return this.rowPartitionTypes[dimension];\n }\n }\n // Returns the relationship between dimension and dimension + 1.\n getRowPartitionTensor(dimension) {\n if (this.rowPartitionTypes[0] === RowPartitionType2.FIRST_DIM_SIZE) {\n return this.rowPartitionValues[dimension + 1];\n } else {\n return this.rowPartitionValues[dimension];\n }\n }\n getMaxWidth(dimension) {\n const rowPartitionTensor = this.getRowPartitionTensor(dimension - 1);\n switch (this.getRowPartitionTypeByDimension(dimension - 1)) {\n case RowPartitionType2.VALUE_ROWIDS:\n return _RaggedTensorToTensorOp.getMaxWidthValueRowID(rowPartitionTensor);\n case RowPartitionType2.ROW_SPLITS:\n return _RaggedTensorToTensorOp.getMaxWidthRowSplit(rowPartitionTensor);\n default:\n throw new Error(`Cannot handle partition type ${RowPartitionType2[this.getRowPartitionTypeByDimension(dimension - 1)]}`);\n }\n }\n static getMaxWidthRowSplit(rowSplit) {\n const tensorLength = rowSplit.length;\n if (tensorLength === 0 || tensorLength === 1) {\n return 0;\n }\n let maxWidth = 0;\n for (let i = 0; i < tensorLength - 1; ++i) {\n const currentWidth = rowSplit[i + 1] - rowSplit[i];\n if (currentWidth > maxWidth) {\n maxWidth = currentWidth;\n }\n }\n return maxWidth;\n }\n static getMaxWidthValueRowID(valueRowIds) {\n const indexLength = valueRowIds.length;\n if (indexLength === 0) {\n return 0;\n }\n let firstEqualIndex = 0;\n let firstEqualIndexValue = valueRowIds[0];\n let maxWidth = 0;\n for (let i = 1; i < indexLength; ++i) {\n const value = valueRowIds[i];\n if (value !== firstEqualIndexValue) {\n firstEqualIndexValue = value;\n maxWidth = Math.max(i - firstEqualIndex, maxWidth);\n firstEqualIndex = i;\n }\n }\n return Math.max(indexLength - firstEqualIndex, maxWidth);\n }\n tensorShapeFromTensor(t, tShape, isPartial = true) {\n if (tShape.length === 0) {\n if (t[0] === -1) {\n return [];\n }\n throw new Error(`The only valid scalar shape tensor is the fully unknown shape specified as -1.`);\n }\n return makeShape(t, isPartial);\n }\n calculateOutputSize(firstDim) {\n const valueShape = this.valuesShape;\n const defaultValueShape = this.defaultValueShape;\n backend_util_exports.validateDefaultValueShape(defaultValueShape, valueShape);\n const shape = this.tensorShapeFromTensor(this.shape, this.shapeShape);\n const outputShape = backend_util_exports.combineRaggedTensorToTensorShapes(this.raggedRank, shape, valueShape);\n const result = outputShape;\n if (result[0] < 0) {\n result[0] = firstDim;\n }\n for (let i = 1; i <= this.raggedRank; ++i) {\n if (result[i] < 0) {\n result[i] = this.getMaxWidth(i);\n }\n }\n return result;\n }\n /**\n * The outputIndex represents the index in the output tensor\n * where the first element of a particular dimension would be written.\n * If it is -1, it indicates that the index is out of scope.\n * Example, given firstDimension = 10, firstDimensionOutput = 6,\n * and outputIndexMultiplier = 100:\n * result = [0 100 200 300 400 500 -1 -1 -1 -1]\n * If firstDimensionOutput = 11 instead, then:\n * result = [0 100 200 300 400 500 600 700 800 900]\n */\n calculateFirstParentOutputIndex(firstDimension, outputIndexMultiplier, firstDimensionOutput) {\n const minDimension = Math.min(firstDimension, firstDimensionOutput);\n const result = [];\n let currentOutputIndex = 0;\n for (let i = 0; i < minDimension; ++i, currentOutputIndex += outputIndexMultiplier) {\n result.push(currentOutputIndex);\n }\n for (let i = minDimension; i < firstDimension; ++i) {\n result.push(-1);\n }\n util_exports.assert(result.length === firstDimension, () => \"Final length of result must be equal to firstDimension.\");\n return result;\n }\n calculateOutputIndexRowSplit(rowSplit, parentOutputIndex, outputIndexMultiplier, outputSize) {\n const rowSplitSize = rowSplit.length;\n const result = [];\n for (let i = 0; i < rowSplitSize - 1; ++i) {\n const rowLength = rowSplit[i + 1] - rowSplit[i];\n let realLength = Math.min(outputSize, rowLength);\n let parentOutputIndexCurrent = parentOutputIndex[i];\n if (parentOutputIndexCurrent === -1) {\n realLength = 0;\n }\n for (let j = 0; j < realLength; ++j) {\n result.push(parentOutputIndexCurrent);\n parentOutputIndexCurrent += outputIndexMultiplier;\n }\n for (let j = 0; j < rowLength - realLength; ++j) {\n result.push(-1);\n }\n }\n if (rowSplitSize > 0 && result.length !== rowSplit[rowSplitSize - 1]) {\n throw new Error(\"Invalid row split size.\");\n }\n return result;\n }\n // Calculate the output index of the first element of a list.\n // The parentOutputIndex is the same computation for the previous list.\n // -1 indicates an element or list that is out of range.\n // The outputIndexMultiplier is the number of output indices one moves\n // forward for each column.\n // E.g., given:\n // valueRowIds:[0 1 2 2 2 3 5 5 6]\n // parentOutputIndex:[1000 1100 2000 2100 -1 3000 4000]\n // outputIndexMultiplier: 10\n // outputSize: 2\n // You get:\n // result = [1000 1100 2000 2010 -1 2100 -1 -1 3000]\n // result[0] = parentOutputIndex[valueRowIds[0]]\n // result[1] = parentOutputIndex[valueRowIds[1]]\n // result[2] = parentOutputIndex[valueRowIds[2]]\n // result[3] = parentOutputIndex[valueRowIds[2] + 10]\n // result[4] = -1 because it is the third element the size is 2.\n // result[5] = parentOutputIndex[valueRowIds[3]]\n // result[6] = -1 because parentOutputIndex[valueRowIds[6]] == -1\n // result[7] = -1 because parentOutputIndex[valueRowIds[6]] == -1\n // result[8] = parentOutputIndex[valueRowIds[7]]\n calculateOutputIndexValueRowID(valueRowIds, parentOutputIndex, outputIndexMultiplier, outputSize) {\n const indexSize = valueRowIds.length;\n const result = [];\n if (indexSize === 0) {\n return [];\n }\n let currentOutputColumn = 0;\n let currentValueRowId = valueRowIds[0];\n if (currentValueRowId >= parentOutputIndex.length) {\n throw new Error(`Got currentValueRowId=${currentValueRowId}, which is not less than ${parentOutputIndex.length}`);\n }\n let currentOutputIndex = parentOutputIndex[currentValueRowId];\n result.push(currentOutputIndex);\n for (let i = 1; i < indexSize; ++i) {\n const nextValueRowId = valueRowIds[i];\n if (nextValueRowId === currentValueRowId) {\n if (currentOutputIndex >= 0) {\n ++currentOutputColumn;\n if (currentOutputColumn < outputSize) {\n currentOutputIndex += outputIndexMultiplier;\n } else {\n currentOutputIndex = -1;\n }\n }\n } else {\n currentOutputColumn = 0;\n currentValueRowId = nextValueRowId;\n if (nextValueRowId >= parentOutputIndex.length) {\n throw new Error(`Got nextValueRowId=${nextValueRowId} which is not less than ${parentOutputIndex.length}`);\n }\n currentOutputIndex = parentOutputIndex[nextValueRowId];\n }\n result.push(currentOutputIndex);\n }\n if (result.length !== valueRowIds.length) {\n throw new Error(\"Invalid row ids.\");\n }\n return result;\n }\n calculateOutputIndex(dimension, parentOutputIndex, outputIndexMultiplier, outputSize) {\n const rowPartitionTensor = this.getRowPartitionTensor(dimension);\n const partitionType = this.getRowPartitionTypeByDimension(dimension);\n switch (partitionType) {\n case RowPartitionType2.VALUE_ROWIDS:\n return this.calculateOutputIndexValueRowID(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize);\n case RowPartitionType2.ROW_SPLITS:\n if (rowPartitionTensor.length - 1 > parentOutputIndex.length) {\n throw new Error(`Row partition size is greater than output size: ${rowPartitionTensor.length - 1} > ${parentOutputIndex.length}`);\n }\n return this.calculateOutputIndexRowSplit(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize);\n default:\n throw new Error(`Unsupported partition type: ${RowPartitionType2[partitionType]}`);\n }\n }\n getFirstDimensionSize() {\n const firstPartitionTensor = this.rowPartitionValues[0];\n if (this.rowPartitionTypes.length === 0) {\n throw new Error(\"No row_partition_types given.\");\n }\n const firstPartitionType = this.rowPartitionTypes[0];\n switch (firstPartitionType) {\n case RowPartitionType2.FIRST_DIM_SIZE:\n return firstPartitionTensor[0];\n case RowPartitionType2.VALUE_ROWIDS:\n throw new Error(\"Cannot handle VALUE_ROWIDS in first dimension.\");\n case RowPartitionType2.ROW_SPLITS:\n return this.rowPartitionValuesShapes[0][0] - 1;\n default:\n throw new Error(`Cannot handle type ${RowPartitionType2[firstPartitionType]}`);\n }\n }\n compute() {\n const firstPartitionTensor = this.rowPartitionValues[0];\n if (firstPartitionTensor.length <= 0) {\n throw new Error(\"Invalid first partition input. Tensor requires at least one element.\");\n }\n const firstDimension = this.getFirstDimensionSize();\n const outputSize = this.calculateOutputSize(firstDimension);\n const multiplier = new Array(this.raggedRank + 1);\n multiplier[multiplier.length - 1] = 1;\n for (let i = multiplier.length - 2; i >= 0; --i) {\n multiplier[i] = multiplier[i + 1] * outputSize[i + 1];\n }\n const outputShape = makeShape(outputSize, false);\n const outputTensor = util_exports.getArrayFromDType(this.valuesDType, util_exports.sizeFromShape(outputShape));\n const fullSize = multiplier[0] * outputSize[0];\n if (fullSize > 0) {\n let outputIndex = this.calculateFirstParentOutputIndex(firstDimension, multiplier[0], outputSize[0]);\n for (let i = 1; i <= this.raggedRank; ++i) {\n const newOutputIndex = this.calculateOutputIndex(i - 1, outputIndex, multiplier[i], outputSize[i]);\n outputIndex = newOutputIndex;\n }\n this.setOutput(this.raggedRank, outputIndex, outputTensor, outputShape);\n }\n return [outputShape, outputTensor];\n }\n setOutput(raggedRank, outputIndex, outputTensor, outputShape) {\n if (outputTensor.length === 0) {\n return;\n }\n const valuesBase = this.values;\n const outputBase = outputTensor;\n let elementShape = outputShape.slice();\n elementShape = elementShape.slice(raggedRank + 1);\n const valueElementSize = util_exports.sizeFromShape(elementShape);\n const outputIndexSize = outputIndex.length;\n let defaultValue = this.defaultValue;\n if (defaultValue.length !== valueElementSize && defaultValue.length !== 1) {\n const srcShape = this.defaultValueShape;\n tidy(() => {\n const defaultValueTensor = reshape(defaultValue, srcShape);\n const bCastDefault = broadcastTo(defaultValueTensor, elementShape);\n defaultValue = bCastDefault.dataSync();\n });\n }\n let srcStart = 0;\n let dstStart = 0;\n let dstEnd = 0;\n for (let srcI = 0; srcI <= outputIndexSize; ++srcI) {\n let dstI = srcI < outputIndexSize ? outputIndex[srcI] : -1;\n if (dstI === dstEnd) {\n ++dstEnd;\n continue;\n }\n if (dstStart < dstEnd) {\n const src = valuesBase.subarray(srcStart * valueElementSize);\n const dst = outputBase.subarray(dstStart * valueElementSize);\n const nVals = (dstEnd - dstStart) * valueElementSize;\n copyArray(dst, src, nVals);\n }\n if (srcI >= outputIndexSize) {\n const outputSize = outputTensor.length;\n dstI = Math.floor(outputSize / valueElementSize);\n }\n if (dstI > dstEnd) {\n if (this.defaultValue.length === 1) {\n outputBase.subarray(dstEnd * valueElementSize, dstI * valueElementSize).fill(this.defaultValue[0]);\n dstEnd = dstI;\n } else {\n while (dstI > dstEnd) {\n const dst = outputBase.slice(dstEnd * valueElementSize);\n copyArray(dst, defaultValue, valueElementSize);\n ++dstEnd;\n }\n }\n }\n if (dstI < 0) {\n srcStart = srcI + 1;\n dstStart = dstEnd;\n } else {\n srcStart = srcI;\n dstStart = dstEnd;\n dstEnd = dstStart + 1;\n }\n }\n }\n};\nfunction copyArray(dst, src, size) {\n for (let i = 0; i < size; i++) {\n dst[i] = src[i];\n }\n}\nfunction makeShape(shape, isPartial) {\n const out = [];\n for (let dim of shape) {\n if (dim < 0) {\n if (!isPartial) {\n throw new Error(`Dimension ${dim} must be >= 0`);\n }\n if (dim < -1) {\n throw new Error(`Dimension ${dim} must be >= -1`);\n }\n dim = -1;\n }\n out.push(dim);\n }\n return out;\n}\nfunction raggedTensorToTensorImpl(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes) {\n return new RaggedTensorToTensorOp(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes).compute();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js\nfunction rangeImpl(start, stop, step5, dtype) {\n const sameStartStop = start === stop;\n const increasingRangeNegativeStep = start < stop && step5 < 0;\n const decreasingRangePositiveStep = stop < start && step5 > 1;\n if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {\n return util_exports.makeZerosTypedArray(0, dtype);\n }\n const numElements = Math.abs(Math.ceil((stop - start) / step5));\n const values = util_exports.makeZerosTypedArray(numElements, dtype);\n if (stop < start && step5 === 1) {\n step5 = -1;\n }\n values[0] = start;\n for (let i = 1; i < values.length; i++) {\n values[i] = values[i - 1] + step5;\n }\n return values;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Rsqrt.js\nvar rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nvar rsqrt2 = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nvar rsqrtConfig = {\n kernelName: Rsqrt,\n backendName: \"cpu\",\n kernelFunc: rsqrt2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Scatter_impl.js\nfunction scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n const flattenShape = [outputSize / sliceSize, sliceSize];\n const indicesData = indices.values;\n const updatesData = updates.values;\n if (outputSize === 0) {\n return buffer(shape, updates.dtype);\n }\n const outBuf = defaultValue instanceof TensorBuffer ? defaultValue : buffer(flattenShape, updates.dtype);\n if (typeof defaultValue === \"string\") {\n outBuf.values.fill(defaultValue);\n } else if (typeof defaultValue === \"number\") {\n outBuf.values.fill(defaultValue);\n } else if (typeof defaultValue === \"boolean\") {\n outBuf.values.fill(+defaultValue);\n }\n for (let i = 0; i < numUpdates; i++) {\n const index = [];\n let flattenIndex = 0;\n for (let j = 0; j < sliceRank; j++) {\n const dim = indicesData[i * sliceRank + j];\n index.push(dim);\n flattenIndex += dim * strides[j];\n }\n if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n }\n for (let k = 0; k < sliceSize; k++) {\n if (sumDupeIndices) {\n outBuf.values[flattenIndex * sliceSize + k] += updatesData[i * sliceSize + k];\n } else {\n outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i * sliceSize + k];\n }\n }\n }\n return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sigmoid.js\nvar sigmoidImpl = createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi)));\nvar sigmoid2 = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nvar sigmoidConfig = {\n kernelName: Sigmoid,\n backendName: \"cpu\",\n kernelFunc: sigmoid2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js\nfunction sliceImpl(vals, begin, size, shape, dtype) {\n const isContinous = slice_util_exports.isSliceContinous(shape, begin, size);\n const length = util_exports.sizeFromShape(size);\n const xStrides = util_exports.computeStrides(shape);\n if (isContinous) {\n const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides);\n if (dtype === \"string\") {\n return vals.slice(flatOffset, flatOffset + length);\n }\n return vals.subarray(flatOffset, flatOffset + length);\n }\n const decodedData = dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;\n const inBuf = buffer(shape, dtype, decodedData);\n const outBuf = buffer(size, dtype);\n for (let i = 0; i < outBuf.size; ++i) {\n const outLoc = outBuf.indexToLoc(i);\n const inLoc = outLoc.map((idx, j) => idx + begin[j]);\n outBuf.set(inBuf.get(...inLoc), ...outLoc);\n }\n if (dtype === \"string\") {\n return backend_util_exports.fromStringArrayToUint8(outBuf.values);\n }\n return outBuf.values;\n}\nfunction slice2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { begin, size } = attrs;\n assertNotComplex(x, \"slice\");\n const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n slice_util_exports.assertParamsValid(x, $begin, $size);\n const vals = backend2.data.get(x.dataId).values;\n const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n return backend2.makeTensorInfo($size, x.dtype, outVals);\n}\nvar sliceConfig = {\n kernelName: Slice,\n backendName: \"cpu\",\n kernelFunc: slice2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows_impl.js\nfunction sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {\n const indicesCount = indicesShape[0];\n const denseRows = denseShape[0];\n const emptyRowIndicator = new Array(denseRows);\n const reverseIndexMap = new Array(indicesCount);\n const rank = indicesShape[1];\n if (denseRows === 0) {\n if (indicesCount !== 0) {\n throw new Error(backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount));\n }\n const outputIndices = util_exports.getArrayFromDType(indicesDType, 0);\n const outputValues = util_exports.getArrayFromDType(valuesDType, 0);\n return [\n outputIndices,\n [0, rank],\n outputValues,\n emptyRowIndicator,\n reverseIndexMap\n ];\n }\n let rowsAreOrdered = true;\n let lastIndicesRow = 0;\n const csrOffset = new Array(denseRows).fill(0);\n for (let i = 0; i < indicesCount; ++i) {\n const row = indices[i * rank];\n if (row < 0) {\n throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row));\n }\n if (row >= denseRows) {\n throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows));\n }\n ++csrOffset[row];\n rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow;\n lastIndicesRow = row;\n }\n let allRowsFull = true;\n for (let row = 0; row < denseRows; ++row) {\n const rowEmpty = csrOffset[row] === 0;\n emptyRowIndicator[row] = rowEmpty;\n allRowsFull = allRowsFull && !rowEmpty;\n csrOffset[row] = Math.max(csrOffset[row], 1);\n if (row > 0) {\n csrOffset[row] += csrOffset[row - 1];\n }\n }\n if (allRowsFull && rowsAreOrdered) {\n const outputIndices = indices;\n const outputValues = values;\n for (let i = 0; i < indicesCount; ++i) {\n reverseIndexMap[i] = i;\n }\n return [\n outputIndices,\n [indicesCount, rank],\n outputValues,\n emptyRowIndicator,\n reverseIndexMap\n ];\n } else {\n const fullIndicesCount = csrOffset[denseRows - 1];\n const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank);\n const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount);\n const filledCount = new Array(denseRows).fill(0);\n for (let i = 0; i < indicesCount; ++i) {\n const row = indices[i * rank];\n const offset = filledCount[row];\n const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset;\n filledCount[row]++;\n for (let j = 0; j < rank; ++j) {\n outputIndices[outputI * rank + j] = indices[i * rank + j];\n }\n outputValues[outputI] = values[i];\n reverseIndexMap[i] = outputI;\n }\n for (let row = 0; row < denseRows; ++row) {\n const rowCount = filledCount[row];\n if (rowCount === 0) {\n const startingIndex = row === 0 ? 0 : csrOffset[row - 1];\n outputIndices[startingIndex * rank + 0] = row;\n for (let col = 1; col < rank; ++col) {\n outputIndices[startingIndex * rank + col] = 0;\n }\n outputValues[startingIndex] = defaultValue;\n }\n }\n return [\n outputIndices,\n [fullIndicesCount, rank],\n outputValues,\n emptyRowIndicator,\n reverseIndexMap\n ];\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape_impl.js\nfunction sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {\n const denseSize = util_exports.sizeFromShape(inputShape);\n const nnz = inputIndicesShape[0];\n const outputRank = targetShape.length;\n const outputShape = [];\n let product = 1;\n let unknownIndex = -1;\n for (let d = 0; d < outputRank; ++d) {\n const size = targetShape[d];\n if (size === -1) {\n if (unknownIndex !== -1) {\n throw new Error(backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d));\n }\n unknownIndex = d;\n outputShape.push(1);\n } else {\n if (size < 0) {\n throw new Error(backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(d, size));\n }\n product *= size;\n outputShape.push(size);\n }\n }\n if (unknownIndex !== -1) {\n if (product <= 0) {\n throw new Error(backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());\n }\n const missing = Math.trunc(denseSize / product);\n if (product * missing !== denseSize) {\n throw new Error(backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape));\n }\n outputShape[unknownIndex] = missing;\n }\n const outputSize = util_exports.sizeFromShape(outputShape);\n if (outputSize !== denseSize) {\n throw new Error(backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape));\n }\n const inputRank = inputShape.length;\n const inputStrides = [];\n if (inputRank > 0) {\n inputStrides[inputRank - 1] = 1;\n for (let d = inputRank - 2; d >= 0; --d) {\n inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];\n }\n }\n const outputStrides = [];\n if (outputRank > 0) {\n outputStrides[outputRank - 1] = 1;\n for (let d = outputRank - 2; d >= 0; --d) {\n outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];\n }\n }\n const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank);\n for (let i = 0; i < nnz; ++i) {\n let id = 0;\n for (let j = 0; j < inputRank; ++j) {\n id += inputIndices[i * inputRank + j] * inputStrides[j];\n }\n for (let j = 0; j < outputRank; ++j) {\n newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);\n id %= outputStrides[j];\n }\n }\n return [newIndices, [nnz, outputRank], outputShape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentReduction_impl.js\nfunction sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {\n const numIndices = indices.length;\n const inputFlat = [inputShape[0], input2.length / inputShape[0]];\n const numCol = inputFlat[1];\n const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;\n const outputRows = lastSegmentIdPlusOne;\n if (outputRows < 0) {\n throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n }\n const outputShape = inputShape.slice();\n outputShape[0] = outputRows;\n const outputLength = outputShape.reduce((product, value) => product * value, 1);\n const output = util_exports.getArrayFromDType(inputDType, outputLength);\n if (numIndices === 0) {\n if (outputRows > 0) {\n output.fill(defaultValue);\n }\n return [output, outputShape];\n }\n if (outputRows <= 0) {\n throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n }\n let start = 0, end = 1;\n let uninitializedIndex = 0;\n let outIndex = segmentIds[start];\n while (true) {\n let nextIndex = 0;\n if (end < numIndices) {\n nextIndex = segmentIds[end];\n if (outIndex === nextIndex) {\n ++end;\n continue;\n }\n if (outIndex >= nextIndex) {\n throw new Error(backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());\n }\n }\n if (outIndex < 0 || outIndex >= outputRows) {\n throw new Error(backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows));\n }\n if (outIndex > uninitializedIndex) {\n output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);\n }\n for (let i = start; i < end; ++i) {\n const index = indices[i];\n if (index < 0 || index >= inputFlat[0]) {\n throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0]));\n }\n for (let j = 0; j < numCol; j++) {\n output[outIndex * numCol + j] += input2[index * numCol + j];\n }\n }\n if (isMean) {\n for (let j = 0; j < numCol; j++) {\n output[outIndex * numCol + j] /= end - start;\n }\n }\n start = end;\n ++end;\n uninitializedIndex = outIndex + 1;\n outIndex = nextIndex;\n if (end > numIndices) {\n break;\n }\n }\n if (uninitializedIndex < outputRows) {\n output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);\n }\n return [output, outputShape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sqrt.js\nvar sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi));\nvar sqrt2 = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nvar sqrtConfig = {\n kernelName: Sqrt,\n backendName: \"cpu\",\n kernelFunc: sqrt2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SquaredDifference.js\nvar squaredDifferenceImpl = createSimpleBinaryKernelImpl((a, b) => {\n const diff = a - b;\n return diff * diff;\n});\nvar squaredDifference2 = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nvar squaredDifferenceConfig = {\n kernelName: SquaredDifference,\n backendName: \"cpu\",\n kernelFunc: squaredDifference2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StaticRegexReplace.js\nvar staticRegexReplaceImpl = createSimpleUnaryImpl((x, attrs) => {\n const { pattern, replaceGlobal, rewrite } = attrs;\n return x.replace(new RegExp(pattern, replaceGlobal ? \"g\" : \"\"), rewrite);\n});\nvar staticRegexReplace2 = unaryKernelFuncFromImpl(StaticRegexReplace, staticRegexReplaceImpl);\nvar staticRegexReplaceConfig = {\n kernelName: StaticRegexReplace,\n backendName: \"cpu\",\n kernelFunc: staticRegexReplace2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js\nfunction stridedSliceImpl(outShape, xBuf, strides, begin) {\n const outBuf = buffer(outShape, xBuf.dtype);\n for (let i = 0; i < outBuf.size; i++) {\n const loc = outBuf.indexToLoc(i);\n const newLoc = new Array(loc.length);\n for (let j = 0; j < newLoc.length; j++) {\n newLoc[j] = loc[j] * strides[j] + begin[j];\n }\n outBuf.set(xBuf.get(...newLoc), ...loc);\n }\n return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams_impl.js\nvar StringNGramsOp = class {\n constructor(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n this.separator = util_exports.encodeString(separator);\n this.nGramWidths = nGramWidths;\n this.leftPad = util_exports.encodeString(leftPad);\n this.rightPad = util_exports.encodeString(rightPad2);\n this.padWidth = padWidth;\n this.preserveShort = preserveShortSequences;\n }\n getPadWidth(nGramWidth) {\n return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);\n }\n getNumNGrams(length, nGramWidth) {\n const padWidth = this.getPadWidth(nGramWidth);\n return Math.max(0, length + 2 * padWidth - nGramWidth + 1);\n }\n createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {\n for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {\n const padWidth = this.getPadWidth(nGramWidth);\n const leftPadding = Math.max(0, padWidth - nGramIndex);\n const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));\n const numTokens = nGramWidth - (leftPadding + rightPadding);\n const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);\n let nGramSize = 0;\n nGramSize += leftPadding * this.leftPad.length;\n for (let n = 0; n < numTokens; ++n) {\n nGramSize += data[dataStartIndex + n].length;\n }\n nGramSize += rightPadding * this.rightPad.length;\n const numSeparators = leftPadding + rightPadding + numTokens - 1;\n nGramSize += numSeparators * this.separator.length;\n output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);\n const nGram = output[outputStartIndex + nGramIndex];\n let nextNGramIndex = 0;\n const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);\n for (let n = 0; n < leftPadding; ++n) {\n appendToNGram(this.leftPad);\n appendToNGram(this.separator);\n }\n for (let n = 0; n < numTokens - 1; ++n) {\n appendToNGram(data[dataStartIndex + n]);\n appendToNGram(this.separator);\n }\n if (numTokens > 0) {\n appendToNGram(data[dataStartIndex + numTokens - 1]);\n for (let n = 0; n < rightPadding; ++n) {\n appendToNGram(this.separator);\n appendToNGram(this.rightPad);\n }\n } else {\n for (let n = 0; n < rightPadding - 1; ++n) {\n appendToNGram(this.rightPad);\n appendToNGram(this.separator);\n }\n appendToNGram(this.rightPad);\n }\n }\n }\n // Data and splits together form the definition of the ragged tensor,\n // where data is 1 dimensional and contains the values of the tensor\n // and splits denotes the indices at which each row starts.\n compute(data, splits) {\n const inputDataSize = data.length;\n const splitsSize = splits.length;\n if (splitsSize > 0) {\n let prevSplit = splits[0];\n if (prevSplit !== 0) {\n throw new Error(`First split value must be 0, got ${prevSplit}`);\n }\n for (let i = 1; i < splitsSize; ++i) {\n let validSplits = splits[i] >= prevSplit;\n validSplits = validSplits && splits[i] <= inputDataSize;\n if (!validSplits) {\n throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`);\n }\n prevSplit = splits[i];\n }\n if (prevSplit !== inputDataSize) {\n throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);\n }\n }\n const numBatchItems = splitsSize - 1;\n const nGramsSplits = util_exports.getArrayFromDType(\"int32\", splitsSize);\n if (inputDataSize === 0 || splitsSize === 0) {\n const empty = new Array(inputDataSize);\n for (let i = 0; i <= numBatchItems; ++i) {\n nGramsSplits[i] = 0;\n }\n return [empty, nGramsSplits];\n }\n nGramsSplits[0] = 0;\n for (let i = 1; i <= numBatchItems; ++i) {\n const length = splits[i] - splits[i - 1];\n let numNGrams = 0;\n this.nGramWidths.forEach((nGramWidth) => {\n numNGrams += this.getNumNGrams(length, nGramWidth);\n });\n if (this.preserveShort && length > 0 && numNGrams === 0) {\n numNGrams = 1;\n }\n nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;\n }\n const nGrams = new Array(nGramsSplits[numBatchItems]);\n for (let i = 0; i < numBatchItems; ++i) {\n const splitIndex = splits[i];\n let outputStartIdx = nGramsSplits[i];\n this.nGramWidths.forEach((nGramWidth) => {\n const length = splits[i + 1] - splits[i];\n const numNGrams = this.getNumNGrams(length, nGramWidth);\n this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n outputStartIdx += numNGrams;\n });\n if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {\n const dataLength = splits[i + 1] - splits[i];\n if (dataLength === 0) {\n continue;\n }\n const nGramWidth = dataLength + 2 * this.padWidth;\n const numNGrams = 1;\n this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n }\n }\n return [nGrams, nGramsSplits];\n }\n};\nfunction stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences).compute(data, dataSplits);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit_impl.js\nfunction split3(str, delimiters, skipEmpty, result) {\n if (!str.length) {\n return;\n }\n if (delimiters.length === 0) {\n for (let i = 0; i < str.length; ++i) {\n result.push(str.subarray(i, i + 1));\n }\n return;\n }\n if (delimiters.length === 1) {\n const delimiter = delimiters[0];\n let f = str.indexOf(delimiter);\n while (f !== -1) {\n const token = str.subarray(0, f);\n if (!skipEmpty || token.length !== 0) {\n result.push(token);\n }\n str = str.subarray(f + 1);\n f = str.indexOf(delimiter);\n }\n if (!skipEmpty || str.length !== 0) {\n result.push(str);\n }\n return;\n }\n let tokenStart = 0;\n for (let i = 0; i < str.length + 1; i++) {\n if (i === str.length || delimiters.indexOf(str[i]) !== -1) {\n const token = str.subarray(tokenStart, i);\n if (!skipEmpty || token.length !== 0) {\n result.push(token);\n }\n tokenStart = i + 1;\n }\n }\n}\nfunction stringSplitImpl(input2, delimiter, skipEmpty) {\n const batchSize = input2.length;\n const tokens = [];\n let outputSize = 0;\n let maxNumEntries = 0;\n const numIndices = new Array(batchSize);\n for (let i = 0; i < batchSize; ++i) {\n const prevTokensLength = tokens.length;\n split3(input2[i], delimiter, skipEmpty, tokens);\n const nEntries = tokens.length - prevTokensLength;\n numIndices[i] = nEntries;\n outputSize += nEntries;\n maxNumEntries = Math.max(maxNumEntries, nEntries);\n }\n const indices = util_exports.getArrayFromDType(\"int32\", outputSize * 2);\n const values = new Array(outputSize);\n const shape = [batchSize, maxNumEntries];\n let c = 0;\n for (let i = 0; i < batchSize; ++i) {\n for (let j = 0; j < numIndices[i]; ++j) {\n indices[c * 2] = i;\n indices[c * 2 + 1] = j;\n values[c] = tokens[c];\n ++c;\n }\n }\n return [indices, values, shape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast_impl.js\nfunction stringToHashBucketFastImpl(input2, numBuckets) {\n const output = util_exports.getArrayFromDType(\"int32\", input2.length);\n for (let i = 0; i < input2.length; ++i) {\n output[i] = util_exports.fingerPrint64(input2[i]).modulo(numBuckets).getLowBitsUnsigned();\n }\n return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sub.js\nvar subImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue - bValue);\nvar subComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n return { real: aReal - bReal, imag: aImag - bImag };\n});\nvar sub2 = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nvar subConfig = {\n kernelName: Sub,\n backendName: \"cpu\",\n kernelFunc: sub2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js\nfunction tileImpl(xBuf, reps) {\n const newShape = new Array(xBuf.rank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = xBuf.shape[i] * reps[i];\n }\n const result = buffer(newShape, xBuf.dtype);\n for (let i = 0; i < result.values.length; ++i) {\n const newLoc = result.indexToLoc(i);\n const originalLoc = new Array(xBuf.rank);\n for (let j = 0; j < originalLoc.length; j++) {\n originalLoc[j] = newLoc[j] % xBuf.shape[j];\n }\n const originalIndex = xBuf.locToIndex(originalLoc);\n result.values[i] = xBuf.values[originalIndex];\n }\n return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK_impl.js\nvar comparePair = (a, b) => {\n const valueDiff = b.value - a.value;\n return valueDiff === 0 ? a.index - b.index : valueDiff;\n};\nfunction select(array2, k, left = 0, right = array2.length - 1) {\n while (right > left) {\n if (right - left > 600) {\n const n = right - left + 1;\n const i2 = k - left + 1;\n const z = Math.log(n);\n const s = 0.5 * Math.exp(2 * z / 3);\n const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i2 - n / 2);\n const newLeft = Math.max(left, Math.floor(k - i2 * s / n + sd));\n const newRight = Math.min(right, Math.floor(k + (n - i2) * s / n + sd));\n select(array2, k, newLeft, newRight);\n }\n const t = array2[k];\n let i = left;\n let j = right;\n util_exports.swap(array2, left, k);\n if (comparePair(array2[right], t) > 0) {\n util_exports.swap(array2, left, right);\n }\n while (i < j) {\n util_exports.swap(array2, i, j);\n i++;\n j--;\n while (comparePair(array2[i], t) < 0) {\n i = i + 1;\n }\n while (comparePair(array2[j], t) > 0) {\n j = j - 1;\n }\n }\n if (comparePair(array2[left], t) === 0) {\n util_exports.swap(array2, left, j);\n } else {\n j = j + 1;\n util_exports.swap(array2, j, right);\n }\n if (j <= k) {\n left = j + 1;\n }\n if (k <= j) {\n right = j - 1;\n }\n }\n}\nfunction topKImpl(x, xShape, xDtype, k, sorted) {\n const lastDim = xShape[xShape.length - 1];\n const [batch, size] = [x.length / lastDim, lastDim];\n const allTopKVals = util_exports.getTypedArrayFromDType(xDtype, batch * k);\n const allTopKIndices = util_exports.getTypedArrayFromDType(\"int32\", batch * k);\n for (let b = 0; b < batch; b++) {\n const offset = b * size;\n const vals = x.subarray(offset, offset + size);\n let valAndInd = new Array(vals.length);\n vals.forEach((value, index) => valAndInd[index] = { value, index });\n if (k < valAndInd.length) {\n select(valAndInd, k);\n valAndInd = valAndInd.slice(0, k);\n }\n if (sorted) {\n valAndInd.sort(comparePair);\n }\n const outOffset = b * k;\n const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n for (let i = 0; i < k; i++) {\n topKVals[i] = valAndInd[i].value;\n topKIndices[i] = valAndInd[i].index;\n }\n }\n const outputShape = xShape.slice();\n outputShape[outputShape.length - 1] = k;\n return [\n buffer(outputShape, xDtype, allTopKVals),\n buffer(outputShape, \"int32\", allTopKIndices)\n ];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique_impl.js\nfunction uniqueImpl(values, axis, shape, dtype) {\n const $axis = util_exports.parseAxisParam(axis, shape)[0];\n const newShape = [1, shape[0], 1];\n for (let i = 0; i < $axis; i++) {\n newShape[0] *= shape[i];\n }\n newShape[1] = shape[$axis];\n for (let i = $axis + 1; i < shape.length; i++) {\n newShape[2] *= shape[i];\n }\n const uniqueElements = /* @__PURE__ */ new Map();\n const indices = new Int32Array(shape[$axis]);\n const inputBuffer = new TensorBuffer(newShape, dtype, values);\n const uniqueIndices = [];\n const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n for (let i = 0; i < shape[$axis]; i++) {\n let element;\n if (is1DTensor) {\n element = values[i].toString();\n } else {\n const axisValues = [];\n for (let m = 0; m < newShape[0]; m++) {\n for (let n = 0; n < newShape[2]; n++) {\n axisValues.push(inputBuffer.get(m, i, n));\n }\n }\n element = axisValues.join(\",\");\n }\n const existingIndex = uniqueElements.get(element);\n if (existingIndex != null) {\n indices[i] = existingIndex;\n } else {\n const uniqueIndex = uniqueElements.size;\n uniqueElements.set(element, uniqueIndex);\n indices[i] = uniqueIndex;\n uniqueIndices.push(i);\n }\n }\n const outputTmpShape = newShape.slice();\n outputTmpShape[1] = uniqueElements.size;\n const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n uniqueIndices.forEach((uniqueElementIndex, i) => {\n for (let m = 0; m < newShape[0]; m++) {\n for (let n = 0; n < newShape[2]; n++) {\n outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n }\n }\n });\n const outputShape = shape.slice();\n outputShape[$axis] = outputTmpShape[1];\n return {\n outputValues: outputBuffer.values,\n outputShape,\n indices\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/version.js\nvar version5 = \"4.16.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/base.js\nregisterBackend(\n \"cpu\",\n () => new MathBackendCPU(),\n 1\n /* priority */\n);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Elu.js\nvar elu4 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1);\nvar eluConfig = {\n kernelName: Elu,\n backendName: \"cpu\",\n kernelFunc: elu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LeakyRelu.js\nfunction leakyRelu2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { alpha } = attrs;\n assertNotComplex([x], \"leakyRelu\");\n const xSize = util_exports.sizeFromShape(x.shape);\n const xVals = backend2.data.get(x.dataId).values;\n const outVals = util_exports.getTypedArrayFromDType(\"float32\", xSize);\n for (let i = 0; i < xVals.length; i++) {\n outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i];\n }\n return backend2.makeTensorInfo(x.shape, \"float32\", outVals);\n}\nvar leakyReluConfig = {\n kernelName: LeakyRelu,\n backendName: \"cpu\",\n kernelFunc: leakyRelu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prelu.js\nvar preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\nfunction prelu3(args) {\n const { inputs, backend: backend2 } = args;\n const { x, alpha } = inputs;\n assertNotComplex([x, alpha], \"prelu\");\n const aVals = backend2.data.get(x.dataId).values;\n const bVals = backend2.data.get(alpha.dataId).values;\n const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, \"float32\");\n return backend2.makeTensorInfo(resultShape, \"float32\", resultData);\n}\nvar preluConfig = {\n kernelName: Prelu,\n backendName: \"cpu\",\n kernelFunc: prelu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu.js\nvar relu2 = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));\nvar reluConfig = {\n kernelName: Relu,\n backendName: \"cpu\",\n kernelFunc: relu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu6.js\nvar relu62 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));\nvar relu6Config = {\n kernelName: Relu6,\n backendName: \"cpu\",\n kernelFunc: relu62\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fused_utils.js\nfunction applyActivation2(backend2, x, activation2, preluActivationWeights, leakyreluAlpha) {\n if (activation2 === \"linear\") {\n return identity2({ inputs: { x }, backend: backend2 });\n } else if (activation2 === \"relu\") {\n return relu2({ inputs: { x }, backend: backend2 });\n } else if (activation2 === \"elu\") {\n return elu4({ inputs: { x }, backend: backend2 });\n } else if (activation2 === \"relu6\") {\n return relu62({ inputs: { x }, backend: backend2 });\n } else if (activation2 === \"prelu\") {\n return prelu3({ inputs: { x, alpha: preluActivationWeights }, backend: backend2 });\n } else if (activation2 === \"leakyrelu\") {\n return leakyRelu2({ inputs: { x }, backend: backend2, attrs: { alpha: leakyreluAlpha } });\n } else if (activation2 === \"sigmoid\") {\n return sigmoid2({ inputs: { x }, backend: backend2 });\n }\n throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reshape.js\nfunction reshape3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { shape } = attrs;\n const xSize = util_exports.sizeFromShape(x.shape);\n const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n const $xSize = util_exports.sizeFromShape($shape);\n util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n backend2.incRef(x.dataId);\n const xData = backend2.data.get(x.dataId);\n if (xData.complexTensorInfos != null) {\n const real4 = xData.complexTensorInfos.real;\n const imag4 = xData.complexTensorInfos.imag;\n real4.shape = $shape;\n imag4.shape = $shape;\n }\n return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig = {\n kernelName: Reshape,\n backendName: \"cpu\",\n kernelFunc: reshape3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchMatMul.js\nfunction batchMatMul(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { a, b } = inputs;\n const { transposeA, transposeB } = attrs;\n assertNotComplex([a, b], \"matMul\");\n const aRank = a.shape.length;\n const bRank = b.shape.length;\n const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n const outerDimsA = a.shape.slice(0, -2);\n const outerDimsB = b.shape.slice(0, -2);\n const batchDimA = util_exports.sizeFromShape(outerDimsA);\n const batchDimB = util_exports.sizeFromShape(outerDimsB);\n const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n const a3d = reshape3({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n const b3d = reshape3({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n const batchDim = Math.max(batchDimA, batchDimB);\n const a3dValues = backend2.data.get(a3d.dataId).values;\n const b3dValues = backend2.data.get(b3d.dataId).values;\n const a3dStrides = util_exports.computeStrides(a3d.shape);\n const b3dStrides = util_exports.computeStrides(b3d.shape);\n const [aBatch, aOuterStep, aInnerStep] = transposeA ? [a3dStrides[0], 1, a3dStrides[1]] : [a3dStrides[0], a3dStrides[1], 1];\n const [bInnerStep, bOuterStep, bBatch] = transposeB ? [1, b3dStrides[1], b3dStrides[0]] : [b3dStrides[1], 1, b3dStrides[0]];\n const size = leftDim * rightDim;\n const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);\n const resVals = result.values;\n const blockSize = backend2.blockSize;\n for (let bi = 0; bi < batchDim; bi++) {\n const batchIndexA = bi % batchDimA;\n const batchIndexB = bi % batchDimB;\n for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n const iBlock = Math.min(i0 + blockSize, leftDim);\n for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n const jBlock = Math.min(j0 + blockSize, rightDim);\n for (let k02 = 0; k02 < sharedDim; k02 += blockSize) {\n const kBlock = Math.min(k02 + blockSize, sharedDim);\n for (let i = i0; i < iBlock; i++) {\n for (let j = j0; j < jBlock; j++) {\n let sum6 = 0;\n for (let k = k02; k < kBlock; k++) {\n const aVal = (\n // tslint:disable-next-line: max-line-length\n a3dValues[batchIndexA * aBatch + i * aOuterStep + k * aInnerStep]\n );\n const bVal = (\n // tslint:disable-next-line: max-line-length\n b3dValues[k * bInnerStep + j * bOuterStep + batchIndexB * bBatch]\n );\n sum6 += aVal * bVal;\n }\n resVals[bi * size + (i * rightDim + j)] += sum6;\n }\n }\n }\n }\n }\n }\n backend2.disposeIntermediateTensorInfo(a3d);\n backend2.disposeIntermediateTensorInfo(b3d);\n return backend2.makeTensorInfo(outShape, result.dtype, result.values);\n}\nvar batchMatMulConfig = {\n kernelName: BatchMatMul,\n backendName: \"cpu\",\n kernelFunc: batchMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { a, b, bias, preluActivationWeights } = inputs;\n const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n let current;\n let addRes;\n let activationRes;\n const intermediates = [];\n const matMulRes = batchMatMul({ inputs: { a, b }, attrs: { transposeA, transposeB }, backend: backend2 });\n current = matMulRes;\n if (bias) {\n addRes = add4({ inputs: { a: current, b: bias }, backend: backend2 });\n intermediates.push(current);\n current = addRes;\n }\n if (activation2) {\n activationRes = applyActivation2(backend2, current, activation2, preluActivationWeights, leakyreluAlpha);\n intermediates.push(current);\n current = activationRes;\n }\n for (const i of intermediates) {\n backend2.disposeIntermediateTensorInfo(i);\n }\n return current;\n}\nvar _fusedMatMulConfig = {\n kernelName: _FusedMatMul,\n backendName: \"cpu\",\n kernelFunc: _fusedMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acos.js\nvar acos2 = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nvar acosConfig = {\n kernelName: Acos,\n backendName: \"cpu\",\n kernelFunc: acos2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acosh.js\nvar acosh2 = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nvar acoshConfig = {\n kernelName: Acosh,\n backendName: \"cpu\",\n kernelFunc: acosh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AddN.js\nfunction addN2(args) {\n const { inputs, backend: backend2 } = args;\n const tensors = inputs;\n assertNotComplex(inputs, \"addN\");\n const vals = tensors.map((t) => backend2.data.get(t.dataId).values);\n const outBuf = buffer(tensors[0].shape, tensors[0].dtype);\n const outVals = outBuf.values;\n for (let i = 0; i < tensors.length; i++) {\n const currVals = vals[i];\n for (let j = 0; j < outVals.length; j++) {\n outVals[j] += currVals[j];\n }\n }\n return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar addNConfig = {\n kernelName: AddN,\n backendName: \"cpu\",\n kernelFunc: addN2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/All.js\nfunction all2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n assertNotComplex(x, \"all\");\n const origAxes = util_exports.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n if (permutedAxes != null) {\n $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, $x.shape.length);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n const aVals = backend2.data.get($x.dataId).values;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let all5 = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n all5 = all5 && value;\n }\n vals[i] = all5;\n }\n if (permutedAxes != null) {\n backend2.disposeIntermediateTensorInfo($x);\n }\n const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n if (keepDims) {\n const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n backend2.disposeIntermediateTensorInfo(result);\n return reshapedResult;\n }\n return result;\n}\nvar allConfig = {\n kernelName: All,\n backendName: \"cpu\",\n kernelFunc: all2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Any.js\nfunction any2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n assertNotComplex(x, \"any\");\n const origAxes = util_exports.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n if (permutedAxes != null) {\n $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, $x.shape.length);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n const aVals = backend2.data.get($x.dataId).values;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let anyVal = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n anyVal = anyVal || value;\n }\n vals[i] = anyVal;\n }\n if (permutedAxes != null) {\n backend2.disposeIntermediateTensorInfo($x);\n }\n const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n if (keepDims) {\n const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n backend2.disposeIntermediateTensorInfo(result);\n return reshapedResult;\n }\n return result;\n}\nvar anyConfig = {\n kernelName: Any,\n backendName: \"cpu\",\n kernelFunc: any2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMax.js\nfunction argMax2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis } = attrs;\n assertNotComplex(x, \"argMax\");\n let axes = util_exports.parseAxisParam(axis, x.shape);\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n const intermediateTensorInfos = [];\n if (permutedAxes != null) {\n $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n intermediateTensorInfos.push($x);\n axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n }\n axes = [axes[0]];\n backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", axes, $x.shape.length);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n const outSize = util_exports.sizeFromShape(outShape);\n const vals = util_exports.makeZerosTypedArray(outSize, \"int32\");\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const aVals = backend2.data.get($x.dataId).values;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let max6 = aVals[offset];\n let maxIndex = 0;\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (value > max6) {\n max6 = value;\n maxIndex = j;\n }\n }\n vals[i] = maxIndex;\n }\n intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return backend2.makeTensorInfo(outShape, \"int32\", vals);\n}\nvar argMaxConfig = {\n kernelName: ArgMax,\n backendName: \"cpu\",\n kernelFunc: argMax2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMin.js\nfunction argMin2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis } = attrs;\n assertNotComplex(x, \"argMin\");\n let axes = util_exports.parseAxisParam(axis, x.shape);\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n const intermediateTensorInfos = [];\n if (permutedAxes != null) {\n $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n intermediateTensorInfos.push($x);\n axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n }\n axes = [axes[0]];\n backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", axes, $x.shape.length);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n const outSize = util_exports.sizeFromShape(outShape);\n const vals = util_exports.makeZerosTypedArray(outSize, \"int32\");\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const aVals = backend2.data.get($x.dataId).values;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let min6 = aVals[offset];\n let minIndex = 0;\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (value < min6) {\n min6 = value;\n minIndex = j;\n }\n }\n vals[i] = minIndex;\n }\n intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return backend2.makeTensorInfo(outShape, \"int32\", vals);\n}\nvar argMinConfig = {\n kernelName: ArgMin,\n backendName: \"cpu\",\n kernelFunc: argMin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asin.js\nvar asin2 = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nvar asinConfig = {\n kernelName: Asin,\n backendName: \"cpu\",\n kernelFunc: asin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asinh.js\nvar asinh2 = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nvar asinhConfig = {\n kernelName: Asinh,\n backendName: \"cpu\",\n kernelFunc: asinh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan.js\nvar atan3 = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nvar atanConfig = {\n kernelName: Atan,\n backendName: \"cpu\",\n kernelFunc: atan3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan2.js\nvar atan2Impl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.atan2(aValue, bValue));\nvar atan22 = binaryKernelFunc(Atan2, atan2Impl);\nvar atan2Config = {\n kernelName: Atan2,\n backendName: \"cpu\",\n kernelFunc: atan22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atanh.js\nvar atanh2 = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nvar atanhConfig = {\n kernelName: Atanh,\n backendName: \"cpu\",\n kernelFunc: atanh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/pool_utils.js\nfunction pool2(xValues, xShape, dtype, strides, convInfo, poolType) {\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const initialValue = poolType === \"max\" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;\n const output = buffer(convInfo.outShape, dtype);\n const outputVals = output.values;\n const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n const outputColStrides = convInfo.outShape[3];\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const outputBatchOffset = b * outputBatchStrides;\n const inputBatchOffset = b * strides[0];\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const xRCorner = yR * strideHeight - padTop;\n const xRMin = Math.max(0, xRCorner);\n const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const xCCorner = yC * strideWidth - padLeft;\n const xCMin = Math.max(0, xCCorner);\n const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n let minMaxValue = initialValue;\n let avgValue = 0;\n let count2 = 0;\n for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n const xROffset = inputBatchOffset + xR * strides[1];\n for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n const xCOffset = xROffset + xC * strides[2];\n const pixel = xValues[xCOffset + d];\n if (poolType === \"max\" && pixel > minMaxValue) {\n minMaxValue = pixel;\n } else if (poolType === \"avg\") {\n avgValue += pixel;\n count2++;\n }\n }\n if (isNaN(minMaxValue)) {\n break;\n }\n }\n const outputOffset = outputRowOffset + yC * outputColStrides + d;\n outputVals[outputOffset] = poolType === \"avg\" ? avgValue / count2 : minMaxValue;\n }\n }\n }\n }\n return output;\n}\nfunction maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n const maxPositions = buffer(convInfo.outShape, \"int32\");\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const xBuf = buffer(xShape, dtype, xValues);\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const xRCorner = yR * strideHeight - padTop;\n let xRMin = xRCorner;\n while (xRMin < 0) {\n xRMin += dilationHeight;\n }\n const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const xCCorner = yC * strideWidth - padLeft;\n let xCMin = xCCorner;\n while (xCMin < 0) {\n xCMin += dilationWidth;\n }\n const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n let maxValue = Number.NEGATIVE_INFINITY;\n let maxPosition = -1;\n for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n const wR = xR - xRCorner;\n for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n const wC = xC - xCCorner;\n const pixel = xBuf.get(b, xR, xC, d);\n if (pixel > maxValue) {\n maxValue = pixel;\n if (flattenPositions) {\n maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n } else {\n maxPosition = wR * effectiveFilterWidth + wC;\n }\n }\n }\n }\n maxPositions.set(maxPosition, b, yR, yC, d);\n }\n }\n }\n }\n return maxPositions;\n}\nfunction pool3d2(xValues, xShape, dtype, strides, convInfo, poolType) {\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = convInfo.padInfo.front;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const initialValue = poolType === \"max\" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;\n const output = buffer(convInfo.outShape, dtype);\n const outputVals = output.values;\n const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n const outputColStrides = convInfo.outShape[4];\n for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n const outputBatchOffset = batch * outputBatchStrides;\n const inputBatchOffset = batch * strides[0];\n for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n const xDepthCorner = yDepth * strideDepth - padFront;\n let xDepthMin = xDepthCorner;\n while (xDepthMin < 0) {\n xDepthMin += dilationDepth;\n }\n const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n const xRowCorner = yRow * strideHeight - padTop;\n let xRowMin = xRowCorner;\n while (xRowMin < 0) {\n xRowMin += dilationHeight;\n }\n const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n const xColCorner = yCol * strideWidth - padLeft;\n let xColMin = xColCorner;\n while (xColMin < 0) {\n xColMin += dilationWidth;\n }\n const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n const outputColOffset = outputRowOffset + yCol * outputColStrides;\n let minMaxValue = initialValue;\n let avgValue = 0;\n let count2 = 0;\n for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n const xDepthOffset = inputBatchOffset + xDepth * strides[1];\n for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n const xRowOffset = xDepthOffset + xRow * strides[2];\n for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n const xColOffset = xRowOffset + xCol * strides[3];\n const pixel = xValues[xColOffset + channel];\n if (poolType === \"max\" && pixel > minMaxValue) {\n minMaxValue = pixel;\n } else if (poolType === \"avg\") {\n avgValue += pixel;\n count2++;\n }\n if (isNaN(minMaxValue)) {\n break;\n }\n }\n if (isNaN(minMaxValue)) {\n break;\n }\n }\n if (isNaN(minMaxValue)) {\n break;\n }\n }\n const outputOffset = outputColOffset + channel;\n outputVals[outputOffset] = poolType === \"avg\" ? avgValue / Math.max(count2, 1) : minMaxValue;\n }\n }\n }\n }\n }\n return output;\n}\nfunction maxPool3dPositions(xBuf, convInfo) {\n const maxPositions = buffer(convInfo.outShape, \"int32\");\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = convInfo.padInfo.front;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n const xDepthCorner = yDepth * strideDepth - padFront;\n let xDepthMin = xDepthCorner;\n while (xDepthMin < 0) {\n xDepthMin += dilationDepth;\n }\n const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n const xRowCorner = yRow * strideHeight - padTop;\n let xRowMin = xRowCorner;\n while (xRowMin < 0) {\n xRowMin += dilationHeight;\n }\n const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n const xColCorner = yCol * strideWidth - padLeft;\n let xColMin = xColCorner;\n while (xColMin < 0) {\n xColMin += dilationWidth;\n }\n const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n let maxValue = Number.NEGATIVE_INFINITY;\n let maxPosition = -1;\n for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n const wDepth = xDepth - xDepthCorner;\n for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n const wRow = xRow - xRowCorner;\n for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n const wCol = xCol - xColCorner;\n const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n if (pixel >= maxValue) {\n maxValue = pixel;\n maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol;\n }\n }\n }\n }\n maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n }\n }\n }\n }\n }\n return maxPositions;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool.js\nfunction avgPool2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n assertNotComplex(x, \"avgPool\");\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const dilations = 1;\n util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n let res;\n if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n res = identity2({ inputs: { x }, backend: backend2 });\n } else {\n const xValues = backend2.data.get(x.dataId).values;\n const strides2 = util_exports.computeStrides(x.shape);\n const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, \"avg\");\n res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);\n }\n return res;\n}\nvar avgPoolConfig = {\n kernelName: AvgPool,\n backendName: \"cpu\",\n kernelFunc: avgPool2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3D.js\nfunction avgPool3D(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n assertNotComplex(x, \"avgPool3d\");\n const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);\n const xValues = backend2.data.get(x.dataId).values;\n const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, \"avg\");\n return backend2.makeTensorInfo(outBuf.shape, \"float32\", outBuf.values);\n}\nvar avgPool3DConfig = {\n kernelName: AvgPool3D,\n backendName: \"cpu\",\n kernelFunc: avgPool3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3DGrad.js\nfunction avgPool3DGrad(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2 } = inputs;\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n assertNotComplex([dy, input2], \"avgPool3DGrad\");\n const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const dx = buffer(input2.shape, \"float32\");\n const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n const dyBuf = backend2.bufferSync(dy);\n for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n const dyDepthCorner = dxDepth - padFront;\n const dyRowCorner = dxRow - padTop;\n const dyColCorner = dxCol - padLeft;\n let dotProd = 0;\n for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {\n continue;\n }\n for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n const dyRow = (dyRowCorner + wRow) / strideHeight;\n if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {\n continue;\n }\n for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n const dyCol = (dyColCorner + wCol) / strideWidth;\n if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {\n continue;\n }\n const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n dotProd += pixel;\n }\n }\n }\n dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n }\n }\n }\n }\n }\n return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar avgPool3DGradConfig2 = {\n kernelName: AvgPool3DGrad,\n backendName: \"cpu\",\n kernelFunc: avgPool3DGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPoolGrad.js\nfunction avgPoolGrad2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2 } = inputs;\n const x = input2;\n assertNotComplex([dy, input2], \"avgPoolGrad\");\n const { filterSize, strides, pad: pad3 } = attrs;\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const dx = buffer(x.shape, \"float32\");\n const avgMultiplier = 1 / (filterHeight * filterWidth);\n const dyData = backend2.data.get(dy.dataId).values;\n const dyBuf = buffer(dy.shape, \"float32\", dyData);\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n const dyRCorner = dxR - padTop;\n const dyCCorner = dxC - padLeft;\n let dotProd = 0;\n for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n const dyR = (dyRCorner + wR) / strideHeight;\n if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {\n continue;\n }\n for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n const dyC = (dyCCorner + wC) / strideWidth;\n if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {\n continue;\n }\n const pixel = dyBuf.get(b, dyR, dyC, d);\n dotProd += pixel;\n }\n }\n dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n }\n }\n }\n }\n return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar avgPoolGradConfig2 = {\n kernelName: AvgPoolGrad,\n backendName: \"cpu\",\n kernelFunc: avgPoolGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchNorm.js\nfunction batchNorm2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, scale: scale2, offset, mean: mean4, variance } = inputs;\n util_exports.assert(mean4.shape.length === variance.shape.length, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n util_exports.assert(offset == null || mean4.shape.length === offset.shape.length, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n util_exports.assert(scale2 == null || mean4.shape.length === scale2.shape.length, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n assertNotComplex([x, mean4, variance, scale2, offset], \"batchNorm\");\n let { varianceEpsilon } = attrs;\n if (varianceEpsilon == null) {\n varianceEpsilon = 1e-3;\n }\n const xVals = backend2.data.get(x.dataId).values;\n const mVals = backend2.data.get(mean4.dataId).values;\n const varVals = backend2.data.get(variance.dataId).values;\n const sVals = scale2 ? backend2.data.get(scale2.dataId).values : new Float32Array([1]);\n const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]);\n const outVals = new Float32Array(xVals.length);\n const offValsLength = offVals.length;\n const sValsLength = sVals.length;\n const varValsLength = varVals.length;\n const mValsLength = mVals.length;\n let offi = 0;\n let mi = 0;\n let si = 0;\n let vi = 0;\n for (let i = 0; i < xVals.length; ++i) {\n outVals[i] = offVals[offi++] + (xVals[i] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);\n if (offi >= offValsLength) {\n offi = 0;\n }\n if (mi >= mValsLength) {\n mi = 0;\n }\n if (si >= sValsLength) {\n si = 0;\n }\n if (vi >= varValsLength) {\n vi = 0;\n }\n }\n return backend2.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nvar batchNormConfig = {\n kernelName: FusedBatchNorm,\n backendName: \"cpu\",\n kernelFunc: batchNorm2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchToSpaceND.js\nfunction batchToSpaceND2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { blockShape, crops } = attrs;\n assertNotComplex([x], \"batchToSpaceND\");\n const prod5 = blockShape.reduce((a, b) => a * b);\n const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5);\n const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5);\n const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n const xReshaped = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n const xTransposed = transpose2({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });\n const xTransposedReshaped = reshape3({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });\n const result = slice2({\n inputs: { x: xTransposedReshaped },\n backend: backend2,\n attrs: { begin: sliceBeginCoords, size: sliceSize }\n });\n backend2.disposeIntermediateTensorInfo(xReshaped);\n backend2.disposeIntermediateTensorInfo(xTransposed);\n backend2.disposeIntermediateTensorInfo(xTransposedReshaped);\n return result;\n}\nvar batchToSpaceNDConfig = {\n kernelName: BatchToSpaceND,\n backendName: \"cpu\",\n kernelFunc: batchToSpaceND2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount.js\nfunction bincount2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, weights } = inputs;\n const { size } = attrs;\n const xVals = backend2.data.get(x.dataId).values;\n const weightsVals = backend2.data.get(weights.dataId).values;\n const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);\n return backend2.makeTensorInfo([size], weights.dtype, outVals);\n}\nvar bincountConfig = {\n kernelName: Bincount,\n backendName: \"cpu\",\n kernelFunc: bincount2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BroadcastArgs.js\nfunction broadcastArgs2(args) {\n const { inputs, backend: backend2 } = args;\n const { s0, s1 } = inputs;\n const s0Vals = backend2.data.get(s0.dataId).values;\n const s1Vals = backend2.data.get(s1.dataId).values;\n const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));\n return backend2.makeTensorInfo([broadcastShape.length], \"int32\", Int32Array.from(broadcastShape));\n}\nvar broadcastArgsConfig = {\n kernelName: BroadcastArgs,\n backendName: \"cpu\",\n kernelFunc: broadcastArgs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ClipByValue.js\nvar clipByValue2 = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n const clipAttrs = attrs;\n if (xi > clipAttrs.clipValueMax) {\n return clipAttrs.clipValueMax;\n }\n return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nvar clipByValueConfig = {\n kernelName: ClipByValue,\n backendName: \"cpu\",\n kernelFunc: clipByValue2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ComplexAbs.js\nvar complexAbs = (args) => {\n const { x } = args.inputs;\n const cpuBackend = args.backend;\n const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));\n const complexVals = cpuBackend.data.get(x.dataId);\n const real4 = complexVals.complexTensorInfos.real;\n const imag4 = complexVals.complexTensorInfos.imag;\n const realVals = cpuBackend.data.get(real4.dataId).values;\n const imagVals = cpuBackend.data.get(imag4.dataId).values;\n for (let i = 0; i < realVals.length; i++) {\n const real5 = realVals[i];\n const imag5 = imagVals[i];\n resultValues[i] = Math.hypot(real5, imag5);\n }\n return cpuBackend.makeOutput(resultValues, x.shape, \"float32\");\n};\nvar complexAbsConfig = {\n kernelName: ComplexAbs,\n backendName: \"cpu\",\n kernelFunc: complexAbs\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Imag.js\nfunction imag2(args) {\n const { inputs, backend: backend2 } = args;\n const { input: input2 } = inputs;\n const imag4 = backend2.data.get(input2.dataId).complexTensorInfos.imag;\n const imagVal = backend2.data.get(imag4.dataId).values;\n return backend2.makeTensorInfo(imag4.shape, imag4.dtype, imagVal);\n}\nvar imagConfig = {\n kernelName: Imag,\n backendName: \"cpu\",\n kernelFunc: imag2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat.js\nfunction concat2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { axis } = attrs;\n const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n const shapes = inputs.map((t) => t.shape);\n backend_util_exports.assertParamsConsistent(shapes, $axis);\n let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);\n if (util_exports.sizeFromShape(outShape) === 0) {\n return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n }\n const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);\n if ($inputs.length === 1) {\n return identity2({ inputs: { x: $inputs[0] }, backend: backend2 });\n }\n if ($inputs[0].dtype === \"complex64\") {\n const reals = $inputs.map((t) => real2({ inputs: { input: t }, backend: backend2 }));\n const imags = $inputs.map((t) => imag2({ inputs: { input: t }, backend: backend2 }));\n const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } });\n const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } });\n const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));\n imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));\n backend2.disposeIntermediateTensorInfo(realConcated);\n backend2.disposeIntermediateTensorInfo(imagConcated);\n return result;\n }\n const inputs2D = $inputs.map((t) => {\n const innerSize = util_exports.sizeFromShape(t.shape.slice($axis));\n const shape = [-1, innerSize];\n return reshape3({ inputs: { x: t }, backend: backend2, attrs: { shape } });\n });\n const inputsValShapes = inputs2D.map((t) => {\n return { vals: backend2.data.get(t.dataId).values, shape: t.shape };\n });\n outShape = backend_util_exports.computeOutShape(\n inputs2D.map((t) => t.shape),\n 1\n /* axis */\n );\n const simplyConcat = inputs2D[0].shape[0] === 1;\n const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);\n const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), $axis);\n const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n inputs2D.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return outInfo;\n}\nvar concatConfig = {\n kernelName: Concat,\n backendName: \"cpu\",\n kernelFunc: concat2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2D.js\nfunction conv2D(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter } = inputs;\n const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n assertNotComplex([x, filter], \"conv2d\");\n const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const padLeft = convInfo.padInfo.left;\n const padTop = convInfo.padInfo.top;\n const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n const y = new TensorBuffer(convInfo.outShape, x.dtype);\n const xStrides = util_exports.computeStrides(x.shape);\n const filterStrides = util_exports.computeStrides(filter.shape);\n const xBatchStride = xStrides[0];\n const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];\n const xColStride = isChannelsLast ? xStrides[2] : 1;\n const xChannelStride = isChannelsLast ? 1 : xStrides[1];\n const yBatchStride = y.strides[0];\n const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n const yColStride = isChannelsLast ? y.strides[2] : 1;\n const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n const xVals = backend2.data.get(x.dataId).values;\n const wVals = backend2.data.get(filter.dataId).values;\n const yVals = y.values;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * xBatchStride;\n const yOffset1 = b * yBatchStride;\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const yOffset2 = yOffset1 + yR * yRowStride;\n const xRCorner = yR * convInfo.strideHeight - padTop;\n for (let wR = 0; wR < filterHeight; ++wR) {\n const xR = xRCorner + wR * dilationHeight;\n if (xR < 0 || xR >= convInfo.inHeight) {\n continue;\n }\n const wOffset1 = wR * filterStrides[0];\n const xOffset2 = xOffset1 + xR * xRowStride;\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const yOffset3 = yOffset2 + yC * yColStride;\n const xCCorner = yC * convInfo.strideWidth - padLeft;\n for (let wC = 0; wC < filterWidth; ++wC) {\n const xC = xCCorner + wC * dilationWidth;\n if (xC < 0 || xC >= convInfo.inWidth) {\n continue;\n }\n const wOffset2 = wOffset1 + wC * filterStrides[1];\n const xOffset3 = xOffset2 + xC * xColStride;\n let wOffset3 = wOffset2;\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const xVal = xVals[xOffset3 + d1 * xChannelStride];\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2];\n }\n wOffset3 += convInfo.outChannels;\n }\n }\n }\n }\n }\n }\n return backend2.makeTensorInfo(y.shape, y.dtype, yVals);\n}\nvar conv2DConfig = {\n kernelName: Conv2D,\n backendName: \"cpu\",\n kernelFunc: conv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropFilter.js\nfunction conv2DBackpropFilter2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, dy } = inputs;\n const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;\n assertNotComplex([x, dy], \"conv2dBackpropFilter\");\n const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n const dW = new TensorBuffer(convInfo.filterShape, \"float32\");\n const leftPad = convInfo.padInfo.left;\n const topPad = convInfo.padInfo.top;\n const xVals = backend2.data.get(x.dataId).values;\n const dyVals = backend2.data.get(dy.dataId).values;\n const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n for (let wR = 0; wR < filterHeight; ++wR) {\n const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n for (let wC = 0; wC < filterWidth; ++wC) {\n const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n let dotProd = 0;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let yR = yRMin; yR < yRMax; ++yR) {\n const xR = wR + yR * strideHeight - topPad;\n for (let yC = yCMin; yC < yCMax; ++yC) {\n const xC = wC + yC * strideWidth - leftPad;\n if (isChannelsLast) {\n dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n } else {\n dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);\n }\n }\n }\n }\n dW.set(dotProd, wR, wC, d1, d2);\n }\n }\n }\n }\n return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nvar conv2DBackpropFilterConfig = {\n kernelName: Conv2DBackpropFilter,\n backendName: \"cpu\",\n kernelFunc: conv2DBackpropFilter2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, filter } = inputs;\n const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n assertNotComplex([dy, filter], \"conv2dBackpropInput\");\n const filterStrides = util_exports.computeStrides(filter.shape);\n const dyStrides = util_exports.computeStrides(dy.shape);\n let $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n const dxValues = dx.values;\n const dyValues = backend2.data.get(dy.dataId).values;\n const fltValues = backend2.data.get(filter.dataId).values;\n const [fltS0, fltS1, fltS2] = filterStrides;\n const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n $dataFormat = convInfo.dataFormat;\n const topPad = filterHeight - 1 - convInfo.padInfo.top;\n const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n const isChannelsLast = $dataFormat === \"channelsLast\";\n const xBatchStride = dx.strides[0];\n const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n const xColStride = isChannelsLast ? dx.strides[2] : 1;\n const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n const yBatchStride = dyStrides[0];\n const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n const yColStride = isChannelsLast ? dyStrides[2] : 1;\n const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n for (let b = 0; b < batchSize; ++b) {\n for (let d1 = 0; d1 < inChannels; ++d1) {\n for (let xR = 0; xR < inHeight; ++xR) {\n const xRCorner = xR - topPad;\n const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n for (let xC = 0; xC < inWidth; ++xC) {\n const xCCorner = xC - leftPad;\n const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n let dotProd = 0;\n for (let yR = xRMin; yR < yRMax; ++yR) {\n const wR = yR * strideHeight - xRCorner;\n for (let yC = xCMin; yC < yCMax; ++yC) {\n const wC = yC * strideWidth - xCCorner;\n const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n for (let d2 = 0; d2 < outChannels; ++d2) {\n const pixel = dyValues[dyOffset + yChannelStride * d2];\n const weight = fltValues[fltOffset + d2];\n dotProd += pixel * weight;\n }\n }\n }\n const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1;\n dxValues[dxOffset] = dotProd;\n }\n }\n }\n }\n return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar conv2DBackpropInputConfig = {\n kernelName: Conv2DBackpropInput,\n backendName: \"cpu\",\n kernelFunc: conv2DBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3D.js\nfunction conv3D(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter } = inputs;\n const { strides, pad: pad3, dilations } = attrs;\n assertNotComplex([x, filter], \"conv3d\");\n const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);\n const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;\n const padFront = padInfo.front;\n const padLeft = padInfo.left;\n const padTop = padInfo.top;\n const y = new TensorBuffer(convInfo.outShape, x.dtype);\n const xVals = backend2.data.get(x.dataId).values;\n const wVals = backend2.data.get(filter.dataId).values;\n const yVals = y.values;\n const xStrides = util_exports.computeStrides(x.shape);\n const filterStrides = util_exports.computeStrides(filter.shape);\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * xStrides[0];\n const yOffset1 = b * y.strides[0];\n for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n const yOffset2 = yOffset1 + yF * y.strides[1];\n const xFCorner = yF * convInfo.strideDepth - padFront;\n for (let wF = 0; wF < filterDepth; ++wF) {\n const xF = xFCorner + wF * dilationDepth;\n if (xF < 0 || xF >= convInfo.inDepth) {\n continue;\n }\n const wOffset1 = wF * filterStrides[0];\n const xOffset2 = xOffset1 + xF * xStrides[1];\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const yOffset3 = yOffset2 + yR * y.strides[2];\n const xRCorner = yR * convInfo.strideHeight - padTop;\n for (let wR = 0; wR < filterHeight; ++wR) {\n const xR = xRCorner + wR * dilationHeight;\n if (xR < 0 || xR >= convInfo.inHeight) {\n continue;\n }\n const wOffset2 = wOffset1 + wR * filterStrides[1];\n const xOffset3 = xOffset2 + xR * xStrides[2];\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n const xCCorner = yC * convInfo.strideWidth - padLeft;\n for (let wC = 0; wC < filterWidth; ++wC) {\n const xC = xCCorner + wC * dilationWidth;\n if (xC < 0 || xC >= convInfo.inWidth) {\n continue;\n }\n const wOffset3 = wOffset2 + wC * filterStrides[2];\n const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n let wOffset4 = wOffset3;\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const xVal = xVals[xOffset4 + d1];\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n }\n wOffset4 += convInfo.outChannels;\n }\n }\n }\n }\n }\n }\n }\n }\n return backend2.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nvar conv3DConfig = {\n kernelName: Conv3D,\n backendName: \"cpu\",\n kernelFunc: conv3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropFilterV2.js\nfunction conv3DBackpropFilterV2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, dy } = inputs;\n const { strides, pad: pad3, filterShape } = attrs;\n assertNotComplex([x, dy], \"conv3dBackpropFilterV2\");\n const xStrides = util_exports.computeStrides(x.shape);\n const dyStrides = util_exports.computeStrides(dy.shape);\n const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dw = new TensorBuffer(convInfo.filterShape, \"float32\");\n const dwValues = dw.values;\n const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n const dyValues = backend2.data.get(dy.dataId).values;\n const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n const xValues = backend2.data.get(x.dataId).values;\n const [xS0, xS1, xS2, xS3] = xStrides;\n const frontPad = convInfo.padInfo.front;\n const leftPad = convInfo.padInfo.left;\n const topPad = convInfo.padInfo.top;\n for (let wF = 0; wF < filterDepth; ++wF) {\n const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n const wOffset1 = wF * dwS0;\n for (let wR = 0; wR < filterHeight; ++wR) {\n const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n const wOffset2 = wR * dwS1 + wOffset1;\n for (let wC = 0; wC < filterWidth; ++wC) {\n const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n const wOffset3 = wC * dwS2 + wOffset2;\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const wOffset4 = d1 * dwS3 + wOffset3;\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n let dotProd = 0;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * xS0;\n const yOffset1 = b * dyS0;\n for (let yF = yFMin; yF < yFMax; ++yF) {\n const xF = wF + yF * strideDepth - frontPad;\n const xOffset2 = xF * xS1 + xOffset1;\n const yOffset2 = yF * dyS1 + yOffset1;\n for (let yR = yRMin; yR < yRMax; ++yR) {\n const xR = wR + yR * strideHeight - topPad;\n const xOffset3 = xR * xS2 + xOffset2;\n const yOffset3 = yR * dyS2 + yOffset2;\n for (let yC = yCMin; yC < yCMax; ++yC) {\n const xC = wC + yC * strideWidth - leftPad;\n const xOffset4 = xC * xS3 + xOffset3;\n const yOffset4 = yC * dyS3 + yOffset3;\n dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n }\n }\n }\n }\n dwValues[wOffset4 + d2] = dotProd;\n }\n }\n }\n }\n }\n return backend2.makeTensorInfo(dw.shape, dw.dtype, dw.values);\n}\nvar conv3DBackpropFilterV2Config = {\n kernelName: Conv3DBackpropFilterV2,\n backendName: \"cpu\",\n kernelFunc: conv3DBackpropFilterV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropInputV2.js\nfunction conv3DBackpropInputV2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, filter } = inputs;\n const { pad: pad3, strides, inputShape } = attrs;\n assertNotComplex([dy], \"conv3dBackpropInputV2\");\n const dyStrides = util_exports.computeStrides(dy.shape);\n const filterStrides = util_exports.computeStrides(filter.shape);\n const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);\n const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n const dxValues = dx.values;\n const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n const dyValues = backend2.data.get(dy.dataId).values;\n const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n const fltValues = backend2.data.get(filter.dataId).values;\n const [fltS0, fltS1, fltS2, fltS3] = filterStrides;\n const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n const topPad = filterHeight - 1 - convInfo.padInfo.top;\n const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n for (let b = 0; b < batchSize; ++b) {\n for (let d1 = 0; d1 < inChannels; ++d1) {\n for (let xF = 0; xF < inDepth; ++xF) {\n const xFCorner = xF - frontPad;\n const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n for (let xR = 0; xR < inHeight; ++xR) {\n const xRCorner = xR - topPad;\n const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n for (let xC = 0; xC < inWidth; ++xC) {\n const xCCorner = xC - leftPad;\n const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n let dotProd = 0;\n for (let yF = xFMin; yF < yFMax; ++yF) {\n const wF = yF * strideDepth - xFCorner;\n for (let yR = xRMin; yR < yRMax; ++yR) {\n const wR = yR * strideHeight - xRCorner;\n for (let yC = xCMin; yC < yCMax; ++yC) {\n const wC = yC * strideWidth - xCCorner;\n const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n for (let d2 = 0; d2 < outChannels; ++d2) {\n const pixel = dyValues[dyOffset + d2];\n const weight = fltValues[fltOffset + d2];\n dotProd += pixel * weight;\n }\n }\n }\n }\n dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd;\n }\n }\n }\n }\n }\n return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar conv3DBackpropInputV2Config = {\n kernelName: Conv3DBackpropInputV2,\n backendName: \"cpu\",\n kernelFunc: conv3DBackpropInputV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cos.js\nvar cos2 = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nvar cosConfig = {\n kernelName: Cos,\n backendName: \"cpu\",\n kernelFunc: cos2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cosh.js\nvar cosh2 = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nvar coshConfig = {\n kernelName: Cosh,\n backendName: \"cpu\",\n kernelFunc: cosh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/CropAndResize.js\nfunction cropAndResize3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { image: image2, boxes, boxInd } = inputs;\n const { cropSize, method, extrapolationValue } = attrs;\n const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n const numBoxes = boxes.shape[0];\n const [cropHeight, cropWidth] = cropSize;\n const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], \"float32\");\n const boxVals = backend2.data.get(boxes.dataId).values;\n const boxIndVals = backend2.data.get(boxInd.dataId).values;\n const imageVals = backend2.data.get(image2.dataId).values;\n const inStride = util_exports.computeStrides(image2.shape);\n const outStride = util_exports.computeStrides(output.shape);\n for (let b = 0; b < numBoxes; b++) {\n const startInd = b * 4;\n const y1 = boxVals[startInd];\n const x1 = boxVals[startInd + 1];\n const y2 = boxVals[startInd + 2];\n const x2 = boxVals[startInd + 3];\n const bInd = boxIndVals[b];\n if (bInd >= batch) {\n continue;\n }\n const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;\n const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n for (let y = 0; y < cropHeight; y++) {\n const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1);\n if (yInd < 0 || yInd > imageHeight - 1) {\n for (let x = 0; x < cropWidth; x++) {\n for (let c = 0; c < numChannels; c++) {\n const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = extrapolationValue;\n }\n }\n continue;\n }\n if (method === \"bilinear\") {\n const topInd = Math.floor(yInd);\n const bottomInd = Math.ceil(yInd);\n const yLerp = yInd - topInd;\n for (let x = 0; x < cropWidth; x++) {\n const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);\n if (xInd < 0 || xInd > imageWidth - 1) {\n for (let c = 0; c < numChannels; c++) {\n const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = extrapolationValue;\n }\n continue;\n }\n const leftInd = Math.floor(xInd);\n const rightInd = Math.ceil(xInd);\n const xLerp = xInd - leftInd;\n for (let c = 0; c < numChannels; c++) {\n let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];\n const topLeft = imageVals[ind];\n ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];\n const topRight = imageVals[ind];\n ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];\n const bottomLeft = imageVals[ind];\n ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];\n const bottomRight = imageVals[ind];\n const top = topLeft + (topRight - topLeft) * xLerp;\n const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = top + (bottom - top) * yLerp;\n }\n }\n } else {\n for (let x = 0; x < cropWidth; ++x) {\n const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);\n if (xInd < 0 || xInd > imageWidth - 1) {\n for (let c = 0; c < numChannels; c++) {\n const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = extrapolationValue;\n }\n continue;\n }\n const closestX = Math.round(xInd);\n const closestY = Math.round(yInd);\n for (let c = 0; c < numChannels; c++) {\n const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0];\n const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[outInd] = imageVals[inInd];\n }\n }\n }\n }\n }\n return backend2.makeTensorInfo(output.shape, output.dtype, output.values);\n}\nvar cropAndResizeConfig = {\n kernelName: CropAndResize,\n backendName: \"cpu\",\n kernelFunc: cropAndResize3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumprod.js\nfunction cumprod2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, exclusive, reverse: reverse5 } = attrs;\n assertNotComplex(x, \"cumprod\");\n const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);\n let $x = x;\n if (permutation != null) {\n $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n }\n const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];\n if (permutedAxis !== $x.shape.length - 1) {\n throw new Error(`backend.cumprod in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);\n }\n const resultDtype = upcastType($x.dtype, \"int32\");\n const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);\n const aVals = backend2.data.get($x.dataId).values;\n const finalDim = $x.shape[$x.shape.length - 1];\n const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;\n for (let i = 0; i < aVals.length; i += finalDim) {\n for (let j = 0; j < finalDim; j++) {\n const idx = indexAdjuster(i, j);\n if (j === 0) {\n vals[idx] = exclusive ? 1 : aVals[idx];\n } else {\n const prevIdx = indexAdjuster(i, j - 1);\n vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx];\n }\n }\n }\n const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);\n if (permutation != null) {\n const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n backend2.disposeIntermediateTensorInfo(result);\n backend2.disposeIntermediateTensorInfo($x);\n return reverseTransposedResult;\n }\n return result;\n}\nvar cumprodConfig = {\n kernelName: Cumprod,\n backendName: \"cpu\",\n kernelFunc: cumprod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumsum.js\nfunction cumsum2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, exclusive, reverse: reverse5 } = attrs;\n assertNotComplex(x, \"cumsum\");\n const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);\n let $x = x;\n if (permutation != null) {\n $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n }\n const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];\n if (permutedAxis !== $x.shape.length - 1) {\n throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);\n }\n const resultDtype = upcastType($x.dtype, \"int32\");\n const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);\n const aVals = backend2.data.get($x.dataId).values;\n const finalDim = $x.shape[$x.shape.length - 1];\n const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;\n for (let i = 0; i < aVals.length; i += finalDim) {\n for (let j = 0; j < finalDim; j++) {\n const idx = indexAdjuster(i, j);\n if (j === 0) {\n vals[idx] = exclusive ? 0 : aVals[idx];\n } else {\n const prevIdx = indexAdjuster(i, j - 1);\n vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];\n }\n }\n }\n const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);\n if (permutation != null) {\n const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n backend2.disposeIntermediateTensorInfo(result);\n backend2.disposeIntermediateTensorInfo($x);\n return reverseTransposedResult;\n }\n return result;\n}\nvar cumsumConfig = {\n kernelName: Cumsum,\n backendName: \"cpu\",\n kernelFunc: cumsum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DenseBincount.js\nfunction denseBincount2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, weights } = inputs;\n const { size, binaryOutput } = attrs;\n if (x.shape.length === 1) {\n const xVals = backend2.data.get(x.dataId).values;\n const weightsVals = backend2.data.get(weights.dataId).values;\n const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);\n return backend2.makeTensorInfo([size], weights.dtype, outVals);\n } else if (x.shape.length === 2) {\n const xBuf = backend2.bufferSync(x);\n const weightsBuf = backend2.bufferSync(weights);\n const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput);\n return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);\n }\n throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);\n}\nvar denseBincountConfig = {\n kernelName: DenseBincount,\n backendName: \"cpu\",\n kernelFunc: denseBincount2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthToSpace.js\nfunction depthToSpace2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { blockSize, dataFormat } = attrs;\n util_exports.assert(dataFormat === \"NHWC\", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n const batchSize = x.shape[0];\n const inputHeight = x.shape[1];\n const inputWidth = x.shape[2];\n const inputDepth = x.shape[3];\n const outputHeight = inputHeight * blockSize;\n const outputWidth = inputWidth * blockSize;\n const outputDepth = inputDepth / (blockSize * blockSize);\n const xValues = backend2.data.get(x.dataId).values;\n const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n let outputIdx = 0;\n for (let b = 0; b < batchSize; ++b) {\n for (let h = 0; h < outputHeight; ++h) {\n const inH = Math.floor(h / blockSize);\n const offsetH = h % blockSize;\n for (let w = 0; w < outputWidth; ++w) {\n const inW = Math.floor(w / blockSize);\n const offsetW = w % blockSize;\n const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n for (let d = 0; d < outputDepth; ++d) {\n const inD = d + offsetD;\n const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n result[outputIdx++] = xValues[inputIdx];\n }\n }\n }\n }\n return backend2.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);\n}\nvar depthToSpaceConfig = {\n kernelName: DepthToSpace,\n backendName: \"cpu\",\n kernelFunc: depthToSpace2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter } = inputs;\n const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;\n assertNotComplex([x, filter], \"depthwiseConv2DNative\");\n const xStrides = util_exports.computeStrides(x.shape);\n const filterStrides = util_exports.computeStrides(filter.shape);\n let $dilations = dilations;\n if ($dilations == null) {\n $dilations = [1, 1];\n }\n util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n const convInfo = backend_util_exports.computeConv2DInfo(\n x.shape,\n filter.shape,\n strides,\n $dilations,\n pad3,\n dimRoundingMode,\n true\n /* depthwise */\n );\n const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;\n const padLeft = padInfo.left;\n const padTop = padInfo.top;\n const chMul = convInfo.outChannels / convInfo.inChannels;\n const y = new TensorBuffer(convInfo.outShape, x.dtype);\n const xVals = backend2.data.get(x.dataId).values;\n const wVals = backend2.data.get(filter.dataId).values;\n const yVals = y.values;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * xStrides[0];\n const yOffset1 = b * y.strides[0];\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const yOffset2 = yOffset1 + yR * y.strides[1];\n const xRCorner = yR * convInfo.strideHeight - padTop;\n for (let wR = 0; wR < filterHeight; ++wR) {\n const xR = xRCorner + wR * dilationHeight;\n if (xR < 0 || xR >= convInfo.inHeight) {\n continue;\n }\n const wOffset1 = wR * filterStrides[0];\n const xOffset2 = xOffset1 + xR * xStrides[1];\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const yOffset3 = yOffset2 + yC * y.strides[2];\n const xCCorner = yC * convInfo.strideWidth - padLeft;\n for (let wC = 0; wC < filterWidth; ++wC) {\n const xC = xCCorner + wC * dilationWidth;\n if (xC < 0 || xC >= convInfo.inWidth) {\n continue;\n }\n const wOffset2 = wOffset1 + wC * filterStrides[1];\n const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n let yOffset4 = yOffset3;\n let wOffset3 = wOffset2;\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const xVal = xVals[xOffset3 + d1];\n for (let q = 0; q < chMul; ++q) {\n yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n }\n yOffset4 += chMul;\n wOffset3 += chMul;\n }\n }\n }\n }\n }\n }\n return backend2.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nvar depthwiseConv2dNativeConfig = {\n kernelName: DepthwiseConv2dNative,\n backendName: \"cpu\",\n kernelFunc: depthwiseConv2dNative\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js\nfunction depthwiseConv2dNativeBackpropFilter2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, dy } = inputs;\n const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;\n assertNotComplex([x, dy], \"depthwiseConv2dNativeBackpropFilter\");\n const convInfo = backend_util_exports.computeConv2DInfo(\n x.shape,\n filterShape,\n strides,\n dilations,\n pad3,\n dimRoundingMode,\n true\n /* depthwise */\n );\n const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n const dW = new TensorBuffer(convInfo.filterShape, \"float32\");\n const leftPad = convInfo.padInfo.left;\n const topPad = convInfo.padInfo.top;\n const chMul = convInfo.outChannels / convInfo.inChannels;\n const xVals = backend2.data.get(x.dataId).values;\n const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n const dyVals = backend2.data.get(dy.dataId).values;\n const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n for (let wR = 0; wR < filterHeight; ++wR) {\n const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n for (let wC = 0; wC < filterWidth; ++wC) {\n const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n const d1 = Math.trunc(d2 / chMul);\n const dm = d2 % chMul;\n let dotProd = 0;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let yR = yRMin; yR < yRMax; ++yR) {\n const xR = wR + yR * strideHeight - topPad;\n for (let yC = yCMin; yC < yCMax; ++yC) {\n const xC = wC + yC * strideWidth - leftPad;\n dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n }\n }\n }\n dW.set(dotProd, wR, wC, d1, dm);\n }\n }\n }\n return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nvar depthwiseConv2dNativeBackpropFilterConfig = {\n kernelName: DepthwiseConv2dNativeBackpropFilter,\n backendName: \"cpu\",\n kernelFunc: depthwiseConv2dNativeBackpropFilter2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropInput.js\nfunction depthwiseConv2dNativeBackpropInput2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, filter } = inputs;\n const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;\n assertNotComplex([dy, filter], \"depthwiseConv2DNativeBackpropInput\");\n const dyStrides = util_exports.computeStrides(dy.shape);\n const filterStrides = util_exports.computeStrides(filter.shape);\n const convInfo = backend_util_exports.computeConv2DInfo(\n inputShape,\n filter.shape,\n strides,\n dilations,\n pad3,\n dimRoundingMode,\n true\n /* depthwise */\n );\n const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n const dxValues = dx.values;\n const [dxS0, dxS1, dxS2] = dx.strides;\n const dyValues = backend2.data.get(dy.dataId).values;\n const [dyS0, dyS1, dyS2] = dyStrides;\n const fltValues = backend2.data.get(filter.dataId).values;\n const [fltS0, fltS1, fltS2] = filterStrides;\n const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n const topPad = filterHeight - 1 - convInfo.padInfo.top;\n const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n const chMul = outChannels / inChannels;\n for (let b = 0; b < batchSize; ++b) {\n for (let d1 = 0; d1 < inChannels; ++d1) {\n for (let xR = 0; xR < inHeight; ++xR) {\n const xRCorner = xR - topPad;\n const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n for (let xC = 0; xC < inWidth; ++xC) {\n const xCCorner = xC - leftPad;\n const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n let dotProd = 0;\n for (let yR = xRMin; yR < yRMax; ++yR) {\n const wR = yR * strideHeight - xRCorner;\n for (let yC = xCMin; yC < yCMax; ++yC) {\n const wC = yC * strideWidth - xCCorner;\n const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n for (let dm = 0; dm < chMul; ++dm) {\n const d2 = d1 * chMul + dm;\n const pixel = dyValues[dyOffset + d2];\n const weight = fltValues[fltOffset + dm];\n dotProd += pixel * weight;\n }\n }\n }\n dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n }\n }\n }\n }\n return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar depthwiseConv2dNativeBackpropInputConfig = {\n kernelName: DepthwiseConv2dNativeBackpropInput,\n backendName: \"cpu\",\n kernelFunc: depthwiseConv2dNativeBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Diag.js\nfunction diag2(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n const xSize = util_exports.sizeFromShape(x.shape);\n const xVals = backend2.data.get(x.dataId).values;\n const outBuf = buffer([xSize, xSize], x.dtype);\n const vals = outBuf.values;\n for (let i = 0; i < xVals.length; i++) {\n vals[i * xSize + i] = xVals[i];\n }\n const outShape = [...x.shape, ...x.shape];\n return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);\n}\nvar diagConfig = {\n kernelName: Diag,\n backendName: \"cpu\",\n kernelFunc: diag2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2D.js\nvar dilation2DConfig = {\n kernelName: Dilation2D,\n backendName: \"cpu\",\n kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n const { x, filter } = inputs;\n const { strides, pad: pad3, dilations } = attrs;\n const cpuBackend = backend2;\n const xVals = cpuBackend.data.get(x.dataId).values;\n const xRank = x.shape.length;\n const filterVals = cpuBackend.data.get(filter.dataId).values;\n const filterRank = filter.shape.length;\n const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n const outSize = util_exports.sizeFromShape(outShape);\n const outRank = outShape.length;\n const outputVals = util_exports.getArrayFromDType(x.dtype, outSize);\n for (let b = 0; b < batchSize; ++b) {\n for (let hOut = 0; hOut < outHeight; ++hOut) {\n const hBeg = hOut * strideHeight - padInfo.top;\n for (let wOut = 0; wOut < outWidth; ++wOut) {\n const wBeg = wOut * strideWidth - padInfo.left;\n for (let d = 0; d < inChannels; ++d) {\n let curVal = Number.MIN_SAFE_INTEGER;\n for (let h = 0; h < filterHeight; ++h) {\n const hIn = hBeg + h * dilationHeight;\n if (hIn >= 0 && hIn < inHeight) {\n for (let w = 0; w < filterWidth; ++w) {\n const wIn = wBeg + w * dilationWidth;\n if (wIn >= 0 && wIn < inWidth) {\n const xIndex = util_exports.locToIndex([b, hIn, wIn, d], xRank, util_exports.computeStrides(x.shape));\n const filterIndex = util_exports.locToIndex([h, w, d], filterRank, util_exports.computeStrides(filter.shape));\n const val = xVals[xIndex] + filterVals[filterIndex];\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n const outputIndex = util_exports.locToIndex([b, hOut, wOut, d], outRank, util_exports.computeStrides(outShape));\n outputVals[outputIndex] = curVal;\n }\n }\n }\n }\n const dataId = cpuBackend.write(util_exports.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n return { dataId, shape: outShape, dtype: x.dtype };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropFilter.js\nvar dilation2DBackpropFilterConfig = {\n kernelName: Dilation2DBackpropFilter,\n backendName: \"cpu\",\n kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n const { x, filter, dy } = inputs;\n const { strides, pad: pad3, dilations } = attrs;\n const cpuBackend = backend2;\n const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);\n const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n for (let b = 0; b < batchSize; ++b) {\n for (let hOut = 0; hOut < outHeight; ++hOut) {\n const hBeg = hOut * strideHeight - padInfo.top;\n for (let wOut = 0; wOut < outWidth; ++wOut) {\n const wBeg = wOut * strideWidth - padInfo.left;\n for (let d = 0; d < inChannels; ++d) {\n let curVal = Number.MIN_SAFE_INTEGER;\n let hMax = 0;\n let wMax = 0;\n for (let h = 0; h < filterHeight; ++h) {\n const hIn = hBeg + h * dilationHeight;\n if (hIn >= 0 && hIn < inHeight) {\n for (let w = 0; w < filterWidth; ++w) {\n const wIn = wBeg + w * dilationWidth;\n if (wIn >= 0 && wIn < inWidth) {\n const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n if (val > curVal) {\n curVal = val;\n hMax = h;\n wMax = w;\n }\n }\n }\n }\n }\n gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n }\n }\n }\n }\n const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n return { dataId, shape: filter.shape, dtype: filter.dtype };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropInput.js\nvar dilation2DBackpropInputConfig = {\n kernelName: Dilation2DBackpropInput,\n backendName: \"cpu\",\n kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n const { x, filter, dy } = inputs;\n const { strides, pad: pad3, dilations } = attrs;\n const cpuBackend = backend2;\n const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);\n const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype);\n for (let b = 0; b < batchSize; ++b) {\n for (let hOut = 0; hOut < outHeight; ++hOut) {\n const hBeg = hOut * strideHeight - padInfo.top;\n for (let wOut = 0; wOut < outWidth; ++wOut) {\n const wBeg = wOut * strideWidth - padInfo.left;\n for (let d = 0; d < inChannels; ++d) {\n let curVal = Number.MIN_SAFE_INTEGER;\n let hInMax = hBeg < 0 ? 0 : hBeg;\n let wInMax = wBeg < 0 ? 0 : wBeg;\n for (let h = 0; h < filterHeight; ++h) {\n const hIn = hBeg + h * dilationHeight;\n if (hIn >= 0 && hIn < inHeight) {\n for (let w = 0; w < filterWidth; ++w) {\n const wIn = wBeg + w * dilationWidth;\n if (wIn >= 0 && wIn < inWidth) {\n const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n if (val > curVal) {\n curVal = val;\n hInMax = hIn;\n wInMax = wIn;\n }\n }\n }\n }\n }\n gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n }\n }\n }\n }\n const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n return { dataId, shape: x.shape, dtype: x.dtype };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Draw.js\nfunction draw2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { image: image2 } = inputs;\n const { canvas, options } = attrs;\n const { contextOptions, imageOptions } = options || {};\n const alpha = (imageOptions === null || imageOptions === void 0 ? void 0 : imageOptions.alpha) || 1;\n const contextType = (contextOptions === null || contextOptions === void 0 ? void 0 : contextOptions.contextType) || \"2d\";\n if (contextType !== \"2d\") {\n throw new Error(`Context type ${contextOptions.contextType} is not supported by the CPU backend.`);\n }\n const ctx = canvas.getContext(contextType, (contextOptions === null || contextOptions === void 0 ? void 0 : contextOptions.contextAttributes) || {});\n if (ctx == null) {\n throw new Error(`Could not get the context with ${contextType} type.`);\n }\n const [height, width] = image2.shape.slice(0, 2);\n const depth = image2.shape.length === 2 ? 1 : image2.shape[2];\n const data = backend2.data.get(image2.dataId).values;\n const multiplier = image2.dtype === \"float32\" ? 255 : 1;\n const bytes = new Uint8ClampedArray(width * height * 4);\n for (let i = 0; i < height * width; ++i) {\n const rgba = [0, 0, 0, 255 * alpha];\n for (let d = 0; d < depth; d++) {\n const value = data[i * depth + d];\n if (image2.dtype === \"float32\") {\n if (value < 0 || value > 1) {\n throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);\n }\n } else if (image2.dtype === \"int32\") {\n if (value < 0 || value > 255) {\n throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);\n }\n }\n if (depth === 1) {\n rgba[0] = value * multiplier;\n rgba[1] = value * multiplier;\n rgba[2] = value * multiplier;\n } else {\n rgba[d] = value * multiplier;\n }\n }\n const j = i * 4;\n bytes[j + 0] = Math.round(rgba[0]);\n bytes[j + 1] = Math.round(rgba[1]);\n bytes[j + 2] = Math.round(rgba[2]);\n bytes[j + 3] = Math.round(rgba[3]);\n }\n canvas.width = width;\n canvas.height = height;\n const imageData = new ImageData(bytes, width, height);\n ctx.putImageData(imageData, 0, 0);\n return image2;\n}\nvar drawConfig = {\n kernelName: Draw,\n backendName: \"cpu\",\n kernelFunc: draw2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sum.js\nfunction sum3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n assertNotComplex(x, \"sum\");\n let $x;\n if (x.dtype === \"bool\") {\n $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"int32\" } });\n } else {\n $x = identity2({ inputs: { x }, backend: backend2 });\n }\n const xRank = $x.shape.length;\n const axes = util_exports.parseAxisParam(axis, $x.shape);\n const permutation = backend_util_exports.getAxesPermutation(axes, xRank);\n let reductionAxes = axes;\n let permutedX = $x;\n if (permutation != null) {\n permutedX = transpose2({ inputs: { x: $x }, backend: backend2, attrs: { perm: permutation } });\n reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"sum\", reductionAxes, permutedX.shape.length);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, reductionAxes);\n const resultDtype = backend_util_exports.upcastType(permutedX.dtype, \"int32\");\n let result = zeros3(backend2, outShape, resultDtype);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const vals = backend2.data.get(result.dataId).values;\n const aVals = backend2.data.get(permutedX.dataId).values;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let sum6 = 0;\n for (let j = 0; j < reduceSize; ++j) {\n sum6 += aVals[offset + j];\n }\n vals[i] = sum6;\n }\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes);\n const oldResult = result;\n result = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: newShape } });\n backend2.disposeIntermediateTensorInfo(oldResult);\n }\n backend2.disposeIntermediateTensorInfo($x);\n if (permutation != null) {\n backend2.disposeIntermediateTensorInfo(permutedX);\n }\n return result;\n}\nvar sumConfig = {\n kernelName: Sum,\n backendName: \"cpu\",\n kernelFunc: sum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Einsum.js\nfunction einsum2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { equation } = attrs;\n const tensors = inputs;\n const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n const nSteps = steps.length;\n let out = null;\n let numDimsRemaining = allDims.length;\n const tensorsToDispose = [];\n for (let i = 0; i < nSteps; ++i) {\n for (const idTerm of steps[i]) {\n const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n let x;\n if (backend_util_exports.isIdentityPermutation(perm)) {\n x = tensors[idTerm];\n } else {\n x = transpose2({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n tensorsToDispose.push(x);\n }\n const targetShape = x.shape.slice();\n for (let k = 0; k < dimsToExpand.length; ++k) {\n targetShape.splice(dimsToExpand[k], 0, 1);\n }\n if (!util_exports.arraysEqual(x.shape, targetShape)) {\n x = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n tensorsToDispose.push(x);\n }\n if (out === null) {\n out = x;\n } else {\n out = multiply2({ inputs: { a: x, b: out }, backend: backend2 });\n tensorsToDispose.push(out);\n }\n }\n if (i < nSteps - 1) {\n if (path[i] >= 0) {\n out = sum3({\n inputs: { x: out },\n backend: backend2,\n attrs: {\n axis: path[i] - (allDims.length - numDimsRemaining),\n keepDims: false\n }\n });\n tensorsToDispose.push(out);\n }\n numDimsRemaining--;\n }\n }\n for (const tensorInfo of tensorsToDispose) {\n if (tensorInfo === out) {\n continue;\n }\n backend2.disposeIntermediateTensorInfo(tensorInfo);\n }\n return out;\n}\nvar einsumConfig = {\n kernelName: Einsum,\n backendName: \"cpu\",\n kernelFunc: einsum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/EluGrad.js\nfunction eluGrad(args) {\n const { inputs, backend: backend2 } = args;\n const { dy, y } = inputs;\n assertNotComplex([dy, y], \"eluGrad\");\n const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape));\n const values = backend2.data.get(y.dataId).values;\n const dyValues = backend2.data.get(dy.dataId).values;\n for (let i = 0; i < values.length; ++i) {\n const v = values[i];\n if (v >= 0) {\n resultValues[i] = dyValues[i];\n } else {\n resultValues[i] = dyValues[i] * (v + 1);\n }\n }\n return backend2.makeTensorInfo(y.shape, \"float32\", resultValues);\n}\nvar eluGradConfig2 = {\n kernelName: EluGrad,\n backendName: \"cpu\",\n kernelFunc: eluGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Erf.js\nvar p = backend_util_exports.ERF_P;\nvar a1 = backend_util_exports.ERF_A1;\nvar a2 = backend_util_exports.ERF_A2;\nvar a3 = backend_util_exports.ERF_A3;\nvar a4 = backend_util_exports.ERF_A4;\nvar a5 = backend_util_exports.ERF_A5;\nvar erf2 = unaryKernelFunc(Erf, (xi) => {\n const sign4 = Math.sign(xi);\n const v = Math.abs(xi);\n const t = 1 / (1 + p * v);\n return sign4 * (1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-v * v));\n});\nvar erfConfig = {\n kernelName: Erf,\n backendName: \"cpu\",\n kernelFunc: erf2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ExpandDims.js\nfunction expandDims3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { input: input2 } = inputs;\n const { dim } = attrs;\n const inputRank = input2.shape.length;\n const newShape = input2.shape.slice();\n let $dim = dim;\n if (dim < 0) {\n util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n $dim = inputRank + dim + 1;\n }\n newShape.splice($dim, 0, 1);\n return reshape3({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig = {\n kernelName: ExpandDims,\n backendName: \"cpu\",\n kernelFunc: expandDims3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RealDiv.js\nvar realDivImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nvar div2 = binaryKernelFunc(RealDiv, realDivImpl);\nvar realDivConfig = {\n kernelName: RealDiv,\n backendName: \"cpu\",\n kernelFunc: div2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fft_utils.js\nfunction fftBatch(input2, inverse, cpuBackend) {\n const inputShape = input2.shape;\n const batch = inputShape[0];\n const innerDim = inputShape[1];\n const inputVals = cpuBackend.data.get(input2.dataId);\n const real2D = inputVals.complexTensorInfos.real;\n const imag2D = inputVals.complexTensorInfos.imag;\n const resultShape = [batch, innerDim];\n const resultSize = util_exports.sizeFromShape(resultShape);\n const resultReal = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n const resultImag = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n for (let b = 0; b < batch; b++) {\n const r = slice2({\n inputs: { x: real2D },\n backend: cpuBackend,\n attrs: { begin: [b, 0], size: [1, innerDim] }\n });\n const i = slice2({\n inputs: { x: imag2D },\n backend: cpuBackend,\n attrs: { begin: [b, 0], size: [1, innerDim] }\n });\n const input3 = complex2({ inputs: { real: r, imag: i }, backend: cpuBackend });\n const { real: real4, imag: imag4 } = fftImpl(input3, inverse, cpuBackend);\n const res = backend_util_exports.mergeRealAndImagArrays(real4, imag4);\n for (let d = 0; d < innerDim; d++) {\n const c = backend_util_exports.getComplexWithIndex(res, d);\n resultReal[b * innerDim + d] = c.real;\n resultImag[b * innerDim + d] = c.imag;\n }\n cpuBackend.disposeIntermediateTensorInfo(r);\n cpuBackend.disposeIntermediateTensorInfo(i);\n cpuBackend.disposeIntermediateTensorInfo(input3);\n }\n const $realInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultReal);\n const $imagInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultImag);\n const result = complex2({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n cpuBackend.disposeIntermediateTensorInfo($realInfo);\n cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n return result;\n}\nfunction fftImpl(input2, inverse, cpuBackend) {\n const inputSize = util_exports.sizeFromShape(input2.shape);\n const inputVals = cpuBackend.data.get(input2.dataId);\n const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n if (isExponentOf2(inputSize)) {\n const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n const resultShape = [input2.shape[0], input2.shape[1]];\n if (inverse) {\n const realInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", result.real);\n const imagInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", result.imag);\n const sizeInfo = cpuBackend.makeTensorInfo([], \"float32\", util_exports.createScalarValue(inputSize, \"float32\"));\n const sizeInfoCopy = identity2({ inputs: { x: sizeInfo }, backend: cpuBackend });\n const divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n const divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n cpuBackend.disposeIntermediateTensorInfo(realInfo);\n cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n return { real: divRealVals, imag: divImagVals };\n }\n return result;\n } else {\n const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);\n const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n return backend_util_exports.splitRealAndImagArrays(rawOutput);\n }\n}\nfunction isExponentOf2(size) {\n return (size & size - 1) === 0;\n}\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n if (size === 1) {\n return { real: realVals, imag: imagVals };\n }\n const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);\n const half = size / 2;\n const evenComplex = backend_util_exports.complexWithEvenIndex(data);\n const evenRealVals = evenComplex.real;\n const evenImagVals = evenComplex.imag;\n const evenShape = [evenRealVals.length];\n const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, \"float32\", evenRealVals);\n const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, \"float32\", evenImagVals);\n const evenTensorInfo = complex2({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n const oddComplex = backend_util_exports.complexWithOddIndex(data);\n const oddRealVals = oddComplex.real;\n const oddImagVals = oddComplex.imag;\n const oddShape = [oddRealVals.length];\n const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, \"float32\", oddRealVals);\n const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, \"float32\", oddImagVals);\n const oddTensorInfo = complex2({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n const $evenRealVals = $evenComplex.real;\n const $evenImagVals = $evenComplex.imag;\n const $evenShape = [$evenRealVals.length];\n const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, \"float32\", $evenRealVals);\n const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, \"float32\", $evenImagVals);\n const $evenTensorInfo = complex2({\n inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n backend: cpuBackend\n });\n const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n const $oddRealVals = $oddComplex.real;\n const $oddImagVals = $oddComplex.imag;\n const $oddShape = [$oddRealVals.length];\n const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, \"float32\", $oddRealVals);\n const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, \"float32\", $oddImagVals);\n const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n const e = backend_util_exports.exponents(size, inverse);\n const eShape = [e.real.length];\n const eRealInfo = cpuBackend.makeTensorInfo(eShape, \"float32\", e.real);\n const eImagInfo = cpuBackend.makeTensorInfo(eShape, \"float32\", e.imag);\n const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n const exponentInfo = multiply2({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n const addPart = add4({\n inputs: { a: $evenTensorInfo, b: exponentInfo },\n backend: cpuBackend\n });\n const subPart = sub2({\n inputs: { a: $evenTensorInfo, b: exponentInfo },\n backend: cpuBackend\n });\n const addPartReal = real2({ inputs: { input: addPart }, backend: cpuBackend });\n const subPartReal = real2({ inputs: { input: subPart }, backend: cpuBackend });\n const addPartImag = imag2({ inputs: { input: addPart }, backend: cpuBackend });\n const subPartImag = imag2({ inputs: { input: subPart }, backend: cpuBackend });\n const $real = concat2({\n inputs: [addPartReal, subPartReal],\n backend: cpuBackend,\n attrs: { axis: 0 }\n });\n const $imag = concat2({\n inputs: [addPartImag, subPartImag],\n backend: cpuBackend,\n attrs: { axis: 0 }\n });\n const $realVals = cpuBackend.data.get($real.dataId).values;\n const $imagVals = cpuBackend.data.get($imag.dataId).values;\n cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n cpuBackend.disposeIntermediateTensorInfo(addPart);\n cpuBackend.disposeIntermediateTensorInfo(subPart);\n cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n cpuBackend.disposeIntermediateTensorInfo($real);\n cpuBackend.disposeIntermediateTensorInfo($imag);\n return { real: $realVals, imag: $imagVals };\n}\nfunction fourierTransformByMatmul(data, size, inverse) {\n const ret = new Float32Array(size * 2);\n for (let r = 0; r < size; r++) {\n let real4 = 0;\n let imag4 = 0;\n for (let c = 0; c < size; c++) {\n const e = backend_util_exports.exponent(r * c, size, inverse);\n const term = backend_util_exports.getComplexWithIndex(data, c);\n real4 += term.real * e.real - term.imag * e.imag;\n imag4 += term.real * e.imag + term.imag * e.real;\n }\n if (inverse) {\n real4 /= size;\n imag4 /= size;\n }\n backend_util_exports.assignToTypedArray(ret, real4, imag4, r);\n }\n return ret;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FFT.js\nfunction fft2(args) {\n const { inputs, backend: backend2 } = args;\n const { input: input2 } = inputs;\n const inputSize = util_exports.sizeFromShape(input2.shape);\n const innerDimensionSize = input2.shape[input2.shape.length - 1];\n const batch = inputSize / innerDimensionSize;\n const input2D = reshape3({\n inputs: { x: input2 },\n backend: backend2,\n attrs: { shape: [batch, innerDimensionSize] }\n });\n const result = fftBatch(input2D, false, backend2);\n const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });\n backend2.disposeIntermediateTensorInfo(input2D);\n backend2.disposeIntermediateTensorInfo(result);\n return resultReshaped;\n}\nvar fftConfig = {\n kernelName: FFT,\n backendName: \"cpu\",\n kernelFunc: fft2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Fill.js\nfunction fill2(args) {\n const { backend: backend2, attrs } = args;\n const { shape, value, dtype } = attrs;\n const $dtype = dtype || util_exports.inferDtype(value);\n const values = util_exports.getArrayFromDType($dtype, util_exports.sizeFromShape(shape));\n fillValues(values, value, $dtype);\n return backend2.makeTensorInfo(shape, $dtype, values);\n}\nvar fillConfig = {\n kernelName: Fill,\n backendName: \"cpu\",\n kernelFunc: fill2\n};\nfunction fillValues(values, value, dtype) {\n if (dtype === \"string\") {\n values.fill(value);\n } else {\n values.fill(value);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig = {\n kernelName: FlipLeftRight,\n backendName: \"cpu\",\n kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n const { image: image2 } = inputs;\n const cpuBackend = backend2;\n const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));\n const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n const imageVals = cpuBackend.data.get(image2.dataId).values;\n for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n for (let row = 0; row < imageHeight; row++) {\n const rowOffset = row * (imageWidth * numChannels);\n for (let col = 0; col < imageWidth; col++) {\n const colOffset = col * numChannels;\n for (let channel = 0; channel < numChannels; channel++) {\n const coordX = Math.round(imageWidth - col - 1);\n const outIdx = batchOffset + rowOffset + colOffset + channel;\n let outputValue = imageVals[outIdx];\n if (coordX >= 0 && coordX < imageWidth) {\n const rotatedColOffset = coordX * numChannels;\n const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n outputValue = imageVals[imageIdx];\n }\n output[outIdx] = outputValue;\n }\n }\n }\n }\n const dataId = cpuBackend.write(output, image2.shape, image2.dtype);\n return { dataId, shape: image2.shape, dtype: image2.dtype };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedConv2D.js\nfunction fusedConv2D(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter, bias, preluActivationWeights } = inputs;\n const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n let result = conv2D({\n inputs: { x, filter },\n backend: backend2,\n attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }\n });\n if (bias) {\n const resultOld = result;\n if (dataFormat === \"NCHW\" && bias.shape.length === 1 && bias.shape[0] !== 1) {\n const reshapedBias = reshape3({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } });\n result = add4({ inputs: { a: result, b: reshapedBias }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(reshapedBias);\n } else {\n result = add4({ inputs: { a: result, b: bias }, backend: backend2 });\n }\n backend2.disposeIntermediateTensorInfo(resultOld);\n }\n if (activation2) {\n const resultOld = result;\n if (dataFormat === \"NCHW\" && activation2 === \"prelu\" && preluActivationWeights.shape.length === 1 && preluActivationWeights.shape[0] !== 1) {\n const reshapedAlpha = reshape3({\n inputs: { x: preluActivationWeights },\n backend: backend2,\n attrs: { shape: [preluActivationWeights.shape[0], 1, 1] }\n });\n result = applyActivation2(backend2, result, activation2, reshapedAlpha, leakyreluAlpha);\n backend2.disposeIntermediateTensorInfo(reshapedAlpha);\n } else {\n result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);\n }\n backend2.disposeIntermediateTensorInfo(resultOld);\n }\n return result;\n}\nvar fusedConv2DConfig = {\n kernelName: FusedConv2D,\n backendName: \"cpu\",\n kernelFunc: fusedConv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter, bias, preluActivationWeights } = inputs;\n const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n let result = depthwiseConv2dNative({\n inputs: { x, filter },\n backend: backend2,\n attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }\n });\n if (bias) {\n const oldResult = result;\n result = add4({ inputs: { a: result, b: bias }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(oldResult);\n }\n if (activation2) {\n const oldResult = result;\n result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);\n backend2.disposeIntermediateTensorInfo(oldResult);\n }\n return result;\n}\nvar fusedDepthwiseConv2DConfig = {\n kernelName: FusedDepthwiseConv2D,\n backendName: \"cpu\",\n kernelFunc: fusedDepthwiseConv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd.js\nfunction gatherNd(args) {\n const { inputs, backend: backend2 } = args;\n const { params, indices } = inputs;\n const paramsSize = util_exports.sizeFromShape(params.shape);\n const indicesShape = indices.shape;\n const sliceRank = indicesShape[indicesShape.length - 1];\n const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n if (numSlices === 0) {\n return backend2.makeTensorInfo(resultShape, params.dtype, []);\n }\n const indicesData = backend2.data.get(indices.dataId).values;\n const paramsBuf = backend2.bufferSync(params);\n const outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n return backend2.makeTensorInfo(resultShape, params.dtype, outBuf.values);\n}\nvar gatherNdConfig = {\n kernelName: GatherNd,\n backendName: \"cpu\",\n kernelFunc: gatherNd\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2.js\nfunction gatherV2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, indices } = inputs;\n const { axis, batchDims } = attrs;\n assertNotComplex([x, indices], \"gatherV2\");\n const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n const indicesVals = backend2.data.get(indices.dataId).values;\n const axisDim = x.shape[parsedAxis];\n for (let i = 0; i < indicesVals.length; ++i) {\n const index = indicesVals[i];\n util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n }\n let $batchDims = batchDims;\n if (batchDims == null) {\n $batchDims = 0;\n }\n const indicesSize = util_exports.sizeFromShape(indices.shape);\n const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims);\n const flattenX = reshape3({\n inputs: { x },\n backend: backend2,\n attrs: {\n shape: [\n shapeInfo.batchSize,\n shapeInfo.outerSize,\n shapeInfo.dimSize,\n shapeInfo.sliceSize\n ]\n }\n });\n const flattenIndex = reshape3({\n inputs: { x: indices },\n backend: backend2,\n attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n });\n const flattenOutputShape = [\n shapeInfo.batchSize,\n shapeInfo.outerSize,\n indicesSize / shapeInfo.batchSize,\n shapeInfo.sliceSize\n ];\n const indicesBuf = backend2.bufferSync(flattenIndex);\n const xBuf = backend2.bufferSync(flattenX);\n const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);\n backend2.disposeIntermediateTensorInfo(flattenX);\n backend2.disposeIntermediateTensorInfo(flattenIndex);\n return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n}\nvar gatherV2Config = {\n kernelName: GatherV2,\n backendName: \"cpu\",\n kernelFunc: gatherV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IFFT.js\nfunction ifft2(args) {\n const { inputs, backend: backend2 } = args;\n const { input: input2 } = inputs;\n const inputSize = util_exports.sizeFromShape(input2.shape);\n const innerDimensionSize = input2.shape[input2.shape.length - 1];\n const batch = inputSize / innerDimensionSize;\n const input2D = reshape3({\n inputs: { x: input2 },\n backend: backend2,\n attrs: { shape: [batch, innerDimensionSize] }\n });\n const result = fftBatch(input2D, true, backend2);\n const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });\n backend2.disposeIntermediateTensorInfo(input2D);\n backend2.disposeIntermediateTensorInfo(result);\n return resultReshaped;\n}\nvar ifftConfig = {\n kernelName: IFFT,\n backendName: \"cpu\",\n kernelFunc: ifft2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsFinite.js\nvar isFinite3 = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, \"bool\");\nvar isFiniteConfig = {\n kernelName: IsFinite,\n backendName: \"cpu\",\n kernelFunc: isFinite3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsInf.js\nvar isInf2 = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, \"bool\");\nvar isInfConfig = {\n kernelName: IsInf,\n backendName: \"cpu\",\n kernelFunc: isInf2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsNaN.js\nvar isNaN3 = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, \"bool\");\nvar isNaNConfig = {\n kernelName: IsNan,\n backendName: \"cpu\",\n kernelFunc: isNaN3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace.js\nfunction linSpace(args) {\n const { backend: backend2, attrs } = args;\n const { start, stop, num } = attrs;\n const outVals = linSpaceImpl(start, stop, num);\n return backend2.makeTensorInfo([outVals.length], \"float32\", outVals);\n}\nvar linSpaceConfig = {\n kernelName: LinSpace,\n backendName: \"cpu\",\n kernelFunc: linSpace\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log1p.js\nvar log1p2 = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nvar log1pConfig = {\n kernelName: Log1p,\n backendName: \"cpu\",\n kernelFunc: log1p2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalAnd.js\nvar logicalAndImpl = createSimpleBinaryKernelImpl((a, b) => a && b);\nvar logicalAnd2 = binaryKernelFunc(LogicalAnd, logicalAndImpl, null, \"bool\");\nvar logicalAndConfig = {\n kernelName: LogicalAnd,\n backendName: \"cpu\",\n kernelFunc: logicalAnd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalNot.js\nvar logicalNot2 = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, \"bool\");\nvar logicalNotConfig = {\n kernelName: LogicalNot,\n backendName: \"cpu\",\n kernelFunc: logicalNot2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalOr.js\nvar logicalOrImpl = createSimpleBinaryKernelImpl((a, b) => a || b);\nvar logicalOr2 = binaryKernelFunc(LogicalOr, logicalOrImpl, null, \"bool\");\nvar logicalOrConfig = {\n kernelName: LogicalOr,\n backendName: \"cpu\",\n kernelFunc: logicalOr2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRN.js\nfunction lRN(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { depthRadius, bias, alpha, beta } = attrs;\n assertNotComplex(x, \"LRN\");\n const channels = x.shape[3];\n const maxD = channels - 1;\n const xValues = backend2.data.get(x.dataId).values;\n const size = util_exports.sizeFromShape(x.shape);\n const result = new Float32Array(size);\n function sumAcrossChannels(offset) {\n const currentChannel = offset % channels;\n let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);\n let sum6 = 0;\n for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n const z = xValues[beginSumOffset];\n sum6 += z * z;\n }\n return sum6;\n }\n for (let offset = 0; offset < size; offset++) {\n const sum6 = sumAcrossChannels(offset);\n const val = xValues[offset] * Math.pow(bias + alpha * sum6, -beta);\n result[offset] = val;\n }\n return backend2.makeTensorInfo(x.shape, x.dtype, result);\n}\nvar LRNConfig = {\n kernelName: LRN,\n backendName: \"cpu\",\n kernelFunc: lRN\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRNGrad.js\nfunction lRNGrad(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, y, dy } = inputs;\n const { depthRadius, bias, alpha, beta } = attrs;\n assertNotComplex(dy, \"LRNGrad\");\n const dySize = util_exports.sizeFromShape(dy.shape);\n const channels = dy.shape[3];\n const dyValues = backend2.data.get(dy.dataId).values;\n const xValues = backend2.data.get(x.dataId).values;\n const yValues = backend2.data.get(y.dataId).values;\n const result = new Float32Array(dySize);\n const size = dySize;\n for (let offset = 0; offset < size; offset++) {\n const currentChannel = offset % channels;\n const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1);\n let norm2 = 0;\n for (let k = depthBegin; k < depthEnd; k++) {\n norm2 += Math.pow(xValues[k], 2);\n }\n norm2 = alpha * norm2 + bias;\n for (let k = depthBegin; k < depthEnd; k++) {\n let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm2;\n if (offset === k) {\n dyi += Math.pow(norm2, -beta);\n }\n dyi *= dyValues[offset];\n result[k] += dyi;\n }\n }\n return backend2.makeTensorInfo(dy.shape, x.dtype, result);\n}\nvar LRNGradConfig = {\n kernelName: LRNGrad,\n backendName: \"cpu\",\n kernelFunc: lRNGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max.js\nfunction max3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { reductionIndices, keepDims } = attrs;\n const cpuBackend = backend2;\n let xShape = x.shape;\n const xRank = xShape.length;\n const origAxes = util_exports.parseAxisParam(reductionIndices, xShape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n let xVals = cpuBackend.data.get(x.dataId).values;\n if (permutedAxes != null) {\n const newShape = new Array(xRank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = xShape[permutedAxes[i]];\n }\n xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n xShape = newShape;\n }\n assertNotComplex(x, \"max\");\n backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, xRank);\n const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, axes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n let outShape = maxOutShape;\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);\n outShape = newShape;\n }\n return { dataId, shape: outShape, dtype: x.dtype };\n}\nvar maxConfig = {\n kernelName: Max,\n backendName: \"cpu\",\n kernelFunc: max3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool.js\nfunction maxPool2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n assertNotComplex(x, \"maxPool\");\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const dilations = 1;\n util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n let res;\n if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n res = identity2({ inputs: { x }, backend: backend2 });\n } else {\n const xValues = backend2.data.get(x.dataId).values;\n const strides2 = util_exports.computeStrides(x.shape);\n const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, \"max\");\n res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);\n }\n return res;\n}\nvar maxPoolConfig = {\n kernelName: MaxPool,\n backendName: \"cpu\",\n kernelFunc: maxPool2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3D.js\nfunction maxPool3D(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n assertNotComplex(x, \"maxPool3d\");\n const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);\n const xValues = backend2.data.get(x.dataId).values;\n const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, \"max\");\n return backend2.makeTensorInfo(outBuf.shape, \"float32\", outBuf.values);\n}\nvar maxPool3DConfig = {\n kernelName: MaxPool3D,\n backendName: \"cpu\",\n kernelFunc: maxPool3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3DGrad.js\nfunction maxPool3DGrad(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2 } = inputs;\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n assertNotComplex([dy, input2], \"maxPool3DGrad\");\n const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n const inputBuf = backend2.bufferSync(input2);\n const maxPosBuf = maxPool3dPositions(inputBuf, convInfo);\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const dx = buffer(input2.shape, \"float32\");\n const dyBuf = backend2.bufferSync(dy);\n for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n const dyDepthCorner = dxDepth - padFront;\n const dyRowCorner = dxRow - padTop;\n const dyColCorner = dxCol - padLeft;\n let dotProd = 0;\n for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {\n continue;\n }\n for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n const dyRow = (dyRowCorner + wRow) / strideHeight;\n if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {\n continue;\n }\n for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n const dyCol = (dyColCorner + wCol) / strideWidth;\n if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {\n continue;\n }\n const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol;\n const mask = maxPos === curPos ? 1 : 0;\n if (mask === 0) {\n continue;\n }\n const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n dotProd += pixel * mask;\n }\n }\n }\n dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n }\n }\n }\n }\n }\n return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar maxPool3DGradConfig2 = {\n kernelName: MaxPool3DGrad,\n backendName: \"cpu\",\n kernelFunc: maxPool3DGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolGrad.js\nfunction maxPoolGrad2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2, output } = inputs;\n const x = input2;\n assertNotComplex([input2, output], \"maxPoolGrad\");\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n const xValues = backend2.data.get(x.dataId).values;\n const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const dx = buffer(x.shape, \"float32\");\n const dyData = backend2.data.get(dy.dataId).values;\n const dyBuf = buffer(dy.shape, \"float32\", dyData);\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n const dyRCorner = dxR - padTop;\n const dyCCorner = dxC - padLeft;\n let dotProd = 0;\n for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n const dyR = (dyRCorner + wR) / strideHeight;\n if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {\n continue;\n }\n for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n const dyC = (dyCCorner + wC) / strideWidth;\n if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {\n continue;\n }\n const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d);\n const curPos = wR * effectiveFilterWidth + wC;\n const mask = maxPos === curPos ? 1 : 0;\n if (mask === 0) {\n continue;\n }\n const pixel = dyBuf.get(b, dyR, dyC, d);\n dotProd += pixel * mask;\n }\n }\n dx.set(dotProd, b, dxR, dxC, d);\n }\n }\n }\n }\n return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar maxPoolGradConfig2 = {\n kernelName: MaxPoolGrad,\n backendName: \"cpu\",\n kernelFunc: maxPoolGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax_impl.js\nfunction maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n const strides = util_exports.computeStrides(xShape);\n const maxPools = pool2(xValues, xShape, dtype, strides, convInfo, \"max\");\n const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n return [maxPools.values, maxPositions.values];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax.js\nvar maxPoolWithArgmaxConfig = {\n kernelName: MaxPoolWithArgmax,\n backendName: \"cpu\",\n kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n const { x } = inputs;\n const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;\n const cpuBackend = backend2;\n assertNotComplex(x, \"MaxPoolWithArgmax\");\n const values = cpuBackend.data.get(x.dataId).values;\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3);\n const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n return [\n { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n { dataId: indexesDataId, shape: convInfo.outShape, dtype: \"int32\" }\n ];\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mean.js\nfunction mean2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n const axes = util_exports.parseAxisParam(axis, x.shape);\n const shapes = backend_util_exports.computeOutAndReduceShapes(x.shape, axes);\n const reduceShape = shapes[1];\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const toDispose = [];\n const reduceSizeScalar = backend2.makeTensorInfo([], \"float32\", new Float32Array([reduceSize]));\n toDispose.push(reduceSizeScalar);\n const $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n toDispose.push($x);\n const res = div2({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend2 });\n toDispose.push(res);\n const result = sum3({ inputs: { x: res }, backend: backend2, attrs: { axis, keepDims } });\n toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return result;\n}\nvar meanConfig = {\n kernelName: Mean,\n backendName: \"cpu\",\n kernelFunc: mean2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Min.js\nfunction min3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n assertNotComplex(x, \"min\");\n const origAxes = util_exports.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n if (permutedAxes != null) {\n $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, $x.shape.length);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n const aVals = backend2.data.get($x.dataId).values;\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let min6 = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (Number.isNaN(value) || value < min6) {\n min6 = value;\n }\n }\n vals[i] = min6;\n }\n if (permutedAxes != null) {\n backend2.disposeIntermediateTensorInfo($x);\n }\n const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n if (keepDims) {\n const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n backend2.disposeIntermediateTensorInfo(result);\n return reshapedResult;\n }\n return result;\n}\nvar minConfig = {\n kernelName: Min,\n backendName: \"cpu\",\n kernelFunc: min3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MirrorPad.js\nfunction mirrorPad2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { paddings, mode } = attrs;\n assertNotComplex(x, \"mirrorPad\");\n const outShape = paddings.map(\n (p2, i) => p2[0] + x.shape[i] + p2[1]\n /* afterPad */\n );\n const start = paddings.map((p2) => p2[0]);\n const end = paddings.map((p2, i) => p2[0] + x.shape[i]);\n const offset = mode === \"reflect\" ? 0 : 1;\n const xVals = backend2.data.get(x.dataId).values;\n const xRank = x.shape.length;\n const xStrides = util_exports.computeStrides(x.shape);\n const resultSize = util_exports.sizeFromShape(outShape);\n const resultRank = outShape.length;\n const resultStrides = util_exports.computeStrides(outShape);\n const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);\n for (let i = 0; i < resultSize; i++) {\n let coords2 = util_exports.indexToLoc(i, resultRank, resultStrides);\n for (let i2 = 0; i2 < resultRank; i2++) {\n if (coords2[i2] < start[i2]) {\n coords2[i2] = start[i2] * 2 - coords2[i2] - offset;\n } else if (coords2[i2] >= end[i2]) {\n coords2[i2] = (end[i2] - 1) * 2 - coords2[i2] + offset;\n }\n }\n coords2 = coords2.map((c, i2) => c - start[i2]);\n const inIndex = util_exports.locToIndex(coords2, xRank, xStrides);\n resVals[i] = xVals[inIndex];\n }\n const outId = backend2.write(resVals, outShape, x.dtype);\n return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nvar mirrorPadConfig = {\n kernelName: MirrorPad,\n backendName: \"cpu\",\n kernelFunc: mirrorPad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mod.js\nvar modImpl = createSimpleBinaryKernelImpl((aValue, bValue) => {\n const rem = aValue % bValue;\n if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) {\n return rem;\n } else {\n return (rem + bValue) % bValue;\n }\n});\nvar mod2 = binaryKernelFunc(Mod, modImpl);\nvar modConfig = {\n kernelName: Mod,\n backendName: \"cpu\",\n kernelFunc: mod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js\nvar seedrandom4 = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softmax.js\nfunction softmax3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { logits } = inputs;\n const { dim } = attrs;\n const logitsRank = logits.shape.length;\n let $dim = dim;\n if ($dim === -1) {\n $dim = logitsRank - 1;\n }\n if ($dim !== logitsRank - 1) {\n throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);\n }\n const axes = util_exports.parseAxisParam([$dim], logits.shape);\n const maxLogit = max3({\n inputs: { x: logits },\n backend: backend2,\n attrs: { reductionIndices: axes, keepDims: false }\n });\n const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n const maxLogitReshaped = reshape3({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n const a = sub2({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend2 });\n const b = exp2({ inputs: { x: a }, backend: backend2 });\n const sumExp = sum3({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n const sumReshaped = reshape3({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n const result = div2({ inputs: { a: b, b: sumReshaped }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(maxLogit);\n backend2.disposeIntermediateTensorInfo(maxLogitReshaped);\n backend2.disposeIntermediateTensorInfo(a);\n backend2.disposeIntermediateTensorInfo(b);\n backend2.disposeIntermediateTensorInfo(sumExp);\n backend2.disposeIntermediateTensorInfo(sumReshaped);\n return result;\n}\nvar softmaxConfig = {\n kernelName: Softmax,\n backendName: \"cpu\",\n kernelFunc: softmax3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js\nfunction multinomial2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { logits } = inputs;\n const { numSamples, seed, normalized } = attrs;\n assertNotComplex(logits, \"multinomial\");\n const probabilities = normalized ? logits : softmax3({ inputs: { logits }, backend: backend2, attrs: { dim: -1 } });\n const batchSize = probabilities.shape[0];\n const numEvents = probabilities.shape[1];\n const probVals = backend2.data.get(probabilities.dataId).values;\n const resShape = [batchSize, numSamples];\n const resVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape), \"int32\");\n for (let b = 0; b < batchSize; ++b) {\n const offset = b * numEvents;\n const cdf = new Float32Array(numEvents - 1);\n cdf[0] = probVals[offset];\n for (let event = 1; event < cdf.length; ++event) {\n cdf[event] = cdf[event - 1] + probVals[offset + event];\n }\n const random = seedrandom4.alea(seed.toString());\n const outOffset = b * numSamples;\n for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n const r = random();\n resVals[outOffset + sampleId] = cdf.length;\n for (let event = 0; event < cdf.length; event++) {\n if (r < cdf[event]) {\n resVals[outOffset + sampleId] = event;\n break;\n }\n }\n }\n }\n if (!normalized) {\n backend2.disposeIntermediateTensorInfo(probabilities);\n }\n return backend2.makeTensorInfo(resShape, \"int32\", resVals);\n}\nvar multinomialConfig = {\n kernelName: Multinomial,\n backendName: \"cpu\",\n kernelFunc: multinomial2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV3.js\nvar nonMaxSuppressionV3Impl2 = kernel_impls_exports.nonMaxSuppressionV3Impl;\nfunction nonMaxSuppressionV3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { boxes, scores } = inputs;\n const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n assertNotComplex(boxes, \"NonMaxSuppression\");\n const boxesVals = backend2.data.get(boxes.dataId).values;\n const scoresVals = backend2.data.get(scores.dataId).values;\n const { selectedIndices } = nonMaxSuppressionV3Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config = {\n kernelName: NonMaxSuppressionV3,\n backendName: \"cpu\",\n kernelFunc: nonMaxSuppressionV3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV4.js\nvar nonMaxSuppressionV4Impl2 = kernel_impls_exports.nonMaxSuppressionV4Impl;\nfunction nonMaxSuppressionV4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { boxes, scores } = inputs;\n const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n assertNotComplex(boxes, \"NonMaxSuppressionPadded\");\n const boxesVals = backend2.data.get(boxes.dataId).values;\n const scoresVals = backend2.data.get(scores.dataId).values;\n const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n return [\n backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n backend2.makeTensorInfo([], \"int32\", new Int32Array([validOutputs]))\n ];\n}\nvar nonMaxSuppressionV4Config = {\n kernelName: NonMaxSuppressionV4,\n backendName: \"cpu\",\n kernelFunc: nonMaxSuppressionV4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV5.js\nvar nonMaxSuppressionV5Impl2 = kernel_impls_exports.nonMaxSuppressionV5Impl;\nfunction nonMaxSuppressionV5(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { boxes, scores } = inputs;\n const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n assertNotComplex(boxes, \"NonMaxSuppressionWithScore\");\n const boxesVals = backend2.data.get(boxes.dataId).values;\n const scoresVals = backend2.data.get(scores.dataId).values;\n const maxOutputSizeVal = maxOutputSize;\n const iouThresholdVal = iouThreshold;\n const scoreThresholdVal = scoreThreshold;\n const softNmsSigmaVal = softNmsSigma;\n const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl2(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n return [\n backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n ];\n}\nvar nonMaxSuppressionV5Config = {\n kernelName: NonMaxSuppressionV5,\n backendName: \"cpu\",\n kernelFunc: nonMaxSuppressionV5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OneHot.js\nfunction oneHot2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { indices } = inputs;\n const { dtype, depth, onValue, offValue } = attrs;\n assertNotComplex(indices, \"oneHot\");\n const indicesSize = util_exports.sizeFromShape(indices.shape);\n const res = new Float32Array(indicesSize * depth);\n res.fill(offValue);\n const indicesVal = backend2.data.get(indices.dataId).values;\n for (let event = 0; event < indicesSize; ++event) {\n if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n res[event * depth + indicesVal[event]] = onValue;\n }\n }\n return backend2.makeTensorInfo([...indices.shape, depth], dtype, res);\n}\nvar oneHotConfig = {\n kernelName: OneHot,\n backendName: \"cpu\",\n kernelFunc: oneHot2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ZerosLike.js\nfunction zerosLike2(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n if (x.dtype === \"string\") {\n throw new Error(\"zerosLike is not supported for string tensors\");\n } else if (x.dtype === \"complex64\") {\n const realPart = real2({ inputs: { input: x }, backend: backend2 });\n const r = zerosLike2({ inputs: { x: realPart }, backend: backend2 });\n const imagPart = imag2({ inputs: { input: x }, backend: backend2 });\n const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });\n const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(realPart);\n backend2.disposeIntermediateTensorInfo(r);\n backend2.disposeIntermediateTensorInfo(imagPart);\n backend2.disposeIntermediateTensorInfo(i);\n return result;\n } else {\n return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } });\n }\n}\nvar zerosLikeConfig = {\n kernelName: ZerosLike,\n backendName: \"cpu\",\n kernelFunc: zerosLike2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OnesLike.js\nfunction onesLike2(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n if (x.dtype === \"string\") {\n throw new Error(\"onesLike is not supported for string tensors\");\n } else if (x.dtype === \"complex64\") {\n const realPart = real2({ inputs: { input: x }, backend: backend2 });\n const r = onesLike2({ inputs: { x: realPart }, backend: backend2 });\n const imagPart = imag2({ inputs: { input: x }, backend: backend2 });\n const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });\n const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(realPart);\n backend2.disposeIntermediateTensorInfo(r);\n backend2.disposeIntermediateTensorInfo(imagPart);\n backend2.disposeIntermediateTensorInfo(i);\n return result;\n } else {\n return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } });\n }\n}\nvar onesLikeConfig = {\n kernelName: OnesLike,\n backendName: \"cpu\",\n kernelFunc: onesLike2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pack.js\nfunction pack(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { axis } = attrs;\n if (inputs.length === 1) {\n return expandDims3({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n }\n const shape = inputs[0].shape;\n const dtype = inputs[0].dtype;\n inputs.forEach((t) => {\n util_exports.assertShapesMatch(shape, t.shape, \"All tensors passed to stack must have matching shapes\");\n util_exports.assert(dtype === t.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n });\n const intermediateTensorInfos = [];\n const expandedTensors = inputs.map((t) => {\n const expandedT = expandDims3({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });\n intermediateTensorInfos.push(expandedT);\n return expandedT;\n });\n const result = concat2({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return result;\n}\nvar packConfig = {\n kernelName: Pack,\n backendName: \"cpu\",\n kernelFunc: pack\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/PadV2.js\nfunction padV2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { paddings, constantValue } = attrs;\n assertNotComplex(x, \"pad\");\n const outShape = paddings.map(\n (p2, i) => p2[0] + x.shape[i] + p2[1]\n /* afterPad */\n );\n const start = paddings.map((p2) => p2[0]);\n const xVals = backend2.data.get(x.dataId).values;\n const xSize = util_exports.sizeFromShape(x.shape);\n const xRank = x.shape.length;\n const xStrides = util_exports.computeStrides(x.shape);\n const resultSize = util_exports.sizeFromShape(outShape);\n const resultRank = outShape.length;\n const resultStrides = util_exports.computeStrides(outShape);\n const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);\n if (constantValue !== 0) {\n resVals.fill(constantValue);\n }\n for (let i = 0; i < xSize; i++) {\n const coords2 = util_exports.indexToLoc(i, xRank, xStrides);\n const outCoords = coords2.map((c, i2) => c + start[i2]);\n const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides);\n resVals[outIndex] = xVals[i];\n }\n const outId = backend2.write(resVals, outShape, x.dtype);\n return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nvar padV2Config = {\n kernelName: PadV2,\n backendName: \"cpu\",\n kernelFunc: padV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pow.js\nvar powImpl = createSimpleBinaryKernelImpl((a, b) => Math.pow(a, b));\nvar pow2 = binaryKernelFunc(Pow, powImpl);\nvar powConfig = {\n kernelName: Pow,\n backendName: \"cpu\",\n kernelFunc: pow2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedGather.js\nfunction raggedGather2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { paramsNestedSplits, paramsDenseValues, indices } = inputs;\n const { outputRaggedRank } = attrs;\n const $paramsNestedSplits = paramsNestedSplits.map((t) => backend2.data.get(t.dataId).values);\n const $paramsNestedSplitsShapes = paramsNestedSplits.map((t) => t.shape);\n const $paramsDenseValues = backend2.data.get(paramsDenseValues.dataId).values;\n const $indices = backend2.data.get(indices.dataId).values;\n const [outputNestedSplits, outputDenseValues, outputDenseValuesShape] = raggedGatherImpl($paramsNestedSplits, $paramsNestedSplitsShapes, $paramsDenseValues, paramsDenseValues.shape, paramsDenseValues.dtype, $indices, indices.shape, outputRaggedRank);\n const outputNestedSplitsTensors = outputNestedSplits.map((splits) => backend2.makeTensorInfo([splits.length], \"int32\", splits));\n const outputDenseValuesTensor = backend2.makeTensorInfo(outputDenseValuesShape, paramsDenseValues.dtype, outputDenseValues);\n return outputNestedSplitsTensors.concat([outputDenseValuesTensor]);\n}\nvar raggedGatherConfig = {\n kernelName: RaggedGather,\n backendName: \"cpu\",\n kernelFunc: raggedGather2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedRange.js\nfunction raggedRange2(args) {\n const { inputs, backend: backend2 } = args;\n const { starts, limits, deltas } = inputs;\n const $starts = backend2.data.get(starts.dataId).values;\n const $limits = backend2.data.get(limits.dataId).values;\n const $deltas = backend2.data.get(deltas.dataId).values;\n const [rtNestedSplitsData, rtDenseValuesData] = raggedRangeImpl($starts, starts.shape, starts.dtype, $limits, limits.shape, $deltas, deltas.shape);\n const rtNestedSplits = backend2.makeTensorInfo([rtNestedSplitsData.length], \"int32\", rtNestedSplitsData);\n const rtDenseValues = backend2.makeTensorInfo([rtDenseValuesData.length], starts.dtype, rtDenseValuesData);\n return [rtNestedSplits, rtDenseValues];\n}\nvar raggedRangeConfig = {\n kernelName: RaggedRange,\n backendName: \"cpu\",\n kernelFunc: raggedRange2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedTensorToTensor.js\nfunction raggedTensorToTensor2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { shape, values, defaultValue, rowPartitionTensors } = inputs;\n const { rowPartitionTypes } = attrs;\n const $shape = backend2.data.get(shape.dataId).values;\n const $values = backend2.data.get(values.dataId).values;\n const $defaultValue = backend2.data.get(defaultValue.dataId).values;\n const $rowPartitionValues = rowPartitionTensors.map((t) => backend2.data.get(t.dataId).values);\n const rowPartitionValuesShapes = rowPartitionTensors.map((t) => t.shape);\n const [outputShape, output] = raggedTensorToTensorImpl($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes);\n return backend2.makeTensorInfo(outputShape, values.dtype, output);\n}\nvar raggedTensorToTensorConfig = {\n kernelName: RaggedTensorToTensor,\n backendName: \"cpu\",\n kernelFunc: raggedTensorToTensor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range.js\nfunction range3(args) {\n const { backend: backend2, attrs } = args;\n const { start, stop, dtype, step: step5 } = attrs;\n const values = rangeImpl(start, stop, step5, dtype);\n return backend2.makeTensorInfo([values.length], dtype, values);\n}\nvar rangeConfig = {\n kernelName: Range,\n backendName: \"cpu\",\n kernelFunc: range3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reciprocal.js\nvar reciprocal2 = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nvar reciprocalConfig = {\n kernelName: Reciprocal,\n backendName: \"cpu\",\n kernelFunc: reciprocal2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { images } = inputs;\n const { alignCorners, halfPixelCenters, size } = attrs;\n assertNotComplex(images, \"resizeBilinear\");\n const imagesStrides = util_exports.computeStrides(images.shape);\n const [newHeight, newWidth] = size;\n const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n const xValues = backend2.data.get(images.dataId).values;\n const result = new Float32Array(util_exports.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n const effectiveInputSize = [\n alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n ];\n const effectiveOutputSize = [\n alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n ];\n let outputIdx = 0;\n const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n for (let b = 0; b < batch; b++) {\n for (let r = 0; r < newHeight; r++) {\n let sourceFracRow;\n if (halfPixelCenters) {\n sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5;\n } else {\n sourceFracRow = effectiveRowSizeRatio * r;\n }\n const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));\n const rowFrac = sourceFracRow - sourceRowFloor;\n const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];\n const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];\n for (let c = 0; c < newWidth; c++) {\n let sourceFracCol;\n if (halfPixelCenters) {\n sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;\n } else {\n sourceFracCol = effectiveColSizeRatio * c;\n }\n const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));\n const colFrac = sourceFracCol - sourceColFloor;\n const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];\n const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];\n const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];\n const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];\n for (let d = 0; d < numChannels; d++) {\n const topLeft = xValues[topLeftOffest + d];\n const bottomLeft = xValues[botLeftOffset + d];\n const topRight = xValues[topRightOffset + d];\n const bottomRight = xValues[botRightOffest + d];\n const top = topLeft + (topRight - topLeft) * colFrac;\n const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n const newValue = top + (bottom - top) * rowFrac;\n result[outputIdx++] = newValue;\n }\n }\n }\n }\n return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], \"float32\", result);\n}\nvar resizeBilinearConfig = {\n kernelName: ResizeBilinear,\n backendName: \"cpu\",\n kernelFunc: resizeBilinear3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinearGrad.js\nfunction resizeBilinearGrad(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { images, dy } = inputs;\n const { alignCorners } = attrs;\n assertNotComplex([dy, images], \"resizeBilinearGrad\");\n const imagesStrides = util_exports.computeStrides(images.shape);\n const [batch, xHeight, xWidth, depth] = images.shape;\n const [, yHeight, yWidth] = dy.shape;\n const output = new Float32Array(batch * xHeight * xWidth * depth);\n const effectiveXSize = [\n alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n ];\n const effectiveYSize = [\n alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n ];\n const heightScale = effectiveXSize[0] / effectiveYSize[0];\n const widthScale = effectiveXSize[1] / effectiveYSize[1];\n const dyValues = backend2.data.get(dy.dataId).values;\n let offset = 0;\n for (let b = 0; b < batch; b++) {\n const bOffset = b * imagesStrides[0];\n for (let r = 0; r < yHeight; r++) {\n const dxR = r * heightScale;\n const topDxRIndex = Math.floor(dxR);\n const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];\n const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];\n const dxRLerp = dxR - topDxRIndex;\n const inverseDxRLerp = 1 - dxRLerp;\n for (let c = 0; c < yWidth; c++) {\n const dxC = c * widthScale;\n const leftDxCIndex = Math.floor(dxC);\n const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n const dxCLerp = dxC - leftDxCIndex;\n const inverseDxCLerp = 1 - dxCLerp;\n const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];\n const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2];\n const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2];\n const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2];\n const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n for (let d = 0; d < depth; d++) {\n const dyVal = dyValues[offset++];\n output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp;\n output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;\n output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n }\n }\n }\n }\n return backend2.makeTensorInfo([batch, xWidth, xHeight, depth], \"float32\", output);\n}\nvar resizeBilinearGradConfig2 = {\n kernelName: ResizeBilinearGrad,\n backendName: \"cpu\",\n kernelFunc: resizeBilinearGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { images } = inputs;\n const { alignCorners, halfPixelCenters, size } = attrs;\n assertNotComplex(images, \"resizeNearestNeighbor\");\n const imagesStrides = util_exports.computeStrides(images.shape);\n const [newHeight, newWidth] = size;\n const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n const xValues = backend2.data.get(images.dataId).values;\n const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n const effectiveInputSize = [\n alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n ];\n const effectiveOutputSize = [\n alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n ];\n const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n let outputOffset = 0;\n for (let b = 0; b < batch; b++) {\n const batchOffset = b * imagesStrides[0];\n for (let r = 0; r < newHeight; r++) {\n const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r + 0.5) : effectiveRowSizeRatio * r;\n let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));\n if (halfPixelCenters) {\n sourceNearestRow = Math.max(0, sourceNearestRow);\n }\n const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];\n for (let c = 0; c < newWidth; c++) {\n const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c;\n let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));\n if (halfPixelCenters) {\n sourceNearestCol = Math.max(0, sourceNearestCol);\n }\n const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];\n for (let d = 0; d < numChannels; d++) {\n const newVal = xValues[colOffset + d];\n output[outputOffset++] = newVal;\n }\n }\n }\n }\n return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output);\n}\nvar resizeNearestNeighborConfig = {\n kernelName: ResizeNearestNeighbor,\n backendName: \"cpu\",\n kernelFunc: resizeNearestNeighbor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighborGrad.js\nfunction resizeNearestNeighborGrad(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { images, dy } = inputs;\n const { alignCorners } = attrs;\n assertNotComplex([dy, images], \"resizeNearestNeighborGrad\");\n const imagesStrides = util_exports.computeStrides(images.shape);\n const dyStrides = util_exports.computeStrides(dy.shape);\n const [batch, xHeight, xWidth, depth] = images.shape;\n const [, yHeight, yWidth] = dy.shape;\n const output = new Float32Array(batch * xHeight * xWidth * depth);\n const dyValues = backend2.data.get(dy.dataId).values;\n const effectiveXSize = [\n alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n ];\n const effectiveYSize = [\n alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n ];\n const heightScale = effectiveXSize[0] / effectiveYSize[0];\n const widthScale = effectiveXSize[1] / effectiveYSize[1];\n const invHeightScale = 1 / heightScale;\n const invWidthScale = 1 / widthScale;\n const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n for (let b = 0; b < batch; b++) {\n const batchOffset = b * imagesStrides[0];\n for (let r = 0; r < xHeight; r++) {\n const rowOffset = batchOffset + r * imagesStrides[1];\n const startRLerp = Math.floor(r * invHeightScale);\n const startDyR = Math.floor(startRLerp - winHeight / 2);\n for (let c = 0; c < xWidth; c++) {\n const colOffset = rowOffset + c * imagesStrides[2];\n const startCLerp = Math.floor(c * invWidthScale);\n const startDyC = Math.floor(startCLerp - winWidth / 2);\n for (let d = 0; d < depth; d++) {\n let accum = 0;\n for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n const dyR = dyRIndex + startDyR;\n if (dyR < 0 || dyR >= yHeight) {\n continue;\n }\n const dyROffset = batchOffset + dyR * dyStrides[1];\n const sourceFracRow = dyR * heightScale;\n const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));\n if (r !== sourceNearestRow) {\n continue;\n }\n for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n const dyC = dyCIndex + startDyC;\n if (dyC < 0 || dyC >= yWidth) {\n continue;\n }\n const dyCOffset = dyROffset + dyC * dyStrides[2];\n const sourceFracCol = dyC * widthScale;\n const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));\n if (c === sourceNearestCol) {\n accum += dyValues[dyCOffset + d];\n }\n }\n }\n output[colOffset + d] = accum;\n }\n }\n }\n }\n return backend2.makeTensorInfo(images.shape, images.dtype, output);\n}\nvar resizeNearestNeighborGradConfig2 = {\n kernelName: ResizeNearestNeighborGrad,\n backendName: \"cpu\",\n kernelFunc: resizeNearestNeighborGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reverse.js\nfunction reverse2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { dims } = attrs;\n assertNotComplex(x, \"reverse\");\n const xRank = x.shape.length;\n const $dims = util_exports.parseAxisParam(dims, x.shape);\n if (xRank === 0) {\n return identity2({ inputs: { x }, backend: backend2 });\n }\n const outBuf = new TensorBuffer(x.shape, x.dtype);\n const xBuf = backend2.bufferSync(x);\n for (let i = 0; i < outBuf.size; i++) {\n const outLoc = outBuf.indexToLoc(i);\n const inLoc = outLoc.slice();\n $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]);\n outBuf.set(xBuf.get(...inLoc), ...outLoc);\n }\n return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar reverseConfig = {\n kernelName: Reverse,\n backendName: \"cpu\",\n kernelFunc: reverse2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig = {\n kernelName: RotateWithOffset,\n backendName: \"cpu\",\n kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n const { image: image2 } = inputs;\n const { radians, fillValue, center } = attrs;\n const cpuBackend = backend2;\n const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));\n const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);\n const fullOpacityValue = 255;\n const sinFactor = Math.sin(radians);\n const cosFactor = Math.cos(radians);\n const imageVals = cpuBackend.data.get(image2.dataId).values;\n for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n for (let row = 0; row < imageHeight; row++) {\n const rowOffset = row * (imageWidth * numChannels);\n for (let col = 0; col < imageWidth; col++) {\n const colOffset = col * numChannels;\n for (let channel = 0; channel < numChannels; channel++) {\n const coords2 = [batch, row, col, channel];\n const x = coords2[2];\n const y = coords2[1];\n let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n coordX = Math.round(coordX + centerX);\n coordY = Math.round(coordY + centerY);\n let outputValue = fillValue;\n if (typeof fillValue !== \"number\") {\n if (channel === 3) {\n outputValue = fullOpacityValue;\n } else {\n outputValue = fillValue[channel];\n }\n }\n if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) {\n const rotatedRowOffset = coordY * (imageWidth * numChannels);\n const rotatedColOffset = coordX * numChannels;\n const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n outputValue = imageVals[imageIdx];\n }\n const outIdx = batchOffset + rowOffset + colOffset + channel;\n output[outIdx] = outputValue;\n }\n }\n }\n }\n const dataId = cpuBackend.write(output, image2.shape, image2.dtype);\n return { dataId, shape: image2.shape, dtype: image2.dtype };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Round.js\nvar round3 = unaryKernelFunc(Round, (xi) => {\n const base = Math.floor(xi);\n if (xi - base < 0.5) {\n return Math.floor(xi);\n } else if (xi - base > 0.5) {\n return Math.ceil(xi);\n } else {\n if (base % 2 === 0) {\n return base;\n } else {\n return base + 1;\n }\n }\n});\nvar roundConfig = {\n kernelName: Round,\n backendName: \"cpu\",\n kernelFunc: round3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ScatterNd.js\nfunction scatterNd(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { indices, updates } = inputs;\n const { shape } = attrs;\n const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n const sumDupeIndices = true;\n const indicesBuf = backend2.bufferSync(indices);\n const updatesBuf = backend2.bufferSync(updates);\n const outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0, sumDupeIndices);\n return backend2.makeTensorInfo(shape, outBuf.dtype, outBuf.values);\n}\nvar scatterNdConfig = {\n kernelName: ScatterNd,\n backendName: \"cpu\",\n kernelFunc: scatterNd\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted_impl.js\nfunction lowerBound2(array2, value) {\n let left = 0;\n let right = array2.length;\n let mid = 0;\n while (left < right) {\n mid = Math.floor((left + right) / 2);\n if (array2[mid] < value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n}\nfunction upperBound2(array2, value) {\n let left = 0;\n let right = array2.length;\n let mid = 0;\n while (left < right) {\n mid = Math.floor((left + right) / 2);\n if (array2[mid] <= value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n}\nfunction searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues, side) {\n const output = util_exports.getArrayFromDType(\"int32\", batchSize * numValues);\n for (let b = 0; b < batchSize; ++b) {\n const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs);\n const outputOffset = b * numValues;\n for (let i = 0; i < numValues; ++i) {\n output[outputOffset + i] = side === \"left\" ? lowerBound2(sortedInputsSlice, values[i + outputOffset]) : upperBound2(sortedInputsSlice, values[i + outputOffset]);\n }\n }\n return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted.js\nfunction searchSorted2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { sortedSequence, values } = inputs;\n const { side } = attrs;\n const $sortedSequence = backend2.data.get(sortedSequence.dataId).values;\n const $values = backend2.data.get(values.dataId).values;\n const output = searchSortedImpl($sortedSequence, $values, sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);\n return backend2.makeTensorInfo(values.shape, \"int32\", output);\n}\nvar searchSortedConfig = {\n kernelName: SearchSorted,\n backendName: \"cpu\",\n kernelFunc: searchSorted2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Select.js\nfunction select2(args) {\n const { inputs, backend: backend2 } = args;\n const { condition, t, e } = inputs;\n assertNotComplex([condition, t, e], \"select\");\n const conditionRank = condition.shape.length;\n const values = backend2.data.get(condition.dataId).values;\n const tValues = backend2.data.get(t.dataId).values;\n const eValues = backend2.data.get(e.dataId).values;\n const resultDtype = upcastType(t.dtype, e.dtype);\n const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape), resultDtype);\n let index = 0;\n const offset = conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1));\n for (let i = 0; i < values.length; i++) {\n for (let j = 0; j < offset; j++) {\n if (values[i] === 1) {\n newValues[index++] = tValues[i];\n } else {\n newValues[index++] = eValues[i];\n }\n }\n }\n return backend2.makeTensorInfo(t.shape, resultDtype, newValues);\n}\nvar selectConfig = {\n kernelName: Select,\n backendName: \"cpu\",\n kernelFunc: select2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Selu.js\nvar scaleAlpha = backend_util_exports.SELU_SCALEALPHA;\nvar scale = backend_util_exports.SELU_SCALE;\nvar selu2 = unaryKernelFunc(Selu, (xi) => {\n if (xi >= 0) {\n return scale * xi;\n } else {\n return scaleAlpha * (Math.exp(xi) - 1);\n }\n});\nvar seluConfig = {\n kernelName: Selu,\n backendName: \"cpu\",\n kernelFunc: selu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sign.js\nvar sign2 = unaryKernelFunc(Sign, (xi) => {\n if (xi < 0) {\n return -1;\n } else if (xi > 0) {\n return 1;\n } else {\n return 0;\n }\n});\nvar signConfig = {\n kernelName: Sign,\n backendName: \"cpu\",\n kernelFunc: sign2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sin.js\nvar sin2 = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nvar sinConfig = {\n kernelName: Sin,\n backendName: \"cpu\",\n kernelFunc: sin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sinh.js\nvar sinh2 = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nvar sinhConfig = {\n kernelName: Sinh,\n backendName: \"cpu\",\n kernelFunc: sinh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softplus.js\nvar epsilon2 = 11920928955078125e-23;\nvar threshold2 = Math.log(epsilon2) + 2;\nvar softplus2 = unaryKernelFunc(Softplus, (xi) => {\n const tooLarge = xi > -threshold2;\n const tooSmall = xi < threshold2;\n const expX = Math.exp(xi);\n let result;\n if (tooSmall) {\n result = expX;\n } else if (tooLarge) {\n result = xi;\n } else {\n result = Math.log(1 + expX);\n }\n return result;\n});\nvar softplusConfig = {\n kernelName: Softplus,\n backendName: \"cpu\",\n kernelFunc: softplus2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SpaceToBatchND.js\nfunction spaceToBatchND2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { blockShape, paddings } = attrs;\n assertNotComplex([x], \"spaceToBatchND\");\n const prod5 = util_exports.sizeFromShape(blockShape);\n const completePaddings = [[0, 0]];\n completePaddings.push(...paddings);\n for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n completePaddings.push([0, 0]);\n }\n const paddedX = padV2Config.kernelFunc({\n inputs: { x },\n backend: backend2,\n attrs: { paddings: completePaddings, constantValue: 0 }\n });\n const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false);\n const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false);\n const reshapeInputs = { x: paddedX };\n const reshapeAttrs = { shape: reshapedPaddedShape };\n const paddedXReshaped = reshape3({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });\n const transposeInputs = { x: paddedXReshaped };\n const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n const paddedXT = transpose2({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });\n const resultReshapeInputs = { x: paddedXT };\n const resultReshapeAttrs = { shape: flattenShape };\n const result = reshape3({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });\n backend2.disposeIntermediateTensorInfo(paddedX);\n backend2.disposeIntermediateTensorInfo(paddedXReshaped);\n backend2.disposeIntermediateTensorInfo(paddedXT);\n return result;\n}\nvar spaceToBatchNDConfig = {\n kernelName: SpaceToBatchND,\n backendName: \"cpu\",\n kernelFunc: spaceToBatchND2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows.js\nfunction sparseFillEmptyRows2(args) {\n const { inputs, backend: backend2 } = args;\n const { indices, values, denseShape, defaultValue } = inputs;\n if (denseShape.shape.length !== 1) {\n throw new Error(`Dense shape must be a vector, saw:\n ${denseShape.shape}`);\n }\n if (indices.shape.length !== 2) {\n throw new Error(`Indices must be a matrix, saw:\n ${indices.shape}`);\n }\n if (values.shape.length !== 1) {\n throw new Error(`Values must be a vector, saw:\n ${values.shape}`);\n }\n if (defaultValue.shape.length !== 0) {\n throw new Error(`Default value must be a scalar, saw:\n ${defaultValue.shape}`);\n }\n const $indices = backend2.data.get(indices.dataId).values;\n const $values = backend2.data.get(values.dataId).values;\n const $denseShape = backend2.data.get(denseShape.dataId).values;\n const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);\n return [\n backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),\n backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),\n backend2.makeTensorInfo([emptyRowIndicator.length], \"bool\", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),\n backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))\n ];\n}\nvar sparseFillEmptyRowsConfig = {\n kernelName: SparseFillEmptyRows,\n backendName: \"cpu\",\n kernelFunc: sparseFillEmptyRows2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape.js\nfunction sparseReshape2(args) {\n const { inputs, backend: backend2 } = args;\n const { inputIndices, inputShape, newShape } = inputs;\n if (inputIndices.shape.length !== 2) {\n throw new Error(`Input indices should be a matrix but received shape\n ${inputIndices.shape}`);\n }\n if (inputShape.shape.length !== 1) {\n throw new Error(`Input shape should be a vector but received shape\n ${inputShape.shape}`);\n }\n if (newShape.shape.length !== 1) {\n throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n }\n const $inputShape = Array.from(backend2.data.get(inputShape.dataId).values);\n const $inputIndices = backend2.data.get(inputIndices.dataId).values;\n const targetShape = Array.from(backend2.data.get(newShape.dataId).values);\n const [newIndices, indicesShape, outputShape] = sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);\n return [\n backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),\n backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))\n ];\n}\nvar sparseReshapeConfig = {\n kernelName: SparseReshape,\n backendName: \"cpu\",\n kernelFunc: sparseReshape2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean2(args) {\n const { inputs, backend: backend2 } = args;\n const { data, indices, segmentIds } = inputs;\n if (data.shape.length < 1) {\n throw new Error(`Data should be at least 1 dimensional but received scalar`);\n }\n if (indices.shape.length !== 1) {\n throw new Error(`Indices should be a vector but received shape\n ${indices.shape}`);\n }\n if (segmentIds.shape.length !== 1) {\n throw new Error(`Segment ids should be a vector but received shape\n ${segmentIds.shape}`);\n }\n if (indices.shape[0] !== segmentIds.shape[0]) {\n throw new Error(`segmentIds and indices should have same size.`);\n }\n const $data = backend2.data.get(data.dataId).values;\n const $indices = backend2.data.get(indices.dataId).values;\n const $segmentIds = backend2.data.get(segmentIds.dataId).values;\n const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true);\n return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentMeanConfig = {\n kernelName: SparseSegmentMean,\n backendName: \"cpu\",\n kernelFunc: sparseSegmentMean2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum2(args) {\n const { inputs, backend: backend2 } = args;\n const { data, indices, segmentIds } = inputs;\n if (data.shape.length < 1) {\n throw new Error(`Data should be at least 1 dimensional but received scalar`);\n }\n if (indices.shape.length !== 1) {\n throw new Error(`Indices should be a vector but received shape\n ${indices.shape}`);\n }\n if (segmentIds.shape.length !== 1) {\n throw new Error(`Segment ids should be a vector but received shape\n ${segmentIds.shape}`);\n }\n if (indices.shape[0] !== segmentIds.shape[0]) {\n throw new Error(`segmentIds and indices should have same size.`);\n }\n const $data = backend2.data.get(data.dataId).values;\n const $indices = backend2.data.get(indices.dataId).values;\n const $segmentIds = backend2.data.get(segmentIds.dataId).values;\n const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds);\n return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentSumConfig = {\n kernelName: SparseSegmentSum,\n backendName: \"cpu\",\n kernelFunc: sparseSegmentSum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseToDense.js\nfunction sparseToDense2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { sparseIndices, sparseValues, defaultValue } = inputs;\n const { outputShape } = attrs;\n const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n const sumDupeIndices = false;\n const indicesBuf = backend2.bufferSync(sparseIndices);\n let outBuf;\n switch (sparseValues.dtype) {\n case \"bool\": {\n const updatesBuf = backend2.bufferSync(sparseValues);\n const $defaultValue = Boolean(backend2.data.get(defaultValue.dataId).values[0]);\n outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n break;\n }\n case \"float32\": {\n const updatesBuf = backend2.bufferSync(sparseValues);\n const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n break;\n }\n case \"int32\": {\n const updatesBuf = backend2.bufferSync(sparseValues);\n const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n break;\n }\n case \"string\": {\n const updatesBuf = backend2.bufferSync(sparseValues);\n const $defaultValue = util_exports.decodeString(backend2.data.get(defaultValue.dataId).values[0]);\n outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n break;\n }\n default:\n throw new Error(`Unsupported type ${sparseValues.dtype}`);\n }\n return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n}\nvar sparseToDenseConfig = {\n kernelName: SparseToDense,\n backendName: \"cpu\",\n kernelFunc: sparseToDense2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SplitV.js\nfunction splitV(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { numOrSizeSplits, axis } = attrs;\n const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n const begin = new Array(x.shape.length).fill(0);\n const size = x.shape.slice();\n return splitSizes.map((s) => {\n const sliceSize = [...size];\n sliceSize[$axis] = s;\n const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n begin[$axis] += s;\n return sliceT;\n });\n}\nvar splitVConfig = {\n kernelName: SplitV,\n backendName: \"cpu\",\n kernelFunc: splitV\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Square.js\nvar squareConfig = {\n kernelName: Square,\n backendName: \"cpu\",\n kernelFunc: ({ inputs, backend: backend2 }) => {\n const { x } = inputs;\n const cpuBackend = backend2;\n assertNotComplex(x, \"square\");\n const values = cpuBackend.data.get(x.dataId).values;\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n const value = values[i];\n newValues[i] = value * value;\n }\n const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n return { dataId, shape: x.shape, dtype: x.dtype };\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Step.js\nvar step2 = unaryKernelFunc(Step, (xi, attrs) => {\n const stepAttrs = attrs;\n if (isNaN(xi)) {\n return NaN;\n } else {\n return xi > 0 ? 1 : stepAttrs.alpha;\n }\n});\nvar stepConfig = {\n kernelName: Step,\n backendName: \"cpu\",\n kernelFunc: step2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice.js\nfunction stridedSlice2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n assertNotComplex(x, \"stridedSlice\");\n const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n let result;\n if (isIdentity) {\n result = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n } else if (sliceDim0 || isSimpleSlice) {\n util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n const sliced = slice2({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n result = reshape3({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n backend2.disposeIntermediateTensorInfo(sliced);\n } else {\n const xBuf = backend2.bufferSync(x);\n const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin);\n result = backend2.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);\n }\n return result;\n}\nvar stridedSliceConfig = {\n kernelName: StridedSlice,\n backendName: \"cpu\",\n kernelFunc: stridedSlice2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams.js\nfunction stringNGrams2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n const { data, dataSplits } = inputs;\n const $data = backend2.data.get(data.dataId).values;\n const $dataSplits = backend2.data.get(dataSplits.dataId).values;\n const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n return [\n backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n ];\n}\nvar stringNGramsConfig = {\n kernelName: StringNGrams,\n backendName: \"cpu\",\n kernelFunc: stringNGrams2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit.js\nfunction stringSplit2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { skipEmpty } = attrs;\n const { input: input2, delimiter } = inputs;\n if (input2.dtype !== \"string\") {\n throw new Error(\"Input must be of datatype string\");\n }\n if (input2.shape.length !== 1) {\n throw new Error(`Input must be a vector, got shape: ${input2.shape}`);\n }\n if (delimiter.shape.length !== 0) {\n throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);\n }\n const $input = backend2.data.get(input2.dataId).values;\n const $delimiter = backend2.data.get(delimiter.dataId).values[0];\n const [indices, values, shape] = stringSplitImpl($input, $delimiter, skipEmpty);\n const outputSize = values.length;\n return [\n backend2.makeTensorInfo([outputSize, 2], \"int32\", indices),\n backend2.makeTensorInfo([outputSize], \"string\", values),\n backend2.makeTensorInfo([2], \"int32\", new Int32Array(shape))\n ];\n}\nvar stringSplitConfig = {\n kernelName: StringSplit,\n backendName: \"cpu\",\n kernelFunc: stringSplit2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { numBuckets } = attrs;\n const { input: input2 } = inputs;\n if (input2.dtype !== \"string\") {\n throw new Error(\"Input must be of datatype string\");\n }\n if (numBuckets <= 0) {\n throw new Error(`Number of buckets must be at least 1`);\n }\n const $input = backend2.data.get(input2.dataId).values;\n const output = stringToHashBucketFastImpl($input, numBuckets);\n return backend2.makeTensorInfo(input2.shape, \"int32\", output);\n}\nvar stringToHashBucketFastConfig = {\n kernelName: StringToHashBucketFast,\n backendName: \"cpu\",\n kernelFunc: stringToHashBucketFast2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tan.js\nvar tan2 = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nvar tanConfig = {\n kernelName: Tan,\n backendName: \"cpu\",\n kernelFunc: tan2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tanh.js\nvar tanh3 = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nvar tanhConfig = {\n kernelName: Tanh,\n backendName: \"cpu\",\n kernelFunc: tanh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TensorScatterUpdate.js\nfunction tensorScatterUpdate2(args) {\n const { inputs, backend: backend2 } = args;\n const { tensor: tensor2, indices, updates } = inputs;\n const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, tensor2.shape);\n const sumDupeIndices = false;\n const indicesBuf = backend2.bufferSync(indices);\n const updatesBuf = backend2.bufferSync(updates);\n const tensorBuf = backend2.bufferSync(tensor2);\n const outBuf = scatterImpl(indicesBuf, updatesBuf, tensor2.shape, outputSize, sliceSize, numUpdates, sliceRank, strides, tensorBuf, sumDupeIndices);\n return backend2.makeTensorInfo(tensor2.shape, outBuf.dtype, outBuf.values);\n}\nvar tensorScatterUpdateConfig = {\n kernelName: TensorScatterUpdate,\n backendName: \"cpu\",\n kernelFunc: tensorScatterUpdate2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile.js\nfunction tile3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { reps } = attrs;\n assertNotComplex(x, \"tile\");\n const outBuf = tileImpl(backend2.bufferSync(x), reps);\n return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar tileConfig = {\n kernelName: Tile,\n backendName: \"cpu\",\n kernelFunc: tile3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK.js\nfunction topK(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { k, sorted } = attrs;\n assertNotComplex(x, \"topk\");\n const xVals = backend2.data.get(x.dataId).values;\n const [allTopKVals, allTopKIndices] = topKImpl(xVals, x.shape, x.dtype, k, sorted);\n return [\n backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n ];\n}\nvar topKConfig = {\n kernelName: TopK,\n backendName: \"cpu\",\n kernelFunc: topK\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transform.js\nfunction transform2(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { image: image2, transforms } = inputs;\n const { interpolation, fillMode, fillValue, outputShape } = attrs;\n const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n const outShape = [batch, outHeight, outWidth, numChannels];\n const inStrides = util_exports.computeStrides(image2.shape);\n const batchInStride = inStrides[0];\n const rowInStride = inStrides[1];\n const colInStride = inStrides[2];\n const outStrides = util_exports.computeStrides(outShape);\n const batchOutStride = outStrides[0];\n const rowOutStride = outStrides[1];\n const colOutStride = outStrides[2];\n const outVals = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(outShape));\n outVals.fill(fillValue);\n const imageVals = backend2.data.get(image2.dataId).values;\n const transformVals = backend2.data.get(transforms.dataId).values;\n for (let b = 0; b < batch; ++b) {\n const transform5 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8);\n for (let outY = 0; outY < outHeight; ++outY) {\n for (let outX = 0; outX < outWidth; ++outX) {\n for (let channel = 0; channel < numChannels; ++channel) {\n let val;\n const projection = transform5[6] * outX + transform5[7] * outY + 1;\n if (projection === 0) {\n continue;\n }\n const inX = (transform5[0] * outX + transform5[1] * outY + transform5[2]) / projection;\n const inY = (transform5[3] * outX + transform5[4] * outY + transform5[5]) / projection;\n const x = mapCoord(inX, imageWidth, fillMode);\n const y = mapCoord(inY, imageHeight, fillMode);\n switch (interpolation) {\n case \"nearest\":\n val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue);\n break;\n case \"bilinear\":\n val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue);\n break;\n default:\n throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${interpolation}`);\n }\n const ind = b * batchOutStride + outY * rowOutStride + outX * colOutStride + channel;\n outVals[ind] = val;\n }\n }\n }\n return backend2.makeTensorInfo(outShape, image2.dtype, outVals);\n }\n const dataId = backend2.write(outVals, outShape, image2.dtype);\n return { dataId, shape: image2.shape, dtype: image2.dtype };\n}\nvar transformConfig = {\n kernelName: Transform,\n backendName: \"cpu\",\n kernelFunc: transform2\n};\nfunction mapCoord(outCoord, len, mode) {\n switch (mode) {\n case \"reflect\":\n return mapCoordReflect(outCoord, len);\n case \"wrap\":\n return mapCoordWrap(outCoord, len);\n case \"nearest\":\n return mapCoordNearest(outCoord, len);\n case \"constant\":\n default:\n return mapCoordConstant(outCoord, len);\n }\n}\nfunction mapCoordReflect(outCoord, len) {\n let inCoord = outCoord;\n if (inCoord < 0) {\n if (len <= 1) {\n inCoord = 0;\n } else {\n const sz2 = 2 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;\n }\n } else if (inCoord > len - 1) {\n if (len <= 1) {\n inCoord = 0;\n } else {\n const sz2 = 2 * len;\n inCoord -= sz2 * Math.trunc(inCoord / sz2);\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1;\n }\n }\n }\n return util_exports.clamp(0, inCoord, len - 1);\n}\nfunction mapCoordWrap(outCoord, len) {\n let inCoord = outCoord;\n if (inCoord < 0) {\n if (len <= 1) {\n inCoord = 0;\n } else {\n const sz = len - 1;\n inCoord += len * (Math.trunc(-inCoord / sz) + 1);\n }\n } else if (inCoord > len - 1) {\n if (len <= 1) {\n inCoord = 0;\n } else {\n const sz = len - 1;\n inCoord -= len * Math.trunc(inCoord / sz);\n }\n }\n return util_exports.clamp(0, inCoord, len - 1);\n}\nfunction mapCoordConstant(outCoord, len) {\n return outCoord;\n}\nfunction mapCoordNearest(outCoord, len) {\n return util_exports.clamp(0, outCoord, len - 1);\n}\nfunction readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n const ind = batch * batchStride + y * rowStride + x * colStride + channel;\n if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {\n return imageVals[ind];\n } else {\n return fillValue;\n }\n}\nfunction nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n const $y = Math.round(y);\n const $x = Math.round(x);\n return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);\n}\nfunction bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n const yFloor = Math.floor(y);\n const xFloor = Math.floor(x);\n const yCeil = yFloor + 1;\n const xCeil = xFloor + 1;\n const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);\n const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);\n return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique.js\nfunction unique3(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { axis } = attrs;\n const { x } = inputs;\n assertNotComplex(x, \"unique\");\n const values = backend2.data.get(x.dataId).values;\n const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n return [\n backend2.makeTensorInfo(outputShape, x.dtype, outputValues),\n backend2.makeTensorInfo([indices.length], \"int32\", indices)\n ];\n}\nvar uniqueConfig = {\n kernelName: Unique,\n backendName: \"cpu\",\n kernelFunc: unique3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unpack.js\nfunction unpack(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { value } = inputs;\n let { axis } = attrs;\n if (axis < 0) {\n axis += value.shape.length;\n }\n const valueRank = value.shape.length;\n const num = value.shape[axis];\n const outShape = new Array(valueRank - 1);\n let outIndex = 0;\n for (let i = 0; i < valueRank; i++) {\n if (i !== axis) {\n outShape[outIndex++] = value.shape[i];\n }\n }\n const begin = new Array(valueRank).fill(0);\n const size = value.shape.slice();\n size[axis] = 1;\n const res = new Array(num);\n for (let i = 0; i < res.length; i++) {\n begin[axis] = i;\n const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size } });\n res[i] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });\n backend2.disposeIntermediateTensorInfo(tempRes);\n }\n return res;\n}\nvar unpackConfig = {\n kernelName: Unpack,\n backendName: \"cpu\",\n kernelFunc: unpack\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/UnsortedSegmentSum.js\nfunction unsortedSegmentSum2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, segmentIds } = inputs;\n const { numSegments } = attrs;\n assertNotComplex(x, \"unsortedSegmentSum\");\n const xRank = x.shape.length;\n const segmentIdsRank = segmentIds.shape.length;\n const res = [];\n const intermediates = [];\n const numIters = xRank - segmentIdsRank;\n let $segmentIds = segmentIds;\n for (let i = 0; i < numIters; ++i) {\n const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i + 1 } });\n $segmentIds = expanded;\n intermediates.push(expanded);\n }\n for (let i = 0; i < numSegments; ++i) {\n const scalarValue = util_exports.createScalarValue(i, \"int32\");\n const segmentId = backend2.makeTensorInfo([], \"int32\", scalarValue);\n const mask = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 });\n const maskCasted = cast3({ inputs: { x: mask }, backend: backend2, attrs: { dtype: \"float32\" } });\n const mul2 = multiply2({ inputs: { a: maskCasted, b: x }, backend: backend2 });\n const sumTensorInfo = sum3({ inputs: { x: mul2 }, backend: backend2, attrs: { axis: 0, keepDims: false } });\n res.push(sumTensorInfo);\n intermediates.push(segmentId);\n intermediates.push(mask);\n intermediates.push(maskCasted);\n intermediates.push(mul2);\n intermediates.push(sumTensorInfo);\n }\n const result = pack({ inputs: res, backend: backend2, attrs: { axis: 0 } });\n intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return result;\n}\nvar unsortedSegmentSumConfig = {\n kernelName: UnsortedSegmentSum,\n backendName: \"cpu\",\n kernelFunc: unsortedSegmentSum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/register_all_kernels.js\nvar kernelConfigs = [\n _fusedMatMulConfig,\n absConfig,\n acosConfig,\n acoshConfig,\n addConfig,\n addNConfig,\n allConfig,\n anyConfig,\n argMaxConfig,\n argMinConfig,\n asinConfig,\n asinhConfig,\n atanConfig,\n atan2Config,\n atanhConfig,\n avgPoolConfig,\n avgPool3DConfig,\n avgPool3DGradConfig2,\n avgPoolGradConfig2,\n batchMatMulConfig,\n batchNormConfig,\n batchToSpaceNDConfig,\n bincountConfig,\n bitwiseAndConfig,\n broadcastArgsConfig,\n castConfig,\n ceilConfig,\n clipByValueConfig,\n complexConfig,\n complexAbsConfig,\n concatConfig,\n conv2DConfig,\n conv2DBackpropFilterConfig,\n conv2DBackpropInputConfig,\n conv3DConfig,\n conv3DBackpropFilterV2Config,\n conv3DBackpropInputV2Config,\n cosConfig,\n coshConfig,\n cropAndResizeConfig,\n cumprodConfig,\n cumsumConfig,\n denseBincountConfig,\n depthToSpaceConfig,\n depthwiseConv2dNativeConfig,\n depthwiseConv2dNativeBackpropFilterConfig,\n depthwiseConv2dNativeBackpropInputConfig,\n diagConfig,\n dilation2DConfig,\n dilation2DBackpropFilterConfig,\n dilation2DBackpropInputConfig,\n drawConfig,\n einsumConfig,\n eluConfig,\n eluGradConfig2,\n equalConfig,\n erfConfig,\n expConfig,\n expandDimsConfig,\n expm1Config,\n fftConfig,\n fillConfig,\n flipLeftRightConfig,\n floorConfig,\n floorDivConfig,\n fusedConv2DConfig,\n fusedDepthwiseConv2DConfig,\n gatherNdConfig,\n gatherV2Config,\n greaterConfig,\n greaterEqualConfig,\n identityConfig,\n ifftConfig,\n imagConfig,\n isFiniteConfig,\n isInfConfig,\n isNaNConfig,\n leakyReluConfig,\n lessConfig,\n lessEqualConfig,\n linSpaceConfig,\n logConfig,\n log1pConfig,\n logicalAndConfig,\n logicalNotConfig,\n logicalOrConfig,\n LRNConfig,\n LRNGradConfig,\n maxConfig,\n maximumConfig,\n maxPoolConfig,\n maxPool3DConfig,\n maxPool3DGradConfig2,\n maxPoolGradConfig2,\n maxPoolWithArgmaxConfig,\n meanConfig,\n minConfig,\n minimumConfig,\n mirrorPadConfig,\n modConfig,\n multinomialConfig,\n multiplyConfig,\n negConfig,\n nonMaxSuppressionV3Config,\n nonMaxSuppressionV4Config,\n nonMaxSuppressionV5Config,\n notEqualConfig,\n oneHotConfig,\n onesLikeConfig,\n packConfig,\n padV2Config,\n powConfig,\n preluConfig,\n prodConfig,\n raggedGatherConfig,\n raggedRangeConfig,\n raggedTensorToTensorConfig,\n rangeConfig,\n realConfig,\n realDivConfig,\n reciprocalConfig,\n reluConfig,\n relu6Config,\n reshapeConfig,\n resizeBilinearConfig,\n resizeBilinearGradConfig2,\n resizeNearestNeighborConfig,\n resizeNearestNeighborGradConfig2,\n reverseConfig,\n rotateWithOffsetConfig,\n roundConfig,\n rsqrtConfig,\n scatterNdConfig,\n searchSortedConfig,\n selectConfig,\n seluConfig,\n sigmoidConfig,\n signConfig,\n sinConfig,\n sinhConfig,\n sliceConfig,\n softmaxConfig,\n softplusConfig,\n spaceToBatchNDConfig,\n sparseFillEmptyRowsConfig,\n sparseReshapeConfig,\n sparseSegmentMeanConfig,\n sparseSegmentSumConfig,\n sparseToDenseConfig,\n splitVConfig,\n sqrtConfig,\n squareConfig,\n squaredDifferenceConfig,\n staticRegexReplaceConfig,\n stepConfig,\n stridedSliceConfig,\n stringNGramsConfig,\n stringSplitConfig,\n stringToHashBucketFastConfig,\n subConfig,\n sumConfig,\n tanConfig,\n tanhConfig,\n tensorScatterUpdateConfig,\n tileConfig,\n topKConfig,\n transformConfig,\n transposeConfig,\n uniqueConfig,\n unpackConfig,\n unsortedSegmentSumConfig,\n zerosLikeConfig\n];\nfor (const kernelConfig of kernelConfigs) {\n registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js\nvar webgl_util_exports = {};\n__export(webgl_util_exports, {\n assertNotComplex: () => assertNotComplex2,\n bindCanvasToFramebuffer: () => bindCanvasToFramebuffer,\n bindColorTextureToFramebuffer: () => bindColorTextureToFramebuffer,\n bindTextureToProgramUniformSampler: () => bindTextureToProgramUniformSampler,\n bindTextureUnit: () => bindTextureUnit,\n bindVertexBufferToProgramAttribute: () => bindVertexBufferToProgramAttribute,\n callAndCheck: () => callAndCheck,\n canBeRepresented: () => canBeRepresented,\n createFragmentShader: () => createFragmentShader,\n createFramebuffer: () => createFramebuffer,\n createProgram: () => createProgram,\n createStaticIndexBuffer: () => createStaticIndexBuffer,\n createStaticVertexBuffer: () => createStaticVertexBuffer,\n createTexture: () => createTexture,\n createVertexShader: () => createVertexShader,\n getBatchDim: () => getBatchDim,\n getExtensionOrThrow: () => getExtensionOrThrow,\n getFramebufferErrorMessage: () => getFramebufferErrorMessage,\n getMaxTexturesInShader: () => getMaxTexturesInShader,\n getNumChannels: () => getNumChannels,\n getProgramUniformLocation: () => getProgramUniformLocation,\n getProgramUniformLocationOrThrow: () => getProgramUniformLocationOrThrow,\n getRowsCols: () => getRowsCols,\n getShapeAs3D: () => getShapeAs3D,\n getTextureShapeFromLogicalShape: () => getTextureShapeFromLogicalShape,\n getWebGLDisjointQueryTimerVersion: () => getWebGLDisjointQueryTimerVersion,\n getWebGLErrorMessage: () => getWebGLErrorMessage,\n getWebGLMaxTextureSize: () => getWebGLMaxTextureSize,\n hasExtension: () => hasExtension,\n isCapableOfRenderingToFloatTexture: () => isCapableOfRenderingToFloatTexture,\n isDownloadFloatTextureEnabled: () => isDownloadFloatTextureEnabled,\n isReshapeFree: () => isReshapeFree,\n isWebGLFenceEnabled: () => isWebGLFenceEnabled,\n isWebGLVersionEnabled: () => isWebGLVersionEnabled,\n linkProgram: () => linkProgram,\n logShaderSourceAndInfoLog: () => logShaderSourceAndInfoLog,\n resetMaxTextureSize: () => resetMaxTextureSize,\n resetMaxTexturesInShader: () => resetMaxTexturesInShader,\n unbindColorTextureFromFramebuffer: () => unbindColorTextureFromFramebuffer,\n unbindTextureUnit: () => unbindTextureUnit,\n validateFramebuffer: () => validateFramebuffer,\n validateProgram: () => validateProgram,\n validateTextureSize: () => validateTextureSize\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/canvas_util.js\nvar contexts = {};\nvar WEBGL_ATTRIBUTES = {\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: true\n};\nfunction setWebGLContext(webGLVersion, gl) {\n contexts[webGLVersion] = gl;\n}\nfunction getWebGLContext(webGLVersion, customCanvas) {\n if (!(webGLVersion in contexts) || customCanvas != null) {\n const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas);\n if (newCtx !== null) {\n contexts[webGLVersion] = newCtx;\n } else {\n console.log(\"Could not get context for WebGL version\", webGLVersion);\n return null;\n }\n }\n const gl = contexts[webGLVersion];\n if (gl == null || gl.isContextLost()) {\n delete contexts[webGLVersion];\n return getWebGLContext(webGLVersion);\n }\n gl.disable(gl.DEPTH_TEST);\n gl.disable(gl.STENCIL_TEST);\n gl.disable(gl.BLEND);\n gl.disable(gl.DITHER);\n gl.disable(gl.POLYGON_OFFSET_FILL);\n gl.disable(gl.SAMPLE_COVERAGE);\n gl.enable(gl.SCISSOR_TEST);\n gl.enable(gl.CULL_FACE);\n gl.cullFace(gl.BACK);\n return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n if (!env().getBool(\"IS_SAFARI\") && typeof OffscreenCanvas !== \"undefined\" && webGLVersion === 2) {\n return new OffscreenCanvas(300, 150);\n } else if (typeof document !== \"undefined\") {\n return document.createElement(\"canvas\");\n } else {\n throw new Error(\"Cannot create a canvas in this context\");\n }\n}\nfunction getWebGLRenderingContext(webGLVersion, customCanvas) {\n if (webGLVersion !== 1 && webGLVersion !== 2) {\n throw new Error(\"Cannot get WebGL rendering context, WebGL is disabled.\");\n }\n const canvas = customCanvas == null ? createCanvas(webGLVersion) : customCanvas;\n canvas.addEventListener(\"webglcontextlost\", (ev) => {\n ev.preventDefault();\n delete contexts[webGLVersion];\n }, false);\n if (env().getBool(\"SOFTWARE_WEBGL_ENABLED\")) {\n WEBGL_ATTRIBUTES.failIfMajorPerformanceCaveat = false;\n }\n if (webGLVersion === 1) {\n return (\n // tslint:disable-next-line\n canvas.getContext(\"webgl\", WEBGL_ATTRIBUTES) || canvas.getContext(\"experimental-webgl\", WEBGL_ATTRIBUTES)\n );\n }\n return canvas.getContext(\"webgl2\", WEBGL_ATTRIBUTES);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/tex_util.js\nvar PackingScheme;\n(function(PackingScheme2) {\n PackingScheme2[PackingScheme2[\"DENSE\"] = 0] = \"DENSE\";\n PackingScheme2[PackingScheme2[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nvar TextureUsage;\n(function(TextureUsage2) {\n TextureUsage2[TextureUsage2[\"RENDER\"] = 0] = \"RENDER\";\n TextureUsage2[TextureUsage2[\"UPLOAD\"] = 1] = \"UPLOAD\";\n TextureUsage2[TextureUsage2[\"PIXELS\"] = 2] = \"PIXELS\";\n TextureUsage2[TextureUsage2[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nvar PhysicalTextureType;\n(function(PhysicalTextureType2) {\n PhysicalTextureType2[PhysicalTextureType2[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n PhysicalTextureType2[PhysicalTextureType2[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n PhysicalTextureType2[PhysicalTextureType2[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n PhysicalTextureType2[PhysicalTextureType2[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n PhysicalTextureType2[PhysicalTextureType2[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nfunction getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n return [columns, rows];\n}\nfunction getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n return matrixSize * channelsPerTexture;\n}\nfunction getDenseTexShape(shape) {\n const size = util_exports.sizeFromShape(shape);\n const texelsNeeded = Math.ceil(size / 4);\n return util_exports.sizeToSquarishShape(texelsNeeded);\n}\nfunction getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n return [\n Math.max(1, Math.ceil(columns / 2)),\n Math.max(1, Math.ceil(rows / 2))\n ];\n}\nfunction getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n return w * h * 4;\n}\nfunction getTextureConfig(gl, textureHalfFloatExtension) {\n const glany = gl;\n let internalFormatFloat;\n let internalFormatHalfFloat;\n let internalFormatPackedHalfFloat;\n let internalFormatPackedFloat;\n let textureFormatFloat;\n let downloadTextureFormat;\n let downloadUnpackNumChannels;\n let defaultNumChannels;\n let textureTypeHalfFloat;\n let textureTypeFloat;\n if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n internalFormatFloat = glany.R32F;\n internalFormatHalfFloat = glany.R16F;\n internalFormatPackedHalfFloat = glany.RGBA16F;\n internalFormatPackedFloat = glany.RGBA32F;\n textureFormatFloat = glany.RED;\n downloadUnpackNumChannels = 4;\n defaultNumChannels = 1;\n textureTypeHalfFloat = glany.HALF_FLOAT;\n textureTypeFloat = glany.FLOAT;\n downloadTextureFormat = glany.RGBA8;\n } else {\n internalFormatFloat = gl.RGBA;\n internalFormatHalfFloat = gl.RGBA;\n internalFormatPackedHalfFloat = gl.RGBA;\n internalFormatPackedFloat = glany.RGBA;\n textureFormatFloat = gl.RGBA;\n downloadUnpackNumChannels = 4;\n defaultNumChannels = 4;\n textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null;\n textureTypeFloat = gl.FLOAT;\n downloadTextureFormat = gl.RGBA;\n }\n return {\n internalFormatFloat,\n internalFormatHalfFloat,\n internalFormatPackedHalfFloat,\n internalFormatPackedFloat,\n textureFormatFloat,\n downloadTextureFormat,\n downloadUnpackNumChannels,\n defaultNumChannels,\n textureTypeHalfFloat,\n textureTypeFloat\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js\nfunction callAndCheck(gl, func2) {\n const returnValue = func2();\n if (env().getBool(\"DEBUG\")) {\n checkWebGLError(gl);\n }\n return returnValue;\n}\nfunction checkWebGLError(gl) {\n const error = gl.getError();\n if (error !== gl.NO_ERROR) {\n throw new Error(\"WebGL Error: \" + getWebGLErrorMessage(gl, error));\n }\n}\nvar MIN_FLOAT16 = 596e-10;\nvar MAX_FLOAT16 = 65504;\nfunction canBeRepresented(num) {\n if (env().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) {\n return true;\n }\n return false;\n}\nfunction getWebGLErrorMessage(gl, status) {\n switch (status) {\n case gl.NO_ERROR:\n return \"NO_ERROR\";\n case gl.INVALID_ENUM:\n return \"INVALID_ENUM\";\n case gl.INVALID_VALUE:\n return \"INVALID_VALUE\";\n case gl.INVALID_OPERATION:\n return \"INVALID_OPERATION\";\n case gl.INVALID_FRAMEBUFFER_OPERATION:\n return \"INVALID_FRAMEBUFFER_OPERATION\";\n case gl.OUT_OF_MEMORY:\n return \"OUT_OF_MEMORY\";\n case gl.CONTEXT_LOST_WEBGL:\n return \"CONTEXT_LOST_WEBGL\";\n default:\n return `Unknown error code ${status}`;\n }\n}\nfunction getExtensionOrThrow(gl, extensionName) {\n return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nfunction createVertexShader(gl, vertexShaderSource) {\n const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), \"Unable to create vertex WebGLShader.\");\n callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n callAndCheck(gl, () => gl.compileShader(vertexShader));\n if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n console.log(gl.getShaderInfoLog(vertexShader));\n throw new Error(\"Failed to compile vertex shader.\");\n }\n return vertexShader;\n}\nfunction createFragmentShader(gl, fragmentShaderSource) {\n const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), \"Unable to create fragment WebGLShader.\");\n callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n callAndCheck(gl, () => gl.compileShader(fragmentShader));\n if (env().get(\"ENGINE_COMPILE_ONLY\")) {\n return fragmentShader;\n }\n if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n throw new Error(\"Failed to compile fragment shader.\");\n }\n return fragmentShader;\n}\nvar lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n if (lineNumberRegexResult == null) {\n console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n console.log(shaderSource);\n return;\n }\n const lineNumber = +lineNumberRegexResult[1];\n const shaderLines = shaderSource.split(\"\\n\");\n const pad3 = shaderLines.length.toString().length + 2;\n const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line);\n let maxLineLength = 0;\n for (let i = 0; i < linesWithLineNumbers.length; i++) {\n maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n }\n const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n console.log(beforeErrorLines.join(\"\\n\"));\n console.log(shaderInfoLog.split(\"\\n\")[0]);\n console.log(`%c ${util_exports.rightPad(errorLine[0], maxLineLength)}`, \"border:1px solid red; background-color:#e3d2d2; color:#a61717\");\n console.log(afterErrorLines.join(\"\\n\"));\n}\nfunction createProgram(gl) {\n return throwIfNull(gl, () => gl.createProgram(), \"Unable to create WebGLProgram.\");\n}\nfunction linkProgram(gl, program) {\n callAndCheck(gl, () => gl.linkProgram(program));\n if (env().get(\"ENGINE_COMPILE_ONLY\")) {\n return;\n }\n if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n console.log(gl.getProgramInfoLog(program));\n throw new Error(\"Failed to link vertex and fragment shaders.\");\n }\n}\nfunction validateProgram(gl, program) {\n callAndCheck(gl, () => gl.validateProgram(program));\n if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n console.log(gl.getProgramInfoLog(program));\n throw new Error(\"Shader program validation failed.\");\n }\n}\nfunction createStaticVertexBuffer(gl, data) {\n const buffer2 = throwIfNull(gl, () => gl.createBuffer(), \"Unable to create WebGLBuffer\");\n callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));\n callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n return buffer2;\n}\nfunction createStaticIndexBuffer(gl, data) {\n const buffer2 = throwIfNull(gl, () => gl.createBuffer(), \"Unable to create WebGLBuffer\");\n callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer2));\n callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n return buffer2;\n}\nfunction getNumChannels() {\n if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n return 1;\n }\n return 4;\n}\nfunction createTexture(gl) {\n return throwIfNull(gl, () => gl.createTexture(), \"Unable to create WebGLTexture.\");\n}\nfunction validateTextureSize(width, height) {\n const maxTextureSize = env().getNumber(\"WEBGL_MAX_TEXTURE_SIZE\");\n if (width <= 0 || height <= 0) {\n const requested = `[${width}x${height}]`;\n throw new Error(\"Requested texture size \" + requested + \" is invalid.\");\n }\n if (width > maxTextureSize || height > maxTextureSize) {\n const requested = `[${width}x${height}]`;\n const max6 = `[${maxTextureSize}x${maxTextureSize}]`;\n throw new Error(\"Requested texture size \" + requested + \" greater than WebGL maximum on this browser / GPU \" + max6 + \".\");\n }\n}\nfunction createFramebuffer(gl) {\n return throwIfNull(gl, () => gl.createFramebuffer(), \"Unable to create WebGLFramebuffer.\");\n}\nfunction bindVertexBufferToProgramAttribute(gl, program, attribute, buffer2, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n const loc = gl.getAttribLocation(program, attribute);\n if (loc === -1) {\n return false;\n }\n callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));\n callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n return true;\n}\nfunction bindTextureUnit(gl, texture, textureUnit) {\n validateTextureUnit(gl, textureUnit);\n callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nfunction unbindTextureUnit(gl, textureUnit) {\n validateTextureUnit(gl, textureUnit);\n callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction getProgramUniformLocationOrThrow(gl, program, uniformName) {\n return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nfunction getProgramUniformLocation(gl, program, uniformName) {\n return gl.getUniformLocation(program, uniformName);\n}\nfunction bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nfunction bindCanvasToFramebuffer(gl) {\n callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nfunction bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nfunction unbindColorTextureFromFramebuffer(gl, framebuffer) {\n callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nfunction validateFramebuffer(gl) {\n const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n if (status !== gl.FRAMEBUFFER_COMPLETE) {\n throw new Error(\"Error binding framebuffer: \" + getFramebufferErrorMessage(gl, status));\n }\n}\nfunction getFramebufferErrorMessage(gl, status) {\n switch (status) {\n case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n return \"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\";\n case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n return \"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\";\n case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n return \"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\";\n case gl.FRAMEBUFFER_UNSUPPORTED:\n return \"FRAMEBUFFER_UNSUPPORTED\";\n default:\n return `unknown error ${status}`;\n }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n const tOrNull = callAndCheck(gl, () => returnTOrNull());\n if (tOrNull == null) {\n throw new Error(failureMessage);\n }\n return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n const glTextureUnit = textureUnit + gl.TEXTURE0;\n if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n }\n}\nfunction getBatchDim(shape, dimsToSkip = 2) {\n return util_exports.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nfunction getRowsCols(shape) {\n if (shape.length === 0) {\n throw Error(\"Cannot get rows and columns of an empty shape array.\");\n }\n return [\n shape.length > 1 ? shape[shape.length - 2] : 1,\n shape[shape.length - 1]\n ];\n}\nfunction getShapeAs3D(shape) {\n let shapeAs3D = [1, 1, 1];\n const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1;\n if (!isScalar) {\n shapeAs3D = [getBatchDim(shape), ...getRowsCols(shape)];\n }\n return shapeAs3D;\n}\nfunction getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n let maxTexSize = env().getNumber(\"WEBGL_MAX_TEXTURE_SIZE\");\n let maxSizeForNarrowTex = env().getNumber(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\");\n if (maxSizeForNarrowTex === Infinity && env().getBool(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\")) {\n maxSizeForNarrowTex = maxTexSize / 2;\n }\n if (isPacked) {\n maxTexSize = maxTexSize * 2;\n maxSizeForNarrowTex = maxSizeForNarrowTex * 2;\n logShape = logShape.map((d, i) => i >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i]) : logShape[i]);\n if (logShape.length === 1) {\n logShape = [2, logShape[0]];\n }\n }\n if (logShape.length !== 2) {\n const squeezeResult = util_exports.squeezeShape(logShape);\n logShape = squeezeResult.newShape;\n }\n let size = util_exports.sizeFromShape(logShape);\n let textureShape = null;\n if (logShape.length <= 1 && size <= maxTexSize) {\n textureShape = [1, size];\n } else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) {\n textureShape = logShape;\n } else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) {\n textureShape = [logShape[0] * logShape[1], logShape[2]];\n } else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) {\n textureShape = [logShape[0], logShape[1] * logShape[2]];\n } else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) {\n textureShape = [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n } else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n textureShape = [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n }\n const isLongNarrowTex = textureShape != null && Math.max(...textureShape) > maxSizeForNarrowTex && Math.min(...textureShape) <= (isPacked ? 2 : 1) && Math.min(...textureShape) > 0;\n if (textureShape == null || isLongNarrowTex) {\n if (isPacked) {\n const batchDim = getBatchDim(logShape);\n let rows = 2, cols = 2;\n if (logShape.length) {\n [rows, cols] = getRowsCols(logShape);\n }\n size = batchDim * (rows / 2) * (cols / 2);\n textureShape = util_exports.sizeToSquarishShape(size).map((d) => d * 2);\n } else {\n textureShape = util_exports.sizeToSquarishShape(size);\n }\n }\n return textureShape;\n}\nfunction isEven(n) {\n return n % 2 === 0;\n}\nfunction isReshapeFree(shape1, shape2) {\n shape1 = shape1.slice(-2);\n shape2 = shape2.slice(-2);\n if (util_exports.arraysEqual(shape1, shape2)) {\n return true;\n }\n if (!shape1.length || !shape2.length) {\n return true;\n }\n if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) {\n return true;\n }\n if (shape1.length !== shape2.length) {\n const shape1Cols = shape1[shape1.length - 1];\n const shape2Cols = shape2[shape2.length - 1];\n if (shape1Cols === shape2Cols) {\n return true;\n }\n if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) {\n return true;\n }\n }\n return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\nvar MAX_TEXTURE_SIZE;\nvar MAX_TEXTURES_IN_SHADER;\nfunction getWebGLMaxTextureSize(webGLVersion) {\n if (MAX_TEXTURE_SIZE == null) {\n const gl = getWebGLContext(webGLVersion);\n MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n }\n return MAX_TEXTURE_SIZE;\n}\nfunction resetMaxTextureSize() {\n MAX_TEXTURE_SIZE = null;\n}\nfunction resetMaxTexturesInShader() {\n MAX_TEXTURES_IN_SHADER = null;\n}\nfunction getMaxTexturesInShader(webGLVersion) {\n if (MAX_TEXTURES_IN_SHADER == null) {\n const gl = getWebGLContext(webGLVersion);\n MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n }\n return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nfunction getWebGLDisjointQueryTimerVersion(webGLVersion) {\n if (webGLVersion === 0) {\n return 0;\n }\n let queryTimerVersion;\n const gl = getWebGLContext(webGLVersion);\n if (hasExtension(gl, \"EXT_disjoint_timer_query_webgl2\") && webGLVersion === 2) {\n queryTimerVersion = 2;\n } else if (hasExtension(gl, \"EXT_disjoint_timer_query\")) {\n queryTimerVersion = 1;\n } else {\n queryTimerVersion = 0;\n }\n return queryTimerVersion;\n}\nfunction hasExtension(gl, extensionName) {\n const ext = gl.getExtension(extensionName);\n return ext != null;\n}\nfunction isWebGLVersionEnabled(webGLVersion) {\n try {\n const gl = getWebGLContext(webGLVersion);\n if (gl != null) {\n return true;\n }\n } catch (e) {\n console.log(\"Error when getting WebGL context: \", e);\n return false;\n }\n return false;\n}\nfunction isCapableOfRenderingToFloatTexture(webGLVersion) {\n if (webGLVersion === 0) {\n return false;\n }\n const gl = getWebGLContext(webGLVersion);\n if (webGLVersion === 1) {\n if (!hasExtension(gl, \"OES_texture_float\")) {\n return false;\n }\n } else {\n if (!hasExtension(gl, \"EXT_color_buffer_float\")) {\n return false;\n }\n }\n const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n return isFrameBufferComplete;\n}\nfunction isDownloadFloatTextureEnabled(webGLVersion) {\n if (webGLVersion === 0) {\n return false;\n }\n const gl = getWebGLContext(webGLVersion);\n if (webGLVersion === 1) {\n if (!hasExtension(gl, \"OES_texture_float\")) {\n return false;\n }\n if (!hasExtension(gl, \"WEBGL_color_buffer_float\")) {\n return false;\n }\n } else {\n if (hasExtension(gl, \"EXT_color_buffer_float\")) {\n return createFloatTextureAndBindToFramebuffer(gl);\n }\n const COLOR_BUFFER_HALF_FLOAT = \"EXT_color_buffer_half_float\";\n if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n }\n return false;\n }\n const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n const texConfig = getTextureConfig(gl);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n const width = 1;\n const height = 1;\n gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n const frameBuffer = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n gl.deleteTexture(texture);\n gl.deleteFramebuffer(frameBuffer);\n return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension) {\n const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n const width = 1;\n const height = 1;\n gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n const frameBuffer = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n gl.deleteTexture(texture);\n gl.deleteFramebuffer(frameBuffer);\n return isFrameBufferComplete;\n}\nfunction isWebGLFenceEnabled(webGLVersion) {\n if (webGLVersion !== 2) {\n return false;\n }\n const gl = getWebGLContext(webGLVersion);\n const isEnabled = gl.fenceSync != null;\n return isEnabled;\n}\nfunction assertNotComplex2(tensor2, opName) {\n if (!Array.isArray(tensor2)) {\n tensor2 = [tensor2];\n }\n tensor2.forEach((t) => {\n if (t != null) {\n util_exports.assert(t.dtype !== \"complex64\", () => `${opName} does not support complex64 tensors in the WebGL backend.`);\n }\n });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/flags_webgl.js\nvar ENV5 = env();\nENV5.registerFlag(\"HAS_WEBGL\", () => ENV5.getNumber(\"WEBGL_VERSION\") > 0);\nENV5.registerFlag(\"WEBGL_VERSION\", () => {\n if (isWebGLVersionEnabled(2)) {\n return 2;\n } else if (isWebGLVersionEnabled(1)) {\n return 1;\n }\n return 0;\n});\nENV5.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\", () => false);\nENV5.registerFlag(\"WEBGL_BUFFER_SUPPORTED\", () => ENV5.get(\"WEBGL_VERSION\") === 2);\nENV5.registerFlag(\"WEBGL_CPU_FORWARD\", () => true);\nENV5.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\", () => false);\nENV5.registerFlag(\"WEBGL_PACK\", () => ENV5.getBool(\"HAS_WEBGL\"));\nENV5.registerFlag(\"WEBGL_PACK_NORMALIZATION\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_CLIP\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_REDUCE\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_LAZILY_UNPACK\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_CONV_IM2COL\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_CONV2DTRANSPOSE\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\", () => getWebGLMaxTextureSize(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\", () => getMaxTexturesInShader(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\", () => {\n const webGLVersion = ENV5.getNumber(\"WEBGL_VERSION\");\n if (webGLVersion === 0) {\n return 0;\n }\n return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\nENV5.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\", () => ENV5.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0 && !device_util_exports.isMobile());\nENV5.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\", () => isCapableOfRenderingToFloatTexture(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\", () => {\n return ENV5.getBool(\"WEBGL_FORCE_F16_TEXTURES\") ? false : ENV5.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\");\n});\nENV5.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\", () => isDownloadFloatTextureEnabled(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_FENCE_API_ENABLED\", () => isWebGLFenceEnabled(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\", () => {\n const useUniforms = ENV5.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\");\n return useUniforms ? 4 : 0;\n});\nENV5.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\", () => {\n return -1;\n}, (threshold3) => {\n if (!(typeof threshold3 === \"number\")) {\n throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${threshold3}.`);\n }\n if (threshold3 < 0 && threshold3 !== -1) {\n throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold3}.`);\n }\n});\nENV5.registerFlag(\"WEBGL_FLUSH_THRESHOLD\", () => {\n return device_util_exports.isMobile() ? 1 : -1;\n}, (threshold3) => {\n if (!(typeof threshold3 === \"number\")) {\n throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${threshold3}.`);\n }\n if (threshold3 < 0 && threshold3 !== -1) {\n throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold3}.`);\n }\n});\nENV5.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\", () => 128);\nENV5.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\", () => false);\nENV5.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\", () => 1e5);\nENV5.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\", () => 128);\nENV5.registerFlag(\"WEBGL_EXP_CONV\", () => false);\nENV5.registerFlag(\"SOFTWARE_WEBGL_ENABLED\", () => ENV5.getBool(\"IS_TEST\"));\nENV5.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\", () => Infinity);\nENV5.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\", () => false);\nENV5.registerFlag(\"WEBGL2_ISNAN_CUSTOM\", () => false);\nENV5.registerFlag(\"ENGINE_COMPILE_ONLY\", () => false);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/glsl_version.js\nfunction getGlslDifferences() {\n let version10;\n let attribute;\n let varyingVs;\n let varyingFs;\n let texture2D;\n let output;\n let defineOutput;\n let defineSpecialNaN;\n let defineSpecialInf;\n let defineRound;\n if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n version10 = \"#version 300 es\";\n attribute = \"in\";\n varyingVs = \"out\";\n varyingFs = \"in\";\n texture2D = \"texture\";\n output = \"outputColor\";\n defineOutput = \"out vec4 outputColor;\";\n defineSpecialNaN = env().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 ` : \"\";\n defineSpecialInf = ``;\n defineRound = `\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 `;\n } else {\n version10 = \"\";\n attribute = \"attribute\";\n varyingVs = \"varying\";\n varyingFs = \"varying\";\n texture2D = \"texture2D\";\n output = \"gl_FragColor\";\n defineOutput = \"\";\n defineSpecialNaN = `\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 `;\n defineSpecialInf = `\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 `;\n defineRound = `\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 `;\n }\n return {\n version: version10,\n attribute,\n varyingVs,\n varyingFs,\n texture2D,\n output,\n defineOutput,\n defineSpecialNaN,\n defineSpecialInf,\n defineRound\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler_util.js\nfunction getLogicalCoordinatesFromFlatIndex(coords2, shape, index = \"index\") {\n const strides = util_exports.computeStrides(shape);\n return strides.map((stride, i) => {\n const line1 = `int ${coords2[i]} = ${index} / ${stride}`;\n const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${stride}` : `index -= ${coords2[i]} * ${stride}`;\n return `${line1}; ${line2};`;\n }).join(\"\");\n}\nfunction getOutputLogicalCoordinatesFromFlatIndexByUniform(coords2, shape, index = \"index\") {\n const strides = util_exports.computeStrides(shape);\n return strides.map((_, i) => {\n const line1 = `int ${coords2[i]} = ${index} / outShapeStrides[${i}]`;\n const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * outShapeStrides[${i}]` : `index -= ${coords2[i]} * outShapeStrides[${i}]`;\n return `${line1}; ${line2};`;\n }).join(\"\");\n}\nfunction symbolicallyComputeStrides(indicesArr, variableName) {\n const numCoords = indicesArr.length;\n const shape = indicesArr.map((d) => `${variableName}[${d}]`);\n const strides = new Array(numCoords - 1);\n strides[numCoords - 2] = shape[numCoords - 1];\n for (let i = numCoords - 3; i >= 0; --i) {\n strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`;\n }\n return strides;\n}\nfunction getLogicalCoordinatesFromFlatIndexByUniform(coords2, variableName, index = \"index\") {\n const indicesArray = coords2.map((_, i) => i);\n const strides = symbolicallyComputeStrides(indicesArray, variableName);\n return strides.map((_, i) => {\n const line1 = `int ${coords2[i]} = ${index} / ${strides[i]}`;\n const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${strides[i]}` : `index -= ${coords2[i]} * ${strides[i]}`;\n return `${line1}; ${line2};`;\n }).join(\"\");\n}\nfunction getFlatIndexFrom3D(shape) {\n const strides = util_exports.computeStrides(shape).map((d) => d.toString());\n return `\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n }\n`;\n}\nfunction getFlatIndexFrom3DOutput() {\n return `\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`;\n}\nvar ENCODE_FLOAT_SNIPPET = `\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`;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler.js\nvar { getBroadcastDims: getBroadcastDims2 } = backend_util_exports;\nfunction makeShader(inputsInfo, outputShape, program) {\n const prefixSnippets = [];\n inputsInfo.forEach((x) => {\n const size = util_exports.sizeFromShape(x.shapeInfo.logicalShape);\n if (x.shapeInfo.isUniform) {\n prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : \"\"};`);\n } else {\n prefixSnippets.push(`uniform sampler2D ${x.name};`);\n prefixSnippets.push(`uniform int offset${x.name};`);\n }\n if (program.enableShapeUniforms) {\n const { uniformShape } = getUniformInfoFromShape(program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape);\n switch (uniformShape.length) {\n case 1:\n prefixSnippets.push(`uniform int ${x.name}Shape;`);\n break;\n case 2:\n prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`);\n break;\n case 3:\n prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`);\n break;\n case 4:\n prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`);\n break;\n default:\n break;\n }\n prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`);\n }\n });\n if (program.enableShapeUniforms) {\n switch (outputShape.logicalShape.length) {\n case 1:\n prefixSnippets.push(`uniform int outShape;`);\n break;\n case 2:\n prefixSnippets.push(`uniform ivec2 outShape;`);\n prefixSnippets.push(`uniform int outShapeStrides;`);\n break;\n case 3:\n prefixSnippets.push(`uniform ivec3 outShape;`);\n prefixSnippets.push(`uniform ivec2 outShapeStrides;`);\n break;\n case 4:\n prefixSnippets.push(`uniform ivec4 outShape;`);\n prefixSnippets.push(`uniform ivec3 outShapeStrides;`);\n break;\n default:\n break;\n }\n prefixSnippets.push(`uniform ivec2 outTexShape;`);\n }\n if (program.customUniforms) {\n program.customUniforms.forEach((d) => {\n prefixSnippets.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : \"\"};`);\n });\n }\n const inputPrefixSnippet = prefixSnippets.join(\"\\n\");\n const inputSamplingSnippet = inputsInfo.map((x) => getInputSamplingSnippet(x, outputShape, program.packedInputs, program.enableShapeUniforms)).join(\"\\n\");\n const outTexShape = outputShape.texShape;\n const glsl = getGlslDifferences();\n const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n let outputSamplingSnippet;\n let floatTextureSetOutputSnippet;\n let shaderPrefix = getShaderPrefix(glsl);\n if (outputShape.isPacked) {\n outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);\n floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n } else {\n outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);\n floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n }\n if (program.packedInputs) {\n shaderPrefix += SHADER_PACKED_PREFIX;\n }\n const source = [\n shaderPrefix,\n floatTextureSampleSnippet,\n floatTextureSetOutputSnippet,\n inputPrefixSnippet,\n outputSamplingSnippet,\n inputSamplingSnippet,\n program.userCode\n ].join(\"\\n\");\n return source;\n}\nfunction getSamplerFromInInfo(inInfo, enableShapeUniforms = false) {\n const shape = inInfo.shapeInfo.logicalShape;\n switch (shape.length) {\n case 0:\n return getSamplerScalar(inInfo, enableShapeUniforms);\n case 1:\n return getSampler1D(inInfo, enableShapeUniforms);\n case 2:\n return getSampler2D(inInfo, enableShapeUniforms);\n case 3:\n return getSampler3D(inInfo, enableShapeUniforms);\n case 4:\n return getSampler4D(inInfo, enableShapeUniforms);\n case 5:\n return getSampler5D(inInfo);\n case 6:\n return getSampler6D(inInfo);\n default:\n throw new Error(`${shape.length}-D input sampling is not yet supported`);\n }\n}\nfunction getPackedSamplerFromInInfo(inInfo, enableShapeUniforms) {\n const shape = inInfo.shapeInfo.logicalShape;\n switch (shape.length) {\n case 0:\n return getPackedSamplerScalar(inInfo);\n case 1:\n return getPackedSampler1D(inInfo, enableShapeUniforms);\n case 2:\n return getPackedSampler2D(inInfo, enableShapeUniforms);\n case 3:\n return getPackedSampler3D(inInfo, enableShapeUniforms);\n default:\n return getPackedSamplerND(inInfo, enableShapeUniforms);\n }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false, enableShapeUniforms) {\n let res = \"\";\n if (usesPackedTextures) {\n res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms);\n } else {\n res += getSamplerFromInInfo(inInfo, enableShapeUniforms);\n }\n const inShape = inInfo.shapeInfo.logicalShape;\n const outShape = outShapeInfo.logicalShape;\n if (inShape.length <= outShape.length) {\n if (usesPackedTextures) {\n res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n } else {\n res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n }\n }\n return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {\n switch (outShape.length) {\n case 0:\n return getOutputScalarCoords();\n case 1:\n return getOutputPacked1DCoords(outShape, outTexShape, enableShapeUniforms);\n case 2:\n return getOutputPacked2DCoords(outShape, outTexShape, enableShapeUniforms);\n case 3:\n return getOutputPacked3DCoords(outShape, outTexShape, enableShapeUniforms);\n default:\n return getOutputPackedNDCoords(outShape, outTexShape, enableShapeUniforms);\n }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {\n switch (outShape.length) {\n case 0:\n return getOutputScalarCoords();\n case 1:\n return getOutput1DCoords(outShape, outTexShape, enableShapeUniforms);\n case 2:\n return getOutput2DCoords(outShape, outTexShape, enableShapeUniforms);\n case 3:\n return getOutput3DCoords(outShape, outTexShape, enableShapeUniforms);\n case 4:\n return getOutput4DCoords(outShape, outTexShape, enableShapeUniforms);\n case 5:\n return getOutput5DCoords(outShape, outTexShape);\n case 6:\n return getOutput6DCoords(outShape, outTexShape);\n default:\n throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n return `\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${glsl.texture2D}(textureSampler, uv).r;\n }\n `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n return `\n void setOutput(float val) {\n ${glsl.output} = vec4(val, 0, 0, 0);\n }\n `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n return `\n void setOutput(vec4 val) {\n ${glsl.output} = val;\n }\n `;\n}\nfunction getShaderPrefix(glsl) {\n const SHADER_PREFIX = `${glsl.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${glsl.varyingFs} vec2 resultUV;\n ${glsl.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 ${glsl.defineSpecialNaN}\n ${glsl.defineSpecialInf}\n ${glsl.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 ${SAMPLE_1D_SNIPPET}\n ${SAMPLE_2D_SNIPPET}\n ${SAMPLE_3D_SNIPPET}\n `;\n return SHADER_PREFIX;\n}\nvar SAMPLE_1D_SNIPPET = `\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`;\nvar SAMPLE_2D_SNIPPET = `\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`;\nvar SAMPLE_3D_SNIPPET = `\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`;\nvar SHADER_PACKED_PREFIX = `\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`;\nfunction getOutputScalarCoords() {\n return `\n int getOutputCoords() {\n return 0;\n }\n `;\n}\nfunction getOutputPacked1DCoords(shape, texShape, enableShapeUniforms) {\n const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n if (packedTexShape[0] === 1) {\n if (enableShapeUniforms) {\n return `\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `;\n }\n return `\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n }\n `;\n }\n if (packedTexShape[1] === 1) {\n if (enableShapeUniforms) {\n return `\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `;\n }\n return `\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n }\n `;\n }\n if (enableShapeUniforms) {\n return `\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `;\n }\n return `\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n }\n `;\n}\nfunction getOutput1DCoords(shape, texShape, enableShapeUniforms) {\n if (texShape[0] === 1) {\n if (enableShapeUniforms) {\n return `\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `;\n }\n return `\n int getOutputCoords() {\n return int(resultUV.x * ${texShape[1]}.0);\n }\n `;\n }\n if (texShape[1] === 1) {\n if (enableShapeUniforms) {\n return `\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `;\n }\n return `\n int getOutputCoords() {\n return int(resultUV.y * ${texShape[0]}.0);\n }\n `;\n }\n if (enableShapeUniforms) {\n return `\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `;\n }\n return `\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n return resTexRC.x * ${texShape[1]} + resTexRC.y;\n }\n `;\n}\nfunction getOutputPacked3DCoords(shape, texShape, enableShapeUniforms) {\n if (enableShapeUniforms) {\n return `\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;\n }\n const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n return `\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${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 `;\n}\nfunction getOutput3DCoords(shape, texShape, enableShapeUniforms) {\n if (enableShapeUniforms) {\n const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], shape);\n 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 ${coordsFromIndexSnippet2}\n return ivec3(r, c, d);\n }\n`;\n }\n const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], shape);\n return `\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n ${coordsFromIndexSnippet}\n return ivec3(r, c, d);\n }\n `;\n}\nfunction getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) {\n if (enableShapeUniforms) {\n return `\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n `;\n }\n const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n let texelsInBatchN = texelsInBatch;\n let batches = ``;\n let coords2 = \"b, r, c\";\n for (let b = 2; b < shape.length - 1; b++) {\n texelsInBatchN *= shape[shape.length - b - 1];\n batches = `\n int b${b} = index / ${texelsInBatchN};\n index -= b${b} * ${texelsInBatchN};\n ` + batches;\n coords2 = `b${b}, ` + coords2;\n }\n return `\n ivec${shape.length} getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n ${batches}\n\n int b = index / ${texelsInBatch};\n index -= b * ${texelsInBatch};\n\n int r = 2 * (index / ${texelsInLogicalRow});\n int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n return ivec${shape.length}(${coords2});\n }\n `;\n}\nfunction getOutput4DCoords(shape, texShape, enableShapeUniforms) {\n if (enableShapeUniforms) {\n const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\", \"d2\"], shape);\n return `\n ivec4 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${coordsFromIndexSnippet2}\n return ivec4(r, c, d, d2);\n }\n `;\n }\n const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\"], shape);\n return `\n ivec4 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n ${coordsFromIndexSnippet}\n return ivec4(r, c, d, d2);\n }\n `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\", \"d3\"], shape);\n return `\n ivec5 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n ${texShape[1]}));\n\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n ${coordsFromIndexSnippet}\n\n ivec5 outShape = ivec5(r, c, d, d2, d3);\n return outShape;\n }\n `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\", \"d3\", \"d4\"], shape);\n return `\n ivec6 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n ${coordsFromIndexSnippet}\n\n ivec6 result = ivec6(r, c, d, d2, d3, d4);\n return result;\n }\n `;\n}\nfunction getOutputPacked2DCoords(shape, texShape, enableShapeUniforms) {\n const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n if (util_exports.arraysEqual(shape, texShape)) {\n if (enableShapeUniforms) {\n return `\n ivec2 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n }\n `;\n }\n return `\n ivec2 getOutputCoords() {\n return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n }\n `;\n }\n const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n if (enableShapeUniforms) {\n return `\n ivec2 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec2(r, c);\n }\n `;\n }\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n int r = 2 * (index / ${texelsInLogicalRow});\n int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n return ivec2(r, c);\n }\n `;\n}\nfunction getOutput2DCoords(shape, texShape, enableShapeUniforms) {\n if (util_exports.arraysEqual(shape, texShape)) {\n if (enableShapeUniforms) {\n return `\n ivec2 getOutputCoords() {\n return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n }\n `;\n }\n return `\n ivec2 getOutputCoords() {\n return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n }\n `;\n }\n if (shape[1] === 1) {\n if (enableShapeUniforms) {\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n return ivec2(index, 0);\n }\n `;\n }\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n return ivec2(index, 0);\n }\n `;\n }\n if (shape[0] === 1) {\n if (enableShapeUniforms) {\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n return ivec2(0, index);\n }\n `;\n }\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n return ivec2(0, index);\n }\n `;\n }\n if (enableShapeUniforms) {\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n int r = index / outShape[1];\n int c = index - r * outShape[1];\n return ivec2(r, c);\n }\n `;\n }\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n int r = index / ${shape[1]};\n int c = index - r * ${shape[1]};\n return ivec2(r, c);\n }\n `;\n}\nfunction getFlatOffsetUniformName(texName) {\n return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n const glsl = getGlslDifferences();\n return `\n vec4 ${funcName}() {\n return ${glsl.texture2D}(${texName}, halfCR);\n }\n `;\n}\nfunction getSamplerScalar(inputInfo, enableShapeUniforms) {\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n if (inputInfo.shapeInfo.isUniform) {\n return `float ${funcName}() {return ${texName};}`;\n }\n const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n if (texNumR === 1 && texNumC === 1) {\n return `\n float ${funcName}() {\n return sampleTexture(${texName}, halfCR);\n }\n `;\n }\n const offset = getFlatOffsetUniformName(texName);\n if (enableShapeUniforms) {\n return `\n float ${funcName}() {\n vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], ${offset});\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n return `\n float ${funcName}() {\n vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n return sampleTexture(${texName}, uv);\n }\n `;\n}\nfunction getPackedSampler1D(inputInfo, enableShapeUniforms) {\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n const texShape = inputInfo.shapeInfo.texShape;\n const glsl = getGlslDifferences();\n if (enableShapeUniforms) {\n return `\n vec4 ${funcName}(int index) {\n ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n vec2 uv = packedUVfrom1D(\n packedTexShape[0], packedTexShape[1], index);\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n }\n const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n return `\n vec4 ${funcName}(int index) {\n vec2 uv = packedUVfrom1D(\n ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n}\nfunction getSampler1D(inputInfo, enableShapeUniforms) {\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n if (inputInfo.shapeInfo.isUniform) {\n return `\n float ${funcName}(int index) {\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n const texShape = inputInfo.shapeInfo.texShape;\n const tNumR = texShape[0];\n const tNumC = texShape[1];\n if (tNumC === 1 && tNumR === 1) {\n return `\n float ${funcName}(int index) {\n return sampleTexture(${texName}, halfCR);\n }\n `;\n }\n const offset = getFlatOffsetUniformName(texName);\n if (tNumC === 1) {\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int index) {\n vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / float(${texName}TexShape[0]));\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int index) {\n vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (tNumR === 1) {\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int index) {\n vec2 uv = vec2((float(index + ${offset}) + 0.5) / float(${texName}TexShape[1]), 0.5);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int index) {\n vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int index) {\n vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int index) {\n vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n return sampleTexture(${texName}, uv);\n }\n `;\n}\nfunction getPackedSampler2D(inputInfo, enableShapeUniforms) {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n const texShape = inputInfo.shapeInfo.texShape;\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n const glsl = getGlslDifferences();\n if (texShape != null && util_exports.arraysEqual(shape, texShape)) {\n if (enableShapeUniforms) {\n return `\n vec4 ${funcName}(int row, int col) {\n vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n }\n return `\n vec4 ${funcName}(int row, int col) {\n vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n }\n if (enableShapeUniforms) {\n return `\n vec4 ${funcName}(int row, int col) {\n ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n int valuesPerRow = int(ceil(float(${texName}Shape[1]) / 2.0));\n vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n }\n const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n const valuesPerRow = Math.ceil(shape[1] / 2);\n return `\n vec4 ${funcName}(int row, int col) {\n vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n}\nfunction getSampler2D(inputInfo, enableShapeUniforms) {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n const texShape = inputInfo.shapeInfo.texShape;\n if (texShape != null && util_exports.arraysEqual(shape, texShape)) {\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int row, int col) {\n vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n const texNumR2 = texShape[0];\n const texNumC2 = texShape[1];\n return `\n float ${funcName}(int row, int col) {\n vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n const { newShape, keptDims } = util_exports.squeezeShape(shape);\n const squeezedShape = newShape;\n if (squeezedShape.length < shape.length) {\n const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n const params = [\"row\", \"col\"];\n return `\n ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n float ${funcName}(int row, int col) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n if (inputInfo.shapeInfo.isUniform) {\n return `\n float ${funcName}(int row, int col) {\n int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n const offset = getFlatOffsetUniformName(texName);\n if (texNumC === 1) {\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int row, int col) {\n float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));\n vec2 uv = vec2(0.5, (index + 0.5) / float(${texName}TexShape[0]));\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int row, int col) {\n float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (texNumR === 1) {\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int row, int col) {\n float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));\n vec2 uv = vec2((index + 0.5) / float(${texName}TexShape[1]), 0.5);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int row, int col) {\n float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int row, int col) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${texName}Shape[1] + col + ${offset};\n vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int row, int col) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${shape[1]} + col + ${offset};\n vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n return sampleTexture(${texName}, uv);\n }\n`;\n}\nfunction getPackedSampler3D(inputInfo, enableShapeUniforms) {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n const texShape = inputInfo.shapeInfo.texShape;\n const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n if (shape[0] === 1) {\n const squeezedShape = shape.slice(1);\n const keptDims = [1, 2];\n const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n const params = [\"b\", \"row\", \"col\"];\n return `\n ${getPackedSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n vec4 ${funcName}(int b, int row, int col) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n const glsl = getGlslDifferences();\n if (enableShapeUniforms) {\n return `\n vec4 ${funcName}(int b, int row, int col) {\n ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n int valuesPerRow = int(ceil(float(${texName}Shape[2]) / 2.0));\n int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[1]) / 2.0));\n vec2 uv = packedUVfrom3D(\n packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n }\n const texNumR = packedTexShape[0];\n const texNumC = packedTexShape[1];\n const valuesPerRow = Math.ceil(shape[2] / 2);\n const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n return `\n vec4 ${funcName}(int b, int row, int col) {\n vec2 uv = packedUVfrom3D(\n ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n}\nfunction getSampler3D(inputInfo, enableShapeUniforms) {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n const stride0 = shape[1] * shape[2];\n const stride1 = shape[2];\n const { newShape, keptDims } = util_exports.squeezeShape(shape);\n const squeezedShape = newShape;\n if (squeezedShape.length < shape.length) {\n const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n const params = [\"row\", \"col\", \"depth\"];\n return `\n ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n float ${funcName}(int row, int col, int depth) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n if (inputInfo.shapeInfo.isUniform) {\n return `\n float ${funcName}(int row, int col, int depth) {\n int index = round(dot(vec3(row, col, depth),\n vec3(${stride0}, ${stride1}, 1)));\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n const texShape = inputInfo.shapeInfo.texShape;\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n const flatOffset = inputInfo.shapeInfo.flatOffset;\n if (texNumC === stride0 && flatOffset == null) {\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int row, int col, int depth) {\n int stride1 = ${texName}Shape[2];\n float texR = float(row);\n float texC = dot(vec2(col, depth), vec2(stride1, 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int row, int col, int depth) {\n float texR = float(row);\n float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (texNumC === stride1 && flatOffset == null) {\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int row, int col, int depth) {\n float texR = dot(vec2(row, col), vec2(${texName}Shape[1], 1));\n float texC = float(depth);\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int row, int col, int depth) {\n float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n float texC = float(depth);\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n const offset = getFlatOffsetUniformName(texName);\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int row, int col, int depth) {\n // Explicitly use integer operations as dot() only works on floats.\n int stride0 = ${texName}Shape[1] * ${texName}Shape[2];\n int stride1 = ${texName}Shape[2];\n int index = row * stride0 + col * stride1 + depth + ${offset};\n vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int row, int col, int depth) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n return sampleTexture(${texName}, uv);\n }\n `;\n}\nfunction getPackedSamplerND(inputInfo, enableShapeUniforms) {\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n const glsl = getGlslDifferences();\n if (enableShapeUniforms) {\n return `\n vec4 ${funcName}(int b2, int b, int row, int col) {\n int valuesPerRow = int(ceil(float(${texName}Shape[3]) / 2.0));\n int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[2]) / 2.0));\n int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n texelsInBatch *= ${texName}Shape[1];\n index = b2 * texelsInBatch + index;\n ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n int texR = index / packedTexShape[1];\n int texC = index - texR * packedTexShape[1];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n }\n const shape = inputInfo.shapeInfo.logicalShape;\n const rank = shape.length;\n const texShape = inputInfo.shapeInfo.texShape;\n const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n const texNumR = packedTexShape[0];\n const texNumC = packedTexShape[1];\n const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n let params = `int b, int row, int col`;\n let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n for (let b = 2; b < rank - 1; b++) {\n params = `int b${b}, ` + params;\n texelsInBatch *= shape[rank - b - 1];\n index = `b${b} * ${texelsInBatch} + ` + index;\n }\n return `\n vec4 ${funcName}(${params}) {\n int index = ${index};\n int texR = index / ${texNumC};\n int texC = index - texR * ${texNumC};\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n}\nfunction getSampler4D(inputInfo, enableShapeUniforms) {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n const stride2 = shape[3];\n const stride1 = shape[2] * stride2;\n const stride0 = shape[1] * stride1;\n const { newShape, keptDims } = util_exports.squeezeShape(shape);\n if (newShape.length < shape.length) {\n const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n const params = [\"row\", \"col\", \"depth\", \"depth2\"];\n return `\n ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n float ${funcName}(int row, int col, int depth, int depth2) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n if (inputInfo.shapeInfo.isUniform) {\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n int index = round(dot(vec4(row, col, depth, depth2),\n vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n const flatOffset = inputInfo.shapeInfo.flatOffset;\n const texShape = inputInfo.shapeInfo.texShape;\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n const stride2Str = `int stride2 = ${texName}Shape[3];`;\n const stride1Str = `int stride1 = ${texName}Shape[2] * stride2;`;\n const stride0Str = `int stride0 = ${texName}Shape[1] * stride1;`;\n if (texNumC === stride0 && flatOffset == null) {\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n ${stride2Str}\n ${stride1Str}\n float texR = float(row);\n float texC =\n dot(vec3(col, depth, depth2),\n vec3(stride1, stride2, 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n float texR = float(row);\n float texC =\n dot(vec3(col, depth, depth2),\n vec3(${stride1}, ${stride2}, 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (texNumC === stride2 && flatOffset == null) {\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n float texR = dot(vec3(row, col, depth),\n vec3(${texName}Shape[1] * ${texName}Shape[2], ${texName}Shape[2], 1));\n float texC = float(depth2);\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n float texR = dot(vec3(row, col, depth),\n vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n float texC = float(depth2);\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n const offset = getFlatOffsetUniformName(texName);\n if (enableShapeUniforms) {\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n // Explicitly use integer operations as dot() only works on floats.\n ${stride2Str}\n ${stride1Str}\n ${stride0Str}\n int index = row * stride0 + col * stride1 +\n depth * stride2 + depth2;\n vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${stride0} + col * ${stride1} +\n depth * ${stride2} + depth2;\n vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n return sampleTexture(${texName}, uv);\n }\n `;\n}\nfunction getSampler5D(inputInfo) {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n const stride3 = shape[4];\n const stride2 = shape[3] * stride3;\n const stride1 = shape[2] * stride2;\n const stride0 = shape[1] * stride1;\n const { newShape, keptDims } = util_exports.squeezeShape(shape);\n if (newShape.length < shape.length) {\n const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n const params = [\"row\", \"col\", \"depth\", \"depth2\", \"depth3\"];\n return `\n ${getSamplerFromInInfo(newInputInfo)}\n float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n if (inputInfo.shapeInfo.isUniform) {\n return `\n float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n float index = dot(\n vec4(row, col, depth, depth2),\n vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n depth3;\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n const flatOffset = inputInfo.shapeInfo.flatOffset;\n const texShape = inputInfo.shapeInfo.texShape;\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n if (texNumC === stride0 && flatOffset == null) {\n return `\n float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n int texR = row;\n float texC = dot(vec4(col, depth, depth2, depth3),\n vec4(${stride1}, ${stride2}, ${stride3}, 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (texNumC === stride3 && flatOffset == null) {\n return `\n float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n float texR = dot(\n vec4(row, col, depth, depth2),\n vec4(${shape[1] * shape[2] * shape[3]},\n ${shape[2] * shape[3]}, ${shape[3]}, 1));\n int texC = depth3;\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n const offset = getFlatOffsetUniformName(texName);\n return `\n float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n depth2 * ${stride3} + depth3 + ${offset};\n vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n return sampleTexture(${texName}, uv);\n }\n `;\n}\nfunction getSampler6D(inputInfo) {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n const { newShape, keptDims } = util_exports.squeezeShape(shape);\n if (newShape.length < shape.length) {\n const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n const params = [\"row\", \"col\", \"depth\", \"depth2\", \"depth3\", \"depth4\"];\n return `\n ${getSamplerFromInInfo(newInputInfo)}\n float ${funcName}(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n const stride4 = shape[5];\n const stride3 = shape[4] * stride4;\n const stride2 = shape[3] * stride3;\n const stride1 = shape[2] * stride2;\n const stride0 = shape[1] * stride1;\n if (inputInfo.shapeInfo.isUniform) {\n return `\n float ${funcName}(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n int index = round(dot(\n vec4(row, col, depth, depth2),\n vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n dot(\n vec2(depth3, depth4),\n vec2(${stride4}, 1)));\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n const flatOffset = inputInfo.shapeInfo.flatOffset;\n const texShape = inputInfo.shapeInfo.texShape;\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n if (texNumC === stride0 && flatOffset == null) {\n return `\n float ${funcName}(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n int texR = row;\n float texC = dot(vec4(col, depth, depth2, depth3),\n vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n float(depth4);\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (texNumC === stride4 && flatOffset == null) {\n return `\n float ${funcName}(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n float texR = dot(vec4(row, col, depth, depth2),\n vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n ${shape[2] * shape[3] * shape[4]},\n ${shape[3] * shape[4]},\n ${shape[4]})) + float(depth3);\n int texC = depth4;\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n const offset = getFlatOffsetUniformName(texName);\n return `\n float ${funcName}(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n return sampleTexture(${texName}, uv);\n }\n `;\n}\nfunction getUniformSampler(inputInfo) {\n const texName = inputInfo.name;\n const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n if (inSize < 2) {\n return `return ${texName};`;\n }\n return `\n for (int i = 0; i < ${inSize}; i++) {\n if (i == index) {\n return ${texName}[i];\n }\n }\n `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n const texName = inputInfo.name;\n const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n const funcName = \"get\" + texFuncSnippet + \"AtOutCoords\";\n const inRank = inputInfo.shapeInfo.logicalShape.length;\n const outRank = outShapeInfo.logicalShape.length;\n const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n const type = getCoordsDataType(outRank);\n const rankDiff = outRank - inRank;\n let coordsSnippet;\n const fields = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n if (inRank === 0) {\n coordsSnippet = \"\";\n } else if (outRank < 2 && broadcastDims.length >= 1) {\n coordsSnippet = \"coords = 0;\";\n } else {\n coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join(\"\\n\");\n }\n let unpackedCoordsSnippet = \"\";\n if (outRank < 2 && inRank > 0) {\n unpackedCoordsSnippet = \"coords\";\n } else {\n unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(\", \");\n }\n let output = `return outputValue;`;\n const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n const isInputScalar = inSize === 1;\n const outSize = util_exports.sizeFromShape(outShapeInfo.logicalShape);\n const isOutputScalar = outSize === 1;\n if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n output = `\n return vec4(outputValue.xy, outputValue.xy);\n `;\n } else if (isInputScalar && !isOutputScalar) {\n if (outRank === 1) {\n output = `\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n `;\n } else {\n output = `\n return vec4(outputValue.x);\n `;\n }\n } else if (broadcastDims.length) {\n const rows = inRank - 2;\n const cols = inRank - 1;\n if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n output = `return vec4(outputValue.x);`;\n } else if (broadcastDims.indexOf(rows) > -1) {\n output = `return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);`;\n } else if (broadcastDims.indexOf(cols) > -1) {\n output = `return vec4(outputValue.xx, outputValue.zz);`;\n }\n }\n return `\n vec4 ${funcName}() {\n ${type} coords = getOutputCoords();\n ${coordsSnippet}\n vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n ${output}\n }\n `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n const texName = inputInfo.name;\n const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n const funcName = \"get\" + texFuncSnippet + \"AtOutCoords\";\n const outTexShape = outShapeInfo.texShape;\n const inTexShape = inputInfo.shapeInfo.texShape;\n const inRank = inputInfo.shapeInfo.logicalShape.length;\n const outRank = outShapeInfo.logicalShape.length;\n if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && util_exports.arraysEqual(inTexShape, outTexShape)) {\n return `\n float ${funcName}() {\n return sampleTexture(${texName}, resultUV);\n }\n `;\n }\n const type = getCoordsDataType(outRank);\n const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n const rankDiff = outRank - inRank;\n let coordsSnippet;\n const fields = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n if (inRank === 0) {\n coordsSnippet = \"\";\n } else if (outRank < 2 && broadcastDims.length >= 1) {\n coordsSnippet = \"coords = 0;\";\n } else {\n coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join(\"\\n\");\n }\n let unpackedCoordsSnippet = \"\";\n if (outRank < 2 && inRank > 0) {\n unpackedCoordsSnippet = \"coords\";\n } else {\n unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(\", \");\n }\n return `\n float ${funcName}() {\n ${type} coords = getOutputCoords();\n ${coordsSnippet}\n return get${texFuncSnippet}(${unpackedCoordsSnippet});\n }\n `;\n}\nfunction getCoordsDataType(rank) {\n if (rank <= 1) {\n return \"int\";\n } else if (rank === 2) {\n return \"ivec2\";\n } else if (rank === 3) {\n return \"ivec3\";\n } else if (rank === 4) {\n return \"ivec4\";\n } else if (rank === 5) {\n return \"ivec5\";\n } else if (rank === 6) {\n return \"ivec6\";\n } else {\n throw Error(`GPU for rank ${rank} is not yet supported`);\n }\n}\nfunction getUniformInfoFromShape(isPacked, shape, texShape) {\n const { newShape, keptDims } = util_exports.squeezeShape(shape);\n const rank = shape.length;\n const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1;\n const squeezeShape2 = useSqueezePackedShape ? shape.slice(1) : newShape;\n const useSqueezeShape = !isPacked && rank > 1 && !util_exports.arraysEqual(shape, texShape) && newShape.length < rank || useSqueezePackedShape;\n const uniformShape = useSqueezeShape ? squeezeShape2 : shape;\n return { useSqueezeShape, uniformShape, keptDims };\n}\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n newInputInfo.shapeInfo.logicalShape = squeezedShape;\n return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n return keptDims.map((d) => params[d]).join(\", \");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js\nfunction compileProgram(gpgpu, program, inputs, output) {\n const inputInfos = inputs.map((input2, i) => {\n const shapeInfo = {\n logicalShape: input2.shape,\n texShape: input2.isUniform ? null : input2.texData.texShape,\n isUniform: input2.isUniform,\n isPacked: input2.isUniform ? false : input2.texData.isPacked,\n flatOffset: null\n };\n if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) {\n shapeInfo.flatOffset = input2.texData.slice.flatOffset;\n }\n return { name: program.variableNames[i], shapeInfo };\n });\n const inShapeInfos = inputInfos.map((x) => x.shapeInfo);\n const outShapeInfo = {\n logicalShape: output.shape,\n texShape: output.texData.texShape,\n isUniform: false,\n isPacked: output.texData.isPacked,\n flatOffset: null\n };\n const source = makeShader(inputInfos, outShapeInfo, program);\n const fragmentShader = createFragmentShader(gpgpu.gl, source);\n const webGLProgram = gpgpu.createProgram(fragmentShader);\n if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n gpgpu.buildVao(webGLProgram);\n return Object.assign({\n program,\n fragmentShader,\n source,\n webGLProgram,\n inShapeInfos,\n outShapeInfo\n }, getUniformLocations(gpgpu, program, webGLProgram));\n } else {\n return {\n program,\n fragmentShader,\n source,\n webGLProgram,\n inShapeInfos,\n outShapeInfo,\n variablesLocations: null,\n customUniformLocations: null,\n infLoc: null,\n nanLoc: null,\n outShapeLocation: null,\n outShapeStridesLocation: null,\n outTexShapeLocation: null\n };\n }\n}\nfunction getUniformLocations(gpgpu, program, webGLProgram) {\n const variablesLocations = [];\n const customUniformLocations = [];\n let outShapeLocation;\n let outTexShapeLocation;\n let outShapeStridesLocation;\n let infLoc = null;\n let nanLoc = null;\n nanLoc = gpgpu.getUniformLocation(webGLProgram, \"NAN\", false);\n if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n infLoc = gpgpu.getUniformLocation(webGLProgram, \"INFINITY\", false);\n }\n const shouldThrow = false;\n for (const varName of program.variableNames) {\n const varLocs = {\n name: varName,\n uniform: gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow),\n offset: gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow)\n };\n if (program.enableShapeUniforms) {\n varLocs.shape = gpgpu.getUniformLocation(webGLProgram, `${varName}Shape`, shouldThrow);\n varLocs.texShape = gpgpu.getUniformLocation(webGLProgram, `${varName}TexShape`, shouldThrow);\n }\n variablesLocations.push(varLocs);\n }\n if (program.enableShapeUniforms) {\n outShapeLocation = gpgpu.getUniformLocation(webGLProgram, \"outShape\", shouldThrow);\n outShapeStridesLocation = gpgpu.getUniformLocation(webGLProgram, \"outShapeStrides\", shouldThrow);\n outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, \"outTexShape\", shouldThrow);\n }\n if (program.customUniforms) {\n for (const d of program.customUniforms) {\n customUniformLocations.push(gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow));\n }\n }\n return {\n variablesLocations,\n customUniformLocations,\n infLoc,\n nanLoc,\n outShapeLocation,\n outShapeStridesLocation,\n outTexShapeLocation\n };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n if (shapeInfos.length !== inputs.length) {\n throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);\n }\n shapeInfos.forEach((s, i) => {\n const shapeA = s.logicalShape;\n const input2 = inputs[i];\n const shapeB = input2.shape;\n if (!util_exports.arraysEqual(shapeA, shapeB)) {\n throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);\n }\n if (s.isUniform && input2.isUniform) {\n return;\n }\n const texShapeA = s.texShape;\n const texShapeB = input2.isUniform ? null : input2.texData.texShape;\n if (!util_exports.arraysEqual(texShapeA, texShapeB)) {\n throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`);\n }\n });\n}\nfunction runProgram(gpgpu, binary, inputs, output, customUniformValues) {\n if (!binary.program.enableShapeUniforms) {\n validateBinaryAndProgram(binary.inShapeInfos, inputs);\n validateBinaryAndProgram([binary.outShapeInfo], [output]);\n }\n const outTex = output.texData.texture;\n const outTexShape = output.texData.texShape;\n if (output.texData.isPacked) {\n gpgpu.setOutputPackedMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);\n } else {\n gpgpu.setOutputMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);\n }\n gpgpu.setProgram(binary.webGLProgram);\n gpgpu.bindVertexArray(binary.webGLProgram.vao);\n if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n if (binary.infLoc !== null) {\n gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n }\n }\n if (binary.nanLoc !== null) {\n gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n }\n for (let i = 0; i < inputs.length; ++i) {\n const input2 = inputs[i];\n const { uniform: varLoc, offset: varOffsetLoc, shape: varShapeLoc, texShape: varTexShapeLoc } = binary.variablesLocations[i];\n if (varShapeLoc) {\n const { uniformShape } = getUniformInfoFromShape(binary.program.packedInputs, input2.shape, input2.texData.texShape);\n switch (uniformShape.length) {\n case 1:\n gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape));\n break;\n case 2:\n gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape));\n break;\n case 3:\n gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape));\n break;\n case 4:\n gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape));\n break;\n default:\n break;\n }\n }\n if (varTexShapeLoc) {\n gpgpu.gl.uniform2i(varTexShapeLoc, input2.texData.texShape[0], input2.texData.texShape[1]);\n }\n if (varLoc == null) {\n continue;\n }\n if (input2.isUniform) {\n if (util_exports.sizeFromShape(input2.shape) < 2) {\n gpgpu.gl.uniform1f(varLoc, input2.uniformValues[0]);\n } else {\n let vals = input2.uniformValues;\n if (!(vals instanceof Float32Array)) {\n vals = new Float32Array(vals);\n }\n gpgpu.gl.uniform1fv(varLoc, vals);\n }\n continue;\n }\n if (input2.texData.slice != null && varOffsetLoc != null) {\n gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset);\n }\n gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i);\n }\n const outShapeLoc = binary.outShapeLocation;\n if (outShapeLoc) {\n switch (output.shape.length) {\n case 1:\n gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape));\n break;\n case 2:\n gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape));\n break;\n case 3:\n gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape));\n break;\n case 4:\n gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape));\n break;\n default:\n break;\n }\n }\n if (binary.outShapeStridesLocation) {\n const strides = util_exports.computeStrides(output.shape);\n switch (output.shape.length) {\n case 2:\n gpgpu.gl.uniform1iv(binary.outShapeStridesLocation, new Int32Array(strides));\n break;\n case 3:\n gpgpu.gl.uniform2iv(binary.outShapeStridesLocation, new Int32Array(strides));\n break;\n case 4:\n gpgpu.gl.uniform3iv(binary.outShapeStridesLocation, new Int32Array(strides));\n break;\n default:\n break;\n }\n }\n if (binary.outTexShapeLocation) {\n gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]);\n }\n if (binary.program.customUniforms && customUniformValues) {\n for (let i = 0; i < binary.program.customUniforms.length; ++i) {\n const d = binary.program.customUniforms[i];\n const customLoc = binary.customUniformLocations[i];\n const customValue = customUniformValues[i];\n if (d.type === \"float\") {\n gpgpu.gl.uniform1fv(customLoc, customValue);\n } else if (d.type === \"vec2\") {\n gpgpu.gl.uniform2fv(customLoc, customValue);\n } else if (d.type === \"vec3\") {\n gpgpu.gl.uniform3fv(customLoc, customValue);\n } else if (d.type === \"vec4\") {\n gpgpu.gl.uniform4fv(customLoc, customValue);\n } else if (d.type === \"int\") {\n gpgpu.gl.uniform1iv(customLoc, customValue);\n } else if (d.type === \"ivec2\") {\n gpgpu.gl.uniform2iv(customLoc, customValue);\n } else if (d.type === \"ivec3\") {\n gpgpu.gl.uniform3iv(customLoc, customValue);\n } else if (d.type === \"ivec4\") {\n gpgpu.gl.uniform4iv(customLoc, customValue);\n } else {\n throw Error(`uniform type ${d.type} is not supported yet.`);\n }\n }\n }\n gpgpu.executeProgram();\n}\nfunction makeShaderKey(program, inputs, output) {\n let keyInputs = \"\";\n inputs.concat(output).forEach((x) => {\n const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0;\n if (program.enableShapeUniforms && !x.isUniform) {\n const xTexShape = x.texData.texShape;\n const { useSqueezeShape, uniformShape, keptDims } = getUniformInfoFromShape(program.packedInputs, x.shape, xTexShape);\n let rank1 = \"\", rank2 = \"\", rank34 = \"\";\n if (uniformShape.length === 1 && program.packedInputs) {\n const packedTexShape = [Math.ceil(xTexShape[0] / 2), Math.ceil(xTexShape[1] / 2)];\n rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`;\n } else if (uniformShape.length === 2 && !program.packedInputs) {\n rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`;\n } else if (uniformShape.length > 2 && !program.packedInputs) {\n const strides = util_exports.computeStrides(uniformShape);\n rank34 = `${strides[0] === xTexShape[1]}_${strides[strides.length - 1] === xTexShape[1]}`;\n }\n const xRank = x.shape.length;\n const isLogicalShapTexShapeEqual = uniformShape.length === 2 && util_exports.arraysEqual(x.shape, xTexShape);\n const isScalar = util_exports.sizeFromShape(x.shape) === 1;\n const broadcastDims = backend_util_exports.getBroadcastDims(x.shape, output.shape);\n const isInOutTexShapeEqual = !program.packedInputs && xRank === output.shape.length && util_exports.arraysEqual(xTexShape, output.texData.texShape);\n const isTexShapeGreaterThanOne = program.packedInputs || uniformShape.length > 2 ? \"\" : `${xTexShape[0] > 1}_${xTexShape[1] > 1}`;\n keyInputs += `${xRank}_${isInOutTexShapeEqual}_${useSqueezeShape ? keptDims : \"\"}_${uniformShape.length}_${isScalar}_${broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`;\n } else {\n const texShape = x.isUniform ? \"uniform\" : x.texData.texShape;\n keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n }\n });\n const keyUserCode = program.userCode;\n let key = program.constructor.name;\n key += \"_\" + keyInputs + \"_\" + keyUserCode + `${env().getNumber(\"WEBGL_VERSION\")}`;\n return key;\n}\nfunction useShapeUniforms(rank) {\n return env().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\") && rank <= 4;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_gpu.js\nvar DecodeMatrixProgram = class {\n constructor(outputShape) {\n this.variableNames = [\"A\"];\n this.packedInputs = false;\n this.packedOutput = true;\n this.outPackingScheme = PackingScheme.DENSE;\n this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n this.userCode = `\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], outputShape) : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], outputShape)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], 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] = getA(rc.x, rc.y, rc.z);\n }\n\n ${glsl.output} = result;\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_packed_gpu.js\nvar DecodeMatrixPackedProgram = class {\n constructor(outputShape) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.outPackingScheme = PackingScheme.DENSE;\n this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n this.userCode = `\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], outputShape) : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], outputShape)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], 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 ${glsl.output} = result;\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_gpu.js\nvar EncodeFloatProgram = class {\n constructor(outputShape) {\n this.variableNames = [\"A\"];\n this.outTexUsage = TextureUsage.DOWNLOAD;\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n this.userCode = `\n ${ENCODE_FLOAT_SNIPPET}\n\n void main() {\n float x = getAAtOutCoords();\n ${glsl.output} = encode_float(x);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_packed_gpu.js\nvar EncodeFloatPackedProgram = class {\n constructor(outputShape) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = false;\n this.outTexUsage = TextureUsage.DOWNLOAD;\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n this.userCode = `\n ${ENCODE_FLOAT_SNIPPET}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${glsl.output} = encode_float(x);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_gpu.js\nvar CHANNEL_CHAR_TO_INDEX_MAP = {\n \"R\": 0,\n \"G\": 1,\n \"B\": 2,\n \"A\": 3\n};\nvar EncodeMatrixProgram = class {\n constructor(outputShape, inputIsUnsignedByte = false, usedChannels = \"RGBA\") {\n this.variableNames = [\"A\"];\n this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n let output = `result`;\n if (inputIsUnsignedByte) {\n output = `floor(result * 255. + 0.5)`;\n }\n let mainLoop = \"\";\n for (let usedChannelIndex = 0; usedChannelIndex < usedChannels.length; usedChannelIndex++) {\n const curChannel = usedChannels[usedChannelIndex];\n mainLoop += `\n if(offset == ${usedChannelIndex}) {\n result = values[${CHANNEL_CHAR_TO_INDEX_MAP[curChannel]}];\n }`;\n }\n this.userCode = `\n ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n int flatIndex = getFlatIndex(coords);\n float result = 0.;\n int offset = imod(flatIndex, ${usedChannels.length});\n\n flatIndex = idiv(flatIndex, ${usedChannels.length}, 1.);\n\n int r = flatIndex / texShape[1];\n if (r < texShape[0]) {\n int c = imod(flatIndex, texShape[1]);\n vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n vec4 values = ${glsl.texture2D}(A, uv);\n ${mainLoop}\n }\n ${glsl.output} = vec4(${output}, 0., 0., 0.);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_packed_gpu.js\nvar EncodeMatrixPackedProgram = class {\n constructor(outputShape, inputIsUnsignedByte = false) {\n this.variableNames = [\"A\"];\n this.packedInputs = false;\n this.packedOutput = true;\n this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n let mainLoop = \"\";\n let output = \"result\";\n if (inputIsUnsignedByte) {\n output = \"floor(result * 255. + 0.5)\";\n }\n for (let row = 0; row <= 1; row++) {\n for (let col = 0; col <= 1; col++) {\n const channel = row * 2 + col;\n mainLoop += `\n localCoords = coords;\n if(localCoords[2] + ${col} < ${this.enableShapeUniforms ? \"outShape[2]\" : `${outputShape[2]}`}) {\n localCoords[2] += ${col};\n if (localCoords[1] + ${row} < ${this.enableShapeUniforms ? \"outShape[1]\" : `${outputShape[1]}`}) {\n localCoords[1] += ${row};\n\n flatIndex = getFlatIndex(localCoords);\n offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n int r = flatIndex / texShape[1];\n int c = imod(flatIndex, texShape[1]);\n vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n values = ${glsl.texture2D}(A, uv);\n\n if (offset == 0) {\n result[${channel}] = values[0];\n } else if (offset == 1) {\n result[${channel}] = values[1];\n } else if (offset == 2) {\n result[${channel}] = values[2];\n } else {\n result[${channel}] = values[3];\n }\n }\n }\n `;\n }\n }\n this.userCode = `\n ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n vec4 result = vec4(0.);\n int flatIndex, r, c, offset;\n ivec3 localCoords;\n vec2 uv;\n vec4 values;\n\n ${mainLoop}\n\n ${glsl.output} = ${output};\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_util.js\nvar gpgpu_util_exports = {};\n__export(gpgpu_util_exports, {\n bindVertexProgramAttributeStreams: () => bindVertexProgramAttributeStreams,\n createBufferFromOutputTexture: () => createBufferFromOutputTexture,\n createFloat16MatrixTexture: () => createFloat16MatrixTexture,\n createFloat16PackedMatrixTexture: () => createFloat16PackedMatrixTexture,\n createFloat32MatrixTexture: () => createFloat32MatrixTexture,\n createIndexBuffer: () => createIndexBuffer,\n createPackedMatrixTexture: () => createPackedMatrixTexture,\n createUnsignedBytesMatrixTexture: () => createUnsignedBytesMatrixTexture,\n createVertexBuffer: () => createVertexBuffer,\n createVertexShader: () => createVertexShader2,\n downloadByteEncodedFloatMatrixFromOutputTexture: () => downloadByteEncodedFloatMatrixFromOutputTexture,\n downloadFloat32MatrixFromBuffer: () => downloadFloat32MatrixFromBuffer,\n downloadMatrixFromPackedOutputTexture: () => downloadMatrixFromPackedOutputTexture,\n downloadPackedMatrixFromBuffer: () => downloadPackedMatrixFromBuffer,\n getInternalFormatForFloat16MatrixTexture: () => getInternalFormatForFloat16MatrixTexture,\n getInternalFormatForFloat16PackedMatrixTexture: () => getInternalFormatForFloat16PackedMatrixTexture,\n getInternalFormatForFloat32MatrixTexture: () => getInternalFormatForFloat32MatrixTexture,\n getInternalFormatForPackedMatrixTexture: () => getInternalFormatForPackedMatrixTexture,\n getInternalFormatForUnsignedBytesMatrixTexture: () => getInternalFormatForUnsignedBytesMatrixTexture,\n uploadDenseMatrixToTexture: () => uploadDenseMatrixToTexture,\n uploadPixelDataToTexture: () => uploadPixelDataToTexture\n});\nfunction createVertexShader2(gl) {\n const glsl = getGlslDifferences();\n const vertexShaderSource = `${glsl.version}\n precision highp float;\n ${glsl.attribute} vec3 clipSpacePos;\n ${glsl.attribute} vec2 uv;\n ${glsl.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = vec4(clipSpacePos, 1);\n resultUV = uv;\n }`;\n return createVertexShader(gl, vertexShaderSource);\n}\nfunction createVertexBuffer(gl) {\n const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n return createStaticVertexBuffer(gl, vertexArray);\n}\nfunction createIndexBuffer(gl) {\n const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n return createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n validateTextureSize(width, height);\n const texture = createTexture(gl);\n const tex2d = gl.TEXTURE_2D;\n callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n } else {\n callAndCheck(gl, () => gl.texStorage2D(tex2d, 1, internalFormat, width, height));\n }\n callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n return { texture, texShape: [height, width] };\n}\nfunction getInternalFormatForFloat32MatrixTexture(textureConfig) {\n return textureConfig.internalFormatFloat;\n}\nfunction createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nfunction getInternalFormatForFloat16MatrixTexture(textureConfig) {\n return textureConfig.internalFormatHalfFloat;\n}\nfunction createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nfunction getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n return textureConfig.downloadTextureFormat;\n}\nfunction createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nfunction getInternalFormatForPackedMatrixTexture(textureConfig) {\n return textureConfig.internalFormatPackedFloat;\n}\nfunction createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nfunction getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n return textureConfig.internalFormatPackedHalfFloat;\n}\nfunction createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nfunction bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n const posOffset = 0;\n const uvOffset = 3 * 4;\n const stride = 3 * 4 + 2 * 4;\n callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n const success = bindVertexBufferToProgramAttribute(gl, program, \"clipSpacePos\", vertexBuffer, 3, stride, posOffset);\n return success && bindVertexBufferToProgramAttribute(gl, program, \"uv\", vertexBuffer, 2, stride, uvOffset);\n}\nfunction uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n let dataForUpload, texelDataType, internalFormat;\n if (data instanceof Uint8Array) {\n dataForUpload = new Uint8Array(width * height * 4);\n texelDataType = gl.UNSIGNED_BYTE;\n internalFormat = gl.RGBA;\n } else {\n dataForUpload = new Float32Array(width * height * 4);\n texelDataType = gl.FLOAT;\n internalFormat = textureConfig.internalFormatPackedFloat;\n }\n dataForUpload.set(data);\n if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload));\n } else {\n callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n }\n callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction uploadPixelDataToTexture(gl, texture, pixels) {\n callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n if (pixels.data instanceof Uint8Array) {\n if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n } else {\n callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n }\n } else {\n if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n } else {\n callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n }\n }\n callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n const buffer2 = gl2.createBuffer();\n callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2));\n const bytesPerFloat = 4;\n const valuesPerTexel = 4;\n const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n return buffer2;\n}\nfunction downloadFloat32MatrixFromBuffer(gl, buffer2, size) {\n const gl2 = gl;\n const downloadTarget = new Float32Array(size);\n gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);\n gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n return downloadTarget;\n}\nfunction downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n const [w, h] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n const numChannels = 4;\n const downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n return new Float32Array(downloadTarget.buffer);\n}\nfunction downloadPackedMatrixFromBuffer(gl, buffer2, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n const gl2 = gl;\n const downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);\n gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n return downloadTarget;\n}\nfunction downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n return packedRGBA;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_context.js\nvar GPGPUContext = class {\n constructor(gl) {\n this.outputTexture = null;\n this.program = null;\n this.disposed = false;\n this.itemsToPoll = [];\n const glVersion = env().getNumber(\"WEBGL_VERSION\");\n if (gl != null) {\n this.gl = gl;\n setWebGLContext(glVersion, gl);\n } else {\n this.gl = getWebGLContext(glVersion);\n }\n gl = this.gl;\n if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n const gl2 = gl;\n this.createVertexArray = () => {\n return callAndCheck(gl2, () => gl2.createVertexArray());\n };\n this.bindVertexArray = (vao) => {\n return callAndCheck(gl2, () => gl2.bindVertexArray(vao));\n };\n this.deleteVertexArray = (vao) => {\n return callAndCheck(gl2, () => gl2.deleteVertexArray(vao));\n };\n this.getVertexArray = () => {\n return callAndCheck(gl2, () => gl2.getParameter(gl2.VERTEX_ARRAY_BINDING));\n };\n } else if (gl != null) {\n const ext = gl.getExtension(\"OES_vertex_array_object\");\n if (ext == null) {\n throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");\n }\n this.createVertexArray = () => {\n return callAndCheck(gl, () => ext.createVertexArrayOES());\n };\n this.bindVertexArray = (vao) => {\n return callAndCheck(gl, () => ext.bindVertexArrayOES(vao));\n };\n this.deleteVertexArray = (vao) => {\n return callAndCheck(gl, () => ext.deleteVertexArrayOES(vao));\n };\n this.getVertexArray = () => {\n return callAndCheck(gl, () => gl.getParameter(ext.VERTEX_ARRAY_BINDING_OES));\n };\n }\n let COLOR_BUFFER_FLOAT = \"WEBGL_color_buffer_float\";\n const COLOR_BUFFER_HALF_FLOAT = \"EXT_color_buffer_half_float\";\n this.parallelCompilationExtension = this.gl.getExtension(\"KHR_parallel_shader_compile\");\n if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n const TEXTURE_FLOAT = \"OES_texture_float\";\n const TEXTURE_HALF_FLOAT = \"OES_texture_half_float\";\n this.textureFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n } else if (env().get(\"WEBGL_FORCE_F16_TEXTURES\")) {\n throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");\n }\n this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n } else if (env().get(\"WEBGL_FORCE_F16_TEXTURES\")) {\n throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");\n }\n } else {\n COLOR_BUFFER_FLOAT = \"EXT_color_buffer_float\";\n if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n } else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n } else {\n throw new Error(\"GL context does not support color renderable floats\");\n }\n }\n this.vertexBuffer = createVertexBuffer(this.gl);\n this.indexBuffer = createIndexBuffer(this.gl);\n this.framebuffer = createFramebuffer(this.gl);\n this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension);\n }\n get debug() {\n return env().getBool(\"DEBUG\");\n }\n dispose() {\n if (this.disposed) {\n return;\n }\n if (this.program != null) {\n 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.\");\n }\n if (this.outputTexture != null) {\n 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.\");\n }\n const gl = this.gl;\n callAndCheck(gl, () => gl.finish());\n callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n this.disposed = true;\n }\n createFloat32MatrixTexture(rows, columns) {\n this.throwIfDisposed();\n return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n }\n createFloat16MatrixTexture(rows, columns) {\n this.throwIfDisposed();\n return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n }\n createUnsignedBytesMatrixTexture(rows, columns) {\n this.throwIfDisposed();\n return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n }\n uploadPixelDataToTexture(texture, pixels) {\n this.throwIfDisposed();\n uploadPixelDataToTexture(this.gl, texture, pixels);\n }\n uploadDenseMatrixToTexture(texture, width, height, data) {\n this.throwIfDisposed();\n uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n }\n createFloat16PackedMatrixTexture(rows, columns) {\n this.throwIfDisposed();\n return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n }\n createPackedMatrixTexture(rows, columns) {\n this.throwIfDisposed();\n return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n }\n deleteMatrixTexture(texture) {\n this.throwIfDisposed();\n if (this.outputTexture === texture) {\n unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n this.outputTexture = null;\n }\n callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n }\n downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n return this.downloadMatrixDriver(texture, () => downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n }\n downloadPackedMatrixFromBuffer(buffer2, batch, rows, columns, physicalRows, physicalCols) {\n return downloadPackedMatrixFromBuffer(this.gl, buffer2, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n }\n downloadFloat32MatrixFromBuffer(buffer2, size) {\n return downloadFloat32MatrixFromBuffer(this.gl, buffer2, size);\n }\n createBufferFromTexture(texture, rows, columns) {\n this.bindTextureToFrameBuffer(texture);\n const result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n this.unbindTextureToFrameBuffer();\n return result;\n }\n createAndWaitForFence() {\n const fenceContext = this.createFence(this.gl);\n return this.pollFence(fenceContext);\n }\n createFence(gl) {\n let query;\n let isFencePassed;\n if (env().getBool(\"WEBGL_FENCE_API_ENABLED\")) {\n const gl2 = gl;\n const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n gl.flush();\n isFencePassed = () => {\n const status = gl2.clientWaitSync(sync, 0, 0);\n return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED;\n };\n query = sync;\n } else if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0) {\n query = this.beginQuery();\n this.endQuery();\n isFencePassed = () => this.isQueryAvailable(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"));\n } else {\n isFencePassed = () => true;\n }\n return { query, isFencePassed };\n }\n downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n }\n createProgram(fragmentShader) {\n this.throwIfDisposed();\n const gl = this.gl;\n if (this.vertexShader == null) {\n this.vertexShader = createVertexShader2(gl);\n }\n const program = createProgram(gl);\n callAndCheck(gl, () => gl.attachShader(program, this.vertexShader));\n callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n linkProgram(gl, program);\n const program2 = Object.assign(program, { vao: this.createVertexArray() });\n if (this.debug) {\n validateProgram(gl, program2);\n }\n return program2;\n }\n buildVao(program) {\n this.setProgram(program);\n this.bindVertexArray(program.vao);\n const gl = this.gl;\n callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, this.indexBuffer));\n bindVertexProgramAttributeStreams(gl, program, this.vertexBuffer);\n }\n deleteProgram(program) {\n this.throwIfDisposed();\n if (program === this.program) {\n this.program = null;\n }\n if (program != null) {\n callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n this.deleteVertexArray(program.vao);\n }\n }\n setProgram(program) {\n this.throwIfDisposed();\n this.program = program;\n if (this.program != null) {\n if (this.debug) {\n validateProgram(this.gl, this.program);\n }\n }\n callAndCheck(this.gl, () => this.gl.useProgram(program));\n }\n getUniformLocation(program, uniformName, shouldThrow = true) {\n this.throwIfDisposed();\n if (shouldThrow) {\n return getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n } else {\n return getProgramUniformLocation(this.gl, program, uniformName);\n }\n }\n getAttributeLocation(program, attribute) {\n this.throwIfDisposed();\n return callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n }\n getUniformLocationNoThrow(program, uniformName) {\n this.throwIfDisposed();\n return this.gl.getUniformLocation(program, uniformName);\n }\n setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n this.throwIfDisposed();\n this.throwIfNoProgram();\n bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n }\n setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n }\n setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n this.throwIfDisposed();\n const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n }\n setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n }\n setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\");\n }\n debugValidate() {\n if (this.program != null) {\n validateProgram(this.gl, this.program);\n }\n validateFramebuffer(this.gl);\n }\n executeProgram() {\n this.throwIfDisposed();\n this.throwIfNoProgram();\n const gl = this.gl;\n if (this.debug) {\n const boundVao = this.getVertexArray();\n console.assert(boundVao === this.program.vao, \"VAO changed between setProgram and executeProgram!\");\n this.debugValidate();\n }\n callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n }\n blockUntilAllProgramsCompleted() {\n this.throwIfDisposed();\n callAndCheck(this.gl, () => this.gl.finish());\n }\n getQueryTimerExtension() {\n if (this.disjointQueryTimerExtension == null) {\n this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2 ? \"EXT_disjoint_timer_query_webgl2\" : \"EXT_disjoint_timer_query\");\n }\n return this.disjointQueryTimerExtension;\n }\n getQueryTimerExtensionWebGL2() {\n return this.getQueryTimerExtension();\n }\n getQueryTimerExtensionWebGL1() {\n return this.getQueryTimerExtension();\n }\n beginQuery() {\n if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2) {\n const gl2 = this.gl;\n const ext2 = this.getQueryTimerExtensionWebGL2();\n const query2 = gl2.createQuery();\n gl2.beginQuery(ext2.TIME_ELAPSED_EXT, query2);\n return query2;\n }\n const ext = this.getQueryTimerExtensionWebGL1();\n const query = ext.createQueryEXT();\n ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n return query;\n }\n endQuery() {\n if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2) {\n const gl2 = this.gl;\n const ext2 = this.getQueryTimerExtensionWebGL2();\n gl2.endQuery(ext2.TIME_ELAPSED_EXT);\n return;\n }\n const ext = this.getQueryTimerExtensionWebGL1();\n ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n }\n async waitForQueryAndGetTime(query) {\n await util_exports.repeatedTry(() => this.disposed || // while testing contexts are created / disposed\n // in rapid succession, so without this check we\n // may poll for the query timer indefinitely\n this.isQueryAvailable(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")));\n return this.getQueryTime(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"));\n }\n getQueryTime(query, queryTimerVersion) {\n if (queryTimerVersion === 0) {\n return null;\n }\n if (queryTimerVersion === 2) {\n const gl2 = this.gl;\n const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n return timeElapsedNanos / 1e6;\n } else {\n const ext = this.getQueryTimerExtensionWebGL1();\n const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n return timeElapsedNanos / 1e6;\n }\n }\n isQueryAvailable(query, queryTimerVersion) {\n if (queryTimerVersion === 0) {\n return true;\n }\n if (queryTimerVersion === 2) {\n const gl2 = this.gl;\n const ext = this.getQueryTimerExtensionWebGL2();\n const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n if (this.disjoint == null) {\n this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n }\n return available && !this.disjoint;\n } else {\n const ext = this.getQueryTimerExtensionWebGL1();\n const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n if (this.disjoint == null) {\n this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n }\n return available && !this.disjoint;\n }\n }\n pollFence(fenceContext) {\n return new Promise((resolve) => {\n this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n });\n }\n pollItems() {\n const index = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn));\n for (let i = 0; i <= index; ++i) {\n const { resolveFn } = this.itemsToPoll[i];\n resolveFn();\n }\n this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n }\n addItemToPoll(isDoneFn, resolveFn) {\n this.itemsToPoll.push({ isDoneFn, resolveFn });\n if (this.itemsToPoll.length > 1) {\n return;\n }\n let scheduleFn = void 0;\n if (\"setTimeoutCustom\" in env().platform) {\n scheduleFn = env().platform.setTimeoutCustom.bind(env().platform);\n }\n util_exports.repeatedTry(() => {\n this.pollItems();\n return this.itemsToPoll.length === 0;\n }, () => 0, null, scheduleFn);\n }\n bindTextureToFrameBuffer(texture) {\n this.throwIfDisposed();\n bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n if (this.debug) {\n validateFramebuffer(this.gl);\n }\n }\n unbindTextureToFrameBuffer() {\n if (this.outputTexture != null) {\n bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n if (this.debug) {\n validateFramebuffer(this.gl);\n }\n } else {\n unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n }\n }\n downloadMatrixDriver(texture, downloadAndDecode) {\n this.bindTextureToFrameBuffer(texture);\n const result = downloadAndDecode();\n this.unbindTextureToFrameBuffer();\n return result;\n }\n setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n this.throwIfDisposed();\n const gl = this.gl;\n bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n if (this.debug) {\n validateFramebuffer(gl);\n }\n this.outputTexture = outputMatrixTextureMaybePacked;\n callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n }\n setOutputMatrixWriteRegionDriver(x, y, width, height) {\n this.throwIfDisposed();\n callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n }\n throwIfDisposed() {\n if (this.disposed) {\n throw new Error(\"Attempted to use disposed GPGPUContext.\");\n }\n }\n throwIfNoProgram() {\n if (this.program == null) {\n throw new Error(\"No GPU program is currently set.\");\n }\n }\n};\nfunction linearSearchLastTrue(arr) {\n let i = 0;\n for (; i < arr.length; ++i) {\n const isDone = arr[i]();\n if (!isDone) {\n break;\n }\n }\n return i - 1;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/shared.js\nvar { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, bitwiseAndImpl: bitwiseAndImplCPU, castImpl: castImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, raggedGatherImpl: raggedGatherImplCPU, raggedRangeImpl: raggedRangeImplCPU, raggedTensorToTensorImpl: raggedTensorToTensorImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, staticRegexReplaceImpl: staticRegexReplaceImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/packing_util.js\nfunction getVecChannels(name, rank) {\n return [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, rank).map((d) => `${name}.${d}`);\n}\nfunction getChannels(name, rank) {\n if (rank === 1) {\n return [name];\n }\n return getVecChannels(name, rank);\n}\nfunction getSourceCoords(rank, dims) {\n if (rank === 1) {\n return \"rc\";\n }\n let coords2 = \"\";\n for (let i = 0; i < rank; i++) {\n coords2 += dims[i];\n if (i < rank - 1) {\n coords2 += \",\";\n }\n }\n return coords2;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pack_gpu.js\nvar PackProgram = class {\n constructor(outputShape) {\n this.variableNames = [\"A\"];\n this.packedInputs = false;\n this.packedOutput = true;\n this.outputShape = outputShape;\n this.rank = outputShape.length;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n if (this.rank === 0) {\n this.userCode = `\n void main() {\n setOutput(vec4(getA(), 0., 0., 0.));\n }\n `;\n } else {\n const channels = getChannels(\"rc\", this.rank);\n const dtype = getCoordsDataType(this.rank);\n const outOfBoundsCondition = this.getOutOfBoundsCondition(channels);\n const setup76 = this.getSetup(channels);\n const output = this.getOutput(channels);\n this.userCode = `\n void main() {\n ${dtype} rc = getOutputCoords();\n\n if(${outOfBoundsCondition}) {\n setOutput(vec4(0));\n } else {\n ${setup76}\n\n setOutput(vec4(${output}));\n }\n }\n `;\n }\n }\n getSourceCoordsArr(dims) {\n const coords2 = [];\n for (let row = 0; row <= 1; row++) {\n for (let col = 0; col <= 1; col++) {\n let coord = `${row === 0 ? \"r\" : \"rp1\"}, ${col === 0 ? \"c\" : \"cp1\"}`;\n for (let d = 2; d < this.rank; d++) {\n coord = `${dims[dims.length - 1 - d]},` + coord;\n }\n coords2.push(coord);\n }\n }\n return coords2;\n }\n getOutOfBoundsCondition(dims) {\n if (this.rank === 1) {\n return `rc > ${this.enableShapeUniforms ? \"outShape\" : this.outputShape[0]}`;\n }\n let cond = \"\";\n for (let i = this.rank - 2; i < this.rank; i++) {\n cond += `${dims[i]} >= ${this.enableShapeUniforms ? `outShape[${i}]` : this.outputShape[i]}`;\n if (i < this.rank - 1) {\n cond += \"||\";\n }\n }\n return cond;\n }\n getSetup(dims) {\n if (this.rank === 1) {\n return \"\";\n }\n const innerDims = dims.slice(-2);\n const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1];\n const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2];\n return `\n int r = ${innerDims[0]};\n int c = ${innerDims[1]};\n int rp1 = r + 1;\n int cp1 = c + 1;\n\n bool cEdge = cp1 >= ${col};\n bool rEdge = rp1 >= ${row};\n `;\n }\n getOutput(dims) {\n const sourceCoords = this.getSourceCoordsArr(dims);\n if (this.rank === 1) {\n const outShape = this.enableShapeUniforms ? \"outShape\" : this.outputShape[0];\n return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`;\n }\n return `getA(${sourceCoords[0]}),\n cEdge ? 0. : getA(${sourceCoords[1]}),\n rEdge ? 0. : getA(${sourceCoords[2]}),\n rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reshape_packed_gpu.js\nvar ReshapePackedProgram = class {\n constructor(outputShape, inputShape) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.customUniforms = [{ name: \"inputShape\", type: \"ivec3\" }];\n this.outputShape = outputShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n let mainLoop = ``;\n for (let i = 0; i < 4; i++) {\n let thisRC = `thisRC = rc;`;\n if (i % 2 === 1) {\n thisRC += `thisRC.z += 1;`;\n }\n if (i > 1) {\n thisRC += `thisRC.y += 1;`;\n }\n mainLoop += `\n ${thisRC}\n ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : \"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${i}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${i > 0 ? \"}\" : \"\"}\n `;\n }\n this.userCode = `\n ${getReshapedInputCoords(inputShape, this.enableShapeUniforms)}\n ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\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]\" : outputShape[1]};\n int cols = ${this.enableShapeUniforms ? \"outShape[2]\" : outputShape[2]};\n\n ${mainLoop}\n\n setOutput(result);\n }\n `;\n }\n};\nfunction getReshapedInputCoords(shape, enableShapeUniforms) {\n const coordsFromIndexSnippet = enableShapeUniforms ? getLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], \"inputShape\") : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], shape);\n return `\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${coordsFromIndexSnippet}\n return ivec3(r, c, d);\n }\n `;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/texture_manager.js\nvar TextureManager = class {\n constructor(gpgpu) {\n this.gpgpu = gpgpu;\n this.numUsedTextures = 0;\n this.numFreeTextures = 0;\n this._numBytesAllocated = 0;\n this._numBytesFree = 0;\n this.freeTextures = {};\n this.usedTextures = {};\n this.logEnabled = false;\n }\n acquireTexture(shapeRC, usage, isPacked) {\n const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n if (!(shapeKey in this.freeTextures)) {\n this.freeTextures[shapeKey] = [];\n }\n if (!(shapeKey in this.usedTextures)) {\n this.usedTextures[shapeKey] = [];\n }\n const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n if (this.freeTextures[shapeKey].length > 0) {\n this.numFreeTextures--;\n this.numUsedTextures++;\n this._numBytesFree -= texBytes;\n this.log();\n const newTexture2 = this.freeTextures[shapeKey].pop();\n this.usedTextures[shapeKey].push(newTexture2);\n return newTexture2;\n }\n let newTexture;\n if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n } else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n }\n this.usedTextures[shapeKey].push(newTexture);\n this.numUsedTextures++;\n this._numBytesAllocated += texBytes;\n this.log();\n return newTexture;\n }\n releaseTexture(texture, shape, logicalTexType, isPacked) {\n if (this.freeTextures == null) {\n return;\n }\n const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n if (!(shapeKey in this.freeTextures)) {\n this.freeTextures[shapeKey] = [];\n }\n const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n const deleteTexThreshold = env().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");\n if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) {\n this.gpgpu.deleteMatrixTexture(texture.texture);\n this._numBytesAllocated -= texBytes;\n } else {\n this.freeTextures[shapeKey].push(texture);\n this.numFreeTextures++;\n this._numBytesFree += texBytes;\n }\n this.numUsedTextures--;\n const texList = this.usedTextures[shapeKey];\n const texIndex = texList && texList.indexOf(texture);\n if (texIndex == null || texIndex < 0) {\n throw new Error(\"Cannot release a texture that was never provided by this texture manager\");\n }\n texList[texIndex] = texList[texList.length - 1];\n texList.pop();\n this.log();\n }\n log() {\n if (!this.logEnabled) {\n return;\n }\n const total = this.numFreeTextures + this.numUsedTextures;\n console.log(\"Free/Used\", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n const freeRatio = this._numBytesFree / this._numBytesAllocated;\n console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n }\n get numBytesAllocated() {\n return this._numBytesAllocated;\n }\n get numBytesFree() {\n return this._numBytesFree;\n }\n getNumUsedTextures() {\n return this.numUsedTextures;\n }\n getNumFreeTextures() {\n return this.numFreeTextures;\n }\n dispose() {\n if (this.freeTextures == null) {\n return;\n }\n for (const texShape in this.freeTextures) {\n this.freeTextures[texShape].forEach((tex) => {\n this.gpgpu.deleteMatrixTexture(tex.texture);\n });\n }\n for (const texShape in this.usedTextures) {\n this.usedTextures[texShape].forEach((tex) => {\n this.gpgpu.deleteMatrixTexture(tex.texture);\n });\n }\n this.freeTextures = null;\n this.usedTextures = null;\n this.numUsedTextures = 0;\n this.numFreeTextures = 0;\n this._numBytesAllocated = 0;\n this._numBytesFree = 0;\n }\n};\nfunction numBytesForInternalFormat(gl, internalFormat) {\n const glany = gl;\n if (internalFormat === glany.R32F) {\n return 4;\n } else if (internalFormat === glany.R16F) {\n return 2;\n } else if (internalFormat === glany.RGBA32F) {\n return 16;\n } else if (internalFormat === gl.RGBA) {\n return 16;\n } else if (internalFormat === glany.RGBA16F) {\n return 8;\n } else if (internalFormat === glany.RGBA8) {\n return 4;\n }\n throw new Error(`Unknown internal format ${internalFormat}`);\n}\nfunction computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n let numElements;\n if (isPacked) {\n const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n numElements = packedWidth * packedHeight;\n } else {\n const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n numElements = width * height;\n }\n const bytesPerElement2 = numBytesForInternalFormat(gl, internalFormat);\n return numElements * bytesPerElement2;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n switch (physicalTexType) {\n case PhysicalTextureType.PACKED_2X2_FLOAT32:\n return getInternalFormatForPackedMatrixTexture(textureConfig);\n case PhysicalTextureType.PACKED_2X2_FLOAT16:\n return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n case PhysicalTextureType.UNPACKED_FLOAT32:\n return getInternalFormatForFloat32MatrixTexture(textureConfig);\n case PhysicalTextureType.UNPACKED_FLOAT16:\n return getInternalFormatForFloat16MatrixTexture(textureConfig);\n case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n default:\n throw new Error(`Unknown physical texture type ${physicalTexType}`);\n }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n if (env().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")) {\n if (isPacked) {\n return PhysicalTextureType.PACKED_2X2_FLOAT32;\n }\n return PhysicalTextureType.UNPACKED_FLOAT32;\n }\n if (isPacked) {\n return PhysicalTextureType.PACKED_2X2_FLOAT16;\n }\n return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n if (logicalTexType === TextureUsage.UPLOAD) {\n return PhysicalTextureType.PACKED_2X2_FLOAT32;\n } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n return getPhysicalTextureForRendering(isPacked);\n } else if (logicalTexType === TextureUsage.DOWNLOAD || logicalTexType === TextureUsage.PIXELS) {\n return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n }\n throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_gpu.js\nvar UnaryOpProgram = class {\n constructor(aShape, opSnippet) {\n this.variableNames = [\"A\"];\n this.outputShape = aShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n this.userCode = `\n float unaryOperation(float x) {\n ${opSnippet}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `;\n }\n};\nvar CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nvar LINEAR = `return x;`;\nvar ABS = `return abs(x);`;\nvar ELU2 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nvar RELU = CHECK_NAN_SNIPPET + `\n return (x < 0.0) ? 0.0 : x;\n`;\nvar RELU6 = CHECK_NAN_SNIPPET + `\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nvar CLONE = \"return x;\";\nvar SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_packed_gpu.js\nvar LINEAR2 = `return x;`;\nvar ELU3 = `\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`;\nvar RELU2 = `\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`;\nvar RELU62 = `\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`;\nvar SIGMOID2 = `return 1.0 / (1.0 + exp(-1.0 * x));`;\nvar UnaryOpPackedProgram = class {\n constructor(aShape, opSnippet) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.outputShape = aShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n this.userCode = `\n vec4 unaryOperation(vec4 x) {\n ${opSnippet}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unpack_gpu.js\nvar UnpackProgram = class {\n constructor(outputShape) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = false;\n this.outputShape = outputShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n const rank = outputShape.length;\n const channels = getChannels(\"rc\", rank);\n const dtype = getCoordsDataType(rank);\n const sourceCoords = getSourceCoords(rank, channels);\n const innerDims = channels.slice(-2);\n const coords2 = rank <= 1 ? \"rc\" : `vec2(${innerDims.join(\",\")})`;\n this.userCode = `\n void main() {\n ${dtype} rc = getOutputCoords();\n vec4 packedInput = getA(${sourceCoords});\n\n setOutput(getChannel(packedInput, ${coords2}));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js\nvar whereImpl3 = kernel_impls_exports.whereImpl;\nvar EPSILON_FLOAT322 = 1e-7;\nvar EPSILON_FLOAT162 = 1e-4;\nvar binaryCaches = {};\nfunction getBinaryCache(webGLVersion) {\n if (webGLVersion in binaryCaches) {\n return binaryCaches[webGLVersion];\n }\n binaryCaches[webGLVersion] = {};\n return binaryCaches[webGLVersion];\n}\nvar CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\");\nvar BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n if (env().global.screen == null) {\n return 1024;\n }\n return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\nvar MathBackendWebGL = class _MathBackendWebGL extends KernelBackend {\n nextDataId() {\n return _MathBackendWebGL.nextDataId++;\n }\n constructor(gpuResource) {\n super();\n this.pendingRead = /* @__PURE__ */ new WeakMap();\n this.pendingDisposal = /* @__PURE__ */ new WeakSet();\n this.dataRefCount = /* @__PURE__ */ new WeakMap();\n this.numBytesInGPU = 0;\n this.uploadWaitMs = 0;\n this.downloadWaitMs = 0;\n this.lastGlFlushTime = 0;\n this.warnedAboutMemory = false;\n this.pendingDeletes = 0;\n this.disposed = false;\n if (!env().getBool(\"HAS_WEBGL\")) {\n throw new Error(\"WebGL is not supported on this device\");\n }\n let newGPGPU;\n if (gpuResource != null) {\n if (gpuResource instanceof GPGPUContext) {\n newGPGPU = gpuResource;\n } else {\n const gl = getWebGLContext(env().getNumber(\"WEBGL_VERSION\"), gpuResource);\n newGPGPU = new GPGPUContext(gl);\n }\n this.binaryCache = {};\n this.gpgpuCreatedLocally = false;\n } else {\n const gl = getWebGLContext(env().getNumber(\"WEBGL_VERSION\"));\n newGPGPU = new GPGPUContext(gl);\n this.binaryCache = getBinaryCache(env().getNumber(\"WEBGL_VERSION\"));\n this.gpgpuCreatedLocally = true;\n }\n this.gpgpu = newGPGPU;\n this.canvas = this.gpgpu.gl.canvas;\n this.textureManager = new TextureManager(this.gpgpu);\n this.numMBBeforeWarning = numMBBeforeWarning();\n this.texData = new DataStorage(this, engine());\n }\n numDataIds() {\n return this.texData.numDataIds() - this.pendingDeletes;\n }\n // Writes a new entry to the data store with a WebGL texture, and registers it\n // to the texture manager.\n writeTexture(texture, shape, dtype, texHeight, texWidth, channels) {\n const input2 = this.makeTensorInfo(shape, dtype);\n const inData = this.texData.get(input2.dataId);\n inData.isPacked = false;\n inData.texture = { texture, texShape: [texHeight, texWidth] };\n inData.texShape = [texHeight, texWidth];\n const shapeAs3D = getShapeAs3D(shape);\n const program = new EncodeMatrixProgram(shapeAs3D, false, channels);\n const output = this.runWebGLProgram(program, [input2], dtype, [[texHeight, texWidth]]);\n output.shape = shape;\n inData.texture = null;\n this.disposeIntermediateTensorInfo(input2);\n return output.dataId;\n }\n write(values, shape, dtype) {\n if (env().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\") || env().getBool(\"DEBUG\")) {\n this.checkNumericalProblems(values);\n }\n if (dtype === \"complex64\" && values != null) {\n throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n }\n const dataId = { id: this.nextDataId() };\n this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n return dataId;\n }\n /** Return refCount of a `TensorData`. */\n refCount(dataId) {\n if (this.texData.has(dataId)) {\n const tensorData = this.texData.get(dataId);\n return tensorData.refCount;\n }\n return 0;\n }\n /** Increase refCount of a `TextureData`. */\n incRef(dataId) {\n const texData = this.texData.get(dataId);\n texData.refCount++;\n }\n /** Decrease refCount of a `TextureData`. */\n decRef(dataId) {\n if (this.texData.has(dataId)) {\n const texData = this.texData.get(dataId);\n texData.refCount--;\n }\n }\n move(dataId, values, shape, dtype, refCount) {\n if (env().getBool(\"DEBUG\")) {\n this.checkNumericalProblems(values);\n }\n if (dtype === \"complex64\") {\n throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n }\n this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount });\n }\n disposeIntermediateTensorInfo(tensorInfo) {\n this.disposeData(tensorInfo.dataId);\n }\n readSync(dataId) {\n const texData = this.texData.get(dataId);\n const { values, dtype, complexTensorInfos, slice: slice5, shape, isPacked } = texData;\n if (slice5 != null) {\n let program;\n if (isPacked) {\n program = new UnaryOpPackedProgram(shape, CLONE);\n } else {\n program = new UnaryOpProgram(shape, CLONE);\n }\n const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n const data = this.readSync(res.dataId);\n this.disposeIntermediateTensorInfo(res);\n return data;\n }\n if (values != null) {\n return this.convertAndCacheOnCPU(dataId);\n }\n if (dtype === \"string\") {\n return values;\n }\n const shouldTimeProgram = this.activeTimers != null;\n let start;\n if (shouldTimeProgram) {\n start = util_exports.now();\n }\n let result;\n if (dtype === \"complex64\") {\n const realValues = this.readSync(complexTensorInfos.real.dataId);\n const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n result = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n } else {\n result = this.getValuesFromTexture(dataId);\n }\n if (shouldTimeProgram) {\n this.downloadWaitMs += util_exports.now() - start;\n }\n return this.convertAndCacheOnCPU(dataId, result);\n }\n async read(dataId) {\n if (this.pendingRead.has(dataId)) {\n const subscribers2 = this.pendingRead.get(dataId);\n return new Promise((resolve) => subscribers2.push(resolve));\n }\n const texData = this.texData.get(dataId);\n const { values, shape, slice: slice5, dtype, complexTensorInfos, isPacked } = texData;\n if (slice5 != null) {\n let program;\n if (isPacked) {\n program = new UnaryOpPackedProgram(shape, CLONE);\n } else {\n program = new UnaryOpProgram(shape, CLONE);\n }\n const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n const data = this.read(res.dataId);\n this.disposeIntermediateTensorInfo(res);\n return data;\n }\n if (values != null) {\n return this.convertAndCacheOnCPU(dataId);\n }\n if (env().getBool(\"DEBUG\")) {\n if (!env().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\") && env().getNumber(\"WEBGL_VERSION\") === 2) {\n throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`);\n }\n }\n let buffer2 = null;\n let tmpDownloadTarget;\n if (dtype !== \"complex64\" && env().get(\"WEBGL_BUFFER_SUPPORTED\")) {\n tmpDownloadTarget = this.decode(dataId);\n const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n buffer2 = this.gpgpu.createBufferFromTexture(tmpData.texture.texture, ...getDenseTexShape(shape));\n }\n this.pendingRead.set(dataId, []);\n if (dtype !== \"complex64\") {\n await this.gpgpu.createAndWaitForFence();\n }\n let vals;\n if (dtype === \"complex64\") {\n const ps = await Promise.all([\n this.read(complexTensorInfos.real.dataId),\n this.read(complexTensorInfos.imag.dataId)\n ]);\n const realValues = ps[0];\n const imagValues = ps[1];\n vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n } else if (buffer2 == null) {\n vals = this.getValuesFromTexture(dataId);\n } else {\n const size = util_exports.sizeFromShape(shape);\n vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2, size);\n }\n if (tmpDownloadTarget != null) {\n this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n }\n if (buffer2 != null) {\n const gl = this.gpgpu.gl;\n callAndCheck(gl, () => gl.deleteBuffer(buffer2));\n }\n const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n const subscribers = this.pendingRead.get(dataId);\n this.pendingRead.delete(dataId);\n subscribers.forEach((resolve) => resolve(dTypeVals));\n if (this.pendingDisposal.has(dataId)) {\n this.pendingDisposal.delete(dataId);\n if (this.disposeData(dataId)) {\n engine().removeDataId(dataId, this);\n }\n this.pendingDeletes--;\n }\n return dTypeVals;\n }\n /**\n * Read tensor to a new texture that is densely packed for ease of use.\n * @param dataId The source tensor.\n * @param options\n * customTexShape: Optional. If set, will use the user defined texture\n * shape to create the texture.\n */\n readToGPU(dataId, options = {}) {\n const texData = this.texData.get(dataId);\n const { values, shape, slice: slice5, dtype, isPacked, texture } = texData;\n if (dtype === \"complex64\") {\n throw new Error(\"Does not support reading texture for complex64 dtype.\");\n }\n if (slice5 != null) {\n let program;\n if (isPacked) {\n program = new UnaryOpPackedProgram(shape, CLONE);\n } else {\n program = new UnaryOpProgram(shape, CLONE);\n }\n const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n const gpuResouorce = this.readToGPU(res, options);\n this.disposeIntermediateTensorInfo(res);\n return gpuResouorce;\n }\n if (texture == null) {\n if (values != null) {\n throw new Error(\"Data is not on GPU but on CPU.\");\n } else {\n throw new Error(\"There is no data on GPU or CPU.\");\n }\n }\n const tmpTarget = this.decode(dataId, options.customTexShape);\n const tensorRef = engine().makeTensorFromTensorInfo(tmpTarget);\n const tmpData = this.texData.get(tmpTarget.dataId);\n return Object.assign({ tensorRef }, tmpData.texture);\n }\n bufferSync(t) {\n const data = this.readSync(t.dataId);\n if (t.dtype === \"string\") {\n try {\n const strings = data.map((d) => util_exports.decodeString(d));\n return buffer(t.shape, t.dtype, strings);\n } catch (_a) {\n throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n }\n }\n return buffer(t.shape, t.dtype, data);\n }\n checkNumericalProblems(values) {\n if (values == null) {\n return;\n }\n for (let i = 0; i < values.length; i++) {\n const num = values[i];\n if (!canBeRepresented(num)) {\n if (env().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")) {\n throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n }\n throw Error(`The value ${num} cannot be represented on this device.`);\n }\n }\n }\n getValuesFromTexture(dataId) {\n const { shape, dtype, isPacked } = this.texData.get(dataId);\n const size = util_exports.sizeFromShape(shape);\n if (env().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")) {\n const tmpTarget = this.decode(dataId);\n const tmpData2 = this.texData.get(tmpTarget.dataId);\n const vals2 = this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture.texture, ...getDenseTexShape(shape)).subarray(0, size);\n this.disposeIntermediateTensorInfo(tmpTarget);\n return vals2;\n }\n const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK\") && isPacked === true;\n const outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape;\n const program = shouldUsePackedProgram ? new EncodeFloatPackedProgram(outputShape) : new EncodeFloatProgram(outputShape);\n const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], \"float32\");\n const tmpData = this.texData.get(output.dataId);\n const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size);\n this.disposeIntermediateTensorInfo(output);\n return vals;\n }\n timerAvailable() {\n return env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0;\n }\n time(f) {\n const oldActiveTimers = this.activeTimers;\n const newActiveTimers = [];\n let outerMostTime = false;\n if (this.programTimersStack == null) {\n this.programTimersStack = newActiveTimers;\n outerMostTime = true;\n } else {\n this.activeTimers.push(newActiveTimers);\n }\n this.activeTimers = newActiveTimers;\n f();\n const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);\n const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);\n this.activeTimers = oldActiveTimers;\n if (outerMostTime) {\n this.programTimersStack = null;\n }\n const res = {\n uploadWaitMs: this.uploadWaitMs,\n downloadWaitMs: this.downloadWaitMs,\n kernelMs: null,\n wallMs: null\n // will be filled by the engine\n };\n return (async () => {\n if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n res[\"kernelMs\"] = util_exports.sum(kernelMs);\n res[\"getExtraProfileInfo\"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(\", \");\n } else {\n res[\"kernelMs\"] = {\n error: \"WebGL query timers are not supported in this environment.\"\n };\n }\n this.uploadWaitMs = 0;\n this.downloadWaitMs = 0;\n return res;\n })();\n }\n memory() {\n return {\n unreliable: false,\n numBytesInGPU: this.numBytesInGPU,\n numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n numBytesInGPUFree: this.textureManager.numBytesFree\n };\n }\n startTimer() {\n if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n return this.gpgpu.beginQuery();\n }\n return { startMs: util_exports.now(), endMs: null };\n }\n endTimer(query) {\n if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n this.gpgpu.endQuery();\n return query;\n }\n query.endMs = util_exports.now();\n return query;\n }\n async getQueryTime(query) {\n if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n return this.gpgpu.waitForQueryAndGetTime(query);\n }\n const timerQuery = query;\n return timerQuery.endMs - timerQuery.startMs;\n }\n /**\n * Decrease the RefCount on the dataId and dispose the memory if the dataId\n * has 0 refCount. If there are pending read on the data, the disposal would\n * added to the pending delete queue. Return true if the dataId is removed\n * from backend or the backend does not contain the dataId, false if the\n * dataId is not removed. Memory may or may not be released even when dataId\n * is removed, which also depends on dataRefCount, see `releaseGPU`.\n * @param dataId\n * @oaram force Optional, remove the data regardless of refCount\n */\n disposeData(dataId, force = false) {\n if (this.pendingDisposal.has(dataId)) {\n return false;\n }\n if (!this.texData.has(dataId)) {\n return true;\n }\n if (force) {\n this.texData.get(dataId).refCount = 0;\n } else {\n this.texData.get(dataId).refCount--;\n }\n if (!force && this.texData.get(dataId).refCount > 0) {\n return false;\n }\n if (this.pendingRead.has(dataId)) {\n this.pendingDisposal.add(dataId);\n this.pendingDeletes++;\n return false;\n }\n this.releaseGPUData(dataId);\n const { complexTensorInfos } = this.texData.get(dataId);\n if (complexTensorInfos != null) {\n this.disposeData(complexTensorInfos.real.dataId, force);\n this.disposeData(complexTensorInfos.imag.dataId, force);\n }\n this.texData.delete(dataId);\n return true;\n }\n releaseGPUData(dataId) {\n const { texture, dtype, texShape, usage, isPacked, slice: slice5 } = this.texData.get(dataId);\n const key = slice5 && slice5.origDataId || dataId;\n const refCount = this.dataRefCount.get(key);\n if (refCount > 1) {\n this.dataRefCount.set(key, refCount - 1);\n } else {\n this.dataRefCount.delete(key);\n if (texture != null) {\n this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n }\n }\n const texData = this.texData.get(dataId);\n texData.texture = null;\n texData.texShape = null;\n texData.isPacked = false;\n texData.slice = null;\n }\n getTexture(dataId) {\n this.uploadToGPU(dataId);\n return this.texData.get(dataId).texture.texture;\n }\n /**\n * Returns internal information for the specific data bucket. Used in unit\n * tests.\n */\n getDataInfo(dataId) {\n return this.texData.get(dataId);\n }\n /*\n Tests whether all the inputs to an op are small and on the CPU. This heuristic\n determines when it would be faster to execute a kernel on the CPU. WebGL\n kernels opt into running this check and forwarding when appropriate.\n TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n sustainable strategy for optimizing backend execution of ops.\n */\n shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n return env().getBool(\"WEBGL_CPU_FORWARD\") && inputs.every((input2) => this.texData.get(input2.dataId).texture == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);\n }\n getGPGPUContext() {\n return this.gpgpu;\n }\n where(condition) {\n backend_util_exports.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");\n const condVals = condition.dataSync();\n return whereImpl3(condition.shape, condVals);\n }\n packedUnaryOp(x, op2, dtype) {\n const program = new UnaryOpPackedProgram(x.shape, op2);\n const outInfo = this.compileAndRun(program, [x], dtype);\n return engine().makeTensorFromTensorInfo(outInfo);\n }\n // TODO(msoulanille) remove this once the backend has been modularized\n // a copy is needed here to break a circular dependency.\n // Also remove the op from unary_op.\n abs(x) {\n if (this.shouldExecuteOnCPU([x]) && x.dtype !== \"complex64\") {\n const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n return this.makeOutput(x.shape, x.dtype, outValues);\n }\n if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n return this.packedUnaryOp(x, ABS, x.dtype);\n }\n const program = new UnaryOpProgram(x.shape, ABS);\n const outInfo = this.compileAndRun(program, [x]);\n return engine().makeTensorFromTensorInfo(outInfo);\n }\n makeTensorInfo(shape, dtype, values) {\n let dataId;\n if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n const encodedValues = values.map((d) => util_exports.encodeString(d));\n dataId = this.write(encodedValues, shape, dtype);\n } else {\n dataId = this.write(values, shape, dtype);\n }\n this.texData.get(dataId).usage = null;\n return { dataId, shape, dtype };\n }\n makeOutput(shape, dtype, values) {\n return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);\n }\n unpackTensor(input2) {\n const program = new UnpackProgram(input2.shape);\n return this.runWebGLProgram(program, [input2], input2.dtype);\n }\n packTensor(input2) {\n const program = new PackProgram(input2.shape);\n const preventEagerUnpackingOutput = true;\n return this.runWebGLProgram(program, [input2], input2.dtype, null, preventEagerUnpackingOutput);\n }\n packedReshape(input2, afterShape) {\n const input3DShape = [\n getBatchDim(input2.shape),\n ...getRowsCols(input2.shape)\n ];\n const input3D = {\n dtype: input2.dtype,\n shape: input3DShape,\n dataId: input2.dataId\n };\n const afterShapeAs3D = [\n getBatchDim(afterShape),\n ...getRowsCols(afterShape)\n ];\n const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n const preventEagerUnpackingOfOutput = true;\n const customValues = [input3DShape];\n const output = this.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);\n return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n }\n decode(dataId, customTexShape) {\n const texData = this.texData.get(dataId);\n const { isPacked, shape, dtype } = texData;\n if (customTexShape != null) {\n const size = util_exports.sizeFromShape(shape);\n const texSize = customTexShape[0] * customTexShape[1] * 4;\n util_exports.assert(size <= texSize, () => \"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\");\n }\n const shapeAs3D = getShapeAs3D(shape);\n let program;\n if (isPacked) {\n program = new DecodeMatrixPackedProgram(shapeAs3D);\n } else {\n program = new DecodeMatrixProgram(shapeAs3D);\n }\n const preventEagerUnpackingOfOutput = true;\n const customValues = [customTexShape != null ? customTexShape : getDenseTexShape(shapeAs3D)];\n const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, customValues, preventEagerUnpackingOfOutput, customTexShape);\n return { dtype, shape, dataId: out.dataId };\n }\n runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false, customTexShape) {\n const output = this.makeTensorInfo(program.outputShape, outputDtype);\n const outData = this.texData.get(output.dataId);\n if (program.packedOutput) {\n outData.isPacked = true;\n }\n if (program.outPackingScheme === PackingScheme.DENSE) {\n const texelShape = customTexShape != null ? customTexShape : getDenseTexShape(program.outputShape);\n outData.texShape = texelShape.map((d) => d * 2);\n }\n if (program.outTexUsage != null) {\n outData.usage = program.outTexUsage;\n }\n if (util_exports.sizeFromShape(output.shape) === 0) {\n outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);\n return output;\n }\n const dataToDispose = [];\n const inputsData = inputs.map((input2) => {\n if (input2.dtype === \"complex64\") {\n throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);\n }\n let texData = this.texData.get(input2.dataId);\n if (texData.texture == null) {\n if (!program.packedInputs && util_exports.sizeFromShape(input2.shape) <= env().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\")) {\n return {\n shape: input2.shape,\n texData: null,\n isUniform: true,\n uniformValues: texData.values\n };\n }\n if (program.packedInputs) {\n texData.isPacked = true;\n texData.shape = input2.shape;\n }\n }\n this.uploadToGPU(input2.dataId);\n if (!!texData.isPacked !== !!program.packedInputs) {\n input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2);\n dataToDispose.push(input2);\n texData = this.texData.get(input2.dataId);\n } else if (texData.isPacked && !isReshapeFree(texData.shape, input2.shape)) {\n const savedInput = input2;\n const targetShape = input2.shape;\n input2.shape = texData.shape;\n input2 = this.packedReshape(input2, targetShape);\n dataToDispose.push(input2);\n texData = this.texData.get(input2.dataId);\n savedInput.shape = targetShape;\n }\n return { shape: input2.shape, texData, isUniform: false };\n });\n this.uploadToGPU(output.dataId);\n const outputData = { shape: output.shape, texData: outData, isUniform: false };\n const key = makeShaderKey(program, inputsData, outputData);\n const binary = this.getAndSaveBinary(key, () => {\n return compileProgram(this.gpgpu, program, inputsData, outputData);\n });\n const shouldTimeProgram = this.activeTimers != null;\n let query;\n if (shouldTimeProgram) {\n query = this.startTimer();\n }\n if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n runProgram(this.gpgpu, binary, inputsData, outputData, customUniformValues);\n }\n dataToDispose.forEach((info) => this.disposeIntermediateTensorInfo(info));\n if (shouldTimeProgram) {\n query = this.endTimer(query);\n this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n }\n const glFlushThreshold = env().getNumber(\"WEBGL_FLUSH_THRESHOLD\");\n if (glFlushThreshold > 0) {\n const time2 = util_exports.now();\n if (time2 - this.lastGlFlushTime > glFlushThreshold) {\n this.gpgpu.gl.flush();\n this.lastGlFlushTime = time2;\n }\n }\n if (!env().getBool(\"WEBGL_LAZILY_UNPACK\") && outData.isPacked && preventEagerUnpackingOfOutput === false) {\n const unpacked = this.unpackTensor(output);\n this.disposeIntermediateTensorInfo(output);\n return unpacked;\n }\n return output;\n }\n compileAndRun(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false) {\n outputDtype = outputDtype || inputs[0].dtype;\n const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput);\n return outInfo;\n }\n getAndSaveBinary(key, getBinary) {\n if (!(key in this.binaryCache)) {\n this.binaryCache[key] = getBinary();\n }\n return this.binaryCache[key];\n }\n getTextureManager() {\n return this.textureManager;\n }\n dispose() {\n if (this.disposed) {\n return;\n }\n if (!env().getBool(\"IS_TEST\")) {\n const allKeys = Object.keys(this.binaryCache);\n allKeys.forEach((key) => {\n this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n delete this.binaryCache[key];\n });\n }\n this.textureManager.dispose();\n if (this.canvas != null && (typeof HTMLCanvasElement !== \"undefined\" && this.canvas instanceof HTMLCanvasElement)) {\n this.canvas.remove();\n } else {\n this.canvas = null;\n }\n if (this.gpgpuCreatedLocally) {\n this.gpgpu.program = null;\n this.gpgpu.dispose();\n }\n this.disposed = true;\n }\n floatPrecision() {\n if (this.floatPrecisionValue == null) {\n this.floatPrecisionValue = tidy(() => {\n if (!env().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")) {\n const debugFlag = env().getBool(\"DEBUG\");\n env().set(\"DEBUG\", false);\n const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n env().set(\"DEBUG\", debugFlag);\n if (underflowCheckValue > 0) {\n return 32;\n }\n }\n return 16;\n });\n }\n return this.floatPrecisionValue;\n }\n /** Returns the smallest representable number. */\n epsilon() {\n return this.floatPrecision() === 32 ? EPSILON_FLOAT322 : EPSILON_FLOAT162;\n }\n uploadToGPU(dataId) {\n const texData = this.texData.get(dataId);\n const { shape, dtype, values, texture, usage, isPacked } = texData;\n if (texture != null) {\n return;\n }\n const shouldTimeProgram = this.activeTimers != null;\n let start;\n if (shouldTimeProgram) {\n start = util_exports.now();\n }\n let texShape = texData.texShape;\n if (texShape == null) {\n texShape = getTextureShapeFromLogicalShape(shape, isPacked);\n texData.texShape = texShape;\n }\n if (values != null) {\n const shapeAs3D = getShapeAs3D(shape);\n let program;\n let width = texShape[1], height = texShape[0];\n const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray;\n if (isPacked || !isByteArray) {\n [width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n }\n if (isPacked) {\n program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray);\n } else {\n program = new EncodeMatrixProgram(shapeAs3D, isByteArray);\n }\n const tempDenseInputTexShape = isByteArray ? [height, width] : texShape;\n const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype);\n const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId);\n if (isByteArray) {\n tempDenseInputTexData.usage = TextureUsage.PIXELS;\n } else {\n tempDenseInputTexData.usage = TextureUsage.UPLOAD;\n }\n tempDenseInputTexData.texShape = tempDenseInputTexShape;\n this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n const customValues = [[height, width]];\n const preventEagerUnpacking = true;\n const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, customValues, preventEagerUnpacking);\n const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n texData.texShape = outputTexData.texShape;\n texData.isPacked = outputTexData.isPacked;\n texData.usage = outputTexData.usage;\n if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n texData.texture = outputTexData.texture;\n texData.values = null;\n this.texData.delete(encodedOutputTarget.dataId);\n } else {\n this.disposeData(encodedOutputTarget.dataId);\n }\n this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n if (shouldTimeProgram) {\n this.uploadWaitMs += util_exports.now() - start;\n }\n } else {\n const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n texData.texture = newTexture;\n }\n }\n convertAndCacheOnCPU(dataId, float32Values) {\n const texData = this.texData.get(dataId);\n const { dtype } = texData;\n if (float32Values != null) {\n texData.values = float32ToTypedArray(float32Values, dtype);\n }\n return texData.values;\n }\n acquireTexture(texShape, texType, dtype, isPacked) {\n this.numBytesInGPU += this.computeBytes(texShape, dtype);\n if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n this.warnedAboutMemory = true;\n console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`);\n }\n return this.textureManager.acquireTexture(texShape, texType, isPacked);\n }\n computeBytes(shape, dtype) {\n return shape[0] * shape[1] * util_exports.bytesPerElement(dtype);\n }\n checkCompileCompletion() {\n for (const [, binary] of Object.entries(this.binaryCache)) {\n this.checkCompletion_(binary);\n }\n }\n async checkCompileCompletionAsync() {\n const ps = [];\n if (this.gpgpu.parallelCompilationExtension) {\n for (const [, binary] of Object.entries(this.binaryCache)) {\n ps.push(this.checkCompletionAsync_(binary));\n }\n return Promise.all(ps);\n } else {\n for (const [, binary] of Object.entries(this.binaryCache)) {\n const p2 = new Promise((resolve) => {\n try {\n this.checkCompletion_(binary);\n resolve(true);\n } catch (error) {\n throw error;\n }\n });\n ps.push(p2);\n }\n return Promise.all(ps);\n }\n }\n async checkCompletionAsync_(binary) {\n if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) {\n return this.checkCompletion_(binary);\n } else {\n await nextFrame();\n return this.checkCompletionAsync_(binary);\n }\n }\n checkCompletion_(binary) {\n if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) {\n console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram));\n if (this.gpgpu.gl.getShaderParameter(binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) {\n logShaderSourceAndInfoLog(binary.source, this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader));\n throw new Error(\"Failed to compile fragment shader.\");\n }\n throw new Error(\"Failed to link vertex and fragment shaders.\");\n }\n return true;\n }\n getUniformLocations() {\n for (const binary of Object.values(this.binaryCache)) {\n this.gpgpu.buildVao(binary.webGLProgram);\n const { variablesLocations, customUniformLocations, infLoc, nanLoc, outShapeLocation, outShapeStridesLocation, outTexShapeLocation } = getUniformLocations(this.gpgpu, binary.program, binary.webGLProgram);\n binary.variablesLocations = variablesLocations;\n binary.customUniformLocations = customUniformLocations;\n binary.infLoc = infLoc;\n binary.nanLoc = nanLoc;\n binary.outShapeLocation = outShapeLocation;\n binary.outShapeStridesLocation = outShapeStridesLocation;\n binary.outTexShapeLocation = outTexShapeLocation;\n }\n }\n /**\n * Create a TF.js tensor out of an existing WebGL texture. A new texture will\n * be created.\n */\n createTensorFromGPUData(values, shape, dtype) {\n values.channels = values.channels || \"RGBA\";\n const { texture, height, width, channels } = values;\n const backend2 = engine().backend;\n if (!backend2.gpgpu.gl.isTexture(texture)) {\n 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)'.`);\n }\n const dataId = backend2.writeTexture(texture, shape, dtype, height, width, channels);\n return engine().makeTensorFromDataId(dataId, shape, dtype, backend2);\n }\n};\nMathBackendWebGL.nextDataId = 0;\nfunction float32ToTypedArray(a, dtype) {\n if (dtype === \"float32\" || dtype === \"complex64\") {\n return a;\n } else if (dtype === \"int32\" || dtype === \"bool\") {\n const result = dtype === \"int32\" ? new Int32Array(a.length) : new Uint8Array(a.length);\n for (let i = 0; i < result.length; ++i) {\n result[i] = Math.round(a[i]);\n }\n return result;\n } else {\n throw new Error(`Unknown dtype ${dtype}`);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/version.js\nvar version6 = \"4.16.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl.js\nfunction forceHalfFloat() {\n env().set(\"WEBGL_FORCE_F16_TEXTURES\", true);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/base.js\nif (device_util_exports.isBrowser()) {\n registerBackend(\n \"webgl\",\n () => new MathBackendWebGL(),\n 2\n /* priority */\n );\n}\nvar webgl = { forceHalfFloat };\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_gpu.js\nvar CHECK_NAN_SNIPPET2 = `\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;\nvar BinaryOpProgram = class {\n constructor(op2, aShape, bShape) {\n this.variableNames = [\"A\", \"B\"];\n this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n this.userCode = `\n float binaryOperation(float a, float b) {\n ${op2}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_packed_gpu.js\nvar CHECK_NAN_SNIPPET_PACKED = `\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`;\nvar BinaryOpPackedProgram = class {\n constructor(op2, aShape, bShape, checkOutOfBounds = false) {\n this.variableNames = [\"A\", \"B\"];\n this.supportsBroadcasting = true;\n this.packedInputs = true;\n this.packedOutput = true;\n this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n const rank = this.outputShape.length;\n this.enableShapeUniforms = useShapeUniforms(rank);\n let checkOutOfBoundsString = \"\";\n if (checkOutOfBounds) {\n if (rank === 0 || util_exports.sizeFromShape(this.outputShape) === 1) {\n checkOutOfBoundsString = `\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;\n } else {\n const dtype = getCoordsDataType(rank);\n checkOutOfBoundsString = `\n ${dtype} coords = getOutputCoords();\n `;\n if (rank === 1) {\n if (this.enableShapeUniforms) {\n checkOutOfBoundsString += `\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;\n } else {\n checkOutOfBoundsString += `\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;\n }\n } else {\n const channels = getChannels(\"coords\", rank);\n if (this.enableShapeUniforms) {\n checkOutOfBoundsString += `\n bool nextRowOutOfBounds =\n (${channels[rank - 2]} + 1) >= outShape[${rank} - 2];\n bool nextColOutOfBounds =\n (${channels[rank - 1]} + 1) >= outShape[${rank} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `;\n } else {\n checkOutOfBoundsString += `\n bool nextRowOutOfBounds =\n (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n bool nextColOutOfBounds =\n (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `;\n }\n }\n }\n }\n this.userCode = `\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${op2}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${checkOutOfBoundsString}\n\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Identity.js\nfunction identity3(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n backend2.incRef(x.dataId);\n return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig2 = {\n kernelName: Identity,\n backendName: \"webgl\",\n kernelFunc: identity3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Complex.js\nfunction complex3(args) {\n const { inputs, backend: backend2 } = args;\n const { real: real4, imag: imag4 } = inputs;\n const complexInfo = backend2.makeTensorInfo(real4.shape, \"complex64\");\n const complex4 = backend2.texData.get(complexInfo.dataId);\n const realTensorInfo = identity3({ inputs: { x: real4 }, backend: backend2 });\n const imagTensorInfo = identity3({ inputs: { x: imag4 }, backend: backend2 });\n complex4.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };\n return complexInfo;\n}\nvar complexConfig2 = {\n kernelName: Complex,\n backendName: \"webgl\",\n kernelFunc: complex3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LeakyRelu.js\nvar LEAKYRELU = `return (a < 0.) ? b * a : a;`;\nvar LEAKYRELU_PACKED = `\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nfunction leakyRelu3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { alpha } = attrs;\n const $alpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(alpha, \"float32\"));\n const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);\n const result = backend2.runWebGLProgram(program, [x, $alpha], \"float32\");\n backend2.disposeIntermediateTensorInfo($alpha);\n return result;\n}\nvar leakyReluConfig2 = {\n kernelName: LeakyRelu,\n backendName: \"webgl\",\n kernelFunc: leakyRelu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prelu.js\nvar PRELU = `return (a < 0.) ? b * a : a;`;\nvar PRELU_PACKED = `\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nfunction prelu4(args) {\n const { inputs, backend: backend2 } = args;\n const { x, alpha } = inputs;\n const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) : new BinaryOpProgram(PRELU, x.shape, alpha.shape);\n return backend2.runWebGLProgram(program, [x, alpha], \"float32\");\n}\nvar preluConfig2 = {\n kernelName: Prelu,\n backendName: \"webgl\",\n kernelFunc: prelu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/kernel_funcs_utils.js\nvar CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nfunction unaryKernelFunc2({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) {\n return ({ inputs, backend: backend2 }) => {\n const { x } = inputs;\n const webglBackend = backend2;\n const $dtype = dtype || x.dtype;\n if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {\n const xData = webglBackend.texData.get(x.dataId);\n const outValues = cpuKernelImpl(xData.values, $dtype);\n return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);\n }\n const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\") && packedOpSnippet != null;\n let program;\n if (shouldUsePackedProgram) {\n program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);\n } else {\n program = new UnaryOpProgram(x.shape, opSnippet);\n }\n return webglBackend.runWebGLProgram(program, [x], $dtype);\n };\n}\nfunction binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {\n return ({ inputs, backend: backend2 }) => {\n const { a, b } = inputs;\n const webglBackend = backend2;\n if (supportsComplex && a.dtype === \"complex64\") {\n const aData = webglBackend.texData.get(a.dataId);\n const bData = webglBackend.texData.get(b.dataId);\n const [real4, imag4] = [\n [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n ].map((complexParts) => {\n const [aPart, bPart] = complexParts;\n const aHandle = {\n dataId: aPart.dataId,\n dtype: aPart.dtype,\n shape: a.shape\n };\n const bHandle = {\n dataId: bPart.dataId,\n dtype: bPart.dtype,\n shape: b.shape\n };\n const program2 = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n });\n const complexOutput = complex3({ inputs: { real: real4, imag: imag4 }, backend: webglBackend });\n webglBackend.disposeIntermediateTensorInfo(real4);\n webglBackend.disposeIntermediateTensorInfo(imag4);\n return complexOutput;\n }\n const $dtype = dtype || upcastType(a.dtype, b.dtype);\n if ((a.dtype === \"string\" || b.dtype === \"string\" || webglBackend.shouldExecuteOnCPU([a, b])) && cpuKernelImpl != null) {\n const aVals = webglBackend.texData.get(a.dataId).values;\n const bVals = webglBackend.texData.get(b.dataId).values;\n const decodedAVals = a.dtype === \"string\" ? (\n // tslint:disable-next-line: no-any\n backend_util_exports.fromUint8ToStringArray(aVals)\n ) : aVals;\n const decodedBVals = a.dtype === \"string\" ? (\n // tslint:disable-next-line: no-any\n backend_util_exports.fromUint8ToStringArray(bVals)\n ) : bVals;\n const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n const out = webglBackend.makeTensorInfo(outShape, $dtype);\n const outData = webglBackend.texData.get(out.dataId);\n outData.values = outValues;\n return out;\n }\n const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") && packedOpSnippet != null;\n let program;\n if (shouldUsePackedProgram) {\n program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds);\n } else {\n program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n }\n return webglBackend.runWebGLProgram(program, [a, b], $dtype);\n };\n}\nfunction mapActivationToShaderProgram(activation2, packed = false) {\n if (activation2 === \"linear\") {\n if (packed) {\n return LINEAR2;\n }\n return LINEAR;\n } else if (activation2 === \"relu\") {\n if (packed) {\n return RELU2;\n }\n return RELU;\n } else if (activation2 === \"elu\") {\n if (packed) {\n return ELU3;\n }\n return ELU2;\n } else if (activation2 === \"relu6\") {\n if (packed) {\n return RELU62;\n }\n return RELU6;\n } else if (activation2 === \"prelu\") {\n if (packed) {\n return PRELU_PACKED;\n }\n return PRELU;\n } else if (activation2 === \"leakyrelu\") {\n if (packed) {\n return LEAKYRELU_PACKED;\n }\n return LEAKYRELU;\n } else if (activation2 === \"sigmoid\") {\n if (packed) {\n return SIGMOID2;\n }\n return SIGMOID;\n }\n throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mulmat_packed_gpu.js\nvar MatMulPackedProgram = class {\n constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyreluActivation = false) {\n this.variableNames = [\"matrixA\", \"matrixB\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.outputShape = outputShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n const sharedDim = transposeA ? aShape[1] : aShape[2];\n const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n const aSample = transposeA ? \"i * 2, rc.y\" : \"rc.y, i * 2\";\n const bSample = transposeB ? \"rc.z, i * 2\" : \"i * 2, rc.z\";\n const aSwizzle = transposeA ? [\"a.xxyy\", \"a.zzww\"] : [\"a.xxzz\", \"a.yyww\"];\n const bSwizzle = transposeB ? [\"b.xzxz\", \"b.ywyw\"] : [\"b.xyxy\", \"b.zwzw\"];\n let activationSnippet = \"\", applyActivationSnippet = \"\";\n if (activation2) {\n if (hasPreluActivation) {\n activationSnippet = `vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${activation2}\n }`;\n } else if (hasLeakyreluActivation) {\n activationSnippet = `vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${activation2}\n }`;\n } else {\n activationSnippet = `vec4 activation(vec4 x) {\n ${activation2}\n }`;\n }\n applyActivationSnippet = `result = activation(result);`;\n }\n const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n if (addBias) {\n this.variableNames.push(\"bias\");\n }\n if (hasPreluActivation) {\n this.variableNames.push(\"preluActivationWeights\");\n }\n if (hasLeakyreluActivation) {\n this.variableNames.push(\"leakyreluAlpha\");\n }\n let batchASnippet = \"rc.x\";\n let batchBSnippet = \"rc.x\";\n if (aShape[0] < bShape[0]) {\n batchASnippet = `imod(rc.x, ${aShape[0]})`;\n } else if (bShape[0] < aShape[0]) {\n batchBSnippet = `imod(rc.x, ${bShape[0]})`;\n }\n this.userCode = `\n ${activationSnippet}\n // Don't use uniform for sharedDimensionPacked for performance.\n const float sharedDimension = ${sharedDimensionPacked}.0;\n\n vec4 dot2x2ARowBCol(ivec3 rc) {\n vec4 result = vec4(0);\n int batchA = ${batchASnippet};\n int batchB = ${batchBSnippet};\n for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n vec4 a = getMatrixA(batchA, ${aSample});\n vec4 b = getMatrixB(batchB, ${bSample});\n\n // These swizzled products need to be separately added.\n // See: https://github.com/tensorflow/tfjs/issues/1735\n result += (${aSwizzle[0]} * ${bSwizzle[0]});\n result += (${aSwizzle[1]} * ${bSwizzle[1]});\n }\n return result;\n }\n\n void main() {\n ivec3 rc = getOutputCoords();\n vec4 result = dot2x2ARowBCol(rc);\n\n ${addBiasSnippet}\n\n ${applyActivationSnippet}\n\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_complex_gpu.js\nvar COMPLEX_MULTIPLY = {\n REAL: \"return areal * breal - aimag * bimag;\",\n IMAG: \"return areal * bimag + aimag * breal;\"\n};\nvar BinaryOpComplexProgram = class {\n constructor(op2, aShape, bShape) {\n this.variableNames = [\"AReal\", \"AImag\", \"BReal\", \"BImag\"];\n this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n this.userCode = `\n float binaryOpComplex(\n float areal, float aimag, float breal, float bimag) {\n ${op2}\n }\n\n void main() {\n float areal = getARealAtOutCoords();\n float aimag = getAImagAtOutCoords();\n float breal = getBRealAtOutCoords();\n float bimag = getBImagAtOutCoords();\n setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multiply.js\nvar MUL = \"return a * b;\";\nfunction multiply3(args) {\n const { inputs, backend: backend2 } = args;\n const { a, b } = inputs;\n const dtype = backend_util_exports.upcastType(a.dtype, b.dtype);\n if (a.dtype === \"complex64\") {\n const aData = backend2.texData.get(a.dataId);\n const bData = backend2.texData.get(b.dataId);\n const realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n const imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n const inputs2 = [\n {\n dataId: aData.complexTensorInfos.real.dataId,\n dtype: aData.complexTensorInfos.real.dtype,\n shape: a.shape\n },\n {\n dataId: aData.complexTensorInfos.imag.dataId,\n dtype: aData.complexTensorInfos.imag.dtype,\n shape: a.shape\n },\n {\n dataId: bData.complexTensorInfos.real.dataId,\n dtype: bData.complexTensorInfos.real.dtype,\n shape: b.shape\n },\n {\n dataId: bData.complexTensorInfos.imag.dataId,\n dtype: bData.complexTensorInfos.imag.dtype,\n shape: b.shape\n }\n ];\n const realPart = backend2.runWebGLProgram(realProgram, inputs2, \"float32\");\n const imagPart = backend2.runWebGLProgram(imagProgram, inputs2, \"float32\");\n const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(realPart);\n backend2.disposeIntermediateTensorInfo(imagPart);\n return complexOutput;\n }\n if (backend2.shouldExecuteOnCPU([a, b])) {\n const aData = backend2.texData.get(a.dataId);\n const bData = backend2.texData.get(b.dataId);\n const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n const out = backend2.makeTensorInfo(outShape, dtype);\n const outData = backend2.texData.get(out.dataId);\n outData.values = outValues;\n return out;\n }\n let program;\n if (env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")) {\n program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);\n } else {\n program = new BinaryOpProgram(MUL, a.shape, b.shape);\n }\n return backend2.runWebGLProgram(program, [a, b], dtype);\n}\nvar multiplyConfig2 = {\n kernelName: Multiply,\n backendName: \"webgl\",\n kernelFunc: multiply3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reshape.js\nfunction packedReshape(input2, afterShape, backend2) {\n const input3DShape = [\n getBatchDim(input2.shape),\n ...getRowsCols(input2.shape)\n ];\n const input3D = {\n dtype: input2.dtype,\n shape: input3DShape,\n dataId: input2.dataId\n };\n const afterShapeAs3D = [\n getBatchDim(afterShape),\n ...getRowsCols(afterShape)\n ];\n const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n const preventEagerUnpackingOfOutput = true;\n const customValues = [input3DShape];\n const output = backend2.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);\n return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reshape.js\nfunction reshape4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { shape } = attrs;\n const webglBackend = backend2;\n const xSize = util_exports.sizeFromShape(x.shape);\n const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n const $xSize = util_exports.sizeFromShape($shape);\n util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n const xTexData = webglBackend.texData.get(x.dataId);\n if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) && !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n return packedReshape(x, $shape, webglBackend);\n }\n webglBackend.incRef(x.dataId);\n return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig2 = {\n kernelName: Reshape,\n backendName: \"webgl\",\n kernelFunc: reshape4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mean_gpu.js\nvar MeanProgram = class {\n constructor(reduceInfo, divisor) {\n this.variableNames = [\"x\"];\n const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n this.outputShape = [batchSize, outSize];\n const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n const windowSizeVec4Remainder = windowSize % 4;\n let updateSnippet = `sumValue += dot(values, ones);`;\n if (divisor != null) {\n const denominator = 1 / divisor;\n updateSnippet = `sumValue += dot(values * ${util_exports.isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`;\n }\n let checkOutOfBounds = \"\";\n if (inSize % windowSize > 0) {\n checkOutOfBounds = `\n if (inIdx < 0 || inIdx >= ${inSize}) {\n return 0.0;\n }\n `;\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 ${checkOutOfBounds}\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 * ${windowSize};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${windowSizeNearestVec4}; 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 ${updateSnippet}\n }\n\n int inIdx = inOffset + ${windowSizeNearestVec4};\n if (${windowSizeVec4Remainder === 1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${updateSnippet}\n } else if (${windowSizeVec4Remainder === 2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${updateSnippet}\n } else if (${windowSizeVec4Remainder === 3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${updateSnippet}\n }\n setOutput(sumValue);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reduce_gpu.js\nvar ReduceProgram = class {\n constructor(reduceInfo, reduceType) {\n this.variableNames = [\"x\"];\n const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n this.outputShape = [batchSize, outSize];\n let initializationValue = \"0.0\";\n let compareOp = ``;\n if (reduceType === \"prod\") {\n initializationValue = \"1.0\";\n } else if (reduceType === \"min\") {\n initializationValue = \"1.0 / 1e-20\";\n compareOp = `min`;\n } else if (reduceType === \"max\") {\n initializationValue = \"-1.0 / 1e-20\";\n compareOp = `max`;\n }\n let returnValue = `${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n if (reduceType === \"sum\") {\n returnValue = `sumValue`;\n } else if (reduceType === \"prod\") {\n returnValue = `prodValue`;\n } else if (reduceType === \"all\") {\n returnValue = `allValue`;\n } else if (reduceType === \"any\") {\n returnValue = `anyValue`;\n }\n const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n const windowSizeVec4Remainder = windowSize % 4;\n let updateSnippet = `\n if (${reduceType === \"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${reduceType === \"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${compareOp}(values, minMaxValue);\n if (${reduceType === \"min\"} || ${reduceType === \"max\"}) {\n minMaxValue = ${compareOp}(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 `;\n let vecType = `vec4`;\n if (reduceType === \"all\") {\n initializationValue = \"1.0\";\n updateSnippet = `\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `;\n vecType = `bvec4`;\n } else if (reduceType === \"any\") {\n initializationValue = \"0.0\";\n updateSnippet = `\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `;\n vecType = `bvec4`;\n }\n let checkOutOfBounds = \"\";\n if (inSize % windowSize > 0) {\n checkOutOfBounds = `\n if (inIdx < 0 || inIdx >= ${inSize}) {\n return initializationValue;\n }\n `;\n }\n this.userCode = `\n const float initializationValue = ${initializationValue};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${checkOutOfBounds}\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 * ${windowSize};\n\n vec4 minMaxValue = vec4(${initializationValue});\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 < ${windowSizeNearestVec4}; i += 4) {\n int inIdx = inOffset + i;\n ${vecType} values = ${vecType}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${updateSnippet}\n }\n\n int inIdx = inOffset + ${windowSizeNearestVec4};\n if (${windowSizeVec4Remainder === 1}) {\n ${vecType} values = ${vecType}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${updateSnippet}\n } else if (${windowSizeVec4Remainder === 2}) {\n ${vecType} values = ${vecType}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${updateSnippet}\n } else if (${windowSizeVec4Remainder === 3}) {\n ${vecType} values = ${vecType}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${updateSnippet}\n }\n setOutput(${returnValue});\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reduce.js\nfunction getReductionStages(inShape) {\n const stages = [];\n while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n const windowSize = backend_util_exports.computeOptimalWindowSize(outSize);\n stages.push({\n inSize: outSize,\n windowSize,\n outSize: Math.ceil(outSize / windowSize)\n });\n }\n return stages;\n}\nfunction reduce(x, dtype, reductionType, backend2) {\n const reductionStages = getReductionStages(x.shape);\n let result = x;\n for (let i = 0; i < reductionStages.length; i++) {\n const { inSize, windowSize, outSize } = reductionStages[i];\n let program;\n let previousResult;\n if (reductionType === \"mean\") {\n program = i === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });\n } else {\n program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n }\n previousResult = result;\n result = backend2.runWebGLProgram(program, [result], dtype);\n if (previousResult.dataId !== x.dataId) {\n backend2.disposeIntermediateTensorInfo(previousResult);\n }\n }\n return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_gpu.js\nvar TransposeProgram = class {\n constructor(aShape, newDim) {\n this.variableNames = [\"A\"];\n const outputShape = new Array(aShape.length);\n for (let i = 0; i < outputShape.length; i++) {\n outputShape[i] = aShape[newDim[i]];\n }\n this.outputShape = outputShape;\n this.rank = outputShape.length;\n const dtype = getCoordsDataType(this.rank);\n const switched = getSwitchedCoords(newDim);\n this.userCode = `\n void main() {\n ${dtype} resRC = getOutputCoords();\n setOutput(getA(${switched}));\n }\n `;\n }\n};\nfunction getSwitchedCoords(newDim) {\n const rank = newDim.length;\n if (rank > 6) {\n throw Error(`Transpose for rank ${rank} is not yet supported`);\n }\n const originalOrder = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\", \"resRC.u\", \"resRC.v\"];\n const switchedCoords = new Array(rank);\n for (let i = 0; i < newDim.length; i++) {\n switchedCoords[newDim[i]] = originalOrder[i];\n }\n return switchedCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_packed_gpu.js\nvar TransposePackedProgram = class {\n constructor(aShape, newDim) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = true;\n const outputShape = new Array(aShape.length);\n for (let i = 0; i < outputShape.length; i++) {\n outputShape[i] = aShape[newDim[i]];\n }\n this.outputShape = outputShape;\n this.rank = outputShape.length;\n if (this.rank > 6) {\n throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n }\n const dtype = getCoordsDataType(this.rank);\n const outputOrder = getVecChannels(\"rc\", this.rank);\n const switchedOrder = new Array(this.rank);\n for (let i = 0; i < newDim.length; i++) {\n switchedOrder[newDim[i]] = outputOrder[i];\n }\n const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n this.userCode = `\n void main() {\n ${dtype} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result[0] = ${getc};\n if(${nextColumn}) {\n result[1] = ${getc};\n }\n --${outputOrder[this.rank - 1]};\n if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n result[2] = ${getc};\n if(${nextColumn}) {\n result[3] = ${getc};\n }\n }\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose_impl.js\nfunction transposeImpl2(x, perm, backend2) {\n const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new TransposePackedProgram(x.shape, perm) : new TransposeProgram(x.shape, perm);\n return backend2.runWebGLProgram(program, [x], x.dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum_impl.js\nfunction sumImpl(x, axis, keepDims, backend2) {\n const reductionIndices = axis;\n const xRank = x.shape.length;\n const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n const sumInputIsTransposed = permutedAxes != null;\n let sumInput = x;\n if (sumInputIsTransposed) {\n sumInput = transposeImpl2(x, permutedAxes, backend2);\n axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"sum\", axes, xRank);\n const [sumOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(sumInput.shape, axes);\n let outShape = sumOutShape;\n if (keepDims) {\n outShape = backend_util_exports.expandShapeToKeepDim(sumOutShape, origAxes);\n }\n const inSize = util_exports.sizeFromShape(reduceShape);\n const xSize = util_exports.sizeFromShape(x.shape);\n const batchSize = xSize / inSize;\n const reshapedInput = reshape4({ inputs: { x: sumInput }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n const outType = sumOutType(x.dtype);\n const reduced = reduce(reshapedInput, outType, \"sum\", backend2);\n const out = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(reshapedInput);\n backend2.disposeIntermediateTensorInfo(reduced);\n if (sumInputIsTransposed) {\n backend2.disposeIntermediateTensorInfo(sumInput);\n }\n return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum.js\nfunction sum4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n return sumImpl(x, axis, keepDims, backend2);\n}\nvar sumConfig2 = {\n kernelName: Sum,\n backendName: \"webgl\",\n kernelFunc: sum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose.js\nfunction transpose3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { perm } = attrs;\n const webglBackend = backend2;\n const xRank = x.shape.length;\n const newShape = new Array(xRank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = x.shape[perm[i]];\n }\n let out;\n if (webglBackend.shouldExecuteOnCPU([x])) {\n const xTexData = webglBackend.texData.get(x.dataId);\n const values = xTexData.values;\n const outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape);\n out = webglBackend.makeTensorInfo(newShape, x.dtype);\n const outData = webglBackend.texData.get(out.dataId);\n outData.values = outValues;\n } else {\n out = transposeImpl2(x, perm, webglBackend);\n }\n return out;\n}\nvar transposeConfig2 = {\n kernelName: Transpose,\n backendName: \"webgl\",\n kernelFunc: transpose3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul_impl.js\nvar MATMUL_SHARED_DIM_THRESHOLD = 1e3;\nfunction batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n const aRank = a.shape.length;\n const bRank = b.shape.length;\n const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n const outerDimsA = a.shape.slice(0, -2);\n const outerDimsB = b.shape.slice(0, -2);\n const batchDimA = util_exports.sizeFromShape(outerDimsA);\n const batchDimB = util_exports.sizeFromShape(outerDimsB);\n const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n const a3d = reshape4({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n const b3d = reshape4({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n const intermediates = [a3d, b3d];\n const batchDim = Math.max(batchDimA, batchDimB);\n const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n const hasBias = bias != null;\n const hasPreluActivationWeights = preluActivationWeights != null;\n const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n const fusedActivation = activation2 != null ? mapActivationToShaderProgram(activation2, true) : null;\n const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null;\n let out;\n if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {\n let aVec = a3d;\n let bVec = b3d;\n if (transposeA) {\n aVec = transpose3({ inputs: { x: a3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });\n intermediates.push(aVec);\n }\n if (transposeB) {\n bVec = transpose3({ inputs: { x: b3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });\n intermediates.push(bVec);\n }\n const shouldReshapeA = outerShapeB !== 1;\n const shouldReshapeB = outerShapeB === 1;\n let aVec3d = aVec;\n if (shouldReshapeA) {\n aVec3d = reshape4({\n inputs: { x: aVec },\n backend: backend2,\n attrs: { shape: [batchDim, sharedDim, 1] }\n });\n intermediates.push(aVec3d);\n }\n const axis = outerShapeB === 1 ? 2 : 1;\n let bVec3d = bVec;\n if (shouldReshapeB) {\n bVec3d = reshape4({\n inputs: { x: bVec },\n backend: backend2,\n attrs: { shape: [batchDim, 1, sharedDim] }\n });\n intermediates.push(bVec3d);\n }\n const product = multiply3({ inputs: { a: aVec3d, b: bVec3d }, backend: backend2 });\n out = sum4({ inputs: { x: product }, backend: backend2, attrs: { axis, keepDims: true } });\n intermediates.push(product);\n } else {\n const dtype = upcastType(a.dtype, b.dtype);\n const program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n const inputs = [a3d, b3d];\n if (bias != null) {\n inputs.push(bias);\n }\n if (hasPreluActivationWeights) {\n inputs.push(preluActivationWeights);\n }\n if (hasLeakyreluAlpha) {\n const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n inputs.push($leakyreluAlpha);\n intermediates.push($leakyreluAlpha);\n }\n out = backend2.runWebGLProgram(program, inputs, dtype);\n }\n const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });\n intermediates.push(out);\n for (const i of intermediates) {\n backend2.disposeIntermediateTensorInfo(i);\n }\n return outReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { a, b, bias, preluActivationWeights } = inputs;\n const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n return batchMatMulImpl({\n a,\n b,\n transposeA,\n transposeB,\n backend: backend2,\n bias,\n preluActivationWeights,\n leakyreluAlpha,\n activation: activation2\n });\n}\nvar _fusedMatMulConfig2 = {\n kernelName: _FusedMatMul,\n backendName: \"webgl\",\n kernelFunc: _fusedMatMul2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Abs.js\nvar ABS2 = `return abs(x);`;\nfunction abs3(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n if (backend2.shouldExecuteOnCPU([x]) && x.dtype !== \"complex64\") {\n const xData = backend2.texData.get(x.dataId);\n const outValues = simpleAbsImplCPU(xData.values);\n return backend2.makeTensorInfo(x.shape, x.dtype, outValues);\n }\n let program;\n if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n program = new UnaryOpPackedProgram(x.shape, ABS2);\n } else {\n program = new UnaryOpProgram(x.shape, ABS2);\n }\n return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar absConfig2 = {\n kernelName: Abs,\n backendName: \"webgl\",\n kernelFunc: abs3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acos.js\nvar ACOS = CHECK_NAN_SNIPPET + `\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`;\nvar acos3 = unaryKernelFunc2({ opSnippet: ACOS });\nvar acosConfig2 = {\n kernelName: Acos,\n backendName: \"webgl\",\n kernelFunc: acos3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acosh.js\nvar ACOSH = CHECK_NAN_SNIPPET + `\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`;\nvar acosh3 = unaryKernelFunc2({ opSnippet: ACOSH });\nvar acoshConfig2 = {\n kernelName: Acosh,\n backendName: \"webgl\",\n kernelFunc: acosh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Add.js\nvar ADD = \"return a + b;\";\nvar addKernelFunc = binaryKernelFunc2({\n opSnippet: ADD,\n packedOpSnippet: ADD,\n supportsComplex: true,\n cpuKernelImpl: addImplCPU\n});\nvar addConfig2 = {\n kernelName: Add,\n backendName: \"webgl\",\n kernelFunc: addKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_gpu.js\nvar AddNProgram = class {\n constructor(outputShape, shapes) {\n this.outputShape = [];\n this.outputShape = outputShape;\n this.variableNames = shapes.map((_, i) => `T${i}`);\n const snippets = [];\n this.variableNames.forEach((variable2) => {\n snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`);\n });\n const operation = this.variableNames.map((variable2) => {\n return `v${variable2}`;\n }).join(\" + \");\n this.userCode = `\n void main() {\n ${snippets.join(\"\\n \")}\n\n float result = ${operation};\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_packed_gpu.js\nvar AddNPackedProgram = class {\n constructor(outputShape, shapes) {\n this.outputShape = [];\n this.packedInputs = true;\n this.packedOutput = true;\n this.outputShape = outputShape;\n this.variableNames = shapes.map((_, i) => `T${i}`);\n const snippets = [];\n this.variableNames.forEach((variable2) => {\n snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`);\n });\n const operation = this.variableNames.map((variable2) => {\n return `v${variable2}`;\n }).join(\" + \");\n this.userCode = `\n void main() {\n ${snippets.join(\"\\n \")}\n\n vec4 result = ${operation};\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AddN.js\nfunction addN3(args) {\n const { inputs, backend: backend2 } = args;\n const tensors = inputs;\n if (tensors.length === 1) {\n return identity3({ inputs: { x: tensors[0] }, backend: backend2 });\n }\n if (tensors.length > env().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")) {\n const midIndex = Math.floor(tensors.length / 2);\n const leftSide = addN3({ inputs: tensors.slice(0, midIndex), backend: backend2 });\n const rightSide = addN3({ inputs: tensors.slice(midIndex), backend: backend2 });\n return addN3({ inputs: [leftSide, rightSide], backend: backend2 });\n }\n const dtype = tensors.map((t) => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n const shapes = tensors.map((t) => t.shape);\n const usePackedOp = env().getBool(\"WEBGL_PACK\");\n const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes);\n return backend2.runWebGLProgram(program, tensors, dtype);\n}\nvar addNConfig2 = {\n kernelName: AddN,\n backendName: \"webgl\",\n kernelFunc: addN3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/All.js\nfunction all3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n const xRank = x.shape.length;\n const origAxes = util_exports.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n let permutedX = x;\n if (permutedAxes != null) {\n permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, xRank);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n const inSize = util_exports.sizeFromShape(reduceShape);\n const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n const reduced = reduce(a2D, a2D.dtype, \"all\", backend2);\n let res;\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n } else {\n res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n }\n backend2.disposeIntermediateTensorInfo(a2D);\n backend2.disposeIntermediateTensorInfo(reduced);\n if (permutedAxes != null) {\n backend2.disposeIntermediateTensorInfo(permutedX);\n }\n return res;\n}\nvar allConfig2 = {\n kernelName: All,\n backendName: \"webgl\",\n kernelFunc: all3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Any.js\nfunction any3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n const xRank = x.shape.length;\n const origAxes = util_exports.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n let permutedX = x;\n if (permutedAxes != null) {\n permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, xRank);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n const inSize = util_exports.sizeFromShape(reduceShape);\n const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n const reduced = reduce(a2D, a2D.dtype, \"any\", backend2);\n let res;\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n } else {\n res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n }\n backend2.disposeIntermediateTensorInfo(a2D);\n backend2.disposeIntermediateTensorInfo(reduced);\n if (permutedAxes != null) {\n backend2.disposeIntermediateTensorInfo(permutedX);\n }\n return res;\n}\nvar anyConfig2 = {\n kernelName: Any,\n backendName: \"webgl\",\n kernelFunc: any3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_gpu.js\nvar ArgMinMaxProgram = class {\n constructor(reduceInfo, op2, firstPass) {\n this.variableNames = [\"A\"];\n const { windowSize, batchSize, outSize } = reduceInfo;\n if (!firstPass) {\n this.variableNames.push(\"bestIndicesA\");\n }\n this.outputShape = [batchSize, outSize];\n const compOp = op2 === \"max\" ? \">\" : \"<\";\n const indexSnippet = firstPass ? \"inOffset + i;\" : \"round(getBestIndicesA(batch, inOffset + i));\";\n this.userCode = `\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${windowSize};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${windowSize}; i++) {\n int inIdx = ${indexSnippet};\n float candidate = getA(batch, inIdx);\n if (candidate ${compOp} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_packed_gpu.js\nvar ArgMinMaxPackedProgram = class {\n constructor(shape, windowSize, op2, firstPass) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = true;\n util_exports.assert(shape.length > 2, () => `Packed arg${op2.charAt(0).toUpperCase() + op2.slice(1)} supports only inputs with rank above 2.`);\n const inSize = shape[shape.length - 1];\n const outSize = Math.ceil(inSize / windowSize);\n this.outputShape = shape.slice(0, -1);\n if (outSize > 1) {\n this.outputShape.push(outSize);\n }\n if (!firstPass) {\n this.variableNames.push(\"bestIndicesA\");\n }\n const outShape = this.outputShape;\n const rank = outShape.length;\n const dtype = getCoordsDataType(rank);\n const coords2 = getChannels(\"coords\", rank);\n let sourceLocSetup;\n let sourceRank;\n if (outSize === 1) {\n sourceRank = rank + 1;\n const sourceLocDType = getCoordsDataType(sourceRank);\n sourceLocSetup = `\n ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords2.join()}, 0);\n ++${coords2[rank - 1]};\n ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords2.join()}, 0);\n ++${coords2[rank - 2]};\n ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords2.join()}, 0);\n --${coords2[rank - 1]};\n ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords2.join()}, 0);\n --${coords2[rank - 2]};`;\n } else {\n sourceRank = rank;\n sourceLocSetup = `\n ${dtype} sourceLocR = coords;\n ++${coords2[rank - 1]};\n ${dtype} sourceLocG = coords;\n ++${coords2[rank - 2]};\n ${dtype} sourceLocA = coords;\n --${coords2[rank - 1]};\n ${dtype} sourceLocB = coords;\n --${coords2[rank - 2]};`;\n }\n const channels = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, sourceRank);\n const inChannel = \".\" + channels[sourceRank - 1];\n const intChannels = channels.map((x) => \"int \" + x);\n const srcRCoords = getChannels(\"sourceLocR\", sourceRank - 1).concat(\"inIdx.r\");\n const srcGCoords = getChannels(\"sourceLocG\", sourceRank - 1).concat(\"inIdx.g\");\n const srcBCoords = getChannels(\"sourceLocB\", sourceRank - 1).concat(\"inIdx.b\");\n const srcACoords = getChannels(\"sourceLocA\", sourceRank - 1).concat(\"inIdx.a\");\n const compOp = op2 === \"max\" ? \"greaterThan\" : \"lessThan\";\n const fetchCandidateIdx = firstPass ? \"\" : `\n inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n getBestIndicesAChannel(${srcGCoords.join()}),\n getBestIndicesAChannel(${srcBCoords.join()}),\n getBestIndicesAChannel(${srcACoords.join()})));`;\n const fetchValue = `vec4(\n getAChannel(${srcRCoords.join()}),\n hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n const getBestIndicesAChannelSnippet = firstPass ? \"\" : `\n float getBestIndicesAChannel(${intChannels.join()}) {\n return getChannel(getBestIndicesA(${channels.join()}),\n vec2(${channels.slice(-2).join()}));\n }`;\n this.userCode = `\n float getAChannel(${intChannels.join()}) {\n return getChannel(getA(${channels.join()}),\n vec2(${channels.slice(-2).join()}));\n }\n ${getBestIndicesAChannelSnippet}\n void main() {\n ${dtype} coords = getOutputCoords();\n bool hasNextCol = ${coords2[rank - 1]} < ${outShape[rank - 1] - 1};\n bool hasNextRow = ${coords2[rank - 2]} < ${outShape[rank - 2] - 1};\n ${sourceLocSetup}\n ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${fetchValue};\n\n for (int i = 0; i < ${windowSize}; i++) {\n inIdx = srcIdx;\n ${fetchCandidateIdx}\n vec4 candidate = ${fetchValue};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${compOp}(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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/arg_min_max.js\nfunction argReduce(backend2, x, reduceType, bestIndicesA = null) {\n let batchSize = x.shape[0];\n let inSize = x.shape[1];\n if (bestIndicesA != null) {\n batchSize = bestIndicesA.shape[0];\n inSize = bestIndicesA.shape[1];\n }\n const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);\n const reduceInfo = { windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize) };\n const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n const inputs = [x];\n if (bestIndicesA != null) {\n inputs.push(bestIndicesA);\n }\n const output = backend2.runWebGLProgram(program, inputs, \"int32\");\n if (output.shape[1] === 1) {\n return output;\n }\n const result = argReduce(backend2, x, reduceType, output);\n backend2.disposeIntermediateTensorInfo(output);\n return result;\n}\nfunction argReducePacked(backend2, x, reduceType, bestIndicesA = null) {\n const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n const inSize = inShape[inShape.length - 1];\n const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);\n const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n const output = backend2.runWebGLProgram(program, inputs, \"int32\");\n if (output.shape.length === x.shape.length) {\n const result = argReducePacked(backend2, x, reduceType, output);\n backend2.disposeIntermediateTensorInfo(output);\n return result;\n }\n return output;\n}\nfunction argMinMaxReduce(backend2, x, axis, reduceType) {\n const axes = [axis];\n backend_util_exports.assertAxesAreInnerMostDims(\"arg\" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length);\n if (!env().getBool(\"WEBGL_PACK_REDUCE\") || x.shape.length <= 2) {\n const intermediateTensorInfos = [];\n const xtexData = backend2.texData.get(x.dataId);\n const xIsPacked = xtexData !== null && xtexData.isPacked;\n let xUnPacked = x;\n if (xIsPacked) {\n xUnPacked = backend2.unpackTensor(x);\n intermediateTensorInfos.push(xUnPacked);\n }\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xUnPacked.shape, axes);\n const inSize = util_exports.sizeFromShape(reduceShape);\n const a2D = reshape4({ inputs: { x: xUnPacked }, backend: backend2, attrs: { shape: [-1, inSize] } });\n intermediateTensorInfos.push(a2D);\n const reduced = argReduce(backend2, a2D, reduceType);\n intermediateTensorInfos.push(reduced);\n const reshaped = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return reshaped;\n }\n return argReducePacked(backend2, x, reduceType);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMax.js\nfunction argMax3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis } = attrs;\n let axes = util_exports.parseAxisParam(axis, x.shape);\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n const intermediateTensorInfos = [];\n if (permutedAxes != null) {\n $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n intermediateTensorInfos.push($x);\n axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", [axes[0]], $x.shape.length);\n const out = argMinMaxReduce(backend2, $x, axes[0], \"max\");\n intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return out;\n}\nvar argMaxConfig2 = {\n kernelName: ArgMax,\n backendName: \"webgl\",\n kernelFunc: argMax3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMin.js\nfunction argMin3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis } = attrs;\n let axes = util_exports.parseAxisParam(axis, x.shape);\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n let $x = x;\n const intermediateTensorInfos = [];\n if (permutedAxes != null) {\n $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n intermediateTensorInfos.push($x);\n axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", [axes[0]], $x.shape.length);\n const out = argMinMaxReduce(backend2, $x, axes[0], \"min\");\n intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return out;\n}\nvar argMinConfig2 = {\n kernelName: ArgMin,\n backendName: \"webgl\",\n kernelFunc: argMin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asin.js\nvar ASIN = CHECK_NAN_SNIPPET + `\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`;\nvar asin3 = unaryKernelFunc2({ opSnippet: ASIN });\nvar asinConfig2 = {\n kernelName: Asin,\n backendName: \"webgl\",\n kernelFunc: asin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asinh.js\nvar ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nvar asinh3 = unaryKernelFunc2({ opSnippet: ASINH });\nvar asinhConfig2 = {\n kernelName: Asinh,\n backendName: \"webgl\",\n kernelFunc: asinh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan.js\nvar ATAN = CHECK_NAN_SNIPPET + `\n return atan(x);\n`;\nvar atan4 = unaryKernelFunc2({ opSnippet: ATAN });\nvar atanConfig2 = {\n kernelName: Atan,\n backendName: \"webgl\",\n kernelFunc: atan4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan2.js\nvar ATAN2 = CHECK_NAN_SNIPPET2 + `\n return atan(a, b);\n`;\nvar ATAN2_PACKED = `\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 ` + CHECK_NAN_SNIPPET_PACKED + `\n return result;\n`;\nvar atan23 = binaryKernelFunc2({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });\nvar atan2Config2 = {\n kernelName: Atan2,\n backendName: \"webgl\",\n kernelFunc: atan23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atanh.js\nvar ATANH = CHECK_NAN_SNIPPET + `\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nvar atanh3 = unaryKernelFunc2({ opSnippet: ATANH });\nvar atanhConfig2 = {\n kernelName: Atanh,\n backendName: \"webgl\",\n kernelFunc: atanh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pool_gpu.js\nvar Pool2DProgram = class {\n constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n this.variableNames = [\"x\"];\n if (poolType === \"avg\" && computePositions) {\n throw new Error(\"Cannot compute positions for average pool.\");\n }\n const filterWidth = convInfo.filterWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n this.outputShape = convInfo.outShape;\n const isAvgPool = poolType === \"avg\";\n const batchFlattenPositionStr = `((batch * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n let initializationValue = \"0.0\";\n if (!isAvgPool) {\n initializationValue = \"-1.0 / 1e-20\";\n }\n if (computePositions) {\n const compareOp2 = \">=\";\n this.userCode = `\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\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 < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${effectiveFilterWidth};\n wC += ${dilationWidth}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${convInfo.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 ${compareOp2} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;\n return;\n }\n const compareOp = \"max\";\n let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n if (poolType === \"avg\") {\n returnValue = `avgValue / max(count, 1.0)`;\n }\n const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n const filterWidthVec4Remainder = filterWidth % 4;\n const updateSnippet = `\n if (${isAvgPool}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${compareOp}(values, minMaxValue);\n }\n `;\n this.userCode = `\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n const float initializationValue = ${initializationValue};\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 >= ${convInfo.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${initializationValue});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n int xC = xCCorner + wC * ${dilationWidth};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${dilationWidth}, d),\n getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n );\n\n ${updateSnippet}\n }\n\n int xC = xCCorner + ${filterWidthNearestVec4};\n if (${filterWidthVec4Remainder === 1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${updateSnippet}\n } else if (${filterWidthVec4Remainder === 2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${dilationWidth}, d),\n initializationValue,\n initializationValue\n );\n\n ${updateSnippet}\n } else if (${filterWidthVec4Remainder === 3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${dilationWidth}, d),\n getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n initializationValue\n );\n\n ${updateSnippet}\n }\n }\n setOutput(${returnValue});\n }\n `;\n }\n};\nvar Pool3DProgram = class {\n constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n this.variableNames = [\"x\"];\n if (poolType === \"avg\" && computePositions) {\n throw new Error(\"Cannot compute positions for average pool.\");\n }\n const filterWidth = convInfo.filterWidth;\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = convInfo.padInfo.front;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n this.outputShape = convInfo.outShape;\n const isAvgPool = poolType === \"avg\";\n let initializationValue = \"0.0\";\n if (!isAvgPool) {\n initializationValue = \"-1.0 / 1e-20\";\n }\n if (computePositions) {\n const compareOp2 = \">=\";\n this.userCode = `\n const ivec3 strides =\n ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\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 < ${effectiveFilterDepth};\n wD += ${dilationDepth}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${convInfo.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${effectiveFilterWidth};\n wC += ${dilationWidth}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${convInfo.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 ${compareOp2} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n wR * ${effectiveFilterWidth} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;\n return;\n }\n const compareOp = \"max\";\n let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n if (poolType === \"avg\") {\n returnValue = `avgValue / max(count, 1.0)`;\n }\n const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n const filterWidthVec4Remainder = filterWidth % 4;\n const updateSnippet = `\n if (${isAvgPool}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${compareOp}(values, minMaxValue);\n }\n `;\n this.userCode = `\n const ivec3 strides =\n ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n const float initializationValue = ${initializationValue};\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 >= ${convInfo.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${initializationValue});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${effectiveFilterDepth};\n wD += ${dilationDepth}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${convInfo.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n int xC = xCCorner + wC * ${dilationWidth};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n );\n\n ${updateSnippet}\n }\n\n int xC = xCCorner + ${filterWidthNearestVec4};\n if (${filterWidthVec4Remainder === 1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${updateSnippet}\n } else if (${filterWidthVec4Remainder === 2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n initializationValue,\n initializationValue\n );\n\n ${updateSnippet}\n } else if (${filterWidthVec4Remainder === 3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n initializationValue\n );\n\n ${updateSnippet}\n }\n }\n }\n setOutput(${returnValue});\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool.js\nfunction avgPool3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n assertNotComplex2(x, \"avgPool\");\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const dilations = 1;\n util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n return identity3({ inputs: { x }, backend: backend2 });\n }\n const avgPoolProgram = new Pool2DProgram(convInfo, \"avg\", false);\n return backend2.runWebGLProgram(avgPoolProgram, [x], \"float32\");\n}\nvar avgPoolConfig2 = {\n kernelName: AvgPool,\n backendName: \"webgl\",\n kernelFunc: avgPool3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3D.js\nfunction avgPool3D2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n const dilations = [1, 1, 1];\n const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);\n const avgPoolProgram = new Pool3DProgram(convInfo, \"avg\", false);\n return backend2.runWebGLProgram(avgPoolProgram, [x], \"float32\");\n}\nvar avgPool3DConfig2 = {\n kernelName: AvgPool3D,\n backendName: \"webgl\",\n kernelFunc: avgPool3D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/avg_pool_backprop_gpu.js\nvar AvgPool2DBackpropProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"dy\"];\n this.outputShape = convInfo.inShape;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const avgMultiplier = 1 / (filterHeight * filterWidth);\n this.userCode = `\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n const float avgMultiplier = float(${avgMultiplier});\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 < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${effectiveFilterWidth};\n wC+= ${dilationWidth}) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n};\nvar AvgPool3DBackpropProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"dy\"];\n this.outputShape = convInfo.inShape;\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n this.userCode = `\n const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n const float avgMultiplier = float(${avgMultiplier});\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 < ${effectiveFilterDepth};\n wD += ${dilationDepth}) {\n float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${effectiveFilterWidth};\n wC += ${dilationWidth}) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3DGrad.js\nfunction avgPool3DGrad2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2 } = inputs;\n const x = input2;\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const dilations = [1, 1, 1];\n const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nvar avgPool3DGradConfig3 = {\n kernelName: AvgPool3DGrad,\n backendName: \"webgl\",\n kernelFunc: avgPool3DGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPoolGrad.js\nfunction avgPoolGrad3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2 } = inputs;\n const x = input2;\n assertNotComplex2([dy, input2], \"avgPoolGrad\");\n const { filterSize, strides, pad: pad3 } = attrs;\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);\n const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nvar avgPoolGradConfig3 = {\n kernelName: AvgPoolGrad,\n backendName: \"webgl\",\n kernelFunc: avgPoolGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul.js\nfunction batchMatMul2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { a, b } = inputs;\n const { transposeA, transposeB } = attrs;\n return batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2 });\n}\nvar batchMatMulConfig2 = {\n kernelName: BatchMatMul,\n backendName: \"webgl\",\n kernelFunc: batchMatMul2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_gpu.js\nvar BatchNormProgram = class {\n constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n this.outputShape = [];\n this.variableNames = [\"x\", \"mean\", \"variance\"];\n backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n let offsetSnippet = \"0.0\";\n if (offsetShape != null) {\n backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n this.variableNames.push(\"offset\");\n offsetSnippet = \"getOffsetAtOutCoords()\";\n }\n let scaleSnippet = \"1.0\";\n if (scaleShape != null) {\n backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n this.variableNames.push(\"scale\");\n scaleSnippet = \"getScaleAtOutCoords()\";\n }\n this.outputShape = xShape;\n this.userCode = `\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${offsetSnippet};\n float scale = ${scaleSnippet};\n float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_packed_gpu.js\nvar BatchNormPackedProgram = class {\n constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n this.packedInputs = true;\n this.packedOutput = true;\n this.variableNames = [\"x\", \"mean\", \"variance\"];\n backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n let offsetSnippet = \"vec4(0.0)\";\n if (offsetShape != null) {\n backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n this.variableNames.push(\"offset\");\n offsetSnippet = \"getOffsetAtOutCoords()\";\n }\n let scaleSnippet = \"vec4(1.0)\";\n if (scaleShape != null) {\n backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n this.variableNames.push(\"scale\");\n scaleSnippet = \"getScaleAtOutCoords()\";\n }\n this.outputShape = xShape;\n this.userCode = `\n void main() {\n vec4 offset = ${offsetSnippet};\n vec4 scale = ${scaleSnippet};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n setOutput((x - mean) * inv + offset);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchNorm.js\nvar batchNorm3 = ({ inputs, backend: backend2, attrs }) => {\n const { x, mean: mean4, variance, offset, scale: scale2 } = inputs;\n util_exports.assert(mean4.shape.length === variance.shape.length, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n util_exports.assert(offset == null || mean4.shape.length === offset.shape.length, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n util_exports.assert(scale2 == null || mean4.shape.length === scale2.shape.length, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n let { varianceEpsilon } = attrs;\n if (varianceEpsilon == null) {\n varianceEpsilon = 1e-3;\n }\n const finalInputs = [x, mean4, variance];\n let offsetShape = null;\n if (offset != null) {\n offsetShape = offset.shape;\n finalInputs.push(offset);\n }\n let scaleShape = null;\n if (scale2 != null) {\n scaleShape = scale2.shape;\n finalInputs.push(scale2);\n }\n const program = env().getBool(\"WEBGL_PACK_NORMALIZATION\") ? new BatchNormPackedProgram(x.shape, mean4.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean4.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n return output;\n};\nvar batchNormConfig2 = {\n kernelName: FusedBatchNorm,\n backendName: \"webgl\",\n kernelFunc: batchNorm3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_gpu.js\nvar SliceProgram = class {\n constructor(destSize) {\n this.variableNames = [\"source\"];\n this.outputShape = destSize;\n this.rank = destSize.length;\n const dtype = getCoordsDataType(this.rank);\n this.customUniforms = [{ name: \"start\", arrayIndex: this.rank, type: \"int\" }];\n const sourceCoords = getCoords(this.rank);\n let body;\n const coordSum = destSize.map((_, i) => {\n return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n });\n body = `\n ${dtype} sourceLoc;\n ${dtype} coords = getOutputCoords();\n ${coordSum.join(\"\\n\")}\n `;\n this.userCode = `\n void main() {\n ${body}\n setOutput(getSource(${sourceCoords}));\n }\n `;\n }\n};\nvar coords = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\nfunction getCoords(rank) {\n if (rank === 1) {\n return \"sourceLoc\";\n } else if (rank <= 6) {\n return coords.slice(0, rank).map((x) => \"sourceLoc.\" + x).join(\",\");\n } else {\n throw Error(`Slicing for rank ${rank} is not yet supported`);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_packed_gpu.js\nvar SlicePackedProgram = class {\n constructor(destSize) {\n this.variableNames = [\"source\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.outputShape = destSize;\n this.rank = destSize.length;\n this.customUniforms = [{ name: \"start\", arrayIndex: this.rank, type: \"int\" }];\n const dtype = getCoordsDataType(this.rank);\n const coords2 = getChannels(\"coords\", this.rank);\n const sourceLoc = getChannels(\"sourceLoc\", this.rank);\n const innerDims = this.rank === 1 ? \"sourceLoc\" : `vec2(${sourceLoc.slice(-2).join()})`;\n const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n const upperRow = `\n result.x = ${getChannel};\n if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n ++${sourceLoc[this.rank - 1]};\n result.y = ${getChannel};\n --${sourceLoc[this.rank - 1]};\n }\n `;\n const lowerRow = this.rank === 1 ? \"\" : `\n --${coords2[this.rank - 1]};\n if (++${coords2[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n ++${sourceLoc[this.rank - 2]};\n result.z = ${getChannel};\n if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n ++${sourceLoc[this.rank - 1]};\n result.w = ${getChannel};\n }\n }\n `;\n const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +\n ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` : destSize.map((_, i) => `${sourceLoc[i]} = ${coords2[i]} + start[${i}];`).join(\"\\n\");\n this.userCode = `\n void main() {\n ${dtype} coords = getOutputCoords();\n ${dtype} sourceLoc;\n ${sourceLocSetup}\n vec4 result = vec4(0.);\n ${upperRow}\n ${lowerRow}\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js\nfunction shallowSlice(x, begin, size, backend2) {\n const xTexData = backend2.texData.get(x.dataId);\n const t = backend2.makeTensorInfo(size, x.dtype);\n const newTexData = backend2.texData.get(t.dataId);\n Object.assign(newTexData, xTexData);\n newTexData.refCount = 1;\n newTexData.shape = size;\n newTexData.dtype = x.dtype;\n let flatOffset = slice_util_exports.computeFlatOffset(begin, util_exports.computeStrides(x.shape));\n if (xTexData.slice) {\n flatOffset += xTexData.slice.flatOffset;\n }\n newTexData.slice = {\n flatOffset,\n // Point to the original dataId, which is used to do ref counting.\n origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n };\n const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1;\n backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n return t;\n}\nfunction slice3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { begin, size } = attrs;\n const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n slice_util_exports.assertParamsValid(x, $begin, $size);\n if (util_exports.sizeFromShape($size) === 0) {\n return backend2.makeTensorInfo($size, x.dtype, []);\n }\n if (backend2.shouldExecuteOnCPU([x]) || x.dtype === \"string\") {\n const xTexData = backend2.texData.get(x.dataId);\n const outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype);\n return backend2.makeTensorInfo($size, x.dtype, outValues);\n }\n const { isPacked } = backend2.texData.get(x.dataId);\n const isContinous = slice_util_exports.isSliceContinous(x.shape, $begin, $size);\n if (isPacked || !isContinous) {\n const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new SlicePackedProgram($size) : new SliceProgram($size);\n const customValues = [$begin];\n return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n }\n backend2.uploadToGPU(x.dataId);\n return shallowSlice(x, $begin, $size, backend2);\n}\nvar sliceConfig2 = {\n kernelName: Slice,\n backendName: \"webgl\",\n kernelFunc: slice3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchToSpaceND.js\nvar batchToSpaceND3 = (args) => {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { blockShape, crops } = attrs;\n util_exports.assert(x.shape.length <= 4, () => \"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");\n const prod5 = blockShape.reduce((a, b) => a * b);\n const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5);\n const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5);\n const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n const toDispose = [];\n const reshapedIntermediate = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n const transposedIntermediate = transpose3({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });\n const reshapedIntermediate2 = reshape4({\n inputs: { x: transposedIntermediate },\n backend: backend2,\n attrs: { shape: reshapedPermuted }\n });\n const sliced = slice3({\n inputs: { x: reshapedIntermediate2 },\n backend: backend2,\n attrs: { begin: sliceBeginCoords, size: sliceSize }\n });\n toDispose.push(reshapedIntermediate);\n toDispose.push(transposedIntermediate);\n toDispose.push(reshapedIntermediate2);\n toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return sliced;\n};\nvar batchToSpaceNDConfig2 = {\n kernelName: BatchToSpaceND,\n backendName: \"webgl\",\n kernelFunc: batchToSpaceND3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Bincount.js\nfunction bincount3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, weights } = inputs;\n const { size } = attrs;\n const xVals = backend2.readSync(x.dataId);\n const weightsVals = backend2.readSync(weights.dataId);\n const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);\n return backend2.makeTensorInfo([size], weights.dtype, outVals);\n}\nvar bincountConfig2 = {\n kernelName: Bincount,\n backendName: \"webgl\",\n kernelFunc: bincount3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BitwiseAnd.js\nvar BITWISEAND = `\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`;\nvar BITWISEAND_UNPACKED = `\n return float(int(a.r) & int(b.r));\n`;\nfunction bitwiseAnd3(args) {\n const { inputs, backend: backend2 } = args;\n const { a, b } = inputs;\n const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\");\n const versionNumber = env().getNumber(\"WEBGL_VERSION\");\n if (backend2.shouldExecuteOnCPU([a, b]) || versionNumber === 1) {\n const aVals = backend2.texData.get(a.dataId).values;\n const bVals = backend2.texData.get(b.dataId).values;\n const [outValues, outShape] = bitwiseAndImplCPU(a.shape, b.shape, aVals, bVals, a.dtype);\n const out = backend2.makeTensorInfo(outShape, a.dtype);\n const outData = backend2.texData.get(out.dataId);\n outData.values = outValues;\n return out;\n }\n let program;\n if (shouldUsePackedProgram) {\n program = new BinaryOpPackedProgram(BITWISEAND, a.shape, b.shape, false);\n } else {\n program = new BinaryOpProgram(BITWISEAND_UNPACKED, a.shape, b.shape);\n }\n return backend2.runWebGLProgram(program, [a, b], a.dtype);\n}\nvar bitwiseAndConfig2 = {\n kernelName: BitwiseAnd,\n backendName: \"webgl\",\n kernelFunc: bitwiseAnd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BroadcastArgs.js\nfunction broadcastArgs3(args) {\n const { inputs, backend: backend2 } = args;\n const { s0, s1 } = inputs;\n const s0Vals = backend2.readSync(s0.dataId);\n const s1Vals = backend2.readSync(s1.dataId);\n const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));\n return backend2.makeTensorInfo([broadcastShape.length], \"int32\", Int32Array.from(broadcastShape));\n}\nvar broadcastArgsConfig2 = {\n kernelName: BroadcastArgs,\n backendName: \"webgl\",\n kernelFunc: broadcastArgs3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NotEqual.js\nvar NOT_EQUAL = `return float(a != b);`;\nvar notEqual3 = binaryKernelFunc2({ opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: \"bool\" });\nvar notEqualConfig2 = {\n kernelName: NotEqual,\n backendName: \"webgl\",\n kernelFunc: notEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Real.js\nfunction real3(args) {\n const { inputs, backend: backend2 } = args;\n const { input: input2 } = inputs;\n const inputData = backend2.texData.get(input2.dataId);\n return identity3({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });\n}\nvar realConfig2 = {\n kernelName: Real,\n backendName: \"webgl\",\n kernelFunc: real3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/int.js\nvar TO_INT = `return float(int(x));`;\nfunction int(input2, backend2) {\n const program = new UnaryOpProgram(input2.shape, TO_INT);\n const output = backend2.runWebGLProgram(program, [input2], \"int32\");\n return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cast.js\nfunction cast4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { dtype } = attrs;\n if (dtype === \"complex64\") {\n if (x.dtype === \"complex64\") {\n return identity3({ inputs: { x }, backend: backend2 });\n }\n const zerosTensor = zeros(x.shape);\n const floatX = cast4({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n const result = complex3({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });\n zerosTensor.dispose();\n backend2.disposeIntermediateTensorInfo(floatX);\n return result;\n }\n if (x.dtype === \"complex64\") {\n const realPart = real3({ inputs: { input: x }, backend: backend2 });\n const result = cast4({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n backend2.disposeIntermediateTensorInfo(realPart);\n return result;\n }\n if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n const result = identity3({ inputs: { x }, backend: backend2 });\n return { dataId: result.dataId, shape: result.shape, dtype };\n }\n if (backend2.shouldExecuteOnCPU([x])) {\n const values = backend2.texData.get(x.dataId).values;\n const [resultShape, resultType, resultData] = castImplCPU(values, x.shape, x.dtype, dtype);\n return backend2.makeTensorInfo(resultShape, resultType, resultData);\n }\n if (dtype === \"int32\") {\n return int(x, backend2);\n }\n if (dtype === \"bool\") {\n const zerosTensorInfo = backend2.makeTensorInfo([], \"bool\", util_exports.getTypedArrayFromDType(\"bool\", 1));\n const binaryInputs = { a: x, b: zerosTensorInfo };\n const result = notEqual3({ inputs: binaryInputs, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(zerosTensorInfo);\n return result;\n }\n throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nvar castConfig2 = {\n kernelName: Cast,\n backendName: \"webgl\",\n kernelFunc: cast4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Ceil.js\nvar CEIL = `return ceil(x);`;\nvar ceil3 = unaryKernelFunc2({ opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU });\nvar ceilConfig2 = {\n kernelName: Ceil,\n backendName: \"webgl\",\n kernelFunc: ceil3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_gpu.js\nvar ClipProgram = class {\n constructor(aShape) {\n this.variableNames = [\"A\"];\n this.customUniforms = [\n { name: \"minVal\", type: \"float\" },\n { name: \"maxVal\", type: \"float\" }\n ];\n this.outputShape = aShape;\n 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_packed_gpu.js\nvar ClipPackedProgram = class {\n constructor(aShape) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.customUniforms = [\n { name: \"minVal\", type: \"float\" },\n { name: \"maxVal\", type: \"float\" }\n ];\n this.outputShape = aShape;\n 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ClipByValue.js\nfunction clipByValue3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { clipValueMin, clipValueMax } = attrs;\n let program;\n if (env().getBool(\"WEBGL_PACK_CLIP\")) {\n program = new ClipPackedProgram(x.shape);\n } else {\n program = new ClipProgram(x.shape);\n }\n const customValues = [[clipValueMin], [clipValueMax]];\n return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n}\nvar clipByValueConfig2 = {\n kernelName: ClipByValue,\n backendName: \"webgl\",\n kernelFunc: clipByValue3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/complex_abs_gpu.js\nvar ComplexAbsProgram = class {\n constructor(shape) {\n this.variableNames = [\"real\", \"imag\"];\n this.outputShape = shape;\n 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ComplexAbs.js\nfunction makeComplexComponentTensorInfo(complexTensor, complexPart) {\n return {\n dataId: complexPart.dataId,\n dtype: complexPart.dtype,\n shape: complexTensor.shape\n };\n}\nfunction complexAbs2(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n const xData = backend2.texData.get(x.dataId);\n const program = new ComplexAbsProgram(x.shape);\n const programInputs = [\n makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),\n makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag)\n ];\n return backend2.runWebGLProgram(program, programInputs, programInputs[0].dtype);\n}\nvar complexAbsConfig2 = {\n kernelName: ComplexAbs,\n backendName: \"webgl\",\n kernelFunc: complexAbs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_gpu.js\nvar ConcatProgram = class {\n // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n constructor(shapes) {\n this.outputShape = [];\n this.outputShape = backend_util_exports.computeOutShape(\n shapes,\n 1\n /* axis */\n );\n this.variableNames = shapes.map((_, i) => `T${i}`);\n const offsets = new Array(shapes.length - 1);\n offsets[0] = shapes[0][1];\n for (let i = 1; i < offsets.length; i++) {\n offsets[i] = offsets[i - 1] + shapes[i][1];\n }\n const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n for (let i = 1; i < offsets.length; i++) {\n const shift = offsets[i - 1];\n snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`);\n }\n const lastIndex = offsets.length;\n const lastShift = offsets[offsets.length - 1];\n snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n this.userCode = `\n void main() {\n ivec2 coords = getOutputCoords();\n int yR = coords.x;\n int yC = coords.y;\n\n ${snippets.join(\"\\n \")}\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_packed_gpu.js\nvar ConcatPackedProgram = class {\n constructor(shapes, axis) {\n this.packedInputs = true;\n this.packedOutput = true;\n this.outputShape = [];\n this.outputShape = backend_util_exports.computeOutShape(shapes, axis);\n const shape = this.outputShape;\n const rank = shape.length;\n const dtype = getCoordsDataType(rank);\n const coords2 = getChannels(\"coords\", rank);\n const channels = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, rank);\n this.variableNames = shapes.map((_, i) => `T${i}`);\n const offsets = new Array(shapes.length - 1);\n offsets[0] = shapes[0][axis];\n for (let i = 1; i < offsets.length; i++) {\n offsets[i] = offsets[i - 1] + shapes[i][axis];\n }\n const channel = channels[axis];\n const lastChannels = channels.slice(-2);\n const allChannels = channels.join();\n let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n return getChannel(\n getT0(${allChannels}), vec2(${lastChannels.join()}));\n }`;\n for (let i = 1; i < offsets.length; i++) {\n const shift2 = offsets[i - 1];\n getValueSnippet += `\n if (${channel} < ${offsets[i]} && ${channel} >= ${offsets[i - 1]}) {\n return getChannel(\n getT${i}(${shiftedChannels(channels, channel, shift2)}),\n vec2(${shiftedChannels(lastChannels, channel, shift2)}));\n }`;\n }\n const lastIndex = offsets.length;\n const shift = offsets[offsets.length - 1];\n getValueSnippet += `\n return getChannel(\n getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n this.userCode = `\n float getValue(${channels.map((x) => \"int \" + x)}) {\n ${getValueSnippet}\n }\n\n void main() {\n ${dtype} coords = getOutputCoords();\n vec4 result = vec4(getValue(${coords2}), 0., 0., 0.);\n\n ${coords2[rank - 1]} = ${coords2[rank - 1]} + 1;\n if (${coords2[rank - 1]} < ${shape[rank - 1]}) {\n result.g = getValue(${coords2});\n }\n\n ${coords2[rank - 2]} = ${coords2[rank - 2]} + 1;\n if (${coords2[rank - 2]} < ${shape[rank - 2]}) {\n result.a = getValue(${coords2});\n }\n\n ${coords2[rank - 1]} = ${coords2[rank - 1]} - 1;\n if (${coords2[rank - 2]} < ${shape[rank - 2]} &&\n ${coords2[rank - 1]} < ${shape[rank - 1]}) {\n result.b = getValue(${coords2});\n }\n setOutput(result);\n }\n `;\n }\n};\nfunction shiftedChannels(channels, channel, shift) {\n const channelIdx = channels.indexOf(channel);\n const res = channels.map((c, idx) => {\n if (idx === channelIdx) {\n return `${c} - ${shift}`;\n } else {\n return c;\n }\n });\n return res.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Imag.js\nfunction imag3(args) {\n const { inputs, backend: backend2 } = args;\n const { input: input2 } = inputs;\n const inputData = backend2.texData.get(input2.dataId);\n return identity3({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });\n}\nvar imagConfig2 = {\n kernelName: Imag,\n backendName: \"webgl\",\n kernelFunc: imag3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js\nfunction concatImpl2(inputs, axis, backend2) {\n const dtype = inputs[0].dtype;\n if (dtype === \"complex64\") {\n const reals = inputs.map((t) => real3({ inputs: { input: t }, backend: backend2 }));\n const imags = inputs.map((t) => imag3({ inputs: { input: t }, backend: backend2 }));\n const realConcated = concatImpl2(reals, axis, backend2);\n const imagConcated = concatImpl2(imags, axis, backend2);\n const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));\n imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));\n backend2.disposeIntermediateTensorInfo(realConcated);\n backend2.disposeIntermediateTensorInfo(imagConcated);\n return result2;\n }\n let runOnCpu = backend2.shouldExecuteOnCPU(inputs);\n if (dtype === \"string\") {\n runOnCpu = true;\n }\n if (runOnCpu) {\n const tensors2D2 = inputs.map((t) => {\n const innerSize = util_exports.sizeFromShape(t.shape.slice(axis));\n const shape = [-1, innerSize];\n return reshape4({ inputs: { x: t }, backend: backend2, attrs: { shape } });\n });\n const inputsValShapes = tensors2D2.map((t) => {\n return { vals: backend2.readSync(t.dataId), shape: t.shape };\n });\n const outShape2 = backend_util_exports.computeOutShape(\n tensors2D2.map((t) => t.shape),\n 1\n /* axis */\n );\n const simplyConcat = tensors2D2[0].shape[0] === 1;\n const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat);\n const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);\n const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);\n tensors2D2.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return outInfo;\n }\n const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);\n const shouldPack = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") && $inputs[0].shape.length > 1;\n if ($inputs.length === 1) {\n const program2 = shouldPack ? new UnaryOpProgram(inputs[0].shape, CLONE) : new UnaryOpPackedProgram(inputs[0].shape, CLONE);\n return backend2.runWebGLProgram(program2, inputs, dtype);\n }\n const maxTexturesInShader = env().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");\n if ($inputs.length > maxTexturesInShader) {\n const reducedInputs = [];\n for (let i = 0; i < $inputs.length; i += maxTexturesInShader) {\n const subArray = $inputs.slice(i, i + maxTexturesInShader);\n reducedInputs.push(concatImpl2(subArray, axis, backend2));\n }\n const result2 = concatImpl2(reducedInputs, axis, backend2);\n for (const i of reducedInputs) {\n backend2.disposeIntermediateTensorInfo(i);\n }\n return result2;\n }\n if (shouldPack) {\n const program2 = new ConcatPackedProgram($inputs.map((t) => t.shape), axis);\n return backend2.runWebGLProgram(program2, $inputs, dtype);\n }\n const { tensors2D, outShape } = computeTensors2D($inputs, axis, backend2);\n const program = new ConcatProgram(tensors2D.map((t) => t.shape));\n const result = backend2.runWebGLProgram(program, tensors2D, dtype);\n tensors2D.forEach((r) => backend2.disposeIntermediateTensorInfo(r));\n const reshapedResult = reshape4({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(result);\n return reshapedResult;\n}\nfunction computeTensors2D(inputs, axis, backend2) {\n const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);\n const tensors2D = inputs.map((x) => reshape4({\n inputs: { x },\n attrs: { shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))] },\n backend: backend2\n }));\n return { tensors2D, outShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js\nfunction concat3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { axis } = attrs;\n const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n const shapes = inputs.map((t) => t.shape);\n backend_util_exports.assertParamsConsistent(shapes, $axis);\n const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);\n if (util_exports.sizeFromShape(outShape) === 0) {\n return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n }\n const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);\n if ($inputs.length === 1) {\n return identity3({ inputs: { x: $inputs[0] }, backend: backend2 });\n }\n return concatImpl2($inputs, $axis, backend2);\n}\nvar concatConfig2 = {\n kernelName: Concat,\n backendName: \"webgl\",\n kernelFunc: concat3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu.js\nvar Conv2DProgram = class {\n constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) {\n this.variableNames = [\"x\", \"W\"];\n this.outputShape = convInfo.outShape;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n const inputDepthVec4Remainder = convInfo.inChannels % 4;\n const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n const rowDim = isChannelsLast ? 1 : 2;\n const colDim = isChannelsLast ? 2 : 3;\n const channelDim = isChannelsLast ? 3 : 1;\n let activationSnippet = \"\", applyActivationSnippet = \"\";\n if (activation2) {\n if (hasPreluActivationWeights) {\n activationSnippet = `float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${activation2}\n }`;\n } else if (hasLeakyreluAlpha) {\n activationSnippet = `float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${activation2}\n }`;\n } else {\n activationSnippet = `\n float activation(float x) {\n ${activation2}\n }\n `;\n }\n applyActivationSnippet = `result = activation(result);`;\n }\n const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n if (addBias) {\n this.variableNames.push(\"bias\");\n }\n if (hasPreluActivationWeights) {\n this.variableNames.push(\"preluActivationWeights\");\n }\n if (hasLeakyreluAlpha) {\n this.variableNames.push(\"leakyreluAlpha\");\n }\n this.userCode = `\n ${activationSnippet}\n\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${channelDim}];\n\n ivec2 xRCCorner =\n ivec2(coords[${rowDim}], coords[${colDim}]) * 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 < ${filterHeight}; wR++) {\n int xR = xRCorner + wR * ${dilationHeight};\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n int xC = xCCorner + wC * ${dilationWidth};\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${isChannelsLast}) {\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 (${inputDepthVec4Remainder === 1}) {\n\n if (${isChannelsLast}) {\n dotProd +=\n getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n getW(wR, wC, ${inputDepthNearestVec4}, d2);\n } else {\n dotProd +=\n getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n getW(wR, wC, ${inputDepthNearestVec4}, d2);\n }\n\n } else if (${inputDepthVec4Remainder === 2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${inputDepthNearestVec4}, d2),\n getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n );\n\n if (${isChannelsLast}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${inputDepthNearestVec4}),\n getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${inputDepthNearestVec4}, xR, xC),\n getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${inputDepthVec4Remainder === 3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${inputDepthNearestVec4}, d2),\n getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n );\n\n if (${isChannelsLast}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${inputDepthNearestVec4}),\n getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${inputDepthNearestVec4}, xR, xC),\n getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${addBiasSnippet}\n ${applyActivationSnippet}\n setOutput(result);\n }\n `;\n }\n};\nvar Conv3DProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"x\", \"W\"];\n this.outputShape = convInfo.outShape;\n const padFront = convInfo.padInfo.front;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n const inputDepthVec4Remainder = convInfo.inChannels % 4;\n this.userCode = `\n const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\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 < ${filterDepth}; wF++) {\n int xF = xFCorner + wF * ${dilationDepth};\n\n if (xF < 0 || xF >= ${convInfo.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${filterHeight}; wR++) {\n int xR = xRCorner + wR * ${dilationHeight};\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n int xC = xCCorner + wC * ${dilationWidth};\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${inputDepthVec4Remainder === 1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n } else if (${inputDepthVec4Remainder === 2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${inputDepthVec4Remainder === 3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu.js\nvar Conv2DPackedProgram = class {\n constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n this.variableNames = [\"x\", \"W\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.customUniforms = [\n { name: \"pads\", type: \"ivec2\" },\n { name: \"strides\", type: \"ivec2\" },\n { name: \"dilations\", type: \"ivec2\" },\n { name: \"inDims\", type: \"ivec2\" }\n ];\n this.outputShape = convInfo.outShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n const padLeft = convInfo.padInfo.left;\n const strideWidth = convInfo.strideWidth;\n const dilationWidth = convInfo.dilationWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const texelsAcross = filterWidth;\n let mainLoop = `\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;\n for (let c = 0; c < filterWidth; c++) {\n mainLoop += `\n vec4 xTexelC${c * 2};\n int xTexelC${c * 2}Ready;\n vec4 xTexelC${c * 2 + 1};\n int xTexelC${c * 2 + 1}Ready;\n vec4 xC${c};`;\n }\n mainLoop += `\n for (int r = 0; r < ${filterHeight}; r++) {\n for (int d1 = 0; d1 < ${convInfo.inChannels}; d1 += 2) {\n `;\n for (let c = 0; c < filterWidth; c++) {\n mainLoop += `\n xTexelC${c * 2} = vec4(0.0);\n xTexelC${c * 2}Ready = 0;\n xTexelC${c * 2 + 1} = vec4(0.0);\n xTexelC${c * 2 + 1}Ready = 0;\n xC${c} = vec4(0.0);`;\n }\n mainLoop += `\n xR = xRCorner + r * dilations[0];\n if (xR >=0 && xR < inDims[0]) {\n `;\n for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {\n const colIndex = texelC * 2;\n mainLoop += `\n xC = xCCorner + ${colIndex * dilationWidth};\n `;\n if (strideWidth === 1) {\n if (colIndex < filterWidth) {\n if (padLeft % 2 === 1) {\n mainLoop += `\n xCOffset = xC + 1;\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${colIndex}.zw = vec2(0.0);\n }\n xTexelC${colIndex}Ready = 1;\n }\n `;\n if (dilationWidth === 1 && colIndex > 0) {\n mainLoop += `\n xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);\n `;\n } else {\n mainLoop += `\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${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);\n } else {\n xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);\n }\n `;\n }\n } else {\n mainLoop += `\n if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n xTexelC${colIndex} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${colIndex}.zw = vec2(0.0);\n }\n xTexelC${colIndex}Ready = 1;\n }\n\n xC${colIndex} = xTexelC${colIndex};\n `;\n }\n if (colIndex + 1 < filterWidth) {\n const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;\n if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {\n mainLoop += `\n xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${colIndex + 1}.zw = vec2(0.0);\n }\n xTexelC${colIndex + 1}Ready = 1;\n }\n `;\n if (dilationWidth > 1) {\n mainLoop += `\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy);\n } else {\n xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy);\n }\n `;\n } else {\n mainLoop += `\n xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);\n `;\n }\n } else {\n if (nextTexelOffset === 1) {\n mainLoop += `\n xC${colIndex + 1} = xTexelC${colIndex};\n `;\n } else {\n mainLoop += `\n xCOffset = xC + ${nextTexelOffset};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${colIndex + 1}.zw = vec2(0.0);\n }\n xTexelC${colIndex + 1}Ready = 1;\n }\n\n xC${colIndex + 1} = xTexelC${colIndex + 1};\n `;\n }\n }\n }\n }\n } else {\n if (colIndex < filterWidth) {\n if (padLeft % 2 === 1) {\n mainLoop += `\n xCOffset = xC + 1 - strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${colIndex}.zw = vec2(0.0);\n }\n xTexelC${colIndex}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${colIndex + 1}.zw = vec2(0.0);\n }\n xTexelC${colIndex + 1}Ready = 1;\n }\n\n xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n `;\n if (colIndex + 1 < filterWidth) {\n mainLoop += `\n final = vec4(0.0);\n xCOffset = xC + 1 + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);\n `;\n }\n } else {\n mainLoop += `\n if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n xTexelC${colIndex} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${colIndex}.zw = vec2(0.0);\n }\n xTexelC${colIndex}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${colIndex + 1}.zw = vec2(0.);\n }\n xTexelC${colIndex + 1}Ready = 1;\n }\n\n xC${colIndex} = vec4(\n xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);\n `;\n if (colIndex + 1 < filterWidth) {\n mainLoop += `\n xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n `;\n }\n }\n }\n }\n if (colIndex < filterWidth) {\n mainLoop += `\n wTexel = getW(r, ${colIndex}, d1, d2);\n dotProd += xC${colIndex}.xxzz * vec4(wTexel.xy, wTexel.xy);\n if(d1 + 1 < ${convInfo.inChannels}) {\n dotProd += xC${colIndex}.yyww * vec4(wTexel.zw, wTexel.zw);\n }\n `;\n if (colIndex + 1 < filterWidth) {\n mainLoop += `\n wTexel = getW(r, ${colIndex + 1}, d1, d2);\n dotProd += xC${colIndex + 1}.xxzz * vec4(wTexel.xy, wTexel.xy);\n if(d1 + 1 < ${convInfo.inChannels}) {\n dotProd += xC${colIndex + 1}.yyww * vec4(wTexel.zw, wTexel.zw);\n }\n `;\n }\n }\n }\n mainLoop += `\n }\n `;\n mainLoop += `\n }\n `;\n mainLoop += `\n }\n `;\n let activationSnippet = \"\", applyActivationSnippet = \"\";\n if (activation2) {\n if (hasPreluActivation) {\n activationSnippet = `vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${activation2}\n }`;\n } else if (hasLeakyReluAlpha) {\n activationSnippet = `vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${activation2}\n }`;\n } else {\n activationSnippet = `vec4 activation(vec4 x) {\n ${activation2}\n }`;\n }\n applyActivationSnippet = `result = activation(result);`;\n }\n const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n if (addBias) {\n this.variableNames.push(\"bias\");\n }\n if (hasPreluActivation) {\n this.variableNames.push(\"preluActivationWeights\");\n }\n if (hasLeakyReluAlpha) {\n this.variableNames.push(\"leakyreluAlpha\");\n }\n this.userCode = `\n ${activationSnippet}\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 xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n vec4 dotProd = vec4(0.000000000000001);\n\n ${mainLoop}\n\n vec4 result = dotProd - vec4(0.000000000000001);\n ${addBiasSnippet}\n ${applyActivationSnippet}\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/im2col_packed_gpu.js\nvar Im2ColPackedProgram = class {\n constructor(outputShape, convInfo) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.customUniforms = [\n { name: \"inputShape\", type: \"ivec4\" },\n { name: \"pad\", type: \"ivec2\" },\n { name: \"stride\", type: \"ivec2\" },\n { name: \"dilation\", type: \"ivec2\" },\n { name: \"inChannels\", type: \"int\" },\n { name: \"itemsPerBlockRow\", type: \"int\" },\n { name: \"outWidth\", type: \"int\" }\n ];\n this.outputShape = outputShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n const { dataFormat } = convInfo;\n const glsl = getGlslDifferences();\n const isChannelsLast = dataFormat === \"channelsLast\";\n const rowDim = isChannelsLast ? 1 : 2;\n const colDim = isChannelsLast ? 2 : 3;\n const boundsCheckingSnippet = this.enableShapeUniforms ? \"if(blockIndex < outShape[2] && pos < outShape[1]) {\" : `if(blockIndex < ${outputShape[2]} && pos < ${outputShape[1]}) {`;\n let unrolled = ``;\n for (let row = 0; row <= 1; row++) {\n for (let col = 0; col <= 1; col++) {\n unrolled += `\n blockIndex = rc.z + ${col};\n pos = rc.y + ${row};\n\n ${boundsCheckingSnippet}\n offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n if(d0 < inputShape[${rowDim}] && d0 >= 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[${colDim}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${isChannelsLast}) {\n innerDims = vec2(d1, ch);\n result[${row * 2 + col}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${row * 2 + col}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\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 ${unrolled}\n\n ${glsl.output} = result;\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js\nfunction getShapeForBatchMatMul(shape, isChannelsLast) {\n const length = shape.length;\n if (length >= 3) {\n return isChannelsLast ? [\n ...shape.slice(0, -3),\n shape[length - 3] * shape[length - 2],\n shape[length - 1]\n /* channel */\n ] : [\n ...shape.slice(0, -3),\n shape[length - 3],\n shape[length - 2] * shape[length - 1]\n /* height * width */\n ];\n } else if (!isChannelsLast && length === 1 && shape[0] > 1) {\n return [shape[0], 1];\n } else {\n return null;\n }\n}\nfunction conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n const xShape = x.shape;\n const xTexData = backend2.texData.get(x.dataId);\n const sharedMatMulDim = convInfo.inChannels;\n const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n const outerShapeFilter = convInfo.outChannels;\n const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n const transposeA = false;\n const transposeB = false;\n let out;\n const intermediates = [];\n if (preluActivationWeights != null) {\n const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);\n if (targetShape != null) {\n preluActivationWeights = reshape4({\n inputs: { x: preluActivationWeights },\n backend: backend2,\n attrs: { shape: targetShape }\n });\n intermediates.push(preluActivationWeights);\n }\n }\n if (bias != null) {\n const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);\n if (targetShape != null) {\n bias = reshape4({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } });\n intermediates.push(bias);\n }\n }\n const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked && isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 && util_exports.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));\n if (canOptimize) {\n const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);\n const xReshaped = {\n dataId: x.dataId,\n shape: [1, targetShape, convInfo.inChannels],\n dtype: x.dtype\n };\n const originalXTexDataShape = xTexData.shape;\n xTexData.shape = xTexData.shape.slice();\n xTexData.shape[xTexData.shape.length - 2]++;\n util_exports.assert(isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n const filterReshaped = reshape4({\n inputs: { x: filter },\n backend: backend2,\n attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n });\n intermediates.push(filterReshaped);\n const pointwiseConv = batchMatMulImpl({\n a: xReshaped,\n b: filterReshaped,\n backend: backend2,\n transposeA,\n transposeB,\n bias,\n activation: activation2,\n preluActivationWeights,\n leakyreluAlpha\n });\n const pointwiseConvTexData = backend2.texData.get(pointwiseConv.dataId);\n util_exports.assert(pointwiseConvTexData.isPacked, () => \"batchMatMul result is expected to be packed\");\n xTexData.shape = originalXTexDataShape;\n pointwiseConvTexData.shape = convInfo.outShape;\n out = identity3({ inputs: { x: pointwiseConv }, backend: backend2 });\n out.shape = convInfo.outShape;\n intermediates.push(pointwiseConv);\n } else {\n const numCols = convInfo.outHeight * convInfo.outWidth;\n const xReshaped = reshape4({\n inputs: { x },\n backend: backend2,\n attrs: {\n shape: isChannelsLast ? [convInfo.batchSize, numCols, convInfo.inChannels] : [convInfo.batchSize, convInfo.inChannels, numCols]\n }\n });\n const filterReshaped = reshape4({\n inputs: { x: filter },\n backend: backend2,\n attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n });\n const result = batchMatMulImpl({\n a: isChannelsLast ? xReshaped : filterReshaped,\n b: isChannelsLast ? filterReshaped : xReshaped,\n transposeA: !isChannelsLast,\n transposeB,\n backend: backend2,\n bias,\n activation: activation2,\n preluActivationWeights,\n leakyreluAlpha\n });\n out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });\n intermediates.push(xReshaped);\n intermediates.push(filterReshaped);\n intermediates.push(result);\n }\n for (const i of intermediates) {\n backend2.disposeIntermediateTensorInfo(i);\n }\n return out;\n}\nfunction conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n const isChannelsLast = dataFormat === \"channelsLast\";\n const sharedDim = filterWidth * filterHeight * inChannels;\n const numCols = outHeight * outWidth;\n const x2ColShape = [convInfo.batchSize, sharedDim, numCols];\n const transposeA = true;\n const transposeB = false;\n const intermediates = [];\n if (preluActivationWeights != null) {\n const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);\n if (targetShape != null) {\n preluActivationWeights = reshape4({\n inputs: { x: preluActivationWeights },\n backend: backend2,\n attrs: { shape: targetShape }\n });\n intermediates.push(preluActivationWeights);\n }\n }\n if (bias != null) {\n const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);\n if (targetShape != null) {\n bias = reshape4({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } });\n intermediates.push(bias);\n }\n }\n const w2Row = reshape4({\n inputs: { x: filter },\n backend: backend2,\n attrs: { shape: [1, sharedDim, util_exports.sizeFromShape(filter.shape) / sharedDim] }\n });\n intermediates.push(w2Row);\n const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo);\n const customValues = [\n x.shape,\n [convInfo.padInfo.top, convInfo.padInfo.left],\n [convInfo.strideHeight, convInfo.strideWidth],\n [convInfo.dilationHeight, convInfo.dilationWidth],\n [convInfo.inChannels],\n [convInfo.filterWidth * convInfo.inChannels],\n [convInfo.outWidth]\n ];\n const im2Col = backend2.runWebGLProgram(im2ColProgram, [x], \"float32\", customValues);\n const im2ColReshaped = reshape4({ inputs: { x: im2Col }, backend: backend2, attrs: { shape: x2ColShape } });\n intermediates.push(im2Col);\n intermediates.push(im2ColReshaped);\n const hasBias = bias != null;\n const hasPreluActivationWeights = preluActivationWeights != null;\n const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null;\n const matmulProgram = new MatMulPackedProgram(isChannelsLast ? im2ColReshaped.shape : w2Row.shape, isChannelsLast ? w2Row.shape : im2ColReshaped.shape, isChannelsLast ? [convInfo.batchSize, numCols, convInfo.outChannels] : [convInfo.batchSize, convInfo.outChannels, numCols], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n const inputs = isChannelsLast ? [im2ColReshaped, w2Row] : [w2Row, im2ColReshaped];\n if (bias) {\n inputs.push(bias);\n }\n if (hasPreluActivationWeights) {\n inputs.push(preluActivationWeights);\n }\n if (hasLeakyreluAlpha) {\n const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n inputs.push($leakyreluAlpha);\n intermediates.push($leakyreluAlpha);\n }\n const product = backend2.runWebGLProgram(matmulProgram, inputs, \"float32\");\n const out = reshape4({ inputs: { x: product }, backend: backend2, attrs: { shape: convInfo.outShape } });\n intermediates.push(product);\n for (const i of intermediates) {\n backend2.disposeIntermediateTensorInfo(i);\n }\n return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js\nfunction conv2d4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter } = inputs;\n const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n let out;\n if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n out = conv2dByMatMul({ x, filter, convInfo, backend: backend2 });\n } else if (convInfo.strideWidth <= 2 && $dataFormat === \"channelsLast\" && env().getBool(\"WEBGL_EXP_CONV\")) {\n const program = new Conv2DPackedProgram(convInfo);\n const customValues = [\n [convInfo.padInfo.top, convInfo.padInfo.left],\n [convInfo.strideHeight, convInfo.strideWidth],\n [convInfo.dilationHeight, convInfo.dilationWidth],\n [convInfo.inHeight, convInfo.inWidth]\n ];\n out = backend2.runWebGLProgram(program, [x, filter], \"float32\", customValues);\n } else if (env().getBool(\"WEBGL_CONV_IM2COL\")) {\n out = conv2dWithIm2Row({ x, filter, convInfo, backend: backend2 });\n } else {\n const program = new Conv2DProgram(convInfo);\n out = backend2.runWebGLProgram(program, [x, filter], \"float32\");\n }\n const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n backend2.disposeIntermediateTensorInfo(out);\n return outReshaped;\n}\nvar conv2DConfig2 = {\n kernelName: Conv2D,\n backendName: \"webgl\",\n kernelFunc: conv2d4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu.js\nvar Conv2DDerFilterProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"x\", \"dy\"];\n this.outputShape = convInfo.filterShape;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n 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 < ${convInfo.batchSize}; b++) {\n for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n int xR = wR + yR * ${strideHeight} - ${padTop};\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n ${isChannelsLast ? `float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);` : `float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n};\nvar Conv2DDerInputProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"dy\", \"W\"];\n this.outputShape = convInfo.inShape;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n const padTop = filterHeight - 1 - convInfo.padInfo.top;\n const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n const rowDim = isChannelsLast ? 1 : 2;\n const colDim = isChannelsLast ? 2 : 3;\n const channelDim = isChannelsLast ? 3 : 1;\n this.userCode = `\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${channelDim}];\n\n ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - 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 < ${filterHeight}; wR++) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${filterHeight} - 1 - wR;\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${filterWidth} - 1 - wC;\n\n for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n if (${isChannelsLast}) {\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 `;\n }\n};\nvar Conv3DDerFilterProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"x\", \"dy\"];\n this.outputShape = convInfo.filterShape;\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const padFront = convInfo.padInfo.front;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n 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 < ${convInfo.batchSize}; b++) {\n for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n int xF = wF + yF * ${strideDepth} - ${padFront};\n\n if (xF < 0 || xF >= ${convInfo.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n int xR = wR + yR * ${strideHeight} - ${padTop};\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n};\nvar Conv3DDerInputProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"dy\", \"W\"];\n this.outputShape = convInfo.inShape;\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const padFront = filterDepth - 1 - convInfo.padInfo.front;\n const padTop = filterHeight - 1 - convInfo.padInfo.top;\n const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n this.userCode = `\n const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\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 < ${filterDepth}; wF++) {\n float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${filterDepth} - 1 - wF;\n\n for (int wR = 0; wR < ${filterHeight}; wR++) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${filterHeight} - 1 - wR;\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${filterWidth} - 1 - wC;\n\n for (int d2 = 0; d2 < ${convInfo.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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropFilter.js\nfunction conv2DBackpropFilter3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, dy } = inputs;\n const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;\n const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n const program = new Conv2DDerFilterProgram(convInfo);\n return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar conv2DBackpropFilterConfig2 = {\n kernelName: Conv2DBackpropFilter,\n backendName: \"webgl\",\n kernelFunc: conv2DBackpropFilter3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_packed_gpu.js\nvar Conv2DDerInputPackedProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"dy\", \"W\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.customUniforms = [\n { name: \"strides\", type: \"vec2\" }\n ];\n this.outputShape = convInfo.inShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const padTop = filterHeight - 1 - convInfo.padInfo.top;\n const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n this.userCode = `\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\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 < ${filterHeight}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${filterHeight} - 1 - wR;\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n int wCPerm = ${filterWidth} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${convInfo.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 < ${convInfo.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${convInfo.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 < ${convInfo.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 < ${convInfo.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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, filter } = inputs;\n const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n if (env().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\") && $dataFormat === \"channelsLast\") {\n const customValues = [\n [convInfo.strideHeight, convInfo.strideWidth]\n ];\n const program = new Conv2DDerInputPackedProgram(convInfo);\n return backend2.runWebGLProgram(program, [dy, filter], \"float32\", customValues);\n } else {\n const program = new Conv2DDerInputProgram(convInfo);\n return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n }\n}\nvar conv2DBackpropInputConfig2 = {\n kernelName: Conv2DBackpropInput,\n backendName: \"webgl\",\n kernelFunc: conv2DBackpropInput3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3D.js\nfunction conv3D2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter } = inputs;\n const { strides, pad: pad3, dilations } = attrs;\n const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);\n const program = new Conv3DProgram(convInfo);\n return backend2.runWebGLProgram(program, [x, filter], \"float32\");\n}\nvar conv3DConfig2 = {\n kernelName: Conv3D,\n backendName: \"webgl\",\n kernelFunc: conv3D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropFilterV2.js\nfunction conv3DBackpropFilterV22(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, dy } = inputs;\n const { strides, pad: pad3, filterShape } = attrs;\n const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);\n const program = new Conv3DDerFilterProgram(convInfo);\n return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar conv3DBackpropFilterV2Config2 = {\n kernelName: Conv3DBackpropFilterV2,\n backendName: \"webgl\",\n kernelFunc: conv3DBackpropFilterV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropInputV2.js\nfunction conv3DBackpropInput2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, filter } = inputs;\n const { pad: pad3, strides, inputShape } = attrs;\n const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);\n const program = new Conv3DDerInputProgram(convInfo);\n return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar conv3DBackpropInputConfig = {\n kernelName: Conv3DBackpropInputV2,\n backendName: \"webgl\",\n kernelFunc: conv3DBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cos.js\nvar COS = CHECK_NAN_SNIPPET_UNARY + `\n return cos(x);\n`;\nvar COS_PACKED = `\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${CHECK_NAN_SNIPPET_PACKED}\n return result;\n`;\nvar cos3 = unaryKernelFunc2({ opSnippet: COS, packedOpSnippet: COS_PACKED });\nvar cosConfig2 = {\n kernelName: Cos,\n backendName: \"webgl\",\n kernelFunc: cos3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cosh.js\nvar COSH = `\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`;\nvar cosh3 = unaryKernelFunc2({ opSnippet: COSH });\nvar coshConfig2 = {\n kernelName: Cosh,\n backendName: \"webgl\",\n kernelFunc: cosh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/crop_and_resize_gpu.js\nvar CropAndResizeProgram = class {\n constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n this.variableNames = [\"Image\", \"Boxes\", \"BoxInd\"];\n this.outputShape = [];\n const [batch, imageHeight, imageWidth, depth] = imageShape;\n const [numBoxes] = boxShape;\n const [cropHeight, cropWidth] = cropSize;\n this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n const methodId = method === \"bilinear\" ? 1 : 0;\n const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n const [heightRatio, heightScale, inY] = cropHeight > 1 ? [\n `${(imageHeight - 1) / (cropHeight - 1)}`,\n \"(y2-y1) * height_ratio\",\n `y1*${inputHeightFloat} + float(y)*(height_scale)`\n ] : [\n \"0.0\",\n \"0.0\",\n `0.5 * (y1+y2) * ${inputHeightFloat}`\n ];\n const [widthRatio, widthScale, inX] = cropWidth > 1 ? [\n `${(imageWidth - 1) / (cropWidth - 1)}`,\n \"(x2-x1) * width_ratio\",\n `x1*${inputWidthFloat} + float(x)*(width_scale)`\n ] : [\n \"0.0\",\n \"0.0\",\n `0.5 * (x1+x2) * ${inputWidthFloat}`\n ];\n this.userCode = `\n const float height_ratio = float(${heightRatio});\n const float width_ratio = float(${widthRatio});\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 >= ${batch}) {\n return;\n }\n\n float height_scale = ${heightScale};\n float width_scale = ${widthScale};\n\n float in_y = ${inY};\n if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n setOutput(float(${extrapolationValue}));\n return;\n }\n float in_x = ${inX};\n if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n setOutput(float(${extrapolationValue}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${methodId} == 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js\nvar cropAndResize4 = (args) => {\n const { inputs, backend: backend2, attrs } = args;\n const { image: image2, boxes, boxInd } = inputs;\n const { cropSize, method, extrapolationValue } = attrs;\n const program = new CropAndResizeProgram(image2.shape, boxes.shape, cropSize, method, extrapolationValue);\n return backend2.runWebGLProgram(program, [image2, boxes, boxInd], \"float32\");\n};\nvar cropAndResizeConfig2 = {\n kernelName: CropAndResize,\n backendName: \"webgl\",\n kernelFunc: cropAndResize4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/cum_gpu.js\nvar CumOpType;\n(function(CumOpType2) {\n CumOpType2[\"Prod\"] = \"*\";\n CumOpType2[\"Sum\"] = \"+\";\n})(CumOpType || (CumOpType = {}));\nvar CumProgram = class {\n constructor(op2, outputShape, exclusive, reverse5) {\n this.op = op2;\n this.outputShape = outputShape;\n this.variableNames = [\"x\"];\n this.customUniforms = [{ name: \"index\", type: \"float\" }];\n const rank = this.outputShape.length;\n const initVal = this.op === CumOpType.Prod ? \"1.0\" : \"0.0\";\n const val = exclusive ? initVal : `getX(${getCoords2(rank, \"coords\", this.op)})`;\n const length = this.outputShape[this.outputShape.length - 1];\n let condition = \"\";\n let idxString = \"\";\n if (exclusive) {\n condition = reverse5 ? `end != ${length - 1}` : \"end != 0\";\n idxString = reverse5 ? \"end + 1\" : \"end - 1\";\n } else {\n condition = reverse5 ? `end + pow2 < ${length}` : \"end >= pow2\";\n idxString = reverse5 ? \"end + pow2\" : \"end - pow2\";\n }\n this.userCode = `\n void main() {\n ${getCoordsDataType(rank)} coords = getOutputCoords();\n int end = ${getFinalCoord(rank, \"coords\", this.op)};\n float val = ${val};\n int pow2 = int(pow(2.0, index));\n if (${condition}) {\n int idx = ${idxString};\n ${getFinalCoord(rank, \"coords\", this.op)} = idx;\n val ${this.op}= getX(${getCoords2(rank, \"coords\", this.op)});\n }\n setOutput(val);\n }\n `;\n }\n};\nfunction getCoords2(rank, name, op2) {\n if (rank === 1) {\n return `${name}`;\n } else if (rank === 2) {\n return `${name}.x, ${name}.y`;\n } else if (rank === 3) {\n return `${name}.x, ${name}.y, ${name}.z`;\n } else if (rank === 4) {\n return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n } else {\n throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n }\n}\nfunction getFinalCoord(rank, name, op2) {\n if (rank === 1) {\n return `${name}`;\n } else if (rank === 2) {\n return `${name}.y`;\n } else if (rank === 3) {\n return `${name}.z`;\n } else if (rank === 4) {\n return `${name}.w`;\n } else {\n throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cum_impl.js\nfunction cumImpl(op2, x, backend2, axis, exclusive, reverse5) {\n const xRank = x.shape.length;\n const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n let permutedX = x;\n if (permutation != null) {\n permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n }\n const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n if (permutedAxis !== xRank - 1) {\n throw new Error(`WebGL cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);\n }\n const size = permutedX.shape[permutedAxis];\n let result = identity3({ inputs: { x: permutedX }, backend: backend2 });\n for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n const program = new CumProgram(op2, permutedX.shape, false, reverse5);\n const customValues = [[i]];\n const prevResult = result;\n result = backend2.runWebGLProgram(program, [result], result.dtype, customValues);\n backend2.disposeIntermediateTensorInfo(prevResult);\n }\n if (exclusive) {\n const program = new CumProgram(op2, permutedX.shape, exclusive, reverse5);\n const prevResult = result;\n result = backend2.runWebGLProgram(program, [result], result.dtype);\n backend2.disposeIntermediateTensorInfo(prevResult);\n }\n if (permutation != null) {\n const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n const reverseTransposedResult = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n backend2.disposeIntermediateTensorInfo(result);\n backend2.disposeIntermediateTensorInfo(permutedX);\n return reverseTransposedResult;\n }\n return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumprod.js\nfunction cumprod3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, exclusive, reverse: reverse5 } = attrs;\n return cumImpl(CumOpType.Prod, x, backend2, axis, exclusive, reverse5);\n}\nvar cumprodConfig2 = {\n kernelName: Cumprod,\n backendName: \"webgl\",\n kernelFunc: cumprod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumsum.js\nfunction cumsum3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, exclusive, reverse: reverse5 } = attrs;\n return cumImpl(CumOpType.Sum, x, backend2, axis, exclusive, reverse5);\n}\nvar cumsumConfig2 = {\n kernelName: Cumsum,\n backendName: \"webgl\",\n kernelFunc: cumsum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DenseBincount.js\nfunction denseBincount3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, weights } = inputs;\n const { size, binaryOutput } = attrs;\n if (x.shape.length === 1) {\n const xVals = backend2.readSync(x.dataId);\n const weightsVals = backend2.readSync(weights.dataId);\n const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);\n return backend2.makeTensorInfo([size], weights.dtype, outVals);\n } else if (x.shape.length === 2) {\n const xBuf = backend2.bufferSync(x);\n const weightsBuf = backend2.bufferSync(weights);\n const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput);\n return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);\n }\n throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);\n}\nvar denseBincountConfig2 = {\n kernelName: DenseBincount,\n backendName: \"webgl\",\n kernelFunc: denseBincount3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/depth_to_space_gpu.js\nvar DepthToSpaceProgram = class {\n constructor(outputShape, blockSize, dataFormat) {\n this.variableNames = [\"x\"];\n this.outputShape = [];\n this.outputShape = outputShape;\n this.blockSize = blockSize;\n this.dataFormat = 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 / ${blockSize};\n int offset_h = imod(h, ${blockSize});\n int in_w = w / ${blockSize};\n int offset_w = imod(w, ${blockSize});\n int offset_d = (offset_h * ${blockSize} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `;\n }\n getHeightCoordString() {\n if (this.dataFormat === \"NHWC\") {\n return `coords[1]`;\n } else {\n return `coords[2]`;\n }\n }\n getWidthCoordString() {\n if (this.dataFormat === \"NHWC\") {\n return `coords[2]`;\n } else {\n return `coords[3]`;\n }\n }\n getDepthCoordString() {\n if (this.dataFormat === \"NHWC\") {\n return `coords[3]`;\n } else {\n return `coords[1]`;\n }\n }\n getOutputDepthSize() {\n if (this.dataFormat === \"NHWC\") {\n return this.outputShape[3];\n } else {\n return this.outputShape[1];\n }\n }\n getInputSamplingString() {\n if (this.dataFormat === \"NHWC\") {\n return `getX(b, in_h, in_w, in_d)`;\n } else {\n return `getX(b, in_d, in_h, in_w)`;\n }\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthToSpace.js\nfunction depthToSpace3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { blockSize, dataFormat } = attrs;\n const batchSize = x.shape[0];\n const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n const outputHeight = inputHeight * blockSize;\n const outputWidth = inputWidth * blockSize;\n const outputDepth = inputDepth / (blockSize * blockSize);\n const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar depthToSpaceConfig2 = {\n kernelName: DepthToSpace,\n backendName: \"webgl\",\n kernelFunc: depthToSpace3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu_depthwise.js\nvar DepthwiseConv2DProgram = class {\n constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n this.variableNames = [\"x\", \"W\"];\n this.customUniforms = [\n { name: \"pads\", type: \"ivec2\" },\n { name: \"strides\", type: \"ivec2\" },\n { name: \"dilations\", type: \"ivec2\" },\n { name: \"inDims\", type: \"ivec2\" }\n ];\n this.outputShape = convInfo.outShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const channelMul = convInfo.outChannels / convInfo.inChannels;\n let activationSnippet = \"\", applyActivationSnippet = \"\";\n if (activation2) {\n if (hasPreluActivation) {\n activationSnippet = `float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${activation2}\n }`;\n } else if (hasLeakyReluAlpha) {\n activationSnippet = `float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${activation2}\n }`;\n } else {\n activationSnippet = `\n float activation(float x) {\n ${activation2}\n }\n `;\n }\n applyActivationSnippet = `result = activation(result);`;\n }\n const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n if (addBias) {\n this.variableNames.push(\"bias\");\n }\n if (hasPreluActivation) {\n this.variableNames.push(\"preluActivationWeights\");\n }\n if (hasLeakyReluAlpha) {\n this.variableNames.push(\"leakyreluAlpha\");\n }\n this.userCode = `\n ${activationSnippet}\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 / ${channelMul};\n int q = d2 - d1 * ${channelMul};\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 < ${filterHeight}; 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 < ${filterWidth}; 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 ${addBiasSnippet}\n ${applyActivationSnippet}\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu_depthwise.js\nvar DepthwiseConvPacked2DProgram = class {\n constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n this.variableNames = [\"x\", \"W\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.customUniforms = [\n { name: \"pads\", type: \"ivec2\" },\n { name: \"strides\", type: \"ivec2\" },\n { name: \"dilations\", type: \"ivec2\" },\n { name: \"inDims\", type: \"ivec2\" }\n ];\n this.outputShape = convInfo.outShape;\n this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n const channelMul = convInfo.outChannels / convInfo.inChannels;\n const padLeft = convInfo.padInfo.left;\n const strideWidth = convInfo.strideWidth;\n const dilationWidth = convInfo.dilationWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const texelsAcross = filterWidth;\n let mainLoop = `\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;\n for (let c = 0; c < filterWidth; c++) {\n mainLoop += `\n vec4 xTexelC${c * 2};\n int xTexelC${c * 2}Ready;\n vec4 xTexelC${c * 2 + 1};\n int xTexelC${c * 2 + 1}Ready;\n vec4 xC${c};`;\n }\n mainLoop += `\n for (int r = 0; r < ${filterHeight}; r++) {\n `;\n for (let c = 0; c < filterWidth; c++) {\n mainLoop += `\n xTexelC${c * 2} = vec4(0.0);\n xTexelC${c * 2}Ready = 0;\n xTexelC${c * 2 + 1} = vec4(0.0);\n xTexelC${c * 2 + 1}Ready = 0;\n xC${c} = vec4(0.0);`;\n }\n mainLoop += `\n xR = xRCorner + r * dilations[0];\n if (xR >=0 && xR < inDims[0]) {\n `;\n for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {\n const colIndex = texelC * 2;\n mainLoop += `\n xC = xCCorner + ${colIndex * dilationWidth};\n `;\n if (strideWidth === 1) {\n if (colIndex < filterWidth) {\n if (padLeft % 2 === 1) {\n mainLoop += `\n xCOffset = xC + 1;\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${colIndex}.zw = vec2(0.0);\n }\n xTexelC${colIndex}Ready = 1;\n }\n `;\n if (dilationWidth === 1 && colIndex > 0) {\n mainLoop += `\n xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);\n `;\n } else {\n mainLoop += `\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${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);\n } else {\n xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);\n }\n `;\n }\n } else {\n mainLoop += `\n if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n xTexelC${colIndex} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${colIndex}.zw = vec2(0.0);\n }\n xTexelC${colIndex}Ready = 1;\n }\n\n xC${colIndex} = xTexelC${colIndex};\n `;\n }\n if (colIndex + 1 < filterWidth) {\n const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;\n if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {\n mainLoop += `\n xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${colIndex + 1}.zw = vec2(0.0);\n }\n xTexelC${colIndex + 1}Ready = 1;\n }\n `;\n if (dilationWidth > 1) {\n mainLoop += `\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy);\n } else {\n xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy);\n }\n `;\n } else {\n mainLoop += `\n xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);\n `;\n }\n } else {\n if (nextTexelOffset === 1) {\n mainLoop += `\n xC${colIndex + 1} = xTexelC${colIndex};\n `;\n } else {\n mainLoop += `\n xCOffset = xC + ${nextTexelOffset};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${colIndex + 1}.zw = vec2(0.0);\n }\n xTexelC${colIndex + 1}Ready = 1;\n }\n\n xC${colIndex + 1} = xTexelC${colIndex + 1};\n `;\n }\n }\n }\n }\n } else {\n if (colIndex < filterWidth) {\n if (padLeft % 2 === 1) {\n mainLoop += `\n xCOffset = xC + 1 - strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${colIndex}.zw = vec2(0.0);\n }\n xTexelC${colIndex}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${colIndex + 1}.zw = vec2(0.0);\n }\n xTexelC${colIndex + 1}Ready = 1;\n }\n\n xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n `;\n if (colIndex + 1 < filterWidth) {\n mainLoop += `\n final = vec4(0.0);\n xCOffset = xC + 1 + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);\n `;\n }\n } else {\n mainLoop += `\n if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n xTexelC${colIndex} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${colIndex}.zw = vec2(0.0);\n }\n xTexelC${colIndex}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${colIndex + 1}.zw = vec2(0.);\n }\n xTexelC${colIndex + 1}Ready = 1;\n }\n\n xC${colIndex} = vec4(\n xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);\n `;\n if (colIndex + 1 < filterWidth) {\n mainLoop += `\n xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n `;\n }\n }\n }\n }\n if (colIndex < filterWidth) {\n mainLoop += `\n wTexel = getW(r, ${colIndex}, d1, q);\n dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);\n `;\n if (colIndex + 1 < filterWidth) {\n mainLoop += `\n wTexel = getW(r, ${colIndex + 1}, d1, q);\n dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);\n `;\n }\n }\n }\n mainLoop += `\n }\n `;\n mainLoop += `\n }\n `;\n let activationSnippet = \"\", applyActivationSnippet = \"\";\n if (activation2) {\n if (hasPreluActivation) {\n activationSnippet = `vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${activation2}\n }`;\n } else if (hasLeakyReluAlpha) {\n activationSnippet = `vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${activation2}\n }`;\n } else {\n activationSnippet = `vec4 activation(vec4 x) {\n ${activation2}\n }`;\n }\n applyActivationSnippet = `result = activation(result);`;\n }\n const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n if (addBias) {\n this.variableNames.push(\"bias\");\n }\n if (hasPreluActivation) {\n this.variableNames.push(\"preluActivationWeights\");\n }\n if (hasLeakyReluAlpha) {\n this.variableNames.push(\"leakyreluAlpha\");\n }\n this.userCode = `\n ${activationSnippet}\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 / ${channelMul};\n int q = d2 - d1 * ${channelMul};\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n vec4 dotProd = vec4(0.000000000000001);\n\n ${mainLoop}\n\n vec4 result = dotProd - vec4(0.000000000000001);\n ${addBiasSnippet}\n ${applyActivationSnippet}\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter } = inputs;\n const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;\n let $dilations = dilations;\n if ($dilations == null) {\n $dilations = [1, 1];\n }\n util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n const convInfo = backend_util_exports.computeConv2DInfo(\n x.shape,\n filter.shape,\n strides,\n $dilations,\n pad3,\n dimRoundingMode,\n true\n /* depthwise */\n );\n let program;\n if (env().getBool(\"WEBGL_PACK_DEPTHWISECONV\") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) {\n program = new DepthwiseConvPacked2DProgram(convInfo);\n } else {\n program = new DepthwiseConv2DProgram(convInfo);\n }\n const customValues = [\n [convInfo.padInfo.top, convInfo.padInfo.left],\n [convInfo.strideHeight, convInfo.strideWidth],\n [convInfo.dilationHeight, convInfo.dilationWidth],\n [convInfo.inHeight, convInfo.inWidth]\n ];\n return backend2.runWebGLProgram(program, [x, filter], \"float32\", customValues);\n}\nvar depthwiseConv2dNativeConfig2 = {\n kernelName: DepthwiseConv2dNative,\n backendName: \"webgl\",\n kernelFunc: depthwiseConv2dNative2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu_depthwise.js\nvar DepthwiseConv2DDerFilterProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"x\", \"dy\"];\n this.outputShape = convInfo.filterShape;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const channelMul = convInfo.outChannels / convInfo.inChannels;\n 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 * ${channelMul} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${convInfo.batchSize}; b++) {\n for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n int xR = wR + yR * ${strideHeight} - ${padTop};\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n};\nvar DepthwiseConv2DDerInputProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"dy\", \"W\"];\n this.outputShape = convInfo.inShape;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const padTop = filterHeight - 1 - convInfo.padInfo.top;\n const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n const channelMul = convInfo.outChannels / convInfo.inChannels;\n this.userCode = `\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\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 < ${filterHeight}; wR++) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${filterHeight} - 1 - wR;\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${filterWidth} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${channelMul}; dm++) {\n int d2 = d1 * ${channelMul} + 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js\nfunction depthwiseConv2dNativeBackpropFilter3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, dy } = inputs;\n const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;\n const convInfo = backend_util_exports.computeConv2DInfo(\n x.shape,\n filterShape,\n strides,\n dilations,\n pad3,\n dimRoundingMode,\n true\n /* depthwise */\n );\n const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar depthwiseConv2dNativeBackpropFilterConfig2 = {\n kernelName: DepthwiseConv2dNativeBackpropFilter,\n backendName: \"webgl\",\n kernelFunc: depthwiseConv2dNativeBackpropFilter3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropInput.js\nfunction depthwiseConv2dNativeBackpropInput3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, filter } = inputs;\n const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;\n const convInfo = backend_util_exports.computeConv2DInfo(\n inputShape,\n filter.shape,\n strides,\n dilations,\n pad3,\n dimRoundingMode,\n true\n /* depthwise */\n );\n const program = new DepthwiseConv2DDerInputProgram(convInfo);\n return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar depthwiseConv2dNativeBackpropInputConfig2 = {\n kernelName: DepthwiseConv2dNativeBackpropInput,\n backendName: \"webgl\",\n kernelFunc: depthwiseConv2dNativeBackpropInput3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/diag_gpu.js\nvar DiagProgram = class {\n constructor(size) {\n this.variableNames = [\"X\"];\n this.outputShape = [size, size];\n 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Diag.js\nfunction diag3(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n const outShape = [...x.shape, ...x.shape];\n const xSize = util_exports.sizeFromShape(x.shape);\n const flat = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [xSize] } });\n const program = new DiagProgram(xSize);\n const res = backend2.runWebGLProgram(program, [flat], flat.dtype);\n const out = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });\n backend2.disposeIntermediateTensorInfo(flat);\n backend2.disposeIntermediateTensorInfo(res);\n return out;\n}\nvar diagConfig2 = {\n kernelName: Diag,\n backendName: \"webgl\",\n kernelFunc: diag3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/dilation_gpu.js\nvar Dilation2DProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"x\", \"W\"];\n this.outputShape = convInfo.outShape;\n const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo;\n const { top: padTop, left: padLeft } = padInfo;\n this.userCode = `\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\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 < ${filterHeight}; h++) {\n int hIn = hBeg + h * ${dilationHeight};\n\n if (hIn >= 0 && hIn < ${inHeight}) {\n for (int w = 0; w < ${filterWidth}; w++) {\n int wIn = wBeg + w * ${dilationWidth};\n\n if (wIn >= 0 && wIn < ${inWidth}) {\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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Dilation2D.js\nfunction dilation2D(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter } = inputs;\n const { strides, pad: pad3, dilations } = attrs;\n const convInfo = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n let out;\n const program = new Dilation2DProgram(convInfo);\n out = backend2.runWebGLProgram(program, [x, filter], \"float32\");\n const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n backend2.disposeIntermediateTensorInfo(out);\n return outReshaped;\n}\nvar dilation2DConfig2 = {\n kernelName: Dilation2D,\n backendName: \"webgl\",\n kernelFunc: dilation2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Einsum.js\nfunction einsum3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { equation } = attrs;\n const tensors = inputs;\n const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n const nSteps = steps.length;\n let out = null;\n let numDimsRemaining = allDims.length;\n const tensorsToDispose = [];\n for (let i = 0; i < nSteps; ++i) {\n for (const idTerm of steps[i]) {\n const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n let x;\n if (backend_util_exports.isIdentityPermutation(perm)) {\n x = tensors[idTerm];\n } else {\n x = transpose3({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n tensorsToDispose.push(x);\n }\n const targetShape = x.shape.slice();\n for (let k = 0; k < dimsToExpand.length; ++k) {\n targetShape.splice(dimsToExpand[k], 0, 1);\n }\n if (!util_exports.arraysEqual(x.shape, targetShape)) {\n x = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n tensorsToDispose.push(x);\n }\n if (out === null) {\n out = x;\n } else {\n out = multiply3({ inputs: { a: x, b: out }, backend: backend2 });\n tensorsToDispose.push(out);\n }\n }\n if (i < nSteps - 1) {\n if (path[i] >= 0) {\n out = sum4({\n inputs: { x: out },\n backend: backend2,\n attrs: {\n axis: path[i] - (allDims.length - numDimsRemaining),\n keepDims: false\n }\n });\n tensorsToDispose.push(out);\n }\n numDimsRemaining--;\n }\n }\n for (const tensorInfo of tensorsToDispose) {\n if (tensorInfo === out) {\n continue;\n }\n backend2.disposeIntermediateTensorInfo(tensorInfo);\n }\n return out;\n}\nvar einsumConfig2 = {\n kernelName: Einsum,\n backendName: \"webgl\",\n kernelFunc: einsum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Elu.js\nvar ELU4 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nvar ELU_PACKED = `\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`;\nvar elu5 = unaryKernelFunc2({ opSnippet: ELU4, packedOpSnippet: ELU_PACKED });\nvar eluConfig2 = {\n kernelName: Elu,\n backendName: \"webgl\",\n kernelFunc: elu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/EluGrad.js\nvar ELU_DER = `return (b >= 0.0) ? a : a * (b + 1.0);`;\nvar ELU_DER_PACKED = `\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nvar eluGrad2 = (args) => {\n const { inputs, backend: backend2 } = args;\n const { dy, y } = inputs;\n const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) : new BinaryOpProgram(ELU_DER, dy.shape, y.shape);\n return backend2.runWebGLProgram(program, [dy, y], dy.dtype);\n};\nvar eluGradConfig3 = {\n kernelName: EluGrad,\n backendName: \"webgl\",\n kernelFunc: eluGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Equal.js\nvar PACKED_EQUAL = `\n return vec4(equal(a, b));\n`;\nvar EQUAL = `return float(a == b);`;\nvar equal3 = binaryKernelFunc2({\n opSnippet: EQUAL,\n packedOpSnippet: PACKED_EQUAL,\n dtype: \"bool\",\n cpuKernelImpl: equalImplCPU\n});\nvar equalConfig2 = {\n kernelName: Equal,\n backendName: \"webgl\",\n kernelFunc: equal3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Erf.js\nvar ERF = `\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 = ${backend_util_exports.ERF_P};\n float a1 = ${backend_util_exports.ERF_A1};\n float a2 = ${backend_util_exports.ERF_A2};\n float a3 = ${backend_util_exports.ERF_A3};\n float a4 = ${backend_util_exports.ERF_A4};\n float a5 = ${backend_util_exports.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`;\nvar erf3 = unaryKernelFunc2({ opSnippet: ERF });\nvar erfConfig2 = {\n kernelName: Erf,\n backendName: \"webgl\",\n kernelFunc: erf3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Exp.js\nvar EXP = CHECK_NAN_SNIPPET_UNARY + `\n return exp(x);\n`;\nvar EXP_PACKED = `\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`;\nvar exp3 = unaryKernelFunc2({\n opSnippet: EXP,\n packedOpSnippet: EXP_PACKED,\n cpuKernelImpl: expImplCPU,\n dtype: \"float32\"\n});\nvar expConfig2 = {\n kernelName: Exp,\n backendName: \"webgl\",\n kernelFunc: exp3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ExpandDims.js\nfunction expandDims4(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { dim } = attrs;\n const { input: input2 } = inputs;\n const inputRank = input2.shape.length;\n const newShape = input2.shape.slice();\n let $dim = dim;\n if (dim < 0) {\n util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n $dim = inputRank + dim + 1;\n }\n newShape.splice($dim, 0, 1);\n return reshape4({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig2 = {\n kernelName: ExpandDims,\n backendName: \"webgl\",\n kernelFunc: expandDims4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Expm1.js\nvar EXPM1 = `return exp(x) - 1.0;`;\nvar expm13 = unaryKernelFunc2({ opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU });\nvar expm1Config2 = {\n kernelName: Expm1,\n backendName: \"webgl\",\n kernelFunc: expm13\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/fft_gpu.js\nvar FFTProgram = class {\n constructor(component, inputShape, inverse) {\n this.variableNames = [\"real\", \"imag\"];\n const innerDim = inputShape[1];\n this.outputShape = inputShape;\n const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n const resultDenominator = inverse ? `${innerDim}.0` : \"1.0\";\n let opString;\n if (component === \"real\") {\n opString = \"return real * expR - imag * expI;\";\n } else if (component === \"imag\") {\n opString = \"return real * expI + imag * expR;\";\n } else {\n throw new Error(`FFT component must be either \"real\" or \"imag\", got ${component}.`);\n }\n this.userCode = `\n const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${opString}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${innerDim});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${innerDim}; 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) / ${resultDenominator};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT_impl.js\nfunction fftImpl2(x, inverse, backend2) {\n const xData = backend2.texData.get(x.dataId);\n const inputSize = util_exports.sizeFromShape(x.shape);\n const innerDimensionSize = x.shape[x.shape.length - 1];\n const batch = inputSize / innerDimensionSize;\n const input2D = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [batch, innerDimensionSize] } });\n const xShape = input2D.shape;\n const realProgram = new FFTProgram(\"real\", xShape, inverse);\n const imagProgram = new FFTProgram(\"imag\", xShape, inverse);\n const inputs = [\n {\n dataId: xData.complexTensorInfos.real.dataId,\n dtype: xData.complexTensorInfos.real.dtype,\n shape: xShape\n },\n {\n dataId: xData.complexTensorInfos.imag.dataId,\n dtype: xData.complexTensorInfos.imag.dtype,\n shape: xShape\n }\n ];\n const realPart = backend2.runWebGLProgram(realProgram, inputs, \"float32\");\n const imagPart = backend2.runWebGLProgram(imagProgram, inputs, \"float32\");\n const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(realPart);\n backend2.disposeIntermediateTensorInfo(imagPart);\n const complexOutputReshaped = reshape4({ inputs: { x: complexOutput }, backend: backend2, attrs: { shape: x.shape } });\n backend2.disposeIntermediateTensorInfo(input2D);\n backend2.disposeIntermediateTensorInfo(complexOutput);\n return complexOutputReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT.js\nfunction fft3(args) {\n const { inputs, backend: backend2 } = args;\n const { input: input2 } = inputs;\n return fftImpl2(input2, false, backend2);\n}\nvar fftConfig2 = {\n kernelName: FFT,\n backendName: \"webgl\",\n kernelFunc: fft3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/fill_gpu.js\nvar FillProgram = class {\n constructor(shape, value) {\n this.outputShape = [];\n this.customUniforms = [{ name: \"value\", type: \"float\" }];\n this.variableNames = [\"x\"];\n this.outputShape = shape;\n this.userCode = `\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Fill.js\nfunction fill3(args) {\n const { backend: backend2, attrs } = args;\n const { shape, value } = attrs;\n let { dtype } = attrs;\n dtype = dtype || util_exports.inferDtype(value);\n if (dtype === \"string\") {\n const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));\n values.fill(value);\n return backend2.makeTensorInfo(shape, dtype, values);\n } else {\n const program = new FillProgram(shape, value);\n const customValues = [[value]];\n return backend2.runWebGLProgram(program, [], dtype, customValues);\n }\n}\nvar fillConfig2 = {\n kernelName: Fill,\n backendName: \"webgl\",\n kernelFunc: fill3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/flip_left_right_gpu.js\nvar FlipLeftRightProgram = class {\n constructor(imageShape) {\n this.variableNames = [\"Image\"];\n this.outputShape = [];\n const imageWidth = imageShape[2];\n this.outputShape = imageShape;\n this.userCode = `\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${imageWidth} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${imageWidth}) {\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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig2 = {\n kernelName: FlipLeftRight,\n backendName: \"webgl\",\n kernelFunc: ({ inputs, backend: backend2 }) => {\n const { image: image2 } = inputs;\n const webglBackend = backend2;\n const program = new FlipLeftRightProgram(image2.shape);\n const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype);\n return output;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Floor.js\nvar FLOOR = `return floor(x);`;\nvar floor3 = unaryKernelFunc2({ opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU });\nvar floorConfig2 = {\n kernelName: Floor,\n backendName: \"webgl\",\n kernelFunc: floor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FloorDiv.js\nvar INT_DIV = `\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`;\nvar INT_DIV_PACKED = `\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`;\nvar floorDiv3 = binaryKernelFunc2({ opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: \"int32\" });\nvar floorDivConfig2 = {\n kernelName: FloorDiv,\n backendName: \"webgl\",\n kernelFunc: floorDiv3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_gpu.js\nvar FromPixelsProgram = class {\n constructor(outputShape) {\n this.variableNames = [\"A\"];\n const glsl = getGlslDifferences();\n const [height, width] = outputShape;\n this.outputShape = outputShape;\n 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(${width}.0, ${height}.0);\n\n vec4 values = ${glsl.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_packed_gpu.js\nvar FromPixelsPackedProgram = class {\n constructor(outputShape) {\n this.variableNames = [\"A\"];\n this.packedInputs = false;\n this.packedOutput = true;\n const glsl = getGlslDifferences();\n const [height, width] = outputShape;\n this.outputShape = outputShape;\n this.userCode = `\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${width}.0, ${height}.0);\n vec4 values = ${glsl.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 ${glsl.output} = result;\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels.js\nvar fromPixelsConfig = {\n kernelName: FromPixels,\n backendName: \"webgl\",\n kernelFunc: fromPixels2\n};\nvar fromPixels2DContext2;\nvar willReadFrequently = env().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");\nfunction fromPixels2(args) {\n const { inputs, backend: backend2, attrs } = args;\n let { pixels } = inputs;\n const { numChannels } = attrs;\n const isVideo = typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement;\n const isImage = typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement;\n const [width, height] = isVideo ? [\n pixels.videoWidth,\n pixels.videoHeight\n ] : [pixels.width, pixels.height];\n const texShape = [height, width];\n const outShape = [height, width, numChannels];\n if (isImage || isVideo) {\n const newWillReadFrequently = env().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");\n if (fromPixels2DContext2 == null || newWillReadFrequently !== willReadFrequently) {\n willReadFrequently = newWillReadFrequently;\n fromPixels2DContext2 = document.createElement(\"canvas\").getContext(\"2d\", { willReadFrequently });\n }\n fromPixels2DContext2.canvas.width = width;\n fromPixels2DContext2.canvas.height = height;\n fromPixels2DContext2.drawImage(pixels, 0, 0, width, height);\n pixels = fromPixels2DContext2.canvas;\n }\n const tempPixelHandle = backend2.makeTensorInfo(texShape, \"int32\");\n backend2.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId), pixels);\n const program = env().getBool(\"WEBGL_PACK\") ? new FromPixelsPackedProgram(outShape) : new FromPixelsProgram(outShape);\n const res = backend2.runWebGLProgram(program, [tempPixelHandle], \"int32\");\n backend2.disposeData(tempPixelHandle.dataId);\n return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedConv2D.js\nfunction fusedConv2d(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter, bias, preluActivationWeights } = inputs;\n const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n let out;\n const intermediates = [];\n const hasBias = bias != null;\n const hasPreluActivationWeights = preluActivationWeights != null;\n const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n const prepareInputs = () => {\n const inputs2 = [x, filter];\n const alignInputWithDataFormat = (input2, dataFormat2) => {\n if (dataFormat2 === \"NCHW\" && input2.shape.length === 1 && input2.shape[0] !== 1) {\n const alignedInput = reshape4({\n inputs: { x: input2 },\n backend: backend2,\n attrs: { shape: [input2.shape[0], 1, 1] }\n });\n intermediates.push(alignedInput);\n return alignedInput;\n }\n return input2;\n };\n if (hasBias) {\n inputs2.push(alignInputWithDataFormat(bias, dataFormat));\n }\n if (hasPreluActivationWeights) {\n inputs2.push(alignInputWithDataFormat(preluActivationWeights, dataFormat));\n }\n if (hasLeakyreluAlpha) {\n const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n inputs2.push($leakyreluAlpha);\n intermediates.push($leakyreluAlpha);\n }\n return inputs2;\n };\n if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n out = conv2dByMatMul({\n x,\n filter,\n convInfo,\n backend: backend2,\n bias,\n activation: activation2,\n preluActivationWeights,\n leakyreluAlpha\n });\n } else if (convInfo.strideWidth <= 2 && $dataFormat === \"channelsLast\" && env().getBool(\"WEBGL_EXP_CONV\")) {\n const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null;\n const program = new Conv2DPackedProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n const customValues = [\n [convInfo.padInfo.top, convInfo.padInfo.left],\n [convInfo.strideHeight, convInfo.strideWidth],\n [convInfo.dilationHeight, convInfo.dilationWidth],\n [convInfo.inHeight, convInfo.inWidth]\n ];\n const inputs2 = prepareInputs();\n out = backend2.runWebGLProgram(program, inputs2, \"float32\", customValues);\n } else if (env().getBool(\"WEBGL_CONV_IM2COL\")) {\n out = conv2dWithIm2Row({\n x,\n filter,\n convInfo,\n backend: backend2,\n bias,\n activation: activation2,\n preluActivationWeights,\n leakyreluAlpha\n });\n } else {\n const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, false) : null;\n const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n const inputs2 = prepareInputs();\n out = backend2.runWebGLProgram(program, inputs2, \"float32\");\n }\n const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n intermediates.push(out);\n intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return outReshaped;\n}\nvar fusedConv2DConfig2 = {\n kernelName: FusedConv2D,\n backendName: \"webgl\",\n kernelFunc: fusedConv2d\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter, bias, preluActivationWeights } = inputs;\n const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n const intermediates = [];\n let $dilations = dilations;\n if ($dilations == null) {\n $dilations = [1, 1];\n }\n util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n const convInfo = backend_util_exports.computeConv2DInfo(\n x.shape,\n filter.shape,\n strides,\n $dilations,\n pad3,\n dimRoundingMode,\n true\n /* depthwise */\n );\n const shouldPackDepthwiseConv = env().getBool(\"WEBGL_PACK_DEPTHWISECONV\") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1;\n const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, shouldPackDepthwiseConv) : null;\n const programInputs = [x, filter];\n const hasBias = bias != null;\n const hasPreluActivationWeights = preluActivationWeights != null;\n const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n if (hasBias) {\n programInputs.push(bias);\n }\n if (hasPreluActivationWeights) {\n programInputs.push(preluActivationWeights);\n }\n if (hasLeakyreluAlpha) {\n const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n programInputs.push($leakyreluAlpha);\n intermediates.push($leakyreluAlpha);\n }\n let program;\n if (shouldPackDepthwiseConv) {\n program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n } else {\n program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n }\n const customValues = [\n [convInfo.padInfo.top, convInfo.padInfo.left],\n [convInfo.strideHeight, convInfo.strideWidth],\n [convInfo.dilationHeight, convInfo.dilationWidth],\n [convInfo.inHeight, convInfo.inWidth]\n ];\n const result = backend2.runWebGLProgram(program, programInputs, \"float32\", customValues);\n intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return result;\n}\nvar fusedDepthwiseConv2DConfig2 = {\n kernelName: FusedDepthwiseConv2D,\n backendName: \"webgl\",\n kernelFunc: fusedDepthwiseConv2D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_nd_gpu.js\nvar GatherNDProgram = class {\n constructor(sliceDim, strides, shape, paramsShape) {\n this.sliceDim = sliceDim;\n this.strides = strides;\n this.paramsShape = paramsShape;\n this.variableNames = [\"x\", \"indices\"];\n this.outputShape = shape;\n const dtype = getCoordsDataType(shape.length);\n let mainLoop = `\n int index;`;\n for (let j = 0; j < this.sliceDim; j++) {\n mainLoop += `\n index = round(getIndices(coords[0], ${j}));\n out_of_bounds = out_of_bounds || index < 0;\n out_of_bounds = out_of_bounds || index >= ${this.paramsShape[j]};\n flattenIndex += index * ${this.strides[j]};`;\n }\n this.userCode = `\n void main() {\n ${dtype} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${mainLoop}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js\nfunction gatherNd2(args) {\n const { inputs, backend: backend2 } = args;\n const { params, indices } = inputs;\n const indicesShape = indices.shape;\n const sliceRank = indicesShape[indicesShape.length - 1];\n const paramsSize = util_exports.sizeFromShape(params.shape);\n const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });\n const flattenX = reshape4({\n inputs: { x: params },\n backend: backend2,\n attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }\n });\n if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === \"string\") {\n const indicesData = backend2.readSync(indices.dataId);\n const paramsBuf = backend2.bufferSync(params);\n const outValue = gatherNdImplCPU(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);\n }\n const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize], params.shape);\n const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype);\n const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });\n backend2.disposeIntermediateTensorInfo(flattenIndices);\n backend2.disposeIntermediateTensorInfo(flattenX);\n backend2.disposeIntermediateTensorInfo(res);\n return reshaped;\n}\nvar gatherNdConfig2 = {\n kernelName: GatherNd,\n backendName: \"webgl\",\n kernelFunc: gatherNd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_gpu.js\nvar GatherProgram = class {\n constructor(aShape, outputShape) {\n this.variableNames = [\"A\", \"indices\"];\n this.outputShape = outputShape;\n this.rank = outputShape.length;\n const dtype = getCoordsDataType(this.rank);\n const sourceCoords = getSourceCoords2(aShape, 2);\n this.userCode = `\n void main() {\n ${dtype} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${sourceCoords}));\n }\n `;\n }\n};\nfunction getSourceCoords2(aShape, axis) {\n const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n const sourceCoords = [];\n for (let i = 0; i < aShape.length; i++) {\n if (i === 2) {\n sourceCoords.push(\"index\");\n } else {\n sourceCoords.push(`${currentCoords[i]}`);\n }\n }\n return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherV2.js\nfunction gatherV22(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, indices } = inputs;\n const { axis, batchDims } = attrs;\n const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n if (env().get(\"DEBUG\")) {\n const indicesVals = backend2.readSync(indices.dataId);\n const axisDim = x.shape[parsedAxis];\n for (let i = 0; i < indicesVals.length; ++i) {\n const index = indicesVals[i];\n util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n }\n }\n const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n const indicesSize = util_exports.sizeFromShape(indices.shape);\n const toDispose = [];\n const flattenX = reshape4({\n inputs: { x },\n backend: backend2,\n attrs: {\n shape: [\n shapeInfo.batchSize,\n shapeInfo.outerSize,\n shapeInfo.dimSize,\n shapeInfo.sliceSize\n ]\n }\n });\n const flattenIndex = reshape4({\n inputs: { x: indices },\n backend: backend2,\n attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n });\n toDispose.push(flattenX);\n toDispose.push(flattenIndex);\n const flattenOutputShape = [\n shapeInfo.batchSize,\n shapeInfo.outerSize,\n indicesSize / shapeInfo.batchSize,\n shapeInfo.sliceSize\n ];\n if (backend2.shouldExecuteOnCPU([x, indices]) || x.dtype === \"string\") {\n const indicesBuf = backend2.bufferSync(flattenIndex);\n const xBuf = backend2.bufferSync(flattenX);\n const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);\n toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n }\n const program = new GatherProgram(flattenX.shape, flattenOutputShape);\n const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype);\n toDispose.push(res);\n const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });\n toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return reshaped;\n}\nvar gatherV2Config2 = {\n kernelName: GatherV2,\n backendName: \"webgl\",\n kernelFunc: gatherV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Greater.js\nvar GREATER = `return float(a > b);`;\nvar GREATER_PACKED = `\n return vec4(greaterThan(a, b));\n`;\nvar greater4 = binaryKernelFunc2({\n opSnippet: GREATER,\n packedOpSnippet: GREATER_PACKED,\n cpuKernelImpl: greaterImplCPU,\n dtype: \"bool\"\n});\nvar greaterConfig2 = {\n kernelName: Greater,\n backendName: \"webgl\",\n kernelFunc: greater4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GreaterEqual.js\nvar GREATER_EQUAL = `return float(a >= b);`;\nvar GREATER_EQUAL_PACKED = `\n return vec4(greaterThanEqual(a, b));\n`;\nvar greaterEqual3 = binaryKernelFunc2({\n opSnippet: GREATER_EQUAL,\n packedOpSnippet: GREATER_EQUAL_PACKED,\n dtype: \"bool\",\n cpuKernelImpl: greaterEqualImplCPU\n});\nvar greaterEqualConfig2 = {\n kernelName: GreaterEqual,\n backendName: \"webgl\",\n kernelFunc: greaterEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IFFT.js\nfunction ifft3(args) {\n const { inputs, backend: backend2 } = args;\n const { input: input2 } = inputs;\n return fftImpl2(input2, true, backend2);\n}\nvar ifftConfig2 = {\n kernelName: IFFT,\n backendName: \"webgl\",\n kernelFunc: ifft3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsFinite.js\nvar IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nvar isFinite4 = unaryKernelFunc2({ opSnippet: IS_FINITE, dtype: \"bool\" });\nvar isFiniteConfig2 = {\n kernelName: IsFinite,\n backendName: \"webgl\",\n kernelFunc: isFinite4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsInf.js\nvar IS_INF = `return float(isinf(x));`;\nvar isInf3 = unaryKernelFunc2({ opSnippet: IS_INF, dtype: \"bool\" });\nvar isInfConfig2 = {\n kernelName: IsInf,\n backendName: \"webgl\",\n kernelFunc: isInf3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsNaN.js\nvar IS_NAN = `return float(isnan(x));`;\nvar isNaN4 = unaryKernelFunc2({ opSnippet: IS_NAN, dtype: \"bool\" });\nvar isNaNConfig2 = {\n kernelName: IsNan,\n backendName: \"webgl\",\n kernelFunc: isNaN4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Less.js\nvar LESS = `return float(a < b);`;\nvar LESS_PACKED = `\n return vec4(lessThan(a, b));\n`;\nvar less4 = binaryKernelFunc2({\n opSnippet: LESS,\n packedOpSnippet: LESS_PACKED,\n cpuKernelImpl: lessImplCPU,\n dtype: \"bool\"\n});\nvar lessConfig2 = {\n kernelName: Less,\n backendName: \"webgl\",\n kernelFunc: less4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LessEqual.js\nvar LESS_EQUAL = `return float(a <= b);`;\nvar LESS_EQUAL_PACKED = `\n return vec4(lessThanEqual(a, b));\n`;\nvar lessEqual3 = binaryKernelFunc2({\n opSnippet: LESS_EQUAL,\n packedOpSnippet: LESS_EQUAL_PACKED,\n cpuKernelImpl: lessEqualImplCPU,\n dtype: \"bool\"\n});\nvar lessEqualConfig2 = {\n kernelName: LessEqual,\n backendName: \"webgl\",\n kernelFunc: lessEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LinSpace.js\nfunction linSpace2(args) {\n const { backend: backend2, attrs } = args;\n const { start, stop, num } = attrs;\n const outVals = linSpaceImplCPU(start, stop, num);\n return backend2.makeTensorInfo([outVals.length], \"float32\", outVals);\n}\nvar linSpaceConfig2 = {\n kernelName: LinSpace,\n backendName: \"webgl\",\n kernelFunc: linSpace2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log.js\nvar LOG = CHECK_NAN_SNIPPET_UNARY + `\n return x < 0.0 ? 0./0. : log(x);\n`;\nvar LOG_PACKED = `\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`;\nvar log4 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU });\nvar logConfig2 = {\n kernelName: Log,\n backendName: \"webgl\",\n kernelFunc: log4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log1p.js\nvar LOG1P = CHECK_NAN_SNIPPET_UNARY + `\n return log(1.0 + x);\n`;\nvar log1p3 = unaryKernelFunc2({ opSnippet: LOG1P });\nvar log1pConfig2 = {\n kernelName: Log1p,\n backendName: \"webgl\",\n kernelFunc: log1p3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalAnd.js\nvar LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nvar LOGICAL_AND_PACKED = `\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nvar logicalAnd3 = binaryKernelFunc2({\n opSnippet: LOGICAL_AND,\n packedOpSnippet: LOGICAL_AND_PACKED,\n dtype: \"bool\"\n});\nvar logicalAndConfig2 = {\n kernelName: LogicalAnd,\n backendName: \"webgl\",\n kernelFunc: logicalAnd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalNot.js\nvar LOGICAL_NOT = `return float(!(x >= 1.0));`;\nvar logicalNot3 = unaryKernelFunc2({ opSnippet: LOGICAL_NOT });\nvar logicalNotConfig2 = {\n kernelName: LogicalNot,\n backendName: \"webgl\",\n kernelFunc: logicalNot3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalOr.js\nvar LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nvar LOGICAL_OR_PACKED = `\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`;\nvar logicalOr3 = binaryKernelFunc2({ opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: \"bool\" });\nvar logicalOrConfig2 = {\n kernelName: LogicalOr,\n backendName: \"webgl\",\n kernelFunc: logicalOr3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_gpu.js\nvar LRNProgram = class {\n constructor(xShape, radius, bias, alpha, beta) {\n this.variableNames = [\"x\"];\n this.outputShape = [];\n const rad = radius;\n const maxD = xShape[3] - 1;\n this.outputShape = xShape;\n let powOperator;\n const basis = `float(${bias}) + float(${alpha}) * sum`;\n if (beta === 0.5) {\n powOperator = `inversesqrt(${basis})`;\n } else if (beta === 1) {\n powOperator = `1.0/(${basis})`;\n } else {\n powOperator = `exp(log(${basis}) * float(-${beta}));`;\n }\n this.userCode = `\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${rad}; j <= ${rad}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${maxD}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${powOperator};\n setOutput(val);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_packed_gpu.js\nvar LRNPackedProgram = class {\n constructor(xShape, radius, bias, alpha, beta) {\n this.variableNames = [\"x\"];\n this.outputShape = [];\n this.packedInputs = true;\n this.packedOutput = true;\n const rad = radius;\n const maxD = xShape[3] - 1;\n this.outputShape = xShape;\n let powOperator;\n const basis = `float(${bias}) + float(${alpha}) * sum`;\n if (beta === 0.5) {\n powOperator = `inversesqrt(${basis})`;\n } else if (beta === 1) {\n powOperator = `1.0/(${basis})`;\n } else {\n powOperator = `exp(log(${basis}) * float(-${beta}));`;\n }\n 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 - ${rad};\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 = - ${rad}; j <= ${rad}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\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 * ${powOperator};\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRN.js\nvar lrn = (args) => {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { depthRadius, bias, alpha, beta } = attrs;\n const program = env().getBool(\"WEBGL_PACK_NORMALIZATION\") ? new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) : new LRNProgram(x.shape, depthRadius, bias, alpha, beta);\n return backend2.runWebGLProgram(program, [x], x.dtype);\n};\nvar LRNConfig2 = {\n kernelName: LRN,\n backendName: \"webgl\",\n kernelFunc: lrn\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_grad_gpu.js\nvar LRNGradProgram = class {\n constructor(inputShape, depthRadius, bias, alpha, beta) {\n this.variableNames = [\"inputImage\", \"outputImage\", \"dy\"];\n this.outputShape = [];\n this.outputShape = inputShape;\n this.depth = inputShape[3];\n this.depthRadius = depthRadius;\n this.bias = bias;\n this.alpha = alpha;\n this.beta = beta;\n 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 - ${depthRadius})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${depthRadius} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${alpha}) * norm + float(${bias});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${alpha})\n * float(${beta})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${beta});\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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRNGrad.js\nvar lrnGrad = (args) => {\n const { inputs, backend: backend2, attrs } = args;\n const { x, y, dy } = inputs;\n const { depthRadius, bias, alpha, beta } = attrs;\n const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta);\n return backend2.runWebGLProgram(program, [x, y, dy], x.dtype);\n};\nvar LRNGradConfig2 = {\n kernelName: LRNGrad,\n backendName: \"webgl\",\n kernelFunc: lrnGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max_impl.js\nfunction maxImpl2(x, reduceShape, outShape, backend2) {\n const inSize = util_exports.sizeFromShape(reduceShape);\n const xSize = util_exports.sizeFromShape(x.shape);\n const batchSize = xSize / inSize;\n const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n const reduced = reduce(reshapedInput, x.dtype, \"max\", backend2);\n const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(reshapedInput);\n backend2.disposeIntermediateTensorInfo(reduced);\n return reshapedOutput;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max.js\nfunction max4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { reductionIndices, keepDims } = attrs;\n const xRank = x.shape.length;\n const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n const maxInputIsTransposed = permutedAxes != null;\n const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n let maxInput = x;\n if (maxInputIsTransposed) {\n if (shouldExecuteOnCPU) {\n const xTexData = backend2.texData.get(maxInput.dataId);\n const values = xTexData.values;\n const newShape = new Array(xRank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = x.shape[permutedAxes[i]];\n }\n const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n maxInput = backend2.makeTensorInfo(newShape, x.dtype);\n const maxInputData = backend2.texData.get(maxInput.dataId);\n maxInputData.values = maxInputValues;\n } else {\n maxInput = transposeImpl2(x, permutedAxes, backend2);\n }\n axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, xRank);\n const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(maxInput.shape, axes);\n let outShape = maxOutShape;\n if (keepDims) {\n outShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);\n }\n let out;\n if (shouldExecuteOnCPU) {\n const xTexData = backend2.texData.get(maxInput.dataId);\n const values = xTexData.values;\n const outValues = maxImplCPU(values, util_exports.sizeFromShape(reduceShape), outShape, x.dtype);\n out = backend2.makeTensorInfo(outShape, x.dtype);\n const outData = backend2.texData.get(out.dataId);\n outData.values = outValues;\n } else {\n out = maxImpl2(maxInput, reduceShape, outShape, backend2);\n }\n if (maxInputIsTransposed) {\n backend2.disposeIntermediateTensorInfo(maxInput);\n }\n return out;\n}\nvar maxConfig2 = {\n kernelName: Max,\n backendName: \"webgl\",\n kernelFunc: max4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Maximum.js\nvar MAXIMUM = CHECK_NAN_SNIPPET2 + `\n return max(a, b);\n`;\nvar MAXIMUM_PACKED = `\n vec4 result = vec4(max(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 ` + CHECK_NAN_SNIPPET_PACKED + `\n return result;\n`;\nvar maximum4 = binaryKernelFunc2({\n opSnippet: MAXIMUM,\n packedOpSnippet: MAXIMUM_PACKED,\n cpuKernelImpl: maximumImplCPU\n});\nvar maximumConfig2 = {\n kernelName: Maximum,\n backendName: \"webgl\",\n kernelFunc: maximum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool.js\nfunction maxPool3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n assertNotComplex2(x, \"maxPool\");\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const dilations = 1;\n util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n return identity3({ inputs: { x }, backend: backend2 });\n }\n const maxPoolProgram = new Pool2DProgram(convInfo, \"max\", false);\n return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nvar maxPoolConfig2 = {\n kernelName: MaxPool,\n backendName: \"webgl\",\n kernelFunc: maxPool3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3D.js\nfunction maxPool3d2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { filterSize, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n const dilations = [1, 1, 1];\n const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);\n const maxPoolProgram = new Pool3DProgram(convInfo, \"max\", false);\n return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nvar maxPool3DConfig2 = {\n kernelName: MaxPool3D,\n backendName: \"webgl\",\n kernelFunc: maxPool3d2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/max_pool_backprop_gpu.js\nvar MaxPool2DBackpropProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"dy\", \"maxPos\"];\n this.outputShape = convInfo.inShape;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n this.userCode = `\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\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 < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.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 = ${lastIndex} - 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 * ${effectiveFilterWidth} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n};\nvar MaxPool3DBackpropProgram = class {\n constructor(convInfo) {\n this.variableNames = [\"dy\", \"maxPos\"];\n this.outputShape = convInfo.inShape;\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n this.userCode = `\n const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\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 < ${effectiveFilterDepth};\n wD += ${dilationDepth}) {\n float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${effectiveFilterWidth};\n wC += ${dilationWidth}) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.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 = ${lastIndex} -\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 * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n wR * ${effectiveFilterWidth} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3DGrad.js\nfunction maxPool3DGrad2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2 } = inputs;\n const x = input2;\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const dilations = [1, 1, 1];\n const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n const maxPool3dPositionsProgram = new Pool3DProgram(\n convInfo,\n \"max\",\n true\n /* get positions */\n );\n const maxPool3dPositions2 = backend2.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);\n const maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);\n const result = backend2.runWebGLProgram(maxPoolBackpropProgram, [dy, maxPool3dPositions2], x.dtype);\n backend2.disposeIntermediateTensorInfo(maxPool3dPositions2);\n return result;\n}\nvar maxPool3DGradConfig3 = {\n kernelName: MaxPool3DGrad,\n backendName: \"webgl\",\n kernelFunc: maxPool3DGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolGrad.js\nfunction maxPoolGrad3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2, output } = inputs;\n const x = input2;\n assertNotComplex2([input2, output], \"maxPoolGrad\");\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n const getPositions = true;\n const maxPoolPositionsProgram = new Pool2DProgram(convInfo, \"max\", getPositions);\n const maxPoolPositions2 = backend2.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n const result = backend2.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions2], x.dtype);\n backend2.disposeIntermediateTensorInfo(maxPoolPositions2);\n return result;\n}\nvar maxPoolGradConfig3 = {\n kernelName: MaxPoolGrad,\n backendName: \"webgl\",\n kernelFunc: maxPoolGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax_impl.js\nfunction maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, backend2) {\n let program = new Pool2DProgram(convInfo, \"max\", false);\n const poolOutput = backend2.runWebGLProgram(program, [x], \"float32\");\n program = new Pool2DProgram(convInfo, \"max\", true, true, includeBatchInIndex);\n const indexOutput = backend2.runWebGLProgram(program, [x], \"float32\");\n return [poolOutput, indexOutput];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax.js\nvar maxPoolWithArgmaxConfig2 = {\n kernelName: MaxPoolWithArgmax,\n backendName: \"webgl\",\n kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n const { x } = inputs;\n const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;\n const webglBackend = backend2;\n util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n const dilations = [1, 1];\n util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3);\n const [result, indexes] = maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, webglBackend);\n return [result, indexes];\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean_impl.js\nfunction meanImpl(x, reduceShape, outShape, backend2) {\n const inSize = util_exports.sizeFromShape(reduceShape);\n const xSize = util_exports.sizeFromShape(x.shape);\n const batchSize = xSize / inSize;\n const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n const reduced = reduce(reshapedInput, \"float32\", \"mean\", backend2);\n const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(reshapedInput);\n backend2.disposeIntermediateTensorInfo(reduced);\n return reshapedOutput;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean.js\nvar meanConfig2 = {\n kernelName: Mean,\n backendName: \"webgl\",\n kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n const { x } = inputs;\n const { keepDims, axis } = attrs;\n const webglBackend = backend2;\n const xRank = x.shape.length;\n const origAxes = util_exports.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n const meanInputIsTransposed = permutedAxes != null;\n const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n const intermediates = [];\n let meanInput = x;\n if (meanInputIsTransposed) {\n if (shouldExecuteOnCPU) {\n const xTexData = webglBackend.texData.get(meanInput.dataId);\n const values = xTexData.values;\n const newShape = new Array(xRank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = x.shape[permutedAxes[i]];\n }\n const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n const meanInputData = webglBackend.texData.get(meanInput.dataId);\n meanInputData.values = meanInputValues;\n } else {\n meanInput = transposeImpl2(x, permutedAxes, webglBackend);\n }\n intermediates.push(meanInput);\n axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"sum\", axes, xRank);\n const [meanOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(meanInput.shape, axes);\n let outShape = meanOutShape;\n if (keepDims) {\n outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes);\n }\n const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);\n for (const i of intermediates) {\n webglBackend.disposeIntermediateTensorInfo(i);\n }\n return out;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Min.js\nfunction min4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n const xRank = x.shape.length;\n const origAxes = util_exports.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n let permutedX = x;\n if (permutedAxes != null) {\n permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, xRank);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n const inSize = util_exports.sizeFromShape(reduceShape);\n const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n const reduced = reduce(a2D, a2D.dtype, \"min\", backend2);\n let res;\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n } else {\n res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n }\n backend2.disposeIntermediateTensorInfo(a2D);\n backend2.disposeIntermediateTensorInfo(reduced);\n if (permutedAxes != null) {\n backend2.disposeIntermediateTensorInfo(permutedX);\n }\n return res;\n}\nvar minConfig2 = {\n kernelName: Min,\n backendName: \"webgl\",\n kernelFunc: min4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Minimum.js\nvar MINIMUM = CHECK_NAN_SNIPPET2 + `\n return min(a, b);\n`;\nvar MINIMUM_PACKED = `\n vec4 result = vec4(min(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 ` + CHECK_NAN_SNIPPET_PACKED + `\n return result;\n`;\nvar minimum4 = binaryKernelFunc2({\n opSnippet: MINIMUM,\n packedOpSnippet: MINIMUM_PACKED,\n cpuKernelImpl: minimumImplCPU\n});\nvar minimumConfig2 = {\n kernelName: Minimum,\n backendName: \"webgl\",\n kernelFunc: minimum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_gpu.js\nvar MirrorPadProgram = class {\n constructor(xShape, paddings, mode) {\n this.variableNames = [\"x\"];\n this.outputShape = paddings.map(\n (p2, i) => p2[0] + xShape[i] + p2[1]\n /* afterPad */\n );\n const rank = xShape.length;\n const dtype = getCoordsDataType(rank);\n const start = paddings.map((p2) => p2[0]).join(\",\");\n const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(\",\");\n const unpackedCoords = [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank);\n const offset = mode === \"reflect\" ? 0 : 1;\n if (rank === 1) {\n this.userCode = `\n int start = ${start};\n int end = ${end};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${offset};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${offset};\n }\n setOutput(getX(outC - start));\n }\n `;\n return;\n }\n this.userCode = `\n ${dtype} start = ${dtype}(${start});\n ${dtype} end = ${dtype}(${end});\n\n void main() {\n ${dtype} outC = getOutputCoords();\n for (int i = 0; i < ${rank}; i++) {\n if (outC[i] < start[i]) {\n outC[i] = start[i] * 2 - outC[i] - ${offset};\n } else if(outC[i] >= end[i]) {\n outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};\n }\n }\n ${dtype} coords = outC - start;\n setOutput(getX(${unpackedCoords}));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_packed_gpu.js\nvar MirrorPadPackedProgram = class {\n constructor(xShape, paddings, mode) {\n this.variableNames = [\"x\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.outputShape = paddings.map(\n (p2, i) => p2[0] + xShape[i] + p2[1]\n /* afterPad */\n );\n const rank = xShape.length;\n const dtype = getCoordsDataType(rank);\n const start = paddings.map((p2) => p2[0]).join(\",\");\n const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(\",\");\n const coords2 = getChannels(\"rc\", rank);\n const source = getChannels(\"source\", rank);\n const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`;\n const innerDims = rank === 1 ? \"source\" : `vec2(${source.slice(-2).join()})`;\n const offset = mode === \"reflect\" ? 0 : 1;\n let mainLoop = \"\";\n if (rank === 1) {\n const padSetup = `\n ${dtype} source = rc;\n if (source < start) {\n source = start * 2 - source - ${offset};\n } else if (source >= end) {\n source = (end - 1) * 2 - source + ${offset};\n }\n source -= start;\n `;\n mainLoop = `\n ${dtype} rc = outputLoc;\n ${padSetup}\n result[0] = getChannel(getX(${source.join()}), ${innerDims});\n ${coords2[rank - 1]} += 1;\n if(${cLimit}) {\n ${padSetup}\n result[1] = getChannel(getX(${source.join()}), ${innerDims});\n }\n `;\n } else {\n const padSetup = `\n ${dtype} source = rc;\n ${dtype} lt = ${dtype}(lessThan(source, start));\n ${dtype} gte = ${dtype}(greaterThanEqual(source, end));\n ${dtype} orig = 1 - (lt + gte);\n source = orig * source +\n lt * (start * 2 - source - ${offset}) +\n gte * ((end - 1) * 2 - source + ${offset});\n source -= start;\n `;\n mainLoop = `\n ${dtype} rc = outputLoc;\n ${padSetup}\n result[0] = getChannel(getX(${source.join()}), ${innerDims});\n ${coords2[rank - 1]} += 1;\n if(${cLimit}) {\n ${padSetup}\n result[1] = getChannel(getX(${source.join()}), ${innerDims});\n }\n rc = outputLoc;\n ${coords2[rank - 2]} += 1;\n if(${coords2[rank - 2]} < ${this.outputShape[rank - 2]}) {\n ${padSetup}\n result[2] = getChannel(getX(${source.join()}), ${innerDims});\n ${coords2[rank - 1]} += 1;\n if(${cLimit}) {\n ${padSetup}\n result[3] = getChannel(getX(${source.join()}), ${innerDims});\n }\n }\n `;\n }\n this.userCode = `\n const ${dtype} start = ${dtype}(${start});\n const ${dtype} end = ${dtype}(${end});\n\n void main() {\n ${dtype} outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n ${mainLoop}\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MirrorPad.js\nvar mirrorPadKernelFunc = ({ inputs, backend: backend2, attrs }) => {\n const { x } = inputs;\n const { paddings, mode } = attrs;\n const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new MirrorPadPackedProgram(x.shape, paddings, mode) : new MirrorPadProgram(x.shape, paddings, mode);\n const output = backend2.runWebGLProgram(program, [x], x.dtype);\n return output;\n};\nvar mirrorPadConfig2 = {\n kernelName: MirrorPad,\n backendName: \"webgl\",\n kernelFunc: mirrorPadKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mod.js\nvar MOD = `if (b == 0.0) return NAN;\n return mod(a, b);`;\nvar MOD_PACKED = `\n vec4 result = mod(a, b);\n bvec4 isNaN = equal(b, vec4(0.0));\n ` + CHECK_NAN_SNIPPET_PACKED + `\n return result;\n`;\nvar mod3 = binaryKernelFunc2({\n opSnippet: MOD,\n packedOpSnippet: MOD_PACKED\n});\nvar modConfig2 = {\n kernelName: Mod,\n backendName: \"webgl\",\n kernelFunc: mod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/multinomial_gpu.js\nvar MultinomialProgram = class {\n constructor(batchSize, numOutcomes, numSamples) {\n this.variableNames = [\"probs\"];\n this.customUniforms = [{ name: \"seed\", type: \"float\" }];\n this.outputShape = [batchSize, numSamples];\n this.userCode = `\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < ${numOutcomes - 1}; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float(${numOutcomes - 1}));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RealDiv.js\nvar DIV = `\nif (a == b) {\n return 1.0;\n};\nreturn a / b;`;\nvar DIV_PACKED = `\n // vec4 one = vec4(equal(a, b));\n // return one + (vec4(1.0) - one) * a / b;\n vec4 result = a / b;\n if(a.x == b.x) {\n result.x = 1.;\n }\n if(a.y == b.y) {\n result.y = 1.;\n }\n if(a.z == b.z) {\n result.z = 1.;\n }\n if(a.w == b.w) {\n result.w = 1.;\n }\n\n return result;\n`;\nvar realDiv = binaryKernelFunc2({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });\nvar realDivConfig2 = {\n kernelName: RealDiv,\n backendName: \"webgl\",\n kernelFunc: realDiv\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sub.js\nvar SUB = \"return a - b;\";\nvar sub3 = binaryKernelFunc2({\n opSnippet: SUB,\n packedOpSnippet: SUB,\n supportsComplex: true,\n cpuKernelImpl: subImplCPU\n});\nvar subConfig2 = {\n kernelName: Sub,\n backendName: \"webgl\",\n kernelFunc: sub3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softmax.js\nfunction softmax4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { logits } = inputs;\n const { dim } = attrs;\n const axes = util_exports.parseAxisParam([dim], logits.shape);\n const maxLogit = max4({\n inputs: { x: logits },\n backend: backend2,\n attrs: { reductionIndices: axes, keepDims: false }\n });\n const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n const maxLogitsReshaped = reshape4({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n const a = sub3({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });\n const b = exp3({ inputs: { x: a }, backend: backend2 });\n const sumExp = sum4({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n const sumExpReshaped = reshape4({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n const res = realDiv({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(maxLogit);\n backend2.disposeIntermediateTensorInfo(maxLogitsReshaped);\n backend2.disposeIntermediateTensorInfo(a);\n backend2.disposeIntermediateTensorInfo(b);\n backend2.disposeIntermediateTensorInfo(sumExp);\n backend2.disposeIntermediateTensorInfo(sumExpReshaped);\n return res;\n}\nvar softmaxConfig2 = {\n kernelName: Softmax,\n backendName: \"webgl\",\n kernelFunc: softmax4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multinomial.js\nfunction multinomial3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { logits } = inputs;\n const { numSamples, seed, normalized } = attrs;\n const probs = normalized ? logits : softmax4({ inputs: { logits }, backend: backend2, attrs: { dim: logits.shape.length - 1 } });\n const batchSize = probs.shape[0];\n const numOutcomes = probs.shape[1];\n const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n const customValues = [[seed]];\n const res = backend2.runWebGLProgram(program, [probs], \"int32\", customValues);\n if (!normalized) {\n backend2.disposeIntermediateTensorInfo(probs);\n }\n return res;\n}\nvar multinomialConfig2 = {\n kernelName: Multinomial,\n backendName: \"webgl\",\n kernelFunc: multinomial3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Neg.js\nvar NEG = CHECK_NAN_SNIPPET + `\n return -x;\n`;\nvar NEG_PACKED = `\n vec4 result = -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`;\nfunction neg3(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n if (backend2.shouldExecuteOnCPU([x])) {\n const xData = backend2.texData.get(x.dataId);\n const [outValues, newShape] = negImplCPU(xData.values, x.shape, x.dtype);\n return backend2.makeTensorInfo(newShape, x.dtype, outValues);\n }\n let program;\n if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n program = new UnaryOpPackedProgram(x.shape, NEG_PACKED);\n } else {\n program = new UnaryOpProgram(x.shape, NEG);\n }\n return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar negConfig2 = {\n kernelName: Neg,\n backendName: \"webgl\",\n kernelFunc: neg3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV3.js\nvar nonMaxSuppressionV3Impl3 = kernel_impls_exports.nonMaxSuppressionV3Impl;\nfunction nonMaxSuppressionV32(args) {\n backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n const { inputs, backend: backend2, attrs } = args;\n const { boxes, scores } = inputs;\n const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n const boxesVals = backend2.readSync(boxes.dataId);\n const scoresVals = backend2.readSync(scores.dataId);\n const { selectedIndices } = nonMaxSuppressionV3Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config2 = {\n kernelName: NonMaxSuppressionV3,\n backendName: \"webgl\",\n kernelFunc: nonMaxSuppressionV32\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV4.js\nvar nonMaxSuppressionV4Impl3 = kernel_impls_exports.nonMaxSuppressionV4Impl;\nfunction nonMaxSuppressionV42(args) {\n backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n const { inputs, backend: backend2, attrs } = args;\n const { boxes, scores } = inputs;\n const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n const boxesVals = backend2.readSync(boxes.dataId);\n const scoresVals = backend2.readSync(scores.dataId);\n const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n return [\n backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n backend2.makeTensorInfo([], \"int32\", new Int32Array([validOutputs]))\n ];\n}\nvar nonMaxSuppressionV4Config2 = {\n kernelName: NonMaxSuppressionV4,\n backendName: \"webgl\",\n kernelFunc: nonMaxSuppressionV42\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV5.js\nvar nonMaxSuppressionV5Impl3 = kernel_impls_exports.nonMaxSuppressionV5Impl;\nfunction nonMaxSuppressionV52(args) {\n backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n const { inputs, backend: backend2, attrs } = args;\n const { boxes, scores } = inputs;\n const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n const boxesVals = backend2.readSync(boxes.dataId);\n const scoresVals = backend2.readSync(scores.dataId);\n const maxOutputSizeVal = maxOutputSize;\n const iouThresholdVal = iouThreshold;\n const scoreThresholdVal = scoreThreshold;\n const softNmsSigmaVal = softNmsSigma;\n const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl3(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n return [\n backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n ];\n}\nvar nonMaxSuppressionV5Config2 = {\n kernelName: NonMaxSuppressionV5,\n backendName: \"webgl\",\n kernelFunc: nonMaxSuppressionV52\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/onehot_gpu.js\nvar OneHotProgram = class {\n constructor(numIndices, depth, onValue, offValue) {\n this.variableNames = [\"indices\"];\n this.outputShape = [numIndices, depth];\n this.userCode = `\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float(${offValue}), float(${onValue}),\n float(index == coords.y)));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OneHot.js\nvar oneHot3 = (args) => {\n const { inputs, backend: backend2, attrs } = args;\n const { indices } = inputs;\n const { dtype, depth, onValue, offValue } = attrs;\n const indicesSize = util_exports.sizeFromShape(indices.shape);\n const program = new OneHotProgram(indicesSize, depth, onValue, offValue);\n const reshaped = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [indicesSize] } });\n const result = backend2.runWebGLProgram(program, [reshaped], dtype);\n backend2.disposeIntermediateTensorInfo(reshaped);\n const outShape = [...indices.shape, depth];\n const out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outShape } });\n backend2.disposeIntermediateTensorInfo(result);\n return out;\n};\nvar oneHotConfig2 = {\n kernelName: OneHot,\n backendName: \"webgl\",\n kernelFunc: oneHot3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ZerosLike.js\nfunction zerosLike3(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n if (x.dtype === \"complex64\") {\n const realPart = real3({ inputs: { input: x }, backend: backend2 });\n const r = zerosLike3({ inputs: { x: realPart }, backend: backend2 });\n const imagPart = imag3({ inputs: { input: x }, backend: backend2 });\n const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });\n const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(realPart);\n backend2.disposeIntermediateTensorInfo(r);\n backend2.disposeIntermediateTensorInfo(imagPart);\n backend2.disposeIntermediateTensorInfo(i);\n return result;\n } else {\n return fill3({\n attrs: {\n shape: x.shape,\n dtype: x.dtype,\n value: x.dtype === \"string\" ? \"\" : 0\n },\n backend: backend2\n });\n }\n}\nvar zerosLikeConfig2 = {\n kernelName: ZerosLike,\n backendName: \"webgl\",\n kernelFunc: zerosLike3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OnesLike.js\nfunction onesLike3(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n if (x.dtype === \"string\") {\n throw new Error(\"onesLike is not supported under string dtype\");\n } else if (x.dtype === \"complex64\") {\n const realPart = real3({ inputs: { input: x }, backend: backend2 });\n const r = onesLike3({ inputs: { x: realPart }, backend: backend2 });\n const imagPart = imag3({ inputs: { input: x }, backend: backend2 });\n const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });\n const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 });\n backend2.disposeIntermediateTensorInfo(realPart);\n backend2.disposeIntermediateTensorInfo(r);\n backend2.disposeIntermediateTensorInfo(imagPart);\n backend2.disposeIntermediateTensorInfo(i);\n return result;\n } else {\n return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });\n }\n}\nvar onesLikeConfig2 = {\n kernelName: OnesLike,\n backendName: \"webgl\",\n kernelFunc: onesLike3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pack.js\nfunction pack2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { axis } = attrs;\n if (inputs.length === 1) {\n return expandDims4({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n }\n const shape = inputs[0].shape;\n const dtype = inputs[0].dtype;\n inputs.forEach((t) => {\n util_exports.assertShapesMatch(shape, t.shape, \"All tensors passed to stack must have matching shapes\");\n util_exports.assert(dtype === t.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n });\n const intermediateTensorInfos = [];\n const expandedTensors = inputs.map((t) => {\n const expandedT = expandDims4({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });\n intermediateTensorInfos.push(expandedT);\n return expandedT;\n });\n const result = concat3({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return result;\n}\nvar packConfig2 = {\n kernelName: Pack,\n backendName: \"webgl\",\n kernelFunc: pack2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_gpu.js\nvar PadProgram = class {\n constructor(xShape, paddings, constantValue) {\n this.variableNames = [\"x\"];\n this.customUniforms = [{ name: \"value\", type: \"float\" }];\n this.outputShape = paddings.map(\n (p2, i) => p2[0] + xShape[i] + p2[1]\n /* afterPad */\n );\n const rank = xShape.length;\n const type = getCoordsDataType(rank);\n const start = paddings.map((p2) => p2[0]).join(\",\");\n const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(\",\");\n const unpackedCoords = [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank);\n if (rank === 1) {\n this.userCode = `\n int start = ${start};\n int end = ${end};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `;\n return;\n }\n this.userCode = `\n ${type} start = ${type}(${start});\n ${type} end = ${type}(${end});\n\n void main() {\n ${type} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${type} coords = outC - start;\n setOutput(getX(${unpackedCoords}));\n }\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_packed_gpu.js\nvar PadPackedProgram = class {\n constructor(xShape, paddings, constantValue) {\n this.variableNames = [\"x\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.customUniforms = [{ name: \"value\", type: \"float\" }];\n this.outputShape = paddings.map(\n (p2, i) => p2[0] + xShape[i] + p2[1]\n /* afterPad */\n );\n const rank = xShape.length;\n const dtype = getCoordsDataType(rank);\n const start = paddings.map((p2) => p2[0]).join(\",\");\n const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(\",\");\n const coords2 = getChannels(\"rc\", rank);\n const source = getChannels(\"source\", rank);\n const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`;\n const innerDims = rank === 1 ? \"source\" : `vec2(${source.slice(-2).join()})`;\n const componentSetup = [\n `${dtype} rc = outputLoc;`,\n `${coords2[rank - 1]} += 1;\n if(${cLimit}) {\n `,\n rank === 1 ? \"\" : `}\n rc = outputLoc;\n ${coords2[rank - 2]} += 1;\n if(${coords2[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n rank === 1 ? \"\" : ` ${coords2[rank - 1]} += 1;\n if(${cLimit}) {`\n ];\n const paddingArea = rank === 1 ? \"rc < start || rc >= end\" : \"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\";\n let mainLoop = \"\";\n for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n mainLoop += `\n ${componentSetup[i]}\n if (${paddingArea}) {\n result[${i}] = float(value);\n } else {\n ${dtype} source = rc - start;\n result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n }\n `;\n }\n mainLoop += rank === 1 ? `} ` : `}}`;\n this.userCode = `\n const ${dtype} start = ${dtype}(${start});\n const ${dtype} end = ${dtype}(${end});\n\n void main() {\n ${dtype} outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n ${mainLoop}\n setOutput(result);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/PadV2.js\nvar padV22 = (args) => {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { paddings, constantValue } = attrs;\n if (util_exports.sizeFromShape(x.shape) === 0) {\n const outputShape = paddings.map(\n (p2, i) => p2[0] + x.shape[i] + p2[1]\n /* afterPad */\n );\n return fill3({\n backend: backend2,\n attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }\n });\n }\n const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new PadPackedProgram(x.shape, paddings, constantValue) : new PadProgram(x.shape, paddings, constantValue);\n const customValues = [[constantValue]];\n return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n};\nvar padV2Config2 = {\n kernelName: PadV2,\n backendName: \"webgl\",\n kernelFunc: padV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pow.js\nvar POW = `\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`;\nvar POW_PACKED = `\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 ` + CHECK_NAN_SNIPPET_PACKED + `\n return result;\n`;\nvar pow3 = binaryKernelFunc2({ opSnippet: POW, packedOpSnippet: POW_PACKED });\nvar powConfig2 = {\n kernelName: Pow,\n backendName: \"webgl\",\n kernelFunc: pow3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prod.js\nfunction prod3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, keepDims } = attrs;\n const xRank = x.shape.length;\n const toDispose = [];\n const origAxes = util_exports.parseAxisParam(axis, x.shape);\n let axes = origAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n let permutedX = x;\n if (permutedAxes != null) {\n permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n toDispose.push(permutedX);\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"prod\", axes, xRank);\n let res;\n if (backend2.shouldExecuteOnCPU([permutedX])) {\n const xVals = backend2.texData.get(permutedX.dataId).values;\n const { outVals, outShape, outDtype } = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes);\n res = backend2.makeTensorInfo(outShape, outDtype, outVals);\n } else {\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n const inSize = util_exports.sizeFromShape(reduceShape);\n const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n const outputDType = sumOutType(x.dtype);\n const reduced = reduce(a2D, outputDType, \"prod\", backend2);\n res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n toDispose.push(a2D);\n toDispose.push(reduced);\n }\n if (keepDims) {\n toDispose.push(res);\n const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes);\n res = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: newShape } });\n }\n toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return res;\n}\nvar prodConfig2 = {\n kernelName: Prod,\n backendName: \"webgl\",\n kernelFunc: prod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RaggedGather.js\nfunction raggedGather3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { paramsNestedSplits, paramsDenseValues, indices } = inputs;\n const { outputRaggedRank } = attrs;\n const $paramsNestedSplits = paramsNestedSplits.map((t) => backend2.readSync(t.dataId));\n const $paramsNestedSplitsShapes = paramsNestedSplits.map((t) => t.shape);\n const $paramsDenseValues = backend2.readSync(paramsDenseValues.dataId);\n const $indices = backend2.readSync(indices.dataId);\n const [outputNestedSplits, outputDenseValues, outputDenseValuesShape] = raggedGatherImplCPU($paramsNestedSplits, $paramsNestedSplitsShapes, $paramsDenseValues, paramsDenseValues.shape, paramsDenseValues.dtype, $indices, indices.shape, outputRaggedRank);\n const outputNestedSplitsTensors = outputNestedSplits.map((splits) => backend2.makeTensorInfo([splits.length], \"int32\", splits));\n const outputDenseValuesTensor = backend2.makeTensorInfo(outputDenseValuesShape, paramsDenseValues.dtype, outputDenseValues);\n return outputNestedSplitsTensors.concat([outputDenseValuesTensor]);\n}\nvar raggedGatherConfig2 = {\n kernelName: RaggedGather,\n backendName: \"webgl\",\n kernelFunc: raggedGather3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RaggedRange.js\nfunction raggedRange3(args) {\n const { inputs, backend: backend2 } = args;\n const { starts, limits, deltas } = inputs;\n const $starts = backend2.readSync(starts.dataId);\n const $limits = backend2.readSync(limits.dataId);\n const $deltas = backend2.readSync(deltas.dataId);\n const [rtNestedSplitsData, rtDenseValuesData] = raggedRangeImplCPU($starts, starts.shape, starts.dtype, $limits, limits.shape, $deltas, deltas.shape);\n const rtNestedSplits = backend2.makeTensorInfo([rtNestedSplitsData.length], \"int32\", rtNestedSplitsData);\n const rtDenseValues = backend2.makeTensorInfo([rtDenseValuesData.length], starts.dtype, rtDenseValuesData);\n return [rtNestedSplits, rtDenseValues];\n}\nvar raggedRangeConfig2 = {\n kernelName: RaggedRange,\n backendName: \"webgl\",\n kernelFunc: raggedRange3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RaggedTensorToTensor.js\nfunction raggedTensorToTensor3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { shape, values, defaultValue, rowPartitionTensors } = inputs;\n const { rowPartitionTypes } = attrs;\n const $shape = backend2.readSync(shape.dataId);\n const $values = backend2.readSync(values.dataId);\n const $defaultValue = backend2.readSync(defaultValue.dataId);\n const $rowPartitionValues = rowPartitionTensors.map((t) => backend2.readSync(t.dataId));\n const rowPartitionValuesShapes = rowPartitionTensors.map((t) => t.shape);\n const [outputShape, output] = raggedTensorToTensorImplCPU($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes);\n return backend2.makeTensorInfo(outputShape, values.dtype, output);\n}\nvar raggedTensorToTensorConfig2 = {\n kernelName: RaggedTensorToTensor,\n backendName: \"webgl\",\n kernelFunc: raggedTensorToTensor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Range.js\nvar range4 = (args) => {\n const { backend: backend2, attrs } = args;\n const { start, stop, step: step5, dtype } = attrs;\n const values = rangeImplCPU(start, stop, step5, dtype);\n return backend2.makeTensorInfo([values.length], dtype, values);\n};\nvar rangeConfig2 = {\n kernelName: Range,\n backendName: \"webgl\",\n kernelFunc: range4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reciprocal.js\nvar RECIPROCAL = `return 1.0 / x;`;\nvar reciprocal3 = unaryKernelFunc2({ opSnippet: RECIPROCAL });\nvar reciprocalConfig2 = {\n kernelName: Reciprocal,\n backendName: \"webgl\",\n kernelFunc: reciprocal3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu.js\nvar RELU3 = CHECK_NAN_SNIPPET + `\n return (x < 0.0) ? 0.0 : x;\n`;\nvar RELU_PACKED = `\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`;\nvar relu3 = unaryKernelFunc2({ opSnippet: RELU3, packedOpSnippet: RELU_PACKED });\nvar reluConfig2 = {\n kernelName: Relu,\n backendName: \"webgl\",\n kernelFunc: relu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu6.js\nvar RELU63 = CHECK_NAN_SNIPPET + `\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nvar RELU6_PACKED = `\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`;\nvar relu63 = unaryKernelFunc2({ opSnippet: RELU63, packedOpSnippet: RELU6_PACKED });\nvar relu6Config2 = {\n kernelName: Relu6,\n backendName: \"webgl\",\n kernelFunc: relu63\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_gpu.js\nvar ResizeBilinearProgram = class {\n constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n this.variableNames = [\"A\"];\n this.outputShape = [];\n const [batch, oldHeight, oldWidth, depth] = inputShape;\n this.outputShape = [batch, newHeight, newWidth, depth];\n const effectiveInSize = [\n alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n ];\n const effectiveOutSize = [\n alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n ];\n let sourceFracIndexRC;\n if (halfPixelCenters) {\n sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)`;\n } else {\n sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;\n }\n this.userCode = `\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${effectiveInSize[0] / effectiveOutSize[0]},\n ${effectiveInSize[1] / effectiveOutSize[1]});\n const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.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 = ${sourceFracIndexRC};\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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_packed_gpu.js\nvar ResizeBilinearPackedProgram = class {\n constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.outputShape = [];\n const [batch, oldHeight, oldWidth, depth] = inputShape;\n this.outputShape = [batch, newHeight, newWidth, depth];\n const effectiveInSize = [\n alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n ];\n const effectiveOutSize = [\n alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n ];\n let sourceFracIndexRC;\n if (halfPixelCenters) {\n sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)`;\n } else {\n sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;\n }\n this.userCode = `\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${effectiveInSize[0] / effectiveOutSize[0]},\n ${effectiveInSize[1] / effectiveOutSize[1]},\n ${effectiveInSize[1] / effectiveOutSize[1]});\n const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n ${oldWidth}.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 = ${sourceFracIndexRC};\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 < ${depth - 1};\n bool hasNextRow = coords.z < ${newWidth - 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { images } = inputs;\n const { alignCorners, halfPixelCenters, size } = attrs;\n const [newHeight, newWidth] = size;\n const program = env().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\") ? new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);\n return backend2.runWebGLProgram(program, [images], \"float32\");\n}\nvar resizeBilinearConfig2 = {\n kernelName: ResizeBilinear,\n backendName: \"webgl\",\n kernelFunc: resizeBilinear4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_backprop_gpu.js\nvar ResizeBilinearBackpropProgram = class {\n constructor(dyShape, inputShape, alignCorners) {\n this.variableNames = [\"dy\"];\n this.outputShape = [];\n this.outputShape = inputShape;\n const [, xHeight, xWidth] = inputShape;\n const [, yHeight, yWidth] = dyShape;\n const effectiveXSize = [\n alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n ];\n const effectiveYSize = [\n alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n ];\n const heightScale = effectiveXSize[0] / effectiveYSize[0];\n const widthScale = effectiveXSize[1] / effectiveYSize[1];\n const invHeightScale = 1 / heightScale;\n const invWidthScale = 1 / widthScale;\n const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n 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(${heightScale});\n const float widthScale = float(${widthScale});\n\n const float invHeightScale = float(${invHeightScale});\n const float invWidthScale = float(${invWidthScale});\n\n const int winHeight = int(${winHeight});\n const int winWidth = int(${winWidth});\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 >= ${yHeight}) {\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 >= ${yWidth}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 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), ${xWidth - 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinearGrad.js\nfunction resizeBilinearGrad2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { images, dy } = inputs;\n const { alignCorners } = attrs;\n const program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners);\n return backend2.runWebGLProgram(program, [dy], dy.dtype);\n}\nvar resizeBilinearGradConfig3 = {\n kernelName: ResizeBilinearGrad,\n backendName: \"webgl\",\n kernelFunc: resizeBilinearGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_gpu.js\nvar ResizeNearestNeighborProgram = class {\n constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n this.variableNames = [\"A\"];\n this.outputShape = [];\n const [batch, oldHeight, oldWidth, depth] = inputShape;\n this.outputShape = [batch, newHeight, newWidth, depth];\n const effectiveInSize = [\n alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n ];\n const effectiveOutSize = [\n alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n ];\n const roundBase = alignCorners ? \"0.5\" : \"0.0\";\n let sourceFracIndexRC;\n if (halfPixelCenters) {\n sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`;\n } else {\n sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;\n }\n this.userCode = `\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${effectiveInSize[0] / effectiveOutSize[0]},\n ${effectiveInSize[1] / effectiveOutSize[1]});\n const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.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 = ${sourceFracIndexRC};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_packed_gpu.js\nvar ResizeNearestNeighborPackedProgram = class {\n constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n this.variableNames = [\"A\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.outputShape = [];\n const [batch, oldHeight, oldWidth, depth] = inputShape;\n this.outputShape = [batch, newHeight, newWidth, depth];\n const effectiveInSize = [\n alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n ];\n const effectiveOutSize = [\n alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n ];\n const roundBase = alignCorners ? \"0.5\" : \"0.0\";\n let sourceFracIndexRC;\n if (halfPixelCenters) {\n sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))`;\n } else {\n sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;\n }\n this.userCode = `\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${effectiveInSize[0] / effectiveOutSize[0]},\n ${effectiveInSize[1] / effectiveOutSize[1]},\n ${effectiveInSize[1] / effectiveOutSize[1]});\n const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n ${oldWidth}.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 = ${sourceFracIndexRC};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${depth - 1};\n bool hasNextRow = coords.z < ${newWidth - 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { images } = inputs;\n const { alignCorners, halfPixelCenters, size } = attrs;\n const [newHeight, newWidth] = size;\n const program = env().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\") ? new ResizeNearestNeighborPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);\n return backend2.runWebGLProgram(program, [images], images.dtype);\n}\nvar resizeNearestNeighborConfig2 = {\n kernelName: ResizeNearestNeighbor,\n backendName: \"webgl\",\n kernelFunc: resizeNearestNeighbor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_backprop_gpu.js\nvar ResizeNearestNeigborBackpropProgram = class {\n constructor(dyShape, inputShape, alignCorners) {\n this.variableNames = [\"dy\"];\n this.outputShape = [];\n this.outputShape = inputShape;\n const [, xHeight, xWidth] = inputShape;\n const [, yHeight, yWidth] = dyShape;\n const effectiveXSize = [\n alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n ];\n const effectiveYSize = [\n alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n ];\n const heightScale = effectiveXSize[0] / effectiveYSize[0];\n const widthScale = effectiveXSize[1] / effectiveYSize[1];\n const invHeightScale = 1 / heightScale;\n const invWidthScale = 1 / widthScale;\n const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n 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(${heightScale});\n const float widthScale = float(${widthScale});\n\n const float invHeightScale = float(${invHeightScale});\n const float invWidthScale = float(${invWidthScale});\n\n const int winHeight = int(${winHeight});\n const int winWidth = int(${winWidth});\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 >= ${yHeight}) {\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 >= ${yWidth}) {\n continue;\n }\n\n float sourceFracRow =\n float(${effectiveXSize[0]}) *\n (float(dyR) / float(${effectiveYSize[0]}));\n\n float sourceFracCol =\n float(${effectiveXSize[1]}) *\n (float(dyC) / float(${effectiveYSize[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${xHeight}) - 1),\n ${alignCorners} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${xWidth}) - 1),\n ${alignCorners} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighborGrad.js\nfunction resizeNearestNeighborGrad2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { images, dy } = inputs;\n const { alignCorners } = attrs;\n const program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners);\n return backend2.runWebGLProgram(program, [dy], dy.dtype);\n}\nvar resizeNearestNeighborGradConfig3 = {\n kernelName: ResizeNearestNeighborGrad,\n backendName: \"webgl\",\n kernelFunc: resizeNearestNeighborGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_gpu.js\nvar ReverseProgram = class {\n constructor(xShape, axis) {\n this.variableNames = [\"x\"];\n const rank = xShape.length;\n if (rank > 4) {\n throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n }\n this.outputShape = xShape;\n if (rank === 1) {\n this.userCode = `\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${xShape[0]} - coord - 1));\n }\n `;\n return;\n }\n const getInCoord = (i) => {\n if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n return `${xShape[i]} - coords[${i}] - 1`;\n }\n return `coords[${i}]`;\n };\n const inCoords = xShape.map((_, i) => getInCoord(i)).join(\",\");\n const type = getCoordsDataType(rank);\n this.userCode = `\n void main() {\n ${type} coords = getOutputCoords();\n setOutput(getX(${inCoords}));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_packed_gpu.js\nvar ReversePackedProgram = class {\n constructor(xShape, axis) {\n this.variableNames = [\"x\"];\n this.packedInputs = true;\n this.packedOutput = true;\n const rank = xShape.length;\n if (rank > 4) {\n throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n }\n this.outputShape = xShape;\n const channels = getChannels(\"rc\", rank);\n const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n const type = getCoordsDataType(rank);\n if (rank === 1) {\n this.userCode = `\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${xShape[0]} - rc - 1),\n ${xShape[0]} - rc - 1);\n if(${nextColumn}){\n result.g = getChannel(getX(${xShape[0]} - (rc + 1) - 1),\n ${xShape[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `;\n } else {\n this.userCode = `\n void main() {\n ${type} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${getR(channels.slice())};\n if(${nextColumn}){\n result.g = ${getG(channels.slice())};\n }\n if(${nextRow}) {\n result.b = ${getB(channels.slice())};\n if(${nextColumn}) {\n result.a = ${getA(channels.slice())};\n }\n }\n setOutput(result);\n }\n `;\n }\n function getR(channels2) {\n return getChannel(channels2);\n }\n function getG(channels2) {\n channels2[rank - 1] = \"(\" + channels2[rank - 1] + ` + 1)`;\n return getChannel(channels2);\n }\n function getB(channels2) {\n channels2[rank - 2] = \"(\" + channels2[rank - 2] + ` + 1)`;\n return getChannel(channels2);\n }\n function getA(channels2) {\n channels2[rank - 1] = \"(\" + channels2[rank - 1] + ` + 1)`;\n channels2[rank - 2] = \"(\" + channels2[rank - 2] + ` + 1)`;\n return getChannel(channels2);\n }\n function getChannel(channels2) {\n const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels2));\n const inCoords = inCoordsArray.join(\",\");\n const innerDims = inCoordsArray.slice(-2).join(\",\");\n return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n }\n function getInCoord(i, channels1) {\n if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n return `${xShape[i]} - ${channels1[i]} - 1`;\n } else {\n return `${channels1[i]}`;\n }\n }\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reverse.js\nfunction reverse3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { dims } = attrs;\n const xRank = x.shape.length;\n const $dims = util_exports.parseAxisParam(dims, x.shape);\n if (xRank === 0) {\n return identity3({ inputs: { x }, backend: backend2 });\n }\n const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new ReversePackedProgram(x.shape, $dims) : new ReverseProgram(x.shape, $dims);\n return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar reverseConfig2 = {\n kernelName: Reverse,\n backendName: \"webgl\",\n kernelFunc: reverse3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/rotate_gpu.js\nvar RotateProgram = class {\n constructor(imageShape, fillValue) {\n this.variableNames = [\"Image\"];\n this.outputShape = [];\n this.customUniforms = [{ name: \"params\", type: \"vec4\" }];\n const imageHeight = imageShape[1];\n const imageWidth = imageShape[2];\n this.outputShape = imageShape;\n let fillSnippet = \"\";\n if (typeof fillValue === \"number\") {\n fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n } else {\n fillSnippet = `\n vec3 fill = vec3(${fillValue.join(\",\")});\n float outputValue = fill[coords[3]];`;\n }\n 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 ${fillSnippet}\n if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig2 = {\n kernelName: RotateWithOffset,\n backendName: \"webgl\",\n kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n const { image: image2 } = inputs;\n const { radians, fillValue, center } = attrs;\n const webglBackend = backend2;\n const program = new RotateProgram(image2.shape, fillValue);\n const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);\n const customValues = [[centerX, centerY, Math.sin(radians), Math.cos(radians)]];\n const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype, customValues);\n return output;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Round.js\nvar ROUND = `\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`;\nvar round4 = unaryKernelFunc2({ opSnippet: ROUND });\nvar roundConfig2 = {\n kernelName: Round,\n backendName: \"webgl\",\n kernelFunc: round4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Rsqrt.js\nvar RSQRT = `return inversesqrt(x);`;\nvar rsqrt3 = unaryKernelFunc2({ opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU });\nvar rsqrtConfig2 = {\n kernelName: Rsqrt,\n backendName: \"webgl\",\n kernelFunc: rsqrt3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_gpu.js\nvar ScatterProgram = class {\n constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true, defaultIsTensor = false) {\n this.variableNames = [\"updates\", \"indices\", \"defaultValue\"];\n this.outputShape = shape;\n const stridesType = getCoordsDataType(strides.length);\n const dtype = getCoordsDataType(shape.length);\n let indicesString = \"\";\n if (indicesRank === 1) {\n indicesString = \"i\";\n } else if (indicesRank === 2) {\n indicesString = \"i, j\";\n }\n const indicesSnippet = `getIndices(${indicesString})`;\n let updatesString = \"\";\n if (updatesRank === 1) {\n updatesString = \"i\";\n } else if (updatesRank === 2) {\n updatesString = \"i, coords[1]\";\n }\n const updatesSnippet = `getUpdates(${updatesString})`;\n let defaultValuesString = \"\";\n if (defaultIsTensor) {\n defaultValuesString = \"coords[0], coords[1]\";\n }\n const defaultValueSnippet = `getDefaultValue(${defaultValuesString})`;\n const strideString = sliceDim > 1 ? \"strides[j]\" : \"strides\";\n this.userCode = `\n ${stridesType} strides = ${stridesType}(${strides});\n\n void main() {\n ${dtype} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${updateSize}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${sliceDim}; j++) {\n int index = round(${indicesSnippet});\n flattenedIndex += index * ${strideString};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${updatesSnippet};\n found = true;\n }\n }\n setOutput(mix(${defaultValueSnippet}, sum, float(found)));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_packed_gpu.js\nvar ScatterPackedProgram = class {\n constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true, defaultIsTensor = false) {\n this.variableNames = [\"updates\", \"indices\", \"defaultValue\"];\n this.packedInputs = true;\n this.packedOutput = true;\n this.outputShape = shape;\n const stridesType = getCoordsDataType(strides.length);\n const dtype = getCoordsDataType(shape.length);\n let indicesString = \"\";\n if (indicesRank === 1) {\n indicesString = \"i\";\n } else if (indicesRank === 2) {\n indicesString = \"i, j\";\n }\n const indicesSnippet = `getIndices(${indicesString})`;\n let updatesString = \"\";\n if (updatesRank === 1) {\n updatesString = \"i\";\n } else if (updatesRank === 2) {\n updatesString = \"i, coords[1]\";\n }\n const updatesSnippet = `getUpdates(${updatesString})`;\n let defaultValuesString = \"\";\n if (defaultIsTensor) {\n defaultValuesString = \"coords[0], coords[1]\";\n }\n const defaultValueSnippet = `getDefaultValue(${defaultValuesString})`;\n const strideString = sliceDim > 1 ? \"strides[j]\" : \"strides\";\n const strideString2 = sliceDim > 1 ? \"strides[j + 1]\" : \"strides\";\n this.userCode = `\n ${stridesType} strides = ${stridesType}(${strides});\n\n void main() {\n ${dtype} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${updateSize}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${sliceDim}; j+=2) {\n ivec4 index = round(${indicesSnippet});\n flattenedIndex += index.xz * ${strideString};\n if (j + 1 < ${sliceDim}) {\n flattenedIndex += index.yw * ${strideString2};\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 = ${updatesSnippet};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${defaultValueSnippet}, sum, found));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ScatterNd.js\nfunction scatterNd2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { indices, updates } = inputs;\n const { shape } = attrs;\n const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n const flattenShape = [outputSize / sliceSize, sliceSize];\n if (outputSize === 0) {\n return backend2.makeTensorInfo(shape, indices.dtype);\n }\n const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });\n const flattenX = reshape4({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });\n const defaultValue = backend2.makeTensorInfo([], \"float32\", new Float32Array([0]));\n let program;\n if (env().getBool(\"WEBGL_PACK\")) {\n program = new ScatterPackedProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);\n } else {\n program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);\n }\n const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);\n const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape } });\n backend2.disposeIntermediateTensorInfo(flattenIndices);\n backend2.disposeIntermediateTensorInfo(flattenX);\n backend2.disposeIntermediateTensorInfo(res);\n backend2.disposeIntermediateTensorInfo(defaultValue);\n return reshaped;\n}\nvar scatterNdConfig2 = {\n kernelName: ScatterNd,\n backendName: \"webgl\",\n kernelFunc: scatterNd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/search_sorted_gpu.js\nvar SearchSortedProgram = class {\n constructor(batchSize, numInputs, numValues, side) {\n this.variableNames = [\"sortedSequence\", \"values\"];\n this.customUniforms = [{ name: \"numInputs\", type: \"int\" }];\n this.outputShape = [batchSize, numValues];\n const webGL2LoopHead = \"while (left < right) {\";\n const webGL1LoopHead = `for (int i = 0; i < ${Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`;\n const loopHead = env().getNumber(\"WEBGL_VERSION\") === 2 ? webGL2LoopHead : webGL1LoopHead;\n const boundComparator = side === \"left\" ? \"<\" : \"<=\";\n this.userCode = `\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${loopHead}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${boundComparator} 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SearchSorted.js\nfunction searchSorted3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { sortedSequence, values } = inputs;\n const { side } = attrs;\n const program = new SearchSortedProgram(sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);\n const customValues = [[sortedSequence.shape[1]]];\n return backend2.runWebGLProgram(program, [sortedSequence, values], \"int32\", customValues);\n}\nvar searchSortedConfig2 = {\n kernelName: SearchSorted,\n backendName: \"webgl\",\n kernelFunc: searchSorted3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/select_gpu.js\nvar SelectProgram = class {\n constructor(cRank, shape, rank) {\n this.variableNames = [\"c\", \"a\", \"b\"];\n this.outputShape = shape;\n let cCoords;\n let abCoords;\n if (rank > 4) {\n throw Error(`Where for rank ${rank} is not yet supported`);\n }\n if (rank === 1) {\n abCoords = `resRC`;\n cCoords = `resRC`;\n } else {\n const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n const cCoordVars = [];\n const abCoordVars = [];\n for (let i = 0; i < shape.length; i++) {\n abCoordVars.push(`${currentCoords[i]}`);\n if (i < cRank) {\n cCoordVars.push(`${currentCoords[i]}`);\n }\n }\n cCoords = cCoordVars.join();\n abCoords = abCoordVars.join();\n }\n const dtype = getCoordsDataType(rank);\n this.userCode = `\n void main() {\n ${dtype} resRC = getOutputCoords();\n float cVal = getC(${cCoords});\n if (cVal >= 1.0) {\n setOutput(getA(${abCoords}));\n } else {\n setOutput(getB(${abCoords}));\n }\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js\nfunction select3(args) {\n const { inputs, backend: backend2 } = args;\n const { condition, t, e } = inputs;\n const program = new SelectProgram(condition.shape.length, t.shape, t.shape.length);\n return backend2.runWebGLProgram(program, [condition, t, e], upcastType(t.dtype, e.dtype));\n}\nvar selectConfig2 = {\n kernelName: Select,\n backendName: \"webgl\",\n kernelFunc: select3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Selu.js\nvar SELU = `\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};\n float scale = ${backend_util_exports.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nvar selu3 = unaryKernelFunc2({ opSnippet: SELU });\nvar seluConfig2 = {\n kernelName: Selu,\n backendName: \"webgl\",\n kernelFunc: selu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sigmoid.js\nvar SIGMOID3 = CHECK_NAN_SNIPPET_UNARY + `\n return 1.0 / (1.0 + exp(-1.0 * x));\n`;\nvar SIGMOID_PACKED = `\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`;\nvar sigmoid3 = unaryKernelFunc2({\n opSnippet: SIGMOID3,\n packedOpSnippet: SIGMOID_PACKED,\n cpuKernelImpl: sigmoidImplCPU\n});\nvar sigmoidConfig2 = {\n kernelName: Sigmoid,\n backendName: \"webgl\",\n kernelFunc: sigmoid3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sign.js\nvar SIGN = `\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`;\nvar sign3 = unaryKernelFunc2({ opSnippet: SIGN });\nvar signConfig2 = {\n kernelName: Sign,\n backendName: \"webgl\",\n kernelFunc: sign3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sin.js\nvar SIN = CHECK_NAN_SNIPPET_UNARY + `\n return sin(x);\n`;\nvar SIN_PACKED = `\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${CHECK_NAN_SNIPPET_PACKED}\n return result;\n`;\nvar sin3 = unaryKernelFunc2({ opSnippet: SIN, packedOpSnippet: SIN_PACKED });\nvar sinConfig2 = {\n kernelName: Sin,\n backendName: \"webgl\",\n kernelFunc: sin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sinh.js\nvar SINH = `\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`;\nvar sinh3 = unaryKernelFunc2({ opSnippet: SINH });\nvar sinhConfig2 = {\n kernelName: Sinh,\n backendName: \"webgl\",\n kernelFunc: sinh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softplus.js\nvar SOFTPLUS = `\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`;\nvar softplus3 = unaryKernelFunc2({ opSnippet: SOFTPLUS });\nvar softplusConfig2 = {\n kernelName: Softplus,\n backendName: \"webgl\",\n kernelFunc: softplus3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SpaceToBatchND.js\nvar spaceToBatchND3 = (args) => {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { blockShape, paddings } = attrs;\n util_exports.assert(x.shape.length <= 4, () => \"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");\n const prod5 = blockShape.reduce((a, b) => a * b);\n const completePaddings = [[0, 0]];\n completePaddings.push(...paddings);\n for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n completePaddings.push([0, 0]);\n }\n const toDispose = [];\n const paddedX = padV22({\n inputs: { x },\n backend: backend2,\n attrs: { paddings: completePaddings, constantValue: 0 }\n });\n const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false);\n const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false);\n const reshapedPaddedX = reshape4({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });\n const paddedXT = transpose3({\n inputs: { x: reshapedPaddedX },\n backend: backend2,\n attrs: { perm: permutedReshapedPaddedPermutation }\n });\n const result = reshape4({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });\n toDispose.push(paddedX);\n toDispose.push(reshapedPaddedX);\n toDispose.push(paddedXT);\n toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return result;\n};\nvar spaceToBatchNDConfig2 = {\n kernelName: SpaceToBatchND,\n backendName: \"webgl\",\n kernelFunc: spaceToBatchND3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseFillEmptyRows.js\nfunction sparseFillEmptyRows3(args) {\n const { inputs, backend: backend2 } = args;\n const { indices, values, denseShape, defaultValue } = inputs;\n if (denseShape.shape.length !== 1) {\n throw new Error(`Dense shape must be a vector, saw:\n ${denseShape.shape}`);\n }\n if (indices.shape.length !== 2) {\n throw new Error(`Indices must be a matrix, saw:\n ${indices.shape}`);\n }\n if (values.shape.length !== 1) {\n throw new Error(`Values must be a vector, saw:\n ${values.shape}`);\n }\n if (defaultValue.shape.length !== 0) {\n throw new Error(`Default value must be a scalar, saw:\n ${defaultValue.shape}`);\n }\n const $indices = backend2.readSync(indices.dataId);\n const $values = backend2.readSync(values.dataId);\n const $denseShape = backend2.readSync(denseShape.dataId);\n const $defaultValue = backend2.readSync(defaultValue.dataId)[0];\n const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);\n return [\n backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),\n backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),\n backend2.makeTensorInfo([emptyRowIndicator.length], \"bool\", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),\n backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))\n ];\n}\nvar sparseFillEmptyRowsConfig2 = {\n kernelName: SparseFillEmptyRows,\n backendName: \"webgl\",\n kernelFunc: sparseFillEmptyRows3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseReshape.js\nfunction sparseReshape3(args) {\n const { inputs, backend: backend2 } = args;\n const { inputIndices, inputShape, newShape } = inputs;\n if (inputIndices.shape.length !== 2) {\n throw new Error(`Input indices should be a matrix but received shape ${inputIndices.shape}`);\n }\n if (inputShape.shape.length !== 1) {\n throw new Error(`Input shape should be a vector but received shape ${inputShape.shape}`);\n }\n if (newShape.shape.length !== 1) {\n throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n }\n const $inputShape = Array.from(backend2.readSync(inputShape.dataId));\n const $inputIndices = backend2.readSync(inputIndices.dataId);\n const targetShape = Array.from(backend2.readSync(newShape.dataId));\n const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);\n return [\n backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),\n backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))\n ];\n}\nvar sparseReshapeConfig2 = {\n kernelName: SparseReshape,\n backendName: \"webgl\",\n kernelFunc: sparseReshape3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean3(args) {\n const { inputs, backend: backend2 } = args;\n const { data, indices, segmentIds } = inputs;\n if (data.shape.length < 1) {\n throw new Error(`Data should be at least 1 dimensional but received scalar`);\n }\n if (indices.shape.length !== 1) {\n throw new Error(`Indices should be a vector but received shape\n ${indices.shape}`);\n }\n if (segmentIds.shape.length !== 1) {\n throw new Error(`Segment ids should be a vector but received shape\n ${segmentIds.shape}`);\n }\n const $data = backend2.readSync(data.dataId);\n const $indices = backend2.readSync(indices.dataId);\n const $segmentIds = backend2.readSync(segmentIds.dataId);\n const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds, true);\n return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentMeanConfig2 = {\n kernelName: SparseSegmentMean,\n backendName: \"webgl\",\n kernelFunc: sparseSegmentMean3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum3(args) {\n const { inputs, backend: backend2 } = args;\n const { data, indices, segmentIds } = inputs;\n if (data.shape.length < 1) {\n throw new Error(`Data should be at least 1 dimensional but received scalar`);\n }\n if (indices.shape.length !== 1) {\n throw new Error(`Indices should be a vector but received shape\n ${indices.shape}`);\n }\n if (segmentIds.shape.length !== 1) {\n throw new Error(`Segment ids should be a vector but received shape\n ${segmentIds.shape}`);\n }\n const $data = backend2.readSync(data.dataId);\n const $indices = backend2.readSync(indices.dataId);\n const $segmentIds = backend2.readSync(segmentIds.dataId);\n const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds);\n return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentSumConfig2 = {\n kernelName: SparseSegmentSum,\n backendName: \"webgl\",\n kernelFunc: sparseSegmentSum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseToDense.js\nfunction sparseToDense3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { sparseIndices, sparseValues, defaultValue } = inputs;\n const { outputShape } = attrs;\n const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n const sumDupeIndices = false;\n if (sparseValues.dtype === \"string\") {\n const indicesBuf = backend2.bufferSync(sparseIndices);\n const updatesBuf = backend2.bufferSync(sparseValues);\n const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);\n const outBuf = scatterImplCPU(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n }\n const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);\n const res = backend2.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);\n const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } });\n backend2.disposeIntermediateTensorInfo(res);\n return reshaped;\n}\nvar sparseToDenseConfig2 = {\n kernelName: SparseToDense,\n backendName: \"webgl\",\n kernelFunc: sparseToDense3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SplitV.js\nfunction splitV2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { numOrSizeSplits, axis } = attrs;\n const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n const xRank = x.shape.length;\n const begin = new Array(xRank).fill(0);\n const size = x.shape.slice();\n return splitSizes.map((s) => {\n const sliceSize = [...size];\n sliceSize[$axis] = s;\n const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n begin[$axis] += s;\n return sliceT;\n });\n}\nvar splitVConfig2 = {\n kernelName: SplitV,\n backendName: \"webgl\",\n kernelFunc: splitV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sqrt.js\nvar SQRT = `return sqrt(x);`;\nvar sqrt3 = unaryKernelFunc2({ opSnippet: SQRT, packedOpSnippet: SQRT, cpuKernelImpl: sqrtImplCPU });\nvar sqrtConfig2 = {\n kernelName: Sqrt,\n backendName: \"webgl\",\n kernelFunc: sqrt3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Square.js\nvar SQUARE = `return x * x;`;\nvar square3 = unaryKernelFunc2({ opSnippet: SQUARE });\nvar squareConfig2 = {\n kernelName: Square,\n backendName: \"webgl\",\n kernelFunc: square3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SquaredDifference.js\nvar SQUARED_DIFFERENCE = \"return (a - b) * (a - b);\";\nvar squaredDifference3 = binaryKernelFunc2({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });\nvar squaredDifferenceConfig2 = {\n kernelName: SquaredDifference,\n backendName: \"webgl\",\n kernelFunc: squaredDifference3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StaticRegexReplace.js\nfunction staticRegexReplace3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n if (x.dtype !== \"string\") {\n throw new Error(\"Input must be of datatype string\");\n }\n const $x = backend2.readSync(x.dataId);\n const stringInput = backend_util_exports.fromUint8ToStringArray($x);\n const output = staticRegexReplaceImplCPU(stringInput, \"string\", attrs);\n return backend2.makeTensorInfo(x.shape, \"string\", output);\n}\nvar staticRegexReplaceConfig2 = {\n kernelName: StaticRegexReplace,\n backendName: \"webgl\",\n kernelFunc: staticRegexReplace3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Step.js\nfunction step3({ inputs, attrs, backend: backend2 }) {\n const { x } = inputs;\n const opSnippet = CHECK_NAN_SNIPPET + `\n return x > 0.0 ? 1.0 : float(${attrs.alpha});\n `;\n const program = new UnaryOpProgram(x.shape, opSnippet);\n return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar stepConfig2 = {\n kernelName: Step,\n backendName: \"webgl\",\n kernelFunc: step3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/strided_slice_gpu.js\nvar StridedSliceProgram = class {\n constructor(begin, strides, size) {\n this.variableNames = [\"x\"];\n this.outputShape = size;\n const rank = size.length;\n const inputDtype = getCoordsDataType(size.length);\n const dtype = getCoordsDataType(size.length);\n let newCoords = \"\";\n if (rank === 1) {\n newCoords = \"coords * strides + begin\";\n } else {\n let outputAxis = 0;\n newCoords = size.map((_, i) => {\n outputAxis++;\n return size.length === 1 ? `coords * strides[${i}] + begin[${i}]` : `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n }).join(\",\");\n }\n this.userCode = `\n ${inputDtype} begin = ${inputDtype}(${begin});\n ${inputDtype} strides = ${inputDtype}(${strides});\n\n void main() {\n ${dtype} coords = getOutputCoords();\n setOutput(getX(${newCoords}));\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StridedSlice.js\nfunction stridedSlice3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n let result;\n if (isIdentity) {\n result = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n } else if (sliceDim0 || isSimpleSlice) {\n util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n result = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n backend2.disposeIntermediateTensorInfo(sliced);\n } else {\n const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n if (shouldExecuteOnCPU) {\n const values = backend2.readSync(x.dataId);\n const xBuf = buffer(x.shape, x.dtype, values);\n const resultValues = stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);\n result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);\n } else {\n const program = new StridedSliceProgram($begin, $strides, finalShapeSparse);\n result = backend2.runWebGLProgram(program, [x], x.dtype);\n }\n }\n const resultReshaped = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: finalShape } });\n backend2.disposeIntermediateTensorInfo(result);\n return resultReshaped;\n}\nvar stridedSliceConfig2 = {\n kernelName: StridedSlice,\n backendName: \"webgl\",\n kernelFunc: stridedSlice3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringNGrams.js\nfunction stringNGrams3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n const { data, dataSplits } = inputs;\n const $data = backend2.readSync(data.dataId);\n const $dataSplits = backend2.readSync(dataSplits.dataId);\n const [nGrams, nGramsSplits] = stringNGramsImplCPU($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n return [\n backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n ];\n}\nvar stringNGramsConfig2 = {\n kernelName: StringNGrams,\n backendName: \"webgl\",\n kernelFunc: stringNGrams3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringSplit.js\nfunction stringSplit3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { skipEmpty } = attrs;\n const { input: input2, delimiter } = inputs;\n if (input2.dtype !== \"string\") {\n throw new Error(\"Input must be of datatype string\");\n }\n if (input2.shape.length !== 1) {\n throw new Error(`Input must be a vector, got shape: ${input2.shape}`);\n }\n if (delimiter.shape.length !== 0) {\n throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);\n }\n const $input = backend2.readSync(input2.dataId);\n const $delimiter = backend2.readSync(delimiter.dataId)[0];\n const [indices, values, shape] = stringSplitImplCPU($input, $delimiter, skipEmpty);\n const outputSize = values.length;\n return [\n backend2.makeTensorInfo([outputSize, 2], \"int32\", indices),\n backend2.makeTensorInfo([outputSize], \"string\", values),\n backend2.makeTensorInfo([2], \"int32\", new Int32Array(shape))\n ];\n}\nvar stringSplitConfig2 = {\n kernelName: StringSplit,\n backendName: \"webgl\",\n kernelFunc: stringSplit3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { numBuckets } = attrs;\n const { input: input2 } = inputs;\n if (input2.dtype !== \"string\") {\n throw new Error(\"Input must be of datatype string\");\n }\n if (numBuckets <= 0) {\n throw new Error(`Number of buckets must be at least 1`);\n }\n const $input = backend2.readSync(input2.dataId);\n const output = stringToHashBucketFastImplCPU($input, numBuckets);\n return backend2.makeTensorInfo(input2.shape, \"int32\", output);\n}\nvar stringToHashBucketFastConfig2 = {\n kernelName: StringToHashBucketFast,\n backendName: \"webgl\",\n kernelFunc: stringToHashBucketFast3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tan.js\nvar TAN = `return tan(x);`;\nvar tan3 = unaryKernelFunc2({ opSnippet: TAN });\nvar tanConfig2 = {\n kernelName: Tan,\n backendName: \"webgl\",\n kernelFunc: tan3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tanh.js\nvar TANH = `\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nvar tanh4 = unaryKernelFunc2({ opSnippet: TANH });\nvar tanhConfig2 = {\n kernelName: Tanh,\n backendName: \"webgl\",\n kernelFunc: tanh4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TensorScatterUpdate.js\nfunction tensorScatterUpdate3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { tensor: tensor2, indices, updates } = inputs;\n const {} = attrs;\n const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, tensor2.shape);\n const flattenShape = [outputSize / sliceSize, sliceSize];\n if (outputSize === 0) {\n return backend2.makeTensorInfo(tensor2.shape, indices.dtype);\n }\n const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });\n const flattenX = reshape4({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });\n const flattenTensor = reshape4({ inputs: { x: tensor2 }, backend: backend2, attrs: { shape: flattenShape } });\n const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape, false, true);\n const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, flattenTensor], flattenTensor.dtype);\n const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: tensor2.shape } });\n backend2.disposeIntermediateTensorInfo(flattenIndices);\n backend2.disposeIntermediateTensorInfo(flattenX);\n backend2.disposeIntermediateTensorInfo(flattenTensor);\n backend2.disposeIntermediateTensorInfo(res);\n return reshaped;\n}\nvar tensorScatterUpdateConfig2 = {\n kernelName: TensorScatterUpdate,\n backendName: \"webgl\",\n kernelFunc: tensorScatterUpdate3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/tile_gpu.js\nvar TileProgram = class {\n constructor(aShape, reps) {\n this.variableNames = [\"A\"];\n const outputShape = new Array(aShape.length);\n for (let i = 0; i < outputShape.length; i++) {\n outputShape[i] = aShape[i] * reps[i];\n }\n this.outputShape = outputShape;\n this.rank = outputShape.length;\n const dtype = getCoordsDataType(this.rank);\n const sourceCoords = getSourceCoords3(aShape);\n this.userCode = `\n void main() {\n ${dtype} resRC = getOutputCoords();\n setOutput(getA(${sourceCoords}));\n }\n `;\n }\n};\nfunction getSourceCoords3(aShape) {\n const rank = aShape.length;\n if (rank > 5) {\n throw Error(`Tile for rank ${rank} is not yet supported`);\n }\n if (rank === 1) {\n return `imod(resRC, ${aShape[0]})`;\n }\n const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\", \"resRC.u\"];\n const sourceCoords = [];\n for (let i = 0; i < aShape.length; i++) {\n sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n }\n return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js\nfunction tile4(params) {\n const { inputs, backend: backend2, attrs } = params;\n const { x } = inputs;\n const { reps } = attrs;\n if (x.dtype === \"string\" || x.shape.length > 5) {\n const data = backend2.readSync(x.dataId);\n const value = x.dtype === \"string\" ? data.map((d) => util_exports.decodeString(d)) : data;\n const buf = buffer(x.shape, x.dtype, value);\n const outBuf = tileImplCPU(buf, reps);\n return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n }\n const program = new TileProgram(x.shape, reps);\n const output = backend2.runWebGLProgram(program, [x], x.dtype);\n return output;\n}\nvar tileConfig2 = {\n kernelName: Tile,\n backendName: \"webgl\",\n kernelFunc: tile4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/top_k_gpu.js\nvar SwapProgram = class {\n /**\n * @param shape desired output shape (can be larger than input shape, output\n * will be padded with -Infinity)\n */\n constructor(shape) {\n this.variableNames = [\"x\", \"indices\"];\n this.customUniforms = [\n { name: \"n\", type: \"int\" },\n { name: \"firstPass\", type: \"int\" },\n { name: \"negativeInf\", type: \"float\" },\n { name: \"dir\", type: \"int\" },\n { name: \"inc\", type: \"int\" }\n ];\n this.outputShape = shape;\n 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 `;\n }\n};\nvar MergeProgram = class {\n /**\n * @param shape desired output shape (must be half of the input size)\n */\n constructor(shape) {\n this.variableNames = [\"x\", \"indices\"];\n this.customUniforms = [\n { name: \"n\", type: \"int\" },\n { name: \"firstPass\", type: \"int\" },\n { name: \"k\", type: \"int\" }\n ];\n this.outputShape = shape;\n 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js\nfunction disposeIntermediateTensorInfoOrNull(backend2, tensorInfo) {\n if (tensorInfo !== null) {\n backend2.disposeIntermediateTensorInfo(tensorInfo);\n }\n}\nfunction roundUpToPow2(num) {\n let pow22 = 1;\n while (pow22 < num) {\n pow22 *= 2;\n }\n return pow22;\n}\nfunction topK2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { k, sorted } = attrs;\n const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\");\n const TOPK_K_CPU_HANDOFF_THRESHOLD = env().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\");\n const xShape = x.shape;\n const lastDim = xShape[xShape.length - 1];\n if (backend2.shouldExecuteOnCPU([x]) || lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD || k > TOPK_K_CPU_HANDOFF_THRESHOLD) {\n const xVals = backend2.readSync(x.dataId);\n const [allTopKVals, allTopKIndices] = topKImplCPU(xVals, xShape, x.dtype, k, sorted);\n return [\n backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n ];\n }\n if (k === 0) {\n xShape[xShape.length - 1] = 0;\n return [\n backend2.makeTensorInfo(xShape, x.dtype, []),\n backend2.makeTensorInfo(xShape, \"int32\", [])\n ];\n }\n if (lastDim === 1) {\n return [\n x,\n fill3({ attrs: { shape: xShape, dtype: \"int32\", value: 0 }, backend: backend2 })\n ];\n }\n const xtexData = backend2.texData.get(x.dataId);\n const xIsPacked = xtexData !== null && xtexData.isPacked;\n const xUnPacked = xIsPacked ? backend2.unpackTensor(x) : x;\n const xSize = util_exports.sizeFromShape(xShape);\n const batch = xSize / lastDim;\n const x2D = reshape4({ inputs: { x: xUnPacked }, attrs: { shape: [batch, lastDim] }, backend: backend2 });\n if (xIsPacked) {\n disposeIntermediateTensorInfoOrNull(backend2, xUnPacked);\n }\n const kPow2 = roundUpToPow2(k);\n const lastDimPow2 = roundUpToPow2(lastDim);\n let indices = null;\n const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];\n const runSwap = (dir, inc, shape) => {\n const inputs2 = getInputs();\n const program = new SwapProgram(shape);\n const fistPass = indices === null ? 1 : 0;\n const customValues = [[lastDim], [fistPass], [Number.NEGATIVE_INFINITY], [dir], [inc]];\n const prevIndices2 = indices;\n indices = backend2.runWebGLProgram(program, inputs2, \"int32\", customValues);\n disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);\n };\n for (let len = 1; len < kPow2; len *= 2) {\n const dir = len * 2;\n for (let inc = len; inc >= 1; inc /= 2) {\n runSwap(dir, inc, [batch, lastDimPow2]);\n }\n }\n for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {\n const inputs2 = getInputs();\n const mergeProgram = new MergeProgram([batch, indicesSize / 2]);\n const firstPass = indices === null ? 1 : 0;\n const customValues = [[lastDim], [firstPass], [kPow2]];\n const prevIndices2 = indices;\n indices = backend2.runWebGLProgram(mergeProgram, inputs2, \"int32\", customValues);\n disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);\n const len = kPow2 / 2;\n const dir = len * 2;\n for (let inc = len; inc >= 1; inc /= 2) {\n runSwap(dir, inc, indices.shape);\n }\n }\n let prevIndices = indices;\n indices = slice3({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });\n disposeIntermediateTensorInfoOrNull(backend2, prevIndices);\n let values = gatherV22({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });\n disposeIntermediateTensorInfoOrNull(backend2, x2D);\n const newShape = xShape.slice(0, -1);\n newShape.push(k);\n prevIndices = indices;\n indices = reshape4({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });\n disposeIntermediateTensorInfoOrNull(backend2, prevIndices);\n const prevValues = values;\n values = reshape4({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });\n disposeIntermediateTensorInfoOrNull(backend2, prevValues);\n return [values, indices];\n}\nvar topKConfig2 = {\n kernelName: TopK,\n backendName: \"webgl\",\n kernelFunc: topK2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transform_gpu.js\nvar TransformProgram = class {\n constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) {\n this.variableNames = [\"Image\", \"Transforms\"];\n this.outputShape = outShape;\n const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n let fillModeId;\n switch (fillMode) {\n case \"constant\":\n fillModeId = 1;\n break;\n case \"reflect\":\n fillModeId = 2;\n break;\n case \"wrap\":\n fillModeId = 3;\n break;\n case \"nearest\":\n fillModeId = 4;\n break;\n default:\n fillModeId = 1;\n break;\n }\n this.userCode = `\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${fillModeId} == 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 (${fillModeId} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${fillModeId} == 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 < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${fillValue});\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(${fillValue});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${imageWidth}));\n float mapY = mapCoord(inY, float(${imageHeight}));\n\n if (${interpolationModeId} == 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 `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transform.js\nfunction transform3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { image: image2, transforms } = inputs;\n const { interpolation, fillMode, fillValue, outputShape } = attrs;\n const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n const outShape = [\n batch,\n outHeight,\n outWidth,\n numChannels\n ];\n const program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);\n return backend2.runWebGLProgram(program, [image2, transforms], \"float32\");\n}\nvar transformConfig2 = {\n kernelName: Transform,\n backendName: \"webgl\",\n kernelFunc: transform3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unique.js\nfunction unique4(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { axis } = attrs;\n const { x } = inputs;\n assertNotComplex2(x, \"unique\");\n console.warn(\"WARNING: \", \"UI might be locked temporarily as data is being downloaded\");\n const values = backend2.readSync(x.dataId);\n const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n return [\n backend2.makeTensorInfo(outputShape, x.dtype, outputValues),\n backend2.makeTensorInfo([indices.length], \"int32\", indices)\n ];\n}\nvar uniqueConfig2 = {\n kernelName: Unique,\n backendName: \"webgl\",\n kernelFunc: unique4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unpack.js\nfunction unpack2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { value } = inputs;\n let { axis } = attrs;\n if (axis < 0) {\n axis += value.shape.length;\n }\n const x = value;\n const xRank = x.shape.length;\n const num = value.shape[axis];\n const outShape = new Array(xRank - 1);\n let outIndex = 0;\n for (let i = 0; i < xRank; i++) {\n if (i !== axis) {\n outShape[outIndex++] = x.shape[i];\n }\n }\n const toDispose = [];\n const begin = new Array(xRank).fill(0);\n const size = x.shape.slice();\n size[axis] = 1;\n const res = new Array(num);\n for (let i = 0; i < res.length; i++) {\n begin[axis] = i;\n const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size } });\n const reshaped = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });\n res[i] = reshaped;\n toDispose.push(sliced);\n }\n toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return res;\n}\nvar unpackConfig2 = {\n kernelName: Unpack,\n backendName: \"webgl\",\n kernelFunc: unpack2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/segment_gpu.js\nvar SegmentOpProgram = class {\n constructor(segOpInfo, segOpType) {\n this.variableNames = [\"x\", \"segmentIds\"];\n const windowSize = segOpInfo.windowSize;\n const batchSize = segOpInfo.batchSize;\n const inSize = segOpInfo.inSize;\n const numSegments = segOpInfo.numSegments;\n const outSize = numSegments * Math.ceil(inSize / windowSize);\n this.outputShape = [batchSize, outSize];\n const initializationValue = \"0.0\";\n const returnValue = `sumValue`;\n const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n const windowSizeVec4Remainder = windowSize % 4;\n const updateSnippet = `\n sumValue += dot(values, segFilter);\n `;\n let checkValueOutOfBounds = \"\";\n if (inSize % windowSize > 0) {\n checkValueOutOfBounds = `\n if (inIdx < 0 || inIdx >= ${inSize}) {\n return initializationValue;\n }\n `;\n }\n let checkSegmentIdOutOfBounds = \"\";\n if (inSize % windowSize > 0) {\n checkSegmentIdOutOfBounds = `\n if (inIdx < 0 || inIdx >= ${inSize}) {\n return -1.0;\n }\n `;\n }\n this.userCode = `\n const float initializationValue = ${initializationValue};\n\n float getValue(int batch, int inIdx) {\n ${checkValueOutOfBounds}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${checkSegmentIdOutOfBounds}\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 ${numSegments})) * float(${windowSize}));\n int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${windowSizeNearestVec4}; 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 ${updateSnippet}\n }\n\n int inIdx = inOffset + ${windowSizeNearestVec4};\n if (${windowSizeVec4Remainder === 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 ${updateSnippet}\n } else if (${windowSizeVec4Remainder === 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 ${updateSnippet}\n } else if (${windowSizeVec4Remainder === 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 ${updateSnippet}\n }\n setOutput(${returnValue});\n }\n `;\n }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/UnsortedSegmentSum.js\nfunction unsortedSegmentSum3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, segmentIds } = inputs;\n const { numSegments } = attrs;\n const xRank = x.shape.length;\n const toDispose = [];\n let axis = 0;\n const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n let permutedX = x;\n if (permutation != null) {\n permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n toDispose.push(permutedX);\n axis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n }\n const outShape = backend_util_exports.segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n const inSize = util_exports.sizeFromShape([permutedX.shape[axis]]);\n const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n toDispose.push(a2D);\n const outputDType = sumOutType(x.dtype);\n const segOpCompute = (x2, segOpType, segmentIds2, dtype, numSegments2) => {\n const batchSize = x2.shape[0];\n const inSize2 = x2.shape[1];\n const windowSize = backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2, numSegments2);\n const segOpInfo = { windowSize, inSize: inSize2, batchSize, numSegments: numSegments2 };\n const program = new SegmentOpProgram(segOpInfo, segOpType);\n const output = backend2.compileAndRun(program, [x2, segmentIds2], dtype);\n toDispose.push(output);\n if (output.shape[1] === numSegments2) {\n return output;\n }\n const rangeInfo = range4({\n backend: backend2,\n attrs: { start: 0, stop: numSegments2, step: 1, dtype: \"float32\" }\n });\n const tileInfo = tile4({\n inputs: { x: rangeInfo },\n backend: backend2,\n attrs: { reps: [inSize2 / windowSize] }\n });\n toDispose.push(rangeInfo);\n toDispose.push(tileInfo);\n const result2 = segOpCompute(output, segOpType, tileInfo, dtype, numSegments2);\n return result2;\n };\n const segOpResult = segOpCompute(a2D, \"unsortedSegmentSum\", segmentIds, outputDType, numSegments);\n const reshaped = reshape4({ inputs: { x: segOpResult }, backend: backend2, attrs: { shape: outShape } });\n let result = reshaped;\n if (permutation != null) {\n toDispose.push(reshaped);\n const perm = backend_util_exports.getUndoAxesPermutation(permutation);\n result = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm } });\n }\n toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n return result;\n}\nvar unsortedSegmentSumConfig2 = {\n kernelName: UnsortedSegmentSum,\n backendName: \"webgl\",\n kernelFunc: unsortedSegmentSum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js\nvar kernelConfigs2 = [\n _fusedMatMulConfig2,\n absConfig2,\n acosConfig2,\n acoshConfig2,\n addConfig2,\n addNConfig2,\n allConfig2,\n anyConfig2,\n argMaxConfig2,\n argMinConfig2,\n asinConfig2,\n asinhConfig2,\n atanConfig2,\n atan2Config2,\n atanhConfig2,\n avgPoolConfig2,\n avgPool3DConfig2,\n avgPool3DGradConfig3,\n avgPoolGradConfig3,\n batchMatMulConfig2,\n batchNormConfig2,\n batchToSpaceNDConfig2,\n bincountConfig2,\n bitwiseAndConfig2,\n broadcastArgsConfig2,\n castConfig2,\n ceilConfig2,\n clipByValueConfig2,\n complexConfig2,\n complexAbsConfig2,\n concatConfig2,\n conv2DConfig2,\n conv2DBackpropFilterConfig2,\n conv2DBackpropInputConfig2,\n conv3DConfig2,\n conv3DBackpropFilterV2Config2,\n conv3DBackpropInputConfig,\n cosConfig2,\n coshConfig2,\n cropAndResizeConfig2,\n cumprodConfig2,\n cumsumConfig2,\n denseBincountConfig2,\n depthToSpaceConfig2,\n depthwiseConv2dNativeConfig2,\n depthwiseConv2dNativeBackpropFilterConfig2,\n depthwiseConv2dNativeBackpropInputConfig2,\n diagConfig2,\n dilation2DConfig2,\n einsumConfig2,\n eluConfig2,\n eluGradConfig3,\n equalConfig2,\n erfConfig2,\n expConfig2,\n expandDimsConfig2,\n expm1Config2,\n fftConfig2,\n fillConfig2,\n flipLeftRightConfig2,\n floorConfig2,\n floorDivConfig2,\n fromPixelsConfig,\n fusedConv2DConfig2,\n fusedDepthwiseConv2DConfig2,\n gatherNdConfig2,\n gatherV2Config2,\n greaterConfig2,\n greaterEqualConfig2,\n identityConfig2,\n ifftConfig2,\n imagConfig2,\n isFiniteConfig2,\n isInfConfig2,\n isNaNConfig2,\n leakyReluConfig2,\n lessConfig2,\n lessEqualConfig2,\n linSpaceConfig2,\n logConfig2,\n log1pConfig2,\n logicalAndConfig2,\n logicalNotConfig2,\n logicalOrConfig2,\n LRNConfig2,\n LRNGradConfig2,\n maxConfig2,\n maximumConfig2,\n maxPoolConfig2,\n maxPool3DConfig2,\n maxPool3DGradConfig3,\n maxPoolGradConfig3,\n maxPoolWithArgmaxConfig2,\n meanConfig2,\n minConfig2,\n minimumConfig2,\n mirrorPadConfig2,\n modConfig2,\n multinomialConfig2,\n multiplyConfig2,\n negConfig2,\n nonMaxSuppressionV3Config2,\n nonMaxSuppressionV4Config2,\n nonMaxSuppressionV5Config2,\n notEqualConfig2,\n oneHotConfig2,\n onesLikeConfig2,\n packConfig2,\n padV2Config2,\n powConfig2,\n preluConfig2,\n prodConfig2,\n raggedGatherConfig2,\n raggedRangeConfig2,\n raggedTensorToTensorConfig2,\n rangeConfig2,\n realConfig2,\n realDivConfig2,\n reciprocalConfig2,\n reluConfig2,\n relu6Config2,\n reshapeConfig2,\n resizeBilinearConfig2,\n resizeBilinearGradConfig3,\n resizeNearestNeighborConfig2,\n resizeNearestNeighborGradConfig3,\n reverseConfig2,\n rotateWithOffsetConfig2,\n roundConfig2,\n rsqrtConfig2,\n scatterNdConfig2,\n searchSortedConfig2,\n selectConfig2,\n seluConfig2,\n sigmoidConfig2,\n signConfig2,\n sinConfig2,\n sinhConfig2,\n sliceConfig2,\n softmaxConfig2,\n softplusConfig2,\n spaceToBatchNDConfig2,\n sparseFillEmptyRowsConfig2,\n sparseReshapeConfig2,\n sparseSegmentMeanConfig2,\n sparseSegmentSumConfig2,\n sparseToDenseConfig2,\n splitVConfig2,\n sqrtConfig2,\n squareConfig2,\n squaredDifferenceConfig2,\n staticRegexReplaceConfig2,\n stepConfig2,\n stridedSliceConfig2,\n stringNGramsConfig2,\n stringSplitConfig2,\n stringToHashBucketFastConfig2,\n subConfig2,\n sumConfig2,\n tanConfig2,\n tanhConfig2,\n tensorScatterUpdateConfig2,\n tileConfig2,\n topKConfig2,\n transformConfig2,\n transposeConfig2,\n uniqueConfig2,\n unpackConfig2,\n unsortedSegmentSumConfig2,\n zerosLikeConfig2\n];\nfor (const kernelConfig of kernelConfigs2) {\n registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/types.js\nvar CppDType;\n(function(CppDType2) {\n CppDType2[CppDType2[\"float32\"] = 0] = \"float32\";\n CppDType2[CppDType2[\"int32\"] = 1] = \"int32\";\n CppDType2[CppDType2[\"bool\"] = 2] = \"bool\";\n CppDType2[CppDType2[\"string\"] = 3] = \"string\";\n CppDType2[CppDType2[\"complex64\"] = 4] = \"complex64\";\n})(CppDType || (CppDType = {}));\nvar FusableActivation;\n(function(FusableActivation2) {\n FusableActivation2[FusableActivation2[\"linear\"] = 0] = \"linear\";\n FusableActivation2[FusableActivation2[\"relu\"] = 1] = \"relu\";\n FusableActivation2[FusableActivation2[\"relu6\"] = 2] = \"relu6\";\n FusableActivation2[FusableActivation2[\"prelu\"] = 3] = \"prelu\";\n FusableActivation2[FusableActivation2[\"leakyrelu\"] = 4] = \"leakyrelu\";\n FusableActivation2[FusableActivation2[\"sigmoid\"] = 5] = \"sigmoid\";\n FusableActivation2[FusableActivation2[\"elu\"] = 6] = \"elu\";\n})(FusableActivation || (FusableActivation = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/_FusedMatMul.js\nvar wasmFusedMatMul;\nfunction setup(backend2) {\n wasmFusedMatMul = backend2.wasm.cwrap(_FusedMatMul, null, [\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction fusedBatchMatMul(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { a, b, bias, preluActivationWeights } = inputs;\n if (a.dtype !== \"float32\" || b.dtype !== \"float32\") {\n throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`);\n }\n const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n const aId = backend2.dataIdMap.get(a.dataId).id;\n const bId = backend2.dataIdMap.get(b.dataId).id;\n let biasId = 0;\n if (bias != null) {\n const biasData = backend2.dataIdMap.get(bias.dataId);\n if (biasData.shape.length !== 1) {\n throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n }\n biasId = biasData.id;\n }\n const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n const fusedActivation = FusableActivation[activation2];\n if (fusedActivation == null) {\n throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);\n }\n const leftDim = transposeA ? a.shape[2] : a.shape[1];\n const rightDim = transposeB ? b.shape[1] : b.shape[2];\n const batchDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n const out = backend2.makeOutput([...batchDims, leftDim, rightDim], a.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);\n const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);\n wasmFusedMatMul(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n return out;\n}\nvar _fusedMatMulConfig3 = {\n kernelName: _FusedMatMul,\n backendName: \"wasm\",\n setupFunc: setup,\n kernelFunc: fusedBatchMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/unary_kernel.js\nfunction createUnaryKernelConfig(kernelName, outType) {\n let wasmFunc8;\n function setupFunc3(backend2) {\n wasmFunc8 = backend2.wasm.cwrap(kernelName, null, [\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n }\n function kernelFunc3(args) {\n const { backend: backend2, inputs: { x } } = args;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const out = backend2.makeOutput(x.shape, outType || x.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n if (util_exports.sizeFromShape(out.shape) === 0) {\n return out;\n }\n wasmFunc8(xId, CppDType[x.dtype], outId);\n return out;\n }\n return { kernelName, backendName: \"wasm\", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Abs.js\nvar absConfig3 = createUnaryKernelConfig(Abs);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Acos.js\nvar acosConfig3 = createUnaryKernelConfig(Acos);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Acosh.js\nvar acoshConfig3 = createUnaryKernelConfig(Acosh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/binary_kernel.js\nfunction createBinaryKernelConfig(kernelName, supportsFullBroadcast20, dtype) {\n let wasmFunc8;\n function setupFunc3(backend2) {\n wasmFunc8 = backend2.wasm.cwrap(kernelName, null, [\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n }\n function kernelFunc3(args) {\n const { backend: backend2, inputs } = args;\n const { a, b } = inputs;\n const aId = backend2.dataIdMap.get(a.dataId).id;\n const bId = backend2.dataIdMap.get(b.dataId).id;\n const outputType = dtype != null ? dtype : a.dtype;\n const newShape = backend_util_exports.assertAndGetBroadcastShape(a.shape, b.shape);\n const out = backend2.makeOutput(newShape, outputType);\n if (util_exports.sizeFromShape(newShape) === 0) {\n return out;\n }\n const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);\n const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const kernelFunc4 = () => wasmFunc8(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a.dtype], outId);\n kernelFunc4();\n return out;\n }\n return { kernelName, backendName: \"wasm\", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Add.js\nvar supportsFullBroadcast = true;\nvar addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AddN.js\nvar wasmFunc;\nfunction setupFunc(backend2) {\n wasmFunc = backend2.wasm.cwrap(AddN, null, [\n \"array\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction addn(args) {\n const { inputs, backend: backend2 } = args;\n const out = backend2.makeOutput(inputs[0].shape, inputs[0].dtype);\n if (util_exports.sizeFromShape(out.shape) === 0) {\n return out;\n }\n const inputIds = inputs.map((x) => backend2.dataIdMap.get(x.dataId).id);\n const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);\n return out;\n}\nvar addNConfig3 = {\n kernelName: AddN,\n backendName: \"wasm\",\n setupFunc,\n kernelFunc: addn\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Identity.js\nfunction identity4(args) {\n const { inputs: { x }, backend: backend2 } = args;\n if (x.dtype === \"string\") {\n return tensor(backend2.readSync(x.dataId), x.shape, x.dtype);\n }\n const out = backend2.makeOutput(x.shape, x.dtype);\n const inVals = backend2.typedArrayFromHeap(x);\n const outVals = backend2.typedArrayFromHeap(out);\n outVals.set(inVals);\n return out;\n}\nvar identityConfig3 = {\n kernelName: Identity,\n backendName: \"wasm\",\n kernelFunc: identity4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transpose.js\nvar wasmTranspose;\nfunction setup2(backend2) {\n wasmTranspose = backend2.wasm.cwrap(Transpose, null, [\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"number\"\n // perm.length\n ]);\n}\nfunction transpose4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);\n let permIsNoOp = true;\n for (let i = 0; i < perm.length; i++) {\n if (perm[i] !== i) {\n permIsNoOp = false;\n }\n }\n const outShape = computeOutShape4(inputs.x.shape, attrs.perm);\n const x = {\n dataId: inputs.x.dataId,\n shape: reducedShape,\n dtype: inputs.x.dtype\n };\n if (permIsNoOp) {\n const cloned = identity4({ inputs, backend: backend2 });\n cloned.shape = outShape;\n return cloned;\n }\n const out = backend2.makeOutput(outShape, x.dtype);\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const permBytes = new Uint8Array(new Int32Array(perm).buffer);\n const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length);\n return out;\n}\nfunction computeOutShape4(inShape, perm) {\n const outShape = new Array(inShape.length);\n for (let i = 0; i < outShape.length; i++) {\n outShape[i] = inShape[perm[i]];\n }\n return outShape;\n}\nfunction removeOneSizeDims(shape, perm) {\n const newShape = [];\n const newPerm = [];\n for (let i = 0; i < shape.length; ++i) {\n if (shape[i] !== 1) {\n newShape.push(shape[i]);\n }\n if (shape[perm[i]] !== 1) {\n newPerm.push(perm[i]);\n }\n }\n for (let i = 0; i < newPerm.length; ++i) {\n let minValIdx = -1;\n for (let j = 0; j < newPerm.length; ++j) {\n if (newPerm[j] >= i && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {\n minValIdx = j;\n }\n }\n newPerm[minValIdx] = i;\n }\n return [newShape, newPerm];\n}\nvar transposeConfig3 = {\n kernelName: Transpose,\n backendName: \"wasm\",\n kernelFunc: transpose4,\n setupFunc: setup2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/kernel_utils.js\nfunction permuteAxesAndTranspose(x, axis, backend2) {\n const xShape = x.shape;\n const xRank = x.shape.length;\n const originalAxes = util_exports.parseAxisParam(axis, xShape);\n let axes = originalAxes;\n const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n let xTransposed = null;\n let inputWasTransposed = false;\n if (permutedAxes != null) {\n const newShape = new Array(xRank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = xShape[permutedAxes[i]];\n }\n axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const transposedId = backend2.dataIdMap.get(xTransposed.dataId).id;\n if (transposedId !== xId) {\n inputWasTransposed = true;\n }\n }\n return { transposed: xTransposed, originalAxes, axes, inputWasTransposed };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/All.js\nvar wasmAll;\nfunction setup3(backend2) {\n wasmAll = backend2.wasm.cwrap(All, null, [\"number, number, number\"]);\n}\nfunction all4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { axis, keepDims } = attrs;\n const { x } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n let inputId = xId;\n let input2 = x;\n const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n if (inputWasTransposed) {\n const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n input2 = transposed;\n inputId = transposedId;\n }\n const inputRank = input2.shape.length;\n backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, inputRank);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const out = backend2.makeOutput(outShape, x.dtype);\n if (util_exports.sizeFromShape(input2.shape) !== 0) {\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmAll(inputId, reduceSize, outId);\n }\n if (inputWasTransposed) {\n backend2.disposeData(transposed.dataId);\n }\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n out.shape = newShape;\n }\n return out;\n}\nvar allConfig3 = {\n kernelName: All,\n backendName: \"wasm\",\n setupFunc: setup3,\n kernelFunc: all4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Any.js\nvar wasmAny;\nfunction setup4(backend2) {\n wasmAny = backend2.wasm.cwrap(Any, null, [\"number, number, number\"]);\n}\nfunction any4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { axis, keepDims } = attrs;\n const { x } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n let inputId = xId;\n let input2 = x;\n const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n if (inputWasTransposed) {\n const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n input2 = transposed;\n inputId = transposedId;\n }\n const inputRank = input2.shape.length;\n backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, inputRank);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const out = backend2.makeOutput(outShape, x.dtype);\n if (util_exports.sizeFromShape(input2.shape) !== 0) {\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmAny(inputId, reduceSize, outId);\n }\n if (inputWasTransposed) {\n backend2.disposeData(transposed.dataId);\n }\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n out.shape = newShape;\n }\n return out;\n}\nvar anyConfig3 = {\n kernelName: Any,\n backendName: \"wasm\",\n setupFunc: setup4,\n kernelFunc: any4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/argminmax_kernel.js\nfunction createArgMinMaxKernelConfig(kernelName) {\n let wasmFunc8;\n function setupFunc3(backend2) {\n wasmFunc8 = backend2.wasm.cwrap(kernelName, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n }\n function kernelFunc3(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { axis } = attrs;\n const { x } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n let inputId = xId;\n let input2 = x;\n const { transposed, axes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n if (inputWasTransposed) {\n const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n if (transposedId !== xId) {\n input2 = transposed;\n inputId = transposedId;\n }\n }\n const outShape = input2.shape.slice(0, -1);\n const out = backend2.makeOutput(outShape, \"int32\");\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const outerSize = util_exports.sizeFromShape(out.shape);\n const innerSize = input2.shape[axes[0]];\n wasmFunc8(inputId, CppDType[input2.dtype], outerSize, innerSize, outId);\n if (inputWasTransposed) {\n backend2.disposeData(transposed.dataId);\n }\n return out;\n }\n return {\n kernelName,\n backendName: \"wasm\",\n setupFunc: setupFunc3,\n kernelFunc: kernelFunc3\n };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMax.js\nvar argMaxConfig3 = createArgMinMaxKernelConfig(ArgMax);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMin.js\nvar argMinConfig3 = createArgMinMaxKernelConfig(ArgMin);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Asin.js\nvar asinConfig3 = createUnaryKernelConfig(Asin);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Asinh.js\nvar asinhConfig3 = createUnaryKernelConfig(Asinh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Atan.js\nvar atanConfig3 = createUnaryKernelConfig(Atan);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Atan2.js\nvar atan2Config3 = createBinaryKernelConfig(\n Atan2,\n /*supportsFullBroadcast=*/\n false\n);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Atanh.js\nvar atanhConfig3 = createUnaryKernelConfig(Atanh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool.js\nvar wasmAvgPool;\nfunction setup5(backend2) {\n wasmAvgPool = backend2.wasm.cwrap(AvgPool, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction avgPool4(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const x = inputs.x;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const padTop = convInfo.padInfo.top;\n const padRight = convInfo.padInfo.right;\n const padBottom = convInfo.padInfo.bottom;\n const padLeft = convInfo.padInfo.left;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const channels = convInfo.inChannels;\n if (convInfo.dataFormat !== \"channelsLast\") {\n throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n }\n if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) {\n throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);\n }\n const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId);\n return out;\n}\nvar avgPoolConfig3 = {\n kernelName: AvgPool,\n backendName: \"wasm\",\n setupFunc: setup5,\n kernelFunc: avgPool4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool3D.js\nvar wasmAvgPool3D;\nfunction setup6(backend2) {\n wasmAvgPool3D = backend2.wasm.cwrap(\"AvgPool3D\", null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction avgPool3D3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n const convInfo = backend_util_exports.computePool3DInfo(\n x.shape,\n filterSize,\n strides,\n /*dilations=*/\n 1,\n pad3,\n dimRoundingMode,\n dataFormat\n );\n const out = backend2.makeOutput(convInfo.outShape, x.dtype);\n wasmAvgPool3D(\n backend2.dataIdMap.get(x.dataId).id,\n backend2.dataIdMap.get(out.dataId).id,\n convInfo.batchSize,\n // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in\n // channels should always equal to out channels.\n /*channelSize=*/\n convInfo.inChannels,\n convInfo.inDepth,\n convInfo.inHeight,\n convInfo.inWidth,\n convInfo.outDepth,\n convInfo.outHeight,\n convInfo.outWidth,\n convInfo.strideDepth,\n convInfo.strideHeight,\n convInfo.strideWidth,\n convInfo.dilationDepth,\n convInfo.dilationHeight,\n convInfo.dilationWidth,\n convInfo.effectiveFilterDepth,\n convInfo.effectiveFilterHeight,\n convInfo.effectiveFilterWidth,\n convInfo.padInfo.front,\n convInfo.padInfo.top,\n convInfo.padInfo.left\n );\n return out;\n}\nvar avgPool3DConfig3 = {\n kernelName: AvgPool3D,\n backendName: \"wasm\",\n setupFunc: setup6,\n kernelFunc: avgPool3D3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool3DGrad.js\nvar wasmAvgPool3DGrad;\nfunction setup7(backend2) {\n wasmAvgPool3DGrad = backend2.wasm.cwrap(\"AvgPool3DGrad\", null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // filterWidth\n ]);\n}\nfunction avgPool3DGrad3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2 } = inputs;\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const convInfo = backend_util_exports.computePool3DInfo(\n input2.shape,\n filterSize,\n strides,\n /*dilations=*/\n 1,\n pad3,\n dimRoundingMode\n );\n const dx = backend2.makeOutput(input2.shape, input2.dtype);\n wasmAvgPool3DGrad(\n backend2.dataIdMap.get(dy.dataId).id,\n backend2.dataIdMap.get(dx.dataId).id,\n convInfo.batchSize,\n // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in\n // channels should always equal to out channels.\n /*channelSize=*/\n convInfo.inChannels,\n convInfo.inDepth,\n convInfo.inHeight,\n convInfo.inWidth,\n convInfo.outDepth,\n convInfo.outHeight,\n convInfo.outWidth,\n convInfo.strideDepth,\n convInfo.strideHeight,\n convInfo.strideWidth,\n convInfo.dilationDepth,\n convInfo.dilationHeight,\n convInfo.dilationWidth,\n convInfo.effectiveFilterDepth,\n convInfo.effectiveFilterHeight,\n convInfo.effectiveFilterWidth,\n convInfo.padInfo.front,\n convInfo.padInfo.top,\n convInfo.padInfo.left,\n convInfo.filterDepth,\n convInfo.filterHeight,\n convInfo.filterWidth\n );\n return dx;\n}\nvar avgPool3DGradConfig4 = {\n kernelName: AvgPool3DGrad,\n backendName: \"wasm\",\n setupFunc: setup7,\n kernelFunc: avgPool3DGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPoolGrad.js\nvar wasmAvgPoolGrad;\nfunction setup8(backend2) {\n wasmAvgPoolGrad = backend2.wasm.cwrap(\"AvgPoolGrad\", null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // filterWidth\n ]);\n}\nfunction avgPoolGrad4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2 } = inputs;\n const { filterSize, strides, pad: pad3 } = attrs;\n const convInfo = backend_util_exports.computePool2DInfo(\n input2.shape,\n filterSize,\n strides,\n /*dilations=*/\n 1,\n pad3\n );\n const dx = backend2.makeOutput(input2.shape, input2.dtype);\n wasmAvgPoolGrad(\n backend2.dataIdMap.get(dy.dataId).id,\n backend2.dataIdMap.get(dx.dataId).id,\n convInfo.batchSize,\n // Since Pool ops (AvgPool and MaxPool) support 2D filter only, in\n // channels should always equal to out channels.\n /*channelSize=*/\n convInfo.inChannels,\n convInfo.inHeight,\n convInfo.inWidth,\n convInfo.outHeight,\n convInfo.outWidth,\n convInfo.strideHeight,\n convInfo.strideWidth,\n convInfo.dilationHeight,\n convInfo.dilationWidth,\n convInfo.effectiveFilterHeight,\n convInfo.effectiveFilterWidth,\n convInfo.padInfo.top,\n convInfo.padInfo.left,\n convInfo.filterHeight,\n convInfo.filterWidth\n );\n return dx;\n}\nvar avgPoolGradConfig4 = {\n kernelName: AvgPoolGrad,\n backendName: \"wasm\",\n setupFunc: setup8,\n kernelFunc: avgPoolGrad4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reshape.js\nfunction reshape5(args) {\n const { inputs, attrs } = args;\n const { x } = inputs;\n const { shape } = attrs;\n const xSize = util_exports.sizeFromShape(x.shape);\n const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n util_exports.assert(xSize === util_exports.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`);\n args.backend.incRef(x.dataId);\n return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig3 = {\n kernelName: Reshape,\n backendName: \"wasm\",\n kernelFunc: reshape5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchMatMul.js\nvar wasmBatchMatMul;\nfunction setup9(backend2) {\n wasmBatchMatMul = backend2.wasm.cwrap(BatchMatMul, null, [\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction batchMatMul3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { a, b } = inputs;\n const { transposeA, transposeB } = attrs;\n if (a.dtype !== \"float32\" || b.dtype !== \"float32\") {\n throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`);\n }\n const aRank = a.shape.length;\n const bRank = b.shape.length;\n const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n const outerDimsA = a.shape.slice(0, -2);\n const outerDimsB = b.shape.slice(0, -2);\n const batchDimA = util_exports.sizeFromShape(outerDimsA);\n const batchDimB = util_exports.sizeFromShape(outerDimsB);\n const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n const a3d = reshape5({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n const b3d = reshape5({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n const a3dId = backend2.dataIdMap.get(a3d.dataId).id;\n const b3dId = backend2.dataIdMap.get(b3d.dataId).id;\n const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n const batchDim = Math.max(batchDimA, batchDimB);\n const out = backend2.makeOutput([batchDim, leftDim, rightDim], a3d.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer);\n const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer);\n wasmBatchMatMul(a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes, b3d.shape.length, transposeA, transposeB, outId);\n backend2.disposeData(a3d.dataId);\n backend2.disposeData(b3d.dataId);\n out.shape = outShape;\n return out;\n}\nvar batchMatMulConfig3 = {\n kernelName: BatchMatMul,\n backendName: \"wasm\",\n setupFunc: setup9,\n kernelFunc: batchMatMul3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Slice.js\nfunction slice4(args) {\n const { inputs: { x }, attrs: { begin, size }, backend: backend2 } = args;\n const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size);\n const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_);\n const xVals = backend2.readSync(x.dataId);\n const out = backend2.makeOutput(size_, x.dtype);\n const xStrides = util_exports.computeStrides(x.shape);\n const outData = backend2.dataIdMap.get(out.dataId);\n if (isContinous) {\n const flatOffset = slice_util_exports.computeFlatOffset(begin_, xStrides);\n if (x.dtype === \"string\") {\n outData.stringBytes = xVals.slice(flatOffset, flatOffset + util_exports.sizeFromShape(size_));\n } else {\n const outVals2 = backend2.typedArrayFromHeap(out);\n outVals2.set(xVals.subarray(flatOffset, flatOffset + util_exports.sizeFromShape(size_)));\n }\n return out;\n }\n if (x.dtype === \"string\") {\n const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);\n outData.stringBytes = res;\n return out;\n }\n const outVals = backend2.typedArrayFromHeap(out);\n const rank = x.shape.length;\n if (rank === 2) {\n slice2d2(xVals, xStrides[0], outVals, begin_, size_);\n } else if (rank === 3) {\n slice3d2(xVals, xStrides[0], xStrides[1], outVals, begin_, size_);\n } else if (rank === 4) {\n slice4d2(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_);\n } else {\n const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);\n outVals.set(res);\n }\n return out;\n}\nfunction slice2d2(xVals, xStride, outVals, begin, size) {\n let outOffset = 0;\n const beginI = begin[0];\n const beginJ = begin[1];\n const endI = beginI + size[0];\n for (let i = beginI; i < endI; i++) {\n const xOffset = i * xStride + beginJ;\n outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset);\n outOffset += size[1];\n }\n}\nfunction slice3d2(xVals, xStride1, xStride2, outVals, begin, size) {\n let outOffset = 0;\n const beginI = begin[0];\n const beginJ = begin[1];\n const beginK = begin[2];\n const endI = beginI + size[0];\n const endJ = beginJ + size[1];\n for (let i = beginI; i < endI; i++) {\n for (let j = beginJ; j < endJ; j++) {\n const xOffset = i * xStride1 + j * xStride2 + beginK;\n outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset);\n outOffset += size[2];\n }\n }\n}\nfunction slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size) {\n let outOffset = 0;\n const beginI = begin[0];\n const beginJ = begin[1];\n const beginK = begin[2];\n const endI = beginI + size[0];\n const endJ = beginJ + size[1];\n const endK = beginK + size[2];\n const beginL = begin[3];\n for (let i = beginI; i < endI; i++) {\n for (let j = beginJ; j < endJ; j++) {\n for (let k = beginK; k < endK; k++) {\n const xOffset = i * xStride1 + j * xStride2 + k * xStride3 + beginL;\n outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset);\n outOffset += size[3];\n }\n }\n }\n}\nvar sliceConfig3 = {\n kernelName: Slice,\n backendName: \"wasm\",\n kernelFunc: slice4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchToSpaceND.js\nfunction batchToSpaceND4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { blockShape, crops } = attrs;\n const prod5 = blockShape.reduce((a, b) => a * b);\n const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5);\n const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5);\n const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n const xReshaped = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n const xTransposed = transpose4({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });\n const xTransposedReshaped = reshape5({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });\n const result = slice4({\n inputs: { x: xTransposedReshaped },\n backend: backend2,\n attrs: { begin: sliceBeginCoords, size: sliceSize }\n });\n backend2.disposeData(xReshaped.dataId);\n backend2.disposeData(xTransposed.dataId);\n backend2.disposeData(xTransposedReshaped.dataId);\n return result;\n}\nvar batchToSpaceNDConfig3 = {\n kernelName: BatchToSpaceND,\n backendName: \"wasm\",\n kernelFunc: batchToSpaceND4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Bincount.js\nvar wasmBincount;\nfunction setup10(backend2) {\n wasmBincount = backend2.wasm.cwrap(Bincount, null, [\n \"number\",\n \"number\",\n \"boolean\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction bincount4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { x, weights } = inputs;\n const { size } = attrs;\n const hasWeights = weights.shape.reduce((p2, v) => p2 * v, 1) !== 0;\n const outShape = x.shape.length === 1 ? [size] : [x.shape[0], size];\n const out = backend2.makeOutput(outShape, weights.dtype);\n function tensorId(x2) {\n return backend2.dataIdMap.get(x2.dataId).id;\n }\n wasmBincount(tensorId(x), size, hasWeights, tensorId(weights), CppDType[weights.dtype], tensorId(out));\n return out;\n}\nvar bincountConfig3 = {\n kernelName: Bincount,\n backendName: \"wasm\",\n setupFunc: setup10,\n kernelFunc: bincount4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BitwiseAnd.js\nvar supportsFullBroadcast2 = true;\nvar bitwiseAndConfig3 = createBinaryKernelConfig(BitwiseAnd, supportsFullBroadcast2);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BroadcastArgs.js\nfunction broadcastArgs4(args) {\n const { inputs, backend: backend2 } = args;\n const { s0, s1 } = inputs;\n const s0Vals = backend2.typedArrayFromHeap(s0);\n const s1Vals = backend2.typedArrayFromHeap(s1);\n const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));\n return backend2.makeOutput(\n [broadcastShape.length],\n \"int32\",\n /*memoryOffset=*/\n void 0,\n /*values=*/\n new Int32Array(broadcastShape)\n );\n}\nvar broadcastArgsConfig3 = {\n kernelName: BroadcastArgs,\n backendName: \"wasm\",\n kernelFunc: broadcastArgs4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cast.js\nfunction cast5(args) {\n const { inputs: { x }, attrs: { dtype }, backend: backend2 } = args;\n const out = backend2.makeOutput(x.shape, dtype);\n const inVals = backend2.typedArrayFromHeap(x);\n const outVals = backend2.typedArrayFromHeap(out);\n outVals.set(inVals);\n return out;\n}\nvar castConfig3 = {\n kernelName: Cast,\n backendName: \"wasm\",\n kernelFunc: cast5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Ceil.js\nvar ceilConfig3 = createUnaryKernelConfig(Ceil);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ClipByValue.js\nvar wasmClip;\nfunction setup11(backend2) {\n wasmClip = backend2.wasm.cwrap(ClipByValue, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction clip(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { clipValueMin, clipValueMax } = attrs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const out = backend2.makeOutput(x.shape, x.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmClip(xId, clipValueMin, clipValueMax, outId);\n return out;\n}\nvar clipByValueConfig3 = {\n kernelName: ClipByValue,\n backendName: \"wasm\",\n setupFunc: setup11,\n kernelFunc: clip\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Concat.js\nfunction concat4(args) {\n const { inputs, backend: backend2 } = args;\n const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];\n const shapes = inputs.map((t) => t.shape);\n backend_util_exports.assertParamsConsistent(shapes, axis);\n let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);\n const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);\n if ($inputs.length === 1) {\n return identity4({ inputs: { x: $inputs[0] }, backend: backend2 });\n }\n const out = backend2.makeOutput(outShape, inputs[0].dtype);\n if (util_exports.sizeFromShape(outShape) === 0) {\n return out;\n }\n if ($inputs[0].dtype === \"string\") {\n const inputs2D = $inputs.map((t) => {\n const innerSize = util_exports.sizeFromShape(t.shape.slice(axis));\n const shape = [-1, innerSize];\n return reshape5({ inputs: { x: t }, backend: backend2, attrs: { shape } });\n });\n const inputsValShapes = inputs2D.map((t) => {\n return { vals: backend2.readSync(t.dataId), shape: t.shape };\n });\n outShape = backend_util_exports.computeOutShape(\n inputs2D.map((t) => t.shape),\n 1\n /* axis */\n );\n const simplyConcat = inputs2D[0].shape[0] === 1;\n const outVals2 = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);\n const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), axis);\n out.shape = finalOutShape;\n const outData = backend2.dataIdMap.get(out.dataId);\n outData.stringBytes = backend_util_exports.fromStringArrayToUint8(outVals2);\n inputs2D.forEach((t) => backend2.disposeData(t.dataId));\n return out;\n }\n const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis));\n let sumInnerDims = 0;\n const innerDims = $inputs.map((input2) => {\n const innerDim = util_exports.sizeFromShape(input2.shape.slice(axis));\n sumInnerDims += innerDim;\n return innerDim;\n });\n const inVals = $inputs.map((input2) => backend2.typedArrayFromHeap(input2));\n const outVals = backend2.typedArrayFromHeap(out);\n for (let b = 0; b < batchDim; b++) {\n let outOffset = b * sumInnerDims;\n for (let i = 0; i < inVals.length; i++) {\n const innerDim = innerDims[i];\n const inOffset = b * innerDim;\n const vals = inVals[i].subarray(inOffset, inOffset + innerDim);\n outVals.set(vals, outOffset);\n outOffset += innerDim;\n }\n }\n return out;\n}\nvar concatConfig3 = {\n kernelName: Concat,\n backendName: \"wasm\",\n kernelFunc: concat4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2D.js\nvar wasmConv2d;\nfunction setup12(backend2) {\n wasmConv2d = backend2.wasm.cwrap(Conv2D, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction conv2d5(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { x, filter } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const filterId = backend2.dataIdMap.get(filter.dataId).id;\n const { strides, dilations, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const padTop = convInfo.padInfo.top;\n const padRight = convInfo.padInfo.right;\n const padBottom = convInfo.padInfo.bottom;\n const padLeft = convInfo.padInfo.left;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const inputChannels = convInfo.inChannels;\n const outputChannels = convInfo.outChannels;\n const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n if (convInfo.dataFormat !== \"channelsLast\") {\n throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n }\n const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n return out;\n}\nvar conv2DConfig3 = {\n kernelName: Conv2D,\n backendName: \"wasm\",\n setupFunc: setup12,\n kernelFunc: conv2d5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2DBackpropInput.js\nvar wasmConv2DBackpropInput;\nfunction setup13(backend2) {\n wasmConv2DBackpropInput = backend2.wasm.cwrap(Conv2DBackpropInput, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction conv2DBackpropInput4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { dy, filter } = inputs;\n const { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape } = attrs;\n const dilations = 1;\n const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n const topPad = filterHeight - 1 - convInfo.padInfo.top;\n const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n const dxStrides = util_exports.computeStrides(convInfo.inShape);\n const dyStrides = util_exports.computeStrides(dy.shape);\n const [fltS0, fltS1, fltS2] = util_exports.computeStrides(filter.shape);\n const xBatchStride = dxStrides[0];\n const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2];\n const xColStride = isChannelsLast ? dxStrides[2] : 1;\n const xChannelStride = isChannelsLast ? 1 : dxStrides[1];\n const yBatchStride = dyStrides[0];\n const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n const yColStride = isChannelsLast ? dyStrides[2] : 1;\n const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n const out = backend2.makeOutput(convInfo.inShape, \"float32\");\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const dyId = backend2.dataIdMap.get(dy.dataId).id;\n const filterId = backend2.dataIdMap.get(filter.dataId).id;\n wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId);\n return out;\n}\nvar conv2DBackpropInputConfig3 = {\n kernelName: Conv2DBackpropInput,\n backendName: \"wasm\",\n setupFunc: setup13,\n kernelFunc: conv2DBackpropInput4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv3D.js\nvar wasmConv3D;\nfunction setup14(backend2) {\n wasmConv3D = backend2.wasm.cwrap(Conv3D, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction conv3D3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter } = inputs;\n const { strides, pad: pad3, dilations } = attrs;\n if (x.dtype !== \"float32\") {\n throw new Error(`Tensor x must have dtype float32, got ${x.dtype}`);\n }\n if (filter.dtype !== \"float32\") {\n throw new Error(`Tensor filter must have dtype float32, got ${filter.dtype}`);\n }\n const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);\n const out = backend2.makeOutput(convInfo.outShape, x.dtype);\n wasmConv3D(backend2.dataIdMap.get(x.dataId).id, backend2.dataIdMap.get(filter.dataId).id, backend2.dataIdMap.get(out.dataId).id, convInfo.batchSize, convInfo.inDepth, convInfo.inHeight, convInfo.inWidth, convInfo.inChannels, convInfo.outDepth, convInfo.outHeight, convInfo.outWidth, convInfo.outChannels, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationDepth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left);\n return out;\n}\nvar conv3DConfig3 = {\n kernelName: Conv3D,\n backendName: \"wasm\",\n setupFunc: setup14,\n kernelFunc: conv3D3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv3DBackpropFilterV2.js\nvar wasmConv3DBackpropFilterV2;\nfunction setup15(backend2) {\n wasmConv3DBackpropFilterV2 = backend2.wasm.cwrap(Conv3DBackpropFilterV2, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction conv3DBackpropFilterV23(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, dy } = inputs;\n const { strides, pad: pad3, filterShape } = attrs;\n if (x.dtype !== \"float32\") {\n throw new Error(`Tensor dy must have dtype float32, got ${x.dtype}`);\n }\n if (dy.dtype !== \"float32\") {\n throw new Error(`Tensor filter must have dtype float32, got ${dy.dtype}`);\n }\n const convInfo = backend_util_exports.computeConv3DInfo(\n x.shape,\n filterShape,\n strides,\n /*dilations=*/\n 1,\n pad3\n );\n const dw = backend2.makeOutput(convInfo.filterShape, dy.dtype);\n wasmConv3DBackpropFilterV2(backend2.dataIdMap.get(x.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dw.dataId).id, convInfo.batchSize, convInfo.inDepth, convInfo.inHeight, convInfo.inWidth, convInfo.inChannels, convInfo.outDepth, convInfo.outHeight, convInfo.outWidth, convInfo.outChannels, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationDepth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left);\n return dw;\n}\nvar conv3DBackpropFilterV2Config3 = {\n kernelName: Conv3DBackpropFilterV2,\n backendName: \"wasm\",\n setupFunc: setup15,\n kernelFunc: conv3DBackpropFilterV23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv3DBackpropInputV2.js\nvar wasmConv3DBackpropInputV2;\nfunction setup16(backend2) {\n wasmConv3DBackpropInputV2 = backend2.wasm.cwrap(Conv3DBackpropInputV2, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction conv3DBackpropInputV22(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, filter } = inputs;\n const { pad: pad3, strides, inputShape } = attrs;\n if (dy.dtype !== \"float32\") {\n throw new Error(`Tensor dy must have dtype float32, got ${dy.dtype}`);\n }\n if (filter.dtype !== \"float32\") {\n throw new Error(`Tensor filter must have dtype float32, got ${filter.dtype}`);\n }\n const convInfo = backend_util_exports.computeConv3DInfo(\n inputShape,\n filter.shape,\n strides,\n /*dilations=*/\n 1,\n pad3\n );\n const dx = backend2.makeOutput(convInfo.inShape, dy.dtype);\n wasmConv3DBackpropInputV2(backend2.dataIdMap.get(filter.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dx.dataId).id, convInfo.batchSize, convInfo.inDepth, convInfo.inHeight, convInfo.inWidth, convInfo.inChannels, convInfo.outDepth, convInfo.outHeight, convInfo.outWidth, convInfo.outChannels, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationDepth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left);\n return dx;\n}\nvar conv3DBackpropInputV2Config2 = {\n kernelName: Conv3DBackpropInputV2,\n backendName: \"wasm\",\n setupFunc: setup16,\n kernelFunc: conv3DBackpropInputV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cos.js\nvar cosConfig3 = createUnaryKernelConfig(Cos);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cosh.js\nvar coshConfig3 = createUnaryKernelConfig(Cosh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/CropAndResize.js\nvar InterpolationMethod;\n(function(InterpolationMethod2) {\n InterpolationMethod2[InterpolationMethod2[\"bilinear\"] = 0] = \"bilinear\";\n InterpolationMethod2[InterpolationMethod2[\"nearest\"] = 1] = \"nearest\";\n})(InterpolationMethod || (InterpolationMethod = {}));\nvar wasmCropAndResize;\nfunction setup17(backend2) {\n wasmCropAndResize = backend2.wasm.cwrap(CropAndResize, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out id\n ]);\n}\nfunction cropAndResize5(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { method, extrapolationValue, cropSize } = attrs;\n const { image: image2, boxes, boxInd } = inputs;\n const numBoxes = boxes.shape[0];\n const [cropHeight, cropWidth] = cropSize;\n const outShape = [numBoxes, cropHeight, cropWidth, image2.shape[3]];\n let imagesData = backend2.dataIdMap.get(image2.dataId);\n let castedData;\n if (image2.dtype !== \"float32\") {\n castedData = cast5({ backend: backend2, inputs: { x: image2 }, attrs: { dtype: \"float32\" } });\n imagesData = backend2.dataIdMap.get(castedData.dataId);\n }\n const imagesId = imagesData.id;\n const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n const boxIndId = backend2.dataIdMap.get(boxInd.dataId).id;\n const out = backend2.makeOutput(outShape, \"float32\");\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const imagesShapeBytes = new Uint8Array(new Int32Array(image2.shape).buffer);\n wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId);\n if (castedData != null) {\n backend2.disposeData(castedData.dataId);\n }\n return out;\n}\nvar cropAndResizeConfig3 = {\n kernelName: CropAndResize,\n backendName: \"wasm\",\n setupFunc: setup17,\n kernelFunc: cropAndResize5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumprod.js\nvar wasmCumprod;\nfunction setup18(backend2) {\n wasmCumprod = backend2.wasm.cwrap(Cumprod, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // dtype\n ]);\n}\nfunction cumprod4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, exclusive, reverse: reverse5 } = attrs;\n const xRank = x.shape.length;\n util_exports.assert(x.dtype === \"float32\" || x.dtype === \"int32\", () => `cumprod does not support ${x.dtype} tensors in the WASM backend`);\n const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n let permutedX = x;\n if (permutation !== null) {\n permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });\n }\n const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n backend_util_exports.assertAxesAreInnerMostDims(\"cumprod\", [permutedAxis], xRank);\n const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);\n const finalDim = permutedX.shape[permutedAxis];\n const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;\n const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;\n wasmCumprod(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);\n let out = permutedOut;\n if (permutation !== null) {\n const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });\n backend2.disposeData(permutedX.dataId);\n backend2.disposeData(permutedOut.dataId);\n }\n return out;\n}\nvar cumprodConfig3 = {\n kernelName: Cumprod,\n backendName: \"wasm\",\n setupFunc: setup18,\n kernelFunc: cumprod4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumsum.js\nvar wasmCumsum;\nfunction setup19(backend2) {\n wasmCumsum = backend2.wasm.cwrap(Cumsum, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // dtype\n ]);\n}\nfunction cumsum4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { axis, exclusive, reverse: reverse5 } = attrs;\n const xRank = x.shape.length;\n util_exports.assert(x.dtype === \"float32\" || x.dtype === \"int32\", () => `cumsum does not support ${x.dtype} tensors in the WASM backend`);\n const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n let permutedX = x;\n if (permutation !== null) {\n permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });\n }\n const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n backend_util_exports.assertAxesAreInnerMostDims(\"cumsum\", [permutedAxis], xRank);\n const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);\n const finalDim = permutedX.shape[permutedAxis];\n const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;\n const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;\n wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);\n let out = permutedOut;\n if (permutation !== null) {\n const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });\n backend2.disposeData(permutedX.dataId);\n backend2.disposeData(permutedOut.dataId);\n }\n return out;\n}\nvar cumsumConfig3 = {\n kernelName: Cumsum,\n backendName: \"wasm\",\n setupFunc: setup19,\n kernelFunc: cumsum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DenseBincount.js\nvar wasmDenseBincount;\nfunction setup20(backend2) {\n wasmDenseBincount = backend2.wasm.cwrap(\"DenseBincount\", null, [\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"boolean\",\n \"number\",\n \"number\",\n \"boolean\",\n \"number\"\n // outId\n ]);\n}\nfunction denseBincount4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { x, weights } = inputs;\n const { size, binaryOutput } = attrs;\n const hasWeights = weights.shape.reduce((p2, v) => p2 * v, 1) !== 0;\n const outShape = x.shape.length === 1 ? [size] : [x.shape[0], size];\n const out = backend2.makeOutput(outShape, weights.dtype);\n function tensorId(x2) {\n return backend2.dataIdMap.get(x2.dataId).id;\n }\n wasmDenseBincount(tensorId(x), new Uint8Array(new Int32Array(x.shape).buffer), x.shape.length, size, hasWeights, tensorId(weights), CppDType[weights.dtype], binaryOutput, tensorId(out));\n return out;\n}\nvar denseBincountConfig3 = {\n kernelName: DenseBincount,\n backendName: \"wasm\",\n setupFunc: setup20,\n kernelFunc: denseBincount4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthToSpace.js\nvar wasmDepthToSpace;\nfunction setup21(backend2) {\n wasmDepthToSpace = backend2.wasm.cwrap(DepthToSpace, null, [\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"array\",\n \"array\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction depthToSpace4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { x } = inputs;\n const { blockSize, dataFormat } = attrs;\n const batchSize = x.shape[0];\n const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n const outputHeight = inputHeight * blockSize;\n const outputWidth = inputWidth * blockSize;\n const outputDepth = inputDepth / (blockSize * blockSize);\n const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n const out = backend2.makeOutput(outputShape, \"float32\");\n const xData = backend2.dataIdMap.get(x.dataId);\n const xId = xData.id;\n const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);\n const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer);\n const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const channelsLast = dataFormat === \"NHWC\" ? 1 : 0;\n wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId);\n return out;\n}\nvar depthToSpaceConfig3 = {\n kernelName: DepthToSpace,\n backendName: \"wasm\",\n setupFunc: setup21,\n kernelFunc: depthToSpace4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthwiseConv2dNative.js\nvar wasmDepthwiseConv2d;\nfunction setup22(backend2) {\n wasmDepthwiseConv2d = backend2.wasm.cwrap(DepthwiseConv2dNative, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction depthwiseConv2d5(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { x, filter } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const filterId = backend2.dataIdMap.get(filter.dataId).id;\n const { strides, dilations, pad: pad3, dimRoundingMode } = attrs;\n const $dilations = dilations == null ? [1, 1] : dilations;\n const convInfo = backend_util_exports.computeConv2DInfo(\n x.shape,\n filter.shape,\n strides,\n $dilations,\n pad3,\n dimRoundingMode,\n true\n /* depthwise */\n );\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const padTop = convInfo.padInfo.top;\n const padRight = convInfo.padInfo.right;\n const padBottom = convInfo.padInfo.bottom;\n const padLeft = convInfo.padInfo.left;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const inputChannels = convInfo.inChannels;\n const outputChannels = convInfo.outChannels;\n const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n if (convInfo.dataFormat !== \"channelsLast\") {\n throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n }\n const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n return out;\n}\nvar depthwiseConv2dNativeConfig3 = {\n kernelName: DepthwiseConv2dNative,\n backendName: \"wasm\",\n setupFunc: setup22,\n kernelFunc: depthwiseConv2d5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Diag.js\nvar wasmDiag;\nfunction setup23(backend2) {\n wasmDiag = backend2.wasm.cwrap(\"Diag\", null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction diag4(args) {\n const { inputs, backend: backend2 } = args;\n const { x } = inputs;\n const xSize = util_exports.sizeFromShape(x.shape);\n const out = backend2.makeOutput([...x.shape, ...x.shape], x.dtype);\n wasmDiag(backend2.dataIdMap.get(x.dataId).id, CppDType[x.dtype], xSize, backend2.dataIdMap.get(out.dataId).id);\n return out;\n}\nvar diagConfig3 = {\n kernelName: Diag,\n backendName: \"wasm\",\n setupFunc: setup23,\n kernelFunc: diag4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Dilation2D.js\nvar wasmDilation2D;\nfunction setup24(backend2) {\n wasmDilation2D = backend2.wasm.cwrap(Dilation2D, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction dilation2D2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter } = inputs;\n const { strides, pad: pad3, dilations } = attrs;\n if (x.dtype !== filter.dtype) {\n throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${x.dtype} and ${filter.dtype}`);\n }\n const dilationInfo = backend_util_exports.computeDilation2DInfo(\n x.shape,\n filter.shape,\n strides,\n pad3,\n /*dataFormat=*/\n \"NHWC\",\n dilations\n );\n const out = backend2.makeOutput(dilationInfo.outShape, x.dtype);\n wasmDilation2D(\n backend2.dataIdMap.get(x.dataId).id,\n backend2.dataIdMap.get(filter.dataId).id,\n backend2.dataIdMap.get(out.dataId).id,\n CppDType[x.dtype],\n dilationInfo.batchSize,\n /*depth=*/\n dilationInfo.inChannels,\n dilationInfo.inHeight,\n dilationInfo.inWidth,\n dilationInfo.outHeight,\n dilationInfo.outWidth,\n dilationInfo.strideHeight,\n dilationInfo.strideWidth,\n dilationInfo.dilationHeight,\n dilationInfo.dilationWidth,\n dilationInfo.filterHeight,\n dilationInfo.filterWidth,\n dilationInfo.padInfo.top,\n dilationInfo.padInfo.left\n );\n return out;\n}\nvar dilation2DConfig3 = {\n kernelName: Dilation2D,\n backendName: \"wasm\",\n setupFunc: setup24,\n kernelFunc: dilation2D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Dilation2DBackpropFilter.js\nvar wasmDilation2DBackpropFilter;\nfunction setup25(backend2) {\n wasmDilation2DBackpropFilter = backend2.wasm.cwrap(Dilation2DBackpropFilter, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction dilation2DBackpropFilter(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter, dy } = inputs;\n const { strides, pad: pad3, dilations } = attrs;\n if (x.dtype !== filter.dtype || x.dtype !== dy.dtype) {\n throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${x.dtype}, ${filter.dtype}, and ${dy.dtype}`);\n }\n const dilationInfo = backend_util_exports.computeDilation2DInfo(\n x.shape,\n filter.shape,\n strides,\n pad3,\n /*dataFormat=*/\n \"NHWC\",\n dilations\n );\n const gradients = backend2.makeOutput(filter.shape, filter.dtype);\n wasmDilation2DBackpropFilter(\n backend2.dataIdMap.get(x.dataId).id,\n backend2.dataIdMap.get(filter.dataId).id,\n backend2.dataIdMap.get(dy.dataId).id,\n backend2.dataIdMap.get(gradients.dataId).id,\n CppDType[x.dtype],\n dilationInfo.batchSize,\n /*depth=*/\n dilationInfo.inChannels,\n dilationInfo.inHeight,\n dilationInfo.inWidth,\n dilationInfo.outHeight,\n dilationInfo.outWidth,\n dilationInfo.strideHeight,\n dilationInfo.strideWidth,\n dilationInfo.dilationHeight,\n dilationInfo.dilationWidth,\n dilationInfo.filterHeight,\n dilationInfo.filterWidth,\n dilationInfo.padInfo.top,\n dilationInfo.padInfo.left\n );\n return gradients;\n}\nvar dilation2DBackpropFilterConfig2 = {\n kernelName: Dilation2DBackpropFilter,\n backendName: \"wasm\",\n setupFunc: setup25,\n kernelFunc: dilation2DBackpropFilter\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Dilation2DBackpropInput.js\nvar wasmDilation2DBackpropInput;\nfunction setup26(backend2) {\n wasmDilation2DBackpropInput = backend2.wasm.cwrap(Dilation2DBackpropInput, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction dilation2DBackpropInput(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, filter, dy } = inputs;\n const { strides, pad: pad3, dilations } = attrs;\n if (x.dtype !== filter.dtype || x.dtype !== dy.dtype) {\n throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${x.dtype}, ${filter.dtype}, and ${dy.dtype}`);\n }\n const dilationInfo = backend_util_exports.computeDilation2DInfo(\n x.shape,\n filter.shape,\n strides,\n pad3,\n /*dataFormat=*/\n \"NHWC\",\n dilations\n );\n const gradients = backend2.makeOutput(x.shape, x.dtype);\n wasmDilation2DBackpropInput(\n backend2.dataIdMap.get(x.dataId).id,\n backend2.dataIdMap.get(filter.dataId).id,\n backend2.dataIdMap.get(dy.dataId).id,\n backend2.dataIdMap.get(gradients.dataId).id,\n CppDType[x.dtype],\n dilationInfo.batchSize,\n /*depth=*/\n dilationInfo.inChannels,\n dilationInfo.inHeight,\n dilationInfo.inWidth,\n dilationInfo.outHeight,\n dilationInfo.outWidth,\n dilationInfo.strideHeight,\n dilationInfo.strideWidth,\n dilationInfo.dilationHeight,\n dilationInfo.dilationWidth,\n dilationInfo.filterHeight,\n dilationInfo.filterWidth,\n dilationInfo.padInfo.top,\n dilationInfo.padInfo.left\n );\n return gradients;\n}\nvar dilation2DBackpropInputConfig2 = {\n kernelName: Dilation2DBackpropInput,\n backendName: \"wasm\",\n setupFunc: setup26,\n kernelFunc: dilation2DBackpropInput\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Elu.js\nvar eluConfig3 = createUnaryKernelConfig(Elu);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/EluGrad.js\nvar wasmEluGrad;\nfunction setup27(backend2) {\n wasmEluGrad = backend2.wasm.cwrap(EluGrad, null, [\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction eluGrad3(args) {\n const { inputs, backend: backend2 } = args;\n const { dy, y } = inputs;\n const out = backend2.makeOutput(y.shape, \"float32\");\n const tensorId = (x) => {\n return backend2.dataIdMap.get(x.dataId).id;\n };\n wasmEluGrad(tensorId(y), tensorId(dy), tensorId(out));\n return out;\n}\nvar eluGradConfig4 = {\n kernelName: EluGrad,\n backendName: \"wasm\",\n setupFunc: setup27,\n kernelFunc: eluGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Equal.js\nvar supportsFullBroadcast3 = false;\nvar equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast3, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Erf.js\nvar erfConfig3 = createUnaryKernelConfig(Erf);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Exp.js\nvar expConfig3 = createUnaryKernelConfig(Exp, \"float32\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ExpandDims.js\nfunction expandDims5(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { input: input2 } = inputs;\n const { dim } = attrs;\n const inputRank = input2.shape.length;\n const newShape = input2.shape.slice();\n let $dim = dim;\n if (dim < 0) {\n util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n $dim = inputRank + dim + 1;\n }\n newShape.splice($dim, 0, 1);\n return reshape5({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig3 = {\n kernelName: ExpandDims,\n backendName: \"wasm\",\n kernelFunc: expandDims5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Expm1.js\nvar expm1Config3 = createUnaryKernelConfig(Expm1, \"float32\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Fill.js\nfunction fill4(args) {\n const { attrs: { shape, value }, backend: backend2 } = args;\n let { attrs: { dtype } } = args;\n dtype = dtype || util_exports.inferDtype(value);\n const out = backend2.makeOutput(shape, dtype);\n const outVals = backend2.typedArrayFromHeap(out);\n outVals.fill(value);\n return out;\n}\nvar fillConfig3 = {\n kernelName: Fill,\n backendName: \"wasm\",\n kernelFunc: fill4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FlipLeftRight.js\nvar wasmFlipLeftRight;\nfunction setup28(backend2) {\n wasmFlipLeftRight = backend2.wasm.cwrap(FlipLeftRight, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction flipLeftRight2(args) {\n const { inputs, backend: backend2 } = args;\n const { image: image2 } = inputs;\n const out = backend2.makeOutput(image2.shape, image2.dtype);\n const imageId = backend2.dataIdMap.get(image2.dataId).id;\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId);\n return out;\n}\nvar flipLeftRightConfig3 = {\n kernelName: FlipLeftRight,\n backendName: \"wasm\",\n kernelFunc: flipLeftRight2,\n setupFunc: setup28\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Floor.js\nvar floorConfig3 = createUnaryKernelConfig(Floor);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FloorDiv.js\nvar supportsFullBroadcast4 = false;\nvar floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast4);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedBatchNorm.js\nvar wasmBatchNorm;\nfunction setup29(backend2) {\n wasmBatchNorm = backend2.wasm.cwrap(FusedBatchNorm, null, [\"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\"]);\n}\nfunction fusedBatchNorm(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { varianceEpsilon } = attrs;\n const { x, mean: mean4, variance, offset, scale: scale2 } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const meanId = backend2.dataIdMap.get(mean4.dataId).id;\n const varianceId = backend2.dataIdMap.get(variance.dataId).id;\n const offsetId = offset != null ? backend2.dataIdMap.get(offset.dataId).id : 0;\n const scaleId = scale2 != null ? backend2.dataIdMap.get(scale2.dataId).id : 0;\n const out = backend2.makeOutput(x.shape, x.dtype);\n if (util_exports.sizeFromShape(x.shape) === 0) {\n return out;\n }\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId);\n return out;\n}\nvar fusedBatchNormConfig = {\n kernelName: FusedBatchNorm,\n backendName: \"wasm\",\n setupFunc: setup29,\n kernelFunc: fusedBatchNorm\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedConv2D.js\nvar wasmFusedConv2d;\nfunction setup30(backend2) {\n wasmFusedConv2d = backend2.wasm.cwrap(FusedConv2D, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction fusedConv2d2(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { x, filter, bias, preluActivationWeights } = inputs;\n const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode);\n const fusedActivation = FusableActivation[activation2];\n if (fusedActivation == null) {\n throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);\n }\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const filterId = backend2.dataIdMap.get(filter.dataId).id;\n const outputChannels = convInfo.outChannels;\n let biasId = 0;\n if (bias != null) {\n const biasData = backend2.dataIdMap.get(bias.dataId);\n if (biasData.shape.length !== 1) {\n throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n }\n if (biasData.shape[0] !== outputChannels) {\n throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);\n }\n biasId = biasData.id;\n }\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const padTop = convInfo.padInfo.top;\n const padRight = convInfo.padInfo.right;\n const padBottom = convInfo.padInfo.bottom;\n const padLeft = convInfo.padInfo.left;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const inputChannels = convInfo.inChannels;\n const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n const batchSize = convInfo.batchSize;\n const inHeight = convInfo.inHeight;\n const inWidth = convInfo.inWidth;\n if (dataFormat !== \"NHWC\") {\n throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);\n }\n const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n return out;\n}\nvar fusedConv2DConfig3 = {\n kernelName: FusedConv2D,\n backendName: \"wasm\",\n setupFunc: setup30,\n kernelFunc: fusedConv2d2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedDepthwiseConv2D.js\nvar wasmFusedDepthwiseConv2d;\nfunction setup31(backend2) {\n wasmFusedDepthwiseConv2d = backend2.wasm.cwrap(FusedDepthwiseConv2D, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction fusedDepthwiseConv2d(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { x, filter, bias, preluActivationWeights } = inputs;\n const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n const convInfo = backend_util_exports.computeConv2DInfo(\n x.shape,\n filter.shape,\n strides,\n dilations,\n pad3,\n dimRoundingMode,\n true\n /* depthwise */\n );\n const fusedActivation = FusableActivation[activation2];\n if (fusedActivation == null) {\n throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);\n }\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const filterId = backend2.dataIdMap.get(filter.dataId).id;\n const outputChannels = convInfo.outChannels;\n let biasId = 0;\n if (bias != null) {\n const biasData = backend2.dataIdMap.get(bias.dataId);\n if (biasData.shape.length !== 1) {\n throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n }\n if (biasData.shape[0] !== outputChannels) {\n throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);\n }\n biasId = biasData.id;\n }\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const padTop = convInfo.padInfo.top;\n const padRight = convInfo.padInfo.right;\n const padBottom = convInfo.padInfo.bottom;\n const padLeft = convInfo.padInfo.left;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const inputChannels = convInfo.inChannels;\n const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n const batchSize = convInfo.batchSize;\n const inHeight = convInfo.inHeight;\n const inWidth = convInfo.inWidth;\n if (dataFormat !== \"NHWC\") {\n throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);\n }\n const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n return out;\n}\nvar fusedDepthwiseConv2DConfig3 = {\n kernelName: FusedDepthwiseConv2D,\n backendName: \"wasm\",\n setupFunc: setup31,\n kernelFunc: fusedDepthwiseConv2d\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherNd.js\nvar wasmGatherNd;\nfunction setup32(backend2) {\n wasmGatherNd = backend2.wasm.cwrap(GatherNd, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"number\"\n // outId\n ]);\n}\nfunction gatherNd3(args) {\n const { backend: backend2, inputs } = args;\n const { params, indices } = inputs;\n const [resultShape, numSlices, sliceSize, strides] = gather_nd_util_exports.prepareAndValidate(params, indices);\n const out = backend2.makeOutput(resultShape, params.dtype);\n if (numSlices === 0) {\n return out;\n }\n const indicesShape = indices.shape;\n const sliceRank = indicesShape[indicesShape.length - 1];\n const xData = backend2.dataIdMap.get(params.dataId);\n const xId = xData.id;\n const indicesData = backend2.dataIdMap.get(indices.dataId);\n const indicesId = indicesData.id;\n const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId);\n return out;\n}\nvar gatherNdConfig3 = {\n kernelName: GatherNd,\n backendName: \"wasm\",\n setupFunc: setup32,\n kernelFunc: gatherNd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherV2.js\nvar wasmGather;\nfunction setup33(backend2) {\n wasmGather = backend2.wasm.cwrap(\"Gather\", null, [\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"number\"\n // outId\n ]);\n}\nfunction gatherV23(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { x, indices } = inputs;\n const { axis, batchDims } = attrs;\n const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n const indicesVals = backend2.readSync(indices.dataId);\n const axisDim = x.shape[parsedAxis];\n for (let i = 0; i < indicesVals.length; ++i) {\n const index = indicesVals[i];\n util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n }\n const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n const flattenX = reshape5({\n inputs: { x },\n attrs: {\n shape: [\n shapeInfo.batchSize,\n shapeInfo.outerSize,\n shapeInfo.dimSize,\n shapeInfo.sliceSize\n ]\n },\n backend: backend2\n });\n const indicesSize = util_exports.sizeFromShape(indices.shape);\n const flattenIndex = reshape5({\n inputs: { x: indices },\n attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] },\n backend: backend2\n });\n const flattenOutputShape = [\n shapeInfo.batchSize,\n shapeInfo.outerSize,\n indicesSize / shapeInfo.batchSize,\n shapeInfo.sliceSize\n ];\n const out = backend2.makeOutput(flattenOutputShape, x.dtype);\n if (util_exports.sizeFromShape(x.shape) === 0) {\n return out;\n }\n const stridesSize = flattenX.shape.length - 1;\n const xData = backend2.dataIdMap.get(flattenX.dataId);\n const xId = xData.id;\n const indicesData = backend2.dataIdMap.get(flattenIndex.dataId);\n const indicesId = indicesData.id;\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer);\n const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);\n wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, shapeInfo.batchSize, outStridesBytes, outId);\n backend2.disposeData(flattenX.dataId);\n backend2.disposeData(flattenIndex.dataId);\n out.shape = shapeInfo.outputShape;\n return out;\n}\nvar gatherV2Config3 = {\n kernelName: GatherV2,\n backendName: \"wasm\",\n setupFunc: setup33,\n kernelFunc: gatherV23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Greater.js\nvar supportsFullBroadcast5 = false;\nvar greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast5, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GreaterEqual.js\nvar supportsFullBroadcast6 = false;\nvar greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast6, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/IsFinite.js\nvar isFiniteConfig3 = createUnaryKernelConfig(IsFinite, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/IsInf.js\nvar isInfConfig3 = createUnaryKernelConfig(IsInf, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/IsNan.js\nvar isNaNConfig3 = createUnaryKernelConfig(IsNan, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LeakyRelu.js\nvar wasmFunc2;\nfunction setupFunc2(backend2) {\n wasmFunc2 = backend2.wasm.cwrap(LeakyRelu, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction leakyRelu4(args) {\n const { inputs: { x }, attrs: { alpha }, backend: backend2 } = args;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const out = backend2.makeOutput(x.shape, \"float32\");\n if (util_exports.sizeFromShape(x.shape) !== 0) {\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmFunc2(xId, CppDType[x.dtype], alpha, outId);\n }\n return out;\n}\nvar leakyReluConfig3 = {\n kernelName: LeakyRelu,\n backendName: \"wasm\",\n setupFunc: setupFunc2,\n kernelFunc: leakyRelu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Less.js\nvar supportsFullBroadcast7 = false;\nvar lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast7, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LessEqual.js\nvar supportsFullBroadcast8 = false;\nvar lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast8, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LinSpace.js\nvar wasmLinSpace;\nfunction setup34(backend2) {\n wasmLinSpace = backend2.wasm.cwrap(LinSpace, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // num\n ]);\n}\nfunction linSpace3(args) {\n const { attrs, backend: backend2 } = args;\n const { start, stop, num } = attrs;\n const numInt = Math.floor(num);\n const out = backend2.makeOutput([numInt], \"float32\");\n wasmLinSpace(backend2.dataIdMap.get(out.dataId).id, start, stop, numInt);\n return out;\n}\nvar linSpaceConfig3 = {\n kernelName: LinSpace,\n backendName: \"wasm\",\n setupFunc: setup34,\n kernelFunc: linSpace3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log.js\nvar logConfig3 = createUnaryKernelConfig(Log);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log1p.js\nvar log1pConfig3 = createUnaryKernelConfig(Log1p);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalAnd.js\nvar supportsFullBroadcast9 = false;\nvar logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast9, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalNot.js\nvar logicalNotConfig3 = createUnaryKernelConfig(LogicalNot);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalOr.js\nvar supportsFullBroadcast10 = false;\nvar logicalOrConfig3 = createBinaryKernelConfig(LogicalOr, supportsFullBroadcast10, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalXor.js\nvar supportsFullBroadcast11 = false;\nvar logicalXorConfig = createBinaryKernelConfig(LogicalXor, supportsFullBroadcast11, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LRN.js\nvar wasmLRN;\nfunction setup35(backend2) {\n wasmLRN = backend2.wasm.cwrap(LRN, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // beta\n ]);\n}\nfunction lrn2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { depthRadius, bias, alpha, beta } = attrs;\n if (x.dtype !== \"float32\") {\n throw new Error(\"LRN error: x must have dtype float32\");\n }\n const out = backend2.makeOutput(x.shape, x.dtype);\n wasmLRN(\n backend2.dataIdMap.get(x.dataId).id,\n backend2.dataIdMap.get(out.dataId).id,\n /*channels=*/\n x.shape[3],\n depthRadius,\n bias,\n alpha,\n beta\n );\n return out;\n}\nvar lrnConfig = {\n kernelName: LRN,\n backendName: \"wasm\",\n setupFunc: setup35,\n kernelFunc: lrn2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LRNGrad.js\nvar wasmLRNGrad;\nfunction setup36(backend2) {\n wasmLRNGrad = backend2.wasm.cwrap(LRNGrad, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // beta\n ]);\n}\nfunction lrnGrad2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x, y, dy } = inputs;\n const { depthRadius, bias, alpha, beta } = attrs;\n if (x.dtype !== \"float32\" || y.dtype !== \"float32\" || dy.dtype !== \"float32\") {\n throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");\n }\n const dx = backend2.makeOutput(x.shape, x.dtype);\n wasmLRNGrad(\n backend2.dataIdMap.get(x.dataId).id,\n backend2.dataIdMap.get(y.dataId).id,\n backend2.dataIdMap.get(dy.dataId).id,\n backend2.dataIdMap.get(dx.dataId).id,\n /*channels=*/\n dy.shape[3],\n depthRadius,\n bias,\n alpha,\n beta\n );\n return dx;\n}\nvar lrnGradConfig2 = {\n kernelName: LRNGrad,\n backendName: \"wasm\",\n setupFunc: setup36,\n kernelFunc: lrnGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Max.js\nvar wasmMax;\nfunction setup37(backend2) {\n wasmMax = backend2.wasm.cwrap(Max, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction max5(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { reductionIndices: axis, keepDims } = attrs;\n const { x } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n let inputId = xId;\n let input2 = x;\n const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n if (inputWasTransposed) {\n const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n input2 = transposed;\n inputId = transposedId;\n }\n const inputRank = input2.shape.length;\n backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, inputRank);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const out = backend2.makeOutput(outShape, x.dtype);\n if (util_exports.sizeFromShape(input2.shape) !== 0) {\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmMax(inputId, CppDType[x.dtype], reduceSize, outId);\n }\n if (inputWasTransposed) {\n backend2.disposeData(transposed.dataId);\n }\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n out.shape = newShape;\n }\n return out;\n}\nvar maxConfig3 = {\n kernelName: Max,\n backendName: \"wasm\",\n setupFunc: setup37,\n kernelFunc: max5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Maximum.js\nvar supportsFullBroadcast12 = false;\nvar maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast12);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool.js\nvar wasmMaxPool;\nfunction setup38(backend2) {\n wasmMaxPool = backend2.wasm.cwrap(MaxPool, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction maxPool4(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const x = inputs.x;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n util_exports.assert(x.dtype === \"float32\", () => `Error in MaxPool: only float32 input is supported. Got ${x.dtype}.`);\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const padTop = convInfo.padInfo.top;\n const padRight = convInfo.padInfo.right;\n const padBottom = convInfo.padInfo.bottom;\n const padLeft = convInfo.padInfo.left;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const inputChannels = convInfo.inChannels;\n const outputChannels = convInfo.outChannels;\n if (convInfo.dataFormat !== \"channelsLast\") {\n throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n }\n const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n return out;\n}\nvar maxPoolConfig3 = {\n kernelName: MaxPool,\n backendName: \"wasm\",\n setupFunc: setup38,\n kernelFunc: maxPool4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool3D.js\nvar wasmMaxPool3D;\nfunction setup39(backend2) {\n wasmMaxPool3D = backend2.wasm.cwrap(\"MaxPool3D\", null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction maxPool3D2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n const convInfo = backend_util_exports.computePool3DInfo(\n x.shape,\n filterSize,\n strides,\n /*dilations=*/\n 1,\n pad3,\n dimRoundingMode,\n dataFormat\n );\n const out = backend2.makeOutput(convInfo.outShape, x.dtype);\n wasmMaxPool3D(\n backend2.dataIdMap.get(x.dataId).id,\n backend2.dataIdMap.get(out.dataId).id,\n convInfo.batchSize,\n // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in\n // channels should always equal to out channels.\n /*channelSize=*/\n convInfo.inChannels,\n convInfo.inDepth,\n convInfo.inHeight,\n convInfo.inWidth,\n convInfo.outDepth,\n convInfo.outHeight,\n convInfo.outWidth,\n convInfo.strideDepth,\n convInfo.strideHeight,\n convInfo.strideWidth,\n convInfo.dilationDepth,\n convInfo.dilationHeight,\n convInfo.dilationWidth,\n convInfo.effectiveFilterDepth,\n convInfo.effectiveFilterHeight,\n convInfo.effectiveFilterWidth,\n convInfo.padInfo.front,\n convInfo.padInfo.top,\n convInfo.padInfo.left\n );\n return out;\n}\nvar maxPool3DConfig3 = {\n kernelName: MaxPool3D,\n backendName: \"wasm\",\n setupFunc: setup39,\n kernelFunc: maxPool3D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool3DGrad.js\nvar wasmMaxPool3DGrad;\nfunction setup40(backend2) {\n wasmMaxPool3DGrad = backend2.wasm.cwrap(\"MaxPool3DGrad\", null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction maxPool3DGrad3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2 } = inputs;\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const convInfo = backend_util_exports.computePool3DInfo(\n input2.shape,\n filterSize,\n strides,\n /*dilations=*/\n 1,\n pad3,\n dimRoundingMode\n );\n const dx = backend2.makeOutput(input2.shape, input2.dtype);\n wasmMaxPool3DGrad(\n backend2.dataIdMap.get(input2.dataId).id,\n backend2.dataIdMap.get(dy.dataId).id,\n backend2.dataIdMap.get(dx.dataId).id,\n convInfo.batchSize,\n // Since Pool3D ops (MaxPool3D and MaxPool3D) support 3D filter only, in\n // channels should always equal to out channels.\n /*channelSize=*/\n convInfo.inChannels,\n convInfo.inDepth,\n convInfo.inHeight,\n convInfo.inWidth,\n convInfo.outDepth,\n convInfo.outHeight,\n convInfo.outWidth,\n convInfo.strideDepth,\n convInfo.strideHeight,\n convInfo.strideWidth,\n convInfo.dilationDepth,\n convInfo.dilationHeight,\n convInfo.dilationWidth,\n convInfo.effectiveFilterDepth,\n convInfo.effectiveFilterHeight,\n convInfo.effectiveFilterWidth,\n convInfo.padInfo.front,\n convInfo.padInfo.top,\n convInfo.padInfo.left\n );\n return dx;\n}\nvar maxPool3DGradConfig4 = {\n kernelName: MaxPool3DGrad,\n backendName: \"wasm\",\n setupFunc: setup40,\n kernelFunc: maxPool3DGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPoolGrad.js\nvar wasmMaxPoolGrad;\nfunction setup41(backend2) {\n wasmMaxPoolGrad = backend2.wasm.cwrap(\"MaxPoolGrad\", null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction maxPoolGrad4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { dy, input: input2 } = inputs;\n const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n const convInfo = backend_util_exports.computePool2DInfo(\n input2.shape,\n filterSize,\n strides,\n /*dilations=*/\n 1,\n pad3,\n dimRoundingMode\n );\n const dx = backend2.makeOutput(input2.shape, input2.dtype);\n wasmMaxPoolGrad(\n backend2.dataIdMap.get(input2.dataId).id,\n backend2.dataIdMap.get(dy.dataId).id,\n backend2.dataIdMap.get(dx.dataId).id,\n convInfo.batchSize,\n // Since Pool ops (MaxPool and MaxPool) support 2D filter only, in\n // channels should always equal to out channels.\n /*channelSize=*/\n convInfo.inChannels,\n convInfo.inHeight,\n convInfo.inWidth,\n convInfo.outHeight,\n convInfo.outWidth,\n convInfo.strideHeight,\n convInfo.strideWidth,\n convInfo.dilationHeight,\n convInfo.dilationWidth,\n convInfo.effectiveFilterHeight,\n convInfo.effectiveFilterWidth,\n convInfo.padInfo.top,\n convInfo.padInfo.left\n );\n return dx;\n}\nvar maxPoolGradConfig4 = {\n kernelName: MaxPoolGrad,\n backendName: \"wasm\",\n setupFunc: setup41,\n kernelFunc: maxPoolGrad4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPoolWithArgmax.js\nvar wasmMaxPoolWithArgmax;\nfunction setup42(backend2) {\n wasmMaxPoolWithArgmax = backend2.wasm.cwrap(\"MaxPoolWithArgmax\", null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"boolean\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // padLeft\n ]);\n}\nfunction maxPoolWithArgmax2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;\n util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n const dilations = [1, 1];\n util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3);\n const pooled = backend2.makeOutput(convInfo.outShape, x.dtype);\n const indexes = backend2.makeOutput(convInfo.outShape, \"int32\");\n wasmMaxPoolWithArgmax(backend2.dataIdMap.get(x.dataId).id, backend2.dataIdMap.get(pooled.dataId).id, backend2.dataIdMap.get(indexes.dataId).id, CppDType[x.dtype], includeBatchInIndex, convInfo.batchSize, convInfo.inChannels, convInfo.inHeight, convInfo.inWidth, convInfo.outHeight, convInfo.outWidth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth, convInfo.padInfo.top, convInfo.padInfo.left);\n return [pooled, indexes];\n}\nvar maxPoolWithArgmaxConfig3 = {\n kernelName: MaxPoolWithArgmax,\n backendName: \"wasm\",\n setupFunc: setup42,\n kernelFunc: maxPoolWithArgmax2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Mean.js\nvar wasmMean;\nfunction setup43(backend2) {\n wasmMean = backend2.wasm.cwrap(Mean, null, [\"number, number, number\"]);\n}\nfunction mean3(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { axis, keepDims } = attrs;\n const { x } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n let inputId = xId;\n let input2 = x;\n const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n let reductionAxes = axes;\n if (inputWasTransposed) {\n const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n if (transposedId !== xId) {\n input2 = transposed;\n inputId = transposedId;\n reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n }\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"mean\", reductionAxes, input2.shape.length);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n let castedInput = input2;\n if (input2.dtype !== \"float32\") {\n castedInput = cast5({ backend: backend2, inputs: { x: input2 }, attrs: { dtype: \"float32\" } });\n inputId = backend2.dataIdMap.get(castedInput.dataId).id;\n }\n const out = backend2.makeOutput(outShape, \"float32\");\n if (util_exports.sizeFromShape(input2.shape) !== 0) {\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmMean(inputId, reduceSize, outId);\n }\n if (inputWasTransposed) {\n backend2.disposeData(transposed.dataId);\n }\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n out.shape = newShape;\n }\n if (input2.dtype !== \"float32\") {\n backend2.disposeData(castedInput.dataId);\n }\n return out;\n}\nvar meanConfig3 = {\n kernelName: Mean,\n backendName: \"wasm\",\n setupFunc: setup43,\n kernelFunc: mean3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Min.js\nvar wasmMin;\nfunction setup44(backend2) {\n wasmMin = backend2.wasm.cwrap(Min, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction min5(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { axis, keepDims } = attrs;\n const { x } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n let inputId = xId;\n let input2 = x;\n const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n if (inputWasTransposed) {\n const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n if (transposedId !== xId) {\n input2 = transposed;\n inputId = transposedId;\n }\n }\n const inputRank = input2.shape.length;\n backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, inputRank);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const out = backend2.makeOutput(outShape, input2.dtype);\n if (util_exports.sizeFromShape(input2.shape) !== 0) {\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmMin(inputId, CppDType[x.dtype], reduceSize, outId);\n }\n if (inputWasTransposed) {\n backend2.disposeData(transposed.dataId);\n }\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n out.shape = newShape;\n }\n return out;\n}\nvar minConfig3 = {\n kernelName: Min,\n backendName: \"wasm\",\n setupFunc: setup44,\n kernelFunc: min5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Minimum.js\nvar supportsFullBroadcast13 = false;\nvar minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast13);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MirrorPad.js\nvar MirrorPaddingMode;\n(function(MirrorPaddingMode2) {\n MirrorPaddingMode2[MirrorPaddingMode2[\"reflect\"] = 0] = \"reflect\";\n MirrorPaddingMode2[MirrorPaddingMode2[\"symmetric\"] = 1] = \"symmetric\";\n})(MirrorPaddingMode || (MirrorPaddingMode = {}));\nvar wasmMirrorPad;\nfunction setup45(backend2) {\n wasmMirrorPad = backend2.wasm.cwrap(MirrorPad, null, [\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"array\",\n \"array\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction mirrorPad3(args) {\n const { inputs: { x }, backend: backend2, attrs: { paddings, mode } } = args;\n const outShape = paddings.map(\n (p2, i) => p2[0] + x.shape[i] + p2[1]\n /* afterPad */\n );\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const out = backend2.makeOutput(outShape, x.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);\n const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);\n const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);\n const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);\n wasmMirrorPad(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, MirrorPaddingMode[mode], outId);\n return out;\n}\nvar mirrorPadConfig3 = {\n kernelName: MirrorPad,\n backendName: \"wasm\",\n kernelFunc: mirrorPad3,\n setupFunc: setup45\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softmax.js\nvar wasmFunc3;\nfunction setup46(backend2) {\n wasmFunc3 = backend2.wasm.cwrap(Softmax, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // batch\n ]);\n}\nfunction softmax5(args) {\n const { backend: backend2, inputs: { logits }, attrs: { dim } } = args;\n const xId = backend2.dataIdMap.get(logits.dataId).id;\n const out = backend2.makeOutput(logits.shape, logits.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const channels = logits.shape[dim];\n const batch = util_exports.sizeFromShape(logits.shape) / channels;\n if (util_exports.sizeFromShape(out.shape) === 0) {\n return out;\n }\n wasmFunc3(xId, outId, channels, batch);\n return out;\n}\nvar softmaxConfig3 = {\n kernelName: Softmax,\n backendName: \"wasm\",\n setupFunc: setup46,\n kernelFunc: softmax5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multinomial.js\nvar wasmMultinomial;\nfunction setup47(backend2) {\n wasmMultinomial = backend2.wasm.cwrap(Multinomial, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction multinomial4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { logits } = inputs;\n const { numSamples, seed, normalized } = attrs;\n if (logits.dtype !== \"float32\") {\n throw new Error(`Tensor logits must have dtype float32, got ${logits.dtype}`);\n }\n const probabilities = normalized ? logits : softmax5({\n inputs: { logits },\n backend: backend2,\n attrs: { dim: logits.shape.length - 1 }\n });\n const [batchSize, numEvents] = probabilities.shape;\n const out = backend2.makeOutput([batchSize, numSamples], \"int32\");\n wasmMultinomial(backend2.dataIdMap.get(probabilities.dataId).id, batchSize, numEvents, numSamples, seed, backend2.dataIdMap.get(out.dataId).id);\n if (!normalized) {\n backend2.disposeData(probabilities.dataId);\n }\n return out;\n}\nvar multinomialConfig3 = {\n kernelName: Multinomial,\n backendName: \"wasm\",\n setupFunc: setup47,\n kernelFunc: multinomial4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Mod.js\nvar modConfig3 = createBinaryKernelConfig(\n Mod,\n /*supportsFullBroadcast=*/\n true\n);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multiply.js\nvar supportsFullBroadcast14 = true;\nvar multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast14);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Neg.js\nvar negConfig3 = createUnaryKernelConfig(Neg);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppression_util.js\nfunction parseResultStruct(backend2, resOffset) {\n const result = new Int32Array(backend2.wasm.HEAPU8.buffer, resOffset, 4);\n const pSelectedIndices = result[0];\n const selectedSize = result[1];\n const pSelectedScores = result[2];\n const pValidOutputs = result[3];\n backend2.wasm._free(resOffset);\n return { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV3.js\nvar wasmFunc4;\nfunction setup48(backend2) {\n wasmFunc4 = backend2.wasm.cwrap(\n NonMaxSuppressionV3,\n \"number\",\n // Result*\n [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // scoreThreshold\n ]\n );\n}\nfunction kernelFunc(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { iouThreshold, maxOutputSize, scoreThreshold } = attrs;\n const { boxes, scores } = inputs;\n const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n const resOffset = wasmFunc4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold);\n const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n backend2.wasm._free(pSelectedScores);\n backend2.wasm._free(pValidOutputs);\n const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n return selectedIndicesTensor;\n}\nvar nonMaxSuppressionV3Config3 = {\n kernelName: NonMaxSuppressionV3,\n backendName: \"wasm\",\n setupFunc: setup48,\n kernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV4.js\nvar wasmFunc5;\nfunction setup49(backend2) {\n wasmFunc5 = backend2.wasm.cwrap(\n NonMaxSuppressionV4,\n \"number\",\n // Result*\n [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"bool\"\n // padToMaxOutputSize\n ]\n );\n}\nfunction nonMaxSuppressionV43(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize } = attrs;\n const { boxes, scores } = inputs;\n const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n const resOffset = wasmFunc5(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n backend2.wasm._free(pSelectedScores);\n const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n const validOutputsTensor = backend2.makeOutput([], \"int32\", pValidOutputs);\n return [selectedIndicesTensor, validOutputsTensor];\n}\nvar nonMaxSuppressionV4Config3 = {\n kernelName: NonMaxSuppressionV4,\n backendName: \"wasm\",\n setupFunc: setup49,\n kernelFunc: nonMaxSuppressionV43\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV5.js\nvar wasmFunc6;\nfunction setup50(backend2) {\n wasmFunc6 = backend2.wasm.cwrap(\n NonMaxSuppressionV5,\n \"number\",\n // Result*\n [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // softNmsSigma\n ]\n );\n}\nfunction kernelFunc2(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma } = attrs;\n const { boxes, scores } = inputs;\n const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n const resOffset = wasmFunc6(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n backend2.wasm._free(pValidOutputs);\n const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n const selectedScoresTensor = backend2.makeOutput([selectedSize], \"float32\", pSelectedScores);\n return [selectedIndicesTensor, selectedScoresTensor];\n}\nvar nonMaxSuppressionV5Config3 = {\n kernelName: NonMaxSuppressionV5,\n backendName: \"wasm\",\n setupFunc: setup50,\n kernelFunc: kernelFunc2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NotEqual.js\nvar supportsFullBroadcast15 = false;\nvar notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast15, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OneHot.js\nvar wasmOneHot;\nfunction setup51(backend2) {\n wasmOneHot = backend2.wasm.cwrap(OneHot, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction oneHot4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { indices } = inputs;\n const { dtype, depth, onValue, offValue } = attrs;\n const out = backend2.makeOutput([...indices.shape, depth], dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const indicesData = backend2.dataIdMap.get(indices.dataId);\n const indicesId = indicesData.id;\n wasmOneHot(indicesId, depth, onValue, offValue, outId);\n return out;\n}\nvar oneHotConfig3 = {\n kernelName: OneHot,\n backendName: \"wasm\",\n setupFunc: setup51,\n kernelFunc: oneHot4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OnesLike.js\nfunction onesLike4(args) {\n const { inputs: { x }, backend: backend2 } = args;\n const out = backend2.makeOutput(x.shape, x.dtype);\n const outVals = backend2.typedArrayFromHeap(out);\n outVals.fill(1);\n return out;\n}\nvar onesLikeConfig3 = {\n kernelName: OnesLike,\n backendName: \"wasm\",\n kernelFunc: onesLike4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pack.js\nfunction pack3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { axis } = attrs;\n if (inputs.length === 1) {\n return expandDims5({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n }\n const shape = inputs[0].shape;\n const dtype = inputs[0].dtype;\n inputs.forEach((t) => {\n util_exports.assertShapesMatch(shape, t.shape, \"All tensors passed to stack must have matching shapes\");\n util_exports.assert(dtype === t.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n });\n const intermediateTensorInfos = [];\n const expandedTensors = inputs.map((t) => {\n const expandedT = expandDims5({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });\n intermediateTensorInfos.push(expandedT);\n return expandedT;\n });\n const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId));\n return result;\n}\nvar packConfig3 = {\n kernelName: Pack,\n backendName: \"wasm\",\n kernelFunc: pack3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/PadV2.js\nvar wasmPadV2;\nfunction setup52(backend2) {\n wasmPadV2 = backend2.wasm.cwrap(PadV2, null, [\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"array\",\n \"array\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction pad2(args) {\n const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args;\n const outShape = paddings.map(\n (p2, i) => p2[0] + x.shape[i] + p2[1]\n /* afterPad */\n );\n if (util_exports.sizeFromShape(x.shape) === 0) {\n return fill4({\n backend: backend2,\n attrs: { shape: outShape, value: constantValue, dtype: x.dtype }\n });\n }\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const out = backend2.makeOutput(outShape, x.dtype);\n const outTensorData = backend2.dataIdMap.get(out.dataId);\n const outId = outTensorData.id;\n const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);\n const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);\n const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);\n const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);\n wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId);\n return out;\n}\nvar padV2Config3 = {\n kernelName: PadV2,\n backendName: \"wasm\",\n kernelFunc: pad2,\n setupFunc: setup52\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pow.js\nvar supportsFullBroadcast16 = false;\nvar powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast16);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prelu.js\nvar wasmPrelu;\nfunction setup53(backend2) {\n wasmPrelu = backend2.wasm.cwrap(Prelu, null, [\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction prelu5(args) {\n const { inputs, backend: backend2 } = args;\n const { x, alpha } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const weightsId = backend2.dataIdMap.get(alpha.dataId).id;\n let inputId = xId;\n const input2 = x;\n let castedInput = input2;\n if (input2.dtype !== \"float32\") {\n castedInput = cast5({ backend: backend2, inputs: { x }, attrs: { dtype: \"float32\" } });\n inputId = backend2.dataIdMap.get(castedInput.dataId).id;\n }\n const out = backend2.makeOutput(x.shape, \"float32\");\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmPrelu(inputId, weightsId, outId);\n if (input2.dtype !== \"float32\") {\n backend2.disposeData(castedInput.dataId);\n }\n return out;\n}\nvar preluConfig3 = {\n kernelName: Prelu,\n backendName: \"wasm\",\n setupFunc: setup53,\n kernelFunc: prelu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prod.js\nvar wasmProd;\nfunction setup54(backend2) {\n wasmProd = backend2.wasm.cwrap(Prod, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n ]);\n}\nfunction prod4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { axis, keepDims } = attrs;\n const { x } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n let inputId = xId;\n let input2 = x;\n const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n let reductionAxes = axes;\n if (inputWasTransposed) {\n const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n if (transposedId !== xId) {\n input2 = transposed;\n inputId = transposedId;\n reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n }\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"prod\", reductionAxes, input2.shape.length);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const out = backend2.makeOutput(outShape, input2.dtype);\n if (util_exports.sizeFromShape(input2.shape) !== 0) {\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmProd(inputId, reduceSize, CppDType[out.dtype], outId);\n }\n if (inputWasTransposed) {\n backend2.disposeData(transposed.dataId);\n }\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n out.shape = newShape;\n }\n return out;\n}\nvar prodConfig3 = {\n kernelName: Prod,\n backendName: \"wasm\",\n setupFunc: setup54,\n kernelFunc: prod4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Range.js\nvar range5 = (args) => {\n const { backend: backend2, attrs } = args;\n const { start, stop, step: step5, dtype } = attrs;\n const values = rangeImpl(start, stop, step5, dtype);\n const out = backend2.makeOutput([values.length], dtype);\n const outVals = backend2.typedArrayFromHeap(out);\n outVals.set(values);\n return out;\n};\nvar rangeConfig3 = {\n kernelName: Range,\n backendName: \"wasm\",\n kernelFunc: range5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RealDiv.js\nvar supportsFullBroadcast17 = true;\nvar realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast17);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reciprocal.js\nvar reciprocalConfig3 = createUnaryKernelConfig(Reciprocal);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu.js\nvar reluConfig3 = createUnaryKernelConfig(Relu);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu6.js\nvar relu6Config3 = createUnaryKernelConfig(Relu6);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinear.js\nvar wasmResizeBilinear;\nfunction setup55(backend2) {\n wasmResizeBilinear = backend2.wasm.cwrap(ResizeBilinear, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction resizeBilinear5(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { images } = inputs;\n const { alignCorners, halfPixelCenters, size } = attrs;\n const [newHeight, newWidth] = size;\n const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n const outShape = [batch, newHeight, newWidth, numChannels];\n let xData = backend2.dataIdMap.get(images.dataId);\n let castedData;\n if (xData.dtype !== \"float32\") {\n castedData = cast5({ backend: backend2, inputs: { x: images }, attrs: { dtype: \"float32\" } });\n xData = backend2.dataIdMap.get(castedData.dataId);\n }\n const xId = xData.id;\n const out = backend2.makeOutput(outShape, \"float32\");\n if (util_exports.sizeFromShape(images.shape) === 0) {\n return out;\n }\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);\n if (castedData != null) {\n backend2.disposeData(castedData.dataId);\n }\n return out;\n}\nvar resizeBilinearConfig3 = {\n kernelName: ResizeBilinear,\n backendName: \"wasm\",\n setupFunc: setup55,\n kernelFunc: resizeBilinear5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinearGrad.js\nvar wasmResizeBilinearGrad;\nfunction setup56(backend2) {\n wasmResizeBilinearGrad = backend2.wasm.cwrap(ResizeBilinearGrad, null, [\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"array\",\n \"boolean\"\n // alignCorners\n ]);\n}\nfunction resizeBilinearGrad3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { images, dy } = inputs;\n const { alignCorners } = attrs;\n const dx = backend2.makeOutput(images.shape, \"float32\");\n let xData = backend2.dataIdMap.get(images.dataId);\n let castedData;\n if (xData.dtype !== \"float32\") {\n castedData = cast5({\n backend: backend2,\n inputs: { x: images },\n attrs: { dtype: \"float32\" }\n });\n xData = backend2.dataIdMap.get(castedData.dataId);\n }\n wasmResizeBilinearGrad(backend2.dataIdMap.get(images.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dx.dataId).id, new Uint8Array(new Int32Array(images.shape).buffer), new Uint8Array(new Int32Array(dy.shape).buffer), alignCorners);\n if (castedData != null) {\n backend2.disposeData(castedData.dataId);\n }\n return dx;\n}\nvar resizeBilinearGradConfig4 = {\n kernelName: ResizeBilinearGrad,\n backendName: \"wasm\",\n setupFunc: setup56,\n kernelFunc: resizeBilinearGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeNearestNeighbor.js\nvar wasmResizeNearestNeighbor;\nfunction setup57(backend2) {\n wasmResizeNearestNeighbor = backend2.wasm.cwrap(ResizeNearestNeighbor, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction resizeNearestNeighbor4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { images } = inputs;\n const { alignCorners, halfPixelCenters, size } = attrs;\n const [newHeight, newWidth] = size;\n const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n const outShape = [batch, newHeight, newWidth, numChannels];\n const out = backend2.makeOutput(outShape, \"float32\");\n if (util_exports.sizeFromShape(images.shape) === 0) {\n return out;\n }\n let xData = backend2.dataIdMap.get(images.dataId);\n let castedData;\n if (xData.dtype !== \"float32\") {\n castedData = cast5({\n backend: backend2,\n inputs: { x: images },\n attrs: { dtype: \"float32\" }\n });\n xData = backend2.dataIdMap.get(castedData.dataId);\n }\n const xId = xData.id;\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmResizeNearestNeighbor(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);\n if (castedData != null) {\n backend2.disposeData(castedData.dataId);\n }\n return out;\n}\nvar resizeNearestNeighborConfig3 = {\n kernelName: ResizeNearestNeighbor,\n backendName: \"wasm\",\n setupFunc: setup57,\n kernelFunc: resizeNearestNeighbor4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeNearestNeighborGrad.js\nvar wasmResizeNearestNeighborGrad;\nfunction setup58(backend2) {\n wasmResizeNearestNeighborGrad = backend2.wasm.cwrap(ResizeNearestNeighborGrad, null, [\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"array\",\n \"boolean\"\n // alignCorners\n ]);\n}\nfunction resizeNearestNeighborGrad3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { images, dy } = inputs;\n const { alignCorners } = attrs;\n const dx = backend2.makeOutput(images.shape, \"float32\");\n let xData = backend2.dataIdMap.get(images.dataId);\n let castedData;\n if (xData.dtype !== \"float32\") {\n castedData = cast5({\n backend: backend2,\n inputs: { x: images },\n attrs: { dtype: \"float32\" }\n });\n xData = backend2.dataIdMap.get(castedData.dataId);\n }\n wasmResizeNearestNeighborGrad(backend2.dataIdMap.get(images.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dx.dataId).id, new Uint8Array(new Int32Array(images.shape).buffer), new Uint8Array(new Int32Array(dy.shape).buffer), alignCorners);\n if (castedData != null) {\n backend2.disposeData(castedData.dataId);\n }\n return dx;\n}\nvar resizeNearestNeighborGradConfig4 = {\n kernelName: ResizeNearestNeighborGrad,\n backendName: \"wasm\",\n setupFunc: setup58,\n kernelFunc: resizeNearestNeighborGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reverse.js\nvar wasmReverse;\nfunction setup59(backend2) {\n wasmReverse = backend2.wasm.cwrap(Reverse, null, [\n \"number\",\n \"array\",\n \"number\",\n \"array\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction reverse4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { dims } = attrs;\n const axes = util_exports.parseAxisParam(dims, x.shape);\n if (x.shape.length === 0) {\n return identity4({ inputs: { x }, backend: backend2 });\n }\n const out = backend2.makeOutput(x.shape, x.dtype);\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const axesBytes = new Uint8Array(new Int32Array(axes).buffer);\n const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId);\n const reshaped = reshape5({ inputs: { x: out }, attrs: { shape: x.shape }, backend: backend2 });\n backend2.disposeData(out.dataId);\n return reshaped;\n}\nvar reverseConfig3 = {\n kernelName: Reverse,\n backendName: \"wasm\",\n kernelFunc: reverse4,\n setupFunc: setup59\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RotateWithOffset.js\nvar wasmRotate;\nfunction setup60(backend2) {\n wasmRotate = backend2.wasm.cwrap(RotateWithOffset, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction rotateWithOffset2(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { image: image2 } = inputs;\n const { radians, fillValue, center } = attrs;\n const out = backend2.makeOutput(image2.shape, image2.dtype);\n const imageId = backend2.dataIdMap.get(image2.dataId).id;\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);\n const fillIsBlack = fillValue === 0;\n const fullOpacityValue = 255;\n const fillValues2 = typeof fillValue === \"number\" ? [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] : [...fillValue, fullOpacityValue];\n const fillBytes = new Uint8Array(new Int32Array(fillValues2).buffer);\n wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues2.length, outId);\n return out;\n}\nvar rotateWithOffsetConfig3 = {\n kernelName: RotateWithOffset,\n backendName: \"wasm\",\n kernelFunc: rotateWithOffset2,\n setupFunc: setup60\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Round.js\nvar roundConfig3 = createUnaryKernelConfig(Round);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Rsqrt.js\nvar rsqrtConfig3 = createUnaryKernelConfig(Rsqrt);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ScatterNd.js\nvar wasmScatterNd;\nfunction setup61(backend2) {\n wasmScatterNd = backend2.wasm.cwrap(ScatterNd, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction scatterNd3(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { indices, updates } = inputs;\n const { shape } = attrs;\n const out = backend2.makeOutput(shape, updates.dtype);\n if (util_exports.sizeFromShape(shape) === 0) {\n return out;\n }\n const { sliceRank, numUpdates, sliceSize, strides, outputSize } = scatter_nd_util_exports.calculateShapes(updates, indices, shape);\n const indicesData = backend2.dataIdMap.get(indices.dataId);\n const indicesId = indicesData.id;\n const updatesData = backend2.dataIdMap.get(updates.dataId);\n const updatesId = updatesData.id;\n const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId);\n return out;\n}\nvar scatterNdConfig3 = {\n kernelName: ScatterNd,\n backendName: \"wasm\",\n setupFunc: setup61,\n kernelFunc: scatterNd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SearchSorted.js\nvar wasmSearchSorted;\nfunction setup62(backend2) {\n wasmSearchSorted = backend2.wasm.cwrap(SearchSorted, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"bool\",\n \"number\"\n // outId\n ]);\n}\nfunction searchSorted4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { sortedSequence, values } = inputs;\n const { side } = attrs;\n if (sortedSequence.dtype !== values.dtype) {\n throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${sortedSequence.dtype} and ${values.dtype}`);\n }\n const out = backend2.makeOutput(values.shape, \"int32\");\n function tensorId(x) {\n return backend2.dataIdMap.get(x.dataId).id;\n }\n wasmSearchSorted(\n tensorId(sortedSequence),\n tensorId(values),\n /*batchSize=*/\n sortedSequence.shape[0],\n /*sequenceSize=*/\n sortedSequence.shape[1],\n /*valuesSize=*/\n values.shape[1],\n /*dtype=*/\n CppDType[sortedSequence.dtype],\n /*isSideLeft=*/\n side === \"left\",\n tensorId(out)\n );\n return out;\n}\nvar searchSortedConfig3 = {\n kernelName: SearchSorted,\n backendName: \"wasm\",\n setupFunc: setup62,\n kernelFunc: searchSorted4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Select.js\nvar wasmSelect;\nfunction setup63(backend2) {\n wasmSelect = backend2.wasm.cwrap(\"SelectV2\", null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction select4(args) {\n const { inputs, backend: backend2 } = args;\n const { condition, t, e } = inputs;\n const conditionId = backend2.dataIdMap.get(condition.dataId).id;\n const tId = backend2.dataIdMap.get(t.dataId).id;\n const eId = backend2.dataIdMap.get(e.dataId).id;\n const out = backend2.makeOutput(t.shape, t.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const cRank = condition.shape.length;\n const tRank = t.shape.length;\n const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1));\n wasmSelect(conditionId, tId, eId, offset, outId);\n return out;\n}\nvar selectConfig3 = {\n kernelName: Select,\n backendName: \"wasm\",\n kernelFunc: select4,\n setupFunc: setup63\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Selu.js\nvar seluConfig3 = createUnaryKernelConfig(Selu);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sigmoid.js\nvar wasmFunc7;\nfunction setup64(backend2) {\n wasmFunc7 = backend2.wasm.cwrap(Sigmoid, null, [\"number\", \"number\"]);\n}\nfunction sigmoid4(args) {\n const { backend: backend2, inputs: { x } } = args;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const out = backend2.makeOutput(x.shape, x.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n if (util_exports.sizeFromShape(out.shape) === 0) {\n return out;\n }\n wasmFunc7(xId, outId);\n return out;\n}\nvar sigmoidConfig3 = {\n kernelName: \"Sigmoid\",\n backendName: \"wasm\",\n setupFunc: setup64,\n kernelFunc: sigmoid4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sign.js\nvar signConfig3 = createUnaryKernelConfig(Sign);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sin.js\nvar sinConfig3 = createUnaryKernelConfig(Sin);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sinh.js\nvar sinhConfig3 = createUnaryKernelConfig(Sinh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softplus.js\nvar softplusConfig3 = createUnaryKernelConfig(Softplus);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SpaceToBatchND.js\nfunction spaceToBatchND4(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const { blockShape, paddings } = attrs;\n const prod5 = util_exports.sizeFromShape(blockShape);\n const completePaddings = [[0, 0]];\n completePaddings.push(...paddings);\n for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n completePaddings.push([0, 0]);\n }\n const paddedX = padV2Config3.kernelFunc({\n inputs: { x },\n backend: backend2,\n attrs: { paddings: completePaddings, constantValue: 0 }\n });\n const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false);\n const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false);\n const reshapeInputs = { x: paddedX };\n const reshapeAttrs = { shape: reshapedPaddedShape };\n const paddedXReshaped = reshape5({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });\n const transposeInputs = { x: paddedXReshaped };\n const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n const paddedXT = transpose4({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });\n const resultReshapeInputs = { x: paddedXT };\n const resultReshapeAttrs = { shape: flattenShape };\n const result = reshape5({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });\n backend2.disposeData(paddedX.dataId);\n backend2.disposeData(paddedXReshaped.dataId);\n backend2.disposeData(paddedXT.dataId);\n return result;\n}\nvar spaceToBatchNDConfig3 = {\n kernelName: SpaceToBatchND,\n backendName: \"wasm\",\n kernelFunc: spaceToBatchND4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseFillEmptyRows.js\nvar wasmSparseFillEmptyRows;\nfunction setup65(backend2) {\n wasmSparseFillEmptyRows = backend2.wasm.cwrap(\"SparseFillEmptyRows\", \"number\", [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // exceptionValuesId\n ]);\n}\nfunction sparseFillEmptyRows4(args) {\n const { backend: backend2, inputs } = args;\n const { indices, values, denseShape, defaultValue } = inputs;\n const indicesCount = indices.shape[0];\n const rank = indices.shape[1];\n const denseRows = backend2.readSync(denseShape.dataId)[0];\n const maxOutputIndicesShape = [indicesCount + denseRows, rank];\n const indicesId = backend2.dataIdMap.get(indices.dataId).id;\n const valuesId = backend2.dataIdMap.get(values.dataId).id;\n const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id;\n const outputIndices = backend2.makeOutput(maxOutputIndicesShape, indices.dtype);\n const outputIndicesId = backend2.dataIdMap.get(outputIndices.dataId).id;\n const outputValues = backend2.makeOutput(maxOutputIndicesShape.slice(0, 1), values.dtype);\n const outputValuesId = backend2.dataIdMap.get(outputValues.dataId).id;\n const emptyRowIndicator = backend2.makeOutput([denseRows], \"bool\");\n const emptyRowIndicatorId = backend2.dataIdMap.get(emptyRowIndicator.dataId).id;\n const reverseIndexMap = backend2.makeOutput([indicesCount], indices.dtype);\n const reverseIndexMapId = backend2.dataIdMap.get(reverseIndexMap.dataId).id;\n const exceptionValues = backend2.makeOutput([4], \"int32\");\n const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n const outputRows = wasmSparseFillEmptyRows(indicesId, valuesId, CppDType[values.dtype], indicesCount, denseRows, rank, defaultValueId, outputIndicesId, outputValuesId, emptyRowIndicatorId, reverseIndexMapId, exceptionValuesId);\n const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n let exceptionMessage;\n switch (exceptionValuesArray[0]) {\n case 1: {\n exceptionMessage = backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(exceptionValuesArray[1]);\n break;\n }\n case 2: {\n exceptionMessage = backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n break;\n }\n case 3:\n exceptionMessage = backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);\n break;\n default:\n exceptionMessage = \"\";\n }\n backend2.disposeData(exceptionValues.dataId);\n if (exceptionMessage) {\n backend2.disposeData(outputIndices.dataId);\n backend2.disposeData(outputValues.dataId);\n backend2.disposeData(emptyRowIndicator.dataId);\n backend2.disposeData(reverseIndexMap.dataId);\n throw new Error(exceptionMessage);\n }\n let resizedIndices = outputIndices;\n let resizedValues = outputValues;\n if (outputRows !== maxOutputIndicesShape[0]) {\n resizedIndices = slice4({\n inputs: { x: outputIndices },\n attrs: { begin: 0, size: [outputRows, rank] },\n backend: backend2\n });\n resizedValues = slice4({\n inputs: { x: outputValues },\n attrs: { begin: 0, size: outputRows },\n backend: backend2\n });\n backend2.disposeData(outputIndices.dataId);\n backend2.disposeData(outputValues.dataId);\n }\n return [resizedIndices, resizedValues, emptyRowIndicator, reverseIndexMap];\n}\nvar sparseFillEmptyRowsConfig3 = {\n kernelName: SparseFillEmptyRows,\n backendName: \"wasm\",\n setupFunc: setup65,\n kernelFunc: sparseFillEmptyRows4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseReshape.js\nvar wasmSparseReshape;\nfunction setup66(backend2) {\n wasmSparseReshape = backend2.wasm.cwrap(SparseReshape, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // exceptionValuesId\n ]);\n}\nfunction sparseReshape4(args) {\n const { backend: backend2, inputs } = args;\n const { inputIndices, inputShape, newShape } = inputs;\n if (inputIndices.shape.length !== 2) {\n throw new Error(`Input indices should be a matrix but received shape\n ${inputIndices.shape}`);\n }\n if (inputShape.shape.length !== 1) {\n throw new Error(`Input shape should be a vector but received shape\n ${inputShape.shape}`);\n }\n if (newShape.shape.length !== 1) {\n throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n }\n const inputIndicesId = backend2.dataIdMap.get(inputIndices.dataId).id;\n const inputShapeId = backend2.dataIdMap.get(inputShape.dataId).id;\n const newShapeId = backend2.dataIdMap.get(newShape.dataId).id;\n const nnz = inputIndices.shape[0];\n const outputRank = util_exports.sizeFromShape(newShape.shape);\n const newIndices = backend2.makeOutput([nnz, outputRank], inputIndices.dtype);\n const newIndicesId = backend2.dataIdMap.get(newIndices.dataId).id;\n const outputShape = backend2.makeOutput([outputRank], newShape.dtype);\n const outputShapeId = backend2.dataIdMap.get(outputShape.dataId).id;\n const exceptionValues = backend2.makeOutput([3], \"int32\");\n const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n wasmSparseReshape(inputIndicesId, inputShapeId, newShapeId, nnz, newIndicesId, outputShapeId, exceptionValuesId);\n const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n let exceptionMessage;\n switch (exceptionValuesArray[0]) {\n case 0: {\n exceptionMessage = backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n break;\n }\n case 1: {\n exceptionMessage = backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n break;\n }\n case 2:\n exceptionMessage = backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();\n break;\n case 3: {\n const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));\n exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShapeValues, outputShapeValues);\n break;\n }\n case 4: {\n const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));\n exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShapeValues, outputShapeValues);\n break;\n }\n default:\n exceptionMessage = \"\";\n }\n backend2.disposeData(exceptionValues.dataId);\n if (exceptionMessage) {\n backend2.disposeData(newIndices.dataId);\n backend2.disposeData(outputShape.dataId);\n throw new Error(exceptionMessage);\n }\n return [newIndices, outputShape];\n}\nvar sparseReshapeConfig3 = {\n kernelName: SparseReshape,\n backendName: \"wasm\",\n setupFunc: setup66,\n kernelFunc: sparseReshape4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentReduction.js\nvar wasmSparseSegmentReduction;\nfunction setup67(backend2) {\n wasmSparseSegmentReduction = backend2.wasm.cwrap(\"SparseSegmentReduction\", null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // defaultValue\n ]);\n}\nfunction sparseSegmentReduction(args, isMean) {\n const { backend: backend2, inputs } = args;\n const { data, indices, segmentIds } = inputs;\n const numIndices = indices.shape[0];\n const segmentIdsBack = backend2.readSync(segmentIds.dataId, numIndices - 1, numIndices)[0];\n const lastSegmentIdPlusOne = numIndices > 0 ? segmentIdsBack + 1 : 0;\n const outputRows = lastSegmentIdPlusOne;\n if (outputRows < 0) {\n throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n }\n const outputShape = data.shape.slice();\n outputShape[0] = outputRows;\n const dataId = backend2.dataIdMap.get(data.dataId).id;\n const indicesId = backend2.dataIdMap.get(indices.dataId).id;\n const segmentIdsId = backend2.dataIdMap.get(segmentIds.dataId).id;\n const output = backend2.makeOutput(outputShape, data.dtype);\n const outputId = backend2.dataIdMap.get(output.dataId).id;\n const exceptionValues = backend2.makeOutput([4], \"int32\");\n const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n wasmSparseSegmentReduction(dataId, CppDType[data.dtype], data.shape[0], indicesId, segmentIdsId, outputId, exceptionValuesId, isMean, 0);\n const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n let exceptionMessage;\n switch (exceptionValuesArray[0]) {\n case 0: {\n exceptionMessage = backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();\n break;\n }\n case 1: {\n exceptionMessage = backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();\n break;\n }\n case 2:\n exceptionMessage = backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n break;\n case 3:\n exceptionMessage = backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);\n break;\n default:\n exceptionMessage = \"\";\n }\n backend2.disposeData(exceptionValues.dataId);\n if (exceptionMessage) {\n backend2.disposeData(output.dataId);\n throw new Error(exceptionMessage);\n }\n return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean4(args) {\n return sparseSegmentReduction(args, true);\n}\nvar sparseSegmentMeanConfig3 = {\n kernelName: SparseSegmentMean,\n backendName: \"wasm\",\n setupFunc: setup67,\n kernelFunc: sparseSegmentMean4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum4(args) {\n return sparseSegmentReduction(args, false);\n}\nvar sparseSegmentSumConfig3 = {\n kernelName: SparseSegmentSum,\n backendName: \"wasm\",\n setupFunc: setup67,\n kernelFunc: sparseSegmentSum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseToDense.js\nvar wasmSparseToDense;\nfunction setup68(backend2) {\n wasmSparseToDense = backend2.wasm.cwrap(SparseToDense, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction sparseToDense4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { sparseIndices, sparseValues, defaultValue } = inputs;\n const { outputShape } = attrs;\n const out = backend2.makeOutput(outputShape, defaultValue.dtype);\n if (util_exports.sizeFromShape(outputShape) === 0) {\n return out;\n }\n const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n const sparseIndicesId = backend2.dataIdMap.get(sparseIndices.dataId).id;\n const sparseValuesId = backend2.dataIdMap.get(sparseValues.dataId).id;\n const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id;\n const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmSparseToDense(sparseIndicesId, sparseValuesId, sparseValues.shape.length, defaultValueId, CppDType[defaultValue.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId);\n return out;\n}\nvar sparseToDenseConfig3 = {\n kernelName: SparseToDense,\n backendName: \"wasm\",\n setupFunc: setup68,\n kernelFunc: sparseToDense4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SplitV.js\nfunction splitV3(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { x } = inputs;\n const { numOrSizeSplits, axis } = attrs;\n const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n const begin = new Array(x.shape.length).fill(0);\n const size = x.shape.slice();\n return splitSizes.map((s) => {\n const xSliceSize = [...size];\n xSliceSize[$axis] = s;\n const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 });\n begin[$axis] += s;\n return xSlice;\n });\n}\nvar splitVConfig3 = {\n kernelName: SplitV,\n backendName: \"wasm\",\n kernelFunc: splitV3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sqrt.js\nvar sqrtConfig3 = createUnaryKernelConfig(Sqrt);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Square.js\nvar squareConfig3 = createUnaryKernelConfig(Square);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SquaredDifference.js\nvar supportsFullBroadcast18 = true;\nvar squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast18);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Step.js\nvar wasmStep;\nfunction setup69(backend2) {\n wasmStep = backend2.wasm.cwrap(Step, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction step4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { alpha } = attrs;\n const { x } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const out = backend2.makeOutput(x.shape, x.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmStep(xId, alpha, CppDType[x.dtype], outId);\n return out;\n}\nvar stepConfig3 = {\n kernelName: Step,\n backendName: \"wasm\",\n setupFunc: setup69,\n kernelFunc: step4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StridedSlice.js\nvar wasmStridedSlice;\nfunction setup70(backend2) {\n wasmStridedSlice = backend2.wasm.cwrap(StridedSlice, null, [\n \"number\",\n \"array\",\n \"number\",\n \"array\",\n \"array\",\n \"array\",\n \"array\",\n \"array\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction stridedSlice4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { x } = inputs;\n const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n let result;\n if (isIdentity) {\n result = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n } else if (sliceDim0 || isSimpleSlice) {\n util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n backend2.disposeData(sliced.dataId);\n } else {\n const out = backend2.makeOutput(finalShapeSparse, \"float32\");\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);\n const beginBytes = new Uint8Array(new Int32Array($begin).buffer);\n const endBytes = new Uint8Array(new Int32Array($end).buffer);\n const stridesBytes = new Uint8Array(new Int32Array($strides).buffer);\n const outputShapeBytes = new Uint8Array(new Int32Array(finalShapeSparse).buffer);\n const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(finalShapeSparse)).buffer);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmStridedSlice(xId, xStridesBytes, x.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, finalShapeSparse.length, outId);\n result = reshape5({ inputs: { x: out }, backend: backend2, attrs: { shape: finalShape } });\n backend2.disposeData(out.dataId);\n }\n return result;\n}\nvar stridedSliceConfig3 = {\n kernelName: StridedSlice,\n backendName: \"wasm\",\n setupFunc: setup70,\n kernelFunc: stridedSlice4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringNGrams.js\nfunction stringNGrams4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { data, dataSplits } = inputs;\n const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n const $data = backend2.readSync(data.dataId);\n const $dataSplits = backend2.readSync(dataSplits.dataId);\n const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n const nGramsOut = backend2.makeOutput([nGrams.length], \"string\");\n const nGramsOutData = backend2.dataIdMap.get(nGramsOut.dataId);\n nGramsOutData.stringBytes = nGrams;\n const nGramsSplitsOut = backend2.makeOutput(dataSplits.shape, \"int32\");\n const nGramsSplitsOutVals = backend2.typedArrayFromHeap(nGramsSplitsOut);\n nGramsSplitsOutVals.set(nGramsSplits);\n return [nGramsOut, nGramsSplitsOut];\n}\nvar stringNGramsConfig3 = {\n kernelName: StringNGrams,\n backendName: \"wasm\",\n kernelFunc: stringNGrams4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringSplit.js\nfunction stringSplit4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { input: input2, delimiter } = inputs;\n const { skipEmpty } = attrs;\n const inputVals = backend2.readSync(input2.dataId);\n const delimiterVals = backend2.readSync(delimiter.dataId);\n const [indices, values, shape] = stringSplitImpl(inputVals, delimiterVals[0], skipEmpty);\n const outputSize = values.length;\n const indicesOut = backend2.makeOutput([outputSize, 2], \"int32\");\n const indicesOutVals = backend2.typedArrayFromHeap(indicesOut);\n indicesOutVals.set(indices);\n const valuesOut = backend2.makeOutput([outputSize], \"string\");\n const valuesOutData = backend2.dataIdMap.get(valuesOut.dataId);\n valuesOutData.stringBytes = values;\n const shapeOut = backend2.makeOutput([2], \"int32\");\n const shapeOutVals = backend2.typedArrayFromHeap(shapeOut);\n shapeOutVals.set(shape);\n return [indicesOut, valuesOut, shapeOut];\n}\nvar stringSplitConfig3 = {\n kernelName: StringSplit,\n backendName: \"wasm\",\n kernelFunc: stringSplit4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { input: input2 } = inputs;\n const { numBuckets } = attrs;\n const inputVals = backend2.readSync(input2.dataId);\n const values = stringToHashBucketFastImpl(inputVals, numBuckets);\n const out = backend2.makeOutput(input2.shape, \"int32\");\n const outVals = backend2.typedArrayFromHeap(out);\n outVals.set(values);\n return out;\n}\nvar stringToHashBucketFastConfig3 = {\n kernelName: StringToHashBucketFast,\n backendName: \"wasm\",\n kernelFunc: stringToHashBucketFast4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sub.js\nvar supportsFullBroadcast19 = true;\nvar subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast19);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sum.js\nvar wasmSum;\nfunction setup71(backend2) {\n wasmSum = backend2.wasm.cwrap(Sum, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction sum5(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { axis, keepDims } = attrs;\n const { x } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n let inputId = xId;\n let input2 = x;\n const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n let reductionAxes = axes;\n if (inputWasTransposed) {\n const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n if (transposedId !== xId) {\n input2 = transposed;\n inputId = transposedId;\n reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n }\n }\n backend_util_exports.assertAxesAreInnerMostDims(\"sum\", reductionAxes, input2.shape.length);\n const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n const reduceSize = util_exports.sizeFromShape(reduceShape);\n const out = backend2.makeOutput(outShape, input2.dtype);\n if (util_exports.sizeFromShape(input2.shape) !== 0) {\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmSum(inputId, reduceSize, CppDType[out.dtype], outId);\n }\n if (inputWasTransposed) {\n backend2.disposeData(transposed.dataId);\n }\n if (keepDims) {\n const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n out.shape = newShape;\n }\n return out;\n}\nvar sumConfig3 = {\n kernelName: Sum,\n backendName: \"wasm\",\n setupFunc: setup71,\n kernelFunc: sum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tan.js\nvar tanConfig3 = createUnaryKernelConfig(Tan);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tanh.js\nvar tanhConfig3 = createUnaryKernelConfig(Tanh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/TensorScatterUpdate.js\nvar wasmTensorScatterUpdate;\nfunction setup72(backend2) {\n wasmTensorScatterUpdate = backend2.wasm.cwrap(TensorScatterUpdate, null, [\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"number\"\n // tensorId\n ]);\n}\nfunction tensorScatterUpdate4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { tensor: tensor2, indices, updates } = inputs;\n const {} = attrs;\n const out = backend2.makeOutput(tensor2.shape, tensor2.dtype);\n if (util_exports.sizeFromShape(tensor2.shape) === 0) {\n return out;\n }\n const { sliceRank, numUpdates, sliceSize, strides, outputSize } = scatter_nd_util_exports.calculateShapes(updates, indices, tensor2.shape);\n const indicesData = backend2.dataIdMap.get(indices.dataId);\n const indicesId = indicesData.id;\n const updatesData = backend2.dataIdMap.get(updates.dataId);\n const updatesId = updatesData.id;\n const tensorData = backend2.dataIdMap.get(tensor2.dataId);\n const tensorId = tensorData.id;\n const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmTensorScatterUpdate(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId, tensorId);\n return out;\n}\nvar tensorScatterUpdateConfig3 = {\n kernelName: TensorScatterUpdate,\n backendName: \"wasm\",\n setupFunc: setup72,\n kernelFunc: tensorScatterUpdate4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tile.js\nvar wasmTile;\nfunction setup73(backend2) {\n wasmTile = backend2.wasm.cwrap(Tile, null, [\n \"number\",\n \"array\",\n \"number\",\n \"array\",\n \"number\",\n \"number\"\n // out_id\n ]);\n}\nfunction tile5(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { x } = inputs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const { reps } = attrs;\n const newShape = new Array(x.shape.length);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = x.shape[i] * reps[i];\n }\n const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);\n const out = backend2.makeOutput(newShape, x.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId);\n return out;\n}\nvar tileConfig3 = {\n kernelName: Tile,\n backendName: \"wasm\",\n setupFunc: setup73,\n kernelFunc: tile5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/TopK.js\nvar wasmTopK;\nfunction setup74(backend2) {\n wasmTopK = backend2.wasm.cwrap(TopK, null, [\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"number\",\n \"bool\",\n \"number\",\n \"number\"\n // outIndicesId\n ]);\n}\nvar topk2 = ({ inputs, backend: backend2, attrs }) => {\n const { x } = inputs;\n const { k, sorted } = attrs;\n const xId = backend2.dataIdMap.get(x.dataId).id;\n const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n const outputShape = x.shape.slice();\n outputShape[outputShape.length - 1] = k;\n const outValues = backend2.makeOutput(outputShape, x.dtype);\n const outValuesId = backend2.dataIdMap.get(outValues.dataId).id;\n const outIndices = backend2.makeOutput(outputShape, \"int32\");\n const outIndicesId = backend2.dataIdMap.get(outIndices.dataId).id;\n wasmTopK(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted, outValuesId, outIndicesId);\n return [outValues, outIndices];\n};\nvar topKConfig3 = {\n kernelName: TopK,\n backendName: \"wasm\",\n setupFunc: setup74,\n kernelFunc: topk2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transform.js\nvar wasmTransform;\nfunction setup75(backend2) {\n wasmTransform = backend2.wasm.cwrap(Transform, null, [\n \"number\",\n \"number\",\n \"bool\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"array\",\n \"number\",\n \"array\",\n \"number\",\n \"number\",\n \"number\",\n \"number\",\n \"number\"\n // outId\n ]);\n}\nfunction transform4(args) {\n const { backend: backend2, inputs, attrs } = args;\n const { image: image2, transforms } = inputs;\n const { interpolation, fillMode, fillValue, outputShape } = attrs;\n const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n const outShape = [\n batch,\n outHeight,\n outWidth,\n numChannels\n ];\n const inputStrides = new Uint8Array(new Int32Array(util_exports.computeStrides(image2.shape)).buffer);\n const outputStrides = new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer);\n const out = backend2.makeOutput(outShape, image2.dtype);\n const outId = backend2.dataIdMap.get(out.dataId).id;\n const imageData = backend2.dataIdMap.get(image2.dataId);\n const imageId = imageData.id;\n const transformsData = backend2.dataIdMap.get(transforms.dataId);\n const transformsId = transformsData.id;\n const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n let fillModeId;\n switch (fillMode) {\n case \"constant\":\n fillModeId = 1;\n break;\n case \"reflect\":\n fillModeId = 2;\n break;\n case \"wrap\":\n fillModeId = 3;\n break;\n case \"nearest\":\n fillModeId = 4;\n break;\n default:\n fillModeId = 1;\n break;\n }\n wasmTransform(imageId, transformsId, transforms.shape[0] > 1, batch, outHeight, outWidth, numChannels, imageWidth, imageHeight, inputStrides, image2.shape.length - 1, outputStrides, outShape.length - 1, interpolationModeId, fillModeId, fillValue, outId);\n return out;\n}\nvar transformConfig3 = {\n kernelName: Transform,\n backendName: \"wasm\",\n setupFunc: setup75,\n kernelFunc: transform4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unique.js\nfunction unique5(args) {\n const { inputs, attrs, backend: backend2 } = args;\n const { axis } = attrs;\n const { x } = inputs;\n const { outputValues, outputShape, indices } = uniqueImpl(backend2.readSync(x.dataId), axis, x.shape, x.dtype);\n return [\n backend2.makeOutput(\n outputShape,\n x.dtype,\n /*memoryOffset=*/\n void 0,\n outputValues\n ),\n backend2.makeOutput(\n [indices.length],\n \"int32\",\n /*memoryOffset=*/\n void 0,\n indices\n )\n ];\n}\nvar uniqueConfig3 = {\n kernelName: Unique,\n backendName: \"wasm\",\n kernelFunc: unique5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unpack.js\nfunction unpack3(args) {\n const { inputs, backend: backend2, attrs } = args;\n const { value } = inputs;\n let { axis } = attrs;\n if (axis < 0) {\n axis += value.shape.length;\n }\n const numOutputs = value.shape[axis];\n const rank = value.shape.length;\n const outShape = new Array(rank - 1);\n let outIndex = 0;\n for (let i = 0; i < rank; i++) {\n if (i !== axis) {\n outShape[outIndex++] = value.shape[i];\n }\n }\n const outs = new Array(numOutputs);\n const begin = new Array(rank).fill(0);\n const size = value.shape.slice();\n size[axis] = 1;\n for (let i = 0; i < outs.length; i++) {\n begin[axis] = i;\n outs[i] = slice4({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 });\n }\n return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape }));\n}\nvar unpackConfig3 = {\n kernelName: Unpack,\n backendName: \"wasm\",\n kernelFunc: unpack3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ZerosLike.js\nfunction zerosLike4(args) {\n const { inputs: { x }, backend: backend2 } = args;\n const out = backend2.makeOutput(x.shape, x.dtype);\n const outVals = backend2.typedArrayFromHeap(out);\n outVals.fill(0);\n return out;\n}\nvar zerosLikeConfig3 = {\n kernelName: ZerosLike,\n backendName: \"wasm\",\n kernelFunc: zerosLike4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js\nvar kernelConfigs3 = [\n _fusedMatMulConfig3,\n absConfig3,\n acosConfig3,\n acoshConfig3,\n addConfig3,\n addNConfig3,\n allConfig3,\n anyConfig3,\n argMaxConfig3,\n argMinConfig3,\n asinConfig3,\n asinhConfig3,\n atanConfig3,\n atan2Config3,\n atanhConfig3,\n avgPoolConfig3,\n avgPoolGradConfig4,\n avgPool3DConfig3,\n avgPool3DGradConfig4,\n batchMatMulConfig3,\n batchToSpaceNDConfig3,\n bincountConfig3,\n bitwiseAndConfig3,\n broadcastArgsConfig3,\n castConfig3,\n ceilConfig3,\n clipByValueConfig3,\n concatConfig3,\n conv2DConfig3,\n conv2DBackpropInputConfig3,\n conv3DConfig3,\n conv3DBackpropFilterV2Config3,\n conv3DBackpropInputV2Config2,\n cosConfig3,\n coshConfig3,\n cropAndResizeConfig3,\n cumprodConfig3,\n cumsumConfig3,\n denseBincountConfig3,\n depthToSpaceConfig3,\n depthwiseConv2dNativeConfig3,\n diagConfig3,\n dilation2DConfig3,\n dilation2DBackpropFilterConfig2,\n dilation2DBackpropInputConfig2,\n eluConfig3,\n eluGradConfig4,\n equalConfig3,\n erfConfig3,\n expConfig3,\n expandDimsConfig3,\n expm1Config3,\n fillConfig3,\n flipLeftRightConfig3,\n floorConfig3,\n floorDivConfig3,\n fusedBatchNormConfig,\n fusedConv2DConfig3,\n fusedDepthwiseConv2DConfig3,\n gatherNdConfig3,\n gatherV2Config3,\n greaterConfig3,\n greaterEqualConfig3,\n identityConfig3,\n isFiniteConfig3,\n isInfConfig3,\n isNaNConfig3,\n leakyReluConfig3,\n lessConfig3,\n lessEqualConfig3,\n linSpaceConfig3,\n log1pConfig3,\n logConfig3,\n logicalAndConfig3,\n logicalNotConfig3,\n logicalOrConfig3,\n logicalXorConfig,\n lrnConfig,\n lrnGradConfig2,\n maxConfig3,\n maximumConfig3,\n maxPoolConfig3,\n maxPool3DConfig3,\n maxPool3DGradConfig4,\n maxPoolGradConfig4,\n maxPoolWithArgmaxConfig3,\n meanConfig3,\n minConfig3,\n minimumConfig3,\n mirrorPadConfig3,\n multinomialConfig3,\n modConfig3,\n multiplyConfig3,\n negConfig3,\n nonMaxSuppressionV3Config3,\n nonMaxSuppressionV4Config3,\n nonMaxSuppressionV5Config3,\n notEqualConfig3,\n oneHotConfig3,\n onesLikeConfig3,\n packConfig3,\n padV2Config3,\n powConfig3,\n preluConfig3,\n prodConfig3,\n rangeConfig3,\n realDivConfig3,\n reciprocalConfig3,\n reluConfig3,\n relu6Config3,\n reshapeConfig3,\n resizeBilinearConfig3,\n resizeBilinearGradConfig4,\n resizeNearestNeighborConfig3,\n resizeNearestNeighborGradConfig4,\n reverseConfig3,\n rotateWithOffsetConfig3,\n roundConfig3,\n rsqrtConfig3,\n scatterNdConfig3,\n searchSortedConfig3,\n selectConfig3,\n seluConfig3,\n sigmoidConfig3,\n signConfig3,\n sinConfig3,\n sinhConfig3,\n sliceConfig3,\n softmaxConfig3,\n softplusConfig3,\n spaceToBatchNDConfig3,\n sparseFillEmptyRowsConfig3,\n sparseReshapeConfig3,\n sparseSegmentMeanConfig3,\n sparseSegmentSumConfig3,\n sparseToDenseConfig3,\n splitVConfig3,\n sqrtConfig3,\n squareConfig3,\n squaredDifferenceConfig3,\n stepConfig3,\n stridedSliceConfig3,\n stringNGramsConfig3,\n stringSplitConfig3,\n stringToHashBucketFastConfig3,\n subConfig3,\n sumConfig3,\n tanConfig3,\n tanhConfig3,\n tensorScatterUpdateConfig3,\n tileConfig3,\n topKConfig3,\n transformConfig3,\n transposeConfig3,\n uniqueConfig3,\n unpackConfig3,\n zerosLikeConfig3\n];\nfor (const kernelConfig of kernelConfigs3) {\n registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js\nvar ENV6 = env();\nENV6.registerFlag(\"WASM_HAS_SIMD_SUPPORT\", async () => {\n try {\n return WebAssembly.validate(new Uint8Array([\n 0,\n 97,\n 115,\n 109,\n 1,\n 0,\n 0,\n 0,\n 1,\n 4,\n 1,\n 96,\n 0,\n 0,\n 3,\n 2,\n 1,\n 0,\n 10,\n 9,\n 1,\n 7,\n 0,\n 65,\n 0,\n 253,\n 15,\n 26,\n 11\n ]));\n } catch (e) {\n return false;\n }\n});\nENV6.registerFlag(\"WASM_HAS_MULTITHREAD_SUPPORT\", async () => {\n if (ENV6.get(\"IS_NODE\")) {\n return false;\n }\n try {\n new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));\n return WebAssembly.validate(new Uint8Array([\n 0,\n 97,\n 115,\n 109,\n 1,\n 0,\n 0,\n 0,\n 1,\n 4,\n 1,\n 96,\n 0,\n 0,\n 3,\n 2,\n 1,\n 0,\n 5,\n 4,\n 1,\n 3,\n 1,\n 1,\n 10,\n 11,\n 1,\n 9,\n 0,\n 65,\n 0,\n 254,\n 16,\n 2,\n 0,\n 26,\n 11\n ]));\n } catch (e) {\n return false;\n }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js\nvar wasmFactoryThreadedSimd_import = __toESM(require_tfjs_backend_wasm_threaded_simd());\nvar import_tfjs_backend_wasm_threaded_simd_worker = __toESM(require_tfjs_backend_wasm_threaded_simd_worker());\nvar wasmFactory_import = __toESM(require_tfjs_backend_wasm());\nvar wasmFactoryThreadedSimd = wasmFactoryThreadedSimd_import.default || wasmFactoryThreadedSimd_import;\nvar wasmFactory = wasmFactory_import.default || wasmFactory_import;\nvar BackendWasm = class extends KernelBackend {\n constructor(wasm) {\n super();\n this.wasm = wasm;\n this.dataIdNextNumber = 1;\n this.wasm.tfjs.initWithThreadsCount(threadsCount);\n actualThreadsCount = this.wasm.tfjs.getThreadsCount();\n this.dataIdMap = new DataStorage(this, engine());\n }\n write(values, shape, dtype) {\n const dataId = { id: this.dataIdNextNumber++ };\n this.move(dataId, values, shape, dtype, 1);\n return dataId;\n }\n numDataIds() {\n return this.dataIdMap.numDataIds();\n }\n async time(f) {\n const start = util_exports.now();\n f();\n const kernelMs = util_exports.now() - start;\n return { kernelMs };\n }\n move(dataId, values, shape, dtype, refCount) {\n const id = this.dataIdNextNumber++;\n if (dtype === \"string\") {\n const stringBytes = values;\n this.dataIdMap.set(dataId, { id, stringBytes, shape, dtype, memoryOffset: null, refCount });\n return;\n }\n const size = util_exports.sizeFromShape(shape);\n const numBytes = size * util_exports.bytesPerElement(dtype);\n const memoryOffset = this.wasm._malloc(numBytes) >>> 0;\n this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount });\n this.wasm.tfjs.registerTensor(id, size, memoryOffset);\n if (values != null) {\n this.wasm.HEAPU8.set(new Uint8Array(values.buffer, values.byteOffset, numBytes), memoryOffset);\n }\n }\n async read(dataId) {\n return this.readSync(dataId);\n }\n readSync(dataId, start, end) {\n const { memoryOffset, dtype, shape, stringBytes } = this.dataIdMap.get(dataId);\n if (dtype === \"string\") {\n if ((start == null || start === 0) && (end == null || end >= stringBytes.length)) {\n return stringBytes;\n }\n return stringBytes.slice(start, end);\n }\n start = start || 0;\n end = end || util_exports.sizeFromShape(shape);\n const bytesPerElement2 = util_exports.bytesPerElement(dtype);\n const bytes = this.wasm.HEAPU8.slice(memoryOffset + start * bytesPerElement2, memoryOffset + end * bytesPerElement2);\n return typedArrayFromBuffer(bytes.buffer, dtype);\n }\n /**\n * Dispose the memory if the dataId has 0 refCount. Return true if the memory\n * is released, false otherwise.\n * @param dataId\n * @oaram force Optional, remove the data regardless of refCount\n */\n disposeData(dataId, force = false) {\n if (this.dataIdMap.has(dataId)) {\n const data = this.dataIdMap.get(dataId);\n data.refCount--;\n if (!force && data.refCount > 0) {\n return false;\n }\n this.wasm._free(data.memoryOffset);\n this.wasm.tfjs.disposeData(data.id);\n this.dataIdMap.delete(dataId);\n }\n return true;\n }\n /** Return refCount of a `TensorData`. */\n refCount(dataId) {\n if (this.dataIdMap.has(dataId)) {\n const tensorData = this.dataIdMap.get(dataId);\n return tensorData.refCount;\n }\n return 0;\n }\n incRef(dataId) {\n const data = this.dataIdMap.get(dataId);\n if (data != null) {\n data.refCount++;\n }\n }\n floatPrecision() {\n return 32;\n }\n // Returns the memory offset of a tensor. Useful for debugging and unit\n // testing.\n getMemoryOffset(dataId) {\n return this.dataIdMap.get(dataId).memoryOffset;\n }\n dispose() {\n this.wasm.tfjs.dispose();\n if (\"PThread\" in this.wasm) {\n this.wasm.PThread.terminateAllThreads();\n }\n this.wasm = null;\n }\n memory() {\n return { unreliable: false };\n }\n /**\n * Make a tensor info for the output of an op. If `memoryOffset` is not\n * present, this method allocates memory on the WASM heap. If `memoryOffset`\n * is present, the memory was allocated elsewhere (in c++) and we just record\n * the pointer where that memory lives.\n */\n makeOutput(shape, dtype, memoryOffset, values) {\n let dataId;\n if (memoryOffset == null) {\n dataId = this.write(values !== null && values !== void 0 ? values : null, shape, dtype);\n } else {\n const id = this.dataIdNextNumber++;\n dataId = { id };\n this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount: 1 });\n const size = util_exports.sizeFromShape(shape);\n this.wasm.tfjs.registerTensor(id, size, memoryOffset);\n }\n return { dataId, shape, dtype };\n }\n typedArrayFromHeap({ shape, dtype, dataId }) {\n const buffer2 = this.wasm.HEAPU8.buffer;\n const { memoryOffset } = this.dataIdMap.get(dataId);\n const size = util_exports.sizeFromShape(shape);\n switch (dtype) {\n case \"float32\":\n return new Float32Array(buffer2, memoryOffset, size);\n case \"int32\":\n return new Int32Array(buffer2, memoryOffset, size);\n case \"bool\":\n return new Uint8Array(buffer2, memoryOffset, size);\n default:\n throw new Error(`Unknown dtype ${dtype}`);\n }\n }\n};\nfunction createInstantiateWasmFunc(path) {\n return (imports, callback) => {\n util_exports.fetch(path, { credentials: \"same-origin\" }).then((response) => {\n if (!response[\"ok\"]) {\n imports.env.a(`failed to load wasm binary file at '${path}'`);\n }\n response.arrayBuffer().then((binary) => {\n WebAssembly.instantiate(binary, imports).then((output) => {\n callback(output.instance, output.module);\n });\n });\n });\n return {};\n };\n}\nfunction getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) {\n if (wasmPath != null) {\n return wasmPath;\n }\n let path = \"tfjs-backend-wasm.wasm\";\n if (simdSupported && threadsSupported) {\n path = \"tfjs-backend-wasm-threaded-simd.wasm\";\n } else if (simdSupported) {\n path = \"tfjs-backend-wasm-simd.wasm\";\n }\n if (wasmFileMap != null) {\n if (wasmFileMap[path] != null) {\n return wasmFileMap[path];\n }\n }\n return wasmModuleFolder + path;\n}\nasync function init() {\n const [simdSupported, threadsSupported] = await Promise.all([\n env().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),\n env().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")\n ]);\n return new Promise((resolve, reject) => {\n const factoryConfig = {};\n factoryConfig.locateFile = (path, prefix) => {\n if (path.endsWith(\".worker.js\")) {\n const response = import_tfjs_backend_wasm_threaded_simd_worker.wasmWorkerContents.replace(/\\n/g, \"\\\\n\");\n const blob = new Blob([response], { type: \"application/javascript\" });\n return URL.createObjectURL(blob);\n }\n if (path.endsWith(\".wasm\")) {\n return getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : prefix);\n }\n return prefix + path;\n };\n if (customFetch) {\n factoryConfig.instantiateWasm = createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : \"\"));\n }\n let initialized = false;\n factoryConfig.onAbort = () => {\n if (initialized) {\n return;\n }\n if (initAborted) {\n return;\n }\n initAborted = true;\n const rejectMsg = \"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\";\n reject({ message: rejectMsg });\n };\n let wasm;\n if (threadsSupported && simdSupported && wasmPath == null) {\n factoryConfig.mainScriptUrlOrBlob = new Blob([`var WasmBackendModuleThreadedSimd = ` + wasmFactoryThreadedSimd.toString()], { type: \"text/javascript\" });\n wasm = wasmFactoryThreadedSimd(factoryConfig);\n } else {\n wasm = wasmFactory(factoryConfig);\n }\n wasm.then((module) => {\n initialized = true;\n initAborted = false;\n const voidReturnType = null;\n module.tfjs = {\n init: module.cwrap(\"init\", null, []),\n initWithThreadsCount: module.cwrap(\"init_with_threads_count\", null, [\"number\"]),\n getThreadsCount: module.cwrap(\"get_threads_count\", \"number\", []),\n registerTensor: module.cwrap(\"register_tensor\", null, [\n \"number\",\n \"number\",\n \"number\"\n // memoryOffset\n ]),\n disposeData: module.cwrap(\"dispose_data\", voidReturnType, [\"number\"]),\n dispose: module.cwrap(\"dispose\", voidReturnType, [])\n };\n resolve({ wasm: module });\n }).catch(reject);\n });\n}\nfunction typedArrayFromBuffer(buffer2, dtype) {\n switch (dtype) {\n case \"float32\":\n return new Float32Array(buffer2);\n case \"int32\":\n return new Int32Array(buffer2);\n case \"bool\":\n return new Uint8Array(buffer2);\n default:\n throw new Error(`Unknown dtype ${dtype}`);\n }\n}\nvar wasmBinaryNames = [\n \"tfjs-backend-wasm.wasm\",\n \"tfjs-backend-wasm-simd.wasm\",\n \"tfjs-backend-wasm-threaded-simd.wasm\"\n];\nvar wasmPath = null;\nvar wasmPathPrefix = null;\nvar wasmFileMap = {};\nvar initAborted = false;\nvar customFetch = false;\nfunction setWasmPath(path, usePlatformFetch = false) {\n deprecationWarn(\"setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release.\");\n if (initAborted) {\n throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");\n }\n wasmPath = path;\n customFetch = usePlatformFetch;\n}\nfunction setWasmPaths(prefixOrFileMap, usePlatformFetch = false) {\n if (initAborted) {\n throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`\");\n }\n if (typeof prefixOrFileMap === \"string\") {\n wasmPathPrefix = prefixOrFileMap;\n } else {\n wasmFileMap = prefixOrFileMap;\n const missingPaths = wasmBinaryNames.filter((name) => wasmFileMap[name] == null);\n if (missingPaths.length > 0) {\n throw new Error(`There were no entries found for the following binaries: ${missingPaths.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.`);\n }\n }\n customFetch = usePlatformFetch;\n}\nvar threadsCount = -1;\nvar actualThreadsCount = -1;\nfunction setThreadsCount(numThreads) {\n threadsCount = numThreads;\n}\nfunction getThreadsCount() {\n if (actualThreadsCount === -1) {\n throw new Error(`WASM backend not initialized.`);\n }\n return actualThreadsCount;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/version.js\nvar version8 = \"4.16.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/base.js\nvar WASM_PRIORITY = 2;\nregisterBackend(\"wasm\", async () => {\n const { wasm } = await init();\n return new BackendWasm(wasm);\n}, WASM_PRIORITY);\n\n// dist/tfjs.version.js\nvar version9 = \"4.16.0\";\nvar version22 = \"4.16.0\";\nvar version32 = \"4.16.0\";\nvar version42 = \"4.16.0\";\nvar version52 = \"4.16.0\";\nvar version62 = {\n // tfjs: tfjsVersion,\n tfjs: version9,\n \"tfjs-core\": version9,\n // 'tfjs-data': tfjsDataVersion,\n // 'tfjs-layers': tfjsLayersVersion,\n \"tfjs-converter\": version22,\n \"tfjs-backend-cpu\": version32,\n \"tfjs-backend-webgl\": version42,\n \"tfjs-backend-wasm\": version52\n};\nexport {\n Abs,\n Acos,\n Acosh,\n AdadeltaOptimizer,\n AdagradOptimizer,\n AdamOptimizer,\n AdamaxOptimizer,\n Add,\n AddN,\n All,\n Any,\n ArgMax,\n ArgMin,\n Asin,\n Asinh,\n Atan,\n Atan2,\n Atanh,\n AvgPool,\n AvgPool3D,\n AvgPool3DGrad,\n AvgPoolGrad,\n BackendWasm,\n BatchMatMul,\n BatchToSpaceND,\n Bincount,\n BitwiseAnd,\n BroadcastArgs,\n BroadcastTo,\n Callback,\n CallbackList,\n Cast,\n Ceil,\n ClipByValue,\n Complex,\n ComplexAbs,\n Concat,\n Conv2D,\n Conv2DBackpropFilter,\n Conv2DBackpropInput,\n Conv3D,\n Conv3DBackpropFilterV2,\n Conv3DBackpropInputV2,\n Cos,\n Cosh,\n CropAndResize,\n Cumprod,\n Cumsum,\n CustomCallback,\n DataStorage,\n DenseBincount,\n DepthToSpace,\n DepthwiseConv2dNative,\n DepthwiseConv2dNativeBackpropFilter,\n DepthwiseConv2dNativeBackpropInput,\n Diag,\n Dilation2D,\n Dilation2DBackpropFilter,\n Dilation2DBackpropInput,\n Draw,\n ENV,\n EarlyStopping,\n Einsum,\n Elu,\n EluGrad,\n Environment,\n Equal,\n Erf,\n Exp,\n ExpandDims,\n Expm1,\n FFT,\n Fill,\n FlipLeftRight,\n Floor,\n FloorDiv,\n FromPixels,\n FusedBatchNorm,\n FusedConv2D,\n FusedDepthwiseConv2D,\n GPGPUContext,\n GatherNd,\n GatherV2,\n GraphModel,\n Greater,\n GreaterEqual,\n History,\n IFFT,\n Identity,\n Imag,\n InputSpec,\n IsFinite,\n IsInf,\n IsNan,\n KernelBackend,\n LRN,\n LRNGrad,\n LayerVariable,\n LayersModel,\n LeakyRelu,\n Less,\n LessEqual,\n LinSpace,\n Log,\n Log1p,\n LogSoftmax,\n LogicalAnd,\n LogicalNot,\n LogicalOr,\n LogicalXor,\n LowerBound,\n MathBackendCPU,\n MathBackendWebGL,\n MatrixBandPart,\n Max,\n MaxPool,\n MaxPool3D,\n MaxPool3DGrad,\n MaxPoolGrad,\n MaxPoolWithArgmax,\n Maximum,\n Mean,\n Min,\n Minimum,\n MirrorPad,\n Mod,\n MomentumOptimizer,\n Multinomial,\n Multiply,\n Neg,\n NonMaxSuppressionV3,\n NonMaxSuppressionV4,\n NonMaxSuppressionV5,\n NotEqual,\n OP_SCOPE_SUFFIX,\n OneHot,\n OnesLike,\n Optimizer,\n OptimizerConstructors,\n Pack,\n PadV2,\n Pool,\n Pow,\n Prelu,\n Prod,\n RMSPropOptimizer,\n RNN,\n RaggedGather,\n RaggedRange,\n RaggedTensorToTensor,\n Range,\n Rank,\n Real,\n RealDiv,\n Reciprocal,\n Reduction,\n Relu,\n Relu6,\n Reshape,\n ResizeBilinear,\n ResizeBilinearGrad,\n ResizeNearestNeighbor,\n ResizeNearestNeighborGrad,\n Reverse,\n RotateWithOffset,\n Round,\n Rsqrt,\n SGDOptimizer,\n ScatterNd,\n SearchSorted,\n Select,\n Selu,\n Sequential,\n Sigmoid,\n Sign,\n Sin,\n Sinh,\n Slice,\n Softmax,\n Softplus,\n SpaceToBatchND,\n SparseFillEmptyRows,\n SparseReshape,\n SparseSegmentMean,\n SparseSegmentSum,\n SparseToDense,\n SplitV,\n Sqrt,\n Square,\n SquaredDifference,\n StaticRegexReplace,\n Step,\n StridedSlice,\n StringNGrams,\n StringSplit,\n StringToHashBucketFast,\n Sub,\n Sum,\n SymbolicTensor,\n Tan,\n Tanh,\n Tensor,\n TensorBuffer,\n TensorScatterUpdate,\n Tile,\n TopK,\n Transform,\n Transpose,\n Unique,\n Unpack,\n UnsortedSegmentSum,\n UpperBound,\n Variable,\n ZerosLike,\n _FusedMatMul,\n abs,\n acos,\n acosh,\n add2 as add,\n addN,\n all,\n any,\n argMax,\n argMin,\n asin,\n asinh,\n atan,\n atan2,\n atanh,\n avgPool,\n avgPool3d,\n backend,\n backend_util_exports as backend_util,\n basicLSTMCell,\n batchNorm,\n batchNorm2d,\n batchNorm3d,\n batchNorm4d,\n batchToSpaceND,\n bincount,\n bitwiseAnd,\n booleanMaskAsync,\n broadcastArgs,\n broadcastTo,\n broadcast_util_exports as broadcast_util,\n browser_exports as browser,\n buffer,\n callbacks,\n cast,\n ceil,\n clipByValue,\n clone,\n complex,\n concat,\n concat1d,\n concat2d,\n concat3d,\n concat4d,\n exports_constraints_exports as constraints,\n conv1d,\n conv2d,\n conv2dTranspose,\n conv3d,\n conv3dTranspose,\n copyRegisteredKernels,\n cos,\n cosh,\n cosineWindow,\n cumprod,\n cumsum,\n customGrad,\n dist_exports2 as data,\n denseBincount,\n deprecationWarn,\n depthToSpace,\n depthwiseConv2d,\n deregisterOp,\n device_util_exports as device_util,\n diag,\n dilation2d,\n disableDeprecationWarnings,\n dispose,\n disposeVariables,\n div,\n divNoNan,\n dot,\n dropout,\n einsum,\n elu,\n enableDebugMode,\n enableProdMode,\n enclosingPowerOfTwo,\n engine,\n ensureShape,\n env,\n equal,\n erf,\n euclideanNorm,\n exp,\n expandDims,\n expm1,\n eye,\n fft,\n fill,\n findBackend,\n findBackendFactory,\n floor,\n floorDiv,\n forceHalfFloat,\n fused_ops_exports as fused,\n gather,\n gatherND,\n gather_nd_util_exports as gather_util,\n getBackend,\n getGradient,\n getKernel,\n getKernelsForBackend,\n getThreadsCount,\n gpgpu_util_exports as gpgpu_util,\n grad,\n grads,\n greater,\n greaterEqual,\n ifft,\n imag,\n image,\n inTopKAsync,\n exports_initializers_exports as initializers,\n input,\n io_exports as io,\n irfft,\n isFinite2 as isFinite,\n isInf,\n isNaN2 as isNaN,\n keep,\n kernel_impls_exports as kernel_impls,\n exports_layers_exports as layers,\n leakyRelu,\n less,\n lessEqual,\n linalg,\n linspace,\n loadGraphModel,\n loadGraphModelSync,\n loadLayersModel,\n localResponseNormalization,\n log2 as log,\n log1p,\n logSigmoid,\n logSoftmax,\n logSumExp,\n logicalAnd,\n logicalNot,\n logicalOr,\n logicalXor,\n losses,\n lowerBound,\n matMul,\n math_exports as math,\n max,\n maxPool,\n maxPool3d,\n maxPoolWithArgmax,\n maximum,\n mean,\n memory,\n meshgrid,\n exports_metrics_exports as metrics,\n min,\n minimum,\n mirrorPad,\n mod,\n model,\n exports_models_exports as models,\n moments,\n movingAverage,\n mul,\n multiRNNCell,\n multinomial,\n neg,\n nextFrame,\n norm,\n notEqual,\n oneHot,\n ones2 as ones,\n onesLike,\n op,\n outerProduct,\n pad,\n pad1d,\n pad2d,\n pad3d,\n pad4d,\n pool,\n pow,\n prelu,\n print,\n prod,\n profile,\n raggedGather,\n raggedRange,\n raggedTensorToTensor,\n rand,\n randomGamma,\n randomNormal,\n randomStandardNormal,\n randomUniform,\n randomUniformInt,\n range,\n ready,\n real,\n reciprocal,\n registerBackend,\n registerCallbackConstructor,\n registerGradient,\n registerKernel,\n registerOp,\n exports_regularizers_exports as regularizers,\n relu,\n relu6,\n removeBackend,\n reshape,\n reverse,\n reverse1d,\n reverse2d,\n reverse3d,\n reverse4d,\n rfft,\n round2 as round,\n rsqrt,\n scalar,\n scatterND,\n scatter_nd_util_exports as scatter_util,\n searchSorted,\n selu,\n separableConv2d,\n sequential,\n serialization_exports as serialization,\n setBackend,\n setPlatform,\n setThreadsCount,\n setWasmPath,\n setWasmPaths,\n setWebGLContext,\n setdiff1dAsync,\n shared_exports as shared,\n sigmoid,\n sign,\n signal,\n sin,\n sinh,\n slice,\n slice1d,\n slice2d,\n slice3d,\n slice4d,\n slice_util_exports as slice_util,\n softmax,\n softplus,\n spaceToBatchND,\n sparse,\n sparseToDense,\n spectral,\n split,\n sqrt,\n square,\n squaredDifference,\n squeeze,\n stack,\n step,\n stridedSlice,\n string,\n sub,\n sum2 as sum,\n sumOutType,\n tan,\n tanh2 as tanh,\n tensor,\n tensor1d,\n tensor2d,\n tensor3d,\n tensor4d,\n tensor5d,\n tensor6d,\n tensorScatterUpdate,\n tensor_util_exports as tensor_util,\n test_util_exports as test_util,\n tidy,\n tile,\n time,\n topk,\n train,\n transpose,\n truncatedNormal,\n unique,\n unregisterGradient,\n unregisterKernel,\n unsortedSegmentSum,\n unstack,\n upcastType,\n upperBound,\n util_exports as util,\n valueAndGrad,\n valueAndGrads,\n variable,\n variableGrads,\n version62 as version,\n version3 as version_converter,\n version as version_core,\n version5 as version_cpu,\n version2 as version_layers,\n version8 as version_wasm,\n version6 as version_webgl,\n webgl,\n webgl_util_exports as webgl_util,\n where,\n whereAsync,\n zeros,\n zerosLike\n};\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 // eslint-disable-next-line no-undef\n ? (filePath: string) => new Promise((resolve, reject) => { fs.readFile(filePath, (err: NodeJS.ErrnoException | null, buffer: string | 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 const Canvas: (new () => HTMLCanvasElement) = (global as any)['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n const Video: (new () => HTMLVideoElement) = (global as any)['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 if (canvasArg instanceof CanvasRenderingContext2D) return canvasArg;\n const canvas = resolveInput(canvasArg);\n if (!(canvas instanceof Canvas)) throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n const ctx = canvas.getContext('2d', { willReadFrequently: true });\n if (!ctx) throw new Error('resolveContext2d - canvas 2d context is null');\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) => (typeof buf === 'string' ? Buffer.from(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'] as const;\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 { Point } from '../classes';\nimport { 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 && (obj as any)['landmarks'] instanceof FaceLandmarks\n && (obj as any)['unshiftedLandmarks'] instanceof FaceLandmarks\n && (obj as any)['alignedRect'] instanceof FaceDetection\n );\n}\n\nfunction calculateFaceAngle(mesh: FaceLandmarks) {\n // Helper to convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (radians: number) => (radians * 180) / Math.PI;\n const calcLengthBetweenTwoPoints = (a: Point, b: Point) => 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: Point, midPoint: Point, rightPoint: Point) => {\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: Point, pivot: Point) => {\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: Point, midPoint: Point, rightPoint: Point) => {\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 = new Point((leftPoint.x + rightPoint.x) / 2, (leftPoint.y + rightPoint.y) / 2);\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\": \"1.7.12\",\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\": \"^2.0.0\",\n \"@microsoft/api-extractor\": \"^7.39.1\",\n \"@tensorflow/tfjs\": \"^4.16.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.16.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.16.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.16.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.16.0\",\n \"@tensorflow/tfjs-converter\": \"^4.16.0\",\n \"@tensorflow/tfjs-core\": \"^4.16.0\",\n \"@tensorflow/tfjs-data\": \"^4.16.0\",\n \"@tensorflow/tfjs-layers\": \"^4.16.0\",\n \"@tensorflow/tfjs-node\": \"^4.16.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.16.0\",\n \"@types/node\": \"^20.11.5\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^6.19.0\",\n \"@typescript-eslint/parser\": \"^6.19.0\",\n \"@vladmandic/build\": \"^0.9.3\",\n \"@vladmandic/pilogger\": \"^0.4.9\",\n \"esbuild\": \"^0.19.11\",\n \"eslint\": \"^8.56.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-import\": \"^2.29.1\",\n \"eslint-plugin-json\": \"^3.1.0\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^6.1.1\",\n \"node-fetch\": \"^3.3.2\",\n \"rimraf\": \"^5.0.5\",\n \"seedrandom\": \"^3.0.5\",\n \"tslib\": \"^2.6.2\",\n \"typedoc\": \"^0.25.7\",\n \"typescript\": \"5.3.3\"\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, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow: Record<`main_block_${number}`, MainBlockParams> = {};\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(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow: Record<`main_block_${number}`, MainBlockParams> = {};\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, TinyYolov2ExtractBoxesResult, 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: TinyYolov2ExtractBoxesResult[] = [];\n const scoresData = await scoresTensor.array() as number[][][][];\n const boxesData = await boxesTensor.array() as number[][][][];\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 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ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){lm(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof Mc)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a(athis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return vc.nextTensorId++}nextVariableId(){return 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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}}};tu.className="Sequential";ne.registerClass(tu);function $H(e){return new Dr(e)}function DH(e){return new tu(e)}function Z2(e){return A2(e)}function RH(e,t){Ea.registerCallbackConstructor(e,t)}var Gn=class extends ne.Serializable{getConfig(){return{}}},J2=class extends Gn{apply(e,t=1){return rG(e,t)}};J2.className="elu";ne.registerClass(J2);var Q2=class extends Gn{apply(e){return ff(e)}};Q2.className="selu";ne.registerClass(Q2);var eC=class extends Gn{apply(e){return Ke(e)}};eC.className="relu";ne.registerClass(eC);var tC=class extends Gn{apply(e){return P(()=>fs(6,Ke(e)))}};tC.className="relu6";ne.registerClass(tC);var nC=class extends Gn{apply(e){return e}};nC.className="linear";ne.registerClass(nC);var aC=class extends Gn{apply(e){return fa(e)}};aC.className="sigmoid";ne.registerClass(aC);var rC=class extends Gn{apply(e){return iG(e)}};rC.className="hardSigmoid";ne.registerClass(rC);var sC=class extends Gn{apply(e){return Ho(e)}};sC.className="softplus";ne.registerClass(sC);var iC=class extends Gn{apply(e){return sG(e)}};iC.className="softsign";ne.registerClass(iC);var oC=class extends Gn{apply(e){return mi(e)}};oC.className="tanh";ne.registerClass(oC);var k0=class extends Gn{apply(e,t=-1){return Xa(e,t)}};k0.className="softmax";ne.registerClass(k0);var lC=class extends Gn{apply(e,t=-1){return of(e,t)}};lC.className="logSoftmax";ne.registerClass(lC);var uC=class extends Gn{apply(e,t=1){return P(()=>z(fa(z(e,t)),e))}};uC.className="swish";ne.registerClass(uC);var pC=class extends Gn{apply(e){return P(()=>z(e,mi(Ho(e))))}};pC.className="mish";ne.registerClass(pC);function bs(e){return e.getClassName()}function wx(e,t={}){return Id(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function ys(e){if(e==null){let t={};return t.className="linear",t.config={},wx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},wx(t)}else return e instanceof Gn?e:wx(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 cC=class extends ne.Serializable{},_d=class extends cC{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,Wt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,Nd(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};_d.className="L1L2";ne.registerClass(_d);function MH(e){return I0(e),new _d({l1:e!=null?e.l1:null,l2:0})}function PH(e){return I0(e),new _d({l2:e!=null?e.l2:null,l1:0})}var GI={l1l2:"L1L2"};function mt(e){return t0(e)}function HI(e,t={}){return Id(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ct(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in GI?GI[e]:e,config:{}};return HI(t)}else return e instanceof cC?e:HI(e)}var S0=class extends Be{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=sn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};S0.className="ReLU";ne.registerClass(S0);var N0=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=Ce(e);return hd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};N0.className="LeakyReLU";ne.registerClass(N0);var T0=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=Zt(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=Je(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 dC(e,t){return P(()=>(Pt(t),t==="channelsFirst"?De(e,[0,2,3,4,1]):e))}function OH(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=ef(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ya(o,n)),o})}function qI(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=A0(e,s);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Zl.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 LH(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 ${e.rank}.`);let o=dC(e,s);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=rw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ya(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var F0=class extends Be{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",F0.verifyArgs(t),this.rank=e,an(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Le(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Wl(t.kernelSize,e,"kernelSize"),this.strides=Wl(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=ys(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Tt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Zt(t.biasConstraint),this.biasRegularizer=Ct(t.biasRegularizer),this.activityRegularizer=Ct(t.activityRegularizer),this.dilationRate=Wl(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"&&!n0(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:bs(this.activation),useBias:this.useBias,biasInitializer:At(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Yt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Ed=class extends F0{constructor(e,t){super(e,t),this.kernel=null,Ed.verifyArgs(t),this.filters=t.filters,an(this.filters,"filters"),this.kernelInitializer=Tt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Zt(t.kernelConstraint),this.kernelRegularizer=Ct(t.kernelRegularizer)}build(e){e=Je(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=v2(this.activation.getClassName());if(r!=null&&this.rank===2)n=qI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=OH(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=qI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=LH(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=Je(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)}`)}},Ad=class extends Ed{constructor(e){super(2,e),Ad.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!n0(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)}.`)}};Ad.className="Conv2D";ne.registerClass(Ad);var Fd=class extends Ed{constructor(e){super(3,e),Fd.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)}.`)}};Fd.className="Conv3D";ne.registerClass(Fd);var $0=class extends Ad{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 ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=tf(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=Je(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}};$0.className="Conv2DTranspose";ne.registerClass($0);var D0=class extends Fd{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 ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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),v=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=sw(n,this.kernel.read(),v,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=Je(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}};D0.className="Conv3DTranspose";ne.registerClass(D0);var hC=class extends Ed{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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ct(t.depthwiseRegularizer),this.depthwiseConstraint=Zt(t.depthwiseConstraint),this.pointwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ct(t.pointwiseRegularizer),this.pointwiseConstraint=Zt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Ce(e);let n;if(this.rank===1)throw new Le("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=$s(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=Yt(this.depthwiseConstraint),e.pointwiseConstraint=Yt(this.pointwiseConstraint),e}};hC.className="SeparableConv";var R0=class extends hC{constructor(e){super(2,e)}};R0.className="SeparableConv2D";ne.registerClass(R0);var Uf=class extends Ed{constructor(e){super(1,e),Uf.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"&&!n0(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)}.`)}};Uf.className="Conv1D";ne.registerClass(Uf);var M0=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=Ce(e),this.dataFormat==="channelsLast"){let n=Lh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Lh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Lh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Lh(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}};M0.className="Cropping2D";ne.registerClass(M0);var P0=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,ZU(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}};P0.className="UpSampling2D";ne.registerClass(P0);function zH(e,t,n=[1,1],a="valid",r,s){return P(()=>{r==null&&(r=ja()),Pt(r);let i=A0(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=Es(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var O0=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=Zt(e.depthwiseConstraint),this.depthwiseRegularizer=Ct(e.depthwiseRegularizer)}build(e){if(e=Je(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=zH(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=Je(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=Yt(this.depthwiseRegularizer),e}};O0.className="DepthwiseConv2D";ne.registerClass(O0);function mC(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 fC(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=se(se(r,"bool"),"float32"),r.rank===l-1&&(r=nn(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 v=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=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Dt(p,1)),[d,g,c]})}var br=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 qf({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){Hx(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 Cr("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;aqt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=mC(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=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=fC((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=Sd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Ux(t,[1,n]):t):this.cell.stateSize>1?[Ux(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()===br.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}))}};br.className="RNN";ne.registerClass(br);var $d=class extends Be{},Gf=class extends $d{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,an(this.units,"units"),this.activation=ys(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=Zt(e.kernelConstraint),this.recurrentConstraint=Zt(e.recurrentConstraint),this.biasConstraint=Zt(e.biasConstraint),this.dropout=Ql([1,gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ql([1,gs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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:bs(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:Yt(this.kernelConstraint),recurrentConstraint:Yt(this.recurrentConstraint),biasConstraint:Yt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Gf.className="SimpleRNNCell";ne.registerClass(Gf);var L0=class extends br{constructor(e){e.cell=new Gf(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)}};L0.className="SimpleRNN";ne.registerClass(L0);var Hf=class extends $d{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,an(this.units,"units"),this.activation=ys(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ys(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=Zt(e.kernelConstraint),this.recurrentConstraint=Zt(e.recurrentConstraint),this.biasConstraint=Zt(e.biasConstraint),this.dropout=Ql([1,gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ql([1,gs([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=Je(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)}};z0.className="GRU";ne.registerClass(z0);var Dd=class extends $d{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,an(this.units,"units"),this.activation=ys(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ys(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=Zt(e.kernelConstraint),this.recurrentConstraint=Zt(e.recurrentConstraint),this.biasConstraint=Zt(e.biasConstraint),this.dropout=Ql([1,gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ql([1,gs([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=Je(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 Df().apply([s]),p=r.apply([s*2]);return AI(AI(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)}};W0.className="LSTM";ne.registerClass(W0);var qf=class extends $d{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{si(`RNNCell_${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 qx(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):C2(t(),n),o=()=>Td(i,t,a);return!r||r<=1?qt(o().clone()):Array(r).fill(void 0).map(o).map(l=>qt(l.clone()))}var WH=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 Cr("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. 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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 a0([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 ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0aa(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0aa(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,T]=zn(this.kernel.read(),i,y),[C,E,F,D]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,E,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,T,D,this.padding);let[$,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=WH(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")}};jf.className="ConvLSTM2DCell";ne.registerClass(jf);var B0=class extends gC{constructor(e){let t=new jf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};B0.className="ConvLSTM2D";ne.registerClass(B0);var Kf=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=Ce(e);if(0C2(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()}};Kf.className="Dropout";ne.registerClass(Kf);var V0=class extends Kf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};V0.className="SpatialDropout1D";ne.registerClass(V0);var U0=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,an(this.units,"units"),this.activation=ys(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=Zt(e.kernelConstraint),this.biasConstraint=Zt(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=Je(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=Je(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=v2(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:bs(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:Yt(this.kernelConstraint),biasConstraint:Yt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};U0.className="Dense";ne.registerClass(U0);var G0=class extends Be{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(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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n=Ce(e),a=n.shape,r=a.length;return P(()=>{let{mean:s,variance:i}=gd(n,this.axis,!0),o=yi(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 bC(e,t,n,a,r,s){return P(()=>{Pt(r),k2(s),wa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=dC(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=ww(e,t,n,o):i=jv(e,t,n,o),r==="channelsFirst"&&(i=De(i,[0,4,1,2,3])),i})}var yC=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(an(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 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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}},d1=class extends xC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),wa(a),Xf(e,t,n,a,r,"max")}};d1.className="MaxPooling2D";ne.registerClass(d1);var h1=class extends xC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),wa(a),Xf(e,t,n,a,r,"avg")}};h1.className="AveragePooling2D";ne.registerClass(h1);var vC=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 ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];an(this.poolSize,"poolSize"),an(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),wa(this.padding),this.inputSpec=[new Bt({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),a=Ha(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},m1=class extends vC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),wa(a),bC(e,t,n,a,r,"max")}};m1.className="MaxPooling3D";ne.registerClass(m1);var f1=class extends vC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Pt(r),wa(a),bC(e,t,n,a,r,"avg")}};f1.className="AveragePooling3D";ne.registerClass(f1);var wC=class extends Be{constructor(e){super(e),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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e=Je(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return P(()=>{e=Ce(e),e.dtype!=="int32"&&(e=lr(e,"int32"));let n;if(typeof t.countWeights!="undefined"){if(this.outputMode!=="count")throw new V(`countWeights is not used when outputMode !== count. - Received countWeights=${t.countWeights}`);n=Ce(t.countWeights)}let a=ga(e),r=Hl(e),s=_n(this.numTokens,a).bufferSync().get(0),i=Or(r,0).bufferSync().get(0);if(!(s&&i))throw new V(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return YH(e,this.outputMode,this.numTokens,n)})}};S1.className="CategoryEncoding";ne.registerClass(S1);var ZH=["bilinear","nearest"],jI=new Set(ZH),N1=class extends Be{constructor(e){if(super(e),this.height=e.height,this.width=e.width,e.interpolation)if(jI.has(e.interpolation))this.interpolation=e.interpolation;else throw new V(`Invalid interpolation parameter: ${e.interpolation} 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o=M.registeredVariables[s],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:O(()=>je(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:O(()=>je(o).variable(i))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,p=X(z(c,this.beta1),z(u,1-this.beta1)),d=X(z(l,this.beta2),z(lt(u),1-this.beta2)),h=fe(p,n),f=fe(d,r);c.assign(p),l.assign(d);let g=X(z(fe(h,X(hn(f),this.epsilon)),-this.learningRate),o);o.assign(g)}),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),O(()=>{this.accBeta1.assign(Ds(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ds(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}},Qw=class extends Os{static get className(){return"Adamax"}constructor(e,t,n,r=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],O(()=>{this.iteration=xe(0).variable(),this.accBeta1=xe(t).variable()}),r==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=le(1,this.accBeta1),r=fe(-this.learningRate,X(z(this.iteration,this.decay),1));t.forEach((s,a)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},Em=class extends Os{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=Array.isArray(e)?e[r].tensor:e[n];if(s==null)return;let a=M.registeredVariables[n];O(()=>{let o=X(z(this.c,s),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(xe(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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compiled before being used.");return this.model.evaluate(t,n,r)}async evaluateDataset(t,n){if(!this.built)throw new os("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,n)}predict(t,n={}){return this.model==null&&this.build(),this.model.predict(t,n)}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,n,r={}){if(!this.built)throw new os("The model needs to be compiled before being used.");return this.model.fit(t,n,r)}async fitDataset(t,n){if(!this.built)throw new os("The model needs to be compiled before being used.");return this.model.fitDataset(t,n)}async trainOnBatch(t,n){return this.model.trainOnBatch(t,n)}static fromConfig(t,n,r={},s=!1){let a,o={};if(n instanceof Array){if(n[0].className==null||n[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");a=n}else w.assert(n.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=n.layers,delete n.layers,o=n;let i=new t(o);if(!(i instanceof tx))throw new Be(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let u of a){let l=Vr(u,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(t){if(this.model==null)throw new V("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 V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let n of this.layers){let r={};r.className=n.getClassName(),r.config=n.getConfig(),t.push(r)}return{name:this.name,layers:t}}};Vm.className="Sequential";re.registerClass(Vm);function w6(e){return new Es(e)}function I6(e){return new Vm(e)}function g2(e){return KN(e)}function k6(e,t){kI.registerCallbackConstructor(e,t)}var Vn=class extends re.Serializable{getConfig(){return{}}},b2=class extends Vn{apply(e,t=1){return UG(e,t)}};b2.className="elu";re.registerClass(b2);var y2=class extends Vn{apply(e){return fm(e)}};y2.className="selu";re.registerClass(y2);var v2=class extends Vn{apply(e){return Ke(e)}};v2.className="relu";re.registerClass(v2);var x2=class extends Vn{apply(e){return O(()=>ga(6,Ke(e)))}};x2.className="relu6";re.registerClass(x2);var w2=class extends Vn{apply(e){return e}};w2.className="linear";re.registerClass(w2);var I2=class extends Vn{apply(e){return pr(e)}};I2.className="sigmoid";re.registerClass(I2);var k2=class extends Vn{apply(e){return HG(e)}};k2.className="hardSigmoid";re.registerClass(k2);var S2=class extends Vn{apply(e){return qi(e)}};S2.className="softplus";re.registerClass(S2);var C2=class extends Vn{apply(e){return GG(e)}};C2.className="softsign";re.registerClass(C2);var T2=class extends Vn{apply(e){return bo(e)}};T2.className="tanh";re.registerClass(T2);var EI=class extends Vn{apply(e,t=-1){return Kr(e,t)}};EI.className="softmax";re.registerClass(EI);var N2=class extends Vn{apply(e,t=-1){return im(e,t)}};N2.className="logSoftmax";re.registerClass(N2);var _2=class extends Vn{apply(e,t=1){return O(()=>z(pr(z(e,t)),e))}};_2.className="swish";re.registerClass(_2);var E2=class extends Vn{apply(e){return O(()=>z(e,bo(qi(e))))}};E2.className="mish";re.registerClass(E2);function ya(e){return e.getClassName()}function kv(e,t={}){return Tp(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function va(e){if(e==null){let t={};return t.className="linear",t.config={},kv(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},kv(t)}else return e instanceof Vn?e:kv(e)}function AI(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 A2=class extends re.Serializable{},Dp=class extends A2{constructor(e){super(),AI(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 O(()=>{let t=kt([1]);return this.hasL1&&(t=X(t,ge(z(this.l1,Lt(e))))),this.hasL2&&(t=X(t,ge(z(this.l2,_p(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Dp.className="L1L2";re.registerClass(Dp);function S6(e){return AI(e),new Dp({l1:e!=null?e.l1:null,l2:0})}function C6(e){return AI(e),new Dp({l2:e!=null?e.l2:null,l1:0})}var e1={l1l2:"L1L2"};function mt(e){return iI(e)}function t1(e,t={}){return Tp(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ct(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in e1?e1[e]:e,config:{}};return t1(n)}else return e instanceof A2?e:t1(e)}var DI=class extends ze{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}};DI.className="ReLU";re.registerClass(DI);var $I=class extends ze{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Te(e);return mp(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};$I.className="LeakyReLU";re.registerClass($I);var FI=class extends ze{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(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 r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r{let n=Te(e),r=t.mask;if(r!=null){let s=z(le(On(n.shape),ae(r,n.dtype)),xe(-1e9));n=X(n,s)}return this.axis instanceof Array?this.axis.length>1?fn(le(n,bp(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};OI.className="Softmax";re.registerClass(OI);function Gu(e,t,n){if(typeof e=="number")return wo(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r(Pt(t),t==="channelsFirst"?Re(e,[0,2,3,1]):e))}function D2(e,t){return O(()=>(Pt(t),t==="channelsFirst"?Re(e,[0,2,3,4,1]):e))}function T6(e,t,n,r=1,s="valid",a,o=1){return O(()=>{if(a==null&&(a=Hr()),Pt(a),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(a==="channelsFirst"&&(e=Re(e,[0,2,1])),s==="causal")throw new Be("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=tm(e,t,r,s==="same"?"same":"valid","NWC",o);return n!=null&&(i=Xr(i,n)),i})}function n1(e,t,n,r=[1,1],s="valid",a,o,i=null){return O(()=>{if(a==null&&(a=Hr()),Pt(a),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 u=MI(e,a);if(s==="causal")throw new Be("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=tc.conv2d({x:u,filter:t,strides:r,pad:s==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(u=Re(u,[0,3,1,2])),u})}function N6(e,t,n,r=[1,1,1],s="valid",a,o){return O(()=>{if(a==null&&(a=Hr()),Pt(a),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 i=D2(e,a);if(s==="causal")throw new Be("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=lw(i,t,r,s==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Xr(i,n)),a==="channelsFirst"&&(i=Re(i,[0,4,1,2,3])),i})}var $2=class F2 extends ze{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",F2.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Be(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gu(n.kernelSize,t,"kernelSize"),this.strides=Gu(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,vr(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Pt(this.dataFormat),this.activation=va(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Ct(n.biasRegularizer),this.activityRegularizer=Ct(n.activityRegularizer),this.dilationRate=Gu(n.dilationRate==null?1:n.dilationRate,t,"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(t){if(ss("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!uI(t.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(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ya(this.activation),useBias:this.useBias,biasInitializer:Et(this.biasInitializer),biasRegularizer:mt(this.biasRegularizer),activityRegularizer:mt(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},Um=class R2 extends $2{constructor(t,n){super(t,n),this.kernel=null,R2.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Ct(n.kernelRegularizer)}build(t){t=Qe(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${t[n]}`);let r=t[n],s=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[n]:r}}],this.built=!0}call(t,n){return O(()=>{t=Te(t);let r,s=this.bias==null?null:this.bias.read(),a=LN(this.activation.getClassName());if(a!=null&&this.rank===2)r=n1(t,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=T6(t,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=n1(t,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=N6(t,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Be("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(t){t=Qe(t);let n=[],r=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let a=0;a 0 but got ${JSON.stringify(t.filters)}`)}},Gm=class P2 extends Um{constructor(t){super(2,t),P2.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!uI(t.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(t.kernelSize)}.`)}};Gm.className="Conv2D";re.registerClass(Gm);var Hm=class O2 extends Um{constructor(t){super(3,t),O2.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 V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hm.className="Conv3D";re.registerClass(Hm);var LI=class extends Gm{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 ${this.padding}`)}build(e){if(e=Qe(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=r[a],u=r[o],c=this.kernelSize[0],l=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=as(i,p,c,this.padding),f=as(u,d,l,this.padding),g=[s,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Re(n,[0,2,3,1]));let m=nm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(m=Re(m,[0,3,1,2])),this.bias!=null&&(m=Xr(m,this.bias.read(),this.dataFormat)),this.activation!=null&&(m=this.activation.apply(m)),m})}computeOutputShape(e){e=Qe(e);let t=e.slice(),n,r,s;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3):(n=3,r=1,s=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[r]=as(t[r],i,a,this.padding),t[s]=as(t[s],u,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};LI.className="Conv2DTranspose";re.registerClass(LI);var BI=class extends Hm{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 ${this.padding}`)}build(e){if(e=Qe(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let u=r[i],c=r[a],l=r[o],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],g=this.strides[1],m=this.strides[2],b=as(u,f,p,this.padding),y=as(c,g,d,this.padding),v=as(l,m,h,this.padding),x=[s,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Re(n,[0,2,3,4,1]));let k=dw(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=Re(k,[0,4,1,2,3])),this.bias!==null&&(k=Xr(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=Qe(e);let t=e.slice(),n,r,s,a;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3,a=4):(n=4,r=1,s=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],u=this.kernelSize[2],c=this.strides[0],l=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[r]=as(t[r],c,o,this.padding),t[s]=as(t[s],l,i,this.padding),t[a]=as(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};BI.className="Conv3DTranspose";re.registerClass(BI);var M2=class extends Um{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=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ct(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(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=Te(e);let n;if(this.rank===1)throw new Be("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Re(e,[0,2,3,1])),n=Fa(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Xr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Re(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=Et(this.depthwiseInitializer),e.pointwiseInitializer=Et(this.pointwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.pointwiseRegularizer=mt(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};M2.className="SeparableConv";var zI=class extends M2{constructor(e){super(2,e)}};zI.className="SeparableConv2D";re.registerClass(zI);var WI=class L2 extends Um{constructor(t){super(1,t),L2.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"&&!uI(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};WI.className="Conv1D";re.registerClass(WI);var VI=class extends ze{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Mh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Mh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Mh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Mh(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}};VI.className="Cropping2D";re.registerClass(VI);var UI=class extends ze{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,OG(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 O(()=>{let n=Te(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Re(n,[0,2,3,1]);let s=this.size[0]*r[2],a=this.size[1]*r[3],o=this.interpolation==="nearest"?er.resizeNearestNeighbor(n,[s,a]):er.resizeBilinear(n,[s,a]);return Re(o,[0,3,1,2])}else{let s=this.size[0]*r[1],a=this.size[1]*r[2];return this.interpolation==="nearest"?er.resizeNearestNeighbor(n,[s,a]):er.resizeBilinear(n,[s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};UI.className="UpSampling2D";re.registerClass(UI);function _6(e,t,n=[1,1],r="valid",s,a){return O(()=>{s==null&&(s=Hr()),Pt(s);let o=MI(e,s);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 o=Aa(o,t,n,r==="same"?"same":"valid","NHWC",a),s==="channelsFirst"&&(o=Re(o,[0,3,1,2])),o})}var GI=class extends $2{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(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],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{e=Te(e);let n=_6(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Xr(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],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=Ur(t,this.kernelSize[0],this.padding,this.strides[0]),a=Ur(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,s,a]:[e[0],s,a,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Et(this.depthwiseInitializer),e.depthwiseRegularizer=mt(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};GI.className="DepthwiseConv2D";re.registerClass(GI);function B2(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function s(a){return a==null||Array.isArray(a)?a:[a]}return t=s(t),n=s(n),{inputs:e,initialState:t,constants:n}}function z2(e,t,n,r=!1,s,a,o=!1,i=!1){return O(()=>{let u=t.shape.length;if(u<3)throw new V(`Input should be at least 3D, but is ${u}D.`);let c=[1,0].concat(Gr(2,u));if(t=Re(t,c),a!=null)throw new Be("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),s!=null&&(s=ae(ae(s,"bool"),"float32"),s.rank===u-1&&(s=Gt(s,-1)),s=Re(s,c)),r&&(t=gr(t,0),s!=null&&(s=gr(s,0)));let l=[],p,d=n,h=t.shape[0],f=pt(t),g;s!=null&&(g=pt(s));for(let b=0;be(y,d));if(s==null)p=v[0],d=v[1];else{let x=O(()=>{let k=g[b],S=le(rr(k),k),N=X(z(v[0],k),z(d[0],S)),E=d.map(($,F)=>X(z(v[1][F],k),z($,S)));return{output:N,newStates:E}});p=x.output,d=x.newStates}i&&l.push(p)}let m;return i&&(m=Dt(l,1)),[p,m,d]})}var Ms=class W2 extends ze{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Km({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Bt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Gr(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Xv(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let r=n[0],s;if(this.returnSequences?s=[t[0],t[1],r]:s=[t[0],r],this.returnState){let a=[];for(let o of n)a.push([t[0],o]);return[s].concat(a)}else return s}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let r=this.returnSequences?n:null;if(this.returnState){let s=this.states.map(a=>null);return[r].concat(s)}else return r})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let r=0;ri.shape[i.shape.length-1]),o))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=o.map(i=>new Bt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new ea("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==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(s=>kt([r,s])):this.states_=[kt([r,this.cell.stateSize])];else if(t==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>kt([r,s])):this.states_[0]=kt([r,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;sHt(s.clone()))})}apply(t,n){let r=n==null?null:n.initialState,s=n==null?null:n.constants;n==null&&(n={});let a=B2(t,r,s,this.numConstants);t=a.inputs,r=a.initialState,s=a.constants;let o=[],i=[];if(r!=null){n.initialState=r,o=o.concat(r),this.stateSpec=[];for(let c of r)this.stateSpec.push(new Bt({shape:c.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(n.constants=s,o=o.concat(s),this.numConstants=s.length),o[0]instanceof jr){let c=[t].concat(o),l=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=l;let d=super.apply(c,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let r=n==null?null:n.mask,s=n==null?null:n.training,a=n==null?null:n.initialState;t=Te(t),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(t));let o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==o)throw new V(`RNN Layer has ${o} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},c=z2((f,g)=>{let m=this.cell.call([f].concat(g),i);return[m[0],m.slice(1)]},t,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=c[0],p=c[1],d=c[2];this.stateful&&this.resetStates(d,s);let h=this.returnSequences?p:l;return this.returnState?[h].concat(d):h})}getInitialState(t){return O(()=>{let n=kt(t.shape);return n=ge(n,[1,2]),n=Np(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?qv(n,[1,r]):n):this.cell.stateSize>1?[qv(n,[1,this.cell.stateSize])]:[n]})}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(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===W2.className&&(n.cell={className:this.cell.getClassName(),config:r}),Object.assign(Object.assign(Object.assign({},r),t),n)}static fromConfig(t,n,r={}){let s=n.cell,a=Vr(s,r);return new t(Object.assign(n,{cell:a}))}};Ms.className="RNN";re.registerClass(Ms);var $p=class extends ze{},jm=class extends $p{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,tn(this.units,"units"),this.activation=va(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(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=nc([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,ba([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 O(()=>{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 r=t.training==null?!1:t.training;0rr(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0rr(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));let s,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?s=ls(z(e,a),this.kernel.read()):s=ls(e,this.kernel.read()),this.bias!=null&&(s=Xr(s,this.bias.read())),o!=null&&(n=z(n,o));let i=X(s,ls(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ya(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(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)}};jm.className="SimpleRNNCell";re.registerClass(jm);var HI=class extends Ms{constructor(e){e.cell=new jm(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return new e(t)}};HI.className="SimpleRNN";re.registerClass(HI);var qm=class extends $p{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,tn(this.units,"units"),this.activation=va(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=va(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(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=nc([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,ba([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 O(()=>{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,r=e[1];e=e[0],0rr(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0rr(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,a=this.recurrentDropoutMask,o,i,u;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,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};jI.className="GRU";re.registerClass(jI);var Fp=class extends $p{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,tn(this.units,"units"),this.activation=va(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=va(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(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=nc([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,ba([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 r;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;r=new(t=class extends Pr{apply(i,u){let c=s.apply([a]),l=new $m().apply([a]),p=s.apply([a*2]);return BS(BS(c,l),p)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return O(()=>{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 r=e[1],s=e[2];e=e[0],0rr(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0rr(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,u,c,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,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};qI.className="LSTM";re.registerClass(qI);var Km=class extends $p{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return O(()=>{e=e;let n=e.slice(1),r=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?r.push(n.splice(0,o.stateSize.length)):r.push(n.splice(0,1));r.reverse();let s=[],a;for(let o=0;o{oo(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),r={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),r)}static fromConfig(e,t,n={}){let r=[];for(let s of t.cells)r.push(Vr(s,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Yv(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,s=e.splice(r);for(let a=0;aa!=null?a(t(),n):HN(t(),n),i=()=>Ep(o,t,r);return!s||s<=1?Ht(i().clone()):Array(s).fill(void 0).map(i).map(c=>Ht(c.clone()))}var E6=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var s=0,r=Object.getOwnPropertySymbols(e);s{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,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return O(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)],a=kt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new ea("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.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(()=>kt(s)):this.states_=[kt(s)];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(()=>kt(s)):this.states_[0]=kt(s);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 o=0;oHt(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:s,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",u=e[i?3:2],c=e[i?4:3],l=Ur(u,r[0],s,a[0],o[0]),p=Ur(c,r[1],s,a[1],o[1]);return[...e.slice(0,2),...i?[n,l,p]:[l,p,n]]}};V2.className="ConvRNN2D";var Xm=class extends Fp{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:s,dataFormat:a,dilationRate:o}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,tn(this.filters,"filters"),this.kernelSize=Gu(n,2,"kernelSize"),this.kernelSize.forEach(i=>tn(i,"kernelSize")),this.strides=Gu(r||1,2,"strides"),this.strides.forEach(i=>tn(i,"strides")),this.padding=s||"valid",vr(this.padding),this.dataFormat=a||"channelsLast",Pt(this.dataFormat),this.dilationRate=Gu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>tn(i,"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 r=e[n],s=4,a=this.kernelSize.concat([r,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;i=new(t=class extends Pr{apply(p,d){let h=u.apply([c]),f=On([c]),g=u.apply([c*2]);return cI([h,f,g])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],s=e[1],a=e[2],o=4;0rr(r),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,u=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),c=u(r,i,0),l=u(r,i,1),p=u(r,i,2),d=u(r,i,3);0rr(s),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(s,h,0),g=u(s,h,1),m=u(s,h,2),b=u(s,h,3),y=3,[v,x,k,S]=Mn(this.kernel.read(),o,y),[N,E,$,F]=this.useBias?Mn(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,v,N,this.padding),l=this.inputConv(l,x,E,this.padding),p=this.inputConv(p,k,$,this.padding),d=this.inputConv(d,S,F,this.padding);let[D,R,C,L]=Mn(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,D),g=this.recurrentConv(g,R),m=this.recurrentConv(m,C),b=this.recurrentConv(b,L);let U=this.recurrentActivation.apply(X(c,f)),H=this.recurrentActivation.apply(X(l,g)),K=X(z(H,a),z(U,this.activation.apply(X(p,m)))),q=z(this.recurrentActivation.apply(X(d,b)),this.activation.apply(K));return[q,q,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=E6(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),r)}inputConv(e,t,n,r){let s=Ft(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Xr(s,n,this.dataFormat):s}recurrentConv(e,t){return Ft(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xm.className="ConvLSTM2DCell";re.registerClass(Xm);var KI=class extends V2{constructor(e){let t=new Xm(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};KI.className="ConvLSTM2D";re.registerClass(KI);var Ym=class extends ze{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r{this.invokeCallHook(e,t);let n=Te(e);if(0HN(n,this.rate,s,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Ym.className="Dropout";re.registerClass(Ym);var XI=class extends Ym{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};XI.className="SpatialDropout1D";re.registerClass(XI);var YI=class extends ze{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,tn(this.units,"units"),this.activation=va(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(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 O(()=>{this.invokeCallHook(e,t);let n=Te(e),r=LN(this.activation.getClassName()),s;return r!=null?s=ls(n,this.kernel.read(),r,this.bias?this.bias.read():null):(s=ls(n,this.kernel.read()),this.bias!=null&&(s=Xr(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:ya(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),biasInitializer:Et(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}};YI.className="Dense";re.registerClass(YI);var ZI=class extends ze{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],ca(e,1)]}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let s=2;s{this.invokeCallHook(e,t);let n=Te(e);return this.activation.apply(n)})}getConfig(){let e={activation:ya(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};JI.className="Activation";re.registerClass(JI);var QI=class extends ze{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 O(()=>(e=Te(e),zG(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};QI.className="RepeatVector";re.registerClass(QI);var ek=class extends ze{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=Te(e),r=n.shape,s=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return W(n,s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};ek.className="Reshape";re.registerClass(ek);var tk=class extends ze{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=Gr(1,e.dims.length+1);if(!w.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 Bt({ndim:this.dims.length+1})]}computeOutputShape(e){e=Qe(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return Re(Te(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};tk.className="Permute";re.registerClass(tk);var nk=class extends ze{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=Te(e);return Sd(vo(n,this.maskValue),-1)}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=Sd(vo(n,this.maskValue),-1,!0);return z(n,ae(a,n.dtype))})}};nk.className="Masking";re.registerClass(nk);var rk=class extends ze{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(it(e.inputLength))}this.inputDim=e.inputDim,tn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,tn(this.outputDim,"outputDim"),this.embeddingsInitializer=St(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 O(()=>this.maskZero?(e=Te(e),vo(e,je(e))):null)}computeOutputShape(e){if(e=Qe(e),this.inputLength==null)return[...e,this.outputDim];let t=it(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 r=0;r{this.invokeCallHook(e,t);let n=Te(e);n.dtype!=="int32"&&(n=cs(n,"int32"));let r=GN(this.embeddings.read(),W(n,[n.size]));return W(r,Qe(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Et(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}};rk.className="Embedding";re.registerClass(rk);var Qi=class extends ze{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Be}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: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let s=1;ss.length);e.indexOf(null)===-1&&ua(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return O(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(s=>s.rank);if(r.indexOf(null)===-1){let s=ba(r);for(let a of e){let o=a.rank;for(let i=0;i1){let c=Gr(1,u).concat([0]);n.push(Re(i,c)),s=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(s){if(o==null){let i=a.shape,u=i.length,c=i[u-1],l=[c].concat(i.slice(0,i.length-1));a=W(Re(W(a,[-1,c]),[1,0]),l)}else if(o>1){let i=[o-1].concat(Gr(0,o-1));a=Re(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Gt(r,0));let n=t[0];for(let r=1;r{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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ze{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e);return Ep(()=>X(Dm(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};dk.className="GaussianNoise";re.registerClass(dk);var pk=class extends ze{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e);return this.rate>0&&this.rate<1?Ep(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return z(n,Dm(n.shape,1,s))},()=>n,t.training||!1):n})}};pk.className="GaussianDropout";re.registerClass(pk);var hk=class extends ze{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Et(this.betaInitializer),gammaInitializer:Et(this.gammaInitializer),movingMeanInitializer:Et(this.movingMeanInitializer),movingVarianceInitializer:Et(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}};fk.className="BatchNormalization";re.registerClass(fk);var mk=class extends ze{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),r=n.shape,s=r.length;return O(()=>{let{mean:o,variance:i}=vp(n,this.axis,!0),u=wo(1,s);for(let f of this.axis)u[f]=r[f];let c=f=>f!=null&&f.shape.length!==s?W(f,u):f,l=this.scale?c(this.gamma.read()):null,p=this.center?c(this.beta.read()):null,d=[],h=[];for(let f=0;f{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=Hr()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. 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a==="max"?o=Rt(e,t,n,i):o=br(e,t,n,i),s==="channelsFirst"&&(o=Re(o,[0,3,1,2])),o})}function U2(e,t,n,r,s,a){return O(()=>{Pt(s),zN(a),vr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),s==null&&(s=Hr()),a==null&&(a="max"),e=D2(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=Nw(e,t,n,i):o=Qx(e,t,n,i),s==="channelsFirst"&&(o=Re(o,[0,4,1,2,3])),o})}var G2=class extends ze{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(tn(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)}`);tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,vr(this.padding),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){e=Qe(e);let t=Ur(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return O(()=>{this.invokeCallHook(e,t),e=Np(Te(e),2);let n=this.poolingFunction(Te(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ra(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},bk=class extends G2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),Zm(e,t,n,r,s,"max")}};bk.className="MaxPooling1D";re.registerClass(bk);var yk=class extends G2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),Zm(e,t,n,r,s,"avg")}};yk.className="AveragePooling1D";re.registerClass(yk);var H2=class extends ze{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];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),vr(this.padding),this.inputSpec=[new Bt({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=Ur(t,this.poolSize[0],this.padding,this.strides[0]),n=Ur(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 O(()=>(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}},vk=class extends H2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),Zm(e,t,n,r,s,"max")}};vk.className="MaxPooling2D";re.registerClass(vk);var xk=class extends H2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),Zm(e,t,n,r,s,"avg")}};xk.className="AveragePooling2D";re.registerClass(xk);var j2=class extends ze{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new 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 ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),vr(this.padding),this.inputSpec=[new Bt({ndim:5})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ur(t,this.poolSize[0],this.padding,this.strides[0]),n=Ur(n,this.poolSize[1],this.padding,this.strides[1]),r=Ur(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},wk=class extends j2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),U2(e,t,n,r,s,"max")}};wk.className="MaxPooling3D";re.registerClass(wk);var Ik=class extends j2{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),vr(r),U2(e,t,n,r,s,"avg")}};Ik.className="AveragePooling3D";re.registerClass(Ik);var q2=class extends ze{constructor(e){super(e),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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e=Qe(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return O(()=>{e=Te(e),e.dtype!=="int32"&&(e=cs(e,"int32"));let n;if(typeof t.countWeights!="undefined"){if(this.outputMode!=="count")throw new V(`countWeights is not used when outputMode !== count. + Received countWeights=${t.countWeights}`);n=Te(t.countWeights)}let r=hr(e),s=Xu(e),a=En(this.numTokens,r).bufferSync().get(0),o=Rs(s,0).bufferSync().get(0);if(!(a&&o))throw new V(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return B6(e,this.outputMode,this.numTokens,n)})}};Dk.className="CategoryEncoding";re.registerClass(Dk);var z6=["bilinear","nearest"],r1=new Set(z6),$k=class extends ze{constructor(e){if(super(e),this.height=e.height,this.width=e.width,e.interpolation)if(r1.has(e.interpolation))this.interpolation=e.interpolation;else throw new V(`Invalid interpolation parameter: ${e.interpolation} 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TypeError(`Node type ${e.op} is not implemented`)}};function tS(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=Yh(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 dj=(e,t,n,a=ln)=>{switch(e.op){case"Conv1D":{let 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implemented`)}},vj=(e,t,n,a=ln)=>{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`)}},wj=(e,t,n,a=ln)=>{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`)}},kj=(e,t,n,a=ln)=>{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`)}},Ij=(e,t,n,a=ln)=>{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`)}},Sj=(e,t,n,a=ln)=>{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`)}},Nj=(e,t,n,a=ln)=>{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=w.arraysEqual(u.shape,i);if(!p&&!w.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`)}},Tj=(e,t,n,a=ln)=>{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`)}},Cj=(e,t,n,a=ln)=>{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`)}},_j=(e,t,n,a=ln)=>{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`)}},Ej=(e,t,n,a=ln)=>{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 nS(e,t,n,a,r=P){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>rj(i,o,l));case"basic_math":return r(()=>sj(i,o,l));case"control":return cj(i,o,l);case"convolution":return r(()=>dj(i,o,l));case"creation":return r(()=>hj(i,o,l));case"dynamic":return mj(i,o,l);case"evaluation":return r(()=>fj(i,o,l));case"image":return r(()=>xj(i,o,l));case"graph":return r(()=>gj(i,o,l));case"logical":return r(()=>vj(i,o,l));case"matrices":return r(()=>wj(i,o,l));case"normalization":return r(()=>kj(i,o,l));case"ragged":return r(()=>Ij(i,o,l));case"reduction":return r(()=>Sj(i,o,l));case"slice_join":return r(()=>Nj(i,o,l));case"sparse":return r(()=>Tj(i,o,l));case"spectral":return r(()=>Cj(i,o,l));case"string":return r(()=>_j(i,o,l));case"transformation":return r(()=>Ej(i,o,l));case"hash_table":return yj(i,o,l,a);case"custom":let u=MC(i.op);if(u&&u.customExecutor)return u.customExecutor(new aj(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 w.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var aS=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 rS(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((Qs(c)||Oj(c)||Lj(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 Aj(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=Fj(m,l);return $j(f,l),f}function Fj(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 Bh=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function $j(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 Bh(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Bh(`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 Bh(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Bh(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function Dj(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>Qs(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;oe[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new lv(e.functions[n],this)})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPARATOR)+"--"+a.join(this.SEPARATOR)}compile(e,t){let n=rS(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${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: [${a}]`)}let i=Aj(this.graph,n),o=Dj(i);return{orderedNodes:i,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return qt(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,n])=>[t,this.cloneTensorList(n)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[Zn(d)[0]]),r=t.map(d=>Zn(d)[0]),s=new Set(r),i=r.map(d=>this.graph.nodes[d]);i.length===0&&(i=this._outputs);let o=this.getCompilationKey(a,i),l=this.compiledMap.get(o);l==null&&(l=this.compile(e,i),this.compiledMap.set(o,l));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let u={},p={};return P(()=>{let d=new aS(this.weightMap,u,p,this.functionExecutorMap,this.parseNodeNameCache),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(g=>{let[b,y]=Zn(g,d),x=[];x[y]=e[g],c[b]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(x))});let h=this.getFrozenTensorIds(c),{orderedNodes:m,nodeLiveUntilMap:f}=l;for(let g of m){if(c[g.name])continue;let b=nS(g,c,d,this._resourceManager);if(w.isPromise(b))throw new Error(`The execution of the op '${g.op}' returned a promise. 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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}}},p5=class extends on{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},sS=class extends on{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${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}}},M1=class extends on{constructor(){super(),this.outputQueue=new D1,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}}},c5=class extends M1{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}},u_=class extends on{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}},is;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(is||(is={}));var d5=class extends on{constructor(e,t=is.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 on?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await i_(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case is.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(e={}){if(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new h_(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(w.sizeFromShape(t));return n.set(e,n.length-e.length),bn(n,t)}},m_=class extends on{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 m_(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.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=Ko.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=nn(se(e,"float32"),0),n;n=ea.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},f_=class{},g_=class extends on{split(e){return new y5(this,e)}},y5=class extends g_{constructor(e,t){super(),this.upstream=e,this.impl=new x5(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},x5=class extends M1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},v5=class extends on{decodeUTF8(){return new w5(this)}},w5=class extends g_{constructor(e){super(),this.upstream=e,this.impl=new k5(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},k5=class extends M1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=US();this.decoder=new t("utf8")}}summary(){return`${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}},b_=class extends v5{constructor(e,t={}){super(),this.file=e,this.options=t,w.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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implemented`)}},cq=(e,t,n,r=an)=>{switch(e.op){case"Equal":return[r.equal(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[r.notEqual(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[r.greater(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[r.greaterEqual(I("a",e,t,n),I("b",e,t,n))];case"Less":return[r.less(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[r.lessEqual(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[r.logicalAnd(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[r.logicalNot(I("a",e,t,n))];case"LogicalOr":return[r.logicalOr(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[r.where(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];case"BitwiseAnd":return[r.bitwiseAnd(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},lq=(e,t,n,r=an)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[r.matMul(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[r.einsum(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[r.transpose(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[s,a]=I("fusedOps",e,t,n),o=s==="biasadd",i=a==="prelu",u=I("numArgs",e,t,n),c=I("leakyreluAlpha",e,t,n);if(o){if(i&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[l,p]=I("args",e,t,n);return[r.fused.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:l,activation:a,preluActivationWeights:p,leakyreluAlpha:c})];case"MatrixBandPart":return[r.linalg.bandPart(I("a",e,t,n),I("numLower",e,t,n),I("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},dq=(e,t,n,r=an)=>{switch(e.op){case"EuclideanNorm":return[r.euclideanNorm(I("x",e,t,n),I("axis",e,t,n),I("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[r.batchNorm(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[r.batchNorm(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[r.localResponseNormalization(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[r.softmax(I("x",e,t,n))];case"LogSoftmax":return[r.logSoftmax(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},pq=(e,t,n,r=an)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:s,outputDenseValues:a}=r.raggedGather(I("paramsNestedSplits",e,t,n),I("paramsDenseValues",e,t,n),I("indices",e,t,n),I("outputRaggedRank",e,t,n));return s.concat(a)}case"RaggedRange":{let{rtNestedSplits:s,rtDenseValues:a}=r.raggedRange(I("starts",e,t,n),I("limits",e,t,n),I("splits",e,t,n));return[s,a]}case"RaggedTensorToTensor":return[r.raggedTensorToTensor(I("shape",e,t,n),I("values",e,t,n),I("defaultValue",e,t,n),I("rowPartitionTensors",e,t,n),I("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},hq=(e,t,n,r=an)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),u=I("keepDims",e,t,n);return[r.max(I("x",e,t,n),i,u)]}case"Mean":{let i=I("axis",e,t,n),u=I("keepDims",e,t,n);return[r.mean(I("x",e,t,n),i,u)]}case"Min":{let i=I("axis",e,t,n),u=I("keepDims",e,t,n);return[r.min(I("x",e,t,n),i,u)]}case"Sum":{let i=I("axis",e,t,n),u=I("keepDims",e,t,n);return[r.sum(I("x",e,t,n),i,u)]}case"All":{let i=I("axis",e,t,n),u=I("keepDims",e,t,n);return[r.all(I("x",e,t,n),i,u)]}case"Any":{let i=I("axis",e,t,n),u=I("keepDims",e,t,n);return[r.any(I("x",e,t,n),i,u)]}case"ArgMax":{let i=I("axis",e,t,n);return[r.argMax(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[r.argMin(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),u=I("keepDims",e,t,n);return[r.prod(I("x",e,t,n),i,u)]}case"Cumprod":{let i=I("axis",e,t,n),u=I("exclusive",e,t,n),c=I("reverse",e,t,n);return[r.cumprod(I("x",e,t,n),i,u,c)]}case"Cumsum":{let i=I("axis",e,t,n),u=I("exclusive",e,t,n),c=I("reverse",e,t,n);return[r.cumsum(I("x",e,t,n),i,u,c)]}case"Bincount":let s=I("x",e,t,n),a=I("weights",e,t,n),o=I("size",e,t,n);return[r.bincount(s,a,o)];case"DenseBincount":{let i=I("x",e,t,n),u=I("weights",e,t,n),c=I("size",e,t,n),l=I("binaryOutput",e,t,n);return[r.denseBincount(i,u,c,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},fq=(e,t,n,r=an)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=I("n",e,t,n),a=I("axis",e,t,n),o=I("tensors",e,t,n);return o=o.slice(0,s),[r.concat(o,a)]}case"Gather":{let s=I("x",e,t,n),a=I("indices",e,t,n);return[r.gather(s,r.cast(a,"int32"),0)]}case"GatherV2":{let s=I("axis",e,t,n),a=I("batchDims",e,t,n),o=I("x",e,t,n),i=I("indices",e,t,n);return[r.gather(o,r.cast(i,"int32"),s,a)]}case"Reverse":{let s=I("dims",e,t,n),a=[];for(let i=0;i{let s=I("axis",e,t,n),a=I("tensors",e,t,n),o=a[0].shape,i=r.squeeze(a[0]).shape,u=a.map(c=>{let l=w.arraysEqual(c.shape,o);if(!l&&!w.arraysEqual(r.squeeze(c).shape,i))throw new Error("the input tensors shape does not match");return l?c:r.reshape(c,o)});return[r.stack(u,s)]});case"Unpack":{let s=I("axis",e,t,n),a=I("tensor",e,t,n);return r.unstack(a,s)}case"Tile":{let s=I("reps",e,t,n);return[r.tile(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),a=I("numOrSizeSplits",e,t,n),o=I("x",e,t,n);return r.split(o,a,s)}case"ScatterNd":{let s=I("indices",e,t,n),a=I("values",e,t,n),o=I("shape",e,t,n);return[r.scatterND(s,a,o)]}case"GatherNd":{let s=I("x",e,t,n),a=I("indices",e,t,n);return[r.gatherND(s,a)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),a=I("outputShape",e,t,n),o=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[r.sparseToDense(s,o,a,o.dtype===i.dtype?i:r.cast(i,o.dtype))]}case"TensorScatterUpdate":{let s=I("indices",e,t,n),a=I("values",e,t,n),o=I("tensor",e,t,n);return[r.tensorScatterUpdate(o,s,a)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},mq=(e,t,n,r=an)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:a,emptyRowIndicator:o,reverseIndexMap:i}=r.sparse.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,a,o,i]}case"SparseReshape":{let{outputIndices:s,outputShape:a}=r.sparse.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,a]}case"SparseSegmentMean":return[r.sparse.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[r.sparse.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},gq=(e,t,n,r=an)=>{switch(e.op){case"FFT":return[r.fft(I("x",e,t,n))];case"IFFT":return[r.ifft(I("x",e,t,n))];case"RFFT":return[r.rfft(I("x",e,t,n))];case"IRFFT":return[r.irfft(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bq=(e,t,n,r=an)=>{switch(e.op){case"StaticRegexReplace":return[r.string.staticRegexReplace(I("input",e,t,n),I("pattern",e,t,n),I("rewrite",e,t,n),I("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:s,nGramsSplits:a}=r.string.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,a]}case"StringSplit":{let{indices:s,values:a,shape:o}=r.string.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,a,o]}case"StringToHashBucketFast":return[r.string.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},yq=(e,t,n,r=an)=>{switch(e.op){case"Cast":return[r.cast(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[r.expandDims(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[r.squeeze(I("x",e,t,n),s)]}case"Reshape":return[r.reshape(I("x",e,t,n),I("shape",e,t,n))];case"EnsureShape":return[r.ensureShape(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[r.mirrorPad(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[r.pad(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),a=I("paddings",e,t,n);return[r.spaceToBatchND(I("x",e,t,n),s,a)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),a=I("crops",e,t,n);return[r.batchToSpaceND(I("x",e,t,n),s,a)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),a=I("dataFormat",e,t,n).toUpperCase();return[r.depthToSpace(I("x",e,t,n),s,a)]}case"BroadcastTo":return[r.broadcastTo(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[r.broadcastArgs(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function p1(e,t,n,r,s=O){let a=((o,i,u)=>{switch(o.category){case"arithmetic":return s(()=>q5(o,i,u));case"basic_math":return s(()=>K5(o,i,u));case"control":return eq(o,i,u);case"convolution":return s(()=>tq(o,i,u));case"creation":return s(()=>nq(o,i,u));case"dynamic":return rq(o,i,u);case"evaluation":return s(()=>sq(o,i,u));case"image":return s(()=>uq(o,i,u));case"graph":return s(()=>aq(o,i,u));case"logical":return s(()=>cq(o,i,u));case"matrices":return s(()=>lq(o,i,u));case"normalization":return s(()=>dq(o,i,u));case"ragged":return s(()=>pq(o,i,u));case"reduction":return s(()=>hq(o,i,u));case"slice_join":return s(()=>fq(o,i,u));case"sparse":return s(()=>mq(o,i,u));case"spectral":return s(()=>gq(o,i,u));case"string":return s(()=>bq(o,i,u));case"transformation":return s(()=>yq(o,i,u));case"hash_table":return iq(o,i,u,r);case"custom":let c=i_(o.op);if(c&&c.customExecutor)return c.customExecutor(new j5(o,i,u));throw TypeError(`Custom op ${o.op} is not registered.`);default:throw TypeError(`Unknown op '${o.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(a)?a.then(o=>[].concat(o)):[].concat(a)}var h1=class{constructor(e={},t={},n={},r={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){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 f1(e,t,n,r){let s=new Set,a=[],o=null,i=null,u=new Set,c=new Set(Object.keys(e).map(d=>Jn(d)[0]));r=r||[];let l=new Set(r.map(d=>Jn(d.name)[0])),p=[...t];for(;p.length>0;){let d=p.pop();if((to(d)||Tq(d)||Nq(d))&&o==null&&(o=d,i=o.children.map(h=>h.name).filter(h=>s.has(h))),s.add(d.name),n[d.name]==null&&!c.has(d.name)&&!l.has(d.name)){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:s,missingInputs:a,dynamicNode:o,syncInputs:i}}function vq(e,t){let{usedNodes:n,inputs:r}=t,s=Object.keys(r).map(m=>Jn(m)[0]).map(m=>e.nodes[m]),a=e.initNodes||[],o=m=>n.has(typeof m=="string"?m:m.name);function i(m){return[...new Map(m.map(b=>[b.name,b])).values()]}let u=i([...s,...e.weights,...a]).filter(o),c=i([...u,...Object.values(e.nodes)]).filter(o),l=new Map(c.map(m=>[m.name,m])),p={};for(let m of c){p[m.name]=p[m.name]||0;for(let b of m.children)o(b)||(p[b.name]=Number.POSITIVE_INFINITY),p[b.name]=(p[b.name]||0)+1}let d=Object.entries(p).filter(([,m])=>m===0).map(([m])=>m),h=[...d];for(;d.length>0;){let m=d.pop(),b=l.get(m);for(let y of b.children.filter(o))--p[y.name]===0&&(h.push(y.name),d.push(y.name))}let f=h.map(m=>l.get(m)),g=xq(f,u);return wq(g,u),g}function xq(e,t){let n=new Map(e.map(o=>[o.name,o])),r=t.map(o=>o.name),s=new Set(r);for(;r.length>0;){let o=r.pop(),i=n.get(o);for(let u of i.children)!n.has(u.name)||s.has(u.name)||(s.add(u.name),r.push(u.name))}return e.filter(o=>s.has(o.name))}var zh=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function wq(e,t){let n=new Map(e.map((i,u)=>[i.name,u])),r=new Set(t.map(i=>i.name)),s=i=>r.has(typeof i=="string"?i:i.name),a=new Set(e.map(i=>i.name)),o=i=>a.has(typeof i=="string"?i:i.name);for(let i of e){for(let u of i.children.filter(o)){if(!n.has(u.name))throw new zh(`Child ${u.name} of node ${i.name} is unreachable.`);if(n.get(i.name)>n.get(u.name))throw new zh(`Node ${i.name} is scheduled to run after its child ${u.name}.`)}if(!s(i))for(let u of i.inputs){if(!n.has(u.name))throw new zh(`Input ${u.name} of node ${i.name} is unreachable.`);if(n.get(u.name)>n.get(i.name))throw new zh(`Node ${i.name} is scheduled to run before its input ${u.name}.`)}}}function Iq(e){let t=new Map(e.map((i,u)=>[i.name,u])),n=Number.MAX_SAFE_INTEGER,r=e.map((i,u)=>to(i)?n:u),s=i=>{let u=r[t.get(i.name)];return u==null?-1:u},a=e.map((i,u)=>i.children.map(s).reduce((c,l)=>Math.max(c,l),r[u])),o=new Map;for(let i=0;it[r].map(s=>s.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(r=>{this._functionExecutorMap[r]=new E_(t.functions[r],this)})}getCompilationKey(t,n){let r=t.map(a=>a.name).sort(),s=n.map(a=>a.name).sort();return r.join(this.SEPARATOR)+"--"+s.join(this.SEPARATOR)}compile(t,n){let r=f1(t,n,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:a,syncInputs:o}=r;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${o}]`);if(s.length>0){let c=n.map(p=>p.name),l=Object.keys(t);throw new Error(`Cannot compute the outputs [${c}] from the provided inputs [${l}]. 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You can use model.execute() instead.");let v=c.filter(x=>!to(x)&&!dn(x.name,g,n)).map(x=>x.name);if(v.length>0){let x="";throw d!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${v}] from the provided inputs [${a}]. Consider providing the following inputs: [${p}]. ${x}`)}return g}processStack(t,n,r,s,a,o,i,u,c){let l=[];for(;n.length>0;){let p=n.pop();r.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&I("isConstant",p.node,s,r)&&([d]=Cs(p.node.name,r)),s[p.node.name]==null){let h=p1(p.node,s,r,this._resourceManager);d||([d]=Cs(p.node.name,r));let f=r.currentContext;w.isPromise(h)?l.push(h.then(g=>(s[d]=g,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(g)),r.currentContext=f,this.checkTensorForDisposal(d,p.node,s,r,o,i,u),this.processChildNodes(p.node,n,r,s,a,c),g))):(s[d]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(h)),this.checkTensorForDisposal(d,p.node,s,r,o,i,u),this.processChildNodes(p.node,n,r,s,a,c))}else this.processChildNodes(p.node,n,r,s,a,c)}return l}processChildNodes(t,n,r,s,a,o){t.children.forEach(i=>{let[u]=Cs(i.name,r);a[u]||!o.has(i.name)||(i.op==="Merge"?i.inputNames.some(c=>!!dn(c,s,r))&&(a[u]=!0,n.push({contexts:r.currentContext,node:i})):i.inputNames.every(c=>!!dn(c,s,r))&&(a[u]=!0,n.push({contexts:r.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let r=t[n],[s]=Jn(n),a=this.graph.nodes[s];if(a.attrParams.shape&&a.attrParams.shape.value){let o=a.attrParams.shape.value,i=o.length===r.shape.length&&r.shape.every((u,c)=>o[c]===-1||o[c]===u);w.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${o}], but was [${r.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&w.assert(r.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(t){var n,r;let s={};for(let a in t){let o=(r=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||r===void 0?void 0:r[a];o!=null?s[o.name]=t[a]:s[a]=t[a]}return s}checkInputs(t){let n=Object.keys(t).filter(r=>{let[s]=Jn(r);return this.graph.nodes[s]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var r,s;let a=(s=(r=this._signature)===null||r===void 0?void 0:r.outputs)===null||s===void 0?void 0:s[n];return a!=null?a.name:n},{})}checkOutputs(t){t.forEach(n=>{let[r]=Jn(n);if(!this.graph.nodes[r])throw new Error(`The output '${n}' is not found in the graph`)})}},_q=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return w.isPromise(e)?e.then(t=>t.getWeightStream==null?this.loadSync(t):this.loadStreaming(t)):this.loadSync(e)}loadSync(e){let t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(e.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let t=await RC(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let n=this.artifacts.modelTopology,r=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(r=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=r,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new m1(u1.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=u1.Instance.transformGraph(e.modelInitializer);this.initializer=new m1(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ne?[e]:e,n={};return t.forEach((r,s)=>n[this.structuredOutputKeys[s]]=r),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 Ne)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let o=s[a];o.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[o.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 r=0;return this.inputNodes.reduce((s,a)=>{var o,i,u;let c=(u=(i=(o=this.signature)===null||o===void 0?void 0:o.inputs)===null||i===void 0?void 0:i[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[r++],s},{})}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 r=0;r1?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 Dq(e,t={},n=jt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=Fq(e));let r=new Bk(e,t,n);return await r.load(),r}function $q(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. 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sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}},Zq=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},Jq=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wr.getTensorsInContainer(e.value),n=this.transform(e.value),r=Wr.getTensorsInContainer(n);for(let s of t)Wr.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},Qq=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`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},g1=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=Wr.getTensorsInContainer(n);for(let s of t)Wr.isTensorInList(s,r)||s.dispose();return{value:n,done:!1}}},Wk=class extends sn{constructor(){super(),this.outputQueue=new P_,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},e8=class extends Wk{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=Wr.getTensorsInContainer(e.value),n=this.transform(e.value),r=Wr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of t)Wr.isTensorInList(s,r)||s.dispose();return!0}},L_=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 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F_(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case aa.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(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new V_(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.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 n=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,n.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,n,r=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(r.freqDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(r.timeDataQueue);n=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:n},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],n=[],r=0;return new Promise(s=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:t,timeDataQueue:n}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),n.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),s({freqDataQueue:t,timeDataQueue:n}))},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 n=t[0].length,r=new Float32Array(t.length*n);return t.forEach((s,a)=>r.set(s,a*n)),r}getTensorFromAudioDataArray(t,n){let r=new Float32Array(w.sizeFromShape(n));return r.set(t,r.length-t.length),yn(r,n)}},u8=class U_ extends sn{constructor(t,n){if(super(),this.webcamVideoElement=t,this.webcamConfig=n,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=He([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,o=(1-s)/2,i=a+r,u=s+o;this.cropBox=Dr([o,a,u,i],[1,4])}else this.cropBox=Dr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!G().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let r=new U_(t,n);return await r.start(),r}async start(){this.webcamConfig.facingMode&&w.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=Yi.fromPixels(this.webcamVideoElement)}catch(n){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(n)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(n){throw new Error(`Error thrown cropping the video: ${n.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return O(()=>{let n=Gt(ae(t,"float32"),0),r;r=er.cropAndResize(n,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=r.shape;return W(r,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(n=>n.stop());try{this.webcamVideoElement.srcObject=null}catch(n){console.log(n),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},G_=class{},H_=class extends sn{split(e){return new c8(this,e)}},c8=class extends H_{constructor(e,t){super(),this.upstream=e,this.impl=new l8(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},l8=class extends Wk{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},d8=class extends sn{decodeUTF8(){return new p8(this)}},p8=class extends H_{constructor(e){super(),this.upstream=e,this.impl=new h8(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},h8=class extends Wk{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=tC();this.decoder=new 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i=ze(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,u=a.dilationHeight,p=a.dilationWidth,d=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=ze(t,n,e);for(let g=0;gD&&(D=H,r?$=s?((g*a.inHeight+S)*a.inWidth+B)*a.inChannels+b:(S*a.inWidth+B)*a.inChannels+b:$=M*c+U)}}i.set($,g,y,T,b)}}return i}function fE(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,d=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,b=r.padInfo.left,y=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=ze(r.outShape,n),v=x.values,I=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[3]*r.outShape[4],E=r.outShape[4];for(let F=0;Fbe?be=ft:s==="avg"&&(ke+=ft,Se++),isNaN(be))break}if(isNaN(be))break}if(isNaN(be))break}let We=ue+S;v[We]=s==="avg"?ke/Math.max(Se,1):be}}}}return 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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,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,T=p.effectiveFilterWidth,C=v-1-p.padInfo.front,E=T-1-p.padInfo.left,F=I-1-p.padInfo.top,D=ze(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let re=0;re=p.outHeight||Math.floor(ie)!==ie))for(let ye=0;ye=p.outWidth||Math.floor(ue)!==ue)continue;let be=S.get(M,te,ie,ue,B);ee+=be}}}D.set(ee*$,M,U,H,j,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var F8={kernelName:Lc,backendName:"cpu",kernelFunc:A8};function $8(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,v=b-1-p.padInfo.top,I=ze(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,E=ze(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(j)!==j))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=E.get(F,j,Z,D);U+=J}}I.set(U*T,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var D8={kernelName:Oc,backendName:"cpu",kernelFunc:$8};function R8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.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=vi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var O8={kernelName:mu,backendName:"cpu",kernelFunc:P8};function L8(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=L1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var z8={kernelName:fu,backendName:"cpu",kernelFunc:L8};function W8(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 B8={kernelName:zc,backendName:"cpu",kernelFunc:W8},V8=it(Ss,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.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(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>w.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(v=>xi({inputs:{input:v},backend:n})),g=l.map(v=>su({inputs:{input:v},backend:n})),b=iu({inputs:f,backend:n,attrs:{axis:s}}),y=iu({inputs:g,backend:n,attrs:{axis:s}}),x=Jn({inputs:{real:b,imag:y},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,w.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=z1(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 j8={kernelName:bu,backendName:"cpu",kernelFunc:iu};function gE(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",v=new Vt(c.outShape,r.dtype),I=w.computeStrides(r.shape),T=w.computeStrides(s.shape),C=I[0],E=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,j=v.values;for(let K=0;K=c.inHeight)continue;let ye=re*T[0],ue=Z+ie*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=$+j*E[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],ye=Z+re*E[2];for(let ue=0;ue=u.inWidth)continue;let Ge=ie+Se*F[2],ht=ye+We*u.inChannels,st=Ge;for(let at=0;atMath.cos(e)),iX={kernelName:Bi,backendName:"cpu",kernelFunc:sX},oX=it(Vi,e=>Math.cosh(e)),lX={kernelName:Vi,backendName:"cpu",kernelFunc:oX};function uX(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=ze([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),T=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-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,v=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=$+j*p[1];for(let J=0;J=h.inWidth)continue;let ie=K+te*d[1],ye=Z+re*h.inChannels,ue=ee,be=ie;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:T,filterWidth:C,dilationHeight:E,dilationWidth:F,outShape:D}=N.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(D),S=D.length,M=w.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&ieJ&&(J=be)}}}let ee=w.locToIndex([B,U,j,Z],S,w.computeStrides(D));M[ee]=J}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},CX={kernelName:Ul,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=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.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:v,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Ul}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=ie,Z=ee,J=te)}}}$[Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},_X={kernelName:Vl,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=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.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:v,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:F}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Vl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=ie,Z=ae,J=re)}}}$[S][Z][J][j]+=D[S][M][U][j]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function EX(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{canvas:s,options:i}=a,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let d=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(d==null)throw new Error(`Could not get the context with ${p} type.`);let[c,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=n.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*c*4);for(let x=0;x1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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l=T.computePool3DInfo(a.shape,o,i,1,u,c),p=l.strideDepth,d=l.strideHeight,h=l.strideWidth,f=l.filterDepth,g=l.filterHeight,m=l.filterWidth,b=l.dilationDepth,y=l.dilationHeight,v=l.dilationWidth,x=l.effectiveFilterDepth,k=l.effectiveFilterHeight,S=l.effectiveFilterWidth,N=x-1-l.padInfo.front,E=S-1-l.padInfo.left,$=k-1-l.padInfo.top,F=Me(a.shape,"float32"),D=1/(f*g*m),R=n.bufferSync(s);for(let C=0;C=l.outDepth||Math.floor(te)!==te))for(let oe=0;oe=l.outHeight||Math.floor(ne)!==ne))for(let de=0;de=l.outWidth||Math.floor(ce)!==ce)continue;let we=R.get(C,te,ne,ce,L);ee+=we}}}F.set(ee*D,C,U,H,K,L)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var kX={kernelName:zd,backendName:"cpu",kernelFunc:IX};function SX(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;be([s,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:c}=r,l=T.computePool2DInfo(o.shape,i,u,1,c),p=l.strideHeight,d=l.strideWidth,h=l.filterHeight,f=l.filterWidth,g=l.dilationHeight,m=l.dilationWidth,b=l.effectiveFilterHeight,y=l.effectiveFilterWidth,v=y-1-l.padInfo.left,x=b-1-l.padInfo.top,k=Me(o.shape,"float32"),S=1/(h*f),N=n.data.get(s.dataId).values,E=Me(s.shape,"float32",N);for(let $=0;$=l.outHeight||Math.floor(K)!==K))for(let q=0;q=l.outWidth||Math.floor(Z)!==Z)continue;let J=E.get($,K,Z,F);U+=J}}k.set(U*S,$,D,R,F)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var CX={kernelName:Bd,backendName:"cpu",kernelFunc:SX};function TX(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,scale:a,offset:o,mean:i,variance:u}=t;w.assert(i.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient 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i=a.reduce((b,y)=>b*y),u=T.getReshaped(s.shape,a,i),c=T.getPermuted(u.length,a.length),l=T.getReshapedPermuted(s.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(l,o,a.length),h=yt({inputs:{x:s},backend:n,attrs:{shape:u}}),f=Wn({inputs:{x:h},backend:n,attrs:{perm:c}}),g=yt({inputs:{x:f},backend:n,attrs:{shape:l}}),m=ko({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),m}var EX={kernelName:mc,backendName:"cpu",kernelFunc:_X};function AX(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.data.get(s.dataId).values,u=n.data.get(a.dataId).values,c=Hk(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var DX={kernelName:gc,backendName:"cpu",kernelFunc:AX};function $X(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,i=T.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var FX={kernelName:Wd,backendName:"cpu",kernelFunc:$X},RX=ct(Ca,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(w.sizeFromShape(t.shape)),s=n.data.get(t.dataId),a=s.complexTensorInfos.real,o=s.complexTensorInfos.imag,i=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values;for(let c=0;cg.shape);T.assertParamsConsistent(o,a);let i=T.computeOutShape(t.map(g=>g.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let u=t.filter(g=>w.sizeFromShape(g.shape)>0);if(u.length===1)return hs({inputs:{x:u[0]},backend:n});if(u[0].dtype==="complex64"){let g=u.map(x=>Io({inputs:{input:x},backend:n})),m=u.map(x=>oc({inputs:{input:x},backend:n})),b=ic({inputs:g,backend:n,attrs:{axis:a}}),y=ic({inputs:m,backend:n,attrs:{axis:a}}),v=Qn({inputs:{real:b,imag:y},backend:n});return g.forEach(x=>n.disposeIntermediateTensorInfo(x)),m.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),v}let c=u.map(g=>{let b=[-1,w.sizeFromShape(g.shape.slice(a))];return yt({inputs:{x:g},backend:n,attrs:{shape:b}})}),l=c.map(g=>({vals:n.data.get(g.dataId).values,shape:g.shape}));i=T.computeOutShape(c.map(g=>g.shape),1);let p=c[0].shape[0]===1,d=jk(l,i,t[0].dtype,p),h=T.computeOutShape(u.map(g=>g.shape),a),f=n.makeTensorInfo(h,t[0].dtype,d);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}var BX={kernelName:yc,backendName:"cpu",kernelFunc:ic};function jE(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:c,dimRoundingMode:l}=r;be([s,a],"conv2d");let p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(s.shape,a.shape,o,c,i,l,!1,p),h=d.filterHeight,f=d.filterWidth,g=d.dilationHeight,m=d.dilationWidth,b=d.padInfo.left,y=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new zt(d.outShape,s.dtype),k=w.computeStrides(s.shape),S=w.computeStrides(a.shape),N=k[0],E=v?k[1]:k[2],$=v?k[2]:1,F=v?1:k[1],D=x.strides[0],R=v?x.strides[1]:x.strides[2],C=v?x.strides[2]:1,L=v?1:x.strides[1],U=n.data.get(s.dataId).values,H=n.data.get(a.dataId).values,K=x.values;for(let q=0;q=d.inHeight)continue;let de=oe*S[0],ce=Z+ne*E;for(let we=0;we=d.inWidth)continue;let at=de+Ae*S[1],ft=ce+qe*$,st=at;for(let Je=0;Je=c.inDepth)continue;let q=H*$[0],Z=D+K*E[1];for(let J=0;J=c.inHeight)continue;let ne=q+te*$[1],de=Z+oe*E[2];for(let ce=0;ce=c.inWidth)continue;let qe=ne+Ce*$[2],at=de+Ae*c.inChannels,ft=qe;for(let st=0;stMath.cos(e)),JX={kernelName:Go,backendName:"cpu",kernelFunc:ZX},QX=ct(Ho,e=>Math.cosh(e)),eY={kernelName:Ho,backendName:"cpu",kernelFunc:QX};function tY(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:c}=r,[l,p,d,h]=s.shape,f=a.shape[0],[g,m]=i,b=Me([f,g,m,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(o.dataId).values,x=n.data.get(s.dataId).values,k=w.computeStrides(s.shape),S=w.computeStrides(b.shape);for(let N=0;N=l)continue;let L=g>1?(D-$)*(p-1)/(g-1):0,U=m>1?(R-F)*(d-1)/(m-1):0;for(let H=0;H1?$*(p-1)+H*L:.5*($+D)*(p-1);if(K<0||K>p-1){for(let q=0;q1?F*(d-1)+ee*U:.5*(F+R)*(d-1);if(se<0||se>d-1){for(let de=0;de1?F*(d-1)+q*U:.5*(F+R)*(d-1);if(Z<0||Z>d-1){for(let se=0;seb+f-y-1:(b,y)=>b+y;for(let b=0;bb+f-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`);let i=s.shape[0],u=s.shape[1],c=s.shape[2],l=s.shape[3],p=u*a,d=c*a,h=l/(a*a),f=n.data.get(s.dataId).values,g=new Float32Array(i*p*d*h),m=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);let h=T.computeConv2DInfo(s.shape,a.shape,o,d,i,c,!0),{filterHeight:f,filterWidth:g,dilationHeight:m,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,S=new zt(h.outShape,s.dtype),N=n.data.get(s.dataId).values,E=n.data.get(a.dataId).values,$=S.values;for(let F=0;F=h.inHeight)continue;let q=H*p[0],Z=D+K*l[1];for(let J=0;J=h.inWidth)continue;let ne=q+te*p[1],de=Z+oe*h.inChannels,ce=ee,we=ne;for(let ve=0;ve{let{x:r,filter:s}=e,{strides:a,pad:o,dilations:i}=n,u=t,c=u.data.get(r.dataId).values,l=r.shape.length,p=u.data.get(s.dataId).values,d=s.shape.length,{batchSize:h,inHeight:f,inWidth:g,inChannels:m,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:S,filterWidth:N,dilationHeight:E,dilationWidth:$,outShape:F}=T.computeDilation2DInfo(r.shape,s.shape,a,o,"NHWC",i),D=w.sizeFromShape(F),R=F.length,C=w.getArrayFromDType(r.dtype,D);for(let U=0;U=0&&oe=0&&deee&&(ee=ve)}}}let se=w.locToIndex([U,H,q,J],R,w.computeStrides(F));C[se]=ee}}}return{dataId:u.write(w.toTypedArray(C,r.dtype),F,r.dtype),shape:F,dtype:r.dtype}}},vY={kernelName:qu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:s,dy:a}=e,{strides:o,pad:i,dilations:u}=n,c=t,l=w.toNestedArray(r.shape,c.data.get(r.dataId).values),p=w.toNestedArray(s.shape,c.data.get(s.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:g,outHeight:m,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:S,dilationHeight:N,dilationWidth:E,outShape:$}=T.computeDilation2DInfo(r.shape,s.shape,o,i,"NHWC",u);w.assert(a.rank===$.length,()=>`Error in ${qu}, dy must have the same rank as output ${$.length}, but got ${a.rank}`);let F=w.toNestedArray($,c.data.get(a.dataId).values),D=w.makeZerosNestedTypedArray(s.shape,s.dtype);for(let C=0;C=0&&te=0&&neZ&&(Z=de,J=se,ee=oe)}}}D[J][ee][q]+=F[C][L][H][q]}}}return{dataId:c.write(w.toTypedArray(D,r.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},xY={kernelName:ju,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:s,dy:a}=e,{strides:o,pad:i,dilations:u}=n,c=t,l=w.toNestedArray(r.shape,c.data.get(r.dataId).values),p=w.toNestedArray(s.shape,c.data.get(s.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:g,outHeight:m,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:S,dilationHeight:N,dilationWidth:E,outShape:$}=T.computeDilation2DInfo(r.shape,s.shape,o,i,"NHWC",u);w.assert(a.rank===$.length,()=>`Error in ${ju}, dy must have the same rank as output ${$.length}, but got ${a.rank}`);let F=w.toNestedArray($,c.data.get(a.dataId).values),D=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let C=0;C=0&&te=0&&neZ&&(Z=de,J=te,ee=ne)}}}D[C][J][ee][q]+=F[C][L][H][q]}}}return{dataId:c.write(w.toTypedArray(D,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function wY(e){let{inputs:t,backend:n,attrs:r}=e,{image:s}=t,{canvas:a,options:o}=r,{contextOptions:i,imageOptions:u}=o||{},c=(u==null?void 0:u.alpha)||1,l=(i==null?void 0:i.contextType)||"2d";if(l!=="2d")throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);let p=a.getContext(l,(i==null?void 0:i.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${l} type.`);let[d,h]=s.shape.slice(0,2),f=s.shape.length===2?1:s.shape[2],g=n.data.get(s.dataId).values,m=s.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*d*4);for(let v=0;v1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(o),i}function CA(e){return e!==2?!1:qr(e).fenceSync!=null}function wl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var 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es",t="in",n="out",r="in",s="texture",a="outputColor",o="out vec4 outputColor;",i=G().getBool("WEBGL2_ISNAN_CUSTOM")?` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -78,7 +78,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram } #define isnan(value) isnan_custom(value) - `:"",l="",u=` + `:"",u="",c=` #define round(value) newRound(value) int newRound(float value) { return int(floor(value + 0.5)); @@ -87,7 +87,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram ivec4 newRound(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `):(e="",t="attribute",n="varying",a="varying",r="texture2D",s="gl_FragColor",i="",o=` + `):(e="",t="attribute",n="varying",r="varying",s="texture2D",a="gl_FragColor",o="",i=` #define isnan(value) isnan_custom(value) bool isnan_custom(float val) { return (val > 0. || val < 1. || val == 0.) ? false : true; @@ -95,7 +95,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram bvec4 isnan_custom(vec4 val) { return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w)); } - `,l=` + `,u=` uniform float INFINITY; bool isinf(float val) { @@ -104,7 +104,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram bvec4 isinf(vec4 val) { return equal(abs(val), vec4(INFINITY)); } - `,u=` + `,c=` int round(float value) { return int(floor(value + 0.5)); } @@ -112,15 +112,15 @@ Hi, looks like you are running TensorFlow.js in Node.js. 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 Jo(e,t,n="index"){let a=w.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 Jf(e,t,n="index"){let a=w.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}; ${o};`}).join("")}function wJ(e,t){let n=e.length,a=e.map(s=>`${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 kJ(e,t,n="index"){let a=e.map((s,i)=>i),r=wJ(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 ek(e){let t=w.computeStrides(e).map(n=>n.toString());return` + `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:s,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:u,defineRound:c}}function eu(e,t,n="index"){let r=w.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / ${s}`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${s}`:`index -= ${e[a]} * ${s}`;return`${o}; ${i};`}).join("")}function eg(e,t,n="index"){let r=w.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function hJ(e,t){let n=e.length,r=e.map(a=>`${t}[${a}]`),s=new Array(n-1);s[n-2]=r[n-1];for(let a=n-3;a>=0;--a)s[a]=`(${s[a+1]} * ${r[a+1]})`;return s}function fJ(e,t,n="index"){let r=e.map((a,o)=>o),s=hJ(r,t);return s.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${s[o]}`,u=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${s[o]}`:`index -= ${e[o]} * ${s[o]}`;return`${i}; ${u};`}).join("")}function i0(e){let t=w.computeStrides(e).map(n=>n.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}function tk(){return` +`}function u0(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var YE=` +`}var TA=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -159,22 +159,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`,{getBroadcastDims:ZE}=N;function IJ(e,t,n){let a=[];if(e.forEach(c=>{let h=w.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}=nk(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?`[${c.arrayIndex}]`:""};`)});let r=a.join(` -`),s=e.map(c=>SJ(c,t,n.packedInputs,n.enableShapeUniforms)).join(` -`),i=t.texShape,o=En(),l=CJ(o),u,p,d=AJ(o);return t.isPacked?(u=NJ(t.logicalShape,i,n.enableShapeUniforms),p=EJ(o)):(u=TJ(t.logicalShape,i,n.enableShapeUniforms),p=_J(o)),n.packedInputs&&(d+=RJ),[d,l,p,r,u,s,n.userCode].join(` -`)}function wp(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return qJ(e,t);case 1:return KJ(e,t);case 2:return YJ(e,t);case 3:return JJ(e,t);case 4:return e9(e,t);case 5:return t9(e);case 6:return n9(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function JE(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return HJ(e);case 1:return jJ(e,t);case 2:return XJ(e,t);case 3:return ZJ(e,t);default:return QJ(e,t)}}function SJ(e,t,n=!1,a){let r="";n?r+=JE(e,a):r+=wp(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=a9(e,t):r+=r9(e,t)),r}function NJ(e,t,n){switch(e.length){case 0:return QE();case 1:return MJ(e,t,n);case 2:return UJ(e,t,n);case 3:return OJ(e,t,n);default:return zJ(e,t,n)}}function TJ(e,t,n){switch(e.length){case 0:return QE();case 1:return PJ(e,t,n);case 2:return GJ(e,t,n);case 3:return LJ(e,t,n);case 4:return WJ(e,t,n);case 5:return BJ(e,t);case 6:return VJ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function CJ(e){return` +`,{getBroadcastDims:NA}=T;function mJ(e,t,n){let r=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:g}=c0(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(g.length){case 1:r.push(`uniform int ${h.name}Shape;`);break;case 2:r.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:r.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:r.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}r.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:r.push("uniform int outShape;");break;case 2:r.push("uniform ivec2 outShape;"),r.push("uniform int outShapeStrides;");break;case 3:r.push("uniform ivec3 outShape;"),r.push("uniform ivec2 outShapeStrides;");break;case 4:r.push("uniform ivec4 outShape;"),r.push("uniform ivec3 outShapeStrides;");break;default:break}r.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{r.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let s=r.join(` +`),a=e.map(h=>gJ(h,t,n.packedInputs,n.enableShapeUniforms)).join(` +`),o=t.texShape,i=An(),u=vJ(i),c,l,p=IJ(i);return t.isPacked?(c=bJ(t.logicalShape,o,n.enableShapeUniforms),l=wJ(i)):(c=yJ(t.logicalShape,o,n.enableShapeUniforms),l=xJ(i)),n.packedInputs&&(p+=TJ),[p,u,l,s,c,a,n.userCode].join(` +`)}function Il(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return LJ(e,t);case 1:return zJ(e,t);case 2:return VJ(e,t);case 3:return GJ(e,t);case 4:return jJ(e,t);case 5:return qJ(e);case 6:return KJ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function _A(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return MJ(e);case 1:return BJ(e,t);case 2:return WJ(e,t);case 3:return UJ(e,t);default:return HJ(e,t)}}function gJ(e,t,n=!1,r){let s="";n?s+=_A(e,r):s+=Il(e,r);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?s+=XJ(e,t):s+=YJ(e,t)),s}function bJ(e,t,n){switch(e.length){case 0:return EA();case 1:return NJ(e,t,n);case 2:return PJ(e,t,n);case 3:return EJ(e,t,n);default:return DJ(e,t,n)}}function yJ(e,t,n){switch(e.length){case 0:return EA();case 1:return _J(e,t,n);case 2:return OJ(e,t,n);case 3:return AJ(e,t,n);case 4:return $J(e,t,n);case 5:return FJ(e,t);case 6:return RJ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function vJ(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } - `}function _J(e){return` + `}function xJ(e){return` void setOutput(float val) { ${e.output} = vec4(val, 0, 0, 0); } - `}function EJ(e){return` + `}function wJ(e){return` void setOutput(vec4 val) { ${e.output} = val; } - `}function AJ(e){return`${e.version} + `}function IJ(e){return`${e.version} precision highp float; precision highp int; precision highp sampler2D; @@ -229,10 +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); } - ${FJ} - ${$J} - ${DJ} - `}var FJ=` + ${kJ} + ${SJ} + ${CJ} + `}var kJ=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -244,7 +244,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,$J=` +`,SJ=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -252,7 +252,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,DJ=` +`,CJ=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -261,7 +261,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,RJ=` +`,TJ=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -272,25 +272,25 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function QE(){return` +`;function EA(){return` int getOutputCoords() { return 0; } - `}function MJ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?` + `}function NJ(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return r[0]===1?n?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } `:` int getOutputCoords() { - return 2 * int(resultUV.x * ${a[1]}.0); + return 2 * int(resultUV.x * ${r[1]}.0); } - `:a[1]===1?n?` + `:r[1]===1?n?` int getOutputCoords() { return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0)); } `:` int getOutputCoords() { - return 2 * int(resultUV.y * ${a[0]}.0); + return 2 * int(resultUV.y * ${r[0]}.0); } `:n?` int getOutputCoords() { @@ -302,10 +302,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, `:` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * - vec2(${a[0]}, ${a[1]})); - return 2 * (resTexRC.x * ${a[1]} + resTexRC.y); + vec2(${r[0]}, ${r[1]})); + return 2 * (resTexRC.x * ${r[1]} + resTexRC.y); } - `}function PJ(e,t,n){return t[0]===1?n?` + `}function _J(e,t,n){return t[0]===1?n?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } @@ -333,7 +333,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2(${t[0]}, ${t[1]})); return resTexRC.x * ${t[1]} + resTexRC.y; } - `}function OJ(e,t,n){if(n)return` + `}function EJ(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -350,37 +350,37 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),s=r*Math.ceil(e[1]/2);return` + `;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),a=s*Math.ceil(e[1]/2);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * - vec2(${a[0]}, ${a[1]})); - int index = resTexRC.x * ${a[1]} + resTexRC.y; + vec2(${r[0]}, ${r[1]})); + int index = resTexRC.x * ${r[1]} + resTexRC.y; - int b = index / ${s}; - index -= b * ${s}; + int b = index / ${a}; + index -= b * ${a}; - int r = 2 * (index / ${r}); - int c = imod(index, ${r}) * 2; + int r = 2 * (index / ${s}); + int c = imod(index, ${s}) * 2; return ivec3(b, r, c); } - `}function LJ(e,t,n){if(n)return` + `}function AJ(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; - ${Jf(["r","c","d"],e)} + ${eg(["r","c","d"],e)} return ivec3(r, c, d); } -`;let a=Jo(["r","c","d"],e);return` +`;let r=eu(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); int index = resTexRC.x * ${t[1]} + resTexRC.y; - ${a} + ${r} return ivec3(r, c, d); } - `}function zJ(e,t,n){if(n)return` + `}function DJ(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -402,42 +402,42 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec4(b2, b, r, c); } - `;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),s=r*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` -`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,b)=>`coords.${d[b+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,f=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` + `}function XJ(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=NA(e.shapeInfo.logicalShape,t.logicalShape),u=ht(o),c=o-a,l,p=["x","y","z","w","u","v"];a===0?l="":o<2&&i.length>=1?l="coords = 0;":l=i.map(y=>`coords.${p[y+c]} = 0;`).join(` +`);let d="";o<2&&a>0?d="coords":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+c]}`).join(", ");let h="return outputValue;",g=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!g&&!b)h=` return vec4(outputValue.xy, outputValue.xy); - `;else if(m&&!f)i===1?h=` + `;else if(g&&!b)o===1?h=` return vec4(outputValue.x, outputValue.x, 0., 0.); `:h=` return vec4(outputValue.x); - `;else if(o.length){let g=s-2,b=s-1;o.indexOf(g)>-1&&o.indexOf(b)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(b)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return` - vec4 ${r}() { - ${l} coords = getOutputCoords(); - ${p} - vec4 outputValue = get${a}(${c}); + `;else if(i.length){let y=a-2,v=a-1;i.indexOf(y)>-1&&i.indexOf(v)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(v)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return` + vec4 ${s}() { + ${u} coords = getOutputCoords(); + ${l} + vec4 outputValue = get${r}(${d}); ${h} } - `}function r9(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&&w.arraysEqual(i,s))return` - float ${r}() { + `}function YJ(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return` + float ${s}() { return sampleTexture(${n}, resultUV); } - `;let u=dt(l),p=ZE(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}(${m}); + `;let c=ht(u),l=NA(e.shapeInfo.logicalShape,t.logicalShape),p=u-i,d,h=["x","y","z","w","u","v"];i===0?d="":u<2&&l.length>=1?d="coords = 0;":d=l.map(g=>`coords.${h[g+p]} = 0;`).join(` +`);let f="";return u<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((g,m)=>`coords.${h[m+p]}`).join(", "),` + float ${s}() { + ${c} coords = getOutputCoords(); + ${d} + return get${r}(${f}); } - `}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 ${e} is not yet supported`)}function nk(e,t,n){let{newShape:a,keptDims:r}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!w.arraysEqual(t,n)&&a.lengthe[n]).join(", ")}function s9(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=IJ(r,i,t),l=EE(e.gl,o),u=e.createProgram(l);return G().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},eA(e,t,u)))}function eA(e,t,n){let a=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(n,"NAN",!1),G().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(n,"INFINITY",!1));let p=!1;for(let d of t.variableNames){let c={name:d,uniform:e.getUniformLocation(n,d,p),offset:e.getUniformLocation(n,`offset${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 mS(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(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${i} and ${u} must match`)})}function JJ(e,t,n,r,s){t.program.enableShapeUniforms||(C1(t.inShapeInfos,n),C1([t.outShapeInfo],[r]));let a=r.texData.texture,o=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,o[0],o[1]):e.setOutputMatrixTexture(a.texture,o[0],o[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),G().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let u=o.texData.texShape,{useSqueezeShape:c,uniformShape:l,keptDims:p}=c0(e.packedInputs,o.shape,u),d="",h="",f="";if(l.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(l.length===2&&!e.packedInputs)h=`${l[0]>1}_${l[1]>1}`;else if(l.length>2&&!e.packedInputs){let k=w.computeStrides(l);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let g=o.shape.length,m=l.length===2&&w.arraysEqual(o.shape,u),b=w.sizeFromShape(o.shape)===1,y=T.getBroadcastDims(o.shape,n.shape),v=!e.packedInputs&&g===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||l.length>2?"":`${u[0]>1}_${u[1]>1}`;r+=`${g}_${v}_${c?p:""}_${l.length}_${b}_${y}_${m}_${d}_${h}_${f}_${x}_${i}`}else{let u=o.isUniform?"uniform":o.texData.texShape;r+=`${o.shape}_${u}_${i}`}});let s=e.userCode,a=e.constructor.name;return a+="_"+r+"_"+s+`${G().getNumber("WEBGL_VERSION")}`,a}function xn(e){return G().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var eQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Dd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=An();this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Jf(["r","c","d"],e):Jo(["r","c","d"],e)} + ${this.enableShapeUniforms?eg(["r","c","d"],e):eu(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1008,9 +1008,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},u9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Ec.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + `}},tQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Dd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=An();this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Jf(["r","c","d"],e):Jo(["r","c","d"],e)} + ${this.enableShapeUniforms?eg(["r","c","d"],e):eu(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1028,26 +1028,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},p9=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ha.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` - ${YE} + `}},nQ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=lr.DOWNLOAD;let t=An();this.outputShape=e,this.userCode=` + ${TA} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - `}},c9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ha.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` - ${YE} + `}},rQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=lr.DOWNLOAD;let t=An();this.outputShape=e,this.userCode=` + ${TA} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},d9={R:0,G:1,B:2,A:3},fS=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let a=En();this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;ipA,createBufferFromOutputTexture:()=>hA,createFloat16MatrixTexture:()=>iA,createFloat16PackedMatrixTexture:()=>uA,createFloat32MatrixTexture:()=>sA,createIndexBuffer:()=>rA,createPackedMatrixTexture:()=>lA,createUnsignedBytesMatrixTexture:()=>oA,createVertexBuffer:()=>aA,createVertexShader:()=>nA,downloadByteEncodedFloatMatrixFromOutputTexture:()=>fA,downloadFloat32MatrixFromBuffer:()=>mA,downloadMatrixFromPackedOutputTexture:()=>bA,downloadPackedMatrixFromBuffer:()=>gA,getInternalFormatForFloat16MatrixTexture:()=>rk,getInternalFormatForFloat16PackedMatrixTexture:()=>ok,getInternalFormatForFloat32MatrixTexture:()=>ak,getInternalFormatForPackedMatrixTexture:()=>ik,getInternalFormatForUnsignedBytesMatrixTexture:()=>sk,uploadDenseMatrixToTexture:()=>cA,uploadPixelDataToTexture:()=>dA});function nA(e){let t=En(),n=`${t.version} + `}},DA={};Ee(DA,{bindVertexProgramAttributeStreams:()=>zA,createBufferFromOutputTexture:()=>UA,createFloat16MatrixTexture:()=>OA,createFloat16PackedMatrixTexture:()=>BA,createFloat32MatrixTexture:()=>PA,createIndexBuffer:()=>RA,createPackedMatrixTexture:()=>LA,createUnsignedBytesMatrixTexture:()=>MA,createVertexBuffer:()=>FA,createVertexShader:()=>$A,downloadByteEncodedFloatMatrixFromOutputTexture:()=>HA,downloadFloat32MatrixFromBuffer:()=>GA,downloadMatrixFromPackedOutputTexture:()=>qA,downloadPackedMatrixFromBuffer:()=>jA,getInternalFormatForFloat16MatrixTexture:()=>d0,getInternalFormatForFloat16PackedMatrixTexture:()=>f0,getInternalFormatForFloat32MatrixTexture:()=>l0,getInternalFormatForPackedMatrixTexture:()=>h0,getInternalFormatForUnsignedBytesMatrixTexture:()=>p0,uploadDenseMatrixToTexture:()=>WA,uploadPixelDataToTexture:()=>VA});function $A(e){let t=An(),n=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -1119,47 +1119,47 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, void main() { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; - }`;return _E(e,n)}function aA(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return $E(e,t)}function rA(e){let t=new Uint16Array([0,1,2,2,1,3]);return DE(e,t)}function Pd(e,t,n,a,r,s){ME(t,n);let i=RE(e),o=e.TEXTURE_2D;return de(e,()=>e.bindTexture(o,i)),de(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),de(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),de(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),de(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),G().getNumber("WEBGL_VERSION")===1?de(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)):de(e,()=>e.texStorage2D(o,1,a,t,n)),de(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function ak(e){return e.internalFormatFloat}function sA(e,t,n,a){let[r,s]=Md(t,n);return Pd(e,r,s,ak(a),a.textureFormatFloat,e.FLOAT)}function rk(e){return e.internalFormatHalfFloat}function iA(e,t,n,a){let[r,s]=Md(t,n);return Pd(e,r,s,rk(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function sk(e){return e.downloadTextureFormat}function oA(e,t,n,a){let[r,s]=Md(t,n);return Pd(e,r,s,sk(a),e.RGBA,e.UNSIGNED_BYTE)}function ik(e){return e.internalFormatPackedFloat}function lA(e,t,n,a){let[r,s]=xp(t,n);return Pd(e,r,s,ik(a),e.RGBA,e.FLOAT)}function ok(e){return e.internalFormatPackedHalfFloat}function uA(e,t,n,a){let[r,s]=xp(t,n);return Pd(e,r,s,ok(a),e.RGBA,a.textureTypeHalfFloat)}function pA(e,t,n){return de(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),dv(e,t,"clipSpacePos",n,3,20,0)&&dv(e,t,"uv",n,2,20,12)}function cA(e,t,n,a,r,s){de(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new 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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",dr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(dr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=FA(this.gl),this.indexBuffer=RA(this.gl),this.framebuffer=pA(this.gl),this.textureConfig=a0(this.gl,this.textureHalfFloatExtension)}get debug(){return G().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),BA(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),LA(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(yx(this.gl,this.framebuffer),this.outputTexture=null),he(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>HA(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,s,a){return jA(this.gl,e,t,n,r,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return GA(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=UA(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(G().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,s=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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t=this.gl;he(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),zA(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(he(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Zh(this.gl,this.program),he(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?fA(this.gl,e,t):mA(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),he(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(),gA(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,s]=xl(t,n);this.setOutputMatrixTextureDriver(e,r,s)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Zh(this.gl,this.program),dd(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 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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 w.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,r=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=oQ(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)),w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Jh(this.gl,e,this.framebuffer),this.debug&&dd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Jh(this.gl,this.outputTexture,this.framebuffer),this.debug&&dd(this.gl)):yx(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;Jh(r,e,this.framebuffer),this.debug&&dd(r),this.outputTexture=e,he(r,()=>r.viewport(0,0,t,n)),he(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),he(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function oQ(e){let t=0;for(;t`${e}.${n}`)}function Cn(e,t){return t===1?[e]:ZA(e,t)}function JQ(e,t){if(e===1)return"rc";let n="";for(let r=0;r ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n}; - bool rEdge = rp1 >= ${a}; + bool rEdge = rp1 >= ${r}; `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},kA=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} - ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""} + rEdge || cEdge ? 0. : getA(${t[3]})`}},JA=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let n="";for(let r=0;r<4;r++){let s="thisRC = rc;";r%2===1&&(s+="thisRC.z += 1;"),r>1&&(s+="thisRC.y += 1;"),n+=` + ${s} + ${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex); vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z)); - result[${a}] = + result[${r}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); - ${a>0?"}":""} + ${r>0?"}":""} `}this.userCode=` - ${lQ(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?tk():ek(e)} + ${eee(t,this.enableShapeUniforms)} + ${this.enableShapeUniforms?u0():i0(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1174,12 +1174,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function lQ(e,t){return` + `}};function eee(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?kJ(["r","c","d"],"inputShape"):Jo(["r","c","d"],e)} + ${t?fJ(["r","c","d"],"inputShape"):eu(["r","c","d"],e)} return ivec3(r, c, d); } - `}var uQ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let a=bS(t,n),r=yS(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=gS(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return a===hn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===hn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===hn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===hn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=bS(n,a),s=yS(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=gS(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=G().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 pQ(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 gS(e,t,n,a,r){let s=cQ(t,a),i;if(r){let[l,u]=xp(e[0],e[1]);i=l*u}else{let[l,u]=Md(e[0],e[1]);i=l*u}let o=pQ(n,s);return i*o}function cQ(e,t){switch(e){case hn.PACKED_2X2_FLOAT32:return ik(t);case hn.PACKED_2X2_FLOAT16:return ok(t);case hn.UNPACKED_FLOAT32:return ak(t);case hn.UNPACKED_FLOAT16:return rk(t);case hn.PACKED_4X1_UNSIGNED_BYTE:return sk(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function dQ(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 bS(e,t){if(e===ha.UPLOAD)return hn.PACKED_2X2_FLOAT32;if(e===ha.RENDER||e==null)return dQ(t);if(e===ha.DOWNLOAD||e===ha.PIXELS)return hn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function yS(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ir=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` + `}var tee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){let r=_1(t,n),s=E1(e,r,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=N1(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[s].pop();return this.usedTextures[s].push(i),i}let o;return r===pn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===pn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===pn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===pn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===pn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let s=_1(n,r),a=E1(t,s,r);a in this.freeTextures||(this.freeTextures[a]=[]);let o=N1(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,r),i=G().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(e);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 nee(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function N1(e,t,n,r,s){let a=ree(t,r),o;if(s){let[u,c]=xl(e[0],e[1]);o=u*c}else{let[u,c]=Pp(e[0],e[1]);o=u*c}let i=nee(n,a);return o*i}function ree(e,t){switch(e){case pn.PACKED_2X2_FLOAT32:return h0(t);case pn.PACKED_2X2_FLOAT16:return f0(t);case pn.UNPACKED_FLOAT32:return l0(t);case pn.UNPACKED_FLOAT16:return d0(t);case pn.PACKED_4X1_UNSIGNED_BYTE:return p0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function see(e){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?pn.PACKED_2X2_FLOAT32:pn.UNPACKED_FLOAT32:e?pn.PACKED_2X2_FLOAT16:pn.UNPACKED_FLOAT16}function _1(e,t){if(e===lr.UPLOAD)return pn.PACKED_2X2_FLOAT32;if(e===lr.RENDER||e==null)return see(t);if(e===lr.DOWNLOAD||e===lr.PIXELS)return pn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function E1(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var is=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1190,11 +1190,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Oa="if (isnan(x)) return x;",hQ="return x;",xS="return abs(x);",mQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",fQ=Oa+` + `}},Or="if (isnan(x)) return x;",aee="return x;",A1="return abs(x);",oee="return (x >= 0.0) ? x : (exp(x) - 1.0);",iee=Or+` return (x < 0.0) ? 0.0 : x; -`,gQ=Oa+` +`,uee=Or+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,ns="return x;",bQ="return 1.0 / (1.0 + exp(-1.0 * x));",yQ="return x;",xQ=` +`,ta="return x;",cee="return 1.0 / (1.0 + exp(-1.0 * x));",lee="return x;",dee=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1203,7 +1203,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,vQ=` +`,pee=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1213,7 +1213,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,wQ=` +`,hee=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1223,7 +1223,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,kQ="return 1.0 / (1.0 + exp(-1.0 * x));",os=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` +`,fee="return 1.0 / (1.0 + exp(-1.0 * x));",oa=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1234,17 +1234,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},IQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.length,n=Sn("rc",t),a=dt(t),r=iQ(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},mee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=xn(this.outputShape.length);let t=e.length,n=Cn("rc",t),r=ht(t),s=JQ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` void main() { - ${a} rc = getOutputCoords(); - vec4 packedInput = getA(${r}); + ${r} rc = getOutputCoords(); + vec4 packedInput = getA(${s}); - setOutput(getChannel(packedInput, ${i})); + setOutput(getChannel(packedInput, ${o})); } - `}},SQ=gr.whereImpl,NQ=1e-7,TQ=1e-4,Sx={};function CQ(e){return e in Sx||(Sx[e]={}),Sx[e]}var _Q=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),EQ=600;function AQ(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*EQ/1024/1024}var Qf=class extends Mc{nextDataId(){return Qf.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 tm)t=e;else{let n=Ka(G().getNumber("WEBGL_VERSION"),e);t=new tm(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ka(G().getNumber("WEBGL_VERSION"));t=new tm(n),this.binaryCache=CQ(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new uQ(this.gpgpu),this.numMBBeforeWarning=AQ(),this.texData=new Rm(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=pc(t),u=new fS(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 os(i,ns):d=new ir(i,ns);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=w.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+=w.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 os(a,ns):h=new ir(a,ns);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,...Hh(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=w.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 os(r,ns):c=new ir(r,ns);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=>w.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=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.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=w.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:w.now(),endMs:null}}endTimer(e){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.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=_Q){return G().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)0&&w.isString(n[0])){let r=n.map(s=>w.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 IQ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new oQ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[wi(e.shape),...ki(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[wi(t),...ki(t)],s=new kA(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=w.sizeFromShape(r),c=t[0]*t[1]*4;w.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=pc(r),o;a?o=new u9(i):o=new l9(i);let l=!0,u=[t!=null?t:Hh(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===Ec.DENSE){let g=s!=null?s:Hh(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.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&&w.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&&!Ac(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=o9(e,u,p),c=this.getAndSaveBinary(d,()=>s9(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||i9(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=w.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?NQ:TQ}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=w.now());let p=t.texShape;if(p==null&&(p=UE(n,o),t.texShape=p),r!=null){let d=pc(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=xp(p[0],p[1])),o?c=new h9(d,f):c=new fS(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]],v=!0,I=this.runWebGLProgram(c,[b],a,x,v),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+=w.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=FQ(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]*w.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 Qw(),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?(Q1(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}=eA(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)}};Qf.nextDataId=0;function FQ(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 Qf,2);var DQ={forceHalfFloat:IA},uk=` + `}},gee=gs.whereImpl,bee=1e-7,yee=1e-4,jh={};function vee(e){return e in jh||(jh[e]={}),jh[e]}var xee=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),wee=600;function Iee(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*wee/1024/1024}var g0=class QA extends Md{nextDataId(){return QA.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,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(t!=null){if(t instanceof tf)n=t;else{let r=qr(G().getNumber("WEBGL_VERSION"),t);n=new tf(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=qr(G().getNumber("WEBGL_VERSION"));n=new tf(r),this.binaryCache=vee(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new tee(this.gpgpu),this.numMBBeforeWarning=Iee(),this.texData=new Rf(this,Er())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,n,r,s,a,o){let i=this.makeTensorInfo(n,r),u=this.texData.get(i.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[s,a]},u.texShape=[s,a];let c=pd(n),l=new T1(c,!1,o),p=this.runWebGLProgram(l,[i],r,[[s,a]]);return p.shape=n,u.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(t,n,r){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(t),r==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:n,dtype:r,values:t,usage:lr.UPLOAD,refCount:1}),s}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let n=this.texData.get(t);n.refCount++}decRef(t){if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--}}move(t,n,r,s,a){if(G().getBool("DEBUG")&&this.checkNumericalProblems(n),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:r,dtype:s,values:n,usage:lr.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let n=this.texData.get(t),{values:r,dtype:s,complexTensorInfos:a,slice:o,shape:i,isPacked:u}=n;if(o!=null){let d;u?d=new oa(i,ta):d=new is(i,ta);let h=this.runWebGLProgram(d,[{dataId:t,shape:i,dtype:s}],s),f=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),f}if(r!=null)return this.convertAndCacheOnCPU(t);if(s==="string")return r;let c=this.activeTimers!=null,l;c&&(l=w.now());let p;if(s==="complex64"){let d=this.readSync(a.real.dataId),h=this.readSync(a.imag.dataId);p=T.mergeRealAndImagArrays(d,h)}else p=this.getValuesFromTexture(t);return c&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let f=this.pendingRead.get(t);return new Promise(g=>f.push(g))}let n=this.texData.get(t),{values:r,shape:s,slice:a,dtype:o,complexTensorInfos:i,isPacked:u}=n;if(a!=null){let f;u?f=new oa(s,ta):f=new is(s,ta);let g=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:o}],o),m=this.read(g.dataId);return this.disposeIntermediateTensorInfo(g),m}if(r!=null)return this.convertAndCacheOnCPU(t);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 c=null,l;if(o!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(t);let f=this.texData.get(l.dataId);c=this.gpgpu.createBufferFromTexture(f.texture.texture,...Gh(s))}this.pendingRead.set(t,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(o==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),g=f[0],m=f[1];p=T.mergeRealAndImagArrays(g,m)}else if(c==null)p=this.getValuesFromTexture(t);else{let f=w.sizeFromShape(s);p=this.gpgpu.downloadFloat32MatrixFromBuffer(c,f)}if(l!=null&&this.disposeIntermediateTensorInfo(l),c!=null){let f=this.gpgpu.gl;he(f,()=>f.deleteBuffer(c))}let d=this.convertAndCacheOnCPU(t,p),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(f=>f(d)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Er().removeDataId(t,this),this.pendingDeletes--),d}readToGPU(t,n={}){let r=this.texData.get(t),{values:s,shape:a,slice:o,dtype:i,isPacked:u,texture:c}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(o!=null){let h;u?h=new oa(a,ta):h=new is(a,ta);let f=this.runWebGLProgram(h,[{dataId:t,shape:a,dtype:i}],i),g=this.readToGPU(f,n);return this.disposeIntermediateTensorInfo(f),g}if(c==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(t,n.customTexShape),p=Er().makeTensorFromTensorInfo(l),d=this.texData.get(l.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let r=n.map(s=>w.decodeString(s));return Me(t.shape,t.dtype,r)}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Me(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,r=[],s=!1;this.programTimersStack==null?(this.programTimersStack=r,s=!0):this.activeTimers.push(r),this.activeTimers=r,t();let a=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),o=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=n,s&&(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 u=await Promise.all(a);i.kernelMs=w.sum(u),i.getExtraProfileInfo=()=>u.map((c,l)=>({name:o[l],ms:c})).map(c=>`${c.name}: ${c.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:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&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:r}=this.texData.get(t);return r!=null&&(this.disposeData(r.real.dataId,n),this.disposeData(r.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:r,texShape:s,usage:a,isPacked:o,slice:i}=this.texData.get(t),u=i&&i.origDataId||t,c=this.dataRefCount.get(u);c>1?this.dataRefCount.set(u,c-1):(this.dataRefCount.delete(u),n!=null&&(this.numBytesInGPU-=this.computeBytes(s,r),this.textureManager.releaseTexture(n,s,a,o)));let l=this.texData.get(t);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=xee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(r=>this.texData.get(r.dataId).texture==null&&w.sizeFromShape(r.shape)0&&w.isString(r[0])){let a=r.map(o=>w.encodeString(o));s=this.write(a,t,n)}else s=this.write(r,t,n);return this.texData.get(s).usage=null,{dataId:s,shape:t,dtype:n}}makeOutput(t,n,r){return Er().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,r),this)}unpackTensor(t){let n=new mee(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new QQ(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let r=[So(t.shape),...Co(t.shape)],s={dtype:t.dtype,shape:r,dataId:t.dataId},a=[So(n),...Co(n)],o=new JA(a,r),i=!0,u=[r],c=this.runWebGLProgram(o,[s],t.dtype,u,i);return{dataId:c.dataId,shape:n,dtype:c.dtype}}decode(t,n){let r=this.texData.get(t),{isPacked:s,shape:a,dtype:o}=r;if(n!=null){let d=w.sizeFromShape(a),h=n[0]*n[1]*4;w.assert(d<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=pd(a),u;s?u=new tQ(i):u=new eQ(i);let c=!0,l=[n!=null?n:Gh(i)],p=this.runWebGLProgram(u,[{shape:i,dtype:o,dataId:t}],o,l,c,n);return{dtype:o,shape:a,dataId:p.dataId}}runWebGLProgram(t,n,r,s,a=!1,o){let i=this.makeTensorInfo(t.outputShape,r),u=this.texData.get(i.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Dd.DENSE){let b=o!=null?o:Gh(t.outputShape);u.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),w.sizeFromShape(i.shape)===0)return u.values=w.getTypedArrayFromDType(i.dtype,0),i;let c=[],l=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),c.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!$d(y.shape,b.shape)){let v=b,x=b.shape;b.shape=y.shape,b=this.packedReshape(b,x),c.push(b),y=this.texData.get(b.dataId),v.shape=x}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:u,isUniform:!1},d=QJ(t,l,p),h=this.getAndSaveBinary(d,()=>ZJ(this.gpgpu,t,l,p)),f=this.activeTimers!=null,g;f&&(g=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||JJ(this.gpgpu,h,l,p,s),c.forEach(b=>this.disposeIntermediateTensorInfo(b)),f&&(g=this.endTimer(g),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(g)}));let m=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(m>0){let b=w.now();b-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&a===!1){let b=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),b}return i}compileAndRun(t,n,r,s,a=!1){return r=r||n[0].dtype,this.runWebGLProgram(t,n,r,s,a)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(n=>{this.gpgpu.deleteProgram(this.binaryCache[n].webGLProgram),delete this.binaryCache[n]}),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=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(xe(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?bee:yee}uploadToGPU(t){let n=this.texData.get(t),{shape:r,dtype:s,values:a,texture:o,usage:i,isPacked:u}=n;if(o!=null)return;let c=this.activeTimers!=null,l;c&&(l=w.now());let p=n.texShape;if(p==null&&(p=vA(r,u),n.texShape=p),a!=null){let d=pd(r),h,f=p[1],g=p[0],m=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(u||!m)&&([f,g]=xl(p[0],p[1])),u?h=new aQ(d,m):h=new T1(d,m);let b=m?[g,f]:p,y=this.makeTensorInfo(b,s),v=this.texData.get(y.dataId);m?v.usage=lr.PIXELS:v.usage=lr.UPLOAD,v.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),f,g,a);let x=[[g,f]],S=this.runWebGLProgram(h,[y],s,x,!0),N=this.texData.get(S.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(S.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(S.dataId)),this.disposeIntermediateTensorInfo(y),c&&(this.uploadWaitMs+=w.now()-l)}else{let d=this.acquireTexture(p,i,s,u);n.texture=d}}convertAndCacheOnCPU(t,n){let r=this.texData.get(t),{dtype:s}=r;return n!=null&&(r.values=kee(n,s)),r.values}acquireTexture(t,n,r,s){if(this.numBytesInGPU+=this.computeBytes(t,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,s)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let r=new Promise(s=>{try{this.checkCompletion_(n),s(!0)}catch(a){throw a}});t.push(r)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await aI(),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?(o0(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:n,customUniformLocations:r,infLoc:s,nanLoc:a,outShapeLocation:o,outShapeStridesLocation:i,outTexShapeLocation:u}=AA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=r,t.infLoc=s,t.nanLoc=a,t.outShapeLocation=o,t.outShapeStridesLocation=i,t.outTexShapeLocation=u}}createTensorFromGPUData(t,n,r){t.channels=t.channels||"RGBA";let{texture:s,height:a,width:o,channels:i}=t,u=Er().backend;if(!u.gpgpu.gl.isTexture(s))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 c=u.writeTexture(s,n,r,a,o,i);return Er().makeTensorFromDataId(c,n,r,u)}};g0.nextDataId=0;function kee(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;rnew g0,2);var Cee={forceHalfFloat:eD},b0=` if (isnan(a)) return a; if (isnan(b)) return b; -`,Ii=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` +`,To=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=xn(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1254,38 +1254,38 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},el=` + `}},nu=` 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; -`,Np=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=vn(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=` +`,Tl=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length;this.enableShapeUniforms=xn(s);let a="";if(r)if(s===0||w.sizeFromShape(this.outputShape)===1)a=` result.y = 0.; result.z = 0.; result.w = 0.; - `;else if(s=` - ${dt(r)} coords = getOutputCoords(); - `,r===1)this.enableShapeUniforms?s+=` + `;else if(a=` + ${ht(s)} coords = getOutputCoords(); + `,s===1)this.enableShapeUniforms?a+=` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; result.w = 0.; - `:s+=` + `:a+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; - `;else{let i=Sn("coords",r);this.enableShapeUniforms?s+=` + `;else{let i=Cn("coords",s);this.enableShapeUniforms?a+=` bool nextRowOutOfBounds = - (${i[r-2]} + 1) >= outShape[${r} - 2]; + (${i[s-2]} + 1) >= outShape[${s} - 2]; bool nextColOutOfBounds = - (${i[r-1]} + 1) >= outShape[${r} - 1]; + (${i[s-1]} + 1) >= outShape[${s} - 1]; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; - `:s+=` + `:a+=` bool nextRowOutOfBounds = - (${i[r-2]} + 1) >= ${this.outputShape[r-2]}; + (${i[s-2]} + 1) >= ${this.outputShape[s-2]}; bool nextColOutOfBounds = - (${i[r-1]} + 1) >= ${this.outputShape[r-1]}; + (${i[s-1]} + 1) >= ${this.outputShape[s-1]}; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; @@ -1299,41 +1299,41 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 b = getBAtOutCoords(); vec4 result = binaryOperation(a, b); - ${s} + ${a} setOutput(result); } - `}};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 RQ={kernelName:to,backendName:"webgl",kernelFunc:ra};function Ms(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 MQ={kernelName:Om,backendName:"webgl",kernelFunc:Ms},SA="return (a < 0.) ? b * a : a;",NA=` + `}};function sr(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Tee={kernelName:si,backendName:"webgl",kernelFunc:sr};function Oa(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.texData.get(a.dataId),i=sr({inputs:{x:r},backend:n}),u=sr({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var Nee={kernelName:Mf,backendName:"webgl",kernelFunc:Oa},tD="return (a < 0.) ? b * a : a;",nD=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function PQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Np(NA,r.shape,i.shape):new Ii(SA,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var OQ={kernelName:so,backendName:"webgl",kernelFunc:PQ},TA="return (a < 0.) ? b * a : a;",CA=` +`;function _ee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tl(nD,s.shape,o.shape):new To(tD,s.shape,o.shape),u=n.runWebGLProgram(i,[s,o],"float32");return n.disposeIntermediateTensorInfo(o),u}var Eee={kernelName:ui,backendName:"webgl",kernelFunc:_ee},rD="return (a < 0.) ? b * a : a;",sD=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function LQ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Np(CA,a.shape,r.shape):new Ii(TA,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var zQ={kernelName:ko,backendName:"webgl",kernelFunc:LQ},Tp="if (isnan(x)) return x;";function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let 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 os(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[v,I]=x,T={dataId:v.dataId,dtype:v.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},E=new Ii(e,l.shape,u.shape);return p.runWebGLProgram(E,[T,C],ba(v.dtype,I.dtype))}),y=Ms({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),v=p.makeTensorInfo(x,d),I=p.texData.get(v.dataId);return I.values=y,v}let c=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Np(t,l.shape,u.shape,n):h=new Ii(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Fc(e,t=!1){if(e==="linear")return t?yQ:hQ;if(e==="relu")return t?vQ:fQ;if(e==="elu")return t?xQ:mQ;if(e==="relu6")return t?wQ:gQ;if(e==="prelu")return t?CA:TA;if(e==="leakyrelu")return t?NA:SA;if(e==="sigmoid")return t?kQ:bQ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var _A=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 Aee(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tl(sD,r.shape,s.shape):new To(rD,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],"float32")}var Dee={kernelName:Si,backendName:"webgl",kernelFunc:Aee},Nl="if (isnan(x)) return x;";function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:s,backend:a})=>{let{x:o}=s,i=a,u=r||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let p=i.texData.get(o.dataId),d=n(p.values,u);return i.makeTensorInfo(o.shape,u,d)}let c=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,l;return c?l=new oa(o.shape,t):l=new is(o.shape,e),i.runWebGLProgram(l,[o],u)}}function mn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:s,dtype:a}){return({inputs:o,backend:i})=>{let{a:u,b:c}=o,l=i;if(r&&u.dtype==="complex64"){let f=l.texData.get(u.dataId),g=l.texData.get(c.dataId),[m,b]=[[f.complexTensorInfos.real,g.complexTensorInfos.real],[f.complexTensorInfos.imag,g.complexTensorInfos.imag]].map(v=>{let[x,k]=v,S={dataId:x.dataId,dtype:x.dtype,shape:u.shape},N={dataId:k.dataId,dtype:k.dtype,shape:c.shape},E=new To(e,u.shape,c.shape);return l.runWebGLProgram(E,[S,N],fr(x.dtype,k.dtype))}),y=Oa({inputs:{real:m,imag:b},backend:l});return l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(b),y}let p=a||fr(u.dtype,c.dtype);if((u.dtype==="string"||c.dtype==="string"||l.shouldExecuteOnCPU([u,c]))&&s!=null){let f=l.texData.get(u.dataId).values,g=l.texData.get(c.dataId).values,m=u.dtype==="string"?T.fromUint8ToStringArray(f):f,b=u.dtype==="string"?T.fromUint8ToStringArray(g):g,[y,v]=s(u.shape,c.shape,m,b,p),x=l.makeTensorInfo(v,p),k=l.texData.get(x.dataId);return k.values=y,x}let d=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Tl(t,u.shape,c.shape,n):h=new To(e,u.shape,c.shape),l.runWebGLProgram(h,[u,c],p)}}function Fd(e,t=!1){if(e==="linear")return t?lee:aee;if(e==="relu")return t?pee:iee;if(e==="elu")return t?dee:oee;if(e==="relu6")return t?hee:uee;if(e==="prelu")return t?sD:rD;if(e==="leakyrelu")return t?nD:tD;if(e==="sigmoid")return t?fee:cee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var aD=class{constructor(e,t,n,r=!1,s=!1,a=!1,o=null,i=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=xn(this.outputShape.length);let c=r?e[1]:e[2],l=Math.ceil(c/2),p=r?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",h=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",m="";o&&(i?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); - ${i} - }`:l?f=`vec4 activation(vec4 a) { + ${o} + }`:u?g=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); - ${i} - }`:f=`vec4 activation(vec4 x) { - ${i} - }`,g="result = activation(result);");let b=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let y="rc.x",x="rc.x";e[0]`The new shape (${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&&!Ac(r.shape,l)&&!(p.texture!==null&&Ac(p.shape,l))?BQ(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var VQ={kernelName:qu,backendName:"webgl",kernelFunc:ce},IS=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 * ${w.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` - if (inIdx < 0 || inIdx >= ${r}) { + `}},F1="return a * b;";function y0(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=T.upcastType(r.dtype,s.dtype);if(r.dtype==="complex64"){let i=n.texData.get(r.dataId),u=n.texData.get(s.dataId),c=new $1(D1.REAL,r.shape,s.shape),l=new $1(D1.IMAG,r.shape,s.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:r.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:s.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:s.shape}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(l,p,"float32"),f=Oa({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([r,s])){let i=n.texData.get(r.dataId),u=n.texData.get(s.dataId),[c,l]=_Q(r.shape,s.shape,i.values,u.values,a),p=n.makeTensorInfo(l,a),d=n.texData.get(p.dataId);return d.values=c,p}let o;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Tl(F1,r.shape,s.shape):o=new To(F1,r.shape,s.shape),n.runWebGLProgram(o,[r,s],a)}var $ee={kernelName:xi,backendName:"webgl",kernelFunc:y0};function Fee(e,t,n){let r=[So(e.shape),...Co(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},a=[So(t),...Co(t)],o=new JA(a,r),i=!0,u=[r],c=n.runWebGLProgram(o,[s],e.dtype,u,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function pe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=n,i=w.sizeFromShape(s.shape),u=w.inferFromImplicitShape(a,i),c=w.sizeFromShape(u);w.assert(i===c,()=>`The new shape (${u}) has ${c} elements and the old shape (${s.shape}) has ${i} elements. 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= vec4( getValue(batch, inIdx), @@ -1390,60 +1390,60 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getValue(batch, inIdx + 3) ); - ${l} + ${u} } - int inIdx = inOffset + ${i}; - if (${o===1}) { + int inIdx = inOffset + ${o}; + if (${i===1}) { vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0); - ${l} - } else if (${o===2}) { + ${u} + } else if (${i===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), 0.0, 0.0); - ${l} - } else if (${o===3}) { + ${u} + } else if (${i===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), 0.0); - ${l} + ${u} } setOutput(sumValue); } - `}},UQ=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), 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minMaxValue); bvec4 isNaN = isnan(values); if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) { minMaxValue = vec4(NAN); } } } - `,c="vec4";t==="all"?(i="1.0",d=` + `,d="vec4";t==="all"?(o="1.0",p=` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); - `,c="bvec4"):t==="any"&&(i="0.0",d=` + `,d="bvec4"):t==="any"&&(o="0.0",p=` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); - `,c="bvec4");let h="";r%n>0&&(h=` - if (inIdx < 0 || inIdx >= ${r}) { + `,d="bvec4");let h="";s%n>0&&(h=` + if (inIdx < 0 || inIdx >= ${s}) { return initializationValue; } `),this.userCode=` - const float initializationValue = ${i}; + const float initializationValue = ${o}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float getValue(int batch, int inIdx) { @@ -1457,172 +1457,172 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int outIdx = coords[1]; int inOffset = outIdx * ${n}; - vec4 minMaxValue = vec4(${i}); + vec4 minMaxValue = vec4(${o}); float prodValue = 1.0; float sumValue = 0.0; float allValue = 1.0; float anyValue = 0.0; - for (int i = 0; i < ${u}; i += 4) { + for (int i = 0; i < ${c}; i += 4) { int inIdx = inOffset + i; - ${c} values = ${c}( + ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); - ${d} + ${p} } - int inIdx = inOffset + ${u}; - if (${p===1}) { - ${c} values = ${c}( + int inIdx = inOffset + ${c}; + if (${l===1}) { + ${d} values = ${d}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); - ${d} - } else if (${p===2}) { - ${c} values = ${c}( + ${p} + } else if (${l===2}) { + ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); - ${d} - } else if (${p===3}) { - ${c} values = ${c}( + ${p} + } else if (${l===3}) { + ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); - ${d} + ${p} } - setOutput(${l}); + setOutput(${u}); } - `}};function GQ(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 tl(e,t,n,a){let r=GQ(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=wA("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"],r=new Array(t);for(let s=0;s6)throw Error(`Packed transpose for rank ${this.rank} 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ie=pk({inputs:{a:ae,b:re},backend:r});H=tg({inputs:{x:ie},backend:r,attrs:{axis:te,keepDims:!0}}),E.push(ie)}else{let K=ba(e.dtype,t.dtype),Z=new _A(v,I,[F,h,m],n,a,$,B,S,M),J=[T,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",w.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 ZQ(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 Em({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var JQ={kernelName:oi,backendName:"webgl",kernelFunc:ZQ},SS="return abs(x);";function QQ(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=xA(s.values);return 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ng(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return zee(s,a,o,n)}var Wee={kernelName:Wi,backendName:"webgl",kernelFunc:ng};function Tn(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,u=new Array(i);for(let l=0;l`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let k=n?[b,p,h]:[b,h,p],S=r?[y,f,d]:[y,d,f],N=pe({inputs:{x:e},backend:s,attrs:{shape:k}}),E=pe({inputs:{x:t},backend:s,attrs:{shape:S}}),$=[N,E],F=Math.max(b,y),D=n?N.shape[1]:N.shape[2],R=a!=null,C=o!=null,L=u==="leakyrelu",U=u!=null?Fd(u,!0):null,H=R||C||L||U!=null,K;if((h===1||f===1)&&D>oD&&H===!1){let Z=N,J=E;n&&(Z=Tn({inputs:{x:N},backend:s,attrs:{perm:[0,2,1]}}),$.push(Z)),r&&(J=Tn({inputs:{x:E},backend:s,attrs:{perm:[0,2,1]}}),$.push(J));let ee=f!==1,se=f===1,te=Z;ee&&(te=pe({inputs:{x:Z},backend:s,attrs:{shape:[F,D,1]}}),$.push(te));let 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ote(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),c=u,l=T.getAxesPermutation(c,i),p=s;l!=null&&(p=Tn({inputs:{x:s},backend:n,attrs:{perm:l}}),c=T.getInnerMostAxes(c.length,i)),T.assertAxesAreInnerMostDims("any",c,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,c),f=w.sizeFromShape(h),g=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),m=ru(g,g.dtype,"any",n),b;if(o){let y=T.expandShapeToKeepDim(d,u);b=pe({inputs:{x:m},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:m},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(m),l!=null&&n.disposeIntermediateTensorInfo(p),b}var ite={kernelName:dc,backendName:"webgl",kernelFunc:ote},ute=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; - int inOffset = outIdx * ${a}; + int inOffset = outIdx * ${r}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); - for (int i = 0; i < ${a}; i++) { - int inIdx = ${o}; + for (int i = 0; i < ${r}; i++) { + int inIdx = ${i}; float candidate = getA(batch, inIdx); - if (candidate ${i} bestValue) { + if (candidate ${o} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } - `}},bee=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${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=` - 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round(vec4(getBestIndicesAChannel(${g.join()}), + getBestIndicesAChannel(${m.join()}), getBestIndicesAChannel(${b.join()}), - getBestIndicesAChannel(${y.join()})));`,I=`vec4( - getAChannel(${f.join()}), - hasNextCol ? getAChannel(${g.join()}) : 0., + getBestIndicesAChannel(${y.join()})));`,k=`vec4( + getAChannel(${g.join()}), + hasNextCol ? getAChannel(${m.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., - hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,T=a?"":` - float getBestIndicesAChannel(${m.join()}) { - return getChannel(getBestIndicesA(${c.join()}), - vec2(${c.slice(-2).join()})); + hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,S=r?"":` + float getBestIndicesAChannel(${f.join()}) { + return getChannel(getBestIndicesA(${d.join()}), + vec2(${d.slice(-2).join()})); }`;this.userCode=` - float getAChannel(${m.join()}) { - return getChannel(getA(${c.join()}), - vec2(${c.slice(-2).join()})); + float getAChannel(${f.join()}) { + return getChannel(getA(${d.join()}), + vec2(${d.slice(-2).join()})); } - ${T} + ${S} void main() { - ${l} coords = getOutputCoords(); - bool hasNextCol = ${u[o-1]} < ${i[o-1]-1}; - bool hasNextRow = ${u[o-2]} < ${i[o-2]-1}; - ${p} + ${u} coords = getOutputCoords(); + bool hasNextCol = ${c[i-1]} < ${o[i-1]-1}; + bool hasNextRow = ${c[i-2]} < ${o[i-2]-1}; + ${l} ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h}, sourceLocB${h}, sourceLocA${h}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); - vec4 bestValue = ${I}; + vec4 bestValue = ${k}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; - ${v} - vec4 candidate = ${I}; + ${x} + vec4 candidate = ${k}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( - vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); + vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); bestValue = vec4(replace.x ? candidate.x : bestValue.x, replace.y ? candidate.y : bestValue.y, @@ -1633,27 +1633,27 @@ return log(x + sqrt(x * x - 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-`,Iee=Ze({opSnippet:kee}),See={kernelName:Ei,backendName:"webgl",kernelFunc:Iee},Nee=Oa+"return log(x + sqrt(x * x + 1.0));",Tee=Ze({opSnippet:Nee}),Cee={kernelName:Ai,backendName:"webgl",kernelFunc:Tee},_ee=Oa+` +`,mte=Ze({opSnippet:fte}),gte={kernelName:$o,backendName:"webgl",kernelFunc:mte},bte=Or+"return log(x + sqrt(x * x + 1.0));",yte=Ze({opSnippet:bte}),vte={kernelName:Fo,backendName:"webgl",kernelFunc:yte},xte=Or+` return atan(x); -`,Eee=Ze({opSnippet:_ee}),Aee={kernelName:Fi,backendName:"webgl",kernelFunc:Eee},Fee=uk+` +`,wte=Ze({opSnippet:xte}),Ite={kernelName:Ro,backendName:"webgl",kernelFunc:wte},kte=b0+` return atan(a, b); -`,$ee=` +`,Ste=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - `+el+` + `+nu+` return result; -`,Dee=fn({opSnippet:Fee,packedOpSnippet:$ee}),Ree={kernelName:Di,backendName:"webgl",kernelFunc:Dee},Mee=Oa+` +`,Cte=mn({opSnippet:kte,packedOpSnippet:Ste}),Tte={kernelName:Oo,backendName:"webgl",kernelFunc:Cte},Nte=Or+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernelName:$i,backendName:"webgl",kernelFunc:Pee},$c=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}); +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,_te=Ze({opSnippet:Nte}),Ete={kernelName:Po,backendName:"webgl",kernelFunc:_te},Rd=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,m=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let N=">=";this.userCode=` + const ivec2 strides = ivec2(${o}, ${i}); + const ivec2 pads = ivec2(${d}, ${h}); void main() { ivec4 coords = getOutputCoords(); @@ -1671,16 +1671,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int minMaxPosition = 0; float avgValue = 0.0; - for (int wR = 0; wR < ${p}; - wR += ${l}) { + for (int wR = 0; wR < ${l}; + wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } - for (int wC = 0; wC < ${d}; - wC += ${u}) { + for (int wC = 0; wC < ${p}; + wC += ${c}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { @@ -1693,24 +1693,24 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${C} currMinMaxValue) { + if (value ${N} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; - minMaxPosition = ${a?r?f:g:`wR * ${d} + wC`}; + minMaxPosition = ${r?s?g:m:`wR * ${p} + wC`}; } } } setOutput(float(minMaxPosition)); } - `;return}let y="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / max(count, 1.0)");let v=Math.floor(s/4)*4,I=s%4,T=` - if (${m}) { + `;return}let y="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / max(count, 1.0)");let x=Math.floor(a/4)*4,k=a%4,S=` + if (${f}) { avgValue += dot(values, ones); } else { minMaxValue = ${y}(values, minMaxValue); } `;this.userCode=` - const ivec2 strides = ivec2(${i}, ${o}); - const ivec2 pads = ivec2(${c}, ${h}); + const ivec2 strides = ivec2(${o}, ${i}); + const ivec2 pads = ivec2(${d}, ${h}); const float initializationValue = ${b}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); @@ -1739,29 +1739,29 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel float avgValue = 0.0; count = 0.0; - for (int wR = 0; wR < ${p}; - wR += ${l}) { + for (int wR = 0; wR < ${l}; + wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } - for (int wC = 0; wC < ${v}; wC += 4) { - int xC = xCCorner + wC * ${u}; + for (int wC = 0; wC < ${x}; wC += 4) { + int xC = xCCorner + wC * ${c}; vec4 values = vec4( getValue(batch, xR, xC, d), - getValue(batch, xR, xC + ${u}, d), - getValue(batch, xR, xC + 2 * ${u}, d), - getValue(batch, xR, xC + 3 * ${u}, d) + getValue(batch, xR, xC + ${c}, d), + getValue(batch, xR, xC + 2 * ${c}, d), + getValue(batch, xR, xC + 3 * ${c}, d) ); - ${T} + ${S} } - int xC = xCCorner + ${v}; - if (${I===1}) { + int xC = xCCorner + ${x}; + if (${k===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, @@ -1769,33 +1769,33 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel initializationValue ); - ${T} - } else if (${I===2}) { + ${S} + } else if (${k===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), - getValue(batch, xR, xC + ${u}, d), + getValue(batch, xR, xC + ${c}, d), initializationValue, initializationValue ); - ${T} - } else if (${I===3}) { + ${S} + } else if (${k===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), - getValue(batch, xR, xC + ${u}, d), - getValue(batch, xR, xC + 2 * ${u}, d), + getValue(batch, xR, xC + ${c}, d), + getValue(batch, xR, xC + 2 * ${c}, d), initializationValue ); - ${T} + ${S} } } - setOutput(${x}); + setOutput(${v}); } - `}},ck=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=` + `}},v0=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,g=e.padInfo.front,m=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let $=">=";this.userCode=` const ivec3 strides = - ivec3(${i}, ${o}, ${l}); - const ivec3 pads = ivec3(${f}, ${g}, ${b}); + ivec3(${o}, ${i}, ${u}); + const ivec3 pads = ivec3(${g}, ${m}, ${b}); void main() { ivec5 coords = getOutputCoords(); @@ -1813,8 +1813,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel float minMaxValueFound = 0.0; int minMaxPosition = 0; - for (int wD = 0; wD < ${c}; - wD += ${u}) { + for (int wD = 0; wD < ${d}; + wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { @@ -1822,15 +1822,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } for (int wR = 0; wR < ${h}; - wR += ${p}) { + wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } - for (int wC = 0; wC < ${m}; - wC += ${d}) { + for (int wC = 0; wC < ${f}; + wC += ${p}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { @@ -1843,28 +1843,28 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${F} currMinMaxValue) { + if (value ${$} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; - minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} + - wR * ${m} + wC`}; + minMaxPosition = ${r?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} + + wR * ${f} + wC`}; } } } } setOutput(float(minMaxPosition)); } - `;return}let v="max",I=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(I="avgValue / max(count, 1.0)");let T=Math.floor(s/4)*4,C=s%4,E=` + `;return}let x="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / max(count, 1.0)");let S=Math.floor(a/4)*4,N=a%4,E=` if (${y}) { avgValue += dot(values, ones); } else { - minMaxValue = ${v}(values, minMaxValue); + minMaxValue = ${x}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = - ivec3(${i}, ${o}, ${l}); - const ivec3 pads = ivec3(${f}, ${g}, ${b}); - const float initializationValue = ${x}; + ivec3(${o}, ${i}, ${u}); + const ivec3 pads = ivec3(${g}, ${m}, ${b}); + const float initializationValue = ${v}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; @@ -1889,12 +1889,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined - vec4 minMaxValue = vec4(${x}); + vec4 minMaxValue = vec4(${v}); float avgValue = 0.0; count = 0.0; - for (int wD = 0; wD < ${c}; - wD += ${u}) { + for (int wD = 0; wD < ${d}; + wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { @@ -1902,28 +1902,28 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } for (int wR = 0; wR < ${h}; - wR += ${p}) { + wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } - for (int wC = 0; wC < ${T}; wC += 4) { - int xC = xCCorner + wC * ${d}; + for (int wC = 0; wC < ${S}; wC += 4) { + int xC = xCCorner + wC * ${p}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), - getValue(batch, xD, xR, xC + ${d}, ch), - getValue(batch, xD, xR, xC + 2 * ${d}, ch), - getValue(batch, xD, xR, xC + 3 * ${d}, ch) + getValue(batch, xD, xR, xC + ${p}, ch), + getValue(batch, xD, xR, xC + 2 * ${p}, ch), + getValue(batch, xD, xR, xC + 3 * ${p}, ch) ); ${E} } - int xC = xCCorner + ${T}; - if (${C===1}) { + int xC = xCCorner + ${S}; + if (${N===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, @@ -1932,20 +1932,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel ); ${E} - } else if (${C===2}) { + } else if (${N===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), - getValue(batch, xD, xR, xC + ${d}, ch), + getValue(batch, xD, xR, xC + ${p}, ch), initializationValue, initializationValue ); ${E} - } else if (${C===3}) { + } else if (${N===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), - getValue(batch, xD, xR, xC + ${d}, ch), - getValue(batch, xD, xR, xC + 2 * ${d}, ch), + getValue(batch, xD, xR, xC + ${p}, ch), + getValue(batch, xD, xR, xC + 2 * ${p}, ch), initializationValue ); @@ -1953,11 +1953,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } } } - setOutput(${I}); + setOutput(${k}); } - `}};function Lee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;vp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.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&&w.arraysEqual(p.inShape,p.outShape))return ra({inputs:{x:r},backend:n});let d=new $c(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var zee={kernelName:Ri,backendName:"webgl",kernelFunc:Lee};function Wee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new ck(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Bee={kernelName:hu,backendName:"webgl",kernelFunc:Wee},Vee=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` - const ivec2 pads = ivec2(${u}, ${p}); - const float avgMultiplier = float(${d}); + `}};function Ate(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;wl(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=r,c=1;w.assert(T.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let l=T.computePool2DInfo(s.shape,a,o,c,i,u);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return sr({inputs:{x:s},backend:n});let p=new Rd(l,"avg",!1);return n.runWebGLProgram(p,[s],"float32")}var Dte={kernelName:Mo,backendName:"webgl",kernelFunc:Ate};function $te(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u,dataFormat:c}=r,l=[1,1,1],p=T.computePool3DInfo(s.shape,a,o,l,i,u,c),d=new v0(p,"avg",!1);return n.runWebGLProgram(d,[s],"float32")}var Fte={kernelName:fc,backendName:"webgl",kernelFunc:$te},Rte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.top,l=u-1-e.padInfo.left,p=1/(t*n);this.userCode=` + const ivec2 pads = ivec2(${c}, ${l}); + const float avgMultiplier = float(${p}); void main() { ivec4 coords = getOutputCoords(); @@ -1971,18 +1971,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${o}; - wR += ${s}) { - float dyR = float(dyRCorner + wR) / ${a}.0; + for (int wR = 0; wR < ${i}; + wR += ${a}) { + float dyR = float(dyRCorner + wR) / ${r}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - for (int wC = 0; wC < ${l}; - wC+= ${i}) { - float dyC = float(dyCCorner + wC) / ${r}.0; + for (int wC = 0; wC < ${u}; + wC+= ${o}) { + float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { @@ -1997,9 +1997,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}},Uee=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=p-1-e.padInfo.front,m=d-1-e.padInfo.top,f=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=` - const ivec3 pads = ivec3(${h}, ${m}, ${f}); - const float avgMultiplier = float(${g}); + `}},Pte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=l-1-e.padInfo.front,f=p-1-e.padInfo.top,g=d-1-e.padInfo.left,m=1/(t*n*r);this.userCode=` + const ivec3 pads = ivec3(${h}, ${f}, ${g}); + const float avgMultiplier = float(${m}); void main() { ivec5 coords = getOutputCoords(); @@ -2016,18 +2016,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wD = 0; wD < ${p}; - wD += ${o}) { - float dyD = float(dyDCorner + wD) / ${r}.0; + for (int wD = 0; wD < ${l}; + wD += ${i}) { + float dyD = float(dyDCorner + wD) / ${s}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); - for (int wR = 0; wR < ${d}; - wR += ${l}) { - float dyR = float(dyRCorner + wR) / ${s}.0; + for (int wR = 0; wR < ${p}; + wR += ${u}) { + float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { @@ -2035,9 +2035,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } int idyR = int(dyR); - for (int wC = 0; wC < ${c}; - wC += ${u}) { - float dyC = float(dyCCorner + wC) / ${i}.0; + for (int wC = 0; wC < ${d}; + wC += ${c}) { + float dyC = float(dyCCorner + wC) / ${o}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { @@ -2053,77 +2053,77 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}};function Gee(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 Uee(c);return n.runWebGLProgram(h,[r],i.dtype)}var Hee={kernelName:Lc,backendName:"webgl",kernelFunc:Gee};function qee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;vp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=new Vee(p);return n.runWebGLProgram(d,[r],i.dtype)}var jee={kernelName:Oc,backendName:"webgl",kernelFunc:qee};function Kee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Em({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Xee={kernelName:Mi,backendName:"webgl",kernelFunc:Kee},Yee=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 Ote(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:u,pad:c,dimRoundingMode:l}=r,p=[1,1,1],d=T.computePool3DInfo(o.shape,i,u,p,c,l),h=new Pte(d);return n.runWebGLProgram(h,[s],o.dtype)}var Mte={kernelName:zd,backendName:"webgl",kernelFunc:Ote};function Lte(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;wl([s,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:c}=r,l=T.computePool2DInfo(o.shape,i,u,1,c),p=new Rte(l);return n.runWebGLProgram(p,[s],o.dtype)}var Bte={kernelName:Bd,backendName:"webgl",kernelFunc:Lte};function zte(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return Ef({a:s,b:a,transposeA:o,transposeB:i,backend:n})}var Wte={kernelName:Lo,backendName:"webgl",kernelFunc:zte},Vte=class{constructor(e,t,n,r,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let o="0.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); - float offset = ${i}; - float scale = ${o}; - float inv = scale * inversesqrt(variance + float(${s})); + float offset = ${o}; + float scale = ${i}; + float inv = scale * inversesqrt(variance + float(${a})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},Zee=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=` + `}},Ute=class{constructor(e,t,n,r,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { - vec4 offset = ${i}; - vec4 scale = ${o}; + vec4 offset = ${o}; + vec4 scale = ${i}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); - vec4 inv = scale * inversesqrt(variance + vec4(${s})); + vec4 inv = scale * inversesqrt(variance + vec4(${a})); setOutput((x - mean) * inv + offset); } - `}},Jee=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=G().getBool("WEBGL_PACK_NORMALIZATION")?new Zee(a.shape,r.shape,s.shape,p,d,l):new Yee(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},Qee={kernelName:Qi,backendName:"webgl",kernelFunc:Jee},ete=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=dt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=tte(this.rank),a,r=e.map((s,i)=>`sourceLoc.${gv[i]} = start[${i}] + coords.${gv[i]};`);a=` + `}},Gte=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:s,variance:a,offset:o,scale:i}=e;w.assert(s.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||s.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal 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tte(e){if(e===1)return"sourceLoc";if(e<=6)return gv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var nte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=dt(this.rank),n=Sn("coords",this.rank),a=Sn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` - result.x = ${s}; + `}},wx=["x","y","z","w","u","v"];function qte(e){if(e===1)return"sourceLoc";if(e<=6)return wx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Kte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ht(this.rank),n=Cn("coords",this.rank),r=Cn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,a=`getChannel(getSource(${r.join()}), ${s})`,o=` + result.x = ${a}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { - ++${a[this.rank-1]}; - result.y = ${s}; - --${a[this.rank-1]}; + ++${r[this.rank-1]}; + result.y = ${a}; + --${r[this.rank-1]}; } - `,o=this.rank===1?"":` + `,i=this.rank===1?"":` --${n[this.rank-1]}; if (++${n[this.rank-2]} < ${e[this.rank-2]}) { - ++${a[this.rank-2]}; - result.z = ${s}; + ++${r[this.rank-2]}; + result.z = ${a}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { - ++${a[this.rank-1]}; - result.w = ${s}; + ++${r[this.rank-1]}; + result.w = ${a}; } } - `,l=this.rank<=4?`sourceLoc = coords + - ${t}(${e.map((u,p)=>`start[${p}]`).join()});`:e.map((u,p)=>`${a[p]} = ${n[p]} + start[${p}];`).join(` + `,u=this.rank<=4?`sourceLoc = coords + + ${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${r[l]} = ${n[l]} + start[${l}];`).join(` 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h.push(m),h.push(f),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},ite={kernelName:mu,backendName:"webgl",kernelFunc:ste};function ote(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=yA(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var lte={kernelName:fu,backendName:"webgl",kernelFunc:ote},ute=` + `}};function Xte(e,t,n,r){let s=r.texData.get(e.dataId),a=r.makeTensorInfo(n,e.dtype),o=r.texData.get(a.dataId);Object.assign(o,s),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Kt.computeFlatOffset(t,w.computeStrides(e.shape));s.slice&&(i+=s.slice.flatOffset),o.slice={flatOffset:i,origDataId:s.slice&&s.slice.origDataId||e.dataId};let u=r.dataRefCount.get(o.slice.origDataId)||1;return r.dataRefCount.set(o.slice.origDataId,u+1),a}function _l(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,u]=Kt.parseSliceParams(s,a,o);if(Kt.assertParamsValid(s,i,u),w.sizeFromShape(u)===0)return 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i=n.texData.get(r.dataId).values,[o,l,u]=y9(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return xte(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",w.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 vte={kernelName:Pi,backendName:"webgl",kernelFunc:bv},TS="return ceil(x);",wte=Ze({opSnippet:TS,packedOpSnippet:TS,cpuKernelImpl:x9}),kte={kernelName:Oi,backendName:"webgl",kernelFunc:wte},Ite=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` +`;function rne(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=G().getBool("WEBGL_PACK_BINARY_OPERATIONS"),o=G().getNumber("WEBGL_VERSION");if(n.shouldExecuteOnCPU([r,s])||o===1){let u=n.texData.get(r.dataId).values,c=n.texData.get(s.dataId).values,[l,p]=cQ(r.shape,s.shape,u,c,r.dtype),d=n.makeTensorInfo(p,r.dtype),h=n.texData.get(d.dataId);return h.values=l,d}let i;return a?i=new Tl(tne,r.shape,s.shape,!1):i=new To(nne,r.shape,s.shape),n.runWebGLProgram(i,[r,s],r.dtype)}var sne={kernelName:bc,backendName:"webgl",kernelFunc:rne};function ane(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.readSync(r.dataId),o=n.readSync(s.dataId),i=T.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var one={kernelName:Wd,backendName:"webgl",kernelFunc:ane},ine="return float(a != b);",lD=mn({opSnippet:ine,cpuKernelImpl:AQ,dtype:"bool"}),une={kernelName:Wc,backendName:"webgl",kernelFunc:lD};function Mp(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return sr({inputs:{x:s.complexTensorInfos.real},backend:n})}var cne={kernelName:Xf,backendName:"webgl",kernelFunc:Mp},lne="return float(int(x));";function dne(e,t){let n=new is(e.shape,lne),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Ix(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return sr({inputs:{x:s},backend:n});let o=kt(s.shape),i=Ix({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),u=Oa({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),u}if(s.dtype==="complex64"){let o=Mp({inputs:{input:s},backend:n}),i=Ix({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(s.dtype,a)){let o=sr({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(n.shouldExecuteOnCPU([s])){let o=n.texData.get(s.dataId).values,[i,u,c]=lQ(o,s.shape,s.dtype,a);return n.makeTensorInfo(i,u,c)}if(a==="int32")return dne(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=lD({inputs:{a:s,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),u}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var pne={kernelName:Bo,backendName:"webgl",kernelFunc:Ix},M1="return ceil(x);",hne=Ze({opSnippet:M1,packedOpSnippet:M1,cpuKernelImpl:dQ}),fne={kernelName:zo,backendName:"webgl",kernelFunc:hne},mne=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(); @@ -2134,7 +2134,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel setOutput(clamp(value, minVal, maxVal)); } - `}},Ste=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=` + `}},gne=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(); @@ -2145,7 +2145,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function Nte(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 Ste(r.shape):o=new Ite(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var Tte={kernelName:Ss,backendName:"webgl",kernelFunc:Nte},Cte=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function bne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i;G().getBool("WEBGL_PACK_CLIP")?i=new gne(s.shape):i=new mne(s.shape);let u=[[a],[o]];return n.runWebGLProgram(i,[s],s.dtype,u)}var yne={kernelName:Ca,backendName:"webgl",kernelFunc:bne},vne=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2158,7 +2158,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function CS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function _te(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new Cte(a.shape),i=[CS(a,r.complexTensorInfos.real),CS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var Ete={kernelName:Wc,backendName:"webgl",kernelFunc:_te},Ate=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${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m`T${g}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f= ${o[m-1]}) { + getT0(${l}), vec2(${c.join()})); + }`;for(let f=1;f= ${i[f-1]}) { return getChannel( - getT${m}(${jh(i,l,f)}), - vec2(${jh(u,l,f)})); - }`}let c=o.length,h=o[o.length-1];d+=` + getT${f}(${qh(o,u,g)}), + vec2(${qh(c,u,g)})); + }`}let d=i.length,h=i[i.length-1];p+=` return getChannel( - getT${c}(${jh(i,l,h)}), - vec2(${jh(u,l,h)}));`,this.userCode=` - float getValue(${i.map(m=>"int "+m)}) { - ${d} + getT${d}(${qh(o,u,h)}), + vec2(${qh(c,u,h)}));`,this.userCode=` + float getValue(${o.map(f=>"int "+f)}) { + ${p} } void main() { - ${r} coords = getOutputCoords(); - vec4 result = vec4(getValue(${s}), 0., 0., 0.); + ${s} coords = getOutputCoords(); + vec4 result = vec4(getValue(${a}), 0., 0., 0.); - ${s[a-1]} = ${s[a-1]} + 1; - if (${s[a-1]} < ${n[a-1]}) { - result.g = getValue(${s}); + ${a[r-1]} = ${a[r-1]} + 1; + if (${a[r-1]} < ${n[r-1]}) { + result.g = getValue(${a}); } - ${s[a-2]} = ${s[a-2]} + 1; - if (${s[a-2]} < ${n[a-2]}) { - result.a = getValue(${s}); + ${a[r-2]} = ${a[r-2]} + 1; + if (${a[r-2]} < ${n[r-2]}) { + result.a = getValue(${a}); } - ${s[a-1]} = ${s[a-1]} - 1; - if (${s[a-2]} < ${n[a-2]} && - ${s[a-1]} < ${n[a-1]}) { - result.b = getValue(${s}); + ${a[r-1]} = ${a[r-1]} - 1; + if (${a[r-2]} < ${n[r-2]} && + ${a[r-1]} < ${n[r-1]}) { + result.b = getValue(${a}); } setOutput(result); } - `}};function jh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function ng(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return ra({inputs:{x:r.complexTensorInfos.imag},backend:n})}var $te={kernelName:Gm,backendName:"webgl",kernelFunc:ng};function cc(e,t,n){let a=e[0].dtype;if(a==="complex64"){let h=e.map(y=>Od({inputs:{input:y},backend:n})),m=e.map(y=>ng({inputs:{input:y},backend:n})),f=cc(h,t,n),g=cc(m,t,n),b=Ms({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(v=>{let I=[-1,w.sizeFromShape(v.shape.slice(t))];return ce({inputs:{x:v},backend:n,attrs:{shape:I}})}),m=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape})),f=N.computeOutShape(h.map(v=>v.shape),1),g=h[0].shape[0]===1,b=v9(m,f,a,g),y=N.computeOutShape(e.map(v=>v.shape),t),x=n.makeTensorInfo(y,a,b);return h.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}let s=e.filter(h=>w.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,ns):new os(e[0].shape,ns);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}=Dte(s,t,n),p=new Ate(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 Dte(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,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function RA(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.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(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>w.sizeFromShape(u.shape)>0);return l.length===1?ra({inputs:{x:l[0]},backend:n}):cc(l,s,n)}var Rte={kernelName:bu,backendName:"webgl",kernelFunc:RA},MA=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="",v="";n&&(a?x=`float activation(float a) { + `}};function qh(e,t,n){let r=e.indexOf(t);return e.map((a,o)=>o===r?`${a} - ${n}`:a).join()}function rg(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return sr({inputs:{x:s.complexTensorInfos.imag},backend:n})}var Sne={kernelName:Hf,backendName:"webgl",kernelFunc:rg};function hd(e,t,n){let r=e[0].dtype;if(r==="complex64"){let h=e.map(y=>Mp({inputs:{input:y},backend:n})),f=e.map(y=>rg({inputs:{input:y},backend:n})),g=hd(h,t,n),m=hd(f,t,n),b=Oa({inputs:{real:g,imag:m},backend:n});return h.forEach(y=>n.disposeIntermediateTensorInfo(y)),f.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(m),b}let s=n.shouldExecuteOnCPU(e);if(r==="string"&&(s=!0),s){let h=e.map(x=>{let S=[-1,w.sizeFromShape(x.shape.slice(t))];return pe({inputs:{x},backend:n,attrs:{shape:S}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),g=T.computeOutShape(h.map(x=>x.shape),1),m=h[0].shape[0]===1,b=pQ(f,g,r,m),y=T.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,r,b);return h.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}let a=e.filter(h=>w.sizeFromShape(h.shape)>0),o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&a[0].shape.length>1;if(a.length===1){let h=o?new is(e[0].shape,ta):new oa(e[0].shape,ta);return n.runWebGLProgram(h,e,r)}let i=G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(a.length>i){let h=[];for(let g=0;gf.shape),t);return n.runWebGLProgram(h,a,r)}let{tensors2D:u,outShape:c}=Cne(a,t,n),l=new Ine(u.map(h=>h.shape)),p=n.runWebGLProgram(l,u,r);u.forEach(h=>n.disposeIntermediateTensorInfo(h));let d=pe({inputs:{x:p},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(p),d}function Cne(e,t,n){let r=T.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>pe({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function dD(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=w.parseAxisParam(s,t[0].shape)[0],o=t.map(c=>c.shape);T.assertParamsConsistent(o,a);let i=T.computeOutShape(t.map(c=>c.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let u=t.filter(c=>w.sizeFromShape(c.shape)>0);return u.length===1?sr({inputs:{x:u[0]},backend:n}):hd(u,a,n)}var Tne={kernelName:yc,backendName:"webgl",kernelFunc:dD},pD=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,g=e.dataFormat==="channelsLast",m=g?1:2,b=g?2:3,y=g?3:1,v="",x="";n&&(r?v=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} - }`:r?x=`float activation(float a) { + }`:s?v=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} - }`:x=` + }`:v=` float activation(float x) { ${n} } - `,v="result = activation(result);");let I=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` - ${x} + `,x="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` + ${v} - const ivec2 strides = ivec2(${o}, ${l}); - const ivec2 pads = ivec2(${s}, ${i}); + const ivec2 strides = ivec2(${i}, ${u}); + const ivec2 pads = ivec2(${a}, ${o}); void main() { ivec4 coords = getOutputCoords(); @@ -2226,22 +2226,22 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int d2 = coords[${y}]; ivec2 xRCCorner = - ivec2(coords[${g}], coords[${b}]) * strides - pads; + ivec2(coords[${m}], coords[${b}]) * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${d}; wR++) { - int xR = xRCorner + wR * ${u}; + for (int wR = 0; wR < ${p}; wR++) { + int xR = xRCorner + wR * ${c}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } - for (int wC = 0; wC < ${c}; wC++) { - int xC = xCCorner + wC * ${p}; + for (int wC = 0; wC < ${d}; wC++) { + int xC = xCCorner + wC * ${l}; if (xC < 0 || xC >= ${e.inWidth}) { continue; @@ -2255,7 +2255,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel getW(wR, wC, d1 + 3, d2) ); - if (${f}) { + if (${g}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), @@ -2274,9 +2274,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } } - if (${m===1}) { + if (${f===1}) { - if (${f}) { + if (${g}) { dotProd += getX(batch, xR, xC, ${h}) * getW(wR, wC, ${h}, d2); @@ -2286,13 +2286,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel getW(wR, wC, ${h}, d2); } - } else if (${m===2}) { + } else if (${f===2}) { vec2 wValues = vec2( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2) ); - if (${f}) { + if (${g}) { vec2 xValues = vec2( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1) @@ -2306,14 +2306,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel dotProd += dot(xValues, wValues); } - } else if (${m===3}) { + } else if (${f===3}) { vec3 wValues = vec3( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2), getW(wR, wC, ${h} + 2, d2) ); - if (${f}) { + if (${g}) { vec3 xValues = vec3( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1), @@ -2334,13 +2334,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } float result = dotProd; - ${I} - ${v} + ${k} + ${x} setOutput(result); } - `}},Mte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,d=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` - const ivec3 strides = ivec3(${r}, ${s}, ${i}); - const ivec3 pads = ivec3(${t}, ${n}, ${a}); + `}},Nne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` + const ivec3 strides = ivec3(${s}, ${a}, ${o}); + const ivec3 pads = ivec3(${t}, ${n}, ${r}); void main() { ivec5 coords = getOutputCoords(); @@ -2356,22 +2356,22 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; - for (int wF = 0; wF < ${p}; wF++) { - int xF = xFCorner + wF * ${o}; + for (int wF = 0; wF < ${l}; wF++) { + int xF = xFCorner + wF * ${i}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } - for (int wR = 0; wR < ${d}; wR++) { - int xR = xRCorner + wR * ${l}; + for (int wR = 0; wR < ${p}; wR++) { + int xR = xRCorner + wR * ${u}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } - for (int wC = 0; wC < ${c}; wC++) { - int xC = xCCorner + wC * ${u}; + for (int wC = 0; wC < ${d}; wC++) { + int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${e.inWidth}) { continue; @@ -2394,11 +2394,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel dotProd += dot(xValues, wValues); } - if (${m===1}) { + if (${f===1}) { dotProd += getX(batch, xF, xR, xC, ${h}) * getW(wF, wR, wC, ${h}, d2); - } else if (${m===2}) { + } else if (${f===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1) @@ -2408,7 +2408,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel getW(wF, wR, wC, ${h} + 1, d2) ); dotProd += dot(xValues, wValues); - } else if (${m===3}) { + } else if (${f===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1), @@ -2426,41 +2426,41 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}},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.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,d=` + `}},hD=class{constructor(e,t=!1,n=null,r=!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=e.outShape,this.enableShapeUniforms=xn(this.outputShape.length);let a=e.padInfo.left,o=e.strideWidth,i=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,p=` int xR; int xC; int xCOffset; - vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f=0 && xR < inDims[0]) { - `;for(let f=0;f<(p+1)/2;f++){let g=f*2;if(d+=` - xC = xCCorner + ${g*o}; - `,i===1){if(g= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) { - xTexelC${g} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${m}Ready == 0) { + xTexelC${m} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${g}.zw = vec2(0.0); + xTexelC${m}.zw = vec2(0.0); } - xTexelC${g}Ready = 1; + xTexelC${m}Ready = 1; } - `,o===1&&g>0?d+=` - xC${g} = vec4(xTexelC${g-2}.zw, xTexelC${g}.xy); - `:d+=` + `,i===1&&m>0?p+=` + xC${m} = vec4(xTexelC${m-2}.zw, xTexelC${m}.xy); + `:p+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { @@ -2472,137 +2472,137 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel previous.zw = vec2(0.0); } - xC${g} = vec4(previous.zw, xTexelC${g}.xy); + xC${m} = vec4(previous.zw, xTexelC${m}.xy); } else { - xC${g} = vec4(0.0, 0.0, xTexelC${g}.xy); + xC${m} = vec4(0.0, 0.0, xTexelC${m}.xy); } - `):d+=` - if (xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) { - xTexelC${g} = getX(batch, xR, xC, d1); + `):p+=` + if (xC >= 0 && xC < inDims[1] && xTexelC${m}Ready == 0) { + xTexelC${m} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { - xTexelC${g}.zw = vec2(0.0); + xTexelC${m}.zw = vec2(0.0); } - xTexelC${g}Ready = 1; + xTexelC${m}Ready = 1; } - xC${g} = xTexelC${g}; - `,g+1= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { - xTexelC${g+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${m+1}Ready == 0) { + xTexelC${m+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${g+1}.zw = vec2(0.0); + xTexelC${m+1}.zw = vec2(0.0); } - xTexelC${g+1}Ready = 1; + xTexelC${m+1}Ready = 1; } - `,o>1?d+=` + `,i>1?p+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); - xC${g+1} = vec4(previous.zw, xTexelC${g+1}.xy); + xC${m+1} = vec4(previous.zw, xTexelC${m+1}.xy); } else { - xC${g+1} = vec4(0.0, 0.0, xTexelC${g+1}.xy); + xC${m+1} = vec4(0.0, 0.0, xTexelC${m+1}.xy); } - `:d+=` - xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.xy); - `):b===1?d+=` - xC${g+1} = xTexelC${g}; - `:d+=` + `:p+=` + xC${m+1} = vec4(xTexelC${m}.zw, xTexelC${m+1}.xy); + `):b===1?p+=` + xC${m+1} = xTexelC${m}; + `:p+=` xCOffset = xC + ${b}; - if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { - xTexelC${g+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${m+1}Ready == 0) { + xTexelC${m+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { - xTexelC${g+1}.zw = vec2(0.0); + xTexelC${m+1}.zw = vec2(0.0); } - xTexelC${g+1}Ready = 1; + xTexelC${m+1}Ready = 1; } - xC${g+1} = xTexelC${g+1}; - `}}else g= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) { - xTexelC${g} = getX(batch, xR, xCOffset, d1); + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${m}Ready == 0) { + xTexelC${m} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${g}.zw = vec2(0.0); + xTexelC${m}.zw = vec2(0.0); } - xTexelC${g}Ready = 1; + xTexelC${m}Ready = 1; } - if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${g+1}Ready == 0) { - xTexelC${g+1} = getX(batch, xR, xC + 1, d1); + if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${m+1}Ready == 0) { + xTexelC${m+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { - xTexelC${g+1}.zw = vec2(0.0); + xTexelC${m+1}.zw = vec2(0.0); } - xTexelC${g+1}Ready = 1; + xTexelC${m+1}Ready = 1; } - xC${g} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw); - `,g+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } - xC${g+1} = vec4(xTexelC${g+1}.xy, final.xy); - `)):(d+=` - if(xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) { - xTexelC${g} = getX(batch, xR, xC, d1); + xC${m+1} = vec4(xTexelC${m+1}.xy, final.xy); + `)):(p+=` + if(xC >= 0 && xC < inDims[1] && xTexelC${m}Ready == 0) { + xTexelC${m} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { - xTexelC${g}.zw = vec2(0.0); + xTexelC${m}.zw = vec2(0.0); } - xTexelC${g}Ready = 1; + xTexelC${m}Ready = 1; } xCOffset = xC + strides[1]; - if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { - xTexelC${g+1} = getX(batch, xR, xCOffset, d1); + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${m+1}Ready == 0) { + xTexelC${m+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { - xTexelC${g+1}.zw = vec2(0.); + xTexelC${m+1}.zw = vec2(0.); } - xTexelC${g+1}Ready = 1; + xTexelC${m+1}Ready = 1; } - xC${g} = vec4( - xTexelC${g}.xy, xTexelC${g+1}.xy); - `,g+1= 0) { + if(d0 < inputShape[${a}] && d0 >= 0) { // Use custom imod instead mod. On Intel GPU, mod may generate // unexpected value. // https://github.com/tensorflow/tfjs/issues/5447 @@ -2638,18 +2638,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / inChannels); - if(d1 < inputShape[${i}] && d1 >= 0) { + if(d1 < inputShape[${o}] && d1 >= 0) { ch = imod(pos, inChannels); - if (${r}) { + if (${s}) { innerDims = vec2(d1, ch); - result[${u*2+p}] = getChannel( + result[${c*2+l}] = getChannel( getA(rc.x, d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); - result[${u*2+p}] = getChannel( + result[${c*2+l}] = getChannel( getA(rc.x, ch, int(innerDims.x), int(innerDims.y)), innerDims); } @@ -2665,11 +2665,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; - ${l} + ${u} - ${a.output} = result; + ${r.output} = result; } - `}};function Am(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 OA({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=Am(s.shape,h);y!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:y}}),b.push(s))}if(r!=null){let y=Am(r.shape,h);y!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:y}}),b.push(r))}if(!((d===1||c===1)&&p>EA)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&w.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},v=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Ac(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(I);let T=Em({a:x,b:I,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=a.texData.get(T.dataId);w.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=v,C.shape=n.outShape,g=ra({inputs:{x:T},backend:a}),g.shape=n.outShape,b.push(T)}else{let y=n.outHeight*n.outWidth,x=ce({inputs:{x:e},backend:a,attrs:{shape:h?[n.batchSize,y,n.inChannels]:[n.batchSize,n.inChannels,y]}}),v=ce({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Em({a:h?x:v,b:h?v:x,transposeA:!h,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ce({inputs:{x:I},backend:a,attrs:{shape:n.outShape}}),b.push(x),b.push(v),b.push(I)}for(let y of b)a.disposeIntermediateTensorInfo(y);return g}function LA({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:d,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*u*p,g=c*d,b=[n.batchSize,f,g],y=!0,x=!1,v=[];if(s!=null){let K=Am(s.shape,m);K!=null&&(s=ce({inputs:{x:s},backend:a,attrs:{shape:K}}),v.push(s))}if(r!=null){let K=Am(r.shape,m);K!=null&&(r=ce({inputs:{x:r},backend:a,attrs:{shape:K}}),v.push(r))}let I=ce({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(I);let T=new Pte(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=a.runWebGLProgram(T,[e],"float32",C),F=ce({inputs:{x:E},backend:a,attrs:{shape:b}});v.push(E),v.push(F);let D=r!=null,$=s!=null,S=o==="leakyrelu",M=o?Fc(o,!0):null,B=new _A(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",w.createScalarValue(i,"float32"));U.push(K),v.push(K)}let H=a.runWebGLProgram(B,U,"float32"),j=ce({inputs:{x:H},backend:a,attrs:{shape:n.outShape}});v.push(H);for(let K of v)a.disposeIntermediateTensorInfo(K);return j}function Ote(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=OA({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let f=new PA(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=LA({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 Lte={kernelName:Li,backendName:"webgl",kernelFunc:Ote},zte=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 Af(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 fD({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=e.shape,c=r.texData.get(e.dataId),l=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,g=!1,m,b=[];if(a!=null){let x=Af(a.shape,h);x!=null&&(a=pe({inputs:{x:a},backend:r,attrs:{shape:x}}),b.push(a))}if(s!=null){let x=Af(s.shape,h);x!=null&&(s=pe({inputs:{x:s},backend:r,attrs:{shape:x}}),b.push(s))}if(!((p===1||d===1)&&l>oD)&&c.isPacked&&h&&c.texture!=null&&u[2]%2!==0&&w.arraysEqual(c.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},S=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,w.assert($d(c.shape,k.shape),()=>`packed reshape ${c.shape} to ${k.shape} isn't free`);let N=pe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let E=Ef({a:k,b:N,backend:r,transposeA:f,transposeB:g,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),$=r.texData.get(E.dataId);w.assert($.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=S,$.shape=n.outShape,m=sr({inputs:{x:E},backend:r}),m.shape=n.outShape,b.push(E)}else{let x=n.outHeight*n.outWidth,k=pe({inputs:{x:e},backend:r,attrs:{shape:h?[n.batchSize,x,n.inChannels]:[n.batchSize,n.inChannels,x]}}),S=pe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Ef({a:h?k:S,b:h?S:k,transposeA:!h,transposeB:g,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o});m=pe({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),b.push(k),b.push(S),b.push(N)}for(let x of b)r.disposeIntermediateTensorInfo(x);return m}function mD({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:u,filterHeight:c,inChannels:l,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",g=u*c*l,m=d*p,b=[n.batchSize,g,m],y=!0,v=!1,x=[];if(a!=null){let q=Af(a.shape,f);q!=null&&(a=pe({inputs:{x:a},backend:r,attrs:{shape:q}}),x.push(a))}if(s!=null){let q=Af(s.shape,f);q!=null&&(s=pe({inputs:{x:s},backend:r,attrs:{shape:q}}),x.push(s))}let k=pe({inputs:{x:t},backend:r,attrs:{shape:[1,g,w.sizeFromShape(t.shape)/g]}});x.push(k);let S=new _ne(b,n),N=[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=r.runWebGLProgram(S,[e],"float32",N),$=pe({inputs:{x:E},backend:r,attrs:{shape:b}});x.push(E),x.push($);let F=s!=null,D=a!=null,R=i==="leakyrelu",C=i?Fd(i,!0):null,L=new aD(f?$.shape:k.shape,f?k.shape:$.shape,f?[n.batchSize,m,n.outChannels]:[n.batchSize,n.outChannels,m],y,v,F,C,D,R),U=f?[$,k]:[k,$];if(s&&U.push(s),D&&U.push(a),R){let q=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));U.push(q),x.push(q)}let H=r.runWebGLProgram(L,U,"float32"),K=pe({inputs:{x:H},backend:r,attrs:{shape:n.outShape}});x.push(H);for(let q of x)r.disposeIntermediateTensorInfo(q);return K}function Ene(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:c,dimRoundingMode:l}=r,p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(s.shape,a.shape,o,c,i,l,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=fD({x:s,filter:a,convInfo:d,backend:n});else if(d.strideWidth<=2&&p==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let g=new hD(d),m=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=n.runWebGLProgram(g,[s,a],"float32",m)}else if(G().getBool("WEBGL_CONV_IM2COL"))h=mD({x:s,filter:a,convInfo:d,backend:n});else{let g=new pD(d);h=n.runWebGLProgram(g,[s,a],"float32")}let f=pe({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var Ane={kernelName:Wo,backendName:"webgl",kernelFunc:Ene},Dne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2683,20 +2683,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { - int xR = wR + yR * ${t} - ${a}; + int xR = wR + yR * ${t} - ${r}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { - int xC = wC + yC * ${n} - ${r}; + int xC = wC + yC * ${n} - ${s}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } - ${s?`float dyValue = getDy(b, yR, yC, d2); + ${a?`float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); @@ -2706,15 +2706,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}},Wte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=` - const ivec2 pads = ivec2(${i}, ${o}); + `}},$ne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=` + const ivec2 pads = ivec2(${o}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; - int d1 = coords[${p}]; + int d1 = coords[${l}]; - ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads; + ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; @@ -2722,7 +2722,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { - float dyR = float(dyRCorner + wR) / ${a}.0; + float dyR = float(dyRCorner + wR) / ${r}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; @@ -2732,7 +2732,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { - float dyC = float(dyCCorner + wC) / ${r}.0; + float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { @@ -2744,7 +2744,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel for (int d2 = 0; d2 < ${e.outChannels}; d2++) { - if (${s}) { + if (${a}) { float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; @@ -2759,7 +2759,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}},Bte=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=` + `}},Fne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2772,21 +2772,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel for (int b = 0; b < ${e.batchSize}; b++) { for (int yF = 0; yF < ${e.outDepth}; yF++) { - int xF = wF + yF * ${t} - ${r}; + int xF = wF + yF * ${t} - ${s}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int yR = 0; yR < ${e.outHeight}; yR++) { - int xR = wR + yR * ${n} - ${s}; + int xR = wR + yR * ${n} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { - int xC = wC + yC * ${a} - ${i}; + int xC = wC + yC * ${r} - ${o}; if (xC < 0 || xC >= ${e.inWidth}) { continue; @@ -2801,8 +2801,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}},Vte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=a-1-e.padInfo.left;this.userCode=` - const ivec3 pads = ivec3(${o}, ${l}, ${u}); + `}},Rne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,u=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=` + const ivec3 pads = ivec3(${i}, ${u}, ${c}); void main() { ivec5 coords = getOutputCoords(); @@ -2817,7 +2817,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel float dotProd = 0.0; for (int wF = 0; wF < ${t}; wF++) { - float dyF = float(dyFCorner + wF) / ${r}.0; + float dyF = float(dyFCorner + wF) / ${s}.0; if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { continue; @@ -2827,7 +2827,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int wFPerm = ${t} - 1 - wF; for (int wR = 0; wR < ${n}; wR++) { - float dyR = float(dyRCorner + wR) / ${s}.0; + float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { @@ -2837,8 +2837,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int wRPerm = ${n} - 1 - wR; - for (int wC = 0; wC < ${a}; wC++) { - float dyC = float(dyCCorner + wC) / ${i}.0; + for (int wC = 0; wC < ${r}; wC++) { + float dyC = float(dyCCorner + wC) / ${o}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { @@ -2846,7 +2846,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } int idyC = int(dyC); - int wCPerm = ${a} - 1 - wC; + int wCPerm = ${r} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); @@ -2858,8 +2858,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}};function Ute(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new zte(c);return n.runWebGLProgram(h,[r,s],"float32")}var Gte={kernelName:Lm,backendName:"webgl",kernelFunc:Ute},Hte=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=vn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,a=t-1-e.padInfo.top,r=n-1-e.padInfo.left;this.userCode=` - const ivec2 pads = ivec2(${a}, ${r}); + `}};function Pne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:c,filterShape:l}=r,p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(s.shape,l,o,1,i,c,!1,p),h=new Dne(d);return n.runWebGLProgram(h,[s,a],"float32")}var One={kernelName:Lf,backendName:"webgl",kernelFunc:Pne},Mne=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=xn(this.outputShape.length);let t=e.filterHeight,n=e.filterWidth,r=t-1-e.padInfo.top,s=n-1-e.padInfo.left;this.userCode=` + const ivec2 pads = ivec2(${r}, ${s}); void main() { ivec4 coords = getOutputCoords(); @@ -2932,18 +2932,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(result); } - `}};function qte(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 Hte(c);return n.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new Wte(c);return n.runWebGLProgram(h,[r,s],"float32")}}var jte={kernelName:zi,backendName:"webgl",kernelFunc:qte};function Kte(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 Mte(u);return n.runWebGLProgram(p,[r,s],"float32")}var Xte={kernelName:Wi,backendName:"webgl",kernelFunc:Kte};function Yte(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 Bte(u);return n.runWebGLProgram(p,[r,s],"float32")}var Zte={kernelName:yu,backendName:"webgl",kernelFunc:Yte};function Jte(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 Qte={kernelName:xu,backendName:"webgl",kernelFunc:Jte},ene=Tp+` + `}};function Lne(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:c,dimRoundingMode:l}=r,p=T.convertConv2DDataFormat(c),d=T.computeConv2DInfo(o,a.shape,i,1,u,l,!1,p);if(G().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&p==="channelsLast"){let h=[[d.strideHeight,d.strideWidth]],f=new Mne(d);return n.runWebGLProgram(f,[s,a],"float32",h)}else{let h=new $ne(d);return n.runWebGLProgram(h,[s,a],"float32")}}var Bne={kernelName:Vo,backendName:"webgl",kernelFunc:Lne};function zne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u}=r,c=T.computeConv3DInfo(s.shape,a.shape,o,u,i),l=new Nne(c);return n.runWebGLProgram(l,[s,a],"float32")}var Wne={kernelName:Uo,backendName:"webgl",kernelFunc:zne};function Vne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:u}=r,c=T.computeConv3DInfo(s.shape,u,o,1,i),l=new Fne(c);return n.runWebGLProgram(l,[s,a],"float32")}var Une={kernelName:vc,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:u}=r,c=T.computeConv3DInfo(u,a.shape,i,1,o),l=new Rne(c);return n.runWebGLProgram(l,[s,a],"float32")}var Hne={kernelName:xc,backendName:"webgl",kernelFunc:Gne},jne=Nl+` return cos(x); -`,tne=` +`,qne=` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${el} + ${nu} return result; -`,nne=Ze({opSnippet:ene,packedOpSnippet:tne}),ane={kernelName:Bi,backendName:"webgl",kernelFunc:nne},rne=` +`,Kne=Ze({opSnippet:jne,packedOpSnippet:qne}),Xne={kernelName:Go,backendName:"webgl",kernelFunc:Kne},Yne=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,sne=Ze({opSnippet:rne}),ine={kernelName:Vi,backendName:"webgl",kernelFunc:sne},one=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${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) * ${h}`],[y,x,v]=d>1?[`${(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}); +`,Zne=Ze({opSnippet:Yne}),Jne={kernelName:Ho,backendName:"webgl",kernelFunc:Zne},Qne=class{constructor(e,t,n,r,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,u]=e,[c]=t,[l,p]=n;this.outputShape=[c,l,p,u];let d=r==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[g,m,b]=l>1?[`${(o-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(i-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=` + const float height_ratio = float(${g}); const float width_ratio = float(${y}); void main() { ivec4 coords = getOutputCoords(); @@ -2960,26 +2960,26 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // get image in batch index int bInd = round(getBoxInd(b)); - if(bInd < 0 || bInd >= ${s}) { + if(bInd < 0 || bInd >= ${a}) { return; } - float height_scale = ${g}; - float width_scale = ${x}; + float height_scale = ${m}; + float width_scale = ${v}; float in_y = ${b}; if( in_y < 0.0 || in_y > ${h} ) { - setOutput(float(${r})); + setOutput(float(${s})); return; } - float in_x = ${v}; - if( in_x < 0.0 || in_x > ${m} ) { - setOutput(float(${r})); + float in_x = ${x}; + if( in_x < 0.0 || in_x > ${f} ) { + setOutput(float(${s})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); - if(${c} == 1) { + if(${d} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); @@ -3003,20 +3003,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel setOutput(newValue); } } - `}},lne=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 one(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},une={kernelName:wu,backendName:"webgl",kernelFunc:lne},Dc;(function(e){e.Prod="*",e.Sum="+"})(Dc||(Dc={}));var _S=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===Dc.Prod?"1.0":"0.0",i=n?s:`getX(${ES(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 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` + `}},ere=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:c}=r,l=new Qne(s.shape,a.shape,i,u,c);return n.runWebGLProgram(l,[s,a,o],"float32")},tre={kernelName:Ic,backendName:"webgl",kernelFunc:ere},Pd;(function(e){e.Prod="*",e.Sum="+"})(Pd||(Pd={}));var B1=class{constructor(e,t,n,r){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===Pd.Prod?"1.0":"0.0",o=n?a:`getX(${z1(s,"coords",this.op)})`,i=this.outputShape[this.outputShape.length-1],u="",c="";n?(u=r?`end != ${i-1}`:"end != 0",c=r?"end + 1":"end - 1"):(u=r?`end + pow2 < ${i}`:"end >= pow2",c=r?"end + pow2":"end - pow2"),this.userCode=` void main() { - ${dt(r)} coords = getOutputCoords(); - int end = ${AS(r,"coords",this.op)}; - float val = ${i}; + ${ht(s)} coords = getOutputCoords(); + int end = ${W1(s,"coords",this.op)}; + float val = ${o}; int pow2 = int(pow(2.0, index)); - if (${l}) { - int idx = ${u}; - ${AS(r,"coords",this.op)} = idx; - val ${this.op}= getX(${ES(r,"coords",this.op)}); + if (${u}) { + int idx = ${c}; + ${W1(s,"coords",this.op)} = idx; + val ${this.op}= getX(${z1(s,"coords",this.op)}); } setOutput(val); } - `}};function ES(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 ${e} is not yet supported`)}function AS(e,t,n){if(e===1)return`${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 ${e} is not yet supported`)}function zA(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=Nn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=ra({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new _S(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new _S(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=Nn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function pne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return zA(Dc.Prod,r,n,s,i,o)}var cne={kernelName:vu,backendName:"webgl",kernelFunc:pne};function dne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return zA(Dc.Sum,r,n,s,i,o)}var hne={kernelName:Ui,backendName:"webgl",kernelFunc:dne};function mne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=yA(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=g9(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var fne={kernelName:Bc,backendName:"webgl",kernelFunc:mne},gne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}};function z1(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 ${e} is not yet supported`)}function W1(e,t,n){if(e===1)return`${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 ${e} is not yet supported`)}function gD(e,t,n,r,s,a){let o=t.shape.length,i=T.getAxesPermutation([r],o),u=t;i!=null&&(u=Tn({inputs:{x:t},backend:n,attrs:{perm:i}}));let c=T.getInnerMostAxes(1,o)[0];if(c!==o-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${r}`);let l=u.shape[c],p=sr({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(l))-1;d++){let h=new B1(e,u.shape,!1,a),f=[[d]],g=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(g)}if(s){let d=new B1(e,u.shape,s,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(i!=null){let d=T.getUndoAxesPermutation(i),h=Tn({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),h}return p}function nre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;return gD(Pd.Prod,s,n,a,o,i)}var rre={kernelName:wc,backendName:"webgl",kernelFunc:nre};function sre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;return gD(Pd.Sum,s,n,a,o,i)}var are={kernelName:jo,backendName:"webgl",kernelFunc:sre};function ore(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let u=n.readSync(s.dataId),c=n.readSync(a.dataId),l=KA(u,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}else if(s.shape.length===2){let u=n.bufferSync(s),c=n.bufferSync(a),l=uQ(u,c,o,i);return n.makeTensorInfo(l.shape,a.dtype,l.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var ire={kernelName:Ud,backendName:"webgl",kernelFunc:ore},ure=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]; @@ -3035,26 +3035,26 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel 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 bne(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 gne(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var yne={kernelName:ku,backendName:"webgl",kernelFunc:bne},WA=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 cre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r,i=s.shape[0],u=o==="NHWC"?s.shape[1]:s.shape[2],c=o==="NHWC"?s.shape[2]:s.shape[3],l=o==="NHWC"?s.shape[3]:s.shape[1],p=u*a,d=c*a,h=l/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],g=new ure(f,a,o);return n.runWebGLProgram(g,[s],s.dtype)}var lre={kernelName:kc,backendName:"webgl",kernelFunc:cre},bD=class{constructor(e,t=!1,n=null,r=!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=e.outShape,this.enableShapeUniforms=xn(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,u="",c="";n&&(r?u=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} - }`:r?l=`float activation(float a) { + }`:s?u=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} - }`:l=` + }`:u=` float activation(float x) { ${n} } - `,u="result = activation(result);");let p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` - ${l} + `,c="result = activation(result);");let l=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` + ${u} void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; - int d1 = d2 / ${o}; - int q = d2 - d1 * ${o}; + int d1 = d2 / ${i}; + int q = d2 - d1 * ${i}; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; @@ -3063,14 +3063,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations. - for (int wR = 0; wR < ${s}; wR++) { + for (int wR = 0; wR < ${a}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } - for (int wC = 0; wC < ${i}; wC++) { + for (int wC = 0; wC < ${o}; wC++) { int xC = xCCorner + wC * dilations[1]; if (xC < 0 || xC >= inDims[1]) { @@ -3084,30 +3084,30 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } float result = dotProd; - ${p} - ${u} + ${l} + ${c} setOutput(result); } - `}},BA=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=` + `}},yD=class{constructor(e,t=!1,n=null,r=!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=e.outShape,this.enableShapeUniforms=xn(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,p=l,d=` int xR; int xC; int xCOffset; - vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { - `;for(let g=0;g<(d+1)/2;g++){let b=g*2;if(c+=` - xC = xCCorner + ${b*l}; - `,o===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xCOffset, d1); @@ -3119,9 +3119,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } xTexelC${b}Ready = 1; } - `,l===1&&b>0?c+=` + `,u===1&&b>0?d+=` xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy); - `:c+=` + `:d+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { @@ -3137,7 +3137,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } else { xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy); } - `):c+=` + `):d+=` if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { @@ -3147,7 +3147,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } xC${b} = xTexelC${b}; - `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { @@ -3160,7 +3160,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } xTexelC${b+1}Ready = 1; } - `,l>1?c+=` + `,u>1?d+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); @@ -3168,11 +3168,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } else { xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy); } - `:c+=` + `:d+=` xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy); - `):y===1?c+=` + `):y===1?d+=` xC${b+1} = xTexelC${b}; - `:c+=` + `:d+=` xCOffset = xC + ${y}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { @@ -3184,7 +3184,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } xC${b+1} = xTexelC${b+1}; - `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xCOffset, d1); @@ -3207,14 +3207,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw); - `,b+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy); - `)):(c+=` + `)):(d+=` if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { @@ -3234,27 +3234,27 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel xC${b} = vec4( xTexelC${b}.xy, xTexelC${b+1}.xy); - `,b+1`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 BA(d):c=new WA(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 vne={kernelName:Gi,backendName:"webgl",kernelFunc:xne},wne=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 dre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:c}=r,l=u;l==null&&(l=[1,1]),w.assert(T.eitherStridesOrDilationsAreOne(o,l),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let p=T.computeConv2DInfo(s.shape,a.shape,o,l,i,c,!0),d;G().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new yD(p):d=new bD(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[s,a],"float32",h)}var pre={kernelName:qo,backendName:"webgl",kernelFunc:dre},hre=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int dm = coords.w; - int d2 = d1 * ${s} + dm; + int d2 = d1 * ${a} + dm; float dotProd = 0.0; // TO DO: Vec4 over the batch size for (int b = 0; b < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { - int xR = wR + yR * ${t} - ${a}; + int xR = wR + yR * ${t} - ${r}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { - int xC = wC + yC * ${n} - ${r}; + int xC = wC + yC * ${n} - ${s}; if (xC < 0 || xC >= ${e.inWidth}) { continue; @@ -3312,8 +3312,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}},kne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` - const ivec2 pads = ivec2(${s}, ${i}); + `}},fre=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=` + const ivec2 pads = ivec2(${a}, ${o}); void main() { ivec4 coords = getOutputCoords(); @@ -3326,7 +3326,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { - float dyR = float(dyRCorner + wR) / ${a}.0; + float dyR = float(dyRCorner + wR) / ${r}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; @@ -3336,7 +3336,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { - float dyC = float(dyCCorner + wC) / ${r}.0; + float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { @@ -3347,8 +3347,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int wCPerm = ${n} - 1 - wC; // TO DO: Vec4 over the channelMul - for (int dm = 0; dm < ${o}; dm++) { - int d2 = d1 * ${o} + dm; + for (int dm = 0; dm < ${i}; dm++) { + int d2 = d1 * ${i} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; @@ -3357,15 +3357,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}};function Ine(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 wne(d);return n.runWebGLProgram(c,[r,s],"float32")}var Sne={kernelName:zm,backendName:"webgl",kernelFunc:Ine};function Nne(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 kne(d);return n.runWebGLProgram(c,[r,s],"float32")}var Tne={kernelName:Wm,backendName:"webgl",kernelFunc:Nne},Cne=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function mre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:c,filterShape:l}=r,p=T.computeConv2DInfo(s.shape,l,o,i,u,c,!0),d=new hre(p);return n.runWebGLProgram(d,[s,a],"float32")}var gre={kernelName:Bf,backendName:"webgl",kernelFunc:mre};function bre(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:c,inputShape:l}=r,p=T.computeConv2DInfo(l,a.shape,o,i,u,c,!0),d=new fre(p);return n.runWebGLProgram(d,[s,a],"float32")}var yre={kernelName:zf,backendName:"webgl",kernelFunc:bre},vre=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 _ne(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=ce({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new Cne(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 Ene={kernelName:Vc,backendName:"webgl",kernelFunc:_ne},Ane=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}, ${d}); + `}};function xre(e){let{inputs:t,backend:n}=e,{x:r}=t,s=[...r.shape,...r.shape],a=w.sizeFromShape(r.shape),o=pe({inputs:{x:r},backend:n,attrs:{shape:[a]}}),i=new vre(a),u=n.runWebGLProgram(i,[o],o.dtype),c=pe({inputs:{x:u},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),c}var wre={kernelName:Gd,backendName:"webgl",kernelFunc:xre},Ire=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:s,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:u,dilationWidth:c}=e,{top:l,left:p}=r;this.userCode=` + const ivec2 strides = ivec2(${s}, ${a}); + const ivec2 pads = ivec2(${l}, ${p}); const float neg_infinity = -3.4e38; void main() { @@ -3378,12 +3378,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; - for (int h = 0; h < ${i}; h++) { - int hIn = hBeg + h * ${l}; + for (int h = 0; h < ${o}; h++) { + int hIn = hBeg + h * ${u}; if (hIn >= 0 && hIn < ${t}) { - for (int w = 0; w < ${o}; w++) { - int wIn = wBeg + w * ${u}; + for (int w = 0; w < ${i}; w++) { + int wIn = wBeg + w * ${c}; if (wIn >= 0 && wIn < ${n}) { float xVal = getX(batch, hIn, wIn, d1); @@ -3401,7 +3401,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel float result = curVal; setOutput(result); } - `}};function Fne(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 Ane(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 $ne={kernelName:Hi,backendName:"webgl",kernelFunc:Fne};function Dne(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=tg({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 Rne={kernelName:Bm,backendName:"webgl",kernelFunc:Dne},Mne="return (x >= 0.0) ? x : (exp(x) - 1.0);",Pne=` + `}};function kre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:u}=r,c=T.computeDilation2DInfo(s.shape,a.shape,o,i,"NHWC",u),l,p=new Ire(c);l=n.runWebGLProgram(p,[s,a],"float32");let d=pe({inputs:{x:l},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(l),d}var Sre={kernelName:Ko,backendName:"webgl",kernelFunc:kre};function Cre(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:u}=T.decodeEinsumEquation(s,a.length);T.checkEinsumDimSizes(o.length,u,a);let{path:c,steps:l}=T.getEinsumComputePath(i,u),p=l.length,d=null,h=o.length,f=[];for(let g=0;g=0&&(d=ng({inputs:{x:d},backend:n,attrs:{axis:c[g]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let g of f)g!==d&&n.disposeIntermediateTensorInfo(g);return d}var Tre={kernelName:Vf,backendName:"webgl",kernelFunc:Cre},Nre="return (x >= 0.0) ? x : (exp(x) - 1.0);",_re=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3410,29 +3410,29 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,One=Ze({opSnippet:Mne,packedOpSnippet:Pne}),Lne={kernelName:ji,backendName:"webgl",kernelFunc:One},zne="return (b >= 0.0) ? a : a * (b + 1.0);",Wne=` +`,Ere=Ze({opSnippet:Nre,packedOpSnippet:_re}),Are={kernelName:Yo,backendName:"webgl",kernelFunc:Ere},Dre="return (b >= 0.0) ? a : a * (b + 1.0);",$re=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,Bne=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Np(Wne,a.shape,r.shape):new Ii(zne,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},Vne={kernelName:Iu,backendName:"webgl",kernelFunc:Bne},Une=` +`,Fre=e=>{let{inputs:t,backend:n}=e,{dy:r,y:s}=t,a=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Tl($re,r.shape,s.shape):new To(Dre,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)},Rre={kernelName:Sc,backendName:"webgl",kernelFunc:Fre},Pre=` return vec4(equal(a, b)); -`,Gne="return float(a == b);",Hne=fn({opSnippet:Gne,packedOpSnippet:Une,dtype:"bool",cpuKernelImpl:w9}),qne={kernelName:Su,backendName:"webgl",kernelFunc:Hne},jne=` +`,Ore="return float(a == b);",Mre=mn({opSnippet:Ore,packedOpSnippet:Pre,dtype:"bool",cpuKernelImpl:hQ}),Lre={kernelName:Cc,backendName:"webgl",kernelFunc:Mre},Bre=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. - float p = ${N.ERF_P}; - float a1 = ${N.ERF_A1}; - float a2 = ${N.ERF_A2}; - float a3 = ${N.ERF_A3}; - float a4 = ${N.ERF_A4}; - float a5 = ${N.ERF_A5}; + float p = ${T.ERF_P}; + float a1 = ${T.ERF_A1}; + float a2 = ${T.ERF_A2}; + float a3 = ${T.ERF_A3}; + float a4 = ${T.ERF_A4}; + float a5 = ${T.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,Kne=Ze({opSnippet:jne}),Xne={kernelName:Ki,backendName:"webgl",kernelFunc:Kne},Yne=Tp+` +`,zre=Ze({opSnippet:Bre}),Wre={kernelName:Zo,backendName:"webgl",kernelFunc:zre},Vre=Nl+` return exp(x); -`,Zne=` +`,Ure=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3441,21 +3441,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel result.a = isNaN.a ? x.a : result.a; return result; -`,VA=Ze({opSnippet:Yne,packedOpSnippet:Zne,cpuKernelImpl:k9,dtype:"float32"}),Jne={kernelName:Xi,backendName:"webgl",kernelFunc:VA};function yv(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ce({inputs:{x:s},backend:a,attrs:{shape:o}})}var Qne={kernelName:Nu,backendName:"webgl",kernelFunc:yv},FS="return exp(x) - 1.0;",eae=Ze({opSnippet:FS,packedOpSnippet:FS,cpuKernelImpl:I9}),tae={kernelName:Yi,backendName:"webgl",kernelFunc:eae},$S=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 = ${r}; +`,vD=Ze({opSnippet:Vre,packedOpSnippet:Ure,cpuKernelImpl:fQ,dtype:"float32"}),Gre={kernelName:Jo,backendName:"webgl",kernelFunc:vD};function kx(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=s;return s<0&&(w.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+s+1),i.splice(u,0,1),pe({inputs:{x:a},backend:r,attrs:{shape:i}})}var Hre={kernelName:Tc,backendName:"webgl",kernelFunc:kx},V1="return exp(x) - 1.0;",jre=Ze({opSnippet:V1,packedOpSnippet:V1,cpuKernelImpl:mQ}),qre={kernelName:Qo,backendName:"webgl",kernelFunc:jre},U1=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${r}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` + const float exponentMultiplier = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { - ${i} + ${o} } float mulMatDFT(int batch, int index) { - float indexRatio = float(index) / float(${a}); + float indexRatio = float(index) / float(${r}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; - for (int i = 0; i < ${a}; i++) { + for (int i = 0; i < ${r}; i++) { // x = (-2|2 * PI / N) * index * i; float x = exponentMultiplierTimesIndexRatio * float(i); float expR = cos(x); @@ -3464,7 +3464,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel float imag = getImag(batch, i); result += - unaryOpComplex(real, expR, imag, expI) / ${s}; + unaryOpComplex(real, expR, imag, expI) / ${a}; } return result; @@ -3474,12 +3474,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function UA(e,t,n){let a=n.texData.get(e.dataId),r=w.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 $S("real",l,t),p=new $S("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=Ms({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 nae(e){let{inputs:t,backend:n}=e,{input:a}=t;return UA(a,!1,n)}var aae={kernelName:Vm,backendName:"webgl",kernelFunc:nae},rae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function xD(e,t,n){let r=n.texData.get(e.dataId),s=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=s/a,i=pe({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),u=i.shape,c=new U1("real",u,t),l=new U1("imag",u,t),p=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:u},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(l,p,"float32"),f=Oa({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let g=pe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),g}function Kre(e){let{inputs:t,backend:n}=e,{input:r}=t;return xD(r,!1,n)}var Xre={kernelName:Uf,backendName:"webgl",kernelFunc:Kre},Yre=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 Ld(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new rae(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var sae={kernelName:Uc,backendName:"webgl",kernelFunc:Ld},iae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function Lp(e){let{backend:t,attrs:n}=e,{shape:r,value:s}=n,{dtype:a}=n;if(a=a||w.inferDtype(s),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(r));return o.fill(s),t.makeTensorInfo(r,a,o)}else{let o=new Yre(r,s),i=[[s]];return t.runWebGLProgram(o,[],a,i)}}var Zre={kernelName:Hd,backendName:"webgl",kernelFunc:Lp},Jre=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]; @@ -3493,7 +3493,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(outputValue); } - `}},oae={kernelName:Tu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new iae(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},DS="return floor(x);",lae=Ze({opSnippet:DS,packedOpSnippet:DS,cpuKernelImpl:S9}),uae={kernelName:Zi,backendName:"webgl",kernelFunc:lae},pae=` + `}},Qre={kernelName:Nc,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new Jre(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},G1="return floor(x);",ese=Ze({opSnippet:G1,packedOpSnippet:G1,cpuKernelImpl:gQ}),tse={kernelName:ei,backendName:"webgl",kernelFunc:ese},nse=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3503,7 +3503,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } else { return NAN; } -`,cae=` +`,rse=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3524,13 +3524,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,dae=fn({opSnippet:pae,packedOpSnippet:cae,dtype:"int32"}),hae={kernelName:Ji,backendName:"webgl",kernelFunc:dae},mae=class{constructor(e){this.variableNames=["A"];let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` +`,sse=mn({opSnippet:nse,packedOpSnippet:rse,dtype:"int32"}),ase={kernelName:ti,backendName:"webgl",kernelFunc:sse},ose=class{constructor(e){this.variableNames=["A"];let t=An(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; - vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0); + vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; @@ -3546,7 +3546,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel setOutput(floor(value * 255.0 + 0.5)); } - `}},fae=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` + `}},ise=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=An(),[n,r]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3561,7 +3561,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / - vec2(${a}.0, ${n}.0); + vec2(${r}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { @@ -3580,39 +3580,39 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel ${t.output} = result; } - `}},gae={kernelName:im,backendName:"webgl",kernelFunc:bae},Rl,Nx=G().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function bae(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],d=[u,l,s];if(o||i){let f=G().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Rl==null||f!==Nx)&&(Nx=f,Rl=document.createElement("canvas").getContext("2d",{willReadFrequently:Nx})),Rl.canvas.width=l,Rl.canvas.height=u,Rl.drawImage(r,0,0,l,u),r=Rl.canvas}let c=n.makeTensorInfo(p,"int32");n.texData.get(c.dataId).usage=ha.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=G().getBool("WEBGL_PACK")?new fae(d):new mae(d),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function yae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=N.convertConv2DDataFormat(p),g=N.computeConv2DInfo(r.shape,s.shape,l,d,u,c,!1,f),b,y=[],x=i!=null,v=o!=null,I=h==="leakyrelu",T=()=>{let E=[r,s],F=(D,$)=>{if($==="NCHW"&&D.shape.length===1&&D.shape[0]!==1){let S=ce({inputs:{x:D},backend:n,attrs:{shape:[D.shape[0],1,1]}});return y.push(S),S}return D};if(x&&E.push(F(i,p)),v&&E.push(F(o,p)),I){let D=n.makeTensorInfo([],"float32",w.createScalarValue(m,"float32"));E.push(D),y.push(D)}return E};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=OA({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(g.strideWidth<=2&&f==="channelsLast"&&G().getBool("WEBGL_EXP_CONV")){let E=h?Fc(h,!0):null,F=new PA(g,x,E,v,I),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=T();b=n.runWebGLProgram(F,$,"float32",D)}else if(G().getBool("WEBGL_CONV_IM2COL"))b=LA({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let E=h?Fc(h,!1):null,F=new MA(g,x,E,v,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 xae={kernelName:li,backendName:"webgl",kernelFunc:yae};function vae(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]),w.assert(N.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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+ flattenIndex += index * ${this.strides[o]};`;this.userCode=` void main() { - ${r} coords = getOutputCoords(); + ${s} coords = getOutputCoords(); int flattenIndex = 0; bool out_of_bounds = false; - ${s} + ${a} setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1])); } - `}};function Iae(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=w.sizeFromShape(a.shape),[l,u,p,d]=N.prepareAndValidate(a,r),c=ce({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),h=ce({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape)/p,p]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(a),x=N9(b,y,a.dtype,u,i,p,d,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let m=new kae(i,d,[u,p],a.shape),f=n.runWebGLProgram(m,[h,c],h.dtype),g=ce({inputs:{x:f},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),g}var Sae={kernelName:_u,backendName:"webgl",kernelFunc:Iae},Nae=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=dt(this.rank),a=Tae(e,2);this.userCode=` + `}};function mse(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=s.shape,o=a[a.length-1],i=w.sizeFromShape(r.shape),[u,c,l,p]=T.prepareAndValidate(r,s),d=pe({inputs:{x:s},backend:n,attrs:{shape:[c,o]}}),h=pe({inputs:{x:r},backend:n,attrs:{shape:[w.sizeFromShape(r.shape)/l,l]}});if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.readSync(s.dataId),y=n.bufferSync(r),v=bQ(b,y,r.dtype,c,o,l,p,r.shape,i);return n.makeTensorInfo(u,r.dtype,v.values)}let f=new fse(o,p,[c,l],r.shape),g=n.runWebGLProgram(f,[h,d],h.dtype),m=pe({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(g),m}var gse={kernelName:Ec,backendName:"webgl",kernelFunc:mse},bse=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ht(this.rank),r=yse(e,2);this.userCode=` void main() { ${n} resRC = getOutputCoords(); int index = int(getIndices(resRC.x, resRC.z)); float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0; - setOutput(inBounds * getA(${a})); + setOutput(inBounds * getA(${r})); } - `}};function Tae(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r=0,()=>`GatherV2: the index value ${I} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=w.sizeFromShape(s.shape),d=[],c=ce({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=ce({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,p/u.batchSize]}});d.push(c),d.push(h);let m=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let y=n.bufferSync(h),x=n.bufferSync(c),v=T9(x,y,m);return d.forEach(I=>n.disposeIntermediateTensorInfo(I)),n.makeTensorInfo(u.outputShape,v.dtype,v.values)}let f=new Nae(c.shape,m),g=n.runWebGLProgram(f,[c,h],c.dtype);d.push(g);let b=ce({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var Cae={kernelName:Cu,backendName:"webgl",kernelFunc:GA},_ae="return float(a > b);",Eae=` + `}};function yse(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let c=T.segment_util.collectGatherOpShapeInfo(s,a,u,i),l=w.sizeFromShape(a.shape),p=[],d=pe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=pe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,l/c.batchSize]}});p.push(d),p.push(h);let f=[c.batchSize,c.outerSize,l/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([s,a])||s.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=yQ(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let g=new bse(d.shape,f),m=n.runWebGLProgram(g,[d,h],d.dtype);p.push(m);let b=pe({inputs:{x:m},backend:n,attrs:{shape:c.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var vse={kernelName:_c,backendName:"webgl",kernelFunc:wD},xse="return float(a > b);",wse=` return vec4(greaterThan(a, b)); -`,Aae=fn({opSnippet:_ae,packedOpSnippet:Eae,cpuKernelImpl:C9,dtype:"bool"}),Fae={kernelName:Eu,backendName:"webgl",kernelFunc:Aae},$ae="return float(a >= b);",Dae=` +`,Ise=mn({opSnippet:xse,packedOpSnippet:wse,cpuKernelImpl:vQ,dtype:"bool"}),kse={kernelName:Ac,backendName:"webgl",kernelFunc:Ise},Sse="return float(a >= b);",Cse=` return vec4(greaterThanEqual(a, b)); -`,Rae=fn({opSnippet:$ae,packedOpSnippet:Dae,dtype:"bool",cpuKernelImpl:_9}),Mae={kernelName:eo,backendName:"webgl",kernelFunc:Rae};function Pae(e){let{inputs:t,backend:n}=e,{input:a}=t;return UA(a,!0,n)}var Oae={kernelName:Um,backendName:"webgl",kernelFunc:Pae},Lae="return float(!isnan(x) && !isinf(x));",zae=Ze({opSnippet:Lae,dtype:"bool"}),Wae={kernelName:no,backendName:"webgl",kernelFunc:zae},Bae="return float(isinf(x));",Vae=Ze({opSnippet:Bae,dtype:"bool"}),Uae={kernelName:ao,backendName:"webgl",kernelFunc:Vae},Gae="return float(isnan(x));",Hae=Ze({opSnippet:Gae,dtype:"bool"}),qae={kernelName:ro,backendName:"webgl",kernelFunc:Hae},jae="return float(a < b);",Kae=` +`,Tse=mn({opSnippet:Sse,packedOpSnippet:Cse,dtype:"bool",cpuKernelImpl:xQ}),Nse={kernelName:ri,backendName:"webgl",kernelFunc:Tse};function _se(e){let{inputs:t,backend:n}=e,{input:r}=t;return xD(r,!0,n)}var Ese={kernelName:Gf,backendName:"webgl",kernelFunc:_se},Ase="return float(!isnan(x) && !isinf(x));",Dse=Ze({opSnippet:Ase,dtype:"bool"}),$se={kernelName:ai,backendName:"webgl",kernelFunc:Dse},Fse="return float(isinf(x));",Rse=Ze({opSnippet:Fse,dtype:"bool"}),Pse={kernelName:oi,backendName:"webgl",kernelFunc:Rse},Ose="return float(isnan(x));",Mse=Ze({opSnippet:Ose,dtype:"bool"}),Lse={kernelName:ii,backendName:"webgl",kernelFunc:Mse},Bse="return float(a < b);",zse=` return vec4(lessThan(a, b)); -`,Xae=fn({opSnippet:jae,packedOpSnippet:Kae,cpuKernelImpl:E9,dtype:"bool"}),Yae={kernelName:Au,backendName:"webgl",kernelFunc:Xae},Zae="return float(a <= b);",Jae=` +`,Wse=mn({opSnippet:Bse,packedOpSnippet:zse,cpuKernelImpl:wQ,dtype:"bool"}),Vse={kernelName:Dc,backendName:"webgl",kernelFunc:Wse},Use="return float(a <= b);",Gse=` return vec4(lessThanEqual(a, b)); -`,Qae=fn({opSnippet:Zae,packedOpSnippet:Jae,cpuKernelImpl:A9,dtype:"bool"}),ere={kernelName:Fu,backendName:"webgl",kernelFunc:Qae};function tre(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=F9(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var nre={kernelName:$u,backendName:"webgl",kernelFunc:tre},are=Tp+` +`,Hse=mn({opSnippet:Use,packedOpSnippet:Gse,cpuKernelImpl:IQ,dtype:"bool"}),jse={kernelName:$c,backendName:"webgl",kernelFunc:Hse};function qse(e){let{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=kQ(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}var Kse={kernelName:Fc,backendName:"webgl",kernelFunc:qse},Xse=Nl+` return x < 0.0 ? 0./0. : log(x); -`,rre=` +`,Yse=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -3620,18 +3620,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel 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; -`,sre=Ze({opSnippet:are,packedOpSnippet:rre,cpuKernelImpl:$9}),ire={kernelName:io,backendName:"webgl",kernelFunc:sre},ore=Tp+` +`,Zse=Ze({opSnippet:Xse,packedOpSnippet:Yse,cpuKernelImpl:SQ}),Jse={kernelName:ci,backendName:"webgl",kernelFunc:Zse},Qse=Nl+` return log(1.0 + x); -`,lre=Ze({opSnippet:ore}),ure={kernelName:oo,backendName:"webgl",kernelFunc:lre},pre="return float(a >= 1.0 && b >= 1.0);",cre=` +`,eae=Ze({opSnippet:Qse}),tae={kernelName:li,backendName:"webgl",kernelFunc:eae},nae="return float(a >= 1.0 && b >= 1.0);",rae=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,dre=fn({opSnippet:pre,packedOpSnippet:cre,dtype:"bool"}),hre={kernelName:Du,backendName:"webgl",kernelFunc:dre},mre="return float(!(x >= 1.0));",fre=Ze({opSnippet:mre}),gre={kernelName:Ru,backendName:"webgl",kernelFunc:fre},bre="return float(a >= 1.0 || b >= 1.0);",yre=` +`,sae=mn({opSnippet:nae,packedOpSnippet:rae,dtype:"bool"}),aae={kernelName:Rc,backendName:"webgl",kernelFunc:sae},oae="return float(!(x >= 1.0));",iae=Ze({opSnippet:oae}),uae={kernelName:Pc,backendName:"webgl",kernelFunc:iae},cae="return float(a >= 1.0 || b >= 1.0);",lae=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,xre=fn({opSnippet:bre,packedOpSnippet:yre,dtype:"bool"}),vre={kernelName:Mu,backendName:"webgl",kernelFunc:xre},wre=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,dae=mn({opSnippet:cae,packedOpSnippet:lae,dtype:"bool"}),pae={kernelName:Oc,backendName:"webgl",kernelFunc:dae},hae=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${u})`:s===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3640,17 +3640,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; - for (int j = -${s}; j <= ${s}; j++) { + for (int j = -${a}; j <= ${a}; j++) { int idx = d + j; - if (idx >= 0 && idx <= ${i}) { + if (idx >= 0 && idx <= ${o}) { float z = getX(b, r, c, idx); sum += z * z; } } - float val = x * ${o}; + float val = x * ${i}; setOutput(val); } - `}},kre=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + `}},fae=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${u})`:s===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3674,7 +3674,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); - int firstChannel = d - ${s}; + int firstChannel = d - ${a}; vec2 cache = vec2(0.); if(firstChannel >= 0){ vec4 firstChannelFrag = getX(b, r, c, firstChannel); @@ -3685,10 +3685,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } ivec2 depth = ivec2(d, d + 1); - for (int j = - ${s}; j <= ${s}; j++) { + for (int j = - ${a}; j <= ${a}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); - bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i})); + bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o})); bool depthInRange = aboveLowerBound.x && belowUpperBound.x; bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; @@ -3709,10 +3709,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel sum += z * z; } } - vec4 result = xAtOutputCoords * ${o}; + vec4 result = xAtOutputCoords * ${i}; setOutput(result); } - `}},Ire=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new kre(r.shape,s,i,o,l):new wre(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},Sre={kernelName:lo,backendName:"webgl",kernelFunc:Ire},Nre=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=` + `}},mae=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{depthRadius:a,bias:o,alpha:i,beta:u}=r,c=G().getBool("WEBGL_PACK_NORMALIZATION")?new fae(s.shape,a,o,i,u):new hae(s.shape,a,o,i,u);return n.runWebGLProgram(c,[s],s.dtype)},gae={kernelName:di,backendName:"webgl",kernelFunc:mae},bae=class{constructor(e,t,n,r,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=s,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3741,19 +3741,19 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } } - norm = float(${a}) * norm + float(${n}); + norm = float(${r}) * norm + float(${n}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ - float dyi = -2.0 * float(${a}) - * float(${r}) + float dyi = -2.0 * float(${r}) + * float(${s}) * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { - dyi += pow(norm, -1.0 * ${r}); + dyi += pow(norm, -1.0 * ${s}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); @@ -3767,17 +3767,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(result); } - `}},Tre=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 Nre(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},Cre={kernelName:Pu,backendName:"webgl",kernelFunc:Tre};function _re(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=tl(i,e.dtype,"max",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function HA(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.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:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:u,alpha:c,beta:l}=r,p=new bae(s.shape,i,u,c,l);return n.runWebGLProgram(p,[s,a,o],s.dtype)},vae={kernelName:Mc,backendName:"webgl",kernelFunc:yae};function xae(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=pe({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),u=ru(i,e.dtype,"max",r),c=pe({inputs:{x:u},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(u),c}function ID(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r,i=s.shape.length,u=w.parseAxisParam(a,s.shape),c=u,l=T.getAxesPermutation(c,i),p=l!=null,d=n.shouldExecuteOnCPU([s]),h=s;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(i);for(let N=0;N`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&&w.arraysEqual(p.inShape,p.outShape))return ra({inputs:{x:r},backend:n});let d=new $c(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Mre={kernelName:co,backendName:"webgl",kernelFunc:Rre};function Pre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new ck(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Ore={kernelName:Ou,backendName:"webgl",kernelFunc:Pre},Lre=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` - const ivec2 pads = ivec2(${i}, ${o}); +`,Sae=mn({opSnippet:Iae,packedOpSnippet:kae,cpuKernelImpl:TQ}),Cae={kernelName:hi,backendName:"webgl",kernelFunc:Sae};function Tae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;wl(s,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=r,c=1;w.assert(T.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let l=T.computePool2DInfo(s.shape,a,o,c,i,u);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return sr({inputs:{x:s},backend:n});let p=new Rd(l,"max",!1);return n.runWebGLProgram(p,[s],s.dtype)}var Nae={kernelName:fi,backendName:"webgl",kernelFunc:Tae};function _ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dataFormat:u,dimRoundingMode:c}=r,l=[1,1,1],p=T.computePool3DInfo(s.shape,a,o,l,i,c,u),d=new v0(p,"max",!1);return n.runWebGLProgram(d,[s],s.dtype)}var Eae={kernelName:Lc,backendName:"webgl",kernelFunc:_ae},Aae=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=s-1-e.padInfo.top,i=a-1-e.padInfo.left,u=s*a-1;this.userCode=` + const ivec2 pads = ivec2(${o}, ${i}); void main() { ivec4 coords = getOutputCoords(); @@ -3791,8 +3791,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${r}; - wR += ${a}) { + for (int wR = 0; wR < ${s}; + wR += ${r}) { float dyR = float(dyRCorner + wR) / ${t}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { @@ -3800,7 +3800,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } int idyR = int(dyR); - for (int wC = 0; wC < ${s}; wC++) { + for (int wC = 0; wC < ${a}; wC++) { float dyC = float(dyCCorner + wC) / ${n}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || @@ -3810,11 +3810,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); - int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d)); + int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. - int curPosValue = wR * ${s} + wC; + int curPosValue = wR * ${a} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; @@ -3822,8 +3822,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}},zre=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,d=l-1-e.padInfo.top,c=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` - const ivec3 pads = ivec3(${p}, ${d}, ${c}); + `}},Dae=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=i-1-e.padInfo.front,p=u-1-e.padInfo.top,d=c-1-e.padInfo.left,h=i*u*c-1;this.userCode=` + const ivec3 pads = ivec3(${l}, ${p}, ${d}); void main() { ivec5 coords = getOutputCoords(); @@ -3840,8 +3840,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wD = 0; wD < ${o}; - wD += ${r}) { + for (int wD = 0; wD < ${i}; + wD += ${s}) { float dyD = float(dyDCorner + wD) / ${t}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { @@ -3849,8 +3849,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } int idyD = int(dyD); - for (int wR = 0; wR < ${l}; - wR += ${s}) { + for (int wR = 0; wR < ${u}; + wR += ${a}) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || @@ -3859,9 +3859,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } int idyR = int(dyR); - for (int wC = 0; wC < ${u}; - wC += ${i}) { - float dyC = float(dyCCorner + wC) / ${a}.0; + for (int wC = 0; wC < ${c}; + wC += ${o}) { + float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { @@ -3876,8 +3876,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // Get the current value, check it against the value from the // position matrix. int curPosValue = - wD * ${l} * ${u} + - wR * ${u} + wC; + wD * ${u} * ${c} + + wR * ${c} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; @@ -3886,107 +3886,107 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel } setOutput(dotProd); } - `}};function Wre(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 ck(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new zre(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Bre={kernelName:Hc,backendName:"webgl",kernelFunc:Wre};function Vre(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;vp([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=N.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new $c(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new Lre(c),b=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),b}var Ure={kernelName:Gc,backendName:"webgl",kernelFunc:Vre};function Gre(e,t,n,a){let r=new $c(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new $c(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Hre={kernelName:qc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];w.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]=Gre(a,o,p,l);return[d,c]}};function qre(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ce({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=tl(i,"float32","mean",a),l=ce({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var jre={kernelName:ho,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=w.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,v=new Array(o);for(let C=0;C{let{x:r}=e,{filterSize:s,strides:a,pad:o,includeBatchInIndex:i}=t,u=n;w.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];w.assert(T.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=T.computePool2DInfo(r.shape,s,a,c,o),[p,d]=Oae(r,i,l,u);return[p,d]}};function Lae(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=pe({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),u=ru(i,"float32","mean",r),c=pe({inputs:{x:u},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(u),c}var Bae={kernelName:mi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:s,axis:a}=t,o=n,i=r.shape.length,u=w.parseAxisParam(a,r.shape),c=u,l=T.getAxesPermutation(c,i),p=l!=null,d=o.shouldExecuteOnCPU([r]),h=[],f=r;if(p){if(d){let x=o.texData.get(f.dataId).values,k=new Array(i);for(let E=0;Eu[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}; +`,Gae=mn({opSnippet:Vae,packedOpSnippet:Uae,cpuKernelImpl:NQ}),Hae={kernelName:bi,backendName:"webgl",kernelFunc:Gae},jae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,l)=>c[0]+e[l]+c[1]);let r=e.length,s=ht(r),a=t.map(c=>c[0]).join(","),o=t.map((c,l)=>c[0]+e[l]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),u=n==="reflect"?0:1;if(r===1){this.userCode=` + int start = ${a}; + int end = ${o}; void main() { int outC = getOutputCoords(); if (outC < start) { - outC = start * 2 - outC - ${l}; + outC = start * 2 - outC - ${u}; } else if(outC >= end) { - outC = (end - 1) * 2 - outC + ${l}; + outC = (end - 1) * 2 - outC + ${u}; } setOutput(getX(outC - start)); } `;return}this.userCode=` - ${r} start = ${r}(${s}); - ${r} end = ${r}(${i}); + ${s} start = ${s}(${a}); + ${s} end = ${s}(${o}); void main() { - ${r} outC = getOutputCoords(); - for (int i = 0; i < ${a}; i++) { + ${s} outC = getOutputCoords(); + for (int i = 0; i < ${r}; i++) { if (outC[i] < start[i]) { - outC[i] = start[i] * 2 - outC[i] - ${l}; + outC[i] = start[i] * 2 - outC[i] - ${u}; } else if(outC[i] >= end[i]) { - outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; + outC[i] = (end[i] - 1) * 2 - outC[i] + ${u}; } } - ${r} coords = outC - start; - setOutput(getX(${o})); + ${s} coords = outC - start; + setOutput(getX(${i})); } - `}},tse=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=` - ${r} source = rc; + `}},qae=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let r=e.length,s=ht(r),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Cn("rc",r),u=Cn("source",r),c=`${i[r-1]} < ${this.outputShape[r-1]}`,l=r===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(r===1){let h=` + ${s} source = rc; if (source < start) { - source = start * 2 - source - ${d}; + source = start * 2 - source - ${p}; } else if (source >= end) { - source = (end - 1) * 2 - source + ${d}; + source = (end - 1) * 2 - source + ${p}; } source -= start; - `;c=` - ${r} rc = outputLoc; + `;d=` + ${s} rc = outputLoc; ${h} - result[0] = getChannel(getX(${l.join()}), ${p}); - ${o[a-1]} += 1; - if(${u}) { + result[0] = getChannel(getX(${u.join()}), ${l}); + ${i[r-1]} += 1; + if(${c}) { ${h} - result[1] = getChannel(getX(${l.join()}), ${p}); + result[1] = getChannel(getX(${u.join()}), ${l}); } `}else{let h=` - ${r} source = rc; - ${r} lt = ${r}(lessThan(source, start)); - ${r} gte = ${r}(greaterThanEqual(source, end)); - ${r} orig = 1 - (lt + gte); + ${s} source = rc; + ${s} lt = ${s}(lessThan(source, start)); + ${s} gte = ${s}(greaterThanEqual(source, end)); + ${s} orig = 1 - (lt + gte); source = orig * source + - lt * (start * 2 - source - ${d}) + - gte * ((end - 1) * 2 - source + ${d}); + lt * (start * 2 - source - ${p}) + + gte * ((end - 1) * 2 - source + ${p}); source -= start; - `;c=` - ${r} rc = outputLoc; + `;d=` + ${s} rc = outputLoc; ${h} - result[0] = getChannel(getX(${l.join()}), ${p}); - ${o[a-1]} += 1; - if(${u}) { + result[0] = getChannel(getX(${u.join()}), ${l}); + ${i[r-1]} += 1; + if(${c}) { ${h} - result[1] = getChannel(getX(${l.join()}), ${p}); + result[1] = getChannel(getX(${u.join()}), ${l}); } rc = outputLoc; - ${o[a-2]} += 1; - if(${o[a-2]} < ${this.outputShape[a-2]}) { + ${i[r-2]} += 1; + if(${i[r-2]} < ${this.outputShape[r-2]}) { ${h} - result[2] = getChannel(getX(${l.join()}), ${p}); - ${o[a-1]} += 1; - if(${u}) { + result[2] = getChannel(getX(${u.join()}), ${l}); + ${i[r-1]} += 1; + if(${c}) { ${h} - result[3] = getChannel(getX(${l.join()}), ${p}); + result[3] = getChannel(getX(${u.join()}), ${l}); } } `}this.userCode=` - const ${r} start = ${r}(${s}); - const ${r} end = ${r}(${i}); + const ${s} start = ${s}(${a}); + const ${s} end = ${s}(${o}); void main() { - ${r} outputLoc = getOutputCoords(); + ${s} outputLoc = getOutputCoords(); vec4 result = vec4(0.); - ${c} + ${d} setOutput(result); } - `}},nse=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tse(a.shape,r,s):new ese(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},ase={kernelName:go,backendName:"webgl",kernelFunc:nse},rse=`if (b == 0.0) return NAN; - return mod(a, b);`,sse=` + `}},Kae=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:s,mode:a}=n,o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qae(r.shape,s,a):new jae(r.shape,s,a);return t.runWebGLProgram(o,[r],r.dtype)},Xae={kernelName:yi,backendName:"webgl",kernelFunc:Kae},Yae=`if (b == 0.0) return NAN; + return mod(a, b);`,Zae=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+el+` + `+nu+` return result; -`,ise=fn({opSnippet:rse,packedOpSnippet:sse}),ose={kernelName:bo,backendName:"webgl",kernelFunc:ise},lse=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` +`,Jae=mn({opSnippet:Yae,packedOpSnippet:Zae}),Qae={kernelName:vi,backendName:"webgl",kernelFunc:Jae},eoe=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4006,11 +4006,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Pee=Ze({opSnippet:Mee}),Oee={kernel // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}},use=` + `}},toe=` if (a == b) { return 1.0; }; -return a / b;`,pse=` +return a / b;`,noe=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4028,9 +4028,9 @@ return a / b;`,pse=` } return result; -`,qA=fn({opSnippet:use,packedOpSnippet:pse,checkOutOfBounds:!0}),cse={kernelName:qi,backendName:"webgl",kernelFunc:qA},RS="return a - b;",jA=fn({opSnippet:RS,packedOpSnippet:RS,supportsComplex:!0,cpuKernelImpl:nQ}),dse={kernelName:Vo,backendName:"webgl",kernelFunc:jA};function KA(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=HA({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=jA({inputs:{a:r,b:u},backend:n}),d=VA({inputs:{x:p},backend:n}),c=tg({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=ce({inputs:{x:c},backend:n,attrs:{shape:l}}),m=qA({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 hse={kernelName:Wo,backendName:"webgl",kernelFunc:KA};function mse(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:KA({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new lse(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var fse={kernelName:Lu,backendName:"webgl",kernelFunc:mse},gse=Oa+` +`,kD=mn({opSnippet:toe,packedOpSnippet:noe,checkOutOfBounds:!0}),roe={kernelName:Xo,backendName:"webgl",kernelFunc:kD},H1="return a - b;",SD=mn({opSnippet:H1,packedOpSnippet:H1,supportsComplex:!0,cpuKernelImpl:KQ}),soe={kernelName:Gi,backendName:"webgl",kernelFunc:SD};function CD(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=w.parseAxisParam([a],s.shape),i=ID({inputs:{x:s},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),u=T.expandShapeToKeepDim(i.shape,o),c=pe({inputs:{x:i},backend:n,attrs:{shape:u}}),l=SD({inputs:{a:s,b:c},backend:n}),p=vD({inputs:{x:l},backend:n}),d=ng({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=pe({inputs:{x:d},backend:n,attrs:{shape:u}}),f=kD({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var aoe={kernelName:Vi,backendName:"webgl",kernelFunc:CD};function ooe(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r,u=i?s:CD({inputs:{logits:s},backend:n,attrs:{dim:s.shape.length-1}}),c=u.shape[0],l=u.shape[1],p=new eoe(c,l,a),d=[[o]],h=n.runWebGLProgram(p,[u],"int32",d);return i||n.disposeIntermediateTensorInfo(u),h}var ioe={kernelName:Bc,backendName:"webgl",kernelFunc:ooe},uoe=Or+` return -x; -`,bse=` +`,coe=` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4040,16 +4040,16 @@ return a / b;`,pse=` result.a = isNaN.a ? x.a : result.a; return result; -`;function yse(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=O9(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new os(a.shape,bse):r=new ir(a.shape,gse),n.runWebGLProgram(r,[a],a.dtype)}var xse={kernelName:zu,backendName:"webgl",kernelFunc:yse},vse=gr.nonMaxSuppressionV3Impl;function wse(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}=vse(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var kse={kernelName:Bu,backendName:"webgl",kernelFunc:wse},Ise=gr.nonMaxSuppressionV4Impl;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,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=Ise(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Nse={kernelName:Vu,backendName:"webgl",kernelFunc:Sse},Tse=gr.nonMaxSuppressionV5Impl;function Cse(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}=Tse(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var _se={kernelName:Uu,backendName:"webgl",kernelFunc:Cse},Ese=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`;function loe(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.texData.get(r.dataId),[o,i]=EQ(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new oa(r.shape,coe):s=new is(r.shape,uoe),n.runWebGLProgram(s,[r],r.dtype)}var doe={kernelName:zc,backendName:"webgl",kernelFunc:loe},poe=gs.nonMaxSuppressionV3Impl;function hoe(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=r,c=n.readSync(s.dataId),l=n.readSync(a.dataId),{selectedIndices:p}=poe(c,l,o,i,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var foe={kernelName:Vc,backendName:"webgl",kernelFunc:hoe},moe=gs.nonMaxSuppressionV4Impl;function goe(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,padToMaxOutputSize:c}=r,l=n.readSync(s.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=moe(l,p,o,i,u,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var boe={kernelName:Uc,backendName:"webgl",kernelFunc:goe},yoe=gs.nonMaxSuppressionV5Impl;function voe(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:c}=r,l=n.readSync(s.dataId),p=n.readSync(a.dataId),d=o,h=i,f=u,g=c,{selectedIndices:m,selectedScores:b}=yoe(l,p,d,h,f,g);return[n.makeTensorInfo([m.length],"int32",new Int32Array(m)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var xoe={kernelName:Gc,backendName:"webgl",kernelFunc:voe},woe=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); - setOutput(mix(float(${a}), float(${n}), + setOutput(mix(float(${r}), float(${n}), float(index == coords.y))); } - `}},Ase=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=w.sizeFromShape(r.shape),p=new Ese(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},Fse={kernelName:xo,backendName:"webgl",kernelFunc:Ase};function Fm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Od({inputs:{input:a},backend:n}),s=Fm({inputs:{x:r},backend:n}),i=ng({inputs:{input:a},backend:n}),o=Fm({inputs:{x:i},backend:n}),l=Ms({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Ld({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var $se={kernelName:lp,backendName:"webgl",kernelFunc:Fm};function XA(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=Od({inputs:{input:a},backend:n}),s=XA({inputs:{x:r},backend:n}),i=ng({inputs:{input:a},backend:n}),o=Fm({inputs:{x:i},backend:n}),l=Ms({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Ld({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var Dse={kernelName:Gu,backendName:"webgl",kernelFunc:XA};function Rse(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return yv({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=yv({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=RA({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Mse={kernelName:Hu,backendName:"webgl",kernelFunc:Rse},Pse=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}; + `}},Ioe=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{dtype:a,depth:o,onValue:i,offValue:u}=r,c=w.sizeFromShape(s.shape),l=new woe(c,o,i,u),p=pe({inputs:{x:s},backend:n,attrs:{shape:[c]}}),d=n.runWebGLProgram(l,[p],a);n.disposeIntermediateTensorInfo(p);let h=[...s.shape,o],f=pe({inputs:{x:d},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(d),f},koe={kernelName:wi,backendName:"webgl",kernelFunc:Ioe};function Df(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=Mp({inputs:{input:r},backend:n}),a=Df({inputs:{x:s},backend:n}),o=rg({inputs:{input:r},backend:n}),i=Df({inputs:{x:o},backend:n}),u=Oa({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}else return Lp({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var Soe={kernelName:cl,backendName:"webgl",kernelFunc:Df};function TD(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let s=Mp({inputs:{input:r},backend:n}),a=TD({inputs:{x:s},backend:n}),o=rg({inputs:{input:r},backend:n}),i=Df({inputs:{x:o},backend:n}),u=Oa({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}else return Lp({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var Coe={kernelName:Hc,backendName:"webgl",kernelFunc:TD};function Toe(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return kx({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(l=>{w.assertShapesMatch(a,l.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],u=t.map(l=>{let p=kx({inputs:{input:l},backend:n,attrs:{dim:s}});return i.push(p),p}),c=dD({inputs:u,backend:n,attrs:{axis:s}});return i.forEach(l=>n.disposeIntermediateTensorInfo(l)),c}var Noe={kernelName:jc,backendName:"webgl",kernelFunc:Toe},_oe=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,s=ht(r),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=` + int start = ${a}; + int end = ${o}; void main() { int outC = getOutputCoords(); @@ -4060,43 +4060,43 @@ return a / b;`,pse=` } } `;return}this.userCode=` - ${r} start = ${r}(${s}); - ${r} end = ${r}(${i}); + ${s} start = ${s}(${a}); + ${s} end = ${s}(${o}); void main() { - ${r} outC = getOutputCoords(); + ${s} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { - ${r} coords = outC - start; - setOutput(getX(${o})); + ${s} coords = outC - start; + setOutput(getX(${i})); } } - `}},Ose=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(${u}) { - `,a===1?"":`} + `}},Eoe=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,g)=>f[0]+e[g]+f[1]);let r=e.length,s=ht(r),a=t.map(f=>f[0]).join(","),o=t.map((f,g)=>f[0]+e[g]).join(","),i=Cn("rc",r),u=Cn("source",r),c=`${i[r-1]} < ${this.outputShape[r-1]}`,l=r===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${s} rc = outputLoc;`,`${i[r-1]} += 1; + if(${c}) { + `,r===1?"":`} rc = outputLoc; - ${o[a-2]} += 1; - if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1; - if(${u}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,g=r===1?2:4;f{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return Ld({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ose(r.shape,s,i):new Pse(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},Lse={kernelName:vo,backendName:"webgl",kernelFunc:YA},zse=` + `}},ND=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r;if(w.sizeFromShape(s.shape)===0){let c=a.map((l,p)=>l[0]+s.shape[p]+l[1]);return Lp({backend:n,attrs:{shape:c,value:o,dtype:s.dtype}})}let i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Eoe(s.shape,a,o):new _oe(s.shape,a,o),u=[[o]];return n.runWebGLProgram(i,[s],s.dtype,u)},Aoe={kernelName:Ii,backendName:"webgl",kernelFunc:ND},Doe=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4105,7 +4105,7 @@ return a / b;`,pse=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,Wse=` +`,$oe=` // 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); @@ -4121,11 +4121,11 @@ return a / b;`,pse=` 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); - `+el+` + `+nu+` return result; -`,Bse=fn({opSnippet:zse,packedOpSnippet:Wse}),Vse={kernelName:wo,backendName:"webgl",kernelFunc:Bse};function Use(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=w.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}=z9(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,b,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=w.sizeFromShape(f),b=ce({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),y=Ym(r.dtype),x=tl(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 Gse={kernelName:Io,backendName:"webgl",kernelFunc:Use};function Hse(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]=W9(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 qse={kernelName:Hm,backendName:"webgl",kernelFunc:Hse};function jse(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]=B9(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 Kse={kernelName:qm,backendName:"webgl",kernelFunc:jse};function Xse(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]=V9(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var Yse={kernelName:jm,backendName:"webgl",kernelFunc:Xse},ZA=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=U9(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Zse={kernelName:jc,backendName:"webgl",kernelFunc:ZA},Jse="return 1.0 / x;",Qse=Ze({opSnippet:Jse}),eie={kernelName:So,backendName:"webgl",kernelFunc:Qse},tie=Oa+` +`,Foe=mn({opSnippet:Doe,packedOpSnippet:$oe}),Roe={kernelName:ki,backendName:"webgl",kernelFunc:Foe};function Poe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,u=[],c=w.parseAxisParam(a,s.shape),l=c,p=T.getAxesPermutation(l,i),d=s;p!=null&&(d=Tn({inputs:{x:s},backend:n,attrs:{perm:p}}),l=T.getInnerMostAxes(l.length,i),u.push(d)),T.assertAxesAreInnerMostDims("prod",l,i);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:g,outShape:m,outDtype:b}=DQ(d.shape,d.dtype,f,l);h=n.makeTensorInfo(m,b,g)}else{let[f,g]=T.computeOutAndReduceShapes(d.shape,l),m=w.sizeFromShape(g),b=pe({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),y=Zf(s.dtype),v=ru(b,y,"prod",n);h=pe({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(o){u.push(h);let f=T.expandShapeToKeepDim(h.shape,c);h=pe({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Ooe={kernelName:Ci,backendName:"webgl",kernelFunc:Poe};function Moe(e){let{inputs:t,backend:n,attrs:r}=e,{paramsNestedSplits:s,paramsDenseValues:a,indices:o}=t,{outputRaggedRank:i}=r,u=s.map(b=>n.readSync(b.dataId)),c=s.map(b=>b.shape),l=n.readSync(a.dataId),p=n.readSync(o.dataId),[d,h,f]=$Q(u,c,l,a.shape,a.dtype,p,o.shape,i),g=d.map(b=>n.makeTensorInfo([b.length],"int32",b)),m=n.makeTensorInfo(f,a.dtype,h);return g.concat([m])}var Loe={kernelName:jf,backendName:"webgl",kernelFunc:Moe};function Boe(e){let{inputs:t,backend:n}=e,{starts:r,limits:s,deltas:a}=t,o=n.readSync(r.dataId),i=n.readSync(s.dataId),u=n.readSync(a.dataId),[c,l]=FQ(o,r.shape,r.dtype,i,s.shape,u,a.shape),p=n.makeTensorInfo([c.length],"int32",c),d=n.makeTensorInfo([l.length],r.dtype,l);return[p,d]}var zoe={kernelName:qf,backendName:"webgl",kernelFunc:Boe};function Woe(e){let{inputs:t,backend:n,attrs:r}=e,{shape:s,values:a,defaultValue:o,rowPartitionTensors:i}=t,{rowPartitionTypes:u}=r,c=n.readSync(s.dataId),l=n.readSync(a.dataId),p=n.readSync(o.dataId),d=i.map(m=>n.readSync(m.dataId)),h=i.map(m=>m.shape),[f,g]=RQ(c,s.shape,l,a.shape,a.dtype,p,o.shape,d,h,u);return n.makeTensorInfo(f,a.dtype,g)}var Voe={kernelName:Kf,backendName:"webgl",kernelFunc:Woe},_D=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=PQ(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},Uoe={kernelName:Xd,backendName:"webgl",kernelFunc:_D},Goe="return 1.0 / x;",Hoe=Ze({opSnippet:Goe}),joe={kernelName:Ti,backendName:"webgl",kernelFunc:Hoe},qoe=Or+` return (x < 0.0) ? 0.0 : x; -`,nie=` +`,Koe=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4135,9 +4135,9 @@ return a / b;`,pse=` result.a = isNaN.a ? x.a : result.a; return result; -`,aie=Ze({opSnippet:tie,packedOpSnippet:nie}),rie={kernelName:No,backendName:"webgl",kernelFunc:aie},sie=Oa+` +`,Xoe=Ze({opSnippet:qoe,packedOpSnippet:Koe}),Yoe={kernelName:Ni,backendName:"webgl",kernelFunc:Xoe},Zoe=Or+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,iie=` +`,Joe=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4147,11 +4147,11 @@ return a / b;`,pse=` result.a = isNaN.a ? x.a : result.a; return result; -`,oie=Ze({opSnippet:sie,packedOpSnippet:iie}),lie={kernelName:_o,backendName:"webgl",kernelFunc:oie},uie=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=` +`,Qoe=Ze({opSnippet:Zoe,packedOpSnippet:Joe}),eie={kernelName:Ai,backendName:"webgl",kernelFunc:Qoe},tie=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let c=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],p;s?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( - ${u[0]/p[0]}, - ${u[1]/p[1]}); - const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); + ${c[0]/l[0]}, + ${c[1]/l[1]}); + const vec2 inputShapeRC = vec2(${o}.0, ${i}.0); void main() { ivec4 coords = getOutputCoords(); @@ -4160,7 +4160,7 @@ return a / b;`,pse=` ivec2 yRC = coords.yz; // Fractional source index. - vec2 sourceFracIndexRC = ${d}; + vec2 sourceFracIndexRC = ${p}; // Compute the four integer indices. ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0))); @@ -4180,13 +4180,13 @@ return a / b;`,pse=` setOutput(newValue); } - `}},pie=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=` + `}},nie=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let c=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],p;s?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( - ${u[0]/p[0]}, - ${u[1]/p[1]}, - ${u[1]/p[1]}); - const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, - ${o}.0); + ${c[0]/l[0]}, + ${c[1]/l[1]}, + ${c[1]/l[1]}); + const vec3 inputShapeRC = vec3(${o}.0, ${i}.0, + ${i}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); @@ -4200,7 +4200,7 @@ return a / b;`,pse=` ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. - vec3 sourceFracIndexRC = ${d}; + vec3 sourceFracIndexRC = ${p}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); @@ -4208,7 +4208,7 @@ return a / b;`,pse=` min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); // Should we calculate next column and row elements in 2x2 packed cell. - bool hasNextCol = d < ${l-1}; + bool hasNextCol = d < ${u-1}; bool hasNextRow = coords.z < ${n-1}; // In parallel, construct four corners for all four components in @@ -4257,7 +4257,7 @@ return a / b;`,pse=` setOutput(newValue); } - `}};function cie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new pie(r.shape,l,u,s,i):new uie(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var die={kernelName:Co,backendName:"webgl",kernelFunc:cie},hie=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function rie(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[u,c]=i,l=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new nie(s.shape,u,c,a,o):new tie(s.shape,u,c,a,o);return n.runWebGLProgram(l,[s],"float32")}var sie={kernelName:Ei,backendName:"webgl",kernelFunc:rie},aie=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/u[0],l=i[1]/u[1],p=1/c,d=1/l,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4267,14 +4267,14 @@ return a / b;`,pse=` float accumulator = 0.0; - const float heightScale = float(${u}); - const float widthScale = float(${p}); + const float heightScale = float(${c}); + const float widthScale = float(${l}); - const float invHeightScale = float(${d}); - const float invWidthScale = float(${c}); + const float invHeightScale = float(${p}); + const float invWidthScale = float(${d}); const int winHeight = int(${h}); - const int winWidth = int(${m}); + const int winWidth = int(${f}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); @@ -4288,7 +4288,7 @@ return a / b;`,pse=` int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy - if (dyR < 0 || dyR >= ${s}) { + if (dyR < 0 || dyR >= ${a}) { continue; } @@ -4296,19 +4296,19 @@ return a / b;`,pse=` int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy - if (dyC < 0 || dyC >= ${i}) { + if (dyC < 0 || dyC >= ${o}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); - int bottomDxRIndex = int(min(ceil(dxR), ${a-1}.0)); + int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0)); float dxRLerp = dxR - float(topDxRIndex); float inverseDxRLerp = 1.0 - dxRLerp; float dxC = float(dyC) * widthScale; int leftDxCIndex = int(floor(dxC)); - int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0)); + int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; @@ -4338,11 +4338,11 @@ return a / b;`,pse=` setOutput(accumulator); } - `}};function mie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new hie(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var fie={kernelName:Ku,backendName:"webgl",kernelFunc:mie},gie=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 oie(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new aie(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var iie={kernelName:Xc,backendName:"webgl",kernelFunc:oie},uie=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let c=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( - ${u[0]/p[0]}, - ${u[1]/p[1]}); - const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); + ${c[0]/l[0]}, + ${c[1]/l[1]}); + const vec2 inputShapeRC = vec2(${o}.0, ${i}.0); void main() { ivec4 coords = getOutputCoords(); @@ -4351,22 +4351,22 @@ return a / b;`,pse=` ivec2 yRC = coords.yz; // Fractional source index. - vec2 sourceFracIndexRC = ${c}; + vec2 sourceFracIndexRC = ${d}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( - min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); + min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } - `}},bie=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=` + `}},cie=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let c=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0",d;s?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( - ${u[0]/p[0]}, - ${u[1]/p[1]}, - ${u[1]/p[1]}); - const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, - ${o}.0); + ${c[0]/l[0]}, + ${c[1]/l[1]}, + ${c[1]/l[1]}); + const vec3 inputShapeRC = vec3(${o}.0, ${i}.0, + ${i}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); @@ -4380,14 +4380,14 @@ return a / b;`,pse=` ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. - vec3 sourceFracIndexRC = ${c}; + vec3 sourceFracIndexRC = ${d}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( - min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); + min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p}))); // Should we calculate next column and row elements in 2x2 packed cell. - bool hasNextCol = d < ${l-1}; + bool hasNextCol = d < ${u-1}; bool hasNextRow = coords.z < ${n-1}; vec4 newValue = vec4( @@ -4401,7 +4401,7 @@ return a / b;`,pse=` setOutput(newValue); } - `}};function yie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new bie(r.shape,l,u,s,i):new gie(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var xie={kernelName:To,backendName:"webgl",kernelFunc:yie},vie=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function lie(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[u,c]=i,l=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new cie(s.shape,u,c,a,o):new uie(s.shape,u,c,a,o);return n.runWebGLProgram(l,[s],s.dtype)}var die={kernelName:_i,backendName:"webgl",kernelFunc:lie},pie=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/u[0],l=i[1]/u[1],p=1/c,d=1/l,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4411,14 +4411,14 @@ return a / b;`,pse=` float accumulator = 0.0; - const float heightScale = float(${u}); - const float widthScale = float(${p}); + const float heightScale = float(${c}); + const float widthScale = float(${l}); - const float invHeightScale = float(${d}); - const float invWidthScale = float(${c}); + const float invHeightScale = float(${p}); + const float invWidthScale = float(${d}); const int winHeight = int(${h}); - const int winWidth = int(${m}); + const int winWidth = int(${f}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); @@ -4432,7 +4432,7 @@ return a / b;`,pse=` int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy - if (dyR < 0 || dyR >= ${s}) { + if (dyR < 0 || dyR >= ${a}) { continue; } @@ -4440,25 +4440,25 @@ return a / b;`,pse=` int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy - if (dyC < 0 || dyC >= ${i}) { + if (dyC < 0 || dyC >= ${o}) { continue; } float sourceFracRow = - float(${o[0]}) * - (float(dyR) / float(${l[0]})); + float(${i[0]}) * + (float(dyR) / float(${u[0]})); float sourceFracCol = - float(${o[1]}) * - (float(dyC) / float(${l[1]})); + float(${i[1]}) * + (float(dyC) / float(${u[1]})); int sourceNearestRow = int(min( - float(int(${a}) - 1), + float(int(${r}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( - float(int(${r}) - 1), + float(int(${s}) - 1), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); @@ -4471,23 +4471,23 @@ return a / b;`,pse=` setOutput(accumulator); } - `}};function wie(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new vie(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var kie={kernelName:ju,backendName:"webgl",kernelFunc:wie},Iie=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function hie(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new pie(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var fie={kernelName:Kc,backendName:"webgl",kernelFunc:hie},mie=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)); } - `;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=dt(n);this.userCode=` + `;return}let r=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,s=e.map((o,i)=>r(i)).join(","),a=ht(n);this.userCode=` void main() { - ${s} coords = getOutputCoords(); - setOutput(getX(${r})); + ${a} coords = getOutputCoords(); + setOutput(getX(${s})); } - `}},Sie=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 < ${this.outputShape[n-2]}`,i=dt(n);n===1?this.userCode=` + `}},gie=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=Cn("rc",n),s=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,o=ht(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${e[0]} - rc - 1), ${e[0]} - rc - 1); - if(${r}){ + if(${s}){ result.g = getChannel(getX(${e[0]} - (rc + 1) - 1), ${e[0]} - (rc + 1) - 1); } @@ -4495,21 +4495,21 @@ return a / b;`,pse=` } `:this.userCode=` void main() { - ${i} rc = getOutputCoords(); + ${o} rc = getOutputCoords(); vec4 result = vec4(0.); - result.r = ${o(a.slice())}; - if(${r}){ - result.g = ${l(a.slice())}; + result.r = ${i(r.slice())}; + if(${s}){ + result.g = ${u(r.slice())}; } - if(${s}) { - result.b = ${u(a.slice())}; - if(${r}) { - result.a = ${p(a.slice())}; + if(${a}) { + result.b = ${c(r.slice())}; + if(${s}) { + result.a = ${l(r.slice())}; } } setOutput(result); } - `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((b,y)=>c(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function Nie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return ra({inputs:{x:r},backend:n});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Sie(r.shape,o):new Iie(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var Tie={kernelName:Eo,backendName:"webgl",kernelFunc:Nie},Cie=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 = ${t.toFixed(2)};`:r=` + `;function i(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),g=f.join(","),m=f.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${m}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function bie(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:a}=r,o=s.shape.length,i=w.parseAxisParam(a,s.shape);if(o===0)return sr({inputs:{x:s},backend:n});let u=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gie(s.shape,i):new mie(s.shape,i);return n.runWebGLProgram(u,[s],s.dtype)}var yie={kernelName:Di,backendName:"webgl",kernelFunc:bie},vie=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],r=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4522,13 +4522,13 @@ return a / b;`,pse=` (float(y) - params[1]) * params[3]; int coordX = int(round(coordXFloat + params[0])); int coordY = int(round(coordYFloat + params[1])); - ${r} - if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) { + ${s} + if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } - `}},_ie={kernelName:up,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new Cie(a.shape,s),[u,p]=N.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},Eie=` + `}},xie={kernelName:ll,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,u=new vie(r.shape,a),[c,l]=T.getImageCenter(o,r.shape[1],r.shape[2]),p=[[c,l,Math.sin(s),Math.cos(s)]];return i.runWebGLProgram(u,[r],r.dtype,p)}},wie=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4543,38 +4543,38 @@ return a / b;`,pse=` return base + 1.0; } } -`,Aie=Ze({opSnippet:Eie}),Fie={kernelName:Ao,backendName:"webgl",kernelFunc:Aie},$ie="return inversesqrt(x);",Die=Ze({opSnippet:$ie,cpuKernelImpl:G9}),Rie={kernelName:Fo,backendName:"webgl",kernelFunc:Die},dk=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=dt(r.length),u=dt(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${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}); +`,Iie=Ze({opSnippet:wie}),kie={kernelName:$i,backendName:"webgl",kernelFunc:Iie},Sie="return inversesqrt(x);",Cie=Ze({opSnippet:Sie,cpuKernelImpl:OQ}),Tie={kernelName:Fi,backendName:"webgl",kernelFunc:Cie},x0=class{constructor(e,t,n,r,s,a,o=!0,i=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let u=ht(s.length),c=ht(a.length),l="";n===1?l="i":n===2&&(l="i, j");let p=`getIndices(${l})`,d="";r===1?d="i":r===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,f="";i&&(f="coords[0], coords[1]");let g=`getDefaultValue(${f})`,m=t>1?"strides[j]":"strides";this.userCode=` + ${u} strides = ${u}(${s}); void main() { - ${u} coords = getOutputCoords(); + ${c} coords = getOutputCoords(); float sum = 0.0; bool found = false; for (int i = 0; i < ${e}; i++) { int flattenedIndex = 0; for (int j = 0; j < ${t}; j++) { - int index = round(${d}); - flattenedIndex += index * ${g}; + int index = round(${p}); + flattenedIndex += index * ${m}; } if (flattenedIndex == coords[0]) { sum += ${h}; found = true; } } - setOutput(mix(${f}, sum, float(found))); + setOutput(mix(${g}, sum, float(found))); } - `}},Mie=class{constructor(e,t,n,a,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;let l=dt(r.length),u=dt(s.length),p="";n===1?p="i":n===2&&(p="i, j");let d=`getIndices(${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}); + `}},Nie=class{constructor(e,t,n,r,s,a,o=!0,i=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=ht(s.length),c=ht(a.length),l="";n===1?l="i":n===2&&(l="i, j");let p=`getIndices(${l})`,d="";r===1?d="i":r===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,f="";i&&(f="coords[0], coords[1]");let g=`getDefaultValue(${f})`,m=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` + ${u} strides = ${u}(${s}); void main() { - ${u} coords = getOutputCoords(); + ${c} coords = getOutputCoords(); vec4 sum = vec4(0.); vec4 found = vec4(0.); for (int i = 0; i < ${e}; i+=2) { ivec2 flattenedIndex = ivec2(0); for (int j = 0; j < ${t}; j+=2) { - ivec4 index = round(${d}); - flattenedIndex += index.xz * ${g}; + ivec4 index = round(${p}); + flattenedIndex += index.xz * ${m}; if (j + 1 < ${t}) { flattenedIndex += index.yw * ${b}; } @@ -4598,16 +4598,16 @@ return a / b;`,pse=` } } } - setOutput(mix(${f}, sum, found)); + setOutput(mix(${g}, sum, found)); } - `}};function Pie(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 Mie(l,o,h.shape.length,m.shape.length,p,c):g=new dk(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 Oie={kernelName:Xu,backendName:"webgl",kernelFunc:Pie},Lie=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 _ie(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:u,sliceSize:c,strides:l,outputSize:p}=T.calculateShapes(a,s,o),d=[p/c,c];if(p===0)return n.makeTensorInfo(o,s.dtype);let h=pe({inputs:{x:s},backend:n,attrs:{shape:[u,i]}}),f=pe({inputs:{x:a},backend:n,attrs:{shape:[u,c]}}),g=n.makeTensorInfo([],"float32",new Float32Array([0])),m;G().getBool("WEBGL_PACK")?m=new Nie(u,i,h.shape.length,f.shape.length,l,d):m=new x0(u,i,h.shape.length,f.shape.length,l,d);let b=n.runWebGLProgram(m,[f,h,g],f.dtype),y=pe({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(g),y}var Eie={kernelName:Yc,backendName:"webgl",kernelFunc:_ie},Aie=class{constructor(e,t,n,r){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,o=G().getNumber("WEBGL_VERSION")===2?s:a,i=r==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; int mid; - ${i} + ${o} mid = (left + right) / 2; - if (getSortedSequence(batch, mid) ${o} value) { + if (getSortedSequence(batch, mid) ${i} value) { left = mid + 1; } else { right = mid; @@ -4625,25 +4625,25 @@ return a / b;`,pse=` setOutput(float(findBound(batch, value))); } - `}};function zie(e){let{inputs:t,backend:n,attrs:a}=e,{sortedSequence:r,values:s}=t,{side:i}=a,o=new Lie(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return n.runWebGLProgram(o,[r,s],"int32",l)}var Wie={kernelName:Zu,backendName:"webgl",kernelFunc:zie},Bie=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 ${n} is not yet supported`);if(n===1)s="resRC",r="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],u=[];for(let c=0;c= 1.0) { - setOutput(getA(${r})); + setOutput(getA(${s})); } else { - setOutput(getB(${r})); + setOutput(getB(${s})); } } - `}};function Vie(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new Bie(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ba(r.dtype,s.dtype))}var Uie={kernelName:Ju,backendName:"webgl",kernelFunc:Vie},Gie=` + `}};function Rie(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new Fie(r.shape.length,s.shape,s.shape.length);return n.runWebGLProgram(o,[r,s,a],fr(s.dtype,a.dtype))}var Pie={kernelName:Qc,backendName:"webgl",kernelFunc:Rie},Oie=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 - float scaleAlpha = ${N.SELU_SCALEALPHA}; - float scale = ${N.SELU_SCALE}; + float scaleAlpha = ${T.SELU_SCALEALPHA}; + float scale = ${T.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,Hie=Ze({opSnippet:Gie}),qie={kernelName:$o,backendName:"webgl",kernelFunc:Hie},jie=Tp+` +`,Mie=Ze({opSnippet:Oie}),Lie={kernelName:Ri,backendName:"webgl",kernelFunc:Mie},Bie=Nl+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,Kie=` +`,zie=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4653,20 +4653,20 @@ return a / b;`,pse=` result.a = isNaN.a ? x.a : result.a; return result; -`,Xie=Ze({opSnippet:jie,packedOpSnippet:Kie,cpuKernelImpl:q9}),Yie={kernelName:Po,backendName:"webgl",kernelFunc:Xie},Zie=` +`,Wie=Ze({opSnippet:Bie,packedOpSnippet:zie,cpuKernelImpl:LQ}),Vie={kernelName:Li,backendName:"webgl",kernelFunc:Wie},Uie=` if (isnan(x)) { return 0.0; } return sign(x); -`,Jie=Ze({opSnippet:Zie}),Qie={kernelName:Mo,backendName:"webgl",kernelFunc:Jie},eoe=Tp+` +`,Gie=Ze({opSnippet:Uie}),Hie={kernelName:Mi,backendName:"webgl",kernelFunc:Gie},jie=Nl+` return sin(x); -`,toe=` +`,qie=` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${el} + ${nu} return result; -`,noe=Ze({opSnippet:eoe,packedOpSnippet:toe}),aoe={kernelName:Do,backendName:"webgl",kernelFunc:noe},roe=` +`,Kie=Ze({opSnippet:jie,packedOpSnippet:qie}),Xie={kernelName:Pi,backendName:"webgl",kernelFunc:Kie},Yie=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,soe=Ze({opSnippet:roe}),ioe={kernelName:Ro,backendName:"webgl",kernelFunc:soe},ooe=` +`,Zie=Ze({opSnippet:Yie}),Jie={kernelName:Oi,backendName:"webgl",kernelFunc:Zie},Qie=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4686,33 +4686,33 @@ return a / b;`,pse=` result = log(exp_x + 1.0); } return result; -`,loe=Ze({opSnippet:ooe}),uoe={kernelName:Oo,backendName:"webgl",kernelFunc:loe},poe=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;w.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},coe={kernelName:ep,backendName:"webgl",kernelFunc:poe};function doe(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: - ${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices 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${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=X9(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var foe={kernelName:np,backendName:"webgl",kernelFunc:moe};function goe(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape - ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=vA(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(p,a.dtype,u)}var boe={kernelName:Xc,backendName:"webgl",kernelFunc:goe};function yoe(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data 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c[c.length-1]=0,[n.makeTensorInfo(c,s.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(l===1)return[s,Lp({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let p=n.texData.get(s.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(s):s,g=w.sizeFromShape(c)/l,m=pe({inputs:{x:h},attrs:{shape:[g,l]},backend:n});d&&Xa(n,h);let b=K1(a),y=K1(l),v=null,x=()=>v===null?[m,m]:[m,v],k=(F,D,R)=>{let C=x(),L=new que(R),H=[[l],[v===null?1:0],[Number.NEGATIVE_INFINITY],[F],[D]],K=v;v=n.runWebGLProgram(L,C,"int32",H),Xa(n,K)};for(let F=1;F=1;R/=2)k(D,R,[g,y])}for(let F=y;F>b;F/=2){let D=x(),R=new Kue([g,F/2]),L=[[l],[v===null?1:0],[b]],U=v;v=n.runWebGLProgram(R,D,"int32",L),Xa(n,U);let H=b/2,K=H*2;for(let q=H;q>=1;q/=2)k(K,q,v.shape)}let S=v;v=_l({inputs:{x:v},backend:n,attrs:{begin:0,size:[g,a]}}),Xa(n,S);let N=wD({inputs:{x:m,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Xa(n,m);let E=c.slice(0,-1);E.push(a),S=v,v=pe({inputs:{x:v},attrs:{shape:E},backend:n}),Xa(n,S);let $=N;return N=pe({inputs:{x:N},attrs:{shape:E},backend:n}),Xa(n,$),[N,v]}var Yue={kernelName:ol,backendName:"webgl",kernelFunc:Xue},Zue=class{constructor(e,t,n,r,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(r){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; - if(${o} == 2) { + if(${i} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; @@ -4813,7 +4813,7 @@ return a / b;`,pse=` } } return clamp(inCoord, 0.0, len - 1.0); - } else if (${o} == 3) { + } else if (${i} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; @@ -4830,7 +4830,7 @@ return a / b;`,pse=` } } return clamp(inCoord, 0.0, len - 1.0); - } else if (${o} == 4) { + } else if (${i} == 4) { return clamp(outCoord, 0.0, len - 1.0); } else { return outCoord; @@ -4843,7 +4843,7 @@ return a / b;`,pse=` if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) { outputValue = getImage(batch, coordY, coordX, channel); } else { - outputValue = float(${r}); + outputValue = float(${s}); } return outputValue; } @@ -4867,14 +4867,14 @@ return a / b;`,pse=` float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { - outputValue = float(${r}); + outputValue = float(${s}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${t})); float mapY = mapCoord(inY, float(${e})); - if (${i} == 1) { + if (${o} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, @@ -4898,21 +4898,21 @@ return a / b;`,pse=` } setOutput(outputValue); } - `}};function 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cle={kernelName:op,backendName:"webgl",kernelFunc:ple},dle=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,p=n%4,d=` + `}};function Jue(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:u,outputShape:c}=r,[l,p,d,h]=s.shape,[f,g]=c!=null?c:[p,d],m=[l,f,g,h],b=new Zue(p,d,o,i,u,m);return n.runWebGLProgram(b,[s,a],"float32")}var Que={kernelName:il,backendName:"webgl",kernelFunc:Jue};function ece(e){let{inputs:t,attrs:n,backend:r}=e,{axis:s}=n,{x:a}=t;wl(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=r.readSync(a.dataId),{outputValues:i,outputShape:u,indices:c}=ZQ(o,s,a.shape,a.dtype);return[r.makeTensorInfo(u,a.dtype,i),r.makeTensorInfo([c.length],"int32",c)]}var tce={kernelName:sp,backendName:"webgl",kernelFunc:ece};function nce(e){let{inputs:t,backend:n,attrs:r}=e,{value:s}=t,{axis:a}=r;a<0&&(a+=s.shape.length);let o=s,i=o.shape.length,u=s.shape[a],c=new Array(i-1),l=0;for(let g=0;gn.disposeIntermediateTensorInfo(g)),f}var rce={kernelName:ul,backendName:"webgl",kernelFunc:nce},sce=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,s=e.inSize,a=e.numSegments,o=a*Math.ceil(s/n);this.outputShape=[r,o];let i="0.0",u="sumValue",c=Math.floor(n/4)*4,l=n%4,p=` sumValue += dot(values, segFilter); - `,c="";r%n>0&&(c=` - if (inIdx < 0 || inIdx >= ${r}) { + `,d="";s%n>0&&(d=` + if (inIdx < 0 || inIdx >= ${s}) { return initializationValue; } - `);let h="";r%n>0&&(h=` - if (inIdx < 0 || inIdx >= ${r}) { + `);let h="";s%n>0&&(h=` + if (inIdx < 0 || inIdx >= ${s}) { return -1.0; } `),this.userCode=` - const float initializationValue = ${o}; + const float initializationValue = ${i}; float getValue(int batch, int inIdx) { - ${c} + ${d} return getX(batch, inIdx); } @@ -4926,12 +4926,12 @@ return a / b;`,pse=` int batch = coords[0]; int outIdx = coords[1]; int inOffset = int(floor(float(outIdx) / float( - ${s})) * float(${n})); - int currentSeg = int(mod(float(outIdx), float(${s}))); + ${a})) * float(${n})); + int currentSeg = int(mod(float(outIdx), float(${a}))); float sumValue = 0.0; - for (int i = 0; i < ${u}; i += 4) { + for (int i = 0; i < ${c}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), @@ -4947,11 +4947,11 @@ return a / b;`,pse=` int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 ); - ${d} + ${p} } - int inIdx = inOffset + ${u}; - if (${p===1}) { + int inIdx = inOffset + ${c}; + if (${l===1}) { vec4 values = vec4( getValue(batch, inIdx), initializationValue, @@ -4968,8 +4968,8 @@ return a / b;`,pse=` 0 ); - ${d} - } else if (${p===2}) { + ${p} + } else if (${l===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), @@ -4984,8 +4984,8 @@ return a / b;`,pse=` 0 ); - ${d} - } else if (${p===3}) { + ${p} + } else if (${l===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), @@ -5000,10 +5000,10 @@ return a / b;`,pse=` 0 ); - ${d} + ${p} } - setOutput(${l}); + setOutput(${u}); } - `}};function hle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,p=N.getAxesPermutation([u],o),d=r;p!=null&&(d=Nn({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=N.getInnerMostAxes(1,o)[0]);let c=N.segment_util.computeOutShape(d.shape,u,i),h=w.sizeFromShape([d.shape[u]]),m=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Ym(r.dtype),g=(v,I,T,C,E)=>{let F=v.shape[0],D=v.shape[1],$=N.segment_util.segOpComputeOptimalWindowSize(D,E),S={windowSize:$,inSize:D,batchSize:F,numSegments:E},M=new dle(S,I),B=n.compileAndRun(M,[v,T],C);if(l.push(B),B.shape[1]===E)return B;let U=ZA({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),H=JA({inputs:{x:U},backend:n,attrs:{reps:[D/$]}});return 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t.dtype==="string"?d.stringBytes=l.slice(m,m+w.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+w.sizeFromShape(i))),u}if(t.dtype==="string"){let m=Nm(l,s,i,t.shape,t.dtype);return d.stringBytes=m,u}let c=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)oue(l,p[0],c,s,i);else if(h===3)lue(l,p[0],p[1],c,s,i);else if(h===4)uue(l,p[0],p[1],p[2],c,s,i);else{let m=Nm(l,s,i,t.shape,t.dtype);c.set(m)}return u}function oue(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;ub*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=Wn({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ws({inputs:{x:h},backend:n,attrs:{perm:u}}),f=Wn({inputs:{x:m},backend:n,attrs:{shape:p}}),g=Si({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeData(h.dataId),n.disposeData(m.dataId),n.disposeData(h.dataId),g}var due={kernelName:mu,backendName:"wasm",kernelFunc:cue},pF;function hue(e){pF=e.wasm.cwrap(fu,null,["number","number","boolean","number","number","number"])}function mue(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 pF(p(r),i,o,p(s),Qe[s.dtype],p(u)),u}var fue={kernelName:fu,backendName:"wasm",setupFunc:hue,kernelFunc:mue},gue=!0,bue=Ht(gu,gue);function yue(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 xue={kernelName:zc,backendName:"wasm",kernelFunc:yue};function Os(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 vue={kernelName:Pi,backendName:"wasm",kernelFunc:Os},wue=Xe(Oi),cF;function kue(e){cF=e.wasm.cwrap(Ss,null,["number","number","number","number"])}function Iue(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 cF(o,s,i,u),l}var Sue={kernelName:Ss,backendName:"wasm",setupFunc:kue,kernelFunc:Iue};function dF(e){let{inputs:t,backend:n}=e,a=w.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=>w.sizeFromShape(h.shape)>0);if(i.length===1)return ag({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(w.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let v=[-1,w.sizeFromShape(x.shape.slice(a))];return Wn({inputs:{x},backend:n,attrs:{shape:v}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));s=N.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=z1(m,s,t[0].dtype,f),b=N.computeOutShape(i.map(x=>x.shape),a);o.shape=b;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=N.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=w.sizeFromShape(i[0].shape.slice(0,a)),u=0,p=i.map(h=>{let m=w.sizeFromShape(h.shape.slice(a));return u+=m,m}),d=i.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(o);for(let h=0;h`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=ws({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumprod",[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;xF(m,i?1:0,o?1:0,h,f,Qe[r.dtype]);let g=c;if(u!==null){let b=N.getUndoAxesPermutation(u);g=ws({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Kue={kernelName:vu,backendName:"wasm",setupFunc:que,kernelFunc:jue},vF;function Xue(e){vF=e.wasm.cwrap(Ui,null,["number","number","number","number","number","number"])}function Yue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;w.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=ws({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;vF(m,i?1:0,o?1:0,h,f,Qe[r.dtype]);let g=c;if(u!==null){let b=N.getUndoAxesPermutation(u);g=ws({inputs:{x:c},attrs:{perm:b},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Zue={kernelName:Ui,backendName:"wasm",setupFunc:Xue,kernelFunc:Yue},wF;function Jue(e){wF=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function Que(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 wF(d(r),new Uint8Array(new Int32Array(r.shape).buffer),r.shape.length,i,l,d(s),Qe[s.dtype],o,d(p)),p}var epe={kernelName:Bc,backendName:"wasm",setupFunc:Jue,kernelFunc:Que},kF;function tpe(e){kF=e.wasm.cwrap(ku,null,["number","number","number","array","number","array","array","number","number"])}function npe(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(w.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return kF(g,s,i==="NHWC"?1:0,b,r.shape.length-1,y,x,m.length,v),f}var ape={kernelName:ku,backendName:"wasm",setupFunc:tpe,kernelFunc:npe},IF;function rpe(e){IF=e.wasm.cwrap(Gi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function spe(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,v=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 IF(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,b,y,x,D,v,I,T,C,E,F,S),$}var ipe={kernelName:Gi,backendName:"wasm",setupFunc:rpe,kernelFunc:spe},SF;function ope(e){SF=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function lpe(e){let{inputs:t,backend:n}=e,{x:a}=t,r=w.sizeFromShape(a.shape),s=n.makeOutput([...a.shape,...a.shape],a.dtype);return SF(n.dataIdMap.get(a.dataId).id,Qe[a.dtype],r,n.dataIdMap.get(s.dataId).id),s}var upe={kernelName:Vc,backendName:"wasm",setupFunc:ope,kernelFunc:lpe},NF;function ppe(e){NF=e.wasm.cwrap(Hi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cpe(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 NF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(p.dataId).id,Qe[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 dpe={kernelName:Hi,backendName:"wasm",setupFunc:ppe,kernelFunc:cpe},TF;function hpe(e){TF=e.wasm.cwrap(Ul,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mpe(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 TF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[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 fpe={kernelName:Ul,backendName:"wasm",setupFunc:hpe,kernelFunc:mpe},CF;function gpe(e){CF=e.wasm.cwrap(Vl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bpe(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 ${i.dtype}`);let p=N.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return CF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var ype={kernelName:Vl,backendName:"wasm",setupFunc:gpe,kernelFunc:bpe},xpe=Xe(ji),_F;function vpe(e){_F=e.wasm.cwrap(Iu,null,["number","number","number"])}function wpe(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return _F(i(r),i(a),i(s)),s}var kpe={kernelName:Iu,backendName:"wasm",setupFunc:vpe,kernelFunc:wpe},Ipe=!1,Spe=Ht(Su,Ipe,"bool"),Npe=Xe(Ki),Tpe=Xe(Xi,"float32");function vv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Wn({inputs:{x:r},backend:a,attrs:{shape:o}})}var Cpe={kernelName:Nu,backendName:"wasm",kernelFunc:vv},_pe=Xe(Yi,"float32");function EF(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 Epe={kernelName:Uc,backendName:"wasm",kernelFunc:EF},AF;function Ape(e){AF=e.wasm.cwrap(Tu,null,["number","number","number","number","number","number"])}function Fpe(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 AF(s,o,l,u,p,i),r}var $pe={kernelName:Tu,backendName:"wasm",kernelFunc:Fpe,setupFunc:Ape},Dpe=Xe(Zi),Rpe=!1,Mpe=Ht(Ji,Rpe),FF;function Ppe(e){FF=e.wasm.cwrap(Qi,null,["number","number","number","number","number","number","number"])}function Ope(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return FF(p,d,c,h,m,r,g),f}var Lpe={kernelName:Qi,backendName:"wasm",setupFunc:Ppe,kernelFunc:Ope},$F;function zpe(e){$F=e.wasm.cwrap(li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Wpe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Rc[h];if(g==null)throw new Error(`${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,v=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 (${x})`);v=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=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 $F(b,j,K,Z,y,I,T,v,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Bpe={kernelName:li,backendName:"wasm",setupFunc:zpe,kernelFunc:Wpe},DF;function Vpe(e){DF=e.wasm.cwrap(ui,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 Upe(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=Rc[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,v=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 (${x})`);v=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=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:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return DF(b,j,K,Z,y,I,T,v,C,E,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Gpe={kernelName:ui,backendName:"wasm",setupFunc:Vpe,kernelFunc:Upe},RF;function Hpe(e){RF=e.wasm.cwrap(_u,null,["number","number","number","number","number","number","array","number"])}function qpe(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Jw.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 RF(c,Qe[a.dtype],h,i,d,o,m,f),u}var jpe={kernelName:_u,backendName:"wasm",setupFunc:Hpe,kernelFunc:qpe},MF;function Kpe(e){MF=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Xpe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.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, ${p-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Wn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=w.sizeFromShape(s.shape),m=Wn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,v=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer);return MF(y,Qe[r.dtype],I,b,x,d.batchSize,T,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var Ype={kernelName:Cu,backendName:"wasm",setupFunc:Kpe,kernelFunc:Xpe},Zpe=!1,Jpe=Ht(Eu,Zpe,"bool"),Qpe=!1,ece=Ht(eo,Qpe,"bool"),tce=Xe(no,"bool"),nce=Xe(ao,"bool"),ace=Xe(ro,"bool"),PF;function rce(e){PF=e.wasm.cwrap(so,null,["number","number","number","number"])}function sce(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;PF(r,Qe[t.dtype],n,i)}return s}var ice={kernelName:so,backendName:"wasm",setupFunc:rce,kernelFunc:sce},oce=!1,lce=Ht(Au,oce,"bool"),uce=!1,pce=Ht(Fu,uce,"bool"),OF;function cce(e){OF=e.wasm.cwrap($u,null,["number","number","number","number"])}function dce(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return OF(n.dataIdMap.get(o.dataId).id,a,r,i),o}var hce={kernelName:$u,backendName:"wasm",setupFunc:cce,kernelFunc:dce},mce=Xe(io),fce=Xe(oo),gce=!1,bce=Ht(Du,gce,"bool"),yce=Xe(Ru),xce=!1,vce=Ht(Mu,xce,"bool"),wce=!1,kce=Ht(rN,wce,"bool"),LF;function Ice(e){LF=e.wasm.cwrap(lo,null,["number","number","number","number","number","number","number"])}function Sce(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 LF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var Nce={kernelName:lo,backendName:"wasm",setupFunc:Ice,kernelFunc:Sce},zF;function Tce(e){zF=e.wasm.cwrap(Pu,null,["number","number","number","number","number","number","number","number","number"])}function Cce(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 zF(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 _ce={kernelName:Pu,backendName:"wasm",setupFunc:Tce,kernelFunc:Cce},WF;function Ece(e){WF=e.wasm.cwrap(uo,null,["number","number","number","number"])}function Ace(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}=Ps(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=w.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;WF(o,Qe[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=N.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var Fce={kernelName:uo,backendName:"wasm",setupFunc:Ece,kernelFunc:Ace},$ce=!1,Dce=Ht(po,$ce),BF;function Rce(e){BF=e.wasm.cwrap(co,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Mce(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),E=a.dataIdMap.get(C.dataId).id;return BF(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,b,y,x,v,I,T,E),C}var Pce={kernelName:co,backendName:"wasm",setupFunc:Rce,kernelFunc:Mce},VF;function Oce(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 Lce(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 zce={kernelName:Ou,backendName:"wasm",setupFunc:Oce,kernelFunc:Lce},UF;function Wce(e){UF=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 Bce(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 UF(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 Vce={kernelName:Hc,backendName:"wasm",setupFunc:Wce,kernelFunc:Bce},GF;function Uce(e){GF=e.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Gce(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.computePool2DInfo(s.shape,i,o,1,l,u),d=n.makeOutput(s.shape,s.dtype);return GF(n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),d}var Hce={kernelName:Gc,backendName:"wasm",setupFunc:Uce,kernelFunc:Gce},HF;function qce(e){HF=e.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function jce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,includeBatchInIndex:l}=a;w.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];w.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,[1,1],o),d=n.makeOutput(p.outShape,r.dtype),c=n.makeOutput(p.outShape,"int32");return HF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,n.dataIdMap.get(c.dataId).id,Qe[r.dtype],l,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),[d,c]}var Kce={kernelName:qc,backendName:"wasm",setupFunc:qce,kernelFunc:jce},qF;function Xce(e){qF=e.wasm.cwrap(ho,null,["number, number, number"])}function Yce(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}=Ps(i,r,t),m=d;if(h){let v=t.dataIdMap.get(p.dataId).id;v!==o&&(u=p,l=v,m=N.getInnerMostAxes(m.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=N.computeOutAndReduceShapes(u.shape,m),b=w.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=Os({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(f,"float32");if(w.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;qF(l,b,v)}if(h&&t.disposeData(p.dataId),s){let v=N.expandShapeToKeepDim(x.shape,c);x.shape=v}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var Zce={kernelName:ho,backendName:"wasm",setupFunc:Xce,kernelFunc:Yce},jF;function Jce(e){jF=e.wasm.cwrap(mo,null,["number","number","number","number"])}function Qce(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}=Ps(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=w.sizeFromShape(g),y=t.makeOutput(f,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;jF(l,Qe[i.dtype],b,x)}if(h&&t.disposeData(p.dataId),s){let x=N.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var ede={kernelName:mo,backendName:"wasm",setupFunc:Jce,kernelFunc:Qce},tde=!1,nde=Ht(fo,tde),wv;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(wv||(wv={}));var KF;function ade(e){KF=e.wasm.cwrap(go,null,["number","array","number","number","array","array","number","number"])}function rde(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 KF(i,u,t.shape.length,Qe[t.dtype],c,h,wv[r],l),o}var sde={kernelName:go,backendName:"wasm",kernelFunc:rde,setupFunc:ade},XF;function ide(e){XF=e.wasm.cwrap(Wo,null,["number","number","number","number"])}function YF(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=w.sizeFromShape(n.shape)/o;return w.sizeFromShape(s.shape)===0||XF(r,i,o,l),s}var ode={kernelName:Wo,backendName:"wasm",setupFunc:ide,kernelFunc:YF},ZF;function lde(e){ZF=e.wasm.cwrap(Lu,null,["number","number","number","number","number","number"])}function ude(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:YF({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),[u,p]=l.shape,d=n.makeOutput([u,s],"int32");return 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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 Ur(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var vt=class extends Ur{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 xk(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 vk(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=et([s,i,o],3);return pe(e,l)})}function kk(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=>se(c,"float32"));return et(d,i)})}function nfe(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 zd(e){return 1/(1+Math.exp(-e))}function afe(e){return Math.log(e/(1-e))}var il=class extends ot{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var rfe=.5,sfe=.43,ife=.45,ia=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/ife),l=rl(t),u=Math.floor(Math.max(0,l.x-rfe*o)),p=Math.floor(Math.max(0,l.y-sfe*o));return new il(u,p,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+p))}alignMinBbox(t){let n=vk(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var Ik=class extends ia{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],rl([t[3],t[4]])]}};var ol=class extends ia{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(rl)}};var Ep=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}${t?` (${al(this.distance)})`:""}`}};var Ap=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 vr=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 vr(t.label,n)}};var Sk=class extends Ap{constructor(n,a,r,s){super(n,a);this._score=r,this._classScore=s}static assertIsValidPredictedBox(n,a){if(Ap.assertIsValidLabeledBox(n,a),!_p(n.score)||!_p(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 wr(e){return e.detection instanceof vt}function ll(e,t){return{...e,...{detection:t}}}function Nk(){let e=window.fetch;if(!e)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:e,readFile:()=>{throw new Error("readFile - filesystem not available for browser environment")}}}function Wd(){return typeof global=="object"&&typeof process!="undefined"&&process.versions!=null&&process.versions.node!=null}function ig(e){let t="";if(!e&&Wd())try{e=QD("fs")}catch(a){t=a.toString()}return{readFile:e?a=>new Promise((r,s)=>{e.readFile(a,(i,o)=>i?s(i):r(o))}):()=>{throw new Error(`readFile - failed to require fs in nodejs environment with error: ${t}`)}}}function Tk(){let e=global.Canvas||global.HTMLCanvasElement,t=global.Image||global.HTMLImageElement,n=global.Video||global.HTMLVideoElement,a=()=>{if(e)return new e;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},r=()=>{if(t)return new t;throw new Error("createImageElement - missing Image implementation for nodejs environment")},s=()=>{if(n)return new n;throw new Error("createVideoElement - missing Video implementation for nodejs environment")},i=global.fetch,o=ig();return{Canvas:e||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:t||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:a,createImageElement:r,createVideoElement:s,fetch:i,...o}}function Ck(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var un;function ofe(){if(!un)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return un}function _k(e){un=e}function Ek(){return Ck()?_k(Nk()):Wd()?_k(Tk()):null}function lfe(e){if(un||Ek(),!un)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=un.Canvas,Image:n=un.Image}=e;un.Canvas=t,un.Image=n,un.createCanvasElement=e.createCanvasElement||(()=>new t),un.createImageElement=e.createImageElement||(()=>new n),un.ImageData=e.ImageData||un.ImageData,un.Video=e.Video||un.Video,un.fetch=e.fetch||un.fetch,un.readFile=e.readFile||un.readFile}var tt={getEnv:ofe,setEnv:_k,initialize:Ek,createBrowserEnv:Nk,createFileSystem:ig,createNodejsEnv:Tk,monkeyPatch:lfe,isBrowser:Ck,isNodejs:Wd};Ek();function ul(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=ul(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d",{willReadFrequently:!0});if(!r)throw new Error("resolveContext2d - canvas 2d context is null");return r}var Ak=(r=>(r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT",r))(Ak||{}),Fp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||"TOP_LEFT",this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},Gr=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof Gr?t.text:t,this.anchor=n,this.options=new Fp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a{let m=l+d.x,f=l+d.y+(h+1)*i;a.fillText(c,m,f)})}};var og=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new Fp({...i,...s})}},Bd=class{constructor(t,n={}){this.box=new ot(t),this.options=new og(n)}draw(t){let n=Hn(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 Gr([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function ufe(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof vt?a.score:wr(a)?a.detection.score:void 0,s=a instanceof vt?a.box:wr(a)?a.detection.box:new ot(a),i=r?`${al(r)}`:void 0;new Bd(s,{label:i}).draw(e)})}function Vd(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function Fk(e){return new Promise((t,n)=>{(e instanceof tt.getEnv().Canvas||Vd(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 $k(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=tt.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function pl(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t?new wn(e.naturalWidth,e.naturalHeight):e instanceof n?new wn(e.videoWidth,e.videoHeight):new wn(e.width,e.height)}function cl({width:e,height:t}){let{createCanvasElement:n}=tt.getEnv(),a=n();return a.width=e,a.height=t,a}function Ud(e,t){let{ImageData:n}=tt.getEnv();if(!(e instanceof n)&&!Vd(e))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width:a,height:r}=t||pl(e),s=cl({width:a,height:r});return e instanceof n?Hn(s).putImageData(e,0,0):Hn(s).drawImage(e,0,0,a,r),s}async function Dk(e,t){let n=t||tt.getEnv().createCanvasElement(),[a,r,s]=e.shape.slice(ka(e)?1:0),i=P(()=>e.as3D(a,r,s).toInt());return await Ko.toPixels(i,n),i.dispose(),n}function lg(e){let{Image:t,Canvas:n,Video:a}=tt.getEnv();return e instanceof t||e instanceof n||e instanceof a}function Rk(e,t,n=!1){let{Image:a,Canvas:r}=tt.getEnv();if(!(e instanceof a||e instanceof r))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");if(t<=0)return cl({width:1,height:1});let s=pl(e),i=t/Math.max(s.height,s.width),o=i*s.width,l=i*s.height,u=cl({width:t,height:t}),p=e instanceof r?e:Ud(e),d=Math.abs(o-l)/2,c=n&&o0&&p.height>0&&Hn(u).drawImage(p,c,h,o,l),u}var kr=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(Vr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ka(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:Ud(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 xr(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 bk({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,P(()=>{let a=xr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Te){let o=ka(i)?i:nn(i);return o=kk(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 tt.getEnv().Canvas)return Ko.fromPixels(Rk(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=>se(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function wt(e){if(e instanceof kr)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 ${r}:`:"",a=t.map(ul);return a.forEach((r,s)=>{if(!lg(r)&&!Vr(r)&&!ka(r))throw typeof t[s]=="string"?new Error(`toNetInput -${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(ka(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=>lg(r)&&Fk(r))),new kr(a,Array.isArray(e))}async function $p(e,t){let{Canvas:n}=tt.getEnv(),a=e;if(!(e instanceof n)){let i=await wt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await Dk(o)}let r=Hn(a);return t.map(i=>i instanceof vt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:u})=>{let p=cl({width:l,height:u});return l>0&&u>0&&Hn(p).putImageData(r.getImageData(i,o,l,u),0,0),p})}async function Dp(e,t){if(!Vr(e)&&!ka(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ka(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return P(()=>{let[n,a,r]=e.shape.slice(ka(e)?1:0);return t.map(o=>o instanceof vt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).filter(o=>o.width>0&&o.height>0).map(({x:o,y:l,width:u,height:p})=>qo(e.as3D(n,a,r),[l,o,0],[p,u,r]))})}async function Hr(e,t){let{fetch:n}=tt.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function pfe(e){let t=await Hr(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return $k(n)}async function Mk(e){return(await Hr(e)).json()}async function cfe(e){return new Float32Array(await(await Hr(e)).arrayBuffer())}function F$(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToVideo - expected buf to be of type: Blob"));let a=tt.getEnv().createVideoElement();a.oncanplay=()=>t(a),a.onerror=n,a.playsInline=!0,a.muted=!0,a.src=URL.createObjectURL(e),a.play()})}async function dfe(e){let t=await Hr(e),n=await t.blob();if(!n.type.startsWith("video/"))throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${n.type}, for url: ${t.url}`);return F$(n)}function ug(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function Pk(e,t){let{manifestUri:n,modelBaseUri:a}=ug(e,t),r=await Mk(n);return jt.loadWeights(r,a)}function hfe(e,t,n=!1){let{width:a,height:r}=n?pl(t):t;return e.width=a,e.height=r,{width:a,height:r}}var pn=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return 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a=se(t.toBatchTensor(112,!0),"float32"),s=Ja(a,[122.782,117.001,104.298]).div(255),i=Gd(s,n.dense0,!0);return i=Gd(i,n.dense1),i=Gd(i,n.dense2),i=Gd(i,n.dense3),i=xa(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await wt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return D$(t)}extractParams(t){return $$(t)}};function qd(e,t){return P(()=>X($e(e,t.weights),t.bias))}function R$(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=Fn(e),o=cg(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=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:a("fc")};return An(e,t),{params:r,paramMappings:t}}function fg(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 Lp=class extends 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(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof kr?this.faceFeatureExtractor.forwardInput(n):n;return qd(r.as2D(r.shape[0],-1),a.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return R$(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=fg(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 Ok=["neutral","happy","sad","angry","fearful","disgusted","surprised"],qr=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: ${t.length}`);Ok.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return Ok.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var jd=class extends Lp{constructor(t=new Op){super("FaceExpressionNet",t)}forwardInput(t){return P(()=>Xa(this.runNet(t)))}async forward(t){return this.forwardInput(await wt(t))}async predictExpressions(t){let n=await wt(t),a=await this.forwardInput(n),r=await Promise.all(ct(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new qr(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Lk(e){return e.expressions instanceof qr}function gg(e,t){return{...e,...{expressions:t}}}function mfe(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof qr?s:Lk(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(d=>d.probability>n),u=wr(s)?s.detection.box.bottomLeft:a||new Re(0,0);new Gr(l.map(d=>`${d.expression} (${al(d.probability)})`),u).draw(e)})}function hl(e){return wr(e)&&e.landmarks instanceof ia&&e.unshiftedLandmarks instanceof ia&&e.alignedRect instanceof vt}function ffe(e){let t=l=>l*180/Math.PI,n=(l,u)=>Math.sqrt((l._x-u._x)**2+(l._y-u._y)**2),a={roll:void 0,pitch:void 0,yaw:void 0},r=(l,u,p)=>{let d=Math.floor(l._x-u._x),c=Math.floor(u._x-p._x);return d-c},s=(l,u)=>{let p=Math.hypot(u._x-l._x,u._y-l._y),d=u._y-l._y,c=Math.asin(d/p),h=t(c),m=Math.floor(90-h),f=u._x-l._x<0?-1:1;return m*f},i=(l,u,p)=>{let 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n=Hn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof ol&&(n.strokeStyle=i,n.lineWidth=s,Br(n,this.faceLandmarks.getJawOutline()),Br(n,this.faceLandmarks.getLeftEyeBrow()),Br(n,this.faceLandmarks.getRightEyeBrow()),Br(n,this.faceLandmarks.getNose()),Br(n,this.faceLandmarks.getLeftEye(),!0),Br(n,this.faceLandmarks.getRightEye(),!0),Br(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let u=p=>{n.beginPath(),n.arc(p.x,p.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(u)}}};function gfe(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ia?a:hl(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new yg(r).draw(e)})}var P$="1.7.12";function xfe(e,t){let n=Rp(e,t),a=Mp(e,t);function r(i,o,l){let 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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=se(n.toBatchTensor(112,!0),"float32"),i=Ja(r,[122.782,117.001,104.298]).div(255),o=Ke(z$(i,a.entry_flow.conv_in,[2,2]));return o=Wk(o,a.entry_flow.reduction_block_0,!1),o=Wk(o,a.entry_flow.reduction_block_1),xr(this._numMainBlocks,0,1).forEach(l=>{o=wfe(o,a.middle_flow[`main_block_${l}`])}),o=Wk(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 L$(n,this._numMainBlocks)}extractParams(n){return O$(n,this._numMainBlocks)}};function W$(e){let t=[],{extractWeights:n,getRemainingWeights:a}=Fn(e),r=cg(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: 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Dfe(e){let t=ct(De(e,[1,0])),n=[pe(t[2],t[0]),pe(t[3],t[1])],a=[X(t[0],he(n[0],2)),X(t[1],he(n[1],2))];return{sizes:n,centers:a}}function Rfe(e,t){let{sizes:n,centers:a}=Dfe(e),r=ct(De(t,[1,0])),s=he(z(yn(he(r[2],5)),n[0]),2),i=X(z(he(r[0],10),n[0]),a[0]),o=he(z(yn(he(r[3],5)),n[1]),2),l=X(z(he(r[1],10),n[1]),a[1]);return De(Dt([pe(i,s),pe(l,o),X(i,s),X(l,o)]),[1,0])}function J$(e,t,n){return P(()=>{let a=e.shape[0],r=Rfe(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=fa(Ue(t,[0,0,1],[-1,-1,-1])),i=Ue(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 gl(e,t){return P(()=>{let n=e.shape[0],a=W(dl(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(dl(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function Q$(e,t,n){return P(()=>{let 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l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return X$(t)}extractParams(t){return K$(t)}};function eD(e){let t=new Ls;return t.extractWeights(e),t}function Mfe(e){return eD(e)}var Gk=class extends Ls{};var tD=.4,nD=[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)],aD=[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)],rD=[117.001,114.697,97.404],sD="tiny_yolov2_model",iD="tiny_yolov2_separable_conv_model";var Tg=e=>typeof e=="number";function Hk(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: ${e.withSeparableConvs}`);if(!Tg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: 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n=$s(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Bp(n)})}function Pfe(e,t){let n=Rp(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=Mp(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function oD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=Fn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=Pfe(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=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,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:T,conv3:C,conv4:E,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=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"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:T,conv3:C,conv4:E,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Ofe(e,t){let n=oa(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:u}}let i=Pp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function lD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Ofe(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return An(e,n),{params:i,paramMappings:n}}var er=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 qk=class extends pn{constructor(n){super("TinyYolov2");Hk(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=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=jr(r,a.conv6),r=jr(r,a.conv7),dl(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?Bp(dl(n,a.conv0,"valid",!1)):Kr(n,a.conv0);return r=Mt(r,[2,2],[2,2],"same"),r=Kr(r,a.conv1),r=Mt(r,[2,2],[2,2],"same"),r=Kr(r,a.conv2),r=Mt(r,[2,2],[2,2],"same"),r=Kr(r,a.conv3),r=Mt(r,[2,2],[2,2],"same"),r=Kr(r,a.conv4),r=Mt(r,[2,2],[2,2],"same"),r=Kr(r,a.conv5),r=Mt(r,[2,2],[1,1],"same"),r=a.conv6?Kr(r,a.conv6):r,r=a.conv7?Kr(r,a.conv7):r,dl(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=se(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 wk(d.map(b=>b.rescale(r)),c,this.config.iouThreshold,!0).map(b=>new Ur(c[b],h[b],m[b],d[b],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return lD(n,this.config)}extractParams(n){let a=this.config.filterSizes||qk.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 ${r} filterSizes in config`);return oD(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let y=n.reshape([p,p,d,this.boxEncodingSize]),x=y.slice([0,0,0,0],[p,p,d,4]),v=y.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Xa(y.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):ve(0);return[x,v,I]}),f=[],g=await h.array(),b=await c.array();for(let y=0;yr){let T=(x+zd(b[y][x][v][0]))/p*l,C=(y+zd(b[y][x][v][1]))/p*u,E=Math.exp(b[y][x][v][2])*this.config.anchors[v].x/p*l,F=Math.exp(b[y][x][v][3])*this.config.anchors[v].y/p*u,D=T-E/2,$=C-F/2,S={row:y,col:x,anchor:v},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new sl(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)}},bl=qk;bl.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var yl=class extends bl{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:tD,classes:["face"],...t?{anchors:aD,meanRgb:rD}:{anchors:nD,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?iD:sD}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Lfe(e,t=!0){let n=new yl(t);return n.extractWeights(e),n}var Zd=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 xl(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>hl(l)?r(l):l.detection),i=a||(t instanceof Te?await Dp(t,s):await $p(t,s)),o=await n(i);return i.forEach(l=>l instanceof Te&&l.dispose()),o}async function Vp(e,t,n,a,r){return xl([e],t,async s=>n(s[0]),a,r)}var uD=.4,pD=[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)],cD=[117.001,114.697,97.404];var vl=class extends bl{constructor(){let t={withSeparableConvs:!0,iouThreshold:uD,classes:["face"],anchors:pD,meanRgb:cD,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 Ls,tinyFaceDetector:new vl,tinyYolov2:new yl,faceLandmark68Net:new ml,faceLandmark68TinyNet:new Xd,faceRecognitionNet:new fl,faceExpressionNet:new jd,ageGenderNet:new Kd},dD=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),zfe=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Wfe=(e,t)=>nt.tinyYolov2.locateFaces(e,t),hD=e=>nt.faceLandmark68Net.detectLandmarks(e),Bfe=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Vfe=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),Ufe=e=>nt.faceExpressionNet.predictExpressions(e),Gfe=e=>nt.ageGenderNet.predictAgeAndGender(e),mD=e=>nt.ssdMobilenetv1.load(e),Hfe=e=>nt.tinyFaceDetector.load(e),qfe=e=>nt.tinyYolov2.load(e),jfe=e=>nt.faceLandmark68Net.load(e),Kfe=e=>nt.faceLandmark68TinyNet.load(e),Xfe=e=>nt.faceRecognitionNet.load(e),Yfe=e=>nt.faceExpressionNet.load(e),Zfe=e=>nt.ageGenderNet.load(e),Jfe=mD,Qfe=dD,ege=hD;var Cg=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},wl=class extends Cg{async run(){let t=await this.parentTask,n=await xl(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>gg(a,n[r]))}withAgeAndGender(){return new Il(this,this.input)}},kl=class extends Cg{async run(){let t=await this.parentTask;if(!t)return;let n=await Vp(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return gg(t,n)}withAgeAndGender(){return new Sl(this,this.input)}},zs=class extends wl{withAgeAndGender(){return new Bs(this,this.input)}withFaceDescriptors(){return new Xr(this,this.input)}},Ws=class extends kl{withAgeAndGender(){return new Vs(this,this.input)}withFaceDescriptor(){return new Yr(this,this.input)}};var _g=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Il=class extends _g{async run(){let t=await this.parentTask,n=await xl(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 Sg(Ng(a,i,o),s)})}withFaceExpressions(){return new wl(this,this.input)}},Sl=class 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b=T.getUndoAxesPermutation(c);m=Ia({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(l.dataId),n.disposeData(d.dataId)}return m}var zle={kernelName:wc,backendName:"wasm",setupFunc:Lle,kernelFunc:Ble},ZD;function Wle(e){ZD=e.wasm.cwrap(jo,null,["number","number","number","number","number","number"])}function Vle(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,u=s.shape.length;w.assert(s.dtype==="float32"||s.dtype==="int32",()=>`cumsum does not support ${s.dtype} tensors in the WASM backend`);let c=T.getAxesPermutation([a],u),l=s;c!==null&&(l=Ia({inputs:{x:s},attrs:{perm:c},backend:n}));let p=T.getInnerMostAxes(1,u)[0];T.assertAxesAreInnerMostDims("cumsum",[p],u);let d=n.makeOutput(l.shape,l.dtype),h=l.shape[p],f=n.dataIdMap.get(l.dataId).id,g=n.dataIdMap.get(d.dataId).id;ZD(f,o?1:0,i?1:0,h,g,et[s.dtype]);let m=d;if(c!==null){let b=T.getUndoAxesPermutation(c);m=Ia({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(l.dataId),n.disposeData(d.dataId)}return m}var Ule={kernelName:jo,backendName:"wasm",setupFunc:Wle,kernelFunc:Vle},JD;function Gle(e){JD=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function Hle(e){let{backend:t,inputs:n,attrs:r}=e,{x:s,weights:a}=n,{size:o,binaryOutput:i}=r,u=a.shape.reduce((d,h)=>d*h,1)!==0,c=s.shape.length===1?[o]:[s.shape[0],o],l=t.makeOutput(c,a.dtype);function p(d){return t.dataIdMap.get(d.dataId).id}return JD(p(s),new Uint8Array(new Int32Array(s.shape).buffer),s.shape.length,o,u,p(a),et[a.dtype],i,p(l)),l}var jle={kernelName:Ud,backendName:"wasm",setupFunc:Gle,kernelFunc:Hle},QD;function qle(e){QD=e.wasm.cwrap(kc,null,["number","number","number","array","number","array","array","number","number"])}function 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P0()?O0(F0()):zp()?O0(R0()):null}function ege(e){if(un||M0(),!un)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=un.Canvas,Image:n=un.Image}=e;un.Canvas=t,un.Image=n,un.createCanvasElement=e.createCanvasElement||(()=>new t),un.createImageElement=e.createImageElement||(()=>new n),un.ImageData=e.ImageData||un.ImageData,un.Video=e.Video||un.Video,un.fetch=e.fetch||un.fetch,un.readFile=e.readFile||un.readFile}var nt={getEnv:Qme,setEnv:O0,initialize:M0,createBrowserEnv:F0,createFileSystem:ig,createNodejsEnv:R0,monkeyPatch:ege,isBrowser:P0,isNodejs:zp};M0();function pu(e){return!nt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Gn(e){let{Canvas:t,CanvasRenderingContext2D:n}=nt.getEnv();if(e instanceof n)return e;let r=pu(e);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let s=r.getContext("2d",{willReadFrequently:!0});if(!s)throw new Error("resolveContext2d 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gu(e,t,n="same",r=!1){return O(()=>{let s=X(Ft(e,t.filters,[1,1],n),t.bias);return r?Ke(s):s})}function Dn(e,t){Object.keys(e).forEach(n=>{t.some(r=>r.originalPath===n)||e[n].dispose()})}function Pl(e,t){return(n,r,s,a)=>{let o=Rr(e(n*r*s*s),[s,s,n,r]),i=He(e(r));return t.push({paramPath:`${a}/filters`},{paramPath:`${a}/bias`}),{filters:o,bias:i}}}function pg(e,t){return(n,r,s)=>{let a=Dr(e(n*r),[n,r]),o=He(e(r));return t.push({paramPath:`${s}/weights`},{paramPath:`${s}/bias`}),{weights:a,bias:o}}}var Hp=class{constructor(t,n,r){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=r}};function Ol(e,t){return(n,r,s)=>{let a=Rr(e(9*n),[3,3,n,1]),o=Rr(e(n*r),[1,1,n,r]),i=He(e(r));return t.push({paramPath:`${s}/depthwise_filter`},{paramPath:`${s}/pointwise_filter`},{paramPath:`${s}/bias`}),new Hp(a,o,i)}}function Ml(e){return t=>{let n=e(`${t}/depthwise_filter`,4),r=e(`${t}/pointwise_filter`,4),s=e(`${t}/bias`,1);return new Hp(n,r,s)}}function ir(e,t){return(n,r,s)=>{let 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r=ae(t.toBatchTensor(112,!0),"float32"),a=Zr(r,[122.782,117.001,104.298]).div(255),o=Gp(a,n.dense0,!0);return o=Gp(o,n.dense1),o=Gp(o,n.dense2),o=Gp(o,n.dense3),o=br(o,[7,7],[2,2],"valid"),o})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return lF(t)}extractParams(t){return cF(t)}};function jp(e,t){return O(()=>X(Fe(e,t.weights),t.bias))}function dF(e,t,n){let r=[],{extractWeights:s,getRemainingWeights:a}=$n(e),i=pg(s,r)(t,n,"fc");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:r,params:{fc:i}}}function pF(e){let t=[],n=ir(e,t);function r(a){let o=n(`${a}/weights`,2),i=n(`${a}/bias`,1);return{weights:o,bias:i}}let s={fc:r("fc")};return Dn(e,t),{params:s,paramMappings:t}}function gg(e){let t={},n={};return Object.keys(e).forEach(r=>{let s=r.startsWith("fc")?n:t;s[r]=e[r]}),{featureExtractorMap:t,classifierMap:n}}var Bl=class extends cn{constructor(t,n){super(t),this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let r=t instanceof xs?this.faceFeatureExtractor.forwardInput(t):t;return jp(r.as2D(r.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:r}=this.extractClassifierParams(t);this._params=n,this._paramMappings=r}extractClassifierParams(t){return dF(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:r}=gg(t);return this.faceFeatureExtractor.loadFromWeightMap(n),pF(r)}extractParams(t){let n=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*n+r,a=t.slice(0,t.length-s),o=t.slice(t.length-s);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(o)}};var 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d=a("exit_flow/reduction_block"),h=s("exit_flow/separable_conv"),f={reduction_block:d,separable_conv:h};return Dn(e,n),{params:{entry_flow:l,middle_flow:p,exit_flow:f},paramMappings:n}}function gF(e,t,n){return X(Ft(e,t.filters,n,"same"),t.bias)}function K0(e,t,n=!0){let r=n?Ke(e):e;return r=Hn(r,t.separable_conv0,[1,1]),r=Hn(Ke(r),t.separable_conv1,[1,1]),r=Rt(r,[3,3],[2,2],"same"),r=X(r,gF(e,t.expansion_conv,[2,2])),r}function hge(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 xg=class extends cn{constructor(t){super("TinyXception"),this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return O(()=>{let r=ae(t.toBatchTensor(112,!0),"float32"),a=Zr(r,[122.782,117.001,104.298]).div(255),o=Ke(gF(a,n.entry_flow.conv_in,[2,2]));return o=K0(o,n.entry_flow.reduction_block_0,!1),o=K0(o,n.entry_flow.reduction_block_1),ys(this._numMainBlocks,0,1).forEach(i=>{o=hge(o,n.middle_flow[`main_block_${i}`])}),o=K0(o,n.exit_flow.reduction_block),o=Ke(Hn(o,n.exit_flow.separable_conv,[1,1])),o})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return mF(t,this._numMainBlocks)}extractParams(t){return fF(t,this._numMainBlocks)}};function bF(e){let t=[],{extractWeights:n,getRemainingWeights:r}=$n(e),s=pg(n,t),a=s(512,1,"fc/age"),o=s(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:o}}}}function yF(e){let t=[],n=ir(e,t);function r(a){let o=n(`${a}/weights`,2),i=n(`${a}/bias`,1);return{weights:o,bias:i}}let s={fc:{age:r("fc/age"),gender:r("fc/gender")}};return Dn(e,t),{params:s,paramMappings:t}}var wg=(n=>(n.FEMALE="female",n.MALE="male",n))(wg||{});var Kp=class extends cn{constructor(t=new xg(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let r=t instanceof xs?this.faceFeatureExtractor.forwardInput(t):t,s=br(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=jp(s,n.fc.age).as1D(),o=jp(s,n.fc.gender);return{age:a,gender:o}})}forwardInput(t){return O(()=>{let{age:n,gender:r}=this.runNet(t);return{age:n,gender:Kr(r)}})}async forward(t){return this.forwardInput(await vt(t))}async predictAgeAndGender(t){let n=await vt(t),r=await this.forwardInput(n),s=pt(r.age),a=pt(r.gender),o=s.map((u,c)=>({ageTensor:u,genderTensor:a[c]})),i=await Promise.all(o.map(async({ageTensor:u,genderTensor:c})=>{let l=u.dataSync()[0],p=c.dataSync()[0],d=p>.5,h=d?"male":"female",f=d?p:1-p;return u.dispose(),c.dispose(),{age:l,gender:h,genderProbability:f}}));return r.age.dispose(),r.gender.dispose(),n.isBatchInput?i:i[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:r}=this.extractClassifierParams(t);this._params=n,this._paramMappings=r}extractClassifierParams(t){return bF(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:r}=gg(t);return this.faceFeatureExtractor.loadFromWeightMap(n),yF(r)}extractParams(t){let r=t.slice(0,t.length-1539),s=t.slice(t.length-1539);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(s)}};var Wl=class extends Bl{postProcess(t,n,r){let s=r.map(({width:o,height:i})=>{let u=n/Math.max(i,o);return{width:o*u,height:i*u}}),a=s.length;return O(()=>{let o=(p,d)=>Dt([vn([68],p,"float32"),vn([68],d,"float32")],1).as2D(1,136).as1D(),i=(p,d)=>{let{width:h,height:f}=s[p];return d(h,f)?Math.abs(h-f)/2:0},u=p=>i(p,(d,h)=>di(p,(d,h)=>ho(u(d),c(d))))).div(Dt(Array.from(Array(a),(p,d)=>o(s[d].width,s[d].height))))})}forwardInput(t){return O(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([r,s])=>({height:r,width:s})))})}async forward(t){return this.forwardInput(await vt(t))}async detectLandmarks(t){let n=await vt(t),r=O(()=>pt(this.forwardInput(n))),s=await Promise.all(r.map(async(a,o)=>{let i=Array.from(a.dataSync()),u=i.filter((l,p)=>ag(p)),c=i.filter((l,p)=>!ag(p));return new lu(Array(68).fill(0).map((l,p)=>new Ue(u[p],c[p])),{height:n.getInputHeight(o),width:n.getInputWidth(o)})}));return r.forEach(a=>a.dispose()),n.isBatchInput?s:s[0]}getClassifierChannelsOut(){return 136}};var yu=class extends Wl{constructor(t=new Ll){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function vF(e){let t=[],{extractDenseBlock3Params:n}=mg(e,t),r={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return Dn(e,t),{params:r,paramMappings:t}}function xF(e){let t=[],{extractWeights:n,getRemainingWeights:r}=$n(e),{extractDenseBlock3Params:s}=hg(n,t),a=s(3,32,"dense0",!0),o=s(32,64,"dense1"),i=s(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:a,dense1:o,dense2:i}}}var Ig=class extends cn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return O(()=>{let r=ae(t.toBatchTensor(112,!0),"float32"),a=Zr(r,[122.782,117.001,104.298]).div(255),o=dg(a,n.dense0,!0);return o=dg(o,n.dense1),o=dg(o,n.dense2),o=br(o,[14,14],[2,2],"valid"),o})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return vF(t)}extractParams(t){return xF(t)}};var Xp=class extends Wl{constructor(t=new Ig){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var X0=class extends yu{};function wF(e,t){return X(z(e,t.weights),t.biases)}function Y0(e,t,n,r,s="same"){let{filters:a,bias:o}=t.conv,i=Ft(e,a,n,s);return i=X(i,o),i=wF(i,t.scale),r?Ke(i):i}function IF(e,t){return Y0(e,t,[1,1],!0)}function Z0(e,t){return Y0(e,t,[1,1],!1)}function kg(e,t){return Y0(e,t,[2,2],!0,"valid")}function fge(e,t){function n(i,u,c){let l=e(i),p=l.length/(u*c*c);if(S0(p))throw new Error(`depth has to be an integer: ${p}, weights.length: ${l.length}, numFilters: ${u}, filterSize: ${c}`);return O(()=>Re(Rr(l,[u,p,c,c]),[2,3,1,0]))}function r(i,u,c,l){let p=n(i,u,c),d=He(e(u));return t.push({paramPath:`${l}/filters`},{paramPath:`${l}/bias`}),{filters:p,bias:d}}function s(i,u){let c=He(e(i)),l=He(e(i));return t.push({paramPath:`${u}/weights`},{paramPath:`${u}/biases`}),{weights:c,biases:l}}function a(i,u,c,l){let p=r(i,u,c,`${l}/conv`),d=s(u,`${l}/scale`);return{conv:p,scale:d}}function o(i,u,c,l,p=!1){let d=a((p?.5:1)*i,u,c,`${l}/conv1`),h=a(i,u,c,`${l}/conv2`);return{conv1:d,conv2:h}}return{extractConvLayerParams:a,extractResidualLayerParams:o}}function kF(e){let{extractWeights:t,getRemainingWeights:n}=$n(e),r=[],{extractConvLayerParams:s,extractResidualLayerParams:a}=fge(t,r),o=s(4704,32,7,"conv32_down"),i=a(9216,32,3,"conv32_1"),u=a(9216,32,3,"conv32_2"),c=a(9216,32,3,"conv32_3"),l=a(36864,64,3,"conv64_down",!0),p=a(36864,64,3,"conv64_1"),d=a(36864,64,3,"conv64_2"),h=a(36864,64,3,"conv64_3"),f=a(147456,128,3,"conv128_down",!0),g=a(147456,128,3,"conv128_1"),m=a(147456,128,3,"conv128_2"),b=a(589824,256,3,"conv256_down",!0),y=a(589824,256,3,"conv256_1"),v=a(589824,256,3,"conv256_2"),x=a(589824,256,3,"conv256_down_out"),k=O(()=>Re(Dr(t(256*128),[128,256]),[1,0]));if(r.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:o,conv32_1:i,conv32_2:u,conv32_3:c,conv64_down:l,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:f,conv128_1:g,conv128_2:m,conv256_down:b,conv256_1:y,conv256_2:v,conv256_down_out:x,fc:k},paramMappings:r}}function mge(e,t){let n=ir(e,t);function 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n=[],{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}=Ege(e,n),o;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else o={conv0:s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:s("conv6"),conv7:s("conv7"),conv8:r("conv8")};return Dn(e,n),{params:o,paramMappings:n}}var Qr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 _g=class _g extends cn{constructor(t){super("TinyYolov2"),Q0(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let r=Us(t,n.conv0);return r=Rt(r,[2,2],[2,2],"same"),r=Us(r,n.conv1),r=Rt(r,[2,2],[2,2],"same"),r=Us(r,n.conv2),r=Rt(r,[2,2],[2,2],"same"),r=Us(r,n.conv3),r=Rt(r,[2,2],[2,2],"same"),r=Us(r,n.conv4),r=Rt(r,[2,2],[2,2],"same"),r=Us(r,n.conv5),r=Rt(r,[2,2],[1,1],"same"),r=Us(r,n.conv6),r=Us(r,n.conv7),gu(r,n.conv8,"valid",!1)}runMobilenet(t,n){let r=this.config.isFirstLayerConv2d?Vl(gu(t,n.conv0,"valid",!1)):Gs(t,n.conv0);return r=Rt(r,[2,2],[2,2],"same"),r=Gs(r,n.conv1),r=Rt(r,[2,2],[2,2],"same"),r=Gs(r,n.conv2),r=Rt(r,[2,2],[2,2],"same"),r=Gs(r,n.conv3),r=Rt(r,[2,2],[2,2],"same"),r=Gs(r,n.conv4),r=Rt(r,[2,2],[2,2],"same"),r=Gs(r,n.conv5),r=Rt(r,[2,2],[1,1],"same"),r=n.conv6?Gs(r,n.conv6):r,r=n.conv7?Gs(r,n.conv7):r,gu(r,n.conv8,"valid",!1)}forwardInput(t,n){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let s=ae(t.toBatchTensor(n,!1),"float32");return s=this.config.meanRgb?Zr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:r,scoreThreshold:s}=new Qr(n),a=await vt(t),o=await this.forwardInput(a,r),i=O(()=>pt(o)[0].expandDims()),u={width:a.getInputWidth(0),height:a.getInputHeight(0)},c=await this.extractBoxes(i,a.getReshapedInputDimensions(0),s);o.dispose(),i.dispose();let l=c.map(m=>m.box),p=c.map(m=>m.score),d=c.map(m=>m.classScore),h=c.map(m=>this.config.classes[m.label]);return E0(l.map(m=>m.rescale(r)),p,this.config.iouThreshold,!0).map(m=>new uu(p[m],d[m],h[m],l[m],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return BF(t,this.config)}extractParams(t){let n=this.config.filterSizes||_g.DEFAULT_FILTER_SIZES,r=n?n.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return LF(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,r){let{width:s,height:a}=n,o=Math.max(s,a),i=o/s,u=o/a,c=t.shape[1],l=this.config.anchors.length,[p,d,h]=O(()=>{let b=t.reshape([c,c,l,this.boxEncodingSize]),y=b.slice([0,0,0,0],[c,c,l,4]),v=b.slice([0,0,0,4],[c,c,l,1]),x=this.withClassScores?Kr(b.slice([0,0,0,5],[c,c,l,this.config.classes.length]),3):xe(0);return[y,v,x]}),f=[],g=await d.array(),m=await p.array();for(let b=0;br){let k=(y+Bp(m[b][y][v][0]))/c*i,S=(b+Bp(m[b][y][v][1]))/c*u,N=Math.exp(m[b][y][v][2])*this.config.anchors[v].x/c*i,E=Math.exp(m[b][y][v][3])*this.config.anchors[v].y/c*u,$=k-N/2,F=S-E/2,D={row:b,col:y,anchor:v},{classScore:R,label:C}=this.withClassScores?await this.extractPredictedClass(h,D):{classScore:1,label:0};f.push({box:new iu($,F,$+N,F+E),score:x,classScore:x*R,label:C,...D})}}return p.dispose(),d.dispose(),h.dispose(),f}async extractPredictedClass(t,n){let{row:r,col:s,anchor:a}=n,o=await t.array();return Array(this.config.classes.length).fill(0).map((i,u)=>o[r][s][a][u]).map((i,u)=>({classScore:i,label:u})).reduce((i,u)=>i.classScore>u.classScore?i:u)}};_g.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Ul=_g;var wu=class extends Ul{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:$F,classes:["face"],...t?{anchors:RF,meanRgb:PF}:{anchors:FF,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new Tt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?MF:OF}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Age(e,t=!0){let n=new wu(t);return n.extractWeights(e),n}var Zp=class extends Qr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Ir=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Iu(e,t,n,r,s=({alignedRect:a})=>a){let a=e.map(u=>bu(u)?s(u):u.detection),o=r||(t instanceof Ne?await Rl(t,a):await Fl(t,a)),i=await n(o);return o.forEach(u=>u instanceof Ne&&u.dispose()),i}async function Gl(e,t,n,r,s){return Iu([e],t,async a=>n(a[0]),r,s)}var zF=.4,WF=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],VF=[117.001,114.697,97.404];var ku=class extends Ul{constructor(){let t={withSeparableConvs:!0,iouThreshold:zF,classes:["face"],anchors:WF,meanRgb:VF,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new Tt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new za,tinyFaceDetector:new ku,tinyYolov2:new wu,faceLandmark68Net:new yu,faceLandmark68TinyNet:new Xp,faceRecognitionNet:new vu,faceExpressionNet:new qp,ageGenderNet:new Kp},UF=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),Dge=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),$ge=(e,t)=>rt.tinyYolov2.locateFaces(e,t),GF=e=>rt.faceLandmark68Net.detectLandmarks(e),Fge=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),Rge=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),Pge=e=>rt.faceExpressionNet.predictExpressions(e),Oge=e=>rt.ageGenderNet.predictAgeAndGender(e),HF=e=>rt.ssdMobilenetv1.load(e),Mge=e=>rt.tinyFaceDetector.load(e),Lge=e=>rt.tinyYolov2.load(e),Bge=e=>rt.faceLandmark68Net.load(e),zge=e=>rt.faceLandmark68TinyNet.load(e),Wge=e=>rt.faceRecognitionNet.load(e),Vge=e=>rt.faceExpressionNet.load(e),Uge=e=>rt.ageGenderNet.load(e),Gge=HF,Hge=UF,jge=GF;var Eg=class extends Ir{constructor(n,r,s){super();this.parentTask=n;this.input=r;this.extractedFaces=s}},Su=class extends Eg{async run(){let t=await this.parentTask,n=await Iu(t,this.input,async r=>Promise.all(r.map(s=>rt.faceExpressionNet.predictExpressions(s))),this.extractedFaces);return t.map((r,s)=>bg(r,n[s]))}withAgeAndGender(){return new Tu(this,this.input)}},Cu=class extends Eg{async run(){let t=await this.parentTask;if(!t)return;let n=await Gl(t,this.input,r=>rt.faceExpressionNet.predictExpressions(r),this.extractedFaces);return bg(t,n)}withAgeAndGender(){return new Nu(this,this.input)}},Wa=class extends Su{withAgeAndGender(){return new Ua(this,this.input)}withFaceDescriptors(){return new Hs(this,this.input)}},Va=class extends Cu{withAgeAndGender(){return new Ga(this,this.input)}withFaceDescriptor(){return new js(this,this.input)}};var Ag=class extends Ir{constructor(n,r,s){super();this.parentTask=n;this.input=r;this.extractedFaces=s}},Tu=class extends Ag{async run(){let t=await this.parentTask,n=await Iu(t,this.input,async r=>Promise.all(r.map(s=>rt.ageGenderNet.predictAgeAndGender(s))),this.extractedFaces);return t.map((r,s)=>{let{age:a,gender:o,genderProbability:i}=n[s];return Cg(Tg(r,o,i),a)})}withFaceExpressions(){return new Su(this,this.input)}},Nu=class extends Ag{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:r,genderProbability:s}=await Gl(t,this.input,a=>rt.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Cg(Tg(t,r,s),n)}withFaceExpressions(){return new Cu(this,this.input)}},Ua=class extends Tu{withFaceExpressions(){return new Wa(this,this.input)}withFaceDescriptors(){return new Hs(this,this.input)}},Ga=class extends Nu{withFaceExpressions(){return new Va(this,this.input)}withFaceDescriptor(){return new js(this,this.input)}};var Jp=class extends Ir{constructor(n,r){super();this.parentTask=n;this.input=r}},Hs=class extends Jp{async run(){let t=await this.parentTask;return(await Iu(t,this.input,r=>Promise.all(r.map(s=>rt.faceRecognitionNet.computeFaceDescriptor(s))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,s)=>Sg(t[s],r))}withFaceExpressions(){return new Wa(this,this.input)}withAgeAndGender(){return new Ua(this,this.input)}},js=class extends Jp{async run(){let t=await this.parentTask;if(!t)return;let n=await Gl(t,this.input,r=>rt.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Sg(t,n)}withFaceExpressions(){return new Va(this,this.input)}withAgeAndGender(){return new Ga(this,this.input)}};var Qp=class extends Ir{constructor(n,r,s){super();this.parentTask=n;this.input=r;this.useTinyLandmarkNet=s}get landmarkNet(){return this.useTinyLandmarkNet?rt.faceLandmark68TinyNet:rt.faceLandmark68Net}},eh=class extends Qp{async run(){let t=await this.parentTask,n=t.map(o=>o.detection),r=this.input instanceof Ne?await Rl(this.input,n):await Fl(this.input,n),s=await Promise.all(r.map(o=>this.landmarkNet.detectLandmarks(o)));return r.forEach(o=>o instanceof Ne&&o.dispose()),t.filter((o,i)=>s[i]).map((o,i)=>zl(o,s[i]))}withFaceExpressions(){return new Wa(this,this.input)}withAgeAndGender(){return new Ua(this,this.input)}withFaceDescriptors(){return new Hs(this,this.input)}},th=class extends Qp{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,r=this.input instanceof Ne?await Rl(this.input,[n]):await Fl(this.input,[n]),s=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ne&&a.dispose()),zl(t,s)}withFaceExpressions(){return new Va(this,this.input)}withAgeAndGender(){return new Ga(this,this.input)}withFaceDescriptor(){return new js(this,this.input)}};var nh=class extends Ir{constructor(n,r=new wr){super();this.input=n;this.options=r}},Hl=class extends nh{async run(){let{input:t,options:n}=this,r;if(n instanceof Zp)r=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof wr)r=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Qr)r=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(r=>t(r.map(s=>du({},s)))).catch(r=>n(r))})}withFaceLandmarks(t=!1){return new eh(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Su(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Tu(this.runAndExtendWithFaceDetections(),this.input)}},rh=class extends nh{async run(){let t=await new Hl(this.input,this.options),n=t[0];return t.forEach(r=>{r.score>n.score&&(n=r)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?du({},n):void 0)})}withFaceLandmarks(t=!1){return new th(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Cu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Nu(this.runAndExtendWithFaceDetection(),this.input)}};function qge(e,t=new wr){return new rh(e,t)}function Dg(e,t=new wr){return new Hl(e,t)}async function jF(e,t){return Dg(e,new wr(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Kge(e,t={}){return Dg(e,new Qr(t)).withFaceLandmarks().withFaceDescriptors()}var Xge=jF;function eS(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),r=Array.from(t);return Math.sqrt(n.map((s,a)=>s-r[a]).reduce((s,a)=>s+a*a,0))}var tS=class e{constructor(t,n=.6){this._distanceThreshold=n;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let s=1,a=()=>`person ${s++}`;this._labeledDescriptors=r.map(o=>{if(o instanceof Ba)return o;if(o instanceof Float32Array)return new Ba(a(),[o]);if(o.descriptor&&o.descriptor instanceof Float32Array)return new Ba(a(),[o.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(r=>eS(r,t)).reduce((r,s)=>r+s,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:r})=>new Al(r,this.computeMeanDistance(t,n))).reduce((n,r)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(r=>Ba.fromJSON(r));return new e(n,t.distanceThreshold)}};function Yge(e){let t=new ku;return t.extractWeights(e),t}function qF(e,t){let{width:n,height:r}=new Un(t.width,t.height);if(n<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:r})}`);if(Array.isArray(e))return e.map(s=>qF(s,{width:n,height:r}));if(bu(e)){let s=e.detection.forSize(n,r),a=e.unshiftedLandmarks.forSize(s.box.width,s.box.height);return zl(du(e,s),a)}return vs(e)?du(e,e.detection.forSize(n,r)):e instanceof or||e instanceof Tt?e.forSize(n,r):e}var Zge=hF;return $R(Jge);})(); diff --git 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c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=da(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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 __create = Object.create; +var __defProp = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getProtoOf = Object.getPrototypeOf; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __commonJS = (cb, mod) => function __require() { + return mod || (0, cb[__getOwnPropNames(cb)[0]])((mod = { exports: {} }).exports, mod), mod.exports; +}; +var __export = (target, all) => { + for (var name in all) + __defProp(target, name, { get: all[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __toESM = (mod, isNodeMode, target) => (target = mod != null ? __create(__getProtoOf(mod)) : {}, __copyProps( + // If the importer is in node compatibility mode or this is not an ESM + // file that has been converted to a CommonJS file using a Babel- + // compatible transform (i.e. "__esModule" has not been set), then set + // "default" to the CommonJS "module.exports" for node compatibility. + isNodeMode || !mod || !mod.__esModule ? __defProp(target, "default", { value: mod, enumerable: true }) : target, + mod +)); +var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod); + +// dist/tfjs.esm.js +var require_tfjs_esm = __commonJS({ + "dist/tfjs.esm.js"(exports2, module2) { + "use strict"; + var __defProp2 = Object.defineProperty; + var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; + var __getOwnPropNames2 = Object.getOwnPropertyNames; + var __hasOwnProp2 = Object.prototype.hasOwnProperty; + var __export2 = (target, all) => { + for (var name in all) + __defProp2(target, name, { get: all[name], enumerable: true }); + }; + var __copyProps2 = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames2(from)) + if (!__hasOwnProp2.call(to, key) && key !== except) + __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); + } + return to; + }; + var __reExport = (target, mod, secondTarget) => (__copyProps2(target, mod, "default"), secondTarget && __copyProps2(secondTarget, mod, "default")); + var __toCommonJS2 = (mod) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod); + var tf_node_gpu_exports = {}; + __export2(tf_node_gpu_exports, { + version: () => version6 + }); + module2.exports = __toCommonJS2(tf_node_gpu_exports); + __reExport(tf_node_gpu_exports, require("@tensorflow/tfjs-node-gpu"), module2.exports); + var version3 = "4.16.0"; + var version22 = "4.16.0"; + var version32 = "4.16.0"; + var version4 = "4.16.0"; + var version5 = "4.16.0"; + var version6 = { + // tfjs: tfjsVersion, + tfjs: version3, + "tfjs-core": version3, + // 'tfjs-data': tfjsDataVersion, + // 'tfjs-layers': tfjsLayersVersion, + "tfjs-converter": version22, + "tfjs-backend-cpu": version32, + "tfjs-backend-webgl": version4, + "tfjs-backend-wasm": version5 + }; + } +}); + +// src/index.ts +var src_exports = {}; +__export(src_exports, { + AgeGenderNet: () => AgeGenderNet, + BoundingBox: () => BoundingBox, + Box: () => Box, + ComposableTask: () => ComposableTask, + ComputeAllFaceDescriptorsTask: () => ComputeAllFaceDescriptorsTask, + ComputeFaceDescriptorsTaskBase: () => ComputeFaceDescriptorsTaskBase, + ComputeSingleFaceDescriptorTask: () => ComputeSingleFaceDescriptorTask, + DetectAllFaceLandmarksTask: () => DetectAllFaceLandmarksTask, + DetectAllFacesTask: () => DetectAllFacesTask, + DetectFaceLandmarksTaskBase: () => DetectFaceLandmarksTaskBase, + DetectFacesTaskBase: () => DetectFacesTaskBase, + DetectSingleFaceLandmarksTask: () => DetectSingleFaceLandmarksTask, + DetectSingleFaceTask: () => DetectSingleFaceTask, + Dimensions: () => Dimensions, + FACE_EXPRESSION_LABELS: () => FACE_EXPRESSION_LABELS, + FaceDetection: () => FaceDetection, + FaceDetectionNet: () => FaceDetectionNet, + FaceExpressionNet: () => FaceExpressionNet, + FaceExpressions: () => FaceExpressions, + FaceLandmark68Net: () => FaceLandmark68Net, + FaceLandmark68TinyNet: () => FaceLandmark68TinyNet, + FaceLandmarkNet: () => FaceLandmarkNet, + FaceLandmarks: () => FaceLandmarks, + FaceLandmarks5: () => FaceLandmarks5, + FaceLandmarks68: () => FaceLandmarks68, + FaceMatch: () => FaceMatch, + FaceMatcher: () => FaceMatcher, + FaceRecognitionNet: () => FaceRecognitionNet, + Gender: () => Gender, + LabeledBox: () => LabeledBox, + LabeledFaceDescriptors: () => LabeledFaceDescriptors, + NetInput: () => NetInput, + NeuralNetwork: () => NeuralNetwork, + ObjectDetection: () => ObjectDetection, + Point: () => Point, + PredictedBox: () => PredictedBox, + Rect: () => Rect, + SsdMobilenetv1: () => SsdMobilenetv1, + SsdMobilenetv1Options: () => SsdMobilenetv1Options, + TinyFaceDetector: () => TinyFaceDetector, + TinyFaceDetectorOptions: () => TinyFaceDetectorOptions, + TinyYolov2: () => TinyYolov2, + TinyYolov2Options: () => TinyYolov2Options, + allFaces: () => allFaces, + allFacesSsdMobilenetv1: () => allFacesSsdMobilenetv1, + allFacesTinyYolov2: () => allFacesTinyYolov2, + awaitMediaLoaded: () => awaitMediaLoaded, + bufferToImage: () => bufferToImage, + computeFaceDescriptor: () => computeFaceDescriptor, + createCanvas: () => createCanvas, + createCanvasFromMedia: () => createCanvasFromMedia, + createFaceDetectionNet: () => createFaceDetectionNet, + createFaceRecognitionNet: () => createFaceRecognitionNet, + createSsdMobilenetv1: () => createSsdMobilenetv1, + createTinyFaceDetector: () => createTinyFaceDetector, + createTinyYolov2: () => createTinyYolov2, + detectAllFaces: () => detectAllFaces, + detectFaceLandmarks: () => detectFaceLandmarks, + detectFaceLandmarksTiny: () => detectFaceLandmarksTiny, + detectLandmarks: () => detectLandmarks, + detectSingleFace: () => detectSingleFace, + draw: () => draw_exports, + env: () => env, + euclideanDistance: () => euclideanDistance, + extendWithAge: () => extendWithAge, + extendWithFaceDescriptor: () => extendWithFaceDescriptor, + extendWithFaceDetection: () => extendWithFaceDetection, + extendWithFaceExpressions: () => extendWithFaceExpressions, + extendWithFaceLandmarks: () => extendWithFaceLandmarks, + extendWithGender: () => extendWithGender, + extractFaceTensors: () => extractFaceTensors, + extractFaces: () => extractFaces, + fetchImage: () => fetchImage, + fetchJson: () => fetchJson, + fetchNetWeights: () => fetchNetWeights, + fetchOrThrow: () => fetchOrThrow, + fetchVideo: () => fetchVideo, + getContext2dOrThrow: () => getContext2dOrThrow, + getMediaDimensions: () => getMediaDimensions, + imageTensorToCanvas: () => imageTensorToCanvas, + imageToSquare: () => imageToSquare, + inverseSigmoid: () => inverseSigmoid, + iou: () => iou, + isMediaElement: () => isMediaElement, + isMediaLoaded: () => isMediaLoaded, + isWithAge: () => isWithAge, + isWithFaceDetection: () => isWithFaceDetection, + isWithFaceExpressions: () => isWithFaceExpressions, + isWithFaceLandmarks: () => isWithFaceLandmarks, + isWithGender: () => isWithGender, + loadAgeGenderModel: () => loadAgeGenderModel, + loadFaceDetectionModel: () => loadFaceDetectionModel, + loadFaceExpressionModel: () => loadFaceExpressionModel, + loadFaceLandmarkModel: () => loadFaceLandmarkModel, + loadFaceLandmarkTinyModel: () => loadFaceLandmarkTinyModel, + loadFaceRecognitionModel: () => loadFaceRecognitionModel, + loadSsdMobilenetv1Model: () => loadSsdMobilenetv1Model, + loadTinyFaceDetectorModel: () => loadTinyFaceDetectorModel, + loadTinyYolov2Model: () => loadTinyYolov2Model, + loadWeightMap: () => loadWeightMap, + locateFaces: () => locateFaces, + matchDimensions: () => matchDimensions, + minBbox: () => minBbox, + nets: () => nets, + nonMaxSuppression: () => nonMaxSuppression, + normalize: () => normalize, + padToSquare: () => padToSquare, + predictAgeAndGender: () => predictAgeAndGender, + recognizeFaceExpressions: () => recognizeFaceExpressions, + resizeResults: () => resizeResults, + resolveInput: () => resolveInput, + shuffleArray: () => shuffleArray, + sigmoid: () => sigmoid, + ssdMobilenetv1: () => ssdMobilenetv1, + tf: () => tf42, + tinyFaceDetector: () => tinyFaceDetector, + tinyYolov2: () => tinyYolov2, + toNetInput: () => toNetInput, + utils: () => utils_exports, + validateConfig: () => validateConfig, + version: () => version2 +}); +module.exports = __toCommonJS(src_exports); +var tf42 = __toESM(require_tfjs_esm()); + +// src/draw/index.ts +var draw_exports = {}; +__export(draw_exports, { + AnchorPosition: () => AnchorPosition, + DrawBox: () => DrawBox, + DrawBoxOptions: () => DrawBoxOptions, + DrawFaceLandmarks: () => DrawFaceLandmarks, + DrawFaceLandmarksOptions: () => DrawFaceLandmarksOptions, + DrawTextField: () => DrawTextField, + DrawTextFieldOptions: () => DrawTextFieldOptions, + drawContour: () => drawContour, + drawDetections: () => drawDetections, + drawFaceExpressions: () => drawFaceExpressions, + drawFaceLandmarks: () => drawFaceLandmarks +}); + +// src/draw/drawContour.ts +function drawContour(ctx, points, isClosed = false) { + ctx.beginPath(); + points.slice(1).forEach(({ x, y }, prevIdx) => { + const from = points[prevIdx]; + ctx.moveTo(from.x, from.y); + ctx.lineTo(x, y); + }); + if (isClosed) { + const from = points[points.length - 1]; + const to = points[0]; + if (!from || !to) { + return; + } + ctx.moveTo(from.x, from.y); + ctx.lineTo(to.x, to.y); + } + ctx.stroke(); +} + +// src/utils/index.ts +var utils_exports = {}; +__export(utils_exports, { + computeReshapedDimensions: () => computeReshapedDimensions, + getCenterPoint: () => getCenterPoint, + isDimensions: () => isDimensions, + isEven: () => isEven, + isFloat: () => isFloat, + isTensor: () => isTensor, + isTensor1D: () => isTensor1D, + isTensor2D: () => isTensor2D, + isTensor3D: () => isTensor3D, + isTensor4D: () => isTensor4D, + isValidNumber: () => isValidNumber, + isValidProbablitiy: () => isValidProbablitiy, + range: () => range, + round: () => round +}); +var tf = __toESM(require_tfjs_esm()); + +// src/classes/Dimensions.ts +var Dimensions = class _Dimensions { + constructor(width, height) { + if (!isValidNumber(width) || !isValidNumber(height)) { + throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`); + } + this._width = width; + this._height = height; + } + get width() { + return this._width; + } + get height() { + return this._height; + } + reverse() { + return new _Dimensions(1 / this.width, 1 / this.height); + } +}; + +// src/utils/index.ts +function isTensor(tensor2, dim) { + return tensor2 instanceof tf.Tensor && tensor2.shape.length === dim; +} +function isTensor1D(tensor2) { + return isTensor(tensor2, 1); +} +function isTensor2D(tensor2) { + return isTensor(tensor2, 2); +} +function isTensor3D(tensor2) { + return isTensor(tensor2, 3); +} +function isTensor4D(tensor2) { + return isTensor(tensor2, 4); +} +function isFloat(num) { + return num % 1 !== 0; +} +function isEven(num) { + return num % 2 === 0; +} +function round(num, prec = 2) { + const f = 10 ** prec; + return Math.floor(num * f) / f; +} +function isDimensions(obj) { + return obj && obj.width && obj.height; +} +function computeReshapedDimensions({ width, height }, inputSize) { + const scale2 = inputSize / Math.max(height, width); + return new Dimensions(Math.round(width * scale2), Math.round(height * scale2)); +} +function getCenterPoint(pts) { + return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length)); +} +function range(num, start, step) { + return Array(num).fill(0).map((_, i) => start + i * step); +} +function isValidNumber(num) { + return !!num && num !== Infinity && num !== -Infinity && !Number.isNaN(num) || num === 0; +} +function isValidProbablitiy(num) { + return isValidNumber(num) && num >= 0 && num <= 1; +} + +// src/classes/Point.ts +var Point = class _Point { + constructor(x, y) { + this._x = x; + this._y = y; + } + get x() { + return this._x; + } + get y() { + return this._y; + } + add(pt) { + return new _Point(this.x + pt.x, this.y + pt.y); + } + sub(pt) { + return new _Point(this.x - pt.x, this.y - pt.y); + } + mul(pt) { + return new _Point(this.x * pt.x, this.y * pt.y); + } + div(pt) { + return new _Point(this.x / pt.x, this.y / pt.y); + } + abs() { + return new _Point(Math.abs(this.x), Math.abs(this.y)); + } + magnitude() { + return Math.sqrt(this.x ** 2 + this.y ** 2); + } + floor() { + return new _Point(Math.floor(this.x), Math.floor(this.y)); + } +}; + +// src/classes/Box.ts +var Box = class _Box { + static isRect(rect) { + return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber); + } + static assertIsValidBox(box, callee, allowNegativeDimensions = false) { + if (!_Box.isRect(box)) { + throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`); + } + if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) { + throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`); + } + } + constructor(_box, allowNegativeDimensions = true) { + const box = _box || {}; + const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber); + const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber); + if (!isRect && !isBbox) { + throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`); + } + const [x, y, width, height] = isRect ? [box.x, box.y, box.width, box.height] : [box.left, box.top, box.right - box.left, box.bottom - box.top]; + _Box.assertIsValidBox({ + x, + y, + width, + height + }, "Box.constructor", allowNegativeDimensions); + this._x = x; + this._y = y; + this._width = width; + this._height = height; + } + 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 Point(this.left, this.top); + } + get topRight() { + return new Point(this.right, this.top); + } + get bottomLeft() { + return new Point(this.left, this.bottom); + } + get bottomRight() { + return new Point(this.right, this.bottom); + } + round() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.round(val)); + return new _Box({ + x, + y, + width, + height + }); + } + floor() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.floor(val)); + return new _Box({ + x, + y, + width, + height + }); + } + toSquare() { + let { + x, + y, + width, + height + } = this; + const diff = Math.abs(width - height); + if (width < height) { + x -= diff / 2; + width += diff; + } + if (height < width) { + y -= diff / 2; + height += diff; + } + return new _Box({ x, y, width, height }); + } + rescale(s) { + const scaleX = isDimensions(s) ? s.width : s; + const scaleY = isDimensions(s) ? s.height : s; + return new _Box({ + x: this.x * scaleX, + y: this.y * scaleY, + width: this.width * scaleX, + height: this.height * scaleY + }); + } + pad(padX, padY) { + const [x, y, width, height] = [ + this.x - padX / 2, + this.y - padY / 2, + this.width + padX, + this.height + padY + ]; + return new _Box({ x, y, width, height }); + } + clipAtImageBorders(imgWidth, imgHeight) { + const { x, y, right, bottom } = this; + const clippedX = Math.max(x, 0); + const clippedY = Math.max(y, 0); + const newWidth = right - clippedX; + const newHeight = bottom - clippedY; + const clippedWidth = Math.min(newWidth, imgWidth - clippedX); + const clippedHeight = Math.min(newHeight, imgHeight - clippedY); + return new _Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight }).floor(); + } + shift(sx, sy) { + const { width, height } = this; + const x = this.x + sx; + const y = this.y + sy; + return new _Box({ x, y, width, height }); + } + padAtBorders(imageHeight, imageWidth) { + const w = this.width + 1; + const h = this.height + 1; + const dx = 1; + const dy = 1; + let edx = w; + let edy = h; + let x = this.left; + let y = this.top; + let ex = this.right; + let ey = this.bottom; + if (ex > imageWidth) { + edx = -ex + imageWidth + w; + ex = imageWidth; + } + if (ey > imageHeight) { + edy = -ey + imageHeight + h; + ey = imageHeight; + } + if (x < 1) { + edy = 2 - x; + x = 1; + } + if (y < 1) { + edy = 2 - y; + y = 1; + } + return { dy, edy, dx, edx, y, ey, x, ex, w, h }; + } + calibrate(region) { + return new _Box({ + left: this.left + region.left * this.width, + top: this.top + region.top * this.height, + right: this.right + region.right * this.width, + bottom: this.bottom + region.bottom * this.height + }).toSquare().round(); + } +}; + +// src/classes/BoundingBox.ts +var BoundingBox = class extends Box { + constructor(left, top, right, bottom, allowNegativeDimensions = false) { + super({ left, top, right, bottom }, allowNegativeDimensions); + } +}; + +// src/classes/ObjectDetection.ts +var ObjectDetection = class _ObjectDetection { + constructor(score, classScore, className, relativeBox, imageDims) { + this._imageDims = new Dimensions(imageDims.width, imageDims.height); + this._score = score; + this._classScore = classScore; + this._className = className; + this._box = new Box(relativeBox).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 Box(this._box).rescale(this.imageDims.reverse()); + } + forSize(width, height) { + return new _ObjectDetection( + this.score, + this.classScore, + this.className, + this.relativeBox, + { width, height } + ); + } +}; + +// src/classes/FaceDetection.ts +var FaceDetection = class _FaceDetection extends ObjectDetection { + constructor(score, relativeBox, imageDims) { + super(score, score, "", relativeBox, imageDims); + } + forSize(width, height) { + const { score, relativeBox, imageDims } = super.forSize(width, height); + return new _FaceDetection(score, relativeBox, imageDims); + } +}; + +// src/ops/iou.ts +function iou(box1, box2, isIOU = true) { + const width = Math.max(0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left)); + const height = Math.max(0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top)); + const interSection = width * height; + return isIOU ? interSection / (box1.area + box2.area - interSection) : interSection / Math.min(box1.area, box2.area); +} + +// src/ops/minBbox.ts +function minBbox(pts) { + const xs = pts.map((pt) => pt.x); + const ys = pts.map((pt) => pt.y); + const minX = xs.reduce((min, x) => x < min ? x : min, Infinity); + const minY = ys.reduce((min, y) => y < min ? y : min, Infinity); + const maxX = xs.reduce((max, x) => max < x ? x : max, 0); + const maxY = ys.reduce((max, y) => max < y ? y : max, 0); + return new BoundingBox(minX, minY, maxX, maxY); +} + +// src/ops/nonMaxSuppression.ts +function nonMaxSuppression(boxes, scores, iouThreshold, isIOU = true) { + let indicesSortedByScore = scores.map((score, boxIndex) => ({ score, boxIndex })).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex); + const pick = []; + while (indicesSortedByScore.length > 0) { + const curr = indicesSortedByScore.pop(); + pick.push(curr); + const indices = indicesSortedByScore; + const outputs = []; + for (let i = 0; i < indices.length; i++) { + const idx = indices[i]; + const currBox = boxes[curr]; + const idxBox = boxes[idx]; + outputs.push(iou(currBox, idxBox, isIOU)); + } + indicesSortedByScore = indicesSortedByScore.filter( + (_, j) => outputs[j] <= iouThreshold + ); + } + return pick; +} + +// src/ops/normalize.ts +var tf2 = __toESM(require_tfjs_esm()); +function normalize(x, meanRgb) { + return tf2.tidy(() => { + const [r, g, b] = meanRgb; + const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r, "float32"); + const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g, "float32"); + const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b, "float32"); + const avg_rgb = tf2.concat([avg_r, avg_g, avg_b], 3); + return tf2.sub(x, avg_rgb); + }); +} + +// src/ops/padToSquare.ts +var tf3 = __toESM(require_tfjs_esm()); +function padToSquare(imgTensor, isCenterImage = false) { + return tf3.tidy(() => { + const [height, width] = imgTensor.shape.slice(1); + if (height === width) + return imgTensor; + const dimDiff = Math.abs(height - width); + const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1)); + const paddingAxis = height > width ? 2 : 1; + const createPaddingTensor = (paddingAmountLocal) => { + const paddingTensorShape = imgTensor.shape.slice(); + paddingTensorShape[paddingAxis] = paddingAmountLocal; + return tf3.fill(paddingTensorShape, 0, "float32"); + }; + const paddingTensorAppend = createPaddingTensor(paddingAmount); + const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis]; + const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null; + const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend].filter((t) => !!t).map((t) => tf3.cast(t, "float32")); + return tf3.concat(tensorsToStack, paddingAxis); + }); +} + +// src/ops/shuffleArray.ts +function shuffleArray(inputArray) { + const array = inputArray.slice(); + for (let i = array.length - 1; i > 0; i--) { + const j = Math.floor(Math.random() * (i + 1)); + const x = array[i]; + array[i] = array[j]; + array[j] = x; + } + return array; +} + +// src/ops/index.ts +function sigmoid(x) { + return 1 / (1 + Math.exp(-x)); +} +function inverseSigmoid(x) { + return Math.log(x / (1 - x)); +} + +// src/classes/Rect.ts +var Rect = class extends Box { + constructor(x, y, width, height, allowNegativeDimensions = false) { + super({ x, y, width, height }, allowNegativeDimensions); + } +}; + +// src/classes/FaceLandmarks.ts +var relX = 0.5; +var relY = 0.43; +var relScale = 0.45; +var FaceLandmarks = class { + constructor(relativeFaceLandmarkPositions, imgDims, shift = new Point(0, 0)) { + const { width, height } = imgDims; + this._imgDims = new Dimensions(width, height); + this._shift = shift; + this._positions = relativeFaceLandmarkPositions.map( + (pt) => pt.mul(new Point(width, height)).add(shift) + ); + } + get shift() { + return new Point(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( + (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)) + ); + } + forSize(width, height) { + return new this.constructor( + this.relativePositions, + { width, height } + ); + } + shiftBy(x, y) { + return new this.constructor( + this.relativePositions, + this._imgDims, + new Point(x, y) + ); + } + shiftByPoint(pt) { + return this.shiftBy(pt.x, pt.y); + } + /** + * Aligns the face landmarks after face detection from the relative positions of the faces + * bounding box, or it's current shift. This function should be used to align the face images + * after face detection has been performed, before they are passed to the face recognition net. + * This will make the computed face descriptor more accurate. + * + * @param detection (optional) The bounding box of the face or the face detection result. If + * no argument was passed the position of the face landmarks are assumed to be relative to + * it's current shift. + * @returns The bounding box of the aligned face. + */ + align(detection, options = {}) { + if (detection) { + const box = detection instanceof FaceDetection ? detection.box.floor() : new Box(detection); + return this.shiftBy(box.x, box.y).align(null, options); + } + const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options }; + if (useDlibAlignment) { + return this.alignDlib(); + } + return this.alignMinBbox(minBoxPadding); + } + alignDlib() { + const centers = this.getRefPointsForAlignment(); + const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers; + const distToMouth = (pt) => mouthCenter.sub(pt).magnitude(); + const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2; + const size = Math.floor(eyeToMouthDist / relScale); + const refPoint = getCenterPoint(centers); + const x = Math.floor(Math.max(0, refPoint.x - relX * size)); + const y = Math.floor(Math.max(0, refPoint.y - relY * size)); + return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y)); + } + alignMinBbox(padding) { + const box = minBbox(this.positions); + return box.pad(box.width * padding, box.height * padding); + } + getRefPointsForAlignment() { + throw new Error("getRefPointsForAlignment not implemented by base class"); + } +}; + +// src/classes/FaceLandmarks5.ts +var FaceLandmarks5 = class extends FaceLandmarks { + getRefPointsForAlignment() { + const pts = this.positions; + return [ + pts[0], + pts[1], + getCenterPoint([pts[3], pts[4]]) + ]; + } +}; + +// src/classes/FaceLandmarks68.ts +var FaceLandmarks68 = class extends FaceLandmarks { + 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(getCenterPoint); + } +}; + +// src/classes/FaceMatch.ts +var FaceMatch = class { + constructor(label, distance) { + this._label = label; + this._distance = distance; + } + get label() { + return this._label; + } + get distance() { + return this._distance; + } + toString(withDistance = true) { + return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`; + } +}; + +// src/classes/LabeledBox.ts +var LabeledBox = class extends Box { + static assertIsValidLabeledBox(box, callee) { + Box.assertIsValidBox(box, callee); + if (!isValidNumber(box.label)) { + throw new Error(`${callee} - expected property label (${box.label}) to be a number`); + } + } + constructor(box, label) { + super(box); + this._label = label; + } + get label() { + return this._label; + } +}; + +// src/classes/LabeledFaceDescriptors.ts +var LabeledFaceDescriptors = class _LabeledFaceDescriptors { + constructor(label, descriptors) { + if (!(typeof label === "string")) { + throw new Error("LabeledFaceDescriptors - constructor expected label to be a string"); + } + if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) { + throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array"); + } + this._label = label; + this._descriptors = descriptors; + } + get label() { + return this._label; + } + get descriptors() { + return this._descriptors; + } + toJSON() { + return { + label: this.label, + descriptors: this.descriptors.map((d) => Array.from(d)) + }; + } + static fromJSON(json) { + const descriptors = json.descriptors.map((d) => new Float32Array(d)); + return new _LabeledFaceDescriptors(json.label, descriptors); + } +}; + +// src/classes/PredictedBox.ts +var PredictedBox = class extends LabeledBox { + static assertIsValidPredictedBox(box, callee) { + LabeledBox.assertIsValidLabeledBox(box, callee); + if (!isValidProbablitiy(box.score) || !isValidProbablitiy(box.classScore)) { + throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`); + } + } + constructor(box, label, score, classScore) { + super(box, label); + this._score = score; + this._classScore = classScore; + } + get score() { + return this._score; + } + get classScore() { + return this._classScore; + } +}; + +// src/factories/WithFaceDetection.ts +function isWithFaceDetection(obj) { + return obj.detection instanceof FaceDetection; +} +function extendWithFaceDetection(sourceObj, detection) { + const extension = { detection }; + return { ...sourceObj, ...extension }; +} + +// src/env/createBrowserEnv.ts +function createBrowserEnv() { + const fetch = window.fetch; + if (!fetch) + throw new Error("fetch - missing fetch implementation for browser environment"); + const readFile = () => { + throw new Error("readFile - filesystem not available for browser environment"); + }; + return { + Canvas: HTMLCanvasElement, + CanvasRenderingContext2D, + Image: HTMLImageElement, + ImageData, + Video: HTMLVideoElement, + createCanvasElement: () => document.createElement("canvas"), + createImageElement: () => document.createElement("img"), + createVideoElement: () => document.createElement("video"), + fetch, + readFile + }; +} + +// src/env/isNodejs.ts +function isNodejs() { + return typeof global === "object" && typeof process !== "undefined" && process.versions != null && process.versions.node != null; +} + +// src/env/createFileSystem.ts +function createFileSystem(fs) { + let requireFsError = ""; + if (!fs && isNodejs()) { + try { + fs = require("fs"); + } catch (err) { + requireFsError = err.toString(); + } + } + const readFile = fs ? (filePath) => new Promise((resolve, reject) => { + fs.readFile(filePath, (err, buffer) => err ? reject(err) : resolve(buffer)); + }) : () => { + throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); + }; + return { readFile }; +} + +// src/env/createNodejsEnv.ts +function createNodejsEnv() { + const Canvas = global["Canvas"] || global.HTMLCanvasElement; + const Image = global.Image || global.HTMLImageElement; + const Video = global["Video"] || global.HTMLVideoElement; + const createCanvasElement = () => { + if (Canvas) + return new Canvas(); + throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment"); + }; + const createImageElement = () => { + if (Image) + return new Image(); + throw new Error("createImageElement - missing Image implementation for nodejs environment"); + }; + const createVideoElement = () => { + if (Video) + return new Video(); + throw new Error("createVideoElement - missing Video implementation for nodejs environment"); + }; + const fetch = global.fetch; + const fileSystem = createFileSystem(); + return { + Canvas: Canvas || class { + }, + CanvasRenderingContext2D: global.CanvasRenderingContext2D || class { + }, + Image: Image || class { + }, + ImageData: global.ImageData || class { + }, + Video: global.HTMLVideoElement || class { + }, + createCanvasElement, + createImageElement, + createVideoElement, + fetch, + ...fileSystem + }; +} + +// src/env/isBrowser.ts +function isBrowser() { + return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined"; +} + +// src/env/index.ts +var environment; +function getEnv() { + if (!environment) { + throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()"); + } + return environment; +} +function setEnv(env2) { + environment = env2; +} +function initialize() { + if (isBrowser()) + return setEnv(createBrowserEnv()); + if (isNodejs()) + return setEnv(createNodejsEnv()); + return null; +} +function monkeyPatch(env2) { + if (!environment) { + initialize(); + } + if (!environment) { + throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()"); + } + const { Canvas = environment.Canvas, Image = environment.Image } = env2; + environment.Canvas = Canvas; + environment.Image = Image; + environment.createCanvasElement = env2.createCanvasElement || (() => new Canvas()); + environment.createImageElement = env2.createImageElement || (() => new Image()); + environment.ImageData = env2.ImageData || environment.ImageData; + environment.Video = env2.Video || environment.Video; + environment.fetch = env2.fetch || environment.fetch; + environment.readFile = env2.readFile || environment.readFile; +} +var env = { + getEnv, + setEnv, + initialize, + createBrowserEnv, + createFileSystem, + createNodejsEnv, + monkeyPatch, + isBrowser, + isNodejs +}; +initialize(); + +// src/dom/resolveInput.ts +function resolveInput(arg) { + if (!env.isNodejs() && typeof arg === "string") { + return document.getElementById(arg); + } + return arg; +} + +// src/dom/getContext2dOrThrow.ts +function getContext2dOrThrow(canvasArg) { + const { Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2 } = env.getEnv(); + if (canvasArg instanceof CanvasRenderingContext2D2) + return canvasArg; + const canvas = resolveInput(canvasArg); + if (!(canvas instanceof Canvas)) + throw new Error("resolveContext2d - expected canvas to be of instance of Canvas"); + const ctx = canvas.getContext("2d", { willReadFrequently: true }); + if (!ctx) + throw new Error("resolveContext2d - canvas 2d context is null"); + return ctx; +} + +// src/draw/DrawTextField.ts +var AnchorPosition = /* @__PURE__ */ ((AnchorPosition2) => { + AnchorPosition2["TOP_LEFT"] = "TOP_LEFT"; + AnchorPosition2["TOP_RIGHT"] = "TOP_RIGHT"; + AnchorPosition2["BOTTOM_LEFT"] = "BOTTOM_LEFT"; + AnchorPosition2["BOTTOM_RIGHT"] = "BOTTOM_RIGHT"; + return AnchorPosition2; +})(AnchorPosition || {}); +var DrawTextFieldOptions = class { + constructor(options = {}) { + const { + anchorPosition, + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = options; + this.anchorPosition = anchorPosition || "TOP_LEFT" /* TOP_LEFT */; + this.backgroundColor = backgroundColor || "rgba(0, 0, 0, 0.5)"; + this.fontColor = fontColor || "rgba(255, 255, 255, 1)"; + this.fontSize = fontSize || 14; + this.fontStyle = fontStyle || "Georgia"; + this.padding = padding || 4; + } +}; +var DrawTextField = class _DrawTextField { + constructor(text, anchor, options = {}) { + this.text = typeof text === "string" ? [text] : text instanceof _DrawTextField ? text.text : text; + this.anchor = anchor; + this.options = new DrawTextFieldOptions(options); + } + measureWidth(ctx) { + const { padding } = this.options; + return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + 2 * padding; + } + measureHeight() { + const { fontSize, padding } = this.options; + return this.text.length * fontSize + 2 * padding; + } + getUpperLeft(ctx, canvasDims) { + const { anchorPosition } = this.options; + const isShiftLeft = anchorPosition === "BOTTOM_RIGHT" /* BOTTOM_RIGHT */ || anchorPosition === "TOP_RIGHT" /* TOP_RIGHT */; + const isShiftTop = anchorPosition === "BOTTOM_LEFT" /* BOTTOM_LEFT */ || anchorPosition === "BOTTOM_RIGHT" /* BOTTOM_RIGHT */; + const textFieldWidth = this.measureWidth(ctx); + const textFieldHeight = this.measureHeight(); + const x = isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x; + const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y; + if (canvasDims) { + const { width, height } = canvasDims; + const newX = Math.max(Math.min(x, width - textFieldWidth), 0); + const newY = Math.max(Math.min(y, height - textFieldHeight), 0); + return { x: newX, y: newY }; + } + return { x, y }; + } + draw(canvasArg) { + const canvas = resolveInput(canvasArg); + const ctx = getContext2dOrThrow(canvas); + const { + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = this.options; + ctx.font = `${fontSize}px ${fontStyle}`; + const maxTextWidth = this.measureWidth(ctx); + const textHeight = this.measureHeight(); + ctx.fillStyle = backgroundColor; + const upperLeft = this.getUpperLeft(ctx, canvas); + ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight); + ctx.fillStyle = fontColor; + this.text.forEach((textLine, i) => { + const x = padding + upperLeft.x; + const y = padding + upperLeft.y + (i + 1) * fontSize; + ctx.fillText(textLine, x, y); + }); + } +}; + +// src/draw/DrawBox.ts +var DrawBoxOptions = class { + constructor(options = {}) { + const { + boxColor, + lineWidth, + label, + drawLabelOptions + } = options; + this.boxColor = boxColor || "rgba(0, 0, 255, 1)"; + this.lineWidth = lineWidth || 2; + this.label = label; + const defaultDrawLabelOptions = { + anchorPosition: "BOTTOM_LEFT" /* BOTTOM_LEFT */, + backgroundColor: this.boxColor + }; + this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions }); + } +}; +var DrawBox = class { + constructor(box, options = {}) { + this.box = new Box(box); + this.options = new DrawBoxOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { boxColor, lineWidth } = this.options; + const { + x, + y, + width, + height + } = this.box; + ctx.strokeStyle = boxColor; + ctx.lineWidth = lineWidth; + ctx.strokeRect(x, y, width, height); + const { label } = this.options; + if (label) { + new DrawTextField([label], { x: x - lineWidth / 2, y }, this.options.drawLabelOptions).draw(canvasArg); + } + } +}; + +// src/draw/drawDetections.ts +function drawDetections(canvasArg, detections) { + const detectionsArray = Array.isArray(detections) ? detections : [detections]; + detectionsArray.forEach((det) => { + const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0; + const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det); + const label = score ? `${round(score)}` : void 0; + new DrawBox(box, { label }).draw(canvasArg); + }); +} + +// src/faceExpressionNet/FaceExpressionNet.ts +var tf18 = __toESM(require_tfjs_esm()); + +// src/dom/isMediaLoaded.ts +function isMediaLoaded(media) { + const { Image, Video } = env.getEnv(); + return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3; +} + +// src/dom/awaitMediaLoaded.ts +function awaitMediaLoaded(media) { + return new Promise((resolve, reject) => { + if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) + resolve(null); + function onError(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + reject(e); + } + function onLoad(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + resolve(e); + } + media.addEventListener("load", onLoad); + media.addEventListener("error", onError); + }); +} + +// src/dom/bufferToImage.ts +function bufferToImage(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToImage - expected buf to be of type: Blob")); + const reader = new FileReader(); + reader.onload = () => { + if (typeof reader.result !== "string") + reject(new Error("bufferToImage - expected reader.result to be a string, in onload")); + const img = env.getEnv().createImageElement(); + img.onload = () => resolve(img); + img.onerror = reject; + img.src = reader.result; + }; + reader.onerror = reject; + reader.readAsDataURL(buf); + }); +} + +// src/dom/getMediaDimensions.ts +function getMediaDimensions(input) { + const { Image, Video } = env.getEnv(); + if (input instanceof Image) { + return new Dimensions(input.naturalWidth, input.naturalHeight); + } + if (input instanceof Video) { + return new Dimensions(input.videoWidth, input.videoHeight); + } + return new Dimensions(input.width, input.height); +} + +// src/dom/createCanvas.ts +function createCanvas({ width, height }) { + const { createCanvasElement } = env.getEnv(); + const canvas = createCanvasElement(); + canvas.width = width; + canvas.height = height; + return canvas; +} +function createCanvasFromMedia(media, dims) { + const { ImageData: ImageData2 } = env.getEnv(); + if (!(media instanceof ImageData2) && !isMediaLoaded(media)) { + throw new Error("createCanvasFromMedia - media has not finished loading yet"); + } + const { width, height } = dims || getMediaDimensions(media); + const canvas = createCanvas({ width, height }); + if (media instanceof ImageData2) { + getContext2dOrThrow(canvas).putImageData(media, 0, 0); + } else { + getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height); + } + return canvas; +} + +// src/dom/imageTensorToCanvas.ts +var tf4 = __toESM(require_tfjs_esm()); +async function imageTensorToCanvas(imgTensor, canvas) { + const targetCanvas = canvas || env.getEnv().createCanvasElement(); + const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0); + const imgTensor3D = tf4.tidy(() => imgTensor.as3D(height, width, numChannels).toInt()); + await tf4["browser"].toPixels(imgTensor3D, targetCanvas); + imgTensor3D.dispose(); + return targetCanvas; +} + +// src/dom/isMediaElement.ts +function isMediaElement(input) { + const { Image, Canvas, Video } = env.getEnv(); + return input instanceof Image || input instanceof Canvas || input instanceof Video; +} + +// src/dom/NetInput.ts +var tf5 = __toESM(require_tfjs_esm()); + +// src/dom/imageToSquare.ts +function imageToSquare(input, inputSize, centerImage = false) { + const { Image, Canvas } = env.getEnv(); + if (!(input instanceof Image || input instanceof Canvas)) { + throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement"); + } + if (inputSize <= 0) + return createCanvas({ width: 1, height: 1 }); + const dims = getMediaDimensions(input); + const scale2 = inputSize / Math.max(dims.height, dims.width); + const width = scale2 * dims.width; + const height = scale2 * dims.height; + const targetCanvas = createCanvas({ width: inputSize, height: inputSize }); + const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input); + const offset = Math.abs(width - height) / 2; + const dx = centerImage && width < height ? offset : 0; + const dy = centerImage && height < width ? offset : 0; + if (inputCanvas.width > 0 && inputCanvas.height > 0) + getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height); + return targetCanvas; +} + +// src/dom/NetInput.ts +var NetInput = class { + constructor(inputs, treatAsBatchInput = false) { + this._imageTensors = []; + this._canvases = []; + this._treatAsBatchInput = false; + this._inputDimensions = []; + this._inputSize = 0; + if (!Array.isArray(inputs)) { + throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`); + } + this._treatAsBatchInput = treatAsBatchInput; + this._batchSize = inputs.length; + inputs.forEach((input, idx) => { + if (isTensor3D(input)) { + this._imageTensors[idx] = input; + this._inputDimensions[idx] = input.shape; + return; + } + if (isTensor4D(input)) { + const batchSize = input.shape[0]; + if (batchSize !== 1) { + throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + this._imageTensors[idx] = input; + this._inputDimensions[idx] = input.shape.slice(1); + return; + } + const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input); + this._canvases[idx] = canvas; + this._inputDimensions[idx] = [canvas.height, canvas.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 range(this.batchSize, 0, 1).map( + (_, batchIdx) => this.getReshapedInputDimensions(batchIdx) + ); + } + getInput(batchIdx) { + return this.canvases[batchIdx] || this.imageTensors[batchIdx]; + } + getInputDimensions(batchIdx) { + return this._inputDimensions[batchIdx]; + } + getInputHeight(batchIdx) { + return this._inputDimensions[batchIdx][0]; + } + getInputWidth(batchIdx) { + return this._inputDimensions[batchIdx][1]; + } + getReshapedInputDimensions(batchIdx) { + if (typeof this.inputSize !== "number") { + throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet"); + } + const width = this.getInputWidth(batchIdx); + const height = this.getInputHeight(batchIdx); + return computeReshapedDimensions({ width, height }, this.inputSize); + } + /** + * Create a batch tensor from all input canvases and tensors + * with size [batchSize, inputSize, inputSize, 3]. + * + * @param inputSize Height and width of the tensor. + * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on + * both sides of the minor dimension oof the image. + * @returns The batch tensor. + */ + toBatchTensor(inputSize, isCenterInputs = true) { + this._inputSize = inputSize; + return tf5.tidy(() => { + const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => { + const input = this.getInput(batchIdx); + if (input instanceof tf5.Tensor) { + let imgTensor = isTensor4D(input) ? input : tf5.expandDims(input); + imgTensor = padToSquare(imgTensor, isCenterInputs); + if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) { + imgTensor = tf5["image"].resizeBilinear(imgTensor, [inputSize, inputSize], false, false); + } + return imgTensor.as3D(inputSize, inputSize, 3); + } + if (input instanceof env.getEnv().Canvas) { + return tf5["browser"].fromPixels(imageToSquare(input, inputSize, isCenterInputs)); + } + throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`); + }); + const batchTensor = tf5.stack(inputTensors.map((t) => tf5.cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3); + return batchTensor; + }); + } +}; + +// src/dom/toNetInput.ts +async function toNetInput(inputs) { + if (inputs instanceof NetInput) + return inputs; + const inputArgArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArgArray.length) + throw new Error("toNetInput - empty array passed as input"); + const getIdxHint = (idx) => Array.isArray(inputs) ? ` at input index ${idx}:` : ""; + const inputArray = inputArgArray.map(resolveInput); + inputArray.forEach((input, i) => { + if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) { + if (typeof inputArgArray[i] === "string") + throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`); + throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`); + } + if (isTensor4D(input)) { + const batchSize = input.shape[0]; + if (batchSize !== 1) + throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + }); + await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input))); + return new NetInput(inputArray, Array.isArray(inputs)); +} + +// src/dom/extractFaces.ts +async function extractFaces(input, detections) { + const { Canvas } = env.getEnv(); + let canvas = input; + if (!(input instanceof Canvas)) { + const netInput = await toNetInput(input); + if (netInput.batchSize > 1) + throw new Error("extractFaces - batchSize > 1 not supported"); + const tensorOrCanvas = netInput.getInput(0); + canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas); + } + const ctx = getContext2dOrThrow(canvas); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det).map((box) => box.clipAtImageBorders(canvas.width, canvas.height)); + return boxes.map(({ x, y, width, height }) => { + const faceImg = createCanvas({ width, height }); + if (width > 0 && height > 0) + getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0); + return faceImg; + }); +} + +// src/dom/extractFaceTensors.ts +var tf6 = __toESM(require_tfjs_esm()); +async function extractFaceTensors(imageTensor, detections) { + if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) { + throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D"); + } + if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) { + throw new Error("extractFaceTensors - batchSize > 1 not supported"); + } + return tf6.tidy(() => { + const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight)); + const faceTensors = boxes.filter((box) => box.width > 0 && box.height > 0).map(({ x, y, width, height }) => tf6.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])); + return faceTensors; + }); +} + +// src/dom/fetchOrThrow.ts +async function fetchOrThrow(url, init) { + const { fetch } = env.getEnv(); + const res = await fetch(url, init); + if (!(res.status < 400)) { + throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`); + } + return res; +} + +// src/dom/fetchImage.ts +async function fetchImage(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("image/")) { + throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToImage(blob); +} + +// src/dom/fetchJson.ts +async function fetchJson(uri) { + return (await fetchOrThrow(uri)).json(); +} + +// src/dom/fetchNetWeights.ts +async function fetchNetWeights(uri) { + return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer()); +} + +// src/dom/bufferToVideo.ts +function bufferToVideo(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToVideo - expected buf to be of type: Blob")); + const video = env.getEnv().createVideoElement(); + video.oncanplay = () => resolve(video); + video.onerror = reject; + video.playsInline = true; + video.muted = true; + video.src = URL.createObjectURL(buf); + video.play(); + }); +} + +// src/dom/fetchVideo.ts +async function fetchVideo(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("video/")) { + throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToVideo(blob); +} + +// src/dom/loadWeightMap.ts +var tf7 = __toESM(require_tfjs_esm()); + +// src/common/getModelUris.ts +function getModelUris(uri, defaultModelName) { + const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`; + if (!uri) { + return { + modelBaseUri: "", + manifestUri: defaultManifestFilename + }; + } + if (uri === "/") { + return { + modelBaseUri: "/", + manifestUri: `/${defaultManifestFilename}` + }; + } + const protocol = uri.startsWith("http://") ? "http://" : uri.startsWith("https://") ? "https://" : ""; + uri = uri.replace(protocol, ""); + const parts = uri.split("/").filter((s) => s); + const manifestFile = uri.endsWith(".json") ? parts[parts.length - 1] : defaultManifestFilename; + let modelBaseUri = protocol + (uri.endsWith(".json") ? parts.slice(0, parts.length - 1) : parts).join("/"); + modelBaseUri = uri.startsWith("/") ? `/${modelBaseUri}` : modelBaseUri; + return { + modelBaseUri, + manifestUri: modelBaseUri === "/" ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}` + }; +} + +// src/dom/loadWeightMap.ts +async function loadWeightMap(uri, defaultModelName) { + const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName); + const manifest = await fetchJson(manifestUri); + return tf7["io"].loadWeights(manifest, modelBaseUri); +} + +// src/dom/matchDimensions.ts +function matchDimensions(input, reference, useMediaDimensions = false) { + const { width, height } = useMediaDimensions ? getMediaDimensions(reference) : reference; + input.width = width; + input.height = height; + return { width, height }; +} + +// src/faceFeatureExtractor/FaceFeatureExtractor.ts +var tf15 = __toESM(require_tfjs_esm()); + +// src/NeuralNetwork.ts +var tf8 = __toESM(require_tfjs_esm()); +var NeuralNetwork = class { + constructor(name) { + this._params = void 0; + this._paramMappings = []; + this._name = name; + } + get params() { + return this._params; + } + get paramMappings() { + return this._paramMappings; + } + get isLoaded() { + return !!this.params; + } + getParamFromPath(paramPath) { + const { obj, objProp } = this.traversePropertyPath(paramPath); + return obj[objProp]; + } + reassignParamFromPath(paramPath, tensor2) { + const { obj, objProp } = this.traversePropertyPath(paramPath); + obj[objProp].dispose(); + obj[objProp] = tensor2; + } + getParamList() { + return this._paramMappings.map(({ paramPath }) => ({ + path: paramPath, + tensor: this.getParamFromPath(paramPath) + })); + } + getTrainableParams() { + return this.getParamList().filter((param) => param.tensor instanceof tf8.Variable); + } + getFrozenParams() { + return this.getParamList().filter((param) => !(param.tensor instanceof tf8.Variable)); + } + variable() { + this.getFrozenParams().forEach(({ path, tensor: tensor2 }) => { + this.reassignParamFromPath(path, tensor2.variable()); + }); + } + freeze() { + this.getTrainableParams().forEach(({ path, tensor: variable }) => { + const tensor2 = tf8.tensor(variable.dataSync()); + variable.dispose(); + this.reassignParamFromPath(path, tensor2); + }); + } + dispose(throwOnRedispose = true) { + this.getParamList().forEach((param) => { + if (throwOnRedispose && param.tensor.isDisposed) { + throw new Error(`param tensor has already been disposed for path ${param.path}`); + } + param.tensor.dispose(); + }); + this._params = void 0; + } + serializeParams() { + return new Float32Array( + this.getParamList().map(({ tensor: tensor2 }) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)) + ); + } + async load(weightsOrUrl) { + if (weightsOrUrl instanceof Float32Array) { + this.extractWeights(weightsOrUrl); + return; + } + await this.loadFromUri(weightsOrUrl); + } + async loadFromUri(uri) { + if (uri && typeof uri !== "string") { + throw new Error(`${this._name}.loadFromUri - expected model uri`); + } + const weightMap = await loadWeightMap(uri, this.getDefaultModelName()); + this.loadFromWeightMap(weightMap); + } + async loadFromDisk(filePath) { + if (filePath && typeof filePath !== "string") { + throw new Error(`${this._name}.loadFromDisk - expected model file path`); + } + const { readFile } = env.getEnv(); + const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName()); + const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => typeof buf === "string" ? Buffer.from(buf) : buf.buffer))); + const loadWeights = tf8["io"].weightsLoaderFactory(fetchWeightsFromDisk); + const manifest = JSON.parse((await readFile(manifestUri)).toString()); + const weightMap = await loadWeights(manifest, modelBaseUri); + this.loadFromWeightMap(weightMap); + } + loadFromWeightMap(weightMap) { + const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap); + this._paramMappings = paramMappings; + this._params = params; + } + extractWeights(weights) { + const { paramMappings, params } = this.extractParams(weights); + this._paramMappings = paramMappings; + this._params = params; + } + traversePropertyPath(paramPath) { + if (!this.params) { + throw new Error("traversePropertyPath - model has no loaded params"); + } + const result = paramPath.split("/").reduce((res, objProp2) => { + if (!res.nextObj.hasOwnProperty(objProp2)) { + throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`); + } + return { obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2] }; + }, { nextObj: this.params }); + const { obj, objProp } = result; + if (!obj || !objProp || !(obj[objProp] instanceof tf8.Tensor)) { + throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`); + } + return { obj, objProp }; + } +}; + +// src/faceFeatureExtractor/denseBlock.ts +var tf10 = __toESM(require_tfjs_esm()); + +// src/common/depthwiseSeparableConv.ts +var tf9 = __toESM(require_tfjs_esm()); +function depthwiseSeparableConv(x, params, stride) { + return tf9.tidy(() => { + let out = tf9.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same"); + out = tf9.add(out, params.bias); + return out; + }); +} + +// src/faceFeatureExtractor/denseBlock.ts +function denseBlock3(x, denseBlockParams, isFirstLayer = false) { + return tf10.tidy(() => { + const out1 = tf10.relu( + isFirstLayer ? tf10.add( + tf10.conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), + denseBlockParams.conv0.bias + ) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]) + ); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = tf10.relu(tf10.add(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + return tf10.relu(tf10.add(out1, tf10.add(out2, out3))); + }); +} +function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) { + return tf10.tidy(() => { + const out1 = tf10.relu( + isFirstLayer ? tf10.add( + tf10.conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), + denseBlockParams.conv0.bias + ) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]) + ); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = tf10.relu(tf10.add(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + const in4 = tf10.relu(tf10.add(out1, tf10.add(out2, out3))); + const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]); + return tf10.relu(tf10.add(out1, tf10.add(out2, tf10.add(out3, out4)))); + }); +} + +// src/common/convLayer.ts +var tf11 = __toESM(require_tfjs_esm()); +function convLayer(x, params, padding = "same", withRelu = false) { + return tf11.tidy(() => { + const out = tf11.add( + tf11.conv2d(x, params.filters, [1, 1], padding), + params.bias + ); + return withRelu ? tf11.relu(out) : out; + }); +} + +// src/common/disposeUnusedWeightTensors.ts +function disposeUnusedWeightTensors(weightMap, paramMappings) { + Object.keys(weightMap).forEach((path) => { + if (!paramMappings.some((pm) => pm.originalPath === path)) { + weightMap[path].dispose(); + } + }); +} + +// src/common/extractConvParamsFactory.ts +var tf12 = __toESM(require_tfjs_esm()); +function extractConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, filterSize, mappedPrefix) => { + const filters = tf12.tensor4d( + extractWeights(channelsIn * channelsOut * filterSize * filterSize), + [filterSize, filterSize, channelsIn, channelsOut] + ); + const bias = tf12.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { filters, bias }; + }; +} + +// src/common/extractFCParamsFactory.ts +var tf13 = __toESM(require_tfjs_esm()); +function extractFCParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const fc_weights = tf13.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]); + const fc_bias = tf13.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/weights` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { + weights: fc_weights, + bias: fc_bias + }; + }; +} + +// src/common/extractSeparableConvParamsFactory.ts +var tf14 = __toESM(require_tfjs_esm()); + +// src/common/types.ts +var SeparableConvParams = class { + // eslint-disable-next-line no-useless-constructor + constructor(depthwise_filter, pointwise_filter, bias) { + this.depthwise_filter = depthwise_filter; + this.pointwise_filter = pointwise_filter; + this.bias = bias; + } +}; + +// src/common/extractSeparableConvParamsFactory.ts +function extractSeparableConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const depthwise_filter = tf14.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]); + const pointwise_filter = tf14.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]); + const bias = tf14.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/depthwise_filter` }, + { paramPath: `${mappedPrefix}/pointwise_filter` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return new SeparableConvParams( + depthwise_filter, + pointwise_filter, + bias + ); + }; +} +function loadSeparableConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4); + const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return new SeparableConvParams( + depthwise_filter, + pointwise_filter, + bias + ); + }; +} + +// src/common/extractWeightEntryFactory.ts +function extractWeightEntryFactory(weightMap, paramMappings) { + return (originalPath, paramRank, mappedPath) => { + const tensor2 = weightMap[originalPath]; + if (!isTensor(tensor2, paramRank)) { + throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`); + } + paramMappings.push( + { originalPath, paramPath: mappedPath || originalPath } + ); + return tensor2; + }; +} + +// src/common/extractWeightsFactory.ts +function extractWeightsFactory(weights) { + let remainingWeights = weights; + function extractWeights(numWeights) { + const ret = remainingWeights.slice(0, numWeights); + remainingWeights = remainingWeights.slice(numWeights); + return ret; + } + function getRemainingWeights() { + return remainingWeights; + } + return { + extractWeights, + getRemainingWeights + }; +} + +// src/faceFeatureExtractor/extractorsFactory.ts +function extractorsFactory(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`) : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`); + const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`); + const conv22 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`); + return { conv0, conv1, conv2: conv22 }; + } + function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const { conv0, conv1, conv2: conv22 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer); + const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParams.ts +function extractParams(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock4Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock4Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock4Params(32, 64, "dense1"); + const dense2 = extractDenseBlock4Params(64, 128, "dense2"); + const dense3 = extractDenseBlock4Params(128, 256, "dense3"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { + dense0, + dense1, + dense2, + dense3 + } + }; +} + +// src/common/loadConvParamsFactory.ts +function loadConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { filters, bias }; + }; +} + +// src/faceFeatureExtractor/loadParamsFactory.ts +function loadParamsFactory(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractDenseBlock3Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + return { conv0, conv1, conv2: conv22 }; + } + function extractDenseBlock4Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + const conv3 = extractSeparableConvParams(`${prefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap(weightMap) { + const paramMappings = []; + const { + extractDenseBlock4Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock4Params("dense0", true), + dense1: extractDenseBlock4Params("dense1"), + dense2: extractDenseBlock4Params("dense2"), + dense3: extractDenseBlock4Params("dense3") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceFeatureExtractor/FaceFeatureExtractor.ts +var FaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("FaceFeatureExtractor"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("FaceFeatureExtractor - load model before inference"); + } + return tf15.tidy(() => { + const batchTensor = tf15.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock4(normalized, params.dense0, true); + out = denseBlock4(out, params.dense1); + out = denseBlock4(out, params.dense2); + out = denseBlock4(out, params.dense3); + out = tf15.avgPool(out, [7, 7], [2, 2], "valid"); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "face_feature_extractor_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap(weightMap); + } + extractParams(weights) { + return extractParams(weights); + } +}; + +// src/faceProcessor/FaceProcessor.ts +var tf17 = __toESM(require_tfjs_esm()); + +// src/common/fullyConnectedLayer.ts +var tf16 = __toESM(require_tfjs_esm()); +function fullyConnectedLayer(x, params) { + return tf16.tidy(() => tf16.add( + tf16.matMul(x, params.weights), + params.bias + )); +} + +// src/faceProcessor/extractParams.ts +function extractParams2(weights, channelsIn, channelsOut) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const fc = extractFCParams(channelsIn, channelsOut, "fc"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { fc } + }; +} + +// src/faceProcessor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap2(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { weights, bias }; + } + const params = { + fc: extractFcParams("fc") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceProcessor/util.ts +function seperateWeightMaps(weightMap) { + const featureExtractorMap = {}; + const classifierMap = {}; + Object.keys(weightMap).forEach((key) => { + const map = key.startsWith("fc") ? classifierMap : featureExtractorMap; + map[key] = weightMap[key]; + }); + return { featureExtractorMap, classifierMap }; +} + +// src/faceProcessor/FaceProcessor.ts +var FaceProcessor = class extends NeuralNetwork { + constructor(_name, faceFeatureExtractor) { + super(_name); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input) { + const { params } = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tf17.tidy(() => { + const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input; + return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc); + }); + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const { params, paramMappings } = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams2(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut()); + } + extractParamsFromWeightMap(weightMap) { + const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap2(classifierMap); + } + extractParams(weights) { + const cIn = this.getClassifierChannelsIn(); + const cOut = this.getClassifierChannelsOut(); + const classifierWeightSize = cOut * cIn + cOut; + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceExpressionNet/FaceExpressions.ts +var FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"]; +var FaceExpressions = class { + constructor(probabilities) { + this.neutral = 0; + this.happy = 0; + this.sad = 0; + this.angry = 0; + this.fearful = 0; + this.disgusted = 0; + this.surprised = 0; + if (probabilities.length !== 7) { + throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`); + } + FACE_EXPRESSION_LABELS.forEach((expression, idx) => { + this[expression] = probabilities[idx]; + }); + } + asSortedArray() { + return FACE_EXPRESSION_LABELS.map((expression) => ({ expression, probability: this[expression] })).sort((e0, e1) => e1.probability - e0.probability); + } +}; + +// src/faceExpressionNet/FaceExpressionNet.ts +var FaceExpressionNet = class extends FaceProcessor { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceExpressionNet", faceFeatureExtractor); + } + forwardInput(input) { + return tf18.tidy(() => tf18.softmax(this.runNet(input))); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async predictExpressions(input) { + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput); + const probabilitesByBatch = await Promise.all(tf18.unstack(out).map(async (t) => { + const data = t.dataSync(); + t.dispose(); + return data; + })); + out.dispose(); + const predictionsByBatch = probabilitesByBatch.map((probabilites) => new FaceExpressions(probabilites)); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "face_expression_model"; + } + getClassifierChannelsIn() { + return 256; + } + getClassifierChannelsOut() { + return 7; + } +}; + +// src/factories/WithFaceExpressions.ts +function isWithFaceExpressions(obj) { + return obj.expressions instanceof FaceExpressions; +} +function extendWithFaceExpressions(sourceObj, expressions) { + const extension = { expressions }; + return { ...sourceObj, ...extension }; +} + +// src/draw/drawFaceExpressions.ts +function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, textFieldAnchor) { + const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]; + faceExpressionsArray.forEach((e) => { + const expr = e instanceof FaceExpressions ? e : isWithFaceExpressions(e) ? e.expressions : void 0; + if (!expr) { + throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof"); + } + const sorted = expr.asSortedArray(); + const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence); + const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0); + const drawTextField = new DrawTextField( + resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`), + anchor + ); + drawTextField.draw(canvasArg); + }); +} + +// src/factories/WithFaceLandmarks.ts +function isWithFaceLandmarks(obj) { + return isWithFaceDetection(obj) && obj["landmarks"] instanceof FaceLandmarks && obj["unshiftedLandmarks"] instanceof FaceLandmarks && obj["alignedRect"] instanceof FaceDetection; +} +function calculateFaceAngle(mesh) { + const degrees = (radians) => radians * 180 / Math.PI; + const calcLengthBetweenTwoPoints = (a, b) => Math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2); + const angle = { + roll: void 0, + pitch: void 0, + yaw: void 0 + }; + const calcYaw = (leftPoint, midPoint, rightPoint) => { + const leftToMidpoint = Math.floor(leftPoint.x - midPoint.x); + const rightToMidpoint = Math.floor(midPoint.x - rightPoint.x); + return leftToMidpoint - rightToMidpoint; + }; + const calcRoll = (lever, pivot) => { + const hypotenuse = Math.hypot(pivot.x - lever.x, pivot.y - lever.y); + const opposite = pivot.y - lever.y; + const angleInRadians = Math.asin(opposite / hypotenuse); + const angleInDegrees = degrees(angleInRadians); + const normalizeAngle = Math.floor(90 - angleInDegrees); + const tiltDirection = pivot.x - lever.x < 0 ? -1 : 1; + const result = normalizeAngle * tiltDirection; + return result; + }; + const calcPitch = (leftPoint, midPoint, rightPoint) => { + const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint); + const baseCoords = new Point((leftPoint.x + rightPoint.x) / 2, (leftPoint.y + rightPoint.y) / 2); + const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords); + const angleInRadians = Math.atan(midToBaseLength / base); + const angleInDegrees = Math.floor(degrees(angleInRadians)); + const direction = baseCoords.y - midPoint.y < 0 ? -1 : 1; + const result = angleInDegrees * direction; + return result; + }; + if (!mesh || !mesh.positions || mesh.positions.length !== 68) + return angle; + const pt = mesh.positions; + angle.roll = calcRoll(pt[27], pt[66]); + angle.pitch = calcPitch(pt[14], pt[30], pt[2]); + angle.yaw = calcYaw(pt[14], pt[33], pt[2]); + return angle; +} +function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) { + const { box: shift } = sourceObj.detection; + const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y); + const rect = landmarks.align(); + const { imageDims } = sourceObj.detection; + const alignedRect = new FaceDetection( + sourceObj.detection.score, + rect.rescale(imageDims.reverse()), + imageDims + ); + const angle = calculateFaceAngle(unshiftedLandmarks); + const extension = { landmarks, unshiftedLandmarks, alignedRect, angle }; + return { ...sourceObj, ...extension }; +} + +// src/draw/DrawFaceLandmarks.ts +var DrawFaceLandmarksOptions = class { + constructor(options = {}) { + const { + drawLines = true, + drawPoints = true, + lineWidth, + lineColor, + pointSize, + pointColor + } = options; + this.drawLines = drawLines; + this.drawPoints = drawPoints; + this.lineWidth = lineWidth || 1; + this.pointSize = pointSize || 2; + this.lineColor = lineColor || "rgba(0, 255, 255, 1)"; + this.pointColor = pointColor || "rgba(255, 0, 255, 1)"; + } +}; +var DrawFaceLandmarks = class { + constructor(faceLandmarks, options = {}) { + this.faceLandmarks = faceLandmarks; + this.options = new DrawFaceLandmarksOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { + drawLines, + drawPoints, + lineWidth, + lineColor, + pointSize, + pointColor + } = this.options; + if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) { + ctx.strokeStyle = lineColor; + ctx.lineWidth = lineWidth; + drawContour(ctx, this.faceLandmarks.getJawOutline()); + drawContour(ctx, this.faceLandmarks.getLeftEyeBrow()); + drawContour(ctx, this.faceLandmarks.getRightEyeBrow()); + drawContour(ctx, this.faceLandmarks.getNose()); + drawContour(ctx, this.faceLandmarks.getLeftEye(), true); + drawContour(ctx, this.faceLandmarks.getRightEye(), true); + drawContour(ctx, this.faceLandmarks.getMouth(), true); + } + if (drawPoints) { + ctx.strokeStyle = pointColor; + ctx.fillStyle = pointColor; + const drawPoint = (pt) => { + ctx.beginPath(); + ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI); + ctx.fill(); + }; + this.faceLandmarks.positions.forEach(drawPoint); + } + } +}; +function drawFaceLandmarks(canvasArg, faceLandmarks) { + const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]; + faceLandmarksArray.forEach((f) => { + const landmarks = f instanceof FaceLandmarks ? f : isWithFaceLandmarks(f) ? f.landmarks : void 0; + if (!landmarks) { + throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof"); + } + new DrawFaceLandmarks(landmarks).draw(canvasArg); + }); +} + +// package.json +var version = "1.7.12"; + +// src/ageGenderNet/AgeGenderNet.ts +var tf20 = __toESM(require_tfjs_esm()); + +// src/xception/TinyXception.ts +var tf19 = __toESM(require_tfjs_esm()); + +// src/xception/extractParams.ts +function extractorsFactory2(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`); + return { separable_conv0, separable_conv1, expansion_conv }; + } + function extractMainBlockParams(channels, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`); + return { separable_conv0, separable_conv1, separable_conv2 }; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParams3(weights, numMainBlocks) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = extractorsFactory2(extractWeights, paramMappings); + const entry_flow_conv_in = extractConvParams(3, 32, 3, "entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, "entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, "entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams(256, 512, "exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { entry_flow, middle_flow, exit_flow } + }; +} + +// src/xception/extractParamsFromWeightMap.ts +function loadParamsFactory2(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractReductionBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`); + return { separable_conv0, separable_conv1, expansion_conv }; + } + function extractMainBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`); + return { separable_conv0, separable_conv1, separable_conv2 }; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParamsFromWeightMap3(weightMap, numMainBlocks) { + const paramMappings = []; + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = loadParamsFactory2(weightMap, paramMappings); + const entry_flow_conv_in = extractConvParams("entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams("entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams("entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams("exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }; +} + +// src/xception/TinyXception.ts +function conv(x, params, stride) { + return tf19.add(tf19.conv2d(x, params.filters, stride, "same"), params.bias); +} +function reductionBlock(x, params, isActivateInput = true) { + let out = isActivateInput ? tf19.relu(x) : x; + out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv1, [1, 1]); + out = tf19.maxPool(out, [3, 3], [2, 2], "same"); + out = tf19.add(out, conv(x, params.expansion_conv, [2, 2])); + return out; +} +function mainBlock(x, params) { + let out = depthwiseSeparableConv(tf19.relu(x), params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv1, [1, 1]); + out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv2, [1, 1]); + out = tf19.add(out, x); + return out; +} +var TinyXception = class extends NeuralNetwork { + constructor(numMainBlocks) { + super("TinyXception"); + this._numMainBlocks = numMainBlocks; + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("TinyXception - load model before inference"); + } + return tf19.tidy(() => { + const batchTensor = tf19.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = tf19.relu(conv(normalized, params.entry_flow.conv_in, [2, 2])); + out = reductionBlock(out, params.entry_flow.reduction_block_0, false); + out = reductionBlock(out, params.entry_flow.reduction_block_1); + range(this._numMainBlocks, 0, 1).forEach((idx) => { + out = mainBlock(out, params.middle_flow[`main_block_${idx}`]); + }); + out = reductionBlock(out, params.exit_flow.reduction_block); + out = tf19.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1])); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "tiny_xception_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap3(weightMap, this._numMainBlocks); + } + extractParams(weights) { + return extractParams3(weights, this._numMainBlocks); + } +}; + +// src/ageGenderNet/extractParams.ts +function extractParams4(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const age = extractFCParams(512, 1, "fc/age"); + const gender = extractFCParams(512, 2, "fc/gender"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { fc: { age, gender } } + }; +} + +// src/ageGenderNet/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap4(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { weights, bias }; + } + const params = { + fc: { + age: extractFcParams("fc/age"), + gender: extractFcParams("fc/gender") + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/ageGenderNet/types.ts +var Gender = /* @__PURE__ */ ((Gender2) => { + Gender2["FEMALE"] = "female"; + Gender2["MALE"] = "male"; + return Gender2; +})(Gender || {}); + +// src/ageGenderNet/AgeGenderNet.ts +var AgeGenderNet = class extends NeuralNetwork { + constructor(faceFeatureExtractor = new TinyXception(2)) { + super("AgeGenderNet"); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input) { + const { params } = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tf20.tidy(() => { + const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input; + const pooled = tf20.avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1); + const age = fullyConnectedLayer(pooled, params.fc.age).as1D(); + const gender = fullyConnectedLayer(pooled, params.fc.gender); + return { age, gender }; + }); + } + forwardInput(input) { + return tf20.tidy(() => { + const { age, gender } = this.runNet(input); + return { age, gender: tf20.softmax(gender) }; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async predictAgeAndGender(input) { + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput); + const ages = tf20.unstack(out.age); + const genders = tf20.unstack(out.gender); + const ageAndGenderTensors = ages.map((ageTensor, i) => ({ + ageTensor, + genderTensor: genders[i] + })); + const predictionsByBatch = await Promise.all( + ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => { + const age = ageTensor.dataSync()[0]; + const probMale = genderTensor.dataSync()[0]; + const isMale = probMale > 0.5; + const gender = isMale ? "male" /* MALE */ : "female" /* FEMALE */; + const genderProbability = isMale ? probMale : 1 - probMale; + ageTensor.dispose(); + genderTensor.dispose(); + return { age, gender, genderProbability }; + }) + ); + out.age.dispose(); + out.gender.dispose(); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "age_gender_model"; + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const { params, paramMappings } = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams4(weights); + } + extractParamsFromWeightMap(weightMap) { + const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap4(classifierMap); + } + extractParams(weights) { + const classifierWeightSize = 512 * 1 + 1 + (512 * 2 + 2); + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68NetBase.ts +var tf21 = __toESM(require_tfjs_esm()); +var FaceLandmark68NetBase = class extends FaceProcessor { + postProcess(output, inputSize, originalDimensions) { + const inputDimensions = originalDimensions.map(({ width, height }) => { + const scale2 = inputSize / Math.max(height, width); + return { + width: width * scale2, + height: height * scale2 + }; + }); + const batchSize = inputDimensions.length; + return tf21.tidy(() => { + const createInterleavedTensor = (fillX, fillY) => tf21.stack([tf21.fill([68], fillX, "float32"), tf21.fill([68], fillY, "float32")], 1).as2D(1, 136).as1D(); + const getPadding = (batchIdx, cond) => { + const { width, height } = inputDimensions[batchIdx]; + return cond(width, height) ? Math.abs(width - height) / 2 : 0; + }; + const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h); + const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w); + const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize, "float32")).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor( + getPaddingX(batchIdx), + getPaddingY(batchIdx) + )))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor( + inputDimensions[batchIdx].width, + inputDimensions[batchIdx].height + )))); + return landmarkTensors; + }); + } + forwardInput(input) { + return tf21.tidy(() => { + const out = this.runNet(input); + return this.postProcess( + out, + input.inputSize, + input.inputDimensions.map(([height, width]) => ({ height, width })) + ); + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async detectLandmarks(input) { + const netInput = await toNetInput(input); + const landmarkTensors = tf21.tidy( + () => tf21.unstack(this.forwardInput(netInput)) + ); + const landmarksForBatch = await Promise.all(landmarkTensors.map( + async (landmarkTensor, batchIdx) => { + const landmarksArray = Array.from(landmarkTensor.dataSync()); + const xCoords = landmarksArray.filter((_, i) => isEven(i)); + const yCoords = landmarksArray.filter((_, i) => !isEven(i)); + return new FaceLandmarks68( + Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])), + { + height: netInput.getInputHeight(batchIdx), + width: netInput.getInputWidth(batchIdx) + } + ); + } + )); + landmarkTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? landmarksForBatch : landmarksForBatch[0]; + } + getClassifierChannelsOut() { + return 136; + } +}; + +// src/faceLandmarkNet/FaceLandmark68Net.ts +var FaceLandmark68Net = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceLandmark68Net", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_model"; + } + getClassifierChannelsIn() { + return 256; + } +}; + +// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts +var tf22 = __toESM(require_tfjs_esm()); + +// src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts +function extractParamsFromWeightMapTiny(weightMap) { + const paramMappings = []; + const { + extractDenseBlock3Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock3Params("dense0", true), + dense1: extractDenseBlock3Params("dense1"), + dense2: extractDenseBlock3Params("dense2") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceFeatureExtractor/extractParamsTiny.ts +function extractParamsTiny(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock3Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock3Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock3Params(32, 64, "dense1"); + const dense2 = extractDenseBlock3Params(64, 128, "dense2"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { dense0, dense1, dense2 } + }; +} + +// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts +var TinyFaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("TinyFaceFeatureExtractor"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("TinyFaceFeatureExtractor - load model before inference"); + } + return tf22.tidy(() => { + const batchTensor = tf22.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock3(normalized, params.dense0, true); + out = denseBlock3(out, params.dense1); + out = denseBlock3(out, params.dense2); + out = tf22.avgPool(out, [14, 14], [2, 2], "valid"); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "face_feature_extractor_tiny_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMapTiny(weightMap); + } + extractParams(weights) { + return extractParamsTiny(weights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68TinyNet.ts +var FaceLandmark68TinyNet = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) { + super("FaceLandmark68TinyNet", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_tiny_model"; + } + getClassifierChannelsIn() { + return 128; + } +}; + +// src/faceLandmarkNet/index.ts +var FaceLandmarkNet = class extends FaceLandmark68Net { +}; + +// src/faceRecognitionNet/FaceRecognitionNet.ts +var tf27 = __toESM(require_tfjs_esm()); + +// src/faceRecognitionNet/convLayer.ts +var tf24 = __toESM(require_tfjs_esm()); + +// src/faceRecognitionNet/scaleLayer.ts +var tf23 = __toESM(require_tfjs_esm()); +function scale(x, params) { + return tf23.add(tf23.mul(x, params.weights), params.biases); +} + +// src/faceRecognitionNet/convLayer.ts +function convLayer2(x, params, strides, withRelu, padding = "same") { + const { filters, bias } = params.conv; + let out = tf24.conv2d(x, filters, strides, padding); + out = tf24.add(out, bias); + out = scale(out, params.scale); + return withRelu ? tf24.relu(out) : out; +} +function conv2(x, params) { + return convLayer2(x, params, [1, 1], true); +} +function convNoRelu(x, params) { + return convLayer2(x, params, [1, 1], false); +} +function convDown(x, params) { + return convLayer2(x, params, [2, 2], true, "valid"); +} + +// src/faceRecognitionNet/extractParams.ts +var tf25 = __toESM(require_tfjs_esm()); +function extractorsFactory3(extractWeights, paramMappings) { + function extractFilterValues(numFilterValues, numFilters, filterSize) { + const weights = extractWeights(numFilterValues); + const depth = weights.length / (numFilters * filterSize * filterSize); + if (isFloat(depth)) { + throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`); + } + return tf25.tidy( + () => tf25.transpose( + tf25.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), + [2, 3, 1, 0] + ) + ); + } + function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const filters = extractFilterValues(numFilterValues, numFilters, filterSize); + const bias = tf25.tensor1d(extractWeights(numFilters)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { filters, bias }; + } + function extractScaleLayerParams(numWeights, mappedPrefix) { + const weights = tf25.tensor1d(extractWeights(numWeights)); + const biases = tf25.tensor1d(extractWeights(numWeights)); + paramMappings.push( + { paramPath: `${mappedPrefix}/weights` }, + { paramPath: `${mappedPrefix}/biases` } + ); + return { + weights, + biases + }; + } + function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const conv3 = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`); + const scale2 = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`); + return { conv: conv3, scale: scale2 }; + } + function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) { + const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`); + const conv22 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`); + return { conv1, conv2: conv22 }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParams5(weights) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory3(extractWeights, paramMappings); + const conv32_down = extractConvLayerParams(4704, 32, 7, "conv32_down"); + const conv32_1 = extractResidualLayerParams(9216, 32, 3, "conv32_1"); + const conv32_2 = extractResidualLayerParams(9216, 32, 3, "conv32_2"); + const conv32_3 = extractResidualLayerParams(9216, 32, 3, "conv32_3"); + const conv64_down = extractResidualLayerParams(36864, 64, 3, "conv64_down", true); + const conv64_1 = extractResidualLayerParams(36864, 64, 3, "conv64_1"); + const conv64_2 = extractResidualLayerParams(36864, 64, 3, "conv64_2"); + const conv64_3 = extractResidualLayerParams(36864, 64, 3, "conv64_3"); + const conv128_down = extractResidualLayerParams(147456, 128, 3, "conv128_down", true); + const conv128_1 = extractResidualLayerParams(147456, 128, 3, "conv128_1"); + const conv128_2 = extractResidualLayerParams(147456, 128, 3, "conv128_2"); + const conv256_down = extractResidualLayerParams(589824, 256, 3, "conv256_down", true); + const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1"); + const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2"); + const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out"); + const fc = tf25.tidy( + () => tf25.transpose(tf25.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]) + ); + paramMappings.push({ paramPath: "fc" }); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + return { params, paramMappings }; +} + +// src/faceRecognitionNet/extractParamsFromWeightMap.ts +function extractorsFactory4(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractScaleLayerParams(prefix) { + const weights = extractWeightEntry(`${prefix}/scale/weights`, 1); + const biases = extractWeightEntry(`${prefix}/scale/biases`, 1); + return { weights, biases }; + } + function extractConvLayerParams(prefix) { + const filters = extractWeightEntry(`${prefix}/conv/filters`, 4); + const bias = extractWeightEntry(`${prefix}/conv/bias`, 1); + const scale2 = extractScaleLayerParams(prefix); + return { conv: { filters, bias }, scale: scale2 }; + } + function extractResidualLayerParams(prefix) { + return { + conv1: extractConvLayerParams(`${prefix}/conv1`), + conv2: extractConvLayerParams(`${prefix}/conv2`) + }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParamsFromWeightMap5(weightMap) { + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory4(weightMap, paramMappings); + const conv32_down = extractConvLayerParams("conv32_down"); + const conv32_1 = extractResidualLayerParams("conv32_1"); + const conv32_2 = extractResidualLayerParams("conv32_2"); + const conv32_3 = extractResidualLayerParams("conv32_3"); + const conv64_down = extractResidualLayerParams("conv64_down"); + const conv64_1 = extractResidualLayerParams("conv64_1"); + const conv64_2 = extractResidualLayerParams("conv64_2"); + const conv64_3 = extractResidualLayerParams("conv64_3"); + const conv128_down = extractResidualLayerParams("conv128_down"); + const conv128_1 = extractResidualLayerParams("conv128_1"); + const conv128_2 = extractResidualLayerParams("conv128_2"); + const conv256_down = extractResidualLayerParams("conv256_down"); + const conv256_1 = extractResidualLayerParams("conv256_1"); + const conv256_2 = extractResidualLayerParams("conv256_2"); + const conv256_down_out = extractResidualLayerParams("conv256_down_out"); + const { fc } = weightMap; + paramMappings.push({ originalPath: "fc", paramPath: "fc" }); + if (!isTensor2D(fc)) { + throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceRecognitionNet/residualLayer.ts +var tf26 = __toESM(require_tfjs_esm()); +function residual(x, params) { + let out = conv2(x, params.conv1); + out = convNoRelu(out, params.conv2); + out = tf26.add(out, x); + out = tf26.relu(out); + return out; +} +function residualDown(x, params) { + let out = convDown(x, params.conv1); + out = convNoRelu(out, params.conv2); + let pooled = tf26.avgPool(x, 2, 2, "valid"); + const zeros2 = tf26.zeros(pooled.shape); + const isPad = pooled.shape[3] !== out.shape[3]; + const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]; + if (isAdjustShape) { + const padShapeX = [...out.shape]; + padShapeX[1] = 1; + const zerosW = tf26.zeros(padShapeX); + out = tf26.concat([out, zerosW], 1); + const padShapeY = [...out.shape]; + padShapeY[2] = 1; + const zerosH = tf26.zeros(padShapeY); + out = tf26.concat([out, zerosH], 2); + } + pooled = isPad ? tf26.concat([pooled, zeros2], 3) : pooled; + out = tf26.add(pooled, out); + out = tf26.relu(out); + return out; +} + +// src/faceRecognitionNet/FaceRecognitionNet.ts +var FaceRecognitionNet = class extends NeuralNetwork { + constructor() { + super("FaceRecognitionNet"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("FaceRecognitionNet - load model before inference"); + } + return tf27.tidy(() => { + const batchTensor = tf27.cast(input.toBatchTensor(150, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = convDown(normalized, params.conv32_down); + out = tf27.maxPool(out, 3, 2, "valid"); + out = residual(out, params.conv32_1); + out = residual(out, params.conv32_2); + out = residual(out, params.conv32_3); + out = residualDown(out, params.conv64_down); + out = residual(out, params.conv64_1); + out = residual(out, params.conv64_2); + out = residual(out, params.conv64_3); + out = residualDown(out, params.conv128_down); + out = residual(out, params.conv128_1); + out = residual(out, params.conv128_2); + out = residualDown(out, params.conv256_down); + out = residual(out, params.conv256_1); + out = residual(out, params.conv256_2); + out = residualDown(out, params.conv256_down_out); + const globalAvg = out.mean([1, 2]); + const fullyConnected = tf27.matMul(globalAvg, params.fc); + return fullyConnected; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async computeFaceDescriptor(input) { + var _a; + if ((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a.some((dim) => dim <= 0)) + return new Float32Array(128); + const netInput = await toNetInput(input); + const faceDescriptorTensors = tf27.tidy(() => tf27.unstack(this.forwardInput(netInput))); + const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())); + faceDescriptorTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0]; + } + getDefaultModelName() { + return "face_recognition_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap5(weightMap); + } + extractParams(weights) { + return extractParams5(weights); + } +}; + +// src/faceRecognitionNet/index.ts +function createFaceRecognitionNet(weights) { + const net = new FaceRecognitionNet(); + net.extractWeights(weights); + return net; +} + +// src/factories/WithFaceDescriptor.ts +function extendWithFaceDescriptor(sourceObj, descriptor) { + const extension = { descriptor }; + return { ...sourceObj, ...extension }; +} + +// src/factories/WithAge.ts +function isWithAge(obj) { + return typeof obj.age === "number"; +} +function extendWithAge(sourceObj, age) { + const extension = { age }; + return { ...sourceObj, ...extension }; +} + +// src/factories/WithGender.ts +function isWithGender(obj) { + return (obj.gender === "male" /* MALE */ || obj.gender === "female" /* FEMALE */) && isValidProbablitiy(obj.genderProbability); +} +function extendWithGender(sourceObj, gender, genderProbability) { + const extension = { gender, genderProbability }; + return { ...sourceObj, ...extension }; +} + +// src/ssdMobilenetv1/SsdMobilenetv1.ts +var tf34 = __toESM(require_tfjs_esm()); + +// src/ssdMobilenetv1/extractParams.ts +var tf28 = __toESM(require_tfjs_esm()); +function extractorsFactory5(extractWeights, paramMappings) { + function extractDepthwiseConvParams(numChannels, mappedPrefix) { + const filters = tf28.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]); + const batch_norm_scale = tf28.tensor1d(extractWeights(numChannels)); + const batch_norm_offset = tf28.tensor1d(extractWeights(numChannels)); + const batch_norm_mean = tf28.tensor1d(extractWeights(numChannels)); + const batch_norm_variance = tf28.tensor1d(extractWeights(numChannels)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/batch_norm_scale` }, + { paramPath: `${mappedPrefix}/batch_norm_offset` }, + { paramPath: `${mappedPrefix}/batch_norm_mean` }, + { paramPath: `${mappedPrefix}/batch_norm_variance` } + ); + return { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }; + } + function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) { + const filters = tf28.tensor4d( + extractWeights(channelsIn * channelsOut * filterSize * filterSize), + [filterSize, filterSize, channelsIn, channelsOut] + ); + const bias = tf28.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}` } + ); + return { filters, bias }; + } + function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) { + const { + filters, + bias + } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true); + return { + filters, + batch_norm_offset: bias + }; + } + function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) { + const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`); + const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`); + return { depthwise_conv, pointwise_conv }; + } + function extractMobilenetV1Params() { + const conv_0 = extractPointwiseConvParams(3, 32, 3, "mobilenetv1/conv_0"); + const conv_1 = extractConvPairParams(32, 64, "mobilenetv1/conv_1"); + const conv_2 = extractConvPairParams(64, 128, "mobilenetv1/conv_2"); + const conv_3 = extractConvPairParams(128, 128, "mobilenetv1/conv_3"); + const conv_4 = extractConvPairParams(128, 256, "mobilenetv1/conv_4"); + const conv_5 = extractConvPairParams(256, 256, "mobilenetv1/conv_5"); + const conv_6 = extractConvPairParams(256, 512, "mobilenetv1/conv_6"); + const conv_7 = extractConvPairParams(512, 512, "mobilenetv1/conv_7"); + const conv_8 = extractConvPairParams(512, 512, "mobilenetv1/conv_8"); + const conv_9 = extractConvPairParams(512, 512, "mobilenetv1/conv_9"); + const conv_10 = extractConvPairParams(512, 512, "mobilenetv1/conv_10"); + const conv_11 = extractConvPairParams(512, 512, "mobilenetv1/conv_11"); + const conv_12 = extractConvPairParams(512, 1024, "mobilenetv1/conv_12"); + const conv_13 = extractConvPairParams(1024, 1024, "mobilenetv1/conv_13"); + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + conv_8, + conv_9, + conv_10, + conv_11, + conv_12, + conv_13 + }; + } + function extractPredictionLayerParams() { + const conv_0 = extractPointwiseConvParams(1024, 256, 1, "prediction_layer/conv_0"); + const conv_1 = extractPointwiseConvParams(256, 512, 3, "prediction_layer/conv_1"); + const conv_2 = extractPointwiseConvParams(512, 128, 1, "prediction_layer/conv_2"); + const conv_3 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_3"); + const conv_4 = extractPointwiseConvParams(256, 128, 1, "prediction_layer/conv_4"); + const conv_5 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_5"); + const conv_6 = extractPointwiseConvParams(256, 64, 1, "prediction_layer/conv_6"); + const conv_7 = extractPointwiseConvParams(64, 128, 3, "prediction_layer/conv_7"); + const box_encoding_0_predictor = extractConvParams(512, 12, 1, "prediction_layer/box_predictor_0/box_encoding_predictor"); + const class_predictor_0 = extractConvParams(512, 9, 1, "prediction_layer/box_predictor_0/class_predictor"); + const box_encoding_1_predictor = extractConvParams(1024, 24, 1, "prediction_layer/box_predictor_1/box_encoding_predictor"); + const class_predictor_1 = extractConvParams(1024, 18, 1, "prediction_layer/box_predictor_1/class_predictor"); + const box_encoding_2_predictor = extractConvParams(512, 24, 1, "prediction_layer/box_predictor_2/box_encoding_predictor"); + const class_predictor_2 = extractConvParams(512, 18, 1, "prediction_layer/box_predictor_2/class_predictor"); + const box_encoding_3_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_3/box_encoding_predictor"); + const class_predictor_3 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_3/class_predictor"); + const box_encoding_4_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_4/box_encoding_predictor"); + const class_predictor_4 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_4/class_predictor"); + const box_encoding_5_predictor = extractConvParams(128, 24, 1, "prediction_layer/box_predictor_5/box_encoding_predictor"); + const class_predictor_5 = extractConvParams(128, 18, 1, "prediction_layer/box_predictor_5/class_predictor"); + const box_predictor_0 = { + box_encoding_predictor: box_encoding_0_predictor, + class_predictor: class_predictor_0 + }; + const box_predictor_1 = { + box_encoding_predictor: box_encoding_1_predictor, + class_predictor: class_predictor_1 + }; + const box_predictor_2 = { + box_encoding_predictor: box_encoding_2_predictor, + class_predictor: class_predictor_2 + }; + const box_predictor_3 = { + box_encoding_predictor: box_encoding_3_predictor, + class_predictor: class_predictor_3 + }; + const box_predictor_4 = { + box_encoding_predictor: box_encoding_4_predictor, + class_predictor: class_predictor_4 + }; + const box_predictor_5 = { + box_encoding_predictor: box_encoding_5_predictor, + class_predictor: class_predictor_5 + }; + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + box_predictor_0, + box_predictor_1, + box_predictor_2, + box_predictor_3, + box_predictor_4, + box_predictor_5 + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParams6(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory5(extractWeights, paramMappings); + const mobilenetv1 = extractMobilenetV1Params(); + const prediction_layer = extractPredictionLayerParams(); + const extra_dim = tf28.tensor3d( + extractWeights(5118 * 4), + [1, 5118, 4] + ); + const output_layer = { + extra_dim + }; + paramMappings.push({ paramPath: "output_layer/extra_dim" }); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + params: { + mobilenetv1, + prediction_layer, + output_layer + }, + paramMappings + }; +} + +// src/ssdMobilenetv1/extractParamsFromWeightMap.ts +function extractorsFactory6(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractPointwiseConvParams(prefix, idx, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`); + const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`); + return { filters, batch_norm_offset }; + } + function extractConvPairParams(idx) { + const mappedPrefix = `mobilenetv1/conv_${idx}`; + const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`; + const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`; + const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`; + const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`); + const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`); + const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`); + const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`); + const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`); + return { + depthwise_conv: { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }, + pointwise_conv: extractPointwiseConvParams("MobilenetV1", idx, mappedPrefixPointwiseConv) + }; + } + function extractMobilenetV1Params() { + return { + conv_0: extractPointwiseConvParams("MobilenetV1", 0, "mobilenetv1/conv_0"), + conv_1: extractConvPairParams(1), + conv_2: extractConvPairParams(2), + conv_3: extractConvPairParams(3), + conv_4: extractConvPairParams(4), + conv_5: extractConvPairParams(5), + conv_6: extractConvPairParams(6), + conv_7: extractConvPairParams(7), + conv_8: extractConvPairParams(8), + conv_9: extractConvPairParams(9), + conv_10: extractConvPairParams(10), + conv_11: extractConvPairParams(11), + conv_12: extractConvPairParams(12), + conv_13: extractConvPairParams(13) + }; + } + function extractConvParams(prefix, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`); + const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`); + return { filters, bias }; + } + function extractBoxPredictorParams(idx) { + const box_encoding_predictor = extractConvParams( + `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, + `prediction_layer/box_predictor_${idx}/box_encoding_predictor` + ); + const class_predictor = extractConvParams( + `Prediction/BoxPredictor_${idx}/ClassPredictor`, + `prediction_layer/box_predictor_${idx}/class_predictor` + ); + return { box_encoding_predictor, class_predictor }; + } + function extractPredictionLayerParams() { + return { + conv_0: extractPointwiseConvParams("Prediction", 0, "prediction_layer/conv_0"), + conv_1: extractPointwiseConvParams("Prediction", 1, "prediction_layer/conv_1"), + conv_2: extractPointwiseConvParams("Prediction", 2, "prediction_layer/conv_2"), + conv_3: extractPointwiseConvParams("Prediction", 3, "prediction_layer/conv_3"), + conv_4: extractPointwiseConvParams("Prediction", 4, "prediction_layer/conv_4"), + conv_5: extractPointwiseConvParams("Prediction", 5, "prediction_layer/conv_5"), + conv_6: extractPointwiseConvParams("Prediction", 6, "prediction_layer/conv_6"), + conv_7: extractPointwiseConvParams("Prediction", 7, "prediction_layer/conv_7"), + box_predictor_0: extractBoxPredictorParams(0), + box_predictor_1: extractBoxPredictorParams(1), + box_predictor_2: extractBoxPredictorParams(2), + box_predictor_3: extractBoxPredictorParams(3), + box_predictor_4: extractBoxPredictorParams(4), + box_predictor_5: extractBoxPredictorParams(5) + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParamsFromWeightMap6(weightMap) { + const paramMappings = []; + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory6(weightMap, paramMappings); + const extra_dim = weightMap["Output/extra_dim"]; + paramMappings.push({ originalPath: "Output/extra_dim", paramPath: "output_layer/extra_dim" }); + if (!isTensor3D(extra_dim)) { + throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`); + } + const params = { + mobilenetv1: extractMobilenetV1Params(), + prediction_layer: extractPredictionLayerParams(), + output_layer: { + extra_dim + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/ssdMobilenetv1/mobileNetV1.ts +var tf30 = __toESM(require_tfjs_esm()); + +// src/ssdMobilenetv1/pointwiseConvLayer.ts +var tf29 = __toESM(require_tfjs_esm()); +function pointwiseConvLayer(x, params, strides) { + return tf29.tidy(() => { + let out = tf29.conv2d(x, params.filters, strides, "same"); + out = tf29.add(out, params.batch_norm_offset); + return tf29.clipByValue(out, 0, 6); + }); +} + +// src/ssdMobilenetv1/mobileNetV1.ts +var epsilon = 0.0010000000474974513; +function depthwiseConvLayer(x, params, strides) { + return tf30.tidy(() => { + let out = tf30.depthwiseConv2d(x, params.filters, strides, "same"); + out = tf30.batchNorm( + out, + params.batch_norm_mean, + params.batch_norm_variance, + params.batch_norm_offset, + params.batch_norm_scale, + epsilon + ); + return tf30.clipByValue(out, 0, 6); + }); +} +function getStridesForLayerIdx(layerIdx) { + return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1]; +} +function mobileNetV1(x, params) { + return tf30.tidy(() => { + let conv11; + let out = pointwiseConvLayer(x, params.conv_0, [2, 2]); + const convPairParams = [ + params.conv_1, + params.conv_2, + params.conv_3, + params.conv_4, + params.conv_5, + params.conv_6, + params.conv_7, + params.conv_8, + params.conv_9, + params.conv_10, + params.conv_11, + params.conv_12, + params.conv_13 + ]; + convPairParams.forEach((param, i) => { + const layerIdx = i + 1; + const depthwiseConvStrides = getStridesForLayerIdx(layerIdx); + out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides); + out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]); + if (layerIdx === 11) + conv11 = out; + }); + if (conv11 === null) { + throw new Error("mobileNetV1 - output of conv layer 11 is null"); + } + return { + out, + conv11 + }; + }); +} + +// src/ssdMobilenetv1/nonMaxSuppression.ts +function IOU(boxes, i, j) { + const boxesData = boxes.arraySync(); + const yminI = Math.min(boxesData[i][0], boxesData[i][2]); + const xminI = Math.min(boxesData[i][1], boxesData[i][3]); + const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]); + const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]); + const yminJ = Math.min(boxesData[j][0], boxesData[j][2]); + const xminJ = Math.min(boxesData[j][1], boxesData[j][3]); + const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]); + const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]); + const areaI = (ymaxI - yminI) * (xmaxI - xminI); + const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); + if (areaI <= 0 || areaJ <= 0) + return 0; + const intersectionYmin = Math.max(yminI, yminJ); + const intersectionXmin = Math.max(xminI, xminJ); + const intersectionYmax = Math.min(ymaxI, ymaxJ); + const intersectionXmax = Math.min(xmaxI, xmaxJ); + const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0); + return intersectionArea / (areaI + areaJ - intersectionArea); +} +function nonMaxSuppression2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + const numBoxes = boxes.shape[0]; + const outputSize = Math.min(maxOutputSize, numBoxes); + const candidates = scores.map((score, boxIndex) => ({ score, boxIndex })).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score); + const suppressFunc = (x) => x <= iouThreshold ? 1 : 0; + const selected = []; + candidates.forEach((c) => { + if (selected.length >= outputSize) + return; + const originalScore = c.score; + for (let j = selected.length - 1; j >= 0; --j) { + const iou2 = IOU(boxes, c.boxIndex, selected[j]); + if (iou2 === 0) + continue; + c.score *= suppressFunc(iou2); + if (c.score <= scoreThreshold) + break; + } + if (originalScore === c.score) { + selected.push(c.boxIndex); + } + }); + return selected; +} + +// src/ssdMobilenetv1/outputLayer.ts +var tf31 = __toESM(require_tfjs_esm()); +function getCenterCoordinatesAndSizesLayer(x) { + const vec = tf31.unstack(tf31.transpose(x, [1, 0])); + const sizes = [ + tf31.sub(vec[2], vec[0]), + tf31.sub(vec[3], vec[1]) + ]; + const centers = [ + tf31.add(vec[0], tf31.div(sizes[0], 2)), + tf31.add(vec[1], tf31.div(sizes[1], 2)) + ]; + return { sizes, centers }; +} +function decodeBoxesLayer(x0, x1) { + const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0); + const vec = tf31.unstack(tf31.transpose(x1, [1, 0])); + const div0_out = tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2], 5)), sizes[0]), 2); + const add0_out = tf31.add(tf31.mul(tf31.div(vec[0], 10), sizes[0]), centers[0]); + const div1_out = tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3], 5)), sizes[1]), 2); + const add1_out = tf31.add(tf31.mul(tf31.div(vec[1], 10), sizes[1]), centers[1]); + return tf31.transpose( + tf31.stack([ + tf31.sub(add0_out, div0_out), + tf31.sub(add1_out, div1_out), + tf31.add(add0_out, div0_out), + tf31.add(add1_out, div1_out) + ]), + [1, 0] + ); +} +function outputLayer(boxPredictions, classPredictions, params) { + return tf31.tidy(() => { + const batchSize = boxPredictions.shape[0]; + let boxes = decodeBoxesLayer( + tf31.reshape(tf31.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), + tf31.reshape(boxPredictions, [-1, 4]) + ); + boxes = tf31.reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]); + const scoresAndClasses = tf31.sigmoid(tf31.slice(classPredictions, [0, 0, 1], [-1, -1, -1])); + let scores = tf31.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]); + scores = tf31.reshape(scores, [batchSize, scores.shape[1]]); + const boxesByBatch = tf31.unstack(boxes); + const scoresByBatch = tf31.unstack(scores); + return { boxes: boxesByBatch, scores: scoresByBatch }; + }); +} + +// src/ssdMobilenetv1/predictionLayer.ts +var tf33 = __toESM(require_tfjs_esm()); + +// src/ssdMobilenetv1/boxPredictionLayer.ts +var tf32 = __toESM(require_tfjs_esm()); +function boxPredictionLayer(x, params) { + return tf32.tidy(() => { + const batchSize = x.shape[0]; + const boxPredictionEncoding = tf32.reshape( + convLayer(x, params.box_encoding_predictor), + [batchSize, -1, 1, 4] + ); + const classPrediction = tf32.reshape( + convLayer(x, params.class_predictor), + [batchSize, -1, 3] + ); + return { boxPredictionEncoding, classPrediction }; + }); +} + +// src/ssdMobilenetv1/predictionLayer.ts +function predictionLayer(x, conv11, params) { + return tf33.tidy(() => { + const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]); + const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]); + const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]); + const conv3 = pointwiseConvLayer(conv22, params.conv_3, [2, 2]); + const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]); + const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]); + const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]); + const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]); + const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0); + const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1); + const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2); + const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3); + const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4); + const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5); + const boxPredictions = tf33.concat([ + boxPrediction0.boxPredictionEncoding, + boxPrediction1.boxPredictionEncoding, + boxPrediction2.boxPredictionEncoding, + boxPrediction3.boxPredictionEncoding, + boxPrediction4.boxPredictionEncoding, + boxPrediction5.boxPredictionEncoding + ], 1); + const classPredictions = tf33.concat([ + boxPrediction0.classPrediction, + boxPrediction1.classPrediction, + boxPrediction2.classPrediction, + boxPrediction3.classPrediction, + boxPrediction4.classPrediction, + boxPrediction5.classPrediction + ], 1); + return { + boxPredictions, + classPredictions + }; + }); +} + +// src/ssdMobilenetv1/SsdMobilenetv1Options.ts +var SsdMobilenetv1Options = class { + constructor({ minConfidence, maxResults } = {}) { + this._name = "SsdMobilenetv1Options"; + this._minConfidence = minConfidence || 0.5; + this._maxResults = maxResults || 100; + if (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; + } +}; + +// src/ssdMobilenetv1/SsdMobilenetv1.ts +var SsdMobilenetv1 = class extends NeuralNetwork { + constructor() { + super("SsdMobilenetv1"); + } + forwardInput(input) { + const { params } = this; + if (!params) + throw new Error("SsdMobilenetv1 - load model before inference"); + return tf34.tidy(() => { + const batchTensor = tf34.cast(input.toBatchTensor(512, false), "float32"); + const x = tf34.sub(tf34.div(batchTensor, 127.5), 1); + const features = mobileNetV1(x, params.mobilenetv1); + const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer); + return outputLayer(boxPredictions, classPredictions, params.output_layer); + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async locateFaces(input, options = {}) { + const { maxResults, minConfidence } = new SsdMobilenetv1Options(options); + const netInput = await toNetInput(input); + const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput); + const boxes = _boxes[0]; + const scores = _scores[0]; + for (let i = 1; i < _boxes.length; i++) { + _boxes[i].dispose(); + _scores[i].dispose(); + } + const scoresData = Array.from(scores.dataSync()); + const iouThreshold = 0.5; + const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence); + const reshapedDims = netInput.getReshapedInputDimensions(0); + const inputSize = netInput.inputSize; + const padX = inputSize / reshapedDims.width; + const padY = inputSize / reshapedDims.height; + const boxesData = boxes.arraySync(); + const results = indices.map((idx) => { + const [top, bottom] = [ + Math.max(0, boxesData[idx][0]), + Math.min(1, boxesData[idx][2]) + ].map((val) => val * padY); + const [left, right] = [ + Math.max(0, boxesData[idx][1]), + Math.min(1, boxesData[idx][3]) + ].map((val) => val * padX); + return new FaceDetection( + scoresData[idx], + new Rect(left, top, right - left, bottom - top), + { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) } + ); + }); + boxes.dispose(); + scores.dispose(); + return results; + } + getDefaultModelName() { + return "ssd_mobilenetv1_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap6(weightMap); + } + extractParams(weights) { + return extractParams6(weights); + } +}; + +// src/ssdMobilenetv1/index.ts +function createSsdMobilenetv1(weights) { + const net = new SsdMobilenetv1(); + net.extractWeights(weights); + return net; +} +function createFaceDetectionNet(weights) { + return createSsdMobilenetv1(weights); +} +var FaceDetectionNet = class extends SsdMobilenetv1 { +}; + +// src/tinyYolov2/const.ts +var IOU_THRESHOLD = 0.4; +var BOX_ANCHORS = [ + new Point(0.738768, 0.874946), + new Point(2.42204, 2.65704), + new Point(4.30971, 7.04493), + new Point(10.246, 4.59428), + new Point(12.6868, 11.8741) +]; +var BOX_ANCHORS_SEPARABLE = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404]; +var DEFAULT_MODEL_NAME = "tiny_yolov2_model"; +var DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model"; + +// src/tinyYolov2/TinyYolov2Base.ts +var tf39 = __toESM(require_tfjs_esm()); + +// src/tinyYolov2/config.ts +var isNumber = (arg) => typeof arg === "number"; +function validateConfig(config) { + if (!config) { + throw new Error(`invalid config: ${config}`); + } + if (typeof config.withSeparableConvs !== "boolean") { + throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`); + } + if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1) { + throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`); + } + if (!Array.isArray(config.classes) || !config.classes.length || !config.classes.every((c) => typeof c === "string")) { + throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`); + } + if (!Array.isArray(config.anchors) || !config.anchors.length || !config.anchors.map((a) => a || {}).every((a) => isNumber(a.x) && isNumber(a.y))) { + throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`); + } + if (config.meanRgb && (!Array.isArray(config.meanRgb) || config.meanRgb.length !== 3 || !config.meanRgb.every(isNumber))) { + throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`); + } +} + +// src/tinyYolov2/convWithBatchNorm.ts +var tf36 = __toESM(require_tfjs_esm()); + +// src/tinyYolov2/leaky.ts +var tf35 = __toESM(require_tfjs_esm()); +function leaky(x) { + return tf35.tidy(() => { + const min = tf35.mul(x, tf35.scalar(0.10000000149011612)); + return tf35.add(tf35.relu(tf35.sub(x, min)), min); + }); +} + +// src/tinyYolov2/convWithBatchNorm.ts +function convWithBatchNorm(x, params) { + return tf36.tidy(() => { + let out = tf36.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = tf36.conv2d(out, params.conv.filters, [1, 1], "valid"); + out = tf36.sub(out, params.bn.sub); + out = tf36.mul(out, params.bn.truediv); + out = tf36.add(out, params.conv.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/depthwiseSeparableConv.ts +var tf37 = __toESM(require_tfjs_esm()); +function depthwiseSeparableConv2(x, params) { + return tf37.tidy(() => { + let out = tf37.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = tf37.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid"); + out = tf37.add(out, params.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/extractParams.ts +var tf38 = __toESM(require_tfjs_esm()); +function extractorsFactory7(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + function extractBatchNormParams(size, mappedPrefix) { + const sub6 = tf38.tensor1d(extractWeights(size)); + const truediv = tf38.tensor1d(extractWeights(size)); + paramMappings.push( + { paramPath: `${mappedPrefix}/sub` }, + { paramPath: `${mappedPrefix}/truediv` } + ); + return { sub: sub6, truediv }; + } + function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) { + const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`); + const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`); + return { conv: conv3, bn }; + } + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParams7(weights, config, boxEncodingSize, filterSizes) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory7(extractWeights, paramMappings); + let params; + if (config.withSeparableConvs) { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = config.isFirstLayerConv2d ? extractConvParams(s0, s1, 3, "conv0") : extractSeparableConvParams(s0, s1, "conv0"); + const conv1 = extractSeparableConvParams(s1, s2, "conv1"); + const conv22 = extractSeparableConvParams(s2, s3, "conv2"); + const conv3 = extractSeparableConvParams(s3, s4, "conv3"); + const conv4 = extractSeparableConvParams(s4, s5, "conv4"); + const conv5 = extractSeparableConvParams(s5, s6, "conv5"); + const conv6 = s7 ? extractSeparableConvParams(s6, s7, "conv6") : void 0; + const conv7 = s8 ? extractSeparableConvParams(s7, s8, "conv7") : void 0; + const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } else { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = extractConvWithBatchNormParams(s0, s1, "conv0"); + const conv1 = extractConvWithBatchNormParams(s1, s2, "conv1"); + const conv22 = extractConvWithBatchNormParams(s2, s3, "conv2"); + const conv3 = extractConvWithBatchNormParams(s3, s4, "conv3"); + const conv4 = extractConvWithBatchNormParams(s4, s5, "conv4"); + const conv5 = extractConvWithBatchNormParams(s5, s6, "conv5"); + const conv6 = extractConvWithBatchNormParams(s6, s7, "conv6"); + const conv7 = extractConvWithBatchNormParams(s7, s8, "conv7"); + const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { params, paramMappings }; +} + +// src/tinyYolov2/extractParamsFromWeightMap.ts +function extractorsFactory8(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractBatchNormParams(prefix) { + const sub6 = extractWeightEntry(`${prefix}/sub`, 1); + const truediv = extractWeightEntry(`${prefix}/truediv`, 1); + return { sub: sub6, truediv }; + } + function extractConvParams(prefix) { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { filters, bias }; + } + function extractConvWithBatchNormParams(prefix) { + const conv3 = extractConvParams(`${prefix}/conv`); + const bn = extractBatchNormParams(`${prefix}/bn`); + return { conv: conv3, bn }; + } + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParamsFromWeightMap7(weightMap, config) { + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory8(weightMap, paramMappings); + let params; + if (config.withSeparableConvs) { + const numFilters = config.filterSizes && config.filterSizes.length || 9; + params = { + conv0: config.isFirstLayerConv2d ? extractConvParams("conv0") : extractSeparableConvParams("conv0"), + conv1: extractSeparableConvParams("conv1"), + conv2: extractSeparableConvParams("conv2"), + conv3: extractSeparableConvParams("conv3"), + conv4: extractSeparableConvParams("conv4"), + conv5: extractSeparableConvParams("conv5"), + conv6: numFilters > 7 ? extractSeparableConvParams("conv6") : void 0, + conv7: numFilters > 8 ? extractSeparableConvParams("conv7") : void 0, + conv8: extractConvParams("conv8") + }; + } else { + params = { + conv0: extractConvWithBatchNormParams("conv0"), + conv1: extractConvWithBatchNormParams("conv1"), + conv2: extractConvWithBatchNormParams("conv2"), + conv3: extractConvWithBatchNormParams("conv3"), + conv4: extractConvWithBatchNormParams("conv4"), + conv5: extractConvWithBatchNormParams("conv5"), + conv6: extractConvWithBatchNormParams("conv6"), + conv7: extractConvWithBatchNormParams("conv7"), + conv8: extractConvParams("conv8") + }; + } + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/tinyYolov2/TinyYolov2Options.ts +var TinyYolov2Options = class { + constructor({ inputSize, scoreThreshold } = {}) { + this._name = "TinyYolov2Options"; + this._inputSize = inputSize || 416; + this._scoreThreshold = scoreThreshold || 0.5; + if (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; + } +}; + +// src/tinyYolov2/TinyYolov2Base.ts +var _TinyYolov2Base = class _TinyYolov2Base extends NeuralNetwork { + constructor(config) { + super("TinyYolov2"); + validateConfig(config); + this._config = config; + } + 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(x, params) { + let out = convWithBatchNorm(x, params.conv0); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv1); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv2); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv3); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv4); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv5); + out = tf39.maxPool(out, [2, 2], [1, 1], "same"); + out = convWithBatchNorm(out, params.conv6); + out = convWithBatchNorm(out, params.conv7); + return convLayer(out, params.conv8, "valid", false); + } + runMobilenet(x, params) { + let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv2(x, params.conv0); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv1); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv2); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv3); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv4); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv5); + out = tf39.maxPool(out, [2, 2], [1, 1], "same"); + out = params.conv6 ? depthwiseSeparableConv2(out, params.conv6) : out; + out = params.conv7 ? depthwiseSeparableConv2(out, params.conv7) : out; + return convLayer(out, params.conv8, "valid", false); + } + forwardInput(input, inputSize) { + const { params } = this; + if (!params) { + throw new Error("TinyYolov2 - load model before inference"); + } + return tf39.tidy(() => { + let batchTensor = tf39.cast(input.toBatchTensor(inputSize, false), "float32"); + batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor; + batchTensor = batchTensor.div(255); + return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params); + }); + } + async forward(input, inputSize) { + return this.forwardInput(await toNetInput(input), inputSize); + } + async detect(input, forwardParams = {}) { + const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams); + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput, inputSize); + const out0 = tf39.tidy(() => tf39.unstack(out)[0].expandDims()); + const inputDimensions = { + width: netInput.getInputWidth(0), + height: netInput.getInputHeight(0) + }; + const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold); + out.dispose(); + out0.dispose(); + const boxes = results.map((res) => res.box); + const scores = results.map((res) => res.score); + const classScores = results.map((res) => res.classScore); + const classNames = results.map((res) => this.config.classes[res.label]); + const indices = nonMaxSuppression( + boxes.map((box) => box.rescale(inputSize)), + scores, + this.config.iouThreshold, + true + ); + const detections = indices.map((idx) => new ObjectDetection( + scores[idx], + classScores[idx], + classNames[idx], + boxes[idx], + inputDimensions + )); + return detections; + } + getDefaultModelName() { + return ""; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap7(weightMap, this.config); + } + extractParams(weights) { + const filterSizes = this.config.filterSizes || _TinyYolov2Base.DEFAULT_FILTER_SIZES; + const numFilters = filterSizes ? filterSizes.length : void 0; + if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) { + throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`); + } + return extractParams7(weights, this.config, this.boxEncodingSize, filterSizes); + } + async extractBoxes(outputTensor, inputBlobDimensions, scoreThreshold) { + const { width, height } = inputBlobDimensions; + const inputSize = Math.max(width, height); + const correctionFactorX = inputSize / width; + const correctionFactorY = inputSize / height; + const numCells = outputTensor.shape[1]; + const numBoxes = this.config.anchors.length; + const [boxesTensor, scoresTensor, classScoresTensor] = tf39.tidy(() => { + const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]); + const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]); + const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]); + const classScores = this.withClassScores ? tf39.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : tf39.scalar(0); + return [boxes, scores, classScores]; + }); + const results = []; + const scoresData = await scoresTensor.array(); + const boxesData = await boxesTensor.array(); + for (let row = 0; row < numCells; row++) { + for (let col = 0; col < numCells; col++) { + for (let anchor = 0; anchor < numBoxes; anchor++) { + const score = sigmoid(scoresData[row][col][anchor][0]); + if (!scoreThreshold || score > scoreThreshold) { + const ctX = (col + sigmoid(boxesData[row][col][anchor][0])) / numCells * correctionFactorX; + const ctY = (row + sigmoid(boxesData[row][col][anchor][1])) / numCells * correctionFactorY; + const widthLocal = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX; + const heightLocal = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY; + const x = ctX - widthLocal / 2; + const y = ctY - heightLocal / 2; + const pos = { row, col, anchor }; + const { classScore, label } = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : { classScore: 1, label: 0 }; + results.push({ + box: new BoundingBox(x, y, x + widthLocal, y + heightLocal), + score, + classScore: score * classScore, + label, + ...pos + }); + } + } + } + } + boxesTensor.dispose(); + scoresTensor.dispose(); + classScoresTensor.dispose(); + return results; + } + async extractPredictedClass(classesTensor, pos) { + const { row, col, anchor } = pos; + const classesData = await classesTensor.array(); + return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({ + classScore, + label + })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr); + } +}; +_TinyYolov2Base.DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024]; +var TinyYolov2Base = _TinyYolov2Base; + +// src/tinyYolov2/TinyYolov2.ts +var TinyYolov2 = class extends TinyYolov2Base { + constructor(withSeparableConvs = true) { + const config = { + withSeparableConvs, + iouThreshold: IOU_THRESHOLD, + classes: ["face"], + ...withSeparableConvs ? { + anchors: BOX_ANCHORS_SEPARABLE, + meanRgb: MEAN_RGB_SEPARABLE + } : { + anchors: BOX_ANCHORS, + withClassScores: true + } + }; + super(config); + } + get withSeparableConvs() { + return this.config.withSeparableConvs; + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input, forwardParams) { + const objectDetections = await this.detect(input, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight })); + } + getDefaultModelName() { + return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/tinyYolov2/index.ts +function createTinyYolov2(weights, withSeparableConvs = true) { + const net = new TinyYolov2(withSeparableConvs); + net.extractWeights(weights); + return net; +} + +// src/tinyFaceDetector/TinyFaceDetectorOptions.ts +var TinyFaceDetectorOptions = class extends TinyYolov2Options { + constructor() { + super(...arguments); + this._name = "TinyFaceDetectorOptions"; + } +}; + +// src/globalApi/ComposableTask.ts +var ComposableTask = class { + // eslint-disable-next-line no-unused-vars + async then(onfulfilled) { + return onfulfilled(await this.run()); + } + async run() { + throw new Error("ComposableTask - run is not implemented"); + } +}; + +// src/globalApi/DetectFaceLandmarksTasks.ts +var tf41 = __toESM(require_tfjs_esm()); + +// src/globalApi/extractFacesAndComputeResults.ts +var tf40 = __toESM(require_tfjs_esm()); +async function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment = ({ alignedRect }) => alignedRect) { + const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection); + const faces = extractedFaces || (input instanceof tf40.Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes)); + const results = await computeResults(faces); + faces.forEach((f) => f instanceof tf40.Tensor && f.dispose()); + return results; +} +async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) { + return extractAllFacesAndComputeResults( + [parentResult], + input, + async (faces) => computeResult(faces[0]), + extractedFaces, + getRectForAlignment + ); +} + +// src/tinyFaceDetector/const.ts +var IOU_THRESHOLD2 = 0.4; +var BOX_ANCHORS2 = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB = [117.001, 114.697, 97.404]; + +// src/tinyFaceDetector/TinyFaceDetector.ts +var TinyFaceDetector = class extends TinyYolov2Base { + constructor() { + const config = { + withSeparableConvs: true, + iouThreshold: IOU_THRESHOLD2, + classes: ["face"], + anchors: BOX_ANCHORS2, + meanRgb: MEAN_RGB, + isFirstLayerConv2d: true, + filterSizes: [3, 16, 32, 64, 128, 256, 512] + }; + super(config); + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input, forwardParams) { + const objectDetections = await this.detect(input, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight })); + } + getDefaultModelName() { + return "tiny_face_detector_model"; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/globalApi/nets.ts +var nets = { + ssdMobilenetv1: new SsdMobilenetv1(), + tinyFaceDetector: new TinyFaceDetector(), + tinyYolov2: new TinyYolov2(), + faceLandmark68Net: new FaceLandmark68Net(), + faceLandmark68TinyNet: new FaceLandmark68TinyNet(), + faceRecognitionNet: new FaceRecognitionNet(), + faceExpressionNet: new FaceExpressionNet(), + ageGenderNet: new AgeGenderNet() +}; +var ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options); +var tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options); +var tinyYolov2 = (input, options) => nets.tinyYolov2.locateFaces(input, options); +var detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input); +var detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input); +var computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input); +var recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input); +var predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input); +var loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url); +var loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url); +var loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url); +var loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url); +var loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url); +var loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url); +var loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url); +var loadAgeGenderModel = (url) => nets.ageGenderNet.load(url); +var loadFaceDetectionModel = loadSsdMobilenetv1Model; +var locateFaces = ssdMobilenetv1; +var detectLandmarks = detectFaceLandmarks; + +// src/globalApi/PredictFaceExpressionsTask.ts +var PredictFaceExpressionsTaskBase = class extends ComposableTask { + constructor(parentTask, input, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResults = await this.parentTask; + const faceExpressionsByFace = await extractAllFacesAndComputeResults( + parentResults, + this.input, + async (faces) => Promise.all( + faces.map((face) => nets.faceExpressionNet.predictExpressions(face)) + ), + this.extractedFaces + ); + return parentResults.map( + (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]) + ); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const faceExpressions = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.faceExpressionNet.predictExpressions(face), + this.extractedFaces + ); + return extendWithFaceExpressions(parentResult, faceExpressions); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask(this, this.input); + } +}; +var PredictAllFaceExpressionsWithFaceAlignmentTask = class extends PredictAllFaceExpressionsTask { + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsWithFaceAlignmentTask = class extends PredictSingleFaceExpressionsTask { + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/PredictAgeAndGenderTask.ts +var PredictAgeAndGenderTaskBase = class extends ComposableTask { + constructor(parentTask, input, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResults = await this.parentTask; + const ageAndGenderByFace = await extractAllFacesAndComputeResults( + parentResults, + this.input, + async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face))), + this.extractedFaces + ); + return parentResults.map((parentResult, i) => { + const { age, gender, genderProbability } = ageAndGenderByFace[i]; + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + }); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.ageGenderNet.predictAgeAndGender(face), + this.extractedFaces + ); + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask(this, this.input); + } +}; +var PredictAllAgeAndGenderWithFaceAlignmentTask = class extends PredictAllAgeAndGenderTask { + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderWithFaceAlignmentTask = class extends PredictSingleAgeAndGenderTask { + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/ComputeFaceDescriptorsTasks.ts +var ComputeFaceDescriptorsTaskBase = class extends ComposableTask { + constructor(parentTask, input) { + super(); + this.parentTask = parentTask; + this.input = input; + } +}; +var ComputeAllFaceDescriptorsTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResults = await this.parentTask; + const descriptors = await extractAllFacesAndComputeResults( + parentResults, + this.input, + (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face))), + null, + (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }) + ); + return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor)); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; +var ComputeSingleFaceDescriptorTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const descriptor = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.faceRecognitionNet.computeFaceDescriptor(face), + null, + // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow + (parentResult2) => parentResult2.landmarks.align(null, { useDlibAlignment: true }) + ); + return extendWithFaceDescriptor(parentResult, descriptor); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; + +// src/globalApi/DetectFaceLandmarksTasks.ts +var DetectFaceLandmarksTaskBase = class extends ComposableTask { + constructor(parentTask, input, useTinyLandmarkNet) { + super(); + this.parentTask = parentTask; + this.input = input; + this.useTinyLandmarkNet = useTinyLandmarkNet; + } + get landmarkNet() { + return this.useTinyLandmarkNet ? nets.faceLandmark68TinyNet : nets.faceLandmark68Net; + } +}; +var DetectAllFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResults = await this.parentTask; + const detections = parentResults.map((res) => res.detection); + const faces = this.input instanceof tf41.Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections); + const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))); + faces.forEach((f) => f instanceof tf41.Tensor && f.dispose()); + const result = parentResults.filter((_parentResult, i) => faceLandmarksByFace[i]).map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i])); + return result; + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var DetectSingleFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const { detection } = parentResult; + const faces = this.input instanceof tf41.Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]); + const landmarks = await this.landmarkNet.detectLandmarks(faces[0]); + faces.forEach((f) => f instanceof tf41.Tensor && f.dispose()); + return extendWithFaceLandmarks(parentResult, landmarks); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/DetectFacesTasks.ts +var DetectFacesTaskBase = class extends ComposableTask { + // eslint-disable-next-line no-unused-vars + constructor(input, options = new SsdMobilenetv1Options()) { + super(); + this.input = input; + this.options = options; + } +}; +var DetectAllFacesTask = class extends DetectFacesTaskBase { + async run() { + const { input, options } = this; + let result; + if (options instanceof TinyFaceDetectorOptions) + result = nets.tinyFaceDetector.locateFaces(input, options); + else if (options instanceof SsdMobilenetv1Options) + result = nets.ssdMobilenetv1.locateFaces(input, options); + else if (options instanceof TinyYolov2Options) + result = nets.tinyYolov2.locateFaces(input, options); + else + throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options"); + return result; + } + runAndExtendWithFaceDetections() { + return new Promise((resolve, reject) => { + this.run().then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection)))).catch((err) => reject(err)); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectAllFaceLandmarksTask( + this.runAndExtendWithFaceDetections(), + this.input, + useTinyLandmarkNet + ); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask( + this.runAndExtendWithFaceDetections(), + this.input + ); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask( + this.runAndExtendWithFaceDetections(), + this.input + ); + } +}; +var DetectSingleFaceTask = class extends DetectFacesTaskBase { + async run() { + const faceDetections = await new DetectAllFacesTask(this.input, this.options); + let faceDetectionWithHighestScore = faceDetections[0]; + faceDetections.forEach((faceDetection) => { + if (faceDetection.score > faceDetectionWithHighestScore.score) + faceDetectionWithHighestScore = faceDetection; + }); + return faceDetectionWithHighestScore; + } + runAndExtendWithFaceDetection() { + return new Promise(async (resolve) => { + const detection = await this.run(); + resolve(detection ? extendWithFaceDetection({}, detection) : void 0); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectSingleFaceLandmarksTask( + this.runAndExtendWithFaceDetection(), + this.input, + useTinyLandmarkNet + ); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask( + this.runAndExtendWithFaceDetection(), + this.input + ); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask( + this.runAndExtendWithFaceDetection(), + this.input + ); + } +}; + +// src/globalApi/detectFaces.ts +function detectSingleFace(input, options = new SsdMobilenetv1Options()) { + return new DetectSingleFaceTask(input, options); +} +function detectAllFaces(input, options = new SsdMobilenetv1Options()) { + return new DetectAllFacesTask(input, options); +} + +// src/globalApi/allFaces.ts +async function allFacesSsdMobilenetv1(input, minConfidence) { + return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {})).withFaceLandmarks().withFaceDescriptors(); +} +async function allFacesTinyYolov2(input, forwardParams = {}) { + return detectAllFaces(input, new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors(); +} +var allFaces = allFacesSsdMobilenetv1; + +// src/euclideanDistance.ts +function euclideanDistance(arr1, arr2) { + if (arr1.length !== arr2.length) + throw new Error("euclideanDistance: arr1.length !== arr2.length"); + const desc1 = Array.from(arr1); + const desc2 = Array.from(arr2); + return Math.sqrt( + desc1.map((val, i) => val - desc2[i]).reduce((res, diff) => res + diff * diff, 0) + ); +} + +// src/globalApi/FaceMatcher.ts +var FaceMatcher = class _FaceMatcher { + constructor(inputs, distanceThreshold = 0.6) { + this._distanceThreshold = distanceThreshold; + const inputArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArray.length) + throw new Error("FaceRecognizer.constructor - expected atleast one input"); + let count = 1; + const createUniqueLabel = () => `person ${count++}`; + this._labeledDescriptors = inputArray.map((desc) => { + if (desc instanceof LabeledFaceDescriptors) + return desc; + if (desc instanceof Float32Array) + return new LabeledFaceDescriptors(createUniqueLabel(), [desc]); + if (desc.descriptor && desc.descriptor instanceof Float32Array) + return new LabeledFaceDescriptors(createUniqueLabel(), [desc.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(queryDescriptor, descriptors) { + return descriptors.map((d) => euclideanDistance(d, queryDescriptor)).reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1); + } + matchDescriptor(queryDescriptor) { + return this.labeledDescriptors.map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors))).reduce((best, curr) => best.distance < curr.distance ? best : curr); + } + findBestMatch(queryDescriptor) { + const bestMatch = this.matchDescriptor(queryDescriptor); + return bestMatch.distance < this._distanceThreshold ? bestMatch : new FaceMatch("unknown", bestMatch.distance); + } + toJSON() { + return { + distanceThreshold: this._distanceThreshold, + labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()) + }; + } + static fromJSON(json) { + const labeledDescriptors = json.labeledDescriptors.map((ld) => LabeledFaceDescriptors.fromJSON(ld)); + return new _FaceMatcher(labeledDescriptors, json.distanceThreshold); + } +}; + +// src/tinyFaceDetector/index.ts +function createTinyFaceDetector(weights) { + const net = new TinyFaceDetector(); + net.extractWeights(weights); + return net; +} + +// src/resizeResults.ts +function resizeResults(results, dimensions) { + const { width, height } = new Dimensions(dimensions.width, dimensions.height); + if (width <= 0 || height <= 0) { + throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`); + } + if (Array.isArray(results)) { + return results.map((obj) => resizeResults(obj, { width, height })); + } + if (isWithFaceLandmarks(results)) { + const resizedDetection = results.detection.forSize(width, height); + const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height); + return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks); + } + if (isWithFaceDetection(results)) { + return extendWithFaceDetection(results, results.detection.forSize(width, height)); + } + if (results instanceof FaceLandmarks || results instanceof FaceDetection) { + return results.forSize(width, height); + } + return results; +} + +// src/index.ts +var version2 = version; +// Annotate the CommonJS export names for ESM import in node: +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 24ba028..8f7fefa 100644 --- a/dist/face-api.node-wasm.js +++ b/dist/face-api.node-wasm.js @@ -4,4 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i=mo(i,r.entry_flow.reduction_block_0,!1),i=mo(i,r.entry_flow.reduction_block_1),it(this._numMainBlocks,0,1).forEach(c=>{i=Kn(i,r.middle_flow[`main_block_${c}`])}),i=mo(i,r.exit_flow.reduction_block),i=S.relu(H(i,r.exit_flow.separable_conv,[1,1])),i})}async forward(e){return this.forwardInput(await C(e))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(e){return Wo(e,this._numMainBlocks)}extractParams(e){return Ao(e,this._numMainBlocks)}};function Bo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),n=hr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Ro(o){let t=[],e=Y(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return B(o,t),{params:n,paramMappings:t}}var Pr=(e=>(e.FEMALE="female",e.MALE="male",e))(Pr||{});var He=class extends A{constructor(e=new wr(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:r}=this;if(!r)throw new Error(`${this._name} - load model before inference`);return ft.tidy(()=>{let n=e instanceof ut?this.faceFeatureExtractor.forwardInput(e):e,a=ft.avgPool(n,[7,7],[2,2],"valid").as2D(n.shape[0],-1),s=$e(a,r.fc.age).as1D(),i=$e(a,r.fc.gender);return{age:s,gender:i}})}forwardInput(e){return ft.tidy(()=>{let{age:r,gender:n}=this.runNet(e);return{age:r,gender:ft.softmax(n)}})}async forward(e){return this.forwardInput(await C(e))}async predictAgeAndGender(e){let r=await C(e),n=await this.forwardInput(r),a=ft.unstack(n.age),s=ft.unstack(n.gender),i=a.map((m,p)=>({ageTensor:m,genderTensor:s[p]})),c=await Promise.all(i.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,b=l?"male":"female",y=l?f:1-f;return 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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=Uo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=qo(a.out,a.conv11,e.prediction_layer);return Jo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await C(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new X(e),a=await C(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let _=1;_{let[E,W]=[Math.max(0,h[_][0]),Math.min(1,h[_][2])].map(q=>q*F),[tt,lt]=[Math.max(0,h[_][1]),Math.min(1,h[_][3])].map(q=>q*y);return new M(p[_],new Yt(tt,E,lt-tt,W-E),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return jo(t)}extractParams(t){return Go(t)}};function Zo(o){let t=new St;return t.extractWeights(o),t}function fa(o){return Zo(o)}var lo=class extends St{};var Ko=.4,Qo=[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)],tn=[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)],en=[117.001,114.697,97.404],rn="tiny_yolov2_model",on="tiny_yolov2_separable_conv_model";var N=v(x());var Nr=o=>typeof o=="number";function ho(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(!Nr(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=>Nr(t.x)&&Nr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Nr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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nn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=la(n,s),p;if(t.withSeparableConvs){let[u,f,l,b,y,F,h,T,_]=r,E=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),W=m(f,l,"conv1"),tt=m(l,b,"conv2"),lt=m(b,y,"conv3"),q=m(y,F,"conv4"),Dt=m(F,h,"conv5"),Et=T?m(h,T,"conv6"):void 0,Mt=_?m(T,_,"conv7"):void 0,$t=i(_||T||h,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}else{let[u,f,l,b,y,F,h,T,_]=r,E=c(u,f,"conv0"),W=c(f,l,"conv1"),tt=c(l,b,"conv2"),lt=c(b,y,"conv3"),q=c(y,F,"conv4"),Dt=c(F,h,"conv5"),Et=c(h,T,"conv6"),Mt=c(T,_,"conv7"),$t=i(_,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function da(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function an(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=da(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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}); +"use strict"; +var __create = Object.create; +var __defProp = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getProtoOf = Object.getPrototypeOf; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __commonJS = (cb, mod) => function __require() { + return mod || (0, cb[__getOwnPropNames(cb)[0]])((mod = { exports: {} }).exports, mod), mod.exports; +}; +var __export = (target, all) => { + for (var name in all) + __defProp(target, name, { get: all[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __toESM = (mod, isNodeMode, target) => (target = mod != null ? __create(__getProtoOf(mod)) : {}, __copyProps( + // If the importer is in node compatibility mode or this is not an ESM + // file that has been converted to a CommonJS file using a Babel- + // compatible transform (i.e. "__esModule" has not been set), then set + // "default" to the CommonJS "module.exports" for node compatibility. + isNodeMode || !mod || !mod.__esModule ? __defProp(target, "default", { value: mod, enumerable: true }) : target, + mod +)); +var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod); + +// dist/tfjs.esm.js +var require_tfjs_esm = __commonJS({ + "dist/tfjs.esm.js"(exports2, module2) { + "use strict"; + var __defProp2 = Object.defineProperty; + var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; + var __getOwnPropNames2 = Object.getOwnPropertyNames; + var __hasOwnProp2 = Object.prototype.hasOwnProperty; + var __export2 = (target, all) => { + for (var name in all) + __defProp2(target, name, { get: all[name], enumerable: true }); + }; + var __copyProps2 = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames2(from)) + if (!__hasOwnProp2.call(to, key) && key !== except) + __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); + } + return to; + }; + var __reExport = (target, mod, secondTarget) => (__copyProps2(target, mod, "default"), secondTarget && __copyProps2(secondTarget, mod, "default")); + var __toCommonJS2 = (mod) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod); + var tf_node_wasm_exports = {}; + __export2(tf_node_wasm_exports, { + version: () => version6 + }); + module2.exports = __toCommonJS2(tf_node_wasm_exports); + __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs"), module2.exports); + __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-backend-wasm"), module2.exports); + var version3 = "4.16.0"; + var version22 = "4.16.0"; + var version32 = "4.16.0"; + var version4 = "4.16.0"; + var version5 = "4.16.0"; + var version6 = { + // tfjs: tfjsVersion, + tfjs: version3, + "tfjs-core": version3, + // 'tfjs-data': tfjsDataVersion, + // 'tfjs-layers': tfjsLayersVersion, + "tfjs-converter": version22, + "tfjs-backend-cpu": version32, + "tfjs-backend-webgl": version4, + "tfjs-backend-wasm": version5 + }; + } +}); + +// src/index.ts +var src_exports = {}; +__export(src_exports, { + AgeGenderNet: () => AgeGenderNet, + BoundingBox: () => BoundingBox, + Box: () => Box, + ComposableTask: () => ComposableTask, + ComputeAllFaceDescriptorsTask: () => ComputeAllFaceDescriptorsTask, + ComputeFaceDescriptorsTaskBase: () => ComputeFaceDescriptorsTaskBase, + ComputeSingleFaceDescriptorTask: () => ComputeSingleFaceDescriptorTask, + DetectAllFaceLandmarksTask: () => DetectAllFaceLandmarksTask, + DetectAllFacesTask: () => DetectAllFacesTask, + DetectFaceLandmarksTaskBase: () => DetectFaceLandmarksTaskBase, + DetectFacesTaskBase: () => DetectFacesTaskBase, + DetectSingleFaceLandmarksTask: () => DetectSingleFaceLandmarksTask, + DetectSingleFaceTask: () => DetectSingleFaceTask, + Dimensions: () => Dimensions, + FACE_EXPRESSION_LABELS: () => FACE_EXPRESSION_LABELS, + FaceDetection: () => FaceDetection, + FaceDetectionNet: () => FaceDetectionNet, + FaceExpressionNet: () => FaceExpressionNet, + FaceExpressions: () => FaceExpressions, + FaceLandmark68Net: () => FaceLandmark68Net, + FaceLandmark68TinyNet: () => FaceLandmark68TinyNet, + FaceLandmarkNet: () => FaceLandmarkNet, + FaceLandmarks: () => FaceLandmarks, + FaceLandmarks5: () => FaceLandmarks5, + FaceLandmarks68: () => FaceLandmarks68, + FaceMatch: () => FaceMatch, + FaceMatcher: () => FaceMatcher, + FaceRecognitionNet: () => FaceRecognitionNet, + Gender: () => Gender, + LabeledBox: () => LabeledBox, + LabeledFaceDescriptors: () => LabeledFaceDescriptors, + NetInput: () => NetInput, + NeuralNetwork: () => NeuralNetwork, + ObjectDetection: () => ObjectDetection, + Point: () => Point, + PredictedBox: () => PredictedBox, + Rect: () => Rect, + SsdMobilenetv1: () => SsdMobilenetv1, + SsdMobilenetv1Options: () => SsdMobilenetv1Options, + TinyFaceDetector: () => TinyFaceDetector, + TinyFaceDetectorOptions: () => TinyFaceDetectorOptions, + TinyYolov2: () => TinyYolov2, + TinyYolov2Options: () => TinyYolov2Options, + allFaces: () => allFaces, + allFacesSsdMobilenetv1: () => allFacesSsdMobilenetv1, + allFacesTinyYolov2: () => allFacesTinyYolov2, + awaitMediaLoaded: () => awaitMediaLoaded, + bufferToImage: () => bufferToImage, + computeFaceDescriptor: () => computeFaceDescriptor, + createCanvas: () => createCanvas, + createCanvasFromMedia: () => createCanvasFromMedia, + createFaceDetectionNet: () => createFaceDetectionNet, + createFaceRecognitionNet: () => createFaceRecognitionNet, + createSsdMobilenetv1: () => createSsdMobilenetv1, + createTinyFaceDetector: () => createTinyFaceDetector, + createTinyYolov2: () => createTinyYolov2, + detectAllFaces: () => detectAllFaces, + detectFaceLandmarks: () => detectFaceLandmarks, + detectFaceLandmarksTiny: () => detectFaceLandmarksTiny, + detectLandmarks: () => detectLandmarks, + detectSingleFace: () => detectSingleFace, + draw: () => draw_exports, + env: () => env, + euclideanDistance: () => euclideanDistance, + extendWithAge: () => extendWithAge, + extendWithFaceDescriptor: () => extendWithFaceDescriptor, + extendWithFaceDetection: () => extendWithFaceDetection, + extendWithFaceExpressions: () => extendWithFaceExpressions, + extendWithFaceLandmarks: () => extendWithFaceLandmarks, + extendWithGender: () => extendWithGender, + extractFaceTensors: () => extractFaceTensors, + extractFaces: () => extractFaces, + fetchImage: () => fetchImage, + fetchJson: () => fetchJson, + fetchNetWeights: () => fetchNetWeights, + fetchOrThrow: () => fetchOrThrow, + fetchVideo: () => fetchVideo, + getContext2dOrThrow: () => getContext2dOrThrow, + getMediaDimensions: () => getMediaDimensions, + imageTensorToCanvas: () => imageTensorToCanvas, + imageToSquare: () => imageToSquare, + inverseSigmoid: () => inverseSigmoid, + iou: () => iou, + isMediaElement: () => isMediaElement, + isMediaLoaded: () => isMediaLoaded, + isWithAge: () => isWithAge, + isWithFaceDetection: () => isWithFaceDetection, + isWithFaceExpressions: () => isWithFaceExpressions, + isWithFaceLandmarks: () => isWithFaceLandmarks, + isWithGender: () => isWithGender, + loadAgeGenderModel: () => loadAgeGenderModel, + loadFaceDetectionModel: () => loadFaceDetectionModel, + loadFaceExpressionModel: () => loadFaceExpressionModel, + loadFaceLandmarkModel: () => loadFaceLandmarkModel, + loadFaceLandmarkTinyModel: () => loadFaceLandmarkTinyModel, + loadFaceRecognitionModel: () => loadFaceRecognitionModel, + loadSsdMobilenetv1Model: () => loadSsdMobilenetv1Model, + loadTinyFaceDetectorModel: () => loadTinyFaceDetectorModel, + loadTinyYolov2Model: () => loadTinyYolov2Model, + loadWeightMap: () => loadWeightMap, + locateFaces: () => locateFaces, + matchDimensions: () => matchDimensions, + minBbox: () => minBbox, + nets: () => nets, + nonMaxSuppression: () => nonMaxSuppression, + normalize: () => normalize, + padToSquare: () => padToSquare, + predictAgeAndGender: () => predictAgeAndGender, + recognizeFaceExpressions: () => recognizeFaceExpressions, + resizeResults: () => resizeResults, + resolveInput: () => resolveInput, + shuffleArray: () => shuffleArray, + sigmoid: () => sigmoid, + ssdMobilenetv1: () => ssdMobilenetv1, + tf: () => tf42, + tinyFaceDetector: () => tinyFaceDetector, + tinyYolov2: () => tinyYolov2, + toNetInput: () => toNetInput, + utils: () => utils_exports, + validateConfig: () => validateConfig, + version: () => version2 +}); +module.exports = __toCommonJS(src_exports); +var tf42 = __toESM(require_tfjs_esm()); + +// src/draw/index.ts +var draw_exports = {}; +__export(draw_exports, { + AnchorPosition: () => AnchorPosition, + DrawBox: () => DrawBox, + DrawBoxOptions: () => DrawBoxOptions, + DrawFaceLandmarks: () => DrawFaceLandmarks, + DrawFaceLandmarksOptions: () => DrawFaceLandmarksOptions, + DrawTextField: () => DrawTextField, + DrawTextFieldOptions: () => DrawTextFieldOptions, + drawContour: () => drawContour, + drawDetections: () => drawDetections, + drawFaceExpressions: () => drawFaceExpressions, + drawFaceLandmarks: () => drawFaceLandmarks +}); + +// src/draw/drawContour.ts +function drawContour(ctx, points, isClosed = false) { + ctx.beginPath(); + points.slice(1).forEach(({ x, y }, prevIdx) => { + const from = points[prevIdx]; + ctx.moveTo(from.x, from.y); + ctx.lineTo(x, y); + }); + if (isClosed) { + const from = points[points.length - 1]; + const to = points[0]; + if (!from || !to) { + return; + } + ctx.moveTo(from.x, from.y); + ctx.lineTo(to.x, to.y); + } + ctx.stroke(); +} + +// src/utils/index.ts +var utils_exports = {}; +__export(utils_exports, { + computeReshapedDimensions: () => computeReshapedDimensions, + getCenterPoint: () => getCenterPoint, + isDimensions: () => isDimensions, + isEven: () => isEven, + isFloat: () => isFloat, + isTensor: () => isTensor, + isTensor1D: () => isTensor1D, + isTensor2D: () => isTensor2D, + isTensor3D: () => isTensor3D, + isTensor4D: () => isTensor4D, + isValidNumber: () => isValidNumber, + isValidProbablitiy: () => isValidProbablitiy, + range: () => range, + round: () => round +}); +var tf = __toESM(require_tfjs_esm()); + +// src/classes/Dimensions.ts +var Dimensions = class _Dimensions { + constructor(width, height) { + if (!isValidNumber(width) || !isValidNumber(height)) { + throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`); + } + this._width = width; + this._height = height; + } + get width() { + return this._width; + } + get height() { + return this._height; + } + reverse() { + return new _Dimensions(1 / this.width, 1 / this.height); + } +}; + +// src/utils/index.ts +function isTensor(tensor2, dim) { + return tensor2 instanceof tf.Tensor && tensor2.shape.length === dim; +} +function isTensor1D(tensor2) { + return isTensor(tensor2, 1); +} +function isTensor2D(tensor2) { + return isTensor(tensor2, 2); +} +function isTensor3D(tensor2) { + return isTensor(tensor2, 3); +} +function isTensor4D(tensor2) { + return isTensor(tensor2, 4); +} +function isFloat(num) { + return num % 1 !== 0; +} +function isEven(num) { + return num % 2 === 0; +} +function round(num, prec = 2) { + const f = 10 ** prec; + return Math.floor(num * f) / f; +} +function isDimensions(obj) { + return obj && obj.width && obj.height; +} +function computeReshapedDimensions({ width, height }, inputSize) { + const scale2 = inputSize / Math.max(height, width); + return new Dimensions(Math.round(width * scale2), Math.round(height * scale2)); +} +function getCenterPoint(pts) { + return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length)); +} +function range(num, start, step) { + return Array(num).fill(0).map((_, i) => start + i * step); +} +function isValidNumber(num) { + return !!num && num !== Infinity && num !== -Infinity && !Number.isNaN(num) || num === 0; +} +function isValidProbablitiy(num) { + return isValidNumber(num) && num >= 0 && num <= 1; +} + +// src/classes/Point.ts +var Point = class _Point { + constructor(x, y) { + this._x = x; + this._y = y; + } + get x() { + return this._x; + } + get y() { + return this._y; + } + add(pt) { + return new _Point(this.x + pt.x, this.y + pt.y); + } + sub(pt) { + return new _Point(this.x - pt.x, this.y - pt.y); + } + mul(pt) { + return new _Point(this.x * pt.x, this.y * pt.y); + } + div(pt) { + return new _Point(this.x / pt.x, this.y / pt.y); + } + abs() { + return new _Point(Math.abs(this.x), Math.abs(this.y)); + } + magnitude() { + return Math.sqrt(this.x ** 2 + this.y ** 2); + } + floor() { + return new _Point(Math.floor(this.x), Math.floor(this.y)); + } +}; + +// src/classes/Box.ts +var Box = class _Box { + static isRect(rect) { + return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber); + } + static assertIsValidBox(box, callee, allowNegativeDimensions = false) { + if (!_Box.isRect(box)) { + throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`); + } + if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) { + throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`); + } + } + constructor(_box, allowNegativeDimensions = true) { + const box = _box || {}; + const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber); + const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber); + if (!isRect && !isBbox) { + throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`); + } + const [x, y, width, height] = isRect ? [box.x, box.y, box.width, box.height] : [box.left, box.top, box.right - box.left, box.bottom - box.top]; + _Box.assertIsValidBox({ + x, + y, + width, + height + }, "Box.constructor", allowNegativeDimensions); + this._x = x; + this._y = y; + this._width = width; + this._height = height; + } + 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 Point(this.left, this.top); + } + get topRight() { + return new Point(this.right, this.top); + } + get bottomLeft() { + return new Point(this.left, this.bottom); + } + get bottomRight() { + return new Point(this.right, this.bottom); + } + round() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.round(val)); + return new _Box({ + x, + y, + width, + height + }); + } + floor() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.floor(val)); + return new _Box({ + x, + y, + width, + height + }); + } + toSquare() { + let { + x, + y, + width, + height + } = this; + const diff = Math.abs(width - height); + if (width < height) { + x -= diff / 2; + width += diff; + } + if (height < width) { + y -= diff / 2; + height += diff; + } + return new _Box({ x, y, width, height }); + } + rescale(s) { + const scaleX = isDimensions(s) ? s.width : s; + const scaleY = isDimensions(s) ? s.height : s; + return new _Box({ + x: this.x * scaleX, + y: this.y * scaleY, + width: this.width * scaleX, + height: this.height * scaleY + }); + } + pad(padX, padY) { + const [x, y, width, height] = [ + this.x - padX / 2, + this.y - padY / 2, + this.width + padX, + this.height + padY + ]; + return new _Box({ x, y, width, height }); + } + clipAtImageBorders(imgWidth, imgHeight) { + const { x, y, right, bottom } = this; + const clippedX = Math.max(x, 0); + const clippedY = Math.max(y, 0); + const newWidth = right - clippedX; + const newHeight = bottom - clippedY; + const clippedWidth = Math.min(newWidth, imgWidth - clippedX); + const clippedHeight = Math.min(newHeight, imgHeight - clippedY); + return new _Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight }).floor(); + } + shift(sx, sy) { + const { width, height } = this; + const x = this.x + sx; + const y = this.y + sy; + return new _Box({ x, y, width, height }); + } + padAtBorders(imageHeight, imageWidth) { + const w = this.width + 1; + const h = this.height + 1; + const dx = 1; + const dy = 1; + let edx = w; + let edy = h; + let x = this.left; + let y = this.top; + let ex = this.right; + let ey = this.bottom; + if (ex > imageWidth) { + edx = -ex + imageWidth + w; + ex = imageWidth; + } + if (ey > imageHeight) { + edy = -ey + imageHeight + h; + ey = imageHeight; + } + if (x < 1) { + edy = 2 - x; + x = 1; + } + if (y < 1) { + edy = 2 - y; + y = 1; + } + return { dy, edy, dx, edx, y, ey, x, ex, w, h }; + } + calibrate(region) { + return new _Box({ + left: this.left + region.left * this.width, + top: this.top + region.top * this.height, + right: this.right + region.right * this.width, + bottom: this.bottom + region.bottom * this.height + }).toSquare().round(); + } +}; + +// src/classes/BoundingBox.ts +var BoundingBox = class extends Box { + constructor(left, top, right, bottom, allowNegativeDimensions = false) { + super({ left, top, right, bottom }, allowNegativeDimensions); + } +}; + +// src/classes/ObjectDetection.ts +var ObjectDetection = class _ObjectDetection { + constructor(score, classScore, className, relativeBox, imageDims) { + this._imageDims = new Dimensions(imageDims.width, imageDims.height); + this._score = score; + this._classScore = classScore; + this._className = className; + this._box = new Box(relativeBox).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 Box(this._box).rescale(this.imageDims.reverse()); + } + forSize(width, height) { + return new _ObjectDetection( + this.score, + this.classScore, + this.className, + this.relativeBox, + { width, height } + ); + } +}; + +// src/classes/FaceDetection.ts +var FaceDetection = class _FaceDetection extends ObjectDetection { + constructor(score, relativeBox, imageDims) { + super(score, score, "", relativeBox, imageDims); + } + forSize(width, height) { + const { score, relativeBox, imageDims } = super.forSize(width, height); + return new _FaceDetection(score, relativeBox, imageDims); + } +}; + +// src/ops/iou.ts +function iou(box1, box2, isIOU = true) { + const width = Math.max(0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left)); + const height = Math.max(0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top)); + const interSection = width * height; + return isIOU ? interSection / (box1.area + box2.area - interSection) : interSection / Math.min(box1.area, box2.area); +} + +// src/ops/minBbox.ts +function minBbox(pts) { + const xs = pts.map((pt) => pt.x); + const ys = pts.map((pt) => pt.y); + const minX = xs.reduce((min, x) => x < min ? x : min, Infinity); + const minY = ys.reduce((min, y) => y < min ? y : min, Infinity); + const maxX = xs.reduce((max, x) => max < x ? x : max, 0); + const maxY = ys.reduce((max, y) => max < y ? y : max, 0); + return new BoundingBox(minX, minY, maxX, maxY); +} + +// src/ops/nonMaxSuppression.ts +function nonMaxSuppression(boxes, scores, iouThreshold, isIOU = true) { + let indicesSortedByScore = scores.map((score, boxIndex) => ({ score, boxIndex })).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex); + const pick = []; + while (indicesSortedByScore.length > 0) { + const curr = indicesSortedByScore.pop(); + pick.push(curr); + const indices = indicesSortedByScore; + const outputs = []; + for (let i = 0; i < indices.length; i++) { + const idx = indices[i]; + const currBox = boxes[curr]; + const idxBox = boxes[idx]; + outputs.push(iou(currBox, idxBox, isIOU)); + } + indicesSortedByScore = indicesSortedByScore.filter( + (_, j) => outputs[j] <= iouThreshold + ); + } + return pick; +} + +// src/ops/normalize.ts +var tf2 = __toESM(require_tfjs_esm()); +function normalize(x, meanRgb) { + return tf2.tidy(() => { + const [r, g, b] = meanRgb; + const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r, "float32"); + const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g, "float32"); + const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b, "float32"); + const avg_rgb = tf2.concat([avg_r, avg_g, avg_b], 3); + return tf2.sub(x, avg_rgb); + }); +} + +// src/ops/padToSquare.ts +var tf3 = __toESM(require_tfjs_esm()); +function padToSquare(imgTensor, isCenterImage = false) { + return tf3.tidy(() => { + const [height, width] = imgTensor.shape.slice(1); + if (height === width) + return imgTensor; + const dimDiff = Math.abs(height - width); + const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1)); + const paddingAxis = height > width ? 2 : 1; + const createPaddingTensor = (paddingAmountLocal) => { + const paddingTensorShape = imgTensor.shape.slice(); + paddingTensorShape[paddingAxis] = paddingAmountLocal; + return tf3.fill(paddingTensorShape, 0, "float32"); + }; + const paddingTensorAppend = createPaddingTensor(paddingAmount); + const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis]; + const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null; + const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend].filter((t) => !!t).map((t) => tf3.cast(t, "float32")); + return tf3.concat(tensorsToStack, paddingAxis); + }); +} + +// src/ops/shuffleArray.ts +function shuffleArray(inputArray) { + const array = inputArray.slice(); + for (let i = array.length - 1; i > 0; i--) { + const j = Math.floor(Math.random() * (i + 1)); + const x = array[i]; + array[i] = array[j]; + array[j] = x; + } + return array; +} + +// src/ops/index.ts +function sigmoid(x) { + return 1 / (1 + Math.exp(-x)); +} +function inverseSigmoid(x) { + return Math.log(x / (1 - x)); +} + +// src/classes/Rect.ts +var Rect = class extends Box { + constructor(x, y, width, height, allowNegativeDimensions = false) { + super({ x, y, width, height }, allowNegativeDimensions); + } +}; + +// src/classes/FaceLandmarks.ts +var relX = 0.5; +var relY = 0.43; +var relScale = 0.45; +var FaceLandmarks = class { + constructor(relativeFaceLandmarkPositions, imgDims, shift = new Point(0, 0)) { + const { width, height } = imgDims; + this._imgDims = new Dimensions(width, height); + this._shift = shift; + this._positions = relativeFaceLandmarkPositions.map( + (pt) => pt.mul(new Point(width, height)).add(shift) + ); + } + get shift() { + return new Point(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( + (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)) + ); + } + forSize(width, height) { + return new this.constructor( + this.relativePositions, + { width, height } + ); + } + shiftBy(x, y) { + return new this.constructor( + this.relativePositions, + this._imgDims, + new Point(x, y) + ); + } + shiftByPoint(pt) { + return this.shiftBy(pt.x, pt.y); + } + /** + * Aligns the face landmarks after face detection from the relative positions of the faces + * bounding box, or it's current shift. This function should be used to align the face images + * after face detection has been performed, before they are passed to the face recognition net. + * This will make the computed face descriptor more accurate. + * + * @param detection (optional) The bounding box of the face or the face detection result. If + * no argument was passed the position of the face landmarks are assumed to be relative to + * it's current shift. + * @returns The bounding box of the aligned face. + */ + align(detection, options = {}) { + if (detection) { + const box = detection instanceof FaceDetection ? detection.box.floor() : new Box(detection); + return this.shiftBy(box.x, box.y).align(null, options); + } + const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options }; + if (useDlibAlignment) { + return this.alignDlib(); + } + return this.alignMinBbox(minBoxPadding); + } + alignDlib() { + const centers = this.getRefPointsForAlignment(); + const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers; + const distToMouth = (pt) => mouthCenter.sub(pt).magnitude(); + const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2; + const size = Math.floor(eyeToMouthDist / relScale); + const refPoint = getCenterPoint(centers); + const x = Math.floor(Math.max(0, refPoint.x - relX * size)); + const y = Math.floor(Math.max(0, refPoint.y - relY * size)); + return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y)); + } + alignMinBbox(padding) { + const box = minBbox(this.positions); + return box.pad(box.width * padding, box.height * padding); + } + getRefPointsForAlignment() { + throw new Error("getRefPointsForAlignment not implemented by base class"); + } +}; + +// src/classes/FaceLandmarks5.ts +var FaceLandmarks5 = class extends FaceLandmarks { + getRefPointsForAlignment() { + const pts = this.positions; + return [ + pts[0], + pts[1], + getCenterPoint([pts[3], pts[4]]) + ]; + } +}; + +// src/classes/FaceLandmarks68.ts +var FaceLandmarks68 = class extends FaceLandmarks { + 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(getCenterPoint); + } +}; + +// src/classes/FaceMatch.ts +var FaceMatch = class { + constructor(label, distance) { + this._label = label; + this._distance = distance; + } + get label() { + return this._label; + } + get distance() { + return this._distance; + } + toString(withDistance = true) { + return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`; + } +}; + +// src/classes/LabeledBox.ts +var LabeledBox = class extends Box { + static assertIsValidLabeledBox(box, callee) { + Box.assertIsValidBox(box, callee); + if (!isValidNumber(box.label)) { + throw new Error(`${callee} - expected property label (${box.label}) to be a number`); + } + } + constructor(box, label) { + super(box); + this._label = label; + } + get label() { + return this._label; + } +}; + +// src/classes/LabeledFaceDescriptors.ts +var LabeledFaceDescriptors = class _LabeledFaceDescriptors { + constructor(label, descriptors) { + if (!(typeof label === "string")) { + throw new Error("LabeledFaceDescriptors - constructor expected label to be a string"); + } + if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) { + throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array"); + } + this._label = label; + this._descriptors = descriptors; + } + get label() { + return this._label; + } + get descriptors() { + return this._descriptors; + } + toJSON() { + return { + label: this.label, + descriptors: this.descriptors.map((d) => Array.from(d)) + }; + } + static fromJSON(json) { + const descriptors = json.descriptors.map((d) => new Float32Array(d)); + return new _LabeledFaceDescriptors(json.label, descriptors); + } +}; + +// src/classes/PredictedBox.ts +var PredictedBox = class extends LabeledBox { + static assertIsValidPredictedBox(box, callee) { + LabeledBox.assertIsValidLabeledBox(box, callee); + if (!isValidProbablitiy(box.score) || !isValidProbablitiy(box.classScore)) { + throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`); + } + } + constructor(box, label, score, classScore) { + super(box, label); + this._score = score; + this._classScore = classScore; + } + get score() { + return this._score; + } + get classScore() { + return this._classScore; + } +}; + +// src/factories/WithFaceDetection.ts +function isWithFaceDetection(obj) { + return obj.detection instanceof FaceDetection; +} +function extendWithFaceDetection(sourceObj, detection) { + const extension = { detection }; + return { ...sourceObj, ...extension }; +} + +// src/env/createBrowserEnv.ts +function createBrowserEnv() { + const fetch = window.fetch; + if (!fetch) + throw new Error("fetch - missing fetch implementation for browser environment"); + const readFile = () => { + throw new Error("readFile - filesystem not available for browser environment"); + }; + return { + Canvas: HTMLCanvasElement, + CanvasRenderingContext2D, + Image: HTMLImageElement, + ImageData, + Video: HTMLVideoElement, + createCanvasElement: () => document.createElement("canvas"), + createImageElement: () => document.createElement("img"), + createVideoElement: () => document.createElement("video"), + fetch, + readFile + }; +} + +// src/env/isNodejs.ts +function isNodejs() { + return typeof global === "object" && typeof process !== "undefined" && process.versions != null && process.versions.node != null; +} + +// src/env/createFileSystem.ts +function createFileSystem(fs) { + let requireFsError = ""; + if (!fs && isNodejs()) { + try { + fs = require("fs"); + } catch (err) { + requireFsError = err.toString(); + } + } + const readFile = fs ? (filePath) => new Promise((resolve, reject) => { + fs.readFile(filePath, (err, buffer) => err ? reject(err) : resolve(buffer)); + }) : () => { + throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); + }; + return { readFile }; +} + +// src/env/createNodejsEnv.ts +function createNodejsEnv() { + const Canvas = global["Canvas"] || global.HTMLCanvasElement; + const Image = global.Image || global.HTMLImageElement; + const Video = global["Video"] || global.HTMLVideoElement; + const createCanvasElement = () => { + if (Canvas) + return new Canvas(); + throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment"); + }; + const createImageElement = () => { + if (Image) + return new Image(); + throw new Error("createImageElement - missing Image implementation for nodejs environment"); + }; + const createVideoElement = () => { + if (Video) + return new Video(); + throw new Error("createVideoElement - missing Video implementation for nodejs environment"); + }; + const fetch = global.fetch; + const fileSystem = createFileSystem(); + return { + Canvas: Canvas || class { + }, + CanvasRenderingContext2D: global.CanvasRenderingContext2D || class { + }, + Image: Image || class { + }, + ImageData: global.ImageData || class { + }, + Video: global.HTMLVideoElement || class { + }, + createCanvasElement, + createImageElement, + createVideoElement, + fetch, + ...fileSystem + }; +} + +// src/env/isBrowser.ts +function isBrowser() { + return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined"; +} + +// src/env/index.ts +var environment; +function getEnv() { + if (!environment) { + throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()"); + } + return environment; +} +function setEnv(env2) { + environment = env2; +} +function initialize() { + if (isBrowser()) + return setEnv(createBrowserEnv()); + if (isNodejs()) + return setEnv(createNodejsEnv()); + return null; +} +function monkeyPatch(env2) { + if (!environment) { + initialize(); + } + if (!environment) { + throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()"); + } + const { Canvas = environment.Canvas, Image = environment.Image } = env2; + environment.Canvas = Canvas; + environment.Image = Image; + environment.createCanvasElement = env2.createCanvasElement || (() => new Canvas()); + environment.createImageElement = env2.createImageElement || (() => new Image()); + environment.ImageData = env2.ImageData || environment.ImageData; + environment.Video = env2.Video || environment.Video; + environment.fetch = env2.fetch || environment.fetch; + environment.readFile = env2.readFile || environment.readFile; +} +var env = { + getEnv, + setEnv, + initialize, + createBrowserEnv, + createFileSystem, + createNodejsEnv, + monkeyPatch, + isBrowser, + isNodejs +}; +initialize(); + +// src/dom/resolveInput.ts +function resolveInput(arg) { + if (!env.isNodejs() && typeof arg === "string") { + return document.getElementById(arg); + } + return arg; +} + +// src/dom/getContext2dOrThrow.ts +function getContext2dOrThrow(canvasArg) { + const { Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2 } = env.getEnv(); + if (canvasArg instanceof CanvasRenderingContext2D2) + return canvasArg; + const canvas = resolveInput(canvasArg); + if (!(canvas instanceof Canvas)) + throw new Error("resolveContext2d - expected canvas to be of instance of Canvas"); + const ctx = canvas.getContext("2d", { willReadFrequently: true }); + if (!ctx) + throw new Error("resolveContext2d - canvas 2d context is null"); + return ctx; +} + +// src/draw/DrawTextField.ts +var AnchorPosition = /* @__PURE__ */ ((AnchorPosition2) => { + AnchorPosition2["TOP_LEFT"] = "TOP_LEFT"; + AnchorPosition2["TOP_RIGHT"] = "TOP_RIGHT"; + AnchorPosition2["BOTTOM_LEFT"] = "BOTTOM_LEFT"; + AnchorPosition2["BOTTOM_RIGHT"] = "BOTTOM_RIGHT"; + return AnchorPosition2; +})(AnchorPosition || {}); +var DrawTextFieldOptions = class { + constructor(options = {}) { + const { + anchorPosition, + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = options; + this.anchorPosition = anchorPosition || "TOP_LEFT" /* TOP_LEFT */; + this.backgroundColor = backgroundColor || "rgba(0, 0, 0, 0.5)"; + this.fontColor = fontColor || "rgba(255, 255, 255, 1)"; + this.fontSize = fontSize || 14; + this.fontStyle = fontStyle || "Georgia"; + this.padding = padding || 4; + } +}; +var DrawTextField = class _DrawTextField { + constructor(text, anchor, options = {}) { + this.text = typeof text === "string" ? [text] : text instanceof _DrawTextField ? text.text : text; + this.anchor = anchor; + this.options = new DrawTextFieldOptions(options); + } + measureWidth(ctx) { + const { padding } = this.options; + return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + 2 * padding; + } + measureHeight() { + const { fontSize, padding } = this.options; + return this.text.length * fontSize + 2 * padding; + } + getUpperLeft(ctx, canvasDims) { + const { anchorPosition } = this.options; + const isShiftLeft = anchorPosition === "BOTTOM_RIGHT" /* BOTTOM_RIGHT */ || anchorPosition === "TOP_RIGHT" /* TOP_RIGHT */; + const isShiftTop = anchorPosition === "BOTTOM_LEFT" /* BOTTOM_LEFT */ || anchorPosition === "BOTTOM_RIGHT" /* BOTTOM_RIGHT */; + const textFieldWidth = this.measureWidth(ctx); + const textFieldHeight = this.measureHeight(); + const x = isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x; + const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y; + if (canvasDims) { + const { width, height } = canvasDims; + const newX = Math.max(Math.min(x, width - textFieldWidth), 0); + const newY = Math.max(Math.min(y, height - textFieldHeight), 0); + return { x: newX, y: newY }; + } + return { x, y }; + } + draw(canvasArg) { + const canvas = resolveInput(canvasArg); + const ctx = getContext2dOrThrow(canvas); + const { + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = this.options; + ctx.font = `${fontSize}px ${fontStyle}`; + const maxTextWidth = this.measureWidth(ctx); + const textHeight = this.measureHeight(); + ctx.fillStyle = backgroundColor; + const upperLeft = this.getUpperLeft(ctx, canvas); + ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight); + ctx.fillStyle = fontColor; + this.text.forEach((textLine, i) => { + const x = padding + upperLeft.x; + const y = padding + upperLeft.y + (i + 1) * fontSize; + ctx.fillText(textLine, x, y); + }); + } +}; + +// src/draw/DrawBox.ts +var DrawBoxOptions = class { + constructor(options = {}) { + const { + boxColor, + lineWidth, + label, + drawLabelOptions + } = options; + this.boxColor = boxColor || "rgba(0, 0, 255, 1)"; + this.lineWidth = lineWidth || 2; + this.label = label; + const defaultDrawLabelOptions = { + anchorPosition: "BOTTOM_LEFT" /* BOTTOM_LEFT */, + backgroundColor: this.boxColor + }; + this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions }); + } +}; +var DrawBox = class { + constructor(box, options = {}) { + this.box = new Box(box); + this.options = new DrawBoxOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { boxColor, lineWidth } = this.options; + const { + x, + y, + width, + height + } = this.box; + ctx.strokeStyle = boxColor; + ctx.lineWidth = lineWidth; + ctx.strokeRect(x, y, width, height); + const { label } = this.options; + if (label) { + new DrawTextField([label], { x: x - lineWidth / 2, y }, this.options.drawLabelOptions).draw(canvasArg); + } + } +}; + +// src/draw/drawDetections.ts +function drawDetections(canvasArg, detections) { + const detectionsArray = Array.isArray(detections) ? detections : [detections]; + detectionsArray.forEach((det) => { + const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0; + const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det); + const label = score ? `${round(score)}` : void 0; + new DrawBox(box, { label }).draw(canvasArg); + }); +} + +// src/faceExpressionNet/FaceExpressionNet.ts +var tf18 = __toESM(require_tfjs_esm()); + +// src/dom/isMediaLoaded.ts +function isMediaLoaded(media) { + const { Image, Video } = env.getEnv(); + return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3; +} + +// src/dom/awaitMediaLoaded.ts +function awaitMediaLoaded(media) { + return new Promise((resolve, reject) => { + if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) + resolve(null); + function onError(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + reject(e); + } + function onLoad(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + resolve(e); + } + media.addEventListener("load", onLoad); + media.addEventListener("error", onError); + }); +} + +// src/dom/bufferToImage.ts +function bufferToImage(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToImage - expected buf to be of type: Blob")); + const reader = new FileReader(); + reader.onload = () => { + if (typeof reader.result !== "string") + reject(new Error("bufferToImage - expected reader.result to be a string, in onload")); + const img = env.getEnv().createImageElement(); + img.onload = () => resolve(img); + img.onerror = reject; + img.src = reader.result; + }; + reader.onerror = reject; + reader.readAsDataURL(buf); + }); +} + +// src/dom/getMediaDimensions.ts +function getMediaDimensions(input) { + const { Image, Video } = env.getEnv(); + if (input instanceof Image) { + return new Dimensions(input.naturalWidth, input.naturalHeight); + } + if (input instanceof Video) { + return new Dimensions(input.videoWidth, input.videoHeight); + } + return new Dimensions(input.width, input.height); +} + +// src/dom/createCanvas.ts +function createCanvas({ width, height }) { + const { createCanvasElement } = env.getEnv(); + const canvas = createCanvasElement(); + canvas.width = width; + canvas.height = height; + return canvas; +} +function createCanvasFromMedia(media, dims) { + const { ImageData: ImageData2 } = env.getEnv(); + if (!(media instanceof ImageData2) && !isMediaLoaded(media)) { + throw new Error("createCanvasFromMedia - media has not finished loading yet"); + } + const { width, height } = dims || getMediaDimensions(media); + const canvas = createCanvas({ width, height }); + if (media instanceof ImageData2) { + getContext2dOrThrow(canvas).putImageData(media, 0, 0); + } else { + getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height); + } + return canvas; +} + +// src/dom/imageTensorToCanvas.ts +var tf4 = __toESM(require_tfjs_esm()); +async function imageTensorToCanvas(imgTensor, canvas) { + const targetCanvas = canvas || env.getEnv().createCanvasElement(); + const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0); + const imgTensor3D = tf4.tidy(() => imgTensor.as3D(height, width, numChannels).toInt()); + await tf4["browser"].toPixels(imgTensor3D, targetCanvas); + imgTensor3D.dispose(); + return targetCanvas; +} + +// src/dom/isMediaElement.ts +function isMediaElement(input) { + const { Image, Canvas, Video } = env.getEnv(); + return input instanceof Image || input instanceof Canvas || input instanceof Video; +} + +// src/dom/NetInput.ts +var tf5 = __toESM(require_tfjs_esm()); + +// src/dom/imageToSquare.ts +function imageToSquare(input, inputSize, centerImage = false) { + const { Image, Canvas } = env.getEnv(); + if (!(input instanceof Image || input instanceof Canvas)) { + throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement"); + } + if (inputSize <= 0) + return createCanvas({ width: 1, height: 1 }); + const dims = getMediaDimensions(input); + const scale2 = inputSize / Math.max(dims.height, dims.width); + const width = scale2 * dims.width; + const height = scale2 * dims.height; + const targetCanvas = createCanvas({ width: inputSize, height: inputSize }); + const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input); + const offset = Math.abs(width - height) / 2; + const dx = centerImage && width < height ? offset : 0; + const dy = centerImage && height < width ? offset : 0; + if (inputCanvas.width > 0 && inputCanvas.height > 0) + getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height); + return targetCanvas; +} + +// src/dom/NetInput.ts +var NetInput = class { + constructor(inputs, treatAsBatchInput = false) { + this._imageTensors = []; + this._canvases = []; + this._treatAsBatchInput = false; + this._inputDimensions = []; + this._inputSize = 0; + if (!Array.isArray(inputs)) { + throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`); + } + this._treatAsBatchInput = treatAsBatchInput; + this._batchSize = inputs.length; + inputs.forEach((input, idx) => { + if (isTensor3D(input)) { + this._imageTensors[idx] = input; + this._inputDimensions[idx] = input.shape; + return; + } + if (isTensor4D(input)) { + const batchSize = input.shape[0]; + if (batchSize !== 1) { + throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + this._imageTensors[idx] = input; + this._inputDimensions[idx] = input.shape.slice(1); + return; + } + const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input); + this._canvases[idx] = canvas; + this._inputDimensions[idx] = [canvas.height, canvas.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 range(this.batchSize, 0, 1).map( + (_, batchIdx) => this.getReshapedInputDimensions(batchIdx) + ); + } + getInput(batchIdx) { + return this.canvases[batchIdx] || this.imageTensors[batchIdx]; + } + getInputDimensions(batchIdx) { + return this._inputDimensions[batchIdx]; + } + getInputHeight(batchIdx) { + return this._inputDimensions[batchIdx][0]; + } + getInputWidth(batchIdx) { + return this._inputDimensions[batchIdx][1]; + } + getReshapedInputDimensions(batchIdx) { + if (typeof this.inputSize !== "number") { + throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet"); + } + const width = this.getInputWidth(batchIdx); + const height = this.getInputHeight(batchIdx); + return computeReshapedDimensions({ width, height }, this.inputSize); + } + /** + * Create a batch tensor from all input canvases and tensors + * with size [batchSize, inputSize, inputSize, 3]. + * + * @param inputSize Height and width of the tensor. + * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on + * both sides of the minor dimension oof the image. + * @returns The batch tensor. + */ + toBatchTensor(inputSize, isCenterInputs = true) { + this._inputSize = inputSize; + return tf5.tidy(() => { + const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => { + const input = this.getInput(batchIdx); + if (input instanceof tf5.Tensor) { + let imgTensor = isTensor4D(input) ? input : tf5.expandDims(input); + imgTensor = padToSquare(imgTensor, isCenterInputs); + if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) { + imgTensor = tf5["image"].resizeBilinear(imgTensor, [inputSize, inputSize], false, false); + } + return imgTensor.as3D(inputSize, inputSize, 3); + } + if (input instanceof env.getEnv().Canvas) { + return tf5["browser"].fromPixels(imageToSquare(input, inputSize, isCenterInputs)); + } + throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`); + }); + const batchTensor = tf5.stack(inputTensors.map((t) => tf5.cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3); + return batchTensor; + }); + } +}; + +// src/dom/toNetInput.ts +async function toNetInput(inputs) { + if (inputs instanceof NetInput) + return inputs; + const inputArgArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArgArray.length) + throw new Error("toNetInput - empty array passed as input"); + const getIdxHint = (idx) => Array.isArray(inputs) ? ` at input index ${idx}:` : ""; + const inputArray = inputArgArray.map(resolveInput); + inputArray.forEach((input, i) => { + if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) { + if (typeof inputArgArray[i] === "string") + throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`); + throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`); + } + if (isTensor4D(input)) { + const batchSize = input.shape[0]; + if (batchSize !== 1) + throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + }); + await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input))); + return new NetInput(inputArray, Array.isArray(inputs)); +} + +// src/dom/extractFaces.ts +async function extractFaces(input, detections) { + const { Canvas } = env.getEnv(); + let canvas = input; + if (!(input instanceof Canvas)) { + const netInput = await toNetInput(input); + if (netInput.batchSize > 1) + throw new Error("extractFaces - batchSize > 1 not supported"); + const tensorOrCanvas = netInput.getInput(0); + canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas); + } + const ctx = getContext2dOrThrow(canvas); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det).map((box) => box.clipAtImageBorders(canvas.width, canvas.height)); + return boxes.map(({ x, y, width, height }) => { + const faceImg = createCanvas({ width, height }); + if (width > 0 && height > 0) + getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0); + return faceImg; + }); +} + +// src/dom/extractFaceTensors.ts +var tf6 = __toESM(require_tfjs_esm()); +async function extractFaceTensors(imageTensor, detections) { + if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) { + throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D"); + } + if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) { + throw new Error("extractFaceTensors - batchSize > 1 not supported"); + } + return tf6.tidy(() => { + const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight)); + const faceTensors = boxes.filter((box) => box.width > 0 && box.height > 0).map(({ x, y, width, height }) => tf6.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])); + return faceTensors; + }); +} + +// src/dom/fetchOrThrow.ts +async function fetchOrThrow(url, init) { + const { fetch } = env.getEnv(); + const res = await fetch(url, init); + if (!(res.status < 400)) { + throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`); + } + return res; +} + +// src/dom/fetchImage.ts +async function fetchImage(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("image/")) { + throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToImage(blob); +} + +// src/dom/fetchJson.ts +async function fetchJson(uri) { + return (await fetchOrThrow(uri)).json(); +} + +// src/dom/fetchNetWeights.ts +async function fetchNetWeights(uri) { + return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer()); +} + +// src/dom/bufferToVideo.ts +function bufferToVideo(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToVideo - expected buf to be of type: Blob")); + const video = env.getEnv().createVideoElement(); + video.oncanplay = () => resolve(video); + video.onerror = reject; + video.playsInline = true; + video.muted = true; + video.src = URL.createObjectURL(buf); + video.play(); + }); +} + +// src/dom/fetchVideo.ts +async function fetchVideo(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("video/")) { + throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToVideo(blob); +} + +// src/dom/loadWeightMap.ts +var tf7 = __toESM(require_tfjs_esm()); + +// src/common/getModelUris.ts +function getModelUris(uri, defaultModelName) { + const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`; + if (!uri) { + return { + modelBaseUri: "", + manifestUri: defaultManifestFilename + }; + } + if (uri === "/") { + return { + modelBaseUri: "/", + manifestUri: `/${defaultManifestFilename}` + }; + } + const protocol = uri.startsWith("http://") ? "http://" : uri.startsWith("https://") ? "https://" : ""; + uri = uri.replace(protocol, ""); + const parts = uri.split("/").filter((s) => s); + const manifestFile = uri.endsWith(".json") ? parts[parts.length - 1] : defaultManifestFilename; + let modelBaseUri = protocol + (uri.endsWith(".json") ? parts.slice(0, parts.length - 1) : parts).join("/"); + modelBaseUri = uri.startsWith("/") ? `/${modelBaseUri}` : modelBaseUri; + return { + modelBaseUri, + manifestUri: modelBaseUri === "/" ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}` + }; +} + +// src/dom/loadWeightMap.ts +async function loadWeightMap(uri, defaultModelName) { + const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName); + const manifest = await fetchJson(manifestUri); + return tf7["io"].loadWeights(manifest, modelBaseUri); +} + +// src/dom/matchDimensions.ts +function matchDimensions(input, reference, useMediaDimensions = false) { + const { width, height } = useMediaDimensions ? getMediaDimensions(reference) : reference; + input.width = width; + input.height = height; + return { width, height }; +} + +// src/faceFeatureExtractor/FaceFeatureExtractor.ts +var tf15 = __toESM(require_tfjs_esm()); + +// src/NeuralNetwork.ts +var tf8 = __toESM(require_tfjs_esm()); +var NeuralNetwork = class { + constructor(name) { + this._params = void 0; + this._paramMappings = []; + this._name = name; + } + get params() { + return this._params; + } + get paramMappings() { + return this._paramMappings; + } + get isLoaded() { + return !!this.params; + } + getParamFromPath(paramPath) { + const { obj, objProp } = this.traversePropertyPath(paramPath); + return obj[objProp]; + } + reassignParamFromPath(paramPath, tensor2) { + const { obj, objProp } = this.traversePropertyPath(paramPath); + obj[objProp].dispose(); + obj[objProp] = tensor2; + } + getParamList() { + return this._paramMappings.map(({ paramPath }) => ({ + path: paramPath, + tensor: this.getParamFromPath(paramPath) + })); + } + getTrainableParams() { + return this.getParamList().filter((param) => param.tensor instanceof tf8.Variable); + } + getFrozenParams() { + return this.getParamList().filter((param) => !(param.tensor instanceof tf8.Variable)); + } + variable() { + this.getFrozenParams().forEach(({ path, tensor: tensor2 }) => { + this.reassignParamFromPath(path, tensor2.variable()); + }); + } + freeze() { + this.getTrainableParams().forEach(({ path, tensor: variable }) => { + const tensor2 = tf8.tensor(variable.dataSync()); + variable.dispose(); + this.reassignParamFromPath(path, tensor2); + }); + } + dispose(throwOnRedispose = true) { + this.getParamList().forEach((param) => { + if (throwOnRedispose && param.tensor.isDisposed) { + throw new Error(`param tensor has already been disposed for path ${param.path}`); + } + param.tensor.dispose(); + }); + this._params = void 0; + } + serializeParams() { + return new Float32Array( + this.getParamList().map(({ tensor: tensor2 }) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)) + ); + } + async load(weightsOrUrl) { + if (weightsOrUrl instanceof Float32Array) { + this.extractWeights(weightsOrUrl); + return; + } + await this.loadFromUri(weightsOrUrl); + } + async loadFromUri(uri) { + if (uri && typeof uri !== "string") { + throw new Error(`${this._name}.loadFromUri - expected model uri`); + } + const weightMap = await loadWeightMap(uri, this.getDefaultModelName()); + this.loadFromWeightMap(weightMap); + } + async loadFromDisk(filePath) { + if (filePath && typeof filePath !== "string") { + throw new Error(`${this._name}.loadFromDisk - expected model file path`); + } + const { readFile } = env.getEnv(); + const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName()); + const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => typeof buf === "string" ? Buffer.from(buf) : buf.buffer))); + const loadWeights = tf8["io"].weightsLoaderFactory(fetchWeightsFromDisk); + const manifest = JSON.parse((await readFile(manifestUri)).toString()); + const weightMap = await loadWeights(manifest, modelBaseUri); + this.loadFromWeightMap(weightMap); + } + loadFromWeightMap(weightMap) { + const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap); + this._paramMappings = paramMappings; + this._params = params; + } + extractWeights(weights) { + const { paramMappings, params } = this.extractParams(weights); + this._paramMappings = paramMappings; + this._params = params; + } + traversePropertyPath(paramPath) { + if (!this.params) { + throw new Error("traversePropertyPath - model has no loaded params"); + } + const result = paramPath.split("/").reduce((res, objProp2) => { + if (!res.nextObj.hasOwnProperty(objProp2)) { + throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`); + } + return { obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2] }; + }, { nextObj: this.params }); + const { obj, objProp } = result; + if (!obj || !objProp || !(obj[objProp] instanceof tf8.Tensor)) { + throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`); + } + return { obj, objProp }; + } +}; + +// src/faceFeatureExtractor/denseBlock.ts +var tf10 = __toESM(require_tfjs_esm()); + +// src/common/depthwiseSeparableConv.ts +var tf9 = __toESM(require_tfjs_esm()); +function depthwiseSeparableConv(x, params, stride) { + return tf9.tidy(() => { + let out = tf9.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same"); + out = tf9.add(out, params.bias); + return out; + }); +} + +// src/faceFeatureExtractor/denseBlock.ts +function denseBlock3(x, denseBlockParams, isFirstLayer = false) { + return tf10.tidy(() => { + const out1 = tf10.relu( + isFirstLayer ? tf10.add( + tf10.conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), + denseBlockParams.conv0.bias + ) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]) + ); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = tf10.relu(tf10.add(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + return tf10.relu(tf10.add(out1, tf10.add(out2, out3))); + }); +} +function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) { + return tf10.tidy(() => { + const out1 = tf10.relu( + isFirstLayer ? tf10.add( + tf10.conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), + denseBlockParams.conv0.bias + ) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]) + ); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = tf10.relu(tf10.add(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + const in4 = tf10.relu(tf10.add(out1, tf10.add(out2, out3))); + const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]); + return tf10.relu(tf10.add(out1, tf10.add(out2, tf10.add(out3, out4)))); + }); +} + +// src/common/convLayer.ts +var tf11 = __toESM(require_tfjs_esm()); +function convLayer(x, params, padding = "same", withRelu = false) { + return tf11.tidy(() => { + const out = tf11.add( + tf11.conv2d(x, params.filters, [1, 1], padding), + params.bias + ); + return withRelu ? tf11.relu(out) : out; + }); +} + +// src/common/disposeUnusedWeightTensors.ts +function disposeUnusedWeightTensors(weightMap, paramMappings) { + Object.keys(weightMap).forEach((path) => { + if (!paramMappings.some((pm) => pm.originalPath === path)) { + weightMap[path].dispose(); + } + }); +} + +// src/common/extractConvParamsFactory.ts +var tf12 = __toESM(require_tfjs_esm()); +function extractConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, filterSize, mappedPrefix) => { + const filters = tf12.tensor4d( + extractWeights(channelsIn * channelsOut * filterSize * filterSize), + [filterSize, filterSize, channelsIn, channelsOut] + ); + const bias = tf12.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { filters, bias }; + }; +} + +// src/common/extractFCParamsFactory.ts +var tf13 = __toESM(require_tfjs_esm()); +function extractFCParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const fc_weights = tf13.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]); + const fc_bias = tf13.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/weights` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { + weights: fc_weights, + bias: fc_bias + }; + }; +} + +// src/common/extractSeparableConvParamsFactory.ts +var tf14 = __toESM(require_tfjs_esm()); + +// src/common/types.ts +var SeparableConvParams = class { + // eslint-disable-next-line no-useless-constructor + constructor(depthwise_filter, pointwise_filter, bias) { + this.depthwise_filter = depthwise_filter; + this.pointwise_filter = pointwise_filter; + this.bias = bias; + } +}; + +// src/common/extractSeparableConvParamsFactory.ts +function extractSeparableConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const depthwise_filter = tf14.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]); + const pointwise_filter = tf14.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]); + const bias = tf14.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/depthwise_filter` }, + { paramPath: `${mappedPrefix}/pointwise_filter` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return new SeparableConvParams( + depthwise_filter, + pointwise_filter, + bias + ); + }; +} +function loadSeparableConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4); + const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return new SeparableConvParams( + depthwise_filter, + pointwise_filter, + bias + ); + }; +} + +// src/common/extractWeightEntryFactory.ts +function extractWeightEntryFactory(weightMap, paramMappings) { + return (originalPath, paramRank, mappedPath) => { + const tensor2 = weightMap[originalPath]; + if (!isTensor(tensor2, paramRank)) { + throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`); + } + paramMappings.push( + { originalPath, paramPath: mappedPath || originalPath } + ); + return tensor2; + }; +} + +// src/common/extractWeightsFactory.ts +function extractWeightsFactory(weights) { + let remainingWeights = weights; + function extractWeights(numWeights) { + const ret = remainingWeights.slice(0, numWeights); + remainingWeights = remainingWeights.slice(numWeights); + return ret; + } + function getRemainingWeights() { + return remainingWeights; + } + return { + extractWeights, + getRemainingWeights + }; +} + +// src/faceFeatureExtractor/extractorsFactory.ts +function extractorsFactory(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`) : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`); + const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`); + const conv22 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`); + return { conv0, conv1, conv2: conv22 }; + } + function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const { conv0, conv1, conv2: conv22 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer); + const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParams.ts +function extractParams(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock4Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock4Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock4Params(32, 64, "dense1"); + const dense2 = extractDenseBlock4Params(64, 128, "dense2"); + const dense3 = extractDenseBlock4Params(128, 256, "dense3"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { + dense0, + dense1, + dense2, + dense3 + } + }; +} + +// src/common/loadConvParamsFactory.ts +function loadConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { filters, bias }; + }; +} + +// src/faceFeatureExtractor/loadParamsFactory.ts +function loadParamsFactory(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractDenseBlock3Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + return { conv0, conv1, conv2: conv22 }; + } + function extractDenseBlock4Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + const conv3 = extractSeparableConvParams(`${prefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap(weightMap) { + const paramMappings = []; + const { + extractDenseBlock4Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock4Params("dense0", true), + dense1: extractDenseBlock4Params("dense1"), + dense2: extractDenseBlock4Params("dense2"), + dense3: extractDenseBlock4Params("dense3") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceFeatureExtractor/FaceFeatureExtractor.ts +var FaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("FaceFeatureExtractor"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("FaceFeatureExtractor - load model before inference"); + } + return tf15.tidy(() => { + const batchTensor = tf15.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock4(normalized, params.dense0, true); + out = denseBlock4(out, params.dense1); + out = denseBlock4(out, params.dense2); + out = denseBlock4(out, params.dense3); + out = tf15.avgPool(out, [7, 7], [2, 2], "valid"); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "face_feature_extractor_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap(weightMap); + } + extractParams(weights) { + return extractParams(weights); + } +}; + +// src/faceProcessor/FaceProcessor.ts +var tf17 = __toESM(require_tfjs_esm()); + +// src/common/fullyConnectedLayer.ts +var tf16 = __toESM(require_tfjs_esm()); +function fullyConnectedLayer(x, params) { + return tf16.tidy(() => tf16.add( + tf16.matMul(x, params.weights), + params.bias + )); +} + +// src/faceProcessor/extractParams.ts +function extractParams2(weights, channelsIn, channelsOut) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const fc = extractFCParams(channelsIn, channelsOut, "fc"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { fc } + }; +} + +// src/faceProcessor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap2(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { weights, bias }; + } + const params = { + fc: extractFcParams("fc") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceProcessor/util.ts +function seperateWeightMaps(weightMap) { + const featureExtractorMap = {}; + const classifierMap = {}; + Object.keys(weightMap).forEach((key) => { + const map = key.startsWith("fc") ? classifierMap : featureExtractorMap; + map[key] = weightMap[key]; + }); + return { featureExtractorMap, classifierMap }; +} + +// src/faceProcessor/FaceProcessor.ts +var FaceProcessor = class extends NeuralNetwork { + constructor(_name, faceFeatureExtractor) { + super(_name); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input) { + const { params } = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tf17.tidy(() => { + const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input; + return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc); + }); + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const { params, paramMappings } = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams2(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut()); + } + extractParamsFromWeightMap(weightMap) { + const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap2(classifierMap); + } + extractParams(weights) { + const cIn = this.getClassifierChannelsIn(); + const cOut = this.getClassifierChannelsOut(); + const classifierWeightSize = cOut * cIn + cOut; + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceExpressionNet/FaceExpressions.ts +var FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"]; +var FaceExpressions = class { + constructor(probabilities) { + this.neutral = 0; + this.happy = 0; + this.sad = 0; + this.angry = 0; + this.fearful = 0; + this.disgusted = 0; + this.surprised = 0; + if (probabilities.length !== 7) { + throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`); + } + FACE_EXPRESSION_LABELS.forEach((expression, idx) => { + this[expression] = probabilities[idx]; + }); + } + asSortedArray() { + return FACE_EXPRESSION_LABELS.map((expression) => ({ expression, probability: this[expression] })).sort((e0, e1) => e1.probability - e0.probability); + } +}; + +// src/faceExpressionNet/FaceExpressionNet.ts +var FaceExpressionNet = class extends FaceProcessor { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceExpressionNet", faceFeatureExtractor); + } + forwardInput(input) { + return tf18.tidy(() => tf18.softmax(this.runNet(input))); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async predictExpressions(input) { + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput); + const probabilitesByBatch = await Promise.all(tf18.unstack(out).map(async (t) => { + const data = t.dataSync(); + t.dispose(); + return data; + })); + out.dispose(); + const predictionsByBatch = probabilitesByBatch.map((probabilites) => new FaceExpressions(probabilites)); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "face_expression_model"; + } + getClassifierChannelsIn() { + return 256; + } + getClassifierChannelsOut() { + return 7; + } +}; + +// src/factories/WithFaceExpressions.ts +function isWithFaceExpressions(obj) { + return obj.expressions instanceof FaceExpressions; +} +function extendWithFaceExpressions(sourceObj, expressions) { + const extension = { expressions }; + return { ...sourceObj, ...extension }; +} + +// src/draw/drawFaceExpressions.ts +function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, textFieldAnchor) { + const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]; + faceExpressionsArray.forEach((e) => { + const expr = e instanceof FaceExpressions ? e : isWithFaceExpressions(e) ? e.expressions : void 0; + if (!expr) { + throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof"); + } + const sorted = expr.asSortedArray(); + const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence); + const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0); + const drawTextField = new DrawTextField( + resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`), + anchor + ); + drawTextField.draw(canvasArg); + }); +} + +// src/factories/WithFaceLandmarks.ts +function isWithFaceLandmarks(obj) { + return isWithFaceDetection(obj) && obj["landmarks"] instanceof FaceLandmarks && obj["unshiftedLandmarks"] instanceof FaceLandmarks && obj["alignedRect"] instanceof FaceDetection; +} +function calculateFaceAngle(mesh) { + const degrees = (radians) => radians * 180 / Math.PI; + const calcLengthBetweenTwoPoints = (a, b) => Math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2); + const angle = { + roll: void 0, + pitch: void 0, + yaw: void 0 + }; + const calcYaw = (leftPoint, midPoint, rightPoint) => { + const leftToMidpoint = Math.floor(leftPoint.x - midPoint.x); + const rightToMidpoint = Math.floor(midPoint.x - rightPoint.x); + return leftToMidpoint - rightToMidpoint; + }; + const calcRoll = (lever, pivot) => { + const hypotenuse = Math.hypot(pivot.x - lever.x, pivot.y - lever.y); + const opposite = pivot.y - lever.y; + const angleInRadians = Math.asin(opposite / hypotenuse); + const angleInDegrees = degrees(angleInRadians); + const normalizeAngle = Math.floor(90 - angleInDegrees); + const tiltDirection = pivot.x - lever.x < 0 ? -1 : 1; + const result = normalizeAngle * tiltDirection; + return result; + }; + const calcPitch = (leftPoint, midPoint, rightPoint) => { + const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint); + const baseCoords = new Point((leftPoint.x + rightPoint.x) / 2, (leftPoint.y + rightPoint.y) / 2); + const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords); + const angleInRadians = Math.atan(midToBaseLength / base); + const angleInDegrees = Math.floor(degrees(angleInRadians)); + const direction = baseCoords.y - midPoint.y < 0 ? -1 : 1; + const result = angleInDegrees * direction; + return result; + }; + if (!mesh || !mesh.positions || mesh.positions.length !== 68) + return angle; + const pt = mesh.positions; + angle.roll = calcRoll(pt[27], pt[66]); + angle.pitch = calcPitch(pt[14], pt[30], pt[2]); + angle.yaw = calcYaw(pt[14], pt[33], pt[2]); + return angle; +} +function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) { + const { box: shift } = sourceObj.detection; + const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y); + const rect = landmarks.align(); + const { imageDims } = sourceObj.detection; + const alignedRect = new FaceDetection( + sourceObj.detection.score, + rect.rescale(imageDims.reverse()), + imageDims + ); + const angle = calculateFaceAngle(unshiftedLandmarks); + const extension = { landmarks, unshiftedLandmarks, alignedRect, angle }; + return { ...sourceObj, ...extension }; +} + +// src/draw/DrawFaceLandmarks.ts +var DrawFaceLandmarksOptions = class { + constructor(options = {}) { + const { + drawLines = true, + drawPoints = true, + lineWidth, + lineColor, + pointSize, + pointColor + } = options; + this.drawLines = drawLines; + this.drawPoints = drawPoints; + this.lineWidth = lineWidth || 1; + this.pointSize = pointSize || 2; + this.lineColor = lineColor || "rgba(0, 255, 255, 1)"; + this.pointColor = pointColor || "rgba(255, 0, 255, 1)"; + } +}; +var DrawFaceLandmarks = class { + constructor(faceLandmarks, options = {}) { + this.faceLandmarks = faceLandmarks; + this.options = new DrawFaceLandmarksOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { + drawLines, + drawPoints, + lineWidth, + lineColor, + pointSize, + pointColor + } = this.options; + if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) { + ctx.strokeStyle = lineColor; + ctx.lineWidth = lineWidth; + drawContour(ctx, this.faceLandmarks.getJawOutline()); + drawContour(ctx, this.faceLandmarks.getLeftEyeBrow()); + drawContour(ctx, this.faceLandmarks.getRightEyeBrow()); + drawContour(ctx, this.faceLandmarks.getNose()); + drawContour(ctx, this.faceLandmarks.getLeftEye(), true); + drawContour(ctx, this.faceLandmarks.getRightEye(), true); + drawContour(ctx, this.faceLandmarks.getMouth(), true); + } + if (drawPoints) { + ctx.strokeStyle = pointColor; + ctx.fillStyle = pointColor; + const drawPoint = (pt) => { + ctx.beginPath(); + ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI); + ctx.fill(); + }; + this.faceLandmarks.positions.forEach(drawPoint); + } + } +}; +function drawFaceLandmarks(canvasArg, faceLandmarks) { + const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]; + faceLandmarksArray.forEach((f) => { + const landmarks = f instanceof FaceLandmarks ? f : isWithFaceLandmarks(f) ? f.landmarks : void 0; + if (!landmarks) { + throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof"); + } + new DrawFaceLandmarks(landmarks).draw(canvasArg); + }); +} + +// package.json +var version = "1.7.12"; + +// src/ageGenderNet/AgeGenderNet.ts +var tf20 = __toESM(require_tfjs_esm()); + +// src/xception/TinyXception.ts +var tf19 = __toESM(require_tfjs_esm()); + +// src/xception/extractParams.ts +function extractorsFactory2(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`); + return { separable_conv0, separable_conv1, expansion_conv }; + } + function extractMainBlockParams(channels, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`); + return { separable_conv0, separable_conv1, separable_conv2 }; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParams3(weights, numMainBlocks) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = extractorsFactory2(extractWeights, paramMappings); + const entry_flow_conv_in = extractConvParams(3, 32, 3, "entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, "entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, "entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams(256, 512, "exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { entry_flow, middle_flow, exit_flow } + }; +} + +// src/xception/extractParamsFromWeightMap.ts +function loadParamsFactory2(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractReductionBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`); + return { separable_conv0, separable_conv1, expansion_conv }; + } + function extractMainBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`); + return { separable_conv0, separable_conv1, separable_conv2 }; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParamsFromWeightMap3(weightMap, numMainBlocks) { + const paramMappings = []; + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = loadParamsFactory2(weightMap, paramMappings); + const entry_flow_conv_in = extractConvParams("entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams("entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams("entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams("exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }; +} + +// src/xception/TinyXception.ts +function conv(x, params, stride) { + return tf19.add(tf19.conv2d(x, params.filters, stride, "same"), params.bias); +} +function reductionBlock(x, params, isActivateInput = true) { + let out = isActivateInput ? tf19.relu(x) : x; + out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv1, [1, 1]); + out = tf19.maxPool(out, [3, 3], [2, 2], "same"); + out = tf19.add(out, conv(x, params.expansion_conv, [2, 2])); + return out; +} +function mainBlock(x, params) { + let out = depthwiseSeparableConv(tf19.relu(x), params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv1, [1, 1]); + out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv2, [1, 1]); + out = tf19.add(out, x); + return out; +} +var TinyXception = class extends NeuralNetwork { + constructor(numMainBlocks) { + super("TinyXception"); + this._numMainBlocks = numMainBlocks; + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("TinyXception - load model before inference"); + } + return tf19.tidy(() => { + const batchTensor = tf19.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = tf19.relu(conv(normalized, params.entry_flow.conv_in, [2, 2])); + out = reductionBlock(out, params.entry_flow.reduction_block_0, false); + out = reductionBlock(out, params.entry_flow.reduction_block_1); + range(this._numMainBlocks, 0, 1).forEach((idx) => { + out = mainBlock(out, params.middle_flow[`main_block_${idx}`]); + }); + out = reductionBlock(out, params.exit_flow.reduction_block); + out = tf19.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1])); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "tiny_xception_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap3(weightMap, this._numMainBlocks); + } + extractParams(weights) { + return extractParams3(weights, this._numMainBlocks); + } +}; + +// src/ageGenderNet/extractParams.ts +function extractParams4(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const age = extractFCParams(512, 1, "fc/age"); + const gender = extractFCParams(512, 2, "fc/gender"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { fc: { age, gender } } + }; +} + +// src/ageGenderNet/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap4(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { weights, bias }; + } + const params = { + fc: { + age: extractFcParams("fc/age"), + gender: extractFcParams("fc/gender") + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/ageGenderNet/types.ts +var Gender = /* @__PURE__ */ ((Gender2) => { + Gender2["FEMALE"] = "female"; + Gender2["MALE"] = "male"; + return Gender2; +})(Gender || {}); + +// src/ageGenderNet/AgeGenderNet.ts +var AgeGenderNet = class extends NeuralNetwork { + constructor(faceFeatureExtractor = new TinyXception(2)) { + super("AgeGenderNet"); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input) { + const { params } = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tf20.tidy(() => { + const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input; + const pooled = tf20.avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1); + const age = fullyConnectedLayer(pooled, params.fc.age).as1D(); + const gender = fullyConnectedLayer(pooled, params.fc.gender); + return { age, gender }; + }); + } + forwardInput(input) { + return tf20.tidy(() => { + const { age, gender } = this.runNet(input); + return { age, gender: tf20.softmax(gender) }; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async predictAgeAndGender(input) { + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput); + const ages = tf20.unstack(out.age); + const genders = tf20.unstack(out.gender); + const ageAndGenderTensors = ages.map((ageTensor, i) => ({ + ageTensor, + genderTensor: genders[i] + })); + const predictionsByBatch = await Promise.all( + ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => { + const age = ageTensor.dataSync()[0]; + const probMale = genderTensor.dataSync()[0]; + const isMale = probMale > 0.5; + const gender = isMale ? "male" /* MALE */ : "female" /* FEMALE */; + const genderProbability = isMale ? probMale : 1 - probMale; + ageTensor.dispose(); + genderTensor.dispose(); + return { age, gender, genderProbability }; + }) + ); + out.age.dispose(); + out.gender.dispose(); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "age_gender_model"; + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const { params, paramMappings } = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams4(weights); + } + extractParamsFromWeightMap(weightMap) { + const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap4(classifierMap); + } + extractParams(weights) { + const classifierWeightSize = 512 * 1 + 1 + (512 * 2 + 2); + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68NetBase.ts +var tf21 = __toESM(require_tfjs_esm()); +var FaceLandmark68NetBase = class extends FaceProcessor { + postProcess(output, inputSize, originalDimensions) { + const inputDimensions = originalDimensions.map(({ width, height }) => { + const scale2 = inputSize / Math.max(height, width); + return { + width: width * scale2, + height: height * scale2 + }; + }); + const batchSize = inputDimensions.length; + return tf21.tidy(() => { + const createInterleavedTensor = (fillX, fillY) => tf21.stack([tf21.fill([68], fillX, "float32"), tf21.fill([68], fillY, "float32")], 1).as2D(1, 136).as1D(); + const getPadding = (batchIdx, cond) => { + const { width, height } = inputDimensions[batchIdx]; + return cond(width, height) ? Math.abs(width - height) / 2 : 0; + }; + const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h); + const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w); + const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize, "float32")).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor( + getPaddingX(batchIdx), + getPaddingY(batchIdx) + )))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor( + inputDimensions[batchIdx].width, + inputDimensions[batchIdx].height + )))); + return landmarkTensors; + }); + } + forwardInput(input) { + return tf21.tidy(() => { + const out = this.runNet(input); + return this.postProcess( + out, + input.inputSize, + input.inputDimensions.map(([height, width]) => ({ height, width })) + ); + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async detectLandmarks(input) { + const netInput = await toNetInput(input); + const landmarkTensors = tf21.tidy( + () => tf21.unstack(this.forwardInput(netInput)) + ); + const landmarksForBatch = await Promise.all(landmarkTensors.map( + async (landmarkTensor, batchIdx) => { + const landmarksArray = Array.from(landmarkTensor.dataSync()); + const xCoords = landmarksArray.filter((_, i) => isEven(i)); + const yCoords = landmarksArray.filter((_, i) => !isEven(i)); + return new FaceLandmarks68( + Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])), + { + height: netInput.getInputHeight(batchIdx), + width: netInput.getInputWidth(batchIdx) + } + ); + } + )); + landmarkTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? landmarksForBatch : landmarksForBatch[0]; + } + getClassifierChannelsOut() { + return 136; + } +}; + +// src/faceLandmarkNet/FaceLandmark68Net.ts +var FaceLandmark68Net = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceLandmark68Net", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_model"; + } + getClassifierChannelsIn() { + return 256; + } +}; + +// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts +var tf22 = __toESM(require_tfjs_esm()); + +// src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts +function extractParamsFromWeightMapTiny(weightMap) { + const paramMappings = []; + const { + extractDenseBlock3Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock3Params("dense0", true), + dense1: extractDenseBlock3Params("dense1"), + dense2: extractDenseBlock3Params("dense2") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceFeatureExtractor/extractParamsTiny.ts +function extractParamsTiny(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock3Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock3Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock3Params(32, 64, "dense1"); + const dense2 = extractDenseBlock3Params(64, 128, "dense2"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { dense0, dense1, dense2 } + }; +} + +// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts +var TinyFaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("TinyFaceFeatureExtractor"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("TinyFaceFeatureExtractor - load model before inference"); + } + return tf22.tidy(() => { + const batchTensor = tf22.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock3(normalized, params.dense0, true); + out = denseBlock3(out, params.dense1); + out = denseBlock3(out, params.dense2); + out = tf22.avgPool(out, [14, 14], [2, 2], "valid"); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "face_feature_extractor_tiny_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMapTiny(weightMap); + } + extractParams(weights) { + return extractParamsTiny(weights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68TinyNet.ts +var FaceLandmark68TinyNet = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) { + super("FaceLandmark68TinyNet", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_tiny_model"; + } + getClassifierChannelsIn() { + return 128; + } +}; + +// src/faceLandmarkNet/index.ts +var FaceLandmarkNet = class extends FaceLandmark68Net { +}; + +// src/faceRecognitionNet/FaceRecognitionNet.ts +var tf27 = __toESM(require_tfjs_esm()); + +// src/faceRecognitionNet/convLayer.ts +var tf24 = __toESM(require_tfjs_esm()); + +// src/faceRecognitionNet/scaleLayer.ts +var tf23 = __toESM(require_tfjs_esm()); +function scale(x, params) { + return tf23.add(tf23.mul(x, params.weights), params.biases); +} + +// src/faceRecognitionNet/convLayer.ts +function convLayer2(x, params, strides, withRelu, padding = "same") { + const { filters, bias } = params.conv; + let out = tf24.conv2d(x, filters, strides, padding); + out = tf24.add(out, bias); + out = scale(out, params.scale); + return withRelu ? tf24.relu(out) : out; +} +function conv2(x, params) { + return convLayer2(x, params, [1, 1], true); +} +function convNoRelu(x, params) { + return convLayer2(x, params, [1, 1], false); +} +function convDown(x, params) { + return convLayer2(x, params, [2, 2], true, "valid"); +} + +// src/faceRecognitionNet/extractParams.ts +var tf25 = __toESM(require_tfjs_esm()); +function extractorsFactory3(extractWeights, paramMappings) { + function extractFilterValues(numFilterValues, numFilters, filterSize) { + const weights = extractWeights(numFilterValues); + const depth = weights.length / (numFilters * filterSize * filterSize); + if (isFloat(depth)) { + throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`); + } + return tf25.tidy( + () => tf25.transpose( + tf25.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), + [2, 3, 1, 0] + ) + ); + } + function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const filters = extractFilterValues(numFilterValues, numFilters, filterSize); + const bias = tf25.tensor1d(extractWeights(numFilters)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { filters, bias }; + } + function extractScaleLayerParams(numWeights, mappedPrefix) { + const weights = tf25.tensor1d(extractWeights(numWeights)); + const biases = tf25.tensor1d(extractWeights(numWeights)); + paramMappings.push( + { paramPath: `${mappedPrefix}/weights` }, + { paramPath: `${mappedPrefix}/biases` } + ); + return { + weights, + biases + }; + } + function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const conv3 = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`); + const scale2 = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`); + return { conv: conv3, scale: scale2 }; + } + function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) { + const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`); + const conv22 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`); + return { conv1, conv2: conv22 }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParams5(weights) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory3(extractWeights, paramMappings); + const conv32_down = extractConvLayerParams(4704, 32, 7, "conv32_down"); + const conv32_1 = extractResidualLayerParams(9216, 32, 3, "conv32_1"); + const conv32_2 = extractResidualLayerParams(9216, 32, 3, "conv32_2"); + const conv32_3 = extractResidualLayerParams(9216, 32, 3, "conv32_3"); + const conv64_down = extractResidualLayerParams(36864, 64, 3, "conv64_down", true); + const conv64_1 = extractResidualLayerParams(36864, 64, 3, "conv64_1"); + const conv64_2 = extractResidualLayerParams(36864, 64, 3, "conv64_2"); + const conv64_3 = extractResidualLayerParams(36864, 64, 3, "conv64_3"); + const conv128_down = extractResidualLayerParams(147456, 128, 3, "conv128_down", true); + const conv128_1 = extractResidualLayerParams(147456, 128, 3, "conv128_1"); + const conv128_2 = extractResidualLayerParams(147456, 128, 3, "conv128_2"); + const conv256_down = extractResidualLayerParams(589824, 256, 3, "conv256_down", true); + const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1"); + const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2"); + const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out"); + const fc = tf25.tidy( + () => tf25.transpose(tf25.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]) + ); + paramMappings.push({ paramPath: "fc" }); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + return { params, paramMappings }; +} + +// src/faceRecognitionNet/extractParamsFromWeightMap.ts +function extractorsFactory4(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractScaleLayerParams(prefix) { + const weights = extractWeightEntry(`${prefix}/scale/weights`, 1); + const biases = extractWeightEntry(`${prefix}/scale/biases`, 1); + return { weights, biases }; + } + function extractConvLayerParams(prefix) { + const filters = extractWeightEntry(`${prefix}/conv/filters`, 4); + const bias = extractWeightEntry(`${prefix}/conv/bias`, 1); + const scale2 = extractScaleLayerParams(prefix); + return { conv: { filters, bias }, scale: scale2 }; + } + function extractResidualLayerParams(prefix) { + return { + conv1: extractConvLayerParams(`${prefix}/conv1`), + conv2: extractConvLayerParams(`${prefix}/conv2`) + }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParamsFromWeightMap5(weightMap) { + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory4(weightMap, paramMappings); + const conv32_down = extractConvLayerParams("conv32_down"); + const conv32_1 = extractResidualLayerParams("conv32_1"); + const conv32_2 = extractResidualLayerParams("conv32_2"); + const conv32_3 = extractResidualLayerParams("conv32_3"); + const conv64_down = extractResidualLayerParams("conv64_down"); + const conv64_1 = extractResidualLayerParams("conv64_1"); + const conv64_2 = extractResidualLayerParams("conv64_2"); + const conv64_3 = extractResidualLayerParams("conv64_3"); + const conv128_down = extractResidualLayerParams("conv128_down"); + const conv128_1 = extractResidualLayerParams("conv128_1"); + const conv128_2 = extractResidualLayerParams("conv128_2"); + const conv256_down = extractResidualLayerParams("conv256_down"); + const conv256_1 = extractResidualLayerParams("conv256_1"); + const conv256_2 = extractResidualLayerParams("conv256_2"); + const conv256_down_out = extractResidualLayerParams("conv256_down_out"); + const { fc } = weightMap; + paramMappings.push({ originalPath: "fc", paramPath: "fc" }); + if (!isTensor2D(fc)) { + throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceRecognitionNet/residualLayer.ts +var tf26 = __toESM(require_tfjs_esm()); +function residual(x, params) { + let out = conv2(x, params.conv1); + out = convNoRelu(out, params.conv2); + out = tf26.add(out, x); + out = tf26.relu(out); + return out; +} +function residualDown(x, params) { + let out = convDown(x, params.conv1); + out = convNoRelu(out, params.conv2); + let pooled = tf26.avgPool(x, 2, 2, "valid"); + const zeros2 = tf26.zeros(pooled.shape); + const isPad = pooled.shape[3] !== out.shape[3]; + const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]; + if (isAdjustShape) { + const padShapeX = [...out.shape]; + padShapeX[1] = 1; + const zerosW = tf26.zeros(padShapeX); + out = tf26.concat([out, zerosW], 1); + const padShapeY = [...out.shape]; + padShapeY[2] = 1; + const zerosH = tf26.zeros(padShapeY); + out = tf26.concat([out, zerosH], 2); + } + pooled = isPad ? tf26.concat([pooled, zeros2], 3) : pooled; + out = tf26.add(pooled, out); + out = tf26.relu(out); + return out; +} + +// src/faceRecognitionNet/FaceRecognitionNet.ts +var FaceRecognitionNet = class extends NeuralNetwork { + constructor() { + super("FaceRecognitionNet"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("FaceRecognitionNet - load model before inference"); + } + return tf27.tidy(() => { + const batchTensor = tf27.cast(input.toBatchTensor(150, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = convDown(normalized, params.conv32_down); + out = tf27.maxPool(out, 3, 2, "valid"); + out = residual(out, params.conv32_1); + out = residual(out, params.conv32_2); + out = residual(out, params.conv32_3); + out = residualDown(out, params.conv64_down); + out = residual(out, params.conv64_1); + out = residual(out, params.conv64_2); + out = residual(out, params.conv64_3); + out = residualDown(out, params.conv128_down); + out = residual(out, params.conv128_1); + out = residual(out, params.conv128_2); + out = residualDown(out, params.conv256_down); + out = residual(out, params.conv256_1); + out = residual(out, params.conv256_2); + out = residualDown(out, params.conv256_down_out); + const globalAvg = out.mean([1, 2]); + const fullyConnected = tf27.matMul(globalAvg, params.fc); + return fullyConnected; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async computeFaceDescriptor(input) { + var _a; + if ((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a.some((dim) => dim <= 0)) + return new Float32Array(128); + const netInput = await toNetInput(input); + const faceDescriptorTensors = tf27.tidy(() => tf27.unstack(this.forwardInput(netInput))); + const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())); + faceDescriptorTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0]; + } + getDefaultModelName() { + return "face_recognition_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap5(weightMap); + } + extractParams(weights) { + return extractParams5(weights); + } +}; + +// src/faceRecognitionNet/index.ts +function createFaceRecognitionNet(weights) { + const net = new FaceRecognitionNet(); + net.extractWeights(weights); + return net; +} + +// src/factories/WithFaceDescriptor.ts +function extendWithFaceDescriptor(sourceObj, descriptor) { + const extension = { descriptor }; + return { ...sourceObj, ...extension }; +} + +// src/factories/WithAge.ts +function isWithAge(obj) { + return typeof obj.age === "number"; +} +function extendWithAge(sourceObj, age) { + const extension = { age }; + return { ...sourceObj, ...extension }; +} + +// src/factories/WithGender.ts +function isWithGender(obj) { + return (obj.gender === "male" /* MALE */ || obj.gender === "female" /* FEMALE */) && isValidProbablitiy(obj.genderProbability); +} +function extendWithGender(sourceObj, gender, genderProbability) { + const extension = { gender, genderProbability }; + return { ...sourceObj, ...extension }; +} + +// src/ssdMobilenetv1/SsdMobilenetv1.ts +var tf34 = __toESM(require_tfjs_esm()); + +// src/ssdMobilenetv1/extractParams.ts +var tf28 = __toESM(require_tfjs_esm()); +function extractorsFactory5(extractWeights, paramMappings) { + function extractDepthwiseConvParams(numChannels, mappedPrefix) { + const filters = tf28.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]); + const batch_norm_scale = tf28.tensor1d(extractWeights(numChannels)); + const batch_norm_offset = tf28.tensor1d(extractWeights(numChannels)); + const batch_norm_mean = tf28.tensor1d(extractWeights(numChannels)); + const batch_norm_variance = tf28.tensor1d(extractWeights(numChannels)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/batch_norm_scale` }, + { paramPath: `${mappedPrefix}/batch_norm_offset` }, + { paramPath: `${mappedPrefix}/batch_norm_mean` }, + { paramPath: `${mappedPrefix}/batch_norm_variance` } + ); + return { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }; + } + function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) { + const filters = tf28.tensor4d( + extractWeights(channelsIn * channelsOut * filterSize * filterSize), + [filterSize, filterSize, channelsIn, channelsOut] + ); + const bias = tf28.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}` } + ); + return { filters, bias }; + } + function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) { + const { + filters, + bias + } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true); + return { + filters, + batch_norm_offset: bias + }; + } + function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) { + const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`); + const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`); + return { depthwise_conv, pointwise_conv }; + } + function extractMobilenetV1Params() { + const conv_0 = extractPointwiseConvParams(3, 32, 3, "mobilenetv1/conv_0"); + const conv_1 = extractConvPairParams(32, 64, "mobilenetv1/conv_1"); + const conv_2 = extractConvPairParams(64, 128, "mobilenetv1/conv_2"); + const conv_3 = extractConvPairParams(128, 128, "mobilenetv1/conv_3"); + const conv_4 = extractConvPairParams(128, 256, "mobilenetv1/conv_4"); + const conv_5 = extractConvPairParams(256, 256, "mobilenetv1/conv_5"); + const conv_6 = extractConvPairParams(256, 512, "mobilenetv1/conv_6"); + const conv_7 = extractConvPairParams(512, 512, "mobilenetv1/conv_7"); + const conv_8 = extractConvPairParams(512, 512, "mobilenetv1/conv_8"); + const conv_9 = extractConvPairParams(512, 512, "mobilenetv1/conv_9"); + const conv_10 = extractConvPairParams(512, 512, "mobilenetv1/conv_10"); + const conv_11 = extractConvPairParams(512, 512, "mobilenetv1/conv_11"); + const conv_12 = extractConvPairParams(512, 1024, "mobilenetv1/conv_12"); + const conv_13 = extractConvPairParams(1024, 1024, "mobilenetv1/conv_13"); + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + conv_8, + conv_9, + conv_10, + conv_11, + conv_12, + conv_13 + }; + } + function extractPredictionLayerParams() { + const conv_0 = extractPointwiseConvParams(1024, 256, 1, "prediction_layer/conv_0"); + const conv_1 = extractPointwiseConvParams(256, 512, 3, "prediction_layer/conv_1"); + const conv_2 = extractPointwiseConvParams(512, 128, 1, "prediction_layer/conv_2"); + const conv_3 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_3"); + const conv_4 = extractPointwiseConvParams(256, 128, 1, "prediction_layer/conv_4"); + const conv_5 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_5"); + const conv_6 = extractPointwiseConvParams(256, 64, 1, "prediction_layer/conv_6"); + const conv_7 = extractPointwiseConvParams(64, 128, 3, "prediction_layer/conv_7"); + const box_encoding_0_predictor = extractConvParams(512, 12, 1, "prediction_layer/box_predictor_0/box_encoding_predictor"); + const class_predictor_0 = extractConvParams(512, 9, 1, "prediction_layer/box_predictor_0/class_predictor"); + const box_encoding_1_predictor = extractConvParams(1024, 24, 1, "prediction_layer/box_predictor_1/box_encoding_predictor"); + const class_predictor_1 = extractConvParams(1024, 18, 1, "prediction_layer/box_predictor_1/class_predictor"); + const box_encoding_2_predictor = extractConvParams(512, 24, 1, "prediction_layer/box_predictor_2/box_encoding_predictor"); + const class_predictor_2 = extractConvParams(512, 18, 1, "prediction_layer/box_predictor_2/class_predictor"); + const box_encoding_3_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_3/box_encoding_predictor"); + const class_predictor_3 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_3/class_predictor"); + const box_encoding_4_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_4/box_encoding_predictor"); + const class_predictor_4 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_4/class_predictor"); + const box_encoding_5_predictor = extractConvParams(128, 24, 1, "prediction_layer/box_predictor_5/box_encoding_predictor"); + const class_predictor_5 = extractConvParams(128, 18, 1, "prediction_layer/box_predictor_5/class_predictor"); + const box_predictor_0 = { + box_encoding_predictor: box_encoding_0_predictor, + class_predictor: class_predictor_0 + }; + const box_predictor_1 = { + box_encoding_predictor: box_encoding_1_predictor, + class_predictor: class_predictor_1 + }; + const box_predictor_2 = { + box_encoding_predictor: box_encoding_2_predictor, + class_predictor: class_predictor_2 + }; + const box_predictor_3 = { + box_encoding_predictor: box_encoding_3_predictor, + class_predictor: class_predictor_3 + }; + const box_predictor_4 = { + box_encoding_predictor: box_encoding_4_predictor, + class_predictor: class_predictor_4 + }; + const box_predictor_5 = { + box_encoding_predictor: box_encoding_5_predictor, + class_predictor: class_predictor_5 + }; + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + box_predictor_0, + box_predictor_1, + box_predictor_2, + box_predictor_3, + box_predictor_4, + box_predictor_5 + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParams6(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory5(extractWeights, paramMappings); + const mobilenetv1 = extractMobilenetV1Params(); + const prediction_layer = extractPredictionLayerParams(); + const extra_dim = tf28.tensor3d( + extractWeights(5118 * 4), + [1, 5118, 4] + ); + const output_layer = { + extra_dim + }; + paramMappings.push({ paramPath: "output_layer/extra_dim" }); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + params: { + mobilenetv1, + prediction_layer, + output_layer + }, + paramMappings + }; +} + +// src/ssdMobilenetv1/extractParamsFromWeightMap.ts +function extractorsFactory6(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractPointwiseConvParams(prefix, idx, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`); + const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`); + return { filters, batch_norm_offset }; + } + function extractConvPairParams(idx) { + const mappedPrefix = `mobilenetv1/conv_${idx}`; + const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`; + const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`; + const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`; + const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`); + const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`); + const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`); + const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`); + const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`); + return { + depthwise_conv: { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }, + pointwise_conv: extractPointwiseConvParams("MobilenetV1", idx, mappedPrefixPointwiseConv) + }; + } + function extractMobilenetV1Params() { + return { + conv_0: extractPointwiseConvParams("MobilenetV1", 0, "mobilenetv1/conv_0"), + conv_1: extractConvPairParams(1), + conv_2: extractConvPairParams(2), + conv_3: extractConvPairParams(3), + conv_4: extractConvPairParams(4), + conv_5: extractConvPairParams(5), + conv_6: extractConvPairParams(6), + conv_7: extractConvPairParams(7), + conv_8: extractConvPairParams(8), + conv_9: extractConvPairParams(9), + conv_10: extractConvPairParams(10), + conv_11: extractConvPairParams(11), + conv_12: extractConvPairParams(12), + conv_13: extractConvPairParams(13) + }; + } + function extractConvParams(prefix, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`); + const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`); + return { filters, bias }; + } + function extractBoxPredictorParams(idx) { + const box_encoding_predictor = extractConvParams( + `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, + `prediction_layer/box_predictor_${idx}/box_encoding_predictor` + ); + const class_predictor = extractConvParams( + `Prediction/BoxPredictor_${idx}/ClassPredictor`, + `prediction_layer/box_predictor_${idx}/class_predictor` + ); + return { box_encoding_predictor, class_predictor }; + } + function extractPredictionLayerParams() { + return { + conv_0: extractPointwiseConvParams("Prediction", 0, "prediction_layer/conv_0"), + conv_1: extractPointwiseConvParams("Prediction", 1, "prediction_layer/conv_1"), + conv_2: extractPointwiseConvParams("Prediction", 2, "prediction_layer/conv_2"), + conv_3: extractPointwiseConvParams("Prediction", 3, "prediction_layer/conv_3"), + conv_4: extractPointwiseConvParams("Prediction", 4, "prediction_layer/conv_4"), + conv_5: extractPointwiseConvParams("Prediction", 5, "prediction_layer/conv_5"), + conv_6: extractPointwiseConvParams("Prediction", 6, "prediction_layer/conv_6"), + conv_7: extractPointwiseConvParams("Prediction", 7, "prediction_layer/conv_7"), + box_predictor_0: extractBoxPredictorParams(0), + box_predictor_1: extractBoxPredictorParams(1), + box_predictor_2: extractBoxPredictorParams(2), + box_predictor_3: extractBoxPredictorParams(3), + box_predictor_4: extractBoxPredictorParams(4), + box_predictor_5: extractBoxPredictorParams(5) + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParamsFromWeightMap6(weightMap) { + const paramMappings = []; + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory6(weightMap, paramMappings); + const extra_dim = weightMap["Output/extra_dim"]; + paramMappings.push({ originalPath: "Output/extra_dim", paramPath: "output_layer/extra_dim" }); + if (!isTensor3D(extra_dim)) { + throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`); + } + const params = { + mobilenetv1: extractMobilenetV1Params(), + prediction_layer: extractPredictionLayerParams(), + output_layer: { + extra_dim + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/ssdMobilenetv1/mobileNetV1.ts +var tf30 = __toESM(require_tfjs_esm()); + +// src/ssdMobilenetv1/pointwiseConvLayer.ts +var tf29 = __toESM(require_tfjs_esm()); +function pointwiseConvLayer(x, params, strides) { + return tf29.tidy(() => { + let out = tf29.conv2d(x, params.filters, strides, "same"); + out = tf29.add(out, params.batch_norm_offset); + return tf29.clipByValue(out, 0, 6); + }); +} + +// src/ssdMobilenetv1/mobileNetV1.ts +var epsilon = 0.0010000000474974513; +function depthwiseConvLayer(x, params, strides) { + return tf30.tidy(() => { + let out = tf30.depthwiseConv2d(x, params.filters, strides, "same"); + out = tf30.batchNorm( + out, + params.batch_norm_mean, + params.batch_norm_variance, + params.batch_norm_offset, + params.batch_norm_scale, + epsilon + ); + return tf30.clipByValue(out, 0, 6); + }); +} +function getStridesForLayerIdx(layerIdx) { + return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1]; +} +function mobileNetV1(x, params) { + return tf30.tidy(() => { + let conv11; + let out = pointwiseConvLayer(x, params.conv_0, [2, 2]); + const convPairParams = [ + params.conv_1, + params.conv_2, + params.conv_3, + params.conv_4, + params.conv_5, + params.conv_6, + params.conv_7, + params.conv_8, + params.conv_9, + params.conv_10, + params.conv_11, + params.conv_12, + params.conv_13 + ]; + convPairParams.forEach((param, i) => { + const layerIdx = i + 1; + const depthwiseConvStrides = getStridesForLayerIdx(layerIdx); + out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides); + out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]); + if (layerIdx === 11) + conv11 = out; + }); + if (conv11 === null) { + throw new Error("mobileNetV1 - output of conv layer 11 is null"); + } + return { + out, + conv11 + }; + }); +} + +// src/ssdMobilenetv1/nonMaxSuppression.ts +function IOU(boxes, i, j) { + const boxesData = boxes.arraySync(); + const yminI = Math.min(boxesData[i][0], boxesData[i][2]); + const xminI = Math.min(boxesData[i][1], boxesData[i][3]); + const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]); + const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]); + const yminJ = Math.min(boxesData[j][0], boxesData[j][2]); + const xminJ = Math.min(boxesData[j][1], boxesData[j][3]); + const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]); + const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]); + const areaI = (ymaxI - yminI) * (xmaxI - xminI); + const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); + if (areaI <= 0 || areaJ <= 0) + return 0; + const intersectionYmin = Math.max(yminI, yminJ); + const intersectionXmin = Math.max(xminI, xminJ); + const intersectionYmax = Math.min(ymaxI, ymaxJ); + const intersectionXmax = Math.min(xmaxI, xmaxJ); + const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0); + return intersectionArea / (areaI + areaJ - intersectionArea); +} +function nonMaxSuppression2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + const numBoxes = boxes.shape[0]; + const outputSize = Math.min(maxOutputSize, numBoxes); + const candidates = scores.map((score, boxIndex) => ({ score, boxIndex })).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score); + const suppressFunc = (x) => x <= iouThreshold ? 1 : 0; + const selected = []; + candidates.forEach((c) => { + if (selected.length >= outputSize) + return; + const originalScore = c.score; + for (let j = selected.length - 1; j >= 0; --j) { + const iou2 = IOU(boxes, c.boxIndex, selected[j]); + if (iou2 === 0) + continue; + c.score *= suppressFunc(iou2); + if (c.score <= scoreThreshold) + break; + } + if (originalScore === c.score) { + selected.push(c.boxIndex); + } + }); + return selected; +} + +// src/ssdMobilenetv1/outputLayer.ts +var tf31 = __toESM(require_tfjs_esm()); +function getCenterCoordinatesAndSizesLayer(x) { + const vec = tf31.unstack(tf31.transpose(x, [1, 0])); + const sizes = [ + tf31.sub(vec[2], vec[0]), + tf31.sub(vec[3], vec[1]) + ]; + const centers = [ + tf31.add(vec[0], tf31.div(sizes[0], 2)), + tf31.add(vec[1], tf31.div(sizes[1], 2)) + ]; + return { sizes, centers }; +} +function decodeBoxesLayer(x0, x1) { + const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0); + const vec = tf31.unstack(tf31.transpose(x1, [1, 0])); + const div0_out = tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2], 5)), sizes[0]), 2); + const add0_out = tf31.add(tf31.mul(tf31.div(vec[0], 10), sizes[0]), centers[0]); + const div1_out = tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3], 5)), sizes[1]), 2); + const add1_out = tf31.add(tf31.mul(tf31.div(vec[1], 10), sizes[1]), centers[1]); + return tf31.transpose( + tf31.stack([ + tf31.sub(add0_out, div0_out), + tf31.sub(add1_out, div1_out), + tf31.add(add0_out, div0_out), + tf31.add(add1_out, div1_out) + ]), + [1, 0] + ); +} +function outputLayer(boxPredictions, classPredictions, params) { + return tf31.tidy(() => { + const batchSize = boxPredictions.shape[0]; + let boxes = decodeBoxesLayer( + tf31.reshape(tf31.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), + tf31.reshape(boxPredictions, [-1, 4]) + ); + boxes = tf31.reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]); + const scoresAndClasses = tf31.sigmoid(tf31.slice(classPredictions, [0, 0, 1], [-1, -1, -1])); + let scores = tf31.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]); + scores = tf31.reshape(scores, [batchSize, scores.shape[1]]); + const boxesByBatch = tf31.unstack(boxes); + const scoresByBatch = tf31.unstack(scores); + return { boxes: boxesByBatch, scores: scoresByBatch }; + }); +} + +// src/ssdMobilenetv1/predictionLayer.ts +var tf33 = __toESM(require_tfjs_esm()); + +// src/ssdMobilenetv1/boxPredictionLayer.ts +var tf32 = __toESM(require_tfjs_esm()); +function boxPredictionLayer(x, params) { + return tf32.tidy(() => { + const batchSize = x.shape[0]; + const boxPredictionEncoding = tf32.reshape( + convLayer(x, params.box_encoding_predictor), + [batchSize, -1, 1, 4] + ); + const classPrediction = tf32.reshape( + convLayer(x, params.class_predictor), + [batchSize, -1, 3] + ); + return { boxPredictionEncoding, classPrediction }; + }); +} + +// src/ssdMobilenetv1/predictionLayer.ts +function predictionLayer(x, conv11, params) { + return tf33.tidy(() => { + const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]); + const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]); + const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]); + const conv3 = pointwiseConvLayer(conv22, params.conv_3, [2, 2]); + const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]); + const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]); + const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]); + const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]); + const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0); + const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1); + const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2); + const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3); + const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4); + const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5); + const boxPredictions = tf33.concat([ + boxPrediction0.boxPredictionEncoding, + boxPrediction1.boxPredictionEncoding, + boxPrediction2.boxPredictionEncoding, + boxPrediction3.boxPredictionEncoding, + boxPrediction4.boxPredictionEncoding, + boxPrediction5.boxPredictionEncoding + ], 1); + const classPredictions = tf33.concat([ + boxPrediction0.classPrediction, + boxPrediction1.classPrediction, + boxPrediction2.classPrediction, + boxPrediction3.classPrediction, + boxPrediction4.classPrediction, + boxPrediction5.classPrediction + ], 1); + return { + boxPredictions, + classPredictions + }; + }); +} + +// src/ssdMobilenetv1/SsdMobilenetv1Options.ts +var SsdMobilenetv1Options = class { + constructor({ minConfidence, maxResults } = {}) { + this._name = "SsdMobilenetv1Options"; + this._minConfidence = minConfidence || 0.5; + this._maxResults = maxResults || 100; + if (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; + } +}; + +// src/ssdMobilenetv1/SsdMobilenetv1.ts +var SsdMobilenetv1 = class extends NeuralNetwork { + constructor() { + super("SsdMobilenetv1"); + } + forwardInput(input) { + const { params } = this; + if (!params) + throw new Error("SsdMobilenetv1 - load model before inference"); + return tf34.tidy(() => { + const batchTensor = tf34.cast(input.toBatchTensor(512, false), "float32"); + const x = tf34.sub(tf34.div(batchTensor, 127.5), 1); + const features = mobileNetV1(x, params.mobilenetv1); + const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer); + return outputLayer(boxPredictions, classPredictions, params.output_layer); + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async locateFaces(input, options = {}) { + const { maxResults, minConfidence } = new SsdMobilenetv1Options(options); + const netInput = await toNetInput(input); + const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput); + const boxes = _boxes[0]; + const scores = _scores[0]; + for (let i = 1; i < _boxes.length; i++) { + _boxes[i].dispose(); + _scores[i].dispose(); + } + const scoresData = Array.from(scores.dataSync()); + const iouThreshold = 0.5; + const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence); + const reshapedDims = netInput.getReshapedInputDimensions(0); + const inputSize = netInput.inputSize; + const padX = inputSize / reshapedDims.width; + const padY = inputSize / reshapedDims.height; + const boxesData = boxes.arraySync(); + const results = indices.map((idx) => { + const [top, bottom] = [ + Math.max(0, boxesData[idx][0]), + Math.min(1, boxesData[idx][2]) + ].map((val) => val * padY); + const [left, right] = [ + Math.max(0, boxesData[idx][1]), + Math.min(1, boxesData[idx][3]) + ].map((val) => val * padX); + return new FaceDetection( + scoresData[idx], + new Rect(left, top, right - left, bottom - top), + { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) } + ); + }); + boxes.dispose(); + scores.dispose(); + return results; + } + getDefaultModelName() { + return "ssd_mobilenetv1_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap6(weightMap); + } + extractParams(weights) { + return extractParams6(weights); + } +}; + +// src/ssdMobilenetv1/index.ts +function createSsdMobilenetv1(weights) { + const net = new SsdMobilenetv1(); + net.extractWeights(weights); + return net; +} +function createFaceDetectionNet(weights) { + return createSsdMobilenetv1(weights); +} +var FaceDetectionNet = class extends SsdMobilenetv1 { +}; + +// src/tinyYolov2/const.ts +var IOU_THRESHOLD = 0.4; +var BOX_ANCHORS = [ + new Point(0.738768, 0.874946), + new Point(2.42204, 2.65704), + new Point(4.30971, 7.04493), + new Point(10.246, 4.59428), + new Point(12.6868, 11.8741) +]; +var BOX_ANCHORS_SEPARABLE = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404]; +var DEFAULT_MODEL_NAME = "tiny_yolov2_model"; +var DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model"; + +// src/tinyYolov2/TinyYolov2Base.ts +var tf39 = __toESM(require_tfjs_esm()); + +// src/tinyYolov2/config.ts +var isNumber = (arg) => typeof arg === "number"; +function validateConfig(config) { + if (!config) { + throw new Error(`invalid config: ${config}`); + } + if (typeof config.withSeparableConvs !== "boolean") { + throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`); + } + if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1) { + throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`); + } + if (!Array.isArray(config.classes) || !config.classes.length || !config.classes.every((c) => typeof c === "string")) { + throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`); + } + if (!Array.isArray(config.anchors) || !config.anchors.length || !config.anchors.map((a) => a || {}).every((a) => isNumber(a.x) && isNumber(a.y))) { + throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`); + } + if (config.meanRgb && (!Array.isArray(config.meanRgb) || config.meanRgb.length !== 3 || !config.meanRgb.every(isNumber))) { + throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`); + } +} + +// src/tinyYolov2/convWithBatchNorm.ts +var tf36 = __toESM(require_tfjs_esm()); + +// src/tinyYolov2/leaky.ts +var tf35 = __toESM(require_tfjs_esm()); +function leaky(x) { + return tf35.tidy(() => { + const min = tf35.mul(x, tf35.scalar(0.10000000149011612)); + return tf35.add(tf35.relu(tf35.sub(x, min)), min); + }); +} + +// src/tinyYolov2/convWithBatchNorm.ts +function convWithBatchNorm(x, params) { + return tf36.tidy(() => { + let out = tf36.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = tf36.conv2d(out, params.conv.filters, [1, 1], "valid"); + out = tf36.sub(out, params.bn.sub); + out = tf36.mul(out, params.bn.truediv); + out = tf36.add(out, params.conv.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/depthwiseSeparableConv.ts +var tf37 = __toESM(require_tfjs_esm()); +function depthwiseSeparableConv2(x, params) { + return tf37.tidy(() => { + let out = tf37.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = tf37.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid"); + out = tf37.add(out, params.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/extractParams.ts +var tf38 = __toESM(require_tfjs_esm()); +function extractorsFactory7(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + function extractBatchNormParams(size, mappedPrefix) { + const sub6 = tf38.tensor1d(extractWeights(size)); + const truediv = tf38.tensor1d(extractWeights(size)); + paramMappings.push( + { paramPath: `${mappedPrefix}/sub` }, + { paramPath: `${mappedPrefix}/truediv` } + ); + return { sub: sub6, truediv }; + } + function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) { + const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`); + const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`); + return { conv: conv3, bn }; + } + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParams7(weights, config, boxEncodingSize, filterSizes) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory7(extractWeights, paramMappings); + let params; + if (config.withSeparableConvs) { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = config.isFirstLayerConv2d ? extractConvParams(s0, s1, 3, "conv0") : extractSeparableConvParams(s0, s1, "conv0"); + const conv1 = extractSeparableConvParams(s1, s2, "conv1"); + const conv22 = extractSeparableConvParams(s2, s3, "conv2"); + const conv3 = extractSeparableConvParams(s3, s4, "conv3"); + const conv4 = extractSeparableConvParams(s4, s5, "conv4"); + const conv5 = extractSeparableConvParams(s5, s6, "conv5"); + const conv6 = s7 ? extractSeparableConvParams(s6, s7, "conv6") : void 0; + const conv7 = s8 ? extractSeparableConvParams(s7, s8, "conv7") : void 0; + const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } else { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = extractConvWithBatchNormParams(s0, s1, "conv0"); + const conv1 = extractConvWithBatchNormParams(s1, s2, "conv1"); + const conv22 = extractConvWithBatchNormParams(s2, s3, "conv2"); + const conv3 = extractConvWithBatchNormParams(s3, s4, "conv3"); + const conv4 = extractConvWithBatchNormParams(s4, s5, "conv4"); + const conv5 = extractConvWithBatchNormParams(s5, s6, "conv5"); + const conv6 = extractConvWithBatchNormParams(s6, s7, "conv6"); + const conv7 = extractConvWithBatchNormParams(s7, s8, "conv7"); + const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { params, paramMappings }; +} + +// src/tinyYolov2/extractParamsFromWeightMap.ts +function extractorsFactory8(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractBatchNormParams(prefix) { + const sub6 = extractWeightEntry(`${prefix}/sub`, 1); + const truediv = extractWeightEntry(`${prefix}/truediv`, 1); + return { sub: sub6, truediv }; + } + function extractConvParams(prefix) { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { filters, bias }; + } + function extractConvWithBatchNormParams(prefix) { + const conv3 = extractConvParams(`${prefix}/conv`); + const bn = extractBatchNormParams(`${prefix}/bn`); + return { conv: conv3, bn }; + } + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParamsFromWeightMap7(weightMap, config) { + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory8(weightMap, paramMappings); + let params; + if (config.withSeparableConvs) { + const numFilters = config.filterSizes && config.filterSizes.length || 9; + params = { + conv0: config.isFirstLayerConv2d ? extractConvParams("conv0") : extractSeparableConvParams("conv0"), + conv1: extractSeparableConvParams("conv1"), + conv2: extractSeparableConvParams("conv2"), + conv3: extractSeparableConvParams("conv3"), + conv4: extractSeparableConvParams("conv4"), + conv5: extractSeparableConvParams("conv5"), + conv6: numFilters > 7 ? extractSeparableConvParams("conv6") : void 0, + conv7: numFilters > 8 ? extractSeparableConvParams("conv7") : void 0, + conv8: extractConvParams("conv8") + }; + } else { + params = { + conv0: extractConvWithBatchNormParams("conv0"), + conv1: extractConvWithBatchNormParams("conv1"), + conv2: extractConvWithBatchNormParams("conv2"), + conv3: extractConvWithBatchNormParams("conv3"), + conv4: extractConvWithBatchNormParams("conv4"), + conv5: extractConvWithBatchNormParams("conv5"), + conv6: extractConvWithBatchNormParams("conv6"), + conv7: extractConvWithBatchNormParams("conv7"), + conv8: extractConvParams("conv8") + }; + } + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/tinyYolov2/TinyYolov2Options.ts +var TinyYolov2Options = class { + constructor({ inputSize, scoreThreshold } = {}) { + this._name = "TinyYolov2Options"; + this._inputSize = inputSize || 416; + this._scoreThreshold = scoreThreshold || 0.5; + if (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; + } +}; + +// src/tinyYolov2/TinyYolov2Base.ts +var _TinyYolov2Base = class _TinyYolov2Base extends NeuralNetwork { + constructor(config) { + super("TinyYolov2"); + validateConfig(config); + this._config = config; + } + 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(x, params) { + let out = convWithBatchNorm(x, params.conv0); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv1); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv2); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv3); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv4); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv5); + out = tf39.maxPool(out, [2, 2], [1, 1], "same"); + out = convWithBatchNorm(out, params.conv6); + out = convWithBatchNorm(out, params.conv7); + return convLayer(out, params.conv8, "valid", false); + } + runMobilenet(x, params) { + let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv2(x, params.conv0); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv1); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv2); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv3); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv4); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv5); + out = tf39.maxPool(out, [2, 2], [1, 1], "same"); + out = params.conv6 ? depthwiseSeparableConv2(out, params.conv6) : out; + out = params.conv7 ? depthwiseSeparableConv2(out, params.conv7) : out; + return convLayer(out, params.conv8, "valid", false); + } + forwardInput(input, inputSize) { + const { params } = this; + if (!params) { + throw new Error("TinyYolov2 - load model before inference"); + } + return tf39.tidy(() => { + let batchTensor = tf39.cast(input.toBatchTensor(inputSize, false), "float32"); + batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor; + batchTensor = batchTensor.div(255); + return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params); + }); + } + async forward(input, inputSize) { + return this.forwardInput(await toNetInput(input), inputSize); + } + async detect(input, forwardParams = {}) { + const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams); + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput, inputSize); + const out0 = tf39.tidy(() => tf39.unstack(out)[0].expandDims()); + const inputDimensions = { + width: netInput.getInputWidth(0), + height: netInput.getInputHeight(0) + }; + const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold); + out.dispose(); + out0.dispose(); + const boxes = results.map((res) => res.box); + const scores = results.map((res) => res.score); + const classScores = results.map((res) => res.classScore); + const classNames = results.map((res) => this.config.classes[res.label]); + const indices = nonMaxSuppression( + boxes.map((box) => box.rescale(inputSize)), + scores, + this.config.iouThreshold, + true + ); + const detections = indices.map((idx) => new ObjectDetection( + scores[idx], + classScores[idx], + classNames[idx], + boxes[idx], + inputDimensions + )); + return detections; + } + getDefaultModelName() { + return ""; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap7(weightMap, this.config); + } + extractParams(weights) { + const filterSizes = this.config.filterSizes || _TinyYolov2Base.DEFAULT_FILTER_SIZES; + const numFilters = filterSizes ? filterSizes.length : void 0; + if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) { + throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`); + } + return extractParams7(weights, this.config, this.boxEncodingSize, filterSizes); + } + async extractBoxes(outputTensor, inputBlobDimensions, scoreThreshold) { + const { width, height } = inputBlobDimensions; + const inputSize = Math.max(width, height); + const correctionFactorX = inputSize / width; + const correctionFactorY = inputSize / height; + const numCells = outputTensor.shape[1]; + const numBoxes = this.config.anchors.length; + const [boxesTensor, scoresTensor, classScoresTensor] = tf39.tidy(() => { + const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]); + const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]); + const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]); + const classScores = this.withClassScores ? tf39.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : tf39.scalar(0); + return [boxes, scores, classScores]; + }); + const results = []; + const scoresData = await scoresTensor.array(); + const boxesData = await boxesTensor.array(); + for (let row = 0; row < numCells; row++) { + for (let col = 0; col < numCells; col++) { + for (let anchor = 0; anchor < numBoxes; anchor++) { + const score = sigmoid(scoresData[row][col][anchor][0]); + if (!scoreThreshold || score > scoreThreshold) { + const ctX = (col + sigmoid(boxesData[row][col][anchor][0])) / numCells * correctionFactorX; + const ctY = (row + sigmoid(boxesData[row][col][anchor][1])) / numCells * correctionFactorY; + const widthLocal = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX; + const heightLocal = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY; + const x = ctX - widthLocal / 2; + const y = ctY - heightLocal / 2; + const pos = { row, col, anchor }; + const { classScore, label } = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : { classScore: 1, label: 0 }; + results.push({ + box: new BoundingBox(x, y, x + widthLocal, y + heightLocal), + score, + classScore: score * classScore, + label, + ...pos + }); + } + } + } + } + boxesTensor.dispose(); + scoresTensor.dispose(); + classScoresTensor.dispose(); + return results; + } + async extractPredictedClass(classesTensor, pos) { + const { row, col, anchor } = pos; + const classesData = await classesTensor.array(); + return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({ + classScore, + label + })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr); + } +}; +_TinyYolov2Base.DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024]; +var TinyYolov2Base = _TinyYolov2Base; + +// src/tinyYolov2/TinyYolov2.ts +var TinyYolov2 = class extends TinyYolov2Base { + constructor(withSeparableConvs = true) { + const config = { + withSeparableConvs, + iouThreshold: IOU_THRESHOLD, + classes: ["face"], + ...withSeparableConvs ? { + anchors: BOX_ANCHORS_SEPARABLE, + meanRgb: MEAN_RGB_SEPARABLE + } : { + anchors: BOX_ANCHORS, + withClassScores: true + } + }; + super(config); + } + get withSeparableConvs() { + return this.config.withSeparableConvs; + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input, forwardParams) { + const objectDetections = await this.detect(input, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight })); + } + getDefaultModelName() { + return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/tinyYolov2/index.ts +function createTinyYolov2(weights, withSeparableConvs = true) { + const net = new TinyYolov2(withSeparableConvs); + net.extractWeights(weights); + return net; +} + +// src/tinyFaceDetector/TinyFaceDetectorOptions.ts +var TinyFaceDetectorOptions = class extends TinyYolov2Options { + constructor() { + super(...arguments); + this._name = "TinyFaceDetectorOptions"; + } +}; + +// src/globalApi/ComposableTask.ts +var ComposableTask = class { + // eslint-disable-next-line no-unused-vars + async then(onfulfilled) { + return onfulfilled(await this.run()); + } + async run() { + throw new Error("ComposableTask - run is not implemented"); + } +}; + +// src/globalApi/DetectFaceLandmarksTasks.ts +var tf41 = __toESM(require_tfjs_esm()); + +// src/globalApi/extractFacesAndComputeResults.ts +var tf40 = __toESM(require_tfjs_esm()); +async function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment = ({ alignedRect }) => alignedRect) { + const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection); + const faces = extractedFaces || (input instanceof tf40.Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes)); + const results = await computeResults(faces); + faces.forEach((f) => f instanceof tf40.Tensor && f.dispose()); + return results; +} +async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) { + return extractAllFacesAndComputeResults( + [parentResult], + input, + async (faces) => computeResult(faces[0]), + extractedFaces, + getRectForAlignment + ); +} + +// src/tinyFaceDetector/const.ts +var IOU_THRESHOLD2 = 0.4; +var BOX_ANCHORS2 = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB = [117.001, 114.697, 97.404]; + +// src/tinyFaceDetector/TinyFaceDetector.ts +var TinyFaceDetector = class extends TinyYolov2Base { + constructor() { + const config = { + withSeparableConvs: true, + iouThreshold: IOU_THRESHOLD2, + classes: ["face"], + anchors: BOX_ANCHORS2, + meanRgb: MEAN_RGB, + isFirstLayerConv2d: true, + filterSizes: [3, 16, 32, 64, 128, 256, 512] + }; + super(config); + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input, forwardParams) { + const objectDetections = await this.detect(input, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight })); + } + getDefaultModelName() { + return "tiny_face_detector_model"; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/globalApi/nets.ts +var nets = { + ssdMobilenetv1: new SsdMobilenetv1(), + tinyFaceDetector: new TinyFaceDetector(), + tinyYolov2: new TinyYolov2(), + faceLandmark68Net: new FaceLandmark68Net(), + faceLandmark68TinyNet: new FaceLandmark68TinyNet(), + faceRecognitionNet: new FaceRecognitionNet(), + faceExpressionNet: new FaceExpressionNet(), + ageGenderNet: new AgeGenderNet() +}; +var ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options); +var tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options); +var tinyYolov2 = (input, options) => nets.tinyYolov2.locateFaces(input, options); +var detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input); +var detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input); +var computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input); +var recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input); +var predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input); +var loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url); +var loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url); +var loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url); +var loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url); +var loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url); +var loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url); +var loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url); +var loadAgeGenderModel = (url) => nets.ageGenderNet.load(url); +var loadFaceDetectionModel = loadSsdMobilenetv1Model; +var locateFaces = ssdMobilenetv1; +var detectLandmarks = detectFaceLandmarks; + +// src/globalApi/PredictFaceExpressionsTask.ts +var PredictFaceExpressionsTaskBase = class extends ComposableTask { + constructor(parentTask, input, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResults = await this.parentTask; + const faceExpressionsByFace = await extractAllFacesAndComputeResults( + parentResults, + this.input, + async (faces) => Promise.all( + faces.map((face) => nets.faceExpressionNet.predictExpressions(face)) + ), + this.extractedFaces + ); + return parentResults.map( + (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]) + ); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const faceExpressions = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.faceExpressionNet.predictExpressions(face), + this.extractedFaces + ); + return extendWithFaceExpressions(parentResult, faceExpressions); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask(this, this.input); + } +}; +var PredictAllFaceExpressionsWithFaceAlignmentTask = class extends PredictAllFaceExpressionsTask { + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsWithFaceAlignmentTask = class extends PredictSingleFaceExpressionsTask { + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/PredictAgeAndGenderTask.ts +var PredictAgeAndGenderTaskBase = class extends ComposableTask { + constructor(parentTask, input, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResults = await this.parentTask; + const ageAndGenderByFace = await extractAllFacesAndComputeResults( + parentResults, + this.input, + async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face))), + this.extractedFaces + ); + return parentResults.map((parentResult, i) => { + const { age, gender, genderProbability } = ageAndGenderByFace[i]; + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + }); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.ageGenderNet.predictAgeAndGender(face), + this.extractedFaces + ); + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask(this, this.input); + } +}; +var PredictAllAgeAndGenderWithFaceAlignmentTask = class extends PredictAllAgeAndGenderTask { + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderWithFaceAlignmentTask = class extends PredictSingleAgeAndGenderTask { + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/ComputeFaceDescriptorsTasks.ts +var ComputeFaceDescriptorsTaskBase = class extends ComposableTask { + constructor(parentTask, input) { + super(); + this.parentTask = parentTask; + this.input = input; + } +}; +var ComputeAllFaceDescriptorsTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResults = await this.parentTask; + const descriptors = await extractAllFacesAndComputeResults( + parentResults, + this.input, + (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face))), + null, + (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }) + ); + return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor)); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; +var ComputeSingleFaceDescriptorTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const descriptor = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.faceRecognitionNet.computeFaceDescriptor(face), + null, + // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow + (parentResult2) => parentResult2.landmarks.align(null, { useDlibAlignment: true }) + ); + return extendWithFaceDescriptor(parentResult, descriptor); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; + +// src/globalApi/DetectFaceLandmarksTasks.ts +var DetectFaceLandmarksTaskBase = class extends ComposableTask { + constructor(parentTask, input, useTinyLandmarkNet) { + super(); + this.parentTask = parentTask; + this.input = input; + this.useTinyLandmarkNet = useTinyLandmarkNet; + } + get landmarkNet() { + return this.useTinyLandmarkNet ? nets.faceLandmark68TinyNet : nets.faceLandmark68Net; + } +}; +var DetectAllFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResults = await this.parentTask; + const detections = parentResults.map((res) => res.detection); + const faces = this.input instanceof tf41.Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections); + const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))); + faces.forEach((f) => f instanceof tf41.Tensor && f.dispose()); + const result = parentResults.filter((_parentResult, i) => faceLandmarksByFace[i]).map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i])); + return result; + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var DetectSingleFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const { detection } = parentResult; + const faces = this.input instanceof tf41.Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]); + const landmarks = await this.landmarkNet.detectLandmarks(faces[0]); + faces.forEach((f) => f instanceof tf41.Tensor && f.dispose()); + return extendWithFaceLandmarks(parentResult, landmarks); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/DetectFacesTasks.ts +var DetectFacesTaskBase = class extends ComposableTask { + // eslint-disable-next-line no-unused-vars + constructor(input, options = new SsdMobilenetv1Options()) { + super(); + this.input = input; + this.options = options; + } +}; +var DetectAllFacesTask = class extends DetectFacesTaskBase { + async run() { + const { input, options } = this; + let result; + if (options instanceof TinyFaceDetectorOptions) + result = nets.tinyFaceDetector.locateFaces(input, options); + else if (options instanceof SsdMobilenetv1Options) + result = nets.ssdMobilenetv1.locateFaces(input, options); + else if (options instanceof TinyYolov2Options) + result = nets.tinyYolov2.locateFaces(input, options); + else + throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options"); + return result; + } + runAndExtendWithFaceDetections() { + return new Promise((resolve, reject) => { + this.run().then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection)))).catch((err) => reject(err)); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectAllFaceLandmarksTask( + this.runAndExtendWithFaceDetections(), + this.input, + useTinyLandmarkNet + ); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask( + this.runAndExtendWithFaceDetections(), + this.input + ); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask( + this.runAndExtendWithFaceDetections(), + this.input + ); + } +}; +var DetectSingleFaceTask = class extends DetectFacesTaskBase { + async run() { + const faceDetections = await new DetectAllFacesTask(this.input, this.options); + let faceDetectionWithHighestScore = faceDetections[0]; + faceDetections.forEach((faceDetection) => { + if (faceDetection.score > faceDetectionWithHighestScore.score) + faceDetectionWithHighestScore = faceDetection; + }); + return faceDetectionWithHighestScore; + } + runAndExtendWithFaceDetection() { + return new Promise(async (resolve) => { + const detection = await this.run(); + resolve(detection ? extendWithFaceDetection({}, detection) : void 0); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectSingleFaceLandmarksTask( + this.runAndExtendWithFaceDetection(), + this.input, + useTinyLandmarkNet + ); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask( + this.runAndExtendWithFaceDetection(), + this.input + ); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask( + this.runAndExtendWithFaceDetection(), + this.input + ); + } +}; + +// src/globalApi/detectFaces.ts +function detectSingleFace(input, options = new SsdMobilenetv1Options()) { + return new DetectSingleFaceTask(input, options); +} +function detectAllFaces(input, options = new SsdMobilenetv1Options()) { + return new DetectAllFacesTask(input, options); +} + +// src/globalApi/allFaces.ts +async function allFacesSsdMobilenetv1(input, minConfidence) { + return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {})).withFaceLandmarks().withFaceDescriptors(); +} +async function allFacesTinyYolov2(input, forwardParams = {}) { + return detectAllFaces(input, new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors(); +} +var allFaces = allFacesSsdMobilenetv1; + +// src/euclideanDistance.ts +function euclideanDistance(arr1, arr2) { + if (arr1.length !== arr2.length) + throw new Error("euclideanDistance: arr1.length !== arr2.length"); + const desc1 = Array.from(arr1); + const desc2 = Array.from(arr2); + return Math.sqrt( + desc1.map((val, i) => val - desc2[i]).reduce((res, diff) => res + diff * diff, 0) + ); +} + +// src/globalApi/FaceMatcher.ts +var FaceMatcher = class _FaceMatcher { + constructor(inputs, distanceThreshold = 0.6) { + this._distanceThreshold = distanceThreshold; + const inputArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArray.length) + throw new Error("FaceRecognizer.constructor - expected atleast one input"); + let count = 1; + const createUniqueLabel = () => `person ${count++}`; + this._labeledDescriptors = inputArray.map((desc) => { + if (desc instanceof LabeledFaceDescriptors) + return desc; + if (desc instanceof Float32Array) + return new LabeledFaceDescriptors(createUniqueLabel(), [desc]); + if (desc.descriptor && desc.descriptor instanceof Float32Array) + return new LabeledFaceDescriptors(createUniqueLabel(), [desc.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(queryDescriptor, descriptors) { + return descriptors.map((d) => euclideanDistance(d, queryDescriptor)).reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1); + } + matchDescriptor(queryDescriptor) { + return this.labeledDescriptors.map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors))).reduce((best, curr) => best.distance < curr.distance ? best : curr); + } + findBestMatch(queryDescriptor) { + const bestMatch = this.matchDescriptor(queryDescriptor); + return bestMatch.distance < this._distanceThreshold ? bestMatch : new FaceMatch("unknown", bestMatch.distance); + } + toJSON() { + return { + distanceThreshold: this._distanceThreshold, + labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()) + }; + } + static fromJSON(json) { + const labeledDescriptors = json.labeledDescriptors.map((ld) => LabeledFaceDescriptors.fromJSON(ld)); + return new _FaceMatcher(labeledDescriptors, json.distanceThreshold); + } +}; + +// src/tinyFaceDetector/index.ts +function createTinyFaceDetector(weights) { + const net = new TinyFaceDetector(); + net.extractWeights(weights); + return net; +} + +// src/resizeResults.ts +function resizeResults(results, dimensions) { + const { width, height } = new Dimensions(dimensions.width, dimensions.height); + if (width <= 0 || height <= 0) { + throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`); + } + if (Array.isArray(results)) { + return results.map((obj) => resizeResults(obj, { width, height })); + } + if (isWithFaceLandmarks(results)) { + const resizedDetection = results.detection.forSize(width, height); + const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height); + return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks); + } + if (isWithFaceDetection(results)) { + return extendWithFaceDetection(results, results.detection.forSize(width, height)); + } + if (results instanceof FaceLandmarks || results instanceof FaceDetection) { + return results.forSize(width, height); + } + return results; +} + +// src/index.ts +var version2 = version; +// Annotate the CommonJS export names for ESM import in node: +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 a6d9a6e..8048b18 100644 --- a/dist/face-api.node.js +++ b/dist/face-api.node.js @@ -4,4 +4,4951 @@ author: ' */ 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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}); +"use strict"; +var __create = Object.create; +var __defProp = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getProtoOf = Object.getPrototypeOf; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __commonJS = (cb, mod) => function __require() { + return mod || (0, cb[__getOwnPropNames(cb)[0]])((mod = { exports: {} }).exports, mod), mod.exports; +}; +var __export = (target, all) => { + for (var name in all) + __defProp(target, name, { get: all[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __toESM = (mod, isNodeMode, target) => (target = mod != null ? __create(__getProtoOf(mod)) : {}, __copyProps( + // If the importer is in node compatibility mode or this is not an ESM + // file that has been converted to a CommonJS file using a Babel- + // compatible transform (i.e. "__esModule" has not been set), then set + // "default" to the CommonJS "module.exports" for node compatibility. + isNodeMode || !mod || !mod.__esModule ? __defProp(target, "default", { value: mod, enumerable: true }) : target, + mod +)); +var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod); + +// dist/tfjs.esm.js +var require_tfjs_esm = __commonJS({ + "dist/tfjs.esm.js"(exports2, module2) { + "use strict"; + var __defProp2 = Object.defineProperty; + var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; + var __getOwnPropNames2 = Object.getOwnPropertyNames; + var __hasOwnProp2 = Object.prototype.hasOwnProperty; + var __export2 = (target, all) => { + for (var name in all) + __defProp2(target, name, { get: all[name], enumerable: true }); + }; + var __copyProps2 = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames2(from)) + if (!__hasOwnProp2.call(to, key) && key !== except) + __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); + } + return to; + }; + var __reExport = (target, mod, secondTarget) => (__copyProps2(target, mod, "default"), secondTarget && __copyProps2(secondTarget, mod, "default")); + var __toCommonJS2 = (mod) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod); + var tf_node_exports = {}; + __export2(tf_node_exports, { + version: () => version6 + }); + module2.exports = __toCommonJS2(tf_node_exports); + __reExport(tf_node_exports, require("@tensorflow/tfjs-node"), module2.exports); + var version3 = "4.16.0"; + var version22 = "4.16.0"; + var version32 = "4.16.0"; + var version4 = "4.16.0"; + var version5 = "4.16.0"; + var version6 = { + // tfjs: tfjsVersion, + tfjs: version3, + "tfjs-core": version3, + // 'tfjs-data': tfjsDataVersion, + // 'tfjs-layers': tfjsLayersVersion, + "tfjs-converter": version22, + "tfjs-backend-cpu": version32, + "tfjs-backend-webgl": version4, + "tfjs-backend-wasm": version5 + }; + } +}); + +// src/index.ts +var src_exports = {}; +__export(src_exports, { + AgeGenderNet: () => AgeGenderNet, + BoundingBox: () => BoundingBox, + Box: () => Box, + ComposableTask: () => ComposableTask, + ComputeAllFaceDescriptorsTask: () => ComputeAllFaceDescriptorsTask, + ComputeFaceDescriptorsTaskBase: () => ComputeFaceDescriptorsTaskBase, + ComputeSingleFaceDescriptorTask: () => ComputeSingleFaceDescriptorTask, + DetectAllFaceLandmarksTask: () => DetectAllFaceLandmarksTask, + DetectAllFacesTask: () => DetectAllFacesTask, + DetectFaceLandmarksTaskBase: () => DetectFaceLandmarksTaskBase, + DetectFacesTaskBase: () => DetectFacesTaskBase, + DetectSingleFaceLandmarksTask: () => DetectSingleFaceLandmarksTask, + DetectSingleFaceTask: () => DetectSingleFaceTask, + Dimensions: () => Dimensions, + FACE_EXPRESSION_LABELS: () => FACE_EXPRESSION_LABELS, + FaceDetection: () => FaceDetection, + FaceDetectionNet: () => FaceDetectionNet, + FaceExpressionNet: () => FaceExpressionNet, + FaceExpressions: () => FaceExpressions, + FaceLandmark68Net: () => FaceLandmark68Net, + FaceLandmark68TinyNet: () => FaceLandmark68TinyNet, + FaceLandmarkNet: () => FaceLandmarkNet, + FaceLandmarks: () => FaceLandmarks, + FaceLandmarks5: () => FaceLandmarks5, + FaceLandmarks68: () => FaceLandmarks68, + FaceMatch: () => FaceMatch, + FaceMatcher: () => FaceMatcher, + FaceRecognitionNet: () => FaceRecognitionNet, + Gender: () => Gender, + LabeledBox: () => LabeledBox, + LabeledFaceDescriptors: () => LabeledFaceDescriptors, + NetInput: () => NetInput, + NeuralNetwork: () => NeuralNetwork, + ObjectDetection: () => ObjectDetection, + Point: () => Point, + PredictedBox: () => PredictedBox, + Rect: () => Rect, + SsdMobilenetv1: () => SsdMobilenetv1, + SsdMobilenetv1Options: () => SsdMobilenetv1Options, + TinyFaceDetector: () => TinyFaceDetector, + TinyFaceDetectorOptions: () => TinyFaceDetectorOptions, + TinyYolov2: () => TinyYolov2, + TinyYolov2Options: () => TinyYolov2Options, + allFaces: () => allFaces, + allFacesSsdMobilenetv1: () => allFacesSsdMobilenetv1, + allFacesTinyYolov2: () => allFacesTinyYolov2, + awaitMediaLoaded: () => awaitMediaLoaded, + bufferToImage: () => bufferToImage, + computeFaceDescriptor: () => computeFaceDescriptor, + createCanvas: () => createCanvas, + createCanvasFromMedia: () => createCanvasFromMedia, + createFaceDetectionNet: () => createFaceDetectionNet, + createFaceRecognitionNet: () => createFaceRecognitionNet, + createSsdMobilenetv1: () => createSsdMobilenetv1, + createTinyFaceDetector: () => createTinyFaceDetector, + createTinyYolov2: () => createTinyYolov2, + detectAllFaces: () => detectAllFaces, + detectFaceLandmarks: () => detectFaceLandmarks, + detectFaceLandmarksTiny: () => detectFaceLandmarksTiny, + detectLandmarks: () => detectLandmarks, + detectSingleFace: () => detectSingleFace, + draw: () => draw_exports, + env: () => env, + euclideanDistance: () => euclideanDistance, + extendWithAge: () => extendWithAge, + extendWithFaceDescriptor: () => extendWithFaceDescriptor, + extendWithFaceDetection: () => extendWithFaceDetection, + extendWithFaceExpressions: () => extendWithFaceExpressions, + extendWithFaceLandmarks: () => extendWithFaceLandmarks, + extendWithGender: () => extendWithGender, + extractFaceTensors: () => extractFaceTensors, + extractFaces: () => extractFaces, + fetchImage: () => fetchImage, + fetchJson: () => fetchJson, + fetchNetWeights: () => fetchNetWeights, + fetchOrThrow: () => fetchOrThrow, + fetchVideo: () => fetchVideo, + getContext2dOrThrow: () => getContext2dOrThrow, + getMediaDimensions: () => getMediaDimensions, + imageTensorToCanvas: () => imageTensorToCanvas, + imageToSquare: () => imageToSquare, + inverseSigmoid: () => inverseSigmoid, + iou: () => iou, + isMediaElement: () => isMediaElement, + isMediaLoaded: () => isMediaLoaded, + isWithAge: () => isWithAge, + isWithFaceDetection: () => isWithFaceDetection, + isWithFaceExpressions: () => isWithFaceExpressions, + isWithFaceLandmarks: () => isWithFaceLandmarks, + isWithGender: () => isWithGender, + loadAgeGenderModel: () => loadAgeGenderModel, + loadFaceDetectionModel: () => loadFaceDetectionModel, + loadFaceExpressionModel: () => loadFaceExpressionModel, + loadFaceLandmarkModel: () => loadFaceLandmarkModel, + loadFaceLandmarkTinyModel: () => loadFaceLandmarkTinyModel, + loadFaceRecognitionModel: () => loadFaceRecognitionModel, + loadSsdMobilenetv1Model: () => loadSsdMobilenetv1Model, + loadTinyFaceDetectorModel: () => loadTinyFaceDetectorModel, + loadTinyYolov2Model: () => loadTinyYolov2Model, + loadWeightMap: () => loadWeightMap, + locateFaces: () => locateFaces, + matchDimensions: () => matchDimensions, + minBbox: () => minBbox, + nets: () => nets, + nonMaxSuppression: () => nonMaxSuppression, + normalize: () => normalize, + padToSquare: () => padToSquare, + predictAgeAndGender: () => predictAgeAndGender, + recognizeFaceExpressions: () => recognizeFaceExpressions, + resizeResults: () => resizeResults, + resolveInput: () => resolveInput, + shuffleArray: () => shuffleArray, + sigmoid: () => sigmoid, + ssdMobilenetv1: () => ssdMobilenetv1, + tf: () => tf42, + tinyFaceDetector: () => tinyFaceDetector, + tinyYolov2: () => tinyYolov2, + toNetInput: () => toNetInput, + utils: () => utils_exports, + validateConfig: () => validateConfig, + version: () => version2 +}); +module.exports = __toCommonJS(src_exports); +var tf42 = __toESM(require_tfjs_esm()); + +// src/draw/index.ts +var draw_exports = {}; +__export(draw_exports, { + AnchorPosition: () => AnchorPosition, + DrawBox: () => DrawBox, + DrawBoxOptions: () => DrawBoxOptions, + DrawFaceLandmarks: () => DrawFaceLandmarks, + DrawFaceLandmarksOptions: () => DrawFaceLandmarksOptions, + DrawTextField: () => DrawTextField, + DrawTextFieldOptions: () => DrawTextFieldOptions, + drawContour: () => drawContour, + drawDetections: () => drawDetections, + drawFaceExpressions: () => drawFaceExpressions, + drawFaceLandmarks: () => drawFaceLandmarks +}); + +// src/draw/drawContour.ts +function drawContour(ctx, points, isClosed = false) { + ctx.beginPath(); + points.slice(1).forEach(({ x, y }, prevIdx) => { + const from = points[prevIdx]; + ctx.moveTo(from.x, from.y); + ctx.lineTo(x, y); + }); + if (isClosed) { + const from = points[points.length - 1]; + const to = points[0]; + if (!from || !to) { + return; + } + ctx.moveTo(from.x, from.y); + ctx.lineTo(to.x, to.y); + } + ctx.stroke(); +} + +// src/utils/index.ts +var utils_exports = {}; +__export(utils_exports, { + computeReshapedDimensions: () => computeReshapedDimensions, + getCenterPoint: () => getCenterPoint, + isDimensions: () => isDimensions, + isEven: () => isEven, + isFloat: () => isFloat, + isTensor: () => isTensor, + isTensor1D: () => isTensor1D, + isTensor2D: () => isTensor2D, + isTensor3D: () => isTensor3D, + isTensor4D: () => isTensor4D, + isValidNumber: () => isValidNumber, + isValidProbablitiy: () => isValidProbablitiy, + range: () => range, + round: () => round +}); +var tf = __toESM(require_tfjs_esm()); + +// src/classes/Dimensions.ts +var Dimensions = class _Dimensions { + constructor(width, height) { + if (!isValidNumber(width) || !isValidNumber(height)) { + throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`); + } + this._width = width; + this._height = height; + } + get width() { + return this._width; + } + get height() { + return this._height; + } + reverse() { + return new _Dimensions(1 / this.width, 1 / this.height); + } +}; + +// src/utils/index.ts +function isTensor(tensor2, dim) { + return tensor2 instanceof tf.Tensor && tensor2.shape.length === dim; +} +function isTensor1D(tensor2) { + return isTensor(tensor2, 1); +} +function isTensor2D(tensor2) { + return isTensor(tensor2, 2); +} +function isTensor3D(tensor2) { + return isTensor(tensor2, 3); +} +function isTensor4D(tensor2) { + return isTensor(tensor2, 4); +} +function isFloat(num) { + return num % 1 !== 0; +} +function isEven(num) { + return num % 2 === 0; +} +function round(num, prec = 2) { + const f = 10 ** prec; + return Math.floor(num * f) / f; +} +function isDimensions(obj) { + return obj && obj.width && obj.height; +} +function computeReshapedDimensions({ width, height }, inputSize) { + const scale2 = inputSize / Math.max(height, width); + return new Dimensions(Math.round(width * scale2), Math.round(height * scale2)); +} +function getCenterPoint(pts) { + return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length)); +} +function range(num, start, step) { + return Array(num).fill(0).map((_, i) => start + i * step); +} +function isValidNumber(num) { + return !!num && num !== Infinity && num !== -Infinity && !Number.isNaN(num) || num === 0; +} +function isValidProbablitiy(num) { + return isValidNumber(num) && num >= 0 && num <= 1; +} + +// src/classes/Point.ts +var Point = class _Point { + constructor(x, y) { + this._x = x; + this._y = y; + } + get x() { + return this._x; + } + get y() { + return this._y; + } + add(pt) { + return new _Point(this.x + pt.x, this.y + pt.y); + } + sub(pt) { + return new _Point(this.x - pt.x, this.y - pt.y); + } + mul(pt) { + return new _Point(this.x * pt.x, this.y * pt.y); + } + div(pt) { + return new _Point(this.x / pt.x, this.y / pt.y); + } + abs() { + return new _Point(Math.abs(this.x), Math.abs(this.y)); + } + magnitude() { + return Math.sqrt(this.x ** 2 + this.y ** 2); + } + floor() { + return new _Point(Math.floor(this.x), Math.floor(this.y)); + } +}; + +// src/classes/Box.ts +var Box = class _Box { + static isRect(rect) { + return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber); + } + static assertIsValidBox(box, callee, allowNegativeDimensions = false) { + if (!_Box.isRect(box)) { + throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`); + } + if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) { + throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`); + } + } + constructor(_box, allowNegativeDimensions = true) { + const box = _box || {}; + const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber); + const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber); + if (!isRect && !isBbox) { + throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`); + } + const [x, y, width, height] = isRect ? [box.x, box.y, box.width, box.height] : [box.left, box.top, box.right - box.left, box.bottom - box.top]; + _Box.assertIsValidBox({ + x, + y, + width, + height + }, "Box.constructor", allowNegativeDimensions); + this._x = x; + this._y = y; + this._width = width; + this._height = height; + } + 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 Point(this.left, this.top); + } + get topRight() { + return new Point(this.right, this.top); + } + get bottomLeft() { + return new Point(this.left, this.bottom); + } + get bottomRight() { + return new Point(this.right, this.bottom); + } + round() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.round(val)); + return new _Box({ + x, + y, + width, + height + }); + } + floor() { + const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.floor(val)); + return new _Box({ + x, + y, + width, + height + }); + } + toSquare() { + let { + x, + y, + width, + height + } = this; + const diff = Math.abs(width - height); + if (width < height) { + x -= diff / 2; + width += diff; + } + if (height < width) { + y -= diff / 2; + height += diff; + } + return new _Box({ x, y, width, height }); + } + rescale(s) { + const scaleX = isDimensions(s) ? s.width : s; + const scaleY = isDimensions(s) ? s.height : s; + return new _Box({ + x: this.x * scaleX, + y: this.y * scaleY, + width: this.width * scaleX, + height: this.height * scaleY + }); + } + pad(padX, padY) { + const [x, y, width, height] = [ + this.x - padX / 2, + this.y - padY / 2, + this.width + padX, + this.height + padY + ]; + return new _Box({ x, y, width, height }); + } + clipAtImageBorders(imgWidth, imgHeight) { + const { x, y, right, bottom } = this; + const clippedX = Math.max(x, 0); + const clippedY = Math.max(y, 0); + const newWidth = right - clippedX; + const newHeight = bottom - clippedY; + const clippedWidth = Math.min(newWidth, imgWidth - clippedX); + const clippedHeight = Math.min(newHeight, imgHeight - clippedY); + return new _Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight }).floor(); + } + shift(sx, sy) { + const { width, height } = this; + const x = this.x + sx; + const y = this.y + sy; + return new _Box({ x, y, width, height }); + } + padAtBorders(imageHeight, imageWidth) { + const w = this.width + 1; + const h = this.height + 1; + const dx = 1; + const dy = 1; + let edx = w; + let edy = h; + let x = this.left; + let y = this.top; + let ex = this.right; + let ey = this.bottom; + if (ex > imageWidth) { + edx = -ex + imageWidth + w; + ex = imageWidth; + } + if (ey > imageHeight) { + edy = -ey + imageHeight + h; + ey = imageHeight; + } + if (x < 1) { + edy = 2 - x; + x = 1; + } + if (y < 1) { + edy = 2 - y; + y = 1; + } + return { dy, edy, dx, edx, y, ey, x, ex, w, h }; + } + calibrate(region) { + return new _Box({ + left: this.left + region.left * this.width, + top: this.top + region.top * this.height, + right: this.right + region.right * this.width, + bottom: this.bottom + region.bottom * this.height + }).toSquare().round(); + } +}; + +// src/classes/BoundingBox.ts +var BoundingBox = class extends Box { + constructor(left, top, right, bottom, allowNegativeDimensions = false) { + super({ left, top, right, bottom }, allowNegativeDimensions); + } +}; + +// src/classes/ObjectDetection.ts +var ObjectDetection = class _ObjectDetection { + constructor(score, classScore, className, relativeBox, imageDims) { + this._imageDims = new Dimensions(imageDims.width, imageDims.height); + this._score = score; + this._classScore = classScore; + this._className = className; + this._box = new Box(relativeBox).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 Box(this._box).rescale(this.imageDims.reverse()); + } + forSize(width, height) { + return new _ObjectDetection( + this.score, + this.classScore, + this.className, + this.relativeBox, + { width, height } + ); + } +}; + +// src/classes/FaceDetection.ts +var FaceDetection = class _FaceDetection extends ObjectDetection { + constructor(score, relativeBox, imageDims) { + super(score, score, "", relativeBox, imageDims); + } + forSize(width, height) { + const { score, relativeBox, imageDims } = super.forSize(width, height); + return new _FaceDetection(score, relativeBox, imageDims); + } +}; + +// src/ops/iou.ts +function iou(box1, box2, isIOU = true) { + const width = Math.max(0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left)); + const height = Math.max(0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top)); + const interSection = width * height; + return isIOU ? interSection / (box1.area + box2.area - interSection) : interSection / Math.min(box1.area, box2.area); +} + +// src/ops/minBbox.ts +function minBbox(pts) { + const xs = pts.map((pt) => pt.x); + const ys = pts.map((pt) => pt.y); + const minX = xs.reduce((min, x) => x < min ? x : min, Infinity); + const minY = ys.reduce((min, y) => y < min ? y : min, Infinity); + const maxX = xs.reduce((max, x) => max < x ? x : max, 0); + const maxY = ys.reduce((max, y) => max < y ? y : max, 0); + return new BoundingBox(minX, minY, maxX, maxY); +} + +// src/ops/nonMaxSuppression.ts +function nonMaxSuppression(boxes, scores, iouThreshold, isIOU = true) { + let indicesSortedByScore = scores.map((score, boxIndex) => ({ score, boxIndex })).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex); + const pick = []; + while (indicesSortedByScore.length > 0) { + const curr = indicesSortedByScore.pop(); + pick.push(curr); + const indices = indicesSortedByScore; + const outputs = []; + for (let i = 0; i < indices.length; i++) { + const idx = indices[i]; + const currBox = boxes[curr]; + const idxBox = boxes[idx]; + outputs.push(iou(currBox, idxBox, isIOU)); + } + indicesSortedByScore = indicesSortedByScore.filter( + (_, j) => outputs[j] <= iouThreshold + ); + } + return pick; +} + +// src/ops/normalize.ts +var tf2 = __toESM(require_tfjs_esm()); +function normalize(x, meanRgb) { + return tf2.tidy(() => { + const [r, g, b] = meanRgb; + const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r, "float32"); + const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g, "float32"); + const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b, "float32"); + const avg_rgb = tf2.concat([avg_r, avg_g, avg_b], 3); + return tf2.sub(x, avg_rgb); + }); +} + +// src/ops/padToSquare.ts +var tf3 = __toESM(require_tfjs_esm()); +function padToSquare(imgTensor, isCenterImage = false) { + return tf3.tidy(() => { + const [height, width] = imgTensor.shape.slice(1); + if (height === width) + return imgTensor; + const dimDiff = Math.abs(height - width); + const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1)); + const paddingAxis = height > width ? 2 : 1; + const createPaddingTensor = (paddingAmountLocal) => { + const paddingTensorShape = imgTensor.shape.slice(); + paddingTensorShape[paddingAxis] = paddingAmountLocal; + return tf3.fill(paddingTensorShape, 0, "float32"); + }; + const paddingTensorAppend = createPaddingTensor(paddingAmount); + const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis]; + const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null; + const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend].filter((t) => !!t).map((t) => tf3.cast(t, "float32")); + return tf3.concat(tensorsToStack, paddingAxis); + }); +} + +// src/ops/shuffleArray.ts +function shuffleArray(inputArray) { + const array = inputArray.slice(); + for (let i = array.length - 1; i > 0; i--) { + const j = Math.floor(Math.random() * (i + 1)); + const x = array[i]; + array[i] = array[j]; + array[j] = x; + } + return array; +} + +// src/ops/index.ts +function sigmoid(x) { + return 1 / (1 + Math.exp(-x)); +} +function inverseSigmoid(x) { + return Math.log(x / (1 - x)); +} + +// src/classes/Rect.ts +var Rect = class extends Box { + constructor(x, y, width, height, allowNegativeDimensions = false) { + super({ x, y, width, height }, allowNegativeDimensions); + } +}; + +// src/classes/FaceLandmarks.ts +var relX = 0.5; +var relY = 0.43; +var relScale = 0.45; +var FaceLandmarks = class { + constructor(relativeFaceLandmarkPositions, imgDims, shift = new Point(0, 0)) { + const { width, height } = imgDims; + this._imgDims = new Dimensions(width, height); + this._shift = shift; + this._positions = relativeFaceLandmarkPositions.map( + (pt) => pt.mul(new Point(width, height)).add(shift) + ); + } + get shift() { + return new Point(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( + (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)) + ); + } + forSize(width, height) { + return new this.constructor( + this.relativePositions, + { width, height } + ); + } + shiftBy(x, y) { + return new this.constructor( + this.relativePositions, + this._imgDims, + new Point(x, y) + ); + } + shiftByPoint(pt) { + return this.shiftBy(pt.x, pt.y); + } + /** + * Aligns the face landmarks after face detection from the relative positions of the faces + * bounding box, or it's current shift. This function should be used to align the face images + * after face detection has been performed, before they are passed to the face recognition net. + * This will make the computed face descriptor more accurate. + * + * @param detection (optional) The bounding box of the face or the face detection result. If + * no argument was passed the position of the face landmarks are assumed to be relative to + * it's current shift. + * @returns The bounding box of the aligned face. + */ + align(detection, options = {}) { + if (detection) { + const box = detection instanceof FaceDetection ? detection.box.floor() : new Box(detection); + return this.shiftBy(box.x, box.y).align(null, options); + } + const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options }; + if (useDlibAlignment) { + return this.alignDlib(); + } + return this.alignMinBbox(minBoxPadding); + } + alignDlib() { + const centers = this.getRefPointsForAlignment(); + const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers; + const distToMouth = (pt) => mouthCenter.sub(pt).magnitude(); + const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2; + const size = Math.floor(eyeToMouthDist / relScale); + const refPoint = getCenterPoint(centers); + const x = Math.floor(Math.max(0, refPoint.x - relX * size)); + const y = Math.floor(Math.max(0, refPoint.y - relY * size)); + return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y)); + } + alignMinBbox(padding) { + const box = minBbox(this.positions); + return box.pad(box.width * padding, box.height * padding); + } + getRefPointsForAlignment() { + throw new Error("getRefPointsForAlignment not implemented by base class"); + } +}; + +// src/classes/FaceLandmarks5.ts +var FaceLandmarks5 = class extends FaceLandmarks { + getRefPointsForAlignment() { + const pts = this.positions; + return [ + pts[0], + pts[1], + getCenterPoint([pts[3], pts[4]]) + ]; + } +}; + +// src/classes/FaceLandmarks68.ts +var FaceLandmarks68 = class extends FaceLandmarks { + 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(getCenterPoint); + } +}; + +// src/classes/FaceMatch.ts +var FaceMatch = class { + constructor(label, distance) { + this._label = label; + this._distance = distance; + } + get label() { + return this._label; + } + get distance() { + return this._distance; + } + toString(withDistance = true) { + return `${this.label}${withDistance ? ` (${round(this.distance)})` : ""}`; + } +}; + +// src/classes/LabeledBox.ts +var LabeledBox = class extends Box { + static assertIsValidLabeledBox(box, callee) { + Box.assertIsValidBox(box, callee); + if (!isValidNumber(box.label)) { + throw new Error(`${callee} - expected property label (${box.label}) to be a number`); + } + } + constructor(box, label) { + super(box); + this._label = label; + } + get label() { + return this._label; + } +}; + +// src/classes/LabeledFaceDescriptors.ts +var LabeledFaceDescriptors = class _LabeledFaceDescriptors { + constructor(label, descriptors) { + if (!(typeof label === "string")) { + throw new Error("LabeledFaceDescriptors - constructor expected label to be a string"); + } + if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) { + throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array"); + } + this._label = label; + this._descriptors = descriptors; + } + get label() { + return this._label; + } + get descriptors() { + return this._descriptors; + } + toJSON() { + return { + label: this.label, + descriptors: this.descriptors.map((d) => Array.from(d)) + }; + } + static fromJSON(json) { + const descriptors = json.descriptors.map((d) => new Float32Array(d)); + return new _LabeledFaceDescriptors(json.label, descriptors); + } +}; + +// src/classes/PredictedBox.ts +var PredictedBox = class extends LabeledBox { + static assertIsValidPredictedBox(box, callee) { + LabeledBox.assertIsValidLabeledBox(box, callee); + if (!isValidProbablitiy(box.score) || !isValidProbablitiy(box.classScore)) { + throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`); + } + } + constructor(box, label, score, classScore) { + super(box, label); + this._score = score; + this._classScore = classScore; + } + get score() { + return this._score; + } + get classScore() { + return this._classScore; + } +}; + +// src/factories/WithFaceDetection.ts +function isWithFaceDetection(obj) { + return obj.detection instanceof FaceDetection; +} +function extendWithFaceDetection(sourceObj, detection) { + const extension = { detection }; + return { ...sourceObj, ...extension }; +} + +// src/env/createBrowserEnv.ts +function createBrowserEnv() { + const fetch = window.fetch; + if (!fetch) + throw new Error("fetch - missing fetch implementation for browser environment"); + const readFile = () => { + throw new Error("readFile - filesystem not available for browser environment"); + }; + return { + Canvas: HTMLCanvasElement, + CanvasRenderingContext2D, + Image: HTMLImageElement, + ImageData, + Video: HTMLVideoElement, + createCanvasElement: () => document.createElement("canvas"), + createImageElement: () => document.createElement("img"), + createVideoElement: () => document.createElement("video"), + fetch, + readFile + }; +} + +// src/env/isNodejs.ts +function isNodejs() { + return typeof global === "object" && typeof process !== "undefined" && process.versions != null && process.versions.node != null; +} + +// src/env/createFileSystem.ts +function createFileSystem(fs) { + let requireFsError = ""; + if (!fs && isNodejs()) { + try { + fs = require("fs"); + } catch (err) { + requireFsError = err.toString(); + } + } + const readFile = fs ? (filePath) => new Promise((resolve, reject) => { + fs.readFile(filePath, (err, buffer) => err ? reject(err) : resolve(buffer)); + }) : () => { + throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); + }; + return { readFile }; +} + +// src/env/createNodejsEnv.ts +function createNodejsEnv() { + const Canvas = global["Canvas"] || global.HTMLCanvasElement; + const Image = global.Image || global.HTMLImageElement; + const Video = global["Video"] || global.HTMLVideoElement; + const createCanvasElement = () => { + if (Canvas) + return new Canvas(); + throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment"); + }; + const createImageElement = () => { + if (Image) + return new Image(); + throw new Error("createImageElement - missing Image implementation for nodejs environment"); + }; + const createVideoElement = () => { + if (Video) + return new Video(); + throw new Error("createVideoElement - missing Video implementation for nodejs environment"); + }; + const fetch = global.fetch; + const fileSystem = createFileSystem(); + return { + Canvas: Canvas || class { + }, + CanvasRenderingContext2D: global.CanvasRenderingContext2D || class { + }, + Image: Image || class { + }, + ImageData: global.ImageData || class { + }, + Video: global.HTMLVideoElement || class { + }, + createCanvasElement, + createImageElement, + createVideoElement, + fetch, + ...fileSystem + }; +} + +// src/env/isBrowser.ts +function isBrowser() { + return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined"; +} + +// src/env/index.ts +var environment; +function getEnv() { + if (!environment) { + throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()"); + } + return environment; +} +function setEnv(env2) { + environment = env2; +} +function initialize() { + if (isBrowser()) + return setEnv(createBrowserEnv()); + if (isNodejs()) + return setEnv(createNodejsEnv()); + return null; +} +function monkeyPatch(env2) { + if (!environment) { + initialize(); + } + if (!environment) { + throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()"); + } + const { Canvas = environment.Canvas, Image = environment.Image } = env2; + environment.Canvas = Canvas; + environment.Image = Image; + environment.createCanvasElement = env2.createCanvasElement || (() => new Canvas()); + environment.createImageElement = env2.createImageElement || (() => new Image()); + environment.ImageData = env2.ImageData || environment.ImageData; + environment.Video = env2.Video || environment.Video; + environment.fetch = env2.fetch || environment.fetch; + environment.readFile = env2.readFile || environment.readFile; +} +var env = { + getEnv, + setEnv, + initialize, + createBrowserEnv, + createFileSystem, + createNodejsEnv, + monkeyPatch, + isBrowser, + isNodejs +}; +initialize(); + +// src/dom/resolveInput.ts +function resolveInput(arg) { + if (!env.isNodejs() && typeof arg === "string") { + return document.getElementById(arg); + } + return arg; +} + +// src/dom/getContext2dOrThrow.ts +function getContext2dOrThrow(canvasArg) { + const { Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2 } = env.getEnv(); + if (canvasArg instanceof CanvasRenderingContext2D2) + return canvasArg; + const canvas = resolveInput(canvasArg); + if (!(canvas instanceof Canvas)) + throw new Error("resolveContext2d - expected canvas to be of instance of Canvas"); + const ctx = canvas.getContext("2d", { willReadFrequently: true }); + if (!ctx) + throw new Error("resolveContext2d - canvas 2d context is null"); + return ctx; +} + +// src/draw/DrawTextField.ts +var AnchorPosition = /* @__PURE__ */ ((AnchorPosition2) => { + AnchorPosition2["TOP_LEFT"] = "TOP_LEFT"; + AnchorPosition2["TOP_RIGHT"] = "TOP_RIGHT"; + AnchorPosition2["BOTTOM_LEFT"] = "BOTTOM_LEFT"; + AnchorPosition2["BOTTOM_RIGHT"] = "BOTTOM_RIGHT"; + return AnchorPosition2; +})(AnchorPosition || {}); +var DrawTextFieldOptions = class { + constructor(options = {}) { + const { + anchorPosition, + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = options; + this.anchorPosition = anchorPosition || "TOP_LEFT" /* TOP_LEFT */; + this.backgroundColor = backgroundColor || "rgba(0, 0, 0, 0.5)"; + this.fontColor = fontColor || "rgba(255, 255, 255, 1)"; + this.fontSize = fontSize || 14; + this.fontStyle = fontStyle || "Georgia"; + this.padding = padding || 4; + } +}; +var DrawTextField = class _DrawTextField { + constructor(text, anchor, options = {}) { + this.text = typeof text === "string" ? [text] : text instanceof _DrawTextField ? text.text : text; + this.anchor = anchor; + this.options = new DrawTextFieldOptions(options); + } + measureWidth(ctx) { + const { padding } = this.options; + return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + 2 * padding; + } + measureHeight() { + const { fontSize, padding } = this.options; + return this.text.length * fontSize + 2 * padding; + } + getUpperLeft(ctx, canvasDims) { + const { anchorPosition } = this.options; + const isShiftLeft = anchorPosition === "BOTTOM_RIGHT" /* BOTTOM_RIGHT */ || anchorPosition === "TOP_RIGHT" /* TOP_RIGHT */; + const isShiftTop = anchorPosition === "BOTTOM_LEFT" /* BOTTOM_LEFT */ || anchorPosition === "BOTTOM_RIGHT" /* BOTTOM_RIGHT */; + const textFieldWidth = this.measureWidth(ctx); + const textFieldHeight = this.measureHeight(); + const x = isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x; + const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y; + if (canvasDims) { + const { width, height } = canvasDims; + const newX = Math.max(Math.min(x, width - textFieldWidth), 0); + const newY = Math.max(Math.min(y, height - textFieldHeight), 0); + return { x: newX, y: newY }; + } + return { x, y }; + } + draw(canvasArg) { + const canvas = resolveInput(canvasArg); + const ctx = getContext2dOrThrow(canvas); + const { + backgroundColor, + fontColor, + fontSize, + fontStyle, + padding + } = this.options; + ctx.font = `${fontSize}px ${fontStyle}`; + const maxTextWidth = this.measureWidth(ctx); + const textHeight = this.measureHeight(); + ctx.fillStyle = backgroundColor; + const upperLeft = this.getUpperLeft(ctx, canvas); + ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight); + ctx.fillStyle = fontColor; + this.text.forEach((textLine, i) => { + const x = padding + upperLeft.x; + const y = padding + upperLeft.y + (i + 1) * fontSize; + ctx.fillText(textLine, x, y); + }); + } +}; + +// src/draw/DrawBox.ts +var DrawBoxOptions = class { + constructor(options = {}) { + const { + boxColor, + lineWidth, + label, + drawLabelOptions + } = options; + this.boxColor = boxColor || "rgba(0, 0, 255, 1)"; + this.lineWidth = lineWidth || 2; + this.label = label; + const defaultDrawLabelOptions = { + anchorPosition: "BOTTOM_LEFT" /* BOTTOM_LEFT */, + backgroundColor: this.boxColor + }; + this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions }); + } +}; +var DrawBox = class { + constructor(box, options = {}) { + this.box = new Box(box); + this.options = new DrawBoxOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { boxColor, lineWidth } = this.options; + const { + x, + y, + width, + height + } = this.box; + ctx.strokeStyle = boxColor; + ctx.lineWidth = lineWidth; + ctx.strokeRect(x, y, width, height); + const { label } = this.options; + if (label) { + new DrawTextField([label], { x: x - lineWidth / 2, y }, this.options.drawLabelOptions).draw(canvasArg); + } + } +}; + +// src/draw/drawDetections.ts +function drawDetections(canvasArg, detections) { + const detectionsArray = Array.isArray(detections) ? detections : [detections]; + detectionsArray.forEach((det) => { + const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0; + const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det); + const label = score ? `${round(score)}` : void 0; + new DrawBox(box, { label }).draw(canvasArg); + }); +} + +// src/faceExpressionNet/FaceExpressionNet.ts +var tf18 = __toESM(require_tfjs_esm()); + +// src/dom/isMediaLoaded.ts +function isMediaLoaded(media) { + const { Image, Video } = env.getEnv(); + return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3; +} + +// src/dom/awaitMediaLoaded.ts +function awaitMediaLoaded(media) { + return new Promise((resolve, reject) => { + if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) + resolve(null); + function onError(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + reject(e); + } + function onLoad(e) { + if (!e.currentTarget) + return; + e.currentTarget.removeEventListener("load", onLoad); + e.currentTarget.removeEventListener("error", onError); + resolve(e); + } + media.addEventListener("load", onLoad); + media.addEventListener("error", onError); + }); +} + +// src/dom/bufferToImage.ts +function bufferToImage(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToImage - expected buf to be of type: Blob")); + const reader = new FileReader(); + reader.onload = () => { + if (typeof reader.result !== "string") + reject(new Error("bufferToImage - expected reader.result to be a string, in onload")); + const img = env.getEnv().createImageElement(); + img.onload = () => resolve(img); + img.onerror = reject; + img.src = reader.result; + }; + reader.onerror = reject; + reader.readAsDataURL(buf); + }); +} + +// src/dom/getMediaDimensions.ts +function getMediaDimensions(input) { + const { Image, Video } = env.getEnv(); + if (input instanceof Image) { + return new Dimensions(input.naturalWidth, input.naturalHeight); + } + if (input instanceof Video) { + return new Dimensions(input.videoWidth, input.videoHeight); + } + return new Dimensions(input.width, input.height); +} + +// src/dom/createCanvas.ts +function createCanvas({ width, height }) { + const { createCanvasElement } = env.getEnv(); + const canvas = createCanvasElement(); + canvas.width = width; + canvas.height = height; + return canvas; +} +function createCanvasFromMedia(media, dims) { + const { ImageData: ImageData2 } = env.getEnv(); + if (!(media instanceof ImageData2) && !isMediaLoaded(media)) { + throw new Error("createCanvasFromMedia - media has not finished loading yet"); + } + const { width, height } = dims || getMediaDimensions(media); + const canvas = createCanvas({ width, height }); + if (media instanceof ImageData2) { + getContext2dOrThrow(canvas).putImageData(media, 0, 0); + } else { + getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height); + } + return canvas; +} + +// src/dom/imageTensorToCanvas.ts +var tf4 = __toESM(require_tfjs_esm()); +async function imageTensorToCanvas(imgTensor, canvas) { + const targetCanvas = canvas || env.getEnv().createCanvasElement(); + const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0); + const imgTensor3D = tf4.tidy(() => imgTensor.as3D(height, width, numChannels).toInt()); + await tf4["browser"].toPixels(imgTensor3D, targetCanvas); + imgTensor3D.dispose(); + return targetCanvas; +} + +// src/dom/isMediaElement.ts +function isMediaElement(input) { + const { Image, Canvas, Video } = env.getEnv(); + return input instanceof Image || input instanceof Canvas || input instanceof Video; +} + +// src/dom/NetInput.ts +var tf5 = __toESM(require_tfjs_esm()); + +// src/dom/imageToSquare.ts +function imageToSquare(input, inputSize, centerImage = false) { + const { Image, Canvas } = env.getEnv(); + if (!(input instanceof Image || input instanceof Canvas)) { + throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement"); + } + if (inputSize <= 0) + return createCanvas({ width: 1, height: 1 }); + const dims = getMediaDimensions(input); + const scale2 = inputSize / Math.max(dims.height, dims.width); + const width = scale2 * dims.width; + const height = scale2 * dims.height; + const targetCanvas = createCanvas({ width: inputSize, height: inputSize }); + const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input); + const offset = Math.abs(width - height) / 2; + const dx = centerImage && width < height ? offset : 0; + const dy = centerImage && height < width ? offset : 0; + if (inputCanvas.width > 0 && inputCanvas.height > 0) + getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height); + return targetCanvas; +} + +// src/dom/NetInput.ts +var NetInput = class { + constructor(inputs, treatAsBatchInput = false) { + this._imageTensors = []; + this._canvases = []; + this._treatAsBatchInput = false; + this._inputDimensions = []; + this._inputSize = 0; + if (!Array.isArray(inputs)) { + throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`); + } + this._treatAsBatchInput = treatAsBatchInput; + this._batchSize = inputs.length; + inputs.forEach((input, idx) => { + if (isTensor3D(input)) { + this._imageTensors[idx] = input; + this._inputDimensions[idx] = input.shape; + return; + } + if (isTensor4D(input)) { + const batchSize = input.shape[0]; + if (batchSize !== 1) { + throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + this._imageTensors[idx] = input; + this._inputDimensions[idx] = input.shape.slice(1); + return; + } + const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input); + this._canvases[idx] = canvas; + this._inputDimensions[idx] = [canvas.height, canvas.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 range(this.batchSize, 0, 1).map( + (_, batchIdx) => this.getReshapedInputDimensions(batchIdx) + ); + } + getInput(batchIdx) { + return this.canvases[batchIdx] || this.imageTensors[batchIdx]; + } + getInputDimensions(batchIdx) { + return this._inputDimensions[batchIdx]; + } + getInputHeight(batchIdx) { + return this._inputDimensions[batchIdx][0]; + } + getInputWidth(batchIdx) { + return this._inputDimensions[batchIdx][1]; + } + getReshapedInputDimensions(batchIdx) { + if (typeof this.inputSize !== "number") { + throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet"); + } + const width = this.getInputWidth(batchIdx); + const height = this.getInputHeight(batchIdx); + return computeReshapedDimensions({ width, height }, this.inputSize); + } + /** + * Create a batch tensor from all input canvases and tensors + * with size [batchSize, inputSize, inputSize, 3]. + * + * @param inputSize Height and width of the tensor. + * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on + * both sides of the minor dimension oof the image. + * @returns The batch tensor. + */ + toBatchTensor(inputSize, isCenterInputs = true) { + this._inputSize = inputSize; + return tf5.tidy(() => { + const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => { + const input = this.getInput(batchIdx); + if (input instanceof tf5.Tensor) { + let imgTensor = isTensor4D(input) ? input : tf5.expandDims(input); + imgTensor = padToSquare(imgTensor, isCenterInputs); + if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) { + imgTensor = tf5["image"].resizeBilinear(imgTensor, [inputSize, inputSize], false, false); + } + return imgTensor.as3D(inputSize, inputSize, 3); + } + if (input instanceof env.getEnv().Canvas) { + return tf5["browser"].fromPixels(imageToSquare(input, inputSize, isCenterInputs)); + } + throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`); + }); + const batchTensor = tf5.stack(inputTensors.map((t) => tf5.cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3); + return batchTensor; + }); + } +}; + +// src/dom/toNetInput.ts +async function toNetInput(inputs) { + if (inputs instanceof NetInput) + return inputs; + const inputArgArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArgArray.length) + throw new Error("toNetInput - empty array passed as input"); + const getIdxHint = (idx) => Array.isArray(inputs) ? ` at input index ${idx}:` : ""; + const inputArray = inputArgArray.map(resolveInput); + inputArray.forEach((input, i) => { + if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) { + if (typeof inputArgArray[i] === "string") + throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`); + throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`); + } + if (isTensor4D(input)) { + const batchSize = input.shape[0]; + if (batchSize !== 1) + throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`); + } + }); + await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input))); + return new NetInput(inputArray, Array.isArray(inputs)); +} + +// src/dom/extractFaces.ts +async function extractFaces(input, detections) { + const { Canvas } = env.getEnv(); + let canvas = input; + if (!(input instanceof Canvas)) { + const netInput = await toNetInput(input); + if (netInput.batchSize > 1) + throw new Error("extractFaces - batchSize > 1 not supported"); + const tensorOrCanvas = netInput.getInput(0); + canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas); + } + const ctx = getContext2dOrThrow(canvas); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det).map((box) => box.clipAtImageBorders(canvas.width, canvas.height)); + return boxes.map(({ x, y, width, height }) => { + const faceImg = createCanvas({ width, height }); + if (width > 0 && height > 0) + getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0); + return faceImg; + }); +} + +// src/dom/extractFaceTensors.ts +var tf6 = __toESM(require_tfjs_esm()); +async function extractFaceTensors(imageTensor, detections) { + if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) { + throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D"); + } + if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) { + throw new Error("extractFaceTensors - batchSize > 1 not supported"); + } + return tf6.tidy(() => { + const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0); + const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight)); + const faceTensors = boxes.filter((box) => box.width > 0 && box.height > 0).map(({ x, y, width, height }) => tf6.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])); + return faceTensors; + }); +} + +// src/dom/fetchOrThrow.ts +async function fetchOrThrow(url, init) { + const { fetch } = env.getEnv(); + const res = await fetch(url, init); + if (!(res.status < 400)) { + throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`); + } + return res; +} + +// src/dom/fetchImage.ts +async function fetchImage(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("image/")) { + throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToImage(blob); +} + +// src/dom/fetchJson.ts +async function fetchJson(uri) { + return (await fetchOrThrow(uri)).json(); +} + +// src/dom/fetchNetWeights.ts +async function fetchNetWeights(uri) { + return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer()); +} + +// src/dom/bufferToVideo.ts +function bufferToVideo(buf) { + return new Promise((resolve, reject) => { + if (!(buf instanceof Blob)) + reject(new Error("bufferToVideo - expected buf to be of type: Blob")); + const video = env.getEnv().createVideoElement(); + video.oncanplay = () => resolve(video); + video.onerror = reject; + video.playsInline = true; + video.muted = true; + video.src = URL.createObjectURL(buf); + video.play(); + }); +} + +// src/dom/fetchVideo.ts +async function fetchVideo(uri) { + const res = await fetchOrThrow(uri); + const blob = await res.blob(); + if (!blob.type.startsWith("video/")) { + throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`); + } + return bufferToVideo(blob); +} + +// src/dom/loadWeightMap.ts +var tf7 = __toESM(require_tfjs_esm()); + +// src/common/getModelUris.ts +function getModelUris(uri, defaultModelName) { + const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`; + if (!uri) { + return { + modelBaseUri: "", + manifestUri: defaultManifestFilename + }; + } + if (uri === "/") { + return { + modelBaseUri: "/", + manifestUri: `/${defaultManifestFilename}` + }; + } + const protocol = uri.startsWith("http://") ? "http://" : uri.startsWith("https://") ? "https://" : ""; + uri = uri.replace(protocol, ""); + const parts = uri.split("/").filter((s) => s); + const manifestFile = uri.endsWith(".json") ? parts[parts.length - 1] : defaultManifestFilename; + let modelBaseUri = protocol + (uri.endsWith(".json") ? parts.slice(0, parts.length - 1) : parts).join("/"); + modelBaseUri = uri.startsWith("/") ? `/${modelBaseUri}` : modelBaseUri; + return { + modelBaseUri, + manifestUri: modelBaseUri === "/" ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}` + }; +} + +// src/dom/loadWeightMap.ts +async function loadWeightMap(uri, defaultModelName) { + const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName); + const manifest = await fetchJson(manifestUri); + return tf7["io"].loadWeights(manifest, modelBaseUri); +} + +// src/dom/matchDimensions.ts +function matchDimensions(input, reference, useMediaDimensions = false) { + const { width, height } = useMediaDimensions ? getMediaDimensions(reference) : reference; + input.width = width; + input.height = height; + return { width, height }; +} + +// src/faceFeatureExtractor/FaceFeatureExtractor.ts +var tf15 = __toESM(require_tfjs_esm()); + +// src/NeuralNetwork.ts +var tf8 = __toESM(require_tfjs_esm()); +var NeuralNetwork = class { + constructor(name) { + this._params = void 0; + this._paramMappings = []; + this._name = name; + } + get params() { + return this._params; + } + get paramMappings() { + return this._paramMappings; + } + get isLoaded() { + return !!this.params; + } + getParamFromPath(paramPath) { + const { obj, objProp } = this.traversePropertyPath(paramPath); + return obj[objProp]; + } + reassignParamFromPath(paramPath, tensor2) { + const { obj, objProp } = this.traversePropertyPath(paramPath); + obj[objProp].dispose(); + obj[objProp] = tensor2; + } + getParamList() { + return this._paramMappings.map(({ paramPath }) => ({ + path: paramPath, + tensor: this.getParamFromPath(paramPath) + })); + } + getTrainableParams() { + return this.getParamList().filter((param) => param.tensor instanceof tf8.Variable); + } + getFrozenParams() { + return this.getParamList().filter((param) => !(param.tensor instanceof tf8.Variable)); + } + variable() { + this.getFrozenParams().forEach(({ path, tensor: tensor2 }) => { + this.reassignParamFromPath(path, tensor2.variable()); + }); + } + freeze() { + this.getTrainableParams().forEach(({ path, tensor: variable }) => { + const tensor2 = tf8.tensor(variable.dataSync()); + variable.dispose(); + this.reassignParamFromPath(path, tensor2); + }); + } + dispose(throwOnRedispose = true) { + this.getParamList().forEach((param) => { + if (throwOnRedispose && param.tensor.isDisposed) { + throw new Error(`param tensor has already been disposed for path ${param.path}`); + } + param.tensor.dispose(); + }); + this._params = void 0; + } + serializeParams() { + return new Float32Array( + this.getParamList().map(({ tensor: tensor2 }) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)) + ); + } + async load(weightsOrUrl) { + if (weightsOrUrl instanceof Float32Array) { + this.extractWeights(weightsOrUrl); + return; + } + await this.loadFromUri(weightsOrUrl); + } + async loadFromUri(uri) { + if (uri && typeof uri !== "string") { + throw new Error(`${this._name}.loadFromUri - expected model uri`); + } + const weightMap = await loadWeightMap(uri, this.getDefaultModelName()); + this.loadFromWeightMap(weightMap); + } + async loadFromDisk(filePath) { + if (filePath && typeof filePath !== "string") { + throw new Error(`${this._name}.loadFromDisk - expected model file path`); + } + const { readFile } = env.getEnv(); + const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName()); + const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => typeof buf === "string" ? Buffer.from(buf) : buf.buffer))); + const loadWeights = tf8["io"].weightsLoaderFactory(fetchWeightsFromDisk); + const manifest = JSON.parse((await readFile(manifestUri)).toString()); + const weightMap = await loadWeights(manifest, modelBaseUri); + this.loadFromWeightMap(weightMap); + } + loadFromWeightMap(weightMap) { + const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap); + this._paramMappings = paramMappings; + this._params = params; + } + extractWeights(weights) { + const { paramMappings, params } = this.extractParams(weights); + this._paramMappings = paramMappings; + this._params = params; + } + traversePropertyPath(paramPath) { + if (!this.params) { + throw new Error("traversePropertyPath - model has no loaded params"); + } + const result = paramPath.split("/").reduce((res, objProp2) => { + if (!res.nextObj.hasOwnProperty(objProp2)) { + throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`); + } + return { obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2] }; + }, { nextObj: this.params }); + const { obj, objProp } = result; + if (!obj || !objProp || !(obj[objProp] instanceof tf8.Tensor)) { + throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`); + } + return { obj, objProp }; + } +}; + +// src/faceFeatureExtractor/denseBlock.ts +var tf10 = __toESM(require_tfjs_esm()); + +// src/common/depthwiseSeparableConv.ts +var tf9 = __toESM(require_tfjs_esm()); +function depthwiseSeparableConv(x, params, stride) { + return tf9.tidy(() => { + let out = tf9.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same"); + out = tf9.add(out, params.bias); + return out; + }); +} + +// src/faceFeatureExtractor/denseBlock.ts +function denseBlock3(x, denseBlockParams, isFirstLayer = false) { + return tf10.tidy(() => { + const out1 = tf10.relu( + isFirstLayer ? tf10.add( + tf10.conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), + denseBlockParams.conv0.bias + ) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]) + ); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = tf10.relu(tf10.add(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + return tf10.relu(tf10.add(out1, tf10.add(out2, out3))); + }); +} +function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) { + return tf10.tidy(() => { + const out1 = tf10.relu( + isFirstLayer ? tf10.add( + tf10.conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), + denseBlockParams.conv0.bias + ) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]) + ); + const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]); + const in3 = tf10.relu(tf10.add(out1, out2)); + const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]); + const in4 = tf10.relu(tf10.add(out1, tf10.add(out2, out3))); + const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]); + return tf10.relu(tf10.add(out1, tf10.add(out2, tf10.add(out3, out4)))); + }); +} + +// src/common/convLayer.ts +var tf11 = __toESM(require_tfjs_esm()); +function convLayer(x, params, padding = "same", withRelu = false) { + return tf11.tidy(() => { + const out = tf11.add( + tf11.conv2d(x, params.filters, [1, 1], padding), + params.bias + ); + return withRelu ? tf11.relu(out) : out; + }); +} + +// src/common/disposeUnusedWeightTensors.ts +function disposeUnusedWeightTensors(weightMap, paramMappings) { + Object.keys(weightMap).forEach((path) => { + if (!paramMappings.some((pm) => pm.originalPath === path)) { + weightMap[path].dispose(); + } + }); +} + +// src/common/extractConvParamsFactory.ts +var tf12 = __toESM(require_tfjs_esm()); +function extractConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, filterSize, mappedPrefix) => { + const filters = tf12.tensor4d( + extractWeights(channelsIn * channelsOut * filterSize * filterSize), + [filterSize, filterSize, channelsIn, channelsOut] + ); + const bias = tf12.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { filters, bias }; + }; +} + +// src/common/extractFCParamsFactory.ts +var tf13 = __toESM(require_tfjs_esm()); +function extractFCParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const fc_weights = tf13.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]); + const fc_bias = tf13.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/weights` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { + weights: fc_weights, + bias: fc_bias + }; + }; +} + +// src/common/extractSeparableConvParamsFactory.ts +var tf14 = __toESM(require_tfjs_esm()); + +// src/common/types.ts +var SeparableConvParams = class { + // eslint-disable-next-line no-useless-constructor + constructor(depthwise_filter, pointwise_filter, bias) { + this.depthwise_filter = depthwise_filter; + this.pointwise_filter = pointwise_filter; + this.bias = bias; + } +}; + +// src/common/extractSeparableConvParamsFactory.ts +function extractSeparableConvParamsFactory(extractWeights, paramMappings) { + return (channelsIn, channelsOut, mappedPrefix) => { + const depthwise_filter = tf14.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]); + const pointwise_filter = tf14.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]); + const bias = tf14.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/depthwise_filter` }, + { paramPath: `${mappedPrefix}/pointwise_filter` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return new SeparableConvParams( + depthwise_filter, + pointwise_filter, + bias + ); + }; +} +function loadSeparableConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4); + const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return new SeparableConvParams( + depthwise_filter, + pointwise_filter, + bias + ); + }; +} + +// src/common/extractWeightEntryFactory.ts +function extractWeightEntryFactory(weightMap, paramMappings) { + return (originalPath, paramRank, mappedPath) => { + const tensor2 = weightMap[originalPath]; + if (!isTensor(tensor2, paramRank)) { + throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`); + } + paramMappings.push( + { originalPath, paramPath: mappedPath || originalPath } + ); + return tensor2; + }; +} + +// src/common/extractWeightsFactory.ts +function extractWeightsFactory(weights) { + let remainingWeights = weights; + function extractWeights(numWeights) { + const ret = remainingWeights.slice(0, numWeights); + remainingWeights = remainingWeights.slice(numWeights); + return ret; + } + function getRemainingWeights() { + return remainingWeights; + } + return { + extractWeights, + getRemainingWeights + }; +} + +// src/faceFeatureExtractor/extractorsFactory.ts +function extractorsFactory(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`) : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`); + const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`); + const conv22 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`); + return { conv0, conv1, conv2: conv22 }; + } + function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) { + const { conv0, conv1, conv2: conv22 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer); + const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParams.ts +function extractParams(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock4Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock4Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock4Params(32, 64, "dense1"); + const dense2 = extractDenseBlock4Params(64, 128, "dense2"); + const dense3 = extractDenseBlock4Params(128, 256, "dense3"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { + dense0, + dense1, + dense2, + dense3 + } + }; +} + +// src/common/loadConvParamsFactory.ts +function loadConvParamsFactory(extractWeightEntry) { + return (prefix) => { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { filters, bias }; + }; +} + +// src/faceFeatureExtractor/loadParamsFactory.ts +function loadParamsFactory(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractDenseBlock3Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + return { conv0, conv1, conv2: conv22 }; + } + function extractDenseBlock4Params(prefix, isFirstLayer = false) { + const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`); + const conv1 = extractSeparableConvParams(`${prefix}/conv1`); + const conv22 = extractSeparableConvParams(`${prefix}/conv2`); + const conv3 = extractSeparableConvParams(`${prefix}/conv3`); + return { + conv0, + conv1, + conv2: conv22, + conv3 + }; + } + return { + extractDenseBlock3Params, + extractDenseBlock4Params + }; +} + +// src/faceFeatureExtractor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap(weightMap) { + const paramMappings = []; + const { + extractDenseBlock4Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock4Params("dense0", true), + dense1: extractDenseBlock4Params("dense1"), + dense2: extractDenseBlock4Params("dense2"), + dense3: extractDenseBlock4Params("dense3") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceFeatureExtractor/FaceFeatureExtractor.ts +var FaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("FaceFeatureExtractor"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("FaceFeatureExtractor - load model before inference"); + } + return tf15.tidy(() => { + const batchTensor = tf15.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock4(normalized, params.dense0, true); + out = denseBlock4(out, params.dense1); + out = denseBlock4(out, params.dense2); + out = denseBlock4(out, params.dense3); + out = tf15.avgPool(out, [7, 7], [2, 2], "valid"); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "face_feature_extractor_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap(weightMap); + } + extractParams(weights) { + return extractParams(weights); + } +}; + +// src/faceProcessor/FaceProcessor.ts +var tf17 = __toESM(require_tfjs_esm()); + +// src/common/fullyConnectedLayer.ts +var tf16 = __toESM(require_tfjs_esm()); +function fullyConnectedLayer(x, params) { + return tf16.tidy(() => tf16.add( + tf16.matMul(x, params.weights), + params.bias + )); +} + +// src/faceProcessor/extractParams.ts +function extractParams2(weights, channelsIn, channelsOut) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const fc = extractFCParams(channelsIn, channelsOut, "fc"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { fc } + }; +} + +// src/faceProcessor/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap2(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { weights, bias }; + } + const params = { + fc: extractFcParams("fc") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceProcessor/util.ts +function seperateWeightMaps(weightMap) { + const featureExtractorMap = {}; + const classifierMap = {}; + Object.keys(weightMap).forEach((key) => { + const map = key.startsWith("fc") ? classifierMap : featureExtractorMap; + map[key] = weightMap[key]; + }); + return { featureExtractorMap, classifierMap }; +} + +// src/faceProcessor/FaceProcessor.ts +var FaceProcessor = class extends NeuralNetwork { + constructor(_name, faceFeatureExtractor) { + super(_name); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input) { + const { params } = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tf17.tidy(() => { + const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input; + return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc); + }); + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const { params, paramMappings } = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams2(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut()); + } + extractParamsFromWeightMap(weightMap) { + const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap2(classifierMap); + } + extractParams(weights) { + const cIn = this.getClassifierChannelsIn(); + const cOut = this.getClassifierChannelsOut(); + const classifierWeightSize = cOut * cIn + cOut; + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceExpressionNet/FaceExpressions.ts +var FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"]; +var FaceExpressions = class { + constructor(probabilities) { + this.neutral = 0; + this.happy = 0; + this.sad = 0; + this.angry = 0; + this.fearful = 0; + this.disgusted = 0; + this.surprised = 0; + if (probabilities.length !== 7) { + throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`); + } + FACE_EXPRESSION_LABELS.forEach((expression, idx) => { + this[expression] = probabilities[idx]; + }); + } + asSortedArray() { + return FACE_EXPRESSION_LABELS.map((expression) => ({ expression, probability: this[expression] })).sort((e0, e1) => e1.probability - e0.probability); + } +}; + +// src/faceExpressionNet/FaceExpressionNet.ts +var FaceExpressionNet = class extends FaceProcessor { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceExpressionNet", faceFeatureExtractor); + } + forwardInput(input) { + return tf18.tidy(() => tf18.softmax(this.runNet(input))); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async predictExpressions(input) { + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput); + const probabilitesByBatch = await Promise.all(tf18.unstack(out).map(async (t) => { + const data = t.dataSync(); + t.dispose(); + return data; + })); + out.dispose(); + const predictionsByBatch = probabilitesByBatch.map((probabilites) => new FaceExpressions(probabilites)); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "face_expression_model"; + } + getClassifierChannelsIn() { + return 256; + } + getClassifierChannelsOut() { + return 7; + } +}; + +// src/factories/WithFaceExpressions.ts +function isWithFaceExpressions(obj) { + return obj.expressions instanceof FaceExpressions; +} +function extendWithFaceExpressions(sourceObj, expressions) { + const extension = { expressions }; + return { ...sourceObj, ...extension }; +} + +// src/draw/drawFaceExpressions.ts +function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, textFieldAnchor) { + const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]; + faceExpressionsArray.forEach((e) => { + const expr = e instanceof FaceExpressions ? e : isWithFaceExpressions(e) ? e.expressions : void 0; + if (!expr) { + throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof"); + } + const sorted = expr.asSortedArray(); + const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence); + const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0); + const drawTextField = new DrawTextField( + resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`), + anchor + ); + drawTextField.draw(canvasArg); + }); +} + +// src/factories/WithFaceLandmarks.ts +function isWithFaceLandmarks(obj) { + return isWithFaceDetection(obj) && obj["landmarks"] instanceof FaceLandmarks && obj["unshiftedLandmarks"] instanceof FaceLandmarks && obj["alignedRect"] instanceof FaceDetection; +} +function calculateFaceAngle(mesh) { + const degrees = (radians) => radians * 180 / Math.PI; + const calcLengthBetweenTwoPoints = (a, b) => Math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2); + const angle = { + roll: void 0, + pitch: void 0, + yaw: void 0 + }; + const calcYaw = (leftPoint, midPoint, rightPoint) => { + const leftToMidpoint = Math.floor(leftPoint.x - midPoint.x); + const rightToMidpoint = Math.floor(midPoint.x - rightPoint.x); + return leftToMidpoint - rightToMidpoint; + }; + const calcRoll = (lever, pivot) => { + const hypotenuse = Math.hypot(pivot.x - lever.x, pivot.y - lever.y); + const opposite = pivot.y - lever.y; + const angleInRadians = Math.asin(opposite / hypotenuse); + const angleInDegrees = degrees(angleInRadians); + const normalizeAngle = Math.floor(90 - angleInDegrees); + const tiltDirection = pivot.x - lever.x < 0 ? -1 : 1; + const result = normalizeAngle * tiltDirection; + return result; + }; + const calcPitch = (leftPoint, midPoint, rightPoint) => { + const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint); + const baseCoords = new Point((leftPoint.x + rightPoint.x) / 2, (leftPoint.y + rightPoint.y) / 2); + const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords); + const angleInRadians = Math.atan(midToBaseLength / base); + const angleInDegrees = Math.floor(degrees(angleInRadians)); + const direction = baseCoords.y - midPoint.y < 0 ? -1 : 1; + const result = angleInDegrees * direction; + return result; + }; + if (!mesh || !mesh.positions || mesh.positions.length !== 68) + return angle; + const pt = mesh.positions; + angle.roll = calcRoll(pt[27], pt[66]); + angle.pitch = calcPitch(pt[14], pt[30], pt[2]); + angle.yaw = calcYaw(pt[14], pt[33], pt[2]); + return angle; +} +function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) { + const { box: shift } = sourceObj.detection; + const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y); + const rect = landmarks.align(); + const { imageDims } = sourceObj.detection; + const alignedRect = new FaceDetection( + sourceObj.detection.score, + rect.rescale(imageDims.reverse()), + imageDims + ); + const angle = calculateFaceAngle(unshiftedLandmarks); + const extension = { landmarks, unshiftedLandmarks, alignedRect, angle }; + return { ...sourceObj, ...extension }; +} + +// src/draw/DrawFaceLandmarks.ts +var DrawFaceLandmarksOptions = class { + constructor(options = {}) { + const { + drawLines = true, + drawPoints = true, + lineWidth, + lineColor, + pointSize, + pointColor + } = options; + this.drawLines = drawLines; + this.drawPoints = drawPoints; + this.lineWidth = lineWidth || 1; + this.pointSize = pointSize || 2; + this.lineColor = lineColor || "rgba(0, 255, 255, 1)"; + this.pointColor = pointColor || "rgba(255, 0, 255, 1)"; + } +}; +var DrawFaceLandmarks = class { + constructor(faceLandmarks, options = {}) { + this.faceLandmarks = faceLandmarks; + this.options = new DrawFaceLandmarksOptions(options); + } + draw(canvasArg) { + const ctx = getContext2dOrThrow(canvasArg); + const { + drawLines, + drawPoints, + lineWidth, + lineColor, + pointSize, + pointColor + } = this.options; + if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) { + ctx.strokeStyle = lineColor; + ctx.lineWidth = lineWidth; + drawContour(ctx, this.faceLandmarks.getJawOutline()); + drawContour(ctx, this.faceLandmarks.getLeftEyeBrow()); + drawContour(ctx, this.faceLandmarks.getRightEyeBrow()); + drawContour(ctx, this.faceLandmarks.getNose()); + drawContour(ctx, this.faceLandmarks.getLeftEye(), true); + drawContour(ctx, this.faceLandmarks.getRightEye(), true); + drawContour(ctx, this.faceLandmarks.getMouth(), true); + } + if (drawPoints) { + ctx.strokeStyle = pointColor; + ctx.fillStyle = pointColor; + const drawPoint = (pt) => { + ctx.beginPath(); + ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI); + ctx.fill(); + }; + this.faceLandmarks.positions.forEach(drawPoint); + } + } +}; +function drawFaceLandmarks(canvasArg, faceLandmarks) { + const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]; + faceLandmarksArray.forEach((f) => { + const landmarks = f instanceof FaceLandmarks ? f : isWithFaceLandmarks(f) ? f.landmarks : void 0; + if (!landmarks) { + throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof"); + } + new DrawFaceLandmarks(landmarks).draw(canvasArg); + }); +} + +// package.json +var version = "1.7.12"; + +// src/ageGenderNet/AgeGenderNet.ts +var tf20 = __toESM(require_tfjs_esm()); + +// src/xception/TinyXception.ts +var tf19 = __toESM(require_tfjs_esm()); + +// src/xception/extractParams.ts +function extractorsFactory2(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`); + return { separable_conv0, separable_conv1, expansion_conv }; + } + function extractMainBlockParams(channels, mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`); + return { separable_conv0, separable_conv1, separable_conv2 }; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParams3(weights, numMainBlocks) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = extractorsFactory2(extractWeights, paramMappings); + const entry_flow_conv_in = extractConvParams(3, 32, 3, "entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, "entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, "entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams(256, 512, "exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { entry_flow, middle_flow, exit_flow } + }; +} + +// src/xception/extractParamsFromWeightMap.ts +function loadParamsFactory2(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + const extractConvParams = loadConvParamsFactory(extractWeightEntry); + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + function extractReductionBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`); + return { separable_conv0, separable_conv1, expansion_conv }; + } + function extractMainBlockParams(mappedPrefix) { + const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`); + const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`); + const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`); + return { separable_conv0, separable_conv1, separable_conv2 }; + } + return { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + }; +} +function extractParamsFromWeightMap3(weightMap, numMainBlocks) { + const paramMappings = []; + const { + extractConvParams, + extractSeparableConvParams, + extractReductionBlockParams, + extractMainBlockParams + } = loadParamsFactory2(weightMap, paramMappings); + const entry_flow_conv_in = extractConvParams("entry_flow/conv_in"); + const entry_flow_reduction_block_0 = extractReductionBlockParams("entry_flow/reduction_block_0"); + const entry_flow_reduction_block_1 = extractReductionBlockParams("entry_flow/reduction_block_1"); + const entry_flow = { + conv_in: entry_flow_conv_in, + reduction_block_0: entry_flow_reduction_block_0, + reduction_block_1: entry_flow_reduction_block_1 + }; + const middle_flow = {}; + range(numMainBlocks, 0, 1).forEach((idx) => { + middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`); + }); + const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block"); + const exit_flow_separable_conv = extractSeparableConvParams("exit_flow/separable_conv"); + const exit_flow = { + reduction_block: exit_flow_reduction_block, + separable_conv: exit_flow_separable_conv + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }; +} + +// src/xception/TinyXception.ts +function conv(x, params, stride) { + return tf19.add(tf19.conv2d(x, params.filters, stride, "same"), params.bias); +} +function reductionBlock(x, params, isActivateInput = true) { + let out = isActivateInput ? tf19.relu(x) : x; + out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv1, [1, 1]); + out = tf19.maxPool(out, [3, 3], [2, 2], "same"); + out = tf19.add(out, conv(x, params.expansion_conv, [2, 2])); + return out; +} +function mainBlock(x, params) { + let out = depthwiseSeparableConv(tf19.relu(x), params.separable_conv0, [1, 1]); + out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv1, [1, 1]); + out = depthwiseSeparableConv(tf19.relu(out), params.separable_conv2, [1, 1]); + out = tf19.add(out, x); + return out; +} +var TinyXception = class extends NeuralNetwork { + constructor(numMainBlocks) { + super("TinyXception"); + this._numMainBlocks = numMainBlocks; + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("TinyXception - load model before inference"); + } + return tf19.tidy(() => { + const batchTensor = tf19.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = tf19.relu(conv(normalized, params.entry_flow.conv_in, [2, 2])); + out = reductionBlock(out, params.entry_flow.reduction_block_0, false); + out = reductionBlock(out, params.entry_flow.reduction_block_1); + range(this._numMainBlocks, 0, 1).forEach((idx) => { + out = mainBlock(out, params.middle_flow[`main_block_${idx}`]); + }); + out = reductionBlock(out, params.exit_flow.reduction_block); + out = tf19.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1])); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "tiny_xception_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap3(weightMap, this._numMainBlocks); + } + extractParams(weights) { + return extractParams3(weights, this._numMainBlocks); + } +}; + +// src/ageGenderNet/extractParams.ts +function extractParams4(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings); + const age = extractFCParams(512, 1, "fc/age"); + const gender = extractFCParams(512, 2, "fc/gender"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { fc: { age, gender } } + }; +} + +// src/ageGenderNet/extractParamsFromWeightMap.ts +function extractParamsFromWeightMap4(weightMap) { + const paramMappings = []; + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractFcParams(prefix) { + const weights = extractWeightEntry(`${prefix}/weights`, 2); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { weights, bias }; + } + const params = { + fc: { + age: extractFcParams("fc/age"), + gender: extractFcParams("fc/gender") + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/ageGenderNet/types.ts +var Gender = /* @__PURE__ */ ((Gender2) => { + Gender2["FEMALE"] = "female"; + Gender2["MALE"] = "male"; + return Gender2; +})(Gender || {}); + +// src/ageGenderNet/AgeGenderNet.ts +var AgeGenderNet = class extends NeuralNetwork { + constructor(faceFeatureExtractor = new TinyXception(2)) { + super("AgeGenderNet"); + this._faceFeatureExtractor = faceFeatureExtractor; + } + get faceFeatureExtractor() { + return this._faceFeatureExtractor; + } + runNet(input) { + const { params } = this; + if (!params) { + throw new Error(`${this._name} - load model before inference`); + } + return tf20.tidy(() => { + const bottleneckFeatures = input instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input) : input; + const pooled = tf20.avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1); + const age = fullyConnectedLayer(pooled, params.fc.age).as1D(); + const gender = fullyConnectedLayer(pooled, params.fc.gender); + return { age, gender }; + }); + } + forwardInput(input) { + return tf20.tidy(() => { + const { age, gender } = this.runNet(input); + return { age, gender: tf20.softmax(gender) }; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async predictAgeAndGender(input) { + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput); + const ages = tf20.unstack(out.age); + const genders = tf20.unstack(out.gender); + const ageAndGenderTensors = ages.map((ageTensor, i) => ({ + ageTensor, + genderTensor: genders[i] + })); + const predictionsByBatch = await Promise.all( + ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => { + const age = ageTensor.dataSync()[0]; + const probMale = genderTensor.dataSync()[0]; + const isMale = probMale > 0.5; + const gender = isMale ? "male" /* MALE */ : "female" /* FEMALE */; + const genderProbability = isMale ? probMale : 1 - probMale; + ageTensor.dispose(); + genderTensor.dispose(); + return { age, gender, genderProbability }; + }) + ); + out.age.dispose(); + out.gender.dispose(); + return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0]; + } + getDefaultModelName() { + return "age_gender_model"; + } + dispose(throwOnRedispose = true) { + this.faceFeatureExtractor.dispose(throwOnRedispose); + super.dispose(throwOnRedispose); + } + loadClassifierParams(weights) { + const { params, paramMappings } = this.extractClassifierParams(weights); + this._params = params; + this._paramMappings = paramMappings; + } + extractClassifierParams(weights) { + return extractParams4(weights); + } + extractParamsFromWeightMap(weightMap) { + const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap); + this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap); + return extractParamsFromWeightMap4(classifierMap); + } + extractParams(weights) { + const classifierWeightSize = 512 * 1 + 1 + (512 * 2 + 2); + const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize); + const classifierWeights = weights.slice(weights.length - classifierWeightSize); + this.faceFeatureExtractor.extractWeights(featureExtractorWeights); + return this.extractClassifierParams(classifierWeights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68NetBase.ts +var tf21 = __toESM(require_tfjs_esm()); +var FaceLandmark68NetBase = class extends FaceProcessor { + postProcess(output, inputSize, originalDimensions) { + const inputDimensions = originalDimensions.map(({ width, height }) => { + const scale2 = inputSize / Math.max(height, width); + return { + width: width * scale2, + height: height * scale2 + }; + }); + const batchSize = inputDimensions.length; + return tf21.tidy(() => { + const createInterleavedTensor = (fillX, fillY) => tf21.stack([tf21.fill([68], fillX, "float32"), tf21.fill([68], fillY, "float32")], 1).as2D(1, 136).as1D(); + const getPadding = (batchIdx, cond) => { + const { width, height } = inputDimensions[batchIdx]; + return cond(width, height) ? Math.abs(width - height) / 2 : 0; + }; + const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h); + const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w); + const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize, "float32")).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor( + getPaddingX(batchIdx), + getPaddingY(batchIdx) + )))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor( + inputDimensions[batchIdx].width, + inputDimensions[batchIdx].height + )))); + return landmarkTensors; + }); + } + forwardInput(input) { + return tf21.tidy(() => { + const out = this.runNet(input); + return this.postProcess( + out, + input.inputSize, + input.inputDimensions.map(([height, width]) => ({ height, width })) + ); + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async detectLandmarks(input) { + const netInput = await toNetInput(input); + const landmarkTensors = tf21.tidy( + () => tf21.unstack(this.forwardInput(netInput)) + ); + const landmarksForBatch = await Promise.all(landmarkTensors.map( + async (landmarkTensor, batchIdx) => { + const landmarksArray = Array.from(landmarkTensor.dataSync()); + const xCoords = landmarksArray.filter((_, i) => isEven(i)); + const yCoords = landmarksArray.filter((_, i) => !isEven(i)); + return new FaceLandmarks68( + Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])), + { + height: netInput.getInputHeight(batchIdx), + width: netInput.getInputWidth(batchIdx) + } + ); + } + )); + landmarkTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? landmarksForBatch : landmarksForBatch[0]; + } + getClassifierChannelsOut() { + return 136; + } +}; + +// src/faceLandmarkNet/FaceLandmark68Net.ts +var FaceLandmark68Net = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new FaceFeatureExtractor()) { + super("FaceLandmark68Net", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_model"; + } + getClassifierChannelsIn() { + return 256; + } +}; + +// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts +var tf22 = __toESM(require_tfjs_esm()); + +// src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts +function extractParamsFromWeightMapTiny(weightMap) { + const paramMappings = []; + const { + extractDenseBlock3Params + } = loadParamsFactory(weightMap, paramMappings); + const params = { + dense0: extractDenseBlock3Params("dense0", true), + dense1: extractDenseBlock3Params("dense1"), + dense2: extractDenseBlock3Params("dense2") + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceFeatureExtractor/extractParamsTiny.ts +function extractParamsTiny(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractDenseBlock3Params + } = extractorsFactory(extractWeights, paramMappings); + const dense0 = extractDenseBlock3Params(3, 32, "dense0", true); + const dense1 = extractDenseBlock3Params(32, 64, "dense1"); + const dense2 = extractDenseBlock3Params(64, 128, "dense2"); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + paramMappings, + params: { dense0, dense1, dense2 } + }; +} + +// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts +var TinyFaceFeatureExtractor = class extends NeuralNetwork { + constructor() { + super("TinyFaceFeatureExtractor"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("TinyFaceFeatureExtractor - load model before inference"); + } + return tf22.tidy(() => { + const batchTensor = tf22.cast(input.toBatchTensor(112, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = denseBlock3(normalized, params.dense0, true); + out = denseBlock3(out, params.dense1); + out = denseBlock3(out, params.dense2); + out = tf22.avgPool(out, [14, 14], [2, 2], "valid"); + return out; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + getDefaultModelName() { + return "face_feature_extractor_tiny_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMapTiny(weightMap); + } + extractParams(weights) { + return extractParamsTiny(weights); + } +}; + +// src/faceLandmarkNet/FaceLandmark68TinyNet.ts +var FaceLandmark68TinyNet = class extends FaceLandmark68NetBase { + constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) { + super("FaceLandmark68TinyNet", faceFeatureExtractor); + } + getDefaultModelName() { + return "face_landmark_68_tiny_model"; + } + getClassifierChannelsIn() { + return 128; + } +}; + +// src/faceLandmarkNet/index.ts +var FaceLandmarkNet = class extends FaceLandmark68Net { +}; + +// src/faceRecognitionNet/FaceRecognitionNet.ts +var tf27 = __toESM(require_tfjs_esm()); + +// src/faceRecognitionNet/convLayer.ts +var tf24 = __toESM(require_tfjs_esm()); + +// src/faceRecognitionNet/scaleLayer.ts +var tf23 = __toESM(require_tfjs_esm()); +function scale(x, params) { + return tf23.add(tf23.mul(x, params.weights), params.biases); +} + +// src/faceRecognitionNet/convLayer.ts +function convLayer2(x, params, strides, withRelu, padding = "same") { + const { filters, bias } = params.conv; + let out = tf24.conv2d(x, filters, strides, padding); + out = tf24.add(out, bias); + out = scale(out, params.scale); + return withRelu ? tf24.relu(out) : out; +} +function conv2(x, params) { + return convLayer2(x, params, [1, 1], true); +} +function convNoRelu(x, params) { + return convLayer2(x, params, [1, 1], false); +} +function convDown(x, params) { + return convLayer2(x, params, [2, 2], true, "valid"); +} + +// src/faceRecognitionNet/extractParams.ts +var tf25 = __toESM(require_tfjs_esm()); +function extractorsFactory3(extractWeights, paramMappings) { + function extractFilterValues(numFilterValues, numFilters, filterSize) { + const weights = extractWeights(numFilterValues); + const depth = weights.length / (numFilters * filterSize * filterSize); + if (isFloat(depth)) { + throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`); + } + return tf25.tidy( + () => tf25.transpose( + tf25.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), + [2, 3, 1, 0] + ) + ); + } + function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const filters = extractFilterValues(numFilterValues, numFilters, filterSize); + const bias = tf25.tensor1d(extractWeights(numFilters)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/bias` } + ); + return { filters, bias }; + } + function extractScaleLayerParams(numWeights, mappedPrefix) { + const weights = tf25.tensor1d(extractWeights(numWeights)); + const biases = tf25.tensor1d(extractWeights(numWeights)); + paramMappings.push( + { paramPath: `${mappedPrefix}/weights` }, + { paramPath: `${mappedPrefix}/biases` } + ); + return { + weights, + biases + }; + } + function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) { + const conv3 = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`); + const scale2 = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`); + return { conv: conv3, scale: scale2 }; + } + function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) { + const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`); + const conv22 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`); + return { conv1, conv2: conv22 }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParams5(weights) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory3(extractWeights, paramMappings); + const conv32_down = extractConvLayerParams(4704, 32, 7, "conv32_down"); + const conv32_1 = extractResidualLayerParams(9216, 32, 3, "conv32_1"); + const conv32_2 = extractResidualLayerParams(9216, 32, 3, "conv32_2"); + const conv32_3 = extractResidualLayerParams(9216, 32, 3, "conv32_3"); + const conv64_down = extractResidualLayerParams(36864, 64, 3, "conv64_down", true); + const conv64_1 = extractResidualLayerParams(36864, 64, 3, "conv64_1"); + const conv64_2 = extractResidualLayerParams(36864, 64, 3, "conv64_2"); + const conv64_3 = extractResidualLayerParams(36864, 64, 3, "conv64_3"); + const conv128_down = extractResidualLayerParams(147456, 128, 3, "conv128_down", true); + const conv128_1 = extractResidualLayerParams(147456, 128, 3, "conv128_1"); + const conv128_2 = extractResidualLayerParams(147456, 128, 3, "conv128_2"); + const conv256_down = extractResidualLayerParams(589824, 256, 3, "conv256_down", true); + const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1"); + const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2"); + const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out"); + const fc = tf25.tidy( + () => tf25.transpose(tf25.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]) + ); + paramMappings.push({ paramPath: "fc" }); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + return { params, paramMappings }; +} + +// src/faceRecognitionNet/extractParamsFromWeightMap.ts +function extractorsFactory4(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractScaleLayerParams(prefix) { + const weights = extractWeightEntry(`${prefix}/scale/weights`, 1); + const biases = extractWeightEntry(`${prefix}/scale/biases`, 1); + return { weights, biases }; + } + function extractConvLayerParams(prefix) { + const filters = extractWeightEntry(`${prefix}/conv/filters`, 4); + const bias = extractWeightEntry(`${prefix}/conv/bias`, 1); + const scale2 = extractScaleLayerParams(prefix); + return { conv: { filters, bias }, scale: scale2 }; + } + function extractResidualLayerParams(prefix) { + return { + conv1: extractConvLayerParams(`${prefix}/conv1`), + conv2: extractConvLayerParams(`${prefix}/conv2`) + }; + } + return { + extractConvLayerParams, + extractResidualLayerParams + }; +} +function extractParamsFromWeightMap5(weightMap) { + const paramMappings = []; + const { + extractConvLayerParams, + extractResidualLayerParams + } = extractorsFactory4(weightMap, paramMappings); + const conv32_down = extractConvLayerParams("conv32_down"); + const conv32_1 = extractResidualLayerParams("conv32_1"); + const conv32_2 = extractResidualLayerParams("conv32_2"); + const conv32_3 = extractResidualLayerParams("conv32_3"); + const conv64_down = extractResidualLayerParams("conv64_down"); + const conv64_1 = extractResidualLayerParams("conv64_1"); + const conv64_2 = extractResidualLayerParams("conv64_2"); + const conv64_3 = extractResidualLayerParams("conv64_3"); + const conv128_down = extractResidualLayerParams("conv128_down"); + const conv128_1 = extractResidualLayerParams("conv128_1"); + const conv128_2 = extractResidualLayerParams("conv128_2"); + const conv256_down = extractResidualLayerParams("conv256_down"); + const conv256_1 = extractResidualLayerParams("conv256_1"); + const conv256_2 = extractResidualLayerParams("conv256_2"); + const conv256_down_out = extractResidualLayerParams("conv256_down_out"); + const { fc } = weightMap; + paramMappings.push({ originalPath: "fc", paramPath: "fc" }); + if (!isTensor2D(fc)) { + throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`); + } + const params = { + conv32_down, + conv32_1, + conv32_2, + conv32_3, + conv64_down, + conv64_1, + conv64_2, + conv64_3, + conv128_down, + conv128_1, + conv128_2, + conv256_down, + conv256_1, + conv256_2, + conv256_down_out, + fc + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/faceRecognitionNet/residualLayer.ts +var tf26 = __toESM(require_tfjs_esm()); +function residual(x, params) { + let out = conv2(x, params.conv1); + out = convNoRelu(out, params.conv2); + out = tf26.add(out, x); + out = tf26.relu(out); + return out; +} +function residualDown(x, params) { + let out = convDown(x, params.conv1); + out = convNoRelu(out, params.conv2); + let pooled = tf26.avgPool(x, 2, 2, "valid"); + const zeros2 = tf26.zeros(pooled.shape); + const isPad = pooled.shape[3] !== out.shape[3]; + const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]; + if (isAdjustShape) { + const padShapeX = [...out.shape]; + padShapeX[1] = 1; + const zerosW = tf26.zeros(padShapeX); + out = tf26.concat([out, zerosW], 1); + const padShapeY = [...out.shape]; + padShapeY[2] = 1; + const zerosH = tf26.zeros(padShapeY); + out = tf26.concat([out, zerosH], 2); + } + pooled = isPad ? tf26.concat([pooled, zeros2], 3) : pooled; + out = tf26.add(pooled, out); + out = tf26.relu(out); + return out; +} + +// src/faceRecognitionNet/FaceRecognitionNet.ts +var FaceRecognitionNet = class extends NeuralNetwork { + constructor() { + super("FaceRecognitionNet"); + } + forwardInput(input) { + const { params } = this; + if (!params) { + throw new Error("FaceRecognitionNet - load model before inference"); + } + return tf27.tidy(() => { + const batchTensor = tf27.cast(input.toBatchTensor(150, true), "float32"); + const meanRgb = [122.782, 117.001, 104.298]; + const normalized = normalize(batchTensor, meanRgb).div(255); + let out = convDown(normalized, params.conv32_down); + out = tf27.maxPool(out, 3, 2, "valid"); + out = residual(out, params.conv32_1); + out = residual(out, params.conv32_2); + out = residual(out, params.conv32_3); + out = residualDown(out, params.conv64_down); + out = residual(out, params.conv64_1); + out = residual(out, params.conv64_2); + out = residual(out, params.conv64_3); + out = residualDown(out, params.conv128_down); + out = residual(out, params.conv128_1); + out = residual(out, params.conv128_2); + out = residualDown(out, params.conv256_down); + out = residual(out, params.conv256_1); + out = residual(out, params.conv256_2); + out = residualDown(out, params.conv256_down_out); + const globalAvg = out.mean([1, 2]); + const fullyConnected = tf27.matMul(globalAvg, params.fc); + return fullyConnected; + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async computeFaceDescriptor(input) { + var _a; + if ((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a.some((dim) => dim <= 0)) + return new Float32Array(128); + const netInput = await toNetInput(input); + const faceDescriptorTensors = tf27.tidy(() => tf27.unstack(this.forwardInput(netInput))); + const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())); + faceDescriptorTensors.forEach((t) => t.dispose()); + return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0]; + } + getDefaultModelName() { + return "face_recognition_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap5(weightMap); + } + extractParams(weights) { + return extractParams5(weights); + } +}; + +// src/faceRecognitionNet/index.ts +function createFaceRecognitionNet(weights) { + const net = new FaceRecognitionNet(); + net.extractWeights(weights); + return net; +} + +// src/factories/WithFaceDescriptor.ts +function extendWithFaceDescriptor(sourceObj, descriptor) { + const extension = { descriptor }; + return { ...sourceObj, ...extension }; +} + +// src/factories/WithAge.ts +function isWithAge(obj) { + return typeof obj.age === "number"; +} +function extendWithAge(sourceObj, age) { + const extension = { age }; + return { ...sourceObj, ...extension }; +} + +// src/factories/WithGender.ts +function isWithGender(obj) { + return (obj.gender === "male" /* MALE */ || obj.gender === "female" /* FEMALE */) && isValidProbablitiy(obj.genderProbability); +} +function extendWithGender(sourceObj, gender, genderProbability) { + const extension = { gender, genderProbability }; + return { ...sourceObj, ...extension }; +} + +// src/ssdMobilenetv1/SsdMobilenetv1.ts +var tf34 = __toESM(require_tfjs_esm()); + +// src/ssdMobilenetv1/extractParams.ts +var tf28 = __toESM(require_tfjs_esm()); +function extractorsFactory5(extractWeights, paramMappings) { + function extractDepthwiseConvParams(numChannels, mappedPrefix) { + const filters = tf28.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]); + const batch_norm_scale = tf28.tensor1d(extractWeights(numChannels)); + const batch_norm_offset = tf28.tensor1d(extractWeights(numChannels)); + const batch_norm_mean = tf28.tensor1d(extractWeights(numChannels)); + const batch_norm_variance = tf28.tensor1d(extractWeights(numChannels)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/batch_norm_scale` }, + { paramPath: `${mappedPrefix}/batch_norm_offset` }, + { paramPath: `${mappedPrefix}/batch_norm_mean` }, + { paramPath: `${mappedPrefix}/batch_norm_variance` } + ); + return { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }; + } + function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) { + const filters = tf28.tensor4d( + extractWeights(channelsIn * channelsOut * filterSize * filterSize), + [filterSize, filterSize, channelsIn, channelsOut] + ); + const bias = tf28.tensor1d(extractWeights(channelsOut)); + paramMappings.push( + { paramPath: `${mappedPrefix}/filters` }, + { paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}` } + ); + return { filters, bias }; + } + function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) { + const { + filters, + bias + } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true); + return { + filters, + batch_norm_offset: bias + }; + } + function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) { + const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`); + const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`); + return { depthwise_conv, pointwise_conv }; + } + function extractMobilenetV1Params() { + const conv_0 = extractPointwiseConvParams(3, 32, 3, "mobilenetv1/conv_0"); + const conv_1 = extractConvPairParams(32, 64, "mobilenetv1/conv_1"); + const conv_2 = extractConvPairParams(64, 128, "mobilenetv1/conv_2"); + const conv_3 = extractConvPairParams(128, 128, "mobilenetv1/conv_3"); + const conv_4 = extractConvPairParams(128, 256, "mobilenetv1/conv_4"); + const conv_5 = extractConvPairParams(256, 256, "mobilenetv1/conv_5"); + const conv_6 = extractConvPairParams(256, 512, "mobilenetv1/conv_6"); + const conv_7 = extractConvPairParams(512, 512, "mobilenetv1/conv_7"); + const conv_8 = extractConvPairParams(512, 512, "mobilenetv1/conv_8"); + const conv_9 = extractConvPairParams(512, 512, "mobilenetv1/conv_9"); + const conv_10 = extractConvPairParams(512, 512, "mobilenetv1/conv_10"); + const conv_11 = extractConvPairParams(512, 512, "mobilenetv1/conv_11"); + const conv_12 = extractConvPairParams(512, 1024, "mobilenetv1/conv_12"); + const conv_13 = extractConvPairParams(1024, 1024, "mobilenetv1/conv_13"); + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + conv_8, + conv_9, + conv_10, + conv_11, + conv_12, + conv_13 + }; + } + function extractPredictionLayerParams() { + const conv_0 = extractPointwiseConvParams(1024, 256, 1, "prediction_layer/conv_0"); + const conv_1 = extractPointwiseConvParams(256, 512, 3, "prediction_layer/conv_1"); + const conv_2 = extractPointwiseConvParams(512, 128, 1, "prediction_layer/conv_2"); + const conv_3 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_3"); + const conv_4 = extractPointwiseConvParams(256, 128, 1, "prediction_layer/conv_4"); + const conv_5 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_5"); + const conv_6 = extractPointwiseConvParams(256, 64, 1, "prediction_layer/conv_6"); + const conv_7 = extractPointwiseConvParams(64, 128, 3, "prediction_layer/conv_7"); + const box_encoding_0_predictor = extractConvParams(512, 12, 1, "prediction_layer/box_predictor_0/box_encoding_predictor"); + const class_predictor_0 = extractConvParams(512, 9, 1, "prediction_layer/box_predictor_0/class_predictor"); + const box_encoding_1_predictor = extractConvParams(1024, 24, 1, "prediction_layer/box_predictor_1/box_encoding_predictor"); + const class_predictor_1 = extractConvParams(1024, 18, 1, "prediction_layer/box_predictor_1/class_predictor"); + const box_encoding_2_predictor = extractConvParams(512, 24, 1, "prediction_layer/box_predictor_2/box_encoding_predictor"); + const class_predictor_2 = extractConvParams(512, 18, 1, "prediction_layer/box_predictor_2/class_predictor"); + const box_encoding_3_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_3/box_encoding_predictor"); + const class_predictor_3 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_3/class_predictor"); + const box_encoding_4_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_4/box_encoding_predictor"); + const class_predictor_4 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_4/class_predictor"); + const box_encoding_5_predictor = extractConvParams(128, 24, 1, "prediction_layer/box_predictor_5/box_encoding_predictor"); + const class_predictor_5 = extractConvParams(128, 18, 1, "prediction_layer/box_predictor_5/class_predictor"); + const box_predictor_0 = { + box_encoding_predictor: box_encoding_0_predictor, + class_predictor: class_predictor_0 + }; + const box_predictor_1 = { + box_encoding_predictor: box_encoding_1_predictor, + class_predictor: class_predictor_1 + }; + const box_predictor_2 = { + box_encoding_predictor: box_encoding_2_predictor, + class_predictor: class_predictor_2 + }; + const box_predictor_3 = { + box_encoding_predictor: box_encoding_3_predictor, + class_predictor: class_predictor_3 + }; + const box_predictor_4 = { + box_encoding_predictor: box_encoding_4_predictor, + class_predictor: class_predictor_4 + }; + const box_predictor_5 = { + box_encoding_predictor: box_encoding_5_predictor, + class_predictor: class_predictor_5 + }; + return { + conv_0, + conv_1, + conv_2, + conv_3, + conv_4, + conv_5, + conv_6, + conv_7, + box_predictor_0, + box_predictor_1, + box_predictor_2, + box_predictor_3, + box_predictor_4, + box_predictor_5 + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParams6(weights) { + const paramMappings = []; + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory5(extractWeights, paramMappings); + const mobilenetv1 = extractMobilenetV1Params(); + const prediction_layer = extractPredictionLayerParams(); + const extra_dim = tf28.tensor3d( + extractWeights(5118 * 4), + [1, 5118, 4] + ); + const output_layer = { + extra_dim + }; + paramMappings.push({ paramPath: "output_layer/extra_dim" }); + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { + params: { + mobilenetv1, + prediction_layer, + output_layer + }, + paramMappings + }; +} + +// src/ssdMobilenetv1/extractParamsFromWeightMap.ts +function extractorsFactory6(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractPointwiseConvParams(prefix, idx, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`); + const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`); + return { filters, batch_norm_offset }; + } + function extractConvPairParams(idx) { + const mappedPrefix = `mobilenetv1/conv_${idx}`; + const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`; + const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`; + const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`; + const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`); + const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`); + const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`); + const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`); + const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`); + return { + depthwise_conv: { + filters, + batch_norm_scale, + batch_norm_offset, + batch_norm_mean, + batch_norm_variance + }, + pointwise_conv: extractPointwiseConvParams("MobilenetV1", idx, mappedPrefixPointwiseConv) + }; + } + function extractMobilenetV1Params() { + return { + conv_0: extractPointwiseConvParams("MobilenetV1", 0, "mobilenetv1/conv_0"), + conv_1: extractConvPairParams(1), + conv_2: extractConvPairParams(2), + conv_3: extractConvPairParams(3), + conv_4: extractConvPairParams(4), + conv_5: extractConvPairParams(5), + conv_6: extractConvPairParams(6), + conv_7: extractConvPairParams(7), + conv_8: extractConvPairParams(8), + conv_9: extractConvPairParams(9), + conv_10: extractConvPairParams(10), + conv_11: extractConvPairParams(11), + conv_12: extractConvPairParams(12), + conv_13: extractConvPairParams(13) + }; + } + function extractConvParams(prefix, mappedPrefix) { + const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`); + const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`); + return { filters, bias }; + } + function extractBoxPredictorParams(idx) { + const box_encoding_predictor = extractConvParams( + `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, + `prediction_layer/box_predictor_${idx}/box_encoding_predictor` + ); + const class_predictor = extractConvParams( + `Prediction/BoxPredictor_${idx}/ClassPredictor`, + `prediction_layer/box_predictor_${idx}/class_predictor` + ); + return { box_encoding_predictor, class_predictor }; + } + function extractPredictionLayerParams() { + return { + conv_0: extractPointwiseConvParams("Prediction", 0, "prediction_layer/conv_0"), + conv_1: extractPointwiseConvParams("Prediction", 1, "prediction_layer/conv_1"), + conv_2: extractPointwiseConvParams("Prediction", 2, "prediction_layer/conv_2"), + conv_3: extractPointwiseConvParams("Prediction", 3, "prediction_layer/conv_3"), + conv_4: extractPointwiseConvParams("Prediction", 4, "prediction_layer/conv_4"), + conv_5: extractPointwiseConvParams("Prediction", 5, "prediction_layer/conv_5"), + conv_6: extractPointwiseConvParams("Prediction", 6, "prediction_layer/conv_6"), + conv_7: extractPointwiseConvParams("Prediction", 7, "prediction_layer/conv_7"), + box_predictor_0: extractBoxPredictorParams(0), + box_predictor_1: extractBoxPredictorParams(1), + box_predictor_2: extractBoxPredictorParams(2), + box_predictor_3: extractBoxPredictorParams(3), + box_predictor_4: extractBoxPredictorParams(4), + box_predictor_5: extractBoxPredictorParams(5) + }; + } + return { + extractMobilenetV1Params, + extractPredictionLayerParams + }; +} +function extractParamsFromWeightMap6(weightMap) { + const paramMappings = []; + const { + extractMobilenetV1Params, + extractPredictionLayerParams + } = extractorsFactory6(weightMap, paramMappings); + const extra_dim = weightMap["Output/extra_dim"]; + paramMappings.push({ originalPath: "Output/extra_dim", paramPath: "output_layer/extra_dim" }); + if (!isTensor3D(extra_dim)) { + throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`); + } + const params = { + mobilenetv1: extractMobilenetV1Params(), + prediction_layer: extractPredictionLayerParams(), + output_layer: { + extra_dim + } + }; + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/ssdMobilenetv1/mobileNetV1.ts +var tf30 = __toESM(require_tfjs_esm()); + +// src/ssdMobilenetv1/pointwiseConvLayer.ts +var tf29 = __toESM(require_tfjs_esm()); +function pointwiseConvLayer(x, params, strides) { + return tf29.tidy(() => { + let out = tf29.conv2d(x, params.filters, strides, "same"); + out = tf29.add(out, params.batch_norm_offset); + return tf29.clipByValue(out, 0, 6); + }); +} + +// src/ssdMobilenetv1/mobileNetV1.ts +var epsilon = 0.0010000000474974513; +function depthwiseConvLayer(x, params, strides) { + return tf30.tidy(() => { + let out = tf30.depthwiseConv2d(x, params.filters, strides, "same"); + out = tf30.batchNorm( + out, + params.batch_norm_mean, + params.batch_norm_variance, + params.batch_norm_offset, + params.batch_norm_scale, + epsilon + ); + return tf30.clipByValue(out, 0, 6); + }); +} +function getStridesForLayerIdx(layerIdx) { + return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1]; +} +function mobileNetV1(x, params) { + return tf30.tidy(() => { + let conv11; + let out = pointwiseConvLayer(x, params.conv_0, [2, 2]); + const convPairParams = [ + params.conv_1, + params.conv_2, + params.conv_3, + params.conv_4, + params.conv_5, + params.conv_6, + params.conv_7, + params.conv_8, + params.conv_9, + params.conv_10, + params.conv_11, + params.conv_12, + params.conv_13 + ]; + convPairParams.forEach((param, i) => { + const layerIdx = i + 1; + const depthwiseConvStrides = getStridesForLayerIdx(layerIdx); + out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides); + out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]); + if (layerIdx === 11) + conv11 = out; + }); + if (conv11 === null) { + throw new Error("mobileNetV1 - output of conv layer 11 is null"); + } + return { + out, + conv11 + }; + }); +} + +// src/ssdMobilenetv1/nonMaxSuppression.ts +function IOU(boxes, i, j) { + const boxesData = boxes.arraySync(); + const yminI = Math.min(boxesData[i][0], boxesData[i][2]); + const xminI = Math.min(boxesData[i][1], boxesData[i][3]); + const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]); + const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]); + const yminJ = Math.min(boxesData[j][0], boxesData[j][2]); + const xminJ = Math.min(boxesData[j][1], boxesData[j][3]); + const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]); + const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]); + const areaI = (ymaxI - yminI) * (xmaxI - xminI); + const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); + if (areaI <= 0 || areaJ <= 0) + return 0; + const intersectionYmin = Math.max(yminI, yminJ); + const intersectionXmin = Math.max(xminI, xminJ); + const intersectionYmax = Math.min(ymaxI, ymaxJ); + const intersectionXmax = Math.min(xmaxI, xmaxJ); + const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0); + return intersectionArea / (areaI + areaJ - intersectionArea); +} +function nonMaxSuppression2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + const numBoxes = boxes.shape[0]; + const outputSize = Math.min(maxOutputSize, numBoxes); + const candidates = scores.map((score, boxIndex) => ({ score, boxIndex })).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score); + const suppressFunc = (x) => x <= iouThreshold ? 1 : 0; + const selected = []; + candidates.forEach((c) => { + if (selected.length >= outputSize) + return; + const originalScore = c.score; + for (let j = selected.length - 1; j >= 0; --j) { + const iou2 = IOU(boxes, c.boxIndex, selected[j]); + if (iou2 === 0) + continue; + c.score *= suppressFunc(iou2); + if (c.score <= scoreThreshold) + break; + } + if (originalScore === c.score) { + selected.push(c.boxIndex); + } + }); + return selected; +} + +// src/ssdMobilenetv1/outputLayer.ts +var tf31 = __toESM(require_tfjs_esm()); +function getCenterCoordinatesAndSizesLayer(x) { + const vec = tf31.unstack(tf31.transpose(x, [1, 0])); + const sizes = [ + tf31.sub(vec[2], vec[0]), + tf31.sub(vec[3], vec[1]) + ]; + const centers = [ + tf31.add(vec[0], tf31.div(sizes[0], 2)), + tf31.add(vec[1], tf31.div(sizes[1], 2)) + ]; + return { sizes, centers }; +} +function decodeBoxesLayer(x0, x1) { + const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0); + const vec = tf31.unstack(tf31.transpose(x1, [1, 0])); + const div0_out = tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2], 5)), sizes[0]), 2); + const add0_out = tf31.add(tf31.mul(tf31.div(vec[0], 10), sizes[0]), centers[0]); + const div1_out = tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3], 5)), sizes[1]), 2); + const add1_out = tf31.add(tf31.mul(tf31.div(vec[1], 10), sizes[1]), centers[1]); + return tf31.transpose( + tf31.stack([ + tf31.sub(add0_out, div0_out), + tf31.sub(add1_out, div1_out), + tf31.add(add0_out, div0_out), + tf31.add(add1_out, div1_out) + ]), + [1, 0] + ); +} +function outputLayer(boxPredictions, classPredictions, params) { + return tf31.tidy(() => { + const batchSize = boxPredictions.shape[0]; + let boxes = decodeBoxesLayer( + tf31.reshape(tf31.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), + tf31.reshape(boxPredictions, [-1, 4]) + ); + boxes = tf31.reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]); + const scoresAndClasses = tf31.sigmoid(tf31.slice(classPredictions, [0, 0, 1], [-1, -1, -1])); + let scores = tf31.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]); + scores = tf31.reshape(scores, [batchSize, scores.shape[1]]); + const boxesByBatch = tf31.unstack(boxes); + const scoresByBatch = tf31.unstack(scores); + return { boxes: boxesByBatch, scores: scoresByBatch }; + }); +} + +// src/ssdMobilenetv1/predictionLayer.ts +var tf33 = __toESM(require_tfjs_esm()); + +// src/ssdMobilenetv1/boxPredictionLayer.ts +var tf32 = __toESM(require_tfjs_esm()); +function boxPredictionLayer(x, params) { + return tf32.tidy(() => { + const batchSize = x.shape[0]; + const boxPredictionEncoding = tf32.reshape( + convLayer(x, params.box_encoding_predictor), + [batchSize, -1, 1, 4] + ); + const classPrediction = tf32.reshape( + convLayer(x, params.class_predictor), + [batchSize, -1, 3] + ); + return { boxPredictionEncoding, classPrediction }; + }); +} + +// src/ssdMobilenetv1/predictionLayer.ts +function predictionLayer(x, conv11, params) { + return tf33.tidy(() => { + const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]); + const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]); + const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]); + const conv3 = pointwiseConvLayer(conv22, params.conv_3, [2, 2]); + const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]); + const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]); + const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]); + const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]); + const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0); + const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1); + const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2); + const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3); + const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4); + const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5); + const boxPredictions = tf33.concat([ + boxPrediction0.boxPredictionEncoding, + boxPrediction1.boxPredictionEncoding, + boxPrediction2.boxPredictionEncoding, + boxPrediction3.boxPredictionEncoding, + boxPrediction4.boxPredictionEncoding, + boxPrediction5.boxPredictionEncoding + ], 1); + const classPredictions = tf33.concat([ + boxPrediction0.classPrediction, + boxPrediction1.classPrediction, + boxPrediction2.classPrediction, + boxPrediction3.classPrediction, + boxPrediction4.classPrediction, + boxPrediction5.classPrediction + ], 1); + return { + boxPredictions, + classPredictions + }; + }); +} + +// src/ssdMobilenetv1/SsdMobilenetv1Options.ts +var SsdMobilenetv1Options = class { + constructor({ minConfidence, maxResults } = {}) { + this._name = "SsdMobilenetv1Options"; + this._minConfidence = minConfidence || 0.5; + this._maxResults = maxResults || 100; + if (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; + } +}; + +// src/ssdMobilenetv1/SsdMobilenetv1.ts +var SsdMobilenetv1 = class extends NeuralNetwork { + constructor() { + super("SsdMobilenetv1"); + } + forwardInput(input) { + const { params } = this; + if (!params) + throw new Error("SsdMobilenetv1 - load model before inference"); + return tf34.tidy(() => { + const batchTensor = tf34.cast(input.toBatchTensor(512, false), "float32"); + const x = tf34.sub(tf34.div(batchTensor, 127.5), 1); + const features = mobileNetV1(x, params.mobilenetv1); + const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer); + return outputLayer(boxPredictions, classPredictions, params.output_layer); + }); + } + async forward(input) { + return this.forwardInput(await toNetInput(input)); + } + async locateFaces(input, options = {}) { + const { maxResults, minConfidence } = new SsdMobilenetv1Options(options); + const netInput = await toNetInput(input); + const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput); + const boxes = _boxes[0]; + const scores = _scores[0]; + for (let i = 1; i < _boxes.length; i++) { + _boxes[i].dispose(); + _scores[i].dispose(); + } + const scoresData = Array.from(scores.dataSync()); + const iouThreshold = 0.5; + const indices = nonMaxSuppression2(boxes, scoresData, maxResults, iouThreshold, minConfidence); + const reshapedDims = netInput.getReshapedInputDimensions(0); + const inputSize = netInput.inputSize; + const padX = inputSize / reshapedDims.width; + const padY = inputSize / reshapedDims.height; + const boxesData = boxes.arraySync(); + const results = indices.map((idx) => { + const [top, bottom] = [ + Math.max(0, boxesData[idx][0]), + Math.min(1, boxesData[idx][2]) + ].map((val) => val * padY); + const [left, right] = [ + Math.max(0, boxesData[idx][1]), + Math.min(1, boxesData[idx][3]) + ].map((val) => val * padX); + return new FaceDetection( + scoresData[idx], + new Rect(left, top, right - left, bottom - top), + { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) } + ); + }); + boxes.dispose(); + scores.dispose(); + return results; + } + getDefaultModelName() { + return "ssd_mobilenetv1_model"; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap6(weightMap); + } + extractParams(weights) { + return extractParams6(weights); + } +}; + +// src/ssdMobilenetv1/index.ts +function createSsdMobilenetv1(weights) { + const net = new SsdMobilenetv1(); + net.extractWeights(weights); + return net; +} +function createFaceDetectionNet(weights) { + return createSsdMobilenetv1(weights); +} +var FaceDetectionNet = class extends SsdMobilenetv1 { +}; + +// src/tinyYolov2/const.ts +var IOU_THRESHOLD = 0.4; +var BOX_ANCHORS = [ + new Point(0.738768, 0.874946), + new Point(2.42204, 2.65704), + new Point(4.30971, 7.04493), + new Point(10.246, 4.59428), + new Point(12.6868, 11.8741) +]; +var BOX_ANCHORS_SEPARABLE = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404]; +var DEFAULT_MODEL_NAME = "tiny_yolov2_model"; +var DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model"; + +// src/tinyYolov2/TinyYolov2Base.ts +var tf39 = __toESM(require_tfjs_esm()); + +// src/tinyYolov2/config.ts +var isNumber = (arg) => typeof arg === "number"; +function validateConfig(config) { + if (!config) { + throw new Error(`invalid config: ${config}`); + } + if (typeof config.withSeparableConvs !== "boolean") { + throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`); + } + if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1) { + throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`); + } + if (!Array.isArray(config.classes) || !config.classes.length || !config.classes.every((c) => typeof c === "string")) { + throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`); + } + if (!Array.isArray(config.anchors) || !config.anchors.length || !config.anchors.map((a) => a || {}).every((a) => isNumber(a.x) && isNumber(a.y))) { + throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`); + } + if (config.meanRgb && (!Array.isArray(config.meanRgb) || config.meanRgb.length !== 3 || !config.meanRgb.every(isNumber))) { + throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`); + } +} + +// src/tinyYolov2/convWithBatchNorm.ts +var tf36 = __toESM(require_tfjs_esm()); + +// src/tinyYolov2/leaky.ts +var tf35 = __toESM(require_tfjs_esm()); +function leaky(x) { + return tf35.tidy(() => { + const min = tf35.mul(x, tf35.scalar(0.10000000149011612)); + return tf35.add(tf35.relu(tf35.sub(x, min)), min); + }); +} + +// src/tinyYolov2/convWithBatchNorm.ts +function convWithBatchNorm(x, params) { + return tf36.tidy(() => { + let out = tf36.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = tf36.conv2d(out, params.conv.filters, [1, 1], "valid"); + out = tf36.sub(out, params.bn.sub); + out = tf36.mul(out, params.bn.truediv); + out = tf36.add(out, params.conv.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/depthwiseSeparableConv.ts +var tf37 = __toESM(require_tfjs_esm()); +function depthwiseSeparableConv2(x, params) { + return tf37.tidy(() => { + let out = tf37.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]); + out = tf37.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid"); + out = tf37.add(out, params.bias); + return leaky(out); + }); +} + +// src/tinyYolov2/extractParams.ts +var tf38 = __toESM(require_tfjs_esm()); +function extractorsFactory7(extractWeights, paramMappings) { + const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); + function extractBatchNormParams(size, mappedPrefix) { + const sub6 = tf38.tensor1d(extractWeights(size)); + const truediv = tf38.tensor1d(extractWeights(size)); + paramMappings.push( + { paramPath: `${mappedPrefix}/sub` }, + { paramPath: `${mappedPrefix}/truediv` } + ); + return { sub: sub6, truediv }; + } + function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) { + const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`); + const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`); + return { conv: conv3, bn }; + } + const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParams7(weights, config, boxEncodingSize, filterSizes) { + const { + extractWeights, + getRemainingWeights + } = extractWeightsFactory(weights); + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory7(extractWeights, paramMappings); + let params; + if (config.withSeparableConvs) { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = config.isFirstLayerConv2d ? extractConvParams(s0, s1, 3, "conv0") : extractSeparableConvParams(s0, s1, "conv0"); + const conv1 = extractSeparableConvParams(s1, s2, "conv1"); + const conv22 = extractSeparableConvParams(s2, s3, "conv2"); + const conv3 = extractSeparableConvParams(s3, s4, "conv3"); + const conv4 = extractSeparableConvParams(s4, s5, "conv4"); + const conv5 = extractSeparableConvParams(s5, s6, "conv5"); + const conv6 = s7 ? extractSeparableConvParams(s6, s7, "conv6") : void 0; + const conv7 = s8 ? extractSeparableConvParams(s7, s8, "conv7") : void 0; + const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } else { + const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes; + const conv0 = extractConvWithBatchNormParams(s0, s1, "conv0"); + const conv1 = extractConvWithBatchNormParams(s1, s2, "conv1"); + const conv22 = extractConvWithBatchNormParams(s2, s3, "conv2"); + const conv3 = extractConvWithBatchNormParams(s3, s4, "conv3"); + const conv4 = extractConvWithBatchNormParams(s4, s5, "conv4"); + const conv5 = extractConvWithBatchNormParams(s5, s6, "conv5"); + const conv6 = extractConvWithBatchNormParams(s6, s7, "conv6"); + const conv7 = extractConvWithBatchNormParams(s7, s8, "conv7"); + const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, "conv8"); + params = { + conv0, + conv1, + conv2: conv22, + conv3, + conv4, + conv5, + conv6, + conv7, + conv8 + }; + } + if (getRemainingWeights().length !== 0) { + throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`); + } + return { params, paramMappings }; +} + +// src/tinyYolov2/extractParamsFromWeightMap.ts +function extractorsFactory8(weightMap, paramMappings) { + const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); + function extractBatchNormParams(prefix) { + const sub6 = extractWeightEntry(`${prefix}/sub`, 1); + const truediv = extractWeightEntry(`${prefix}/truediv`, 1); + return { sub: sub6, truediv }; + } + function extractConvParams(prefix) { + const filters = extractWeightEntry(`${prefix}/filters`, 4); + const bias = extractWeightEntry(`${prefix}/bias`, 1); + return { filters, bias }; + } + function extractConvWithBatchNormParams(prefix) { + const conv3 = extractConvParams(`${prefix}/conv`); + const bn = extractBatchNormParams(`${prefix}/bn`); + return { conv: conv3, bn }; + } + const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry); + return { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + }; +} +function extractParamsFromWeightMap7(weightMap, config) { + const paramMappings = []; + const { + extractConvParams, + extractConvWithBatchNormParams, + extractSeparableConvParams + } = extractorsFactory8(weightMap, paramMappings); + let params; + if (config.withSeparableConvs) { + const numFilters = config.filterSizes && config.filterSizes.length || 9; + params = { + conv0: config.isFirstLayerConv2d ? extractConvParams("conv0") : extractSeparableConvParams("conv0"), + conv1: extractSeparableConvParams("conv1"), + conv2: extractSeparableConvParams("conv2"), + conv3: extractSeparableConvParams("conv3"), + conv4: extractSeparableConvParams("conv4"), + conv5: extractSeparableConvParams("conv5"), + conv6: numFilters > 7 ? extractSeparableConvParams("conv6") : void 0, + conv7: numFilters > 8 ? extractSeparableConvParams("conv7") : void 0, + conv8: extractConvParams("conv8") + }; + } else { + params = { + conv0: extractConvWithBatchNormParams("conv0"), + conv1: extractConvWithBatchNormParams("conv1"), + conv2: extractConvWithBatchNormParams("conv2"), + conv3: extractConvWithBatchNormParams("conv3"), + conv4: extractConvWithBatchNormParams("conv4"), + conv5: extractConvWithBatchNormParams("conv5"), + conv6: extractConvWithBatchNormParams("conv6"), + conv7: extractConvWithBatchNormParams("conv7"), + conv8: extractConvParams("conv8") + }; + } + disposeUnusedWeightTensors(weightMap, paramMappings); + return { params, paramMappings }; +} + +// src/tinyYolov2/TinyYolov2Options.ts +var TinyYolov2Options = class { + constructor({ inputSize, scoreThreshold } = {}) { + this._name = "TinyYolov2Options"; + this._inputSize = inputSize || 416; + this._scoreThreshold = scoreThreshold || 0.5; + if (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; + } +}; + +// src/tinyYolov2/TinyYolov2Base.ts +var _TinyYolov2Base = class _TinyYolov2Base extends NeuralNetwork { + constructor(config) { + super("TinyYolov2"); + validateConfig(config); + this._config = config; + } + 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(x, params) { + let out = convWithBatchNorm(x, params.conv0); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv1); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv2); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv3); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv4); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = convWithBatchNorm(out, params.conv5); + out = tf39.maxPool(out, [2, 2], [1, 1], "same"); + out = convWithBatchNorm(out, params.conv6); + out = convWithBatchNorm(out, params.conv7); + return convLayer(out, params.conv8, "valid", false); + } + runMobilenet(x, params) { + let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv2(x, params.conv0); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv1); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv2); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv3); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv4); + out = tf39.maxPool(out, [2, 2], [2, 2], "same"); + out = depthwiseSeparableConv2(out, params.conv5); + out = tf39.maxPool(out, [2, 2], [1, 1], "same"); + out = params.conv6 ? depthwiseSeparableConv2(out, params.conv6) : out; + out = params.conv7 ? depthwiseSeparableConv2(out, params.conv7) : out; + return convLayer(out, params.conv8, "valid", false); + } + forwardInput(input, inputSize) { + const { params } = this; + if (!params) { + throw new Error("TinyYolov2 - load model before inference"); + } + return tf39.tidy(() => { + let batchTensor = tf39.cast(input.toBatchTensor(inputSize, false), "float32"); + batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor; + batchTensor = batchTensor.div(255); + return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params); + }); + } + async forward(input, inputSize) { + return this.forwardInput(await toNetInput(input), inputSize); + } + async detect(input, forwardParams = {}) { + const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams); + const netInput = await toNetInput(input); + const out = await this.forwardInput(netInput, inputSize); + const out0 = tf39.tidy(() => tf39.unstack(out)[0].expandDims()); + const inputDimensions = { + width: netInput.getInputWidth(0), + height: netInput.getInputHeight(0) + }; + const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold); + out.dispose(); + out0.dispose(); + const boxes = results.map((res) => res.box); + const scores = results.map((res) => res.score); + const classScores = results.map((res) => res.classScore); + const classNames = results.map((res) => this.config.classes[res.label]); + const indices = nonMaxSuppression( + boxes.map((box) => box.rescale(inputSize)), + scores, + this.config.iouThreshold, + true + ); + const detections = indices.map((idx) => new ObjectDetection( + scores[idx], + classScores[idx], + classNames[idx], + boxes[idx], + inputDimensions + )); + return detections; + } + getDefaultModelName() { + return ""; + } + extractParamsFromWeightMap(weightMap) { + return extractParamsFromWeightMap7(weightMap, this.config); + } + extractParams(weights) { + const filterSizes = this.config.filterSizes || _TinyYolov2Base.DEFAULT_FILTER_SIZES; + const numFilters = filterSizes ? filterSizes.length : void 0; + if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) { + throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`); + } + return extractParams7(weights, this.config, this.boxEncodingSize, filterSizes); + } + async extractBoxes(outputTensor, inputBlobDimensions, scoreThreshold) { + const { width, height } = inputBlobDimensions; + const inputSize = Math.max(width, height); + const correctionFactorX = inputSize / width; + const correctionFactorY = inputSize / height; + const numCells = outputTensor.shape[1]; + const numBoxes = this.config.anchors.length; + const [boxesTensor, scoresTensor, classScoresTensor] = tf39.tidy(() => { + const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]); + const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]); + const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]); + const classScores = this.withClassScores ? tf39.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : tf39.scalar(0); + return [boxes, scores, classScores]; + }); + const results = []; + const scoresData = await scoresTensor.array(); + const boxesData = await boxesTensor.array(); + for (let row = 0; row < numCells; row++) { + for (let col = 0; col < numCells; col++) { + for (let anchor = 0; anchor < numBoxes; anchor++) { + const score = sigmoid(scoresData[row][col][anchor][0]); + if (!scoreThreshold || score > scoreThreshold) { + const ctX = (col + sigmoid(boxesData[row][col][anchor][0])) / numCells * correctionFactorX; + const ctY = (row + sigmoid(boxesData[row][col][anchor][1])) / numCells * correctionFactorY; + const widthLocal = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX; + const heightLocal = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY; + const x = ctX - widthLocal / 2; + const y = ctY - heightLocal / 2; + const pos = { row, col, anchor }; + const { classScore, label } = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : { classScore: 1, label: 0 }; + results.push({ + box: new BoundingBox(x, y, x + widthLocal, y + heightLocal), + score, + classScore: score * classScore, + label, + ...pos + }); + } + } + } + } + boxesTensor.dispose(); + scoresTensor.dispose(); + classScoresTensor.dispose(); + return results; + } + async extractPredictedClass(classesTensor, pos) { + const { row, col, anchor } = pos; + const classesData = await classesTensor.array(); + return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({ + classScore, + label + })).reduce((max, curr) => max.classScore > curr.classScore ? max : curr); + } +}; +_TinyYolov2Base.DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024]; +var TinyYolov2Base = _TinyYolov2Base; + +// src/tinyYolov2/TinyYolov2.ts +var TinyYolov2 = class extends TinyYolov2Base { + constructor(withSeparableConvs = true) { + const config = { + withSeparableConvs, + iouThreshold: IOU_THRESHOLD, + classes: ["face"], + ...withSeparableConvs ? { + anchors: BOX_ANCHORS_SEPARABLE, + meanRgb: MEAN_RGB_SEPARABLE + } : { + anchors: BOX_ANCHORS, + withClassScores: true + } + }; + super(config); + } + get withSeparableConvs() { + return this.config.withSeparableConvs; + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input, forwardParams) { + const objectDetections = await this.detect(input, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight })); + } + getDefaultModelName() { + return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/tinyYolov2/index.ts +function createTinyYolov2(weights, withSeparableConvs = true) { + const net = new TinyYolov2(withSeparableConvs); + net.extractWeights(weights); + return net; +} + +// src/tinyFaceDetector/TinyFaceDetectorOptions.ts +var TinyFaceDetectorOptions = class extends TinyYolov2Options { + constructor() { + super(...arguments); + this._name = "TinyFaceDetectorOptions"; + } +}; + +// src/globalApi/ComposableTask.ts +var ComposableTask = class { + // eslint-disable-next-line no-unused-vars + async then(onfulfilled) { + return onfulfilled(await this.run()); + } + async run() { + throw new Error("ComposableTask - run is not implemented"); + } +}; + +// src/globalApi/DetectFaceLandmarksTasks.ts +var tf41 = __toESM(require_tfjs_esm()); + +// src/globalApi/extractFacesAndComputeResults.ts +var tf40 = __toESM(require_tfjs_esm()); +async function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment = ({ alignedRect }) => alignedRect) { + const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection); + const faces = extractedFaces || (input instanceof tf40.Tensor ? await extractFaceTensors(input, faceBoxes) : await extractFaces(input, faceBoxes)); + const results = await computeResults(faces); + faces.forEach((f) => f instanceof tf40.Tensor && f.dispose()); + return results; +} +async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) { + return extractAllFacesAndComputeResults( + [parentResult], + input, + async (faces) => computeResult(faces[0]), + extractedFaces, + getRectForAlignment + ); +} + +// src/tinyFaceDetector/const.ts +var IOU_THRESHOLD2 = 0.4; +var BOX_ANCHORS2 = [ + new Point(1.603231, 2.094468), + new Point(6.041143, 7.080126), + new Point(2.882459, 3.518061), + new Point(4.266906, 5.178857), + new Point(9.041765, 10.66308) +]; +var MEAN_RGB = [117.001, 114.697, 97.404]; + +// src/tinyFaceDetector/TinyFaceDetector.ts +var TinyFaceDetector = class extends TinyYolov2Base { + constructor() { + const config = { + withSeparableConvs: true, + iouThreshold: IOU_THRESHOLD2, + classes: ["face"], + anchors: BOX_ANCHORS2, + meanRgb: MEAN_RGB, + isFirstLayerConv2d: true, + filterSizes: [3, 16, 32, 64, 128, 256, 512] + }; + super(config); + } + get anchors() { + return this.config.anchors; + } + async locateFaces(input, forwardParams) { + const objectDetections = await this.detect(input, forwardParams); + return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight })); + } + getDefaultModelName() { + return "tiny_face_detector_model"; + } + extractParamsFromWeightMap(weightMap) { + return super.extractParamsFromWeightMap(weightMap); + } +}; + +// src/globalApi/nets.ts +var nets = { + ssdMobilenetv1: new SsdMobilenetv1(), + tinyFaceDetector: new TinyFaceDetector(), + tinyYolov2: new TinyYolov2(), + faceLandmark68Net: new FaceLandmark68Net(), + faceLandmark68TinyNet: new FaceLandmark68TinyNet(), + faceRecognitionNet: new FaceRecognitionNet(), + faceExpressionNet: new FaceExpressionNet(), + ageGenderNet: new AgeGenderNet() +}; +var ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options); +var tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options); +var tinyYolov2 = (input, options) => nets.tinyYolov2.locateFaces(input, options); +var detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input); +var detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input); +var computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input); +var recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input); +var predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input); +var loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url); +var loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url); +var loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url); +var loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url); +var loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url); +var loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url); +var loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url); +var loadAgeGenderModel = (url) => nets.ageGenderNet.load(url); +var loadFaceDetectionModel = loadSsdMobilenetv1Model; +var locateFaces = ssdMobilenetv1; +var detectLandmarks = detectFaceLandmarks; + +// src/globalApi/PredictFaceExpressionsTask.ts +var PredictFaceExpressionsTaskBase = class extends ComposableTask { + constructor(parentTask, input, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResults = await this.parentTask; + const faceExpressionsByFace = await extractAllFacesAndComputeResults( + parentResults, + this.input, + async (faces) => Promise.all( + faces.map((face) => nets.faceExpressionNet.predictExpressions(face)) + ), + this.extractedFaces + ); + return parentResults.map( + (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]) + ); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const faceExpressions = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.faceExpressionNet.predictExpressions(face), + this.extractedFaces + ); + return extendWithFaceExpressions(parentResult, faceExpressions); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask(this, this.input); + } +}; +var PredictAllFaceExpressionsWithFaceAlignmentTask = class extends PredictAllFaceExpressionsTask { + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleFaceExpressionsWithFaceAlignmentTask = class extends PredictSingleFaceExpressionsTask { + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/PredictAgeAndGenderTask.ts +var PredictAgeAndGenderTaskBase = class extends ComposableTask { + constructor(parentTask, input, extractedFaces) { + super(); + this.parentTask = parentTask; + this.input = input; + this.extractedFaces = extractedFaces; + } +}; +var PredictAllAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResults = await this.parentTask; + const ageAndGenderByFace = await extractAllFacesAndComputeResults( + parentResults, + this.input, + async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face))), + this.extractedFaces + ); + return parentResults.map((parentResult, i) => { + const { age, gender, genderProbability } = ageAndGenderByFace[i]; + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + }); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.ageGenderNet.predictAgeAndGender(face), + this.extractedFaces + ); + return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask(this, this.input); + } +}; +var PredictAllAgeAndGenderWithFaceAlignmentTask = class extends PredictAllAgeAndGenderTask { + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var PredictSingleAgeAndGenderWithFaceAlignmentTask = class extends PredictSingleAgeAndGenderTask { + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/ComputeFaceDescriptorsTasks.ts +var ComputeFaceDescriptorsTaskBase = class extends ComposableTask { + constructor(parentTask, input) { + super(); + this.parentTask = parentTask; + this.input = input; + } +}; +var ComputeAllFaceDescriptorsTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResults = await this.parentTask; + const descriptors = await extractAllFacesAndComputeResults( + parentResults, + this.input, + (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face))), + null, + (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }) + ); + return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor)); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; +var ComputeSingleFaceDescriptorTask = class extends ComputeFaceDescriptorsTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) + return void 0; + const descriptor = await extractSingleFaceAndComputeResult( + parentResult, + this.input, + (face) => nets.faceRecognitionNet.computeFaceDescriptor(face), + null, + // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow + (parentResult2) => parentResult2.landmarks.align(null, { useDlibAlignment: true }) + ); + return extendWithFaceDescriptor(parentResult, descriptor); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } +}; + +// src/globalApi/DetectFaceLandmarksTasks.ts +var DetectFaceLandmarksTaskBase = class extends ComposableTask { + constructor(parentTask, input, useTinyLandmarkNet) { + super(); + this.parentTask = parentTask; + this.input = input; + this.useTinyLandmarkNet = useTinyLandmarkNet; + } + get landmarkNet() { + return this.useTinyLandmarkNet ? nets.faceLandmark68TinyNet : nets.faceLandmark68Net; + } +}; +var DetectAllFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResults = await this.parentTask; + const detections = parentResults.map((res) => res.detection); + const faces = this.input instanceof tf41.Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections); + const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))); + faces.forEach((f) => f instanceof tf41.Tensor && f.dispose()); + const result = parentResults.filter((_parentResult, i) => faceLandmarksByFace[i]).map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i])); + return result; + } + withFaceExpressions() { + return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptors() { + return new ComputeAllFaceDescriptorsTask(this, this.input); + } +}; +var DetectSingleFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase { + async run() { + const parentResult = await this.parentTask; + if (!parentResult) { + return void 0; + } + const { detection } = parentResult; + const faces = this.input instanceof tf41.Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]); + const landmarks = await this.landmarkNet.detectLandmarks(faces[0]); + faces.forEach((f) => f instanceof tf41.Tensor && f.dispose()); + return extendWithFaceLandmarks(parentResult, landmarks); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input); + } + withFaceDescriptor() { + return new ComputeSingleFaceDescriptorTask(this, this.input); + } +}; + +// src/globalApi/DetectFacesTasks.ts +var DetectFacesTaskBase = class extends ComposableTask { + // eslint-disable-next-line no-unused-vars + constructor(input, options = new SsdMobilenetv1Options()) { + super(); + this.input = input; + this.options = options; + } +}; +var DetectAllFacesTask = class extends DetectFacesTaskBase { + async run() { + const { input, options } = this; + let result; + if (options instanceof TinyFaceDetectorOptions) + result = nets.tinyFaceDetector.locateFaces(input, options); + else if (options instanceof SsdMobilenetv1Options) + result = nets.ssdMobilenetv1.locateFaces(input, options); + else if (options instanceof TinyYolov2Options) + result = nets.tinyYolov2.locateFaces(input, options); + else + throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options"); + return result; + } + runAndExtendWithFaceDetections() { + return new Promise((resolve, reject) => { + this.run().then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection)))).catch((err) => reject(err)); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectAllFaceLandmarksTask( + this.runAndExtendWithFaceDetections(), + this.input, + useTinyLandmarkNet + ); + } + withFaceExpressions() { + return new PredictAllFaceExpressionsTask( + this.runAndExtendWithFaceDetections(), + this.input + ); + } + withAgeAndGender() { + return new PredictAllAgeAndGenderTask( + this.runAndExtendWithFaceDetections(), + this.input + ); + } +}; +var DetectSingleFaceTask = class extends DetectFacesTaskBase { + async run() { + const faceDetections = await new DetectAllFacesTask(this.input, this.options); + let faceDetectionWithHighestScore = faceDetections[0]; + faceDetections.forEach((faceDetection) => { + if (faceDetection.score > faceDetectionWithHighestScore.score) + faceDetectionWithHighestScore = faceDetection; + }); + return faceDetectionWithHighestScore; + } + runAndExtendWithFaceDetection() { + return new Promise(async (resolve) => { + const detection = await this.run(); + resolve(detection ? extendWithFaceDetection({}, detection) : void 0); + }); + } + withFaceLandmarks(useTinyLandmarkNet = false) { + return new DetectSingleFaceLandmarksTask( + this.runAndExtendWithFaceDetection(), + this.input, + useTinyLandmarkNet + ); + } + withFaceExpressions() { + return new PredictSingleFaceExpressionsTask( + this.runAndExtendWithFaceDetection(), + this.input + ); + } + withAgeAndGender() { + return new PredictSingleAgeAndGenderTask( + this.runAndExtendWithFaceDetection(), + this.input + ); + } +}; + +// src/globalApi/detectFaces.ts +function detectSingleFace(input, options = new SsdMobilenetv1Options()) { + return new DetectSingleFaceTask(input, options); +} +function detectAllFaces(input, options = new SsdMobilenetv1Options()) { + return new DetectAllFacesTask(input, options); +} + +// src/globalApi/allFaces.ts +async function allFacesSsdMobilenetv1(input, minConfidence) { + return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {})).withFaceLandmarks().withFaceDescriptors(); +} +async function allFacesTinyYolov2(input, forwardParams = {}) { + return detectAllFaces(input, new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors(); +} +var allFaces = allFacesSsdMobilenetv1; + +// src/euclideanDistance.ts +function euclideanDistance(arr1, arr2) { + if (arr1.length !== arr2.length) + throw new Error("euclideanDistance: arr1.length !== arr2.length"); + const desc1 = Array.from(arr1); + const desc2 = Array.from(arr2); + return Math.sqrt( + desc1.map((val, i) => val - desc2[i]).reduce((res, diff) => res + diff * diff, 0) + ); +} + +// src/globalApi/FaceMatcher.ts +var FaceMatcher = class _FaceMatcher { + constructor(inputs, distanceThreshold = 0.6) { + this._distanceThreshold = distanceThreshold; + const inputArray = Array.isArray(inputs) ? inputs : [inputs]; + if (!inputArray.length) + throw new Error("FaceRecognizer.constructor - expected atleast one input"); + let count = 1; + const createUniqueLabel = () => `person ${count++}`; + this._labeledDescriptors = inputArray.map((desc) => { + if (desc instanceof LabeledFaceDescriptors) + return desc; + if (desc instanceof Float32Array) + return new LabeledFaceDescriptors(createUniqueLabel(), [desc]); + if (desc.descriptor && desc.descriptor instanceof Float32Array) + return new LabeledFaceDescriptors(createUniqueLabel(), [desc.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(queryDescriptor, descriptors) { + return descriptors.map((d) => euclideanDistance(d, queryDescriptor)).reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1); + } + matchDescriptor(queryDescriptor) { + return this.labeledDescriptors.map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors))).reduce((best, curr) => best.distance < curr.distance ? best : curr); + } + findBestMatch(queryDescriptor) { + const bestMatch = this.matchDescriptor(queryDescriptor); + return bestMatch.distance < this._distanceThreshold ? bestMatch : new FaceMatch("unknown", bestMatch.distance); + } + toJSON() { + return { + distanceThreshold: this._distanceThreshold, + labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()) + }; + } + static fromJSON(json) { + const labeledDescriptors = json.labeledDescriptors.map((ld) => LabeledFaceDescriptors.fromJSON(ld)); + return new _FaceMatcher(labeledDescriptors, json.distanceThreshold); + } +}; + +// src/tinyFaceDetector/index.ts +function createTinyFaceDetector(weights) { + const net = new TinyFaceDetector(); + net.extractWeights(weights); + return net; +} + +// src/resizeResults.ts +function resizeResults(results, dimensions) { + const { width, height } = new Dimensions(dimensions.width, dimensions.height); + if (width <= 0 || height <= 0) { + throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`); + } + if (Array.isArray(results)) { + return results.map((obj) => resizeResults(obj, { width, height })); + } + if (isWithFaceLandmarks(results)) { + const resizedDetection = results.detection.forSize(width, height); + const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height); + return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks); + } + if (isWithFaceDetection(results)) { + return extendWithFaceDetection(results, results.detection.forSize(width, height)); + } + if (results instanceof FaceLandmarks || results instanceof FaceDetection) { + return results.forSize(width, height); + } + return results; +} + +// src/index.ts +var version2 = version; +// Annotate the CommonJS export names for ESM import in node: +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/tfjs.esm.js b/dist/tfjs.esm.js index 7f988f5..d1fa8c1 100644 --- a/dist/tfjs.esm.js +++ b/dist/tfjs.esm.js @@ -4,69 +4,54566 @@ author: ' */ -var 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A0;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(A0||(A0={}));var D0;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(D0||(D0={}));var hK={float32:A0,int32:_0,bool:E0,complex64:D0};function ur(r,t){if(r==="string"||t==="string"){if(r==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${r} with ${t}`)}return hK[r][t]}function xc(r){return ur(r,"int32")}function ox(r){return r!=null&&typeof r=="object"&&"texture"in r&&r.texture instanceof WebGLTexture}function sx(r){return typeof GPUBuffer!="undefined"&&r!=null&&typeof r=="object"&&"buffer"in r&&r.buffer instanceof GPUBuffer}function jt(r,t){if(r.dtype===t.dtype)return[r,t];let e=ur(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function $0(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function gK(r,t){return t.some(e=>e.id===r.id)}function ph(r){let t=[];return X_(r,t,new Set),t}function X_(r,t,e){if(r==null)return;if(r instanceof Ot){t.push(r);return}if(!xK(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),X_(s,t,e))}}function xK(r){return Array.isArray(r)||typeof r=="object"}function R0(r){return r.kernelName!=null}var ix=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(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},Iu=class{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ix}async 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){Jg(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 ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof Uo)&&typeof n.then=="function"){let o=++this.pendingBackendInitId,s=n.then(i=>o(othis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return Iu.nextTensorId++}nextVariableId(){return Iu.nextVariableId++}clone(t){let e=T.runKernel(bo,{x:t}),n={x:t},o=i=>({x:()=>{let a="float32",u={x:i},l={dtype:a};return T.runKernel(xo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(ih(t,this.backendName)!=null))throw new Error(`Kernel '${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=R0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(R0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=ih(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=R0(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=b0(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"&&Ho(t[0])&&(s=t.map(u=>wu(u)));let i=o.write(s,e,n),a=new Ot(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n==="string"){let u=this.state.tensorInfo.get(i),l=h0(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 Ot(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 gl(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++;let n=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(n=t.size*Pp(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof gl||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!=="complex64"&&t.dtype!=="string"){let 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e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=ph(t),n=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(_(e.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));_(s instanceof Ot,()=>"The result y returned by f() must be a tensor.");let i=V_(this.state.activeTape,e,s);if(!o&&i.length===0&&e.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let a={};a[s.id]=n==null?yK(s.shape):n,G_(a,i,l=>this.tidy(l),bK);let u=e.map(l=>a[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:u}})}customGrad(t){return _(Ai(t),()=>"The f passed in customGrad(f) must be a function."),(...e)=>{_(e.every(a=>a instanceof Ot),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,o={};e.forEach((a,u)=>{o[u]=a});let s=(a,u)=>(n=t(...e,u),_(n.value instanceof Ot,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),_(Ai(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),i=(a,u)=>{let l=n.gradFunc(a,u),c=Array.isArray(l)?l:[l];_(c.length===e.length,()=>"The function f passed in customGrad(f) must 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p=C(r,"x","conv2d","float32"),m=C(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]}.`),_(Rr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`);let g=wc(f.shape,m.shape,e,s,n,i),x;a!=null&&(x=C(a,"bias","fused conv2d"),[x]=jt(x,p),o==="NHWC"?Mt(g.outShape,x.shape):(_(x.shape.length<=1,()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`),_(x.shape.length===0||x.shape[0]===g.outChannels||x.shape[0]===1,()=>`Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`)));let b;if(l!=null){let E=l.shape;if(_(E.length<=1||E.length===3,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${E.length}.`),E.length===1)_(E[0]===1||E[0]===g.outChannels,()=>`Error in fused conv2d: PReLU activation weights (${E}) is not compatible with the number of output channels (${g.outChannels}).`);else if(E.length===3)try{Mt(E,g.outShape)}catch(A){let D=`Error in fused conv2d: PReLU activation weights (${E}) is not compatible with the output shape of the conv2d (${g.outShape}).`;throw Error(D)}b=C(l,"prelu weights","fused conv2d")}let w=(E,A)=>{_(o==="NHWC",()=>`Error in gradient of fused conv2D: got dataFormat of ${o} but only NHWC is currently supported.`);let[D,F,P,V]=A,G=_c(E,P,u);_(co(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let W=pm(F.shape,G,D,e,n),q=$m(F,G,D.shape,e,n),H=[W,q];if(V!=null){let K=Ec(V,G);H.push(K)}return H},I={x:f,filter:m,bias:x,preluActivationWeights:b},N={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?fn((A,D,F)=>{let P=T.runKernel(Ji,I,N);return F([D,A,P]),d&&(P=R(P,[P.shape[1],P.shape[2],P.shape[3]])),{value:P,gradFunc:w}})(f,m):fn((A,D,F,P)=>{let V=T.runKernel(Ji,I,N);return P([D,A,V,F]),d&&(V=R(V,[V.shape[1],V.shape[2],V.shape[3]])),{value:V,gradFunc:w}})(f,m,x)}var DA=k({fusedConv2d_:X5});function Y5(r,t,e,n,o,s=[1,1],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]]));let l={x:a,dy:u},c={strides:n,pad:o,dimRoundingMode:i,dilations:s,filterShape:e};return T.runKernel(Vp,l,c)}var yy=k({depthwiseConv2dNativeBackpropFilter_:Y5});function Z5(r,t,e,n,o,s=[1,1],i){let a=t,u=!1;t.rank===3&&(u=!0,a=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let l={dy:a,filter:e},c={strides:n,pad:o,dimRoundingMode:i,dilations:s,inputShape:r},p=T.runKernel(Gp,l,c);return u?R(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var by=k({depthwiseConv2dNativeBackpropInput_:Z5});function J5({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(Dc(T.state.gradientDepth,u)===!1){let N=ua(r,t,e,n,o,s,i);return a!=null&&(N=Y(N,a)),Ac(N,u,l,c)}let p=C(r,"x","depthwiseConv2d","float32"),m=C(t,"filter","depthwiseConv2d","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 depthwiseConv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`),_(f.shape[3]===m.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${f.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`),s==null&&(s=[1,1]),_(Rr(e,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),Se("fused depthwiseConv2d",n,i);let h=wc(f.shape,m.shape,e,s,n,i,!0),g;a!=null&&(g=C(a,"bias","fused conv2d"),[g]=jt(g,p),Mt(h.outShape,g.shape));let x;l!=null&&(x=C(l,"prelu weights","fused depthwiseConv2d"));let b=(N,E)=>{_(co(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=_c(N,F,u),G=by(D.shape,V,A,e,n,s,i),W=yy(D,V,A.shape,e,n,s,i);if(P!=null){let q=Ec(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?fn((E,A,D)=>{let F=T.runKernel(Qi,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):fn((E,A,D,F)=>{let P=T.runKernel(Qi,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 $A=k({fusedDepthwiseConv2d_:J5});function Q5({a:r,b:t,transposeA:e=!1,transposeB:n=!1,bias:o,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:a=.2}){if(Dc(T.state.gradientDepth,s)===!1){let V=Bt(r,t,e,n);return o!=null&&(V=Y(V,o)),Ac(V,s,i,a)}let u=C(r,"a","fused matMul"),l=C(t,"b","fused matMul");[u,l]=jt(u,l);let c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=te(d),x=te(h);_(c===p,()=>`Error in fused matMul: inner shapes (${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=C(o,"bias","fused matMul"),[E]=jt(E,u),Mt(w,E.shape));let A;i!=null&&(A=C(i,"prelu weights","fused matMul"));let D=(V,G)=>{let[W,q,H,K]=G,X=_c(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=Ec(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?fn((G,W,q)=>{let H=T.runKernel(Zi,F,P);return q([G,W,H]),{value:R(H,w),gradFunc:D}})(I,N):fn((G,W,q,H)=>{let K=T.runKernel(Zi,F,P);return H([G,W,K,q]),{value:R(K,w),gradFunc:D}})(I,N,E)}var RA=k({fusedMatMul_:Q5});function t8(r){return Ih(r,.54,.46)}var FA=k({hammingWindow_:t8});function e8(r){return Ih(r,.5,.5)}var wy=k({hannWindow_:e8});function r8(r,t,e,n=!1,o=0){let s=0,i=[];for(;s+t<=r.size;)i.push(Pt(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(Ba,c,p)}var PA=k({cropAndResize_:o8});function s8(r){let t=C(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(Ua,e,{})}var MA=k({flipLeftRight_:s8});function i8(r){let t=C(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,Or(t,o)}var LA=k({grayscaleToRGB_:i8});function a8(r,t,e=0,n=.5){let o=C(r,"image","rotateWithOffset","float32");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(hl,s,i)}var zA=k({rotateWithOffset_:a8});function _o(r,t,e,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=r.shape[0];return e=Math.min(e,i),_(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${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(GA);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=m8(r,b,p[N]);if(E>=n){I=!0;break}if(g.score=g.score*f8(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&&VA(l,g,GA))}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 m8(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 f8(r,t,e){let n=Math.exp(t*e*e);return e<=r?n:0}function GA(r,t){return r.score-t.score||r.score===t.score&&t.boxIndex-r.boxIndex}async function d8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY){let s=C(r,"boxes","nonMaxSuppressionAsync"),i=C(t,"scores","nonMaxSuppressionAsync"),a=_o(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}=Cy(l,c,e,n,o);return s!==r&&s.dispose(),i!==t&&i.dispose(),Ke(p,"int32")}var WA=d8;function h8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,"boxes","nonMaxSuppression"),a=C(t,"scores","nonMaxSuppression"),u=_o(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(ol,l,c);return{selectedIndices:p[0],selectedScores:p[1]}}var UA=k({nonMaxSuppressionWithScore_:h8});async function g8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,"boxes","nonMaxSuppressionAsync"),a=C(t,"scores","nonMaxSuppressionAsync"),u=_o(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}=Sy(c,p,e,n,o,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Ke(m,"int32"),selectedScores:Ke(f)}}var HA=g8;function x8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,"boxes","nonMaxSuppression"),a=C(t,"scores","nonMaxSuppression"),u=_o(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(nl,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var qA=k({nonMaxSuppressionPadded_:x8});async function y8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,"boxes","nonMaxSuppressionAsync"),a=C(t,"scores","nonMaxSuppressionAsync"),u=_o(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,[m,f]=await Promise.all([i.data(),a.data()]),{selectedIndices:d,validOutputs:h}=vy(m,f,l,c,p,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Ke(d,"int32"),validOutputs:ft(h,"int32")}}var KA=y8;function b8(r,t,e=!1,n=!1){let o=C(r,"images","resizeBilinear");_(o.rank===3||o.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${o.rank}.`),_(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),_(n===!1||e===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=o,i=!1;o.rank===3&&(i=!0,s=R(o,[1,o.shape[0],o.shape[1],o.shape[2]]));let[]=t,a={images:s},u={alignCorners:e,halfPixelCenters:n,size:t},l=T.runKernel(Us,a,u);return i?R(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var Ny=k({resizeBilinear_:b8});function 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T.runKernel(dl,u,l)}var XA=k({transform_:v8});function S8(r,t,e){let n=C(r,"a","bandPart");_(n.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${n.rank}.`);let o=n.shape,[s,i]=n.shape.slice(-2),a,u;typeof t=="number"?(_(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),_(t<=s,()=>`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`),a=C(t<0?s:t,"numLower","bandPart")):(_(t.dtype==="int32",()=>"bandPart(): numLower's dtype must be an int32."),a=be(Il(t,0),s,mo(t,s))),typeof e=="number"?(_(e%1===0,()=>`bandPart(): numUpper must be an integer, got ${e}.`),_(e<=i,()=>`bandPart(): numUpper (${e}) must not be greater than the number of columns (${i}).`),u=C(e<0?i:e,"numUpper","bandPart")):(_(e.dtype==="int32",()=>"bandPart(): numUpper's dtype must be an int32."),u=be(Il(e,0),i,mo(e,i)));let l=R(da(0,s,1,"int32"),[-1,1]),c=da(0,i,1,"int32"),p=lt(l,c),m=Pr(Un(p,a),mn(p,Ut(u))),f=Te([s,i],n.dtype);return R(qe(xr(R(n,[-1,s,i])).map(d=>be(m,d,f))),o)}var YA=k({bandPart_:S8});function N8(r){let t;if(Array.isArray(r)){t=!1,_(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let o=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${o})`)}else t=!0,r=gr(r,r.shape[0],0).map(o=>qn(o,[0]));_(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let e=[],n=r;for(let o=0;o{let s=n[o];if(o>0)for(let i=0;i=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return JA(r,t);{let e=r.shape.slice(0,r.shape.length-2).reduce((u,l)=>u*l),n=xr(R(r,[e,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),o=[],s=[];n.forEach(u=>{let[l,c]=JA(u,t);o.push(l),s.push(c)});let i=R(qe(o,0),r.shape),a=R(qe(s,0),r.shape);return[i,a]}}function JA(r,t=!1){return T.tidy(()=>{_(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let e=r.shape[0],n=r.shape[1],o=Cc(e),s=cn(r),i=fi([[1]],[1,1]),a=cn(i),u=e>=n?n:e;for(let l=0;l{let f=Pt(s,[l,l],[e-l,1]),d=wl(f),h=Pt(s,[l,l],[1,1]),g=be(Fe(h,0),fi([[-1]]),fi([[1]])),x=lt(h,$(g,d)),b=ct(f,x);b.shape[0]===1?a=cn(i):a=ie([i,Pt(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let w=Ut(ct(Bt(g,x),d)),I=Pt(s,[l,0],[e-l,n]),N=$(w,a),E=Vt(a);if(l===0)s=lt(I,Bt(N,Bt(E,I)));else{let F=lt(I,Bt(N,Bt(E,I)));s=ie([Pt(s,[0,0],[l,n]),F],0)}let A=Vt(N),D=Pt(o,[0,l],[e,o.shape[1]-l]);if(l===0)o=lt(D,Bt(Bt(D,a),A));else{let F=lt(D,Bt(Bt(D,a),A));o=ie([Pt(o,[0,0],[e,l]),F],1)}return[a,s,o]}),Tt([c,p,m])}return!t&&e>n&&(o=Pt(o,[0,0],[e,n]),s=Pt(s,[0,0],[n,n])),[o,s]})}var QA=k({qr_:k8});var Ze;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Ze||(Ze={}));function T8(r,t,e=Ze.SUM_BY_NONZERO_WEIGHTS){let 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s=C(r,"labels","cosineDistance"),i=C(t,"predictions","cosineDistance"),a=null;n!=null&&(a=C(n,"weights","cosineDistance")),Re(s.shape,i.shape,"Error in cosineDistance: ");let u=ft(1),l=lt(u,pt($(s,i),e,!0));return qr(l,a,o)}var e2=k({cosineDistance_:E8});function A8(r,t,e,n=Ze.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","hingeLoss"),s=C(t,"predictions","hingeLoss"),i=null;e!=null&&(i=C(e,"weights","hingeLoss")),Re(o.shape,s.shape,"Error in hingeLoss: ");let a=ft(1);o=lt($(ft(2),o),a);let u=Mr(lt(a,$(o,s)));return qr(u,i,n)}var r2=k({hingeLoss_:A8});function D8(r,t,e,n=1,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","huberLoss"),i=C(t,"predictions","huberLoss"),a=null;e!=null&&(a=C(e,"weights","huberLoss")),Re(s.shape,i.shape,"Error in huberLoss: ");let u=ft(n),l=Ee(lt(i,s)),c=mo(l,u),p=lt(l,c),m=Y($(ft(.5),Wt(c)),$(u,p));return qr(m,a,o)}var n2=k({huberLoss_:D8});function $8(r,t,e,n=1e-7,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","logLoss"),i=C(t,"predictions","logLoss"),a=null;e!=null&&(a=C(e,"weights","logLoss")),Re(s.shape,i.shape,"Error in logLoss: ");let u=ft(1),l=ft(n),c=Ut($(s,kr(Y(i,l)))),p=$(lt(u,s),kr(Y(lt(u,i),l))),m=lt(c,p);return qr(m,a,o)}var o2=k({logLoss_:$8});function R8(r,t,e,n=Ze.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","meanSquaredError"),s=C(t,"predictions","meanSquaredError"),i=null;e!=null&&(i=C(e,"weights","meanSquaredError")),Re(o.shape,s.shape,"Error in meanSquaredError: ");let a=_m(o,s);return qr(a,i,n)}var s2=k({meanSquaredError_:R8});function F8(r,t){let e=C(r,"labels","sigmoidCrossEntropyWithLogits"),n=C(t,"logits","sigmoidCrossEntropyWithLogits");Re(e.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Mr(n),s=$(n,e),i=Eu(ir(Ut(Ee(n))));return Y(lt(o,s),i)}function O8(r,t,e,n=0,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"multiClassLabels","sigmoidCrossEntropy"),i=C(t,"logits","sigmoidCrossEntropy"),a=null;if(e!=null&&(a=C(e,"weights","sigmoidCrossEntropy")),Re(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),n>0){let l=ft(n),c=ft(1),p=ft(.5);s=Y($(s,lt(c,l)),$(p,l))}let u=F8(s,i);return qr(u,a,o)}var i2=k({sigmoidCrossEntropy_:O8});function P8(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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i={inputIndices:n,inputShape:o,newShape:s},a=T.runKernel(cl,i);return{outputIndices:a[0],outputShape:a[1]}}var u2=k({sparseReshape_:z8});function B8(r,t,e){let n=C(r,"data","sparseSegmentMean"),o=C(t,"indices","sparseSegmentMean","int32"),s=C(e,"segmentIds","sparseSegmentMean","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape - ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape - ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(pu,i)}var c2=k({sparseSegmentMean_:B8});function V8(r,t,e){let n=C(r,"data","sparseSegmentSum"),o=C(t,"indices","sparseSegmentSum","int32"),s=C(e,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape - 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cD={kernelName:tu,inputsToSave:["x"],gradFunc:Ry.gradFunc};var pD={kernelName:Mi,saveAllInputs:!0,gradFunc:(r,t,e)=>{let n=t.map(u=>u.shape),{axis:o}=e,s=fr(o,t[0].shape)[0],i=n.map(u=>u[s]);return gr(r,i,s).map(u=>()=>u)}};var mD={kernelName:ns,inputsToSave:["x","filter"],gradFunc:(r,t,e)=>{let[n,o]=t,{dilations:s,strides:i,pad:a,dataFormat:u}=e;return _(co(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. 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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 Ia))throw new kt(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=Cn(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Ia.className="Sequential";J.registerClass(Ia);function JZ(r){return new jn(r)}function QZ(r){return new Ia(r)}function KN(r){return qy(r)}function tJ(r,t){In.registerCallbackConstructor(r,t)}var on=class extends J.Serializable{getConfig(){return{}}},fb=class extends on{apply(t,e=1){return tR(t,e)}};fb.className="elu";J.registerClass(fb);var db=class extends on{apply(t){return Im(t)}};db.className="selu";J.registerClass(db);var hb=class extends on{apply(t){return Mr(t)}};hb.className="relu";J.registerClass(hb);var gb=class extends on{apply(t){return B(()=>mo(6,Mr(t)))}};gb.className="relu6";J.registerClass(gb);var xb=class extends on{apply(t){return t}};xb.className="linear";J.registerClass(xb);var yb=class extends on{apply(t){return en(t)}};yb.className="sigmoid";J.registerClass(yb);var bb=class extends on{apply(t){return rR(t)}};bb.className="hardSigmoid";J.registerClass(bb);var wb=class extends on{apply(t){return pi(t)}};wb.className="softplus";J.registerClass(wb);var Ib=class extends on{apply(t){return eR(t)}};Ib.className="softsign";J.registerClass(Ib);var Cb=class extends on{apply(t){return ia(t)}};Cb.className="tanh";J.registerClass(Cb);var nf=class extends on{apply(t,e=-1){return Fu(t,e)}};nf.className="softmax";J.registerClass(nf);var vb=class extends on{apply(t,e=-1){return hm(t,e)}};vb.className="logSoftmax";J.registerClass(vb);var Sb=class extends on{apply(t,e=1){return B(()=>$(en($(t,e)),t))}};Sb.className="swish";J.registerClass(Sb);var Nb=class extends on{apply(t){return B(()=>$(t,ia(pi(t))))}};Nb.className="mish";J.registerClass(Nb);function yi(r){return r.getClassName()}function jN(r,t={}){return xa(r,J.SerializationMap.getMap().classNameMap,t,"activation")}function bi(r){if(r==null){let t={};return t.className="linear",t.config={},jN(t)}if(typeof r=="string"){let t={};return t.className=r,t.config={},jN(t)}else return r instanceof on?r:jN(r)}function XN(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var kb=class extends J.Serializable{},Wu=class extends kb{constructor(t){super(),XN(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,pt($(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,pt($(this.l2,Vc(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Wu.className="L1L2";J.registerClass(Wu);function MR(r){return XN(r),new Wu({l1:r!=null?r.l1:null,l2:0})}function LR(r){return XN(r),new Wu({l2:r!=null?r.l2:null,l1:0})}var OR={l1l2:"L1L2"};function me(r){return Fm(r)}function PR(r,t={}){return xa(r,J.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ce(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in OR?OR[r]:r,config:{}};return PR(e)}else return r instanceof kb?r:PR(r)}var of=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=Mr(t);return this.maxValue!=null&&(n=Sr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};of.className="ReLU";J.registerClass(of);var sf=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 _u(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};sf.className="LeakyReLU";J.registerClass(sf);var af=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 YN(r,t){return B(()=>(Oe(t),t==="channelsFirst"?Vt(r,[0,2,3,4,1]):r))}function rJ(r,t,e,n=1,o="valid",s,i=1){return B(()=>{if(s==null&&(s=yn()),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 kt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=cm(r,t,n,o==="same"?"same":"valid","NWC",i);return e!=null&&(a=bn(a,e)),a})}function zR(r,t,e,n=[1,1],o="valid",s,i,a=null){return B(()=>{if(s==null&&(s=yn()),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 kt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Lu.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 nJ(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=yn()),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 ${r.rank}.`);let a=YN(r,s);if(o==="causal")throw new kt("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=Rx(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=bn(a,e)),s==="channelsFirst"&&(a=Vt(a,[0,4,1,2,3])),a})}var Jc=class extends _t{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Jc.verifyArgs(e),this.rank=t,Qe(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new kt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Uu(e.kernelSize,t,"kernelSize"),this.strides=Uu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,gn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Oe(this.dataFormat),this.activation=bi(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=Uu(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(fo("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:yi(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}},Hu=class extends Jc{constructor(t,e){super(t,e),this.kernel=null,Hu.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=zR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=rJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=zR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=nJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new kt("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)}`)}},Dl=class extends Hu{constructor(t){super(2,t),Dl.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)}.`)}};Dl.className="Conv2D";J.registerClass(Dl);var $l=class extends Hu{constructor(t){super(3,t),$l.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 ${JSON.stringify(t.kernelSize)}.`)}};$l.className="Conv3D";J.registerClass($l);var pf=class extends Dl{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=wi(u,m,c,this.padding),h=wi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,1]));let x=mm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=bn(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]=wi(e[o],u,i,this.padding),e[s]=wi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};pf.className="Conv2DTranspose";J.registerClass(pf);var mf=class extends $l{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=wi(l,h,m,this.padding),w=wi(c,g,f,this.padding),I=wi(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=Ox(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=bn(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]=wi(e[o],c,a,this.padding),e[s]=wi(e[s],p,u,this.padding),e[i]=wi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};mf.className="Conv3DTranspose";J.registerClass(mf);var Tb=class extends Hu{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 kt("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Vt(t,[0,2,3,1])),n=Cm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=bn(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 ff=class extends Tb{constructor(t){super(2,t)}};ff.className="SeparableConv2D";J.registerClass(ff);var qu=class extends Hu{constructor(t){super(1,t),qu.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 ${JSON.stringify(t.kernelSize)}.`)}};qu.className="Conv1D";J.registerClass(qu);var df=class extends _t{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=Ah(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ah(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ah(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ah(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};df.className="Cropping2D";J.registerClass(df);var hf=class extends _t{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,j$(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"?hn.resizeNearestNeighbor(n,[s,i]):hn.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"?hn.resizeNearestNeighbor(n,[s,i]):hn.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}};hf.className="UpSampling2D";J.registerClass(hf);function oJ(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=yn()),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=ua(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var gf=class extends Jc{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=oJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=bn(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=An(e,this.kernelSize[0],this.padding,this.strides[0]),i=An(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}};gf.className="DepthwiseConv2D";J.registerClass(gf);function ZN(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 JN(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 ${u}D.`);let l=[1,0].concat(xn(2,u));if(t=Vt(t,l),s!=null)throw new kt("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=Q(Q(o,"bool"),"float32"),o.rank===u-1&&(o=ar(o,-1)),o=Vt(o,l)),n&&(t=hr(t,0),o!=null&&(o=hr(o,0)));let c=[],p,m=e,f=t.shape[0],d=xr(t),h;o!=null&&(h=xr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=lt(Ir(N),N),A=Y($(w[0],N),$(m[0],E)),D=m.map((F,P)=>Y($(w[1][P],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var Dn=class extends _t{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new ep({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ie({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return xn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Hy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${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 En("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=ZN(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 nn){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=JN((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=_l(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()===Dn.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=Cn(o,n);return new t(Object.assign(e,{cell:s}))}};Dn.className="RNN";J.registerClass(Dn);var Rl=class extends _t{},Qc=class extends Rl{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=bi(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=Bc([1,gi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Bc([1,gi([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 ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0Ir(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0Ir(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Fo($(t,i),this.kernel.read()):s=Fo(t,this.kernel.read()),this.bias!=null&&(s=bn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=Y(s,Fo(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:yi(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)}};Qc.className="SimpleRNNCell";J.registerClass(Qc);var xf=class extends Dn{constructor(t){t.cell=new Qc(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)}};xf.className="SimpleRNN";J.registerClass(xf);var tp=class extends Rl{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=bi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=bi(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=Bc([1,gi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Bc([1,gi([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)}};yf.className="GRU";J.registerClass(yf);var Fl=class extends Rl{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=bi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=bi(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=Bc([1,gi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Bc([1,gi([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 wn{apply(u,l){let c=s.apply([i]),p=new Vu().apply([i]),m=s.apply([i*2]);return MN(MN(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 ${t.length}.`);let o=t[1],s=t[2];t=t[0],0Ir(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0Ir(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};bf.className="LSTM";J.registerClass(bf);var ep=class extends Rl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{hi(`RNNCell_${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(Cn(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 $h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Uy(t(),e),a=()=>Bu(i,t,n);return!o||o<=1?$e(a().clone()):Array(o).fill(void 0).map(a).map(l=>$e(l.clone()))}var sJ=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 En("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=An(l,o[0],s,i[0],a[0]),m=An(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};_b.className="ConvRNN2D";var rp=class extends Fl{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=Uu(n,2,"kernelSize"),this.kernelSize.forEach(u=>Qe(u,"kernelSize")),this.strides=Uu(o||1,2,"strides"),this.strides.forEach(u=>Qe(u,"strides")),this.padding=s||"valid",gn(this.padding),this.dataFormat=i||"channelsLast",Oe(this.dataFormat),this.dilationRate=Uu(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 wn{apply(m,f){let d=l.apply([c]),h=dr([c]),g=l.apply([c*2]);return Pm([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=sJ(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=Tn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?bn(s,n,this.dataFormat):s}recurrentConv(t,e){return Tn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};rp.className="ConvLSTM2DCell";J.registerClass(rp);var wf=class extends _b{constructor(t){let e=new rp(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};wf.className="ConvLSTM2D";J.registerClass(wf);var np=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()}};np.className="Dropout";J.registerClass(np);var If=class extends np{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};If.className="SpatialDropout1D";J.registerClass(If);var Cf=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=bi(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=Fo(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Fo(n,this.kernel.read()),this.bias!=null&&(s=bn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:yi(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}};Cf.className="Dense";J.registerClass(Cf);var vf=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 ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],Ro(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:yi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};Sf.className="Activation";J.registerClass(Sf);var Nf=class extends _t{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),Z$(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};Nf.className="RepeatVector";J.registerClass(Nf);var kf=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}};kf.className="Reshape";J.registerClass(kf);var Tf=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=xn(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}};Tf.className="Permute";J.registerClass(Tf);var _f=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 bc(mi(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=-1,s=!0,i=bc(mi(n,this.maskValue),o,s);return $(n,Q(i,n.dtype))})}};_f.className="Masking";J.registerClass(_f);var Ef=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),mi(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=rn(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}};Ef.className="Embedding";J.registerClass(Ef);var Pl=class extends _t{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new kt}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: ${JSON.stringify(t)}.`);let n=t[0]==null?null:t[0].slice(1);for(let s=1;ss.length);t.indexOf(null)===-1&&$o(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=gi(o);for(let i of t){let a=i.rank;for(let u=0;u1){let c=xn(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(xn(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 ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:ar(o,0));let n=e[0];for(let o=1;o{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0];for(let n=1;n{let e=t[0];for(let n=1;n1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}mergeFunction(t){return B(()=>Pm(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let e=t,n=e[0].slice(),o=this.axis<0?n.length+this.axis:this.axis;for(let s of e.slice(1)){if(n[o]==null||s[o]==null){n[o]=null;break}n[o]+=s[o]}return n}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new z("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new z(`Mismatch in the length of mask (${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 kt("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 kt("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 kt("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])),iJ(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 kt("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}};Pf.className="Dot";J.registerClass(Pf);var Mf=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 Bu(()=>Y(Mm(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Mf.className="GaussianNoise";J.registerClass(Mf);var Lf=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?Bu(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Mm(n.shape,1,s))},()=>n,e.training||!1):n})}};Lf.className="GaussianDropout";J.registerClass(Lf);var zf=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 ${JSON.stringify(t)}.`);this.inputSpec=[new Ie({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=xn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=Ao(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,xn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),I=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return Gh(o,b,w,I,N,this.epsilon)}else return Gh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=uJ(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,I)=>{B(()=>{let N=1-I,E=b.read(),A=$(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}};Bf.className="BatchNormalization";J.registerClass(Bf);var Vf=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: ${s}`);if(this.axis.length!==$o(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}=vc(n,this.axis,!0),l=Ao(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=yn()),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(()=>cJ(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Gf.className="ZeroPadding2D";J.registerClass(Gf);function Fb(r,t,e,n,o,s){return B(()=>{Oe(o),RN(s),gn(n),e==null&&(e=[1,1]),n==null&&(n="valid"),o==null&&(o=yn()),s==null&&(s="max"),r=Bh(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Du(r,t,e,a):i=Su(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),RN(s),gn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=yn()),s==null&&(s="max"),r=YN(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Jx(r,t,e,a):i=vx(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,gn(this.padding),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=An(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=_l(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return qn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Wf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,"max")}};Wf.className="MaxPooling1D";J.registerClass(Wf);var Uf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,"avg")}};Uf.className="AveragePooling1D";J.registerClass(Uf);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),gn(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=An(e,this.poolSize[0],this.padding,this.strides[0]),n=An(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}},Hf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,"max")}};Hf.className="MaxPooling2D";J.registerClass(Hf);var qf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),Fb(t,e,n,o,s,"avg")}};qf.className="AveragePooling2D";J.registerClass(qf);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),gn(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=An(e,this.poolSize[0],this.padding,this.strides[0]),n=An(n,this.poolSize[1],this.padding,this.strides[1]),o=An(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}},Kf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),BR(t,e,n,o,s,"max")}};Kf.className="MaxPooling3D";J.registerClass(Kf);var jf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),gn(o),BR(t,e,n,o,s,"avg")}};jf.className="AveragePooling3D";J.registerClass(jf);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 kt}},Xf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return ke(n,1)})}};Xf.className="GlobalAveragePooling1D";J.registerClass(Xf);var Yf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Nr(n,1)})}};Yf.className="GlobalMaxPooling1D";J.registerClass(Yf);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 kt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Zf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?ke(n,[1,2]):ke(n,[2,3])})}};Zf.className="GlobalAveragePooling2D";J.registerClass(Zf);var Jf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Nr(n,[1,2]):Nr(n,[2,3])})}};Jf.className="GlobalMaxPooling2D";J.registerClass(Jf);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=Cn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Qf=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 ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),JN((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Qf.className="TimeDistributed";J.registerClass(Qf);function pJ(r){ya(q$,"BidirectionalMergeMode",r)}var mJ="concat",td=class extends Ob{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=Cn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=Cn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?mJ:t.mergeMode,pJ(this.mergeMode),t.weights)throw new kt("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()):Tr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=ZN(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 kt("Support for constants in Bidirectional layers is not implemented yet.");let u=i[0]instanceof nn;for(let l of i)if(l instanceof nn!==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=Pm([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){hi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),hi(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=Cn(e.layer);if(delete e.layer,e.numConstants!=null)throw new kt("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=n,new t(o)}};td.className="Bidirectional";J.registerClass(td);var ed=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=rn(t,"float32")),Y($(t,this.scale),this.offset)))}};ed.className="Rescaling";J.registerClass(ed);var{resizeBilinear:fJ,cropAndResize:dJ}=hn,rd=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=fJ(t,[e,n]);return rn(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}};rd.className="CenterCrop";J.registerClass(rd);function VR(r,t,e,n){let o=St(r);if(o.dtype!=="int32"&&(o=rn(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 $(u,n);throw new z("When outputMode is 'tfIdf', weights must be provided.")}var nd=class extends _t{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode="multiHot"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode==="oneHot"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!=="int32"&&(t=rn(t,"int32"));let n;if(typeof e.countWeights!="undefined"){if(this.outputMode!=="count")throw new z(`countWeights is not used when outputMode !== count. - Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Nr(t),s=bl(t),i=Fe(this.numTokens,o).bufferSync().get(0),a=mn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return VR(t,this.outputMode,this.numTokens,n)})}};nd.className="CategoryEncoding";J.registerClass(nd);var gJ=["bilinear","nearest"],GR=new Set(gJ),od=class extends _t{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(GR.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 hn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation==="nearest")return hn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...GR]} are supported`)})}};od.className="Resizing";J.registerClass(od);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 xJ=["bilinear","nearest"],WR=new Set(xJ),sd=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}. Must be positive number or tuple of 2 numbers`);if(this.widthLower<-1||this.widthUpper<-1)throw new z(`factor must have values larger than -1. Got: ${this.factor}`);if(this.widthUpper function __require() { + return mod4 || (0, cb[__getOwnPropNames(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports; +}; +var __export = (target, all5) => { + for (var name in all5) + __defProp(target, name, { get: all5[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __toESM = (mod4, isNodeMode, target) => (target = mod4 != null ? __create(__getProtoOf(mod4)) : {}, __copyProps( + // If the importer is in node compatibility mode or this is not an ESM + // file that has been converted to a CommonJS file using a Babel- + // compatible transform (i.e. "__esModule" has not been set), then set + // "default" to the CommonJS "module.exports" for node compatibility. + isNodeMode || !mod4 || !mod4.__esModule ? __defProp(target, "default", { value: mod4, enumerable: true }) : target, + mod4 +)); + +// node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js +var require_long = __commonJS({ + "node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js"(exports, module) { + "use strict"; + module.exports = Long2; + var wasm = null; + try { + wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([ + 0, + 97, + 115, + 109, + 1, + 0, + 0, + 0, + 1, + 13, + 2, + 96, + 0, + 1, + 127, + 96, + 4, + 127, + 127, + 127, + 127, + 1, + 127, + 3, + 7, + 6, + 0, + 1, + 1, + 1, + 1, + 1, + 6, + 6, + 1, + 127, + 1, + 65, + 0, + 11, + 7, + 50, + 6, + 3, + 109, + 117, + 108, + 0, + 1, + 5, + 100, + 105, + 118, + 95, + 115, + 0, + 2, + 5, + 100, + 105, + 118, + 95, + 117, + 0, + 3, + 5, + 114, + 101, + 109, + 95, + 115, + 0, + 4, + 5, + 114, + 101, + 109, + 95, + 117, + 0, + 5, + 8, + 103, + 101, + 116, + 95, + 104, + 105, + 103, + 104, + 0, + 0, + 10, + 191, + 1, + 6, + 4, + 0, + 35, + 0, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 126, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 127, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 128, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 129, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11, + 36, + 1, + 1, + 126, + 32, + 0, + 173, + 32, + 1, + 173, + 66, + 32, + 134, + 132, + 32, + 2, + 173, + 32, + 3, + 173, + 66, + 32, + 134, + 132, + 130, + 34, + 4, + 66, + 32, + 135, + 167, + 36, + 0, + 32, + 4, + 167, + 11 + ])), {}).exports; + } catch (e) { + } + function Long2(low, high, unsigned) { + this.low = low | 0; + this.high = high | 0; + this.unsigned = !!unsigned; + } + Long2.prototype.__isLong__; + Object.defineProperty(Long2.prototype, "__isLong__", { value: true }); + function isLong(obj) { + return (obj && obj["__isLong__"]) === true; + } + Long2.isLong = isLong; + var INT_CACHE = {}; + var UINT_CACHE = {}; + function fromInt(value, unsigned) { + var obj, cachedObj, cache; + if (unsigned) { + value >>>= 0; + if (cache = 0 <= value && value < 256) { + cachedObj = UINT_CACHE[value]; + if (cachedObj) + return cachedObj; + } + obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true); + if (cache) + UINT_CACHE[value] = obj; + return obj; + } else { + value |= 0; + if (cache = -128 <= value && value < 128) { + cachedObj = INT_CACHE[value]; + if (cachedObj) + return cachedObj; + } + obj = fromBits(value, value < 0 ? -1 : 0, false); + if (cache) + INT_CACHE[value] = obj; + return obj; + } + } + Long2.fromInt = fromInt; + function fromNumber(value, unsigned) { + if (isNaN(value)) + return unsigned ? UZERO : ZERO; + if (unsigned) { + if (value < 0) + return UZERO; + if (value >= TWO_PWR_64_DBL) + return MAX_UNSIGNED_VALUE; + } else { + if (value <= -TWO_PWR_63_DBL) + return MIN_VALUE; + if (value + 1 >= TWO_PWR_63_DBL) + return MAX_VALUE; + } + if (value < 0) + return fromNumber(-value, unsigned).neg(); + return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned); + } + Long2.fromNumber = fromNumber; + function fromBits(lowBits, highBits, unsigned) { + return new Long2(lowBits, highBits, unsigned); + } + Long2.fromBits = fromBits; + var pow_dbl = Math.pow; + function fromString(str, unsigned, radix) { + if (str.length === 0) + throw Error("empty string"); + if (str === "NaN" || str === "Infinity" || str === "+Infinity" || str === "-Infinity") + return ZERO; + if (typeof unsigned === "number") { + radix = unsigned, unsigned = false; + } else { + unsigned = !!unsigned; + } + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError("radix"); + var p2; + if ((p2 = str.indexOf("-")) > 0) + throw Error("interior hyphen"); + else if (p2 === 0) { + return fromString(str.substring(1), unsigned, radix).neg(); + } + var radixToPower = fromNumber(pow_dbl(radix, 8)); + var result = ZERO; + for (var i = 0; i < str.length; i += 8) { + var size = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size), radix); + if (size < 8) { + var power = fromNumber(pow_dbl(radix, size)); + result = result.mul(power).add(fromNumber(value)); + } else { + result = result.mul(radixToPower); + result = result.add(fromNumber(value)); + } + } + result.unsigned = unsigned; + return result; + } + Long2.fromString = fromString; + function fromValue(val, unsigned) { + if (typeof val === "number") + return fromNumber(val, unsigned); + if (typeof val === "string") + return fromString(val, unsigned); + return fromBits(val.low, val.high, typeof unsigned === "boolean" ? unsigned : val.unsigned); + } + Long2.fromValue = fromValue; + var TWO_PWR_16_DBL = 1 << 16; + var TWO_PWR_24_DBL = 1 << 24; + var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL; + var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL; + var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2; + var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL); + var ZERO = fromInt(0); + Long2.ZERO = ZERO; + var UZERO = fromInt(0, true); + Long2.UZERO = UZERO; + var ONE = fromInt(1); + Long2.ONE = ONE; + var UONE = fromInt(1, true); + Long2.UONE = UONE; + var NEG_ONE = fromInt(-1); + Long2.NEG_ONE = NEG_ONE; + var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false); + Long2.MAX_VALUE = MAX_VALUE; + var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true); + Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE; + var MIN_VALUE = fromBits(0, 2147483648 | 0, false); + Long2.MIN_VALUE = MIN_VALUE; + var LongPrototype = Long2.prototype; + LongPrototype.toInt = function toInt() { + return this.unsigned ? this.low >>> 0 : this.low; + }; + LongPrototype.toNumber = function toNumber() { + if (this.unsigned) + return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0); + return this.high * TWO_PWR_32_DBL + (this.low >>> 0); + }; + LongPrototype.toString = function toString(radix) { + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError("radix"); + if (this.isZero()) + return "0"; + if (this.isNegative()) { + if (this.eq(MIN_VALUE)) { + var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this); + return div3.toString(radix) + rem1.toInt().toString(radix); + } else + return "-" + this.neg().toString(radix); + } + var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this; + var result = ""; + while (true) { + var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix); + rem = remDiv; + if (rem.isZero()) + return digits + result; + else { + while (digits.length < 6) + digits = "0" + digits; + result = "" + digits + result; + } + } + }; + LongPrototype.getHighBits = function getHighBits() { + return this.high; + }; + LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() { + return this.high >>> 0; + }; + LongPrototype.getLowBits = function getLowBits() { + return this.low; + }; + LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() { + return this.low >>> 0; + }; + LongPrototype.getNumBitsAbs = function getNumBitsAbs() { + if (this.isNegative()) + return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs(); + var val = this.high != 0 ? this.high : this.low; + for (var bit = 31; bit > 0; bit--) + if ((val & 1 << bit) != 0) + break; + return this.high != 0 ? bit + 33 : bit + 1; + }; + LongPrototype.isZero = function isZero() { + return this.high === 0 && this.low === 0; + }; + LongPrototype.eqz = LongPrototype.isZero; + LongPrototype.isNegative = function isNegative() { + return !this.unsigned && this.high < 0; + }; + LongPrototype.isPositive = function isPositive() { + return this.unsigned || this.high >= 0; + }; + LongPrototype.isOdd = function isOdd() { + return (this.low & 1) === 1; + }; + LongPrototype.isEven = function isEven2() { + return (this.low & 1) === 0; + }; + LongPrototype.equals = function equals(other) { + if (!isLong(other)) + other = fromValue(other); + if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1) + return false; + return this.high === other.high && this.low === other.low; + }; + LongPrototype.eq = LongPrototype.equals; + LongPrototype.notEquals = function notEquals(other) { + return !this.eq( + /* validates */ + other + ); + }; + LongPrototype.neq = LongPrototype.notEquals; + LongPrototype.ne = LongPrototype.notEquals; + LongPrototype.lessThan = function lessThan(other) { + return this.comp( + /* validates */ + other + ) < 0; + }; + LongPrototype.lt = LongPrototype.lessThan; + LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) { + return this.comp( + /* validates */ + other + ) <= 0; + }; + LongPrototype.lte = LongPrototype.lessThanOrEqual; + LongPrototype.le = LongPrototype.lessThanOrEqual; + LongPrototype.greaterThan = function greaterThan(other) { + return this.comp( + /* validates */ + other + ) > 0; + }; + LongPrototype.gt = LongPrototype.greaterThan; + LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) { + return this.comp( + /* validates */ + other + ) >= 0; + }; + LongPrototype.gte = LongPrototype.greaterThanOrEqual; + LongPrototype.ge = LongPrototype.greaterThanOrEqual; + LongPrototype.compare = function compare(other) { + if (!isLong(other)) + other = fromValue(other); + if (this.eq(other)) + return 0; + var thisNeg = this.isNegative(), otherNeg = other.isNegative(); + if (thisNeg && !otherNeg) + return -1; + if (!thisNeg && otherNeg) + return 1; + if (!this.unsigned) + return this.sub(other).isNegative() ? -1 : 1; + return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1; + }; + LongPrototype.comp = LongPrototype.compare; + LongPrototype.negate = function negate() { + if (!this.unsigned && this.eq(MIN_VALUE)) + return MIN_VALUE; + return this.not().add(ONE); + }; + LongPrototype.neg = LongPrototype.negate; + LongPrototype.add = function add5(addend) { + if (!isLong(addend)) + addend = fromValue(addend); + var a48 = this.high >>> 16; + var a32 = this.high & 65535; + var a16 = this.low >>> 16; + var a00 = this.low & 65535; + var b48 = addend.high >>> 16; + var b32 = addend.high & 65535; + var b16 = addend.low >>> 16; + var b00 = addend.low & 65535; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 + b00; + c16 += c00 >>> 16; + c00 &= 65535; + c16 += a16 + b16; + c32 += c16 >>> 16; + c16 &= 65535; + c32 += a32 + b32; + c48 += c32 >>> 16; + c32 &= 65535; + c48 += a48 + b48; + c48 &= 65535; + return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned); + }; + LongPrototype.subtract = function subtract(subtrahend) { + if (!isLong(subtrahend)) + subtrahend = fromValue(subtrahend); + return this.add(subtrahend.neg()); + }; + LongPrototype.sub = LongPrototype.subtract; + LongPrototype.multiply = function multiply4(multiplier) { + if (this.isZero()) + return ZERO; + if (!isLong(multiplier)) + multiplier = fromValue(multiplier); + if (wasm) { + var low = wasm.mul( + this.low, + this.high, + multiplier.low, + multiplier.high + ); + return fromBits(low, wasm.get_high(), this.unsigned); + } + if (multiplier.isZero()) + return ZERO; + if (this.eq(MIN_VALUE)) + return multiplier.isOdd() ? MIN_VALUE : ZERO; + if (multiplier.eq(MIN_VALUE)) + return this.isOdd() ? MIN_VALUE : ZERO; + if (this.isNegative()) { + if (multiplier.isNegative()) + return this.neg().mul(multiplier.neg()); + else + return this.neg().mul(multiplier).neg(); + } else if (multiplier.isNegative()) + return this.mul(multiplier.neg()).neg(); + if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24)) + return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned); + var a48 = this.high >>> 16; + var a32 = this.high & 65535; + var a16 = this.low >>> 16; + var a00 = this.low & 65535; + var b48 = multiplier.high >>> 16; + var b32 = multiplier.high & 65535; + var b16 = multiplier.low >>> 16; + var b00 = multiplier.low & 65535; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 * b00; + c16 += c00 >>> 16; + c00 &= 65535; + c16 += a16 * b00; + c32 += c16 >>> 16; + c16 &= 65535; + c16 += a00 * b16; + c32 += c16 >>> 16; + c16 &= 65535; + c32 += a32 * b00; + c48 += c32 >>> 16; + c32 &= 65535; + c32 += a16 * b16; + c48 += c32 >>> 16; + c32 &= 65535; + c32 += a00 * b32; + c48 += c32 >>> 16; + c32 &= 65535; + c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48; + c48 &= 65535; + return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned); + }; + LongPrototype.mul = LongPrototype.multiply; + LongPrototype.divide = function divide(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + if (divisor.isZero()) + throw Error("division by zero"); + if (wasm) { + if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) { + return this; + } + var low = (this.unsigned ? wasm.div_u : wasm.div_s)( + this.low, + this.high, + divisor.low, + divisor.high + ); + return fromBits(low, wasm.get_high(), this.unsigned); + } + if (this.isZero()) + return this.unsigned ? UZERO : ZERO; + var approx, rem, res; + if (!this.unsigned) { + if (this.eq(MIN_VALUE)) { + if (divisor.eq(ONE) || divisor.eq(NEG_ONE)) + return MIN_VALUE; + else if (divisor.eq(MIN_VALUE)) + return ONE; + else { + var halfThis = this.shr(1); + approx = halfThis.div(divisor).shl(1); + if (approx.eq(ZERO)) { + return divisor.isNegative() ? ONE : NEG_ONE; + } else { + rem = this.sub(divisor.mul(approx)); + res = approx.add(rem.div(divisor)); + return res; + } + } + } else if (divisor.eq(MIN_VALUE)) + return this.unsigned ? UZERO : ZERO; + if (this.isNegative()) { + if (divisor.isNegative()) + return this.neg().div(divisor.neg()); + return this.neg().div(divisor).neg(); + } else if (divisor.isNegative()) + return this.div(divisor.neg()).neg(); + res = ZERO; + } else { + if (!divisor.unsigned) + divisor = divisor.toUnsigned(); + if (divisor.gt(this)) + return UZERO; + if (divisor.gt(this.shru(1))) + return UONE; + res = UZERO; + } + rem = this; + while (rem.gte(divisor)) { + approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber())); + var log22 = Math.ceil(Math.log(approx) / Math.LN2), delta = log22 <= 48 ? 1 : pow_dbl(2, log22 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor); + while (approxRem.isNegative() || approxRem.gt(rem)) { + approx -= delta; + approxRes = fromNumber(approx, this.unsigned); + approxRem = approxRes.mul(divisor); + } + if (approxRes.isZero()) + approxRes = ONE; + res = res.add(approxRes); + rem = rem.sub(approxRem); + } + return res; + }; + LongPrototype.div = LongPrototype.divide; + LongPrototype.modulo = function modulo(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + if (wasm) { + var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)( + this.low, + this.high, + divisor.low, + divisor.high + ); + return fromBits(low, wasm.get_high(), this.unsigned); + } + return this.sub(this.div(divisor).mul(divisor)); + }; + LongPrototype.mod = LongPrototype.modulo; + LongPrototype.rem = LongPrototype.modulo; + LongPrototype.not = function not() { + return fromBits(~this.low, ~this.high, this.unsigned); + }; + LongPrototype.and = function and(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low & other.low, this.high & other.high, this.unsigned); + }; + LongPrototype.or = function or(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low | other.low, this.high | other.high, this.unsigned); + }; + LongPrototype.xor = function xor(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned); + }; + LongPrototype.shiftLeft = function shiftLeft(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + if ((numBits &= 63) === 0) + return this; + else if (numBits < 32) + return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned); + else + return fromBits(0, this.low << numBits - 32, this.unsigned); + }; + LongPrototype.shl = LongPrototype.shiftLeft; + LongPrototype.shiftRight = function shiftRight(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + if ((numBits &= 63) === 0) + return this; + else if (numBits < 32) + return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned); + else + return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned); + }; + LongPrototype.shr = LongPrototype.shiftRight; + LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + numBits &= 63; + if (numBits === 0) + return this; + else { + var high = this.high; + if (numBits < 32) { + var low = this.low; + return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned); + } else if (numBits === 32) + return fromBits(high, 0, this.unsigned); + else + return fromBits(high >>> numBits - 32, 0, this.unsigned); + } + }; + LongPrototype.shru = LongPrototype.shiftRightUnsigned; + LongPrototype.shr_u = LongPrototype.shiftRightUnsigned; + LongPrototype.toSigned = function toSigned() { + if (!this.unsigned) + return this; + return fromBits(this.low, this.high, false); + }; + LongPrototype.toUnsigned = function toUnsigned() { + if (this.unsigned) + return this; + return fromBits(this.low, this.high, true); + }; + LongPrototype.toBytes = function toBytes(le) { + return le ? this.toBytesLE() : this.toBytesBE(); + }; + LongPrototype.toBytesLE = function toBytesLE() { + var hi = this.high, lo = this.low; + return [ + lo & 255, + lo >>> 8 & 255, + lo >>> 16 & 255, + lo >>> 24, + hi & 255, + hi >>> 8 & 255, + hi >>> 16 & 255, + hi >>> 24 + ]; + }; + LongPrototype.toBytesBE = function toBytesBE() { + var hi = this.high, lo = this.low; + return [ + hi >>> 24, + hi >>> 16 & 255, + hi >>> 8 & 255, + hi & 255, + lo >>> 24, + lo >>> 16 & 255, + lo >>> 8 & 255, + lo & 255 + ]; + }; + Long2.fromBytes = function fromBytes(bytes, unsigned, le) { + return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned); + }; + Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) { + return new Long2( + bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24, + bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24, + unsigned + ); + }; + Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) { + return new Long2( + bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7], + bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3], + unsigned + ); + }; + } +}); + +// (disabled):node_modules/.pnpm/node-fetch@2.6.13/node_modules/node-fetch/browser.js +var require_browser = __commonJS({ + "(disabled):node_modules/.pnpm/node-fetch@2.6.13/node_modules/node-fetch/browser.js"() { + "use strict"; + } +}); + +// (disabled):util +var require_util = __commonJS({ + "(disabled):util"() { + "use strict"; + } +}); + +// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js +var require_alea = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function Alea(seed) { + var me = this, mash = Mash(); + me.next = function() { + var t = 2091639 * me.s0 + me.c * 23283064365386963e-26; + me.s0 = me.s1; + me.s1 = me.s2; + return me.s2 = t - (me.c = t | 0); + }; + me.c = 1; + me.s0 = mash(" "); + me.s1 = mash(" "); + me.s2 = mash(" "); + me.s0 -= mash(seed); + if (me.s0 < 0) { + me.s0 += 1; + } + me.s1 -= mash(seed); + if (me.s1 < 0) { + me.s1 += 1; + } + me.s2 -= mash(seed); + if (me.s2 < 0) { + me.s2 += 1; + } + mash = null; + } + function copy(f, t) { + t.c = f.c; + t.s0 = f.s0; + t.s1 = f.s1; + t.s2 = f.s2; + return t; + } + function impl(seed, opts) { + var xg = new Alea(seed), state = opts && opts.state, prng = xg.next; + prng.int32 = function() { + return xg.next() * 4294967296 | 0; + }; + prng.double = function() { + return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32; + }; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + function Mash() { + var n = 4022871197; + var mash = function(data) { + data = String(data); + for (var i = 0; i < data.length; i++) { + n += data.charCodeAt(i); + var h = 0.02519603282416938 * n; + n = h >>> 0; + h -= n; + h *= n; + n = h >>> 0; + h -= n; + n += h * 4294967296; + } + return (n >>> 0) * 23283064365386963e-26; + }; + return mash; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.alea = impl; + } + })( + exports, + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); + +// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js +var require_xor128 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.next = function() { + var t = me.x ^ me.x << 11; + me.x = me.y; + me.y = me.z; + me.z = me.w; + return me.w ^= me.w >>> 19 ^ t ^ t >>> 8; + }; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor128 = impl; + } + })( + exports, + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); + +// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js +var require_xorwow = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var t = me.x ^ me.x >>> 2; + me.x = me.y; + me.y = me.z; + me.z = me.w; + me.w = me.v; + return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0; + }; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.v = 0; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + if (k == strseed.length) { + me.d = me.x << 10 ^ me.x >>> 4; + } + me.next(); + } + } + function copy(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + t.v = f.v; + t.d = f.d; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorwow = impl; + } + })( + exports, + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); + +// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js +var require_xorshift7 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var X = me.x, i = me.i, t, v, w; + t = X[i]; + t ^= t >>> 7; + v = t ^ t << 24; + t = X[i + 1 & 7]; + v ^= t ^ t >>> 10; + t = X[i + 3 & 7]; + v ^= t ^ t >>> 3; + t = X[i + 4 & 7]; + v ^= t ^ t << 7; + t = X[i + 7 & 7]; + t = t ^ t << 13; + v ^= t ^ t << 9; + X[i] = v; + me.i = i + 1 & 7; + return v; + }; + function init2(me2, seed2) { + var j, w, X = []; + if (seed2 === (seed2 | 0)) { + w = X[0] = seed2; + } else { + seed2 = "" + seed2; + for (j = 0; j < seed2.length; ++j) { + X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13; + } + } + while (X.length < 8) + X.push(0); + for (j = 0; j < 8 && X[j] === 0; ++j) + ; + if (j == 8) + w = X[7] = -1; + else + w = X[j]; + me2.x = X; + me2.i = 0; + for (j = 256; j > 0; --j) { + me2.next(); + } + } + init2(me, seed); + } + function copy(f, t) { + t.x = f.x.slice(); + t.i = f.i; + return t; + } + function impl(seed, opts) { + if (seed == null) + seed = +/* @__PURE__ */ new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.x) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorshift7 = impl; + } + })( + exports, + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); + +// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js +var require_xor4096 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var w = me.w, X = me.X, i = me.i, t, v; + me.w = w = w + 1640531527 | 0; + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + v = X[i] = v ^ t; + me.i = i; + return v + (w ^ w >>> 16) | 0; + }; + function init2(me2, seed2) { + var t, v, i, j, w, X = [], limit = 128; + if (seed2 === (seed2 | 0)) { + v = seed2; + seed2 = null; + } else { + seed2 = seed2 + "\0"; + v = 0; + limit = Math.max(limit, seed2.length); + } + for (i = 0, j = -32; j < limit; ++j) { + if (seed2) + v ^= seed2.charCodeAt((j + 32) % seed2.length); + if (j === 0) + w = v; + v ^= v << 10; + v ^= v >>> 15; + v ^= v << 4; + v ^= v >>> 13; + if (j >= 0) { + w = w + 1640531527 | 0; + t = X[j & 127] ^= v + w; + i = 0 == t ? i + 1 : 0; + } + } + if (i >= 128) { + X[(seed2 && seed2.length || 0) & 127] = -1; + } + i = 127; + for (j = 4 * 128; j > 0; --j) { + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + X[i] = v ^ t; + } + me2.w = w; + me2.X = X; + me2.i = i; + } + init2(me, seed); + } + function copy(f, t) { + t.i = f.i; + t.w = f.w; + t.X = f.X.slice(); + return t; + } + ; + function impl(seed, opts) { + if (seed == null) + seed = +/* @__PURE__ */ new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.X) + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor4096 = impl; + } + })( + exports, + // window object or global + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); + +// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js +var require_tychei = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(exports, module) { + "use strict"; + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var b = me.b, c = me.c, d = me.d, a = me.a; + b = b << 25 ^ b >>> 7 ^ c; + c = c - d | 0; + d = d << 24 ^ d >>> 8 ^ a; + a = a - b | 0; + me.b = b = b << 20 ^ b >>> 12 ^ c; + me.c = c = c - d | 0; + me.d = d << 16 ^ c >>> 16 ^ a; + return me.a = a - b | 0; + }; + me.a = 0; + me.b = 0; + me.c = 2654435769 | 0; + me.d = 1367130551; + if (seed === Math.floor(seed)) { + me.a = seed / 4294967296 | 0; + me.b = seed | 0; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 20; k++) { + me.b ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy(f, t) { + t.a = f.a; + t.b = f.b; + t.c = f.c; + t.d = f.d; + return t; + } + ; + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy(state, xg); + prng.state = function() { + return copy(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.tychei = impl; + } + })( + exports, + typeof module == "object" && module, + // present in node.js + typeof define == "function" && define + // present with an AMD loader + ); + } +}); + +// (disabled):crypto +var require_crypto = __commonJS({ + "(disabled):crypto"() { + "use strict"; + } +}); + +// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js +var require_seedrandom = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js"(exports, module) { + "use strict"; + (function(global2, pool3, math) { + var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; + function seedrandom5(seed, options, callback) { + var key = []; + options = options == true ? { entropy: true } : options || {}; + var shortseed = mixkey(flatten4( + options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, + 3 + ), key); + var arc4 = new ARC4(key); + var prng = function() { + var n = arc4.g(chunks), d = startdenom, x = 0; + while (n < significance) { + n = (n + x) * width; + d *= width; + x = arc4.g(1); + } + while (n >= overflow) { + n /= 2; + d /= 2; + x >>>= 1; + } + return (n + x) / d; + }; + prng.int32 = function() { + return arc4.g(4) | 0; + }; + prng.quick = function() { + return arc4.g(4) / 4294967296; + }; + prng.double = prng; + mixkey(tostring(arc4.S), pool3); + return (options.pass || callback || function(prng2, seed2, is_math_call, state) { + if (state) { + if (state.S) { + copy(state, arc4); + } + prng2.state = function() { + return copy(arc4, {}); + }; + } + if (is_math_call) { + math[rngname] = prng2; + return seed2; + } else + return prng2; + })( + prng, + shortseed, + "global" in options ? options.global : this == math, + options.state + ); + } + function ARC4(key) { + var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = []; + if (!keylen) { + key = [keylen++]; + } + while (i < width) { + s[i] = i++; + } + for (i = 0; i < width; i++) { + s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])]; + s[j] = t; + } + (me.g = function(count2) { + var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S; + while (count2--) { + t2 = s2[i2 = mask & i2 + 1]; + r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)]; + } + me.i = i2; + me.j = j2; + return r; + })(width); + } + function copy(f, t) { + t.i = f.i; + t.j = f.j; + t.S = f.S.slice(); + return t; + } + ; + function flatten4(obj, depth) { + var result = [], typ = typeof obj, prop; + if (depth && typ == "object") { + for (prop in obj) { + try { + result.push(flatten4(obj[prop], depth - 1)); + } catch (e) { + } + } + } + return result.length ? result : typ == "string" ? obj : obj + "\0"; + } + function mixkey(seed, key) { + var stringseed = seed + "", smear, j = 0; + while (j < stringseed.length) { + key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++); + } + return tostring(key); + } + function autoseed() { + try { + var out; + if (nodecrypto && (out = nodecrypto.randomBytes)) { + out = out(width); + } else { + out = new Uint8Array(width); + (global2.crypto || global2.msCrypto).getRandomValues(out); + } + return tostring(out); + } catch (e) { + var browser = global2.navigator, plugins = browser && browser.plugins; + return [+/* @__PURE__ */ new Date(), global2, plugins, global2.screen, tostring(pool3)]; + } + } + function tostring(a) { + return String.fromCharCode.apply(0, a); + } + mixkey(math.random(), pool3); + if (typeof module == "object" && module.exports) { + module.exports = seedrandom5; + try { + nodecrypto = require_crypto(); + } catch (ex) { + } + } else if (typeof define == "function" && define.amd) { + define(function() { + return seedrandom5; + }); + } else { + math["seed" + rngname] = seedrandom5; + } + })( + // global: `self` in browsers (including strict mode and web workers), + // otherwise `this` in Node and other environments + typeof self !== "undefined" ? self : exports, + [], + // pool: entropy pool starts empty + Math + // math: package containing random, pow, and seedrandom + ); + } +}); + +// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js +var require_seedrandom2 = __commonJS({ + "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(exports, module) { + "use strict"; + var alea5 = require_alea(); + var xor128 = require_xor128(); + var xorwow = require_xorwow(); + var xorshift7 = require_xorshift7(); + var xor4096 = require_xor4096(); + var tychei = require_tychei(); + var sr = require_seedrandom(); + sr.alea = alea5; + sr.xor128 = xor128; + sr.xorwow = xorwow; + sr.xorshift7 = xorshift7; + sr.xor4096 = xor4096; + sr.tychei = tychei; + module.exports = sr; + } +}); + +// (disabled):node_modules/.pnpm/string_decoder@1.3.0/node_modules/string_decoder/lib/string_decoder.js +var require_string_decoder = __commonJS({ + "(disabled):node_modules/.pnpm/string_decoder@1.3.0/node_modules/string_decoder/lib/string_decoder.js"() { + "use strict"; + } +}); + +// (disabled):fs +var require_fs = __commonJS({ + "(disabled):fs"() { + "use strict"; + } +}); + +// (disabled):path +var require_path = __commonJS({ + "(disabled):path"() { + "use strict"; + } +}); + +// (disabled):worker_threads +var require_worker_threads = __commonJS({ + "(disabled):worker_threads"() { + "use strict"; + } +}); + +// (disabled):perf_hooks +var require_perf_hooks = __commonJS({ + "(disabled):perf_hooks"() { + "use strict"; + } +}); + +// (disabled):os +var require_os = __commonJS({ + "(disabled):os"() { + "use strict"; + } +}); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js +var require_tfjs_backend_wasm_threaded_simd = __commonJS({ + "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(exports, module) { + "use strict"; + var WasmBackendModuleThreadedSimd2 = (() => { + var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; + if (typeof __filename !== "undefined") + _scriptDir = _scriptDir || __filename; + return function(WasmBackendModuleThreadedSimd3) { + WasmBackendModuleThreadedSimd3 = WasmBackendModuleThreadedSimd3 || {}; + function GROWABLE_HEAP_I8() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP8; + } + function GROWABLE_HEAP_U8() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPU8; + } + function GROWABLE_HEAP_I16() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP16; + } + function GROWABLE_HEAP_I32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP32; + } + function GROWABLE_HEAP_U32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPU32; + } + function GROWABLE_HEAP_F32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPF32; + } + function GROWABLE_HEAP_F64() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPF64; + } + var Module = typeof WasmBackendModuleThreadedSimd3 != "undefined" ? WasmBackendModuleThreadedSimd3 : {}; + var readyPromiseResolve, readyPromiseReject; + Module["ready"] = new Promise(function(resolve, reject) { + readyPromiseResolve = resolve; + readyPromiseReject = reject; + }); + var beforeListeners; + if (typeof process !== "undefined" && process.listeners) { + beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }; + } + var moduleOverrides = Object.assign({}, Module); + var arguments_ = []; + var thisProgram = "./this.program"; + var quit_ = (status, toThrow) => { + throw toThrow; + }; + var ENVIRONMENT_IS_WEB = typeof window == "object"; + var ENVIRONMENT_IS_WORKER = typeof importScripts == "function"; + var ENVIRONMENT_IS_NODE = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string"; + var ENVIRONMENT_IS_PTHREAD = Module["ENVIRONMENT_IS_PTHREAD"] || false; + var scriptDirectory = ""; + function locateFile(path) { + if (Module["locateFile"]) { + return Module["locateFile"](path, scriptDirectory); + } + return scriptDirectory + path; + } + var read_, readAsync, readBinary, setWindowTitle; + function logExceptionOnExit(e) { + if (e instanceof ExitStatus) + return; + let toLog = e; + err("exiting due to exception: " + toLog); + } + if (ENVIRONMENT_IS_NODE) { + var fs = require_fs(); + var nodePath = require_path(); + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = nodePath.dirname(scriptDirectory) + "/"; + } else { + scriptDirectory = __dirname + "/"; + } + read_ = (filename, binary) => { + filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename); + return fs.readFileSync(filename, binary ? void 0 : "utf8"); + }; + readBinary = (filename) => { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + return ret; + }; + readAsync = (filename, onload, onerror) => { + filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename); + fs.readFile(filename, function(err2, data) { + if (err2) + onerror(err2); + else + onload(data.buffer); + }); + }; + if (process["argv"].length > 1) { + thisProgram = process["argv"][1].replace(/\\/g, "/"); + } + arguments_ = process["argv"].slice(2); + process["on"]("uncaughtException", function(ex) { + if (!(ex instanceof ExitStatus)) { + throw ex; + } + }); + process["on"]("unhandledRejection", function(reason) { + throw reason; + }); + quit_ = (status, toThrow) => { + if (keepRuntimeAlive()) { + process["exitCode"] = status; + throw toThrow; + } + logExceptionOnExit(toThrow); + process["exit"](status); + }; + Module["inspect"] = function() { + return "[Emscripten Module object]"; + }; + let nodeWorkerThreads; + try { + nodeWorkerThreads = require_worker_threads(); + } catch (e) { + console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'); + throw e; + } + global.Worker = nodeWorkerThreads.Worker; + } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = self.location.href; + } else if (typeof document != "undefined" && document.currentScript) { + scriptDirectory = document.currentScript.src; + } + if (typeof _scriptDir !== "undefined" && _scriptDir) { + scriptDirectory = _scriptDir; + } + if (scriptDirectory.indexOf("blob:") !== 0) { + scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1); + } else { + scriptDirectory = ""; + } + if (!ENVIRONMENT_IS_NODE) { + read_ = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.send(null); + return xhr.responseText; + }; + if (ENVIRONMENT_IS_WORKER) { + readBinary = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.responseType = "arraybuffer"; + xhr.send(null); + return new Uint8Array(xhr.response); + }; + } + readAsync = (url, onload, onerror) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, true); + xhr.responseType = "arraybuffer"; + xhr.onload = () => { + if (xhr.status == 200 || xhr.status == 0 && xhr.response) { + onload(xhr.response); + return; + } + onerror(); + }; + xhr.onerror = onerror; + xhr.send(null); + }; + } + setWindowTitle = (title) => document.title = title; + } else { + } + if (ENVIRONMENT_IS_NODE) { + if (typeof performance == "undefined") { + global.performance = require_perf_hooks().performance; + } + } + var defaultPrint = console.log.bind(console); + var defaultPrintErr = console.warn.bind(console); + if (ENVIRONMENT_IS_NODE) { + defaultPrint = (str) => fs.writeSync(1, str + "\n"); + defaultPrintErr = (str) => fs.writeSync(2, str + "\n"); + } + var out = Module["print"] || defaultPrint; + var err = Module["printErr"] || defaultPrintErr; + Object.assign(Module, moduleOverrides); + moduleOverrides = null; + if (Module["arguments"]) + arguments_ = Module["arguments"]; + if (Module["thisProgram"]) + thisProgram = Module["thisProgram"]; + if (Module["quit"]) + quit_ = Module["quit"]; + var POINTER_SIZE = 4; + var Atomics_load = Atomics.load; + var Atomics_store = Atomics.store; + var Atomics_compareExchange = Atomics.compareExchange; + var wasmBinary; + if (Module["wasmBinary"]) + wasmBinary = Module["wasmBinary"]; + var noExitRuntime = Module["noExitRuntime"] || true; + if (typeof WebAssembly != "object") { + abort("no native wasm support detected"); + } + var wasmMemory; + var wasmModule; + var ABORT = false; + var EXITSTATUS; + function assert3(condition, text) { + if (!condition) { + abort(text); + } + } + var UTF8Decoder = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; + function UTF8ArrayToString(heapOrArray, idx, maxBytesToRead) { + idx >>>= 0; + var endIdx = idx + maxBytesToRead; + var endPtr = idx; + while (heapOrArray[endPtr] && !(endPtr >= endIdx)) + ++endPtr; + if (endPtr - idx > 16 && heapOrArray.buffer && UTF8Decoder) { + return UTF8Decoder.decode(heapOrArray.buffer instanceof SharedArrayBuffer ? heapOrArray.slice(idx, endPtr) : heapOrArray.subarray(idx, endPtr)); + } + var str = ""; + while (idx < endPtr) { + var u0 = heapOrArray[idx++]; + if (!(u0 & 128)) { + str += String.fromCharCode(u0); + continue; + } + var u1 = heapOrArray[idx++] & 63; + if ((u0 & 224) == 192) { + str += String.fromCharCode((u0 & 31) << 6 | u1); + continue; + } + var u2 = heapOrArray[idx++] & 63; + if ((u0 & 240) == 224) { + u0 = (u0 & 15) << 12 | u1 << 6 | u2; + } else { + u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heapOrArray[idx++] & 63; + } + if (u0 < 65536) { + str += String.fromCharCode(u0); + } else { + var ch = u0 - 65536; + str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023); + } + } + return str; + } + function UTF8ToString(ptr, maxBytesToRead) { + ptr >>>= 0; + return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : ""; + } + function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) { + outIdx >>>= 0; + if (!(maxBytesToWrite > 0)) + return 0; + var startIdx = outIdx; + var endIdx = outIdx + maxBytesToWrite - 1; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) { + var u1 = str.charCodeAt(++i); + u = 65536 + ((u & 1023) << 10) | u1 & 1023; + } + if (u <= 127) { + if (outIdx >= endIdx) + break; + heap[outIdx++ >>> 0] = u; + } else if (u <= 2047) { + if (outIdx + 1 >= endIdx) + break; + heap[outIdx++ >>> 0] = 192 | u >> 6; + heap[outIdx++ >>> 0] = 128 | u & 63; + } else if (u <= 65535) { + if (outIdx + 2 >= endIdx) + break; + heap[outIdx++ >>> 0] = 224 | u >> 12; + heap[outIdx++ >>> 0] = 128 | u >> 6 & 63; + heap[outIdx++ >>> 0] = 128 | u & 63; + } else { + if (outIdx + 3 >= endIdx) + break; + heap[outIdx++ >>> 0] = 240 | u >> 18; + heap[outIdx++ >>> 0] = 128 | u >> 12 & 63; + heap[outIdx++ >>> 0] = 128 | u >> 6 & 63; + heap[outIdx++ >>> 0] = 128 | u & 63; + } + } + heap[outIdx >>> 0] = 0; + return outIdx - startIdx; + } + function stringToUTF8(str, outPtr, maxBytesToWrite) { + return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite); + } + var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; + if (ENVIRONMENT_IS_PTHREAD) { + buffer2 = Module["buffer"]; + } + function updateGlobalBufferAndViews(buf) { + buffer2 = buf; + Module["HEAP8"] = HEAP8 = new Int8Array(buf); + Module["HEAP16"] = HEAP16 = new Int16Array(buf); + Module["HEAP32"] = HEAP32 = new Int32Array(buf); + Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf); + Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf); + Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf); + Module["HEAPF32"] = HEAPF32 = new Float32Array(buf); + Module["HEAPF64"] = HEAPF64 = new Float64Array(buf); + } + var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216; + if (ENVIRONMENT_IS_PTHREAD) { + wasmMemory = Module["wasmMemory"]; + buffer2 = Module["buffer"]; + } else { + if (Module["wasmMemory"]) { + wasmMemory = Module["wasmMemory"]; + } else { + wasmMemory = new WebAssembly.Memory({ "initial": INITIAL_MEMORY / 65536, "maximum": 4294967296 / 65536, "shared": true }); + if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) { + err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"); + if (ENVIRONMENT_IS_NODE) { + err("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and/or recent version)"); + } + throw Error("bad memory"); + } + } + } + if (wasmMemory) { + buffer2 = wasmMemory.buffer; + } + INITIAL_MEMORY = buffer2.byteLength; + updateGlobalBufferAndViews(buffer2); + var wasmTable; + var __ATPRERUN__ = []; + var __ATINIT__ = []; + var __ATPOSTRUN__ = []; + var runtimeInitialized = false; + function keepRuntimeAlive() { + return noExitRuntime; + } + function preRun() { + if (Module["preRun"]) { + if (typeof Module["preRun"] == "function") + Module["preRun"] = [Module["preRun"]]; + while (Module["preRun"].length) { + addOnPreRun(Module["preRun"].shift()); + } + } + callRuntimeCallbacks(__ATPRERUN__); + } + function initRuntime() { + runtimeInitialized = true; + if (ENVIRONMENT_IS_PTHREAD) + return; + callRuntimeCallbacks(__ATINIT__); + } + function postRun() { + if (ENVIRONMENT_IS_PTHREAD) + return; + if (Module["postRun"]) { + if (typeof Module["postRun"] == "function") + Module["postRun"] = [Module["postRun"]]; + while (Module["postRun"].length) { + addOnPostRun(Module["postRun"].shift()); + } + } + callRuntimeCallbacks(__ATPOSTRUN__); + } + function addOnPreRun(cb) { + __ATPRERUN__.unshift(cb); + } + function addOnInit(cb) { + __ATINIT__.unshift(cb); + } + function addOnPostRun(cb) { + __ATPOSTRUN__.unshift(cb); + } + var runDependencies = 0; + var runDependencyWatcher = null; + var dependenciesFulfilled = null; + function addRunDependency(id) { + runDependencies++; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + } + function removeRunDependency(id) { + runDependencies--; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + if (runDependencies == 0) { + if (runDependencyWatcher !== null) { + clearInterval(runDependencyWatcher); + runDependencyWatcher = null; + } + if (dependenciesFulfilled) { + var callback = dependenciesFulfilled; + dependenciesFulfilled = null; + callback(); + } + } + } + function abort(what) { + if (Module["onAbort"]) { + Module["onAbort"](what); + } + what = "Aborted(" + what + ")"; + err(what); + ABORT = true; + EXITSTATUS = 1; + what += ". Build with -sASSERTIONS for more info."; + var e = new WebAssembly.RuntimeError(what); + readyPromiseReject(e); + throw e; + } + var dataURIPrefix = "data:application/octet-stream;base64,"; + function isDataURI(filename) { + return filename.startsWith(dataURIPrefix); + } + function isFileURI(filename) { + return filename.startsWith("file://"); + } + var wasmBinaryFile; + wasmBinaryFile = "tfjs-backend-wasm-threaded-simd.wasm"; + if (!isDataURI(wasmBinaryFile)) { + wasmBinaryFile = locateFile(wasmBinaryFile); + } + function getBinary(file) { + try { + if (file == wasmBinaryFile && wasmBinary) { + return new Uint8Array(wasmBinary); + } + if (readBinary) { + return readBinary(file); + } + throw "both async and sync fetching of the wasm failed"; + } catch (err2) { + abort(err2); + } + } + function getBinaryPromise() { + if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) { + if (typeof fetch == "function" && !isFileURI(wasmBinaryFile)) { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + if (!response["ok"]) { + throw "failed to load wasm binary file at '" + wasmBinaryFile + "'"; + } + return response["arrayBuffer"](); + }).catch(function() { + return getBinary(wasmBinaryFile); + }); + } else { + if (readAsync) { + return new Promise(function(resolve, reject) { + readAsync(wasmBinaryFile, function(response) { + resolve(new Uint8Array(response)); + }, reject); + }); + } + } + } + return Promise.resolve().then(function() { + return getBinary(wasmBinaryFile); + }); + } + function createWasm() { + var info = { "env": asmLibraryArg, "wasi_snapshot_preview1": asmLibraryArg }; + function receiveInstance(instance, module2) { + var exports3 = instance.exports; + Module["asm"] = exports3; + registerTLSInit(Module["asm"]["_emscripten_tls_init"]); + wasmTable = Module["asm"]["__indirect_function_table"]; + addOnInit(Module["asm"]["__wasm_call_ctors"]); + wasmModule = module2; + if (!ENVIRONMENT_IS_PTHREAD) { + var numWorkersToLoad = PThread.unusedWorkers.length; + PThread.unusedWorkers.forEach(function(w) { + PThread.loadWasmModuleToWorker(w, function() { + if (!--numWorkersToLoad) + removeRunDependency("wasm-instantiate"); + }); + }); + } + } + if (!ENVIRONMENT_IS_PTHREAD) { + addRunDependency("wasm-instantiate"); + } + function receiveInstantiationResult(result) { + receiveInstance(result["instance"], result["module"]); + } + function instantiateArrayBuffer(receiver) { + return getBinaryPromise().then(function(binary) { + return WebAssembly.instantiate(binary, info); + }).then(function(instance) { + return instance; + }).then(receiver, function(reason) { + err("failed to asynchronously prepare wasm: " + reason); + abort(reason); + }); + } + function instantiateAsync() { + if (!wasmBinary && typeof WebAssembly.instantiateStreaming == "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && !ENVIRONMENT_IS_NODE && typeof fetch == "function") { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + var result = WebAssembly.instantiateStreaming(response, info); + return result.then(receiveInstantiationResult, function(reason) { + err("wasm streaming compile failed: " + reason); + err("falling back to ArrayBuffer instantiation"); + return instantiateArrayBuffer(receiveInstantiationResult); + }); + }); + } else { + return instantiateArrayBuffer(receiveInstantiationResult); + } + } + if (Module["instantiateWasm"]) { + try { + var exports2 = Module["instantiateWasm"](info, receiveInstance); + return exports2; + } catch (e) { + err("Module.instantiateWasm callback failed with error: " + e); + readyPromiseReject(e); + } + } + instantiateAsync().catch(readyPromiseReject); + return {}; + } + var tempDouble; + var tempI64; + var ASM_CONSTS = {}; + function ExitStatus(status) { + this.name = "ExitStatus"; + this.message = "Program terminated with exit(" + status + ")"; + this.status = status; + } + function killThread(pthread_ptr) { + var worker = PThread.pthreads[pthread_ptr]; + delete PThread.pthreads[pthread_ptr]; + worker.terminate(); + __emscripten_thread_free_data(pthread_ptr); + PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1); + worker.pthread_ptr = 0; + } + function cancelThread(pthread_ptr) { + var worker = PThread.pthreads[pthread_ptr]; + worker.postMessage({ "cmd": "cancel" }); + } + function cleanupThread(pthread_ptr) { + var worker = PThread.pthreads[pthread_ptr]; + assert3(worker); + PThread.returnWorkerToPool(worker); + } + function spawnThread(threadParams) { + var worker = PThread.getNewWorker(); + if (!worker) { + return 6; + } + PThread.runningWorkers.push(worker); + PThread.pthreads[threadParams.pthread_ptr] = worker; + worker.pthread_ptr = threadParams.pthread_ptr; + var msg = { "cmd": "run", "start_routine": threadParams.startRoutine, "arg": threadParams.arg, "pthread_ptr": threadParams.pthread_ptr }; + worker.runPthread = () => { + if (ENVIRONMENT_IS_NODE) { + worker.ref(); + } + worker.postMessage(msg, threadParams.transferList); + delete worker.runPthread; + }; + if (worker.loaded) { + worker.runPthread(); + } + return 0; + } + var SYSCALLS = { varargs: void 0, get: function() { + SYSCALLS.varargs += 4; + var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >>> 2]; + return ret; + }, getStr: function(ptr) { + var ret = UTF8ToString(ptr); + return ret; + } }; + function _proc_exit(code) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(1, 1, code); + EXITSTATUS = code; + if (!keepRuntimeAlive()) { + PThread.terminateAllThreads(); + if (Module["onExit"]) + Module["onExit"](code); + ABORT = true; + } + quit_(code, new ExitStatus(code)); + } + function exitJS(status, implicit) { + EXITSTATUS = status; + if (!implicit) { + if (ENVIRONMENT_IS_PTHREAD) { + exitOnMainThread(status); + throw "unwind"; + } else { + } + } + _proc_exit(status); + } + var _exit = exitJS; + function handleException(e) { + if (e instanceof ExitStatus || e == "unwind") { + return EXITSTATUS; + } + quit_(1, e); + } + var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], pthreads: {}, init: function() { + if (ENVIRONMENT_IS_PTHREAD) { + PThread.initWorker(); + } else { + PThread.initMainThread(); + } + }, initMainThread: function() { + var pthreadPoolSize = 8; + while (pthreadPoolSize--) { + PThread.allocateUnusedWorker(); + } + }, initWorker: function() { + noExitRuntime = false; + }, setExitStatus: function(status) { + EXITSTATUS = status; + }, terminateAllThreads: function() { + for (var worker of Object.values(PThread.pthreads)) { + PThread.returnWorkerToPool(worker); + } + for (var worker of PThread.unusedWorkers) { + worker.terminate(); + } + PThread.unusedWorkers = []; + }, returnWorkerToPool: function(worker) { + var pthread_ptr = worker.pthread_ptr; + delete PThread.pthreads[pthread_ptr]; + PThread.unusedWorkers.push(worker); + PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1); + worker.pthread_ptr = 0; + if (ENVIRONMENT_IS_NODE) { + worker.unref(); + } + __emscripten_thread_free_data(pthread_ptr); + }, receiveObjectTransfer: function(data) { + }, threadInitTLS: function() { + PThread.tlsInitFunctions.forEach((f) => f()); + }, loadWasmModuleToWorker: function(worker, onFinishedLoading) { + worker.onmessage = (e) => { + var d = e["data"]; + var cmd = d["cmd"]; + if (worker.pthread_ptr) + PThread.currentProxiedOperationCallerThread = worker.pthread_ptr; + if (d["targetThread"] && d["targetThread"] != _pthread_self()) { + var targetWorker = PThread.pthreads[d.targetThread]; + if (targetWorker) { + targetWorker.postMessage(d, d["transferList"]); + } else { + err('Internal error! Worker sent a message "' + cmd + '" to target pthread ' + d["targetThread"] + ", but that thread no longer exists!"); + } + PThread.currentProxiedOperationCallerThread = void 0; + return; + } + if (cmd === "processProxyingQueue") { + executeNotifiedProxyingQueue(d["queue"]); + } else if (cmd === "spawnThread") { + spawnThread(d); + } else if (cmd === "cleanupThread") { + cleanupThread(d["thread"]); + } else if (cmd === "killThread") { + killThread(d["thread"]); + } else if (cmd === "cancelThread") { + cancelThread(d["thread"]); + } else if (cmd === "loaded") { + worker.loaded = true; + if (ENVIRONMENT_IS_NODE) { + worker.unref(); + } + if (onFinishedLoading) + onFinishedLoading(worker); + if (worker.runPthread) { + worker.runPthread(); + } + } else if (cmd === "print") { + out("Thread " + d["threadId"] + ": " + d["text"]); + } else if (cmd === "printErr") { + err("Thread " + d["threadId"] + ": " + d["text"]); + } else if (cmd === "alert") { + alert("Thread " + d["threadId"] + ": " + d["text"]); + } else if (d.target === "setimmediate") { + worker.postMessage(d); + } else if (cmd === "callHandler") { + Module[d["handler"]](...d["args"]); + } else if (cmd) { + err("worker sent an unknown command " + cmd); + } + PThread.currentProxiedOperationCallerThread = void 0; + }; + worker.onerror = (e) => { + var message = "worker sent an error!"; + err(message + " " + e.filename + ":" + e.lineno + ": " + e.message); + throw e; + }; + if (ENVIRONMENT_IS_NODE) { + worker.on("message", function(data) { + worker.onmessage({ data }); + }); + worker.on("error", function(e) { + worker.onerror(e); + }); + worker.on("detachedExit", function() { + }); + } + var handlers = []; + var knownHandlers = ["onExit", "onAbort", "print", "printErr"]; + for (var handler of knownHandlers) { + if (Module.hasOwnProperty(handler)) { + handlers.push(handler); + } + } + worker.postMessage({ "cmd": "load", "handlers": handlers, "urlOrBlob": Module["mainScriptUrlOrBlob"] || _scriptDir, "wasmMemory": wasmMemory, "wasmModule": wasmModule }); + }, allocateUnusedWorker: function() { + var worker; + var pthreadMainJs = locateFile("tfjs-backend-wasm-threaded-simd.worker.js"); + worker = new Worker(pthreadMainJs); + PThread.unusedWorkers.push(worker); + }, getNewWorker: function() { + if (PThread.unusedWorkers.length == 0) { + PThread.allocateUnusedWorker(); + PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]); + } + return PThread.unusedWorkers.pop(); + } }; + Module["PThread"] = PThread; + function callRuntimeCallbacks(callbacks2) { + while (callbacks2.length > 0) { + callbacks2.shift()(Module); + } + } + function establishStackSpace() { + var pthread_ptr = _pthread_self(); + var stackTop = GROWABLE_HEAP_I32()[pthread_ptr + 52 >>> 2]; + var stackSize = GROWABLE_HEAP_I32()[pthread_ptr + 56 >>> 2]; + var stackMax = stackTop - stackSize; + _emscripten_stack_set_limits(stackTop, stackMax); + stackRestore(stackTop); + } + Module["establishStackSpace"] = establishStackSpace; + function exitOnMainThread(returnCode) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(2, 0, returnCode); + try { + _exit(returnCode); + } catch (e) { + handleException(e); + } + } + var wasmTableMirror = []; + function getWasmTableEntry(funcPtr) { + var func2 = wasmTableMirror[funcPtr]; + if (!func2) { + if (funcPtr >= wasmTableMirror.length) + wasmTableMirror.length = funcPtr + 1; + wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr); + } + return func2; + } + function invokeEntryPoint(ptr, arg) { + var result = getWasmTableEntry(ptr)(arg); + if (keepRuntimeAlive()) { + PThread.setExitStatus(result); + } else { + __emscripten_thread_exit(result); + } + } + Module["invokeEntryPoint"] = invokeEntryPoint; + function registerTLSInit(tlsInitFunc) { + PThread.tlsInitFunctions.push(tlsInitFunc); + } + function ___emscripten_init_main_thread_js(tb) { + __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1, !ENVIRONMENT_IS_WEB); + PThread.threadInitTLS(); + } + function ___emscripten_thread_cleanup(thread) { + if (!ENVIRONMENT_IS_PTHREAD) + cleanupThread(thread); + else + postMessage({ "cmd": "cleanupThread", "thread": thread }); + } + function pthreadCreateProxied(pthread_ptr, attr, startRoutine, arg) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(3, 1, pthread_ptr, attr, startRoutine, arg); + return ___pthread_create_js(pthread_ptr, attr, startRoutine, arg); + } + function ___pthread_create_js(pthread_ptr, attr, startRoutine, arg) { + if (typeof SharedArrayBuffer == "undefined") { + err("Current environment does not support SharedArrayBuffer, pthreads are not available!"); + return 6; + } + var transferList = []; + var error = 0; + if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) { + return pthreadCreateProxied(pthread_ptr, attr, startRoutine, arg); + } + if (error) + return error; + var threadParams = { startRoutine, pthread_ptr, arg, transferList }; + if (ENVIRONMENT_IS_PTHREAD) { + threadParams.cmd = "spawnThread"; + postMessage(threadParams, transferList); + return 0; + } + return spawnThread(threadParams); + } + function __emscripten_default_pthread_stack_size() { + return 65536; + } + var nowIsMonotonic = true; + function __emscripten_get_now_is_monotonic() { + return nowIsMonotonic; + } + function executeNotifiedProxyingQueue(queue) { + Atomics.store(GROWABLE_HEAP_I32(), queue >> 2, 1); + if (_pthread_self()) { + __emscripten_proxy_execute_task_queue(queue); + } + Atomics.compareExchange(GROWABLE_HEAP_I32(), queue >> 2, 1, 0); + } + Module["executeNotifiedProxyingQueue"] = executeNotifiedProxyingQueue; + function __emscripten_notify_task_queue(targetThreadId, currThreadId, mainThreadId, queue) { + if (targetThreadId == currThreadId) { + setTimeout(() => executeNotifiedProxyingQueue(queue)); + } else if (ENVIRONMENT_IS_PTHREAD) { + postMessage({ "targetThread": targetThreadId, "cmd": "processProxyingQueue", "queue": queue }); + } else { + var worker = PThread.pthreads[targetThreadId]; + if (!worker) { + return; + } + worker.postMessage({ "cmd": "processProxyingQueue", "queue": queue }); + } + return 1; + } + function __emscripten_set_offscreencanvas_size(target, width, height) { + return -1; + } + function _abort() { + abort(""); + } + function warnOnce(text) { + if (!warnOnce.shown) + warnOnce.shown = {}; + if (!warnOnce.shown[text]) { + warnOnce.shown[text] = 1; + if (ENVIRONMENT_IS_NODE) + text = "warning: " + text; + err(text); + } + } + function _emscripten_check_blocking_allowed() { + if (ENVIRONMENT_IS_NODE) + return; + if (ENVIRONMENT_IS_WORKER) + return; + warnOnce("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread"); + } + function _emscripten_date_now() { + return Date.now(); + } + function getHeapMax() { + return 4294901760; + } + function _emscripten_get_heap_max() { + return getHeapMax(); + } + var _emscripten_get_now; + if (ENVIRONMENT_IS_NODE) { + _emscripten_get_now = () => { + var t = process["hrtime"](); + return t[0] * 1e3 + t[1] / 1e6; + }; + } else + _emscripten_get_now = () => performance.timeOrigin + performance.now(); + function _emscripten_memcpy_big(dest, src, num) { + GROWABLE_HEAP_U8().copyWithin(dest >>> 0, src >>> 0, src + num >>> 0); + } + function _emscripten_num_logical_cores() { + if (ENVIRONMENT_IS_NODE) + return require_os().cpus().length; + return navigator["hardwareConcurrency"]; + } + function withStackSave(f) { + var stack2 = stackSave(); + var ret = f(); + stackRestore(stack2); + return ret; + } + function _emscripten_proxy_to_main_thread_js(index, sync) { + var numCallArgs = arguments.length - 2; + var outerArgs = arguments; + return withStackSave(() => { + var serializedNumCallArgs = numCallArgs; + var args = stackAlloc(serializedNumCallArgs * 8); + var b = args >> 3; + for (var i = 0; i < numCallArgs; i++) { + var arg = outerArgs[2 + i]; + GROWABLE_HEAP_F64()[b + i >>> 0] = arg; + } + return _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync); + }); + } + var _emscripten_receive_on_main_thread_js_callArgs = []; + function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) { + _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs; + var b = args >> 3; + for (var i = 0; i < numCallArgs; i++) { + _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i >>> 0]; + } + var isEmAsmConst = index < 0; + var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1]; + return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs); + } + function emscripten_realloc_buffer(size) { + try { + wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16); + updateGlobalBufferAndViews(wasmMemory.buffer); + return 1; + } catch (e) { + } + } + function _emscripten_resize_heap(requestedSize) { + var oldSize = GROWABLE_HEAP_U8().length; + requestedSize = requestedSize >>> 0; + if (requestedSize <= oldSize) { + return false; + } + var maxHeapSize = getHeapMax(); + if (requestedSize > maxHeapSize) { + return false; + } + let alignUp = (x, multiple) => x + (multiple - x % multiple) % multiple; + for (var cutDown = 1; cutDown <= 4; cutDown *= 2) { + var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown); + overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296); + var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536)); + var replacement = emscripten_realloc_buffer(newSize); + if (replacement) { + return true; + } + } + return false; + } + function _emscripten_unwind_to_js_event_loop() { + throw "unwind"; + } + function _fd_close(fd) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(4, 1, fd); + return 52; + } + function _fd_seek(fd, offset_low, offset_high, whence, newOffset) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(5, 1, fd, offset_low, offset_high, whence, newOffset); + return 70; + } + var printCharBuffers = [null, [], []]; + function printChar(stream, curr) { + var buffer3 = printCharBuffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0)); + buffer3.length = 0; + } else { + buffer3.push(curr); + } + } + function _fd_write(fd, iov, iovcnt, pnum) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(6, 1, fd, iov, iovcnt, pnum); + var num = 0; + for (var i = 0; i < iovcnt; i++) { + var ptr = GROWABLE_HEAP_U32()[iov >>> 2]; + var len = GROWABLE_HEAP_U32()[iov + 4 >>> 2]; + iov += 8; + for (var j = 0; j < len; j++) { + printChar(fd, GROWABLE_HEAP_U8()[ptr + j >>> 0]); + } + num += len; + } + GROWABLE_HEAP_U32()[pnum >>> 2] = num; + return 0; + } + function getCFunc(ident) { + var func2 = Module["_" + ident]; + return func2; + } + function writeArrayToMemory(array2, buffer3) { + GROWABLE_HEAP_I8().set(array2, buffer3 >>> 0); + } + function ccall(ident, returnType, argTypes, args, opts) { + var toC = { "string": (str) => { + var ret2 = 0; + if (str !== null && str !== void 0 && str !== 0) { + var len = (str.length << 2) + 1; + ret2 = stackAlloc(len); + stringToUTF8(str, ret2, len); + } + return ret2; + }, "array": (arr) => { + var ret2 = stackAlloc(arr.length); + writeArrayToMemory(arr, ret2); + return ret2; + } }; + function convertReturnValue(ret2) { + if (returnType === "string") { + return UTF8ToString(ret2); + } + if (returnType === "boolean") + return Boolean(ret2); + return ret2; + } + var func2 = getCFunc(ident); + var cArgs = []; + var stack2 = 0; + if (args) { + for (var i = 0; i < args.length; i++) { + var converter = toC[argTypes[i]]; + if (converter) { + if (stack2 === 0) + stack2 = stackSave(); + cArgs[i] = converter(args[i]); + } else { + cArgs[i] = args[i]; + } + } + } + var ret = func2.apply(null, cArgs); + function onDone(ret2) { + if (stack2 !== 0) + stackRestore(stack2); + return convertReturnValue(ret2); + } + ret = onDone(ret); + return ret; + } + function cwrap(ident, returnType, argTypes, opts) { + argTypes = argTypes || []; + var numericArgs = argTypes.every((type) => type === "number" || type === "boolean"); + var numericRet = returnType !== "string"; + if (numericRet && numericArgs && !opts) { + return getCFunc(ident); + } + return function() { + return ccall(ident, returnType, argTypes, arguments, opts); + }; + } + PThread.init(); + var proxiedFunctionTable = [null, _proc_exit, exitOnMainThread, pthreadCreateProxied, _fd_close, _fd_seek, _fd_write]; + var asmLibraryArg = { "__emscripten_init_main_thread_js": ___emscripten_init_main_thread_js, "__emscripten_thread_cleanup": ___emscripten_thread_cleanup, "__pthread_create_js": ___pthread_create_js, "_emscripten_default_pthread_stack_size": __emscripten_default_pthread_stack_size, "_emscripten_get_now_is_monotonic": __emscripten_get_now_is_monotonic, "_emscripten_notify_task_queue": __emscripten_notify_task_queue, "_emscripten_set_offscreencanvas_size": __emscripten_set_offscreencanvas_size, "abort": _abort, "emscripten_check_blocking_allowed": _emscripten_check_blocking_allowed, "emscripten_date_now": _emscripten_date_now, "emscripten_get_heap_max": _emscripten_get_heap_max, "emscripten_get_now": _emscripten_get_now, "emscripten_memcpy_big": _emscripten_memcpy_big, "emscripten_num_logical_cores": _emscripten_num_logical_cores, "emscripten_receive_on_main_thread_js": _emscripten_receive_on_main_thread_js, "emscripten_resize_heap": _emscripten_resize_heap, "emscripten_unwind_to_js_event_loop": _emscripten_unwind_to_js_event_loop, "exit": _exit, "fd_close": _fd_close, "fd_seek": _fd_seek, "fd_write": _fd_write, "memory": wasmMemory || Module["wasmMemory"] }; + var asm = createWasm(); + var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() { + return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["__wasm_call_ctors"]).apply(null, arguments); + }; + var _init = Module["_init"] = function() { + return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments); + }; + var _init_with_threads_count = Module["_init_with_threads_count"] = function() { + return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["init_with_threads_count"]).apply(null, arguments); + }; + var _get_threads_count = Module["_get_threads_count"] = function() { + return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["get_threads_count"]).apply(null, arguments); + }; + var _register_tensor = Module["_register_tensor"] = function() { + return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments); + }; + var _dispose_data = Module["_dispose_data"] = function() { + return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments); + }; + var _dispose = Module["_dispose"] = function() { + return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments); + }; + var _Abs = Module["_Abs"] = function() { + return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments); + }; + var _Acos = Module["_Acos"] = function() { + return (_Acos = Module["_Acos"] = Module["asm"]["Acos"]).apply(null, arguments); + }; + var _Acosh = Module["_Acosh"] = function() { + return (_Acosh = Module["_Acosh"] = Module["asm"]["Acosh"]).apply(null, arguments); + }; + var _Add = Module["_Add"] = function() { + return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments); + }; + var _AddN = Module["_AddN"] = function() { + return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments); + }; + var _All = Module["_All"] = function() { + return (_All = Module["_All"] = Module["asm"]["All"]).apply(null, arguments); + }; + var _Any = Module["_Any"] = function() { + return (_Any = Module["_Any"] = Module["asm"]["Any"]).apply(null, arguments); + }; + var _ArgMax = Module["_ArgMax"] = function() { + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments); + }; + var _ArgMin = Module["_ArgMin"] = function() { + return (_ArgMin = Module["_ArgMin"] = Module["asm"]["ArgMin"]).apply(null, arguments); + }; + var _Asin = Module["_Asin"] = function() { + return (_Asin = Module["_Asin"] = Module["asm"]["Asin"]).apply(null, arguments); + }; + var _Asinh = Module["_Asinh"] = function() { + return (_Asinh = Module["_Asinh"] = Module["asm"]["Asinh"]).apply(null, arguments); + }; + var _Atan = Module["_Atan"] = function() { + return (_Atan = Module["_Atan"] = Module["asm"]["Atan"]).apply(null, arguments); + }; + var _Atan2 = Module["_Atan2"] = function() { + return (_Atan2 = Module["_Atan2"] = Module["asm"]["Atan2"]).apply(null, arguments); + }; + var _Atanh = Module["_Atanh"] = function() { + return (_Atanh = Module["_Atanh"] = Module["asm"]["Atanh"]).apply(null, arguments); + }; + var _AvgPool = Module["_AvgPool"] = function() { + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments); + }; + var _AvgPool3D = Module["_AvgPool3D"] = function() { + return (_AvgPool3D = Module["_AvgPool3D"] = Module["asm"]["AvgPool3D"]).apply(null, arguments); + }; + var _AvgPool3DGrad = Module["_AvgPool3DGrad"] = function() { + return (_AvgPool3DGrad = Module["_AvgPool3DGrad"] = Module["asm"]["AvgPool3DGrad"]).apply(null, arguments); + }; + var _AvgPoolGrad = Module["_AvgPoolGrad"] = function() { + return (_AvgPoolGrad = Module["_AvgPoolGrad"] = Module["asm"]["AvgPoolGrad"]).apply(null, arguments); + }; + var _BatchMatMul = Module["_BatchMatMul"] = function() { + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments); + }; + var _Bincount = Module["_Bincount"] = function() { + return (_Bincount = Module["_Bincount"] = Module["asm"]["Bincount"]).apply(null, arguments); + }; + var _BitwiseAnd = Module["_BitwiseAnd"] = function() { + return (_BitwiseAnd = Module["_BitwiseAnd"] = Module["asm"]["BitwiseAnd"]).apply(null, arguments); + }; + var _Ceil = Module["_Ceil"] = function() { + return (_Ceil = Module["_Ceil"] = Module["asm"]["Ceil"]).apply(null, arguments); + }; + var _ClipByValue = Module["_ClipByValue"] = function() { + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments); + }; + var _Conv2D = Module["_Conv2D"] = function() { + return (_Conv2D = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments); + }; + var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments); + }; + var _Conv3D = Module["_Conv3D"] = function() { + return (_Conv3D = Module["_Conv3D"] = Module["asm"]["Conv3D"]).apply(null, arguments); + }; + var _Conv3DBackpropFilterV2 = Module["_Conv3DBackpropFilterV2"] = function() { + return (_Conv3DBackpropFilterV2 = Module["_Conv3DBackpropFilterV2"] = Module["asm"]["Conv3DBackpropFilterV2"]).apply(null, arguments); + }; + var _Conv3DBackpropInputV2 = Module["_Conv3DBackpropInputV2"] = function() { + return (_Conv3DBackpropInputV2 = Module["_Conv3DBackpropInputV2"] = Module["asm"]["Conv3DBackpropInputV2"]).apply(null, arguments); + }; + var _Cos = Module["_Cos"] = function() { + return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments); + }; + var _Cosh = Module["_Cosh"] = function() { + return (_Cosh = Module["_Cosh"] = Module["asm"]["Cosh"]).apply(null, arguments); + }; + var _CropAndResize = Module["_CropAndResize"] = function() { + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments); + }; + var _Cumprod = Module["_Cumprod"] = function() { + return (_Cumprod = Module["_Cumprod"] = Module["asm"]["Cumprod"]).apply(null, arguments); + }; + var _Cumsum = Module["_Cumsum"] = function() { + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments); + }; + var _DenseBincount = Module["_DenseBincount"] = function() { + return (_DenseBincount = Module["_DenseBincount"] = Module["asm"]["DenseBincount"]).apply(null, arguments); + }; + var _DepthToSpace = Module["_DepthToSpace"] = function() { + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments); + }; + var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments); + }; + var _Diag = Module["_Diag"] = function() { + return (_Diag = Module["_Diag"] = Module["asm"]["Diag"]).apply(null, arguments); + }; + var _Dilation2D = Module["_Dilation2D"] = function() { + return (_Dilation2D = Module["_Dilation2D"] = Module["asm"]["Dilation2D"]).apply(null, arguments); + }; + var _Dilation2DBackpropFilter = Module["_Dilation2DBackpropFilter"] = function() { + return (_Dilation2DBackpropFilter = Module["_Dilation2DBackpropFilter"] = Module["asm"]["Dilation2DBackpropFilter"]).apply(null, arguments); + }; + var _Dilation2DBackpropInput = Module["_Dilation2DBackpropInput"] = function() { + return (_Dilation2DBackpropInput = Module["_Dilation2DBackpropInput"] = Module["asm"]["Dilation2DBackpropInput"]).apply(null, arguments); + }; + var _Elu = Module["_Elu"] = function() { + return (_Elu = Module["_Elu"] = Module["asm"]["Elu"]).apply(null, arguments); + }; + var _EluGrad = Module["_EluGrad"] = function() { + return (_EluGrad = Module["_EluGrad"] = Module["asm"]["EluGrad"]).apply(null, arguments); + }; + var _Equal = Module["_Equal"] = function() { + return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments); + }; + var _Erf = Module["_Erf"] = function() { + return (_Erf = Module["_Erf"] = Module["asm"]["Erf"]).apply(null, arguments); + }; + var _Exp = Module["_Exp"] = function() { + return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments); + }; + var _Expm1 = Module["_Expm1"] = function() { + return (_Expm1 = Module["_Expm1"] = Module["asm"]["Expm1"]).apply(null, arguments); + }; + var _FlipLeftRight = Module["_FlipLeftRight"] = function() { + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments); + }; + var _Floor = Module["_Floor"] = function() { + return (_Floor = Module["_Floor"] = Module["asm"]["Floor"]).apply(null, arguments); + }; + var _FloorDiv = Module["_FloorDiv"] = function() { + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments); + }; + var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments); + }; + var _FusedConv2D = Module["_FusedConv2D"] = function() { + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments); + }; + var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments); + }; + var _Gather = Module["_Gather"] = function() { + return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments); + }; + var _GatherNd = Module["_GatherNd"] = function() { + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments); + }; + var _Greater = Module["_Greater"] = function() { + return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments); + }; + var _GreaterEqual = Module["_GreaterEqual"] = function() { + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments); + }; + var _IsFinite = Module["_IsFinite"] = function() { + return (_IsFinite = Module["_IsFinite"] = Module["asm"]["IsFinite"]).apply(null, arguments); + }; + var _IsInf = Module["_IsInf"] = function() { + return (_IsInf = Module["_IsInf"] = Module["asm"]["IsInf"]).apply(null, arguments); + }; + var _IsNan = Module["_IsNan"] = function() { + return (_IsNan = Module["_IsNan"] = Module["asm"]["IsNan"]).apply(null, arguments); + }; + var _LRN = Module["_LRN"] = function() { + return (_LRN = Module["_LRN"] = Module["asm"]["LRN"]).apply(null, arguments); + }; + var _LRNGrad = Module["_LRNGrad"] = function() { + return (_LRNGrad = Module["_LRNGrad"] = Module["asm"]["LRNGrad"]).apply(null, arguments); + }; + var _LeakyRelu = Module["_LeakyRelu"] = function() { + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["LeakyRelu"]).apply(null, arguments); + }; + var _Less = Module["_Less"] = function() { + return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments); + }; + var _LessEqual = Module["_LessEqual"] = function() { + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments); + }; + var _LinSpace = Module["_LinSpace"] = function() { + return (_LinSpace = Module["_LinSpace"] = Module["asm"]["LinSpace"]).apply(null, arguments); + }; + var _Log = Module["_Log"] = function() { + return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments); + }; + var _Log1p = Module["_Log1p"] = function() { + return (_Log1p = Module["_Log1p"] = Module["asm"]["Log1p"]).apply(null, arguments); + }; + var _LogicalAnd = Module["_LogicalAnd"] = function() { + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments); + }; + var _LogicalNot = Module["_LogicalNot"] = function() { + return (_LogicalNot = Module["_LogicalNot"] = Module["asm"]["LogicalNot"]).apply(null, arguments); + }; + var _LogicalOr = Module["_LogicalOr"] = function() { + return (_LogicalOr = Module["_LogicalOr"] = Module["asm"]["LogicalOr"]).apply(null, arguments); + }; + var _LogicalXor = Module["_LogicalXor"] = function() { + return (_LogicalXor = Module["_LogicalXor"] = Module["asm"]["LogicalXor"]).apply(null, arguments); + }; + var _Max = Module["_Max"] = function() { + return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments); + }; + var _MaxPool = Module["_MaxPool"] = function() { + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments); + }; + var _MaxPool3D = Module["_MaxPool3D"] = function() { + return (_MaxPool3D = Module["_MaxPool3D"] = Module["asm"]["MaxPool3D"]).apply(null, arguments); + }; + var _MaxPool3DGrad = Module["_MaxPool3DGrad"] = function() { + return (_MaxPool3DGrad = Module["_MaxPool3DGrad"] = Module["asm"]["MaxPool3DGrad"]).apply(null, arguments); + }; + var _MaxPoolGrad = Module["_MaxPoolGrad"] = function() { + return (_MaxPoolGrad = Module["_MaxPoolGrad"] = Module["asm"]["MaxPoolGrad"]).apply(null, arguments); + }; + var _MaxPoolWithArgmax = Module["_MaxPoolWithArgmax"] = function() { + return (_MaxPoolWithArgmax = Module["_MaxPoolWithArgmax"] = Module["asm"]["MaxPoolWithArgmax"]).apply(null, arguments); + }; + var _Maximum = Module["_Maximum"] = function() { + return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments); + }; + var _Mean = Module["_Mean"] = function() { + return (_Mean = Module["_Mean"] = Module["asm"]["Mean"]).apply(null, arguments); + }; + var _Min = Module["_Min"] = function() { + return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments); + }; + var _Minimum = Module["_Minimum"] = function() { + return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments); + }; + var _MirrorPad = Module["_MirrorPad"] = function() { + return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["MirrorPad"]).apply(null, arguments); + }; + var _Mod = Module["_Mod"] = function() { + return (_Mod = Module["_Mod"] = Module["asm"]["Mod"]).apply(null, arguments); + }; + var _Multinomial = Module["_Multinomial"] = function() { + return (_Multinomial = Module["_Multinomial"] = Module["asm"]["Multinomial"]).apply(null, arguments); + }; + var _Multiply = Module["_Multiply"] = function() { + return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments); + }; + var _Neg = Module["_Neg"] = function() { + return (_Neg = Module["_Neg"] = Module["asm"]["Neg"]).apply(null, arguments); + }; + var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments); + }; + var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments); + }; + var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments); + }; + var _NotEqual = Module["_NotEqual"] = function() { + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments); + }; + var _OneHot = Module["_OneHot"] = function() { + return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments); + }; + var _PadV2 = Module["_PadV2"] = function() { + return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments); + }; + var _Pow = Module["_Pow"] = function() { + return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments); + }; + var _Prelu = Module["_Prelu"] = function() { + return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments); + }; + var _Prod = Module["_Prod"] = function() { + return (_Prod = Module["_Prod"] = Module["asm"]["Prod"]).apply(null, arguments); + }; + var _RealDiv = Module["_RealDiv"] = function() { + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["RealDiv"]).apply(null, arguments); + }; + var _Reciprocal = Module["_Reciprocal"] = function() { + return (_Reciprocal = Module["_Reciprocal"] = Module["asm"]["Reciprocal"]).apply(null, arguments); + }; + var _Relu = Module["_Relu"] = function() { + return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments); + }; + var _Relu6 = Module["_Relu6"] = function() { + return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments); + }; + var _ResizeBilinear = Module["_ResizeBilinear"] = function() { + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments); + }; + var _ResizeBilinearGrad = Module["_ResizeBilinearGrad"] = function() { + return (_ResizeBilinearGrad = Module["_ResizeBilinearGrad"] = Module["asm"]["ResizeBilinearGrad"]).apply(null, arguments); + }; + var _ResizeNearestNeighbor = Module["_ResizeNearestNeighbor"] = function() { + return (_ResizeNearestNeighbor = Module["_ResizeNearestNeighbor"] = Module["asm"]["ResizeNearestNeighbor"]).apply(null, arguments); + }; + var _ResizeNearestNeighborGrad = Module["_ResizeNearestNeighborGrad"] = function() { + return (_ResizeNearestNeighborGrad = Module["_ResizeNearestNeighborGrad"] = Module["asm"]["ResizeNearestNeighborGrad"]).apply(null, arguments); + }; + var _Reverse = Module["_Reverse"] = function() { + return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments); + }; + var _RotateWithOffset = Module["_RotateWithOffset"] = function() { + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments); + }; + var _Round = Module["_Round"] = function() { + return (_Round = Module["_Round"] = Module["asm"]["Round"]).apply(null, arguments); + }; + var _Rsqrt = Module["_Rsqrt"] = function() { + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments); + }; + var _ScatterNd = Module["_ScatterNd"] = function() { + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments); + }; + var _SearchSorted = Module["_SearchSorted"] = function() { + return (_SearchSorted = Module["_SearchSorted"] = Module["asm"]["SearchSorted"]).apply(null, arguments); + }; + var _SelectV2 = Module["_SelectV2"] = function() { + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments); + }; + var _Selu = Module["_Selu"] = function() { + return (_Selu = Module["_Selu"] = Module["asm"]["Selu"]).apply(null, arguments); + }; + var _Sigmoid = Module["_Sigmoid"] = function() { + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments); + }; + var _Sign = Module["_Sign"] = function() { + return (_Sign = Module["_Sign"] = Module["asm"]["Sign"]).apply(null, arguments); + }; + var _Sin = Module["_Sin"] = function() { + return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments); + }; + var _Sinh = Module["_Sinh"] = function() { + return (_Sinh = Module["_Sinh"] = Module["asm"]["Sinh"]).apply(null, arguments); + }; + var _Softmax = Module["_Softmax"] = function() { + return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments); + }; + var _Softplus = Module["_Softplus"] = function() { + return (_Softplus = Module["_Softplus"] = Module["asm"]["Softplus"]).apply(null, arguments); + }; + var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() { + return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["SparseFillEmptyRows"]).apply(null, arguments); + }; + var _SparseReshape = Module["_SparseReshape"] = function() { + return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["SparseReshape"]).apply(null, arguments); + }; + var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() { + return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["SparseSegmentReduction"]).apply(null, arguments); + }; + var _SparseToDense = Module["_SparseToDense"] = function() { + return (_SparseToDense = Module["_SparseToDense"] = Module["asm"]["SparseToDense"]).apply(null, arguments); + }; + var _Sqrt = Module["_Sqrt"] = function() { + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments); + }; + var _Square = Module["_Square"] = function() { + return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments); + }; + var _SquaredDifference = Module["_SquaredDifference"] = function() { + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments); + }; + var _Step = Module["_Step"] = function() { + return (_Step = Module["_Step"] = Module["asm"]["Step"]).apply(null, arguments); + }; + var _StridedSlice = Module["_StridedSlice"] = function() { + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments); + }; + var _Sub = Module["_Sub"] = function() { + return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments); + }; + var _Sum = Module["_Sum"] = function() { + return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments); + }; + var _Tan = Module["_Tan"] = function() { + return (_Tan = Module["_Tan"] = Module["asm"]["Tan"]).apply(null, arguments); + }; + var _Tanh = Module["_Tanh"] = function() { + return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments); + }; + var _TensorScatterUpdate = Module["_TensorScatterUpdate"] = function() { + return (_TensorScatterUpdate = Module["_TensorScatterUpdate"] = Module["asm"]["TensorScatterUpdate"]).apply(null, arguments); + }; + var _Tile = Module["_Tile"] = function() { + return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments); + }; + var _TopK = Module["_TopK"] = function() { + return (_TopK = Module["_TopK"] = Module["asm"]["TopK"]).apply(null, arguments); + }; + var _Transform = Module["_Transform"] = function() { + return (_Transform = Module["_Transform"] = Module["asm"]["Transform"]).apply(null, arguments); + }; + var _Transpose = Module["_Transpose"] = function() { + return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments); + }; + var __FusedMatMul = Module["__FusedMatMul"] = function() { + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments); + }; + var _malloc = Module["_malloc"] = function() { + return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments); + }; + var _free = Module["_free"] = function() { + return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments); + }; + var __emscripten_tls_init = Module["__emscripten_tls_init"] = function() { + return (__emscripten_tls_init = Module["__emscripten_tls_init"] = Module["asm"]["_emscripten_tls_init"]).apply(null, arguments); + }; + var _pthread_self = Module["_pthread_self"] = function() { + return (_pthread_self = Module["_pthread_self"] = Module["asm"]["pthread_self"]).apply(null, arguments); + }; + var ___errno_location = Module["___errno_location"] = function() { + return (___errno_location = Module["___errno_location"] = Module["asm"]["__errno_location"]).apply(null, arguments); + }; + var __emscripten_thread_init = Module["__emscripten_thread_init"] = function() { + return (__emscripten_thread_init = Module["__emscripten_thread_init"] = Module["asm"]["_emscripten_thread_init"]).apply(null, arguments); + }; + var __emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = function() { + return (__emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = Module["asm"]["_emscripten_thread_crashed"]).apply(null, arguments); + }; + var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() { + return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["emscripten_main_thread_process_queued_calls"]).apply(null, arguments); + }; + var _emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = function() { + return (_emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = Module["asm"]["emscripten_main_browser_thread_id"]).apply(null, arguments); + }; + var _emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = function() { + return (_emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = Module["asm"]["emscripten_run_in_main_runtime_thread_js"]).apply(null, arguments); + }; + var _emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = function() { + return (_emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = Module["asm"]["emscripten_dispatch_to_thread_"]).apply(null, arguments); + }; + var __emscripten_proxy_execute_task_queue = Module["__emscripten_proxy_execute_task_queue"] = function() { + return (__emscripten_proxy_execute_task_queue = Module["__emscripten_proxy_execute_task_queue"] = Module["asm"]["_emscripten_proxy_execute_task_queue"]).apply(null, arguments); + }; + var __emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = function() { + return (__emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = Module["asm"]["_emscripten_thread_free_data"]).apply(null, arguments); + }; + var __emscripten_thread_exit = Module["__emscripten_thread_exit"] = function() { + return (__emscripten_thread_exit = Module["__emscripten_thread_exit"] = Module["asm"]["_emscripten_thread_exit"]).apply(null, arguments); + }; + var _emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = function() { + return (_emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = Module["asm"]["emscripten_stack_set_limits"]).apply(null, arguments); + }; + var stackSave = Module["stackSave"] = function() { + return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments); + }; + var stackRestore = Module["stackRestore"] = function() { + return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments); + }; + var stackAlloc = Module["stackAlloc"] = function() { + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments); + }; + var dynCall_iijjiiii = Module["dynCall_iijjiiii"] = function() { + return (dynCall_iijjiiii = Module["dynCall_iijjiiii"] = Module["asm"]["dynCall_iijjiiii"]).apply(null, arguments); + }; + var dynCall_jiji = Module["dynCall_jiji"] = function() { + return (dynCall_jiji = Module["dynCall_jiji"] = Module["asm"]["dynCall_jiji"]).apply(null, arguments); + }; + Module["keepRuntimeAlive"] = keepRuntimeAlive; + Module["wasmMemory"] = wasmMemory; + Module["cwrap"] = cwrap; + Module["ExitStatus"] = ExitStatus; + Module["PThread"] = PThread; + var calledRun; + dependenciesFulfilled = function runCaller() { + if (!calledRun) + run(); + if (!calledRun) + dependenciesFulfilled = runCaller; + }; + function run(args) { + args = args || arguments_; + if (runDependencies > 0) { + return; + } + if (ENVIRONMENT_IS_PTHREAD) { + readyPromiseResolve(Module); + initRuntime(); + startWorker(Module); + return; + } + preRun(); + if (runDependencies > 0) { + return; + } + function doRun() { + if (calledRun) + return; + calledRun = true; + Module["calledRun"] = true; + if (ABORT) + return; + initRuntime(); + readyPromiseResolve(Module); + if (Module["onRuntimeInitialized"]) + Module["onRuntimeInitialized"](); + postRun(); + } + if (Module["setStatus"]) { + Module["setStatus"]("Running..."); + setTimeout(function() { + setTimeout(function() { + Module["setStatus"](""); + }, 1); + doRun(); + }, 1); + } else { + doRun(); + } + } + if (Module["preInit"]) { + if (typeof Module["preInit"] == "function") + Module["preInit"] = [Module["preInit"]]; + while (Module["preInit"].length > 0) { + Module["preInit"].pop()(); + } + } + run(); + var listenersAdded; + if (beforeListeners) { + listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) { + return !beforeListeners.uncaughtException.indexOf(listener) > -1; + }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) { + return !beforeListeners.unhandledRejection.indexOf(listener) > -1; + }) }; + } + var actualModule; + if (typeof WasmBackendModule !== "undefined") { + actualModule = WasmBackendModule; + } else if (typeof WasmBackendModuleThreadedSimd3 !== "undefined") { + actualModule = WasmBackendModuleThreadedSimd3; + } else { + throw new Error("Could not find wasm module in post.js"); + } + if (listenersAdded) { + var tmpDispose = actualModule["_dispose"]; + actualModule["_dispose"] = function() { + tmpDispose(); + listenersAdded.uncaughtException.forEach(function(listener) { + process.removeListener("uncaughtException", listener); + }); + listenersAdded.unhandledRejection.forEach(function(listener) { + process.removeListener("unhandledRejection", listener); + }); + }; + } + return WasmBackendModuleThreadedSimd3.ready; + }; + })(); + if (typeof exports === "object" && typeof module === "object") + module.exports = WasmBackendModuleThreadedSimd2; + else if (typeof define === "function" && define["amd"]) + define([], function() { + return WasmBackendModuleThreadedSimd2; + }); + else if (typeof exports === "object") + exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd2; + } +}); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js +var require_tfjs_backend_wasm_threaded_simd_worker = __commonJS({ + "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js"(exports, module) { + "use strict"; + module.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+" +");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.startWorker=instance=>{Module=instance;postMessage({"cmd":"loaded"})};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:"callHandler",handler:handler,args:[...arguments]})}}Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module)}else if(e.data.cmd==="run"){Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};`; + } +}); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js +var require_tfjs_backend_wasm = __commonJS({ + "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(exports, module) { + "use strict"; + var WasmBackendModule2 = (() => { + var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; + if (typeof __filename !== "undefined") + _scriptDir = _scriptDir || __filename; + return function(WasmBackendModule3) { + WasmBackendModule3 = WasmBackendModule3 || {}; + var Module = typeof WasmBackendModule3 != "undefined" ? WasmBackendModule3 : {}; + var readyPromiseResolve, readyPromiseReject; + Module["ready"] = new Promise(function(resolve, reject) { + readyPromiseResolve = resolve; + readyPromiseReject = reject; + }); + var beforeListeners; + if (typeof process !== "undefined" && process.listeners) { + beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }; + } + var moduleOverrides = Object.assign({}, Module); + var arguments_ = []; + var thisProgram = "./this.program"; + var quit_ = (status, toThrow) => { + throw toThrow; + }; + var ENVIRONMENT_IS_WEB = typeof window == "object"; + var ENVIRONMENT_IS_WORKER = typeof importScripts == "function"; + var ENVIRONMENT_IS_NODE = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string"; + var scriptDirectory = ""; + function locateFile(path) { + if (Module["locateFile"]) { + return Module["locateFile"](path, scriptDirectory); + } + return scriptDirectory + path; + } + var read_, readAsync, readBinary, setWindowTitle; + function logExceptionOnExit(e) { + if (e instanceof ExitStatus) + return; + let toLog = e; + err("exiting due to exception: " + toLog); + } + if (ENVIRONMENT_IS_NODE) { + var fs = require_fs(); + var nodePath = require_path(); + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = nodePath.dirname(scriptDirectory) + "/"; + } else { + scriptDirectory = __dirname + "/"; + } + read_ = (filename, binary) => { + filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename); + return fs.readFileSync(filename, binary ? void 0 : "utf8"); + }; + readBinary = (filename) => { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + return ret; + }; + readAsync = (filename, onload, onerror) => { + filename = isFileURI(filename) ? new URL(filename) : nodePath.normalize(filename); + fs.readFile(filename, function(err2, data) { + if (err2) + onerror(err2); + else + onload(data.buffer); + }); + }; + if (process["argv"].length > 1) { + thisProgram = process["argv"][1].replace(/\\/g, "/"); + } + arguments_ = process["argv"].slice(2); + process["on"]("uncaughtException", function(ex) { + if (!(ex instanceof ExitStatus)) { + throw ex; + } + }); + process["on"]("unhandledRejection", function(reason) { + throw reason; + }); + quit_ = (status, toThrow) => { + if (keepRuntimeAlive()) { + process["exitCode"] = status; + throw toThrow; + } + logExceptionOnExit(toThrow); + process["exit"](status); + }; + Module["inspect"] = function() { + return "[Emscripten Module object]"; + }; + } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = self.location.href; + } else if (typeof document != "undefined" && document.currentScript) { + scriptDirectory = document.currentScript.src; + } + if (_scriptDir) { + scriptDirectory = _scriptDir; + } + if (scriptDirectory.indexOf("blob:") !== 0) { + scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1); + } else { + scriptDirectory = ""; + } + { + read_ = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.send(null); + return xhr.responseText; + }; + if (ENVIRONMENT_IS_WORKER) { + readBinary = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.responseType = "arraybuffer"; + xhr.send(null); + return new Uint8Array(xhr.response); + }; + } + readAsync = (url, onload, onerror) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, true); + xhr.responseType = "arraybuffer"; + xhr.onload = () => { + if (xhr.status == 200 || xhr.status == 0 && xhr.response) { + onload(xhr.response); + return; + } + onerror(); + }; + xhr.onerror = onerror; + xhr.send(null); + }; + } + setWindowTitle = (title) => document.title = title; + } else { + } + var out = Module["print"] || console.log.bind(console); + var err = Module["printErr"] || console.warn.bind(console); + Object.assign(Module, moduleOverrides); + moduleOverrides = null; + if (Module["arguments"]) + arguments_ = Module["arguments"]; + if (Module["thisProgram"]) + thisProgram = Module["thisProgram"]; + if (Module["quit"]) + quit_ = Module["quit"]; + var POINTER_SIZE = 4; + var wasmBinary; + if (Module["wasmBinary"]) + wasmBinary = Module["wasmBinary"]; + var noExitRuntime = Module["noExitRuntime"] || true; + if (typeof WebAssembly != "object") { + abort("no native wasm support detected"); + } + var wasmMemory; + var ABORT = false; + var EXITSTATUS; + function assert3(condition, text) { + if (!condition) { + abort(text); + } + } + var UTF8Decoder = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; + function UTF8ArrayToString(heapOrArray, idx, maxBytesToRead) { + idx >>>= 0; + var endIdx = idx + maxBytesToRead; + var endPtr = idx; + while (heapOrArray[endPtr] && !(endPtr >= endIdx)) + ++endPtr; + if (endPtr - idx > 16 && heapOrArray.buffer && UTF8Decoder) { + return UTF8Decoder.decode(heapOrArray.subarray(idx, endPtr)); + } + var str = ""; + while (idx < endPtr) { + var u0 = heapOrArray[idx++]; + if (!(u0 & 128)) { + str += String.fromCharCode(u0); + continue; + } + var u1 = heapOrArray[idx++] & 63; + if ((u0 & 224) == 192) { + str += String.fromCharCode((u0 & 31) << 6 | u1); + continue; + } + var u2 = heapOrArray[idx++] & 63; + if ((u0 & 240) == 224) { + u0 = (u0 & 15) << 12 | u1 << 6 | u2; + } else { + u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heapOrArray[idx++] & 63; + } + if (u0 < 65536) { + str += String.fromCharCode(u0); + } else { + var ch = u0 - 65536; + str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023); + } + } + return str; + } + function UTF8ToString(ptr, maxBytesToRead) { + ptr >>>= 0; + return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : ""; + } + function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) { + outIdx >>>= 0; + if (!(maxBytesToWrite > 0)) + return 0; + var startIdx = outIdx; + var endIdx = outIdx + maxBytesToWrite - 1; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) { + var u1 = str.charCodeAt(++i); + u = 65536 + ((u & 1023) << 10) | u1 & 1023; + } + if (u <= 127) { + if (outIdx >= endIdx) + break; + heap[outIdx++ >>> 0] = u; + } else if (u <= 2047) { + if (outIdx + 1 >= endIdx) + break; + heap[outIdx++ >>> 0] = 192 | u >> 6; + heap[outIdx++ >>> 0] = 128 | u & 63; + } else if (u <= 65535) { + if (outIdx + 2 >= endIdx) + break; + heap[outIdx++ >>> 0] = 224 | u >> 12; + heap[outIdx++ >>> 0] = 128 | u >> 6 & 63; + heap[outIdx++ >>> 0] = 128 | u & 63; + } else { + if (outIdx + 3 >= endIdx) + break; + heap[outIdx++ >>> 0] = 240 | u >> 18; + heap[outIdx++ >>> 0] = 128 | u >> 12 & 63; + heap[outIdx++ >>> 0] = 128 | u >> 6 & 63; + heap[outIdx++ >>> 0] = 128 | u & 63; + } + } + heap[outIdx >>> 0] = 0; + return outIdx - startIdx; + } + function stringToUTF8(str, outPtr, maxBytesToWrite) { + return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite); + } + var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; + function updateGlobalBufferAndViews(buf) { + buffer2 = buf; + Module["HEAP8"] = HEAP8 = new Int8Array(buf); + Module["HEAP16"] = HEAP16 = new Int16Array(buf); + Module["HEAP32"] = HEAP32 = new Int32Array(buf); + Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf); + Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf); + Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf); + Module["HEAPF32"] = HEAPF32 = new Float32Array(buf); + Module["HEAPF64"] = HEAPF64 = new Float64Array(buf); + } + var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216; + var wasmTable; + var __ATPRERUN__ = []; + var __ATINIT__ = []; + var __ATPOSTRUN__ = []; + var runtimeInitialized = false; + function keepRuntimeAlive() { + return noExitRuntime; + } + function preRun() { + if (Module["preRun"]) { + if (typeof Module["preRun"] == "function") + Module["preRun"] = [Module["preRun"]]; + while (Module["preRun"].length) { + addOnPreRun(Module["preRun"].shift()); + } + } + callRuntimeCallbacks(__ATPRERUN__); + } + function initRuntime() { + runtimeInitialized = true; + callRuntimeCallbacks(__ATINIT__); + } + function postRun() { + if (Module["postRun"]) { + if (typeof Module["postRun"] == "function") + Module["postRun"] = [Module["postRun"]]; + while (Module["postRun"].length) { + addOnPostRun(Module["postRun"].shift()); + } + } + callRuntimeCallbacks(__ATPOSTRUN__); + } + function addOnPreRun(cb) { + __ATPRERUN__.unshift(cb); + } + function addOnInit(cb) { + __ATINIT__.unshift(cb); + } + function addOnPostRun(cb) { + __ATPOSTRUN__.unshift(cb); + } + var runDependencies = 0; + var runDependencyWatcher = null; + var dependenciesFulfilled = null; + function addRunDependency(id) { + runDependencies++; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + } + function removeRunDependency(id) { + runDependencies--; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + if (runDependencies == 0) { + if (runDependencyWatcher !== null) { + clearInterval(runDependencyWatcher); + runDependencyWatcher = null; + } + if (dependenciesFulfilled) { + var callback = dependenciesFulfilled; + dependenciesFulfilled = null; + callback(); + } + } + } + function abort(what) { + if (Module["onAbort"]) { + Module["onAbort"](what); + } + what = "Aborted(" + what + ")"; + err(what); + ABORT = true; + EXITSTATUS = 1; + what += ". Build with -sASSERTIONS for more info."; + var e = new WebAssembly.RuntimeError(what); + readyPromiseReject(e); + throw e; + } + var dataURIPrefix = "data:application/octet-stream;base64,"; + function isDataURI(filename) { + return filename.startsWith(dataURIPrefix); + } + function isFileURI(filename) { + return filename.startsWith("file://"); + } + var wasmBinaryFile; + wasmBinaryFile = "tfjs-backend-wasm.wasm"; + if (!isDataURI(wasmBinaryFile)) { + wasmBinaryFile = locateFile(wasmBinaryFile); + } + function getBinary(file) { + try { + if (file == wasmBinaryFile && wasmBinary) { + return new Uint8Array(wasmBinary); + } + if (readBinary) { + return readBinary(file); + } + throw "both async and sync fetching of the wasm failed"; + } catch (err2) { + abort(err2); + } + } + function getBinaryPromise() { + if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) { + if (typeof fetch == "function" && !isFileURI(wasmBinaryFile)) { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + if (!response["ok"]) { + throw "failed to load wasm binary file at '" + wasmBinaryFile + "'"; + } + return response["arrayBuffer"](); + }).catch(function() { + return getBinary(wasmBinaryFile); + }); + } else { + if (readAsync) { + return new Promise(function(resolve, reject) { + readAsync(wasmBinaryFile, function(response) { + resolve(new Uint8Array(response)); + }, reject); + }); + } + } + } + return Promise.resolve().then(function() { + return getBinary(wasmBinaryFile); + }); + } + function createWasm() { + var info = { "env": asmLibraryArg, "wasi_snapshot_preview1": asmLibraryArg }; + function receiveInstance(instance, module2) { + var exports3 = instance.exports; + Module["asm"] = exports3; + wasmMemory = Module["asm"]["memory"]; + updateGlobalBufferAndViews(wasmMemory.buffer); + wasmTable = Module["asm"]["__indirect_function_table"]; + addOnInit(Module["asm"]["__wasm_call_ctors"]); + removeRunDependency("wasm-instantiate"); + } + addRunDependency("wasm-instantiate"); + function receiveInstantiationResult(result) { + receiveInstance(result["instance"]); + } + function instantiateArrayBuffer(receiver) { + return getBinaryPromise().then(function(binary) { + return WebAssembly.instantiate(binary, info); + }).then(function(instance) { + return instance; + }).then(receiver, function(reason) { + err("failed to asynchronously prepare wasm: " + reason); + abort(reason); + }); + } + function instantiateAsync() { + if (!wasmBinary && typeof WebAssembly.instantiateStreaming == "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && !ENVIRONMENT_IS_NODE && typeof fetch == "function") { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + var result = WebAssembly.instantiateStreaming(response, info); + return result.then(receiveInstantiationResult, function(reason) { + err("wasm streaming compile failed: " + reason); + err("falling back to ArrayBuffer instantiation"); + return instantiateArrayBuffer(receiveInstantiationResult); + }); + }); + } else { + return instantiateArrayBuffer(receiveInstantiationResult); + } + } + if (Module["instantiateWasm"]) { + try { + var exports2 = Module["instantiateWasm"](info, receiveInstance); + return exports2; + } catch (e) { + err("Module.instantiateWasm callback failed with error: " + e); + readyPromiseReject(e); + } + } + instantiateAsync().catch(readyPromiseReject); + return {}; + } + var tempDouble; + var tempI64; + function ExitStatus(status) { + this.name = "ExitStatus"; + this.message = "Program terminated with exit(" + status + ")"; + this.status = status; + } + function callRuntimeCallbacks(callbacks2) { + while (callbacks2.length > 0) { + callbacks2.shift()(Module); + } + } + function _abort() { + abort(""); + } + function getHeapMax() { + return 4294901760; + } + function _emscripten_get_heap_max() { + return getHeapMax(); + } + function _emscripten_memcpy_big(dest, src, num) { + HEAPU8.copyWithin(dest >>> 0, src >>> 0, src + num >>> 0); + } + function emscripten_realloc_buffer(size) { + try { + wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16); + updateGlobalBufferAndViews(wasmMemory.buffer); + return 1; + } catch (e) { + } + } + function _emscripten_resize_heap(requestedSize) { + var oldSize = HEAPU8.length; + requestedSize = requestedSize >>> 0; + var maxHeapSize = getHeapMax(); + if (requestedSize > maxHeapSize) { + return false; + } + let alignUp = (x, multiple) => x + (multiple - x % multiple) % multiple; + for (var cutDown = 1; cutDown <= 4; cutDown *= 2) { + var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown); + overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296); + var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536)); + var replacement = emscripten_realloc_buffer(newSize); + if (replacement) { + return true; + } + } + return false; + } + var SYSCALLS = { varargs: void 0, get: function() { + SYSCALLS.varargs += 4; + var ret = HEAP32[SYSCALLS.varargs - 4 >>> 2]; + return ret; + }, getStr: function(ptr) { + var ret = UTF8ToString(ptr); + return ret; + } }; + function _fd_close(fd) { + return 52; + } + function _fd_seek(fd, offset_low, offset_high, whence, newOffset) { + return 70; + } + var printCharBuffers = [null, [], []]; + function printChar(stream, curr) { + var buffer3 = printCharBuffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0)); + buffer3.length = 0; + } else { + buffer3.push(curr); + } + } + function _fd_write(fd, iov, iovcnt, pnum) { + var num = 0; + for (var i = 0; i < iovcnt; i++) { + var ptr = HEAPU32[iov >>> 2]; + var len = HEAPU32[iov + 4 >>> 2]; + iov += 8; + for (var j = 0; j < len; j++) { + printChar(fd, HEAPU8[ptr + j >>> 0]); + } + num += len; + } + HEAPU32[pnum >>> 2] = num; + return 0; + } + function getCFunc(ident) { + var func2 = Module["_" + ident]; + return func2; + } + function writeArrayToMemory(array2, buffer3) { + HEAP8.set(array2, buffer3 >>> 0); + } + function ccall(ident, returnType, argTypes, args, opts) { + var toC = { "string": (str) => { + var ret2 = 0; + if (str !== null && str !== void 0 && str !== 0) { + var len = (str.length << 2) + 1; + ret2 = stackAlloc(len); + stringToUTF8(str, ret2, len); + } + return ret2; + }, "array": (arr) => { + var ret2 = stackAlloc(arr.length); + writeArrayToMemory(arr, ret2); + return ret2; + } }; + function convertReturnValue(ret2) { + if (returnType === "string") { + return UTF8ToString(ret2); + } + if (returnType === "boolean") + return Boolean(ret2); + return ret2; + } + var func2 = getCFunc(ident); + var cArgs = []; + var stack2 = 0; + if (args) { + for (var i = 0; i < args.length; i++) { + var converter = toC[argTypes[i]]; + if (converter) { + if (stack2 === 0) + stack2 = stackSave(); + cArgs[i] = converter(args[i]); + } else { + cArgs[i] = args[i]; + } + } + } + var ret = func2.apply(null, cArgs); + function onDone(ret2) { + if (stack2 !== 0) + stackRestore(stack2); + return convertReturnValue(ret2); + } + ret = onDone(ret); + return ret; + } + function cwrap(ident, returnType, argTypes, opts) { + argTypes = argTypes || []; + var numericArgs = argTypes.every((type) => type === "number" || type === "boolean"); + var numericRet = returnType !== "string"; + if (numericRet && numericArgs && !opts) { + return getCFunc(ident); + } + return function() { + return ccall(ident, returnType, argTypes, arguments, opts); + }; + } + var asmLibraryArg = { "abort": _abort, "emscripten_get_heap_max": _emscripten_get_heap_max, "emscripten_memcpy_big": _emscripten_memcpy_big, "emscripten_resize_heap": _emscripten_resize_heap, "fd_close": _fd_close, "fd_seek": _fd_seek, "fd_write": _fd_write }; + var asm = createWasm(); + var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() { + return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["__wasm_call_ctors"]).apply(null, arguments); + }; + var _init = Module["_init"] = function() { + return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments); + }; + var _init_with_threads_count = Module["_init_with_threads_count"] = function() { + return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["init_with_threads_count"]).apply(null, arguments); + }; + var _get_threads_count = Module["_get_threads_count"] = function() { + return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["get_threads_count"]).apply(null, arguments); + }; + var _register_tensor = Module["_register_tensor"] = function() { + return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments); + }; + var _dispose_data = Module["_dispose_data"] = function() { + return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments); + }; + var _dispose = Module["_dispose"] = function() { + return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments); + }; + var _Abs = Module["_Abs"] = function() { + return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments); + }; + var _Acos = Module["_Acos"] = function() { + return (_Acos = Module["_Acos"] = Module["asm"]["Acos"]).apply(null, arguments); + }; + var _Acosh = Module["_Acosh"] = function() { + return (_Acosh = Module["_Acosh"] = Module["asm"]["Acosh"]).apply(null, arguments); + }; + var _Add = Module["_Add"] = function() { + return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments); + }; + var _AddN = Module["_AddN"] = function() { + return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments); + }; + var _All = Module["_All"] = function() { + return (_All = Module["_All"] = Module["asm"]["All"]).apply(null, arguments); + }; + var _Any = Module["_Any"] = function() { + return (_Any = Module["_Any"] = Module["asm"]["Any"]).apply(null, arguments); + }; + var _ArgMax = Module["_ArgMax"] = function() { + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments); + }; + var _ArgMin = Module["_ArgMin"] = function() { + return (_ArgMin = Module["_ArgMin"] = Module["asm"]["ArgMin"]).apply(null, arguments); + }; + var _Asin = Module["_Asin"] = function() { + return (_Asin = Module["_Asin"] = Module["asm"]["Asin"]).apply(null, arguments); + }; + var _Asinh = Module["_Asinh"] = function() { + return (_Asinh = Module["_Asinh"] = Module["asm"]["Asinh"]).apply(null, arguments); + }; + var _Atan = Module["_Atan"] = function() { + return (_Atan = Module["_Atan"] = Module["asm"]["Atan"]).apply(null, arguments); + }; + var _Atan2 = Module["_Atan2"] = function() { + return (_Atan2 = Module["_Atan2"] = Module["asm"]["Atan2"]).apply(null, arguments); + }; + var _Atanh = Module["_Atanh"] = function() { + return (_Atanh = Module["_Atanh"] = Module["asm"]["Atanh"]).apply(null, arguments); + }; + var _AvgPool = Module["_AvgPool"] = function() { + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments); + }; + var _AvgPool3D = Module["_AvgPool3D"] = function() { + return (_AvgPool3D = Module["_AvgPool3D"] = Module["asm"]["AvgPool3D"]).apply(null, arguments); + }; + var _AvgPool3DGrad = Module["_AvgPool3DGrad"] = function() { + return (_AvgPool3DGrad = Module["_AvgPool3DGrad"] = Module["asm"]["AvgPool3DGrad"]).apply(null, arguments); + }; + var _AvgPoolGrad = Module["_AvgPoolGrad"] = function() { + return (_AvgPoolGrad = Module["_AvgPoolGrad"] = Module["asm"]["AvgPoolGrad"]).apply(null, arguments); + }; + var _BatchMatMul = Module["_BatchMatMul"] = function() { + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments); + }; + var _Bincount = Module["_Bincount"] = function() { + return (_Bincount = Module["_Bincount"] = Module["asm"]["Bincount"]).apply(null, arguments); + }; + var _BitwiseAnd = Module["_BitwiseAnd"] = function() { + return (_BitwiseAnd = Module["_BitwiseAnd"] = Module["asm"]["BitwiseAnd"]).apply(null, arguments); + }; + var _Ceil = Module["_Ceil"] = function() { + return (_Ceil = Module["_Ceil"] = Module["asm"]["Ceil"]).apply(null, arguments); + }; + var _ClipByValue = Module["_ClipByValue"] = function() { + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments); + }; + var _Conv2D = Module["_Conv2D"] = function() { + return (_Conv2D = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments); + }; + var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments); + }; + var _Conv3D = Module["_Conv3D"] = function() { + return (_Conv3D = Module["_Conv3D"] = Module["asm"]["Conv3D"]).apply(null, arguments); + }; + var _Conv3DBackpropFilterV2 = Module["_Conv3DBackpropFilterV2"] = function() { + return (_Conv3DBackpropFilterV2 = Module["_Conv3DBackpropFilterV2"] = Module["asm"]["Conv3DBackpropFilterV2"]).apply(null, arguments); + }; + var _Conv3DBackpropInputV2 = Module["_Conv3DBackpropInputV2"] = function() { + return (_Conv3DBackpropInputV2 = Module["_Conv3DBackpropInputV2"] = Module["asm"]["Conv3DBackpropInputV2"]).apply(null, arguments); + }; + var _Cos = Module["_Cos"] = function() { + return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments); + }; + var _Cosh = Module["_Cosh"] = function() { + return (_Cosh = Module["_Cosh"] = Module["asm"]["Cosh"]).apply(null, arguments); + }; + var _CropAndResize = Module["_CropAndResize"] = function() { + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments); + }; + var _Cumprod = Module["_Cumprod"] = function() { + return (_Cumprod = Module["_Cumprod"] = Module["asm"]["Cumprod"]).apply(null, arguments); + }; + var _Cumsum = Module["_Cumsum"] = function() { + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments); + }; + var _DenseBincount = Module["_DenseBincount"] = function() { + return (_DenseBincount = Module["_DenseBincount"] = Module["asm"]["DenseBincount"]).apply(null, arguments); + }; + var _DepthToSpace = Module["_DepthToSpace"] = function() { + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments); + }; + var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments); + }; + var _Diag = Module["_Diag"] = function() { + return (_Diag = Module["_Diag"] = Module["asm"]["Diag"]).apply(null, arguments); + }; + var _Dilation2D = Module["_Dilation2D"] = function() { + return (_Dilation2D = Module["_Dilation2D"] = Module["asm"]["Dilation2D"]).apply(null, arguments); + }; + var _Dilation2DBackpropFilter = Module["_Dilation2DBackpropFilter"] = function() { + return (_Dilation2DBackpropFilter = Module["_Dilation2DBackpropFilter"] = Module["asm"]["Dilation2DBackpropFilter"]).apply(null, arguments); + }; + var _Dilation2DBackpropInput = Module["_Dilation2DBackpropInput"] = function() { + return (_Dilation2DBackpropInput = Module["_Dilation2DBackpropInput"] = Module["asm"]["Dilation2DBackpropInput"]).apply(null, arguments); + }; + var _Elu = Module["_Elu"] = function() { + return (_Elu = Module["_Elu"] = Module["asm"]["Elu"]).apply(null, arguments); + }; + var _EluGrad = Module["_EluGrad"] = function() { + return (_EluGrad = Module["_EluGrad"] = Module["asm"]["EluGrad"]).apply(null, arguments); + }; + var _Equal = Module["_Equal"] = function() { + return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments); + }; + var _Erf = Module["_Erf"] = function() { + return (_Erf = Module["_Erf"] = Module["asm"]["Erf"]).apply(null, arguments); + }; + var _Exp = Module["_Exp"] = function() { + return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments); + }; + var _Expm1 = Module["_Expm1"] = function() { + return (_Expm1 = Module["_Expm1"] = Module["asm"]["Expm1"]).apply(null, arguments); + }; + var _FlipLeftRight = Module["_FlipLeftRight"] = function() { + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments); + }; + var _Floor = Module["_Floor"] = function() { + return (_Floor = Module["_Floor"] = Module["asm"]["Floor"]).apply(null, arguments); + }; + var _FloorDiv = Module["_FloorDiv"] = function() { + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments); + }; + var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments); + }; + var _FusedConv2D = Module["_FusedConv2D"] = function() { + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments); + }; + var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments); + }; + var _Gather = Module["_Gather"] = function() { + return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments); + }; + var _GatherNd = Module["_GatherNd"] = function() { + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments); + }; + var _Greater = Module["_Greater"] = function() { + return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments); + }; + var _GreaterEqual = Module["_GreaterEqual"] = function() { + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments); + }; + var _IsFinite = Module["_IsFinite"] = function() { + return (_IsFinite = Module["_IsFinite"] = Module["asm"]["IsFinite"]).apply(null, arguments); + }; + var _IsInf = Module["_IsInf"] = function() { + return (_IsInf = Module["_IsInf"] = Module["asm"]["IsInf"]).apply(null, arguments); + }; + var _IsNan = Module["_IsNan"] = function() { + return (_IsNan = Module["_IsNan"] = Module["asm"]["IsNan"]).apply(null, arguments); + }; + var _LRN = Module["_LRN"] = function() { + return (_LRN = Module["_LRN"] = Module["asm"]["LRN"]).apply(null, arguments); + }; + var _LRNGrad = Module["_LRNGrad"] = function() { + return (_LRNGrad = Module["_LRNGrad"] = Module["asm"]["LRNGrad"]).apply(null, arguments); + }; + var _LeakyRelu = Module["_LeakyRelu"] = function() { + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["LeakyRelu"]).apply(null, arguments); + }; + var _Less = Module["_Less"] = function() { + return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments); + }; + var _LessEqual = Module["_LessEqual"] = function() { + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments); + }; + var _LinSpace = Module["_LinSpace"] = function() { + return (_LinSpace = Module["_LinSpace"] = Module["asm"]["LinSpace"]).apply(null, arguments); + }; + var _Log = Module["_Log"] = function() { + return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments); + }; + var _Log1p = Module["_Log1p"] = function() { + return (_Log1p = Module["_Log1p"] = Module["asm"]["Log1p"]).apply(null, arguments); + }; + var _LogicalAnd = Module["_LogicalAnd"] = function() { + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments); + }; + var _LogicalNot = Module["_LogicalNot"] = function() { + return (_LogicalNot = Module["_LogicalNot"] = Module["asm"]["LogicalNot"]).apply(null, arguments); + }; + var _LogicalOr = Module["_LogicalOr"] = function() { + return (_LogicalOr = Module["_LogicalOr"] = Module["asm"]["LogicalOr"]).apply(null, arguments); + }; + var _LogicalXor = Module["_LogicalXor"] = function() { + return (_LogicalXor = Module["_LogicalXor"] = Module["asm"]["LogicalXor"]).apply(null, arguments); + }; + var _Max = Module["_Max"] = function() { + return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments); + }; + var _MaxPool = Module["_MaxPool"] = function() { + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments); + }; + var _MaxPool3D = Module["_MaxPool3D"] = function() { + return (_MaxPool3D = Module["_MaxPool3D"] = Module["asm"]["MaxPool3D"]).apply(null, arguments); + }; + var _MaxPool3DGrad = Module["_MaxPool3DGrad"] = function() { + return (_MaxPool3DGrad = Module["_MaxPool3DGrad"] = Module["asm"]["MaxPool3DGrad"]).apply(null, arguments); + }; + var _MaxPoolGrad = Module["_MaxPoolGrad"] = function() { + return (_MaxPoolGrad = Module["_MaxPoolGrad"] = Module["asm"]["MaxPoolGrad"]).apply(null, arguments); + }; + var _MaxPoolWithArgmax = Module["_MaxPoolWithArgmax"] = function() { + return (_MaxPoolWithArgmax = Module["_MaxPoolWithArgmax"] = Module["asm"]["MaxPoolWithArgmax"]).apply(null, arguments); + }; + var _Maximum = Module["_Maximum"] = function() { + return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments); + }; + var _Mean = Module["_Mean"] = function() { + return (_Mean = Module["_Mean"] = Module["asm"]["Mean"]).apply(null, arguments); + }; + var _Min = Module["_Min"] = function() { + return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments); + }; + var _Minimum = Module["_Minimum"] = function() { + return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments); + }; + var _MirrorPad = Module["_MirrorPad"] = function() { + return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["MirrorPad"]).apply(null, arguments); + }; + var _Mod = Module["_Mod"] = function() { + return (_Mod = Module["_Mod"] = Module["asm"]["Mod"]).apply(null, arguments); + }; + var _Multinomial = Module["_Multinomial"] = function() { + return (_Multinomial = Module["_Multinomial"] = Module["asm"]["Multinomial"]).apply(null, arguments); + }; + var _Multiply = Module["_Multiply"] = function() { + return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments); + }; + var _Neg = Module["_Neg"] = function() { + return (_Neg = Module["_Neg"] = Module["asm"]["Neg"]).apply(null, arguments); + }; + var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments); + }; + var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments); + }; + var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments); + }; + var _NotEqual = Module["_NotEqual"] = function() { + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments); + }; + var _OneHot = Module["_OneHot"] = function() { + return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments); + }; + var _PadV2 = Module["_PadV2"] = function() { + return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments); + }; + var _Pow = Module["_Pow"] = function() { + return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments); + }; + var _Prelu = Module["_Prelu"] = function() { + return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments); + }; + var _Prod = Module["_Prod"] = function() { + return (_Prod = Module["_Prod"] = Module["asm"]["Prod"]).apply(null, arguments); + }; + var _RealDiv = Module["_RealDiv"] = function() { + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["RealDiv"]).apply(null, arguments); + }; + var _Reciprocal = Module["_Reciprocal"] = function() { + return (_Reciprocal = Module["_Reciprocal"] = Module["asm"]["Reciprocal"]).apply(null, arguments); + }; + var _Relu = Module["_Relu"] = function() { + return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments); + }; + var _Relu6 = Module["_Relu6"] = function() { + return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments); + }; + var _ResizeBilinear = Module["_ResizeBilinear"] = function() { + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments); + }; + var _ResizeBilinearGrad = Module["_ResizeBilinearGrad"] = function() { + return (_ResizeBilinearGrad = Module["_ResizeBilinearGrad"] = Module["asm"]["ResizeBilinearGrad"]).apply(null, arguments); + }; + var _ResizeNearestNeighbor = Module["_ResizeNearestNeighbor"] = function() { + return (_ResizeNearestNeighbor = Module["_ResizeNearestNeighbor"] = Module["asm"]["ResizeNearestNeighbor"]).apply(null, arguments); + }; + var _ResizeNearestNeighborGrad = Module["_ResizeNearestNeighborGrad"] = function() { + return (_ResizeNearestNeighborGrad = Module["_ResizeNearestNeighborGrad"] = Module["asm"]["ResizeNearestNeighborGrad"]).apply(null, arguments); + }; + var _Reverse = Module["_Reverse"] = function() { + return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments); + }; + var _RotateWithOffset = Module["_RotateWithOffset"] = function() { + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments); + }; + var _Round = Module["_Round"] = function() { + return (_Round = Module["_Round"] = Module["asm"]["Round"]).apply(null, arguments); + }; + var _Rsqrt = Module["_Rsqrt"] = function() { + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments); + }; + var _ScatterNd = Module["_ScatterNd"] = function() { + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments); + }; + var _SearchSorted = Module["_SearchSorted"] = function() { + return (_SearchSorted = Module["_SearchSorted"] = Module["asm"]["SearchSorted"]).apply(null, arguments); + }; + var _SelectV2 = Module["_SelectV2"] = function() { + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments); + }; + var _Selu = Module["_Selu"] = function() { + return (_Selu = Module["_Selu"] = Module["asm"]["Selu"]).apply(null, arguments); + }; + var _Sigmoid = Module["_Sigmoid"] = function() { + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments); + }; + var _Sign = Module["_Sign"] = function() { + return (_Sign = Module["_Sign"] = Module["asm"]["Sign"]).apply(null, arguments); + }; + var _Sin = Module["_Sin"] = function() { + return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments); + }; + var _Sinh = Module["_Sinh"] = function() { + return (_Sinh = Module["_Sinh"] = Module["asm"]["Sinh"]).apply(null, arguments); + }; + var _Softmax = Module["_Softmax"] = function() { + return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments); + }; + var _Softplus = Module["_Softplus"] = function() { + return (_Softplus = Module["_Softplus"] = Module["asm"]["Softplus"]).apply(null, arguments); + }; + var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() { + return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["SparseFillEmptyRows"]).apply(null, arguments); + }; + var _SparseReshape = Module["_SparseReshape"] = function() { + return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["SparseReshape"]).apply(null, arguments); + }; + var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() { + return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["SparseSegmentReduction"]).apply(null, arguments); + }; + var _SparseToDense = Module["_SparseToDense"] = function() { + return (_SparseToDense = Module["_SparseToDense"] = Module["asm"]["SparseToDense"]).apply(null, arguments); + }; + var _Sqrt = Module["_Sqrt"] = function() { + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments); + }; + var _Square = Module["_Square"] = function() { + return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments); + }; + var _SquaredDifference = Module["_SquaredDifference"] = function() { + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments); + }; + var _Step = Module["_Step"] = function() { + return (_Step = Module["_Step"] = Module["asm"]["Step"]).apply(null, arguments); + }; + var _StridedSlice = Module["_StridedSlice"] = function() { + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments); + }; + var _Sub = Module["_Sub"] = function() { + return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments); + }; + var _Sum = Module["_Sum"] = function() { + return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments); + }; + var _Tan = Module["_Tan"] = function() { + return (_Tan = Module["_Tan"] = Module["asm"]["Tan"]).apply(null, arguments); + }; + var _Tanh = Module["_Tanh"] = function() { + return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments); + }; + var _TensorScatterUpdate = Module["_TensorScatterUpdate"] = function() { + return (_TensorScatterUpdate = Module["_TensorScatterUpdate"] = Module["asm"]["TensorScatterUpdate"]).apply(null, arguments); + }; + var _Tile = Module["_Tile"] = function() { + return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments); + }; + var _TopK = Module["_TopK"] = function() { + return (_TopK = Module["_TopK"] = Module["asm"]["TopK"]).apply(null, arguments); + }; + var _Transform = Module["_Transform"] = function() { + return (_Transform = Module["_Transform"] = Module["asm"]["Transform"]).apply(null, arguments); + }; + var _Transpose = Module["_Transpose"] = function() { + return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments); + }; + var __FusedMatMul = Module["__FusedMatMul"] = function() { + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments); + }; + var _malloc = Module["_malloc"] = function() { + return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments); + }; + var _free = Module["_free"] = function() { + return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments); + }; + var ___errno_location = Module["___errno_location"] = function() { + return (___errno_location = Module["___errno_location"] = Module["asm"]["__errno_location"]).apply(null, arguments); + }; + var stackSave = Module["stackSave"] = function() { + return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments); + }; + var stackRestore = Module["stackRestore"] = function() { + return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments); + }; + var stackAlloc = Module["stackAlloc"] = function() { + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments); + }; + var dynCall_iijjiiii = Module["dynCall_iijjiiii"] = function() { + return (dynCall_iijjiiii = Module["dynCall_iijjiiii"] = Module["asm"]["dynCall_iijjiiii"]).apply(null, arguments); + }; + var dynCall_jiji = Module["dynCall_jiji"] = function() { + return (dynCall_jiji = Module["dynCall_jiji"] = Module["asm"]["dynCall_jiji"]).apply(null, arguments); + }; + Module["cwrap"] = cwrap; + var calledRun; + dependenciesFulfilled = function runCaller() { + if (!calledRun) + run(); + if (!calledRun) + dependenciesFulfilled = runCaller; + }; + function run(args) { + args = args || arguments_; + if (runDependencies > 0) { + return; + } + preRun(); + if (runDependencies > 0) { + return; + } + function doRun() { + if (calledRun) + return; + calledRun = true; + Module["calledRun"] = true; + if (ABORT) + return; + initRuntime(); + readyPromiseResolve(Module); + if (Module["onRuntimeInitialized"]) + Module["onRuntimeInitialized"](); + postRun(); + } + if (Module["setStatus"]) { + Module["setStatus"]("Running..."); + setTimeout(function() { + setTimeout(function() { + Module["setStatus"](""); + }, 1); + doRun(); + }, 1); + } else { + doRun(); + } + } + if (Module["preInit"]) { + if (typeof Module["preInit"] == "function") + Module["preInit"] = [Module["preInit"]]; + while (Module["preInit"].length > 0) { + Module["preInit"].pop()(); + } + } + run(); + var listenersAdded; + if (beforeListeners) { + listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) { + return !beforeListeners.uncaughtException.indexOf(listener) > -1; + }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) { + return !beforeListeners.unhandledRejection.indexOf(listener) > -1; + }) }; + } + var actualModule; + if (typeof WasmBackendModule3 !== "undefined") { + actualModule = WasmBackendModule3; + } else if (typeof WasmBackendModuleThreadedSimd !== "undefined") { + actualModule = WasmBackendModuleThreadedSimd; + } else { + throw new Error("Could not find wasm module in post.js"); + } + if (listenersAdded) { + var tmpDispose = actualModule["_dispose"]; + actualModule["_dispose"] = function() { + tmpDispose(); + listenersAdded.uncaughtException.forEach(function(listener) { + process.removeListener("uncaughtException", listener); + }); + listenersAdded.unhandledRejection.forEach(function(listener) { + process.removeListener("unhandledRejection", listener); + }); + }; + } + return WasmBackendModule3.ready; + }; + })(); + if (typeof exports === "object" && typeof module === "object") + module.exports = WasmBackendModule2; + else if (typeof define === "function" && define["amd"]) + define([], function() { + return WasmBackendModule2; + }); + else if (typeof exports === "object") + exports["WasmBackendModule"] = WasmBackendModule2; + } +}); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js +var EPSILON_FLOAT32 = 1e-7; +var EPSILON_FLOAT16 = 1e-4; +var DataStorage = class { + constructor(backend2, dataMover) { + this.backend = backend2; + this.dataMover = dataMover; + this.data = /* @__PURE__ */ new WeakMap(); + this.dataIdsCount = 0; + } + get(dataId) { + if (!this.data.has(dataId)) { + this.dataMover.moveData(this.backend, dataId); + } + return this.data.get(dataId); + } + set(dataId, value) { + this.dataIdsCount++; + this.data.set(dataId, value); + } + has(dataId) { + return this.data.has(dataId); + } + delete(dataId) { + this.dataIdsCount--; + return this.data.delete(dataId); + } + numDataIds() { + return this.dataIdsCount; + } +}; +var KernelBackend = class { + refCount(dataId) { + return notYetImplemented("refCount"); + } + incRef(dataId) { + return notYetImplemented("incRef"); + } + timerAvailable() { + return true; + } + time(f) { + return notYetImplemented("time"); + } + read(dataId) { + return notYetImplemented("read"); + } + readSync(dataId) { + return notYetImplemented("readSync"); + } + readToGPU(dataId, options) { + return notYetImplemented("readToGPU"); + } + numDataIds() { + return notYetImplemented("numDataIds"); + } + disposeData(dataId, force) { + return notYetImplemented("disposeData"); + } + write(values, shape, dtype) { + return notYetImplemented("write"); + } + move(dataId, values, shape, dtype, refCount) { + return notYetImplemented("move"); + } + createTensorFromGPUData(values, shape, dtype) { + return notYetImplemented("createTensorFromGPUData"); + } + memory() { + return notYetImplemented("memory"); + } + /** Returns the highest precision for floats in bits (e.g. 16 or 32) */ + floatPrecision() { + return notYetImplemented("floatPrecision"); + } + /** Returns the smallest representable number. */ + epsilon() { + return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16; + } + dispose() { + return notYetImplemented("dispose"); + } +}; +function notYetImplemented(kernelName) { + throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/util_base.js +function shuffle(array2) { + let counter = array2.length; + let index = 0; + while (counter > 0) { + index = Math.random() * counter | 0; + counter--; + swap(array2, counter, index); + } +} +function shuffleCombo(array2, array22) { + if (array2.length !== array22.length) { + throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`); + } + let counter = array2.length; + let index = 0; + while (counter > 0) { + index = Math.random() * counter | 0; + counter--; + swap(array2, counter, index); + swap(array22, counter, index); + } +} +function clamp(min6, x, max6) { + return Math.max(min6, Math.min(x, max6)); +} +function nearestLargerEven(val) { + return val % 2 === 0 ? val : val + 1; +} +function swap(object, left, right) { + const temp = object[left]; + object[left] = object[right]; + object[right] = temp; +} +function sum(arr) { + let sum6 = 0; + for (let i = 0; i < arr.length; i++) { + sum6 += arr[i]; + } + return sum6; +} +function randUniform(a, b) { + const r = Math.random(); + return b * r + (1 - r) * a; +} +function distSquared(a, b) { + let result = 0; + for (let i = 0; i < a.length; i++) { + const diff = Number(a[i]) - Number(b[i]); + result += diff * diff; + } + return result; +} +function assert(expr, msg) { + if (!expr) { + throw new Error(typeof msg === "string" ? msg : msg()); + } +} +function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") { + assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); +} +function assertNonNull(a) { + assert(a != null, () => `The input to the tensor constructor must be a non-null value.`); +} +function sizeFromShape(shape) { + if (shape.length === 0) { + return 1; + } + let size = shape[0]; + for (let i = 1; i < shape.length; i++) { + size *= shape[i]; + } + return size; +} +function isScalarShape(shape) { + return shape.length === 0; +} +function arraysEqualWithNull(n1, n2) { + if (n1 === n2) { + return true; + } + if (n1 == null || n2 == null) { + return false; + } + if (n1.length !== n2.length) { + return false; + } + for (let i = 0; i < n1.length; i++) { + if (n1[i] !== null && n2[i] !== null && n1[i] !== n2[i]) { + return false; + } + } + return true; +} +function arraysEqual(n1, n2) { + if (n1 === n2) { + return true; + } + if (n1 == null || n2 == null) { + return false; + } + if (n1.length !== n2.length) { + return false; + } + for (let i = 0; i < n1.length; i++) { + if (n1[i] !== n2[i]) { + return false; + } + } + return true; +} +function isInt(a) { + return a % 1 === 0; +} +function tanh(x) { + if (Math.tanh != null) { + return Math.tanh(x); + } + if (x === Infinity) { + return 1; + } else if (x === -Infinity) { + return -1; + } else { + const e2x = Math.exp(2 * x); + return (e2x - 1) / (e2x + 1); + } +} +function sizeToSquarishShape(size) { + const width = Math.ceil(Math.sqrt(size)); + return [width, Math.ceil(size / width)]; +} +function createShuffledIndices(n) { + const shuffledIndices = new Uint32Array(n); + for (let i = 0; i < n; ++i) { + shuffledIndices[i] = i; + } + shuffle(shuffledIndices); + return shuffledIndices; +} +function rightPad(a, size) { + if (size <= a.length) { + return a; + } + return a + " ".repeat(size - a.length); +} +function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter, scheduleFn) { + return new Promise((resolve, reject) => { + let tryCount = 0; + const tryFn = () => { + if (checkFn()) { + resolve(); + return; + } + tryCount++; + const nextBackoff = delayFn(tryCount); + if (maxCounter != null && tryCount >= maxCounter) { + reject(); + return; + } + if (scheduleFn != null) { + scheduleFn(tryFn, nextBackoff); + } else { + setTimeout(tryFn, nextBackoff); + } + }; + tryFn(); + }); +} +function inferFromImplicitShape(shape, size) { + let shapeProd = 1; + let implicitIdx = -1; + for (let i = 0; i < shape.length; ++i) { + if (shape[i] >= 0) { + shapeProd *= shape[i]; + } else if (shape[i] === -1) { + if (implicitIdx !== -1) { + throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`); + } + implicitIdx = i; + } else if (shape[i] < 0) { + throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`); + } + } + if (implicitIdx === -1) { + if (size > 0 && size !== shapeProd) { + throw Error(`Size(${size}) must match the product of shape ${shape}`); + } + return shape; + } + if (shapeProd === 0) { + throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`); + } + if (size % shapeProd !== 0) { + throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`); + } + const newShape = shape.slice(); + newShape[implicitIdx] = size / shapeProd; + return newShape; +} +function parseAxisParam(axis, shape) { + const rank = shape.length; + axis = axis == null ? shape.map((s, i) => i) : [].concat(axis); + assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`); + assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`); + return axis.map((a) => a < 0 ? rank + a : a); +} +function squeezeShape(shape, axis) { + const newShape = []; + const keptDims = []; + const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0; + const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort(); + let j = 0; + for (let i = 0; i < shape.length; ++i) { + if (axes != null) { + if (axes[j] === i && shape[i] !== 1) { + throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`); + } + if ((axes[j] == null || axes[j] > i) && shape[i] === 1) { + newShape.push(shape[i]); + keptDims.push(i); + } + if (axes[j] <= i) { + j++; + } + } + if (shape[i] !== 1) { + newShape.push(shape[i]); + keptDims.push(i); + } + } + return { newShape, keptDims }; +} +function getTypedArrayFromDType(dtype, size) { + return getArrayFromDType(dtype, size); +} +function getArrayFromDType(dtype, size) { + let values = null; + if (dtype == null || dtype === "float32") { + values = new Float32Array(size); + } else if (dtype === "int32") { + values = new Int32Array(size); + } else if (dtype === "bool") { + values = new Uint8Array(size); + } else if (dtype === "string") { + values = new Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } + return values; +} +function checkConversionForErrors(vals, dtype) { + for (let i = 0; i < vals.length; i++) { + const num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`); + } + } +} +function isValidDtype(dtype) { + return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string"; +} +function hasEncodingLoss(oldType, newType) { + if (newType === "complex64") { + return false; + } + if (newType === "float32" && oldType !== "complex64") { + return false; + } + if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") { + return false; + } + if (newType === "bool" && oldType === "bool") { + return false; + } + return true; +} +function bytesPerElement(dtype) { + if (dtype === "float32" || dtype === "int32") { + return 4; + } else if (dtype === "complex64") { + return 8; + } else if (dtype === "bool") { + return 1; + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} +function bytesFromStringArray(arr) { + if (arr == null) { + return 0; + } + let bytes = 0; + arr.forEach((x) => bytes += x.length); + return bytes; +} +function isString(value) { + return typeof value === "string" || value instanceof String; +} +function isBoolean(value) { + return typeof value === "boolean"; +} +function isNumber(value) { + return typeof value === "number"; +} +function inferDtype(values) { + if (Array.isArray(values)) { + return inferDtype(values[0]); + } + if (values instanceof Float32Array) { + return "float32"; + } else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) { + return "int32"; + } else if (isNumber(values)) { + return "float32"; + } else if (isString(values)) { + return "string"; + } else if (isBoolean(values)) { + return "bool"; + } + return "float32"; +} +function isFunction(f) { + return !!(f && f.constructor && f.call && f.apply); +} +function nearestDivisor(size, start) { + for (let i = start; i < size; ++i) { + if (size % i === 0) { + return i; + } + } + return size; +} +function computeStrides(shape) { + const rank = shape.length; + if (rank < 2) { + return []; + } + const strides = new Array(rank - 1); + strides[rank - 2] = shape[rank - 1]; + for (let i = rank - 3; i >= 0; --i) { + strides[i] = strides[i + 1] * shape[i + 1]; + } + return strides; +} +function createNestedArray(offset, shape, a, isComplex = false) { + const ret = new Array(); + if (shape.length === 1) { + const d = shape[0] * (isComplex ? 2 : 1); + for (let i = 0; i < d; i++) { + ret[i] = a[offset + i]; + } + } else { + const d = shape[0]; + const rest = shape.slice(1); + const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1); + for (let i = 0; i < d; i++) { + ret[i] = createNestedArray(offset + i * len, rest, a, isComplex); + } + } + return ret; +} +function toNestedArray(shape, a, isComplex = false) { + if (shape.length === 0) { + return a[0]; + } + const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1); + if (size === 0) { + return []; + } + if (size !== a.length) { + throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? " for a complex tensor" : ""}.`); + } + return createNestedArray(0, shape, a, isComplex); +} +function convertBackendValuesAndArrayBuffer(data, dtype) { + if (Array.isArray(data)) { + return data; + } + if (dtype === "float32") { + return data instanceof Float32Array ? data : new Float32Array(data); + } else if (dtype === "int32") { + return data instanceof Int32Array ? data : new Int32Array(data); + } else if (dtype === "bool" || dtype === "string") { + return Uint8Array.from(new Int32Array(data)); + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} +function makeOnesTypedArray(size, dtype) { + const array2 = makeZerosTypedArray(size, dtype); + for (let i = 0; i < array2.length; i++) { + array2[i] = 1; + } + return array2; +} +function makeZerosTypedArray(size, dtype) { + if (dtype == null || dtype === "float32" || dtype === "complex64") { + return new Float32Array(size); + } else if (dtype === "int32") { + return new Int32Array(size); + } else if (dtype === "bool") { + return new Uint8Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function makeZerosNestedTypedArray(shape, dtype) { + const size = shape.reduce((prev, curr) => prev * curr, 1); + if (dtype == null || dtype === "float32") { + return toNestedArray(shape, new Float32Array(size)); + } else if (dtype === "int32") { + return toNestedArray(shape, new Int32Array(size)); + } else if (dtype === "bool") { + return toNestedArray(shape, new Uint8Array(size)); + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function assertNonNegativeIntegerDimensions(shape) { + shape.forEach((dimSize) => { + assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`); + }); +} +function locToIndex(locs, rank, strides) { + if (rank === 0) { + return 0; + } else if (rank === 1) { + return locs[0]; + } + let index = locs[locs.length - 1]; + for (let i = 0; i < locs.length - 1; ++i) { + index += strides[i] * locs[i]; + } + return index; +} +function indexToLoc(index, rank, strides) { + if (rank === 0) { + return []; + } else if (rank === 1) { + return [index]; + } + const locs = new Array(rank); + for (let i = 0; i < locs.length - 1; ++i) { + locs[i] = Math.floor(index / strides[i]); + index -= locs[i] * strides[i]; + } + locs[locs.length - 1] = index; + return locs; +} +function isPromise(object) { + return object && object.then && typeof object.then === "function"; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/environment.js +var TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags"; +var Environment = class { + // tslint:disable-next-line: no-any + constructor(global2) { + this.global = global2; + this.flags = {}; + this.flagRegistry = {}; + this.urlFlags = {}; + this.getQueryParams = getQueryParams; + this.populateURLFlags(); + } + setPlatform(platformName, platform) { + if (this.platform != null) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platformName}.`); + } + } + this.platformName = platformName; + this.platform = platform; + } + registerFlag(flagName, evaluationFn, setHook) { + this.flagRegistry[flagName] = { evaluationFn, setHook }; + if (this.urlFlags[flagName] != null) { + const flagValue = this.urlFlags[flagName]; + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`); + } + this.set(flagName, flagValue); + } + } + async getAsync(flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + this.flags[flagName] = await this.evaluateFlag(flagName); + return this.flags[flagName]; + } + get(flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + const flagValue = this.evaluateFlag(flagName); + if (isPromise(flagValue)) { + throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`); + } + this.flags[flagName] = flagValue; + return this.flags[flagName]; + } + getNumber(flagName) { + return this.get(flagName); + } + getBool(flagName) { + return this.get(flagName); + } + getString(flagName) { + return this.get(flagName); + } + getFlags() { + return this.flags; + } + // For backwards compatibility. + get features() { + return this.flags; + } + set(flagName, value) { + if (this.flagRegistry[flagName] == null) { + throw new Error(`Cannot set flag ${flagName} as it has not been registered.`); + } + this.flags[flagName] = value; + if (this.flagRegistry[flagName].setHook != null) { + this.flagRegistry[flagName].setHook(value); + } + } + evaluateFlag(flagName) { + if (this.flagRegistry[flagName] == null) { + throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`); + } + return this.flagRegistry[flagName].evaluationFn(); + } + setFlags(flags) { + this.flags = Object.assign({}, flags); + } + reset() { + this.flags = {}; + this.urlFlags = {}; + this.populateURLFlags(); + } + populateURLFlags() { + if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") { + return; + } + const urlParams = this.getQueryParams(this.global.location.search); + if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) { + const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(","); + keyValues.forEach((keyValue) => { + const [key, value] = keyValue.split(":"); + this.urlFlags[key] = parseValue(key, value); + }); + } + } +}; +function getQueryParams(queryString) { + const params = {}; + queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => { + decodeParam(params, t[0], t[1]); + return t.join("="); + }); + return params; +} +function decodeParam(params, name, value) { + params[decodeURIComponent(name)] = decodeURIComponent(value || ""); +} +function parseValue(flagName, value) { + const lowerCaseValue = value.toLowerCase(); + if (lowerCaseValue === "true" || lowerCaseValue === "false") { + return lowerCaseValue === "true"; + } else if (`${+lowerCaseValue}` === lowerCaseValue) { + return +lowerCaseValue; + } else { + return value; + } +} +function env() { + return ENV; +} +var ENV = null; +function setEnvironmentGlobal(environment) { + ENV = environment; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/global_util.js +var globalNameSpace; +function getGlobalNamespace() { + if (globalNameSpace == null) { + let ns; + if (typeof window !== "undefined") { + ns = window; + } else if (typeof global !== "undefined") { + ns = global; + } else if (typeof process !== "undefined") { + ns = process; + } else if (typeof self !== "undefined") { + ns = self; + } else { + throw new Error("Could not find a global object"); + } + globalNameSpace = ns; + } + return globalNameSpace; +} +function getGlobalMap() { + const ns = getGlobalNamespace(); + if (ns._tfGlobals == null) { + ns._tfGlobals = /* @__PURE__ */ new Map(); + } + return ns._tfGlobals; +} +function getGlobal(key, init2) { + const globalMap = getGlobalMap(); + if (globalMap.has(key)) { + return globalMap.get(key); + } else { + const singleton = init2(); + globalMap.set(key, singleton); + return globalMap.get(key); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js +var Abs = "Abs"; +var Acos = "Acos"; +var Acosh = "Acosh"; +var Add = "Add"; +var AddN = "AddN"; +var All = "All"; +var Any = "Any"; +var ArgMax = "ArgMax"; +var ArgMin = "ArgMin"; +var Asin = "Asin"; +var Asinh = "Asinh"; +var Atan = "Atan"; +var Atanh = "Atanh"; +var Atan2 = "Atan2"; +var AvgPool = "AvgPool"; +var AvgPoolGrad = "AvgPoolGrad"; +var AvgPool3D = "AvgPool3D"; +var AvgPool3DGrad = "AvgPool3DGrad"; +var BatchMatMul = "BatchMatMul"; +var BatchToSpaceND = "BatchToSpaceND"; +var Bincount = "Bincount"; +var BitwiseAnd = "BitwiseAnd"; +var BroadcastTo = "BroadcastTo"; +var BroadcastArgs = "BroadcastArgs"; +var Cast = "Cast"; +var Ceil = "Ceil"; +var ClipByValue = "ClipByValue"; +var Complex = "Complex"; +var ComplexAbs = "ComplexAbs"; +var Concat = "Concat"; +var Conv2D = "Conv2D"; +var Conv2DBackpropFilter = "Conv2DBackpropFilter"; +var Conv2DBackpropInput = "Conv2DBackpropInput"; +var Conv3D = "Conv3D"; +var Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2"; +var Conv3DBackpropInputV2 = "Conv3DBackpropInputV2"; +var Cos = "Cos"; +var Cosh = "Cosh"; +var Cumprod = "Cumprod"; +var Cumsum = "Cumsum"; +var CropAndResize = "CropAndResize"; +var DenseBincount = "DenseBincount"; +var DepthToSpace = "DepthToSpace"; +var DepthwiseConv2dNative = "DepthwiseConv2dNative"; +var DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter"; +var DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput"; +var Diag = "Diag"; +var Dilation2D = "Dilation2D"; +var Dilation2DBackpropInput = "Dilation2DBackpropInput"; +var Dilation2DBackpropFilter = "Dilation2DBackpropFilter"; +var Draw = "Draw"; +var RealDiv = "RealDiv"; +var Einsum = "Einsum"; +var Elu = "Elu"; +var EluGrad = "EluGrad"; +var Erf = "Erf"; +var Equal = "Equal"; +var Exp = "Exp"; +var ExpandDims = "ExpandDims"; +var Expm1 = "Expm1"; +var FFT = "FFT"; +var Fill = "Fill"; +var FlipLeftRight = "FlipLeftRight"; +var Floor = "Floor"; +var FloorDiv = "FloorDiv"; +var FusedBatchNorm = "FusedBatchNorm"; +var GatherV2 = "GatherV2"; +var GatherNd = "GatherNd"; +var Greater = "Greater"; +var GreaterEqual = "GreaterEqual"; +var Identity = "Identity"; +var IFFT = "IFFT"; +var Imag = "Imag"; +var IsFinite = "IsFinite"; +var IsInf = "IsInf"; +var IsNan = "IsNan"; +var LeakyRelu = "LeakyRelu"; +var Less = "Less"; +var LessEqual = "LessEqual"; +var LinSpace = "LinSpace"; +var Log = "Log"; +var Log1p = "Log1p"; +var LogicalAnd = "LogicalAnd"; +var LogicalNot = "LogicalNot"; +var LogicalOr = "LogicalOr"; +var LogicalXor = "LogicalXor"; +var LogSoftmax = "LogSoftmax"; +var LowerBound = "LowerBound"; +var LRN = "LRN"; +var LRNGrad = "LRNGrad"; +var MatrixBandPart = "MatrixBandPart"; +var Max = "Max"; +var Maximum = "Maximum"; +var MaxPool = "MaxPool"; +var MaxPoolGrad = "MaxPoolGrad"; +var MaxPool3D = "MaxPool3D"; +var MaxPool3DGrad = "MaxPool3DGrad"; +var MaxPoolWithArgmax = "MaxPoolWithArgmax"; +var Mean = "Mean"; +var Min = "Min"; +var Minimum = "Minimum"; +var MirrorPad = "MirrorPad"; +var Mod = "Mod"; +var Multinomial = "Multinomial"; +var Multiply = "Multiply"; +var Neg = "Neg"; +var NotEqual = "NotEqual"; +var NonMaxSuppressionV3 = "NonMaxSuppressionV3"; +var NonMaxSuppressionV4 = "NonMaxSuppressionV4"; +var NonMaxSuppressionV5 = "NonMaxSuppressionV5"; +var OnesLike = "OnesLike"; +var OneHot = "OneHot"; +var Pack = "Pack"; +var PadV2 = "PadV2"; +var Pool = "Pool"; +var Pow = "Pow"; +var Prelu = "Prelu"; +var Prod = "Prod"; +var RaggedGather = "RaggedGather"; +var RaggedRange = "RaggedRange"; +var RaggedTensorToTensor = "RaggedTensorToTensor"; +var Range = "Range"; +var Real = "Real"; +var Reciprocal = "Reciprocal"; +var Relu = "Relu"; +var Reshape = "Reshape"; +var ResizeNearestNeighbor = "ResizeNearestNeighbor"; +var ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad"; +var ResizeBilinear = "ResizeBilinear"; +var ResizeBilinearGrad = "ResizeBilinearGrad"; +var Relu6 = "Relu6"; +var Reverse = "Reverse"; +var Round = "Round"; +var Rsqrt = "Rsqrt"; +var ScatterNd = "ScatterNd"; +var TensorScatterUpdate = "TensorScatterUpdate"; +var SearchSorted = "SearchSorted"; +var Select = "Select"; +var Selu = "Selu"; +var Slice = "Slice"; +var Sin = "Sin"; +var Sinh = "Sinh"; +var Sign = "Sign"; +var Sigmoid = "Sigmoid"; +var Softplus = "Softplus"; +var Sqrt = "Sqrt"; +var Sum = "Sum"; +var SpaceToBatchND = "SpaceToBatchND"; +var SplitV = "SplitV"; +var Softmax = "Softmax"; +var SparseFillEmptyRows = "SparseFillEmptyRows"; +var SparseReshape = "SparseReshape"; +var SparseSegmentMean = "SparseSegmentMean"; +var SparseSegmentSum = "SparseSegmentSum"; +var SparseToDense = "SparseToDense"; +var SquaredDifference = "SquaredDifference"; +var Square = "Square"; +var StaticRegexReplace = "StaticRegexReplace"; +var StridedSlice = "StridedSlice"; +var StringNGrams = "StringNGrams"; +var StringSplit = "StringSplit"; +var StringToHashBucketFast = "StringToHashBucketFast"; +var Sub = "Sub"; +var Tan = "Tan"; +var Tanh = "Tanh"; +var Tile = "Tile"; +var TopK = "TopK"; +var Transform = "Transform"; +var Transpose = "Transpose"; +var Unique = "Unique"; +var Unpack = "Unpack"; +var UnsortedSegmentSum = "UnsortedSegmentSum"; +var UpperBound = "UpperBound"; +var ZerosLike = "ZerosLike"; +var Step = "Step"; +var FromPixels = "FromPixels"; +var RotateWithOffset = "RotateWithOffset"; +var _FusedMatMul = "_FusedMatMul"; +var FusedConv2D = "FusedConv2D"; +var FusedDepthwiseConv2D = "FusedDepthwiseConv2D"; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/log.js +function warn(...msg) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(...msg); + } +} +function log(...msg) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.log(...msg); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js +var kernelRegistry = getGlobal("kernelRegistry", () => /* @__PURE__ */ new Map()); +var gradRegistry = getGlobal("gradRegistry", () => /* @__PURE__ */ new Map()); +function getKernel(kernelName, backendName) { + const key = makeKey(kernelName, backendName); + return kernelRegistry.get(key); +} +function getGradient(kernelName) { + return gradRegistry.get(kernelName); +} +function getKernelsForBackend(backendName) { + const it = kernelRegistry.entries(); + const result = []; + while (true) { + const { done, value } = it.next(); + if (done) { + break; + } + const [key, config] = value; + const [backend2] = key.split("_"); + if (backend2 === backendName) { + result.push(config); + } + } + return result; +} +function registerKernel(config) { + const { kernelName, backendName } = config; + const key = makeKey(kernelName, backendName); + if (kernelRegistry.has(key)) { + warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`); + } + kernelRegistry.set(key, config); +} +function registerGradient(config) { + const { kernelName } = config; + if (gradRegistry.has(kernelName)) { + if (env().getBool("DEBUG")) { + warn(`Overriding the gradient for '${kernelName}'`); + } + } + gradRegistry.set(kernelName, config); +} +function unregisterKernel(kernelName, backendName) { + const key = makeKey(kernelName, backendName); + if (!kernelRegistry.has(key)) { + throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`); + } + kernelRegistry.delete(key); +} +function unregisterGradient(kernelName) { + if (!gradRegistry.has(kernelName)) { + throw new Error(`The gradient '${kernelName}' for backend is not registered`); + } + gradRegistry.delete(kernelName); +} +function copyRegisteredKernels(registeredBackendName, newBackendName) { + const kernels = getKernelsForBackend(registeredBackendName); + kernels.forEach((kernelConfig) => { + const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName }); + registerKernel(newKernelConfig); + }); +} +function makeKey(kernelName, backendName) { + return `${backendName}_${kernelName}`; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/util.js +var util_exports = {}; +__export(util_exports, { + arraysEqual: () => arraysEqual, + arraysEqualWithNull: () => arraysEqualWithNull, + assert: () => assert, + assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions, + assertNonNull: () => assertNonNull, + assertShapesMatch: () => assertShapesMatch, + bytesFromStringArray: () => bytesFromStringArray, + bytesPerElement: () => bytesPerElement, + checkConversionForErrors: () => checkConversionForErrors, + clamp: () => clamp, + computeStrides: () => computeStrides, + convertBackendValuesAndArrayBuffer: () => convertBackendValuesAndArrayBuffer, + createScalarValue: () => createScalarValue, + createShuffledIndices: () => createShuffledIndices, + decodeString: () => decodeString, + distSquared: () => distSquared, + encodeString: () => encodeString, + fetch: () => fetch3, + fingerPrint64: () => fingerPrint64, + flatten: () => flatten, + getArrayFromDType: () => getArrayFromDType, + getTypedArrayFromDType: () => getTypedArrayFromDType, + hasEncodingLoss: () => hasEncodingLoss, + hexToLong: () => hexToLong, + indexToLoc: () => indexToLoc, + inferDtype: () => inferDtype, + inferFromImplicitShape: () => inferFromImplicitShape, + isBoolean: () => isBoolean, + isFunction: () => isFunction, + isInt: () => isInt, + isNumber: () => isNumber, + isPromise: () => isPromise, + isScalarShape: () => isScalarShape, + isString: () => isString, + isTypedArray: () => isTypedArray, + isValidDtype: () => isValidDtype, + locToIndex: () => locToIndex, + makeOnesTypedArray: () => makeOnesTypedArray, + makeZerosNestedTypedArray: () => makeZerosNestedTypedArray, + makeZerosTypedArray: () => makeZerosTypedArray, + nearestDivisor: () => nearestDivisor, + nearestLargerEven: () => nearestLargerEven, + now: () => now, + parseAxisParam: () => parseAxisParam, + randUniform: () => randUniform, + repeatedTry: () => repeatedTry, + rightPad: () => rightPad, + shuffle: () => shuffle, + shuffleCombo: () => shuffleCombo, + sizeFromShape: () => sizeFromShape, + sizeToSquarishShape: () => sizeToSquarishShape, + squeezeShape: () => squeezeShape, + sum: () => sum, + swap: () => swap, + tanh: () => tanh, + toNestedArray: () => toNestedArray, + toTypedArray: () => toTypedArray +}); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/platforms/is_typed_array_browser.js +function isTypedArrayBrowser(a) { + return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array || a instanceof Uint8ClampedArray; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/hash_util.js +var LongExports = __toESM(require_long()); +var Long = ( + // tslint:disable-next-line + LongExports.default || LongExports +); +function hexToLong(hex) { + return Long.fromString(hex, true, 16); +} +var k0 = hexToLong("c3a5c85c97cb3127"); +var k1 = hexToLong("b492b66fbe98f273"); +var k2 = hexToLong("9ae16a3b2f90404f"); +function shiftMix(val) { + return val.xor(val.shru(47)); +} +function fetch2(s, offset, numBytes) { + const bytes = s.slice(offset, offset + numBytes); + return Long.fromBytes(Array.from(bytes), true, true); +} +function fetch64(s, offset) { + return fetch2(s, offset, 8); +} +function fetch32(s, offset) { + return fetch2(s, offset, 4); +} +function rotate64(val, shift) { + return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift)); +} +function hashLen16(u, v, mul2 = hexToLong("9ddfea08eb382d69")) { + let a = u.xor(v).mul(mul2); + a = a.xor(a.shru(47)); + let b = v.xor(a).mul(mul2); + b = b.xor(b.shru(47)); + b = b.mul(mul2); + return b; +} +function weakHashLen32WithSeeds(w, x, y, z, a, b) { + a = a.add(w); + b = rotate64(b.add(a).add(z), 21); + const c = a; + a = a.add(x); + a = a.add(y); + b = b.add(rotate64(a, 44)); + return [a.add(z), b.add(c)]; +} +function weakHashLen32WithSeedsStr(s, offset, a, b) { + return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b); +} +function hashLen0to16(s, len = s.length) { + if (len >= 8) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).add(k2); + const b = fetch64(s, len - 8); + const c = rotate64(b, 37).mul(mul2).add(a); + const d = rotate64(a, 25).add(b).mul(mul2); + return hashLen16(c, d, mul2); + } + if (len >= 4) { + const mul2 = k2.add(len * 2); + const a = fetch32(s, 0); + return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul2); + } + if (len > 0) { + const a = s[0]; + const b = s[len >> 1]; + const c = s[len - 1]; + const y = a + (b << 8); + const z = len + (c << 2); + return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2); + } + return k2; +} +function hashLen17to32(s, len = s.length) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).mul(k1); + const b = fetch64(s, 8); + const c = fetch64(s, len - 8).mul(mul2); + const d = fetch64(s, len - 16).mul(k2); + return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul2); +} +function hashLen33to64(s, len = s.length) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).mul(k2); + const b = fetch64(s, 8); + const c = fetch64(s, len - 8).mul(mul2); + const d = fetch64(s, len - 16).mul(k2); + const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d); + const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul2); + const e = fetch64(s, 16).mul(mul2); + const f = fetch64(s, 24); + const g = y.add(fetch64(s, len - 32)).mul(mul2); + const h = z.add(fetch64(s, len - 24)).mul(mul2); + return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul2); +} +function fingerPrint64(s, len = s.length) { + const seed = Long.fromNumber(81, true); + if (len <= 32) { + if (len <= 16) { + return hashLen0to16(s, len); + } else { + return hashLen17to32(s, len); + } + } else if (len <= 64) { + return hashLen33to64(s, len); + } + let x = seed; + let y = seed.mul(k1).add(113); + let z = shiftMix(y.mul(k2).add(113)).mul(k2); + let v = [Long.UZERO, Long.UZERO]; + let w = [Long.UZERO, Long.UZERO]; + x = x.mul(k2).add(fetch64(s, 0)); + let offset = 0; + const end = (len - 1 >> 6) * 64; + const last64 = end + (len - 1 & 63) - 63; + do { + x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1); + y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1); + x = x.xor(w[1]); + y = y.add(v[0]).add(fetch64(s, offset + 40)); + z = rotate64(z.add(w[0]), 33).mul(k1); + v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0])); + w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16))); + [z, x] = [x, z]; + offset += 64; + } while (offset !== end); + const mul2 = k1.add(z.and(255).shl(1)); + offset = last64; + w[0] = w[0].add(len - 1 & 63); + v[0] = v[0].add(w[0]); + w[0] = w[0].add(v[0]); + x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul2); + y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul2); + x = x.xor(w[1].mul(9)); + y = y.add(v[0].mul(9).add(fetch64(s, offset + 40))); + z = rotate64(z.add(w[0]), 33).mul(mul2); + v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul2), x.add(w[0])); + w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16))); + [z, x] = [x, z]; + return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/util.js +function createScalarValue(value, dtype) { + if (dtype === "string") { + return encodeString(value); + } + return toTypedArray([value], dtype); +} +function noConversionNeeded(a, dtype) { + return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool"; +} +function toTypedArray(a, dtype) { + if (dtype === "string") { + throw new Error("Cannot convert a string[] to a TypedArray"); + } + if (Array.isArray(a)) { + a = flatten(a); + } + if (env().getBool("DEBUG")) { + checkConversionForErrors(a, dtype); + } + if (noConversionNeeded(a, dtype)) { + return a; + } + if (dtype == null || dtype === "float32" || dtype === "complex64") { + return new Float32Array(a); + } else if (dtype === "int32") { + return new Int32Array(a); + } else if (dtype === "bool") { + const bool = new Uint8Array(a.length); + for (let i = 0; i < bool.length; ++i) { + if (Math.round(a[i]) !== 0) { + bool[i] = 1; + } + } + return bool; + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function now() { + return env().platform.now(); +} +function fetch3(path, requestInits) { + return env().platform.fetch(path, requestInits); +} +function encodeString(s, encoding = "utf-8") { + encoding = encoding || "utf-8"; + return env().platform.encode(s, encoding); +} +function decodeString(bytes, encoding = "utf-8") { + encoding = encoding || "utf-8"; + return env().platform.decode(bytes, encoding); +} +function isTypedArray(a) { + if (env().platform.isTypedArray != null) { + return env().platform.isTypedArray(a); + } else { + return isTypedArrayBrowser(a); + } +} +function flatten(arr, result = [], skipTypedArray = false) { + if (result == null) { + result = []; + } + if (typeof arr === "boolean" || typeof arr === "number" || typeof arr === "string" || isPromise(arr) || arr == null || isTypedArray(arr) && skipTypedArray) { + result.push(arr); + } else if (Array.isArray(arr) || isTypedArray(arr)) { + for (let i = 0; i < arr.length; ++i) { + flatten(arr[i], result, skipTypedArray); + } + } else { + let maxIndex = -1; + for (const key of Object.keys(arr)) { + if (/^([1-9]+[0-9]*|0)$/.test(key)) { + maxIndex = Math.max(maxIndex, Number(key)); + } + } + for (let i = 0; i <= maxIndex; i++) { + flatten(arr[i], result, skipTypedArray); + } + } + return result; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/profiler.js +var Profiler = class { + constructor(backendTimer, logger) { + this.backendTimer = backendTimer; + this.logger = logger; + if (logger == null) { + this.logger = new Logger(); + } + } + profileKernel(kernelName, inputs, f) { + let outputs; + const holdResultWrapperFn = () => { + outputs = f(); + }; + let timer; + const start = now(); + if (this.backendTimer.timerAvailable()) { + timer = this.backendTimer.time(holdResultWrapperFn); + } else { + holdResultWrapperFn(); + for (const output of outputs) { + output.dataSync(); + } + timer = Promise.resolve({ kernelMs: now() - start }); + } + if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) { + for (let i = 0; i < outputs.length; i++) { + const output = outputs[i]; + output.data().then((tensorVals) => { + checkComputationForErrors(tensorVals, output.dtype, kernelName); + }); + } + } + const kernelProfile = { + kernelName, + outputs, + inputs, + timeMs: timer.then((timing) => timing.kernelMs), + extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "") + }; + return kernelProfile; + } + logKernelProfile(kernelProfile) { + const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile; + outputs.forEach((result) => { + Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => { + this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]); + }); + }); + } +}; +function checkComputationForErrors(vals, dtype, kernelName) { + if (dtype !== "float32") { + return false; + } + for (let i = 0; i < vals.length; i++) { + const num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + console.warn(`Found ${num} in the result of '${kernelName}'`); + return true; + } + } + return false; +} +var Logger = class { + logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) { + const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"]; + const paddedName = rightPad(name, 25); + const rank = result.rank; + const size = result.size; + const shape = rightPad(result.shape.toString(), 14); + let inputShapesDescription = ""; + for (const name2 in inputs) { + const input2 = inputs[name2]; + if (input2 != null) { + const inputShape = input2.shape || result.shape; + const inputRank = inputShape.length; + inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `; + } + } + console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue"); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tape.js +function getFilteredNodesXToY(tape, xs, y) { + const tensorsFromX = {}; + const nodesFromX = {}; + for (let i = 0; i < xs.length; i++) { + tensorsFromX[xs[i].id] = true; + } + for (let i = 0; i < tape.length; i++) { + const node = tape[i]; + const nodeInputs = node.inputs; + for (const inputName in nodeInputs) { + const input2 = nodeInputs[inputName]; + let anyInputFromX = false; + for (let j = 0; j < xs.length; j++) { + if (tensorsFromX[input2.id]) { + node.outputs.forEach((output) => tensorsFromX[output.id] = true); + anyInputFromX = true; + nodesFromX[node.id] = true; + break; + } + } + if (anyInputFromX) { + break; + } + } + } + const tensorsLeadToY = {}; + tensorsLeadToY[y.id] = true; + const nodesToY = {}; + for (let i = tape.length - 1; i >= 0; i--) { + const node = tape[i]; + const nodeInputs = node.inputs; + for (let j = 0; j < node.outputs.length; j++) { + if (tensorsLeadToY[node.outputs[j].id]) { + for (const inputName in nodeInputs) { + tensorsLeadToY[nodeInputs[inputName].id] = true; + nodesToY[node.id] = true; + } + break; + } + } + } + const filteredTape = []; + for (let i = 0; i < tape.length; i++) { + const node = tape[i]; + if (nodesFromX[node.id] && nodesToY[node.id]) { + const prunedInputs = {}; + for (const inputName in node.inputs) { + const nodeInput = node.inputs[inputName]; + if (tensorsFromX[nodeInput.id]) { + prunedInputs[inputName] = nodeInput; + } + } + const prunedNode = Object.assign({}, node); + prunedNode.inputs = prunedInputs; + prunedNode.outputs = node.outputs; + filteredTape.push(prunedNode); + } + } + return filteredTape; +} +function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) { + for (let i = filteredTape.length - 1; i >= 0; i--) { + const node = filteredTape[i]; + const dys = []; + node.outputs.forEach((o) => { + const gradTensor = tensorAccumulatedGradientMap[o.id]; + if (gradTensor != null) { + dys.push(gradTensor); + } else { + dys.push(null); + } + }); + if (node.gradient == null) { + throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`); + } + const inputGradients = node.gradient(dys); + for (const inputName in node.inputs) { + if (!(inputName in inputGradients)) { + throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`); + } + const dx = tidy2(() => inputGradients[inputName]()); + if (dx.dtype !== "float32") { + throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`); + } + const x = node.inputs[inputName]; + if (!arraysEqual(dx.shape, x.shape)) { + throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`); + } + if (tensorAccumulatedGradientMap[x.id] == null) { + tensorAccumulatedGradientMap[x.id] = dx; + } else { + const curGradient = tensorAccumulatedGradientMap[x.id]; + tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx); + curGradient.dispose(); + } + } + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js +var FORMAT_LIMIT_NUM_VALS = 20; +var FORMAT_NUM_FIRST_LAST_VALS = 3; +var FORMAT_NUM_SIG_DIGITS = 7; +function tensorToString(vals, shape, dtype, verbose) { + const strides = computeStrides(shape); + const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides); + const rank = shape.length; + const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol); + const lines = ["Tensor"]; + if (verbose) { + lines.push(` dtype: ${dtype}`); + lines.push(` rank: ${rank}`); + lines.push(` shape: [${shape}]`); + lines.push(` values:`); + } + lines.push(valsLines.map((l) => " " + l).join("\n")); + return lines.join("\n"); +} +function computeMaxSizePerColumn(vals, shape, dtype, strides) { + const n = sizeFromShape(shape); + const numCols = strides[strides.length - 1]; + const padPerCol = new Array(numCols).fill(0); + const rank = shape.length; + const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals; + if (rank > 1) { + for (let row = 0; row < n / numCols; row++) { + const offset = row * numCols; + for (let j = 0; j < numCols; j++) { + padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length); + } + } + } + return padPerCol; +} +function valToString(val, pad3, dtype) { + let valStr; + if (Array.isArray(val)) { + valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`; + } else if (isString(val)) { + valStr = `'${val}'`; + } else if (dtype === "bool") { + valStr = boolNumToString(val); + } else { + valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(); + } + return rightPad(valStr, pad3); +} +function boolNumToString(v) { + return v === 0 ? "false" : "true"; +} +function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) { + const storagePerElement = dtype === "complex64" ? 2 : 1; + const size = shape[0]; + const rank = shape.length; + if (rank === 0) { + if (dtype === "complex64") { + const complexTuple = createComplexTuples(vals); + return [valToString(complexTuple[0], 0, dtype)]; + } + if (dtype === "bool") { + return [boolNumToString(vals[0])]; + } + return [vals[0].toString()]; + } + if (rank === 1) { + if (size > FORMAT_LIMIT_NUM_VALS) { + const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement; + let firstVals = Array.from(vals.slice(0, firstValsSize)); + let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement)); + if (dtype === "complex64") { + firstVals = createComplexTuples(firstVals); + lastVals = createComplexTuples(lastVals); + } + return [ + "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]" + ]; + } + const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals); + return [ + "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]" + ]; + } + const subshape = shape.slice(1); + const substrides = strides.slice(1); + const stride = strides[0] * storagePerElement; + const lines = []; + if (size > FORMAT_LIMIT_NUM_VALS) { + for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) { + const start = i * stride; + const end = start + stride; + lines.push(...subTensorToString( + vals.slice(start, end), + subshape, + dtype, + substrides, + padPerCol, + false + /* isLast */ + )); + } + lines.push("..."); + for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) { + const start = i * stride; + const end = start + stride; + lines.push(...subTensorToString( + vals.slice(start, end), + subshape, + dtype, + substrides, + padPerCol, + i === size - 1 + /* isLast */ + )); + } + } else { + for (let i = 0; i < size; i++) { + const start = i * stride; + const end = start + stride; + lines.push(...subTensorToString( + vals.slice(start, end), + subshape, + dtype, + substrides, + padPerCol, + i === size - 1 + /* isLast */ + )); + } + } + const sep = rank === 2 ? "," : ""; + lines[0] = "[" + (size > 0 ? lines[0] + sep : ""); + for (let i = 1; i < lines.length - 1; i++) { + lines[i] = " " + lines[i] + sep; + } + let newLineSep = ",\n"; + for (let i = 2; i < rank; i++) { + newLineSep += "\n"; + } + lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep); + return lines; +} +function createComplexTuples(vals) { + const complexTuples = []; + for (let i = 0; i < vals.length; i += 2) { + complexTuples.push([vals[i], vals[i + 1]]); + } + return complexTuples; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tensor.js +var TensorBuffer = class { + constructor(shape, dtype, values) { + this.dtype = dtype; + this.shape = shape.slice(); + this.size = sizeFromShape(shape); + if (values != null) { + const n = values.length; + assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`); + } + if (dtype === "complex64") { + throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`); + } + this.values = values || getArrayFromDType(dtype, this.size); + this.strides = computeStrides(shape); + } + /** + * Sets a value in the buffer at a given location. + * + * @param value The value to set. + * @param locs The location indices. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + set(value, ...locs) { + if (locs.length === 0) { + locs = [0]; + } + assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`); + const index = this.locToIndex(locs); + this.values[index] = value; + } + /** + * Returns the value in the buffer at the provided location. + * + * @param locs The location indices. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + get(...locs) { + if (locs.length === 0) { + locs = [0]; + } + let i = 0; + for (const loc of locs) { + if (loc < 0 || loc >= this.shape[i]) { + const msg = `Requested out of range element at ${locs}. Buffer shape=${this.shape}`; + throw new Error(msg); + } + i++; + } + let index = locs[locs.length - 1]; + for (let i2 = 0; i2 < locs.length - 1; ++i2) { + index += this.strides[i2] * locs[i2]; + } + return this.values[index]; + } + locToIndex(locs) { + if (this.rank === 0) { + return 0; + } else if (this.rank === 1) { + return locs[0]; + } + let index = locs[locs.length - 1]; + for (let i = 0; i < locs.length - 1; ++i) { + index += this.strides[i] * locs[i]; + } + return index; + } + indexToLoc(index) { + if (this.rank === 0) { + return []; + } else if (this.rank === 1) { + return [index]; + } + const locs = new Array(this.shape.length); + for (let i = 0; i < locs.length - 1; ++i) { + locs[i] = Math.floor(index / this.strides[i]); + index -= locs[i] * this.strides[i]; + } + locs[locs.length - 1] = index; + return locs; + } + get rank() { + return this.shape.length; + } + /** + * Creates an immutable `tf.Tensor` object from the buffer. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + toTensor() { + return trackerFn().makeTensor(this.values, this.shape, this.dtype); + } +}; +var trackerFn = null; +var opHandler = null; +var deprecationWarningFn = null; +function setTensorTracker(fn) { + trackerFn = fn; +} +function setOpHandler(handler) { + opHandler = handler; +} +function setDeprecationWarningFn(fn) { + deprecationWarningFn = fn; +} +var Tensor = class { + constructor(shape, dtype, dataId, id) { + this.kept = false; + this.isDisposedInternal = false; + this.shape = shape.slice(); + this.dtype = dtype || "float32"; + this.size = sizeFromShape(shape); + this.strides = computeStrides(shape); + this.dataId = dataId; + this.id = id; + this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; + } + get rank() { + return this.shape.length; + } + /** + * Returns a promise of `tf.TensorBuffer` that holds the underlying data. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + async buffer() { + const vals = await this.data(); + return opHandler.buffer(this.shape, this.dtype, vals); + } + /** + * Returns a `tf.TensorBuffer` that holds the underlying data. + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + bufferSync() { + return opHandler.buffer(this.shape, this.dtype, this.dataSync()); + } + /** + * Returns the tensor data as a nested array. The transfer of data is done + * asynchronously. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + async array() { + const vals = await this.data(); + return toNestedArray(this.shape, vals, this.dtype === "complex64"); + } + /** + * Returns the tensor data as a nested array. The transfer of data is done + * synchronously. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + arraySync() { + return toNestedArray(this.shape, this.dataSync(), this.dtype === "complex64"); + } + /** + * Asynchronously downloads the values from the `tf.Tensor`. Returns a + * promise of `TypedArray` that resolves when the computation has finished. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + async data() { + this.throwIfDisposed(); + const data = trackerFn().read(this.dataId); + if (this.dtype === "string") { + const bytes = await data; + try { + return bytes.map((b) => decodeString(b)); + } catch (_a) { + throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); + } + } + return data; + } + /** + * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()` + * and `data()`, this method prevents data from being downloaded to CPU. + * + * For WebGL backend, the data will be stored on a densely packed texture. + * This means that the texture will use the RGBA channels to store value. + * + * For WebGPU backend, the data will be stored on a buffer. There is no + * parameter, so can not use a user-defined size to create the buffer. + * + * @param options: + * For WebGL, + * - customTexShape: Optional. If set, will use the user defined + * texture shape to create the texture. + * + * @returns For WebGL backend, a GPUData contains the new texture and + * its information. + * { + * tensorRef: The tensor that is associated with this texture, + * texture: WebGLTexture, + * texShape: [number, number] // [height, width] + * } + * + * For WebGPU backend, a GPUData contains the new buffer. + * { + * tensorRef: The tensor that is associated with this buffer, + * buffer: GPUBuffer, + * } + * + * Remember to dispose the GPUData after it is used by + * `res.tensorRef.dispose()`. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + dataToGPU(options) { + this.throwIfDisposed(); + return trackerFn().readToGPU(this.dataId, options); + } + /** + * Synchronously downloads the values from the `tf.Tensor`. This blocks the + * UI thread until the values are ready, which can cause performance issues. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + dataSync() { + this.throwIfDisposed(); + const data = trackerFn().readSync(this.dataId); + if (this.dtype === "string") { + try { + return data.map((b) => decodeString(b)); + } catch (_a) { + throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); + } + } + return data; + } + /** Returns the underlying bytes of the tensor's data. */ + async bytes() { + this.throwIfDisposed(); + const data = await trackerFn().read(this.dataId); + if (this.dtype === "string") { + return data; + } else { + return new Uint8Array(data.buffer); + } + } + /** + * Disposes `tf.Tensor` from memory. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + dispose() { + if (this.isDisposed) { + return; + } + if (this.kerasMask) { + this.kerasMask.dispose(); + } + trackerFn().disposeTensor(this); + this.isDisposedInternal = true; + } + get isDisposed() { + return this.isDisposedInternal; + } + throwIfDisposed() { + if (this.isDisposed) { + throw new Error(`Tensor is disposed.`); + } + } + /** + * Prints the `tf.Tensor`. See `tf.print` for details. + * + * @param verbose Whether to print verbose information about the tensor, + * including dtype and size. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + print(verbose = false) { + return opHandler.print(this, verbose); + } + /** + * Returns a copy of the tensor. See `tf.clone` for details. + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + clone() { + this.throwIfDisposed(); + return opHandler.clone(this); + } + /** + * Returns a human-readable description of the tensor. Useful for logging. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + toString(verbose = false) { + const vals = this.dataSync(); + return tensorToString(vals, this.shape, this.dtype, verbose); + } + cast(dtype) { + this.throwIfDisposed(); + return opHandler.cast(this, dtype); + } + variable(trainable = true, name, dtype) { + this.throwIfDisposed(); + return trackerFn().makeVariable(this, trainable, name, dtype); + } +}; +Object.defineProperty(Tensor, Symbol.hasInstance, { + value: (instance) => { + return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null; + } +}); +function getGlobalTensorClass() { + return getGlobal("Tensor", () => { + return Tensor; + }); +} +getGlobalTensorClass(); +var Variable = class extends Tensor { + constructor(initialValue, trainable, name, tensorId) { + super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId); + this.trainable = trainable; + this.name = name; + } + /** + * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have + * the same shape and dtype as the old `tf.Tensor`. + * + * @param newValue New tensor to be assigned to this variable. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + assign(newValue) { + if (newValue.dtype !== this.dtype) { + throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`); + } + if (!arraysEqual(newValue.shape, this.shape)) { + throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`); + } + trackerFn().disposeTensor(this); + this.dataId = newValue.dataId; + trackerFn().incRef( + this, + null + /* backend */ + ); + } + dispose() { + trackerFn().disposeVariable(this); + this.isDisposedInternal = true; + } +}; +Object.defineProperty(Variable, Symbol.hasInstance, { + value: (instance) => { + return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function; + } +}); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js +var tensor_util_exports = {}; +__export(tensor_util_exports, { + assertTypesMatch: () => assertTypesMatch, + getTensorsInContainer: () => getTensorsInContainer, + isTensorInList: () => isTensorInList, + makeTypesMatch: () => makeTypesMatch +}); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/types.js +var Rank; +(function(Rank2) { + Rank2["R0"] = "R0"; + Rank2["R1"] = "R1"; + Rank2["R2"] = "R2"; + Rank2["R3"] = "R3"; + Rank2["R4"] = "R4"; + Rank2["R5"] = "R5"; + Rank2["R6"] = "R6"; +})(Rank || (Rank = {})); +var UpcastInt32AndMap; +(function(UpcastInt32AndMap2) { + UpcastInt32AndMap2["float32"] = "float32"; + UpcastInt32AndMap2["int32"] = "int32"; + UpcastInt32AndMap2["bool"] = "int32"; + UpcastInt32AndMap2["complex64"] = "complex64"; +})(UpcastInt32AndMap || (UpcastInt32AndMap = {})); +var UpcastBoolAndMap; +(function(UpcastBoolAndMap2) { + UpcastBoolAndMap2["float32"] = "float32"; + UpcastBoolAndMap2["int32"] = "int32"; + UpcastBoolAndMap2["bool"] = "bool"; + UpcastBoolAndMap2["complex64"] = "complex64"; +})(UpcastBoolAndMap || (UpcastBoolAndMap = {})); +var UpcastFloat32AndMap; +(function(UpcastFloat32AndMap2) { + UpcastFloat32AndMap2["float32"] = "float32"; + UpcastFloat32AndMap2["int32"] = "float32"; + UpcastFloat32AndMap2["bool"] = "float32"; + UpcastFloat32AndMap2["complex64"] = "complex64"; +})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {})); +var UpcastComplex64AndMap; +(function(UpcastComplex64AndMap2) { + UpcastComplex64AndMap2["float32"] = "complex64"; + UpcastComplex64AndMap2["int32"] = "complex64"; + UpcastComplex64AndMap2["bool"] = "complex64"; + UpcastComplex64AndMap2["complex64"] = "complex64"; +})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {})); +var upcastTypeMap = { + "float32": UpcastFloat32AndMap, + "int32": UpcastInt32AndMap, + "bool": UpcastBoolAndMap, + "complex64": UpcastComplex64AndMap +}; +function upcastType(typeA, typeB) { + if (typeA === "string" || typeB === "string") { + if (typeA === "string" && typeB === "string") { + return "string"; + } + throw new Error(`Can not upcast ${typeA} with ${typeB}`); + } + return upcastTypeMap[typeA][typeB]; +} +function sumOutType(type) { + return upcastType(type, "int32"); +} +function isWebGLData(values) { + return values != null && typeof values === "object" && "texture" in values && values.texture instanceof WebGLTexture; +} +function isWebGPUData(values) { + return typeof GPUBuffer !== "undefined" && values != null && typeof values === "object" && "buffer" in values && values.buffer instanceof GPUBuffer; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js +function makeTypesMatch(a, b) { + if (a.dtype === b.dtype) { + return [a, b]; + } + const dtype = upcastType(a.dtype, b.dtype); + return [a.cast(dtype), b.cast(dtype)]; +} +function assertTypesMatch(a, b) { + assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`); +} +function isTensorInList(tensor2, tensorList) { + return tensorList.some((x) => x.id === tensor2.id); +} +function getTensorsInContainer(result) { + const list = []; + const seen = /* @__PURE__ */ new Set(); + walkTensorContainer(result, list, seen); + return list; +} +function walkTensorContainer(container, list, seen) { + if (container == null) { + return; + } + if (container instanceof Tensor) { + list.push(container); + return; + } + if (!isIterable(container)) { + return; + } + const iterable = container; + for (const k in iterable) { + const val = iterable[k]; + if (!seen.has(val)) { + seen.add(val); + walkTensorContainer(val, list, seen); + } + } +} +function isIterable(obj) { + return Array.isArray(obj) || typeof obj === "object"; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/engine.js +function isRegisteredKernelInvocation(kernelInvocation) { + return kernelInvocation.kernelName != null; +} +var EngineState = 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 = /* @__PURE__ */ new WeakMap(); + this.profiling = false; + this.activeProfile = { + newBytes: 0, + newTensors: 0, + peakBytes: 0, + kernels: [], + result: null, + get kernelNames() { + return Array.from(new Set(this.kernels.map((k) => k.name))); + } + }; + } + dispose() { + for (const variableName in this.registeredVariables) { + this.registeredVariables[variableName].dispose(); + } + } +}; +var Engine = class _Engine { + constructor(ENV7) { + this.ENV = ENV7; + this.registry = {}; + this.registryFactory = {}; + this.pendingBackendInitId = 0; + this.state = new EngineState(); + } + async ready() { + if (this.pendingBackendInit != null) { + return this.pendingBackendInit.then(() => { + }); + } + if (this.backendInstance != null) { + return; + } + const sortedBackends = this.getSortedBackends(); + for (let i = 0; i < sortedBackends.length; i++) { + const backendName = sortedBackends[i]; + const success = await this.initializeBackend(backendName).success; + if (success) { + await this.setBackend(backendName); + return; + } + } + throw new Error(`Could not initialize any backends, all backend initializations failed.`); + } + get backend() { + if (this.pendingBackendInit != null) { + throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); + } + if (this.backendInstance == null) { + const { name, asyncInit } = this.initializeBackendsAndReturnBest(); + if (asyncInit) { + throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); + } + this.setBackend(name); + } + return this.backendInstance; + } + backendNames() { + return Object.keys(this.registryFactory); + } + findBackend(backendName) { + if (!(backendName in this.registry)) { + if (backendName in this.registryFactory) { + const { asyncInit } = this.initializeBackend(backendName); + if (asyncInit) { + return null; + } + } else { + return null; + } + } + return this.registry[backendName]; + } + findBackendFactory(backendName) { + if (!(backendName in this.registryFactory)) { + return null; + } + return this.registryFactory[backendName].factory; + } + registerBackend(backendName, factory, priority = 1) { + if (backendName in this.registryFactory) { + warn(`${backendName} backend was already registered. Reusing existing backend factory.`); + return false; + } + this.registryFactory[backendName] = { factory, priority }; + return true; + } + async setBackend(backendName) { + if (this.registryFactory[backendName] == null) { + throw new Error(`Backend name '${backendName}' not found in registry`); + } + this.backendName = backendName; + if (this.registry[backendName] == null) { + this.backendInstance = null; + const { success, asyncInit } = this.initializeBackend(backendName); + const result = asyncInit ? await success : success; + if (!result) { + return false; + } + } + this.backendInstance = this.registry[backendName]; + this.setupRegisteredKernels(); + this.profiler = new Profiler(this.backendInstance); + return true; + } + setupRegisteredKernels() { + const kernels = getKernelsForBackend(this.backendName); + kernels.forEach((kernel) => { + if (kernel.setupFunc != null) { + kernel.setupFunc(this.backendInstance); + } + }); + } + disposeRegisteredKernels(backendName) { + const kernels = getKernelsForBackend(backendName); + kernels.forEach((kernel) => { + if (kernel.disposeFunc != null) { + kernel.disposeFunc(this.registry[backendName]); + } + }); + } + /** + * Initializes a backend by looking up the backend name in the factory + * registry and calling the factory method. Returns a boolean representing + * whether the initialization of the backend suceeded. Throws an error if + * there is no backend in the factory registry. + */ + initializeBackend(backendName) { + const registryFactoryEntry = this.registryFactory[backendName]; + if (registryFactoryEntry == null) { + throw new Error(`Cannot initialize backend ${backendName}, no registration found.`); + } + try { + const backend2 = registryFactoryEntry.factory(); + if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === "function") { + const promiseId = ++this.pendingBackendInitId; + const success = backend2.then((backendInstance) => { + if (promiseId < this.pendingBackendInitId) { + return false; + } + this.registry[backendName] = backendInstance; + this.pendingBackendInit = null; + return true; + }).catch((err) => { + if (promiseId < this.pendingBackendInitId) { + return false; + } + this.pendingBackendInit = null; + warn(`Initialization of backend ${backendName} failed`); + warn(err.stack || err.message); + return false; + }); + this.pendingBackendInit = success; + return { success, asyncInit: true }; + } else { + this.registry[backendName] = backend2; + return { success: true, asyncInit: false }; + } + } catch (err) { + warn(`Initialization of backend ${backendName} failed`); + warn(err.stack || err.message); + return { success: false, asyncInit: false }; + } + } + removeBackend(backendName) { + if (!(backendName in this.registryFactory)) { + throw new Error(`${backendName} backend not found in registry`); + } + if (this.backendName === backendName && this.pendingBackendInit != null) { + this.pendingBackendInitId++; + } + if (backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + delete this.registryFactory[backendName]; + if (this.backendName === backendName) { + this.pendingBackendInit = null; + this.backendName = null; + this.backendInstance = null; + } + } + getSortedBackends() { + if (Object.keys(this.registryFactory).length === 0) { + throw new Error("No backend found in registry."); + } + return Object.keys(this.registryFactory).sort((a, b) => { + return this.registryFactory[b].priority - this.registryFactory[a].priority; + }); + } + initializeBackendsAndReturnBest() { + const sortedBackends = this.getSortedBackends(); + for (let i = 0; i < sortedBackends.length; i++) { + const backendName = sortedBackends[i]; + const { success, asyncInit } = this.initializeBackend(backendName); + if (asyncInit || success) { + return { name: backendName, asyncInit }; + } + } + throw new Error(`Could not initialize any backends, all backend initializations failed.`); + } + moveData(backend2, dataId) { + const info = this.state.tensorInfo.get(dataId); + const srcBackend = info.backend; + const values = this.readSync(dataId); + const refCount = srcBackend.refCount(dataId); + srcBackend.disposeData(dataId, true); + info.backend = backend2; + backend2.move(dataId, values, info.shape, info.dtype, refCount); + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; + } + } + tidy(nameOrFn, fn) { + let name = null; + if (fn == null) { + if (typeof nameOrFn !== "function") { + throw new Error("Please provide a function to tidy()"); + } + fn = nameOrFn; + } else { + if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) { + throw new Error("When calling with two arguments, the first argument to tidy() must be a string"); + } + if (typeof fn !== "function") { + throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function"); + } + name = nameOrFn; + } + let result; + return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => { + result = fn(); + if (result instanceof Promise) { + console.error("Cannot return a Promise inside of tidy."); + } + return result; + }); + } + scopedRun(start, end, f) { + start(); + try { + const res = f(); + end(); + return res; + } catch (ex) { + end(); + throw ex; + } + } + nextTensorId() { + return _Engine.nextTensorId++; + } + nextVariableId() { + return _Engine.nextVariableId++; + } + /** + * This method is called instead of the public-facing tensor.clone() when + * saving a tensor for backwards pass. It makes sure to add the clone + * operation to the tape regardless of being called inside a kernel + * execution. + */ + clone(x) { + const y = ENGINE.runKernel(Identity, { x }); + const inputs = { x }; + const grad2 = (dy) => ({ + x: () => { + const dtype = "float32"; + const gradInputs = { x: dy }; + const attrs = { dtype }; + return ENGINE.runKernel( + Cast, + gradInputs, + // tslint:disable-next-line: no-unnecessary-type-assertion + attrs + ); + } + }); + const saved = []; + this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {}); + return y; + } + /** + * Execute a kernel with the given name and return the output tensor. + * + * @param kernelName The name of the kernel to execute. + * @param inputs A map of input names to tensors. + * @param attrs A map of attribute names to their values. An attribute is a + * primitive (non-tensor) input to the kernel. + * @param inputsToSave A list of tensors, inputs to save for the backprop + * computation. + * @param outputsToSave A list of booleans, specifying which output to save + * for the backprop computation. These are booleans since the output + * tensors are not visible to the user. + */ + runKernel(kernelName, inputs, attrs) { + if (this.backendName == null) { + this.backend; + } + const hasKernel = getKernel(kernelName, this.backendName) != null; + if (!hasKernel) { + throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`); + } + return this.runKernelFunc({ kernelName, inputs, attrs }); + } + shouldCheckForMemLeaks() { + return this.ENV.getBool("IS_TEST"); + } + checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) { + const numDataIdsAfter = this.backend.numDataIds(); + let numOutputDataIds = 0; + outInfos.forEach((info) => { + numOutputDataIds += info.dtype === "complex64" ? 3 : 1; + }); + const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]; + const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves; + if (dataIdsLeaked > 0) { + throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`); + } + } + /** + * Internal helper method to execute a kernel Func + * + * Use `runKernel` to execute kernels from outside of engine. + */ + runKernelFunc(kernelParams) { + let outputs; + let saved = []; + const isTapeOn = this.isTapeOn(); + const startingBytecount = this.state.numBytes; + const startingNumTensors = this.state.numTensors; + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack.push(0); + } + let kernelFunc3; + if (this.backendName == null) { + this.backend; + } + let out; + const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ""; + if (isRegisteredKernelInvocation(kernelParams)) { + const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams; + if (this.backendName == null) { + this.backend; + } + const kernel = getKernel(kernelName, this.backendName); + assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`); + kernelFunc3 = () => { + const numDataIdsBefore = this.backend.numDataIds(); + out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend }); + const outInfos = Array.isArray(out) ? out : [out]; + if (this.shouldCheckForMemLeaks()) { + this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos); + } + const outTensors = outInfos.map((outInfo) => { + if (outInfo.rank != null) { + return outInfo; + } + return this.makeTensorFromTensorInfo(outInfo); + }); + if (isTapeOn) { + const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors); + saved = this.saveTensorsForBackwardMode(tensorsToSave); + } + return outTensors; + }; + } else { + const { forwardFunc } = kernelParams; + const saveFunc = (tensors) => { + if (!isTapeOn) { + return; + } + saved = tensors.map((tensor2) => this.keep(this.clone(tensor2))); + }; + kernelFunc3 = () => { + const numDataIdsBefore = this.backend.numDataIds(); + out = this.tidy(() => forwardFunc(this.backend, saveFunc)); + const outs = Array.isArray(out) ? out : [out]; + if (this.shouldCheckForMemLeaks()) { + this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs); + } + return outs; + }; + } + const { inputs, attrs } = kernelParams; + const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc; + let kernelProfile; + this.scopedRun( + // Stop recording to a tape when running a kernel. + () => this.state.kernelDepth++, + () => this.state.kernelDepth--, + () => { + if (!this.ENV.getBool("DEBUG") && !this.state.profiling) { + outputs = kernelFunc3(); + } else { + kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3()); + if (this.ENV.getBool("DEBUG")) { + this.profiler.logKernelProfile(kernelProfile); + } + outputs = kernelProfile.outputs; + } + } + ); + if (isTapeOn) { + this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs); + } + if (this.state.profiling) { + this.state.activeProfile.kernels.push({ + name: kernelOrScopeName, + bytesAdded: this.state.numBytes - startingBytecount, + totalBytesSnapshot: this.state.numBytes, + tensorsAdded: this.state.numTensors - startingNumTensors, + totalTensorsSnapshot: this.state.numTensors, + inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null), + outputShapes: outputs.map((item) => item.shape), + kernelTimeMs: kernelProfile.timeMs, + extraInfo: kernelProfile.extraInfo + }); + } + return Array.isArray(out) ? outputs : outputs[0]; + } + /** + * Saves tensors used in forward mode for use in backward mode. + * + * @param tensors the list of tensors to save. + */ + saveTensorsForBackwardMode(tensors) { + const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2))); + return saved; + } + /** + * Returns a list of tensors to save for a given gradient calculation. + * + * @param kernelName name of kernel to look up gradient for. + * @param inputs a map of input tensors. + * @param outputs an array of output tensors from forward mode of kernel. + */ + getTensorsForGradient(kernelName, inputs, outputs) { + const gradConfig = getGradient(kernelName); + if (gradConfig != null) { + const inputsToSave = gradConfig.inputsToSave || []; + const outputsToSave = gradConfig.outputsToSave || []; + let inputTensorsToSave; + if (gradConfig.saveAllInputs) { + assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array."); + inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]); + } else { + inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]); + } + const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]); + return inputTensorsToSave.concat(outputTensorsToSave); + } + return []; + } + /** + * Internal method used by public APIs for tensor creation. Makes a new + * tensor with the provided shape, dtype and values. It always + * creates a new data id and writes the values to the underlying backend. + */ + makeTensor(values, shape, dtype, backend2) { + if (values == null) { + throw new Error("Values passed to engine.makeTensor() are null"); + } + dtype = dtype || "float32"; + backend2 = backend2 || this.backend; + let backendVals = values; + if (dtype === "string" && isString(values[0])) { + backendVals = values.map((d) => encodeString(d)); + } + const dataId = backend2.write(backendVals, shape, dtype); + const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend2); + if (dtype === "string") { + const info = this.state.tensorInfo.get(dataId); + const newBytes = bytesFromStringArray(backendVals); + this.state.numBytes += newBytes - info.bytes; + info.bytes = newBytes; + } + return t; + } + /** + * Internal method used by backends. Makes a new tensor + * that is a wrapper around an existing data id. It doesn't create + * a new data id, only increments the ref count used in memory tracking. + * @deprecated + */ + makeTensorFromDataId(dataId, shape, dtype, backend2) { + dtype = dtype || "float32"; + const tensorInfo = { dataId, shape, dtype }; + return this.makeTensorFromTensorInfo(tensorInfo, backend2); + } + /** + * Internal method used by backends. Makes a new tensor that is a wrapper + * around an existing data id in TensorInfo. It doesn't create a new data id, + * only increments the ref count used in memory tracking. + */ + makeTensorFromTensorInfo(tensorInfo, backend2) { + const { dataId, shape, dtype } = tensorInfo; + const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend2); + return t; + } + makeVariable(initialValue, trainable = true, name, dtype) { + name = name || this.nextVariableId().toString(); + if (dtype != null && dtype !== initialValue.dtype) { + initialValue = initialValue.cast(dtype); + } + const v = new Variable(initialValue, trainable, name, this.nextTensorId()); + if (this.state.registeredVariables[v.name] != null) { + throw new Error(`Variable with name ${v.name} was already registered`); + } + this.state.registeredVariables[v.name] = v; + this.incRef(v, this.backend); + return v; + } + trackTensor(a, backend2) { + this.state.numTensors++; + if (a.dtype === "string") { + this.state.numStringTensors++; + } + let bytes = 0; + if (a.dtype !== "complex64" && a.dtype !== "string") { + bytes = a.size * bytesPerElement(a.dtype); + } + this.state.numBytes += bytes; + if (!this.state.tensorInfo.has(a.dataId)) { + this.state.numDataBuffers++; + this.state.tensorInfo.set(a.dataId, { + backend: backend2 || this.backend, + dtype: a.dtype, + shape: a.shape, + bytes + }); + } + if (!(a instanceof Variable)) { + this.track(a); + } + } + // Track the tensor by dataId and increase the refCount for the dataId in the + // backend. + // TODO(pyu10055): This is currently used by makeVariable method, to increase + // refCount on the backend for the dataId. It can potentially be replaced with + // Identity op indead of calling backend directly. + incRef(a, backend2) { + this.trackTensor(a, backend2); + this.backend.incRef(a.dataId); + } + removeDataId(dataId, backend2) { + if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) { + this.state.tensorInfo.delete(dataId); + this.state.numDataBuffers--; + } + } + disposeTensor(a) { + if (!this.state.tensorInfo.has(a.dataId)) { + return; + } + const info = this.state.tensorInfo.get(a.dataId); + this.state.numTensors--; + if (a.dtype === "string") { + this.state.numStringTensors--; + this.state.numBytes -= info.bytes; + } + if (a.dtype !== "complex64" && a.dtype !== "string") { + const bytes = a.size * bytesPerElement(a.dtype); + this.state.numBytes -= bytes; + } + if (info.backend.disposeData(a.dataId)) { + this.removeDataId(a.dataId, info.backend); + } + } + disposeVariables() { + for (const varName in this.state.registeredVariables) { + const v = this.state.registeredVariables[varName]; + this.disposeVariable(v); + } + } + disposeVariable(v) { + this.disposeTensor(v); + if (this.state.registeredVariables[v.name] != null) { + delete this.state.registeredVariables[v.name]; + } + } + memory() { + const info = this.backend.memory(); + info.numTensors = this.state.numTensors; + info.numDataBuffers = this.state.numDataBuffers; + info.numBytes = this.state.numBytes; + if (this.state.numStringTensors > 0) { + info.unreliable = true; + if (info.reasons == null) { + info.reasons = []; + } + info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)"); + } + return info; + } + async profile(query) { + this.state.profiling = true; + const startBytes = this.state.numBytes; + const startNumTensors = this.state.numTensors; + this.state.activeProfile.kernels = []; + this.state.activeProfile.result = await query(); + this.state.profiling = false; + this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot)); + this.state.activeProfile.newBytes = this.state.numBytes - startBytes; + this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors; + for (const kernel of this.state.activeProfile.kernels) { + kernel.kernelTimeMs = await kernel.kernelTimeMs; + kernel.extraInfo = await kernel.extraInfo; + } + return this.state.activeProfile; + } + isTapeOn() { + return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; + } + addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) { + const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved }; + const gradConfig = getGradient(kernelName); + if (gradConfig != null) { + gradientsFunc = gradConfig.gradFunc; + } + if (gradientsFunc != null) { + tapeNode.gradient = (dys) => { + dys = dys.map((dy, i) => { + if (dy == null) { + const output = outputs[i]; + const vals = makeZerosTypedArray(output.size, output.dtype); + return this.makeTensor(vals, output.shape, output.dtype); + } + return dy; + }); + return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs); + }; + } + this.state.activeTape.push(tapeNode); + } + keep(result) { + result.kept = true; + return result; + } + startTape() { + if (this.state.gradientDepth === 0) { + this.state.activeTape = []; + } + this.state.gradientDepth++; + } + endTape() { + this.state.gradientDepth--; + } + /** + * Start a scope. Use this with endScope() to achieve the same functionality + * as scope() without the need for a function closure. + */ + startScope(name) { + const scopeInfo = { + track: [], + name: "unnamed scope", + id: this.state.nextScopeId++ + }; + if (name) { + scopeInfo.name = name; + } + this.state.scopeStack.push(scopeInfo); + this.state.activeScope = scopeInfo; + } + /** + * End a scope. Use this with startScope() to achieve the same functionality + * as scope() without the need for a function closure. + */ + endScope(result) { + const tensorsToTrackInParent = getTensorsInContainer(result); + const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id)); + for (let i = 0; i < this.state.activeScope.track.length; i++) { + const tensor2 = this.state.activeScope.track[i]; + if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) { + tensor2.dispose(); + } + } + const oldScope = this.state.scopeStack.pop(); + this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1]; + tensorsToTrackInParent.forEach((tensor2) => { + if (!tensor2.kept && tensor2.scopeId === oldScope.id) { + this.track(tensor2); + } + }); + } + /** + * Returns gradients of `f` with respect to each of the `xs`. The gradients + * returned are of the same length as `xs`, but some might be null if `f` + * was not a function of that `x`. It also takes optional dy to multiply the + * gradient, which defaults to `1`. + */ + gradients(f, xs, dy, allowNoGradients = false) { + assert(xs.length > 0, () => "gradients() received an empty list of xs."); + if (dy != null && dy.dtype !== "float32") { + throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`); + } + const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f)); + assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor."); + const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y); + if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) { + throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y."); + } + return this.tidy("backward", () => { + const accumulatedGradientMap = {}; + accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy; + backpropagateGradients( + accumulatedGradientMap, + filteredTape, + // Pass the tidy function to avoid circular dep with `tape.ts`. + (f2) => this.tidy(f2), + // Pass an add function to avoide a circular dep with `tape.ts`. + add + ); + const grads2 = xs.map((x) => accumulatedGradientMap[x.id]); + if (this.state.gradientDepth === 0) { + this.state.activeTape.forEach((node) => { + for (const tensor2 of node.saved) { + tensor2.dispose(); + } + }); + this.state.activeTape = null; + } + return { value: y, grads: grads2 }; + }); + } + customGrad(f) { + assert(isFunction(f), () => "The f passed in customGrad(f) must be a function."); + return (...inputs) => { + assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); + let res; + const inputMap = {}; + inputs.forEach((input2, i) => { + inputMap[i] = input2; + }); + const forwardFunc = (_, save) => { + res = f(...[...inputs, save]); + assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"); + assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."); + return res.value; + }; + const backwardsFunc = (dy, saved) => { + const gradRes = res.gradFunc(dy, saved); + const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes]; + assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."); + assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); + const gradMap = {}; + grads2.forEach((grad2, i) => { + gradMap[i] = () => grad2; + }); + return gradMap; + }; + return this.runKernelFunc({ + forwardFunc, + backwardsFunc, + inputs: inputMap + }); + }; + } + readSync(dataId) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.readSync(dataId); + } + read(dataId) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.read(dataId); + } + readToGPU(dataId, options) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.readToGPU(dataId, options); + } + async time(query) { + const start = now(); + const timingInfo = await this.backend.time(query); + timingInfo.wallMs = now() - start; + return timingInfo; + } + /** + * Tracks a Tensor in the current scope to be automatically cleaned up + * when the current scope ends, and returns the value. + * + * @param result The Tensor to track in the current scope. + */ + track(result) { + if (this.state.activeScope != null) { + result.scopeId = this.state.activeScope.id; + this.state.activeScope.track.push(result); + } + return result; + } + get registeredVariables() { + return this.state.registeredVariables; + } + /** + * Resets the engine state. Removes all backends but does not remove + * registered backend factories. + */ + reset() { + this.pendingBackendInitId++; + this.state.dispose(); + this.ENV.reset(); + this.state = new EngineState(); + for (const backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + this.backendName = null; + this.backendInstance = null; + this.pendingBackendInit = null; + } +}; +Engine.nextTensorId = 0; +Engine.nextVariableId = 0; +function ones(shape) { + const values = makeOnesTypedArray(sizeFromShape(shape), "float32"); + return ENGINE.makeTensor(values, shape, "float32"); +} +function getOrMakeEngine() { + const ns = getGlobalNamespace(); + if (ns._tfengine == null) { + const environment = new Environment(ns); + ns._tfengine = new Engine(environment); + } + setEnvironmentGlobal(ns._tfengine.ENV); + setTensorTracker(() => ns._tfengine); + return ns._tfengine; +} +var ENGINE = getOrMakeEngine(); +function add(a, b) { + const inputs = { a, b }; + return ENGINE.runKernel(Add, inputs); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/device_util.js +var device_util_exports = {}; +__export(device_util_exports, { + isBrowser: () => isBrowser, + isMobile: () => isMobile, + mockIsMobile: () => mockIsMobile +}); +function _isNavigatorDefined() { + return typeof navigator !== "undefined" && navigator != null; +} +var isMobileMockValue; +function mockIsMobile(value) { + isMobileMockValue = value; +} +function isMobile(nav) { + if (isMobileMockValue !== void 0) { + return isMobileMockValue; + } + if (nav || _isNavigatorDefined()) { + if (!nav) { + nav = navigator; + } + if (nav.product === "ReactNative") { + return true; + } + const a = nav.userAgent || nav.vendor || // tslint:disable-next-line:no-any + (typeof window !== "undefined" ? window.opera : ""); + if (!a) { + const navAny = nav; + return navAny.userAgentData && navAny.userAgentData.mobile; + } + return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || // tslint:disable-next-line:max-line-length + /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4)); + } + return false; +} +function isBrowser() { + return typeof window !== "undefined" && window.document != null || //@ts-ignore + typeof WorkerGlobalScope !== "undefined"; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/flags.js +var ENV2 = env(); +ENV2.registerFlag("DEBUG", () => false, (debugValue) => { + if (debugValue) { + console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance."); + } +}); +ENV2.registerFlag("IS_BROWSER", () => isBrowser()); +ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"); +ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); +ENV2.registerFlag("IS_SAFARI", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Safari/.test(navigator.userAgent) && /Apple/.test(navigator.vendor)); +ENV2.registerFlag("PROD", () => false); +ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG")); +ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true); +ENV2.registerFlag("IS_TEST", () => false); +ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => ENV2.getBool("DEBUG")); +ENV2.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); +ENV2.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", () => false); +ENV2.registerFlag("USE_SETTIMEOUTCUSTOM", () => false); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js +function inferShape(val, dtype) { + let firstElem = val; + if (isTypedArray(val)) { + return dtype === "string" ? [] : [val.length]; + } + if (isWebGLData(val)) { + const usedChannels = val.channels || "RGBA"; + return [val.height, val.width * usedChannels.length]; + } else if (isWebGPUData(val)) { + return [val.buffer.size / (dtype == null ? 4 : bytesPerElement(dtype))]; + } + if (!Array.isArray(val)) { + return []; + } + const shape = []; + while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") { + shape.push(firstElem.length); + firstElem = firstElem[0]; + } + if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) { + deepAssertShapeConsistency(val, shape, []); + } + return shape; +} +function deepAssertShapeConsistency(val, shape, indices) { + indices = indices || []; + if (!Array.isArray(val) && !isTypedArray(val)) { + assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`); + return; + } + assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`); + assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`); + const subShape = shape.slice(1); + for (let i = 0; i < val.length; ++i) { + deepAssertShapeConsistency(val[i], subShape, indices.concat(i)); + } +} +function assertDtype(expectedDtype, actualDType, argName, functionName) { + if (expectedDtype === "string_or_numeric") { + return; + } + if (expectedDtype == null) { + throw new Error(`Expected dtype cannot be null.`); + } + if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") { + throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`); + } +} +function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") { + if (x instanceof getGlobalTensorClass()) { + assertDtype(parseAsDtype, x.dtype, argName, functionName); + return x; + } + let inferredDtype = inferDtype(x); + if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) { + inferredDtype = parseAsDtype; + } + assertDtype(parseAsDtype, inferredDtype, argName, functionName); + if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") { + const type = x == null ? "null" : x.constructor.name; + throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`); + } + const inferredShape = inferShape(x, inferredDtype); + if (!isTypedArray(x) && !Array.isArray(x)) { + x = [x]; + } + const skipTypedArray = true; + const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray); + return ENGINE.makeTensor(values, inferredShape, inferredDtype); +} +function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") { + if (!Array.isArray(arg)) { + throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``); + } + const tensors = arg; + return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype)); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js +var OP_SCOPE_SUFFIX = "__op"; +function op(f) { + const keys = Object.keys(f); + if (keys.length !== 1) { + throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`); + } + let opName = keys[0]; + const fn = f[opName]; + if (opName.endsWith("_")) { + opName = opName.substring(0, opName.length - 1); + } + opName = opName + OP_SCOPE_SUFFIX; + const f2 = (...args) => { + ENGINE.startScope(opName); + try { + const result = fn(...args); + if (isPromise(result)) { + console.error("Cannot return a Promise inside of tidy."); + } + ENGINE.endScope(result); + return result; + } catch (ex) { + ENGINE.endScope(null); + throw ex; + } + }; + Object.defineProperty(f2, "name", { value: opName, configurable: true }); + return f2; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js +function complex_(real4, imag4) { + const $real = convertToTensor(real4, "real", "complex"); + const $imag = convertToTensor(imag4, "imag", "complex"); + assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`); + const inputs = { real: $real, imag: $imag }; + return ENGINE.runKernel(Complex, inputs); +} +var complex = op({ complex_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js +function makeTensor(values, shape, inferredShape, dtype) { + if (dtype == null) { + dtype = inferDtype(values); + } else if (dtype === "complex64") { + throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`); + } + if (isWebGPUData(values) || isWebGLData(values)) { + if (dtype !== "float32" && dtype !== "int32") { + throw new Error(`Creating tensor from GPU data only supports 'float32'|'int32' dtype, while the dtype is ${dtype}.`); + } + return ENGINE.backend.createTensorFromGPUData(values, shape || inferredShape, dtype); + } + if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") { + throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray"); + } + if (shape != null) { + assertNonNegativeIntegerDimensions(shape); + const providedSize = sizeFromShape(shape); + const inferredSize = sizeFromShape(inferredShape); + assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`); + for (let i = 0; i < inferredShape.length; ++i) { + const inferred = inferredShape[i]; + const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true; + assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `); + } + } + if (!isTypedArray(values) && !Array.isArray(values)) { + values = [values]; + } + shape = shape || inferredShape; + values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true); + return ENGINE.makeTensor(values, shape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js +function tensor(values, shape, dtype) { + const inferredShape = inferShape(values, dtype); + return makeTensor(values, shape, inferredShape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/types.js +var DTYPE_VALUE_SIZE_MAP = { + "float32": 4, + "float16": 2, + "int32": 4, + "uint16": 2, + "uint8": 1, + "bool": 1, + "complex64": 8 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/composite_array_buffer.js +var CompositeArrayBuffer = class _CompositeArrayBuffer { + /** + * Concatenate a number of ArrayBuffers into one. + * + * @param buffers An array of ArrayBuffers to concatenate, or a single + * ArrayBuffer. + * @returns Result of concatenating `buffers` in order. + */ + static join(buffers) { + return new _CompositeArrayBuffer(buffers).slice(); + } + constructor(buffers) { + this.shards = []; + this.previousShardIndex = 0; + if (buffers == null) { + return; + } + if (!(buffers instanceof Array)) { + buffers = [buffers]; + } + buffers = buffers.map((bufferOrTypedArray) => { + if (isTypedArray(bufferOrTypedArray)) { + return bufferOrTypedArray.buffer; + } + return bufferOrTypedArray; + }); + if (buffers.length === 0) { + return; + } + this.bufferUniformSize = buffers[0].byteLength; + let start = 0; + for (let i = 0; i < buffers.length; i++) { + const buffer2 = buffers[i]; + if (i !== buffers.length - 1 && buffer2.byteLength !== this.bufferUniformSize) { + this.bufferUniformSize = void 0; + } + const end = start + buffer2.byteLength; + this.shards.push({ buffer: buffer2, start, end }); + start = end; + } + if (this.shards.length === 0) { + this.byteLength = 0; + } + this.byteLength = this.shards[this.shards.length - 1].end; + } + slice(start = 0, end = this.byteLength) { + if (this.shards.length === 0) { + return new ArrayBuffer(0); + } + start = isNaN(Number(start)) ? 0 : start; + end = isNaN(Number(end)) ? 0 : end; + start = Math.max(0, start); + end = Math.min(this.byteLength, end); + if (end <= start) { + return new ArrayBuffer(0); + } + const startShardIndex = this.findShardForByte(start); + if (startShardIndex === -1) { + throw new Error(`Could not find start shard for byte ${start}`); + } + const size = end - start; + const outputBuffer = new ArrayBuffer(size); + const outputArray = new Uint8Array(outputBuffer); + let sliced = 0; + for (let i = startShardIndex; i < this.shards.length; i++) { + const shard = this.shards[i]; + const globalStart = start + sliced; + const localStart = globalStart - shard.start; + const outputStart = sliced; + const globalEnd = Math.min(end, shard.end); + const localEnd = globalEnd - shard.start; + const outputSlice = new Uint8Array(shard.buffer, localStart, localEnd - localStart); + outputArray.set(outputSlice, outputStart); + sliced += outputSlice.length; + if (end < shard.end) { + break; + } + } + return outputBuffer; + } + /** + * Get the index of the shard that contains the byte at `byteIndex`. + */ + findShardForByte(byteIndex) { + if (this.shards.length === 0 || byteIndex < 0 || byteIndex >= this.byteLength) { + return -1; + } + if (this.bufferUniformSize != null) { + this.previousShardIndex = Math.floor(byteIndex / this.bufferUniformSize); + return this.previousShardIndex; + } + function check(shard) { + if (byteIndex < shard.start) { + return -1; + } + if (byteIndex >= shard.end) { + return 1; + } + return 0; + } + if (check(this.shards[this.previousShardIndex]) === 0) { + return this.previousShardIndex; + } + const index = search(this.shards, check); + if (index === -1) { + return -1; + } + this.previousShardIndex = index; + return this.previousShardIndex; + } +}; +function search(sortedArray, compare) { + let min6 = 0; + let max6 = sortedArray.length; + while (min6 <= max6) { + const middle = Math.floor((max6 - min6) / 2) + min6; + const side = compare(sortedArray[middle]); + if (side === 0) { + return middle; + } else if (side < 0) { + max6 = middle; + } else { + min6 = middle + 1; + } + } + return -1; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/globals.js +function enableProdMode() { + env().set("PROD", true); +} +function enableDebugMode() { + env().set("DEBUG", true); +} +function disableDeprecationWarnings() { + env().set("DEPRECATION_WARNINGS_ENABLED", false); + console.warn(`TensorFlow.js deprecation warnings have been disabled.`); +} +function deprecationWarn(msg) { + if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) { + console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings()."); + } +} +setDeprecationWarningFn(deprecationWarn); +function disposeVariables() { + ENGINE.disposeVariables(); +} +function engine() { + return ENGINE; +} +function memory() { + return ENGINE.memory(); +} +function profile(f) { + return ENGINE.profile(f); +} +function tidy(nameOrFn, fn) { + return ENGINE.tidy(nameOrFn, fn); +} +function dispose(container) { + const tensors = getTensorsInContainer(container); + tensors.forEach((tensor2) => tensor2.dispose()); +} +function keep(result) { + return ENGINE.keep(result); +} +function time(f) { + return ENGINE.time(f); +} +function setBackend(backendName) { + return ENGINE.setBackend(backendName); +} +function ready() { + return ENGINE.ready(); +} +function getBackend() { + return ENGINE.backendName; +} +function removeBackend(name) { + ENGINE.removeBackend(name); +} +function findBackend(name) { + return ENGINE.findBackend(name); +} +function findBackendFactory(name) { + return ENGINE.findBackendFactory(name); +} +function registerBackend(name, factory, priority = 1) { + return ENGINE.registerBackend(name, factory, priority); +} +function backend() { + return ENGINE.backend; +} +function setPlatform(platformName, platform) { + env().setPlatform(platformName, platform); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js +var NUM_BYTES_STRING_LENGTH = 4; +async function encodeWeights(tensors, group) { + const specs = []; + const dataPromises = []; + const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors); + for (let i = 0; i < names.length; ++i) { + const name = names[i]; + const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name]; + if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") { + throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`); + } + const spec = { name, shape: t.shape, dtype: t.dtype }; + if (t.dtype === "string") { + const utf8bytes = new Promise(async (resolve) => { + const vals = await t.bytes(); + const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length; + const bytes = new Uint8Array(totalNumBytes); + let offset = 0; + for (let i2 = 0; i2 < vals.length; i2++) { + const val = vals[i2]; + const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer); + bytes.set(bytesOfLength, offset); + offset += NUM_BYTES_STRING_LENGTH; + bytes.set(val, offset); + offset += val.length; + } + resolve(bytes); + }); + dataPromises.push(utf8bytes); + } else { + dataPromises.push(t.data()); + } + if (group != null) { + spec.group = group; + } + specs.push(spec); + } + const tensorValues = await Promise.all(dataPromises); + return { data: concatenateTypedArrays(tensorValues), specs }; +} +function decodeWeights(weightData, specs) { + const compositeBuffer = new CompositeArrayBuffer(weightData); + const out = {}; + let offset = 0; + for (const spec of specs) { + const byteLength = getWeightBytelength(spec, (start, end) => { + return compositeBuffer.slice(offset + start, offset + end); + }); + out[spec.name] = decodeWeight(spec, compositeBuffer.slice(offset, offset + byteLength)); + offset += byteLength; + } + return out; +} +function getWeightBytelength(spec, slice5) { + const size = sizeFromShape(spec.shape); + let bytesPerValue; + if ("quantization" in spec) { + const quantization = spec.quantization; + bytesPerValue = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; + } else if (spec.dtype === "string") { + let byteLength = 0; + for (let i = 0; i < size; i++) { + byteLength += NUM_BYTES_STRING_LENGTH + new Uint32Array(slice5(byteLength, byteLength + NUM_BYTES_STRING_LENGTH))[0]; + } + return byteLength; + } else { + bytesPerValue = DTYPE_VALUE_SIZE_MAP[spec.dtype]; + } + return size * bytesPerValue; +} +async function getWeightBytelengthAsync(spec, slice5) { + const size = sizeFromShape(spec.shape); + let bytesPerValue; + if ("quantization" in spec) { + const quantization = spec.quantization; + bytesPerValue = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; + } else if (spec.dtype === "string") { + let byteLength = 0; + for (let i = 0; i < size; i++) { + byteLength += NUM_BYTES_STRING_LENGTH + new Uint32Array(await slice5(byteLength, byteLength + NUM_BYTES_STRING_LENGTH))[0]; + } + return byteLength; + } else { + bytesPerValue = DTYPE_VALUE_SIZE_MAP[spec.dtype]; + } + return size * bytesPerValue; +} +function decodeWeight(spec, byteBuffer) { + const name = spec.name; + const dtype = spec.dtype; + const shape = spec.shape; + const size = sizeFromShape(shape); + let values; + let offset = 0; + if ("quantization" in spec) { + const quantization = spec.quantization; + if (quantization.dtype === "uint8" || quantization.dtype === "uint16") { + if (!("min" in quantization && "scale" in quantization)) { + throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`); + } + } else if (quantization.dtype === "float16") { + if (dtype !== "float32") { + throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`); + } + } else { + throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`); + } + const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; + const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer); + if (dtype === "float32") { + if (quantization.dtype === "uint8" || quantization.dtype === "uint16") { + values = new Float32Array(quantizedArray.length); + for (let i = 0; i < quantizedArray.length; i++) { + const v = quantizedArray[i]; + values[i] = v * quantization.scale + quantization.min; + } + } else if (quantization.dtype === "float16") { + const float16Decode = getFloat16Decoder(); + values = float16Decode(quantizedArray); + } else { + throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`); + } + } else if (dtype === "int32") { + if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") { + throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`); + } + values = new Int32Array(quantizedArray.length); + for (let i = 0; i < quantizedArray.length; i++) { + const v = quantizedArray[i]; + values[i] = Math.round(v * quantization.scale + quantization.min); + } + } else { + throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); + } + offset += size * quantizationSizeFactor; + } else if (dtype === "string") { + const size2 = sizeFromShape(spec.shape); + values = []; + for (let i = 0; i < size2; i++) { + const byteLength = new Uint32Array(byteBuffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0]; + offset += NUM_BYTES_STRING_LENGTH; + const bytes = new Uint8Array(byteBuffer.slice(offset, offset + byteLength)); + values.push(bytes); + offset += byteLength; + } + } else { + const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype]; + if (dtype === "float32") { + values = new Float32Array(byteBuffer); + } else if (dtype === "int32") { + values = new Int32Array(byteBuffer); + } else if (dtype === "bool") { + values = new Uint8Array(byteBuffer); + } else if (dtype === "complex64") { + values = new Float32Array(byteBuffer); + const real4 = new Float32Array(values.length / 2); + const image2 = new Float32Array(values.length / 2); + for (let i = 0; i < real4.length; i++) { + real4[i] = values[i * 2]; + image2[i] = values[i * 2 + 1]; + } + const realTensor = tensor(real4, shape, "float32"); + const imageTensor = tensor(image2, shape, "float32"); + const complexTensor = complex(realTensor, imageTensor); + realTensor.dispose(); + imageTensor.dispose(); + return complexTensor; + } else { + throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); + } + offset += size * dtypeFactor; + } + return tensor(values, shape, dtype); +} +async function readToLength(reader, initialData, length) { + let data = new Uint8Array(initialData); + while (data.byteLength < length) { + const { done, value } = await reader.read(); + if (done && value == null) { + const missing = length - data.byteLength; + throw new Error(`Reader is done but ${missing} bytes are still expected`); + } + const newData = new Uint8Array(data.length + value.byteLength); + newData.set(data, 0); + newData.set(new Uint8Array(value), data.length); + data = newData; + } + return data.buffer; +} +async function decodeWeightsStream(weightStream, specs) { + const tensors = {}; + const reader = weightStream.getReader(); + let data = new ArrayBuffer(0); + for (const spec of specs) { + const byteLength = await getWeightBytelengthAsync(spec, async (start, end) => { + data = await readToLength(reader, data, end); + return data.slice(start, end); + }); + data = await readToLength(reader, data, byteLength); + const tensorData = data.slice(0, byteLength); + data = data.slice(byteLength); + const weightTensor = decodeWeight(spec, tensorData); + tensors[spec.name] = weightTensor; + if (getBackend() === "webgpu") { + const b = backend(); + if ("uploadToGPU" in b && sizeFromShape(weightTensor.shape) >= env().get("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD")) { + b.uploadToGPU(weightTensor.dataId); + } + } + } + return tensors; +} +function concatenateTypedArrays(xs) { + if (xs === null) { + throw new Error(`Invalid input value: ${JSON.stringify(xs)}`); + } + let totalByteLength = 0; + const normalizedXs = []; + xs.forEach((x) => { + totalByteLength += x.byteLength; + normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x)); + if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) { + throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`); + } + }); + const y = new Uint8Array(totalByteLength); + let offset = 0; + normalizedXs.forEach((x) => { + y.set(new Uint8Array(x.buffer), offset); + offset += x.byteLength; + }); + return y.buffer; +} +var useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined"); +function stringByteLength(str) { + if (useNodeBuffer) { + return Buffer.byteLength(str, "utf8"); + } + return new Blob([str]).size; +} +function arrayBufferToBase64String(buffer2) { + if (useNodeBuffer) { + return Buffer.from(buffer2).toString("base64"); + } + const buf = new Uint8Array(buffer2); + let s = ""; + for (let i = 0, l = buf.length; i < l; i++) { + s += String.fromCharCode(buf[i]); + } + return btoa(s); +} +function base64StringToArrayBuffer(str) { + if (useNodeBuffer) { + const buf = Buffer.from(str, "base64"); + return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength); + } + const s = atob(str); + const buffer2 = new Uint8Array(s.length); + for (let i = 0; i < s.length; ++i) { + buffer2.set([s.charCodeAt(i)], i); + } + return buffer2.buffer; +} +function concatenateArrayBuffers(buffers) { + return CompositeArrayBuffer.join(buffers); +} +function basename(path) { + const SEPARATOR = "/"; + path = path.trim(); + while (path.endsWith(SEPARATOR)) { + path = path.slice(0, path.length - 1); + } + const items = path.split(SEPARATOR); + return items[items.length - 1]; +} +function getModelJSONForModelArtifacts(artifacts, manifest) { + const result = { + modelTopology: artifacts.modelTopology, + format: artifacts.format, + generatedBy: artifacts.generatedBy, + convertedBy: artifacts.convertedBy, + weightsManifest: manifest + }; + if (artifacts.signature != null) { + result.signature = artifacts.signature; + } + if (artifacts.userDefinedMetadata != null) { + result.userDefinedMetadata = artifacts.userDefinedMetadata; + } + if (artifacts.modelInitializer != null) { + result.modelInitializer = artifacts.modelInitializer; + } + if (artifacts.initializerSignature != null) { + result.initializerSignature = artifacts.initializerSignature; + } + if (artifacts.trainingConfig != null) { + result.trainingConfig = artifacts.trainingConfig; + } + return result; +} +function getModelArtifactsForJSONSync(modelJSON, weightSpecs, weightData) { + const modelArtifacts = { + modelTopology: modelJSON.modelTopology, + format: modelJSON.format, + generatedBy: modelJSON.generatedBy, + convertedBy: modelJSON.convertedBy + }; + if (modelJSON.trainingConfig != null) { + modelArtifacts.trainingConfig = modelJSON.trainingConfig; + } + if (modelJSON.weightsManifest != null) { + if (!weightSpecs) { + throw new Error("modelJSON has weightsManifest but weightSpecs is null"); + } + if (!weightData) { + throw new Error("modelJSON has weightsManifest but weightData is null"); + } + modelArtifacts.weightSpecs = weightSpecs; + modelArtifacts.weightData = weightData; + } + if (modelJSON.signature != null) { + modelArtifacts.signature = modelJSON.signature; + } + if (modelJSON.userDefinedMetadata != null) { + modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata; + } + if (modelJSON.modelInitializer != null) { + modelArtifacts.modelInitializer = modelJSON.modelInitializer; + } + if (modelJSON.initializerSignature != null) { + modelArtifacts.initializerSignature = modelJSON.initializerSignature; + } + return modelArtifacts; +} +async function getModelArtifactsForJSON(modelJSON, loadWeights2) { + let weightSpecs; + let weightData; + if (modelJSON.weightsManifest != null) { + [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest); + } + return getModelArtifactsForJSONSync(modelJSON, weightSpecs, weightData); +} +function getModelArtifactsInfoForJSON(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("Expected JSON model topology, received ArrayBuffer."); + } + return { + dateSaved: /* @__PURE__ */ new Date(), + modelTopologyType: "JSON", + modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)), + weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)), + weightDataBytes: modelArtifacts.weightData == null ? 0 : new CompositeArrayBuffer(modelArtifacts.weightData).byteLength + }; +} +function getWeightSpecs(weightsManifest) { + const weightSpecs = []; + for (const entry of weightsManifest) { + weightSpecs.push(...entry.weights); + } + return weightSpecs; +} +function computeFloat16MantisaTable() { + const convertMantissa = (i) => { + let m = i << 13; + let e = 0; + while ((m & 8388608) === 0) { + e -= 8388608; + m <<= 1; + } + m &= ~8388608; + e += 947912704; + return m | e; + }; + const mantisaTable = new Uint32Array(2048); + mantisaTable[0] = 0; + for (let i = 1; i < 1024; i++) { + mantisaTable[i] = convertMantissa(i); + } + for (let i = 1024; i < 2048; i++) { + mantisaTable[i] = 939524096 + (i - 1024 << 13); + } + return mantisaTable; +} +function computeFloat16ExponentTable() { + const exponentTable = new Uint32Array(64); + exponentTable[0] = 0; + exponentTable[31] = 1199570944; + exponentTable[32] = 2147483648; + exponentTable[63] = 3347054592; + for (let i = 1; i < 31; i++) { + exponentTable[i] = i << 23; + } + for (let i = 33; i < 63; i++) { + exponentTable[i] = 2147483648 + (i - 32 << 23); + } + return exponentTable; +} +function computeFloat16OffsetTable() { + const offsetTable = new Uint32Array(64); + for (let i = 0; i < 64; i++) { + offsetTable[i] = 1024; + } + offsetTable[0] = offsetTable[32] = 0; + return offsetTable; +} +function getFloat16Decoder() { + const mantisaTable = computeFloat16MantisaTable(); + const exponentTable = computeFloat16ExponentTable(); + const offsetTable = computeFloat16OffsetTable(); + return (quantizedArray) => { + const buffer2 = new ArrayBuffer(4 * quantizedArray.length); + const bufferUint32View = new Uint32Array(buffer2); + for (let index = 0; index < quantizedArray.length; index++) { + const float16Bits = quantizedArray[index]; + const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10]; + bufferUint32View[index] = float32Bits; + } + return new Float32Array(buffer2); + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js +var IORouterRegistry = class _IORouterRegistry { + constructor() { + this.saveRouters = []; + this.loadRouters = []; + } + static getInstance() { + if (_IORouterRegistry.instance == null) { + _IORouterRegistry.instance = new _IORouterRegistry(); + } + return _IORouterRegistry.instance; + } + /** + * Register a save-handler router. + * + * @param saveRouter A function that maps a URL-like string onto an instance + * of `IOHandler` with the `save` method defined or `null`. + */ + static registerSaveRouter(saveRouter) { + _IORouterRegistry.getInstance().saveRouters.push(saveRouter); + } + /** + * Register a load-handler router. + * + * @param loadRouter A function that maps a URL-like string onto an instance + * of `IOHandler` with the `load` method defined or `null`. + */ + static registerLoadRouter(loadRouter) { + _IORouterRegistry.getInstance().loadRouters.push(loadRouter); + } + /** + * Look up IOHandler for saving, given a URL-like string. + * + * @param url + * @returns If only one match is found, an instance of IOHandler with the + * `save` method defined. If no match is found, `null`. + * @throws Error, if more than one match is found. + */ + static getSaveHandlers(url) { + return _IORouterRegistry.getHandlers(url, "save"); + } + /** + * Look up IOHandler for loading, given a URL-like string. + * + * @param url + * @param loadOptions Optional, custom load options. + * @returns All valid handlers for `url`, given the currently registered + * handler routers. + */ + static getLoadHandlers(url, loadOptions) { + return _IORouterRegistry.getHandlers(url, "load", loadOptions); + } + static getHandlers(url, handlerType, loadOptions) { + const validHandlers = []; + const routers = handlerType === "load" ? _IORouterRegistry.getInstance().loadRouters : _IORouterRegistry.getInstance().saveRouters; + routers.forEach((router) => { + const handler = router(url, loadOptions); + if (handler !== null) { + validHandlers.push(handler); + } + }); + return validHandlers; + } +}; +var registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter); +var registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter); +var getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url); +var getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js +var DATABASE_NAME = "tensorflowjs"; +var DATABASE_VERSION = 1; +var MODEL_STORE_NAME = "models_store"; +var INFO_STORE_NAME = "model_info_store"; +function getIndexedDBFactory() { + if (!env().getBool("IS_BROWSER")) { + throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser."); + } + const theWindow = typeof window === "undefined" ? self : window; + const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB; + if (factory == null) { + throw new Error("The current browser does not appear to support IndexedDB."); + } + return factory; +} +function setUpDatabase(openRequest) { + const db = openRequest.result; + db.createObjectStore(MODEL_STORE_NAME, { keyPath: "modelPath" }); + db.createObjectStore(INFO_STORE_NAME, { keyPath: "modelPath" }); +} +var BrowserIndexedDB = class { + constructor(modelPath) { + this.indexedDB = getIndexedDBFactory(); + if (modelPath == null || !modelPath) { + throw new Error("For IndexedDB, modelPath must not be null, undefined or empty."); + } + this.modelPath = modelPath; + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); + } + return this.databaseAction(this.modelPath, modelArtifacts); + } + async load() { + return this.databaseAction(this.modelPath); + } + /** + * Perform database action to put model artifacts into or read model artifacts + * from IndexedDB object store. + * + * Whether the action is put or get depends on whether `modelArtifacts` is + * specified. If it is specified, the action will be put; otherwise the action + * will be get. + * + * @param modelPath A unique string path for the model. + * @param modelArtifacts If specified, it will be the model artifacts to be + * stored in IndexedDB. + * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise` + * of `ModelArtifacts`, if the action is get. + */ + databaseAction(modelPath, modelArtifacts) { + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + if (modelArtifacts == null) { + const modelTx = db.transaction(MODEL_STORE_NAME, "readonly"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const getRequest = modelStore.get(this.modelPath); + getRequest.onsuccess = () => { + if (getRequest.result == null) { + db.close(); + return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`)); + } else { + resolve(getRequest.result.modelArtifacts); + } + }; + getRequest.onerror = (error) => { + db.close(); + return reject(getRequest.error); + }; + modelTx.oncomplete = () => db.close(); + } else { + modelArtifacts.weightData = CompositeArrayBuffer.join(modelArtifacts.weightData); + const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts); + const infoTx = db.transaction(INFO_STORE_NAME, "readwrite"); + let infoStore = infoTx.objectStore(INFO_STORE_NAME); + let putInfoRequest; + try { + putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo }); + } catch (error) { + return reject(error); + } + let modelTx; + putInfoRequest.onsuccess = () => { + modelTx = db.transaction(MODEL_STORE_NAME, "readwrite"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + let putModelRequest; + try { + putModelRequest = modelStore.put({ + modelPath: this.modelPath, + modelArtifacts, + modelArtifactsInfo + }); + } catch (error) { + return reject(error); + } + putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo }); + putModelRequest.onerror = (error) => { + infoStore = infoTx.objectStore(INFO_STORE_NAME); + const deleteInfoRequest = infoStore.delete(this.modelPath); + deleteInfoRequest.onsuccess = () => { + db.close(); + return reject(putModelRequest.error); + }; + deleteInfoRequest.onerror = (error2) => { + db.close(); + return reject(putModelRequest.error); + }; + }; + }; + putInfoRequest.onerror = (error) => { + db.close(); + return reject(putInfoRequest.error); + }; + infoTx.oncomplete = () => { + if (modelTx == null) { + db.close(); + } else { + modelTx.oncomplete = () => db.close(); + } + }; + } + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } +}; +BrowserIndexedDB.URL_SCHEME = "indexeddb://"; +var indexedDBRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) { + return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(indexedDBRouter); +IORouterRegistry.registerLoadRouter(indexedDBRouter); +function browserIndexedDB(modelPath) { + return new BrowserIndexedDB(modelPath); +} +function maybeStripScheme(key) { + return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key; +} +var BrowserIndexedDBManager = class { + constructor() { + this.indexedDB = getIndexedDBFactory(); + } + async listModels() { + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + const tx = db.transaction(INFO_STORE_NAME, "readonly"); + const store = tx.objectStore(INFO_STORE_NAME); + const getAllInfoRequest = store.getAll(); + getAllInfoRequest.onsuccess = () => { + const out = {}; + for (const item of getAllInfoRequest.result) { + out[item.modelPath] = item.modelArtifactsInfo; + } + resolve(out); + }; + getAllInfoRequest.onerror = (error) => { + db.close(); + return reject(getAllInfoRequest.error); + }; + tx.oncomplete = () => db.close(); + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } + async removeModel(path) { + path = maybeStripScheme(path); + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + const infoTx = db.transaction(INFO_STORE_NAME, "readwrite"); + const infoStore = infoTx.objectStore(INFO_STORE_NAME); + const getInfoRequest = infoStore.get(path); + let modelTx; + getInfoRequest.onsuccess = () => { + if (getInfoRequest.result == null) { + db.close(); + return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`)); + } else { + const deleteInfoRequest = infoStore.delete(path); + const deleteModelData = () => { + modelTx = db.transaction(MODEL_STORE_NAME, "readwrite"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const deleteModelRequest = modelStore.delete(path); + deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo); + deleteModelRequest.onerror = (error) => reject(getInfoRequest.error); + }; + deleteInfoRequest.onsuccess = deleteModelData; + deleteInfoRequest.onerror = (error) => { + deleteModelData(); + db.close(); + return reject(getInfoRequest.error); + }; + } + }; + getInfoRequest.onerror = (error) => { + db.close(); + return reject(getInfoRequest.error); + }; + infoTx.oncomplete = () => { + if (modelTx == null) { + db.close(); + } else { + modelTx.oncomplete = () => db.close(); + } + }; + }; + openRequest.onerror = (error) => reject(openRequest.error); + }); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js +var PATH_SEPARATOR = "/"; +var PATH_PREFIX = "tensorflowjs_models"; +var INFO_SUFFIX = "info"; +var MODEL_TOPOLOGY_SUFFIX = "model_topology"; +var WEIGHT_SPECS_SUFFIX = "weight_specs"; +var WEIGHT_DATA_SUFFIX = "weight_data"; +var MODEL_METADATA_SUFFIX = "model_metadata"; +function getModelKeys(path) { + return { + info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR), + topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR), + weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR), + weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR), + modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR) + }; +} +function removeItems(keys) { + for (const key of Object.values(keys)) { + window.localStorage.removeItem(key); + } +} +function getModelPathFromKey(key) { + const items = key.split(PATH_SEPARATOR); + if (items.length < 3) { + throw new Error(`Invalid key format: ${key}`); + } + return items.slice(1, items.length - 1).join(PATH_SEPARATOR); +} +function maybeStripScheme2(key) { + return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key; +} +var BrowserLocalStorage = class { + constructor(modelPath) { + if (!env().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") { + throw new Error("The current environment does not support local storage."); + } + this.LS = window.localStorage; + if (modelPath == null || !modelPath) { + throw new Error("For local storage, modelPath must not be null, undefined or empty."); + } + this.modelPath = modelPath; + this.keys = getModelKeys(this.modelPath); + } + /** + * Save model artifacts to browser local storage. + * + * See the documentation to `browserLocalStorage` for details on the saved + * artifacts. + * + * @param modelArtifacts The model artifacts to be stored. + * @returns An instance of SaveResult. + */ + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); + } else { + const topology = JSON.stringify(modelArtifacts.modelTopology); + const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs); + const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts); + const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData); + try { + this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo)); + this.LS.setItem(this.keys.topology, topology); + this.LS.setItem(this.keys.weightSpecs, weightSpecs); + this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(weightBuffer)); + const metadata = { + format: modelArtifacts.format, + generatedBy: modelArtifacts.generatedBy, + convertedBy: modelArtifacts.convertedBy, + signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0, + userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0, + modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0, + initializerSignature: modelArtifacts.initializerSignature != null ? modelArtifacts.initializerSignature : void 0, + trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0 + }; + this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata)); + return { modelArtifactsInfo }; + } catch (err) { + removeItems(this.keys); + throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`); + } + } + } + /** + * Load a model from local storage. + * + * See the documentation to `browserLocalStorage` for details on the saved + * artifacts. + * + * @returns The loaded model (if loading succeeds). + */ + async load() { + const info = JSON.parse(this.LS.getItem(this.keys.info)); + if (info == null) { + throw new Error(`In local storage, there is no model with name '${this.modelPath}'`); + } + if (info.modelTopologyType !== "JSON") { + throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet."); + } + const out = {}; + const topology = JSON.parse(this.LS.getItem(this.keys.topology)); + if (topology == null) { + throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`); + } + out.modelTopology = topology; + const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs)); + if (weightSpecs == null) { + throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`); + } + out.weightSpecs = weightSpecs; + const metadataString = this.LS.getItem(this.keys.modelMetadata); + if (metadataString != null) { + const metadata = JSON.parse(metadataString); + out.format = metadata.format; + out.generatedBy = metadata.generatedBy; + out.convertedBy = metadata.convertedBy; + if (metadata.signature != null) { + out.signature = metadata.signature; + } + if (metadata.userDefinedMetadata != null) { + out.userDefinedMetadata = metadata.userDefinedMetadata; + } + if (metadata.modelInitializer != null) { + out.modelInitializer = metadata.modelInitializer; + } + if (metadata.initializerSignature != null) { + out.initializerSignature = metadata.initializerSignature; + } + if (metadata.trainingConfig != null) { + out.trainingConfig = metadata.trainingConfig; + } + } + const weightDataBase64 = this.LS.getItem(this.keys.weightData); + if (weightDataBase64 == null) { + throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`); + } + out.weightData = base64StringToArrayBuffer(weightDataBase64); + return out; + } +}; +BrowserLocalStorage.URL_SCHEME = "localstorage://"; +var localStorageRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) { + return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(localStorageRouter); +IORouterRegistry.registerLoadRouter(localStorageRouter); +function browserLocalStorage(modelPath) { + return new BrowserLocalStorage(modelPath); +} +var BrowserLocalStorageManager = class { + constructor() { + assert(env().getBool("IS_BROWSER"), () => "Current environment is not a web browser"); + assert(typeof window === "undefined" || typeof window.localStorage !== "undefined", () => "Current browser does not appear to support localStorage"); + this.LS = window.localStorage; + } + async listModels() { + const out = {}; + const prefix = PATH_PREFIX + PATH_SEPARATOR; + const suffix = PATH_SEPARATOR + INFO_SUFFIX; + for (let i = 0; i < this.LS.length; ++i) { + const key = this.LS.key(i); + if (key.startsWith(prefix) && key.endsWith(suffix)) { + const modelPath = getModelPathFromKey(key); + out[modelPath] = JSON.parse(this.LS.getItem(key)); + } + } + return out; + } + async removeModel(path) { + path = maybeStripScheme2(path); + const keys = getModelKeys(path); + if (this.LS.getItem(keys.info) == null) { + throw new Error(`Cannot find model at path '${path}'`); + } + const info = JSON.parse(this.LS.getItem(keys.info)); + removeItems(keys); + return info; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js +var URL_SCHEME_SUFFIX = "://"; +var ModelStoreManagerRegistry = class _ModelStoreManagerRegistry { + constructor() { + this.managers = {}; + } + static getInstance() { + if (_ModelStoreManagerRegistry.instance == null) { + _ModelStoreManagerRegistry.instance = new _ModelStoreManagerRegistry(); + } + return _ModelStoreManagerRegistry.instance; + } + /** + * Register a save-handler router. + * + * @param saveRouter A function that maps a URL-like string onto an instance + * of `IOHandler` with the `save` method defined or `null`. + */ + static registerManager(scheme, manager) { + assert(scheme != null, () => "scheme must not be undefined or null."); + if (scheme.endsWith(URL_SCHEME_SUFFIX)) { + scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX)); + } + assert(scheme.length > 0, () => "scheme must not be an empty string."); + const registry = _ModelStoreManagerRegistry.getInstance(); + assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`); + registry.managers[scheme] = manager; + } + static getManager(scheme) { + const manager = _ModelStoreManagerRegistry.getInstance().managers[scheme]; + if (manager == null) { + throw new Error(`Cannot find model manager for scheme '${scheme}'`); + } + return manager; + } + static getSchemes() { + return Object.keys(_ModelStoreManagerRegistry.getInstance().managers); + } +}; +function parseURL(url) { + if (url.indexOf(URL_SCHEME_SUFFIX) === -1) { + throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`); + } + return { + scheme: url.split(URL_SCHEME_SUFFIX)[0], + path: url.split(URL_SCHEME_SUFFIX)[1] + }; +} +async function cloneModelInternal(sourceURL, destURL, deleteSource = false) { + assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`); + const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL); + assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`); + assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`); + const loadHandler = loadHandlers[0]; + const saveHandlers = IORouterRegistry.getSaveHandlers(destURL); + assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`); + assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`); + const saveHandler = saveHandlers[0]; + const sourceScheme = parseURL(sourceURL).scheme; + const sourcePath = parseURL(sourceURL).path; + const sameMedium = sourceScheme === parseURL(sourceURL).scheme; + const modelArtifacts = await loadHandler.load(); + if (deleteSource && sameMedium) { + await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath); + } + const saveResult = await saveHandler.save(modelArtifacts); + if (deleteSource && !sameMedium) { + await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath); + } + return saveResult.modelArtifactsInfo; +} +async function listModels() { + const schemes = ModelStoreManagerRegistry.getSchemes(); + const out = {}; + for (const scheme of schemes) { + const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels(); + for (const path in schemeOut) { + const url = scheme + URL_SCHEME_SUFFIX + path; + out[url] = schemeOut[path]; + } + } + return out; +} +async function removeModel(url) { + const schemeAndPath = parseURL(url); + const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme); + return manager.removeModel(schemeAndPath.path); +} +async function copyModel(sourceURL, destURL) { + const deleteSource = false; + return cloneModelInternal(sourceURL, destURL, deleteSource); +} +async function moveModel(sourceURL, destURL) { + const deleteSource = true; + return cloneModelInternal(sourceURL, destURL, deleteSource); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js +var PlatformBrowser = class { + constructor() { + this.messageName = "setTimeoutCustom"; + this.functionRefs = []; + this.handledMessageCount = 0; + this.hasEventListener = false; + } + fetch(path, init2) { + return fetch(path, init2); + } + now() { + return performance.now(); + } + encode(text, encoding) { + if (encoding !== "utf-8" && encoding !== "utf8") { + throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`); + } + if (this.textEncoder == null) { + this.textEncoder = new TextEncoder(); + } + return this.textEncoder.encode(text); + } + decode(bytes, encoding) { + return new TextDecoder(encoding).decode(bytes); + } + // If the setTimeout nesting level is greater than 5 and timeout is less + // than 4ms, timeout will be clamped to 4ms, which hurts the perf. + // Interleaving window.postMessage and setTimeout will trick the browser and + // avoid the clamp. + setTimeoutCustom(functionRef, delay) { + if (typeof window === "undefined" || !env().getBool("USE_SETTIMEOUTCUSTOM")) { + setTimeout(functionRef, delay); + return; + } + this.functionRefs.push(functionRef); + setTimeout(() => { + window.postMessage({ name: this.messageName, index: this.functionRefs.length - 1 }, "*"); + }, delay); + if (!this.hasEventListener) { + this.hasEventListener = true; + window.addEventListener("message", (event) => { + if (event.source === window && event.data.name === this.messageName) { + event.stopPropagation(); + const functionRef2 = this.functionRefs[event.data.index]; + functionRef2(); + this.handledMessageCount++; + if (this.handledMessageCount === this.functionRefs.length) { + this.functionRefs = []; + this.handledMessageCount = 0; + } + } + }, true); + } + } + isTypedArray(a) { + return isTypedArrayBrowser(a); + } +}; +if (env().get("IS_BROWSER")) { + env().setPlatform("browser", new PlatformBrowser()); + try { + ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager()); + } catch (err) { + } + try { + ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager()); + } catch (err) { + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js +var getNodeFetch = { + // tslint:disable-next-line:no-require-imports + importFetch: () => require_browser() +}; +var systemFetch; +var PlatformNode = class { + constructor() { + this.util = require_util(); + this.textEncoder = new this.util.TextEncoder(); + } + fetch(path, requestInits) { + if (env().global.fetch != null) { + return env().global.fetch(path, requestInits); + } + if (systemFetch == null) { + systemFetch = getNodeFetch.importFetch(); + } + return systemFetch(path, requestInits); + } + now() { + const time2 = process.hrtime(); + return time2[0] * 1e3 + time2[1] / 1e6; + } + encode(text, encoding) { + if (encoding !== "utf-8" && encoding !== "utf8") { + throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`); + } + return this.textEncoder.encode(text); + } + decode(bytes, encoding) { + if (bytes.length === 0) { + return ""; + } + return new this.util.TextDecoder(encoding).decode(bytes); + } + isTypedArray(a) { + return this.util.types.isFloat32Array(a) || this.util.types.isInt32Array(a) || this.util.types.isUint8Array(a) || this.util.types.isUint8ClampedArray(a); + } +}; +if (env().get("IS_NODE") && !env().get("IS_BROWSER")) { + env().setPlatform("node", new PlatformNode()); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js +function buffer(shape, dtype = "float32", values) { + dtype = dtype || "float32"; + assertNonNegativeIntegerDimensions(shape); + return new TensorBuffer(shape, dtype, values); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js +function cast_(x, dtype) { + const $x = convertToTensor(x, "x", "cast"); + if (!isValidDtype(dtype)) { + throw new Error(`Failed to cast to unknown dtype ${dtype}`); + } + if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") { + throw new Error("Only strings can be casted to strings"); + } + const inputs = { x: $x }; + const attrs = { dtype }; + return ENGINE.runKernel(Cast, inputs, attrs); +} +var cast = op({ cast_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js +function clone_(x) { + const $x = convertToTensor(x, "x", "clone", "string_or_numeric"); + const inputs = { x: $x }; + return ENGINE.runKernel(Identity, inputs); +} +var clone = op({ clone_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/print.js +function print(x, verbose = false) { + console.log(x.toString(verbose)); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js +getOrMakeEngine(); +var opHandler2 = { + buffer, + cast, + clone, + print +}; +setOpHandler(opHandler2); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/add.js +function add_(a, b) { + let $a = convertToTensor(a, "a", "add"); + let $b = convertToTensor(b, "b", "add"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Add, inputs); +} +var add2 = op({ add_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js +function floorDiv_(a, b) { + let $a = convertToTensor(a, "a", "floorDiv"); + let $b = convertToTensor(b, "b", "floorDiv"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(FloorDiv, inputs); +} +var floorDiv = op({ floorDiv_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/div.js +function div_(a, b) { + let $a = convertToTensor(a, "a", "div"); + let $b = convertToTensor(b, "b", "div"); + [$a, $b] = makeTypesMatch($a, $b); + if ($a.dtype === "int32" && $b.dtype === "int32") { + return floorDiv($a, $b); + } + const inputs = { a: $a, b: $b }; + const attrs = {}; + return ENGINE.runKernel(RealDiv, inputs, attrs); +} +var div = op({ div_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/mul.js +function mul_(a, b) { + let $a = convertToTensor(a, "a", "mul"); + let $b = convertToTensor(b, "b", "mul"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Multiply, inputs); +} +var mul = op({ mul_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/abs.js +function abs_(x) { + const $x = convertToTensor(x, "x", "abs"); + if ($x.dtype === "complex64") { + const inputs = { x: $x }; + return ENGINE.runKernel(ComplexAbs, inputs); + } else { + const inputs = { x: $x }; + return ENGINE.runKernel(Abs, inputs); + } +} +var abs = op({ abs_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/acos.js +function acos_(x) { + const $x = convertToTensor(x, "x", "acos"); + const inputs = { x: $x }; + return ENGINE.runKernel(Acos, inputs); +} +var acos = op({ acos_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js +function acosh_(x) { + const $x = convertToTensor(x, "x", "acosh"); + const inputs = { x: $x }; + return ENGINE.runKernel(Acosh, inputs); +} +var acosh = op({ acosh_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js +function addN_(tensors) { + assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors"); + assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`); + const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "addN")); + const firstTensor = $tensors[0]; + $tensors.forEach((t) => { + if (t.dtype !== firstTensor.dtype) { + throw new Error("All tensors passed to tf.addN() must have the same dtype"); + } + }); + $tensors.forEach((t) => { + if (!arraysEqual(t.shape, firstTensor.shape)) { + throw new Error("All tensors passed to tf.addN() must have the same shape"); + } + }); + const inputs = $tensors; + return ENGINE.runKernel(AddN, inputs); +} +var addN = op({ addN_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/all.js +function all_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "all", "bool"); + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(All, inputs, attrs); +} +var all = op({ all_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/any.js +function any_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "any", "bool"); + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(Any, inputs, attrs); +} +var any = op({ any_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js +function argMax_(x, axis = 0) { + const $x = convertToTensor(x, "x", "argMax"); + const inputs = { x: $x }; + const attrs = { axis }; + return ENGINE.runKernel(ArgMax, inputs, attrs); +} +var argMax = op({ argMax_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js +function argMin_(x, axis = 0) { + const $x = convertToTensor(x, "x", "argMin"); + const inputs = { x: $x }; + const attrs = { axis }; + return ENGINE.runKernel(ArgMin, inputs, attrs); +} +var argMin = op({ argMin_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/asin.js +function asin_(x) { + const $x = convertToTensor(x, "x", "asin"); + const inputs = { x: $x }; + return ENGINE.runKernel(Asin, inputs); +} +var asin = op({ asin_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js +function asinh_(x) { + const $x = convertToTensor(x, "x", "asinh"); + const inputs = { x: $x }; + return ENGINE.runKernel(Asinh, inputs); +} +var asinh = op({ asinh_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan.js +function atan_(x) { + const $x = convertToTensor(x, "x", "atan"); + const inputs = { x: $x }; + return ENGINE.runKernel(Atan, inputs); +} +var atan = op({ atan_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js +function atan2_(a, b) { + let $a = convertToTensor(a, "a", "atan2"); + let $b = convertToTensor(b, "b", "atan2"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Atan2, inputs); +} +var atan2 = op({ atan2_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js +function atanh_(x) { + const $x = convertToTensor(x, "x", "atanh"); + const inputs = { x: $x }; + return ENGINE.runKernel(Atanh, inputs); +} +var atanh = op({ atanh_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js +function computeDilation2DInfo(inputShape, filterShape, strides, pad3, dataFormat = "NHWC", dilations) { + const inputChannels = inputShape[3]; + const $filterShape = [...filterShape, inputChannels]; + const $dataFormat = convertConv2DDataFormat(dataFormat); + return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad3, null, null, $dataFormat); +} +function computePool2DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = "channelsLast") { + const [filterHeight, filterWidth] = parseTupleParam(filterSize); + let filterShape; + if (dataFormat === "channelsLast") { + filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]]; + } else if (dataFormat === "channelsFirst") { + filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]]; + } else { + throw new Error(`Unknown dataFormat ${dataFormat}`); + } + return computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, false, dataFormat); +} +function computePool3DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = "NDHWC") { + const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize); + let filterShape; + let $dataFormat; + if (dataFormat === "NDHWC") { + $dataFormat = "channelsLast"; + filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]]; + } else if (dataFormat === "NCDHW") { + $dataFormat = "channelsFirst"; + filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]]; + } else { + throw new Error(`Unknown dataFormat ${dataFormat}`); + } + return computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, false, $dataFormat, roundingMode); +} +function computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, depthwise = false, dataFormat = "channelsLast") { + let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1]; + if (dataFormat === "channelsLast") { + [batchSize, inHeight, inWidth, inChannels] = inShape; + } else if (dataFormat === "channelsFirst") { + [batchSize, inChannels, inHeight, inWidth] = inShape; + } else { + throw new Error(`Unknown dataFormat ${dataFormat}`); + } + const [filterHeight, filterWidth, , filterChannels] = filterShape; + const [strideHeight, strideWidth] = parseTupleParam(strides); + const [dilationHeight, dilationWidth] = parseTupleParam(dilations); + const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight); + const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth); + const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat); + const outChannels = depthwise ? filterChannels * inChannels : filterChannels; + let outShape; + if (dataFormat === "channelsFirst") { + outShape = [batchSize, outChannels, outHeight, outWidth]; + } else if (dataFormat === "channelsLast") { + outShape = [batchSize, outHeight, outWidth, outChannels]; + } + return { + batchSize, + dataFormat, + inHeight, + inWidth, + inChannels, + outHeight, + outWidth, + outChannels, + padInfo, + strideHeight, + strideWidth, + filterHeight, + filterWidth, + effectiveFilterHeight, + effectiveFilterWidth, + dilationHeight, + dilationWidth, + inShape, + outShape, + filterShape + }; +} +function computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, depthwise = false, dataFormat = "channelsLast", roundingMode) { + let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1]; + if (dataFormat === "channelsLast") { + [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape; + } else if (dataFormat === "channelsFirst") { + [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape; + } else { + throw new Error(`Unknown dataFormat ${dataFormat}`); + } + const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape; + const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides); + const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations); + const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth); + const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight); + const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth); + const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode); + const outChannels = depthwise ? filterChannels * inChannels : filterChannels; + let outShape; + if (dataFormat === "channelsFirst") { + outShape = [batchSize, outChannels, outDepth, outHeight, outWidth]; + } else if (dataFormat === "channelsLast") { + outShape = [batchSize, outDepth, outHeight, outWidth, outChannels]; + } + return { + batchSize, + dataFormat, + inDepth, + inHeight, + inWidth, + inChannels, + outDepth, + outHeight, + outWidth, + outChannels, + padInfo, + strideDepth, + strideHeight, + strideWidth, + filterDepth, + filterHeight, + filterWidth, + effectiveFilterDepth, + effectiveFilterHeight, + effectiveFilterWidth, + dilationDepth, + dilationHeight, + dilationWidth, + inShape, + outShape, + filterShape + }; +} +function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) { + if (zeroPad == null) { + zeroPad = computeDefaultPad(inShape, fieldSize, stride); + } + const inputRows = inShape[0]; + const inputCols = inShape[1]; + const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode); + const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode); + return [outputRows, outputCols]; +} +function computeOutputShape4D(inShape, filterShape, outChannels, strides, zeroPad, roundingMode) { + if (zeroPad == null) { + zeroPad = computeDefaultPad(inShape, filterShape[0], strides[0]); + } + const outShape = [0, 0, 0, outChannels]; + for (let index = 0; index < 3; index++) { + if (inShape[index] + 2 * zeroPad >= filterShape[index]) { + outShape[index] = round((inShape[index] - filterShape[index] + 2 * zeroPad) / strides[index] + 1, roundingMode); + } + } + return outShape; +} +function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) { + const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation); + return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2); +} +function parseTupleParam(param) { + if (typeof param === "number") { + return [param, param, param]; + } + if (param.length === 2) { + return [param[0], param[1], 1]; + } + return param; +} +function parse3TupleParam(param) { + return typeof param === "number" ? [param, param, param] : param; +} +function getEffectiveFilterSize(filterSize, dilation) { + if (dilation <= 1) { + return filterSize; + } + return filterSize + (filterSize - 1) * (dilation - 1); +} +function getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) { + let padInfo; + let outHeight; + let outWidth; + if (typeof pad3 === "number") { + const padType = pad3 === 0 ? "VALID" : "NUMBER"; + padInfo = { top: pad3, bottom: pad3, left: pad3, right: pad3, type: padType }; + const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad3, roundingMode); + outHeight = outShape[0]; + outWidth = outShape[1]; + } else if (pad3 === "same") { + outHeight = Math.ceil(inHeight / strideHeight); + outWidth = Math.ceil(inWidth / strideWidth); + const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight); + const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth); + const top = Math.floor(padAlongHeight / 2); + const bottom = padAlongHeight - top; + const left = Math.floor(padAlongWidth / 2); + const right = padAlongWidth - left; + padInfo = { top, bottom, left, right, type: "SAME" }; + } else if (pad3 === "valid") { + padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }; + outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight); + outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth); + } else if (typeof pad3 === "object") { + const top = dataFormat === "channelsLast" ? pad3[1][0] : pad3[2][0]; + const bottom = dataFormat === "channelsLast" ? pad3[1][1] : pad3[2][1]; + const left = dataFormat === "channelsLast" ? pad3[2][0] : pad3[3][0]; + const right = dataFormat === "channelsLast" ? pad3[2][1] : pad3[3][1]; + const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT"; + padInfo = { top, bottom, left, right, type: padType }; + outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode); + outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode); + } else { + throw Error(`Unknown padding parameter: ${pad3}`); + } + return { padInfo, outHeight, outWidth }; +} +function get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) { + let padInfo; + let outDepth; + let outHeight; + let outWidth; + if (pad3 === "valid") { + pad3 = 0; + } + if (typeof pad3 === "number") { + const padType = pad3 === 0 ? "VALID" : "NUMBER"; + padInfo = { + top: pad3, + bottom: pad3, + left: pad3, + right: pad3, + front: pad3, + back: pad3, + type: padType + }; + const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], [filterDepth, filterHeight, filterWidth], 1, [strideDepth, strideHeight, strideWidth], pad3, roundingMode); + outDepth = outShape[0]; + outHeight = outShape[1]; + outWidth = outShape[2]; + } else if (pad3 === "same") { + outDepth = Math.ceil(inDepth / strideDepth); + outHeight = Math.ceil(inHeight / strideHeight); + outWidth = Math.ceil(inWidth / strideWidth); + const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth; + const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight; + const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth; + const front = Math.floor(padAlongDepth / 2); + const back = padAlongDepth - front; + const top = Math.floor(padAlongHeight / 2); + const bottom = padAlongHeight - top; + const left = Math.floor(padAlongWidth / 2); + const right = padAlongWidth - left; + padInfo = { top, bottom, left, right, front, back, type: "SAME" }; + } else { + throw Error(`Unknown padding parameter: ${pad3}`); + } + return { padInfo, outDepth, outHeight, outWidth }; +} +function round(value, roundingMode) { + if (!roundingMode) { + return Math.trunc(value); + } + switch (roundingMode) { + case "round": + return Math.round(value); + case "ceil": + return Math.ceil(value); + case "floor": + return Math.floor(value); + default: + throw new Error(`Unknown roundingMode ${roundingMode}`); + } +} +function tupleValuesAreOne(param) { + const [dimA, dimB, dimC] = parseTupleParam(param); + return dimA === 1 && dimB === 1 && dimC === 1; +} +function eitherStridesOrDilationsAreOne(strides, dilations) { + return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations); +} +function stridesOrDilationsArePositive(values) { + return parseTupleParam(values).every((value) => value > 0); +} +function convertConv2DDataFormat(dataFormat) { + if (dataFormat === "NHWC") { + return "channelsLast"; + } else if (dataFormat === "NCHW") { + return "channelsFirst"; + } else { + throw new Error(`Unknown dataFormat ${dataFormat}`); + } +} +function checkPadOnDimRoundingMode(opDesc, pad3, dimRoundingMode) { + if (dimRoundingMode != null) { + if (typeof pad3 === "string") { + throw Error(`Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); + } else if (typeof pad3 === "number") { + assert(isInt(pad3), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); + } else if (typeof pad3 === "object") { + pad3.forEach((p2) => { + p2.forEach((v) => { + assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${v}.`); + }); + }); + } else { + throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad3}`); + } + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js +function reshape_(x, shape) { + const $x = convertToTensor(x, "x", "reshape", "string_or_numeric"); + const inputs = { x: $x }; + const attrs = { shape }; + return ENGINE.runKernel(Reshape, inputs, attrs); +} +var reshape = op({ reshape_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js +function avgPool_(x, filterSize, strides, pad3, dimRoundingMode) { + const $x = convertToTensor(x, "x", "avgPool", "float32"); + const dilations = 1; + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`); + checkPadOnDimRoundingMode("avgPool", pad3, dimRoundingMode); + const inputs = { x: x4D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; + let res = ENGINE.runKernel(AvgPool, inputs, attrs); + res = cast(res, $x.dtype); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var avgPool = op({ avgPool_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js +function avgPool3d_(x, filterSize, strides, pad3, dimRoundingMode, dataFormat = "NDHWC") { + const $x = convertToTensor(x, "x", "avgPool3d", "float32"); + let x5D = $x; + let reshapedTo5D = false; + if ($x.rank === 4) { + reshapedTo5D = true; + x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]); + } + assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`); + assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`); + assert(typeof strides === "number" && strides > 0 || Array.isArray(strides) && strides[0] > 0 && strides[1] > 0 && strides[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${strides}'`); + checkPadOnDimRoundingMode("avgPool3d", pad3, dimRoundingMode); + const inputs = { x: x5D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat }; + let res = ENGINE.runKernel(AvgPool3D, inputs, attrs); + res = cast(res, x5D.dtype); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var avgPool3d = op({ avgPool3d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat.js +function concat_(tensors, axis = 0) { + assert(tensors.length >= 1, () => "Pass at least one tensor to concat"); + const $tensors = convertToTensorArray(tensors, "tensors", "concat", "string_or_numeric"); + if ($tensors[0].dtype === "complex64") { + $tensors.forEach((tensor2) => { + if (tensor2.dtype !== "complex64") { + throw new Error(`Cannot concatenate complex64 tensors with a tensor + with dtype ${tensor2.dtype}. `); + } + }); + } + if ($tensors.length === 1) { + return clone($tensors[0]); + } + const inputs = $tensors; + const attr = { axis }; + return ENGINE.runKernel(Concat, inputs, attr); +} +var concat = op({ concat_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js +function matMul_(a, b, transposeA = false, transposeB = false) { + let $a = convertToTensor(a, "a", "matMul"); + let $b = convertToTensor(b, "b", "matMul"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + const attrs = { transposeA, transposeB }; + return ENGINE.runKernel(BatchMatMul, inputs, attrs); +} +var matMul = op({ matMul_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js +function sigmoid_(x) { + const $x = convertToTensor(x, "x", "sigmoid", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Sigmoid, inputs); +} +var sigmoid = op({ sigmoid_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js +function slice_(x, begin, size) { + const $x = convertToTensor(x, "x", "slice", "string_or_numeric"); + if ($x.rank === 0) { + throw new Error("Slicing scalar is not possible"); + } + const inputs = { x: $x }; + const attrs = { begin, size }; + return ENGINE.runKernel(Slice, inputs, attrs); +} +var slice = op({ slice_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js +function tanh_(x) { + const $x = convertToTensor(x, "x", "tanh", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Tanh, inputs); +} +var tanh2 = op({ tanh_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js +function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) { + const $forgetBias = convertToTensor(forgetBias, "forgetBias", "basicLSTMCell"); + const $lstmKernel = convertToTensor(lstmKernel, "lstmKernel", "basicLSTMCell"); + const $lstmBias = convertToTensor(lstmBias, "lstmBias", "basicLSTMCell"); + const $data = convertToTensor(data, "data", "basicLSTMCell"); + const $c = convertToTensor(c, "c", "basicLSTMCell"); + const $h = convertToTensor(h, "h", "basicLSTMCell"); + const combined = concat([$data, $h], 1); + const weighted = matMul(combined, $lstmKernel); + const res = add2(weighted, $lstmBias); + const batchSize = res.shape[0]; + const sliceCols = res.shape[1] / 4; + const sliceSize = [batchSize, sliceCols]; + const i = slice(res, [0, 0], sliceSize); + const j = slice(res, [0, sliceCols], sliceSize); + const f = slice(res, [0, sliceCols * 2], sliceSize); + const o = slice(res, [0, sliceCols * 3], sliceSize); + const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f)))); + const newH = mul(tanh2(newC), sigmoid(o)); + return [newC, newH]; +} +var basicLSTMCell = op({ basicLSTMCell_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js +function batchToSpaceND_(x, blockShape, crops) { + const $x = convertToTensor(x, "x", "batchToSpaceND"); + const prod5 = blockShape.reduce((a, b) => a * b); + assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`); + assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length ${blockShape.length}`); + assert($x.shape[0] % prod5 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`); + const inputs = { x: $x }; + const attrs = { blockShape, crops }; + return ENGINE.runKernel(BatchToSpaceND, inputs, attrs); +} +var batchToSpaceND = op({ batchToSpaceND_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js +function xAs4D(x) { + let x4D; + if (x.rank === 0 || x.rank === 1) { + x4D = reshape(x, [1, 1, 1, x.size]); + } else if (x.rank === 2) { + x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]); + } else if (x.rank === 3) { + x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); + } else { + x4D = x; + } + return x4D; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js +function batchNorm_(x, mean4, variance, offset, scale2, varianceEpsilon) { + if (varianceEpsilon == null) { + varianceEpsilon = 1e-3; + } + const $x = convertToTensor(x, "x", "batchNorm"); + const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $variance = convertToTensor(variance, "variance", "batchNorm"); + let $scale; + if (scale2 != null) { + $scale = convertToTensor(scale2, "scale", "batchNorm"); + } + let $offset; + if (offset != null) { + $offset = convertToTensor(offset, "offset", "batchNorm"); + } + assert($mean.rank === $variance.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."); + assert($offset == null || $mean.rank === $offset.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."); + assert($scale == null || $mean.rank === $scale.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks."); + const x4D = xAs4D($x); + const inputs = { + x: x4D, + scale: $scale, + offset: $offset, + mean: $mean, + variance: $variance + }; + const attrs = { varianceEpsilon }; + const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs); + return reshape(res, $x.shape); +} +var batchNorm = op({ batchNorm_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js +function batchNorm2d_(x, mean4, variance, offset, scale2, varianceEpsilon) { + const $x = convertToTensor(x, "x", "batchNorm"); + const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $variance = convertToTensor(variance, "variance", "batchNorm"); + let $scale; + if (scale2 != null) { + $scale = convertToTensor(scale2, "scale", "batchNorm"); + } + let $offset; + if (offset != null) { + $offset = convertToTensor(offset, "offset", "batchNorm"); + } + assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`); + assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`); + assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`); + if ($scale != null) { + assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`); + } + if ($offset != null) { + assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`); + } + return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); +} +var batchNorm2d = op({ batchNorm2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js +function batchNorm3d_(x, mean4, variance, offset, scale2, varianceEpsilon) { + const $x = convertToTensor(x, "x", "batchNorm"); + const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $variance = convertToTensor(variance, "variance", "batchNorm"); + let $scale; + if (scale2 != null) { + $scale = convertToTensor(scale2, "scale", "batchNorm"); + } + let $offset; + if (offset != null) { + $offset = convertToTensor(offset, "offset", "batchNorm"); + } + assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`); + assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`); + assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`); + if ($scale != null) { + assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`); + } + if ($offset != null) { + assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`); + } + return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); +} +var batchNorm3d = op({ batchNorm3d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js +function batchNorm4d_(x, mean4, variance, offset, scale2, varianceEpsilon) { + const $x = convertToTensor(x, "x", "batchNorm"); + const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $variance = convertToTensor(variance, "variance", "batchNorm"); + let $scale; + if (scale2 != null) { + $scale = convertToTensor(scale2, "scale", "batchNorm"); + } + let $offset; + if (offset != null) { + $offset = convertToTensor(offset, "offset", "batchNorm"); + } + assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`); + assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`); + assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`); + if ($scale != null) { + assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`); + } + if ($offset != null) { + assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`); + } + return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); +} +var batchNorm4d = op({ batchNorm4d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/bincount.js +function bincount_(x, weights, size) { + const $x = convertToTensor(x, "x", "bincount"); + const $weights = convertToTensor(weights, "weights", "bincount"); + assert($x.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${$x.dtype}`); + assert(size >= 0, () => `size must be non-negative, but got ${size}.`); + assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`); + const inputs = { x: $x, weights: $weights }; + const attrs = { size }; + return ENGINE.runKernel(Bincount, inputs, attrs); +} +var bincount = op({ bincount_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/bitwise_and.js +function bitwiseAnd_(x, y) { + const $x = convertToTensor(x, "x", "bitwiseAnd"); + const $y = convertToTensor(y, "y", "bitwiseAnd"); + if (!arraysEqual($x.shape, $y.shape)) { + throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${$x.shape}, y: ${$y.shape}`); + } + if ($x.dtype !== "int32" || $y.dtype !== "int32") { + throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${$x.dtype} and type of y: ${$y.dtype}`); + } + const inputs = { a: $x, b: $y }; + return ENGINE.runKernel(BitwiseAnd, inputs); +} +var bitwiseAnd = op({ bitwiseAnd_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_args.js +function broadcastArgs_(s0, s1) { + const shape1Input = convertToTensor(s0, "s0", "broadcastArgs", "int32"); + const shape2Input = convertToTensor(s1, "s1", "broadcastArgs", "int32"); + if (shape1Input.rank !== 1) { + throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${shape1Input.rank}`); + } + if (shape2Input.rank !== 1) { + throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${shape2Input.rank}`); + } + const inputs = { s0: shape1Input, s1: shape2Input }; + return ENGINE.runKernel(BroadcastArgs, inputs); +} +var broadcastArgs = op({ broadcastArgs_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js +function broadcastTo_(x, shape) { + let input2 = convertToTensor(x, "broadcastTo", "x"); + const xShape = input2.shape; + assertNonNegativeIntegerDimensions(shape); + if (shape.length < input2.rank) { + throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`); + } + if (shape.length > input2.rank) { + const newShape = input2.shape.slice(); + while (newShape.length < shape.length) { + newShape.unshift(1); + } + input2 = reshape(input2, newShape); + } + const inputShape = input2.shape; + const reps = Array.from(shape); + for (let i = shape.length - 1; i >= 0; i--) { + if (inputShape[i] === shape[i]) { + reps[i] = 1; + } else if (input2.shape[i] !== 1) { + throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`); + } + } + const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0); + if (axes.length === 0) { + return clone(input2); + } + const inputs = { x: input2 }; + const attrs = { reps }; + return ENGINE.runKernel(Tile, inputs, attrs); +} +var broadcastTo = op({ broadcastTo_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js +function ceil_(x) { + const $x = convertToTensor(x, "x", "ceil", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Ceil, inputs); +} +var ceil = op({ ceil_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fill.js +function fill(shape, value, dtype) { + assertNonNegativeIntegerDimensions(shape); + dtype = dtype || inferDtype(value); + const attrs = { shape, value, dtype }; + return ENGINE.runKernel(Fill, {}, attrs); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js +function clipByValue_(x, clipValueMin, clipValueMax) { + const $x = convertToTensor(x, "x", "clipByValue"); + assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`); + if (clipValueMin === clipValueMax) { + return fill($x.shape, clipValueMin, $x.dtype); + } + const inputs = { x: $x }; + const attrs = { clipValueMin, clipValueMax }; + return ENGINE.runKernel(ClipByValue, inputs, attrs); +} +var clipByValue = op({ clipByValue_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js +function concat1d_(tensors) { + return concat( + tensors, + 0 + /* axis */ + ); +} +var concat1d = op({ concat1d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js +function concat2d_(tensors, axis) { + return concat(tensors, axis); +} +var concat2d = op({ concat2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js +function concat3d_(tensors, axis) { + return concat(tensors, axis); +} +var concat3d = op({ concat3d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js +function concat4d_(tensors, axis) { + return concat(tensors, axis); +} +var concat4d = op({ concat4d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js +function conv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) { + const $x = convertToTensor(x, "x", "conv2d", "float32"); + const $filter = convertToTensor(filter, "filter", "conv2d", "float32"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`); + assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`); + checkPadOnDimRoundingMode("conv2d", pad3, dimRoundingMode); + const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1]; + assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`); + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + assert(stridesOrDilationsArePositive(dilations), () => "Error in conv2D: Dilated rates should be larger than 0."); + assert(stridesOrDilationsArePositive(strides), () => "Error in conv2D: Strides should be larger than 0."); + const inputs = { x: x4D, filter: $filter }; + const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }; + const res = ENGINE.runKernel(Conv2D, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var conv2d = op({ conv2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js +function conv1d_(x, filter, stride, pad3, dataFormat = "NWC", dilation = 1, dimRoundingMode) { + const $x = convertToTensor(x, "x", "conv1d"); + const $filter = convertToTensor(filter, "filter", "conv1d"); + let x3D = $x; + let reshapedTo3D = false; + if ($x.rank === 2) { + reshapedTo3D = true; + x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]); + } + assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`); + assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`); + checkPadOnDimRoundingMode("conv1d", pad3, dimRoundingMode); + assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`); + assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`); + assert(stridesOrDilationsArePositive(dilation), () => "Error in conv1D: Dilated rates should be larger than 0."); + assert(stridesOrDilationsArePositive(stride), () => "Error in conv1D: Stride should be larger than 0."); + assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`); + const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]); + const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]); + const strides = [1, stride]; + const dilations = [1, dilation]; + const conv2dDataFormat = "NHWC"; + const res = conv2d(input4D, filter4D, strides, pad3, conv2dDataFormat, dilations, dimRoundingMode); + if (reshapedTo3D) { + return reshape(res, [res.shape[2], res.shape[3]]); + } + return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]); +} +var conv1d = op({ conv1d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js +function conv2DBackpropInput_(xShape, dy, filter, strides, pad3, dataFormat = "NHWC", dimRoundingMode) { + assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`); + let xShape4D = xShape; + let dy4D = dy; + let reshapedTo4D = false; + if (dy.rank === 3) { + reshapedTo4D = true; + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + xShape4D = [1, xShape[0], xShape[1], xShape[2]]; + } + assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`); + assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`); + assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`); + const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1]; + const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1]; + assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`); + assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`); + checkPadOnDimRoundingMode("conv2dDerInput", pad3, dimRoundingMode); + const inputs = { dy: dy4D, filter }; + const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape: xShape4D }; + const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var conv2DBackpropInput = op({ conv2DBackpropInput_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js +function conv2dTranspose_(x, filter, outputShape, strides, pad3, dimRoundingMode) { + const $x = convertToTensor(x, "x", "conv2dTranspose"); + const $filter = convertToTensor(filter, "filter", "conv2dTranspose"); + return conv2DBackpropInput(outputShape, $x, $filter, strides, pad3, "NHWC", dimRoundingMode); +} +var conv2dTranspose = op({ conv2dTranspose_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js +function conv3d_(x, filter, strides, pad3, dataFormat = "NDHWC", dilations = [1, 1, 1]) { + const $x = convertToTensor(x, "x", "conv3d"); + const $filter = convertToTensor(filter, "filter", "conv3d"); + let x5D = $x; + let reshapedTo5D = false; + if ($x.rank === 4) { + reshapedTo5D = true; + x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]); + } + assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`); + assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`); + assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`); + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + assert(dataFormat === "NDHWC", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`); + assert(stridesOrDilationsArePositive(dilations), () => "Error in conv3D: Dilated rates should be larger than 0."); + assert(stridesOrDilationsArePositive(strides), () => "Error in conv3D: Strides should be larger than 0."); + const inputs = { x: x5D, filter: $filter }; + const attrs = { strides, pad: pad3, dataFormat, dilations }; + const res = ENGINE.runKernel(Conv3D, inputs, attrs); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var conv3d = op({ conv3d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js +function conv3DBackpropInput_(xShape, dy, filter, strides, pad3) { + assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`); + let xShape5D = xShape; + let dy5D = dy; + let reshapedTo5D = false; + if (dy.rank === 4) { + reshapedTo5D = true; + dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]); + xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]]; + } + const inDepth = xShape5D[4]; + const outDepth = dy5D.shape[4]; + assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`); + assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`); + assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`); + assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`); + assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`); + const inputs = { dy: dy5D, filter }; + const attrs = { pad: pad3, strides, inputShape: xShape5D }; + const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var conv3DBackpropInput = op({ conv3DBackpropInput_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js +function conv3dTranspose_(x, filter, outputShape, strides, pad3) { + const $x = convertToTensor(x, "x", "conv3dTranspose"); + const $filter = convertToTensor(filter, "filter", "conv3dTranspose"); + return conv3DBackpropInput(outputShape, $x, $filter, strides, pad3); +} +var conv3dTranspose = op({ conv3dTranspose_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/cos.js +function cos_(x) { + const $x = convertToTensor(x, "x", "cos", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Cos, inputs); +} +var cos = op({ cos_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js +function cosh_(x) { + const $x = convertToTensor(x, "x", "cosh", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Cosh, inputs); +} +var cosh = op({ cosh_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumprod.js +function cumprod_(x, axis = 0, exclusive = false, reverse5 = false) { + const $x = convertToTensor(x, "x", "cumprod"); + const inputs = { x: $x }; + const attrs = { axis, exclusive, reverse: reverse5 }; + return ENGINE.runKernel(Cumprod, inputs, attrs); +} +var cumprod = op({ cumprod_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js +function cumsum_(x, axis = 0, exclusive = false, reverse5 = false) { + const $x = convertToTensor(x, "x", "cumsum"); + const inputs = { x: $x }; + const attrs = { axis, exclusive, reverse: reverse5 }; + return ENGINE.runKernel(Cumsum, inputs, attrs); +} +var cumsum = op({ cumsum_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/dense_bincount.js +function denseBincount_(x, weights, size, binaryOutput = false) { + const $x = convertToTensor(x, "x", "denseBincount"); + const $weights = convertToTensor(weights, "weights", "denseBincount"); + assert($x.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`); + assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`); + assert(size >= 0, () => `size must be non-negative, but got ${size}.`); + assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`); + const inputs = { x: $x, weights: $weights }; + const attrs = { size, binaryOutput }; + return ENGINE.runKernel(DenseBincount, inputs, attrs); +} +var denseBincount = op({ denseBincount_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js +function depthToSpace_(x, blockSize, dataFormat = "NHWC") { + const $x = convertToTensor(x, "x", "depthToSpace", "float32"); + const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2]; + const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3]; + const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1]; + assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`); + assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying + ${inputHeight} and ${blockSize} for depthToSpace with input shape + ${$x.shape}`); + assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying + ${inputWidth} and ${blockSize} for depthToSpace with input shape + ${$x.shape}`); + assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`); + const inputs = { x: $x }; + const attrs = { blockSize, dataFormat }; + return ENGINE.runKernel(DepthToSpace, inputs, attrs); +} +var depthToSpace = op({ depthToSpace_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js +function depthwiseConv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) { + const $x = convertToTensor(x, "x", "depthwiseConv2d", "float32"); + const $filter = convertToTensor(filter, "filter", "depthwiseConv2d", "float32"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`); + assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`); + const inChannels = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1]; + assert(inChannels === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${inChannels}) must match the inChannels dimension in filter ${$filter.shape[2]}.`); + checkPadOnDimRoundingMode("depthwiseConv2d", pad3, dimRoundingMode); + const inputs = { x: x4D, filter: $filter }; + const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }; + const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var depthwiseConv2d = op({ depthwiseConv2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/diag.js +function diag_(x) { + const $x = convertToTensor(x, "x", "diag"); + const inputs = { x: $x }; + return ENGINE.runKernel(Diag, inputs); +} +var diag = op({ diag_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js +function dilation2d_(x, filter, strides, pad3, dilations = [1, 1], dataFormat = "NHWC") { + const $x = convertToTensor(x, "x", "dilation2d"); + const $filter = convertToTensor(filter, "filter", "dilation2d"); + assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`); + assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`); + assert(dataFormat === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + reshapedTo4D = true; + } + assert(x4D.shape[3] === $filter.shape[2], () => `Error in dilation2d: input and filter must have the same depth: ${x4D.shape[3]} vs ${$filter.shape[2]}`); + const inputs = { x: x4D, filter: $filter }; + const attrs = { strides, pad: pad3, dilations }; + const res = ENGINE.runKernel(Dilation2D, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var dilation2d = op({ dilation2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js +var broadcast_util_exports = {}; +__export(broadcast_util_exports, { + assertAndGetBroadcastShape: () => assertAndGetBroadcastShape, + getBroadcastDims: () => getBroadcastDims, + getReductionAxes: () => getReductionAxes +}); +function getBroadcastDims(inShape, outShape) { + const inRank = inShape.length; + const dims = []; + for (let i = 0; i < inRank; i++) { + const dim = inRank - 1 - i; + const a = inShape[dim] || 1; + const b = outShape[outShape.length - 1 - i] || 1; + if (b > 1 && a === 1) { + dims.unshift(dim); + } + } + return dims; +} +function getReductionAxes(inShape, outShape) { + const result = []; + for (let i = 0; i < outShape.length; i++) { + const inDim = inShape[inShape.length - i - 1]; + const outAxis = outShape.length - i - 1; + const outDim = outShape[outAxis]; + if (inDim == null || inDim === 1 && outDim > 1) { + result.unshift(outAxis); + } + } + return result; +} +function assertAndGetBroadcastShape(shapeA, shapeB) { + const l = Math.max(shapeA.length, shapeB.length); + const result = new Array(l); + for (let i = 0; i < l; i++) { + let a = shapeA[shapeA.length - i - 1]; + if (a == null) { + a = 1; + } + let b = shapeB[shapeB.length - i - 1]; + if (b == null) { + b = 1; + } + if (a === 1) { + result[l - i - 1] = b; + } else if (b === 1) { + result[l - i - 1] = a; + } else if (a !== b) { + const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`; + throw Error(errMsg); + } else { + result[l - i - 1] = a; + } + } + return result; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/equal.js +function equal_(a, b) { + let $a = convertToTensor(a, "a", "equal", "string_or_numeric"); + let $b = convertToTensor(b, "b", "equal", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Equal, inputs); +} +var equal = op({ equal_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/where.js +function where_(condition, a, b) { + const $a = convertToTensor(a, "a", "where"); + const $b = convertToTensor(b, "b", "where"); + const $condition = convertToTensor(condition, "condition", "where", "bool"); + const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape); + const $broadcastedCondition = broadcastTo($condition, broadcastShape); + const $broadcastedA = broadcastTo($a, broadcastShape); + const $broadcastedB = broadcastTo($b, broadcastShape); + const inputs = { + condition: $broadcastedCondition, + t: $broadcastedA, + e: $broadcastedB + }; + return ENGINE.runKernel(Select, inputs); +} +var where = op({ where_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js +function zerosLike_(x) { + const $x = convertToTensor(x, "x", "zerosLike"); + const inputs = { x: $x }; + return ENGINE.runKernel(ZerosLike, inputs); +} +var zerosLike = op({ zerosLike_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js +function divNoNan_(a, b) { + let $a = convertToTensor(a, "a", "div"); + let $b = convertToTensor(b, "b", "div"); + [$a, $b] = makeTypesMatch($a, $b); + const divResult = div($a, $b); + const zeros4 = zerosLike(divResult); + const bEqualsZero = equal($b, zeros4); + return where(bEqualsZero, zeros4, divResult); +} +var divNoNan = op({ divNoNan_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/dot.js +function dot_(t1, t2) { + const $t1 = convertToTensor(t1, "t1", "dot"); + const $t2 = convertToTensor(t2, "t2", "dot"); + assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`); + const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1]; + const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0]; + assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`); + if ($t1.rank === 1 && $t2.rank === 1) { + const t12D = reshape($t1, [1, -1]); + const t22D = reshape($t2, [-1, 1]); + const t1t2 = matMul(t12D, t22D); + return reshape(t1t2, []); + } else if ($t1.rank === 1 && $t2.rank === 2) { + const t12D = reshape($t1, [1, -1]); + const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]); + const t1t2 = matMul(t12D, t22D); + return reshape(t1t2, [t1t2.size]); + } else if ($t1.rank === 2 && $t2.rank === 1) { + const t22D = reshape($t2, [-1, 1]); + const t1t2 = matMul($t1, t22D); + return reshape(t1t2, [t1t2.size]); + } else { + const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]); + const t1t2 = matMul($t1, t22D); + return t1t2; + } +} +var dot = op({ dot_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/einsum.js +function einsum_(equation, ...tensors) { + const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "einsum")); + const attrs = { equation }; + return ENGINE.runKernel(Einsum, $tensors, attrs); +} +var einsum = op({ einsum_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/elu.js +function elu_(x) { + const $x = convertToTensor(x, "x", "elu", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Elu, inputs); +} +var elu = op({ elu_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ensure_shape.js +function ensureShape_(x, shape) { + const $x = convertToTensor(x, "x", "ensureShape", "string_or_numeric"); + if (!arraysEqualWithNull($x.shape, shape)) { + throw new Error(`EnsureShape: Shape of tensor ${$x.shape} is not compatible with expected shape ${shape}`); + } + return x; +} +var ensureShape = op({ ensureShape_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf.js +function erf_(x) { + let $x = convertToTensor(x, "x", "erf"); + assert($x.dtype === "int32" || $x.dtype === "float32", () => "Input dtype must be `int32` or `float32`."); + if ($x.dtype === "int32") { + $x = cast($x, "float32"); + } + const inputs = { x: $x }; + return ENGINE.runKernel(Erf, inputs); +} +var erf = op({ erf_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js +function axesAreInnerMostDims(axes, rank) { + for (let i = 0; i < axes.length; ++i) { + if (axes[axes.length - i - 1] !== rank - 1 - i) { + return false; + } + } + return true; +} +function combineLocations(outputLoc, reduceLoc, axes) { + const rank = outputLoc.length + reduceLoc.length; + const loc = []; + let outIdx = 0; + let reduceIdx = 0; + for (let dim = 0; dim < rank; dim++) { + if (axes.indexOf(dim) === -1) { + loc.push(outputLoc[outIdx++]); + } else { + loc.push(reduceLoc[reduceIdx++]); + } + } + return loc; +} +function computeOutAndReduceShapes(aShape, axes) { + const outShape = []; + const rank = aShape.length; + for (let dim = 0; dim < rank; dim++) { + if (axes.indexOf(dim) === -1) { + outShape.push(aShape[dim]); + } + } + const reduceShape = axes.map((dim) => aShape[dim]); + return [outShape, reduceShape]; +} +function expandShapeToKeepDim(shape, axes) { + const reduceSubShape = axes.map((x) => 1); + return combineLocations(shape, reduceSubShape, axes); +} +function assertAxesAreInnerMostDims(msg, axes, rank) { + assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`); +} +function getAxesPermutation(axes, rank) { + if (axesAreInnerMostDims(axes, rank)) { + return null; + } + const result = []; + for (let i = 0; i < rank; ++i) { + if (axes.indexOf(i) === -1) { + result.push(i); + } + } + axes.forEach((axis) => result.push(axis)); + return result; +} +function getUndoAxesPermutation(axes) { + return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]); +} +function getInnerMostAxes(numAxes, rank) { + const res = []; + for (let i = rank - numAxes; i < rank; ++i) { + res.push(i); + } + return res; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max.js +function max_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "max"); + const inputs = { x: $x }; + const attrs = { reductionIndices: axis, keepDims }; + return ENGINE.runKernel(Max, inputs, attrs); +} +var max = op({ max_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/min.js +function min_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "min"); + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(Min, inputs, attrs); +} +var min = op({ min_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pow.js +function pow_(base, exp4) { + let $base = convertToTensor(base, "base", "pow"); + let $exp = convertToTensor(exp4, "exp", "pow"); + [$base, $exp] = makeTypesMatch($base, $exp); + const inputs = { a: $base, b: $exp }; + return ENGINE.runKernel(Pow, inputs); +} +var pow = op({ pow_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js +function scalar(value, dtype) { + if ((isTypedArray(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") { + throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)"); + } + if (dtype === "string" && isTypedArray(value) && !(value instanceof Uint8Array)) { + throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`."); + } + const shape = []; + const inferredShape = []; + return makeTensor(value, shape, inferredShape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js +function sqrt_(x) { + const $x = convertToTensor(x, "x", "sqrt", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Sqrt, inputs); +} +var sqrt = op({ sqrt_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/square.js +function square_(x) { + const $x = convertToTensor(x, "x", "square"); + const attrs = {}; + return ENGINE.runKernel("Square", { x: $x }, attrs); +} +var square = op({ square_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sum.js +function sum_(x, axis = null, keepDims = false) { + let $x = convertToTensor(x, "x", "sum"); + if ($x.dtype === "bool") { + $x = cast($x, "int32"); + } + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(Sum, inputs, attrs); +} +var sum2 = op({ sum_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js +function norm_(x, ord = "euclidean", axis = null, keepDims = false) { + x = convertToTensor(x, "x", "norm"); + const norm2 = normImpl(x, ord, axis); + let keepDimsShape = norm2.shape; + if (keepDims) { + const axes = parseAxisParam(axis, x.shape); + keepDimsShape = expandShapeToKeepDim(norm2.shape, axes); + } + return reshape(norm2, keepDimsShape); +} +function normImpl(x, p2, axis = null) { + if (x.rank === 0) { + return abs(x); + } + if (x.rank !== 1 && axis === null) { + return normImpl(reshape(x, [-1]), p2, axis); + } + if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) { + if (p2 === 1) { + return sum2(abs(x), axis); + } + if (p2 === Infinity) { + return max(abs(x), axis); + } + if (p2 === -Infinity) { + return min(abs(x), axis); + } + if (p2 === "euclidean" || p2 === 2) { + return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis)); + } + throw new Error(`Error in norm: invalid ord value: ${p2}`); + } + if (Array.isArray(axis) && axis.length === 2) { + if (p2 === 1) { + return max(sum2(abs(x), axis[0]), axis[1] - 1); + } + if (p2 === Infinity) { + return max(sum2(abs(x), axis[1]), axis[0]); + } + if (p2 === -Infinity) { + return min(sum2(abs(x), axis[1]), axis[0]); + } + if (p2 === "fro" || p2 === "euclidean") { + return sqrt(sum2(square(x), axis)); + } + throw new Error(`Error in norm: invalid ord value: ${p2}`); + } + throw new Error(`Error in norm: invalid axis: ${axis}`); +} +var norm = op({ norm_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/euclidean_norm.js +function euclideanNorm_(x, axis = null, keepDims = false) { + return norm(x, "euclidean", axis, keepDims); +} +var euclideanNorm = op({ euclideanNorm_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/exp.js +function exp_(x) { + const $x = convertToTensor(x, "x", "exp"); + const inputs = { x: $x }; + return ENGINE.runKernel(Exp, inputs); +} +var exp = op({ exp_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js +function expandDims_(x, axis = 0) { + const $x = convertToTensor(x, "x", "expandDims", "string_or_numeric"); + assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor"); + const inputs = { input: $x }; + const attrs = { dim: axis }; + return ENGINE.runKernel(ExpandDims, inputs, attrs); +} +var expandDims = op({ expandDims_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js +function expm1_(x) { + const $x = convertToTensor(x, "x", "expm1"); + const inputs = { x: $x }; + return ENGINE.runKernel(Expm1, inputs); +} +var expm1 = op({ expm1_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tile.js +function tile_(x, reps) { + const $x = convertToTensor(x, "x", "tile", "string_or_numeric"); + assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`); + const inputs = { x: $x }; + const attrs = { reps }; + return ENGINE.runKernel(Tile, inputs, attrs); +} +var tile = op({ tile_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/eye.js +function eye_(numRows, numColumns, batchShape, dtype = "float32") { + if (numColumns == null) { + numColumns = numRows; + } + const buff = buffer([numRows, numColumns], dtype); + const n = numRows <= numColumns ? numRows : numColumns; + for (let i = 0; i < n; ++i) { + buff.set(1, i, i); + } + const out = reshape(buff.toTensor(), [numRows, numColumns]); + if (batchShape == null) { + return out; + } else { + if (batchShape.length === 1) { + return tile(expandDims(out, 0), [batchShape[0], 1, 1]); + } else if (batchShape.length === 2) { + return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]); + } else if (batchShape.length === 3) { + return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [ + batchShape[0], + batchShape[1], + batchShape[2], + 1, + 1 + ]); + } else { + throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`); + } + } +} +var eye = op({ eye_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/floor.js +function floor_(x) { + const $x = convertToTensor(x, "x", "floor", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Floor, inputs); +} +var floor = op({ floor_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather.js +function gather_(x, indices, axis = 0, batchDims = 0) { + const $x = convertToTensor(x, "x", "gather"); + const $indices = convertToTensor(indices, "indices", "gather", "int32"); + const inputs = { x: $x, indices: $indices }; + const attrs = { axis, batchDims }; + return ENGINE.runKernel(GatherV2, inputs, attrs); +} +var gather = op({ gather_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater.js +function greater_(a, b) { + let $a = convertToTensor(a, "a", "greater", "string_or_numeric"); + let $b = convertToTensor(b, "b", "greater", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Greater, inputs); +} +var greater = op({ greater_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js +function greaterEqual_(a, b) { + let $a = convertToTensor(a, "a", "greaterEqual", "string_or_numeric"); + let $b = convertToTensor(b, "b", "greaterEqual", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(GreaterEqual, inputs); +} +var greaterEqual = op({ greaterEqual_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/imag.js +function imag_(input2) { + const $input = convertToTensor(input2, "input", "imag"); + const inputs = { input: $input }; + return ENGINE.runKernel(Imag, inputs); +} +var imag = op({ imag_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js +function isFinite_(x) { + const $x = convertToTensor(x, "x", "isFinite"); + const inputs = { x: $x }; + return ENGINE.runKernel(IsFinite, inputs); +} +var isFinite2 = op({ isFinite_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js +function isInf_(x) { + const $x = convertToTensor(x, "x", "isInf"); + const inputs = { x: $x }; + return ENGINE.runKernel(IsInf, inputs); +} +var isInf = op({ isInf_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js +function isNaN_(x) { + const $x = convertToTensor(x, "x", "isNaN"); + const inputs = { x: $x }; + return ENGINE.runKernel(IsNan, inputs); +} +var isNaN2 = op({ isNaN_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js +function leakyRelu_(x, alpha = 0.2) { + const $x = convertToTensor(x, "x", "leakyRelu"); + const inputs = { x: $x }; + const attrs = { alpha }; + return ENGINE.runKernel(LeakyRelu, inputs, attrs); +} +var leakyRelu = op({ leakyRelu_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/less.js +function less_(a, b) { + let $a = convertToTensor(a, "a", "less", "string_or_numeric"); + let $b = convertToTensor(b, "b", "less", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Less, inputs); +} +var less = op({ less_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js +function lessEqual_(a, b) { + let $a = convertToTensor(a, "a", "lessEqual", "string_or_numeric"); + let $b = convertToTensor(b, "b", "lessEqual", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(LessEqual, inputs); +} +var lessEqual = op({ lessEqual_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js +function linspace(start, stop, num) { + if (num <= 0) { + throw new Error("The number of values should be positive."); + } + const attrs = { start, stop, num }; + return ENGINE.runKernel(LinSpace, {}, attrs); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js +function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) { + const $x = convertToTensor(x, "x", "localResponseNormalization"); + assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got + rank ${$x.rank}.`); + assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + const inputs = { x: x4D }; + const attrs = { depthRadius, bias, alpha, beta }; + const res = ENGINE.runKernel(LRN, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } else { + return res; + } +} +var localResponseNormalization = op({ localResponseNormalization_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/log.js +function log_(x) { + const $x = convertToTensor(x, "x", "log", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Log, inputs); +} +var log2 = op({ log_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js +function log1p_(x) { + const $x = convertToTensor(x, "x", "log1p"); + const inputs = { x: $x }; + return ENGINE.runKernel(Log1p, inputs); +} +var log1p = op({ log1p_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients.js +function grad(f) { + assert(isFunction(f), () => "The f passed in grad(f) must be a function"); + return (x, dy) => { + const $x = convertToTensor(x, "x", "tf.grad", "string_or_numeric"); + const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grad") : null; + return ENGINE.tidy(() => { + const { value, grads: grads2 } = ENGINE.gradients(() => f($x), [$x], $dy); + if ($dy != null) { + assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"); + } + checkGrads(grads2); + return grads2[0]; + }); + }; +} +function grads(f) { + assert(isFunction(f), () => "The f passed in grads(f) must be a function"); + return (args, dy) => { + assert(Array.isArray(args), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s"); + const $args = convertToTensorArray(args, "args", "tf.grads", "string_or_numeric"); + const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grads") : null; + return ENGINE.tidy(() => { + const { value, grads: grads2 } = ENGINE.gradients(() => f(...$args), $args, $dy); + if ($dy != null) { + assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"); + } + checkGrads(grads2); + return grads2; + }); + }; +} +function valueAndGrad(f) { + assert(isFunction(f), () => "The f passed in valueAndGrad(f) must be a function"); + return (x, dy) => { + assert(x instanceof Tensor, () => "The x passed in valueAndGrad(f)(x) must be a tensor"); + assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor"); + const { grads: grads2, value } = ENGINE.gradients(() => f(x), [x], dy); + checkGrads(grads2); + return { grad: grads2[0], value }; + }; +} +function valueAndGrads(f) { + assert(isFunction(f), () => "The f passed in valueAndGrads(f) must be a function"); + return (args, dy) => { + assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"); + assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor"); + const res = ENGINE.gradients(() => f(...args), args, dy); + if (dy != null) { + assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"); + } + checkGrads(res.grads); + return res; + }; +} +function variableGrads(f, varList) { + assert(isFunction(f), () => "The f passed in variableGrads(f) must be a function"); + assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => "The varList passed in variableGrads(f, varList) must be an array of variables"); + const specifiedVarList = varList != null; + if (!specifiedVarList) { + varList = []; + for (const varName in ENGINE.registeredVariables) { + varList.push(ENGINE.registeredVariables[varName]); + } + } + const specifiedNonTrainable = specifiedVarList ? varList.filter((variable2) => !variable2.trainable) : null; + const originalVarCount = varList.length; + varList = varList.filter((variable2) => variable2.trainable); + assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`); + const allowNoGradients = true; + const { value, grads: grads2 } = ENGINE.gradients(f, varList, null, allowNoGradients); + assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."); + assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`); + const namedGrads = {}; + varList.forEach((v, i) => { + if (grads2[i] != null) { + namedGrads[v.name] = grads2[i]; + } + }); + if (specifiedNonTrainable != null) { + specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null); + } + return { value, grads: namedGrads }; +} +function customGrad(f) { + return ENGINE.customGrad(f); +} +function checkGrads(grads2) { + const numNullGradients = grads2.filter((g) => g == null).length; + if (numNullGradients > 0) { + throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that + the f you passed encloses all operations that lead from x to y.`); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/neg.js +function neg_(x) { + const $x = convertToTensor(x, "x", "neg"); + const inputs = { x: $x }; + return ENGINE.runKernel(Neg, inputs); +} +var neg = op({ neg_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js +function softplus_(x) { + const $x = convertToTensor(x, "x", "softplus"); + const inputs = { x: $x }; + return ENGINE.runKernel(Softplus, inputs); +} +var softplus = op({ softplus_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js +function logSigmoid_(x) { + const $x = convertToTensor(x, "x", "logSigmoid"); + const customOp = customGrad((x2) => { + const value = neg(softplus(neg(x2))); + const gradFunc = (dy) => { + const derX = mul(dy, sigmoid(neg(x2))); + return derX; + }; + return { value, gradFunc }; + }); + return customOp($x); +} +var logSigmoid = op({ logSigmoid_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sub.js +function sub_(a, b) { + let $a = convertToTensor(a, "a", "sub"); + let $b = convertToTensor(b, "b", "sub"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Sub, inputs); +} +var sub = op({ sub_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js +function logSoftmax_(logits, axis = -1) { + const $logits = convertToTensor(logits, "logits", "logSoftmax"); + if (axis === -1) { + axis = $logits.rank - 1; + } + if (axis !== $logits.rank - 1) { + throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`); + } + const customOp = customGrad((logits2, save) => { + const keepDims = true; + const xMax = max(logits2, axis, true); + const shifted = sub(logits2, xMax); + const value = sub(cast(shifted, "float32"), log2(sum2(exp(shifted), axis, keepDims))); + save([value]); + const gradFunc = (dy, saved) => { + const [value2] = saved; + const keepDims2 = true; + const softmax6 = exp(value2); + return sub(dy, mul(sum2(dy, axis, keepDims2), softmax6)); + }; + return { value, gradFunc }; + }); + return customOp($logits); +} +var logSoftmax = op({ logSoftmax_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js +function logSumExp_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "logSumExp"); + const axes = parseAxisParam(axis, $x.shape); + const xMax = max( + $x, + axes, + true + /* keepDims */ + ); + const a = sub($x, xMax); + const b = exp(a); + const c = sum2(b, axes); + const d = log2(c); + const res = add2(reshape(xMax, d.shape), d); + if (keepDims) { + const newShape = expandShapeToKeepDim(res.shape, axes); + return reshape(res, newShape); + } + return res; +} +var logSumExp = op({ logSumExp_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js +function logicalAnd_(a, b) { + const $a = convertToTensor(a, "a", "logicalAnd", "bool"); + const $b = convertToTensor(b, "b", "logicalAnd", "bool"); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(LogicalAnd, inputs); +} +var logicalAnd = op({ logicalAnd_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js +function logicalNot_(x) { + const $x = convertToTensor(x, "x", "logicalNot", "bool"); + const inputs = { x: $x }; + return ENGINE.runKernel(LogicalNot, inputs); +} +var logicalNot = op({ logicalNot_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js +function logicalOr_(a, b) { + const $a = convertToTensor(a, "a", "logicalOr", "bool"); + const $b = convertToTensor(b, "b", "logicalOr", "bool"); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(LogicalOr, inputs); +} +var logicalOr = op({ logicalOr_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js +function logicalXor_(a, b) { + const $a = convertToTensor(a, "a", "logicalXor", "bool"); + const $b = convertToTensor(b, "b", "logicalXor", "bool"); + assertAndGetBroadcastShape($a.shape, $b.shape); + return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b))); +} +var logicalXor = op({ logicalXor_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/search_sorted.js +var INT32_MAX = 2147483648; +function searchSorted_(sortedSequence, values, side = "left") { + const $sortedSequence = convertToTensor(sortedSequence, "sortedSequence", "searchSorted"); + const $values = convertToTensor(values, "values", "searchSorted"); + const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1]; + const valuesSize = $values.shape[$values.shape.length - 1]; + const $sortedSequence2D = reshape($sortedSequence, [-1, sequenceSize]); + const $values2D = reshape($values, [-1, valuesSize]); + if ($sortedSequence2D.rank < 2) { + throw new Error(`Sorted input argument must be at least 2-dimensional`); + } + if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) { + throw new Error(`Leading dimension of 'sortedSequence' and 'values' must match.`); + } + if (sizeFromShape($values2D.shape) >= INT32_MAX) { + throw new Error(`values tensor size must less than ${INT32_MAX}`); + } + if ($sortedSequence2D.shape[1] >= INT32_MAX) { + throw new Error(`trailing dim_size must less than ${INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`); + } + const inputs = { + sortedSequence: $sortedSequence2D, + values: $values2D + }; + const attrs = { side }; + return ENGINE.runKernel(SearchSorted, inputs, attrs); +} +var searchSorted = op({ searchSorted_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/lower_bound.js +function lowerBound(sortedSequence, values) { + return searchSorted(sortedSequence, values, "left"); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js +function maxPool_(x, filterSize, strides, pad3, dimRoundingMode) { + const $x = convertToTensor(x, "x", "maxPool"); + const dilations = 1; + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`); + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + checkPadOnDimRoundingMode("maxPool", pad3, dimRoundingMode); + const inputs = { x: x4D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; + const res = ENGINE.runKernel(MaxPool, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var maxPool = op({ maxPool_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js +function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad3, dimRoundingMode, dataFormat = "NDHWC") { + const $x = convertToTensor(x, "x", "maxPool3d"); + let x5D = $x; + let reshapedTo5D = false; + if ($x.rank === 4) { + reshapedTo5D = true; + x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]); + } + assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`); + assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`); + checkPadOnDimRoundingMode("maxPool3d", pad3, dimRoundingMode); + const inputs = { x: x5D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat }; + const res = ENGINE.runKernel(MaxPool3D, inputs, attrs); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var maxPool3d = op({ maxPool3d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js +function maxPoolWithArgmax_(x, filterSize, strides, pad3, includeBatchInIndex = false) { + const $x = convertToTensor(x, "x", "maxPoolWithArgmax"); + const inputs = { x: $x }; + const attrs = { filterSize, strides, pad: pad3, includeBatchInIndex }; + const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs); + return { result: result[0], indexes: result[1] }; +} +var maxPoolWithArgmax = op({ maxPoolWithArgmax_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js +function maximum_(a, b) { + let $a = convertToTensor(a, "a", "maximum"); + let $b = convertToTensor(b, "b", "maximum"); + [$a, $b] = makeTypesMatch($a, $b); + if ($a.dtype === "bool") { + $a = cast($a, "int32"); + $b = cast($b, "int32"); + } + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Maximum, inputs); +} +var maximum = op({ maximum_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/mean.js +function mean_(x, axis = null, keepDims = false) { + const $x = convertToTensor(x, "x", "mean"); + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(Mean, inputs, attrs); +} +var mean = op({ mean_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js +function zeros(shape, dtype = "float32") { + assertNonNegativeIntegerDimensions(shape); + if (dtype === "complex64") { + const real4 = zeros(shape, "float32"); + const imag4 = zeros(shape, "float32"); + return complex(real4, imag4); + } + const values = makeZerosTypedArray(sizeFromShape(shape), dtype); + return ENGINE.makeTensor(values, shape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones.js +function ones2(shape, dtype = "float32") { + assertNonNegativeIntegerDimensions(shape); + if (dtype === "complex64") { + const real4 = ones2(shape, "float32"); + const imag4 = zeros(shape, "float32"); + return complex(real4, imag4); + } + const values = makeOnesTypedArray(sizeFromShape(shape), dtype); + return ENGINE.makeTensor(values, shape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/meshgrid.js +function meshgrid(x, y, { indexing = "xy" } = {}) { + if (indexing !== "xy" && indexing !== "ij") { + throw new TypeError(`${indexing} is not a valid third argument to meshgrid`); + } + if (x === void 0) { + return []; + } + let $x = convertToTensor(x, "x", "meshgrid", x instanceof Tensor ? x.dtype : "float32"); + if (y === void 0) { + return [$x]; + } + let $y = convertToTensor(y, "y", "meshgrid", y instanceof Tensor ? y.dtype : "float32"); + const w = sizeFromShape($x.shape); + const h = sizeFromShape($y.shape); + if (indexing === "xy") { + $x = reshape($x, [1, -1]); + $y = reshape($y, [-1, 1]); + return [ + matMul(ones2([h, 1], $x.dtype), $x), + matMul($y, ones2([1, w], $y.dtype)) + ]; + } + $x = reshape($x, [-1, 1]); + $y = reshape($y, [1, -1]); + return [ + matMul($x, ones2([1, h], $x.dtype)), + matMul(ones2([w, 1], $y.dtype), $y) + ]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js +function minimum_(a, b) { + let $a = convertToTensor(a, "a", "minimum"); + let $b = convertToTensor(b, "b", "minimum"); + [$a, $b] = makeTypesMatch($a, $b); + if ($a.dtype === "bool") { + $a = cast($a, "int32"); + $b = cast($b, "int32"); + } + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Minimum, inputs); +} +var minimum = op({ minimum_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/mirror_pad.js +function mirrorPad_(x, paddings, mode) { + assert(mode === "reflect" || mode === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`); + const $x = convertToTensor(x, "x", "mirrorPad"); + if ($x.rank === 0) { + throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad"); + } + assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`); + const shapeOffset = mode === "reflect" ? 1 : 0; + for (let i = 0; i < $x.rank; i++) { + assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`); + assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset && paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i] - shapeOffset} or less than 0 for input of shape ${$x.shape}`); + } + const attrs = { paddings, mode }; + const inputs = { x: $x }; + return ENGINE.runKernel(MirrorPad, inputs, attrs); +} +var mirrorPad = op({ mirrorPad_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/mod.js +function mod_(a, b) { + let $a = convertToTensor(a, "a", "mod"); + let $b = convertToTensor(b, "b", "mod"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Mod, inputs); +} +var mod = op({ mod_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/moments.js +function moments_(x, axis = null, keepDims = false) { + x = convertToTensor(x, "x", "moments"); + const axes = parseAxisParam(axis, x.shape); + const xMean = mean(x, axes, keepDims); + let keepDimsShape = xMean.shape; + if (!keepDims) { + keepDimsShape = expandShapeToKeepDim(xMean.shape, axes); + } + const devSquared = square(sub(cast(x, "float32"), reshape(xMean, keepDimsShape))); + const variance = mean(devSquared, axes, keepDims); + return { mean: xMean, variance }; +} +var moments = op({ moments_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js +function multiRNNCell_(lstmCells, data, c, h) { + const $data = convertToTensor(data, "data", "multiRNNCell"); + const $c = convertToTensorArray(c, "c", "multiRNNCell"); + const $h = convertToTensorArray(h, "h", "multiRNNCell"); + let input2 = $data; + const newStates = []; + for (let i = 0; i < lstmCells.length; i++) { + const output = lstmCells[i](input2, $c[i], $h[i]); + newStates.push(output[0]); + newStates.push(output[1]); + input2 = output[1]; + } + const newC = []; + const newH = []; + for (let i = 0; i < newStates.length; i += 2) { + newC.push(newStates[i]); + newH.push(newStates[i + 1]); + } + return [newC, newH]; +} +var multiRNNCell = op({ multiRNNCell_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js +function multinomial_(logits, numSamples, seed, normalized = false) { + const $logits = convertToTensor(logits, "logits", "multinomial"); + const numOutcomes = $logits.size; + const origRank = $logits.rank; + if (numOutcomes < 2) { + throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`); + } + if (origRank > 2) { + throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`); + } + seed = seed || Math.random(); + const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits; + const inputs = { logits: logits2D }; + const attrs = { numSamples, seed, normalized }; + const res = ENGINE.runKernel(Multinomial, inputs, attrs); + return origRank === 1 ? reshape(res, [res.size]) : res; +} +var multinomial = op({ multinomial_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js +function notEqual_(a, b) { + let $a = convertToTensor(a, "a", "notEqual", "string_or_numeric"); + let $b = convertToTensor(b, "b", "notEqual", "string_or_numeric"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + return ENGINE.runKernel(NotEqual, inputs); +} +var notEqual = op({ notEqual_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js +function oneHot_(indices, depth, onValue = 1, offValue = 0, dtype = "int32") { + if (depth < 2) { + throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`); + } + const $indices = convertToTensor(indices, "indices", "oneHot", "int32"); + const inputs = { indices: $indices }; + const attrs = { dtype, depth, onValue, offValue }; + return ENGINE.runKernel(OneHot, inputs, attrs); +} +var oneHot = op({ oneHot_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js +function onesLike_(x) { + const $x = convertToTensor(x, "x", "onesLike"); + const inputs = { x: $x }; + return ENGINE.runKernel(OnesLike, inputs); +} +var onesLike = op({ onesLike_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js +function outerProduct_(v1, v2) { + const $v1 = convertToTensor(v1, "v1", "outerProduct"); + const $v2 = convertToTensor(v2, "v2", "outerProduct"); + assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`); + const v12D = reshape($v1, [-1, 1]); + const v22D = reshape($v2, [1, -1]); + return matMul(v12D, v22D); +} +var outerProduct = op({ outerProduct_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad.js +function pad_(x, paddings, constantValue = 0) { + const $x = convertToTensor(x, "x", "pad"); + if ($x.rank === 0) { + throw new Error("pad(scalar) is not defined. Pass non-scalar to pad"); + } + const attrs = { paddings, constantValue }; + const inputs = { x: $x }; + return ENGINE.runKernel(PadV2, inputs, attrs); +} +var pad = op({ pad_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js +function pad1d_(x, paddings, constantValue = 0) { + assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2."); + return pad(x, [paddings], constantValue); +} +var pad1d = op({ pad1d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js +function pad2d_(x, paddings, constantValue = 0) { + assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."); + return pad(x, paddings, constantValue); +} +var pad2d = op({ pad2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js +function pad3d_(x, paddings, constantValue = 0) { + assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."); + return pad(x, paddings, constantValue); +} +var pad3d = op({ pad3d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js +function pad4d_(x, paddings, constantValue = 0) { + assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."); + return pad(x, paddings, constantValue); +} +var pad4d = op({ pad4d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js +function spaceToBatchND_(x, blockShape, paddings) { + const $x = convertToTensor(x, "x", "spaceToBatchND"); + assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`); + assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`); + assert($x.shape.reduce((a, b, i) => { + if (i > 0 && i <= blockShape.length) { + return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0; + } + return a; + }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`); + const inputs = { x: $x }; + const attrs = { blockShape, paddings }; + return ENGINE.runKernel(SpaceToBatchND, inputs, attrs); +} +var spaceToBatchND = op({ spaceToBatchND_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js +function pool_(input2, windowShape, poolingType, pad3, dilations, strides, dimRoundingMode) { + if (dilations == null) { + dilations = [1, 1]; + } + if (strides == null) { + strides = 1; + } + if (pad3 === 0) { + pad3 = "valid"; + } + const $x = convertToTensor(input2, "x", "maxPool"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad3); + const dilation = [convInfo.dilationHeight, convInfo.dilationWidth]; + let basePadding; + if (pad3 === "same") { + basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation); + } else { + basePadding = [[0, 0], [0, 0]]; + } + const isDilationOne = dilation[0] === 1 && dilation[1] === 1; + const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding); + const convertedPad = isDilationOne ? pad3 : "valid"; + const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding); + const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode); + const y = forwardOp(); + const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) { + const padStart = basePadding.map((b) => b[0]); + const origPadEnd = basePadding.map((b) => b[1]); + const fullInputShape = inputShape.concat(padStart, origPadEnd); + const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b); + const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]); + const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]); + const crops = blockShape.map((_, i) => [0, padEndExtra[i]]); + return [paddings, crops]; +} +function withSpaceToBatchBasePaddings(filterShape, dilation) { + const dilatedFilterShape = filterShape.map((s, i) => { + return s + (s - 1) * (dilation[i] - 1); + }); + const padExtraShape = dilatedFilterShape.map((s) => s - 1); + const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2)); + const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]); + return padExtraShape.map((_, i) => { + return [padExtraStart[i], padExtraEnd[i]]; + }); +} +var pool = op({ pool_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js +function prelu_(x, alpha) { + const $x = convertToTensor(x, "x", "prelu"); + const $alpha = convertToTensor(alpha, "alpha", "prelu"); + const inputs = { x: $x, alpha: $alpha }; + return ENGINE.runKernel(Prelu, inputs); +} +var prelu = op({ prelu_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/prod.js +function prod_(x, axis = null, keepDims = false) { + let $x = convertToTensor(x, "x", "prod"); + if ($x.dtype === "bool") { + $x = cast($x, "int32"); + } + const inputs = { x: $x }; + const attrs = { axis, keepDims }; + return ENGINE.runKernel(Prod, inputs, attrs); +} +var prod = op({ prod_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_gather.js +function raggedGather_(paramsNestedSplits, paramsDenseValues, indices, outputRaggedRank) { + const $paramsNestedSplits = paramsNestedSplits.map((t, i) => convertToTensor(t, `tensors${i}`, "raggedGather", "int32")); + const $paramsDenseValues = convertToTensor(paramsDenseValues, "paramsDenseValues", "raggedGather"); + const $indices = convertToTensor(indices, "indices", "raggedGather", "int32"); + const inputs = { + paramsNestedSplits: $paramsNestedSplits, + paramsDenseValues: $paramsDenseValues, + indices: $indices + }; + const attrs = { outputRaggedRank }; + const result = ENGINE.runKernel(RaggedGather, inputs, attrs); + return { + outputNestedSplits: result.slice(0, result.length - 1), + outputDenseValues: result[result.length - 1] + }; +} +var raggedGather = op({ raggedGather_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_range.js +function raggedRange_(starts, limits, deltas) { + const $starts = convertToTensor(starts, "starts", "raggedRange"); + const $limits = convertToTensor(limits, "limits", "raggedRange", $starts.dtype); + const $deltas = convertToTensor(deltas, "deltas", "raggedRange", $starts.dtype); + const inputs = { + starts: $starts, + limits: $limits, + deltas: $deltas + }; + const result = ENGINE.runKernel(RaggedRange, inputs); + return { + rtNestedSplits: result[0], + rtDenseValues: result[1] + }; +} +var raggedRange = op({ raggedRange_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_tensor_to_tensor.js +function raggedTensorToTensor_(shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes) { + const $shape = convertToTensor(shape, "shape", "raggedTensorToTensor", "int32"); + const $values = convertToTensor(values, "values", "raggedTensorToTensor"); + const $defaultValue = convertToTensor(defaultValue, "defaultValue", "raggedTensorToTensor", $values.dtype); + const $rowPartitionTensors = rowPartitionTensors.map((t, i) => convertToTensor(t, `tensors${i}`, "raggedTensorToTensor", "int32")); + const inputs = { + shape: $shape, + values: $values, + defaultValue: $defaultValue, + rowPartitionTensors: $rowPartitionTensors + }; + const attrs = { rowPartitionTypes }; + return ENGINE.runKernel(RaggedTensorToTensor, inputs, attrs); +} +var raggedTensorToTensor = op({ raggedTensorToTensor_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js +function rand_(shape, randFunction, dtype) { + assertNonNegativeIntegerDimensions(shape); + const size = sizeFromShape(shape); + let values = null; + if (dtype == null || dtype === "float32") { + values = new Float32Array(size); + } else if (dtype === "int32") { + values = new Int32Array(size); + } else if (dtype === "bool") { + values = new Uint8Array(size); + } else { + throw new Error(`Unknown data type ${dtype}`); + } + for (let i = 0; i < size; i++) { + values[i] = randFunction(); + } + return ENGINE.makeTensor(values, shape, dtype); +} +var rand = op({ rand_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js +var seedrandom = __toESM(require_seedrandom2()); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/test_util.js +var test_util_exports = {}; +__export(test_util_exports, { + TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16, + createVideoElement: () => createVideoElement, + encodeStrings: () => encodeStrings, + expectArrayBuffersEqual: () => expectArrayBuffersEqual, + expectArraysClose: () => expectArraysClose, + expectArraysEqual: () => expectArraysEqual, + expectNumbersClose: () => expectNumbersClose, + expectPromiseToFail: () => expectPromiseToFail, + expectValuesInRange: () => expectValuesInRange, + play: () => play, + testEpsilon: () => testEpsilon +}); +var TEST_EPSILON_FLOAT32 = 1e-3; +var TEST_EPSILON_FLOAT16 = 0.1; +function expectArraysClose(actual, expected, epsilon3) { + if (epsilon3 == null) { + epsilon3 = testEpsilon(); + } + return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon3)); +} +function testEpsilon() { + return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16; +} +function expectArraysPredicate(actual, expected, predicate) { + let checkClassType = true; + if (isTypedArray(actual) || isTypedArray(expected)) { + checkClassType = false; + } + if (isTypedArray(actual) && isTypedArray(expected)) { + checkClassType = true; + } + if (checkClassType) { + const aType = actual.constructor.name; + const bType = expected.constructor.name; + if (aType !== bType) { + throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`); + } + } + if (Array.isArray(actual) && Array.isArray(expected)) { + const actualShape = inferShape(actual); + const expectedShape = inferShape(expected); + if (!arraysEqual(actualShape, expectedShape)) { + throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`); + } + } + const actualFlat = isTypedArray(actual) ? actual : flatten(actual); + const expectedFlat = isTypedArray(expected) ? expected : flatten(expected); + if (actualFlat.length !== expectedFlat.length) { + throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}. +Actual: ${actualFlat}. +Expected: ${expectedFlat}.`); + } + for (let i = 0; i < expectedFlat.length; ++i) { + const a = actualFlat[i]; + const e = expectedFlat[i]; + if (!predicate(a, e)) { + throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}. +Actual: ${actualFlat}. +Expected: ${expectedFlat}.`); + } + } + if (typeof expect !== "undefined") { + expect().nothing(); + } +} +function expectPromiseToFail(fn, done) { + fn().then(() => done.fail(), () => done()); + if (typeof expect !== "undefined") { + expect().nothing(); + } +} +function expectArraysEqual(actual, expected) { + const exp4 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected; + if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) { + return expectArraysPredicate(actual, exp4, (a, b) => a == b); + } + return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0)); +} +function expectNumbersClose(a, e, epsilon3) { + if (epsilon3 == null) { + epsilon3 = testEpsilon(); + } + if (!areClose(a, e, epsilon3)) { + throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`); + } + if (typeof expect !== "undefined") { + expect().nothing(); + } +} +function areClose(a, e, epsilon3) { + if (!isFinite(a) && !isFinite(e)) { + return true; + } + if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon3) { + return false; + } + return true; +} +function expectValuesInRange(actual, low, high) { + for (let i = 0; i < actual.length; i++) { + if (actual[i] < low || actual[i] > high) { + throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`); + } + } +} +function expectArrayBuffersEqual(actual, expected) { + const actualArray = new Float32Array(actual); + const expectedArray = new Float32Array(expected); + if (actualArray.length !== expectedArray.length) { + throw new Error(`Expected ArrayBuffer to be of length ${expectedArray.length}, but it was ${actualArray.length}`); + } + for (let i = 0; i < expectedArray.length; i++) { + if (actualArray[i] !== expectedArray[i]) { + throw new Error(`Expected ArrayBuffer value at ${i} to be ${expectedArray[i]} but got ${actualArray[i]} instead`); + } + } +} +function encodeStrings(a) { + for (let i = 0; i < a.length; i++) { + const val = a[i]; + if (Array.isArray(val)) { + encodeStrings(val); + } else { + a[i] = encodeString(val); + } + } + return a; +} +function createVideoElement(source) { + const video = document.createElement("video"); + if ("playsInline" in video) { + video.playsInline = true; + } + video.muted = true; + video.loop = true; + video.style.position = "fixed"; + video.style.left = "0px"; + video.style.top = "0px"; + video.preload = "auto"; + video.appendChild(source); + return new Promise((resolve) => { + video.addEventListener("loadeddata", (_) => resolve(video)); + video.load(); + }); +} +async function play(video) { + await video.play(); + if ("requestVideoFrameCallback" in video) { + await new Promise((resolve) => { + video.requestVideoFrameCallback(resolve); + }); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js +var MPRandGauss = class { + constructor(mean4, stdDeviation, dtype, truncated, seed) { + this.mean = mean4; + this.stdDev = stdDeviation; + this.dtype = dtype; + this.nextVal = NaN; + this.truncated = truncated; + if (this.truncated) { + this.upper = this.mean + this.stdDev * 2; + this.lower = this.mean - this.stdDev * 2; + } + const seedValue = seed ? seed : Math.random(); + this.random = seedrandom.alea(seedValue.toString()); + } + /** Returns next sample from a Gaussian distribution. */ + nextValue() { + if (!isNaN(this.nextVal)) { + const value = this.nextVal; + this.nextVal = NaN; + return value; + } + let resultX, resultY; + let isValid = false; + while (!isValid) { + let v1, v2, s; + do { + v1 = 2 * this.random() - 1; + v2 = 2 * this.random() - 1; + s = v1 * v1 + v2 * v2; + } while (s >= 1 || s === 0); + const mul2 = Math.sqrt(-2 * Math.log(s) / s); + resultX = this.mean + this.stdDev * v1 * mul2; + resultY = this.mean + this.stdDev * v2 * mul2; + if (!this.truncated || this.isValidTruncated(resultX)) { + isValid = true; + } + } + if (!this.truncated || this.isValidTruncated(resultY)) { + this.nextVal = this.convertValue(resultY); + } + return this.convertValue(resultX); + } + /** Handles proper rounding for non-floating-point numbers. */ + convertValue(value) { + if (this.dtype == null || this.dtype === "float32") { + return value; + } + return Math.round(value); + } + /** Returns true if less than 2-standard-deviations from the mean. */ + isValidTruncated(value) { + return value <= this.upper && value >= this.lower; + } +}; +var RandGamma = class { + constructor(alpha, beta, dtype, seed) { + this.alpha = alpha; + this.beta = 1 / beta; + this.dtype = dtype; + const seedValue = seed ? seed : Math.random(); + this.randu = seedrandom.alea(seedValue.toString()); + this.randn = new MPRandGauss(0, 1, dtype, false, this.randu()); + if (alpha < 1) { + this.d = alpha + 2 / 3; + } else { + this.d = alpha - 1 / 3; + } + this.c = 1 / Math.sqrt(9 * this.d); + } + /** Returns next sample from a gamma distribution. */ + nextValue() { + let x2, v0, v1, x, u, v; + while (true) { + do { + x = this.randn.nextValue(); + v = 1 + this.c * x; + } while (v <= 0); + v *= v * v; + x2 = x * x; + v0 = 1 - 0.331 * x2 * x2; + v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v)); + u = this.randu(); + if (u < v0 || Math.log(u) < v1) { + break; + } + } + v = 1 / this.beta * this.d * v; + if (this.alpha < 1) { + v *= Math.pow(this.randu(), 1 / this.alpha); + } + return this.convertValue(v); + } + /** Handles proper rounding for non-floating-point numbers. */ + convertValue(value) { + if (this.dtype === "float32") { + return value; + } + return Math.round(value); + } +}; +var UniformRandom = class { + constructor(min6 = 0, max6 = 1, dtype, seed) { + this.canReturnFloat = () => this.dtype == null || this.dtype === "float32"; + this.min = min6; + this.range = max6 - min6; + this.dtype = dtype; + if (seed == null) { + seed = Math.random(); + } + if (typeof seed === "number") { + seed = seed.toString(); + } + if (!this.canReturnFloat() && this.range <= 1) { + throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`); + } + this.random = seedrandom.alea(seed); + } + convertValue(value) { + if (this.canReturnFloat()) { + return value; + } + return Math.round(value); + } + nextValue() { + return this.convertValue(this.min + this.range * this.random()); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js +function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) { + assertNonNegativeIntegerDimensions(shape); + if (beta == null) { + beta = 1; + } + if (dtype == null) { + dtype = "float32"; + } + if (dtype !== "float32" && dtype !== "int32") { + throw new Error(`Unsupported data type ${dtype}`); + } + const rgamma = new RandGamma(alpha, beta, dtype, seed); + const res = buffer(shape, dtype); + for (let i = 0; i < res.values.length; i++) { + res.values[i] = rgamma.nextValue(); + } + return res.toTensor(); +} +var randomGamma = op({ randomGamma_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js +function randomNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) { + assertNonNegativeIntegerDimensions(shape); + if (dtype != null && dtype === "bool") { + throw new Error(`Unsupported data type ${dtype}`); + } + const randGauss = new MPRandGauss(mean4, stdDev, dtype, false, seed); + const res = buffer(shape, dtype); + for (let i = 0; i < res.values.length; i++) { + res.values[i] = randGauss.nextValue(); + } + return res.toTensor(); +} +var randomNormal = op({ randomNormal_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_standard_normal.js +function randomStandardNormal_(shape, dtype, seed) { + if (dtype != null && dtype === "bool") { + throw new Error(`Unsupported data type ${dtype}`); + } + return randomNormal(shape, 0, 1, dtype, seed); +} +var randomStandardNormal = op({ randomStandardNormal_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js +function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) { + assertNonNegativeIntegerDimensions(shape); + const res = buffer(shape, dtype); + const random = new UniformRandom(minval, maxval, null, seed); + for (let i = 0; i < res.values.length; i++) { + res.values[i] = random.nextValue(); + } + return res.toTensor(); +} +var randomUniform = op({ randomUniform_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform_int.js +function randomUniformInt_(shape, minval, maxval, seed) { + return randomUniform(shape, minval, maxval, "int32", seed); +} +var randomUniformInt = op({ randomUniformInt_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/range.js +function range(start, stop, step5 = 1, dtype = "float32") { + if (step5 === 0) { + throw new Error("Cannot have a step of zero"); + } + const attrs = { start, stop, step: step5, dtype }; + return ENGINE.runKernel(Range, {}, attrs); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/real.js +function real_(input2) { + const $input = convertToTensor(input2, "input", "real"); + const inputs = { input: $input }; + return ENGINE.runKernel(Real, inputs); +} +var real = op({ real_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js +function reciprocal_(x) { + const $x = convertToTensor(x, "x", "reciprocal"); + const inputs = { x: $x }; + return ENGINE.runKernel(Reciprocal, inputs); +} +var reciprocal = op({ reciprocal_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu.js +function relu_(x) { + const $x = convertToTensor(x, "x", "relu"); + const inputs = { x: $x }; + return ENGINE.runKernel(Relu, inputs); +} +var relu = op({ relu_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js +function relu6_(x) { + const $x = convertToTensor(x, "x", "relu6"); + const inputs = { x: $x }; + return ENGINE.runKernel(Relu6, inputs); +} +var relu6 = op({ relu6_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js +function reverse_(x, axis) { + const $x = convertToTensor(x, "x", "reverse"); + const inputs = { x: $x }; + const attrs = { dims: axis }; + return ENGINE.runKernel(Reverse, inputs, attrs); +} +var reverse = op({ reverse_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js +function reverse1d_(x) { + const $x = convertToTensor(x, "x", "reverse"); + assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`); + return reverse($x, 0); +} +var reverse1d = op({ reverse1d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js +function reverse2d_(x, axis) { + const $x = convertToTensor(x, "x", "reverse"); + assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`); + return reverse($x, axis); +} +var reverse2d = op({ reverse2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js +function reverse3d_(x, axis) { + const $x = convertToTensor(x, "x", "reverse"); + assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`); + return reverse($x, axis); +} +var reverse3d = op({ reverse3d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js +function reverse4d_(x, axis) { + const $x = convertToTensor(x, "x", "reverse"); + assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`); + return reverse($x, axis); +} +var reverse4d = op({ reverse4d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/round.js +function round_(x) { + const $x = convertToTensor(x, "x", "round"); + const inputs = { x: $x }; + return ENGINE.runKernel(Round, inputs); +} +var round2 = op({ round_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js +function rsqrt_(x) { + const $x = convertToTensor(x, "x", "rsqrt", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Rsqrt, inputs); +} +var rsqrt = op({ rsqrt_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu.js +function selu_(x) { + const $x = convertToTensor(x, "x", "selu"); + const inputs = { x: $x }; + return ENGINE.runKernel(Selu, inputs); +} +var selu = op({ selu_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js +function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad3, dilation = [1, 1], dataFormat = "NHWC") { + const $x = convertToTensor(x, "x", "separableConv2d"); + const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d"); + const $pointwiseFilter = convertToTensor(pointwiseFilter, "pointwiseFilter", "separableConv2d"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + if (dataFormat === "NCHW") { + throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported"); + } + assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`); + assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`); + assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`); + assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[0]}.`); + assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`); + const inChannels = $depthwiseFilter.shape[2]; + const channelMultiplier = $depthwiseFilter.shape[3]; + assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`); + const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad3, dataFormat, dilation); + const pointwiseStride = 1; + const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var separableConv2d = op({ separableConv2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js +async function setdiff1dAsync_(x, y) { + const $x = convertToTensor(x, "x", "setdiff1d"); + const $y = convertToTensor(y, "y", "setdiff1d"); + assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`); + assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`); + assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`); + const xVals = await $x.data(); + const yVals = await $y.data(); + const ySet = new Set(yVals); + let outputSize = 0; + for (let i = 0; i < xVals.length; i++) { + if (!ySet.has(xVals[i])) { + outputSize++; + } + } + const buffer2 = new TensorBuffer([outputSize], $x.dtype); + const indices = new TensorBuffer([outputSize], "int32"); + for (let i = 0, p2 = 0; i < xVals.length; i++) { + if (!ySet.has(xVals[i])) { + buffer2.values[p2] = xVals[i]; + indices.values[p2] = i; + p2++; + } + } + return [buffer2.toTensor(), indices.toTensor()]; +} +var setdiff1dAsync = setdiff1dAsync_; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sign.js +function sign_(x) { + const $x = convertToTensor(x, "x", "sign"); + const inputs = { x: $x }; + return ENGINE.runKernel(Sign, inputs); +} +var sign = op({ sign_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sin.js +function sin_(x) { + const $x = convertToTensor(x, "x", "sin", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Sin, inputs); +} +var sin = op({ sin_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js +function sinh_(x) { + const $x = convertToTensor(x, "x", "sinh"); + const inputs = { x: $x }; + return ENGINE.runKernel(Sinh, inputs); +} +var sinh = op({ sinh_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js +function slice1d_(x, begin, size) { + const $x = convertToTensor(x, "x", "slice1d"); + assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`); + return slice($x, [begin], [size]); +} +var slice1d = op({ slice1d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js +function slice2d_(x, begin, size) { + const $x = convertToTensor(x, "x", "slice2d"); + assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`); + return slice($x, begin, size); +} +var slice2d = op({ slice2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js +function slice3d_(x, begin, size) { + const $x = convertToTensor(x, "x", "slice3d"); + assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`); + return slice($x, begin, size); +} +var slice3d = op({ slice3d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js +function slice4d_(x, begin, size) { + const $x = convertToTensor(x, "x", "slice4d"); + assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`); + return slice($x, begin, size); +} +var slice4d = op({ slice4d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js +function softmax_(logits, dim = -1) { + const $logits = convertToTensor(logits, "logits", "softmax", "float32"); + if (dim === -1) { + dim = $logits.rank - 1; + } + if (dim !== $logits.rank - 1) { + throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`); + } + const inputs = { logits: $logits }; + const attrs = { dim }; + return ENGINE.runKernel(Softmax, inputs, attrs); +} +var softmax = op({ softmax_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js +function fft_(input2) { + assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`); + const inputs = { input: input2 }; + return ENGINE.runKernel(FFT, inputs); +} +var fft = op({ fft_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js +function ifft_(input2) { + assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`); + const inputs = { input: input2 }; + return ENGINE.runKernel(IFFT, inputs); +} +var ifft = op({ ifft_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js +function irfft_(input2) { + const innerDimensionSize = input2.shape[input2.shape.length - 1]; + const batch = input2.size / innerDimensionSize; + let ret; + if (innerDimensionSize <= 2) { + const complexInput = reshape(input2, [batch, innerDimensionSize]); + ret = ifft(complexInput); + } else { + const outputShape = [batch, 2 * (innerDimensionSize - 1)]; + const realInput = reshape(real(input2), [batch, innerDimensionSize]); + const imagInput = reshape(imag(input2), [batch, innerDimensionSize]); + const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1); + const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1)); + const r = concat([realInput, realConjugate], 1); + const i = concat([imagInput, imagConjugate], 1); + const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]); + ret = ifft(complexInput); + } + ret = real(ret); + if (input2.rank === 3 && input2.shape[0] !== 0) { + const temp = ret; + const batch2 = input2.shape[0]; + ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]); + temp.dispose(); + } + return ret; +} +var irfft = op({ irfft_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/split.js +function split_(x, numOrSizeSplits, axis = 0) { + const $x = convertToTensor(x, "x", "split"); + const inputs = { x: $x }; + const attr = { numOrSizeSplits, axis }; + return ENGINE.runKernel(SplitV, inputs, attr); +} +var split = op({ split_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js +function rfft_(input2, fftLength) { + assert(input2.dtype === "float32", () => `The dtype for rfft() must be real value but got ${input2.dtype}`); + let innerDimensionSize = input2.shape[input2.shape.length - 1]; + const batch = input2.size / innerDimensionSize; + let adjustedInput; + if (fftLength != null && fftLength < innerDimensionSize) { + const begin = input2.shape.map((v) => 0); + const size = input2.shape.map((v) => v); + size[input2.shape.length - 1] = fftLength; + adjustedInput = slice(input2, begin, size); + innerDimensionSize = fftLength; + } else if (fftLength != null && fftLength > innerDimensionSize) { + const zerosShape = input2.shape.map((v) => v); + zerosShape[input2.shape.length - 1] = fftLength - innerDimensionSize; + adjustedInput = concat([input2, zeros(zerosShape)], input2.shape.length - 1); + innerDimensionSize = fftLength; + } else { + adjustedInput = input2; + } + const zerosInput = zerosLike(adjustedInput); + const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]); + const ret = fft(complexInput); + const half = Math.floor(innerDimensionSize / 2) + 1; + const realValues = real(ret); + const imagValues = imag(ret); + const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1); + const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1); + const outputShape = adjustedInput.shape.slice(); + outputShape[adjustedInput.shape.length - 1] = half; + return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape); +} +var rfft = op({ rfft_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js +function squaredDifference_(a, b) { + let $a = convertToTensor(a, "a", "squaredDifference"); + let $b = convertToTensor(b, "b", "squaredDifference"); + [$a, $b] = makeTypesMatch($a, $b); + assertAndGetBroadcastShape($a.shape, $b.shape); + const inputs = { a: $a, b: $b }; + const attrs = {}; + return ENGINE.runKernel(SquaredDifference, inputs, attrs); +} +var squaredDifference = op({ squaredDifference_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js +function squeeze_(x, axis) { + const $x = convertToTensor(x, "x", "squeeze", "string_or_numeric"); + return reshape($x, squeezeShape($x.shape, axis).newShape); +} +var squeeze = op({ squeeze_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/stack.js +function stack_(tensors, axis = 0) { + const $tensors = convertToTensorArray(tensors, "tensors", "stack", "string_or_numeric"); + assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack"); + if ($tensors.length > 0) { + assert(axis <= $tensors[0].rank, () => "Axis must be <= rank of the tensor"); + } + const inputs = $tensors; + const attrs = { axis }; + return ENGINE.runKernel(Pack, inputs, attrs); +} +var stack = op({ stack_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/step.js +function step_(x, alpha = 0) { + const $x = convertToTensor(x, "x", "step"); + const inputs = { x: $x }; + const attrs = { alpha }; + return ENGINE.runKernel(Step, inputs, attrs); +} +var step = op({ step_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js +function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) { + const $x = convertToTensor(x, "x", "stridedSlice", "string_or_numeric"); + const inputs = { x: $x }; + const attrs = { + begin, + end, + strides, + beginMask, + endMask, + ellipsisMask, + newAxisMask, + shrinkAxisMask + }; + return ENGINE.runKernel(StridedSlice, inputs, attrs); +} +var stridedSlice = op({ stridedSlice_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tan.js +function tan_(x) { + const $x = convertToTensor(x, "x", "tan", "float32"); + const inputs = { x: $x }; + return ENGINE.runKernel(Tan, inputs); +} +var tan = op({ tan_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js +function tensor1d(values, dtype) { + assertNonNull(values); + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 1) { + throw new Error("tensor1d() requires values to be a flat/TypedArray"); + } + const shape = null; + return makeTensor(values, shape, inferredShape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js +function tensor2d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 2) { + throw new Error("tensor2d() requires shape to have two numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 2 && inferredShape.length !== 1) { + throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray"); + } + return makeTensor(values, shape, inferredShape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js +function tensor3d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 3) { + throw new Error("tensor3d() requires shape to have three numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 3 && inferredShape.length !== 1) { + throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor3d() requires shape to be provided when `values` are a flat array"); + } + return makeTensor(values, shape, inferredShape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js +function tensor4d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 4) { + throw new Error("tensor4d() requires shape to have four numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 4 && inferredShape.length !== 1) { + throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor4d() requires shape to be provided when `values` are a flat array"); + } + return makeTensor(values, shape, inferredShape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js +function tensor5d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 5) { + throw new Error("tensor5d() requires shape to have five numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 5 && inferredShape.length !== 1) { + throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor5d() requires shape to be provided when `values` are a flat array"); + } + return makeTensor(values, shape, inferredShape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js +function tensor6d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 6) { + throw new Error("tensor6d() requires shape to have six numbers"); + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 6 && inferredShape.length !== 1) { + throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray"); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error("tensor6d() requires shape to be provided when `values` are a flat array"); + } + shape = shape || inferredShape; + return makeTensor(values, shape, inferredShape, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js +var scatter_nd_util_exports = {}; +__export(scatter_nd_util_exports, { + calculateShapes: () => calculateShapes, + validateInput: () => validateInput, + validateUpdateShape: () => validateUpdateShape +}); +function validateUpdateShape(shape, indices, updates) { + const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1; + const batchDim = indices.rank > 1 ? indices.rank - 1 : 1; + const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`; + if (updates.rank < batchDim) { + throw new Error(shapeError + ` update.rank < ${batchDim}. `); + } + if (shape.length < sliceDim + (updates.rank - batchDim)) { + throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`); + } + if (updates.rank !== batchDim + shape.length - sliceDim) { + throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`); + } + for (let d = 0; d < batchDim; ++d) { + if (updates.shape[d] !== indices.shape[d]) { + throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`); + } + } + for (let d = 0; d < updates.rank - batchDim; ++d) { + if (updates.shape[d + batchDim] !== shape[d + sliceDim]) { + throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`); + } + } +} +function validateInput(updates, indices, shape) { + if (indices.rank < 1) { + throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`); + } + if (updates.rank < 1) { + throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`); + } + if (indices.dtype !== "int32") { + throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`); + } + if (shape.length < 1) { + throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`); + } + if (shape.length === 0) { + if (indices.size === 0) { + throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`); + } + if (updates.size === 0) { + throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`); + } + } + validateUpdateShape(shape, indices, updates); +} +function calculateShapes(updates, indices, shape) { + const indicesRank = indices.shape.length; + const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1; + const totalNd = shape.length; + let sliceSize = 1; + for (let i = sliceRank; i < totalNd; ++i) { + sliceSize *= shape[i]; + } + const safeSliceDim = sliceRank < 1 ? 1 : sliceRank; + const numUpdates = sizeFromShape(indices.shape) / safeSliceDim; + const strides = [...computeStrides(shape.slice(0, sliceRank)), 1]; + const outputSize = sizeFromShape(shape); + return { sliceRank, numUpdates, sliceSize, strides, outputSize }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_scatter_update.js +function tensorScatterUpdate_(tensor2, indices, updates) { + const $tensor = convertToTensor(tensor2, "tensor", "tensorScatterupdate"); + const $indices = convertToTensor(indices, "indices", "tensorScatterupdate", "int32"); + const $updates = convertToTensor(updates, "updates", "tensorScatterupdate"); + validateInput($updates, $indices, $tensor.shape); + if ($tensor.dtype !== $updates.dtype) { + throw new Error(`tensor and updates must have the same dtype, instead they are ${$tensor.dtype} and ${$updates.dtype}.`); + } + const inputs = { + tensor: $tensor, + indices: $indices, + updates: $updates + }; + const attrs = {}; + return ENGINE.runKernel(TensorScatterUpdate, inputs, attrs); +} +var tensorScatterUpdate = op({ tensorScatterUpdate_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js +function topk_(x, k = 1, sorted = true) { + const $x = convertToTensor(x, "x", "topk"); + if ($x.rank === 0) { + throw new Error("topk() expects the input to be of rank 1 or higher"); + } + const lastDim = $x.shape[$x.shape.length - 1]; + if (k < 0) { + throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`); + } + if (k > lastDim) { + throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`); + } + const inputs = { x: $x }; + const attrs = { k, sorted }; + const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs); + return { values, indices }; +} +var topk = op({ topk_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js +function truncatedNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) { + assertNonNegativeIntegerDimensions(shape); + if (dtype != null && dtype === "bool") { + throw new Error(`Unsupported data type $ { dtype }`); + } + const randGauss = new MPRandGauss(mean4, stdDev, dtype, true, seed); + const res = buffer(shape, dtype); + for (let i = 0; i < res.values.length; i++) { + res.values[i] = randGauss.nextValue(); + } + return res.toTensor(); +} +var truncatedNormal = op({ truncatedNormal_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/unique.js +function unique_(x, axis = 0) { + const $x = convertToTensor(x, "x", "unique", "string_or_numeric"); + assert($x.rank > 0, () => "The input tensor must be at least 1D"); + const inputs = { x: $x }; + const attrs = { axis }; + const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs); + return { values, indices }; +} +var unique = op({ unique_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js +function unsortedSegmentSum_(x, segmentIds, numSegments) { + const $x = convertToTensor(x, "x", "unsortedSegmentSum"); + const $segmentIds = convertToTensor(segmentIds, "segmentIds", "unsortedSegmentSum", "int32"); + assert(isInt(numSegments), () => "numSegments must be of dtype int"); + const inputs = { x: $x, segmentIds: $segmentIds }; + const attrs = { numSegments }; + return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs); +} +var unsortedSegmentSum = op({ unsortedSegmentSum_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js +function unstack_(x, axis = 0) { + const $x = convertToTensor(x, "x", "unstack", "string_or_numeric"); + assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`); + const inputs = { value: $x }; + const attrs = { axis }; + return ENGINE.runKernel(Unpack, inputs, attrs); +} +var unstack = op({ unstack_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/upper_bound.js +function upperBound(sortedSequence, values) { + return searchSorted(sortedSequence, values, "right"); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/variable.js +function variable(initialValue, trainable = true, name, dtype) { + return ENGINE.makeVariable(initialValue, trainable, name, dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js +function whereImpl(condShape, condVals) { + const indices = []; + for (let i = 0; i < condVals.length; i++) { + if (condVals[i]) { + indices.push(i); + } + } + const inBuffer = buffer(condShape, "int32"); + const out = buffer([indices.length, condShape.length], "int32"); + for (let i = 0; i < indices.length; i++) { + const loc = inBuffer.indexToLoc(indices[i]); + const offset = i * condShape.length; + out.values.set(loc, offset); + } + return out.toTensor(); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js +async function whereAsync_(condition) { + const $condition = convertToTensor(condition, "condition", "whereAsync", "bool"); + const vals = await $condition.data(); + const res = whereImpl($condition.shape, vals); + if (condition !== $condition) { + $condition.dispose(); + } + return res; +} +var whereAsync = whereAsync_; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js +async function booleanMaskAsync_(tensor2, mask, axis) { + const $tensor = convertToTensor(tensor2, "tensor", "boolMask"); + const $mask = convertToTensor(mask, "mask", "boolMask", "bool"); + const axisFrom = axis == null ? 0 : axis; + const maskDim = $mask.rank; + const tensorShape = $tensor.shape; + assert(maskDim > 0, () => "mask cannot be scalar"); + assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`); + let leadingSize = 1; + for (let i = axisFrom; i < axisFrom + maskDim; i++) { + leadingSize *= tensorShape[i]; + } + const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim)); + const reshapedTensor = reshape($tensor, targetTensorShape); + const reshapedMask = reshape($mask, [-1]); + const positivePositions = await whereAsync(reshapedMask); + const indices = squeeze(positivePositions, [1]); + const res = gather(reshapedTensor, indices, axisFrom); + if (tensor2 !== $tensor) { + $tensor.dispose(); + } + if (mask !== $mask) { + $mask.dispose(); + } + indices.dispose(); + reshapedTensor.dispose(); + reshapedMask.dispose(); + positivePositions.dispose(); + return res; +} +var booleanMaskAsync = booleanMaskAsync_; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js +function transpose_(x, perm, conjugate) { + const $x = convertToTensor(x, "x", "transpose"); + if (perm == null) { + perm = $x.shape.map((s, i) => i).reverse(); + } + assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`); + perm.forEach((axis) => { + assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`); + }); + if ($x.rank <= 1) { + return $x.clone(); + } + const inputs = { x: $x }; + const attrs = { perm }; + if ($x.dtype === "complex64") { + return tidy(() => { + let $real = real($x); + let $imag = imag($x); + $real = ENGINE.runKernel(Transpose, { x: $real }, attrs); + $imag = ENGINE.runKernel(Transpose, { x: $imag }, attrs); + if (conjugate) { + $imag = neg($imag); + } + return complex($real, $imag); + }); + } + return ENGINE.runKernel(Transpose, inputs, attrs); +} +var transpose = op({ transpose_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js +function movingAverage_(v, x, decay, step5, zeroDebias = true) { + const $v = convertToTensor(v, "v", "movingAverage"); + const $x = convertToTensor(x, "x", "movingAverage"); + const $decay = convertToTensor(decay, "decay", "movingAverage"); + assertTypesMatch($v, $x); + assert(arraysEqual($v.shape, $x.shape), () => "Shape mismatch in v and x"); + const one = scalar(1); + const oneMinusDecay = sub(one, $decay); + let update = mul(sub($x, $v), oneMinusDecay); + if (zeroDebias) { + assert(step5 != null, () => "When using zeroDebias: true, step is required."); + const $step = convertToTensor(step5, "step", "movingAverage"); + update = div(update, sub(one, pow($decay, $step))); + } + return add2($v, update); +} +var movingAverage = op({ movingAverage_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js +function scatterND_(indices, updates, shape) { + assertNonNegativeIntegerDimensions(shape); + const $indices = convertToTensor(indices, "indices", "scatterND", "int32"); + const $updates = convertToTensor(updates, "updates", "scatterND"); + validateInput($updates, $indices, shape); + const inputs = { indices: $indices, updates: $updates }; + const attrs = { shape }; + return ENGINE.runKernel(ScatterNd, inputs, attrs); +} +var scatterND = op({ scatterND_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js +function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) { + if (sparseIndices.dtype !== "int32") { + throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`); + } + if (sparseIndices.rank > 2) { + throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`); + } + const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1; + const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1; + if (outputShape.length !== numDims) { + throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`); + } + const numValues = sparseValues.size; + if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) { + throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`); + } + if (sparseValues.dtype !== defaultValues.dtype) { + throw new Error("sparseValues.dtype must match defaultValues.dtype"); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js +function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) { + assertNonNegativeIntegerDimensions(outputShape); + const $sparseIndices = convertToTensor(sparseIndices, "sparseIndices", "sparseToDense", "int32"); + const $sparseValues = convertToTensor(sparseValues, "sparseValues", "sparseToDense", "string_or_numeric"); + const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype); + validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue); + const inputs = { + sparseIndices: $sparseIndices, + sparseValues: $sparseValues, + defaultValue: $defaultValue + }; + const attrs = { outputShape }; + return ENGINE.runKernel(SparseToDense, inputs, attrs); +} +var sparseToDense = op({ sparseToDense_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js +function gatherND_(x, indices) { + const $indices = convertToTensor(indices, "indices", "gatherND", "int32"); + const $x = convertToTensor(x, "x", "gatherND", "string_or_numeric"); + const inputs = { params: $x, indices: $indices }; + return ENGINE.runKernel(GatherNd, inputs); +} +var gatherND = op({ gatherND_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js +function getNoiseShape(x, noiseShape) { + if (noiseShape == null) { + return x.shape.slice(); + } + if (arraysEqual(x.shape, noiseShape)) { + return noiseShape; + } + if (x.shape.length === noiseShape.length) { + const newDimension = []; + for (let i = 0; i < x.shape.length; i++) { + if (noiseShape[i] == null && x.shape[i] != null) { + newDimension.push(x.shape[i]); + } else { + newDimension.push(noiseShape[i]); + } + } + return newDimension; + } + return noiseShape; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js +function dropout_(x, rate, noiseShape, seed) { + const $x = convertToTensor(x, "x", "dropout"); + assert($x.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`); + assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`); + if (rate === 0) { + return x instanceof Tensor ? $x.clone() : $x; + } + const $noiseShape = getNoiseShape($x, noiseShape); + const keepProb = 1 - rate; + const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb); + return mul($x, multiplier); +} +var dropout = op({ dropout_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js +function enclosingPowerOfTwo(value) { + return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2)))); +} +function cosineWindow(windowLength, a, b) { + const even = 1 - windowLength % 2; + const newValues = new Float32Array(windowLength); + for (let i = 0; i < windowLength; ++i) { + const cosArg = 2 * Math.PI * i / (windowLength + even - 1); + newValues[i] = a - b * Math.cos(cosArg); + } + return tensor1d(newValues, "float32"); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js +async function inTopKAsync_(predictions, targets, k = 1) { + const $predictions = convertToTensor(predictions, "predictions", "inTopK"); + const $targets = convertToTensor(targets, "targets", "inTopK"); + assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`); + assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`); + assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`); + const lastDim = $predictions.shape[$predictions.shape.length - 1]; + assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`); + const predictionsVals = await $predictions.data(); + const targetsVals = await $targets.data(); + const [batch, size] = [predictionsVals.length / lastDim, lastDim]; + const precision3 = getTypedArrayFromDType("bool", batch); + for (let b = 0; b < batch; b++) { + const offset = b * size; + const vals = predictionsVals.subarray(offset, offset + size); + const valAndInd = []; + for (let i = 0; i < vals.length; i++) { + valAndInd.push({ value: vals[i], index: i }); + } + valAndInd.sort((a, b2) => b2.value - a.value); + precision3[b] = 0; + for (let i = 0; i < k; i++) { + if (valAndInd[i].index === targetsVals[b]) { + precision3[b] = 1; + break; + } + } + } + if (predictions !== $predictions) { + $predictions.dispose(); + } + if (targets !== $targets) { + $targets.dispose(); + } + return tensor(precision3, $targets.shape, "bool"); +} +var inTopKAsync = inTopKAsync_; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js +var fused_ops_exports = {}; +__export(fused_ops_exports, { + conv2d: () => conv2d2, + depthwiseConv2d: () => depthwiseConv2d2, + matMul: () => matMul2 +}); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js +function conv2DBackpropFilter_(x, dy, filterShape, strides, pad3, dataFormat = "NHWC", dimRoundingMode) { + let x4D = x; + if (x.rank === 3) { + x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); + } + let dy4D = dy; + if (dy4D.rank === 3) { + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`); + assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`); + assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`); + const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1]; + const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1]; + assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`); + assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`); + checkPadOnDimRoundingMode("conv2dDerFilter", pad3, dimRoundingMode); + const inputs = { x: x4D, dy: dy4D }; + const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape }; + return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs); +} +var conv2DBackpropFilter = op({ conv2DBackpropFilter_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js +function getFusedDyActivation(dy, y, activation2) { + if (activation2 == null || activation2 === "linear") { + return dy; + } + if (activation2 === "relu") { + return mul(dy, step(y)); + } + throw new Error(`Cannot compute gradient for fused activation ${activation2}.`); +} +function getFusedBiasGradient(bias, dyActivation) { + let res = dyActivation; + const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, bias.shape); +} +function applyActivation(x, activation2, preluActivationWeights, leakyreluAlpha) { + if (activation2 === "linear") { + return x; + } else if (activation2 === "relu") { + return relu(x); + } else if (activation2 === "elu") { + return elu(x); + } else if (activation2 === "relu6") { + return relu6(x); + } else if (activation2 === "prelu") { + return prelu(x, preluActivationWeights); + } else if (activation2 === "leakyrelu") { + return leakyRelu(x, leakyreluAlpha); + } else if (activation2 === "sigmoid") { + return sigmoid(x); + } + throw new Error(`Unknown fused activation ${activation2}.`); +} +var shouldFuse = (gradientDepth, activation2) => { + const gradientMode = gradientDepth > 0; + return !gradientMode || activation2 === "linear"; +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js +function fusedConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) { + activation2 = activation2 || "linear"; + if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) { + assert(dataFormat === "NHWC", () => `Error in fused conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`); + let result = conv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); + if (bias != null) { + result = add2(result, bias); + } + return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha); + } + const $x = convertToTensor(x, "x", "conv2d", "float32"); + const $filter = convertToTensor(filter, "filter", "conv2d", "float32"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`); + assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`); + checkPadOnDimRoundingMode("fused conv2d", pad3, dimRoundingMode); + const inputChannels = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1]; + assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) must match input depth for filter ${$filter.shape[2]}.`); + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode); + let $bias; + if (bias != null) { + $bias = convertToTensor(bias, "bias", "fused conv2d"); + [$bias] = makeTypesMatch($bias, $x); + if (dataFormat === "NHWC") { + assertAndGetBroadcastShape(convInfo.outShape, $bias.shape); + } else { + assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${$bias.shape.length}.`); + assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not compatible with the number of output channels (${convInfo.outChannels})`); + } + } + let $preluActivationWeights; + if (preluActivationWeights != null) { + const alphaShape = preluActivationWeights.shape; + assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${alphaShape.length}.`); + if (alphaShape.length === 1) { + assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the number of output channels (${convInfo.outChannels}).`); + } else if (alphaShape.length === 3) { + try { + assertAndGetBroadcastShape(alphaShape, convInfo.outShape); + } catch (e) { + const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`; + throw Error(errMsg); + } + } + $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d"); + } + const grad2 = (dy, saved) => { + assert(dataFormat === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`); + const [$filter2, x4D2, y, $bias2] = saved; + const dyActivation = getFusedDyActivation(dy, y, activation2); + assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`); + const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3); + const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3); + const der = [xDer, filterDer]; + if ($bias2 != null) { + const biasDer = getFusedBiasGradient($bias2, dyActivation); + der.push(biasDer); + } + return der; + }; + const inputs = { + x: x4D, + filter: $filter, + bias: $bias, + preluActivationWeights: $preluActivationWeights + }; + const attrs = { + strides, + pad: pad3, + dataFormat, + dilations, + dimRoundingMode, + activation: activation2, + leakyreluAlpha + }; + if (bias == null) { + const customOp = customGrad((x4D2, filter2, save) => { + let res = ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(FusedConv2D, inputs, attrs) + ); + save([filter2, x4D2, res]); + if (reshapedTo4D) { + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad2 }; + }); + return customOp(x4D, $filter); + } else { + const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => { + let res = ENGINE.runKernel(FusedConv2D, inputs, attrs); + save([filter2, x4D2, res, bias2]); + if (reshapedTo4D) { + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad2 }; + }); + return customOpWithBias(x4D, $filter, $bias); + } +} +var conv2d2 = op({ fusedConv2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js +function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad3, dilations = [1, 1], dimRoundingMode) { + let x4D = x; + if (x.rank === 3) { + x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); + } + let dy4D = dy; + if (dy4D.rank === 3) { + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + } + const inputs = { x: x4D, dy: dy4D }; + const attrs = { strides, pad: pad3, dimRoundingMode, dilations, filterShape }; + return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs); +} +var depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js +function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad3, dilations = [1, 1], dimRoundingMode) { + let dy4D = dy; + let reshapedTo4D = false; + if (dy.rank === 3) { + reshapedTo4D = true; + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + } + const inputs = { dy: dy4D, filter }; + const attrs = { strides, pad: pad3, dimRoundingMode, dilations, inputShape: xShape }; + const res = ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs) + ); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js +function fusedDepthwiseConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) { + if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) { + let result = depthwiseConv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); + if (bias != null) { + result = add2(result, bias); + } + return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha); + } + const $x = convertToTensor(x, "x", "depthwiseConv2d", "float32"); + const $filter = convertToTensor(filter, "filter", "depthwiseConv2d", "float32"); + let x4D = $x; + let reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`); + assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`); + assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`); + if (dilations == null) { + dilations = [1, 1]; + } + assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + checkPadOnDimRoundingMode("fused depthwiseConv2d", pad3, dimRoundingMode); + const convInfo = computeConv2DInfo( + x4D.shape, + $filter.shape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + let $bias; + if (bias != null) { + $bias = convertToTensor(bias, "bias", "fused conv2d"); + [$bias] = makeTypesMatch($bias, $x); + assertAndGetBroadcastShape(convInfo.outShape, $bias.shape); + } + let $preluActivationWeights; + if (preluActivationWeights != null) { + $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d"); + } + const grad2 = (dy, saved) => { + assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`); + const [$filter2, x4D2, y, bias2] = saved; + const dyActivation = getFusedDyActivation(dy, y, activation2); + const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3, dilations, dimRoundingMode); + const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3, dilations, dimRoundingMode); + if (bias2 != null) { + const biasDer = getFusedBiasGradient($bias, dyActivation); + return [xDer, filterDer, biasDer]; + } + return [xDer, filterDer]; + }; + const inputs = { + x: x4D, + filter: $filter, + bias: $bias, + preluActivationWeights: $preluActivationWeights + }; + const attrs = { + strides, + pad: pad3, + dataFormat, + dilations, + dimRoundingMode, + activation: activation2, + leakyreluAlpha + }; + if (bias == null) { + const customOp = customGrad((x4D2, filter2, save) => { + let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs); + save([filter2, x4D2, res]); + if (reshapedTo4D) { + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad2 }; + }); + return customOp(x4D, $filter); + } else { + const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => { + let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs); + save([filter2, x4D2, res, bias2]); + if (reshapedTo4D) { + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad2 }; + }); + return customOpWithBias(x4D, $filter, $bias); + } +} +var depthwiseConv2d2 = op({ fusedDepthwiseConv2d_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js +function fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha = 0.2 }) { + if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) { + let result = matMul(a, b, transposeA, transposeB); + if (bias != null) { + result = add2(result, bias); + } + return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha); + } + let $a = convertToTensor(a, "a", "fused matMul"); + let $b = convertToTensor(b, "b", "fused matMul"); + [$a, $b] = makeTypesMatch($a, $b); + const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1]; + const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2]; + const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2]; + const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1]; + const outerDimsA = $a.shape.slice(0, -2); + const outerDimsB = $b.shape.slice(0, -2); + const batchDimA = sizeFromShape(outerDimsA); + const batchDimB = sizeFromShape(outerDimsB); + assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); + const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2)); + const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]); + const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]); + let $bias; + if (bias != null) { + $bias = convertToTensor(bias, "bias", "fused matMul"); + [$bias] = makeTypesMatch($bias, $a); + assertAndGetBroadcastShape(outShape, $bias.shape); + } + let $preluActivationWeights; + if (preluActivationWeights != null) { + $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused matMul"); + } + const grad2 = (dy, saved) => { + const [a3D2, b3D2, y, $bias2] = saved; + const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation2); + let aDer; + let bDer; + if (!transposeA && !transposeB) { + aDer = matMul(dyActivation, b3D2, false, true); + bDer = matMul(a3D2, dyActivation, true, false); + } else if (!transposeA && transposeB) { + aDer = matMul(dyActivation, b3D2, false, false); + bDer = matMul(dyActivation, a3D2, true, false); + } else if (transposeA && !transposeB) { + aDer = matMul(b3D2, dyActivation, false, true); + bDer = matMul(a3D2, dyActivation, false, false); + } else { + aDer = matMul(b3D2, dyActivation, true, true); + bDer = matMul(dyActivation, a3D2, true, true); + } + if (bias != null) { + const biasDer = getFusedBiasGradient($bias2, dyActivation); + return [aDer, bDer, biasDer]; + } else { + return [aDer, bDer]; + } + }; + const inputs = { + a: a3D, + b: b3D, + bias: $bias, + preluActivationWeights: $preluActivationWeights + }; + const attrs = { transposeA, transposeB, activation: activation2, leakyreluAlpha }; + if (bias == null) { + const customOp = customGrad((a3D2, b3D2, save) => { + const res = ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(_FusedMatMul, inputs, attrs) + ); + save([a3D2, b3D2, res]); + return { value: reshape(res, outShape), gradFunc: grad2 }; + }); + return customOp(a3D, b3D); + } else { + const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => { + const res = ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(_FusedMatMul, inputs, attrs) + ); + save([a3D2, b3D2, res, $bias2]); + return { value: reshape(res, outShape), gradFunc: grad2 }; + }); + return customOpWithBias(a3D, b3D, $bias); + } +} +var matMul2 = op({ fusedMatMul_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js +function hammingWindow_(windowLength) { + return cosineWindow(windowLength, 0.54, 0.46); +} +var hammingWindow = op({ hammingWindow_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js +function hannWindow_(windowLength) { + return cosineWindow(windowLength, 0.5, 0.5); +} +var hannWindow = op({ hannWindow_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js +function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) { + let start = 0; + const output = []; + while (start + frameLength <= signal2.size) { + output.push(slice(signal2, start, frameLength)); + start += frameStep; + } + if (padEnd) { + while (start < signal2.size) { + const padLen = start + frameLength - signal2.size; + const pad3 = concat([ + slice(signal2, start, frameLength - padLen), + fill([padLen], padValue) + ]); + output.push(pad3); + start += frameStep; + } + } + if (output.length === 0) { + return tensor2d([], [0, frameLength]); + } + return reshape(concat(output), [output.length, frameLength]); +} +var frame = op({ frame_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js +function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) { + if (fftLength == null) { + fftLength = enclosingPowerOfTwo(frameLength); + } + const framedSignal = frame(signal2, frameLength, frameStep); + const windowedSignal = mul(framedSignal, windowFn(frameLength)); + return rfft(windowedSignal, fftLength); +} +var stft = op({ stft_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js +function cropAndResize_(image2, boxes, boxInd, cropSize, method = "bilinear", extrapolationValue = 0) { + const $image = convertToTensor(image2, "image", "cropAndResize"); + const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32"); + const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32"); + const numBoxes = $boxes.shape[0]; + assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`); + assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`); + assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`); + assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`); + assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`); + assert(method === "bilinear" || method === "nearest", () => `method must be bilinear or nearest, but was ${method}`); + const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd }; + const attrs = { method, extrapolationValue, cropSize }; + const res = ENGINE.runKernel(CropAndResize, inputs, attrs); + return res; +} +var cropAndResize = op({ cropAndResize_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js +function flipLeftRight_(image2) { + const $image = convertToTensor(image2, "image", "flipLeftRight", "float32"); + assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`); + const inputs = { image: $image }; + const res = ENGINE.runKernel(FlipLeftRight, inputs, {}); + return res; +} +var flipLeftRight = op({ flipLeftRight_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/grayscale_to_rgb.js +function grayscaleToRGB_(image2) { + const $image = convertToTensor(image2, "image", "grayscaleToRGB"); + const lastDimsIdx = $image.rank - 1; + const lastDims = $image.shape[lastDimsIdx]; + assert($image.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${$image.rank}.`); + assert(lastDims === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${lastDims}.`); + const reps = new Array($image.rank); + reps.fill(1, 0, lastDimsIdx); + reps[lastDimsIdx] = 3; + return tile($image, reps); +} +var grayscaleToRGB = op({ grayscaleToRGB_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/rgb_to_grayscale.js +function rgbToGrayscale_(image2) { + const $image = convertToTensor(image2, "image", "RGBToGrayscale"); + const lastDimsIdx = $image.rank - 1; + const lastDims = $image.shape[lastDimsIdx]; + assert($image.rank >= 2, () => `Error in RGBToGrayscale: images must be at least rank 2, but got rank ${$image.rank}.`); + assert(lastDims === 3, () => `Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${lastDims}.`); + const origDtype = $image.dtype; + const fltImage = cast($image, "float32"); + const rgbWeights = tensor1d([0.2989, 0.587, 0.114]); + let grayFloat; + switch ($image.rank) { + case 2: + grayFloat = einsum("ij,j->i", fltImage, rgbWeights); + break; + case 3: + grayFloat = einsum("ijk,k->ij", fltImage, rgbWeights); + break; + case 4: + grayFloat = einsum("ijkl,l->ijk", fltImage, rgbWeights); + break; + case 5: + grayFloat = einsum("ijklm,m->ijkl", fltImage, rgbWeights); + break; + case 6: + grayFloat = einsum("ijklmn,n->ijklm", fltImage, rgbWeights); + break; + default: + throw new Error("Not a valid tensor rank."); + } + grayFloat = expandDims(grayFloat, -1); + return cast(grayFloat, origDtype); +} +var rgbToGrayscale = op({ rgbToGrayscale_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js +function rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) { + const $image = convertToTensor(image2, "image", "rotateWithOffset", "float32"); + assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`); + const inputs = { image: $image }; + const attrs = { radians, fillValue, center }; + const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs); + return res; +} +var rotateWithOffset = op({ rotateWithOffset_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js +function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) { + if (iouThreshold == null) { + iouThreshold = 0.5; + } + if (scoreThreshold == null) { + scoreThreshold = Number.NEGATIVE_INFINITY; + } + if (softNmsSigma == null) { + softNmsSigma = 0; + } + const numBoxes = boxes.shape[0]; + maxOutputSize = Math.min(maxOutputSize, numBoxes); + assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`); + assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`); + assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`); + assert(scores.rank === 1, () => "scores must be a 1D tensor"); + assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`); + assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`); + return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js +function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression", "float32"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppression", "float32"); + const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); + maxOutputSize = inputs.maxOutputSize; + iouThreshold = inputs.iouThreshold; + scoreThreshold = inputs.scoreThreshold; + const attrs = { maxOutputSize, iouThreshold, scoreThreshold }; + return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs); +} +var nonMaxSuppression = op({ nonMaxSuppression_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_util.js +function binaryInsert(arr, element, comparator) { + const index = binarySearch(arr, element, comparator); + const insertionPoint = index < 0 ? -(index + 1) : index; + arr.splice(insertionPoint, 0, element); +} +function binarySearch(arr, target, comparator) { + return binarySearch_(arr, target, comparator || defaultComparator); +} +function defaultComparator(a, b) { + return a > b ? 1 : a < b ? -1 : 0; +} +function binarySearch_(arr, target, comparator) { + let left = 0; + let right = arr.length; + let middle = 0; + let found = false; + while (left < right) { + middle = left + (right - left >>> 1); + const compareResult = comparator(target, arr[middle]); + if (compareResult > 0) { + left = middle + 1; + } else { + right = middle; + found = !compareResult; + } + } + return found ? left : -left - 1; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js +function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + return nonMaxSuppressionImpl_( + boxes, + scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + 0 + /* softNmsSigma */ + ); +} +function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) { + return nonMaxSuppressionImpl_( + boxes, + scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + 0, + false, + padToMaxOutputSize, + true + /* returnValidOutputs */ + ); +} +function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) { + return nonMaxSuppressionImpl_( + boxes, + scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + softNmsSigma, + true + /* returnScoresTensor */ + ); +} +function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) { + const candidates = []; + for (let i = 0; i < scores.length; i++) { + if (scores[i] > scoreThreshold) { + candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 }); + } + } + candidates.sort(ascendingComparator); + const scale2 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0; + const selectedIndices = []; + const selectedScores = []; + while (selectedIndices.length < maxOutputSize && candidates.length > 0) { + const candidate = candidates.pop(); + const { score: originalScore, boxIndex, suppressBeginIndex } = candidate; + if (originalScore < scoreThreshold) { + break; + } + let ignoreCandidate = false; + for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) { + const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]); + if (iou >= iouThreshold) { + ignoreCandidate = true; + break; + } + candidate.score = candidate.score * suppressWeight(iouThreshold, scale2, iou); + if (candidate.score <= scoreThreshold) { + break; + } + } + candidate.suppressBeginIndex = selectedIndices.length; + if (!ignoreCandidate) { + if (candidate.score === originalScore) { + selectedIndices.push(boxIndex); + selectedScores.push(candidate.score); + } else if (candidate.score > scoreThreshold) { + binaryInsert(candidates, candidate, ascendingComparator); + } + } + } + const validOutputs = selectedIndices.length; + const elemsToPad = maxOutputSize - validOutputs; + if (padToMaxOutputSize && elemsToPad > 0) { + selectedIndices.push(...new Array(elemsToPad).fill(0)); + selectedScores.push(...new Array(elemsToPad).fill(0)); + } + const result = { selectedIndices }; + if (returnScoresTensor) { + result["selectedScores"] = selectedScores; + } + if (returnValidOutputs) { + result["validOutputs"] = validOutputs; + } + return result; +} +function intersectionOverUnion(boxes, i, j) { + const iCoord = boxes.subarray(i * 4, i * 4 + 4); + const jCoord = boxes.subarray(j * 4, j * 4 + 4); + const yminI = Math.min(iCoord[0], iCoord[2]); + const xminI = Math.min(iCoord[1], iCoord[3]); + const ymaxI = Math.max(iCoord[0], iCoord[2]); + const xmaxI = Math.max(iCoord[1], iCoord[3]); + const yminJ = Math.min(jCoord[0], jCoord[2]); + const xminJ = Math.min(jCoord[1], jCoord[3]); + const ymaxJ = Math.max(jCoord[0], jCoord[2]); + const xmaxJ = Math.max(jCoord[1], jCoord[3]); + const areaI = (ymaxI - yminI) * (xmaxI - xminI); + const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); + if (areaI <= 0 || areaJ <= 0) { + return 0; + } + const intersectionYmin = Math.max(yminI, yminJ); + const intersectionXmin = Math.max(xminI, xminJ); + const intersectionYmax = Math.min(ymaxI, ymaxJ); + const intersectionXmax = Math.min(xmaxI, xmaxJ); + const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0); + return intersectionArea / (areaI + areaJ - intersectionArea); +} +function suppressWeight(iouThreshold, scale2, iou) { + const weight = Math.exp(scale2 * iou * iou); + return iou <= iouThreshold ? weight : 0; +} +function ascendingComparator(c1, c2) { + return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js +async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync"); + const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); + maxOutputSize = inputs.maxOutputSize; + iouThreshold = inputs.iouThreshold; + scoreThreshold = inputs.scoreThreshold; + const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]); + const boxesVals = boxesAndScores[0]; + const scoresVals = boxesAndScores[1]; + const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold); + if ($boxes !== boxes) { + $boxes.dispose(); + } + if ($scores !== scores) { + $scores.dispose(); + } + return tensor1d(selectedIndices, "int32"); +} +var nonMaxSuppressionAsync = nonMaxSuppressionAsync_; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js +function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppression"); + const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + maxOutputSize = params.maxOutputSize; + iouThreshold = params.iouThreshold; + scoreThreshold = params.scoreThreshold; + softNmsSigma = params.softNmsSigma; + const inputs = { boxes: $boxes, scores: $scores }; + const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma }; + const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs); + return { selectedIndices: result[0], selectedScores: result[1] }; +} +var nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js +async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync"); + const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + maxOutputSize = params.maxOutputSize; + iouThreshold = params.iouThreshold; + scoreThreshold = params.scoreThreshold; + softNmsSigma = params.softNmsSigma; + const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]); + const boxesVals = boxesAndScores[0]; + const scoresVals = boxesAndScores[1]; + const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + if ($boxes !== boxes) { + $boxes.dispose(); + } + if ($scores !== scores) { + $scores.dispose(); + } + return { + selectedIndices: tensor1d(selectedIndices, "int32"), + selectedScores: tensor1d(selectedScores) + }; +} +var nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js +function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppression"); + const params = nonMaxSuppSanityCheck( + $boxes, + $scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + null + /* softNmsSigma */ + ); + const $maxOutputSize = params.maxOutputSize; + const $iouThreshold = params.iouThreshold; + const $scoreThreshold = params.scoreThreshold; + const inputs = { boxes: $boxes, scores: $scores }; + const attrs = { + maxOutputSize: $maxOutputSize, + iouThreshold: $iouThreshold, + scoreThreshold: $scoreThreshold, + padToMaxOutputSize + }; + const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs); + return { selectedIndices: result[0], validOutputs: result[1] }; +} +var nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js +async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) { + const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync"); + const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync"); + const params = nonMaxSuppSanityCheck( + $boxes, + $scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + null + /* softNmsSigma */ + ); + const $maxOutputSize = params.maxOutputSize; + const $iouThreshold = params.iouThreshold; + const $scoreThreshold = params.scoreThreshold; + const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]); + const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize); + if ($boxes !== boxes) { + $boxes.dispose(); + } + if ($scores !== scores) { + $scores.dispose(); + } + return { + selectedIndices: tensor1d(selectedIndices, "int32"), + validOutputs: scalar(validOutputs, "int32") + }; +} +var nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js +function resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) { + const $images = convertToTensor(images, "images", "resizeBilinear"); + assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`); + assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`); + assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.`); + let batchImages = $images; + let reshapedTo4D = false; + if ($images.rank === 3) { + reshapedTo4D = true; + batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]); + } + const [] = size; + const inputs = { images: batchImages }; + const attrs = { alignCorners, halfPixelCenters, size }; + const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var resizeBilinear = op({ resizeBilinear_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js +function resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) { + const $images = convertToTensor(images, "images", "resizeNearestNeighbor"); + assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`); + assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`); + assert($images.dtype === "float32" || $images.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"); + assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.`); + let batchImages = $images; + let reshapedTo4D = false; + if ($images.rank === 3) { + reshapedTo4D = true; + batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]); + } + const [] = size; + const inputs = { images: batchImages }; + const attrs = { alignCorners, halfPixelCenters, size }; + const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var resizeNearestNeighbor = op({ resizeNearestNeighbor_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/threshold.js +function threshold_(image2, method = "binary", inverted = false, threshValue = 0.5) { + const $image = convertToTensor(image2, "image", "threshold"); + const RED_INTENCITY_COEF = 0.2989; + const GREEN_INTENCITY_COEF = 0.587; + const BLUE_INTENCITY_COEF = 0.114; + const totalPixelsInImage = $image.shape[0] * $image.shape[1]; + let $threshold = mul(tensor1d([threshValue]), 255); + let r, g, b, grayscale; + assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`); + assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`); + assert($image.dtype === "int32" || $image.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`); + assert(method === "otsu" || method === "binary", () => `Method must be binary or otsu, but was ${method}`); + if ($image.shape[2] === 3) { + [r, g, b] = split($image, [1, 1, 1], -1); + const $r = mul(r, RED_INTENCITY_COEF); + const $g = mul(g, GREEN_INTENCITY_COEF); + const $b = mul(b, BLUE_INTENCITY_COEF); + grayscale = add2(add2($r, $g), $b); + } else { + grayscale = image2; + } + if (method === "otsu") { + const $histogram = bincount(cast(round2(grayscale), "int32"), tensor([]), 256); + $threshold = otsu($histogram, totalPixelsInImage); + } + const invCondition = inverted ? lessEqual(grayscale, $threshold) : greater(grayscale, $threshold); + const result = cast(mul(invCondition, 255), "int32"); + return result; +} +function otsu(histogram, total) { + let bestThresh = tensor1d([-1]); + let bestInBetVar = tensor1d([0]); + let cInBetVar = tensor1d([0]); + let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack; + for (let index = 0; index < histogram.size - 1; index++) { + classFirst = slice(histogram, 0, index + 1); + classSecond = slice(histogram, index + 1); + weightForeground = div(sum2(classFirst), total); + weightBack = div(sum2(classSecond), total); + const meanFirstDivA = sum2(mul(classFirst, range(0, classFirst.size))); + meanFirst = div(meanFirstDivA, sum2(classFirst)); + const meanSecFill = fill(classSecond.shape, classFirst.size); + const meanSecAdd = add2(range(0, classSecond.size), meanSecFill); + const meanSecMul = mul(classSecond, meanSecAdd); + meanSec = div(sum2(meanSecMul), sum2(classSecond)); + const cInBetVarSubA = sub(meanFirst, meanSec); + const cInBetVarSubB = sub(meanFirst, meanSec); + const cInBetVarMul = mul(weightForeground, weightBack); + cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB); + const condition = greater(cInBetVar, bestInBetVar); + bestInBetVar = where(condition, cInBetVar, bestInBetVar); + bestThresh = where(condition, tensor1d([index]), bestThresh); + } + return bestThresh; +} +var threshold = op({ threshold_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/transform.js +function transform_(image2, transforms, interpolation = "nearest", fillMode = "constant", fillValue = 0, outputShape) { + const $image = convertToTensor(image2, "image", "transform", "float32"); + const $transforms = convertToTensor(transforms, "transforms", "transform", "float32"); + assert($image.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${$image.rank}.`); + assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`); + assert(outputShape == null || outputShape.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${outputShape}.`); + const inputs = { image: $image, transforms: $transforms }; + const attrs = { interpolation, fillMode, fillValue, outputShape }; + return ENGINE.runKernel(Transform, inputs, attrs); +} +var transform = op({ transform_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js +function bandPart_(a, numLower, numUpper) { + const $a = convertToTensor(a, "a", "bandPart"); + assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`); + const shape = $a.shape; + const [M, N] = $a.shape.slice(-2); + let $numLower; + let $numUpper; + if (typeof numLower === "number") { + assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`); + assert(numLower <= M, () => `bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`); + $numLower = convertToTensor(numLower < 0 ? M : numLower, "numLower", "bandPart"); + } else { + assert(numLower.dtype === "int32", () => `bandPart(): numLower's dtype must be an int32.`); + $numLower = where(less(numLower, 0), M, minimum(numLower, M)); + } + if (typeof numUpper === "number") { + assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`); + assert(numUpper <= N, () => `bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`); + $numUpper = convertToTensor(numUpper < 0 ? N : numUpper, "numUpper", "bandPart"); + } else { + assert(numUpper.dtype === "int32", () => `bandPart(): numUpper's dtype must be an int32.`); + $numUpper = where(less(numUpper, 0), N, minimum(numUpper, N)); + } + const i = reshape(range(0, M, 1, "int32"), [-1, 1]); + const j = range(0, N, 1, "int32"); + const ij = sub(i, j); + const inBand = logicalAnd(lessEqual(ij, $numLower), greaterEqual(ij, neg($numUpper))); + const zero = zeros([M, N], $a.dtype); + return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape); +} +var bandPart = op({ bandPart_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js +function gramSchmidt_(xs) { + let inputIsTensor2D; + if (Array.isArray(xs)) { + inputIsTensor2D = false; + assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty"); + const dim = xs[0].shape[0]; + for (let i = 1; i < xs.length; ++i) { + assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`); + } + } else { + inputIsTensor2D = true; + xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0])); + } + assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`); + const ys = []; + const xs1d = xs; + for (let i = 0; i < xs.length; ++i) { + ys.push(ENGINE.tidy(() => { + let x = xs1d[i]; + if (i > 0) { + for (let j = 0; j < i; ++j) { + const proj = mul(sum2(mul(ys[j], x)), ys[j]); + x = sub(x, proj); + } + } + return div(x, norm(x, "euclidean")); + })); + } + if (inputIsTensor2D) { + return stack(ys, 0); + } else { + return ys; + } +} +var gramSchmidt = op({ gramSchmidt_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js +function qr_(x, fullMatrices = false) { + assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`); + if (x.rank === 2) { + return qr2d(x, fullMatrices); + } else { + const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev); + const x2ds = unstack(reshape(x, [ + outerDimsProd, + x.shape[x.shape.length - 2], + x.shape[x.shape.length - 1] + ]), 0); + const q2ds = []; + const r2ds = []; + x2ds.forEach((x2d) => { + const [q2d, r2d] = qr2d(x2d, fullMatrices); + q2ds.push(q2d); + r2ds.push(r2d); + }); + const q = reshape(stack(q2ds, 0), x.shape); + const r = reshape(stack(r2ds, 0), x.shape); + return [q, r]; + } +} +function qr2d(x, fullMatrices = false) { + return ENGINE.tidy(() => { + assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`); + const m = x.shape[0]; + const n = x.shape[1]; + let q = eye(m); + let r = clone(x); + const one2D = tensor2d([[1]], [1, 1]); + let w = clone(one2D); + const iters = m >= n ? n : m; + for (let j = 0; j < iters; ++j) { + const rTemp = r; + const wTemp = w; + const qTemp = q; + [w, r, q] = ENGINE.tidy(() => { + const rjEnd1 = slice(r, [j, j], [m - j, 1]); + const normX = norm(rjEnd1); + const rjj = slice(r, [j, j], [1, 1]); + const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]])); + const u1 = sub(rjj, mul(s, normX)); + const wPre = div(rjEnd1, u1); + if (wPre.shape[0] === 1) { + w = clone(one2D); + } else { + w = concat([ + one2D, + slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) + ], 0); + } + const tau = neg(div(matMul(s, u1), normX)); + const rjEndAll = slice(r, [j, 0], [m - j, n]); + const tauTimesW = mul(tau, w); + const wT = transpose(w); + if (j === 0) { + r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll))); + } else { + const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll))); + r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0); + } + const tawTimesWT = transpose(tauTimesW); + const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]); + if (j === 0) { + q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT)); + } else { + const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT)); + q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1); + } + return [w, r, q]; + }); + dispose([rTemp, wTemp, qTemp]); + } + if (!fullMatrices && m > n) { + q = slice(q, [0, 0], [m, n]); + r = slice(r, [0, 0], [n, n]); + } + return [q, r]; + }); +} +var qr = op({ qr_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js +var Reduction; +(function(Reduction2) { + Reduction2[Reduction2["NONE"] = 0] = "NONE"; + Reduction2[Reduction2["MEAN"] = 1] = "MEAN"; + Reduction2[Reduction2["SUM"] = 2] = "SUM"; + Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS"; +})(Reduction || (Reduction = {})); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js +function computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $losses = convertToTensor(losses2, "losses", "computeWeightedLoss"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "computeWeightedLoss"); + } + const weightedLoss = $weights == null ? $losses : mul($losses, $weights); + if (reduction === Reduction.NONE) { + return weightedLoss; + } + if (reduction === Reduction.SUM) { + return sum2(weightedLoss); + } + if (reduction === Reduction.MEAN) { + if ($weights == null) { + return mean(weightedLoss); + } else { + const broadcastFactor = $losses.size / $weights.size; + const result = div(sum2(weightedLoss), sum2($weights)); + return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result; + } + } + if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) { + if ($weights == null) { + return div(sum2(weightedLoss), scalar($losses.size)); + } else { + const broadcastedWeights = mul($weights, ones2($losses.shape)); + const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), "float32"); + return div(sum2(weightedLoss), numNonZeros); + } + } + throw Error(`Unknown reduction: ${reduction}`); +} +var computeWeightedLoss = op({ computeWeightedLoss_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js +function absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $labels = convertToTensor(labels, "labels", "absoluteDifference"); + const $predictions = convertToTensor(predictions, "predictions", "absoluteDifference"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "absoluteDifference"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in absoluteDifference: "); + const losses2 = abs(sub($labels, $predictions)); + return computeWeightedLoss(losses2, $weights, reduction); +} +var absoluteDifference = op({ absoluteDifference_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js +function cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $labels = convertToTensor(labels, "labels", "cosineDistance"); + const $predictions = convertToTensor(predictions, "predictions", "cosineDistance"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "cosineDistance"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in cosineDistance: "); + const one = scalar(1); + const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true)); + return computeWeightedLoss(losses2, $weights, reduction); +} +var cosineDistance = op({ cosineDistance_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js +function hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + let $labels = convertToTensor(labels, "labels", "hingeLoss"); + const $predictions = convertToTensor(predictions, "predictions", "hingeLoss"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "hingeLoss"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in hingeLoss: "); + const one = scalar(1); + $labels = sub(mul(scalar(2), $labels), one); + const losses2 = relu(sub(one, mul($labels, $predictions))); + return computeWeightedLoss(losses2, $weights, reduction); +} +var hingeLoss = op({ hingeLoss_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js +function huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $labels = convertToTensor(labels, "labels", "huberLoss"); + const $predictions = convertToTensor(predictions, "predictions", "huberLoss"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "huberLoss"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in huberLoss: "); + const deltaScalar = scalar(delta); + const error = abs(sub($predictions, $labels)); + const quadratic = minimum(error, deltaScalar); + const linear = sub(error, quadratic); + const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear)); + return computeWeightedLoss(losses2, $weights, reduction); +} +var huberLoss = op({ huberLoss_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js +function logLoss_(labels, predictions, weights, epsilon3 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $labels = convertToTensor(labels, "labels", "logLoss"); + const $predictions = convertToTensor(predictions, "predictions", "logLoss"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "logLoss"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: "); + const one = scalar(1); + const epsilonScalar = scalar(epsilon3); + const l13 = neg(mul($labels, log2(add2($predictions, epsilonScalar)))); + const l23 = mul(sub(one, $labels), log2(add2(sub(one, $predictions), epsilonScalar))); + const losses2 = sub(l13, l23); + return computeWeightedLoss(losses2, $weights, reduction); +} +var logLoss = op({ logLoss_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js +function meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + const $labels = convertToTensor(labels, "labels", "meanSquaredError"); + const $predictions = convertToTensor(predictions, "predictions", "meanSquaredError"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "meanSquaredError"); + } + assertShapesMatch($labels.shape, $predictions.shape, "Error in meanSquaredError: "); + const losses2 = squaredDifference($labels, $predictions); + return computeWeightedLoss(losses2, $weights, reduction); +} +var meanSquaredError = op({ meanSquaredError_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js +function sigmoidCrossEntropyWithLogits_(labels, logits) { + const $labels = convertToTensor(labels, "labels", "sigmoidCrossEntropyWithLogits"); + const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropyWithLogits"); + assertShapesMatch($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: "); + const maxOutput = relu($logits); + const outputXTarget = mul($logits, $labels); + const sigmoidOutput = log1p(exp(neg(abs($logits)))); + return add2(sub(maxOutput, outputXTarget), sigmoidOutput); +} +function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + let $multiClassLabels = convertToTensor(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy"); + const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropy"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "sigmoidCrossEntropy"); + } + assertShapesMatch($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: "); + if (labelSmoothing > 0) { + const labelSmoothingScalar = scalar(labelSmoothing); + const one = scalar(1); + const half = scalar(0.5); + $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar)); + } + const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits); + return computeWeightedLoss(losses2, $weights, reduction); +} +var sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js +function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) { + if (dim === -1) { + dim = logits.rank - 1; + } + if (dim !== logits.rank - 1) { + throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`); + } + const customOp = customGrad((labels2, logits2, save) => { + const keepDims = true; + const lse = logSumExp(logits2, [dim], keepDims); + const logResult = sub(cast(logits2, "float32"), lse); + save([labels2, logResult]); + const costVector = neg(mul(logResult, labels2)); + const value = sum2(costVector, [dim]); + const gradFunc = (dy, saved) => { + const [labels3, logResult2] = saved; + const dyShape = expandShapeToKeepDim(dy.shape, [dim]); + return [ + mul(reshape(dy, dyShape), sub(cast(labels3, "float32"), exp(logResult2))), + mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, "float32"))) + ]; + }; + return { value, gradFunc }; + }); + return customOp(labels, logits); +} +function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) { + let $onehotLabels = convertToTensor(onehotLabels, "onehotLabels", "softmaxCrossEntropy"); + const $logits = convertToTensor(logits, "logits", "softmaxCrossEntropy"); + let $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, "weights", "softmaxCrossEntropy"); + } + assertShapesMatch($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: "); + if (labelSmoothing > 0) { + const labelSmoothingScalar = scalar(labelSmoothing); + const one = scalar(1); + const numClasses = scalar($onehotLabels.shape[1]); + $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses)); + } + const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits); + return computeWeightedLoss(losses2, $weights, reduction); +} +var softmaxCrossEntropy = op({ softmaxCrossEntropy_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows.js +function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) { + const $indices = convertToTensor(indices, "indices", "sparseFillEmptyRows", "int32"); + const $values = convertToTensor(values, "values", "sparseFillEmptyRows"); + const $denseShape = convertToTensor(denseShape, "denseShape", "sparseFillEmptyRows", "int32"); + const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseFillEmptyRows", $values.dtype); + if ($indices.rank !== 2) { + throw new Error(`Indices should be Tensor2D but received shape + ${$indices.shape}`); + } + if ($values.rank !== 1) { + throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`); + } + if ($denseShape.rank !== 1) { + throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`); + } + if ($defaultValue.rank !== 0) { + throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`); + } + const inputs = { + indices: $indices, + values: $values, + denseShape: $denseShape, + defaultValue: $defaultValue + }; + const result = ENGINE.runKernel(SparseFillEmptyRows, inputs); + return { + outputIndices: result[0], + outputValues: result[1], + emptyRowIndicator: result[2], + reverseIndexMap: result[3] + }; +} +var sparseFillEmptyRows = op({ sparseFillEmptyRows_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape.js +function sparseReshape_(inputIndices, inputShape, newShape) { + const $inputIndices = convertToTensor(inputIndices, "inputIndices", "sparseReshape", "int32"); + const $inputShape = convertToTensor(inputShape, "inputShape", "sparseReshape", "int32"); + const $newShape = convertToTensor(newShape, "newShape", "sparseReshape", "int32"); + if ($inputIndices.rank !== 2) { + throw new Error(`Input indices should be Tensor2D but received shape + ${$inputIndices.shape}`); + } + if ($inputShape.rank !== 1) { + throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`); + } + if ($newShape.rank !== 1) { + throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`); + } + const inputs = { + inputIndices: $inputIndices, + inputShape: $inputShape, + newShape: $newShape + }; + const result = ENGINE.runKernel(SparseReshape, inputs); + return { outputIndices: result[0], outputShape: result[1] }; +} +var sparseReshape = op({ sparseReshape_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_mean.js +function sparseSegmentMean_(data, indices, segmentIds) { + const $data = convertToTensor(data, "data", "sparseSegmentMean"); + const $indices = convertToTensor(indices, "indices", "sparseSegmentMean", "int32"); + const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentMean", "int32"); + if ($data.rank < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if ($indices.rank !== 1) { + throw new Error(`Indices should be Tensor1D but received shape + ${$indices.shape}`); + } + if ($segmentIds.rank !== 1) { + throw new Error(`Segment ids should be Tensor1D but received shape + ${$segmentIds.shape}`); + } + const inputs = { + data: $data, + indices: $indices, + segmentIds: $segmentIds + }; + return ENGINE.runKernel(SparseSegmentMean, inputs); +} +var sparseSegmentMean = op({ sparseSegmentMean_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_sum.js +function sparseSegmentSum_(data, indices, segmentIds) { + const $data = convertToTensor(data, "data", "sparseSegmentSum"); + const $indices = convertToTensor(indices, "indices", "sparseSegmentSum", "int32"); + const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentSum", "int32"); + if ($data.rank < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if ($indices.rank !== 1) { + throw new Error(`Indices should be Tensor1D but received shape + ${$indices.shape}`); + } + if ($segmentIds.rank !== 1) { + throw new Error(`Segment ids should be Tensor1D but received shape + ${$segmentIds.shape}`); + } + const inputs = { + data: $data, + indices: $indices, + segmentIds: $segmentIds + }; + return ENGINE.runKernel(SparseSegmentSum, inputs); +} +var sparseSegmentSum = op({ sparseSegmentSum_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_n_grams.js +function stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) { + const $data = convertToTensor(data, "data", "stringNGrams", "string"); + if ($data.dtype !== "string") { + throw new Error("Data must be of datatype string"); + } + if ($data.shape.length !== 1) { + throw new Error(`Data must be a vector, saw: ${$data.shape}`); + } + const $dataSplits = convertToTensor(dataSplits, "dataSplits", "stringNGrams"); + if ($dataSplits.dtype !== "int32") { + throw new Error("Data splits must be of datatype int32"); + } + const attrs = { + separator, + nGramWidths, + leftPad, + rightPad: rightPad2, + padWidth, + preserveShortSequences + }; + const inputs = { data: $data, dataSplits: $dataSplits }; + const result = ENGINE.runKernel(StringNGrams, inputs, attrs); + return { nGrams: result[0], nGramsSplits: result[1] }; +} +var stringNGrams = op({ stringNGrams_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_split.js +function stringSplit_(input2, delimiter, skipEmpty = true) { + const $input = convertToTensor(input2, "input", "stringSplit", "string"); + const $delimiter = convertToTensor(delimiter, "delimiter", "stringSplit", "string"); + if ($input.rank !== 1) { + throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`); + } + if ($delimiter.rank !== 0) { + throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`); + } + const attrs = { skipEmpty }; + const inputs = { input: $input, delimiter: $delimiter }; + const result = ENGINE.runKernel(StringSplit, inputs, attrs); + return { indices: result[0], values: result[1], shape: result[2] }; +} +var stringSplit = op({ stringSplit_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_to_hash_bucket_fast.js +function stringToHashBucketFast_(input2, numBuckets) { + const $input = convertToTensor(input2, "input", "stringToHashBucketFast", "string"); + const attrs = { numBuckets }; + if (numBuckets <= 0) { + throw new Error(`Number of buckets must be at least 1`); + } + const inputs = { input: $input }; + return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs); +} +var stringToHashBucketFast = op({ stringToHashBucketFast_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/static_regex_replace.js +function staticRegexReplace_(input2, pattern, rewrite, replaceGlobal = true) { + const $input = convertToTensor(input2, "input", "staticRegexReplace", "string"); + const attrs = { pattern, rewrite, replaceGlobal }; + return ENGINE.runKernel(StaticRegexReplace, { x: $input }, attrs); +} +var staticRegexReplace = op({ staticRegexReplace_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js +var spectral = { + fft, + ifft, + rfft, + irfft +}; +var signal = { + hammingWindow, + hannWindow, + frame, + stft +}; +var image = { + flipLeftRight, + grayscaleToRGB, + resizeNearestNeighbor, + resizeBilinear, + rgbToGrayscale, + rotateWithOffset, + cropAndResize, + nonMaxSuppression, + nonMaxSuppressionAsync, + nonMaxSuppressionWithScore, + nonMaxSuppressionWithScoreAsync, + nonMaxSuppressionPadded, + nonMaxSuppressionPaddedAsync, + threshold, + transform +}; +var linalg = { + bandPart, + gramSchmidt, + qr +}; +var losses = { + absoluteDifference, + computeWeightedLoss, + cosineDistance, + hingeLoss, + huberLoss, + logLoss, + meanSquaredError, + sigmoidCrossEntropy, + softmaxCrossEntropy +}; +var sparse = { + sparseFillEmptyRows, + sparseReshape, + sparseSegmentMean, + sparseSegmentSum +}; +var string = { + stringNGrams, + stringSplit, + stringToHashBucketFast, + staticRegexReplace +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/serialization.js +var serialization_exports = {}; +__export(serialization_exports, { + Serializable: () => Serializable, + SerializationMap: () => SerializationMap, + getRegisteredName: () => getRegisteredName, + registerClass: () => registerClass +}); +var GLOBAL_CUSTOM_OBJECT = /* @__PURE__ */ new Map(); +var GLOBAL_CUSTOM_NAMES = /* @__PURE__ */ new Map(); +var Serializable = class { + /** + * Return the class name for this class to use in serialization contexts. + * + * Generally speaking this will be the same thing that constructor.name + * would have returned. However, the class name needs to be robust + * against minification for serialization/deserialization to work properly. + * + * There's also places such as initializers.VarianceScaling, where + * implementation details between different languages led to different + * class hierarchies and a non-leaf node is used for serialization purposes. + */ + getClassName() { + return this.constructor.className; + } + /** + * Creates an instance of T from a ConfigDict. + * + * This works for most descendants of serializable. A few need to + * provide special handling. + * @param cls A Constructor for the class to instantiate. + * @param config The Configuration for the object. + */ + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config); + } +}; +var SerializationMap = class _SerializationMap { + constructor() { + this.classNameMap = {}; + } + /** + * Returns the singleton instance of the map. + */ + static getMap() { + if (_SerializationMap.instance == null) { + _SerializationMap.instance = new _SerializationMap(); + } + return _SerializationMap.instance; + } + /** + * Registers the class as serializable. + */ + static register(cls) { + _SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig]; + } +}; +function registerClass(cls, pkg, name) { + assert(cls.className != null, () => `Class being registered does not have the static className property defined.`); + assert(typeof cls.className === "string", () => `className is required to be a string, but got type ` + typeof cls.className); + assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`); + if (typeof pkg === "undefined") { + pkg = "Custom"; + } + if (typeof name === "undefined") { + name = cls.className; + } + const className = name; + const registerName = pkg + ">" + className; + SerializationMap.register(cls); + GLOBAL_CUSTOM_OBJECT.set(registerName, cls); + GLOBAL_CUSTOM_NAMES.set(cls, registerName); + return cls; +} +function getRegisteredName(cls) { + if (GLOBAL_CUSTOM_NAMES.has(cls)) { + return GLOBAL_CUSTOM_NAMES.get(cls); + } else { + return cls.className; + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js +var Optimizer = class extends Serializable { + /** + * Executes `f()` and minimizes the scalar output of `f()` by computing + * gradients of y with respect to the list of trainable variables provided by + * `varList`. If no list is provided, it defaults to all trainable variables. + * + * @param f The function to execute and whose output to minimize. + * @param returnCost Whether to return the scalar cost value produced by + * executing `f()`. + * @param varList An optional list of variables to update. If specified, only + * the trainable variables in varList will be updated by minimize. Defaults to + * all trainable variables. + * + * @doc {heading: 'Training', subheading: 'Optimizers'} + */ + minimize(f, returnCost = false, varList) { + const { value, grads: grads2 } = this.computeGradients(f, varList); + if (varList != null) { + const gradArray = varList.map((v) => ({ name: v.name, tensor: grads2[v.name] })); + this.applyGradients(gradArray); + } else { + this.applyGradients(grads2); + } + dispose(grads2); + if (returnCost) { + return value; + } else { + value.dispose(); + return null; + } + } + /** + * The number of iterations that this optimizer instance has been invoked for. + */ + get iterations() { + if (this.iterations_ == null) { + this.iterations_ = 0; + } + return this.iterations_; + } + incrementIterations() { + this.iterations_ = this.iterations + 1; + } + /** + * Executes f() and computes the gradient of the scalar output of f() with + * respect to the list of trainable variables provided by `varList`. If no + * list is provided, it defaults to all trainable variables. + * + * @param f The function to execute and whose output to use for computing + * gradients with respect to variables. + * @param varList An optional list of variables to compute gradients with + * respect to. If specified, only the trainable variables in varList will have + * gradients computed with respect to. Defaults to all trainable variables. + * + * @doc {heading: 'Training', subheading: 'Optimizers'} + */ + computeGradients(f, varList) { + return variableGrads(f, varList); + } + /** + * Dispose the variables (if any) owned by this optimizer instance. + */ + dispose() { + if (this.iterations_ != null) { + dispose(this.iterations_); + } + } + async saveIterations() { + if (this.iterations_ == null) { + this.iterations_ = 0; + } + return { + name: "iter", + // TODO(cais): Use 'int64' type when available. + tensor: scalar(this.iterations_, "int32") + }; + } + async getWeights() { + throw new Error("getWeights() is not implemented for this optimizer yet."); + } + async setWeights(weightValues) { + throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`); + } + /** + * Extract the first element of the weight values and set it + * as the iterations counter variable of this instance of optimizer. + * + * @param weightValues + * @returns Weight values with the first element consumed and excluded. + */ + async extractIterations(weightValues) { + this.iterations_ = (await weightValues[0].tensor.data())[0]; + return weightValues.slice(1); + } +}; +Object.defineProperty(Optimizer, Symbol.hasInstance, { + value: (instance) => { + return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null; + } +}); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js +var AdadeltaOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "Adadelta"; + } + constructor(learningRate, rho, epsilon3 = null) { + super(); + this.learningRate = learningRate; + this.rho = rho; + this.epsilon = epsilon3; + this.accumulatedGrads = []; + this.accumulatedUpdates = []; + if (epsilon3 == null) { + this.epsilon = ENGINE.backend.epsilon(); + } + } + applyGradients(variableGradients) { + const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients); + variableNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + const trainable = false; + if (this.accumulatedGrads[i] == null) { + this.accumulatedGrads[i] = { + originalName: `${name}/accum_grad`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + if (this.accumulatedUpdates[i] == null) { + this.accumulatedUpdates[i] = { + originalName: `${name}/accum_var`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const accumulatedGrad = this.accumulatedGrads[i].variable; + const accumulatedUpdate = this.accumulatedUpdates[i].variable; + tidy(() => { + const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho)); + const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient); + const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho)); + accumulatedGrad.assign(newAccumulatedGrad); + accumulatedUpdate.assign(newAccumulatedUpdate); + const newValue = add2(mul(updates, -this.learningRate), value); + value.assign(newValue); + }); + }); + this.incrementIterations(); + } + dispose() { + if (this.accumulatedUpdates != null) { + dispose(this.accumulatedGrads.map((v) => v.variable)); + dispose(this.accumulatedUpdates.map((v) => v.variable)); + } + } + async getWeights() { + const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates]; + return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable }))); + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + const variableCount = weightValues.length / 2; + const trainable = false; + this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + } + getConfig() { + return { + "learningRate": this.learningRate, + "rho": this.rho, + "epsilon": this.epsilon + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["rho"], config["epsilon"]); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js +var AdagradOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "Adagrad"; + } + constructor(learningRate, initialAccumulatorValue = 0.1) { + super(); + this.learningRate = learningRate; + this.initialAccumulatorValue = initialAccumulatorValue; + this.accumulatedGrads = []; + } + applyGradients(variableGradients) { + const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients); + variableNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + if (this.accumulatedGrads[i] == null) { + const trainable = false; + this.accumulatedGrads[i] = { + originalName: `${name}/accumulator`, + variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable)) + }; + } + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const accumulatedGrad = this.accumulatedGrads[i].variable; + tidy(() => { + const newAccumulatedGrad = add2(accumulatedGrad, square(gradient)); + accumulatedGrad.assign(newAccumulatedGrad); + const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value); + value.assign(newValue); + }); + }); + this.incrementIterations(); + } + dispose() { + if (this.accumulatedGrads != null) { + dispose(this.accumulatedGrads.map((v) => v.variable)); + } + } + async getWeights() { + return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({ name: v.originalName, tensor: v.variable }))); + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + const trainable = false; + this.accumulatedGrads = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) })); + } + getConfig() { + return { + "learningRate": this.learningRate, + "initialAccumulatorValue": this.initialAccumulatorValue + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["initialAccumulatorValue"]); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js +var AdamOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "Adam"; + } + constructor(learningRate, beta1, beta2, epsilon3 = null) { + super(); + this.learningRate = learningRate; + this.beta1 = beta1; + this.beta2 = beta2; + this.epsilon = epsilon3; + this.accumulatedFirstMoment = []; + this.accumulatedSecondMoment = []; + tidy(() => { + this.accBeta1 = scalar(beta1).variable(); + this.accBeta2 = scalar(beta2).variable(); + }); + if (epsilon3 == null) { + this.epsilon = ENGINE.backend.epsilon(); + } + } + applyGradients(variableGradients) { + const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients); + tidy(() => { + const oneMinusAccBeta1 = sub(1, this.accBeta1); + const oneMinusAccBeta2 = sub(1, this.accBeta2); + varNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + const trainable = false; + if (this.accumulatedFirstMoment[i] == null) { + this.accumulatedFirstMoment[i] = { + originalName: `${name}/m`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + if (this.accumulatedSecondMoment[i] == null) { + this.accumulatedSecondMoment[i] = { + originalName: `${name}/v`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const firstMoment = this.accumulatedFirstMoment[i].variable; + const secondMoment = this.accumulatedSecondMoment[i].variable; + const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1)); + const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2)); + const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1); + const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2); + firstMoment.assign(newFirstMoment); + secondMoment.assign(newSecondMoment); + const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value); + value.assign(newValue); + }); + this.accBeta1.assign(mul(this.accBeta1, this.beta1)); + this.accBeta2.assign(mul(this.accBeta2, this.beta2)); + }); + this.incrementIterations(); + } + dispose() { + this.accBeta1.dispose(); + this.accBeta2.dispose(); + if (this.accumulatedFirstMoment != null) { + dispose(this.accumulatedFirstMoment.map((v) => v.variable)); + } + if (this.accumulatedSecondMoment != null) { + dispose(this.accumulatedSecondMoment.map((v) => v.variable)); + } + } + async getWeights() { + const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment]; + return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable }))); + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + tidy(() => { + this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1)); + this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1)); + }); + const variableCount = weightValues.length / 2; + const trainable = false; + this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + } + getConfig() { + return { + "learningRate": this.learningRate, + "beta1": this.beta1, + "beta2": this.beta2, + "epsilon": this.epsilon + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"]); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js +var AdamaxOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "Adamax"; + } + constructor(learningRate, beta1, beta2, epsilon3 = null, decay = 0) { + super(); + this.learningRate = learningRate; + this.beta1 = beta1; + this.beta2 = beta2; + this.epsilon = epsilon3; + this.decay = decay; + this.accumulatedFirstMoment = []; + this.accumulatedWeightedInfNorm = []; + tidy(() => { + this.iteration = scalar(0).variable(); + this.accBeta1 = scalar(beta1).variable(); + }); + if (epsilon3 == null) { + this.epsilon = ENGINE.backend.epsilon(); + } + } + applyGradients(variableGradients) { + const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients); + tidy(() => { + const oneMinusAccBeta1 = sub(1, this.accBeta1); + const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1)); + variableNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + const trainable = false; + if (this.accumulatedFirstMoment[i] == null) { + this.accumulatedFirstMoment[i] = { + originalName: `${name}/m`, + variable: zerosLike(value).variable(trainable) + }; + } + if (this.accumulatedWeightedInfNorm[i] == null) { + this.accumulatedWeightedInfNorm[i] = { + originalName: `${name}/v`, + variable: zerosLike(value).variable(trainable) + }; + } + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const firstMoment = this.accumulatedFirstMoment[i].variable; + const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable; + const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1)); + const ut0 = mul(weightedInfNorm, this.beta2); + const ut1 = abs(gradient); + const newWeightedInfNorm = maximum(ut0, ut1); + firstMoment.assign(newFirstMoment); + weightedInfNorm.assign(newWeightedInfNorm); + const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value); + value.assign(newValue); + }); + this.iteration.assign(add2(this.iteration, 1)); + this.accBeta1.assign(mul(this.accBeta1, this.beta1)); + }); + this.incrementIterations(); + } + dispose() { + this.accBeta1.dispose(); + this.iteration.dispose(); + if (this.accumulatedFirstMoment != null) { + dispose(this.accumulatedFirstMoment.map((v) => v.variable)); + } + if (this.accumulatedWeightedInfNorm != null) { + dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable)); + } + } + async getWeights() { + throw new Error("getWeights() is not implemented for Adamax yet."); + } + async setWeights(weightValues) { + 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 + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"], config["decay"]); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js +var SGDOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "SGD"; + } + constructor(learningRate) { + super(); + this.learningRate = learningRate; + this.setLearningRate(learningRate); + } + applyGradients(variableGradients) { + const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients); + varNames.forEach((name, i) => { + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const value = ENGINE.registeredVariables[name]; + tidy(() => { + const newValue = add2(mul(this.c, gradient), value); + value.assign(newValue); + }); + }); + this.incrementIterations(); + } + /** + * Sets the learning rate of the optimizer. + */ + setLearningRate(learningRate) { + this.learningRate = learningRate; + if (this.c != null) { + this.c.dispose(); + } + this.c = keep(scalar(-learningRate)); + } + dispose() { + this.c.dispose(); + } + async getWeights() { + return [await this.saveIterations()]; + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + if (weightValues.length !== 0) { + throw new Error("SGD optimizer does not have settable weights."); + } + } + getConfig() { + return { "learningRate": this.learningRate }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"]); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js +var MomentumOptimizer = class extends SGDOptimizer { + /** @nocollapse */ + // Name matters for Python compatibility. + static get className() { + return "Momentum"; + } + constructor(learningRate, momentum, useNesterov = false) { + super(learningRate); + this.learningRate = learningRate; + this.momentum = momentum; + this.useNesterov = useNesterov; + this.accumulations = []; + this.m = scalar(this.momentum); + } + applyGradients(variableGradients) { + const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients); + variableNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + if (this.accumulations[i] == null) { + const trainable = false; + this.accumulations[i] = { + originalName: `${name}/momentum`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + const accumulation = this.accumulations[i].variable; + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + tidy(() => { + let newValue; + const newAccumulation = add2(mul(this.m, accumulation), gradient); + if (this.useNesterov) { + newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value); + } else { + newValue = add2(mul(this.c, newAccumulation), value); + } + accumulation.assign(newAccumulation); + value.assign(newValue); + }); + }); + this.incrementIterations(); + } + dispose() { + this.m.dispose(); + if (this.accumulations != null) { + dispose(this.accumulations.map((v) => v.variable)); + } + } + /** + * Sets the momentum of the optimizer. + * + * @param momentum + */ + setMomentum(momentum) { + this.momentum = momentum; + } + async getWeights() { + return [await this.saveIterations()].concat(this.accumulations.map((v) => ({ name: v.originalName, tensor: v.variable }))); + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + const trainable = false; + this.accumulations = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) })); + } + getConfig() { + return { + "learningRate": this.learningRate, + "momentum": this.momentum, + "useNesterov": this.useNesterov + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["momentum"], config["useNesterov"]); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js +var RMSPropOptimizer = class extends Optimizer { + /** @nocollapse */ + static get className() { + return "RMSProp"; + } + constructor(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) { + super(); + this.learningRate = learningRate; + this.decay = decay; + this.momentum = momentum; + this.epsilon = epsilon3; + this.accumulatedMeanSquares = []; + this.accumulatedMoments = []; + this.accumulatedMeanGrads = []; + this.centered = centered; + if (epsilon3 == null) { + this.epsilon = ENGINE.backend.epsilon(); + } + if (learningRate == null) { + throw new Error(`learningRate for RMSPropOptimizer must be defined.`); + } + } + applyGradients(variableGradients) { + const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients); + variableNames.forEach((name, i) => { + const value = ENGINE.registeredVariables[name]; + const trainable = false; + if (this.accumulatedMeanSquares[i] == null) { + this.accumulatedMeanSquares[i] = { + originalName: `${name}/rms`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + if (this.accumulatedMoments[i] == null) { + this.accumulatedMoments[i] = { + originalName: `${name}/momentum`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + if (this.accumulatedMeanGrads[i] == null && this.centered) { + this.accumulatedMeanGrads[i] = { + originalName: `${name}/mg`, + variable: tidy(() => zerosLike(value).variable(trainable)) + }; + } + const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name]; + if (gradient == null) { + return; + } + const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable; + const accumulatedMoments = this.accumulatedMoments[i].variable; + tidy(() => { + const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay)); + if (this.centered) { + const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable; + const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay)); + const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon)))); + const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution); + accumulatedMeanSquare.assign(newAccumulatedMeanSquare); + accumulatedMeanGrad.assign(newAccumulatedMeanGrad); + accumulatedMoments.assign(newAccumulatedMoments); + const newValue = sub(value, newAccumulatedMoments); + value.assign(newValue); + } else { + const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay)); + const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon)))); + accumulatedMeanSquare.assign(newAccumulatedMeanSquare2); + accumulatedMoments.assign(newAccumulatedMoments); + const newValue = sub(value, newAccumulatedMoments); + value.assign(newValue); + } + }); + }); + this.incrementIterations(); + } + dispose() { + if (this.accumulatedMeanSquares != null) { + dispose(this.accumulatedMeanSquares.map((v) => v.variable)); + } + if (this.accumulatedMeanGrads != null && this.centered) { + dispose(this.accumulatedMeanGrads.map((v) => v.variable)); + } + if (this.accumulatedMoments != null) { + dispose(this.accumulatedMoments.map((v) => v.variable)); + } + } + async getWeights() { + const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments]; + if (this.centered) { + variables.push(...this.accumulatedMeanGrads); + } + return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable }))); + } + async setWeights(weightValues) { + weightValues = await this.extractIterations(weightValues); + const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2; + const trainable = false; + this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + if (this.centered) { + this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({ + originalName: v.name, + variable: v.tensor.variable(trainable) + })); + } + } + getConfig() { + return { + "learningRate": this.learningRate, + "decay": this.decay, + "momentum": this.momentum, + "epsilon": this.epsilon, + "centered": this.centered + }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config["learningRate"], config["decay"], config["momentum"], config["epsilon"], config["centered"]); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/register_optimizers.js +var OPTIMIZERS = [ + AdadeltaOptimizer, + AdagradOptimizer, + AdamOptimizer, + AdamaxOptimizer, + MomentumOptimizer, + RMSPropOptimizer, + SGDOptimizer +]; +function registerOptimizers() { + for (const optimizer of OPTIMIZERS) { + registerClass(optimizer); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/io.js +var io_exports = {}; +__export(io_exports, { + CompositeArrayBuffer: () => CompositeArrayBuffer, + browserFiles: () => browserFiles, + browserHTTPRequest: () => browserHTTPRequest, + concatenateArrayBuffers: () => concatenateArrayBuffers, + copyModel: () => copyModel, + decodeWeights: () => decodeWeights, + decodeWeightsStream: () => decodeWeightsStream, + encodeWeights: () => encodeWeights, + fromMemory: () => fromMemory, + fromMemorySync: () => fromMemorySync, + getLoadHandlers: () => getLoadHandlers, + getModelArtifactsForJSON: () => getModelArtifactsForJSON, + getModelArtifactsForJSONSync: () => getModelArtifactsForJSONSync, + getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON, + getSaveHandlers: () => getSaveHandlers, + getWeightSpecs: () => getWeightSpecs, + http: () => http, + isHTTPScheme: () => isHTTPScheme, + listModels: () => listModels, + loadWeights: () => loadWeights, + moveModel: () => moveModel, + registerLoadRouter: () => registerLoadRouter, + registerSaveRouter: () => registerSaveRouter, + removeModel: () => removeModel, + weightsLoaderFactory: () => weightsLoaderFactory, + withSaveHandler: () => withSaveHandler, + withSaveHandlerSync: () => withSaveHandlerSync +}); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js +var DEFAULT_FILE_NAME_PREFIX = "model"; +var DEFAULT_JSON_EXTENSION_NAME = ".json"; +var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin"; +function defer(f) { + return new Promise((resolve) => setTimeout(resolve)).then(f); +} +var BrowserDownloads = class _BrowserDownloads { + constructor(fileNamePrefix) { + if (!env().getBool("IS_BROWSER")) { + throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); + } + if (fileNamePrefix.startsWith(_BrowserDownloads.URL_SCHEME)) { + fileNamePrefix = fileNamePrefix.slice(_BrowserDownloads.URL_SCHEME.length); + } + if (fileNamePrefix == null || fileNamePrefix.length === 0) { + fileNamePrefix = DEFAULT_FILE_NAME_PREFIX; + } + this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME; + this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME; + } + async save(modelArtifacts) { + if (typeof document === "undefined") { + throw new Error("Browser downloads are not supported in this environment since `document` is not present"); + } + const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData); + const weightsURL = window.URL.createObjectURL(new Blob([weightBuffer], { type: "application/octet-stream" })); + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet."); + } else { + const weightsManifest = [{ + paths: ["./" + this.weightDataFileName], + weights: modelArtifacts.weightSpecs + }]; + const modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest); + const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: "application/json" })); + const jsonAnchor = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; + jsonAnchor.download = this.modelJsonFileName; + jsonAnchor.href = modelJsonURL; + await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click"))); + if (modelArtifacts.weightData != null) { + const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; + weightDataAnchor.download = this.weightDataFileName; + weightDataAnchor.href = weightsURL; + await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click"))); + } + return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) }; + } + } +}; +BrowserDownloads.URL_SCHEME = "downloads://"; +var BrowserFiles = class { + constructor(files) { + if (files == null || files.length < 1) { + throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`); + } + this.jsonFile = files[0]; + this.weightsFiles = files.slice(1); + } + async load() { + return new Promise((resolve, reject) => { + const jsonReader = new FileReader(); + jsonReader.onload = (event) => { + const modelJSON = JSON.parse(event.target.result); + const modelTopology = modelJSON.modelTopology; + if (modelTopology == null) { + reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`)); + return; + } + const weightsManifest = modelJSON.weightsManifest; + if (weightsManifest == null) { + reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`)); + return; + } + if (this.weightsFiles.length === 0) { + resolve({ modelTopology }); + return; + } + const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2)); + resolve(modelArtifactsPromise); + }; + jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`); + jsonReader.readAsText(this.jsonFile); + }); + } + loadWeights(weightsManifest) { + const weightSpecs = []; + const paths = []; + for (const entry of weightsManifest) { + weightSpecs.push(...entry.weights); + paths.push(...entry.paths); + } + const pathToFile = this.checkManifestAndWeightFiles(weightsManifest); + const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path])); + return Promise.all(promises).then((buffers) => [weightSpecs, buffers]); + } + loadWeightsFile(path, file) { + return new Promise((resolve, reject) => { + const weightFileReader = new FileReader(); + weightFileReader.onload = (event) => { + const weightData = event.target.result; + resolve(weightData); + }; + weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`); + weightFileReader.readAsArrayBuffer(file); + }); + } + /** + * Check the compatibility between weights manifest and weight files. + */ + checkManifestAndWeightFiles(manifest) { + const basenames = []; + const fileNames = this.weightsFiles.map((file) => basename(file.name)); + const pathToFile = {}; + for (const group of manifest) { + group.paths.forEach((path) => { + const pathBasename = basename(path); + if (basenames.indexOf(pathBasename) !== -1) { + throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`); + } + basenames.push(pathBasename); + if (fileNames.indexOf(pathBasename) === -1) { + throw new Error(`Weight file with basename '${pathBasename}' is not provided.`); + } else { + pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)]; + } + }); + } + if (basenames.length !== this.weightsFiles.length) { + throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`); + } + return pathToFile; + } +}; +var browserDownloadsRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) { + return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(browserDownloadsRouter); +function browserDownloads(fileNamePrefix = "model") { + return new BrowserDownloads(fileNamePrefix); +} +function browserFiles(files) { + return new BrowserFiles(files); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/progress.js +function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) { + checkPromises(promises); + startFraction = startFraction == null ? 0 : startFraction; + endFraction = endFraction == null ? 1 : endFraction; + checkFraction(startFraction, endFraction); + let resolvedPromise = 0; + const registerMonitor = (promise) => { + promise.then((value) => { + const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction); + onProgress(fraction); + return value; + }); + return promise; + }; + function checkPromises(promises2) { + assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array"); + } + function checkFraction(startFraction2, endFraction2) { + assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`); + assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`); + assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`); + } + return Promise.all(promises.map(registerMonitor)); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js +async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) { + if (loadOptions == null) { + loadOptions = {}; + } + const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc; + const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true })); + const fetchStartFraction = 0; + const fetchEndFraction = 0.5; + const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction); + const bufferPromises = responses.map((response) => response.arrayBuffer()); + const bufferStartFraction = 0.5; + const bufferEndFraction = 1; + const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction); + return buffers; +} +function streamWeights(fetchURLs, loadOptions) { + var _a; + const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc; + let fetchIndex = 0; + let chunkReader; + (_a = loadOptions.onProgress) === null || _a === void 0 ? void 0 : _a.call(loadOptions, 0); + return new ReadableStream({ + pull: async (controller) => { + var _a2; + while (fetchIndex < fetchURLs.length) { + if (!chunkReader) { + const body = (await fetchFunc(fetchURLs[fetchIndex], loadOptions.requestInit, { isBinary: true })).body; + chunkReader = body.getReader(); + } + const { done, value } = await chunkReader.read(); + if (done) { + fetchIndex++; + chunkReader = void 0; + (_a2 = loadOptions.onProgress) === null || _a2 === void 0 ? void 0 : _a2.call(loadOptions, fetchIndex / fetchURLs.length); + continue; + } + controller.enqueue(value); + return; + } + controller.close(); + } + }); +} +async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) { + const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit }); + const loadWeights2 = weightsLoaderFactory(fetchWeights); + return loadWeights2(manifest, filePathPrefix, weightNames); +} +function weightsLoaderFactory(fetchWeightsFunction) { + return async (manifest, filePathPrefix = "", weightNames) => { + const groupIndicesToFetchMap = manifest.map(() => false); + const groupWeightsToFetch = {}; + const weightsFound = weightNames != null ? weightNames.map(() => false) : []; + const allManifestWeightNames = []; + manifest.forEach((manifestGroupConfig, groupIndex) => { + let groupOffset = 0; + manifestGroupConfig.weights.forEach((weightsEntry) => { + const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype; + const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape); + const enqueueWeightsForFetchingFn = () => { + groupIndicesToFetchMap[groupIndex] = true; + if (groupWeightsToFetch[groupIndex] == null) { + groupWeightsToFetch[groupIndex] = []; + } + groupWeightsToFetch[groupIndex].push({ + manifestEntry: weightsEntry, + groupOffset, + sizeBytes: weightsBytes + }); + }; + if (weightNames != null) { + weightNames.forEach((weightName, weightIndex) => { + if (weightName === weightsEntry.name) { + enqueueWeightsForFetchingFn(); + weightsFound[weightIndex] = true; + } + }); + } else { + enqueueWeightsForFetchingFn(); + } + allManifestWeightNames.push(weightsEntry.name); + groupOffset += weightsBytes; + }); + }); + if (!weightsFound.every((found) => found)) { + const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]); + throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. +Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`); + } + const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => { + if (shouldFetch) { + accumulator.push(i); + } + return accumulator; + }, []); + const fetchUrls = []; + groupIndicesToFetch.forEach((i) => { + manifest[i].paths.forEach((filepath) => { + const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath; + fetchUrls.push(fetchUrl); + }); + }); + const buffers = await fetchWeightsFunction(fetchUrls); + const weightsTensorMap = {}; + let bufferIndexOffset = 0; + groupIndicesToFetch.forEach((i) => { + const numBuffers = manifest[i].paths.length; + const weightsBuffer = new CompositeArrayBuffer(buffers.slice(bufferIndexOffset, bufferIndexOffset + numBuffers)); + const weightsEntries = groupWeightsToFetch[i]; + weightsEntries.forEach((weightsEntry) => { + const byteBuffer = weightsBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes); + const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]); + for (const name in nameToTensorMap) { + weightsTensorMap[name] = nameToTensorMap[name]; + } + }); + bufferIndexOffset += numBuffers; + }); + return weightsTensorMap; + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/http.js +var OCTET_STREAM_MIME_TYPE = "application/octet-stream"; +var JSON_TYPE = "application/json"; +var HTTPRequest = class { + constructor(path, loadOptions) { + this.DEFAULT_METHOD = "POST"; + if (loadOptions == null) { + loadOptions = {}; + } + this.weightPathPrefix = loadOptions.weightPathPrefix; + this.weightUrlConverter = loadOptions.weightUrlConverter; + if (loadOptions.fetchFunc != null) { + assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"); + this.fetch = loadOptions.fetchFunc; + } else { + this.fetch = env().platform.fetch; + } + assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty."); + if (Array.isArray(path)) { + assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`); + } + this.path = path; + if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) { + throw new Error("requestInit is expected to have no pre-existing body, but has one."); + } + this.requestInit = loadOptions.requestInit || {}; + this.loadOptions = loadOptions; + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet."); + } + const init2 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); + init2.body = new FormData(); + const weightsManifest = [{ + paths: ["./model.weights.bin"], + weights: modelArtifacts.weightSpecs + }]; + const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest); + init2.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), "model.json"); + if (modelArtifacts.weightData != null) { + const weightBuffer = CompositeArrayBuffer.join(modelArtifacts.weightData); + init2.body.append("model.weights.bin", new Blob([weightBuffer], { type: OCTET_STREAM_MIME_TYPE }), "model.weights.bin"); + } + const response = await this.fetch(this.path, init2); + if (response.ok) { + return { + modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts), + responses: [response] + }; + } else { + throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`); + } + } + async loadModelJSON() { + const modelConfigRequest = await this.fetch(this.path, this.requestInit); + if (!modelConfigRequest.ok) { + throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`); + } + let modelJSON; + try { + modelJSON = await modelConfigRequest.json(); + } catch (e) { + let message = `Failed to parse model JSON of response from ${this.path}.`; + if (this.path.endsWith(".pb")) { + message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository."; + } else { + message += " Please make sure the server is serving valid JSON for this request."; + } + throw new Error(message); + } + const modelTopology = modelJSON.modelTopology; + const weightsManifest = modelJSON.weightsManifest; + if (modelTopology == null && weightsManifest == null) { + throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`); + } + return modelJSON; + } + /** + * Load model artifacts via HTTP request(s). + * + * See the documentation to `tf.io.http` for details on the saved + * artifacts. + * + * @returns The loaded model artifacts (if loading succeeds). + */ + async load() { + if (this.loadOptions.streamWeights) { + return this.loadStream(); + } + const modelJSON = await this.loadModelJSON(); + return getModelArtifactsForJSON(modelJSON, (weightsManifest) => this.loadWeights(weightsManifest)); + } + async loadStream() { + const modelJSON = await this.loadModelJSON(); + const fetchURLs = await this.getWeightUrls(modelJSON.weightsManifest); + const weightSpecs = getWeightSpecs(modelJSON.weightsManifest); + const stream = () => streamWeights(fetchURLs, this.loadOptions); + return Object.assign(Object.assign({}, modelJSON), { weightSpecs, getWeightStream: stream }); + } + async getWeightUrls(weightsManifest) { + const weightPath = Array.isArray(this.path) ? this.path[1] : this.path; + const [prefix, suffix] = parseUrl(weightPath); + const pathPrefix = this.weightPathPrefix || prefix; + const fetchURLs = []; + const urlPromises = []; + for (const weightsGroup of weightsManifest) { + for (const path of weightsGroup.paths) { + if (this.weightUrlConverter != null) { + urlPromises.push(this.weightUrlConverter(path)); + } else { + fetchURLs.push(pathPrefix + path + suffix); + } + } + } + if (this.weightUrlConverter) { + fetchURLs.push(...await Promise.all(urlPromises)); + } + return fetchURLs; + } + async loadWeights(weightsManifest) { + const fetchURLs = await this.getWeightUrls(weightsManifest); + const weightSpecs = getWeightSpecs(weightsManifest); + const buffers = await loadWeightsAsArrayBuffer(fetchURLs, this.loadOptions); + return [weightSpecs, buffers]; + } +}; +HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//; +function parseUrl(url) { + const lastSlash = url.lastIndexOf("/"); + const lastSearchParam = url.lastIndexOf("?"); + const prefix = url.substring(0, lastSlash); + const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : ""; + return [prefix + "/", suffix]; +} +function isHTTPScheme(url) { + return url.match(HTTPRequest.URL_SCHEME_REGEX) != null; +} +var httpRouter = (url, loadOptions) => { + if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) { + return null; + } else { + let isHTTP = true; + if (Array.isArray(url)) { + isHTTP = url.every((urlItem) => isHTTPScheme(urlItem)); + } else { + isHTTP = isHTTPScheme(url); + } + if (isHTTP) { + return http(url, loadOptions); + } + } + return null; +}; +IORouterRegistry.registerSaveRouter(httpRouter); +IORouterRegistry.registerLoadRouter(httpRouter); +function http(path, loadOptions) { + return new HTTPRequest(path, loadOptions); +} +function browserHTTPRequest(path, loadOptions) { + return http(path, loadOptions); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js +var PassthroughLoader = class { + constructor(modelArtifacts) { + this.modelArtifacts = modelArtifacts; + } + load() { + return this.modelArtifacts; + } +}; +var PassthroughSaver = class { + constructor(saveHandler) { + this.saveHandler = saveHandler; + } + save(modelArtifacts) { + return this.saveHandler(modelArtifacts); + } +}; +var PassthroughAsync = class { + constructor(handler) { + if (handler.load) { + this.load = () => Promise.resolve(handler.load()); + } + if (handler.save) { + this.save = (modelArtifacts) => Promise.resolve(handler.save(modelArtifacts)); + } + } +}; +function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) { + const args = arguments; + return new PassthroughAsync(fromMemorySync(...args)); +} +function fromMemorySync(modelArtifacts, weightSpecs, weightData, trainingConfig) { + if (arguments.length === 1) { + const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null; + if (isModelArtifacts) { + return new PassthroughLoader(modelArtifacts); + } else { + console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."); + return new PassthroughLoader({ modelTopology: modelArtifacts }); + } + } else { + console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."); + return new PassthroughLoader({ + modelTopology: modelArtifacts, + weightSpecs, + weightData, + trainingConfig + }); + } +} +function withSaveHandler(saveHandler) { + return new PassthroughSaver(saveHandler); +} +function withSaveHandlerSync(saveHandler) { + return new PassthroughSaver(saveHandler); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/math.js +var math_exports = {}; +__export(math_exports, { + confusionMatrix: () => confusionMatrix +}); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js +function confusionMatrix_(labels, predictions, numClasses) { + const $labels = convertToTensor(labels, "labels", "confusionMatrix"); + const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix"); + assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`); + assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`); + assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`); + assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`); + assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`); + const oneHotLabels = oneHot(cast($labels, "int32"), numClasses); + const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses); + const oneHotLabelsT = transpose(oneHotLabels); + const product = matMul(oneHotLabelsT, oneHotPredictions); + return cast(product, "int32"); +} +var confusionMatrix = op({ confusionMatrix_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js +var browser_exports = {}; +__export(browser_exports, { + draw: () => draw, + fromPixels: () => fromPixels, + fromPixelsAsync: () => fromPixelsAsync, + toPixels: () => toPixels +}); +var fromPixels2DContext; +var hasToPixelsWarned = false; +function fromPixels_(pixels, numChannels = 3) { + if (numChannels > 4) { + throw new Error("Cannot construct Tensor with more than 4 channels from pixels."); + } + if (pixels == null) { + throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); + } + let isPixelData2 = false; + let isImageData = false; + let isVideo = false; + let isImage = false; + let isCanvasLike = false; + let isImageBitmap = false; + if (pixels.data instanceof Uint8Array) { + isPixelData2 = true; + } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) { + isImageData = true; + } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) { + isVideo = true; + } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) { + isImage = true; + } else if (pixels.getContext != null) { + isCanvasLike = true; + } else if (typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap) { + isImageBitmap = true; + } else { + throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`); + } + const kernel = getKernel(FromPixels, ENGINE.backendName); + if (kernel != null) { + const inputs = { pixels }; + const attrs = { numChannels }; + return ENGINE.runKernel(FromPixels, inputs, attrs); + } + const [width, height] = isVideo ? [ + pixels.videoWidth, + pixels.videoHeight + ] : [pixels.width, pixels.height]; + let vals; + if (isCanvasLike) { + vals = // tslint:disable-next-line:no-any + pixels.getContext("2d").getImageData(0, 0, width, height).data; + } else if (isImageData || isPixelData2) { + vals = pixels.data; + } else if (isImage || isVideo || isImageBitmap) { + if (fromPixels2DContext == null) { + if (typeof document === "undefined") { + if (typeof OffscreenCanvas !== "undefined" && typeof OffscreenCanvasRenderingContext2D !== "undefined") { + fromPixels2DContext = new OffscreenCanvas(1, 1).getContext("2d"); + } else { + throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported."); + } + } else { + fromPixels2DContext = document.createElement("canvas").getContext("2d", { willReadFrequently: true }); + } + } + fromPixels2DContext.canvas.width = width; + fromPixels2DContext.canvas.height = height; + fromPixels2DContext.drawImage(pixels, 0, 0, width, height); + vals = fromPixels2DContext.getImageData(0, 0, width, height).data; + } + let values; + if (numChannels === 4) { + values = new Int32Array(vals); + } else { + const numPixels = width * height; + values = new Int32Array(numPixels * numChannels); + for (let i = 0; i < numPixels; i++) { + for (let channel = 0; channel < numChannels; ++channel) { + values[i * numChannels + channel] = vals[i * 4 + channel]; + } + } + } + const outShape = [height, width, numChannels]; + return tensor3d(values, outShape, "int32"); +} +function isPixelData(pixels) { + return pixels != null && pixels.data instanceof Uint8Array; +} +function isImageBitmapFullySupported() { + return typeof window !== "undefined" && typeof ImageBitmap !== "undefined" && window.hasOwnProperty("createImageBitmap"); +} +function isNonEmptyPixels(pixels) { + return pixels != null && pixels.width !== 0 && pixels.height !== 0; +} +function canWrapPixelsToImageBitmap(pixels) { + return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels); +} +async function fromPixelsAsync(pixels, numChannels = 3) { + let inputs = null; + if (env().getBool("WRAP_TO_IMAGEBITMAP") && canWrapPixelsToImageBitmap(pixels)) { + let imageBitmap; + try { + imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: "none" }); + } catch (e) { + imageBitmap = null; + } + if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) { + inputs = imageBitmap; + } else { + inputs = pixels; + } + } else { + inputs = pixels; + } + return fromPixels_(inputs, numChannels); +} +function validateImgTensor(img) { + if (img.rank !== 2 && img.rank !== 3) { + throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${img.rank}.`); + } + const depth = img.rank === 2 ? 1 : img.shape[2]; + if (depth > 4 || depth === 2) { + throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`); + } + if (img.dtype !== "float32" && img.dtype !== "int32") { + throw new Error(`Unsupported type for toPixels: ${img.dtype}. Please use float32 or int32 tensors.`); + } +} +function validateImageOptions(imageOptions) { + const alpha = (imageOptions === null || imageOptions === void 0 ? void 0 : imageOptions.alpha) || 1; + if (alpha > 1 || alpha < 0) { + throw new Error(`Alpha value ${alpha} is suppoed to be in range [0 - 1].`); + } +} +async function toPixels(img, canvas) { + let $img = convertToTensor(img, "img", "toPixels"); + if (!(img instanceof Tensor)) { + const originalImgTensor = $img; + $img = cast(originalImgTensor, "int32"); + originalImgTensor.dispose(); + } + validateImgTensor($img); + const [height, width] = $img.shape.slice(0, 2); + const depth = $img.rank === 2 ? 1 : $img.shape[2]; + const data = await $img.data(); + const multiplier = $img.dtype === "float32" ? 255 : 1; + const bytes = new Uint8ClampedArray(width * height * 4); + for (let i = 0; i < height * width; ++i) { + const rgba = [0, 0, 0, 255]; + for (let d = 0; d < depth; d++) { + const value = data[i * depth + d]; + if ($img.dtype === "float32") { + if (value < 0 || value > 1) { + throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`); + } + } else if ($img.dtype === "int32") { + if (value < 0 || value > 255) { + throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`); + } + } + if (depth === 1) { + rgba[0] = value * multiplier; + rgba[1] = value * multiplier; + rgba[2] = value * multiplier; + } else { + rgba[d] = value * multiplier; + } + } + const j = i * 4; + bytes[j + 0] = Math.round(rgba[0]); + bytes[j + 1] = Math.round(rgba[1]); + bytes[j + 2] = Math.round(rgba[2]); + bytes[j + 3] = Math.round(rgba[3]); + } + if (canvas != null) { + if (!hasToPixelsWarned) { + const kernel = getKernel(Draw, ENGINE.backendName); + if (kernel != null) { + console.warn("tf.browser.toPixels is not efficient to draw tensor on canvas. Please try tf.browser.draw instead."); + hasToPixelsWarned = true; + } + } + canvas.width = width; + canvas.height = height; + const ctx = canvas.getContext("2d"); + const imageData = new ImageData(bytes, width, height); + ctx.putImageData(imageData, 0, 0); + } + if ($img !== img) { + $img.dispose(); + } + return bytes; +} +function draw(image2, canvas, options) { + let $img = convertToTensor(image2, "img", "draw"); + if (!(image2 instanceof Tensor)) { + const originalImgTensor = $img; + $img = cast(originalImgTensor, "int32"); + originalImgTensor.dispose(); + } + validateImgTensor($img); + validateImageOptions(options === null || options === void 0 ? void 0 : options.imageOptions); + const inputs = { image: $img }; + const attrs = { canvas, options }; + ENGINE.runKernel(Draw, inputs, attrs); +} +var fromPixels = op({ fromPixels_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js +var gather_nd_util_exports = {}; +__export(gather_nd_util_exports, { + prepareAndValidate: () => prepareAndValidate +}); +function prepareAndValidate(tensor2, indices) { + const tensorRank = tensor2.shape.length; + const indicesRank = indices.shape.length; + if (tensorRank < 1) { + throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`); + } + if (indicesRank < 1) { + throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`); + } + if (indices.dtype !== "int32") { + throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`); + } + if (indices.shape[indicesRank - 1] > tensorRank) { + throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank - 1]} vs. ${tensorRank}`); + } + if (sizeFromShape(tensor2.shape) === 0) { + throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`); + } + const indicesShape = indices.shape; + const sliceRank = indicesShape[indicesShape.length - 1]; + let nResult = 1; + for (let i = 0; i < indicesShape.length - 1; ++i) { + nResult *= indicesShape[i]; + } + const inputShape = tensor2.shape; + const resultShape = indicesShape.slice(); + resultShape.pop(); + let sliceSize = 1; + for (let i = sliceRank; i < tensorRank; ++i) { + sliceSize *= inputShape[i]; + resultShape.push(inputShape[i]); + } + const strides = [ + ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize), + 1 + ].slice(0, sliceRank); + return [resultShape, nResult, sliceSize, strides]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js +var slice_util_exports = {}; +__export(slice_util_exports, { + assertParamsValid: () => assertParamsValid, + computeFlatOffset: () => computeFlatOffset, + computeOutShape: () => computeOutShape, + getNormalizedAxes: () => getNormalizedAxes, + isSliceContinous: () => isSliceContinous, + maskToAxes: () => maskToAxes, + parseSliceParams: () => parseSliceParams, + sliceInfo: () => sliceInfo, + startForAxis: () => startForAxis, + startIndicesWithElidedDims: () => startIndicesWithElidedDims, + stopForAxis: () => stopForAxis, + stopIndicesWithElidedDims: () => stopIndicesWithElidedDims, + stridesForAxis: () => stridesForAxis, + stridesWithElidedDims: () => stridesWithElidedDims +}); +var NEW_AXIS = -2; +var SHRINK_AXIS = -1; +function assertParamsValid(input2, begin, size) { + const inputRank = input2.shape.length; + assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`); + assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`); + for (let i = 0; i < inputRank; ++i) { + assert(begin[i] + size[i] <= input2.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`); + } +} +function maskToAxes(mask) { + const axes = []; + let axis = 0; + while (mask > 0) { + if (mask & 1) { + axes.push(axis); + } + mask /= 2; + axis++; + } + return axes; +} +function computeOutShape(begin, end, strides) { + const size = []; + for (let axis = 0; axis < begin.length; axis++) { + size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]); + } + return size; +} +function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) { + const newStrides = [...strides]; + for (let i = newStrides.length; i < inputShape.length; i++) { + newStrides.push(1); + } + for (let i = 0; i < numElidedAxes; i++) { + if (i === 0) { + newStrides[ellipsisInsertionIndex] = 1; + } else { + newStrides.splice( + ellipsisInsertionIndex, + 0, + 1 + /* element to add */ + ); + newStrides.pop(); + } + } + return newStrides; +} +function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) { + if (normalizedAxis <= ellipsisInsertionIndex) { + return normalizedAxis; + } + return normalizedAxis - (numElidedAxes - 1); +} +function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) { + const elidedAxes = []; + for (let i = 0; i < numElidedAxes; i++) { + elidedAxes.push(ellipsisInsertionIndex + i); + } + return elidedAxes; +} +function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) { + const inputRank = inputShape.length; + let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank); + if (ellipsisAxes.length && numInterpolatedAxes > 0) { + const fullIndex = ellipsisAxes[0]; + const numElidedAxes = numInterpolatedAxes + 1; + normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape); + normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape); + normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape); + } else { + for (let axis = 0; axis < inputRank; axis++) { + normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask); + normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask); + normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask); + } + } + return { + begin: normalizedBegin, + end: normalizedEnd, + strides: normalizedStrides + }; +} +function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) { + const newIndices = [...inputShape]; + const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex); + for (let axis = 0; axis < newIndices.length; axis++) { + if (elidedAxes.indexOf(axis) > -1) { + newIndices[axis] = 0; + } else { + const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis); + let originalValue = originalBegin[originalAxis]; + if (beginMask & 1 << originalAxis) { + originalValue = 0; + } + newIndices[axis] = originalValue; + } + } + return newIndices; +} +function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) { + const newIndices = [...inputShape]; + const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex); + for (let axis = 0; axis < newIndices.length; axis++) { + if (elidedAxes.indexOf(axis) > -1) { + newIndices[axis] = Number.MAX_SAFE_INTEGER; + } else { + const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis); + let originalValue = originalEnd[originalAxis]; + if (endMask & 1 << originalAxis) { + originalValue = Number.MAX_SAFE_INTEGER; + } + newIndices[axis] = originalValue; + } + } + for (let i = 0; i < newIndices.length; i++) { + const axisSize = inputShape[i]; + if (newIndices[i] < 0) { + newIndices[i] += axisSize; + } + newIndices[i] = clamp(0, newIndices[i], inputShape[i]); + } + return newIndices; +} +function stridesForAxis(strides, axis, ellipsisMask) { + let stride = strides[axis]; + if (ellipsisMask & 1 << axis || stride == null) { + stride = 1; + } + return stride; +} +function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) { + let start = startIndices[axis]; + const stride = strides[axis] || 1; + if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) { + if (stride > 0) { + start = Number.MIN_SAFE_INTEGER; + } else { + start = Number.MAX_SAFE_INTEGER; + } + } + const axisSize = inputShape[axis]; + if (start < 0) { + start += axisSize; + } + start = clamp(0, start, axisSize - 1); + return start; +} +function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) { + let stop = stopIndices[axis]; + const stride = strides[axis] || 1; + if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) { + if (stride > 0) { + stop = Number.MAX_SAFE_INTEGER; + } else { + stop = Number.MIN_SAFE_INTEGER; + } + } + const axisSize = inputShape[axis]; + if (stop < 0) { + stop += axisSize; + } + if (stride > 0) { + stop = clamp(0, stop, axisSize); + } else { + stop = clamp(-1, stop, axisSize - 1); + } + return stop; +} +function isSliceContinous(shape, begin, size) { + let firstNonOneAxis = size.length; + for (let i = 0; i < size.length; i++) { + if (size[i] > 1) { + firstNonOneAxis = i; + break; + } + } + for (let i = firstNonOneAxis + 1; i < size.length; i++) { + if (begin[i] > 0 || size[i] !== shape[i]) { + return false; + } + } + return true; +} +function computeFlatOffset(begin, strides) { + let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1; + for (let i = 0; i < begin.length - 1; i++) { + flatOffset += begin[i] * strides[i]; + } + return flatOffset; +} +function parseSliceParams(x, begin, size) { + let begin_; + const xRank = x.shape.length; + if (typeof begin === "number") { + begin_ = [begin, ...new Array(xRank - 1).fill(0)]; + } else if (begin.length < xRank) { + begin_ = begin.concat(new Array(xRank - begin.length).fill(0)); + } else { + begin_ = begin.slice(); + } + begin_.forEach((d) => { + assert(d !== -1, () => "slice() does not support negative begin indexing."); + }); + let size_; + if (size == null) { + size_ = new Array(xRank).fill(-1); + } else if (typeof size === "number") { + size_ = [size, ...new Array(xRank - 1).fill(-1)]; + } else if (size.length < xRank) { + size_ = size.concat(new Array(xRank - size.length).fill(-1)); + } else { + size_ = size; + } + size_ = size_.map((d, i) => { + if (d >= 0) { + return d; + } else { + assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`); + return x.shape[i] - begin_[i]; + } + }); + return [begin_, size_]; +} +function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) { + let stridesNonNull; + if (strides == null) { + stridesNonNull = new Array(begin.length); + stridesNonNull.fill(1); + } else { + stridesNonNull = strides; + } + if (ellipsisMask != null && (ellipsisMask & ellipsisMask - 1) !== 0) { + throw new Error("Multiple ellipses in slice is not allowed."); + } + let ellipsisSeen = false; + const sparseSpec = { + dims: stridesNonNull.length, + numAddAxisAfterEllipsis: 0, + begin: begin.slice(), + end: end.slice(), + strides: stridesNonNull.slice(), + beginMask, + endMask, + ellipsisMask, + newAxisMask, + shrinkAxisMask + }; + for (let i = 0; i < sparseSpec.dims; i++) { + if (ellipsisSeen && (1 << i & newAxisMask) !== 0) { + sparseSpec.numAddAxisAfterEllipsis++; + } + if (1 << i & ellipsisMask) { + ellipsisSeen = true; + } + } + if (!ellipsisSeen) { + sparseSpec.ellipsisMask |= 1 << sparseSpec.dims; + sparseSpec.dims++; + } + const denseSpec = { + dims: xShape.length, + beginMask: 0, + endMask: 0, + beginValid: false, + endValid: false + }; + buildDenseSpec(sparseSpec, denseSpec); + let isIdentity = true; + let sliceDim0 = true; + let isSimpleSlice = true; + const processingShape = []; + const finalShape = []; + for (let i = 0; i < xShape.length; ++i) { + if (denseSpec.strides[i] === 0) { + throw Error(`strides[${i}] must be non-zero`); + } + const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i); + const dimI = xShape[i]; + if (dimI === -1) { + processingShape.push(shrinkI ? 1 : -1); + continue; + } + const masks = [denseSpec.beginMask & 1 << i, denseSpec.endMask & 1 << i]; + const validRange = [ + denseSpec.strides[i] > 0 ? 0 : -1, + denseSpec.strides[i] > 0 ? dimI : dimI - 1 + ]; + if (shrinkI && denseSpec.strides[i] <= 0) { + throw Error("only stride 1 allowed on non-range indexing."); + } + isSimpleSlice = isSimpleSlice && denseSpec.strides[i] === 1; + const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i && denseSpec.endMask & 1 << i); + if (denseSpec.beginValid && denseSpec.endValid) { + if (shrinkI) { + const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] : denseSpec.begin[i]; + denseSpec.begin[i] = xFwd; + denseSpec.end[i] = denseSpec.begin[i] + 1; + if (xFwd < 0 || xFwd >= dimI) { + throw Error(`slice index ${denseSpec.begin[i]} of dimension ${i} out of bounds.`); + } + } else { + denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange); + denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange); + } + const takeAllInDimension = denseSpec.strides[i] === 1 && denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI; + isIdentity = isIdentity && takeAllInDimension; + sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || takeAllInDimension); + } else { + isIdentity = isIdentity && (denseSpec.strides[i] === 1 && beginAndEndMasked); + sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || beginAndEndMasked); + } + let intervalLength; + let knownInterval = false; + if (denseSpec.beginValid && denseSpec.endValid) { + intervalLength = denseSpec.end[i] - denseSpec.begin[i]; + knownInterval = true; + } else if (shrinkI) { + intervalLength = 1; + knownInterval = true; + } else if (beginAndEndMasked) { + if (dimI >= 0) { + if (denseSpec.strides[i] < 0) { + intervalLength = -dimI; + } else { + intervalLength = dimI; + } + knownInterval = true; + } + } + if (knownInterval) { + let sizeI; + if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i] < 0) { + sizeI = 0; + } else { + sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) + (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0); + } + processingShape.push(sizeI); + } else { + processingShape.push(-1); + } + } + for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) { + const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim]; + if (gatherIndex >= 0) { + finalShape.push(processingShape[gatherIndex]); + } else if (gatherIndex === NEW_AXIS) { + finalShape.push(1); + } + } + const finalShapeSparse = finalShape.filter((dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS); + return { + finalShapeSparse, + finalShape, + isIdentity, + sliceDim0, + isSimpleSlice, + begin: denseSpec.begin, + end: denseSpec.end, + strides: denseSpec.strides + }; +} +function buildDenseSpec(sparse2, dense2) { + dense2.beginMask = 0; + dense2.endMask = 0; + dense2.shrinkAxisMask = 0; + let fullIndex = 0; + dense2.beginValid = sparse2.begin != null; + dense2.endValid = sparse2.end != null; + dense2.begin = new Array(dense2.dims); + dense2.end = new Array(dense2.dims); + dense2.strides = new Array(dense2.dims); + dense2.finalShapeGatherIndices = []; + dense2.finalShapeGatherIndicesSparse = []; + dense2.inputShapeGatherIndicesSparse = new Array(dense2.dims); + for (let i = 0; i < sparse2.dims; i++) { + if (1 << i & sparse2.ellipsisMask) { + const nextIndex = Math.min(dense2.dims - (sparse2.dims - i) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims); + for (; fullIndex < nextIndex; fullIndex++) { + dense2.begin[fullIndex] = 0; + dense2.end[fullIndex] = 0; + dense2.strides[fullIndex] = 1; + dense2.beginMask |= 1 << fullIndex; + dense2.endMask |= 1 << fullIndex; + dense2.finalShapeGatherIndices.push(fullIndex); + dense2.finalShapeGatherIndicesSparse.push(-1); + dense2.inputShapeGatherIndicesSparse[fullIndex] = i; + } + } else if (1 << i & sparse2.newAxisMask) { + dense2.finalShapeGatherIndices.push(NEW_AXIS); + dense2.finalShapeGatherIndicesSparse.push(-1); + } else { + if (fullIndex === dense2.begin.length) { + throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense2.dims} dims, ${dense2.begin.length}.`); + } + if (sparse2.begin != null) { + dense2.begin[fullIndex] = sparse2.begin[i]; + } + if (sparse2.end != null) { + dense2.end[fullIndex] = sparse2.end[i]; + } + dense2.strides[fullIndex] = sparse2.strides[i]; + if (sparse2.beginMask & 1 << i) { + dense2.beginMask |= 1 << fullIndex; + } + if (sparse2.endMask & 1 << i) { + dense2.endMask |= 1 << fullIndex; + } + if (sparse2.shrinkAxisMask & 1 << i) { + dense2.finalShapeGatherIndices.push(SHRINK_AXIS); + dense2.finalShapeGatherIndicesSparse.push(-1); + dense2.shrinkAxisMask |= 1 << fullIndex; + } else { + dense2.finalShapeGatherIndices.push(fullIndex); + dense2.finalShapeGatherIndicesSparse.push(i); + } + dense2.inputShapeGatherIndicesSparse[fullIndex] = i; + fullIndex++; + } + } +} +function canonical(x, c, strideI, dimI, masks, validRange) { + if (masks[c]) { + return strideI > 0 ? validRange[c] : validRange[c + 1 & 1]; + } else { + const xFwd = x < 0 ? dimI + x : x; + return xFwd < validRange[0] ? validRange[0] : xFwd > validRange[1] ? validRange[1] : xFwd; + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/version.js +var version = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js +var OptimizerConstructors = class { + /** + * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent. + * + * ```js + * // Fit a quadratic function by learning the coefficients a, b, c. + * const xs = tf.tensor1d([0, 1, 2, 3]); + * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]); + * + * const a = tf.scalar(Math.random()).variable(); + * const b = tf.scalar(Math.random()).variable(); + * const c = tf.scalar(Math.random()).variable(); + * + * // y = a * x^2 + b * x + c. + * const f = x => a.mul(x.square()).add(b.mul(x)).add(c); + * const loss = (pred, label) => pred.sub(label).square().mean(); + * + * const learningRate = 0.01; + * const optimizer = tf.train.sgd(learningRate); + * + * // Train the model. + * for (let i = 0; i < 10; i++) { + * optimizer.minimize(() => loss(f(xs), ys)); + * } + * + * // Make predictions. + * console.log( + * `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`); + * const preds = f(xs).dataSync(); + * preds.forEach((pred, i) => { + * console.log(`x: ${i}, pred: ${pred}`); + * }); + * ``` + * + * @param learningRate The learning rate to use for the SGD algorithm. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static sgd(learningRate) { + return new SGDOptimizer(learningRate); + } + /** + * Constructs a `tf.MomentumOptimizer` that uses momentum gradient + * descent. + * + * See + * [http://proceedings.mlr.press/v28/sutskever13.pdf]( + * http://proceedings.mlr.press/v28/sutskever13.pdf) + * + * @param learningRate The learning rate to use for the Momentum gradient + * descent algorithm. + * @param momentum The momentum to use for the momentum gradient descent + * algorithm. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static momentum(learningRate, momentum, useNesterov = false) { + return new MomentumOptimizer(learningRate, momentum, useNesterov); + } + /** + * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient + * descent. This implementation uses plain momentum and is not centered + * version of RMSProp. + * + * See + * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf]( + * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf) + * + * @param learningRate The learning rate to use for the RMSProp gradient + * descent algorithm. + * @param decay The discounting factor for the history/coming gradient. + * @param momentum The momentum to use for the RMSProp gradient descent + * algorithm. + * @param epsilon Small value to avoid zero denominator. + * @param centered If true, gradients are normalized by the estimated + * variance of the gradient. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) { + return new RMSPropOptimizer(learningRate, decay, momentum, epsilon3, centered); + } + /** + * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm. + * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980) + * + * @param learningRate The learning rate to use for the Adam gradient + * descent algorithm. + * @param beta1 The exponential decay rate for the 1st moment estimates. + * @param beta2 The exponential decay rate for the 2nd moment estimates. + * @param epsilon A small constant for numerical stability. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null) { + return new AdamOptimizer(learningRate, beta1, beta2, epsilon3); + } + /** + * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm. + * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701) + * + * @param learningRate The learning rate to use for the Adadelta gradient + * descent algorithm. + * @param rho The learning rate decay over each update. + * @param epsilon A constant epsilon used to better condition the grad + * update. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static adadelta(learningRate = 1e-3, rho = 0.95, epsilon3 = null) { + return new AdadeltaOptimizer(learningRate, rho, epsilon3); + } + /** + * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm. + * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980) + * + * @param learningRate The learning rate to use for the Adamax gradient + * descent algorithm. + * @param beta1 The exponential decay rate for the 1st moment estimates. + * @param beta2 The exponential decay rate for the 2nd moment estimates. + * @param epsilon A small constant for numerical stability. + * @param decay The learning rate decay over each update. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null, decay = 0) { + return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon3, decay); + } + /** + * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm. + * See + * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf]( + * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) + * or + * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad]( + * http://ruder.io/optimizing-gradient-descent/index.html#adagrad) + * + * @param learningRate The learning rate to use for the Adagrad gradient + * descent algorithm. + * @param initialAccumulatorValue Starting value for the accumulators, must be + * positive. + * + * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'} + */ + static adagrad(learningRate, initialAccumulatorValue = 0.1) { + return new AdagradOptimizer(learningRate, initialAccumulatorValue); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/train.js +var train = OptimizerConstructors; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/browser_util.js +var delayCallback = (() => { + if (typeof requestAnimationFrame !== "undefined") { + return requestAnimationFrame; + } else if (typeof setImmediate !== "undefined") { + return setImmediate; + } + return (f) => f(); +})(); +function nextFrame() { + return new Promise((resolve) => delayCallback(() => resolve())); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js +var backend_util_exports = {}; +__export(backend_util_exports, { + ERF_A1: () => ERF_A1, + ERF_A2: () => ERF_A2, + ERF_A3: () => ERF_A3, + ERF_A4: () => ERF_A4, + ERF_A5: () => ERF_A5, + ERF_P: () => ERF_P, + PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD, + RowPartitionType: () => RowPartitionType, + SELU_SCALE: () => SELU_SCALE, + SELU_SCALEALPHA: () => SELU_SCALEALPHA, + applyActivation: () => applyActivation, + assertAndGetBroadcastShape: () => assertAndGetBroadcastShape, + assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims, + assertParamsConsistent: () => assertParamsConsistent, + assignToTypedArray: () => assignToTypedArray, + axesAreInnerMostDims: () => axesAreInnerMostDims, + calculateShapes: () => calculateShapes, + checkEinsumDimSizes: () => checkEinsumDimSizes, + checkPadOnDimRoundingMode: () => checkPadOnDimRoundingMode, + combineLocations: () => combineLocations, + combineRaggedTensorToTensorShapes: () => combineRaggedTensorToTensorShapes, + complexWithEvenIndex: () => complexWithEvenIndex, + complexWithOddIndex: () => complexWithOddIndex, + computeConv2DInfo: () => computeConv2DInfo, + computeConv3DInfo: () => computeConv3DInfo, + computeDefaultPad: () => computeDefaultPad, + computeDilation2DInfo: () => computeDilation2DInfo, + computeOptimalWindowSize: () => computeOptimalWindowSize, + computeOutAndReduceShapes: () => computeOutAndReduceShapes, + computeOutShape: () => computeOutShape2, + computePool2DInfo: () => computePool2DInfo, + computePool3DInfo: () => computePool3DInfo, + convertConv2DDataFormat: () => convertConv2DDataFormat, + decodeEinsumEquation: () => decodeEinsumEquation, + eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne, + expandShapeToKeepDim: () => expandShapeToKeepDim, + exponent: () => exponent, + exponents: () => exponents, + fromStringArrayToUint8: () => fromStringArrayToUint8, + fromUint8ToStringArray: () => fromUint8ToStringArray, + getAxesPermutation: () => getAxesPermutation, + getBroadcastDims: () => getBroadcastDims, + getComplexWithIndex: () => getComplexWithIndex, + getEinsumComputePath: () => getEinsumComputePath, + getEinsumPermutation: () => getEinsumPermutation, + getFusedBiasGradient: () => getFusedBiasGradient, + getFusedDyActivation: () => getFusedDyActivation, + getImageCenter: () => getImageCenter, + getInnerMostAxes: () => getInnerMostAxes, + getPermuted: () => getPermuted, + getRaggedRank: () => getRaggedRank, + getReductionAxes: () => getReductionAxes, + getReshaped: () => getReshaped, + getReshapedPermuted: () => getReshapedPermuted, + getRowPartitionTypesHelper: () => getRowPartitionTypesHelper, + getSliceBeginCoords: () => getSliceBeginCoords, + getSliceSize: () => getSliceSize, + getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => getSparseFillEmptyRowsIndicesDenseShapeMismatch, + getSparseFillEmptyRowsNegativeIndexErrorMessage: () => getSparseFillEmptyRowsNegativeIndexErrorMessage, + getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => getSparseFillEmptyRowsOutOfRangeIndexErrorMessage, + getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => getSparseReshapeEmptyTensorZeroOutputDimErrorMessage, + getSparseReshapeInputOutputMismatchErrorMessage: () => getSparseReshapeInputOutputMismatchErrorMessage, + getSparseReshapeInputOutputMultipleErrorMessage: () => getSparseReshapeInputOutputMultipleErrorMessage, + getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => getSparseReshapeMultipleNegativeOneOutputDimErrorMessage, + getSparseReshapeNegativeOutputDimErrorMessage: () => getSparseReshapeNegativeOutputDimErrorMessage, + getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => getSparseSegmentReductionIndicesOutOfRangeErrorMessage, + getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => getSparseSegmentReductionNegativeSegmentIdsErrorMessage, + getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage, + getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage, + getUndoAxesPermutation: () => getUndoAxesPermutation, + isIdentityPermutation: () => isIdentityPermutation, + log: () => log, + mergeRealAndImagArrays: () => mergeRealAndImagArrays, + prepareAndValidate: () => prepareAndValidate, + prepareSplitSize: () => prepareSplitSize, + segment_util: () => segment_util_exports, + shouldFuse: () => shouldFuse, + slice_util: () => slice_util_exports, + splitRealAndImagArrays: () => splitRealAndImagArrays, + stridesOrDilationsArePositive: () => stridesOrDilationsArePositive, + tupleValuesAreOne: () => tupleValuesAreOne, + upcastType: () => upcastType, + validateDefaultValueShape: () => validateDefaultValueShape, + validateInput: () => validateInput, + validateUpdateShape: () => validateUpdateShape, + warn: () => warn +}); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js +function assertParamsConsistent(shapes, axis) { + const rank = shapes[0].length; + shapes.forEach((shape, i) => { + assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`); + }); + assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`); + const firstShape = shapes[0]; + shapes.forEach((shape, i) => { + for (let r = 0; r < rank; r++) { + assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`); + } + }); +} +function computeOutShape2(shapes, axis) { + const outputShape = shapes[0].slice(); + for (let i = 1; i < shapes.length; i++) { + outputShape[axis] += shapes[i][axis]; + } + return outputShape; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_to_dense_util.js +var RowPartitionType; +(function(RowPartitionType3) { + RowPartitionType3[RowPartitionType3["FIRST_DIM_SIZE"] = 0] = "FIRST_DIM_SIZE"; + RowPartitionType3[RowPartitionType3["VALUE_ROWIDS"] = 1] = "VALUE_ROWIDS"; + RowPartitionType3[RowPartitionType3["ROW_LENGTHS"] = 2] = "ROW_LENGTHS"; + RowPartitionType3[RowPartitionType3["ROW_SPLITS"] = 3] = "ROW_SPLITS"; + RowPartitionType3[RowPartitionType3["ROW_LIMITS"] = 4] = "ROW_LIMITS"; + RowPartitionType3[RowPartitionType3["ROW_STARTS"] = 5] = "ROW_STARTS"; +})(RowPartitionType || (RowPartitionType = {})); +function combineRaggedTensorToTensorShapes(raggedRank, shape, valueShape) { + let outputShape = new Array(); + if (valueShape == null && shape == null) { + return outputShape; + } + if (shape == null) { + while (outputShape.length < raggedRank + valueShape.length) { + outputShape.push(-1); + } + } else { + outputShape = shape.slice(); + } + if (valueShape == null) { + return outputShape; + } + if (raggedRank + valueShape.length !== outputShape.length) { + throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.rank = ${raggedRank + valueShape.length}, but shape.rank = ${outputShape.length}`); + } + for (let i = 1; i < valueShape.length; ++i) { + const valueDim = valueShape[i]; + const outputShapeDimIndex = outputShape[outputShape.length - valueShape.length + i]; + const outputShapeDim = outputShape[outputShapeDimIndex]; + if (valueDim >= 0) { + if (outputShapeDim >= 0) { + if (outputShapeDim !== valueDim) { + throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.shape[${i + raggedRank}] = ${valueDim} but shape[${i + raggedRank}] = ${outputShapeDim}`); + } + } else { + outputShape[outputShapeDimIndex] = valueDim; + } + } + } + return outputShape; +} +function getRowPartitionTypesHelper(rowPartitionTypeStrings) { + const stringToType = { + "FIRST_DIM_SIZE": RowPartitionType.FIRST_DIM_SIZE, + "VALUE_ROWIDS": RowPartitionType.VALUE_ROWIDS, + "ROW_LENGTHS": RowPartitionType.ROW_LENGTHS, + "ROW_SPLITS": RowPartitionType.ROW_SPLITS, + "ROW_LIMITS": RowPartitionType.ROW_LIMITS, + "ROW_STARTS": RowPartitionType.ROW_STARTS + }; + const result = []; + for (const typeStr of rowPartitionTypeStrings) { + if (typeStr in stringToType) { + result.push(stringToType[typeStr]); + } else { + break; + } + } + return result; +} +function getRaggedRank(rowPartitionTypes) { + if (rowPartitionTypes.length === 0) { + return 0; + } + if (rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) { + return rowPartitionTypes.length - 1; + } + return rowPartitionTypes.length; +} +function validateDefaultValueShape(defaultValueShape, valueShape) { + if (defaultValueShape == null || valueShape == null) { + return; + } + const defaultNDims = defaultValueShape.length; + const valuesNDims = valueShape.length; + if (defaultNDims >= valuesNDims) { + throw new Error(`defaultValue.shape=${defaultValueShape} and ragged tensor flatValues.shape=${valueShape}, are incompatible: defaultValue.rank = ${defaultNDims} must be less than ragged tensor input flatValues.rank = ${valuesNDims})`); + } + for (let i = 0; i < Math.min(defaultNDims, valuesNDims - 1); ++i) { + const defaultDim = defaultValueShape[i]; + const valueDim = valueShape[i + 1]; + if (defaultDim >= 0 && valueDim >= 0 && defaultDim !== 1 && defaultDim !== valueDim) { + throw new Error(`defaultValue.shape=${defaultValueShape}, and ragged tensor input flatValues.shape=${valueShape} are incompatible: defaultValue.shape[${i - defaultValueShape.length}] = ${defaultDim} but ragged tensor input.flatValues.shape[${i - defaultValueShape.length}] = ${valueDim}`); + } + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js +var PARALLELIZE_THRESHOLD = 30; +function computeOptimalWindowSize(inSize) { + if (inSize <= PARALLELIZE_THRESHOLD) { + return inSize; + } + return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize))); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js +function getImageCenter(center, imageHeight, imageWidth) { + const centerX = imageWidth * (typeof center === "number" ? center : center[0]); + const centerY = imageHeight * (typeof center === "number" ? center : center[1]); + return [centerX, centerY]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js +function getReshaped(inputShape, blockShape, prod5, batchToSpace = true) { + let reshaped = []; + if (batchToSpace) { + reshaped = reshaped.concat(blockShape.slice(0)); + reshaped.push(inputShape[0] / prod5); + reshaped = reshaped.concat(inputShape.slice(1)); + } else { + reshaped = reshaped.concat(inputShape[0]); + const spatialLength = blockShape.length; + for (let i = 0; i < spatialLength; ++i) { + reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]); + } + reshaped = reshaped.concat(inputShape.slice(spatialLength + 1)); + } + return reshaped; +} +function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) { + const permuted = []; + if (batchToSpace) { + permuted.push(blockShapeRank); + for (let i = blockShapeRank + 1; i < reshapedRank; ++i) { + if (i <= 2 * blockShapeRank) { + permuted.push(i); + permuted.push(i - (blockShapeRank + 1)); + } else { + permuted.push(i); + } + } + } else { + const permutedBeforeBatch = []; + const permutedAfterBatch = []; + for (let i = 1; i < reshapedRank; ++i) { + if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) { + permutedAfterBatch.push(i); + } else { + permutedBeforeBatch.push(i); + } + } + permuted.push(...permutedBeforeBatch); + permuted.push(0); + permuted.push(...permutedAfterBatch); + } + return permuted; +} +function getReshapedPermuted(inputShape, blockShape, prod5, batchToSpace = true) { + const reshapedPermuted = []; + if (batchToSpace) { + reshapedPermuted.push(inputShape[0] / prod5); + } else { + reshapedPermuted.push(inputShape[0] * prod5); + } + for (let i = 1; i < inputShape.length; ++i) { + if (i <= blockShape.length) { + if (batchToSpace) { + reshapedPermuted.push(blockShape[i - 1] * inputShape[i]); + } else { + reshapedPermuted.push(inputShape[i] / blockShape[i - 1]); + } + } else { + reshapedPermuted.push(inputShape[i]); + } + } + return reshapedPermuted; +} +function getSliceBeginCoords(crops, blockShape) { + const sliceBeginCoords = [0]; + for (let i = 0; i < blockShape; ++i) { + sliceBeginCoords.push(crops[i][0]); + } + return sliceBeginCoords; +} +function getSliceSize(uncroppedShape, crops, blockShape) { + const sliceSize = uncroppedShape.slice(0, 1); + for (let i = 0; i < blockShape; ++i) { + sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]); + } + return sliceSize; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js +var SELU_SCALEALPHA = 1.7580993408473768; +var SELU_SCALE = 1.0507009873554805; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js +var ERF_P = 0.3275911; +var ERF_A1 = 0.254829592; +var ERF_A2 = -0.284496736; +var ERF_A3 = 1.421413741; +var ERF_A4 = -1.453152027; +var ERF_A5 = 1.061405429; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js +function mergeRealAndImagArrays(real4, imag4) { + if (real4.length !== imag4.length) { + throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`); + } + const result = new Float32Array(real4.length * 2); + for (let i = 0; i < result.length; i += 2) { + result[i] = real4[i / 2]; + result[i + 1] = imag4[i / 2]; + } + return result; +} +function splitRealAndImagArrays(complex4) { + const real4 = new Float32Array(complex4.length / 2); + const imag4 = new Float32Array(complex4.length / 2); + for (let i = 0; i < complex4.length; i += 2) { + real4[i / 2] = complex4[i]; + imag4[i / 2] = complex4[i + 1]; + } + return { real: real4, imag: imag4 }; +} +function complexWithEvenIndex(complex4) { + const len = Math.ceil(complex4.length / 4); + const real4 = new Float32Array(len); + const imag4 = new Float32Array(len); + for (let i = 0; i < complex4.length; i += 4) { + real4[Math.floor(i / 4)] = complex4[i]; + imag4[Math.floor(i / 4)] = complex4[i + 1]; + } + return { real: real4, imag: imag4 }; +} +function complexWithOddIndex(complex4) { + const len = Math.floor(complex4.length / 4); + const real4 = new Float32Array(len); + const imag4 = new Float32Array(len); + for (let i = 2; i < complex4.length; i += 4) { + real4[Math.floor(i / 4)] = complex4[i]; + imag4[Math.floor(i / 4)] = complex4[i + 1]; + } + return { real: real4, imag: imag4 }; +} +function getComplexWithIndex(complex4, index) { + const real4 = complex4[index * 2]; + const imag4 = complex4[index * 2 + 1]; + return { real: real4, imag: imag4 }; +} +function assignToTypedArray(data, real4, imag4, index) { + data[index * 2] = real4; + data[index * 2 + 1] = imag4; +} +function exponents(n, inverse) { + const real4 = new Float32Array(n / 2); + const imag4 = new Float32Array(n / 2); + for (let i = 0; i < Math.ceil(n / 2); i++) { + const x = (inverse ? 2 : -2) * Math.PI * (i / n); + real4[i] = Math.cos(x); + imag4[i] = Math.sin(x); + } + return { real: real4, imag: imag4 }; +} +function exponent(k, n, inverse) { + const x = (inverse ? 2 : -2) * Math.PI * (k / n); + const real4 = Math.cos(x); + const imag4 = Math.sin(x); + return { real: real4, imag: imag4 }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/einsum_util.js +var ARROW = "->"; +var ARROW_REGEX = /->/g; +var COMMA = ","; +var ELLIPSIS = "..."; +function decodeEinsumEquation(equation, numTensors) { + equation = equation.replace(/\s/g, ""); + const numArrows = (equation.length - equation.replace(ARROW_REGEX, "").length) / ARROW.length; + if (numArrows < 1) { + throw new Error("Equations without an arrow are not supported."); + } else if (numArrows > 1) { + throw new Error(`Equation must contain exactly one arrow ("${ARROW}").`); + } + const [inputString, outputString] = equation.split(ARROW); + assert(inputString.indexOf(ELLIPSIS) === -1, () => `The ellipsis notation ("${ELLIPSIS}") is not supported yet.`); + const inputTerms = inputString.split(COMMA); + const numInputs = inputTerms.length; + if (numTensors !== numInputs) { + throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`); + } + if (numInputs > 2) { + throw new Error("Support for more than 2 input tensors is not implemented yet."); + } + const allDims = []; + for (let i = 0; i < outputString.length; ++i) { + const dimName = outputString[i]; + if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) { + throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`); + } + if (allDims.indexOf(dimName) === -1) { + allDims.push(dimName); + } + } + for (let i = 0; i < inputString.length; ++i) { + const dimName = inputString[i]; + if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) { + allDims.push(dimName); + } + } + const idDims = new Array(inputTerms.length); + for (let i = 0; i < numInputs; ++i) { + if (new Set(inputTerms[i].split("")).size !== inputTerms[i].length) { + throw new Error(`Found duplicate axes in input component ${inputTerms[i]}. Support for duplicate axes in input is not implemented yet.`); + } + idDims[i] = []; + for (let j = 0; j < inputTerms[i].length; ++j) { + idDims[i].push(allDims.indexOf(inputTerms[i][j])); + } + } + const numDims = allDims.length; + const numOutDims = outputString.length; + const summedDims = []; + for (let i = numOutDims; i < numDims; ++i) { + summedDims.push(i); + } + return { allDims, summedDims, idDims }; +} +function getEinsumPermutation(nDims, idDims) { + let permutationIndices = new Array(nDims); + permutationIndices.fill(-1); + for (let i = 0; i < idDims.length; ++i) { + permutationIndices[idDims[i]] = i; + } + const expandDims6 = []; + for (let i = 0; i < nDims; ++i) { + if (permutationIndices[i] === -1) { + expandDims6.push(i); + } + } + permutationIndices = permutationIndices.filter((d) => d !== -1); + return { permutationIndices, expandDims: expandDims6 }; +} +function checkEinsumDimSizes(nDims, idDims, tensors) { + const dimSizes = new Array(nDims); + for (let i = 0; i < tensors.length; ++i) { + const shape = tensors[i].shape; + for (let j = 0; j < idDims[i].length; ++j) { + if (dimSizes[idDims[i][j]] === void 0) { + dimSizes[idDims[i][j]] = shape[j]; + } else { + assert(dimSizes[idDims[i][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`); + } + } + } +} +function getEinsumComputePath(summedDims, idDims) { + const path = summedDims; + const steps = []; + let nSteps = 0; + if (summedDims.length === 0) { + path.push(-1); + } + nSteps = summedDims.length + 1; + for (let i = 0; i < nSteps; ++i) { + steps.push([]); + } + const computedTermIndices = []; + for (let i = 0; i < path.length; ++i) { + const summedDim = path[i]; + const termIndices = findTermsWithDim(idDims, summedDim); + for (const termIndex of termIndices) { + if (computedTermIndices.indexOf(termIndex) === -1) { + steps[i].push(termIndex); + computedTermIndices.push(termIndex); + } + } + } + return { path, steps }; +} +function isIdentityPermutation(perm) { + return perm.every((dim, index) => dim === index); +} +function findTermsWithDim(idDims, dim) { + const termIndices = []; + for (let i = 0; i < idDims.length; ++i) { + if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) { + termIndices.push(i); + } + } + return termIndices; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js +function prepareSplitSize(x, numOrSizeSplits, axis = 0) { + let splitSizes = []; + if (typeof numOrSizeSplits === "number") { + assert(x.shape[axis] % numOrSizeSplits === 0, () => "Number of splits must evenly divide the axis."); + splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits); + } else { + const numOfNegs = numOrSizeSplits.reduce((count2, value) => { + if (value === -1) { + count2 += 1; + } + return count2; + }, 0); + assert(numOfNegs <= 1, () => "There should be only one negative value in split array."); + const negIndex = numOrSizeSplits.indexOf(-1); + if (negIndex !== -1) { + const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a); + numOrSizeSplits[negIndex] = x.shape[axis] - total; + } + assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => "The sum of sizes must match the size of the axis dimension."); + splitSizes = numOrSizeSplits; + } + return splitSizes; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows_util.js +function getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) { + return `Received SparseTensor with denseShape[0] = 0 but + indices.shape[0] = ${indicesLength}`; +} +function getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) { + return `indices(${index}, 0) is invalid: ${value} < 0`; +} +function getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) { + return `indices(${index}, 0) is invalid: ${value} >= ${limit}`; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape_util.js +function getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) { + return `only one output dimension may be -1, not both ${dim1} and ${dim2}`; +} +function getSparseReshapeNegativeOutputDimErrorMessage(dim, value) { + return `size ${dim} must be non-negative, not ${value}`; +} +function getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() { + return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"; +} +function getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) { + const inputSize = sizeFromShape(inputShape); + const outputSize = sizeFromShape(outputShape); + return `Input to reshape is a SparseTensor with ${inputSize} + dense values, but the requested shape requires a multiple of ${outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`; +} +function getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) { + const inputSize = sizeFromShape(inputShape); + const outputSize = sizeFromShape(outputShape); + return `Input to reshape is a tensor with ${inputSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_reduction_util.js +function getSparseSegmentReductionNegativeSegmentIdsErrorMessage() { + return `segment ids must be >= 0`; +} +function getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() { + return `segment ids are not increasing`; +} +function getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) { + return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`; +} +function getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) { + return `Bad: indices[${index}] == ${indexValue} out of range [0, ${inputRows})`; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js +var segment_util_exports = {}; +__export(segment_util_exports, { + collectGatherOpShapeInfo: () => collectGatherOpShapeInfo, + computeOutShape: () => computeOutShape3, + segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize +}); +function segOpComputeOptimalWindowSize(inSize, numSegments) { + let done = false; + let res; + if (inSize <= PARALLELIZE_THRESHOLD) { + res = inSize; + done = true; + } else { + res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize))); + } + while (!done) { + if (res > numSegments || res === inSize) { + done = true; + } else { + res = nearestDivisor(inSize, res + 1); + } + } + return res; +} +function computeOutShape3(aShape, axis, numSegments) { + const outShape = []; + const rank = aShape.length; + for (let dim = 0; dim < rank; dim++) { + if (dim !== axis) { + outShape.push(aShape[dim]); + } else { + outShape.push(numSegments); + } + } + return outShape; +} +function collectGatherOpShapeInfo(x, indices, axis, batchDims) { + const indicesRank = indices.shape.length; + const xRank = x.shape.length; + if (batchDims !== 0) { + if (batchDims < -indicesRank || batchDims > indicesRank) { + throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`); + } + } + if (batchDims < 0) { + batchDims += indicesRank; + } + if (batchDims > xRank) { + throw new Error(`batchDims (${batchDims}) must be less than rank(x) ( + ${xRank}).`); + } + if (axis < batchDims) { + throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`); + } + for (let i = 0; i < batchDims; ++i) { + if (x.shape[i] !== indices.shape[i]) { + throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`); + } + } + const dimSize = x.shape[axis]; + const outputShape = []; + let batchSize = 1; + let outerSize = 1; + let sliceSize = 1; + for (let i = 0; i < batchDims; ++i) { + outputShape.push(x.shape[i]); + batchSize *= x.shape[i]; + } + for (let i = batchDims; i < axis; i++) { + outputShape.push(x.shape[i]); + outerSize *= x.shape[i]; + } + for (let i = batchDims; i < indicesRank; i++) { + outputShape.push(indices.shape[i]); + } + for (let i = axis + 1; i < xRank; i++) { + outputShape.push(x.shape[i]); + sliceSize *= x.shape[i]; + } + return { batchSize, sliceSize, outerSize, dimSize, outputShape }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js +function fromUint8ToStringArray(vals) { + try { + return vals.map((val) => decodeString(val)); + } catch (err) { + throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`); + } +} +function fromStringArrayToUint8(strings) { + return strings.map((s) => encodeString(s)); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js +var kernel_impls_exports = {}; +__export(kernel_impls_exports, { + nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl, + nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl, + nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl, + whereImpl: () => whereImpl +}); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/index.js +registerOptimizers(); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js +var absGradConfig = { + kernelName: Abs, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(dy, step(cast(x, "float32"), -1)) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js +var acosGradConfig = { + kernelName: Acos, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { + x: () => { + const a = square(cast(x, "float32")); + const b = sqrt(sub(scalar(1), a)); + return neg(div(dy, b)); + } + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js +var acoshGradConfig = { + kernelName: Acosh, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { + x: () => { + const a = sqrt(sub(square(cast(x, "float32")), 1)); + return div(dy, a); + } + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js +var addGradConfig = { + kernelName: Add, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + let res = dy; + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, a.shape); + }; + const derB = () => { + let res = dy; + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, b.shape); + }; + return { a: derA, b: derB }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js +var addNGradConfig = { + kernelName: AddN, + saveAllInputs: true, + gradFunc: (dy, saved) => { + const ders = {}; + saved.forEach((_, i) => { + ders[i] = () => dy.clone(); + }); + return ders; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js +var argMaxGradConfig = { + kernelName: ArgMax, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => zerosLike(x) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js +var argMinGradConfig = { + kernelName: ArgMin, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => zerosLike(x) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js +var asinGradConfig = { + kernelName: Asin, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, "float32"))))) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js +var asinhGradConfig = { + kernelName: Asinh, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { + x: () => { + const a = sqrt(add2(scalar(1), square(cast(x, "float32")))); + return div(dy, a); + } + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js +var atan2GradConfig = { + kernelName: Atan2, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + const d = add2(square(a), square(b)); + let res = mul(dy, div(b, d)); + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, a.shape); + }; + const derB = () => { + const d = add2(square(a), square(b)); + let res = neg(mul(dy, div(a, d))); + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, b.shape); + }; + return { a: derA, b: derB }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js +var atanGradConfig = { + kernelName: Atan, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, add2(square(cast(x, "float32")), 1)) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js +var atanhGradConfig = { + kernelName: Atanh, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, sub(scalar(1), square(cast(x, "float32")))) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_grad.js +function avgPool3dGrad_(dy, input2, filterSize, strides, pad3, dimRoundingMode) { + const $dy = convertToTensor(dy, "dy", "avgPool3dGrad"); + const $input = convertToTensor(input2, "input", "avgPool3dGrad"); + let dy5D = $dy; + let input5D = $input; + let reshapedTo5D = false; + if ($input.rank === 4) { + reshapedTo5D = true; + dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]); + input5D = reshape($input, [ + 1, + $input.shape[0], + $input.shape[1], + $input.shape[2], + $input.shape[3] + ]); + } + assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`); + assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`); + checkPadOnDimRoundingMode("avgPool3dGrad", pad3, dimRoundingMode); + const inputs = { dy: dy5D, input: input5D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; + const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var avgPool3dGrad = op({ avgPool3dGrad_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js +var avgPool3DGradConfig = { + kernelName: AvgPool3D, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + return { + x: () => avgPool3dGrad(dy, x, filterSize, strides, pad3, dimRoundingMode) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_grad.js +function avgPoolGrad_(dy, input2, filterSize, strides, pad3) { + const $dy = convertToTensor(dy, "dy", "avgPoolGrad"); + const $input = convertToTensor(input2, "input", "avgPoolGrad"); + assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`); + let input4D = $input; + let dy4D = $dy; + let reshapedTo4D = false; + if ($input.rank === 3) { + reshapedTo4D = true; + input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]); + dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]); + } + assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`); + assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`); + const inputs = { dy: dy4D, input: input4D }; + const attrs = { filterSize, strides, pad: pad3 }; + const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; +} +var avgPoolGrad = op({ avgPoolGrad_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js +var avgPoolGradConfig = { + kernelName: AvgPool, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { filterSize, strides, pad: pad3 } = attrs; + return { x: () => avgPoolGrad(dy, x, filterSize, strides, pad3) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js +var batchMatMulGradConfig = { + kernelName: BatchMatMul, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved, attrs) => { + const [a, b] = saved; + const { transposeA, transposeB } = attrs; + if (!transposeA && !transposeB) { + return { + a: () => matMul(dy, b, false, true), + b: () => matMul(a, dy, true, false) + }; + } else if (!transposeA && transposeB) { + return { + a: () => matMul(dy, b, false, false), + b: () => matMul(dy, a, true, false) + }; + } else if (transposeA && !transposeB) { + return { + a: () => matMul(b, dy, false, true), + b: () => matMul(a, dy, false, false) + }; + } else { + return { + a: () => matMul(b, dy, true, true), + b: () => matMul(dy, a, true, true) + }; + } + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js +var batchToSpaceNDGradConfig = { + kernelName: BatchToSpaceND, + gradFunc: (dy, saved, attrs) => { + const { blockShape, crops } = attrs; + return { x: () => spaceToBatchND(dy, blockShape, crops) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js +var broadcastToGradConfig = { + kernelName: BroadcastTo, + gradFunc: (dy, saved, attrs) => { + const broadCastToAttrs = attrs; + const inputShape = broadCastToAttrs.inputShape; + const outputShape = broadCastToAttrs.shape; + const reps = Array.from(outputShape); + for (let i = inputShape.length - 1; i >= 0; i--) { + if (inputShape[i] === outputShape[i]) { + reps[i] = 1; + } else if (inputShape[i] !== 1) { + throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`); + } + } + const axes = []; + for (let i = 0; i < reps.length; i++) { + if (reps[i] > 1) { + axes.push(i); + } + } + return { x: () => sum2( + dy, + axes, + true + /* keepDims */ + ) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js +var castGradConfig = { + kernelName: Cast, + gradFunc: (dy) => { + return { x: () => dy.clone() }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js +var ceilGradConfig = { + kernelName: Ceil, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js +var clipByValueGradConfig = { + kernelName: ClipByValue, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { clipValueMin, clipValueMax } = attrs; + return { + x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ComplexAbs_grad.js +var complexAbsGradConfig = { + kernelName: ComplexAbs, + inputsToSave: ["x"], + gradFunc: absGradConfig.gradFunc +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js +var concatGradConfig = { + kernelName: Concat, + saveAllInputs: true, + gradFunc: (dy, saved, attrs) => { + const shapes = saved.map((t) => t.shape); + const { axis } = attrs; + const $axis = parseAxisParam(axis, saved[0].shape)[0]; + const sizeSplits = shapes.map((s) => s[$axis]); + const derTensors = split(dy, sizeSplits, $axis); + return derTensors.map((t) => () => t); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js +var conv2DGradConfig = { + kernelName: Conv2D, + inputsToSave: ["x", "filter"], + gradFunc: (dy, saved, attrs) => { + const [x4D, $filter] = saved; + const { dilations, strides, pad: pad3, dataFormat } = attrs; + assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`); + return { + x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad3, dataFormat), + filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad3, dataFormat) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js +var conv2DBackpropInputGradConfig = { + kernelName: Conv2DBackpropInput, + inputsToSave: ["dy", "filter"], + gradFunc: (ddx, saved, attrs) => { + const [dy, filter] = saved; + const { strides, pad: pad3, dataFormat, dimRoundingMode } = attrs; + return { + dy: () => conv2d(ddx, filter, strides, pad3, dataFormat, 1, dimRoundingMode), + filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad3, dataFormat, dimRoundingMode) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js +function conv3DBackpropFilter_(x, dy, filterShape, strides, pad3) { + let x5D = x; + if (x.rank === 4) { + x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]); + } + let dy5D = dy; + if (dy5D.rank === 4) { + dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]); + } + assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`); + assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`); + assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`); + assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`); + assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`); + const inputs = { x: x5D, dy: dy5D }; + const attrs = { strides, pad: pad3, filterShape }; + return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs); +} +var conv3DBackpropFilter = op({ conv3DBackpropFilter_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js +var conv3DGradConfig = { + kernelName: Conv3D, + inputsToSave: ["x", "filter"], + gradFunc: (dy, saved, attrs) => { + const { dilations, strides, pad: pad3 } = attrs; + assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`); + const [x5D, $filter] = saved; + return { + x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad3), + filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad3) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js +var cosGradConfig = { + kernelName: Cos, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(neg(sin(cast(x, "float32"))), dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js +var coshGradConfig = { + kernelName: Cosh, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(sinh(cast(x, "float32")), dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js +var cumsumGradConfig = { + kernelName: Cumsum, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { axis, exclusive, reverse: reverse5 } = attrs; + return { + x: () => { + const permutation = getAxesPermutation([axis], x.rank); + let out = cumsum(dy, axis, exclusive, !reverse5); + if (permutation != null) { + out = transpose(out, permutation); + } + return out; + } + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js +var depthwiseConv2dNativeGradConfig = { + kernelName: DepthwiseConv2dNative, + inputsToSave: ["x", "filter"], + gradFunc: (dy, saved, attrs) => { + const { dilations, strides, pad: pad3, dimRoundingMode } = attrs; + const $dilations = dilations == null ? [1, 1] : dilations; + assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`); + const [x, filter] = saved; + assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`); + assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`); + assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`); + assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'.`); + checkPadOnDimRoundingMode("depthwiseConv2d", pad3, dimRoundingMode); + return { + x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad3, $dilations, dimRoundingMode), + filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad3, $dilations, dimRoundingMode) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js +var dilation2dGradConfig = { + kernelName: Dilation2D, + inputsToSave: ["x", "filter"], + gradFunc: (dy, saved, attrs) => { + const [x, filter] = saved; + const inputInputs = { x, filter, dy }; + const filterInputs = { x, filter, dy }; + return { + x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs), + filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js +var eluGradConfig = { + kernelName: Elu, + outputsToSave: [true], + gradFunc: (dy, saved) => { + const [y] = saved; + const inputs = { dy, y }; + return { x: () => ENGINE.runKernel(EluGrad, inputs) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js +var erfGradConfig = { + kernelName: Erf, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI)); + return { x: () => mul(dy, a) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js +var expGradConfig = { + kernelName: Exp, + outputsToSave: [true], + gradFunc: (dy, saved) => { + const [y] = saved; + return { x: () => mul(dy, y) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ExpandDims_grad.js +var expandDimsGradConfig = { + kernelName: ExpandDims, + inputsToSave: ["input"], + gradFunc: (dy, saved) => { + const [input2] = saved; + return { input: () => reshape(dy, input2.shape) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js +var expm1GradConfig = { + kernelName: Expm1, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(dy, exp(x)) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js +var floorGradConfig = { + kernelName: Floor, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js +var floorDivGradConfig = { + kernelName: FloorDiv, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + const res = div(dy, cast(b, "float32")); + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(res, reduceAxes), a.shape); + } + return res; + }; + const derB = () => { + let res = mul(dy, cast(a, "float32")); + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + res = reshape(sum2(res, reduceAxes), b.shape); + } + const tmp = square(b); + return neg(div(res, cast(tmp, "float32"))); + }; + return { a: derA, b: derB }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js +var fusedBatchNormGradConfig = { + kernelName: FusedBatchNorm, + inputsToSave: ["x", "mean", "variance", "scale"], + gradFunc: (dy, saved, attrs) => { + const { varianceEpsilon } = attrs; + const [x, mean4, variance, scale2] = saved; + const scaleValue = scale2 == null ? scalar(1) : scale2; + const reductionAxes = getReductionAxes(mean4.shape, x.shape); + const tileShape = []; + if (mean4.rank === 1) { + for (let i = 0; i < x.shape.length - 1; ++i) { + tileShape.push(x.shape[i]); + } + tileShape.push(1); + } + const xMinusMean = sub(x, mean4); + const dyTimesScaleValue = mul(dy, scaleValue); + const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon))); + const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5)); + const derX = () => { + if (mean4.rank === 1) { + return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean4.shape[0]]), tileShape)), scaleValue), x.shape); + } else { + return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape); + } + }; + const derMean = () => { + let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue); + if (mean4.rank === 1) { + meanDer = sum2(meanDer, reductionAxes); + } + return reshape(meanDer, mean4.shape); + }; + const derVariance = () => { + let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue); + if (mean4.rank === 1) { + varianceDer = sum2(varianceDer, reductionAxes); + } + return reshape(varianceDer, mean4.shape); + }; + const derScale = () => { + const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance); + let scaleDer = mul(dy, xMinusMean2TimesRsqrt); + if (mean4.rank === 1) { + scaleDer = sum2(scaleDer, reductionAxes); + } + return reshape(scaleDer, mean4.shape); + }; + const derOffset = () => { + let offsetDer = dy; + if (mean4.rank === 1) { + offsetDer = sum2(offsetDer, reductionAxes); + } + return reshape(offsetDer, mean4.shape); + }; + return { + x: derX, + mean: derMean, + variance: derVariance, + scale: derScale, + offset: derOffset + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js +var gatherGradConfig = { + kernelName: GatherV2, + inputsToSave: ["x", "indices"], + gradFunc: (dy, saved, attrs) => { + const [x, indices] = saved; + const { axis, batchDims } = attrs; + const parsedAxis = parseAxisParam(axis, x.shape)[0]; + const derXBatch = (x2, indices2, dy2) => { + return () => { + const paramsShape = x2.shape; + const indicesSize = indices2.size; + const outerShape = paramsShape.slice(0, parsedAxis); + const outerDims = outerShape.length; + const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1); + const innerDims = innerShape.length; + const outerAxesIndices = arrayRange(0, outerDims); + const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims); + const valuesShape = arrayConcat([ + outerShape, + [indicesSize], + innerShape + ]); + const values = reshape(dy2, valuesShape); + const reshapedIndices = reshape(indices2, [indicesSize]); + const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]); + const valuesTranspose = transpose(values, transposeDims); + let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x2.shape[parsedAxis]); + const invertTransposeDims = getUndoAxesPermutation(transposeDims); + paramsGrad = transpose(paramsGrad, invertTransposeDims); + return paramsGrad; + }; + }; + if (batchDims === 1) { + const batchSize = x.shape[0]; + const xBatch = x.split(batchSize, 0); + const derXBatched = () => { + const stacked = stack(xBatch.map((x2, i) => { + return derXBatch(x2, indices.slice(i, 1), dy.slice(i, 1))(); + })); + return stacked.reshape(x.shape); + }; + return { x: derXBatched, indices: () => indices }; + } else { + return { x: derXBatch(x, indices, dy), indices: () => indices }; + } + } +}; +function arrayRange(start, stop) { + const result = []; + for (let i = start; i < stop; ++i) { + result.push(i); + } + return result; +} +function arrayConcat(arrays) { + const result = []; + for (let i = 0; i < arrays.length; ++i) { + for (let j = 0; j < arrays[i].length; ++j) { + result.push(arrays[i][j]); + } + } + return result; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js +var greaterEqualGradConfig = { + kernelName: GreaterEqual, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + return { a: () => zerosLike(a), b: () => zerosLike(b) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js +var identityGradConfig = { + kernelName: Identity, + gradFunc: (dy) => { + return { x: () => cast(dy, "float32") }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js +var isFiniteGradConfig = { + kernelName: IsFinite, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js +var isInfGradConfig = { + kernelName: IsInf, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js +var isNanGradConfig = { + kernelName: IsNan, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LeakyRelu_grad.js +var leakyReluGradConfig = { + kernelName: LeakyRelu, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { alpha } = attrs; + const mask = greater(x, 0); + return { x: () => where(mask, dy, mul(dy, alpha)) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js +var log1pGradConfig = { + kernelName: Log1p, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, add2(x, 1)) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js +var logGradConfig = { + kernelName: Log, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, cast(x, "float32")) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js +var logSoftmaxGradConfig = { + kernelName: LogSoftmax, + inputsToSave: [], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const [value] = saved; + const { axis } = attrs; + return { + logits: () => { + const keepDims = true; + const softmax6 = exp(value); + return sub(dy, mul(sum2(dy, axis, keepDims), softmax6)); + } + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js +function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) { + const inputs = { x, y, dy }; + const attrs = { depthRadius, bias, alpha, beta }; + return ENGINE.runKernel(LRNGrad, inputs, attrs); +} +var localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js +var lrnGradConfig = { + kernelName: LRN, + inputsToSave: ["x"], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const [x, y] = saved; + const { depthRadius, bias, alpha, beta } = attrs; + return { + x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js +function gradForMinAndMax(dy, y, xOrig, origAxes) { + if (y.rank < xOrig.rank) { + y = reshape(y, expandShapeToKeepDim(y.shape, origAxes)); + } + if (dy.rank < xOrig.rank) { + dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes)); + } + return { + x: () => { + const dx = mul(dy, cast(equal(xOrig, y), dy.dtype)); + return dx; + } + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js +var maxGradConfig = { + kernelName: Max, + inputsToSave: ["x"], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const maxAttrs = attrs; + const { reductionIndices } = maxAttrs; + const x = saved[0]; + const y = saved[1]; + const origAxes = parseAxisParam(reductionIndices, x.shape); + const maxGrad = gradForMinAndMax(dy, y, x, origAxes); + return { + x: () => { + return maxGrad["x"](); + } + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js +var maximumGradConfig = { + kernelName: Maximum, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const derA = () => mul(dy, cast(greaterEqual(a, b), "float32")); + const derB = () => mul(dy, cast(less(a, b), "float32")); + return { a: derA, b: derB }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_grad.js +function maxPool3dGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) { + const $dy = convertToTensor(dy, "dy", "maxPool3dGrad"); + const $input = convertToTensor(input2, "input", "maxPool3dGrad"); + const $output = convertToTensor(output, "output", "maxPool3dGrad"); + let dy5D = $dy; + let input5D = $input; + let output5D = $output; + let reshapedTo5D = false; + if ($input.rank === 4) { + reshapedTo5D = true; + dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]); + input5D = reshape($input, [ + 1, + $input.shape[0], + $input.shape[1], + $input.shape[2], + $input.shape[3] + ]); + output5D = reshape($output, [ + 1, + $output.shape[0], + $output.shape[1], + $output.shape[2], + $output.shape[3] + ]); + } + assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`); + assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`); + assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`); + checkPadOnDimRoundingMode("maxPool3dGrad", pad3, dimRoundingMode); + const inputs = { dy: dy5D, input: input5D, output: output5D }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; + const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs); + if (reshapedTo5D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]); + } + return res; +} +var maxPool3dGrad = op({ maxPool3dGrad_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js +var maxPool3DGradConfig = { + kernelName: MaxPool3D, + inputsToSave: ["x"], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const [x, y] = saved; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + return { + x: () => maxPool3dGrad(dy, x, y, filterSize, strides, pad3, dimRoundingMode) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_grad.js +function maxPoolGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) { + const $dy = convertToTensor(dy, "dy", "maxPoolGrad"); + const $input = convertToTensor(input2, "input", "maxPoolGrad"); + const $output = convertToTensor(output, "output", "maxPoolGrad"); + assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`); + assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`); + assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`); + checkPadOnDimRoundingMode("maxPoolGrad", pad3, dimRoundingMode); + const inputs = { dy: $dy, input: $input, output: $output }; + const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; + return ENGINE.runKernel(MaxPoolGrad, inputs, attrs); +} +var maxPoolGrad = op({ maxPoolGrad_ }); + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js +var maxPoolGradConfig = { + kernelName: MaxPool, + inputsToSave: ["x"], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const [x, y] = saved; + const { filterSize, strides, pad: pad3 } = attrs; + return { + x: () => maxPoolGrad(dy, x, y, filterSize, strides, pad3) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mean_grad.js +var meanGradConfig = { + kernelName: Mean, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { axis } = attrs; + const axes = parseAxisParam(axis, x.shape); + const shapes = computeOutAndReduceShapes(x.shape, axes); + const reduceShape = shapes[1]; + const reduceSize = sizeFromShape(reduceShape); + const derX = () => { + const expandedDyShape = x.shape.slice(); + axes.forEach((axis2) => { + expandedDyShape[axis2] = 1; + }); + const expandedDy = reshape(dy, expandedDyShape); + const res = div(mul(expandedDy, ones2(x.shape, "float32")), reduceSize); + return res; + }; + return { x: derX }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js +var minGradConfig = { + kernelName: Min, + inputsToSave: ["x"], + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const minAttrs = attrs; + const { axis } = minAttrs; + const [x, y] = saved; + const origAxes = parseAxisParam(axis, x.shape); + const minGrad = gradForMinAndMax(dy, y, x, origAxes); + return { + x: () => { + return minGrad["x"](); + } + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js +var minimumGradConfig = { + kernelName: Minimum, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const derA = () => mul(dy, cast(lessEqual(a, b), "float32")); + const derB = () => mul(dy, cast(greater(a, b), "float32")); + return { a: derA, b: derB }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MirrorPad_grad.js +var mirrorPadGradConfig = { + kernelName: MirrorPad, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const x = saved[0]; + const { paddings } = attrs; + const begin = paddings.map((p2) => p2[0]); + return { x: () => slice(dy, begin, x.shape) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js +var modGradConfig = { + kernelName: Mod, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(dy, reduceAxes), a.shape); + } + return dy; + }; + const derB = () => { + const res = mul(dy, neg(floor(div(a, b)))); + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(res, reduceAxes), b.shape); + } + return res; + }; + return { a: derA, b: derB }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js +var multiplyGradConfig = { + kernelName: Multiply, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + const res = mul(dy, cast(b, "float32")); + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(res, reduceAxes), a.shape); + } + return res; + }; + const derB = () => { + const res = mul(dy, cast(a, "float32")); + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(res, reduceAxes), b.shape); + } + return res; + }; + return { a: derA, b: derB }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Neg_grad.js +var negGradConfig = { + kernelName: Neg, + gradFunc: (dy) => { + return { x: () => neg(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js +var oneHotGradConfig = { + kernelName: OneHot, + inputsToSave: ["indices"], + gradFunc: (dy, saved) => { + const indices = saved[0]; + return { indices: () => zeros(indices.shape, "float32") }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js +var onesLikeGradConfig = { + kernelName: OnesLike, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pack_grad.js +var packGradConfig = { + kernelName: Pack, + saveAllInputs: true, + gradFunc: (dy, saved, attrs) => { + const { axis } = attrs; + const derTensors = unstack(dy, axis); + return derTensors.map((t) => () => t); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js +var padV2GradConfig = { + kernelName: PadV2, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const x = saved[0]; + const { paddings } = attrs; + const begin = paddings.map((p2) => p2[0]); + return { x: () => slice(dy, begin, x.shape) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js +var powGradConfig = { + kernelName: Pow, + inputsToSave: ["a", "b"], + outputsToSave: [true], + gradFunc: (dy, saved) => { + const [a, b, y] = saved; + const base = a; + const exp4 = b; + const outShape = assertAndGetBroadcastShape(base.shape, exp4.shape); + const derBase = () => { + const expFloat = cast(exp4, "float32"); + let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1))))); + const reduceAxes = getReductionAxes(base.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, base.shape); + }; + const derExp = () => { + const condition = greater(base, 0); + const logBase = where(condition, log2(base), zerosLike(base)); + let res = mul(dy, mul(y, logBase)); + const reduceAxes = getReductionAxes(exp4.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, exp4.shape); + }; + return { a: derBase, b: derExp }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js +var preluGradConfig = { + kernelName: Prelu, + inputsToSave: ["x", "alpha"], + gradFunc: (dy, saved) => { + const [x, alpha] = saved; + const mask = greater(x, 0); + return { + x: () => where(mask, dy, mul(dy, alpha)), + alpha: () => { + let res = where(mask, zerosLike(dy), mul(dy, x)); + const reduceAxes = getReductionAxes(alpha.shape, dy.shape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, alpha.shape); + } + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prod_grad.js +function prodGradFn_(x, dy, axis) { + const expandedYShape = x.shape.slice(); + expandedYShape[axis] = 1; + const expandedDy = reshape(dy, expandedYShape); + const xCumProd = cumprod(x, axis, true, false); + const xCumRevProd = cumprod(x, axis, true, true); + const dx = mul(xCumProd, xCumRevProd); + return mul(expandedDy, dx); +} +function prodsGradFn_(x, dy, axis) { + const xRank = x.shape.length; + const finalProdAxis = xRank - axis.length; + const xPermutation = backend_util_exports.getAxesPermutation(axis, xRank); + let permutedX = x; + if (xPermutation != null) { + permutedX = transpose(x, xPermutation); + } + const newShape = permutedX.shape.slice(); + const removedShape = newShape.splice(xRank - axis.length, axis.length); + const endPartShape = removedShape.reduce((p2, c) => p2 * c, 1); + newShape.push(endPartShape); + const reshapedPermutedX = permutedX.reshape(newShape); + let prodGrad = prodGradFn_(reshapedPermutedX, dy, finalProdAxis); + prodGrad = prodGrad.reshape(permutedX.shape); + if (xPermutation != null) { + const undoPermutation = backend_util_exports.getUndoAxesPermutation(xPermutation); + prodGrad = transpose(prodGrad, undoPermutation); + } + return prodGrad; +} +var prodGradConfig = { + kernelName: Prod, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { axis } = attrs; + let axisArr = []; + if (axis === void 0 || axis === null) { + axisArr = x.shape.map((_, i) => i); + } else if (typeof axis === "number") { + axisArr = [axis]; + } else { + axisArr = axis; + } + return { x: () => prodsGradFn_(x, dy, axisArr) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/RealDiv_grad.js +var divGradConfig = { + kernelName: RealDiv, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + const res = div(dy, cast(b, "float32")); + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + return reshape(sum2(res, reduceAxes), a.shape); + } + return res; + }; + const derB = () => { + let res = mul(dy, cast(a, "float32")); + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + res = reshape(sum2(res, reduceAxes), b.shape); + } + const tmp = square(b); + return neg(div(res, cast(tmp, "float32"))); + }; + return { a: derA, b: derB }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js +var reciprocalGradConfig = { + kernelName: Reciprocal, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, neg(square(x))) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js +var relu6GradConfig = { + kernelName: Relu6, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + const mask = mul(lessEqual(x, 6), step(x)); + return { x: () => mul(dy, cast(mask, "float32")) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js +var reluGradConfig = { + kernelName: Relu, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(dy, cast(step(x), "float32")) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js +var reshapeGradConfig = { + kernelName: Reshape, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => reshape(dy, x.shape) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js +var resizeBilinearGradConfig = { + kernelName: ResizeBilinear, + inputsToSave: ["images"], + gradFunc: (dy, saved, attrs) => { + const [images] = saved; + const inputs = { dy, images }; + const imagesDer = () => ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs) + ); + return { images: imagesDer }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js +var resizeNearestNeighborGradConfig = { + kernelName: ResizeNearestNeighbor, + inputsToSave: ["images"], + gradFunc: (dy, saved, attrs) => { + const [images] = saved; + const inputs = { dy, images }; + const imagesDer = () => ( + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs) + ); + return { images: imagesDer }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js +var reverseGradConfig = { + kernelName: Reverse, + gradFunc: (dy, saved, attrs) => { + const { dims } = attrs; + const axes = parseAxisParam(dims, dy.shape); + return { x: () => reverse(dy, axes) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js +var roundGradConfig = { + kernelName: Round, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js +var rsqrtGradConfig = { + kernelName: Rsqrt, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Select_grad.js +var selectGradConfig = { + kernelName: Select, + inputsToSave: ["condition"], + gradFunc: (dy, saved) => { + const [condition] = saved; + return { + // TODO(julianoks): Return null for condition gradient + // when backprop supports it. + condition: () => cast(zerosLike(condition), "float32"), + t: () => mul(dy, cast(condition, dy.dtype)), + e: () => mul(dy, cast(logicalNot(condition), dy.dtype)) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js +var seluGradConfig = { + kernelName: Selu, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { + x: () => { + const mask = greater(x, scalar(0)); + const scaleAlpha2 = scalar(SELU_SCALEALPHA); + const scale2 = scalar(SELU_SCALE); + const greaterThanZeroDer = mul(dy, scale2); + const lessEqualZeroDer = mul(mul(dy, scaleAlpha2), exp(cast(x, "float32"))); + return where(mask, greaterThanZeroDer, lessEqualZeroDer); + } + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js +var sigmoidGradConfig = { + kernelName: Sigmoid, + outputsToSave: [true], + gradFunc: (dy, saved) => { + const [y] = saved; + return { x: () => mul(dy, mul(y, sub(scalar(1), y))) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js +var signGradConfig = { + kernelName: Sign, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js +var sinGradConfig = { + kernelName: Sin, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(cos(cast(x, "float32")), dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js +var sinhGradConfig = { + kernelName: Sinh, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(cosh(cast(x, "float32")), dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js +var sliceGradConfig = { + kernelName: Slice, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { begin, size } = attrs; + const inputShape = x.shape; + const [begin_, size_] = parseSliceParams(x, begin, size); + const paddings = []; + for (let i = 0; i < dy.rank; i++) { + paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]); + } + return { x: () => pad(dy, paddings) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js +var softmaxGradConfig = { + kernelName: Softmax, + outputsToSave: [true], + gradFunc: (dy, saved, attrs) => { + const [y] = saved; + const { dim } = attrs; + const keepDims = true; + const dyTimesY = mul(dy, y); + return { + logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y)) + }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js +var softplusGradConfig = { + kernelName: Softplus, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(dy, sigmoid(x)) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js +var spaceToBatchNDGradConfig = { + kernelName: SpaceToBatchND, + gradFunc: (dy, saved, attrs) => { + const { blockShape, paddings } = attrs; + return { x: () => batchToSpaceND(dy, blockShape, paddings) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js +var splitVGradConfig = { + kernelName: SplitV, + gradFunc: (dy, saved, attrs) => { + const { axis } = attrs; + return { x: () => concat(dy, axis) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js +var sqrtGradConfig = { + kernelName: Sqrt, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, mul(sqrt(cast(x, "float32")), 2)) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js +var squareGradConfig = { + kernelName: Square, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => mul(dy, mul(cast(x, "float32"), 2)) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js +var squaredDifferenceGradConfig = { + kernelName: SquaredDifference, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const two = scalar(2); + const derA = () => mul(dy, mul(two, sub(a, b))); + const derB = () => mul(dy, mul(two, sub(b, a))); + return { a: derA, b: derB }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js +var stepGradConfig = { + kernelName: Step, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js +var subGradConfig = { + kernelName: Sub, + inputsToSave: ["a", "b"], + gradFunc: (dy, saved) => { + const [a, b] = saved; + const outShape = assertAndGetBroadcastShape(a.shape, b.shape); + const derA = () => { + let res = dy; + const reduceAxes = getReductionAxes(a.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(res, a.shape); + }; + const derB = () => { + let res = dy; + const reduceAxes = getReductionAxes(b.shape, outShape); + if (reduceAxes.length > 0) { + res = sum2(res, reduceAxes); + } + return reshape(neg(res), b.shape); + }; + return { a: derA, b: derB }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js +var sumGradConfig = { + kernelName: Sum, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const expandedDyShape = x.shape.slice(); + const { axis } = attrs; + const axes = parseAxisParam(axis, x.shape); + axes.forEach((axis2) => { + expandedDyShape[axis2] = 1; + }); + const expandedDy = reshape(dy, expandedDyShape); + const derX = mul(expandedDy, ones2(x.shape, "float32")); + return { x: () => derX }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js +var tanGradConfig = { + kernelName: Tan, + inputsToSave: ["x"], + gradFunc: (dy, saved) => { + const [x] = saved; + return { x: () => div(dy, square(cos(x))) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js +var tanhGradConfig = { + kernelName: Tanh, + outputsToSave: [true], + gradFunc: (dy, saved) => { + const [y] = saved; + return { x: () => mul(sub(scalar(1), square(y)), dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js +var tileGradConfig = { + kernelName: Tile, + inputsToSave: ["x"], + gradFunc: (dy, saved, attrs) => { + const [x] = saved; + const { reps } = attrs; + const derX = () => { + let xGrad = zerosLike(x); + if (x.rank === 1) { + for (let i = 0; i < reps[0]; ++i) { + xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]])); + } + } else if (x.rank === 2) { + for (let i = 0; i < reps[0]; ++i) { + for (let j = 0; j < reps[1]; ++j) { + xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [ + x.shape[0], + x.shape[1] + ])); + } + } + } else if (x.rank === 3) { + for (let i = 0; i < reps[0]; ++i) { + for (let j = 0; j < reps[1]; ++j) { + for (let k = 0; k < reps[2]; ++k) { + xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]])); + } + } + } + } else if (x.rank === 4) { + for (let i = 0; i < reps[0]; ++i) { + for (let j = 0; j < reps[1]; ++j) { + for (let k = 0; k < reps[2]; ++k) { + for (let l = 0; l < reps[3]; ++l) { + xGrad = add2(xGrad, slice(dy, [ + i * x.shape[0], + j * x.shape[1], + k * x.shape[2], + l * x.shape[3] + ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]])); + } + } + } + } + } else { + throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`); + } + return xGrad; + }; + return { x: derX }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js +var transposeGradConfig = { + kernelName: Transpose, + gradFunc: (dy, saved, attrs) => { + const transposeAttrs = attrs; + const { perm } = transposeAttrs; + const undoPerm = getUndoAxesPermutation(perm); + return { x: () => transpose(dy, undoPerm) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js +var unpackGradConfig = { + kernelName: Unpack, + gradFunc: (dy, saved, attrs) => { + const unpackAttrs = attrs; + const { axis } = unpackAttrs; + return { value: () => stack(dy, axis) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js +var unsortedSegmentSumGradConfig = { + kernelName: UnsortedSegmentSum, + inputsToSave: ["segmentIds"], + gradFunc: (dy, saved) => { + const [segmentIds] = saved; + const derX = () => { + return gatherDropNegatives(dy, segmentIds); + }; + return { x: derX }; + } +}; +function gatherDropNegatives(x, indices) { + const zeroClippedIndices = maximum(indices, zerosLike(indices)); + const gathered = gather(x, zeroClippedIndices); + let isPositive = greaterEqual(indices, scalar(0, "int32")); + const numIters = gathered.rank - isPositive.rank; + for (let i = 0; i < numIters; ++i) { + isPositive = expandDims(isPositive, i + 1); + } + isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool")); + const zeroSlice = zerosLike(gathered); + return where(isPositive, gathered, zeroSlice); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js +var zerosLikeGradConfig = { + kernelName: ZerosLike, + gradFunc: (dy) => { + return { x: () => zerosLike(dy) }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js +var gradConfigs = [ + absGradConfig, + acosGradConfig, + acoshGradConfig, + addGradConfig, + addNGradConfig, + argMaxGradConfig, + argMinGradConfig, + asinGradConfig, + asinhGradConfig, + atan2GradConfig, + atanGradConfig, + atanhGradConfig, + avgPool3DGradConfig, + avgPoolGradConfig, + batchMatMulGradConfig, + batchToSpaceNDGradConfig, + broadcastToGradConfig, + castGradConfig, + ceilGradConfig, + clipByValueGradConfig, + complexAbsGradConfig, + concatGradConfig, + conv2DBackpropInputGradConfig, + conv2DGradConfig, + conv3DGradConfig, + cosGradConfig, + coshGradConfig, + cumsumGradConfig, + depthwiseConv2dNativeGradConfig, + dilation2dGradConfig, + divGradConfig, + eluGradConfig, + erfGradConfig, + expGradConfig, + expandDimsGradConfig, + expm1GradConfig, + floorDivGradConfig, + floorGradConfig, + fusedBatchNormGradConfig, + gatherGradConfig, + greaterEqualGradConfig, + identityGradConfig, + isFiniteGradConfig, + isInfGradConfig, + isNanGradConfig, + leakyReluGradConfig, + log1pGradConfig, + logGradConfig, + logSoftmaxGradConfig, + lrnGradConfig, + maxGradConfig, + maxGradConfig, + maximumGradConfig, + maxPool3DGradConfig, + maxPoolGradConfig, + meanGradConfig, + minGradConfig, + minimumGradConfig, + mirrorPadGradConfig, + modGradConfig, + multiplyGradConfig, + negGradConfig, + oneHotGradConfig, + onesLikeGradConfig, + packGradConfig, + padV2GradConfig, + padV2GradConfig, + powGradConfig, + preluGradConfig, + prodGradConfig, + reciprocalGradConfig, + relu6GradConfig, + reluGradConfig, + reshapeGradConfig, + resizeBilinearGradConfig, + resizeNearestNeighborGradConfig, + reverseGradConfig, + roundGradConfig, + rsqrtGradConfig, + selectGradConfig, + seluGradConfig, + sigmoidGradConfig, + signGradConfig, + sinGradConfig, + sinhGradConfig, + sliceGradConfig, + softmaxGradConfig, + softplusGradConfig, + spaceToBatchNDGradConfig, + spaceToBatchNDGradConfig, + splitVGradConfig, + splitVGradConfig, + sqrtGradConfig, + squaredDifferenceGradConfig, + squareGradConfig, + stepGradConfig, + subGradConfig, + sumGradConfig, + tanGradConfig, + tanhGradConfig, + tileGradConfig, + transposeGradConfig, + unpackGradConfig, + unsortedSegmentSumGradConfig, + zerosLikeGradConfig +]; +for (const gradientConfig of gradConfigs) { + registerGradient(gradientConfig); +} + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.js +getGlobalTensorClass().prototype.abs = function() { + this.throwIfDisposed(); + return abs(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.js +getGlobalTensorClass().prototype.acos = function() { + this.throwIfDisposed(); + return acos(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.js +getGlobalTensorClass().prototype.acosh = function() { + this.throwIfDisposed(); + return acosh(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.js +getGlobalTensorClass().prototype.add = function(b) { + this.throwIfDisposed(); + return add2(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.js +getGlobalTensorClass().prototype.all = function(axis, keepDims) { + this.throwIfDisposed(); + return all(this, axis, keepDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.js +getGlobalTensorClass().prototype.any = function(axis, keepDims) { + this.throwIfDisposed(); + return any(this, axis, keepDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.js +getGlobalTensorClass().prototype.argMax = function(axis) { + this.throwIfDisposed(); + return argMax(this, axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.js +getGlobalTensorClass().prototype.argMin = function(axis) { + this.throwIfDisposed(); + return argMin(this, axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.js +getGlobalTensorClass().prototype.asScalar = function() { + this.throwIfDisposed(); + assert(this.size === 1, () => "The array must have only 1 element."); + return reshape(this, []); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.js +getGlobalTensorClass().prototype.asType = function(dtype) { + this.throwIfDisposed(); + return cast(this, dtype); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.js +getGlobalTensorClass().prototype.as1D = function() { + this.throwIfDisposed(); + return reshape(this, [this.size]); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.js +getGlobalTensorClass().prototype.as2D = function(rows, columns) { + this.throwIfDisposed(); + return reshape(this, [rows, columns]); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.js +getGlobalTensorClass().prototype.as3D = function(rows, columns, depth) { + this.throwIfDisposed(); + return reshape(this, [rows, columns, depth]); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.js +getGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) { + this.throwIfDisposed(); + return reshape(this, [rows, columns, depth, depth2]); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.js +getGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) { + this.throwIfDisposed(); + return reshape(this, [rows, columns, depth, depth2, depth3]); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.js +getGlobalTensorClass().prototype.asin = function() { + this.throwIfDisposed(); + return asin(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.js +getGlobalTensorClass().prototype.asinh = function() { + this.throwIfDisposed(); + return asinh(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.js +getGlobalTensorClass().prototype.atan = function() { + this.throwIfDisposed(); + return atan(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.js +getGlobalTensorClass().prototype.atan2 = function(b) { + this.throwIfDisposed(); + return atan2(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.js +getGlobalTensorClass().prototype.atanh = function() { + this.throwIfDisposed(); + return atanh(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.js +getGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad3, dimRoundingMode) { + this.throwIfDisposed(); + return avgPool(this, filterSize, strides, pad3, dimRoundingMode); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.js +getGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) { + this.throwIfDisposed(); + return batchToSpaceND(this, blockShape, crops); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.js +getGlobalTensorClass().prototype.batchNorm = function(mean4, variance, offset, scale2, varianceEpsilon) { + this.throwIfDisposed(); + return batchNorm(this, mean4, variance, offset, scale2, varianceEpsilon); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.js +getGlobalTensorClass().prototype.broadcastTo = function(shape) { + this.throwIfDisposed(); + return broadcastTo(this, shape); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.js +getGlobalTensorClass().prototype.cast = function(dtype) { + this.throwIfDisposed(); + return cast(this, dtype); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.js +getGlobalTensorClass().prototype.ceil = function() { + this.throwIfDisposed(); + return ceil(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.js +getGlobalTensorClass().prototype.clipByValue = function(min6, max6) { + this.throwIfDisposed(); + return clipByValue(this, min6, max6); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.js +getGlobalTensorClass().prototype.concat = function(x, axis) { + this.throwIfDisposed(); + if (x instanceof Tensor) { + x = [x]; + } + return concat([this, ...x], axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.js +getGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFormat, dilation, dimRoundingMode) { + this.throwIfDisposed(); + return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.js +getGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides, pad3, dimRoundingMode) { + this.throwIfDisposed(); + return conv2dTranspose(this, filter, outputShape, strides, pad3, dimRoundingMode); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.js +getGlobalTensorClass().prototype.conv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) { + this.throwIfDisposed(); + return conv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.js +getGlobalTensorClass().prototype.cos = function() { + this.throwIfDisposed(); + return cos(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.js +getGlobalTensorClass().prototype.cosh = function() { + this.throwIfDisposed(); + return cosh(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.js +getGlobalTensorClass().prototype.cumprod = function(axis, exclusive, reverse5) { + this.throwIfDisposed(); + return cumprod(this, axis, exclusive, reverse5); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.js +getGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) { + this.throwIfDisposed(); + return cumsum(this, axis, exclusive, reverse5); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.js +getGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) { + this.throwIfDisposed(); + return depthToSpace(this, blockSize, dataFormat); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.js +getGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) { + this.throwIfDisposed(); + return depthwiseConv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.js +getGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad3, dilations, dataFormat) { + this.throwIfDisposed(); + return dilation2d(this, filter, strides, pad3, dilations, dataFormat); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.js +getGlobalTensorClass().prototype.divNoNan = function(b) { + this.throwIfDisposed(); + return divNoNan(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.js +getGlobalTensorClass().prototype.div = function(b) { + this.throwIfDisposed(); + return div(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.js +getGlobalTensorClass().prototype.dot = function(b) { + this.throwIfDisposed(); + return dot(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.js +getGlobalTensorClass().prototype.elu = function() { + this.throwIfDisposed(); + return elu(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.js +getGlobalTensorClass().prototype.equal = function(b) { + this.throwIfDisposed(); + return equal(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.js +getGlobalTensorClass().prototype.erf = function() { + this.throwIfDisposed(); + return erf(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.js +getGlobalTensorClass().prototype.euclideanNorm = function(axis, keepDims) { + this.throwIfDisposed(); + return euclideanNorm(this, axis, keepDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.js +getGlobalTensorClass().prototype.exp = function() { + this.throwIfDisposed(); + return exp(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.js +getGlobalTensorClass().prototype.expandDims = function(axis) { + this.throwIfDisposed(); + return expandDims(this, axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.js +getGlobalTensorClass().prototype.expm1 = function() { + this.throwIfDisposed(); + return expm1(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.js +getGlobalTensorClass().prototype.fft = function() { + this.throwIfDisposed(); + return fft(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.js +getGlobalTensorClass().prototype.flatten = function() { + this.throwIfDisposed(); + return reshape(this, [this.size]); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.js +getGlobalTensorClass().prototype.floor = function() { + this.throwIfDisposed(); + return floor(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.js +getGlobalTensorClass().prototype.floorDiv = function(b) { + this.throwIfDisposed(); + return floorDiv(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.js +getGlobalTensorClass().prototype.gather = function(indices, axis, batchDims) { + this.throwIfDisposed(); + return gather(this, indices, axis, batchDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.js +getGlobalTensorClass().prototype.greaterEqual = function(b) { + this.throwIfDisposed(); + return greaterEqual(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.js +getGlobalTensorClass().prototype.greater = function(b) { + this.throwIfDisposed(); + return greater(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.js +getGlobalTensorClass().prototype.ifft = function() { + this.throwIfDisposed(); + return ifft(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.js +getGlobalTensorClass().prototype.irfft = function() { + this.throwIfDisposed(); + return irfft(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.js +getGlobalTensorClass().prototype.isFinite = function() { + this.throwIfDisposed(); + return isFinite2(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.js +getGlobalTensorClass().prototype.isInf = function() { + this.throwIfDisposed(); + return isInf(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.js +getGlobalTensorClass().prototype.isNaN = function() { + this.throwIfDisposed(); + return isNaN2(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.js +getGlobalTensorClass().prototype.leakyRelu = function(alpha) { + this.throwIfDisposed(); + return leakyRelu(this, alpha); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.js +getGlobalTensorClass().prototype.lessEqual = function(b) { + this.throwIfDisposed(); + return lessEqual(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.js +getGlobalTensorClass().prototype.less = function(b) { + this.throwIfDisposed(); + return less(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.js +getGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha, beta) { + this.throwIfDisposed(); + return localResponseNormalization(this, depthRadius, bias, alpha, beta); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.js +getGlobalTensorClass().prototype.logSigmoid = function() { + this.throwIfDisposed(); + return logSigmoid(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.js +getGlobalTensorClass().prototype.logSoftmax = function(axis) { + this.throwIfDisposed(); + return logSoftmax(this, axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.js +getGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) { + this.throwIfDisposed(); + return logSumExp(this, axis, keepDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.js +getGlobalTensorClass().prototype.log = function() { + this.throwIfDisposed(); + return log2(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.js +getGlobalTensorClass().prototype.log1p = function() { + this.throwIfDisposed(); + return log1p(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.js +getGlobalTensorClass().prototype.logicalAnd = function(b) { + this.throwIfDisposed(); + return logicalAnd(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.js +getGlobalTensorClass().prototype.logicalNot = function() { + this.throwIfDisposed(); + return logicalNot(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.js +getGlobalTensorClass().prototype.logicalOr = function(b) { + this.throwIfDisposed(); + return logicalOr(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.js +getGlobalTensorClass().prototype.logicalXor = function(b) { + this.throwIfDisposed(); + return logicalXor(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.js +getGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) { + this.throwIfDisposed(); + return matMul(this, b, transposeA, transposeB); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.js +getGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad3, dimRoundingMode) { + this.throwIfDisposed(); + return maxPool(this, filterSize, strides, pad3, dimRoundingMode); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.js +getGlobalTensorClass().prototype.max = function(axis, keepDims) { + this.throwIfDisposed(); + return max(this, axis, keepDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.js +getGlobalTensorClass().prototype.maximum = function(b) { + this.throwIfDisposed(); + return maximum(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.js +getGlobalTensorClass().prototype.mean = function(axis, keepDims) { + this.throwIfDisposed(); + return mean(this, axis, keepDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.js +getGlobalTensorClass().prototype.min = function(axis, keepDims) { + this.throwIfDisposed(); + return min(this, axis, keepDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.js +getGlobalTensorClass().prototype.minimum = function(b) { + this.throwIfDisposed(); + return minimum(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.js +getGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) { + this.throwIfDisposed(); + return mirrorPad(this, paddings, mode); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.js +getGlobalTensorClass().prototype.mod = function(b) { + this.throwIfDisposed(); + return mod(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.js +getGlobalTensorClass().prototype.mul = function(b) { + this.throwIfDisposed(); + return mul(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.js +getGlobalTensorClass().prototype.neg = function() { + this.throwIfDisposed(); + return neg(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.js +getGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) { + this.throwIfDisposed(); + return norm(this, ord, axis, keepDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.js +getGlobalTensorClass().prototype.notEqual = function(b) { + this.throwIfDisposed(); + return notEqual(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.js +getGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) { + this.throwIfDisposed(); + return oneHot(this, depth, onValue, offValue); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.js +getGlobalTensorClass().prototype.onesLike = function() { + this.throwIfDisposed(); + return onesLike(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.js +getGlobalTensorClass().prototype.pad = function(paddings, constantValue) { + this.throwIfDisposed(); + return pad(this, paddings, constantValue); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.js +getGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode) { + this.throwIfDisposed(); + return pool(this, windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.js +getGlobalTensorClass().prototype.pow = function(exp4) { + this.throwIfDisposed(); + return pow(this, exp4); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.js +getGlobalTensorClass().prototype.prelu = function(alpha) { + this.throwIfDisposed(); + return prelu(this, alpha); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.js +getGlobalTensorClass().prototype.prod = function(axis, keepDims) { + this.throwIfDisposed(); + return prod(this, axis, keepDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.js +getGlobalTensorClass().prototype.reciprocal = function() { + this.throwIfDisposed(); + return reciprocal(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.js +getGlobalTensorClass().prototype.relu = function() { + this.throwIfDisposed(); + return relu(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.js +getGlobalTensorClass().prototype.relu6 = function() { + this.throwIfDisposed(); + return relu6(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.js +getGlobalTensorClass().prototype.reshapeAs = function(x) { + this.throwIfDisposed(); + return reshape(this, x.shape); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.js +getGlobalTensorClass().prototype.reshape = function(shape) { + this.throwIfDisposed(); + return reshape(this, shape); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.js +getGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) { + this.throwIfDisposed(); + return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.js +getGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) { + this.throwIfDisposed(); + return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.js +getGlobalTensorClass().prototype.reverse = function(axis) { + this.throwIfDisposed(); + return reverse(this, axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.js +getGlobalTensorClass().prototype.rfft = function() { + this.throwIfDisposed(); + return rfft(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.js +getGlobalTensorClass().prototype.round = function() { + this.throwIfDisposed(); + return round2(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.js +getGlobalTensorClass().prototype.rsqrt = function() { + this.throwIfDisposed(); + return rsqrt(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.js +getGlobalTensorClass().prototype.selu = function() { + this.throwIfDisposed(); + return selu(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.js +getGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat) { + this.throwIfDisposed(); + return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.js +getGlobalTensorClass().prototype.sigmoid = function() { + this.throwIfDisposed(); + return sigmoid(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.js +getGlobalTensorClass().prototype.sign = function() { + this.throwIfDisposed(); + return sign(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.js +getGlobalTensorClass().prototype.sin = function() { + this.throwIfDisposed(); + return sin(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.js +getGlobalTensorClass().prototype.sinh = function() { + this.throwIfDisposed(); + return sinh(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.js +getGlobalTensorClass().prototype.slice = function(begin, size) { + this.throwIfDisposed(); + return slice(this, begin, size); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.js +getGlobalTensorClass().prototype.softmax = function(dim) { + this.throwIfDisposed(); + return softmax(this, dim); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.js +getGlobalTensorClass().prototype.softplus = function() { + this.throwIfDisposed(); + return softplus(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.js +getGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) { + this.throwIfDisposed(); + return spaceToBatchND(this, blockShape, paddings); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.js +getGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) { + this.throwIfDisposed(); + return split(this, numOrSizeSplits, axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.js +getGlobalTensorClass().prototype.sqrt = function() { + this.throwIfDisposed(); + return sqrt(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.js +getGlobalTensorClass().prototype.square = function() { + this.throwIfDisposed(); + return square(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.js +getGlobalTensorClass().prototype.squaredDifference = function(b) { + this.throwIfDisposed(); + return squaredDifference(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.js +getGlobalTensorClass().prototype.squeeze = function(axis) { + this.throwIfDisposed(); + return squeeze(this, axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.js +getGlobalTensorClass().prototype.stack = function(x, axis) { + this.throwIfDisposed(); + const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x]; + return stack(tensorsToBeStacked, axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.js +getGlobalTensorClass().prototype.step = function(alpha) { + this.throwIfDisposed(); + return step(this, alpha); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.js +getGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) { + this.throwIfDisposed(); + return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.js +getGlobalTensorClass().prototype.sub = function(b) { + this.throwIfDisposed(); + return sub(this, b); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.js +getGlobalTensorClass().prototype.sum = function(axis, keepDims) { + this.throwIfDisposed(); + return sum2(this, axis, keepDims); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.js +getGlobalTensorClass().prototype.tan = function() { + this.throwIfDisposed(); + return tan(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.js +getGlobalTensorClass().prototype.tanh = function() { + this.throwIfDisposed(); + return tanh2(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.js +getGlobalTensorClass().prototype.tile = function(reps) { + this.throwIfDisposed(); + return tile(this, reps); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.js +getGlobalTensorClass().prototype.toBool = function() { + this.throwIfDisposed(); + return cast(this, "bool"); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.js +getGlobalTensorClass().prototype.toFloat = function() { + this.throwIfDisposed(); + return cast(this, "float32"); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.js +getGlobalTensorClass().prototype.toInt = function() { + this.throwIfDisposed(); + return cast(this, "int32"); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.js +getGlobalTensorClass().prototype.topk = function(k, sorted) { + this.throwIfDisposed(); + return topk(this, k, sorted); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.js +getGlobalTensorClass().prototype.transpose = function(perm) { + this.throwIfDisposed(); + return transpose(this, perm); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.js +getGlobalTensorClass().prototype.unique = function(axis) { + this.throwIfDisposed(); + return unique(this, axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.js +getGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) { + this.throwIfDisposed(); + return unsortedSegmentSum(this, segmentIds, numSegments); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.js +getGlobalTensorClass().prototype.unstack = function(axis) { + this.throwIfDisposed(); + return unstack(this, axis); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.js +getGlobalTensorClass().prototype.where = function(condition, x) { + this.throwIfDisposed(); + return where(condition, this, x); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.js +getGlobalTensorClass().prototype.zerosLike = function() { + this.throwIfDisposed(); + return zerosLike(this); +}; + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/errors.js +var AttributeError = class _AttributeError extends Error { + constructor(message) { + super(message); + Object.setPrototypeOf(this, _AttributeError.prototype); + } +}; +var RuntimeError = class _RuntimeError extends Error { + constructor(message) { + super(message); + Object.setPrototypeOf(this, _RuntimeError.prototype); + } +}; +var ValueError = class _ValueError extends Error { + constructor(message) { + super(message); + Object.setPrototypeOf(this, _ValueError.prototype); + } +}; +var NotImplementedError = class _NotImplementedError extends Error { + constructor(message) { + super(message); + Object.setPrototypeOf(this, _NotImplementedError.prototype); + } +}; +var AssertionError = class _AssertionError extends Error { + constructor(message) { + super(message); + Object.setPrototypeOf(this, _AssertionError.prototype); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/executor_utils.js +var LruCache = class { + constructor(maxEntries) { + this.maxEntries = maxEntries || 100; + this.cache = /* @__PURE__ */ new Map(); + } + /** + * Get the entry for the key and mark it as used recently. + */ + get(key) { + let entry; + if (this.cache.has(key)) { + entry = this.cache.get(key); + this.cache.delete(key); + this.cache.set(key, entry); + } + return entry; + } + /** + * Put the entry into the cache. If the key already existed, mark the key as + * used recently. + */ + put(key, value) { + if (this.cache.has(key)) { + this.cache.delete(key); + } else if (this.cache.size >= this.maxEntries) { + const keyToDelete = this.cache.keys().next().value; + this.cache.delete(keyToDelete); + } + this.cache.set(key, value); + } + /** + * Get the MaxEntries of the cache. + */ + getMaxEntries() { + return this.maxEntries; + } + /** + * Set the MaxEntries of the cache. If the maxEntries is decreased, reduce + * entries in the cache. + */ + setMaxEntries(maxEntries) { + if (maxEntries < 0) { + throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`); + } + if (this.maxEntries > maxEntries) { + for (let i = 0; i < this.maxEntries - maxEntries; i++) { + const keyToDelete = this.cache.keys().next().value; + this.cache.delete(keyToDelete); + } + } + this.maxEntries = maxEntries; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/generic_utils.js +function pyListRepeat(value, numValues) { + if (Array.isArray(value)) { + let newArray = []; + for (let i = 0; i < numValues; i++) { + newArray = newArray.concat(value); + } + return newArray; + } else { + const newArray = new Array(numValues); + newArray.fill(value); + return newArray; + } +} +function assert2(val, message) { + if (!val) { + throw new AssertionError(message); + } +} +function count(array2, refernce) { + let counter = 0; + for (const item of array2) { + if (item === refernce) { + counter++; + } + } + return counter; +} +function singletonOrArray(xs) { + if (xs.length === 1) { + return xs[0]; + } + return xs; +} +function toList(x) { + if (Array.isArray(x)) { + return x; + } + return [x]; +} +function toSnakeCase(name) { + const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, "$1_$2"); + const insecure = intermediate.replace(/([a-z])([A-Z])/g, "$1_$2").toLowerCase(); + if (insecure[0] !== "_") { + return insecure; + } + return "private" + insecure; +} +function toCamelCase(identifier) { + if (identifier.length <= 1) { + return identifier; + } + if (identifier.indexOf("_") === -1) { + return identifier; + } + return identifier.replace(/[_]+(\w|$)/g, (m, p1) => p1.toUpperCase()); +} +var _GLOBAL_CUSTOM_OBJECTS = {}; +function serializeKerasObject(instance) { + if (instance === null || instance === void 0) { + return null; + } + const dict = {}; + dict["className"] = instance.getClassName(); + dict["config"] = instance.getConfig(); + return dict; +} +function convertNDArrayScalarsInConfig(config) { + if (config == null || typeof config !== "object") { + return; + } else if (Array.isArray(config)) { + config.forEach((configItem) => convertNDArrayScalarsInConfig(configItem)); + } else { + const fields = Object.keys(config); + for (const field of fields) { + const value = config[field]; + if (value != null && typeof value === "object") { + if (!Array.isArray(value) && value["type"] === "ndarray" && typeof value["value"] === "number") { + config[field] = value["value"]; + } else { + convertNDArrayScalarsInConfig(value); + } + } + } + } +} +function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = "object", fastWeightInit = false) { + if (typeof identifier === "string") { + const functionName = identifier; + let fn; + if (functionName in customObjects) { + fn = customObjects[functionName]; + } else if (functionName in _GLOBAL_CUSTOM_OBJECTS) { + fn = _GLOBAL_CUSTOM_OBJECTS[functionName]; + } else { + fn = moduleObjects[functionName]; + if (fn == null) { + throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons: +1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. +2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`); + } + } + return fn; + } else { + const config = identifier; + if (config["className"] == null || config["config"] == null) { + throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}. +'className' and 'config' must set.`); + } + const className = config["className"]; + let cls, fromConfig; + if (className in customObjects) { + [cls, fromConfig] = customObjects[className]; + } else if (className in _GLOBAL_CUSTOM_OBJECTS) { + [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS["className"]; + } else if (className in moduleObjects) { + [cls, fromConfig] = moduleObjects[className]; + } + if (cls == null) { + throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons: +1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. +2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`); + } + if (fromConfig != null) { + const customObjectsCombined = {}; + for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) { + customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key]; + } + for (const key of Object.keys(customObjects)) { + customObjectsCombined[key] = customObjects[key]; + } + const nestedConfig = config["config"]; + nestedConfig["customObjects"] = customObjectsCombined; + const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS); + for (const key of Object.keys(customObjects)) { + _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key]; + } + convertNDArrayScalarsInConfig(config["config"]); + const returnObj = fromConfig(cls, config["config"], customObjects, fastWeightInit); + _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects); + return returnObj; + } else { + const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS); + for (const key of Object.keys(customObjects)) { + _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key]; + } + const returnObj = new cls(config["config"]); + _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects); + return returnObj; + } + } +} +function numberCompare(a, b) { + return a < b ? -1 : a > b ? 1 : 0; +} +function reverseNumberCompare(a, b) { + return -1 * numberCompare(a, b); +} +function unique2(xs) { + if (xs == null) { + return xs; + } + const out = []; + for (const x of xs) { + if (out.indexOf(x) === -1) { + out.push(x); + } + } + return out; +} +function isObjectEmpty(obj) { + if (obj == null) { + throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`); + } + for (const key in obj) { + if (obj.hasOwnProperty(key)) { + return false; + } + } + return true; +} +function checkStringTypeUnionValue(values, label, value) { + if (value == null) { + return; + } + if (values.indexOf(value) < 0) { + throw new ValueError(`${value} is not a valid ${label}. Valid values are ${values} or null/undefined.`); + } +} +function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) { + assert2(minLength >= 0); + assert2(maxLength >= minLength); + return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e) => typeof e === expectedType); +} +function assertPositiveInteger(value, name) { + if (Array.isArray(value)) { + util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`); + value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`)); + } else { + util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`); + } +} +function formatAsFriendlyString(value) { + if (value === null) { + return "null"; + } else if (Array.isArray(value)) { + return "[" + value.map((v) => formatAsFriendlyString(v)).join(",") + "]"; + } else if (typeof value === "string") { + return `"${value}"`; + } else { + return `${value}`; + } +} +function debounce(f, waitMs, nowFunc) { + let lastTime = nowFunc != null ? nowFunc() : util_exports.now(); + let lastResult; + const f2 = (...args) => { + const now2 = nowFunc != null ? nowFunc() : util_exports.now(); + if (now2 - lastTime < waitMs) { + return lastResult; + } + lastTime = now2; + lastResult = f(...args); + return lastResult; + }; + return f2; +} +function mapActivationToFusedKernel(activationName) { + if (activationName === "relu") { + return "relu"; + } + if (activationName === "linear") { + return "linear"; + } + if (activationName === "elu") { + return "elu"; + } + return null; +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/backend/state.js +var _nextUniqueTensorId = 0; +function getNextUniqueTensorId() { + return _nextUniqueTensorId++; +} +var _uidPrefixes = {}; +function getUid(prefix = "") { + if (!(prefix in _uidPrefixes)) { + _uidPrefixes[prefix] = 0; + } + _uidPrefixes[prefix] += 1; + return prefix + _uidPrefixes[prefix].toString(); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/keras_format/common.js +var VALID_DATA_FORMAT_VALUES = ["channelsFirst", "channelsLast"]; +var VALID_INTERPOLATION_FORMAT_VALUES = ["nearest", "bilinear"]; +var VALID_PADDING_MODE_VALUES = ["valid", "same", "causal"]; +var VALID_POOL_MODE_VALUES = ["max", "avg"]; +var VALID_BIDIRECTIONAL_MERGE_MODES = ["sum", "mul", "concat", "ave"]; + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/common.js +var nameMap = /* @__PURE__ */ new Map(); +function checkDataFormat(value) { + checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, "DataFormat", value); +} +function checkInterpolationFormat(value) { + checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES, "InterpolationFormat", value); +} +function checkPaddingMode(value) { + checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, "PaddingMode", value); +} +function checkPoolMode(value) { + checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, "PoolMode", value); +} +var _nameScopeStack = []; +var _nameScopeDivider = "/"; +function nameScope(name, fn) { + _nameScopeStack.push(name); + try { + const val = fn(); + _nameScopeStack.pop(); + return val; + } catch (e) { + _nameScopeStack.pop(); + throw e; + } +} +function currentNameScopePrefix() { + if (_nameScopeStack.length === 0) { + return ""; + } else { + return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider; + } +} +function getScopedTensorName(tensorName) { + if (!isValidTensorName(tensorName)) { + throw new Error("Not a valid tensor name: '" + tensorName + "'"); + } + return currentNameScopePrefix() + tensorName; +} +function getUniqueTensorName(scopedName) { + if (!isValidTensorName(scopedName)) { + throw new Error("Not a valid tensor name: '" + scopedName + "'"); + } + if (!nameMap.has(scopedName)) { + nameMap.set(scopedName, 0); + } + const index = nameMap.get(scopedName); + nameMap.set(scopedName, nameMap.get(scopedName) + 1); + if (index > 0) { + const result = `${scopedName}_${index}`; + nameMap.set(result, 1); + return result; + } else { + return scopedName; + } +} +var tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/); +function isValidTensorName(name) { + return !!name.match(tensorNameRegex); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/math_utils.js +function isInteger(x) { + return x === parseInt(x.toString(), 10); +} +function arrayProd(array2, begin, end) { + if (begin == null) { + begin = 0; + } + if (end == null) { + end = array2.length; + } + let prod5 = 1; + for (let i = begin; i < end; ++i) { + prod5 *= array2[i]; + } + return prod5; +} +function min2(array2) { + if (array2.length === 0) { + return Number.NaN; + } + let min6 = Number.POSITIVE_INFINITY; + for (let i = 0; i < array2.length; i++) { + const value = array2[i]; + if (value < min6) { + min6 = value; + } + } + return min6; +} +function max2(array2) { + if (array2.length === 0) { + return Number.NaN; + } + let max6 = Number.NEGATIVE_INFINITY; + for (let i = 0; i < array2.length; i++) { + const value = array2[i]; + if (value > max6) { + max6 = value; + } + } + return max6; +} +function range2(begin, end) { + if (end < begin) { + throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`); + } + const out = []; + for (let i = begin; i < end; ++i) { + out.push(i); + } + return out; +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/backend/common.js +var _epsilon; +function epsilon() { + if (_epsilon == null) { + _epsilon = backend().epsilon(); + } + return _epsilon; +} +function imageDataFormat() { + return "channelsLast"; +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js +function cast2(x, dtype) { + return cast(x, dtype); +} +function expandDims2(x, axis = -1) { + const outShape = x.shape.slice(); + if (axis < 0) { + axis = outShape.length + axis + 1; + } + outShape.splice(axis, 0, 1); + return reshape(x, outShape); +} +function repeat(x, n) { + return tidy(() => { + if (x.shape.length !== 2) { + throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`); + } + const y = expandDims2(x, 1); + return tile2(y, [1, n, 1]); + }); +} +function flatten2(x) { + const newShape = [arrayProd(x.shape)]; + return reshape(x, newShape); +} +function batchFlatten(x) { + if (x.rank <= 1) { + throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`); + } + const newShape = [x.shape[0], arrayProd(x.shape, 1)]; + return reshape(x, newShape); +} +function sliceAlongFirstAxis(array2, start, size) { + return tidy(() => { + switch (array2.rank) { + case 1: + return slice1d(array2, start, size); + case 2: + return slice2d(array2, [start, 0], [size, array2.shape[1]]); + case 3: + return slice3d(array2, [start, 0, 0], [size, array2.shape[1], array2.shape[2]]); + case 4: + return slice4d(array2, [start, 0, 0, 0], [size, array2.shape[1], array2.shape[2], array2.shape[3]]); + case 5: + return slice(array2, [start, 0, 0, 0, 0], [ + size, + array2.shape[1], + array2.shape[2], + array2.shape[3], + array2.shape[4] + ]); + case 6: + return slice(array2, [start, 0, 0, 0, 0, 0], [ + size, + array2.shape[1], + array2.shape[2], + array2.shape[3], + array2.shape[4], + array2.shape[5] + ]); + default: + throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`); + } + }); +} +function sliceAlongLastAxis(array2, start, size) { + return tidy(() => { + switch (array2.rank) { + case 1: + return slice1d(array2, start, size); + case 2: + return slice2d(array2, [0, start], [array2.shape[0], size]); + case 3: + return slice3d(array2, [0, 0, start], [array2.shape[0], array2.shape[1], size]); + case 4: + return slice4d(array2, [0, 0, 0, start], [array2.shape[0], array2.shape[1], array2.shape[2], size]); + default: + throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`); + } + }); +} +function sliceAlongAxis(array2, start, size, axis) { + return tidy(() => { + switch (array2.rank) { + case 1: + return slice1d(array2, start, size); + case 2: + switch (axis) { + case 1: + return sliceAlongFirstAxis(array2, start, size); + case 2: + return sliceAlongLastAxis(array2, start, size); + default: + throw new ValueError(`The axis is not within the rank of the tensor ${axis}`); + } + case 3: + switch (axis) { + case 1: + return sliceAlongFirstAxis(array2, start, size); + case 2: + return slice3d(array2, [0, start, 0], [array2.shape[0], size, array2.shape[2]]); + case 3: + return sliceAlongLastAxis(array2, start, size); + default: + throw new ValueError(`The axis is not within the rank of the tensor ${axis}`); + } + case 4: + switch (axis) { + case 1: + return sliceAlongFirstAxis(array2, start, size); + case 2: + return slice4d(array2, [0, start, 0, 0], [array2.shape[0], size, array2.shape[2], array2.shape[3]]); + case 3: + return slice4d(array2, [0, 0, start, 0], [array2.shape[0], array2.shape[1], size, array2.shape[3]]); + case 4: + return sliceAlongLastAxis(array2, start, size); + default: + throw new ValueError(`The axis is not within the rank of the tensor ${axis}`); + } + default: + throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`); + } + }); +} +function concatenate(tensors, axis = -1) { + let rank; + if (axis < 0) { + rank = tensors[0].rank; + if (rank !== 0) { + axis = rank; + } else { + axis = 0; + } + } + if (axis === tensors[0].rank) { + axis = -1; + } + return concat(tensors, axis); +} +function concatAlongFirstAxis(a, b) { + switch (a.rank) { + case 1: + return concat1d([a, b]); + case 2: + return concat2d([a, b], 0); + case 3: + return concat3d([a, b], 0); + case 4: + return concat4d([a, b], 0); + default: + throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`); + } +} +function tile2(x, n) { + if (!Array.isArray(n)) { + n = [n]; + } + if (x.rank !== n.length) { + throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`); + } + return tile(x, n); +} +function randomNormal2(shape, mean4 = 0, stddev = 1, dtype, seed) { + return randomNormal(shape, mean4, stddev, dtype, seed); +} +function dot2(a, b, activation2, bias) { + if (a.rank < 2 || b.rank < 2) { + throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`); + } + if (b.rank >= 3) { + const xLastDim = a.shape.slice(-1)[0]; + const ySecondLastDim = b.shape.slice(-2)[0]; + if (xLastDim !== ySecondLastDim) { + throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and y shape = ${b.shape}`); + } + } + if (a.rank === 2 && b.rank === 2) { + const transposeA = false; + const transposeB = false; + return fused_ops_exports.matMul({ + a, + b, + transposeA, + transposeB, + bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null, + activation: activation2 + }); + } else { + const aFirstDims = a.shape.slice(); + const aLastDim = aFirstDims.pop(); + a = reshape(a, [-1, aLastDim]); + const bShape = b.shape.slice(); + const bLastDim = bShape.pop(); + const ySecondLastDim = bShape.pop(); + const yOtherDims = [...bShape, bLastDim]; + const perm = Array.from({ length: b.rank }, (_, i) => { + if (i === 0) { + return b.rank - 2; + } else if (i <= b.rank - 2) { + return i - 1; + } + return i; + }); + b = reshape(transpose(b, perm), [ySecondLastDim, -1]); + const outputShape = [...aFirstDims, ...yOtherDims]; + const transposeA = false; + const transposeB = false; + return reshape(fused_ops_exports.matMul({ + a, + b, + transposeA, + transposeB, + bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null, + activation: activation2 + }), outputShape); + } +} +function gather2(reference, indices, axis) { + return tidy(() => { + if (Array.isArray(indices)) { + indices = tensor1d(indices, "int32"); + } else { + indices = cast(indices, "int32"); + } + return gather(reference, indices, axis); + }); +} +function square2(x) { + return mul(x, x); +} +function reshapeBias(xRank, bias, dataFormat) { + const biasShape = bias.shape; + if (bias.rank !== 1 && bias.rank !== xRank) { + throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`); + } + if (xRank === 5) { + if (dataFormat === "channelsFirst") { + if (biasShape.length === 1) { + return reshape(bias, [1, biasShape[0], 1, 1, 1]); + } else { + return reshape(bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]); + } + } else if (dataFormat === "channelsLast") { + if (biasShape.length === 1) { + return reshape(bias, [1, 1, 1, 1, biasShape[0]]); + } else { + return reshape(bias, [1].concat(biasShape)); + } + } + } else if (xRank === 4) { + if (dataFormat === "channelsFirst") { + if (biasShape.length === 1) { + return reshape(bias, [1, biasShape[0], 1, 1]); + } else { + return reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]); + } + } else if (dataFormat === "channelsLast") { + if (biasShape.length === 1) { + return reshape(bias, [1, 1, 1, biasShape[0]]); + } else { + return reshape(bias, [1].concat(biasShape)); + } + } + } else if (xRank === 3) { + if (dataFormat === "channelsFirst") { + if (biasShape.length === 1) { + return reshape(bias, [1, biasShape[0], 1]); + } else { + return reshape(bias, [1, biasShape[1], biasShape[0]]); + } + } else if (dataFormat === "channelsLast") { + if (biasShape.length === 1) { + return reshape(bias, [1, 1, biasShape[0]]); + } else { + return reshape(bias, [1].concat(biasShape)); + } + } + } else if (xRank < 3) { + return bias; + } + throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`); +} +function biasAdd(x, bias, dataFormat) { + return tidy(() => { + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + checkDataFormat(dataFormat); + return add2(x, reshapeBias(x.rank, bias, dataFormat)); + }); +} +function elu2(x, alpha = 1) { + if (alpha !== 1) { + throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`); + } + return elu(x); +} +function softsign(x) { + return tidy(() => div(x, add2(abs(x), 1))); +} +function dropout2(x, level, noiseShape, seed) { + return tidy(() => dropout(x, level, noiseShape, seed)); +} +function hardSigmoid(x) { + return tidy(() => { + const y = add2(0.5, mul(0.2, x)); + return clipByValue(y, 0, 1); + }); +} +function inTrainPhase(x, alt, training = false) { + return training ? x() : alt(); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/keras_format/initializer_config.js +var VALID_FAN_MODE_VALUES = ["fanIn", "fanOut", "fanAvg"]; +var VALID_DISTRIBUTION_VALUES = ["normal", "uniform", "truncatedNormal"]; + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/initializers.js +function checkFanMode(value) { + checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, "FanMode", value); +} +function checkDistribution(value) { + checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, "Distribution", value); +} +var Initializer = class extends serialization_exports.Serializable { + fromConfigUsesCustomObjects() { + return false; + } + getConfig() { + return {}; + } +}; +var Zeros = class extends Initializer { + apply(shape, dtype) { + return zeros(shape, dtype); + } +}; +Zeros.className = "Zeros"; +serialization_exports.registerClass(Zeros); +var Ones = class extends Initializer { + apply(shape, dtype) { + return ones2(shape, dtype); + } +}; +Ones.className = "Ones"; +serialization_exports.registerClass(Ones); +var Constant = class extends Initializer { + constructor(args) { + super(); + if (typeof args !== "object") { + throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`); + } + if (args.value === void 0) { + throw new ValueError(`config must have value set but got ${args}`); + } + this.value = args.value; + } + apply(shape, dtype) { + return tidy(() => mul(scalar(this.value), ones2(shape, dtype))); + } + getConfig() { + return { + value: this.value + }; + } +}; +Constant.className = "Constant"; +serialization_exports.registerClass(Constant); +var RandomUniform = class extends Initializer { + constructor(args) { + super(); + this.DEFAULT_MINVAL = -0.05; + this.DEFAULT_MAXVAL = 0.05; + this.minval = args.minval || this.DEFAULT_MINVAL; + this.maxval = args.maxval || this.DEFAULT_MAXVAL; + this.seed = args.seed; + } + apply(shape, dtype) { + return randomUniform(shape, this.minval, this.maxval, dtype, this.seed); + } + getConfig() { + return { minval: this.minval, maxval: this.maxval, seed: this.seed }; + } +}; +RandomUniform.className = "RandomUniform"; +serialization_exports.registerClass(RandomUniform); +var RandomNormal = class extends Initializer { + constructor(args) { + super(); + this.DEFAULT_MEAN = 0; + this.DEFAULT_STDDEV = 0.05; + this.mean = args.mean || this.DEFAULT_MEAN; + this.stddev = args.stddev || this.DEFAULT_STDDEV; + this.seed = args.seed; + } + apply(shape, dtype) { + dtype = dtype || "float32"; + if (dtype !== "float32" && dtype !== "int32") { + throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`); + } + return randomNormal2(shape, this.mean, this.stddev, dtype, this.seed); + } + getConfig() { + return { mean: this.mean, stddev: this.stddev, seed: this.seed }; + } +}; +RandomNormal.className = "RandomNormal"; +serialization_exports.registerClass(RandomNormal); +var TruncatedNormal = class extends Initializer { + constructor(args) { + super(); + this.DEFAULT_MEAN = 0; + this.DEFAULT_STDDEV = 0.05; + this.mean = args.mean || this.DEFAULT_MEAN; + this.stddev = args.stddev || this.DEFAULT_STDDEV; + this.seed = args.seed; + } + apply(shape, dtype) { + dtype = dtype || "float32"; + if (dtype !== "float32" && dtype !== "int32") { + throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`); + } + return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed); + } + getConfig() { + return { mean: this.mean, stddev: this.stddev, seed: this.seed }; + } +}; +TruncatedNormal.className = "TruncatedNormal"; +serialization_exports.registerClass(TruncatedNormal); +var Identity2 = class extends Initializer { + constructor(args) { + super(); + this.gain = args.gain != null ? args.gain : 1; + } + apply(shape, dtype) { + return tidy(() => { + if (shape.length !== 2 || shape[0] !== shape[1]) { + throw new ValueError("Identity matrix initializer can only be used for 2D square matrices."); + } else { + return mul(this.gain, eye(shape[0])); + } + }); + } + getConfig() { + return { gain: this.gain }; + } +}; +Identity2.className = "Identity"; +serialization_exports.registerClass(Identity2); +function computeFans(shape, dataFormat = "channelsLast") { + let fanIn; + let fanOut; + checkDataFormat(dataFormat); + if (shape.length === 2) { + fanIn = shape[0]; + fanOut = shape[1]; + } else if ([3, 4, 5].indexOf(shape.length) !== -1) { + if (dataFormat === "channelsFirst") { + const receptiveFieldSize = arrayProd(shape, 2); + fanIn = shape[1] * receptiveFieldSize; + fanOut = shape[0] * receptiveFieldSize; + } else if (dataFormat === "channelsLast") { + const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2); + fanIn = shape[shape.length - 2] * receptiveFieldSize; + fanOut = shape[shape.length - 1] * receptiveFieldSize; + } + } else { + const shapeProd = arrayProd(shape); + fanIn = Math.sqrt(shapeProd); + fanOut = Math.sqrt(shapeProd); + } + return [fanIn, fanOut]; +} +var VarianceScaling = class extends Initializer { + /** + * Constructor of VarianceScaling. + * @throws ValueError for invalid value in scale. + */ + constructor(args) { + super(); + if (args.scale < 0) { + throw new ValueError(`scale must be a positive float. Got: ${args.scale}`); + } + this.scale = args.scale == null ? 1 : args.scale; + this.mode = args.mode == null ? "fanIn" : args.mode; + checkFanMode(this.mode); + this.distribution = args.distribution == null ? "normal" : args.distribution; + checkDistribution(this.distribution); + this.seed = args.seed; + } + apply(shape, dtype) { + const fans = computeFans(shape); + const fanIn = fans[0]; + const fanOut = fans[1]; + let scale2 = this.scale; + if (this.mode === "fanIn") { + scale2 /= Math.max(1, fanIn); + } else if (this.mode === "fanOut") { + scale2 /= Math.max(1, fanOut); + } else { + scale2 /= Math.max(1, (fanIn + fanOut) / 2); + } + if (this.distribution === "normal") { + const stddev = Math.sqrt(scale2); + dtype = dtype || "float32"; + if (dtype !== "float32" && dtype !== "int32") { + throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`); + } + return truncatedNormal(shape, 0, stddev, dtype, this.seed); + } else { + const limit = Math.sqrt(3 * scale2); + return randomUniform(shape, -limit, limit, dtype, this.seed); + } + } + getConfig() { + return { + scale: this.scale, + mode: this.mode, + distribution: this.distribution, + seed: this.seed + }; + } +}; +VarianceScaling.className = "VarianceScaling"; +serialization_exports.registerClass(VarianceScaling); +var GlorotUniform = class extends VarianceScaling { + /** + * Constructor of GlorotUniform + * @param scale + * @param mode + * @param distribution + * @param seed + */ + constructor(args) { + super({ + scale: 1, + mode: "fanAvg", + distribution: "uniform", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +GlorotUniform.className = "GlorotUniform"; +serialization_exports.registerClass(GlorotUniform); +var GlorotNormal = class extends VarianceScaling { + /** + * Constructor of GlorotNormal. + * @param scale + * @param mode + * @param distribution + * @param seed + */ + constructor(args) { + super({ + scale: 1, + mode: "fanAvg", + distribution: "normal", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +GlorotNormal.className = "GlorotNormal"; +serialization_exports.registerClass(GlorotNormal); +var HeNormal = class extends VarianceScaling { + constructor(args) { + super({ + scale: 2, + mode: "fanIn", + distribution: "normal", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +HeNormal.className = "HeNormal"; +serialization_exports.registerClass(HeNormal); +var HeUniform = class extends VarianceScaling { + constructor(args) { + super({ + scale: 2, + mode: "fanIn", + distribution: "uniform", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +HeUniform.className = "HeUniform"; +serialization_exports.registerClass(HeUniform); +var LeCunNormal = class extends VarianceScaling { + constructor(args) { + super({ + scale: 1, + mode: "fanIn", + distribution: "normal", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +LeCunNormal.className = "LeCunNormal"; +serialization_exports.registerClass(LeCunNormal); +var LeCunUniform = class extends VarianceScaling { + constructor(args) { + super({ + scale: 1, + mode: "fanIn", + distribution: "uniform", + seed: args == null ? null : args.seed + }); + } + getClassName() { + return VarianceScaling.className; + } +}; +LeCunUniform.className = "LeCunUniform"; +serialization_exports.registerClass(LeCunUniform); +var Orthogonal = class extends Initializer { + constructor(args) { + super(); + this.DEFAULT_GAIN = 1; + this.ELEMENTS_WARN_SLOW = 2e3; + this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain; + this.seed = args.seed; + } + apply(shape, dtype) { + return tidy(() => { + if (shape.length < 2) { + throw new NotImplementedError("Shape must be at least 2D."); + } + if (dtype !== "int32" && dtype !== "float32" && dtype !== void 0) { + throw new TypeError(`Unsupported data type ${dtype}.`); + } + dtype = dtype; + const numRows = util_exports.sizeFromShape(shape.slice(0, -1)); + const numCols = shape[shape.length - 1]; + const numElements = numRows * numCols; + if (numElements > this.ELEMENTS_WARN_SLOW) { + console.warn(`Orthogonal initializer is being called on a matrix with more than ${this.ELEMENTS_WARN_SLOW} (${numElements}) elements: Slowness may result.`); + } + const flatShape = [Math.max(numCols, numRows), Math.min(numCols, numRows)]; + const randNormalMat = randomNormal2(flatShape, 0, 1, dtype, this.seed); + const qr2 = linalg.qr(randNormalMat, false); + let qMat = qr2[0]; + const rMat = qr2[1]; + const diag5 = rMat.flatten().stridedSlice([0], [Math.min(numCols, numRows) * Math.min(numCols, numRows)], [Math.min(numCols, numRows) + 1]); + qMat = mul(qMat, diag5.sign()); + if (numRows < numCols) { + qMat = qMat.transpose(); + } + return mul(scalar(this.gain), qMat.reshape(shape)); + }); + } + getConfig() { + return { + gain: this.gain, + seed: this.seed + }; + } +}; +Orthogonal.className = "Orthogonal"; +serialization_exports.registerClass(Orthogonal); +var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = { + "constant": "Constant", + "glorotNormal": "GlorotNormal", + "glorotUniform": "GlorotUniform", + "heNormal": "HeNormal", + "heUniform": "HeUniform", + "identity": "Identity", + "leCunNormal": "LeCunNormal", + "leCunUniform": "LeCunUniform", + "ones": "Ones", + "orthogonal": "Orthogonal", + "randomNormal": "RandomNormal", + "randomUniform": "RandomUniform", + "truncatedNormal": "TruncatedNormal", + "varianceScaling": "VarianceScaling", + "zeros": "Zeros" +}; +function deserializeInitializer(config, customObjects = {}) { + return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "initializer"); +} +function serializeInitializer(initializer) { + return serializeKerasObject(initializer); +} +function getInitializer(identifier) { + if (typeof identifier === "string") { + const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier; + if (className === "GlorotNormal") { + return new GlorotNormal(); + } else if (className === "GlorotUniform") { + return new GlorotUniform(); + } else if (className === "HeNormal") { + return new HeNormal(); + } else if (className === "HeUniform") { + return new HeUniform(); + } else if (className === "LeCunNormal") { + return new LeCunNormal(); + } else if (className === "LeCunUniform") { + return new LeCunUniform(); + } else { + const config = {}; + config["className"] = className; + config["config"] = {}; + return deserializeInitializer(config); + } + } else if (identifier instanceof Initializer) { + return identifier; + } else { + return deserializeInitializer(identifier); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/types_utils.js +function isArrayOfShapes(x) { + return Array.isArray(x) && Array.isArray(x[0]); +} +function normalizeShapeList(x) { + if (x.length === 0) { + return []; + } + if (!Array.isArray(x[0])) { + return [x]; + } + return x; +} +function getExactlyOneTensor(xs) { + let x; + if (Array.isArray(xs)) { + if (xs.length !== 1) { + throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`); + } + x = xs[0]; + } else { + x = xs; + } + return x; +} +function getExactlyOneShape(shapes) { + if (Array.isArray(shapes) && Array.isArray(shapes[0])) { + if (shapes.length === 1) { + shapes = shapes; + return shapes[0]; + } else { + throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`); + } + } else { + return shapes; + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/variable_utils.js +function countParamsInWeights(weights) { + let count2 = 0; + for (const weight of weights) { + if (weight.shape.length === 0) { + count2 += 1; + } else { + count2 += weight.shape.reduce((a, b) => a * b); + } + } + return count2; +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/variables.js +var DEFAULT_VARIABLE_NAME_PREFIX = "Variable"; +var LayerVariable = class { + /** + * Construct Variable from a `tf.Tensor`. + * + * If not explicitly named, the Variable will be given a name with the + * prefix 'Variable'. Variable names are unique. In the case of name + * collision, suffixies '_' will be added to the name. + * + * @param val Initial value of the Variable. + * @param name Name of the variable. If `null` or `undefined` is provided, it + * will default a name with the prefix 'Variable'. + * @param constraint Optional, projection function to be applied to the + * variable after optimize updates + * @throws ValueError if `name` is `null` or `undefined`. + */ + constructor(val, dtype = "float32", name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) { + this.dtype = dtype == null ? "float32" : dtype; + this.shape = val.shape; + this.id = getNextUniqueTensorId(); + name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name; + this.originalName = getScopedTensorName(name); + this.name = getUniqueTensorName(this.originalName); + this.trainable_ = trainable; + this.constraint = constraint; + this.val = variable(val, this.trainable_, this.name, this.dtype); + } + /** + * Get a snapshot of the Variable's value. + * + * The returned value is a snapshot of the Variable's value at the time of + * the invocation. Future mutations in the value of the tensor will only + * be reflected by future calls to this method. + */ + read() { + this.assertNotDisposed(); + return this.val; + } + /** + * Update the value of the Variable. + * + * @param newVal: The new value to update to. Must be consistent with the + * dtype and shape of the Variable. + * @return This Variable. + */ + write(newVal) { + this.assertNotDisposed(); + checkShapesMatch(this.val, newVal); + if (this.val.id !== newVal.id) { + this.val.assign(newVal); + if (this.constraint != null) { + this.val.assign(this.constraint.apply(this.val)); + } + } + return this; + } + /** + * Dispose this LayersVariable instance from memory. + */ + dispose() { + this.assertNotDisposed(); + this.val.dispose(); + } + assertNotDisposed() { + if (this.val.isDisposed) { + throw new Error(`LayersVariable ${this.name} is already disposed.`); + } + } + get trainable() { + return this.trainable_; + } + set trainable(trainable) { + this.trainable_ = trainable; + this.val.trainable = trainable; + } +}; +function checkShapesMatch(x, y) { + if (x.shape.toString() !== y.shape.toString()) { + throw new Error("Shape mismatch: " + JSON.stringify(x.shape) + " vs. " + JSON.stringify(y.shape)); + } +} +function batchGetValue(xs) { + return xs.map((x) => x.read()); +} +function batchSetValue(variablesAndValues) { + variablesAndValues.forEach((variableAndValue) => { + const variable2 = variableAndValue[0]; + variable2.write(variableAndValue[1]); + }); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js +var InputSpec = class { + constructor(args) { + this.dtype = args.dtype; + this.shape = args.shape; + if (args.shape != null) { + this.ndim = args.shape.length; + } else { + this.ndim = args.ndim; + } + this.maxNDim = args.maxNDim; + this.minNDim = args.minNDim; + this.axes = args.axes || {}; + } +}; +var SymbolicTensor = class { + /** + * + * @param dtype + * @param shape + * @param sourceLayer The Layer that produced this symbolic tensor. + * @param inputs The inputs passed to sourceLayer's __call__() method. + * @param nodeIndex + * @param tensorIndex + * @param callArgs The keyword arguments passed to the __call__() method. + * @param name + * @param outputTensorIndex The index of this tensor in the list of outputs + * returned by apply(). + */ + constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) { + this.dtype = dtype; + this.shape = shape; + this.sourceLayer = sourceLayer; + this.inputs = inputs; + this.callArgs = callArgs; + this.outputTensorIndex = outputTensorIndex; + this.id = getNextUniqueTensorId(); + if (name != null) { + this.originalName = getScopedTensorName(name); + this.name = getUniqueTensorName(this.originalName); + } + this.rank = shape.length; + } +}; +var _nextNodeID = 0; +var Node = class { + constructor(args, callArgs) { + this.callArgs = callArgs; + this.id = _nextNodeID++; + this.outboundLayer = args.outboundLayer; + this.inboundLayers = args.inboundLayers; + this.nodeIndices = args.nodeIndices; + this.tensorIndices = args.tensorIndices; + this.inputTensors = args.inputTensors; + this.outputTensors = args.outputTensors; + this.inputMasks = args.inputMasks; + this.outputMasks = args.outputMasks; + this.inputShapes = args.inputShapes; + this.outputShapes = args.outputShapes; + for (const layer of args.inboundLayers) { + if (layer != null) { + layer.outboundNodes.push(this); + } + } + args.outboundLayer.inboundNodes.push(this); + } + getConfig() { + const inboundNames = []; + for (const layer of this.inboundLayers) { + if (layer != null) { + inboundNames.push(layer.name); + } else { + inboundNames.push(null); + } + } + return { + outboundLayer: this.outboundLayer ? this.outboundLayer.name : null, + inboundLayers: inboundNames, + nodeIndices: this.nodeIndices, + tensorIndices: this.tensorIndices + }; + } +}; +var _nextLayerID = 0; +var Layer = class extends serialization_exports.Serializable { + constructor(args = {}) { + super(); + this._callHook = null; + this._addedWeightNames = []; + this._stateful = false; + this.id = _nextLayerID++; + this.activityRegularizer = null; + this.inputSpec = null; + this.supportsMasking = false; + this._trainableWeights = []; + this._nonTrainableWeights = []; + this._losses = []; + this._updates = []; + this._built = false; + this.inboundNodes = []; + this.outboundNodes = []; + let name = args.name; + if (!name) { + const prefix = this.getClassName(); + name = toSnakeCase(prefix) + "_" + getUid(prefix); + } + this.name = name; + this.trainable_ = args.trainable == null ? true : args.trainable; + if (args.inputShape != null || args.batchInputShape != null) { + let batchInputShape; + if (args.batchInputShape != null) { + batchInputShape = args.batchInputShape; + } else if (args.inputShape != null) { + let batchSize = null; + if (args.batchSize != null) { + batchSize = args.batchSize; + } + batchInputShape = [batchSize].concat(args.inputShape); + } + this.batchInputShape = batchInputShape; + let dtype = args.dtype; + if (dtype == null) { + dtype = args.inputDType; + } + if (dtype == null) { + dtype = "float32"; + } + this.dtype = dtype; + } + if (args.weights != null) { + this.initialWeights = args.weights; + } else { + this.initialWeights = null; + } + this._refCount = null; + this.fastWeightInitDuringBuild = false; + } + /** + * Converts a layer and its index to a unique (immutable type) name. + * This function is used internally with `this.containerNodes`. + * @param layer The layer. + * @param nodeIndex The layer's position (e.g. via enumerate) in a list of + * nodes. + * + * @returns The unique name. + */ + static nodeKey(layer, nodeIndex) { + return layer.name + "_ib-" + nodeIndex.toString(); + } + /** + * Returns this.inboundNode at index nodeIndex. + * + * Porting note: This is a replacement for _get_node_attribute_at_index() + * @param nodeIndex + * @param attrName The name of the attribute related to request for this node. + */ + getNodeAtIndex(nodeIndex, attrName) { + if (this.inboundNodes.length === 0) { + throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`); + } + if (this.inboundNodes.length <= nodeIndex) { + throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`); + } + return this.inboundNodes[nodeIndex]; + } + /** + * Retrieves the input tensor(s) of a layer at a given node. + * + * @param nodeIndex Integer, index of the node from which to retrieve the + * attribute. E.g. `nodeIndex=0` will correspond to the first time the layer + * was called. + * + * @return A tensor (or list of tensors if the layer has multiple inputs). + */ + getInputAt(nodeIndex) { + return singletonOrArray(this.getNodeAtIndex(nodeIndex, "input").inputTensors); + } + /** + * Retrieves the output tensor(s) of a layer at a given node. + * + * @param nodeIndex Integer, index of the node from which to retrieve the + * attribute. E.g. `nodeIndex=0` will correspond to the first time the layer + * was called. + * + * @return A tensor (or list of tensors if the layer has multiple outputs). + */ + getOutputAt(nodeIndex) { + return singletonOrArray(this.getNodeAtIndex(nodeIndex, "output").outputTensors); + } + // Properties + /** + * Retrieves the input tensor(s) of a layer. + * + * Only applicable if the layer has exactly one inbound node, + * i.e. if it is connected to one incoming layer. + * + * @return Input tensor or list of input tensors. + * + * @exception AttributeError if the layer is connected to more than one + * incoming layers. + */ + get input() { + if (this.inboundNodes.length > 1) { + throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`); + } else if (this.inboundNodes.length === 0) { + throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`); + } + return singletonOrArray(this.getNodeAtIndex(0, "input").inputTensors); + } + /** + * Retrieves the output tensor(s) of a layer. + * + * Only applicable if the layer has exactly one inbound node, + * i.e. if it is connected to one incoming layer. + * + * @return Output tensor or list of output tensors. + * + * @exception AttributeError if the layer is connected to more than one + * incoming layers. + */ + get output() { + if (this.inboundNodes.length === 0) { + throw new AttributeError(`Layer ${this.name} has no inbound nodes.`); + } + if (this.inboundNodes.length > 1) { + throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`); + } + return singletonOrArray(this.getNodeAtIndex(0, "output").outputTensors); + } + get losses() { + return this._losses; + } + /** + * Retrieves the Layer's current loss values. + * + * Used for regularizers during training. + */ + calculateLosses() { + return this.losses.map((lossFn) => lossFn()); + } + get updates() { + return this._updates; + } + get built() { + return this._built; + } + set built(built) { + this._built = built; + } + get trainable() { + return this.trainable_; + } + set trainable(trainable) { + this._trainableWeights.forEach((w) => w.trainable = trainable); + this.trainable_ = trainable; + } + get trainableWeights() { + if (this.trainable_) { + return this._trainableWeights.filter((w) => w.trainable); + } else { + return []; + } + } + set trainableWeights(weights) { + this._trainableWeights = weights; + } + get nonTrainableWeights() { + if (this.trainable) { + return this._trainableWeights.filter((w) => !w.trainable).concat(this._nonTrainableWeights); + } else { + return this._trainableWeights.concat(this._nonTrainableWeights); + } + } + set nonTrainableWeights(weights) { + this._nonTrainableWeights = weights; + } + /** + * The concatenation of the lists trainableWeights and nonTrainableWeights + * (in this order). + */ + get weights() { + return this.trainableWeights.concat(this.nonTrainableWeights); + } + get stateful() { + return this._stateful; + } + /** + * Reset the states of the layer. + * + * This method of the base Layer class is essentially a no-op. + * Subclasses that are stateful (e.g., stateful RNNs) should override this + * method. + */ + resetStates() { + if (!this.stateful) { + throw new Error("Cannot call the resetStates() method of a non-stateful Layer object."); + } + } + /** + * Checks compatibility between the layer and provided inputs. + * + * This checks that the tensor(s) `input` + * verify the input assumptions of the layer + * (if any). If not, exceptions are raised. + * + * @param inputs Input tensor or list of input tensors. + * + * @exception ValueError in case of mismatch between + * the provided inputs and the expectations of the layer. + */ + assertInputCompatibility(inputs) { + const inputsList = toList(inputs); + if (this.inputSpec == null || this.inputSpec.length === 0) { + return; + } + const inputSpec = toList(this.inputSpec); + if (inputsList.length !== inputSpec.length) { + throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputsList.length} input tensors. Input received: ${inputs}`); + } + for (let inputIndex = 0; inputIndex < inputsList.length; inputIndex++) { + const x = inputsList[inputIndex]; + const spec = inputSpec[inputIndex]; + if (spec == null) { + continue; + } + const ndim = x.rank; + if (spec.ndim != null) { + if (ndim !== spec.ndim) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`); + } + } + if (spec.maxNDim != null) { + if (ndim > spec.maxNDim) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`); + } + } + if (spec.minNDim != null) { + if (ndim < spec.minNDim) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`); + } + } + if (spec.dtype != null) { + if (x.dtype !== spec.dtype) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`); + } + } + if (spec.axes) { + const xShape = x.shape; + for (const key in spec.axes) { + const axis = Number(key); + const value = spec.axes[key]; + const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis]; + if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`); + } + } + } + if (spec.shape != null) { + for (let i = 0; i < spec.shape.length; ++i) { + const specDim = spec.shape[i]; + const dim = x.shape[i]; + if (specDim != null && dim != null) { + if (specDim !== dim) { + throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`); + } + } + } + } + } + } + /** + * This is where the layer's logic lives. + * + * @param inputs Input tensor, or list/tuple of input tensors. + * @param kwargs Additional keyword arguments. + * + * @return A tensor or list/tuple of tensors. + */ + call(inputs, kwargs) { + return inputs; + } + invokeCallHook(inputs, kwargs) { + if (this._callHook != null) { + this._callHook(inputs, kwargs); + } + } + /** + * Set call hook. + * This is currently used for testing only. + * @param callHook + */ + setCallHook(callHook) { + this._callHook = callHook; + } + /** + * Clear call hook. + * This is currently used for testing only. + */ + clearCallHook() { + this._callHook = null; + } + /** + * Builds or executes a `Layer`'s logic. + * + * When called with `tf.Tensor`(s), execute the `Layer`'s computation and + * return Tensor(s). For example: + * + * ```js + * const denseLayer = tf.layers.dense({ + * units: 1, + * kernelInitializer: 'zeros', + * useBias: false + * }); + * + * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete + * // numeric values). + * const input = tf.ones([2, 2]); + * const output = denseLayer.apply(input); + * + * // The output's value is expected to be [[0], [0]], due to the fact that + * // the dense layer has a kernel initialized to all-zeros and does not have + * // a bias. + * output.print(); + * ``` + * + * When called with `tf.SymbolicTensor`(s), this will prepare the layer for + * future execution. This entails internal book-keeping on shapes of + * expected Tensors, wiring layers together, and initializing weights. + * + * Calling `apply` with `tf.SymbolicTensor`s are typically used during the + * building of non-`tf.Sequential` models. For example: + * + * ```js + * const flattenLayer = tf.layers.flatten(); + * const denseLayer = tf.layers.dense({units: 1}); + * + * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply(). + * const input = tf.input({shape: [2, 2]}); + * const output1 = flattenLayer.apply(input); + * + * // output1.shape is [null, 4]. The first dimension is the undetermined + * // batch size. The second dimension comes from flattening the [2, 2] + * // shape. + * console.log(JSON.stringify(output1.shape)); + * + * // The output SymbolicTensor of the flatten layer can be used to call + * // the apply() of the dense layer: + * const output2 = denseLayer.apply(output1); + * + * // output2.shape is [null, 1]. The first dimension is the undetermined + * // batch size. The second dimension matches the number of units of the + * // dense layer. + * console.log(JSON.stringify(output2.shape)); + * + * // The input and output can be used to construct a model that consists + * // of the flatten and dense layers. + * const model = tf.model({inputs: input, outputs: output2}); + * ``` + * + * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them. + * @param kwargs Additional keyword arguments to be passed to `call()`. + * + * @return Output of the layer's `call` method. + * + * @exception ValueError error in case the layer is missing shape information + * for its `build` call. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + // Porting Note: This is a replacement for __call__() in Python. + apply(inputs, kwargs) { + kwargs = kwargs || {}; + this.assertNotDisposed(); + const inputsList = toList(inputs); + const allAreSymbolic = checkAllSymbolic(inputs); + const noneAreSymbolic = checkNoneSymbolic(inputs); + if (allAreSymbolic === noneAreSymbolic) { + throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors"); + } + return nameScope(this.name, () => { + if (!this.built) { + this.assertInputCompatibility(inputs); + const inputShapes = []; + for (const xElem of toList(inputs)) { + inputShapes.push(xElem.shape); + } + this.build(singletonOrArray(inputShapes)); + this.built = true; + if (this.initialWeights) { + this.setWeights(this.initialWeights); + } + if (this._refCount === null && noneAreSymbolic) { + this._refCount = 1; + } + } + this.assertInputCompatibility(inputs); + if (noneAreSymbolic) { + let output = this.call(inputs, kwargs); + if (this.supportsMasking) { + this.setMaskMetadata(inputs, output); + } + const outputList = toList(output); + const outputListCopy = []; + for (let x of outputList) { + if (inputsList.indexOf(x) !== -1) { + x = x.clone(); + } + outputListCopy.push(x); + } + output = singletonOrArray(outputListCopy); + if (this.activityRegularizer != null) { + throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet."); + } + return output; + } else { + const inputShape = collectInputShape(inputs); + const outputShape = this.computeOutputShape(inputShape); + let output; + const outputDType = guessOutputDType(inputs); + this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] : inputShape); + if (outputShape != null && outputShape.length > 0 && Array.isArray(outputShape[0])) { + output = outputShape.map((shape, index) => new SymbolicTensor(outputDType, shape, this, toList(inputs), kwargs, this.name, index)); + } else { + output = new SymbolicTensor(outputDType, outputShape, this, toList(inputs), kwargs, this.name); + } + this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs); + this._refCount++; + if (this.activityRegularizer != null) { + throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet."); + } + return output; + } + }); + } + /** + * Check compatibility between input shape and this layer's batchInputShape. + * + * Print warning if any incompatibility is found. + * + * @param inputShape Input shape to be checked. + */ + warnOnIncompatibleInputShape(inputShape) { + if (this.batchInputShape == null) { + return; + } else if (inputShape.length !== this.batchInputShape.length) { + console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`); + } else { + let dimMismatch = false; + this.batchInputShape.forEach((dimension, i) => { + if (dimension != null && inputShape[i] != null && inputShape[i] !== dimension) { + dimMismatch = true; + } + }); + if (dimMismatch) { + console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`); + } + } + } + /** + * Retrieves the output shape(s) of a layer. + * + * Only applicable if the layer has only one inbound node, or if all inbound + * nodes have the same output shape. + * + * @returns Output shape or shapes. + * @throws AttributeError: if the layer is connected to more than one incoming + * nodes. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + get outputShape() { + if (this.inboundNodes == null || this.inboundNodes.length === 0) { + throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`); + } + const allOutputShapes = []; + for (const node of this.inboundNodes) { + const shapeString = JSON.stringify(node.outputShapes); + if (allOutputShapes.indexOf(shapeString) === -1) { + allOutputShapes.push(shapeString); + } + } + if (allOutputShapes.length === 1) { + const outputShapes = this.inboundNodes[0].outputShapes; + if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) && outputShapes.length === 1) { + return outputShapes[0]; + } else { + return outputShapes; + } + } else { + throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`); + } + } + /** + * Counts the total number of numbers (e.g., float32, int32) in the + * weights. + * + * @returns An integer count. + * @throws RuntimeError: If the layer is not built yet (in which case its + * weights are not defined yet.) + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + countParams() { + if (!this.built) { + throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`); + } + return countParamsInWeights(this.weights); + } + /** + * Creates the layer weights. + * + * Must be implemented on all layers that have weights. + * + * Called when apply() is called to construct the weights. + * + * @param inputShape A `Shape` or array of `Shape` (unused). + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + build(inputShape) { + this.built = true; + } + /** + * Returns the current values of the weights of the layer. + * + * @param trainableOnly Whether to get the values of only trainable weights. + * @returns Weight values as an `Array` of `tf.Tensor`s. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + getWeights(trainableOnly = false) { + return batchGetValue(trainableOnly ? this.trainableWeights : this.weights); + } + /** + * Sets the weights of the layer, from Tensors. + * + * @param weights a list of Tensors. The number of arrays and their shape + * must match number of the dimensions of the weights of the layer (i.e. + * it should match the output of `getWeights`). + * + * @exception ValueError If the provided weights list does not match the + * layer's specifications. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + setWeights(weights) { + tidy(() => { + const params = this.weights; + if (params.length !== weights.length) { + throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`); + } + if (params.length === 0) { + return; + } + const weightValueTuples = []; + const paramValues = batchGetValue(params); + for (let i = 0; i < paramValues.length; ++i) { + const pv = paramValues[i]; + const p2 = params[i]; + const w = weights[i]; + if (!util_exports.arraysEqual(pv.shape, w.shape)) { + throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`); + } + weightValueTuples.push([p2, w]); + } + batchSetValue(weightValueTuples); + }); + } + /** + * Adds a weight variable to the layer. + * + * @param name Name of the new weight variable. + * @param shape The shape of the weight. + * @param dtype The dtype of the weight. + * @param initializer An initializer instance. + * @param regularizer A regularizer instance. + * @param trainable Whether the weight should be trained via backprop or not + * (assuming that the layer itself is also trainable). + * @param constraint An optional trainable. + * @return The created weight variable. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint, getInitializerFunc) { + if (this._addedWeightNames.indexOf(name) !== -1) { + throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`); + } + this._addedWeightNames.push(name); + if (dtype == null) { + dtype = "float32"; + } + if (this.fastWeightInitDuringBuild) { + initializer = getInitializerFunc != null ? getInitializerFunc() : getInitializer("zeros"); + } + const initValue = initializer.apply(shape, dtype); + const weight = new LayerVariable(initValue, dtype, name, trainable, constraint); + initValue.dispose(); + if (regularizer != null) { + this.addLoss(() => regularizer.apply(weight.read())); + } + if (trainable == null) { + trainable = true; + } + if (trainable) { + this._trainableWeights.push(weight); + } else { + this._nonTrainableWeights.push(weight); + } + return weight; + } + /** + * Set the fast-weight-initialization flag. + * + * In cases where the initialized weight values will be immediately + * overwritten by loaded weight values during model loading, setting + * the flag to `true` saves unnecessary calls to potentially expensive + * initializers and speeds up the loading process. + * + * @param value Target value of the flag. + */ + setFastWeightInitDuringBuild(value) { + this.fastWeightInitDuringBuild = value; + } + /** + * Add losses to the layer. + * + * The loss may potentially be conditional on some inputs tensors, + * for instance activity losses are conditional on the layer's inputs. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + addLoss(losses2) { + if (losses2 == null || Array.isArray(losses2) && losses2.length === 0) { + return; + } + losses2 = toList(losses2); + if (this._losses !== void 0 && this._losses !== null) { + this.losses.push(...losses2); + } + } + /** + * Computes the output shape of the layer. + * + * Assumes that the layer will be built to match that input shape provided. + * + * @param inputShape A shape (tuple of integers) or a list of shape tuples + * (one per output tensor of the layer). Shape tuples can include null for + * free dimensions, instead of an integer. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + computeOutputShape(inputShape) { + return inputShape; + } + /** + * Computes an output mask tensor. + * + * @param inputs Tensor or list of tensors. + * @param mask Tensor or list of tensors. + * + * @return null or a tensor (or list of tensors, one per output tensor of the + * layer). + */ + computeMask(inputs, mask) { + if (!this.supportsMasking) { + if (mask != null) { + if (Array.isArray(mask)) { + mask.forEach((maskElement) => { + if (maskElement != null) { + throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`); + } + }); + } else { + throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`); + } + } + return null; + } + return mask; + } + setMaskMetadata(inputs, outputs, previousMask) { + if (!this.supportsMasking) { + return; + } + const outputMasks = this.computeMask(inputs, previousMask); + const outputsList = toList(outputs); + const outputMasksList = toList(outputMasks); + if (outputsList.length !== outputMasksList.length) { + throw new Error(`${this.name} outputs ${outputsList.length} tensors but ${outputsList.length} masks for those tensors`); + } + for (let i = 0; i < outputsList.length; i++) { + outputsList[i].kerasMask = outputMasksList[i]; + } + } + /** + * Internal method to create an inbound node for the layer. + * + * @param inputTensors List of input tensors. + * @param outputTensors List of output tensors. + * @param inputMasks List of input masks (a mask can be a tensor, or null). + * @param outputMasks List of output masks (a mask can be a tensor, or null). + * @param inputShapes List of input shape tuples. + * @param outputShapes List of output shape tuples. + * @param kwargs Dictionary of keyword arguments that were passed to the + * `call` method of the layer at the call that created the node. + */ + addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) { + const inputTensorList = toList(inputTensors); + outputTensors = toList(outputTensors); + inputMasks = toList(inputMasks); + outputMasks = toList(outputMasks); + inputShapes = normalizeShapeList(inputShapes); + outputShapes = normalizeShapeList(outputShapes); + const inboundLayers = []; + const nodeIndices = []; + const tensorIndices = []; + for (const x of inputTensorList) { + inboundLayers.push(x.sourceLayer); + nodeIndices.push(x.nodeIndex); + tensorIndices.push(x.tensorIndex); + } + new Node({ + outboundLayer: this, + inboundLayers, + nodeIndices, + tensorIndices, + inputTensors: inputTensorList, + outputTensors, + inputMasks, + outputMasks, + inputShapes, + outputShapes + }, kwargs); + for (let i = 0; i < outputTensors.length; i++) { + outputTensors[i].sourceLayer = this; + outputTensors[i].nodeIndex = this.inboundNodes.length - 1; + outputTensors[i].tensorIndex = i; + } + } + /** + * Returns the config of the layer. + * + * A layer config is a TS dictionary (serializable) + * containing the configuration of a layer. + * The same layer can be reinstantiated later + * (without its trained weights) from this configuration. + * + * The config of a layer does not include connectivity + * information, nor the layer class name. These are handled + * by 'Container' (one layer of abstraction above). + * + * Porting Note: The TS dictionary follows TS naming standards for + * keys, and uses tfjs-layers type-safe Enums. Serialization methods + * should use a helper function to convert to the pythonic storage + * standard. (see serialization_utils.convertTsToPythonic) + * + * @returns TS dictionary of configuration. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + getConfig() { + const config = { name: this.name, trainable: this.trainable }; + if (this.batchInputShape != null) { + config["batchInputShape"] = this.batchInputShape; + } + if (this.dtype != null) { + config["dtype"] = this.dtype; + } + return config; + } + /** + * Dispose the weight variables that this Layer instance holds. + * + * @returns {number} Number of disposed variables. + */ + disposeWeights() { + this.weights.forEach((weight) => weight.dispose()); + return this.weights.length; + } + assertNotDisposed() { + if (this._refCount === 0) { + throw new Error(`Layer '${this.name}' is already disposed.`); + } + } + /** + * Attempt to dispose layer's weights. + * + * This method decreases the reference count of the Layer object by 1. + * + * A Layer is reference-counted. Its reference count is incremented by 1 + * the first item its `apply()` method is called and when it becomes a part + * of a new `Node` (through calling the `apply()` method on a + * `tf.SymbolicTensor`). + * + * If the reference count of a Layer becomes 0, all the weights will be + * disposed and the underlying memory (e.g., the textures allocated in WebGL) + * will be freed. + * + * Note: If the reference count is greater than 0 after the decrement, the + * weights of the Layer will *not* be disposed. + * + * After a Layer is disposed, it cannot be used in calls such as `apply()`, + * `getWeights()` or `setWeights()` anymore. + * + * @returns A DisposeResult Object with the following fields: + * - refCountAfterDispose: The reference count of the Container after this + * `dispose()` call. + * - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed + * during this `dispose()` call. + * @throws {Error} If the layer is not built yet, or if the layer has already + * been disposed. + * + * @doc {heading: 'Models', 'subheading': 'Classes'} + */ + dispose() { + if (!this.built) { + throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`); + } + if (this._refCount === null) { + throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`); + } + this.assertNotDisposed(); + let numDisposedVariables = 0; + if (--this._refCount === 0) { + numDisposedVariables = this.disposeWeights(); + } + return { refCountAfterDispose: this._refCount, numDisposedVariables }; + } +}; +function collectInputShape(inputTensors) { + inputTensors = toList(inputTensors); + const shapes = []; + for (const x of inputTensors) { + shapes.push(x.shape); + } + return singletonOrArray(shapes); +} +function guessOutputDType(inputTensors) { + return "float32"; +} +function getSourceInputs(tensor2, layer, nodeIndex) { + if (layer == null || nodeIndex != null && nodeIndex > 0) { + layer = tensor2.sourceLayer; + nodeIndex = tensor2.nodeIndex; + } + if (layer.inboundNodes.length === 0) { + return [tensor2]; + } else { + const node = layer.inboundNodes[nodeIndex]; + if (node.inboundLayers.length === 0) { + return node.inputTensors; + } else { + const sourceTensors = []; + for (let i = 0; i < node.inboundLayers.length; i++) { + const x = node.inputTensors[i]; + const layer2 = node.inboundLayers[i]; + const nodeIndex2 = node.nodeIndices[i]; + const previousSources = getSourceInputs(x, layer2, nodeIndex2); + for (const x2 of previousSources) { + if (sourceTensors.indexOf(x2) === -1) { + sourceTensors.push(x2); + } + } + } + return sourceTensors; + } + } +} +function checkAllSymbolic(tensors) { + let allAreSymbolic = true; + for (const tensor2 of toList(tensors)) { + if (!(tensor2 instanceof SymbolicTensor)) { + allAreSymbolic = false; + break; + } + } + return allAreSymbolic; +} +function checkNoneSymbolic(tensors) { + let noneAreSymbolic = true; + for (const tensor2 of toList(tensors)) { + if (tensor2 instanceof SymbolicTensor) { + noneAreSymbolic = false; + break; + } + } + return noneAreSymbolic; +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js +var InputLayer = class extends Layer { + constructor(args) { + super({ + dtype: args.dtype, + name: args.name != null ? args.name : getUid("input").toString() + }); + if (args.batchSize == null) { + args.batchSize = null; + } + if (args.sparse == null) { + args.sparse = false; + } + this.trainable = false; + this.built = true; + this.sparse = args.sparse; + if (args.inputShape != null && args.batchInputShape != null) { + throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time."); + } + let batchInputShape = args.batchInputShape; + if (batchInputShape == null) { + if (args.inputShape == null) { + throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`."); + } else { + batchInputShape = [args.batchSize].concat(args.inputShape); + } + } else { + if (args.batchSize != null) { + throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer."); + } + } + const dtype = args.dtype || "float32"; + this.batchInputShape = batchInputShape; + this.dtype = dtype; + this.inputSpec = [{ shape: batchInputShape }]; + const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name); + inputTensor.nodeIndex = 0; + inputTensor.tensorIndex = 0; + new Node({ + outboundLayer: this, + inboundLayers: [], + nodeIndices: [], + tensorIndices: [], + inputTensors: [inputTensor], + outputTensors: [inputTensor], + inputMasks: [null], + outputMasks: [null], + inputShapes: [batchInputShape], + outputShapes: [batchInputShape] + }); + } + apply(inputs, kwargs) { + throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`); + } + dispose() { + return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 }; + } + getConfig() { + return { + batchInputShape: this.batchInputShape, + dtype: this.dtype, + sparse: this.sparse, + name: this.name + }; + } +}; +InputLayer.className = "InputLayer"; +serialization_exports.registerClass(InputLayer); +function Input(config) { + if (config.batchShape == null && config.shape == null) { + throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension."); + } + if (config.batchShape != null && config.shape != null) { + throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both."); + } + let batchShape = config.batchShape; + if (config.shape != null && batchShape == null) { + batchShape = [null].concat(config.shape); + } + let dtype = config.dtype; + if (dtype == null) { + dtype = "float32"; + } + const inputLayer2 = new InputLayer({ + batchInputShape: batchShape, + name: config.name, + dtype, + sparse: config.sparse + }); + const outputs = inputLayer2.inboundNodes[0].outputTensors; + return outputs[0]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js +function assertFeedCompatibility(key, val) { + if (key.dtype == null || key.dtype === val.dtype) { + return val; + } + try { + return cast(val, key.dtype); + } catch (err) { + throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`); + } +} +var FeedDict = class _FeedDict { + /** + * Constructor, optionally does copy-construction. + * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case + * copy-construction will be performed. + */ + constructor(feeds) { + this.id2Value = {}; + this.id2Mask = {}; + this.name2Id = {}; + if (feeds instanceof _FeedDict) { + for (const id in feeds.id2Value) { + this.id2Value[id] = feeds.id2Value[id]; + if (id in feeds.id2Mask) { + this.id2Mask[id] = feeds.id2Mask[id]; + } + } + } else { + if (feeds == null) { + return; + } + for (const feed of feeds) { + this.add(feed.key, feed.value); + } + } + } + /** + * Add a key-value pair to the FeedDict. + * + * @param key The key of the feed. + * @param value The value of the tensor feed. + * @param mask The value of the mask feed (optional). + * @returns This `FeedDict`. + * @throws ValueError: If the key `SymbolicTensor` already exists in the + * `FeedDict`. + */ + add(key, value, mask) { + if (this.id2Value[key.id] == null) { + this.id2Value[key.id] = assertFeedCompatibility(key, value); + this.name2Id[key.name] = key.id; + if (mask != null) { + this.id2Mask[key.id] = mask; + } + } else { + throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`); + } + return this; + } + /** + * Add a Feed to the FeedDict. + * @param feed The new `Feed` to add. + * @returns This `FeedDict`. + */ + addFeed(feed) { + this.add(feed.key, feed.value); + } + /** + * Probe whether a key already exists in the FeedDict. + * @param key + */ + hasKey(key) { + return this.id2Value[key.id] != null; + } + /** + * Get all the SymbolicTensor available in this FeedDict. + */ + names() { + return Object.keys(this.name2Id); + } + /** + * Get the feed value for given key. + * @param key The SymbolicTensor, or its name (as a string), of which the + * value is sought. + * @returns If `key` exists, the corresponding feed value. + * @throws ValueError: If `key` does not exist in this `FeedDict`. + */ + getValue(key) { + if (key instanceof SymbolicTensor) { + if (this.id2Value[key.id] == null) { + throw new ValueError(`Nonexistent key: ${key.name}`); + } else { + return this.id2Value[key.id]; + } + } else { + const id = this.name2Id[key]; + if (id == null) { + throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`); + } + return this.id2Value[id]; + } + } + /** + * Get the feed mask for given key. + * @param key The SymbolicTensor, or its name (as a string), of which the + * value is sought. + * @returns If `key` exists, the corresponding feed mask. + * @throws ValueError: If `key` does not exist in this `FeedDict`. + */ + getMask(key) { + if (key instanceof SymbolicTensor) { + if (this.id2Value[key.id] == null) { + throw new ValueError(`Nonexistent key: ${key.name}`); + } else { + return this.id2Mask[key.id]; + } + } else { + const id = this.name2Id[key]; + if (id == null) { + throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`); + } + return this.id2Mask[id]; + } + } + /** Dispose all mask Tensors held by this object. */ + disposeMasks() { + if (this.id2Mask != null) { + dispose(this.id2Mask); + } + } +}; +var cachedSorted = new LruCache(); +var cachedRecipientCounts = new LruCache(); +function updateCacheMaxEntries(maxEntries) { + if (cachedSorted != null) { + cachedSorted.setMaxEntries(maxEntries); + } + if (cachedRecipientCounts != null) { + cachedRecipientCounts.setMaxEntries(maxEntries); + } +} +function execute(fetches, feedDict, kwargs, probe) { + const training = kwargs == null ? false : kwargs["training"]; + const arrayFetches = Array.isArray(fetches); + const fetchArray = arrayFetches ? fetches : [fetches]; + const outputNames = fetchArray.map((t) => t.name); + const finalOutputs = []; + const feedNames = feedDict.names(); + for (const outputName of outputNames) { + if (feedNames.indexOf(outputName) !== -1) { + finalOutputs.push(feedDict.getValue(outputName)); + } else { + finalOutputs.push(null); + } + } + if (probe != null) { + probe.maxNumTensors = -Infinity; + probe.minNumTensors = Infinity; + } + const fetchAndFeedKey = outputNames.join(",") + "|" + feedDict.names().sort().join(","); + let sorted = cachedSorted.get(fetchAndFeedKey); + let recipientCounts; + if (sorted == null) { + const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict); + sorted = out.sorted; + recipientCounts = out.recipientCounts; + cachedSorted.put(fetchAndFeedKey, sorted); + cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts); + } + recipientCounts = {}; + if (!training) { + Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey)); + } + const internalFeedDict = new FeedDict(feedDict); + for (let i = 0; i < sorted.length; ++i) { + if (probe != null) { + const numTensors = memory().numTensors; + if (numTensors > probe.maxNumTensors) { + probe.maxNumTensors = numTensors; + } + if (numTensors < probe.minNumTensors) { + probe.minNumTensors = numTensors; + } + } + const symbolic = sorted[i]; + const srcLayer = symbolic.sourceLayer; + if (srcLayer instanceof InputLayer) { + continue; + } + const inputValues = []; + const inputMasks = []; + const tensorsToDispose = []; + let maskExists = false; + for (const input2 of symbolic.inputs) { + const value = internalFeedDict.getValue(input2); + const mask = internalFeedDict.getMask(input2); + inputValues.push(value); + inputMasks.push(mask); + if (mask != null) { + maskExists = true; + } + if (!training) { + recipientCounts[input2.name]--; + if (recipientCounts[input2.name] === 0 && !feedDict.hasKey(input2) && outputNames.indexOf(input2.name) === -1 && !value.isDisposed && input2.sourceLayer.stateful !== true) { + tensorsToDispose.push(value); + } + } + } + if (maskExists) { + kwargs = kwargs || {}; + kwargs["mask"] = inputMasks[0]; + } + const outputTensors = toList(srcLayer.apply(inputValues, kwargs)); + let outputMask = null; + if (srcLayer.supportsMasking) { + outputMask = srcLayer.computeMask(inputValues, inputMasks); + } + const layerOutputs = getNodeOutputs(symbolic); + const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs]; + for (let i2 = 0; i2 < outputSymbolicTensors.length; ++i2) { + if (!internalFeedDict.hasKey(outputSymbolicTensors[i2])) { + internalFeedDict.add(outputSymbolicTensors[i2], outputTensors[i2], Array.isArray(outputMask) ? outputMask[0] : outputMask); + } + const index = outputNames.indexOf(outputSymbolicTensors[i2].name); + if (index !== -1) { + finalOutputs[index] = outputTensors[i2]; + } + } + if (!training) { + dispose(tensorsToDispose); + } + } + internalFeedDict.disposeMasks(); + return arrayFetches ? finalOutputs : finalOutputs[0]; +} +function getTopologicalSortAndRecipientCounts(fetches, feedDict) { + util_exports.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`); + let finalSorted = []; + let finalRecipientMap = {}; + if (fetches.length === 1) { + const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict); + finalSorted = out.sorted; + finalRecipientMap = out.recipientMap; + } else { + const visited = /* @__PURE__ */ new Set(); + for (const fetch4 of fetches) { + const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict); + for (const symbolicTensor of sorted) { + if (!visited.has(symbolicTensor.name)) { + finalSorted.push(symbolicTensor); + visited.add(symbolicTensor.name); + } + } + for (const name in recipientMap) { + if (finalRecipientMap[name] == null) { + finalRecipientMap[name] = /* @__PURE__ */ new Set(); + } + recipientMap[name].forEach((recipient) => finalRecipientMap[name].add(recipient)); + } + } + } + return { + sorted: finalSorted, + recipientCounts: recipientMap2Counts(finalRecipientMap) + }; +} +function recipientMap2Counts(recipientMap) { + const recipientCounts = {}; + for (const name in recipientMap) { + recipientCounts[name] = recipientMap[name].size; + } + return recipientCounts; +} +function getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict) { + const visited = /* @__PURE__ */ new Set(); + const sorted = []; + const recipientMap = {}; + for (const key of feedDict.names()) { + visited.add(key); + } + const stack2 = []; + const marks = []; + stack2.push(fetch4); + while (stack2.length > 0) { + const top = stack2[stack2.length - 1]; + if (visited.has(top.name)) { + stack2.pop(); + continue; + } + const topIsMarked = marks[marks.length - 1] === stack2.length - 1; + if (top.inputs.length === 0 || topIsMarked) { + stack2.pop(); + sorted.push(top); + visited.add(top.name); + if (topIsMarked) { + marks.pop(); + } + } else { + marks.push(stack2.length - 1); + for (const input2 of top.inputs) { + if (recipientMap[input2.name] == null) { + recipientMap[input2.name] = /* @__PURE__ */ new Set(); + } + recipientMap[input2.name].add(top.name); + if (visited.has(input2.name)) { + continue; + } + stack2.push(input2); + } + } + } + return { sorted, recipientMap }; +} +function getNodeOutputs(fetch4) { + let layerOutputs; + if (fetch4.sourceLayer.inboundNodes.length === 1) { + layerOutputs = fetch4.sourceLayer.output; + } else { + let nodeIndex = null; + for (let i = 0; i < fetch4.sourceLayer.inboundNodes.length; ++i) { + for (const outputTensor of fetch4.sourceLayer.inboundNodes[i].outputTensors) { + if (outputTensor.id === fetch4.id) { + nodeIndex = i; + break; + } + } + } + layerOutputs = fetch4.sourceLayer.getOutputAt(nodeIndex); + } + return layerOutputs; +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/flags_layers.js +var ENV3 = env(); +ENV3.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES", () => 100, updateCacheMaxEntries); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js +var exports_constraints_exports = {}; +__export(exports_constraints_exports, { + maxNorm: () => maxNorm, + minMaxNorm: () => minMaxNorm, + nonNeg: () => nonNeg, + unitNorm: () => unitNorm +}); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/constraints.js +function calcL2Norms(w, axis) { + return tidy(() => sqrt(sum2(mul(w, w), axis, true))); +} +var Constraint = class extends serialization_exports.Serializable { + getConfig() { + return {}; + } +}; +var MaxNorm = class extends Constraint { + constructor(args) { + super(); + this.defaultMaxValue = 2; + this.defaultAxis = 0; + this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue; + this.axis = args.axis != null ? args.axis : this.defaultAxis; + } + apply(w) { + return tidy(() => { + const norms = calcL2Norms(w, this.axis); + const desired = clipByValue(norms, 0, this.maxValue); + return mul(w, div(desired, add2(epsilon(), norms))); + }); + } + getConfig() { + return { maxValue: this.maxValue, axis: this.axis }; + } +}; +MaxNorm.className = "MaxNorm"; +serialization_exports.registerClass(MaxNorm); +var UnitNorm = class extends Constraint { + constructor(args) { + super(); + this.defaultAxis = 0; + this.axis = args.axis != null ? args.axis : this.defaultAxis; + } + apply(w) { + return tidy(() => div(w, add2(epsilon(), calcL2Norms(w, this.axis)))); + } + getConfig() { + return { axis: this.axis }; + } +}; +UnitNorm.className = "UnitNorm"; +serialization_exports.registerClass(UnitNorm); +var NonNeg = class extends Constraint { + apply(w) { + return relu(w); + } +}; +NonNeg.className = "NonNeg"; +serialization_exports.registerClass(NonNeg); +var MinMaxNorm = class extends Constraint { + constructor(args) { + super(); + this.defaultMinValue = 0; + this.defaultMaxValue = 1; + this.defaultRate = 1; + this.defaultAxis = 0; + this.minValue = args.minValue != null ? args.minValue : this.defaultMinValue; + this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue; + this.rate = args.rate != null ? args.rate : this.defaultRate; + this.axis = args.axis != null ? args.axis : this.defaultAxis; + } + apply(w) { + return tidy(() => { + const norms = calcL2Norms(w, this.axis); + const desired = add2(mul(this.rate, clipByValue(norms, this.minValue, this.maxValue)), mul(1 - this.rate, norms)); + return mul(w, div(desired, add2(epsilon(), norms))); + }); + } + getConfig() { + return { + minValue: this.minValue, + maxValue: this.maxValue, + rate: this.rate, + axis: this.axis + }; + } +}; +MinMaxNorm.className = "MinMaxNorm"; +serialization_exports.registerClass(MinMaxNorm); +var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = { + "maxNorm": "MaxNorm", + "minMaxNorm": "MinMaxNorm", + "nonNeg": "NonNeg", + "unitNorm": "UnitNorm" +}; +function serializeConstraint(constraint) { + return serializeKerasObject(constraint); +} +function deserializeConstraint(config, customObjects = {}) { + return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "constraint"); +} +function getConstraint(identifier) { + if (identifier == null) { + return null; + } + if (typeof identifier === "string") { + const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ? CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier; + const config = { className, config: {} }; + return deserializeConstraint(config); + } else if (identifier instanceof Constraint) { + return identifier; + } else { + return deserializeConstraint(identifier); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js +function maxNorm(args) { + return new MaxNorm(args); +} +function unitNorm(args) { + return new UnitNorm(args); +} +function nonNeg() { + return new NonNeg(); +} +function minMaxNorm(config) { + return new MinMaxNorm(config); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js +var exports_initializers_exports = {}; +__export(exports_initializers_exports, { + constant: () => constant, + glorotNormal: () => glorotNormal, + glorotUniform: () => glorotUniform, + heNormal: () => heNormal, + heUniform: () => heUniform, + identity: () => identity, + leCunNormal: () => leCunNormal, + leCunUniform: () => leCunUniform, + ones: () => ones3, + orthogonal: () => orthogonal, + randomNormal: () => randomNormal3, + randomUniform: () => randomUniform2, + truncatedNormal: () => truncatedNormal2, + varianceScaling: () => varianceScaling, + zeros: () => zeros2 +}); +function zeros2() { + return new Zeros(); +} +function ones3() { + return new Ones(); +} +function constant(args) { + return new Constant(args); +} +function randomUniform2(args) { + return new RandomUniform(args); +} +function randomNormal3(args) { + return new RandomNormal(args); +} +function truncatedNormal2(args) { + return new TruncatedNormal(args); +} +function identity(args) { + return new Identity2(args); +} +function varianceScaling(config) { + return new VarianceScaling(config); +} +function glorotUniform(args) { + return new GlorotUniform(args); +} +function glorotNormal(args) { + return new GlorotNormal(args); +} +function heNormal(args) { + return new HeNormal(args); +} +function heUniform(args) { + return new HeUniform(args); +} +function leCunNormal(args) { + return new LeCunNormal(args); +} +function leCunUniform(args) { + return new LeCunUniform(args); +} +function orthogonal(args) { + return new Orthogonal(args); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js +var exports_layers_exports = {}; +__export(exports_layers_exports, { + Layer: () => Layer, + RNN: () => RNN, + RNNCell: () => RNNCell, + activation: () => activation, + add: () => add3, + alphaDropout: () => alphaDropout, + average: () => average, + averagePooling1d: () => averagePooling1d, + averagePooling2d: () => averagePooling2d, + averagePooling3d: () => averagePooling3d, + avgPool1d: () => avgPool1d, + avgPool2d: () => avgPool2d, + avgPool3d: () => avgPool3d2, + avgPooling1d: () => avgPooling1d, + avgPooling2d: () => avgPooling2d, + avgPooling3d: () => avgPooling3d, + batchNormalization: () => batchNormalization2, + bidirectional: () => bidirectional, + categoryEncoding: () => categoryEncoding, + centerCrop: () => centerCrop, + concatenate: () => concatenate2, + conv1d: () => conv1d2, + conv2d: () => conv2d3, + conv2dTranspose: () => conv2dTranspose2, + conv3d: () => conv3d2, + conv3dTranspose: () => conv3dTranspose2, + convLstm2d: () => convLstm2d, + convLstm2dCell: () => convLstm2dCell, + cropping2D: () => cropping2D, + dense: () => dense, + depthwiseConv2d: () => depthwiseConv2d4, + dot: () => dot3, + dropout: () => dropout3, + elu: () => elu3, + embedding: () => embedding, + flatten: () => flatten3, + gaussianDropout: () => gaussianDropout, + gaussianNoise: () => gaussianNoise, + globalAveragePooling1d: () => globalAveragePooling1d, + globalAveragePooling2d: () => globalAveragePooling2d, + globalMaxPool1d: () => globalMaxPool1d, + globalMaxPool2d: () => globalMaxPool2d, + globalMaxPooling1d: () => globalMaxPooling1d, + globalMaxPooling2d: () => globalMaxPooling2d, + gru: () => gru, + gruCell: () => gruCell, + input: () => input, + inputLayer: () => inputLayer, + layerNormalization: () => layerNormalization, + leakyReLU: () => leakyReLU, + lstm: () => lstm, + lstmCell: () => lstmCell, + masking: () => masking, + maxPool1d: () => maxPool1d, + maxPool2d: () => maxPool2d, + maxPooling1d: () => maxPooling1d, + maxPooling2d: () => maxPooling2d, + maxPooling3d: () => maxPooling3d, + maximum: () => maximum2, + minimum: () => minimum2, + multiply: () => multiply, + permute: () => permute, + prelu: () => prelu2, + randomWidth: () => randomWidth, + reLU: () => reLU, + repeatVector: () => repeatVector, + rescaling: () => rescaling, + reshape: () => reshape2, + resizing: () => resizing, + rnn: () => rnn2, + separableConv2d: () => separableConv2d2, + simpleRNN: () => simpleRNN, + simpleRNNCell: () => simpleRNNCell, + softmax: () => softmax2, + spatialDropout1d: () => spatialDropout1d, + stackedRNNCells: () => stackedRNNCells, + thresholdedReLU: () => thresholdedReLU, + timeDistributed: () => timeDistributed, + upSampling2d: () => upSampling2d, + zeroPadding2d: () => zeroPadding2d +}); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/logs.js +async function resolveScalarsInLogs(logs) { + if (logs == null) { + return; + } + const promises = []; + const keys = []; + const scalarsToDispose = []; + for (const key in logs) { + const value = logs[key]; + if (typeof value !== "number") { + const valueScalar = value; + promises.push(valueScalar.data()); + keys.push(key); + scalarsToDispose.push(valueScalar); + } + } + if (promises.length > 0) { + const values = await Promise.all(promises); + for (let i = 0; i < values.length; ++i) { + logs[keys[i]] = values[i][0]; + } + dispose(scalarsToDispose); + } +} +function disposeTensorsInLogs(logs) { + if (logs == null) { + return; + } + for (const key in logs) { + const value = logs[key]; + if (typeof value !== "number") { + value.dispose(); + } + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js +var ModelLoggingVerbosity; +(function(ModelLoggingVerbosity2) { + ModelLoggingVerbosity2[ModelLoggingVerbosity2["SILENT"] = 0] = "SILENT"; + ModelLoggingVerbosity2[ModelLoggingVerbosity2["VERBOSE"] = 1] = "VERBOSE"; +})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {})); +var DEFAULT_YIELD_EVERY_MS = 125; +var BaseCallback = class { + constructor() { + this.validationData = null; + } + setParams(params) { + this.params = params; + } + async onEpochBegin(epoch, logs) { + } + async onEpochEnd(epoch, logs) { + } + async onBatchBegin(batch, logs) { + } + async onBatchEnd(batch, logs) { + } + async onTrainBegin(logs) { + } + async onTrainEnd(logs) { + } + // LayersModel needs to call Callback.setModel(), but cannot actually depend + // on Callback because that creates a cyclic dependency. Providing this no-op + // method on BaseCallback breaks the cycle: this way LayersModel can depend on + // BaseCallback but not on Callback. The argument is typed as `Container` + // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback + // overrides this method and enforces that the argument is really a + // LayersModel. + setModel(model2) { + } +}; +var CallbackList = class { + // TODO(cais): When the need arises, uncomment the following lines and + // implement the queue for time values. + // private deltaTBatch: number; + // private deltaTsBatchBegin: Array; + // private deltaTsBatchEnd: Array; + /** + * Constructor of CallbackList. + * @param callbacks Array of `Callback` instances. + * @param queueLength Queue length for keeping running statistics over + * callback execution time. + */ + constructor(callbacks2, queueLength = 10) { + if (callbacks2 == null) { + callbacks2 = []; + } + this.callbacks = callbacks2; + this.queueLength = queueLength; + } + append(callback) { + this.callbacks.push(callback); + } + setParams(params) { + for (const callback of this.callbacks) { + callback.setParams(params); + } + } + setModel(model2) { + for (const callback of this.callbacks) { + callback.setModel(model2); + } + } + /** + * Called at the start of an epoch. + * @param epoch Index of epoch. + * @param logs Dictionary of logs. + */ + async onEpochBegin(epoch, logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onEpochBegin(epoch, logs); + } + } + /** + * Called at the end of an epoch. + * @param epoch Index of epoch. + * @param logs Dictionary of logs. + */ + async onEpochEnd(epoch, logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onEpochEnd(epoch, logs); + } + } + /** + * Called right before processing a batch. + * @param batch Index of batch within the current epoch. + * @param logs Dictionary of logs. + */ + async onBatchBegin(batch, logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onBatchBegin(batch, logs); + } + } + /** + * Called at the end of a batch. + * @param batch Index of batch within the current epoch. + * @param logs Dictionary of logs. + */ + async onBatchEnd(batch, logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onBatchEnd(batch, logs); + } + } + /** + * Called at the beginning of training. + * @param logs Dictionary of logs. + */ + async onTrainBegin(logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onTrainBegin(logs); + } + } + /** + * Called at the end of training. + * @param logs Dictionary of logs. + */ + async onTrainEnd(logs) { + if (logs == null) { + logs = {}; + } + for (const callback of this.callbacks) { + await callback.onTrainEnd(logs); + } + } +}; +var BaseLogger = class extends BaseCallback { + constructor() { + super(); + } + async onEpochBegin(epoch) { + this.seen = 0; + this.totals = {}; + } + async onBatchEnd(batch, logs) { + if (logs == null) { + logs = {}; + } + const batchSize = logs["size"] == null ? 0 : logs["size"]; + this.seen += batchSize; + for (const key in logs) { + const value = logs[key]; + if (typeof value === "number") { + if (!this.totals.hasOwnProperty(key)) { + this.totals[key] = 0; + } + this.totals[key] = this.totals[key] + value * batchSize; + } else { + let oldTotalsToDispose; + if (key in this.totals) { + oldTotalsToDispose = this.totals[key]; + } else { + this.totals[key] = 0; + } + const total = tidy(() => add2(this.totals[key], mul(value, batchSize))); + this.totals[key] = total; + if (oldTotalsToDispose != null) { + oldTotalsToDispose.dispose(); + } + } + } + } + async onEpochEnd(epoch, logs) { + if (logs != null) { + for (const key of this.params["metrics"]) { + if (this.totals[key] == null) { + continue; + } + if (typeof this.totals[key] === "number") { + logs[key] = this.totals[key] / this.seen; + } else { + tidy(() => { + const log5 = mul(div(1, this.seen), this.totals[key]); + logs[key] = log5; + this.totals[key].dispose(); + keep(logs[key]); + }); + } + } + } + } +}; +var History = class extends BaseCallback { + async onTrainBegin(logs) { + this.epoch = []; + this.history = {}; + } + async onEpochEnd(epoch, logs) { + if (logs == null) { + logs = {}; + } + this.epoch.push(epoch); + for (const key in logs) { + if (this.history[key] == null) { + this.history[key] = []; + } + this.history[key].push(logs[key]); + } + } + /** + * Await the values of all losses and metrics. + */ + async syncData() { + const promises = []; + const keys = []; + const indices = []; + for (const key in this.history) { + const valueArray = this.history[key]; + for (let i = 0; i < valueArray.length; ++i) { + if (typeof valueArray[i] !== "number") { + const valueScalar = valueArray[i]; + promises.push(valueScalar.data()); + keys.push(key); + indices.push(i); + } + } + } + const values = await Promise.all(promises); + for (let n = 0; n < values.length; ++n) { + const tensorToDispose = this.history[keys[n]][indices[n]]; + tensorToDispose.dispose(); + this.history[keys[n]][indices[n]] = values[n][0]; + } + } +}; +var CustomCallback = class extends BaseCallback { + constructor(args, yieldEvery) { + super(); + this.currentEpoch = 0; + this.nowFunc = args.nowFunc; + this.nextFrameFunc = args.nextFrameFunc || nextFrame; + this.yieldEvery = yieldEvery || "auto"; + if (this.yieldEvery === "auto") { + this.yieldEvery = DEFAULT_YIELD_EVERY_MS; + } + if (this.yieldEvery === "never" && args.onYield != null) { + throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback"); + } + if (util_exports.isNumber(this.yieldEvery)) { + this.maybeWait = debounce(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc); + } + this.trainBegin = args.onTrainBegin; + this.trainEnd = args.onTrainEnd; + this.epochBegin = args.onEpochBegin; + this.epochEnd = args.onEpochEnd; + this.batchBegin = args.onBatchBegin; + this.batchEnd = args.onBatchEnd; + this.yield = args.onYield; + } + async maybeWait(epoch, batch, logs) { + const ps = []; + if (this.yield != null) { + await resolveScalarsInLogs(logs); + ps.push(this.yield(epoch, batch, logs)); + } + ps.push(this.nextFrameFunc()); + await Promise.all(ps); + } + async onEpochBegin(epoch, logs) { + this.currentEpoch = epoch; + if (this.epochBegin != null) { + await resolveScalarsInLogs(logs); + await this.epochBegin(epoch, logs); + } + } + async onEpochEnd(epoch, logs) { + const ps = []; + if (this.epochEnd != null) { + await resolveScalarsInLogs(logs); + ps.push(this.epochEnd(epoch, logs)); + } + if (this.yieldEvery === "epoch") { + ps.push(this.nextFrameFunc()); + } + await Promise.all(ps); + } + async onBatchBegin(batch, logs) { + if (this.batchBegin != null) { + await resolveScalarsInLogs(logs); + await this.batchBegin(batch, logs); + } + } + async onBatchEnd(batch, logs) { + const ps = []; + if (this.batchEnd != null) { + await resolveScalarsInLogs(logs); + ps.push(this.batchEnd(batch, logs)); + } + if (this.yieldEvery === "batch") { + ps.push(this.nextFrameFunc()); + } else if (util_exports.isNumber(this.yieldEvery)) { + ps.push(this.maybeWait(this.currentEpoch, batch, logs)); + } + await Promise.all(ps); + } + async onTrainBegin(logs) { + if (this.trainBegin != null) { + await resolveScalarsInLogs(logs); + await this.trainBegin(logs); + } + } + async onTrainEnd(logs) { + if (this.trainEnd != null) { + await resolveScalarsInLogs(logs); + await this.trainEnd(logs); + } + } +}; +function standardizeCallbacks(callbacks2, yieldEvery) { + if (callbacks2 == null) { + callbacks2 = {}; + } + if (callbacks2 instanceof BaseCallback) { + return [callbacks2]; + } + if (Array.isArray(callbacks2) && callbacks2[0] instanceof BaseCallback) { + return callbacks2; + } + const callbackConfigs = toList(callbacks2); + return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery)); +} +var CallbackConstructorRegistry = class _CallbackConstructorRegistry { + /** + * Blocks public access to constructor. + */ + constructor() { + } + /** + * Register a tf.LayersModel.fit() callback constructor. + * + * The registered callback constructor will be used to instantiate + * callbacks for every tf.LayersModel.fit() call afterwards. + * + * @param verbosityLevel Level of verbosity at which the `callbackConstructor` + * is to be reigstered. + * @param callbackConstructor A no-arg constructor for `tf.Callback`. + * @throws Error, if the same callbackConstructor has been registered before, + * either at the same or a different `verbosityLevel`. + */ + static registerCallbackConstructor(verbosityLevel, callbackConstructor) { + util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`); + _CallbackConstructorRegistry.checkForDuplicate(callbackConstructor); + if (_CallbackConstructorRegistry.constructors[verbosityLevel] == null) { + _CallbackConstructorRegistry.constructors[verbosityLevel] = []; + } + _CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor); + } + static checkForDuplicate(callbackConstructor) { + for (const levelName in _CallbackConstructorRegistry.constructors) { + const constructors = _CallbackConstructorRegistry.constructors[+levelName]; + constructors.forEach((ctor) => { + if (ctor === callbackConstructor) { + throw new ValueError("Duplicate callback constructor."); + } + }); + } + } + /** + * Clear all registered callback constructors. + */ + static clear() { + _CallbackConstructorRegistry.constructors = {}; + } + /** + * Create callbacks using the registered callback constructors. + * + * Given `verbosityLevel`, all constructors registered at that level or above + * will be called and the instantiated callbacks will be used. + * + * @param verbosityLevel: Level of verbosity. + */ + static createCallbacks(verbosityLevel) { + const constructors = []; + for (const levelName in _CallbackConstructorRegistry.constructors) { + const level = +levelName; + if (verbosityLevel >= level) { + constructors.push(..._CallbackConstructorRegistry.constructors[level]); + } + } + return constructors.map((ctor) => new ctor()); + } +}; +CallbackConstructorRegistry.constructors = {}; +function configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) { + const history = new History(); + const actualCallbacks = [ + new BaseLogger(), + ...CallbackConstructorRegistry.createCallbacks(verbose) + ]; + if (callbacks2 != null) { + actualCallbacks.push(...callbacks2); + } + actualCallbacks.push(history); + const callbackList = new CallbackList(actualCallbacks); + callbackList.setParams({ + epochs, + initialEpoch, + samples: numTrainSamples, + steps: stepsPerEpoch, + batchSize, + verbose, + doValidation, + metrics: callbackMetrics + }); + return { callbackList, history }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/serialization.js +function deserialize(config, customObjects = {}, fastWeightInit = false) { + return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "layer", fastWeightInit); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/losses.js +function l2Normalize(x, axis) { + return tidy(() => { + if (x.dtype !== "float32") { + x = cast(x, "float32"); + } + const squareSum = sum2(square2(x), axis, true); + const epsilonTensor = fill(squareSum.shape, epsilon()); + const norm2 = sqrt(maximum(squareSum, epsilonTensor)); + return div(x, norm2); + }); +} +function meanSquaredError2(yTrue, yPred) { + return tidy(() => mean(square2(sub(yPred, yTrue)), -1)); +} +function meanAbsoluteError(yTrue, yPred) { + return tidy(() => mean(abs(sub(yPred, yTrue)), -1)); +} +function meanAbsolutePercentageError(yTrue, yPred) { + return tidy(() => { + const diff = sub(yTrue, yPred); + const clippedTrue = clipByValue(abs(yTrue), epsilon(), Number.MAX_VALUE); + const absResult = abs(div(diff, clippedTrue)); + return mul(100, mean(absResult, -1)); + }); +} +function meanSquaredLogarithmicError(yTrue, yPred) { + return tidy(() => { + const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE); + const firstLog = log2(add2(1, clippedPred)); + const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE); + const secondLog = log2(add2(1, clippedTrue)); + return mean(square2(sub(firstLog, secondLog)), -1); + }); +} +function squaredHinge(yTrue, yPred) { + return tidy(() => { + const maxResult = maximum(0, sub(1, mul(yTrue, yPred))); + return mean(square2(maxResult), -1); + }); +} +function hinge(yTrue, yPred) { + return tidy(() => { + const maxResult = maximum(0, sub(1, mul(yTrue, yPred))); + return mean(maxResult, -1); + }); +} +function categoricalHinge(yTrue, yPred) { + return tidy(() => { + const pos = sum2(mul(yTrue, yPred), -1); + const neg4 = max(mul(sub(1, yTrue), yPred), -1); + return maximum(0, add2(1, sub(neg4, pos))); + }); +} +function logcosh(yTrue, yPred) { + return tidy(() => { + const log22 = Math.log(2); + const predictionDiff = sub(yPred, yTrue); + const logcoshResult = sub(add2(predictionDiff, softplus(mul(-2, predictionDiff))), log22); + return mean(logcoshResult, -1); + }); +} +function categoricalCrossentropy(target, output, fromLogits = false) { + return tidy(() => { + if (fromLogits) { + output = softmax(output); + } else { + const outputSum = sum2(output, output.shape.length - 1, true); + output = div(output, outputSum); + } + output = clipByValue(output, epsilon(), 1 - epsilon()); + return neg(sum2(mul(cast(target, "float32"), log2(output)), output.shape.length - 1)); + }); +} +function sparseCategoricalCrossentropy(target, output, fromLogits = false) { + return tidy(() => { + const flatTarget = cast(floor(flatten2(target)), "int32"); + output = clipByValue(output, epsilon(), 1 - epsilon()); + const outputShape = output.shape; + const oneHotTarget = reshape(oneHot(flatTarget, outputShape[outputShape.length - 1]), outputShape); + return categoricalCrossentropy(oneHotTarget, output, fromLogits); + }); +} +function sigmoidCrossEntropyWithLogits(labels, logits) { + if (!util_exports.arraysEqual(labels.shape, logits.shape)) { + throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`); + } + return tidy(() => { + const reluLogits = relu(logits); + const negAbsLogits = neg(abs(logits)); + return add2(sub(reluLogits, mul(logits, labels)), log1p(exp(negAbsLogits))); + }); +} +function binaryCrossentropy(yTrue, yPred) { + return tidy(() => { + let y; + y = clipByValue(yPred, epsilon(), 1 - epsilon()); + y = log2(div(y, sub(1, y))); + return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1); + }); +} +function kullbackLeiblerDivergence(yTrue, yPred) { + return tidy(() => { + const clippedTrue = clipByValue(yTrue, epsilon(), 1); + const clippedPred = clipByValue(yPred, epsilon(), 1); + return sum2(mul(yTrue, log2(div(clippedTrue, clippedPred))), -1); + }); +} +function poisson(yTrue, yPred) { + return tidy(() => { + const logPred = log2(add2(epsilon(), yPred)); + return mean(sub(yPred, mul(yTrue, logPred)), -1); + }); +} +function cosineProximity(yTrue, yPred) { + return tidy(() => { + const trueNormalized = l2Normalize(yTrue, -1); + const predNormalized = l2Normalize(yPred, -1); + const trueXPred = mul(trueNormalized, predNormalized); + return neg(sum2(trueXPred, -1)); + }); +} +var lossesMap = { + meanSquaredError: meanSquaredError2, + meanAbsoluteError, + meanAbsolutePercentageError, + meanSquaredLogarithmicError, + squaredHinge, + hinge, + categoricalHinge, + logcosh, + categoricalCrossentropy, + sparseCategoricalCrossentropy, + binaryCrossentropy, + kullbackLeiblerDivergence, + poisson, + cosineProximity +}; +function get(identifierOrFn) { + if (typeof identifierOrFn === "string") { + if (identifierOrFn in lossesMap) { + return lossesMap[identifierOrFn]; + } + let errMsg = `Unknown loss ${identifierOrFn}`; + if (identifierOrFn.toLowerCase().includes("softmaxcrossentropy")) { + errMsg = `Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`; + } + throw new ValueError(errMsg); + } else { + return identifierOrFn; + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/metrics.js +function binaryAccuracy(yTrue, yPred) { + return tidy(() => { + const threshold3 = mul(0.5, onesLike(yPred)); + const yPredThresholded = cast2(greater(yPred, threshold3), yTrue.dtype); + return mean(equal(yTrue, yPredThresholded), -1); + }); +} +function categoricalAccuracy(yTrue, yPred) { + return tidy(() => cast2(equal(argMax(yTrue, -1), argMax(yPred, -1)), "float32")); +} +function truePositives(yTrue, yPred) { + return tidy(() => { + return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 1))), "float32"); + }); +} +function falseNegatives(yTrue, yPred) { + return tidy(() => { + return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 0))), "float32"); + }); +} +function falsePositives(yTrue, yPred) { + return tidy(() => { + return cast(sum2(logicalAnd(equal(yTrue, 0), equal(yPred, 1))), "float32"); + }); +} +function precision(yTrue, yPred) { + return tidy(() => { + const tp = truePositives(yTrue, yPred); + const fp = falsePositives(yTrue, yPred); + const denominator = add2(tp, fp); + return cast(where(greater(denominator, 0), div(tp, denominator), 0), "float32"); + }); +} +function recall(yTrue, yPred) { + return tidy(() => { + const tp = truePositives(yTrue, yPred); + const fn = falseNegatives(yTrue, yPred); + const denominator = add2(tp, fn); + return cast(where(greater(denominator, 0), div(tp, denominator), 0), "float32"); + }); +} +function binaryCrossentropy2(yTrue, yPred) { + return binaryCrossentropy(yTrue, yPred); +} +function sparseCategoricalAccuracy(yTrue, yPred) { + if (yTrue.rank === yPred.rank) { + yTrue = squeeze(yTrue, [yTrue.rank - 1]); + } + yPred = argMax(yPred, -1); + if (yPred.dtype !== yTrue.dtype) { + yPred = cast(yPred, yTrue.dtype); + } + return cast(equal(yTrue, yPred), "float32"); +} +var mse = meanSquaredError2; +var MSE = meanSquaredError2; +var mae = meanAbsoluteError; +var MAE = meanAbsoluteError; +var mape = meanAbsolutePercentageError; +var MAPE = meanAbsolutePercentageError; +var categoricalCrossentropy2 = categoricalCrossentropy; +var cosine = cosineProximity; +var sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy; +var metricsMap = { + binaryAccuracy, + categoricalAccuracy, + precision, + categoricalCrossentropy: categoricalCrossentropy2, + sparseCategoricalCrossentropy: sparseCategoricalCrossentropy2, + mse, + MSE, + mae, + MAE, + mape, + MAPE, + cosine +}; +function get2(identifier) { + if (typeof identifier === "string" && identifier in metricsMap) { + return metricsMap[identifier]; + } else if (typeof identifier !== "string" && identifier != null) { + return identifier; + } else { + throw new ValueError(`Unknown metric ${identifier}`); + } +} +function getLossOrMetricName(fn) { + assert2(fn !== null, `Unknown LossOrMetricFn ${fn}`); + if (typeof fn === "string") { + return fn; + } else { + let fnName; + for (const key of Object.keys(lossesMap)) { + if (lossesMap[key] === fn) { + fnName = key; + break; + } + } + if (fnName !== void 0) { + return fnName; + } + for (const key of Object.keys(metricsMap)) { + if (metricsMap[key] === fn) { + fnName = key; + break; + } + } + if (fnName !== void 0) { + return fnName; + } + return fn.name; + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/optimizers.js +function getOptimizer(identifier) { + const optimizerMap = { + "Adagrad": () => train.adagrad(0.01), + "Adadelta": () => train.adadelta(1, 0.95, epsilon()), + "Adam": () => train.adam(1e-3, 0.9, 0.999, epsilon()), + "Adamax": () => train.adamax(2e-3, 0.9, 0.999, epsilon(), 0), + "RMSProp": () => train.rmsprop(1e-3, 0.9, 0, epsilon()), + "SGD": () => train.sgd(0.01) + }; + optimizerMap["adagrad"] = optimizerMap["Adagrad"]; + optimizerMap["adadelta"] = optimizerMap["Adadelta"]; + optimizerMap["adam"] = optimizerMap["Adam"]; + optimizerMap["adamax"] = optimizerMap["Adamax"]; + optimizerMap["rmsprop"] = optimizerMap["RMSProp"]; + optimizerMap["sgd"] = optimizerMap["SGD"]; + if (identifier in optimizerMap) { + return optimizerMap[identifier](); + } + throw new ValueError(`Unknown Optimizer ${identifier}`); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/user_defined_metadata.js +var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024; +function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) { + if (userDefinedMetadata == null || typeof userDefinedMetadata !== "object" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) { + throw new Error("User-defined metadata is expected to be a JSON object, but is not."); + } + if (checkSize) { + const out = JSON.stringify(userDefinedMetadata); + if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) { + console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`); + } + } +} +function plainObjectCheck(x) { + if (x === null) { + return true; + } else if (typeof x === "object") { + if (Object.getPrototypeOf(x) === Object.prototype) { + const keys = Object.keys(x); + for (const key of keys) { + if (typeof key !== "string") { + return false; + } + if (!plainObjectCheck(x[key])) { + return false; + } + } + return true; + } else { + if (Array.isArray(x)) { + for (const item of x) { + if (!plainObjectCheck(item)) { + return false; + } + } + return true; + } else { + return false; + } + } + } else { + const xType = typeof x; + return xType === "string" || xType === "number" || xType === "boolean"; + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/layer_utils.js +function printSummary(model2, lineLength, positions, printFn = console.log) { + const sequentialLike = isModelSequentialLike(model2); + const toDisplay = ["Layer (type)", "Input Shape", "Output shape", "Param #"]; + if (sequentialLike) { + lineLength = lineLength || 90; + positions = positions || [0.32, 0.61, 0.89, 1]; + } else { + lineLength = lineLength || 115; + positions = positions || [0.24, 0.48, 0.7, 0.8, 1]; + } + if (positions[positions.length - 1] <= 1) { + positions = positions.map((p2) => Math.floor(lineLength * p2)); + } + let relevantNodes; + if (!sequentialLike) { + toDisplay.push("Receives inputs"); + relevantNodes = []; + for (const depth in model2.nodesByDepth) { + relevantNodes.push(...model2.nodesByDepth[depth]); + } + } + printFn("_".repeat(lineLength)); + printRow(toDisplay, positions, printFn); + printFn("=".repeat(lineLength)); + const layers = model2.layers; + for (let i = 0; i < layers.length; ++i) { + if (sequentialLike) { + printLayerSummary(layers[i], positions, printFn); + } else { + printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn); + } + printFn((i === layers.length - 1 ? "=" : "_").repeat(lineLength)); + } + model2.checkTrainableWeightsConsistency(); + const trainableCount = countTrainableParams(model2); + const nonTrainableCount = countParamsInWeights(model2.nonTrainableWeights); + printFn(`Total params: ${trainableCount + nonTrainableCount}`); + printFn(`Trainable params: ${trainableCount}`); + printFn(`Non-trainable params: ${nonTrainableCount}`); + printFn("_".repeat(lineLength)); +} +function countTrainableParams(model2) { + let trainableCount; + if (model2.collectedTrainableWeights != null) { + trainableCount = countParamsInWeights(model2.collectedTrainableWeights); + } else { + trainableCount = countParamsInWeights(model2.trainableWeights); + } + return trainableCount; +} +function isModelSequentialLike(model2) { + let sequentialLike = true; + const nodesByDepth = []; + const nodes = []; + for (const depth in model2.nodesByDepth) { + nodesByDepth.push(model2.nodesByDepth[depth]); + } + for (const depthNodes of nodesByDepth) { + if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) { + sequentialLike = false; + break; + } + nodes.push(...depthNodes); + } + if (sequentialLike) { + for (const layer of model2.layers) { + let flag = false; + for (const node of layer.inboundNodes) { + if (nodes.indexOf(node) !== -1) { + if (flag) { + sequentialLike = false; + break; + } else { + flag = true; + } + } + } + if (!sequentialLike) { + break; + } + } + } + return sequentialLike; +} +function printRow(fields, positions, printFn = console.log) { + let line = ""; + for (let i = 0; i < fields.length; ++i) { + if (i > 0) { + line = line.slice(0, line.length - 1) + " "; + } + line += fields[i]; + line = line.slice(0, positions[i]); + line += " ".repeat(positions[i] - line.length); + } + printFn(line); +} +function printLayerSummary(layer, positions, printFn) { + let outputShape; + let inputShape; + try { + inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(","); + } catch (err) { + inputShape = "multiple"; + } + try { + outputShape = JSON.stringify(layer.outputShape); + } catch (err) { + outputShape = "multiple"; + } + const name = layer.name; + const className = layer.getClassName(); + const fields = [ + `${name} (${className})`, + inputShape, + outputShape, + layer.countParams().toString() + ]; + printRow(fields, positions, printFn); +} +function printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) { + let outputShape; + let inputShape; + try { + inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(","); + } catch (err) { + inputShape = "multiple"; + } + try { + outputShape = JSON.stringify(layer.outputShape); + } catch (err) { + outputShape = "multiple"; + } + const connections = []; + for (const node of layer.inboundNodes) { + if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node) === -1) { + continue; + } + for (let i = 0; i < node.inboundLayers.length; ++i) { + const inboundLayer = node.inboundLayers[i].name; + const inboundLayerIndex = node.nodeIndices[i]; + const inboundTensorIndex = node.tensorIndices[i]; + connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`); + } + } + const name = layer.name; + const className = layer.getClassName(); + const firstConnection = connections.length === 0 ? "" : connections[0]; + const fields = [ + `${name} (${className})`, + inputShape, + outputShape, + layer.countParams().toString(), + firstConnection + ]; + printRow(fields, positions, printFn); + for (let i = 1; i < connections.length; ++i) { + printRow(["", "", "", "", connections[i]], positions, printFn); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/serialization_utils.js +function isArrayItemInputOrOutputName(key, index, value) { + return (key === "inboundNodes" || key === "outputLayers" || key === "inputLayers") && index === 0 && typeof value === "string"; +} +function convertPythonicToTs(pythonicConfig, key) { + if (pythonicConfig === null) { + return null; + } else if (typeof pythonicConfig === "string") { + return toCamelCase(pythonicConfig); + } else if (typeof pythonicConfig === "number" || typeof pythonicConfig === "boolean") { + return pythonicConfig; + } else if (pythonicConfig instanceof Array) { + const tsArray = []; + const arrayLength = pythonicConfig.length; + for (let i = 0; i < arrayLength; ++i) { + const item = pythonicConfig[i]; + if (isArrayItemInputOrOutputName(key, i, item)) { + tsArray.push(item); + } else { + tsArray.push(convertPythonicToTs(item, key)); + } + } + return tsArray; + } else { + const tsDict = {}; + for (const pythonicKey of Object.keys(pythonicConfig)) { + const pythonicValue = pythonicConfig[pythonicKey]; + if (pythonicKey === "name" && typeof pythonicValue === "string") { + tsDict[pythonicKey] = pythonicValue; + } else { + const tsKey = toCamelCase(pythonicKey); + tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey); + } + } + return tsDict; + } +} +function convertTsToPythonic(tsConfig, key) { + if (tsConfig === null || tsConfig === void 0) { + return null; + } else if (typeof tsConfig === "string") { + return toSnakeCase(tsConfig); + } else if (typeof tsConfig === "number" || typeof tsConfig === "boolean") { + return tsConfig; + } else if (tsConfig instanceof Array) { + const pyArray = []; + const arrayLength = tsConfig.length; + for (let i = 0; i < arrayLength; ++i) { + const item = tsConfig[i]; + if (isArrayItemInputOrOutputName(key, i, item)) { + pyArray.push(item); + } else { + pyArray.push(convertTsToPythonic(item, key)); + } + } + return pyArray; + } else { + const pyDict = {}; + for (const tsKey of Object.keys(tsConfig)) { + const tsValue = tsConfig[tsKey]; + const pyKey = toSnakeCase(tsKey); + if ((tsKey === "name" || tsKey === "className") && typeof tsValue === "string") { + pyDict[pyKey] = tsValue; + } else { + pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey); + } + } + return pyDict; + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/version.js +var version2 = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/container.js +var isKerasSavedModelFormat = (weights) => { + const keys = Object.keys(weights); + if (keys.length === 0) { + return false; + } + const key = keys[0].split("/"); + return !isNaN(parseInt(key[key.length - 1], 10)); +}; +var Container = class _Container extends Layer { + constructor(args) { + super({}); + this.containerNodes = /* @__PURE__ */ new Set(); + this.name = args.name; + if (this.name == null) { + const prefix = this.getClassName().toLowerCase(); + this.name = getUid(prefix); + } + this.supportsMasking = false; + this.trainable_ = true; + if (Array.isArray(args.inputs)) { + this.inputs = args.inputs.slice(); + } else { + this.inputs = [args.inputs]; + } + if (Array.isArray(args.outputs)) { + this.outputs = args.outputs.slice(); + } else { + this.outputs = [args.outputs]; + } + if (unique2(this.inputs).length !== this.inputs.length) { + throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((x) => x.name)}`); + } + if (unique2(this.outputs).length !== this.outputs.length) { + console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((x) => x.name)}`); + } + this.inputLayers = []; + this.inputLayersNodeIndices = []; + this.inputLayersTensorIndices = []; + this.outputLayers = []; + this.outputLayersNodeIndices = []; + this.outputLayersTensorIndices = []; + this.layers = []; + this.internalContainerRefs = []; + for (const x of this.outputs) { + const layer = x.sourceLayer; + const nodeIndex = x.nodeIndex; + const tensorIndex = x.tensorIndex; + this.outputLayers.push(layer); + this.outputLayersNodeIndices.push(nodeIndex); + this.outputLayersTensorIndices.push(tensorIndex); + } + for (const x of this.inputs) { + const layer = x.sourceLayer; + const nodeIndex = x.nodeIndex; + const tensorIndex = x.tensorIndex; + assert2(nodeIndex === 0, "input layer has >1 nodes"); + assert2(tensorIndex === 0, "input layer has >1 tensors"); + this.inputLayers.push(layer); + this.inputLayersNodeIndices.push(nodeIndex); + this.inputLayersTensorIndices.push(tensorIndex); + } + this.inputNames = []; + this.outputNames = []; + this.feedInputShapes = []; + this.feedInputNames = []; + this.feedOutputNames = []; + for (let i = 0; i < this.inputLayers.length; i++) { + const layer = this.inputLayers[i]; + if (!(layer instanceof InputLayer)) { + throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`); + } + this.inputNames.push(layer.name); + this.feedInputShapes.push(layer.batchInputShape); + this.feedInputNames.push(layer.name); + } + for (const layer of this.outputLayers) { + this.outputNames.push(layer.name); + } + this.internalInputShapes = this.inputs.map((x) => x.shape); + this.internalOutputShapes = this.outputs.map((x) => x.shape); + const nodesDepths = {}; + const nodeIDToNode = {}; + const layersDepths = {}; + const layerIDToLayer = {}; + const layerIndices = {}; + const nodesInDecreasingDepth = []; + const buildMapOfGraph = (tensor2, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => { + if (layer == null || nodeIndex == null || tensorIndex == null) { + layer = tensor2.sourceLayer; + nodeIndex = tensor2.nodeIndex; + tensorIndex = tensor2.tensorIndex; + } + const node = layer.inboundNodes[nodeIndex]; + if (nodesInProgress2.indexOf(node) !== -1) { + throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`); + } + if (finishedNodes2.indexOf(node) !== -1) { + return; + } + this.containerNodes.add(_Container.nodeKey(layer, nodeIndex)); + if (!(layer.id in layerIndices)) { + layerIndices[layer.id] = Object.keys(layerIndices).length; + } + if (nodesInProgress2.indexOf(node) === -1) { + nodesInProgress2.push(node); + } + const numInboundLayers = node.inboundLayers.length; + for (let i = 0; i < numInboundLayers; i++) { + const x = node.inputTensors[i]; + const layer2 = node.inboundLayers[i]; + const nodeIndex2 = node.nodeIndices[i]; + const tensorIndex2 = node.tensorIndices[i]; + buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2); + } + finishedNodes2.push(node); + while (nodesInProgress2.indexOf(node) >= 0) { + nodesInProgress2.splice(nodesInProgress2.indexOf(node), 1); + } + nodesInDecreasingDepth.push(node); + }; + const finishedNodes = []; + const nodesInProgress = []; + for (const x of this.outputs) { + buildMapOfGraph(x, finishedNodes, nodesInProgress); + } + const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse(); + for (const node of reversedNodesInDecreasingDepth) { + nodeIDToNode[node.id] = node; + if (!(node.id in nodesDepths)) { + nodesDepths[node.id] = 0; + } + let depth = nodesDepths[node.id]; + const previousDepth = layersDepths[node.outboundLayer.id] == null ? 0 : layersDepths[node.outboundLayer.id]; + depth = Math.max(depth, previousDepth); + layersDepths[node.outboundLayer.id] = depth; + layerIDToLayer[node.outboundLayer.id] = node.outboundLayer; + nodesDepths[node.id] = depth; + for (let i = 0; i < node.inboundLayers.length; i++) { + const inboundLayer = node.inboundLayers[i]; + const nodeIndex = node.nodeIndices[i]; + const inboundNode = inboundLayer.inboundNodes[nodeIndex]; + const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id]; + nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2); + nodeIDToNode[inboundNode.id] = inboundNode; + } + } + const nodesByDepth = {}; + for (const nodeID in nodesDepths) { + const depth = nodesDepths[nodeID]; + if (!(depth in nodesByDepth)) { + nodesByDepth[depth] = []; + } + nodesByDepth[depth].push(nodeIDToNode[nodeID]); + } + const layersByDepth = {}; + for (const layerID in layersDepths) { + const depth = layersDepths[layerID]; + if (!(depth in layersByDepth)) { + layersByDepth[depth] = []; + } + layersByDepth[depth].push(layerIDToLayer[layerID]); + } + let depthKeys = Object.keys(layersByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare); + this.layers = []; + for (const depth of depthKeys) { + const layersForDepth = layersByDepth[depth]; + layersForDepth.sort((a, b) => { + const aIndex = layerIndices[a.id]; + const bIndex = layerIndices[b.id]; + if (aIndex < bIndex) { + return -1; + } + if (aIndex > bIndex) { + return 1; + } + return 0; + }); + for (const layer of layersForDepth) { + if (layer instanceof _Container) { + this.internalContainerRefs.push(layer); + } + this.layers.push(layer); + } + } + this.layersByDepth = layersByDepth; + depthKeys = Object.keys(nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare); + const computableTensors = this.inputs.slice(); + const layersWithCompleteInput = []; + for (const depth of depthKeys) { + for (const node of nodesByDepth[depth]) { + const layer = node.outboundLayer; + if (layer != null) { + for (const x of node.inputTensors) { + if (computableTensors.indexOf(x) === -1) { + throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`); + } + } + for (const x of node.outputTensors) { + computableTensors.push(x); + } + layersWithCompleteInput.push(layer.name); + } + } + } + this.nodesByDepth = nodesByDepth; + const allNames = this.layers.map((x) => x.name); + for (const name of allNames) { + const numOccurrences = allNames.filter((x) => x === name).length; + if (numOccurrences !== 1) { + throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(allNames)); + } + } + this.outboundNodes = []; + this.inboundNodes = []; + new Node({ + outboundLayer: this, + inboundLayers: [], + nodeIndices: [], + tensorIndices: [], + inputTensors: this.inputs, + outputTensors: this.outputs, + inputMasks: this.inputs.map((x) => null), + outputMasks: this.outputs.map((x) => null), + inputShapes: this.inputs.map((x) => x.shape), + outputShapes: this.outputs.map((x) => x.shape) + }); + this.built = true; + this._refCount = 1; + } + assertNotDisposed() { + if (this._refCount === 0) { + throw new Error(`Container '${this.name}' is already disposed.`); + } + } + /** + * Attempt to dispose a LayersModel's weights. + * + * This method decrease the reference count of the LayersModel object by 1. + * + * A LayersModel is reference-counted. Its reference count is incremented by 1 + * when it is first constructed and when it is used as a Layer of another + * LayersModel. + * + * If the reference count of a LayersModel becomes 0, the `dispose` method of + * all its constituent `Layer`s will be called. + * + * Note: If the reference count is greater than 0 after the decrement, the + * `dispose` method of its constituent `Layer`s will *not* be called. + * + * After a LayersModel is disposed, it cannot be used in calls such as + * 'predict`, `evaluate` or `fit` anymore. + * + * @returns A DisposeResult Object with the following fields: + * - refCountAfterDispose: The reference count of the LayersModel after this + * `dispose()` call. + * - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed + * during this `dispose()` call. + * @throws {Error} If the layer is not built yet, or if the LayersModel has + * already been disposed. + */ + dispose() { + this.assertNotDisposed(); + const result = { refCountAfterDispose: null, numDisposedVariables: 0 }; + if (--this._refCount === 0) { + for (const layer of this.layers) { + result.numDisposedVariables += layer.dispose().numDisposedVariables; + } + for (const container of this.internalContainerRefs) { + result.numDisposedVariables += container.dispose().numDisposedVariables; + } + } + result.refCountAfterDispose = this._refCount; + return result; + } + get trainable() { + return this.trainable_; + } + set trainable(trainable) { + this.layers.forEach((layer) => { + layer._trainableWeights.forEach((w) => w.trainable = trainable); + }); + this.trainable_ = trainable; + } + get trainableWeights() { + if (this._trainableWeights.length > 0) { + throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array."); + } + if (!this.trainable) { + return []; + } + let weights = []; + for (const layer of this.layers) { + weights = weights.concat(layer.trainableWeights); + } + return weights; + } + get nonTrainableWeights() { + const weights = []; + for (const layer of this.layers) { + weights.push(...layer.nonTrainableWeights); + } + if (!this.trainable) { + const trainableWeights = []; + for (const layer of this.layers) { + trainableWeights.push(...layer.trainableWeights); + } + return trainableWeights.concat(weights); + } + return weights; + } + get weights() { + return this.trainableWeights.concat(this.nonTrainableWeights); + } + /** + * Loads all layer weights from a JSON object. + * + * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript / + * TypeScript. The utility script at `scripts/pykeras.py` offers means + * to convert them into JSON strings compatible with this method. + * Porting Note: TensorFlow.js Layers supports only loading by name currently. + * + * @param weights A JSON mapping weight names to weight values as nested + * arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight + * names to `tf.Tensor` objects. + * @param strict Require that the provided weights exactly match those + * required by the container. Default: `true`. Passing `false` means that + * extra weights and missing weights will be silently ignored. + */ + loadWeights(weights, strict = true) { + const nameToWeight = {}; + let totalWeightsCount = 0; + const modelIsKerasSavedModelFormat = isKerasSavedModelFormat(weights); + if (modelIsKerasSavedModelFormat) { + this.parseWeights(weights); + } + for (const layer of this.layers) { + for (const [index, weight] of layer.weights.entries()) { + const parsedName = modelIsKerasSavedModelFormat ? `${weight.name.split("/").slice(0, -1).join("/") + "/"}${index}` : weight.originalName; + if (nameToWeight[parsedName] != null) { + throw new ValueError(`Duplicate weight name: ${parsedName}`); + } + nameToWeight[parsedName] = weight; + totalWeightsCount++; + } + } + const weightValueTuples = []; + for (const name in weights) { + let validatedName = name; + if (nameToWeight[name] == null) { + const tokens = name.split("/"); + const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]); + validatedName = shortenNameArray.join("/"); + } + if (nameToWeight[validatedName] != null) { + weightValueTuples.push([nameToWeight[validatedName], weights[name]]); + } else if (strict) { + throw new ValueError(`Provided weight data has no target variable: ${name}`); + } + delete nameToWeight[validatedName]; + } + if (strict) { + const unsetNames = []; + for (const name in nameToWeight) { + unsetNames.push(name); + } + if (unsetNames.length > 0) { + throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`); + } + } + batchSetValue(weightValueTuples); + } + parseWeights(weights) { + for (const key in Object.keys(weights)) { + const listParts = key.split("/"); + const list = ["vars", "layer_checkpoint_dependencies"]; + const newKey = listParts.map((str) => { + if (str.startsWith("_")) { + return str.slice(1); + } + return str; + }).filter((str) => !list.includes(str)).join("/"); + if (newKey !== key) { + weights[newKey] = weights[key]; + delete weights[key]; + } + } + } + /** + * Util shared between different serialization methods. + * @returns LayersModel config with Keras version information added. + */ + updatedConfig() { + const theConfig = this.getConfig(); + const modelConfig = {}; + modelConfig["className"] = this.getClassName(); + modelConfig["config"] = theConfig; + modelConfig["kerasVersion"] = `tfjs-layers ${version2}`; + modelConfig["backend"] = "TensorFlow.js"; + return modelConfig; + } + /** + * Returns a JSON string containing the network configuration. + * + * To load a network from a JSON save file, use + * models.modelFromJSON(jsonString); + * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras + * @param returnString Whether the return value should be stringified + * (default: `true`). + * @returns a JSON string if `returnString` (default), or a JSON object if + * `!returnString`. + */ + // tslint:disable-next-line:no-any + toJSON(unused, returnString = true) { + const modelConfig = convertTsToPythonic(this.updatedConfig()); + return returnString ? JSON.stringify(modelConfig) : modelConfig; + } + /** + * Call the model on new inputs. + * + * In this case `call` just reapplies all ops in the graph to the new inputs + * (e.g. build a new computational graph from the provided inputs). + * + * @param inputs A tensor or list of tensors. + * @param mask A mask or list of masks. A mask can be either a tensor or null + * (no mask). + * + * @return A tensor if there is a single output, or a list of tensors if there + * are more than one outputs. + */ + call(inputs, kwargs) { + return tidy(() => { + inputs = toList(inputs); + const feedDict = new FeedDict(); + for (let i = 0; i < this.inputs.length; ++i) { + feedDict.add(this.inputs[i], inputs[i]); + } + return execute(this.outputs, feedDict, kwargs); + }); + } + /** + * Computes an output mask tensor. + * + * @param inputs Tensor or list of tensors. + * @param mask Tensor or list of tensors. + * + * @return null or a tensor (or list of tensors, one per output tensor of the + * layer). + */ + computeMask(inputs, mask) { + return tidy(() => { + inputs = toList(inputs); + let masks; + if (mask == null) { + masks = pyListRepeat(null, inputs.length); + } else { + masks = toList(mask); + } + return this.runInternalGraph(inputs, masks)[1]; + }); + } + /** + * Computes the output shape of the layer. + * + * Assumes that the layer will be built to match that input shape provided. + * + * @param inputShape A shape (tuple of integers) or a list of shape tuples + * (one per output tensor of the layer). Shape tuples can include null for + * free dimensions, instead of an integer. + */ + computeOutputShape(inputShape) { + const inputShapes = normalizeShapeList(inputShape); + if (inputShapes.length !== this.inputLayers.length) { + throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`); + } + const layersToOutputShapes = {}; + for (let i = 0; i < inputShapes.length; i++) { + const layer = this.inputLayers[i]; + const inputShape2 = inputShapes[i]; + const shapeKey = layer.name + "_0_0"; + layersToOutputShapes[shapeKey] = inputShape2; + } + const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare); + if (depthKeys.length > 1) { + for (const depth of depthKeys) { + const nodes = this.nodesByDepth[depth]; + for (const node of nodes) { + const layer = node.outboundLayer; + if (this.inputLayers.map((x) => x.id).indexOf(layer.id) !== -1) { + continue; + } + const inputShapes2 = []; + for (let j = 0; j < node.inboundLayers.length; j++) { + const inboundLayer = node.inboundLayers[j]; + const nodeIndex2 = node.nodeIndices[j]; + const tensorIndex = node.tensorIndices[j]; + const shapeKey = `${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`; + const inputShape2 = layersToOutputShapes[shapeKey]; + inputShapes2.push(inputShape2); + } + const outputShape = layer.computeOutputShape(singletonOrArray(inputShapes2)); + const outputShapes2 = normalizeShapeList(outputShape); + const nodeIndex = layer.inboundNodes.indexOf(node); + for (let j = 0; j < outputShapes2.length; j++) { + const shapeKey = `${layer.name}_${nodeIndex}_${j}`; + layersToOutputShapes[shapeKey] = outputShapes2[j]; + } + } + } + } + const outputShapes = []; + const outputShapeKeys = []; + for (let i = 0; i < this.outputLayers.length; i++) { + const layer = this.outputLayers[i]; + const nodeIndex = this.outputLayersNodeIndices[i]; + const tensorIndex = this.outputLayersTensorIndices[i]; + const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`; + outputShapeKeys.push(shapeKey); + } + for (let i = 0; i < outputShapeKeys.length; i++) { + const key = outputShapeKeys[i]; + assert2(key in layersToOutputShapes); + outputShapes.push(layersToOutputShapes[key]); + } + return singletonOrArray(outputShapes); + } + /** + * Computes output tensors for new inputs. + * + * Note: + * - Expects `inputs` to be a list (potentially with 1 element). + * + * @param inputs List of tensors + * @param masks List of masks (tensors or null). + * @return Three lists: outputTensors, outputMasks, outputShapes + */ + runInternalGraph(inputs, masks) { + if (masks == null) { + masks = pyListRepeat(null, inputs.length); + } + const tensorMap = {}; + for (let i = 0; i < this.inputs.length; ++i) { + const x = this.inputs[i]; + const y = inputs[i]; + const mask = masks[i]; + tensorMap[x.id] = [y, mask]; + } + const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare); + for (const depth of depthKeys) { + const nodes = this.nodesByDepth[depth]; + for (const node of nodes) { + const layer = node.outboundLayer; + const referenceInputTensors = node.inputTensors; + const referenceOutputTensors = node.outputTensors; + const computedData = new Array(); + for (const x of referenceInputTensors) { + if (x.id in tensorMap) { + computedData.push(tensorMap[x.id]); + } + } + if (computedData.length === referenceInputTensors.length) { + let kwargs = {}; + let computedTensors; + let computedMasks; + let outputTensors2; + let outputMasks2; + if (node.callArgs != null) { + kwargs = node.callArgs; + } + if (computedData.length === 1) { + const [computedTensor, computedMask] = computedData[0]; + if (kwargs["mask"] == null) { + kwargs["mask"] = computedMask; + } + outputTensors2 = toList(layer.call(computedTensor, kwargs)); + outputMasks2 = toList(layer.computeMask(computedTensor, computedMask)); + computedTensors = [computedTensor]; + computedMasks = [computedMask]; + } else { + computedTensors = computedData.map((x) => x[0]); + computedMasks = computedData.map((x) => x[1]); + if (kwargs["mask"] == null) { + kwargs["mask"] = computedMasks; + } + outputTensors2 = toList(layer.call(computedTensors, kwargs)); + outputMasks2 = toList(layer.computeMask(computedTensors, computedMasks)); + } + if (layer.activityRegularizer) { + throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet."); + } + for (let i = 0; i < referenceOutputTensors.length; ++i) { + const x = referenceOutputTensors[i]; + const y = outputTensors2[i]; + const mask = outputMasks2[i]; + tensorMap[x.id] = [y, mask]; + } + } + } + } + const outputTensors = []; + const outputMasks = []; + const outputShapes = []; + for (const x of this.outputs) { + assert2(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`); + const [tensor2, mask] = tensorMap[x.id]; + outputShapes.push(tensor2.shape); + outputTensors.push(tensor2); + outputMasks.push(mask); + } + return [outputTensors, outputMasks, outputShapes]; + } + /** + * Builds a map of internal node keys to node ordering. + * Used in serializaion a node orderings may change as unused nodes are + * dropped. Porting Note: This helper method was pulled out of getConfig to + * improve readability. + * @param layers An array of Layers in the model. + * @returns Map of Node Keys to index order within the layer. + */ + buildNodeConversionMap(layers) { + const nodeConversionMap = {}; + let keptNodes; + for (const layer of this.layers) { + keptNodes = layer instanceof _Container ? 1 : 0; + for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) { + const nodeKey = _Container.nodeKey(layer, originalNodeIndex); + if (this.containerNodes.has(nodeKey)) { + nodeConversionMap[nodeKey] = keptNodes; + keptNodes += 1; + } + } + } + return nodeConversionMap; + } + getLayer(nameOrIndex, index) { + if (index != null) { + return this.findLayer(index); + } else { + if (nameOrIndex == null) { + throw new ValueError("Provide either a layer name or layer index"); + } + if (typeof nameOrIndex === "number") { + return this.findLayer(nameOrIndex); + } + } + for (const layer of this.layers) { + if (layer.name === nameOrIndex) { + return layer; + } + } + throw new ValueError(`No such layer: ${nameOrIndex}`); + } + findLayer(index) { + if (this.layers.length <= index) { + throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`); + } else { + return this.layers[index]; + } + } + /** + * Retrieves the Container's current loss values. + * + * Used for regularizers during training. + */ + calculateLosses() { + return tidy(() => { + const losses2 = []; + for (const layer of this.layers) { + for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) { + const nodeKey = _Container.nodeKey(layer, nodeIndex); + if (this.containerNodes.has(nodeKey)) { + losses2.push(...layer.calculateLosses()); + } + } + } + return losses2; + }); + } + getConfig() { + const config = { name: this.name }; + const nodeConversionMap = this.buildNodeConversionMap(this.layers); + const layerConfigs = []; + for (const layer of this.layers) { + const layerClassName = layer.getClassName(); + const layerConfig = layer.getConfig(); + const filteredInboundNodes = []; + for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) { + const node = layer.inboundNodes[originalNodeIndex]; + const nodeKey = _Container.nodeKey(layer, originalNodeIndex); + let kwargs = {}; + if (this.containerNodes.has(nodeKey)) { + if (node.callArgs) { + try { + JSON.stringify(node.callArgs); + kwargs = node.callArgs; + } catch (err) { + console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`); + kwargs = {}; + } + } + if (node.inboundLayers.length > 0) { + const nodeData = []; + for (let i = 0; i < node.inboundLayers.length; i++) { + const inboundLayer = node.inboundLayers[i]; + const nodeIndex = node.nodeIndices[i]; + const tensorIndex = node.tensorIndices[i]; + const nodeKey2 = _Container.nodeKey(inboundLayer, nodeIndex); + let newNodeIndex = nodeConversionMap[nodeKey2]; + if (newNodeIndex == null) { + newNodeIndex = 0; + } + nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]); + } + filteredInboundNodes.push(nodeData); + } + } + } + const dict = {}; + dict["name"] = layer.name; + dict["className"] = layerClassName; + dict["config"] = layerConfig; + dict["inboundNodes"] = filteredInboundNodes; + layerConfigs.push(dict); + } + config["layers"] = layerConfigs; + const modelInputs = []; + for (let i = 0; i < this.inputLayers.length; i++) { + const layer = this.inputLayers[i]; + const nodeIndex = this.inputLayersNodeIndices[i]; + const nodeKey = _Container.nodeKey(layer, nodeIndex); + if (!this.containerNodes.has(nodeKey)) { + continue; + } + let newNodeIndex = nodeConversionMap[nodeKey]; + if (newNodeIndex === null || newNodeIndex === void 0) { + newNodeIndex = 0; + } + const tensorIndex = this.inputLayersTensorIndices[i]; + modelInputs.push([layer.name, newNodeIndex, tensorIndex]); + } + config["inputLayers"] = modelInputs; + const modelOutputs = []; + for (let i = 0; i < this.outputLayers.length; i++) { + const layer = this.outputLayers[i]; + const nodeIndex = this.outputLayersNodeIndices[i]; + const nodeKey = _Container.nodeKey(layer, nodeIndex); + if (!this.containerNodes.has(nodeKey)) { + continue; + } + let newNodeIndex = nodeConversionMap[nodeKey]; + if (newNodeIndex === null || newNodeIndex === void 0) { + newNodeIndex = 0; + } + const tensorIndex = this.outputLayersTensorIndices[i]; + modelOutputs.push([layer.name, newNodeIndex, tensorIndex]); + } + config["outputLayers"] = modelOutputs; + return config; + } + /** + * Instantiates a LayersModel from its config (output of `get_config()`). + * @param cls the class to create + * @param config LayersModel config dictionary. + * @param customObjects An optional dictionary of custom objects. + * @param fastWeightInit Optional flag to use fast weight initialization + * during deserialization. This is applicable to cases in which + * the initialization will be immediately overwritten by loaded weight + * values. Default: `false`. + * @returns A LayersModel instance. + * @throws ValueError: In case of improperly formatted config dict. + */ + /** @nocollapse */ + static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) { + const createdLayers = {}; + const unprocessedNodes = {}; + function addUnprocessedNode(layer, nodeData) { + if (!(layer.name in unprocessedNodes)) { + unprocessedNodes[layer.name] = [nodeData]; + } else { + unprocessedNodes[layer.name].push(nodeData); + } + } + function processNode(layer, nodeData) { + const inputTensors2 = []; + let kwargs; + for (const inputData of nodeData) { + const inboundLayerName = inputData[0]; + const inboundNodeIndex = inputData[1]; + const inboundTensorIndex = inputData[2]; + kwargs = inputData[3] == null ? {} : inputData[3]; + if (!(inboundLayerName in createdLayers)) { + addUnprocessedNode(layer, nodeData); + return; + } + const inboundLayer = createdLayers[inboundLayerName]; + if (inboundLayer.inboundNodes.length <= inboundNodeIndex) { + addUnprocessedNode(layer, nodeData); + return; + } + const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex]; + inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex]); + } + if (inputTensors2.length > 0) { + layer.apply(singletonOrArray(inputTensors2), kwargs); + } + } + function processLayer(layerData) { + const layerName = layerData["name"]; + const layer = deserialize(layerData, config["customObjects"] != null ? config["customObjects"] : {}); + layer.setFastWeightInitDuringBuild(fastWeightInit); + createdLayers[layerName] = layer; + const inboundNodesData = layerData["inboundNodes"]; + inboundNodesData.forEach((nodeData) => { + if (!(nodeData instanceof Array)) { + throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`); + } + addUnprocessedNode(layer, nodeData); + }); + } + const name = config["name"]; + const layersFromConfig = config["layers"]; + for (const layerData of layersFromConfig) { + processLayer(layerData); + } + while (!isObjectEmpty(unprocessedNodes)) { + for (const layerData of layersFromConfig) { + const layer = createdLayers[layerData["name"]]; + if (layer.name in unprocessedNodes) { + const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name]; + delete unprocessedNodes[layer.name]; + for (const nodeData of currentUnprocessedNodesForLayer) { + processNode(layer, nodeData); + } + } + } + } + const inputTensors = []; + const outputTensors = []; + const inputLayersFromConfig = config["inputLayers"]; + for (const layerData of inputLayersFromConfig) { + const layerName = layerData[0]; + const nodeIndex = layerData[1]; + const tensorIndex = layerData[2]; + assert2(layerName in createdLayers); + const layer = createdLayers[layerName]; + const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors; + inputTensors.push(layerOutputTensors[tensorIndex]); + } + const outputLayersFromConfig = config["outputLayers"]; + for (const layerData of outputLayersFromConfig) { + const layerName = layerData[0]; + const nodeIndex = layerData[1]; + const tensorIndex = layerData[2]; + assert2(layerName in createdLayers); + const layer = createdLayers[layerName]; + const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors; + outputTensors.push(layerOutputTensors[tensorIndex]); + } + return new cls({ inputs: inputTensors, outputs: outputTensors, name }); + } + /** + * Determine whether the container is stateful. + * + * Porting Note: this is the equivalent of the stateful @property of + * the Container class in PyKeras. + */ + get stateful() { + if (this._stateful) { + throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false."); + } + for (const layer of this.layers) { + if (layer.stateful) { + return true; + } + } + return false; + } + /** + * Reset the state of all stateful constituent layers (if any). + * + * Examples of stateful layers include RNN layers whose `stateful` property + * is set as `true`. + */ + resetStates() { + tidy(() => { + this.layers.forEach((layer) => { + if (layer.stateful) { + layer.resetStates(); + } + }); + }); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_utils.js +function standardizeSampleOrClassWeights(xWeight, outputNames, weightType) { + const numOutputs = outputNames.length; + if (xWeight == null || Array.isArray(xWeight) && xWeight.length === 0) { + return outputNames.map((name) => null); + } + if (numOutputs === 1) { + if (Array.isArray(xWeight) && xWeight.length === 1) { + return xWeight; + } else if (typeof xWeight === "object" && outputNames[0] in xWeight) { + return [xWeight[outputNames[0]]]; + } else { + return [xWeight]; + } + } + if (Array.isArray(xWeight)) { + if (xWeight.length !== numOutputs) { + throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`); + } + return xWeight; + } else if (typeof xWeight === "object" && Object.keys(xWeight).length > 0 && typeof xWeight[Object.keys(xWeight)[0]] === "object") { + const output = []; + outputNames.forEach((outputName) => { + if (outputName in xWeight) { + output.push(xWeight[outputName]); + } else { + output.push(null); + } + }); + return output; + } else { + throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`); + } +} +function standardizeClassWeights(classWeight, outputNames) { + return standardizeSampleOrClassWeights(classWeight, outputNames, "classWeight"); +} +async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) { + if (sampleWeight != null || sampleWeightMode != null) { + throw new Error("Support sampleWeight is not implemented yet"); + } + if (classWeight != null) { + const yClasses = tidy(() => { + if (y.shape.length === 1) { + return clone(y); + } else if (y.shape.length === 2) { + if (y.shape[1] > 1) { + const axis = 1; + return argMax(y, axis); + } else if (y.shape[1] === 1) { + return reshape(y, [y.shape[0]]); + } else { + throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`); + } + } else { + throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`); + } + }); + const yClassIndices = Array.from(await yClasses.data()); + dispose(yClasses); + const classSampleWeight = []; + yClassIndices.forEach((classIndex) => { + if (classWeight[classIndex] == null) { + throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`); + } else { + classSampleWeight.push(classWeight[classIndex]); + } + }); + return tensor1d(classSampleWeight, "float32"); + } else { + return null; + } +} +function computeWeightedLoss2(losses2, sampleWeights) { + return mul(losses2, sampleWeights); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js +var DEFAULT_VALIDATION_BATCH_SIZE = 32; +function standardizeDataIteratorOutput(model2, iteratorOut) { + let xs; + let ys; + const iteratorOutObj = iteratorOut; + xs = iteratorOutObj["xs"]; + ys = iteratorOutObj["ys"]; + util_exports.assert(xs != null && ys != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${iteratorOut}`); + const flattenedXs = flattenTensorOrArrayOrMap("input", model2.inputNames, xs); + const flattenedYs = flattenTensorOrArrayOrMap("output", model2.outputNames, ys); + const batchSize = flattenedXs[0].shape[0]; + util_exports.assert(flattenedXs.length === model2.inputs.length, () => `LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model2.inputNames)})`); + util_exports.assert(flattenedYs.length === model2.outputs.length, () => `LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model2.outputNames)})`); + for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) { + util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`); + } + for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) { + util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`); + } + return { xs: flattenedXs, ys: flattenedYs }; +} +function flattenTensorOrArrayOrMap(inputOrOutput, names, values) { + if (values instanceof Tensor) { + return [values]; + } else if (Array.isArray(values)) { + util_exports.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`); + return values; + } else { + const result = []; + for (const name of names) { + if (values[name] == null) { + throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`); + } + result.push(values[name]); + } + return result; + } +} +function standardizeTensorValidationData(data) { + if (data.length === 3) { + throw new NotImplementedError("Validation with sample weights is not implemented yet."); + } + return { xs: data[0], ys: data[1] }; +} +async function fitDataset(model2, dataset, args) { + const hasBatchesPerEpoch = args.batchesPerEpoch != null; + util_exports.assert(model2.optimizer != null, () => "You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."); + util_exports.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.`); + util_exports.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`); + util_exports.assert(!hasBatchesPerEpoch || args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`); + util_exports.assert( + // tslint:disable-next-line:no-any + args["validationSplit"] == null, + () => "`validationSplit` is not supported by `fitDataset()`. Use validationData instead." + ); + if (model2.isTraining) { + throw new Error("Cannot start training because another fit() call is ongoing."); + } + model2.isTraining = true; + try { + const doValidation = args.validationData != null; + let valXs; + let valYs; + if (doValidation) { + if (isDatasetObject(args.validationData)) { + util_exports.assert(args.validationBatches == null || args.validationBatches > 0 && Number.isInteger(args.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`); + } else { + const validationData = standardizeTensorValidationData(args.validationData); + valXs = validationData.xs; + valYs = validationData.ys; + } + } + const trainFunction = model2.makeTrainFunction(); + const outLabels = model2.getDedupedMetricsNames(); + let callbackMetrics; + if (doValidation) { + callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n)); + } else { + callbackMetrics = outLabels.slice(); + } + const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery); + const verbose = args.verbose == null ? 1 : args.verbose; + const { callbackList, history } = configureCallbacks( + callbacks2, + verbose, + args.epochs, + null, + null, + getStepsPerEpoch(dataset, args), + null, + // Batch size determined by the dataset itself. + doValidation, + callbackMetrics + ); + callbackList.setModel(model2); + model2.history = history; + await callbackList.onTrainBegin(); + model2.stopTraining_ = false; + let epoch = args.initialEpoch == null ? 0 : args.initialEpoch; + let dataIterator = await dataset.iterator(); + while (epoch < args.epochs) { + const epochLogs = {}; + await callbackList.onEpochBegin(epoch); + let stepsDone = 0; + let batchIndex = 0; + if (!hasBatchesPerEpoch) { + dataIterator = await dataset.iterator(); + } + while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) { + const iteratorOut = await dataIterator.next(); + if (hasBatchesPerEpoch && iteratorOut.done) { + console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`); + break; + } + if (iteratorOut.value != null) { + const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value); + const batchLogs = {}; + batchLogs["batch"] = batchIndex; + batchLogs["size"] = xs[0].shape[0]; + await callbackList.onBatchBegin(batchIndex, batchLogs); + const sampleWeights = []; + if (args.classWeight != null) { + const standardClassWeights = standardizeClassWeights(args.classWeight, model2.outputNames); + for (let i = 0; i < standardClassWeights.length; ++i) { + sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i])); + } + } + const ins = xs.concat(ys).concat(sampleWeights); + const outs = trainFunction(ins); + dispose(ins); + for (let i = 0; i < outLabels.length; ++i) { + const label = outLabels[i]; + const out = outs[i]; + batchLogs[label] = out; + keep(out); + } + await callbackList.onBatchEnd(batchIndex, batchLogs); + disposeTensorsInLogs(batchLogs); + batchIndex++; + stepsDone++; + } + if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) { + if (doValidation) { + let valOuts; + if (isDatasetObject(args.validationData)) { + valOuts = toList(await model2.evaluateDataset(args.validationData, { batches: args.validationBatches })); + } else { + valOuts = toList(model2.evaluate(valXs, valYs, { + batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize, + verbose: 0 + })); + } + for (let i = 0; i < model2.metricsNames.length; ++i) { + epochLogs[`val_${model2.metricsNames[i]}`] = valOuts[i]; + } + } + break; + } + if (model2.stopTraining_) { + break; + } + } + await callbackList.onEpochEnd(epoch, epochLogs); + epoch++; + if (model2.stopTraining_) { + break; + } + } + await callbackList.onTrainEnd(); + await model2.history.syncData(); + return model2.history; + } finally { + model2.isTraining = false; + } +} +function getStepsPerEpoch(dataset, args) { + let stepsPerEpoch = null; + if (args.batchesPerEpoch != null) { + stepsPerEpoch = args.batchesPerEpoch; + } else if (Number.isFinite(dataset.size)) { + stepsPerEpoch = dataset.size; + } + return stepsPerEpoch; +} +function isDatasetObject(dataset) { + return typeof dataset.iterator === "function"; +} +function isLazyIteratorObject(iterator) { + return typeof iterator.next === "function"; +} +async function evaluateDataset(model2, dataset, args) { + args = args || {}; + const hasBatches = args.batches != null; + const f = model2.testFunction; + let outs = []; + if (args.verbose > 0) { + throw new NotImplementedError("Verbose mode is not implemented yet."); + } + util_exports.assert(!hasBatches || args.batches > 0 && Number.isInteger(args.batches), () => `Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`); + const dataIterator = isLazyIteratorObject(dataset) ? dataset : await dataset.iterator(); + let numExamples = 0; + let batch = 0; + while (hasBatches ? batch < args.batches : true) { + const iteratorOut = await dataIterator.next(); + outs = tidy(() => { + if (iteratorOut.value) { + const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value); + const xsAndYs = xs.concat(ys); + const batchOuts = tidy(() => f(xsAndYs)); + dispose(xsAndYs); + if (batch === 0) { + for (let i = 0; i < batchOuts.length; ++i) { + outs.push(scalar(0)); + } + } + const batchSize = xsAndYs[0].shape[0]; + for (let i = 0; i < batchOuts.length; ++i) { + const batchOut = batchOuts[i]; + const oldScalar = outs[i]; + outs[i] = tidy(() => add2(outs[i], mul(batchSize, batchOut))); + if (batch > 0) { + dispose(oldScalar); + } + } + dispose(batchOuts); + numExamples += batchSize; + ++batch; + } + return outs; + }); + if (iteratorOut.done) { + if (hasBatches) { + console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`); + } + break; + } + } + for (let i = 0; i < outs.length; ++i) { + const oldScalar = outs[i]; + outs[i] = div(outs[i], numExamples); + dispose(oldScalar); + } + return singletonOrArray(outs); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_tensors.js +function checkBatchSize(batchSize) { + util_exports.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`); +} +function sliceArrays(arrays, start, stop) { + if (arrays == null) { + return [null]; + } else if (Array.isArray(arrays)) { + return arrays.map((array2) => sliceAlongFirstAxis(array2, start, stop - start)); + } else { + return sliceAlongFirstAxis(arrays, start, stop - start); + } +} +function sliceArraysByIndices(arrays, indices) { + return tidy(() => { + if (arrays == null) { + return null; + } else if (Array.isArray(arrays)) { + return arrays.map((array2) => sliceArraysByIndices(array2, indices)); + } else { + return gather2(arrays, indices.dtype === "int32" ? indices : cast(indices, "int32")); + } + }); +} +function makeBatches(size, batchSize) { + const output = []; + let batchStart = 0; + let batchEnd = null; + while (batchStart < size) { + batchEnd = batchStart + batchSize; + if (batchEnd >= size) { + batchEnd = size; + } + output.push([batchStart, batchEnd]); + batchStart = batchEnd; + } + return output; +} +function ensureTensorsRank2OrHigher(tensors) { + const outs = []; + if (tensors instanceof Tensor) { + tensors = [tensors]; + } + for (let i = 0; i < tensors.length; ++i) { + const tensor2 = tensors[i]; + if (tensor2.rank === 1) { + outs.push(expandDims2(tensor2, 1)); + } else if (tensor2.rank === 0) { + throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar)."); + } else { + outs.push(tensor2); + } + } + return outs; +} +function disposeNewTensors(tensors, refTensors) { + if (tensors == null) { + return; + } + const oldTensorIds = []; + if (refTensors instanceof Tensor) { + oldTensorIds.push(refTensors.id); + } else if (Array.isArray(refTensors)) { + refTensors.forEach((t) => oldTensorIds.push(t.id)); + } else if (refTensors != null) { + for (const name in refTensors) { + const oldTensor = refTensors[name]; + oldTensorIds.push(oldTensor.id); + } + } + const tensorsToDispose = []; + if (tensors instanceof Tensor) { + if (oldTensorIds.indexOf(tensors.id) === -1) { + tensorsToDispose.push(tensors); + } + } else if (Array.isArray(tensors)) { + tensors.forEach((t) => { + if (oldTensorIds.indexOf(t.id) === -1) { + tensorsToDispose.push(t); + } + }); + } else if (tensors != null) { + for (const name in tensors) { + const tensor2 = tensors[name]; + if (oldTensorIds.indexOf(tensor2.id) === -1) { + tensorsToDispose.push(tensor2); + } + } + } + tensorsToDispose.forEach((t) => { + if (!t.isDisposed) { + t.dispose(); + } + }); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training.js +function isDataTensor(x) { + return x instanceof Tensor; +} +function isDataArray(x) { + return Array.isArray(x); +} +function isDataDict(x) { + return !isDataTensor(x) && !isDataArray(x); +} +function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") { + if (names == null || names.length === 0) { + if (data != null) { + let gotUnexpectedData = false; + if (isDataArray(data) && data.length > 0) { + gotUnexpectedData = true; + } else if (isDataDict(data)) { + for (const key in data) { + if (data.hasOwnProperty(key)) { + gotUnexpectedData = true; + break; + } + } + } else { + gotUnexpectedData = true; + } + if (gotUnexpectedData) { + throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`); + } + } + return []; + } + if (data == null) { + return names.map((name) => null); + } + let arrays; + if (isDataDict(data)) { + data = data; + arrays = []; + for (const name of names) { + if (data[name] == null) { + throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`); + } + arrays.push(data[name]); + } + } else if (isDataArray(data)) { + data = data; + if (data.length !== names.length) { + throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`); + } + arrays = data; + } else { + data = data; + if (names.length > 1) { + throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`); + } + arrays = [data]; + } + arrays = ensureTensorsRank2OrHigher(arrays); + if (shapes != null) { + for (let i = 0; i < names.length; ++i) { + if (shapes[i] == null) { + continue; + } + const array2 = arrays[i]; + if (array2.shape.length !== shapes[i].length) { + throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`); + } + for (let j = 0; j < shapes[i].length; ++j) { + if (j === 0 && !checkBatchAxis) { + continue; + } + const dim = array2.shape[j]; + const refDim = shapes[i][j]; + if (refDim != null && refDim >= 0 && dim !== refDim) { + throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i].slice(1, shapes[i].length)}] (i.e.,tensor shape [*,${shapes[i].slice(1, shapes[i].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`); + } + } + } + } + return arrays; +} +function checkArrayLengths(inputs, targets, weights) { + const setX = unique2(inputs.map((input2) => input2.shape[0])); + setX.sort(); + const setY = unique2(targets.map((target) => target.shape[0])); + setY.sort(); + if (setX.length > 1) { + throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map((input2) => input2.shape))}`); + } + if (setY.length > 1) { + throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map((target) => target.shape))}`); + } + if (setX.length > 0 && setY.length > 0 && !util_exports.arraysEqual(setX, setY)) { + throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`); + } +} +function checkLossAndTargetCompatibility(targets, lossFns, outputShapes) { + const keyLosses = [ + meanSquaredError2, + binaryCrossentropy, + categoricalCrossentropy + ]; + for (let i = 0; i < targets.length; ++i) { + const y = targets[i]; + const loss = lossFns[i]; + const shape = outputShapes[i]; + if (loss == null) { + continue; + } + if (loss === categoricalCrossentropy) { + if (y.shape[y.shape.length - 1] === 1) { + throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`); + } + } + if (keyLosses.indexOf(loss) !== -1) { + const slicedYShape = y.shape.slice(1); + const slicedShape = shape.slice(1); + for (let j = 0; j < slicedYShape.length; ++j) { + const targetDim = slicedYShape[j]; + const outDim = slicedShape[j]; + if (outDim != null && targetDim !== outDim) { + throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`); + } + } + } + } +} +function checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") { + let arrays; + if (Array.isArray(data)) { + if (data.length !== names.length) { + throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`); + } + arrays = data; + } else { + if (names.length > 1) { + throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`); + } + arrays = [data]; + } + if (shapes != null) { + for (let i = 0; i < names.length; ++i) { + if (shapes[i] == null) { + continue; + } + const array2 = arrays[i]; + if (array2.shape.length !== shapes[i].length) { + throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`); + } + for (let j = 0; j < shapes[i].length; ++j) { + if (j === 0 && !checkBatchAxis) { + continue; + } + const dim = array2.shape[j]; + const refDim = shapes[i][j]; + if (refDim != null) { + if (refDim !== dim) { + throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`); + } + } + } + } + } +} +function collectMetrics(metrics, outputNames) { + if (metrics == null || Array.isArray(metrics) && metrics.length === 0) { + return outputNames.map((name) => []); + } + let wrappedMetrics; + if (typeof metrics === "string" || typeof metrics === "function") { + wrappedMetrics = [metrics]; + } else if (Array.isArray(metrics) || typeof metrics === "object") { + wrappedMetrics = metrics; + } else { + throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics}`); + } + if (Array.isArray(wrappedMetrics)) { + return outputNames.map((name) => wrappedMetrics); + } else { + const nestedMetrics = []; + for (const name of outputNames) { + let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : []; + if (!Array.isArray(outputMetrics)) { + outputMetrics = [outputMetrics]; + } + nestedMetrics.push(outputMetrics); + } + return nestedMetrics; + } +} +var LAYERS_MODEL_FORMAT_NAME = "layers-model"; +var LayersModel = class extends Container { + constructor(args) { + super(args); + this.isTraining = false; + } + /** + * Print a text summary of the model's layers. + * + * The summary includes + * - Name and type of all layers that comprise the model. + * - Output shape(s) of the layers + * - Number of weight parameters of each layer + * - If the model has non-sequential-like topology, the inputs each layer + * receives + * - The total number of trainable and non-trainable parameters of the model. + * + * ```js + * const input1 = tf.input({shape: [10]}); + * const input2 = tf.input({shape: [20]}); + * const dense1 = tf.layers.dense({units: 4}).apply(input1); + * const dense2 = tf.layers.dense({units: 8}).apply(input2); + * const concat = tf.layers.concatenate().apply([dense1, dense2]); + * const output = + * tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat); + * + * const model = tf.model({inputs: [input1, input2], outputs: output}); + * model.summary(); + * ``` + * + * @param lineLength Custom line length, in number of characters. + * @param positions Custom widths of each of the columns, as either + * fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number + * of characters (e.g., `[30, 50, 65]`). Each number corresponds to + * right-most (i.e., ending) position of a column. + * @param printFn Custom print function. Can be used to replace the default + * `console.log`. For example, you can use `x => {}` to mute the printed + * messages in the console. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + summary(lineLength, positions, printFn = console.log) { + if (!this.built) { + throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`); + } + printSummary(this, lineLength, positions, printFn); + } + /** + * Configures and prepares the model for training and evaluation. Compiling + * outfits the model with an optimizer, loss, and/or metrics. Calling `fit` + * or `evaluate` on an un-compiled model will throw an error. + * + * @param args a `ModelCompileArgs` specifying the loss, optimizer, and + * metrics to be used for fitting and evaluating this model. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + compile(args) { + if (args.loss == null) { + args.loss = []; + } + this.loss = args.loss; + if (typeof args.optimizer === "string") { + this.optimizer_ = getOptimizer(args.optimizer); + this.isOptimizerOwned = true; + } else { + if (!(args.optimizer instanceof Optimizer)) { + throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`); + } + this.optimizer_ = args.optimizer; + this.isOptimizerOwned = false; + } + let lossFunctions = []; + if (!Array.isArray(args.loss) && typeof args.loss !== "string" && typeof args.loss !== "function") { + args.loss = args.loss; + for (const name in args.loss) { + if (this.outputNames.indexOf(name) === -1) { + throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`); + } + } + for (const name of this.outputNames) { + if (args.loss[name] == null) { + console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`); + } + lossFunctions.push(get(args.loss[name])); + } + } else if (Array.isArray(args.loss)) { + if (args.loss.length !== this.outputs.length) { + throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`); + } + const theLosses = args.loss; + lossFunctions = theLosses.map((l) => get(l)); + } else { + const lossFunction = get(args.loss); + this.outputs.forEach((_) => { + lossFunctions.push(lossFunction); + }); + } + this.lossFunctions = lossFunctions; + this.feedOutputNames = []; + this.feedOutputShapes = []; + this.feedLossFns = []; + for (let i = 0; i < this.outputs.length; ++i) { + const shape = this.internalOutputShapes[i]; + const name = this.outputNames[i]; + this.feedOutputNames.push(name); + this.feedOutputShapes.push(shape); + this.feedLossFns.push(this.lossFunctions[i]); + } + const skipTargetIndices = []; + this.metrics = args.metrics; + this.metricsNames = ["loss"]; + this.metricsTensors = []; + nameScope("loss", () => { + for (let i = 0; i < this.outputs.length; ++i) { + if (skipTargetIndices.indexOf(i) !== -1) { + continue; + } + const weightedLoss = this.lossFunctions[i]; + if (this.outputs.length > 1) { + this.metricsTensors.push([weightedLoss, i]); + this.metricsNames.push(this.outputNames[i] + "_loss"); + } + } + }); + const nestedMetrics = collectMetrics(args.metrics, this.outputNames); + const appendMetric = (outputIndex, metricName, metricTensor) => { + if (this.outputNames.length > 1) { + metricName = this.outputNames[outputIndex] + "_" + metricName; + } + this.metricsNames.push(metricName); + this.metricsTensors.push([metricTensor, outputIndex]); + }; + nameScope("metric", () => { + for (let i = 0; i < this.outputs.length; ++i) { + if (skipTargetIndices.indexOf(i) !== -1) { + continue; + } + const outputMetrics = nestedMetrics[i]; + const handleMetrics = (metrics) => { + const metricNamePrefix = ""; + let metricName; + let accFn; + let weightedMetricFn; + for (const metric of metrics) { + if (typeof metric === "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(metric) !== -1) { + const outputShape = this.internalOutputShapes[i]; + if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i] === binaryCrossentropy) { + if (["accuracy", "acc"].indexOf(metric) !== -1) { + accFn = binaryAccuracy; + } else if (["crossentropy", "ce"].indexOf(metric) !== -1) { + accFn = binaryCrossentropy2; + } + } else if (this.lossFunctions[i] === sparseCategoricalCrossentropy) { + if (["accuracy", "acc"].indexOf(metric) !== -1) { + accFn = sparseCategoricalAccuracy; + } else if (["crossentropy", "ce"].indexOf(metric) !== -1) { + accFn = sparseCategoricalCrossentropy2; + } + } else { + if (["accuracy", "acc"].indexOf(metric) !== -1) { + accFn = categoricalAccuracy; + } else if (["crossentropy", "ce"].indexOf(metric) !== -1) { + accFn = categoricalCrossentropy2; + } + } + let suffix; + if (["accuracy", "acc"].indexOf(metric) !== -1) { + suffix = "acc"; + } else if (["crossentropy", "ce"].indexOf(metric) !== -1) { + suffix = "ce"; + } + weightedMetricFn = accFn; + metricName = metricNamePrefix + suffix; + } else { + const metricFn = get2(metric); + weightedMetricFn = metricFn; + metricName = metricNamePrefix + getLossOrMetricName(metric); + } + let metricResult; + nameScope(metricName, () => { + metricResult = weightedMetricFn; + }); + appendMetric(i, metricName, metricResult); + } + }; + handleMetrics(outputMetrics); + } + }); + this.collectedTrainableWeights = this.trainableWeights; + } + /** + * Check trainable weights count consistency. + * + * This will raise a warning if `this.trainableWeights` and + * `this.collectedTrainableWeights` are inconsistent (i.e., have different + * numbers of parameters). + * Inconsistency will typically arise when one modifies `model.trainable` + * without calling `model.compile()` again. + */ + checkTrainableWeightsConsistency() { + if (this.collectedTrainableWeights == null) { + return; + } + if (this.trainableWeights.length !== this.collectedTrainableWeights.length) { + console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?"); + } + } + /** + * Returns the loss value & metrics values for the model in test mode. + * + * Loss and metrics are specified during `compile()`, which needs to happen + * before calls to `evaluate()`. + * + * Computation is done in batches. + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'}); + * const result = model.evaluate( + * tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4}); + * result.print(); + * ``` + * + * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the + * model has multiple inputs. + * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the + * model has multiple outputs. + * @param args A `ModelEvaluateArgs`, containing optional fields. + * + * @return `Scalar` test loss (if the model has a single output and no + * metrics) or `Array` of `Scalar`s (if the model has multiple outputs + * and/or metrics). The attribute `model.metricsNames` + * will give you the display labels for the scalar outputs. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + evaluate(x, y, args = {}) { + const batchSize = args.batchSize == null ? 32 : args.batchSize; + checkBatchSize(batchSize); + const checkBatchAxis = true; + const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize); + try { + const ins = standardizedOuts[0].concat(standardizedOuts[1]); + this.makeTestFunction(); + const f = this.testFunction; + const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps); + return singletonOrArray(testOuts); + } finally { + disposeNewTensors(standardizedOuts[0], x); + disposeNewTensors(standardizedOuts[1], y); + } + } + // TODO(cais): Add code snippet below once real dataset objects are + // available. + /** + * Evaluate model using a dataset object. + * + * Note: Unlike `evaluate()`, this method is asynchronous (`async`). + * + * @param dataset A dataset object. Its `iterator()` method is expected + * to generate a dataset iterator object, the `next()` method of which + * is expected to produce data batches for evaluation. The return value + * of the `next()` call ought to contain a boolean `done` field and a + * `value` field. The `value` field is expected to be an array of two + * `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former + * case is for models with exactly one input and one output (e.g. + * a sequential model). The latter case is for models with multiple + * inputs and/or multiple outputs. Of the two items in the array, the + * first is the input feature(s) and the second is the output target(s). + * @param args A configuration object for the dataset-based evaluation. + * @returns Loss and metric values as an Array of `Scalar` objects. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async evaluateDataset(dataset, args) { + this.makeTestFunction(); + return evaluateDataset(this, dataset, args); + } + /** + * Get number of samples provided for training, evaluation or prediction. + * + * @param ins Input `tf.Tensor`. + * @param batchSize Integer batch size, optional. + * @param steps Total number of steps (batches of samples) before + * declaring loop finished. Optional. + * @param stepsName The public API's parameter name for `steps`. + * @returns Number of samples provided. + */ + checkNumSamples(ins, batchSize, steps, stepsName = "steps") { + let numSamples; + if (steps != null) { + numSamples = null; + if (batchSize != null) { + throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`); + } + } else if (ins != null) { + if (Array.isArray(ins)) { + numSamples = ins[0].shape[0]; + } else { + numSamples = ins.shape[0]; + } + } else { + throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`); + } + return numSamples; + } + /** + * Execute internal tensors of the model with input data feed. + * @param inputs Input data feed. Must match the inputs of the model. + * @param outputs Names of the output tensors to be fetched. Must match + * names of the SymbolicTensors that belong to the graph. + * @returns Fetched values for `outputs`. + */ + execute(inputs, outputs) { + if (Array.isArray(outputs) && outputs.length === 0) { + throw new ValueError("`outputs` is an empty Array, which is not allowed."); + } + const outputsIsArray = Array.isArray(outputs); + const outputNames = outputsIsArray ? outputs : [outputs]; + const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames); + const feedDict = new FeedDict(); + if (inputs instanceof Tensor) { + inputs = [inputs]; + } + if (Array.isArray(inputs)) { + if (inputs.length !== this.inputs.length) { + throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`); + } + for (let i = 0; i < this.inputs.length; ++i) { + feedDict.add(this.inputs[i], inputs[i]); + } + } else { + for (const input2 of this.inputs) { + const tensorValue = inputs[input2.name]; + if (tensorValue == null) { + throw new ValueError(`No value is provided for the model's input ${input2.name}`); + } + feedDict.add(input2, tensorValue); + } + } + const executeOutputs = execute(outputSymbolicTensors, feedDict); + return outputsIsArray ? executeOutputs : executeOutputs[0]; + } + /** + * Retrieve the model's internal symbolic tensors from symbolic-tensor names. + */ + retrieveSymbolicTensors(symbolicTensorNames) { + const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length); + let outputsRemaining = symbolicTensorNames.length; + for (const layer of this.layers) { + const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output]; + const layerOutputNames = layerOutputs.map((output) => output.name); + for (let i = 0; i < symbolicTensorNames.length; ++i) { + const index = layerOutputNames.indexOf(symbolicTensorNames[i]); + if (index !== -1) { + outputSymbolicTensors[i] = layerOutputs[index]; + outputsRemaining--; + } + if (outputsRemaining === 0) { + break; + } + } + if (outputsRemaining === 0) { + break; + } + } + if (outputsRemaining > 0) { + const remainingNames = []; + outputSymbolicTensors.forEach((tensor2, i) => { + if (tensor2 == null) { + remainingNames.push(symbolicTensorNames[i]); + } + }); + throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`); + } + return outputSymbolicTensors; + } + /** + * Helper method to loop over some data in batches. + * + * Porting Note: Not using the functional approach in the Python equivalent + * due to the imperative backend. + * Porting Note: Does not support step mode currently. + * + * @param ins: input data + * @param batchSize: integer batch size. + * @param verbose: verbosity model + * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of + * `tf.Tensor` (if multipe outputs). + */ + predictLoop(ins, batchSize = 32, verbose = false) { + return tidy(() => { + const numSamples = this.checkNumSamples(ins); + if (verbose) { + throw new NotImplementedError("Verbose predictLoop() is not implemented yet."); + } + const batches = makeBatches(numSamples, batchSize); + const outsBatches = this.outputs.map((output) => []); + for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) { + const batchOuts = tidy(() => { + const batchStart = batches[batchIndex][0]; + const batchEnd = batches[batchIndex][1]; + const insBatch = sliceArrays(ins, batchStart, batchEnd); + const feeds = []; + if (Array.isArray(insBatch)) { + for (let i = 0; i < insBatch.length; ++i) { + feeds.push({ key: this.inputs[i], value: insBatch[i] }); + } + } else { + feeds.push({ key: this.inputs[0], value: insBatch }); + } + const feedDict = new FeedDict(feeds); + return execute(this.outputs, feedDict); + }); + batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut)); + } + return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0))); + }); + } + /** + * Generates output predictions for the input samples. + * + * Computation is done in batches. + * + * Note: the "step" mode of predict() is currently not supported. + * This is because the TensorFlow.js core backend is imperative only. + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.predict(tf.ones([8, 10]), {batchSize: 4}).print(); + * ``` + * + * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if + * the model has multiple inputs. + * @param args A `ModelPredictArgs` object containing optional fields. + * + * @return Prediction results as a `tf.Tensor`(s). + * + * @exception ValueError In case of mismatch between the provided input data + * and the model's expectations, or in case a stateful model receives a + * number of samples that is not a multiple of the batch size. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + predict(x, args = {}) { + const xsRank2OrHigher = ensureTensorsRank2OrHigher(x); + checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false); + try { + const batchSize = args.batchSize == null ? 32 : args.batchSize; + checkBatchSize(batchSize); + return this.predictLoop(xsRank2OrHigher, batchSize); + } finally { + disposeNewTensors(xsRank2OrHigher, x); + } + } + /** + * Returns predictions for a single batch of samples. + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.predictOnBatch(tf.ones([8, 10])).print(); + * ``` + * @param x: Input samples, as a Tensor (for models with exactly one + * input) or an array of Tensors (for models with more than one input). + * @return Tensor(s) of predictions + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + predictOnBatch(x) { + checkInputData(x, this.inputNames, this.feedInputShapes, true); + const batchSize = (Array.isArray(x) ? x[0] : x).shape[0]; + return this.predictLoop(x, batchSize); + } + standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) { + if (this.optimizer_ == null) { + throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs)."); + } + const outputShapes = []; + for (let i = 0; i < this.feedOutputShapes.length; ++i) { + const outputShape = this.feedOutputShapes[i]; + const lossFn = this.feedLossFns[i]; + if (lossFn === sparseCategoricalCrossentropy) { + outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1])); + } else { + outputShapes.push(outputShape); + } + } + x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, "input"); + y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, "target"); + checkArrayLengths(x, y, null); + checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes); + if (this.stateful && batchSize != null && batchSize > 0) { + if (x[0].shape[0] % batchSize !== 0) { + throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`); + } + } + return [x, y]; + } + async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) { + const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize); + if (sampleWeight != null) { + throw new Error("sample weight is not supported yet."); + } + let standardSampleWeights = null; + if (classWeight != null) { + const classWeights = standardizeClassWeights(classWeight, this.outputNames); + standardSampleWeights = []; + for (let i = 0; i < classWeights.length; ++i) { + standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i])); + } + } + return [standardXs, standardYs, standardSampleWeights]; + } + /** + * Loop over some test data in batches. + * @param f A Function returning a list of tensors. + * @param ins Array of tensors to be fed to `f`. + * @param batchSize Integer batch size or `null` / `undefined`. + * @param verbose verbosity mode. + * @param steps Total number of steps (batches of samples) before + * declaring test finished. Ignored with the default value of `null` / + * `undefined`. + * @returns Array of Scalars. + */ + testLoop(f, ins, batchSize, verbose = 0, steps) { + return tidy(() => { + const numSamples = this.checkNumSamples(ins, batchSize, steps, "steps"); + const outs = []; + if (verbose > 0) { + throw new NotImplementedError("Verbose mode is not implemented yet."); + } + if (steps != null) { + throw new NotImplementedError("steps mode in testLoop() is not implemented yet"); + } else { + const batches = makeBatches(numSamples, batchSize); + const indexArray = tensor1d(range2(0, numSamples)); + for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) { + const batchStart = batches[batchIndex][0]; + const batchEnd = batches[batchIndex][1]; + const batchIds = sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart); + const insBatch = sliceArraysByIndices(ins, batchIds); + const batchOuts = f(insBatch); + if (batchIndex === 0) { + for (let i = 0; i < batchOuts.length; ++i) { + outs.push(scalar(0)); + } + } + for (let i = 0; i < batchOuts.length; ++i) { + const batchOut = batchOuts[i]; + outs[i] = add2(outs[i], mul(batchEnd - batchStart, batchOut)); + } + } + for (let i = 0; i < outs.length; ++i) { + outs[i] = div(outs[i], numSamples); + } + } + return outs; + }); + } + getDedupedMetricsNames() { + const outLabels = this.metricsNames; + const dedupedOutLabels = []; + for (let i = 0; i < outLabels.length; ++i) { + const label = outLabels[i]; + let newLabel = label; + if (count(outLabels, label) > 1) { + const dupIndex = count(outLabels.slice(0, i), label); + newLabel += `_${dupIndex}`; + } + dedupedOutLabels.push(newLabel); + } + return dedupedOutLabels; + } + /** + * Creates a function that performs the following actions: + * + * 1. computes the losses + * 2. sums them to get the total loss + * 3. call the optimizer computes the gradients of the LayersModel's + * trainable weights w.r.t. the total loss and update the variables + * 4. calculates the metrics + * 5. returns the values of the losses and metrics. + */ + makeTrainFunction() { + return (data) => { + const lossValues = []; + const inputs = data.slice(0, this.inputs.length); + const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length); + const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2); + const metricsValues = []; + const totalLossFunction = () => { + const feeds = []; + for (let i = 0; i < this.inputs.length; ++i) { + feeds.push({ key: this.inputs[i], value: inputs[i] }); + } + const feedDict = new FeedDict(feeds); + const outputs = execute(this.outputs, feedDict, { "training": true }); + let totalLoss; + for (let i = 0; i < this.lossFunctions.length; ++i) { + const lossFunction = this.lossFunctions[i]; + let loss = lossFunction(targets[i], outputs[i]); + if (sampleWeights[i] != null) { + loss = computeWeightedLoss2(loss, sampleWeights[i]); + } + const meanLoss = mean(loss); + lossValues.push(meanLoss); + if (i === 0) { + totalLoss = loss; + } else { + totalLoss = add2(totalLoss, loss); + } + } + for (let i = 0; i < this.metricsTensors.length; ++i) { + let weightedMetric; + if (this.outputs.length > 1 && i < this.outputs.length) { + weightedMetric = lossValues[i]; + } else { + const metric = this.metricsTensors[i][0]; + const outputIndex = this.metricsTensors[i][1]; + weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex])); + } + keep(weightedMetric); + metricsValues.push(weightedMetric); + } + totalLoss = mean(totalLoss); + this.calculateLosses().forEach((regularizerLoss) => { + totalLoss = add2(totalLoss, regularizerLoss); + }); + return totalLoss; + }; + const variables = this.collectedTrainableWeights.map((param) => param.read()); + const returnCost = true; + const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables); + return [totalLossValue].concat(metricsValues); + }; + } + /** + * Create a function which, when invoked with an array of `tf.Tensor`s as a + * batch of inputs, returns the prespecified loss and metrics of the model + * under the batch of input data. + */ + makeTestFunction() { + this.testFunction = (data) => { + return tidy(() => { + const valOutputs = []; + let totalLoss; + const inputs = data.slice(0, this.inputs.length); + const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length); + const feeds = []; + for (let i = 0; i < this.inputs.length; ++i) { + feeds.push({ key: this.inputs[i], value: inputs[i] }); + } + const feedDict = new FeedDict(feeds); + const outputs = execute(this.outputs, feedDict); + for (let i = 0; i < this.lossFunctions.length; ++i) { + const lossFunction = this.lossFunctions[i]; + const loss = mean(lossFunction(targets[i], outputs[i])); + if (i === 0) { + totalLoss = loss; + } else { + totalLoss = add2(totalLoss, loss); + } + valOutputs.push(totalLoss); + } + for (let i = 0; i < this.metricsTensors.length; ++i) { + const metric = this.metricsTensors[i][0]; + const outputIndex = this.metricsTensors[i][1]; + const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex])); + valOutputs.push(meanMetric); + } + return valOutputs; + }); + }; + } + /** + * Trains the model for a fixed number of epochs (iterations on a + * dataset). + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'}); + * for (let i = 1; i < 5 ; ++i) { + * const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), { + * batchSize: 4, + * epochs: 3 + * }); + * console.log("Loss after Epoch " + i + " : " + h.history.loss[0]); + * } + * ``` + * + * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the + * model has multiple inputs. If all inputs in the model are named, you + * can also pass a dictionary mapping input names to `tf.Tensor`s. + * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if + * the model has multiple outputs. If all outputs in the model are named, + * you can also pass a dictionary mapping output names to `tf.Tensor`s. + * @param args A `ModelFitArgs`, containing optional fields. + * + * @return A `History` instance. Its `history` attribute contains all + * information collected during training. + * + * @exception ValueError In case of mismatch between the provided input + * data and what the model expects. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async fit(x, y, args = {}) { + if (this.isTraining) { + throw new Error("Cannot start training because another fit() call is ongoing."); + } + this.isTraining = true; + let inputs; + let targets; + let originalInputs; + let originalTargets; + let inputValX; + let inputValY; + let valX; + let valY; + let sampleWeights; + try { + const batchSize = args.batchSize == null ? 32 : args.batchSize; + checkBatchSize(batchSize); + const checkBatchAxis = false; + const standardizedOuts = await this.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize); + inputs = standardizedOuts[0]; + targets = standardizedOuts[1]; + sampleWeights = standardizedOuts[2]; + let doValidation = false; + let valIns; + if (args.validationData != null && args.validationData.length > 0) { + doValidation = true; + if (args.validationData.length === 2) { + inputValX = args.validationData[0]; + inputValY = args.validationData[1]; + } else if (args.validationData.length === 3) { + throw new NotImplementedError("validationData including sample weights is not supported yet."); + } else { + throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`); + } + const checkBatchAxis2 = true; + const valStandardized = await this.standardizeUserData( + inputValX, + inputValY, + null, + /** Unused sample weights. */ + null, + /** Unused class weights. */ + checkBatchAxis2, + batchSize + ); + valX = valStandardized[0]; + valY = valStandardized[1]; + valIns = valX.concat(valY); + } else if (args.validationSplit != null && args.validationSplit > 0 && args.validationSplit < 1) { + doValidation = true; + const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit)); + const originalBatchSize = inputs[0].shape[0]; + valX = sliceArrays(inputs, splitAt, originalBatchSize); + originalInputs = inputs; + inputs = sliceArrays(inputs, 0, splitAt); + valY = sliceArrays(targets, splitAt, originalBatchSize); + originalTargets = targets; + targets = sliceArrays(targets, 0, splitAt); + valIns = valX.concat(valY); + } else if (args.validationSteps != null) { + doValidation = true; + } + const ins = inputs.concat(targets).concat(sampleWeights); + this.checkTrainableWeightsConsistency(); + const trainFunction = this.makeTrainFunction(); + const outLabels = this.getDedupedMetricsNames(); + let valFunction; + let callbackMetrics; + if (doValidation) { + this.makeTestFunction(); + valFunction = this.testFunction; + callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n)); + } else { + valFunction = null; + valIns = []; + callbackMetrics = outLabels.slice(); + } + const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery); + const out = await this.fitLoop(trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks2, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null); + return out; + } finally { + this.isTraining = false; + disposeNewTensors(inputs, x); + disposeNewTensors(targets, y); + disposeNewTensors(originalInputs, x); + disposeNewTensors(originalTargets, y); + disposeNewTensors(valX, inputValX); + disposeNewTensors(valY, inputValY); + if (sampleWeights != null) { + dispose(sampleWeights); + } + } + } + /** + * Abstract fit function for `f(ins)`. + * @param f A Function returning a list of tensors. For training, this + * function is expected to perform the updates to the variables. + * @param ins List of tensors to be fed to `f`. + * @param outLabels List of strings, display names of the outputs of `f`. + * @param batchSize Integer batch size or `== null` if unknown. Default : 32. + * @param epochs Number of times to iterate over the data. Default : 1. + * @param verbose Verbosity mode: 0, 1, or 2. Default: 1. + * @param callbacks List of callbacks to be called during training. + * @param valF Function to call for validation. + * @param valIns List of tensors to be fed to `valF`. + * @param shuffle Whether to shuffle the data at the beginning of every + * epoch. Default : true. + * @param callbackMetrics List of strings, the display names of the metrics + * passed to the callbacks. They should be the concatenation of the + * display names of the outputs of `f` and the list of display names + * of the outputs of `valF`. + * @param initialEpoch Epoch at which to start training (useful for + * resuming a previous training run). Default : 0. + * @param stepsPerEpoch Total number of steps (batches on samples) before + * declaring one epoch finished and starting the next epoch. Ignored with + * the default value of `undefined` or `null`. + * @param validationSteps Number of steps to run validation for (only if + * doing validation from data tensors). Not applicable for tfjs-layers. + * @returns A `History` object. + */ + async fitLoop(f, ins, outLabels, batchSize, epochs, verbose, callbacks2, valF, valIns, shuffle2, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) { + if (batchSize == null) { + batchSize = 32; + } + if (epochs == null) { + epochs = 1; + } + if (shuffle2 == null) { + shuffle2 = true; + } + if (initialEpoch == null) { + initialEpoch = 0; + } + let doValidation = false; + if (valF != null && valIns != null) { + doValidation = true; + } + if (validationSteps != null) { + doValidation = true; + if (stepsPerEpoch == null) { + throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set."); + } + } + const numTrainSamples = this.checkNumSamples(ins, batchSize, stepsPerEpoch, "steps_per_epoch"); + let indexArray; + if (numTrainSamples != null) { + indexArray = range2(0, numTrainSamples); + } + if (verbose == null) { + verbose = 1; + } + const { callbackList, history } = configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics); + callbackList.setModel(this); + this.history = history; + await callbackList.onTrainBegin(); + this.stopTraining_ = false; + for (let epoch = initialEpoch; epoch < epochs; ++epoch) { + await callbackList.onEpochBegin(epoch); + const epochLogs = {}; + if (stepsPerEpoch != null) { + throw new NotImplementedError("stepsPerEpoch mode is not implemented yet."); + } else { + if (shuffle2 === "batch") { + throw new NotImplementedError("batch shuffling is not implemneted yet"); + } else if (shuffle2) { + util_exports.shuffle(indexArray); + } + const epochIndexArray1D = tensor1d(indexArray); + const batches = makeBatches(numTrainSamples, batchSize); + for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) { + const batchLogs = {}; + await callbackList.onBatchBegin(batchIndex, batchLogs); + tidy(() => { + const batchStart = batches[batchIndex][0]; + const batchEnd = batches[batchIndex][1]; + const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart); + batchLogs["batch"] = batchIndex; + batchLogs["size"] = batchEnd - batchStart; + const insBatch = sliceArraysByIndices(ins, batchIds); + const outs = f(insBatch); + for (let i = 0; i < outLabels.length; ++i) { + const label = outLabels[i]; + const out = outs[i]; + batchLogs[label] = out; + keep(out); + } + if (batchIndex === batches.length - 1) { + if (doValidation) { + const valOuts = this.testLoop(valF, valIns, batchSize); + for (let i = 0; i < outLabels.length; ++i) { + const label = outLabels[i]; + const out = valOuts[i]; + keep(out); + epochLogs["val_" + label] = out; + } + } + } + }); + await callbackList.onBatchEnd(batchIndex, batchLogs); + disposeTensorsInLogs(batchLogs); + if (this.stopTraining_) { + break; + } + } + epochIndexArray1D.dispose(); + } + await callbackList.onEpochEnd(epoch, epochLogs); + if (this.stopTraining_) { + break; + } + } + await callbackList.onTrainEnd(); + await this.history.syncData(); + return this.history; + } + // TODO(cais): Add code snippet below when it's possible to instantiate + // actual dataset objects. + /** + * Trains the model using a dataset object. + * + * @param dataset A dataset object. Its `iterator()` method is expected + * to generate a dataset iterator object, the `next()` method of which + * is expected to produce data batches for training. The return value + * of the `next()` call ought to contain a boolean `done` field and a + * `value` field. The `value` field is expected to be an array of two + * `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former + * case is for models with exactly one input and one output (e.g. + * a sequential model). The latter case is for models with multiple + * inputs and/or multiple outputs. + * Of the two items in the array, the first is the input feature(s) and + * the second is the output target(s). + * @param args A `ModelFitDatasetArgs`, containing optional fields. + * + * @return A `History` instance. Its `history` attribute contains all + * information collected during training. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async fitDataset(dataset, args) { + return fitDataset(this, dataset, args); + } + /** + * Runs a single gradient update on a single batch of data. + * + * This method differs from `fit()` and `fitDataset()` in the following + * regards: + * - It operates on exactly one batch of data. + * - It returns only the loss and metric values, instead of + * returning the batch-by-batch loss and metric values. + * - It doesn't support fine-grained options such as verbosity and + * callbacks. + * + * @param x Input data. It could be one of the following: + * - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has + * multiple inputs). + * - An Object mapping input names to corresponding `tf.Tensor` (if the + * model has named inputs). + * @param y Target data. It could be either a `tf.Tensor` or multiple + * `tf.Tensor`s. It should be consistent with `x`. + * @returns Training loss or losses (in case the model has + * multiple outputs), along with metrics (if any), as numbers. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async trainOnBatch(x, y) { + const standardizeOut = await this.standardizeUserData(x, y); + const inputs = standardizeOut[0]; + const targets = standardizeOut[1]; + const trainFunction = this.makeTrainFunction(); + const losses2 = trainFunction(inputs.concat(targets)); + const lossValues = []; + for (const loss of losses2) { + const v = await loss.data(); + lossValues.push(v[0]); + } + dispose(losses2); + disposeNewTensors(standardizeOut[0], x); + disposeNewTensors(standardizeOut[1], y); + return singletonOrArray(lossValues); + } + /** + * Extract weight values of the model. + * + * @param config: An instance of `io.SaveConfig`, which specifies + * model-saving options such as whether only trainable weights are to be + * saved. + * @returns A `NamedTensorMap` mapping original weight names (i.e., + * non-uniqueified weight names) to their values. + */ + getNamedWeights(config) { + const namedWeights = []; + const trainableOnly = config != null && config.trainableOnly; + const weights = trainableOnly ? this.trainableWeights : this.weights; + const weightValues = this.getWeights(trainableOnly); + for (let i = 0; i < weights.length; ++i) { + if (trainableOnly && !weights[i].trainable) { + continue; + } + namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] }); + } + return namedWeights; + } + /** + * Setter used for force stopping of LayersModel.fit() (i.e., training). + * + * Example: + * + * ```js + * const input = tf.input({shape: [10]}); + * const output = tf.layers.dense({units: 1}).apply(input); + * const model = tf.model({inputs: [input], outputs: [output]}); + * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'}); + * const xs = tf.ones([8, 10]); + * const ys = tf.zeros([8, 1]); + * + * const history = await model.fit(xs, ys, { + * epochs: 10, + * callbacks: { + * onEpochEnd: async (epoch, logs) => { + * if (epoch === 2) { + * model.stopTraining = true; + * } + * } + * } + * }); + * + * // There should be only 3 values in the loss array, instead of 10 + * values, + * // due to the stopping after 3 epochs. + * console.log(history.history.loss); + * ``` + */ + set stopTraining(stop) { + this.stopTraining_ = stop; + } + get stopTraining() { + return this.stopTraining_; + } + get optimizer() { + return this.optimizer_; + } + set optimizer(optimizer) { + if (this.optimizer_ !== optimizer) { + this.optimizer_ = optimizer; + this.isOptimizerOwned = false; + } + } + dispose() { + const result = super.dispose(); + if (result.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) { + const numTensorsBeforeOptmizerDisposal = memory().numTensors; + this.optimizer_.dispose(); + result.numDisposedVariables += numTensorsBeforeOptmizerDisposal - memory().numTensors; + } + return result; + } + getLossIdentifiers() { + let lossNames; + if (typeof this.loss === "string") { + lossNames = toSnakeCase(this.loss); + } else if (Array.isArray(this.loss)) { + for (const loss of this.loss) { + if (typeof loss !== "string") { + throw new Error("Serialization of non-string loss is not supported."); + } + } + lossNames = this.loss.map((name) => toSnakeCase(name)); + } else { + const outputNames = Object.keys(this.loss); + lossNames = {}; + const losses2 = this.loss; + for (const outputName of outputNames) { + if (typeof losses2[outputName] === "string") { + lossNames[outputName] = toSnakeCase(losses2[outputName]); + } else { + throw new Error("Serialization of non-string loss is not supported."); + } + } + } + return lossNames; + } + getMetricIdentifiers() { + if (typeof this.metrics === "string" || typeof this.metrics === "function") { + return [toSnakeCase(getLossOrMetricName(this.metrics))]; + } else if (Array.isArray(this.metrics)) { + return this.metrics.map((metric) => toSnakeCase(getLossOrMetricName(metric))); + } else { + const metricsIdentifiers = {}; + for (const key in this.metrics) { + metricsIdentifiers[key] = toSnakeCase(getLossOrMetricName(this.metrics[key])); + } + return metricsIdentifiers; + } + } + getTrainingConfig() { + return { + loss: this.getLossIdentifiers(), + metrics: this.getMetricIdentifiers(), + optimizer_config: { + class_name: this.optimizer.getClassName(), + config: this.optimizer.getConfig() + } + }; + } + loadTrainingConfig(trainingConfig) { + if (trainingConfig.weighted_metrics != null) { + throw new Error("Loading weight_metrics is not supported yet."); + } + if (trainingConfig.loss_weights != null) { + throw new Error("Loading loss_weights is not supported yet."); + } + if (trainingConfig.sample_weight_mode != null) { + throw new Error("Loading sample_weight_mode is not supported yet."); + } + const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config); + const optimizer = deserialize(tsConfig); + let loss; + if (typeof trainingConfig.loss === "string") { + loss = toCamelCase(trainingConfig.loss); + } else if (Array.isArray(trainingConfig.loss)) { + loss = trainingConfig.loss.map((lossEntry) => toCamelCase(lossEntry)); + } else if (trainingConfig.loss != null) { + loss = {}; + for (const key in trainingConfig.loss) { + loss[key] = toCamelCase(trainingConfig.loss[key]); + } + } + let metrics; + if (Array.isArray(trainingConfig.metrics)) { + metrics = trainingConfig.metrics.map((metric) => toCamelCase(metric)); + } else if (trainingConfig.metrics != null) { + metrics = {}; + for (const key in trainingConfig.metrics) { + metrics[key] = toCamelCase(trainingConfig.metrics[key]); + } + } + this.compile({ loss, metrics, optimizer }); + } + /** + * Save the configuration and/or weights of the LayersModel. + * + * An `IOHandler` is an object that has a `save` method of the proper + * signature defined. The `save` method manages the storing or + * transmission of serialized data ("artifacts") that represent the + * model's topology and weights onto or via a specific medium, such as + * file downloads, local storage, IndexedDB in the web browser and HTTP + * requests to a server. TensorFlow.js provides `IOHandler` + * implementations for a number of frequently used saving mediums, such as + * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io` + * for more details. + * + * This method also allows you to refer to certain types of `IOHandler`s + * as URL-like string shortcuts, such as 'localstorage://' and + * 'indexeddb://'. + * + * Example 1: Save `model`'s topology and weights to browser [local + * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage); + * then load it back. + * + * ```js + * const model = tf.sequential( + * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]}); + * console.log('Prediction from original model:'); + * model.predict(tf.ones([1, 3])).print(); + * + * const saveResults = await model.save('localstorage://my-model-1'); + * + * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1'); + * console.log('Prediction from loaded model:'); + * loadedModel.predict(tf.ones([1, 3])).print(); + * ``` + * + * Example 2. Saving `model`'s topology and weights to browser + * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API); + * then load it back. + * + * ```js + * const model = tf.sequential( + * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]}); + * console.log('Prediction from original model:'); + * model.predict(tf.ones([1, 3])).print(); + * + * const saveResults = await model.save('indexeddb://my-model-1'); + * + * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1'); + * console.log('Prediction from loaded model:'); + * loadedModel.predict(tf.ones([1, 3])).print(); + * ``` + * + * Example 3. Saving `model`'s topology and weights as two files + * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from + * browser. + * + * ```js + * const model = tf.sequential( + * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]}); + * const saveResults = await model.save('downloads://my-model-1'); + * ``` + * + * Example 4. Send `model`'s topology and weights to an HTTP server. + * See the documentation of `tf.io.http` for more details + * including specifying request parameters and implementation of the + * server. + * + * ```js + * const model = tf.sequential( + * {layers: [tf.layers.dense({units: 1, inputShape: [3]})]}); + * const saveResults = await model.save('http://my-server/model/upload'); + * ``` + * + * @param handlerOrURL An instance of `IOHandler` or a URL-like, + * scheme-based string shortcut for `IOHandler`. + * @param config Options for saving the model. + * @returns A `Promise` of `SaveResult`, which summarizes the result of + * the saving, such as byte sizes of the saved artifacts for the model's + * topology and weight values. + * + * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} + */ + async save(handlerOrURL, config) { + if (typeof handlerOrURL === "string") { + const handlers = io_exports.getSaveHandlers(handlerOrURL); + if (handlers.length === 0) { + throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`); + } else if (handlers.length > 1) { + throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`); + } + handlerOrURL = handlers[0]; + } + if (handlerOrURL.save == null) { + throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined."); + } + const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config)); + const returnString = false; + const unusedArg = null; + const modelConfig = this.toJSON(unusedArg, returnString); + const modelArtifacts = { + modelTopology: modelConfig, + format: LAYERS_MODEL_FORMAT_NAME, + generatedBy: `TensorFlow.js tfjs-layers v${version2}`, + convertedBy: null + }; + const includeOptimizer = config == null ? false : config.includeOptimizer; + if (includeOptimizer && this.optimizer != null) { + modelArtifacts.trainingConfig = this.getTrainingConfig(); + const weightType = "optimizer"; + const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io_exports.encodeWeights(await this.optimizer.getWeights(), weightType); + weightDataAndSpecs.specs.push(...optimizerWeightSpecs); + weightDataAndSpecs.data = io_exports.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]); + } + if (this.userDefinedMetadata != null) { + const checkSize = true; + checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize); + modelArtifacts.userDefinedMetadata = this.userDefinedMetadata; + } + modelArtifacts.weightData = weightDataAndSpecs.data; + modelArtifacts.weightSpecs = weightDataAndSpecs.specs; + return handlerOrURL.save(modelArtifacts); + } + /** + * Set user-defined metadata. + * + * The set metadata will be serialized together with the topology + * and weights of the model during `save()` calls. + * + * @param setUserDefinedMetadata + */ + setUserDefinedMetadata(userDefinedMetadata) { + checkUserDefinedMetadata(userDefinedMetadata, this.name); + this.userDefinedMetadata = userDefinedMetadata; + } + /** + * Get user-defined metadata. + * + * The metadata is supplied via one of the two routes: + * 1. By calling `setUserDefinedMetadata()`. + * 2. Loaded during model loading (if the model is constructed + * via `tf.loadLayersModel()`.) + * + * If no user-defined metadata is available from either of the + * two routes, this function will return `undefined`. + */ + getUserDefinedMetadata() { + return this.userDefinedMetadata; + } +}; +LayersModel.className = "Model"; +serialization_exports.registerClass(LayersModel); +var Functional = class extends LayersModel { +}; +Functional.className = "Functional"; +serialization_exports.registerClass(Functional); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/models.js +async function modelFromJSON(modelAndWeightsConfig, customObjects) { + if (!("modelTopology" in modelAndWeightsConfig)) { + modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig }; + } + modelAndWeightsConfig = modelAndWeightsConfig; + let modelTopology = modelAndWeightsConfig.modelTopology; + if (modelTopology["model_config"] != null) { + modelTopology = modelTopology["model_config"]; + } + const tsConfig = convertPythonicToTs(modelTopology); + const model2 = deserialize(tsConfig, customObjects); + if (modelAndWeightsConfig.weightsManifest != null) { + const weightValues = await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model2.weights.map((weight) => weight.originalName)); + const uniqueWeightValues = {}; + for (const weight of model2.weights) { + uniqueWeightValues[weight.originalName] = weightValues[weight.originalName]; + } + model2.loadWeights(uniqueWeightValues); + dispose(weightValues); + } + return model2; +} +async function loadLayersModel(pathOrIOHandler, options) { + if (options == null) { + options = {}; + } + if (typeof pathOrIOHandler === "string") { + const handlers = io_exports.getLoadHandlers(pathOrIOHandler, options); + if (handlers.length === 0) { + handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler, options)); + } else if (handlers.length > 1) { + throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`); + } + pathOrIOHandler = handlers[0]; + } + return loadLayersModelFromIOHandler(pathOrIOHandler, void 0, options); +} +async function loadLayersModelFromIOHandler(handler, customObjects, options) { + if (options == null) { + options = {}; + } + if (handler.load == null) { + throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented."); + } + const artifacts = await handler.load(); + let modelTopology = artifacts.modelTopology; + if (modelTopology["model_config"] != null) { + modelTopology = modelTopology["model_config"]; + } + const strict = options.strict == null ? true : options.strict; + const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict; + const model2 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit); + const trainingConfig = artifacts.trainingConfig; + if (trainingConfig != null) { + model2.loadTrainingConfig(trainingConfig); + } + if (artifacts.userDefinedMetadata != null) { + model2.setUserDefinedMetadata(artifacts.userDefinedMetadata); + } + if (artifacts.weightData != null) { + if (artifacts.weightSpecs == null) { + throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed."); + } + const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs); + model2.loadWeights(modelWeights, strict); + if (model2.optimizer != null && optimizerWeights.length > 0) { + await model2.optimizer.setWeights(optimizerWeights); + } + dispose(modelWeights); + dispose(optimizerWeights.map((w) => w.tensor)); + } + return model2; +} +function decodeModelAndOptimizerWeights(weightData, specs) { + const name2Tensor = io_exports.decodeWeights(weightData, specs); + const modelWeights = {}; + const optimizerWeights = []; + specs.forEach((spec) => { + if (spec.group === "optimizer") { + optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] }); + } else { + modelWeights[spec.name] = name2Tensor[spec.name]; + } + }); + return { modelWeights, optimizerWeights }; +} +var Sequential = class _Sequential extends LayersModel { + constructor(args) { + super({ inputs: [], outputs: [] }); + args = args || {}; + this.trainable = true; + this.built = false; + this.name = args.name != null ? args.name : getUid("sequential_"); + if (args.layers != null) { + for (const layer of args.layers) { + this.add(layer); + } + } + } + // Helper function to Sequential.add Throws if the new output shape will be + // invalid. + checkShape(layer) { + const shape = layer.inboundNodes[0].outputTensors[0].shape; + if (shape.some((x) => x < 0)) { + throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`); + } + } + /** + * Adds a layer instance on top of the layer stack. + * + * ```js + * const model = tf.sequential(); + * model.add(tf.layers.dense({units: 8, inputShape: [1]})); + * model.add(tf.layers.dense({units: 4, activation: 'relu6'})); + * model.add(tf.layers.dense({units: 1, activation: 'relu6'})); + * // Note that the untrained model is random at this point. + * model.predict(tf.randomNormal([10, 1])).print(); + * ``` + * @param layer Layer instance. + * + * @exception ValueError In case the `layer` argument does not know its + * input shape. + * @exception ValueError In case the `layer` argument has multiple output + * tensors, or is already connected somewhere else (forbidden in + * `Sequential` models). + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + add(layer) { + const isLayerModelInstance = layer instanceof _Sequential || layer instanceof LayersModel; + let modelLayer; + if (isLayerModelInstance) { + modelLayer = layer; + if (modelLayer.outputs.length !== 1) { + throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API."); + } + if (modelLayer.inputs.length !== 1) { + throw new ValueError("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 (layer.inboundNodes.length === 0) { + if (layer.batchInputShape == null) { + throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument."); + } + const x = Input({ + batchShape: layer.batchInputShape, + dtype: layer.dtype, + name: layer.name + "_input" + }); + layer.apply(x); + } + if (isLayerModelInstance) { + this.outputs = modelLayer.outputs; + this.inputs = modelLayer.inputs; + } else { + if (layer.inboundNodes.length !== 1) { + throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`); + } + if (layer.inboundNodes[0].outputTensors.length !== 1) { + throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API."); + } + this.checkShape(layer); + this.outputs = [layer.inboundNodes[0].outputTensors[0]]; + this.inputs = getSourceInputs(this.outputs[0]); + } + this.inboundNodes = []; + new Node({ + outboundLayer: this, + inboundLayers: [], + nodeIndices: [], + tensorIndices: [], + inputTensors: this.inputs, + outputTensors: this.outputs, + // no model-level masking for now + inputMasks: pyListRepeat(null, this.inputs.length), + outputMasks: [null], + inputShapes: this.inputs.map((x) => x.shape), + outputShapes: this.outputs[0].shape + }); + } else { + const outputTensor = layer.apply(this.outputs[0]); + if (Array.isArray(outputTensor)) { + 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(layer); + this.outputs = [outputTensor]; + this.inboundNodes[0].outputTensors = this.outputs; + this.inboundNodes[0].outputShapes = [this.outputs[0].shape]; + } + this.layers.push(layer); + this.built = false; + } + /** + * Removes the last layer in the model. + * + * @exception TypeError if there are no layers in the model. + */ + pop() { + if (this.layers.length === 0) { + throw new TypeError("There are no layers in the model."); + } + this.layers.pop(); + if (this.layers.length === 0) { + this.outputs = []; + this.inboundNodes = []; + this.outboundNodes = []; + } else { + const lastLayerIndex = this.layers.length - 1; + this.layers[lastLayerIndex].outboundNodes = []; + this.outputs = [this.layers[lastLayerIndex].output]; + this.inboundNodes[0].outputTensors = this.outputs; + this.inboundNodes[0].outputShapes = [this.outputs[0].shape]; + } + } + call(inputs, kwargs) { + if (this.model == null) { + this.build(); + } + return this.model.call(inputs, kwargs); + } + build(inputShape) { + getExactlyOneShape(inputShape); + if (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 LayersModel({ + 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 = true; + } + countParams() { + if (!this.built) { + this.build(); + } + return super.countParams(); + } + /** + * Print a text summary of the Sequential model's layers. + * + * The summary includes + * - Name and type of all layers that comprise the model. + * - Output shape(s) of the layers + * - Number of weight parameters of each layer + * - The total number of trainable and non-trainable parameters of the + * model. + * + * ```js + * const model = tf.sequential(); + * model.add( + * tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'})); + * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'})); + * + * model.summary(); + * ``` + * + * @param lineLength Custom line length, in number of characters. + * @param positions Custom widths of each of the columns, as either + * fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number + * of characters (e.g., `[30, 50, 65]`). Each number corresponds to + * right-most (i.e., ending) position of a column. + * @param printFn Custom print function. Can be used to replace the default + * `console.log`. For example, you can use `x => {}` to mute the printed + * messages in the console. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + summary(lineLength, positions, printFn = console.log) { + if (!this.built) { + this.build(); + } + super.summary(lineLength, positions, printFn); + } + /** + * Sets the weights of the model. + * + * @param weights Should be a list of Tensors with shapes and types matching + * the output of `model.getWeights()`. + */ + setWeights(weights) { + if (this.model == null) { + this.build(); + } + this.model.setWeights(weights); + } + /** + * Returns the loss value & metrics values for the model in test mode. + * + * Loss and metrics are specified during `compile()`, which needs to happen + * before calls to `evaluate()`. + * + * Computation is done in batches. + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'}); + * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), { + * batchSize: 4, + * }); + * result.print(); + * ``` + * + * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the + * model has multiple inputs. + * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the + * model has multiple outputs. + * @param args A `ModelEvaluateConfig`, containing optional fields. + * + * @return `Scalar` test loss (if the model has a single output and no + * metrics) or `Array` of `Scalar`s (if the model has multiple outputs + * and/or metrics). The attribute `model.metricsNames` + * will give you the display labels for the scalar outputs. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + evaluate(x, y, args = {}) { + if (!this.built) { + throw new RuntimeError("The model needs to be compiled before being used."); + } + return this.model.evaluate(x, y, args); + } + // TODO(cais): Add code snippet below once real dataset objects are + // available. + /** + * Evaluate model using a dataset object. + * + * Note: Unlike `evaluate()`, this method is asynchronous (`async`). + * + * @param dataset A dataset object. Its `iterator()` method is expected + * to generate a dataset iterator object, the `next()` method of which + * is expected to produce data batches for evaluation. The return value + * of the `next()` call ought to contain a boolean `done` field and a + * `value` field. The `value` field is expected to be an array of two + * `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former + * case is for models with exactly one input and one output (e.g. + * a sequential model). The latter case is for models with multiple + * inputs and/or multiple outputs. Of the two items in the array, the + * first is the input feature(s) and the second is the output target(s). + * @param args A configuration object for the dataset-based evaluation. + * @returns Loss and metric values as an Array of `Scalar` objects. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async evaluateDataset(dataset, args) { + if (!this.built) { + throw new RuntimeError("The model needs to be compiled before being used."); + } + return this.model.evaluateDataset(dataset, args); + } + /** + * Generates output predictions for the input samples. + * + * Computation is done in batches. + * + * Note: the "step" mode of predict() is currently not supported. + * This is because the TensorFlow.js core backend is imperative only. + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.predict(tf.ones([2, 10])).print(); + * ``` + * + * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if + * the model has multiple inputs. + * @param conifg A `ModelPredictConfig` object containing optional fields. + * + * @return `tf.Tensor`(s) of predictions. + * + * @exception ValueError In case of mismatch between the provided input data + * and the model's expectations, or in case a stateful model receives a + * number of samples that is not a multiple of the batch size. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + predict(x, args = {}) { + if (this.model == null) { + this.build(); + } + return this.model.predict(x, args); + } + /** + * Returns predictions for a single batch of samples. + * + * @param x: Input samples, as a Tensor, or list of Tensors (if the model + * has multiple inputs). + * @return Tensor(s) of predictions + */ + predictOnBatch(x) { + if (this.model == null) { + this.build(); + } + return this.model.predictOnBatch(x); + } + /** + * See `LayersModel.compile`. + * + * @param args + */ + compile(args) { + this.build(); + this.model.compile(args); + 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(optimizer) { + this.model.optimizer = optimizer; + } + /** + * Trains the model for a fixed number of epochs (iterations on a dataset). + * + * ```js + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [10]})] + * }); + * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'}); + * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), { + * batchSize: 4, + * epochs: 3 + * }); + * console.log(history.history.loss[0]); + * ``` + * + * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the + * model has multiple inputs. If all inputs in the model are named, you can + * also pass a dictionary mapping input names to `tf.Tensor`s. + * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if + * the model has multiple outputs. If all outputs in the model are named, you + * can also pass a dictionary mapping output names to `tf.Tensor`s. + * @param args A `ModelFitConfig`, containing optional fields. + * + * @return A `History` instance. Its `history` attribute contains all + * information collected during training. + * + * @exception ValueError In case of mismatch between the provided input data + * and what the model expects. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async fit(x, y, args = {}) { + if (!this.built) { + throw new RuntimeError("The model needs to be compiled before being used."); + } + return this.model.fit(x, y, args); + } + /** + * Trains the model using a dataset object. + * + * ```js + * const xArray = [ + * [1, 1, 1, 1, 1, 1, 1, 1, 1], + * [1, 1, 1, 1, 1, 1, 1, 1, 1], + * [1, 1, 1, 1, 1, 1, 1, 1, 1], + * [1, 1, 1, 1, 1, 1, 1, 1, 1], + * ]; + * const yArray = [1, 1, 1, 1]; + * // Create a dataset from the JavaScript array. + * const xDataset = tf.data.array(xArray); + * const yDataset = tf.data.array(yArray); + * // Zip combines the `x` and `y` Datasets into a single Dataset, the + * // iterator of which will return an object containing of two tensors, + * // corresponding to `x` and `y`. The call to `batch(4)` will bundle + * // four such samples into a single object, with the same keys now pointing + * // to tensors that hold 4 examples, organized along the batch dimension. + * // The call to `shuffle(4)` causes each iteration through the dataset to + * // happen in a different order. The size of the shuffle window is 4. + * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset}) + * .batch(4) + * .shuffle(4); + * const model = tf.sequential({ + * layers: [tf.layers.dense({units: 1, inputShape: [9]})] + * }); + * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'}); + * const history = await model.fitDataset(xyDataset, { + * epochs: 4, + * callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)} + * }); + * ``` + * + * @param dataset A dataset object. Its `iterator()` method is expected to + * generate a dataset iterator object, the `next()` method of which is + * expected to produce data batches for evaluation. The return value of the + * `next()` call ought to contain a boolean `done` field and a `value` + * field. + * + * The `value` field is expected to be an object of with fields + * `xs` and `ys`, which point to the feature tensor and the target tensor, + * respectively. This case is for models with exactly one input and one + * output (e.g. a sequential model). For example: + * ```js + * {value: {xs: xsTensor, ys: ysTensor}, done: false} + * ``` + * + * If the model has multiple inputs, the `xs` field of `value` should + * be an object mapping input names to their respective feature tensors. + * For example: + * ```js + * { + * value: { + * xs: { + * input_1: xsTensor1, + * input_2: xsTensor2 + * }, + * ys: ysTensor + * }, + * done: false + * } + * ``` + * If the model has multiple outputs, the `ys` field of `value` should + * be an object mapping output names to their respective target tensors. + * For example: + * ```js + * { + * value: { + * xs: xsTensor, + * ys: { + * output_1: ysTensor1, + * output_2: ysTensor2 + * }, + * }, + * done: false + * } + * ``` + * @param args A `ModelFitDatasetArgs`, containing optional fields. + * + * @return A `History` instance. Its `history` attribute contains all + * information collected during training. + * + * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} + */ + async fitDataset(dataset, args) { + if (!this.built) { + throw new RuntimeError("The model needs to be compiled before being used."); + } + return this.model.fitDataset(dataset, args); + } + /** + * Runs a single gradient update on a single batch of data. + * + * This method differs from `fit()` and `fitDataset()` in the following + * regards: + * - It operates on exactly one batch of data. + * - It returns only the loss and metric values, instead of + * returning the batch-by-batch loss and metric values. + * - It doesn't support fine-grained options such as verbosity and + * callbacks. + * + * @param x Input data. It could be one of the following: + * - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has + * multiple inputs). + * - An Object mapping input names to corresponding `tf.Tensor` (if the + * model has named inputs). + * @param y Target data. It could be either a `tf.Tensor` or multiple + * `tf.Tensor`s. It should be consistent with `x`. + * @returns Training loss or losses (in case the model has + * multiple outputs), along with metrics (if any), as numbers. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async trainOnBatch(x, y) { + return this.model.trainOnBatch(x, y); + } + /* See parent class for JsDoc */ + /** @nocollapse */ + static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) { + let configArray; + let extraModelConfig = {}; + if (config instanceof Array) { + if (!(config[0].className != null) || config[0]["className"] === "Merge") { + throw new ValueError("Legacy serialization format not supported yet."); + } + configArray = config; + } else { + util_exports.assert(config["layers"] != null, () => `When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.`); + configArray = config["layers"]; + delete config["layers"]; + extraModelConfig = config; + } + const model2 = new cls(extraModelConfig); + if (!(model2 instanceof _Sequential)) { + throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`); + } + for (const conf of configArray) { + const customObjects2 = void 0; + const layer = deserialize(conf, customObjects2, fastWeightInit); + if (fastWeightInit) { + layer.setFastWeightInitDuringBuild(true); + } + model2.add(layer); + } + return model2; + } + /** + * Setter used for force stopping of LayersModel.fit() (i.e., training). + * + * Example: + * + * ```js + * const model = tf.sequential(); + * model.add(tf.layers.dense({units: 1, inputShape: [10]})); + * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'}); + * const xs = tf.ones([8, 10]); + * const ys = tf.zeros([8, 1]); + * + * const history = await model.fit(xs, ys, { + * epochs: 10, + * callbacks: { + * onEpochEnd: async (epoch, logs) => { + * if (epoch === 2) { + * model.stopTraining = true; + * } + * } + * } + * }); + * + * // There should be only 3 values in the loss array, instead of 10 values, + * // due to the stopping after 3 epochs. + * console.log(history.history.loss); + * ``` + */ + set stopTraining(stop) { + if (this.model == null) { + throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled."); + } + this.model.stopTraining = stop; + } + get stopTraining() { + if (this.model == null) { + throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled."); + } + return this.model.stopTraining; + } + // TODO(cais): Override get trainableWeights() here + // tslint:disable-next-line:no-any + getConfig() { + const layers = []; + for (const layer of this.layers) { + const dict = {}; + dict["className"] = layer.getClassName(); + dict["config"] = layer.getConfig(); + layers.push(dict); + } + return { name: this.name, layers }; + } +}; +Sequential.className = "Sequential"; +serialization_exports.registerClass(Sequential); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports.js +function model(args) { + return new LayersModel(args); +} +function sequential(config) { + return new Sequential(config); +} +function input(config) { + return Input(config); +} +function registerCallbackConstructor(verbosityLevel, callbackConstructor) { + CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/activations.js +var Activation = class extends serialization_exports.Serializable { + getConfig() { + return {}; + } +}; +var Elu2 = class extends Activation { + /** + * Calculate the activation function. + * + * @param x: Input. + * @param alpha: Scaling factor the negative section. + * @return Output of the ELU activation. + */ + apply(x, alpha = 1) { + return elu2(x, alpha); + } +}; +Elu2.className = "elu"; +serialization_exports.registerClass(Elu2); +var Selu2 = class extends Activation { + apply(x) { + return selu(x); + } +}; +Selu2.className = "selu"; +serialization_exports.registerClass(Selu2); +var Relu2 = class extends Activation { + apply(x) { + return relu(x); + } +}; +Relu2.className = "relu"; +serialization_exports.registerClass(Relu2); +var Relu62 = class extends Activation { + apply(x) { + return tidy(() => minimum(6, relu(x))); + } +}; +Relu62.className = "relu6"; +serialization_exports.registerClass(Relu62); +var Linear = class extends Activation { + apply(x) { + return x; + } +}; +Linear.className = "linear"; +serialization_exports.registerClass(Linear); +var Sigmoid2 = class extends Activation { + apply(x) { + return sigmoid(x); + } +}; +Sigmoid2.className = "sigmoid"; +serialization_exports.registerClass(Sigmoid2); +var HardSigmoid = class extends Activation { + apply(x) { + return hardSigmoid(x); + } +}; +HardSigmoid.className = "hardSigmoid"; +serialization_exports.registerClass(HardSigmoid); +var Softplus2 = class extends Activation { + apply(x) { + return softplus(x); + } +}; +Softplus2.className = "softplus"; +serialization_exports.registerClass(Softplus2); +var Softsign = class extends Activation { + apply(x) { + return softsign(x); + } +}; +Softsign.className = "softsign"; +serialization_exports.registerClass(Softsign); +var Tanh2 = class extends Activation { + apply(x) { + return tanh2(x); + } +}; +Tanh2.className = "tanh"; +serialization_exports.registerClass(Tanh2); +var Softmax2 = class extends Activation { + /** + * Calculate the activation function. + * + * @param x Tensor. + * @param axis Integer, axis along which the softmax normalization is applied. + * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be + * an error. + * + * @returns a Tensor of the same shape as x + * + * @throws ValueError: In case `dim(x) < 2`. + */ + apply(x, axis = -1) { + return softmax(x, axis); + } +}; +Softmax2.className = "softmax"; +serialization_exports.registerClass(Softmax2); +var LogSoftmax2 = class extends Activation { + /** + * Calculate the activation function of log softmax: + * log( exp(x_i) / sum(exp(x)) ) + * + * @param x Tensor. + * @param axis Integer, axis along which the softmax normalization is applied. + * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be + * an error. + * + * @returns a Tensor of the same shape as x + * + * @throws ValueError: In case `dim(x) < 2`. + */ + apply(x, axis = -1) { + return logSoftmax(x, axis); + } +}; +LogSoftmax2.className = "logSoftmax"; +serialization_exports.registerClass(LogSoftmax2); +var Swish = class extends Activation { + /** + * Calculate the activation function. + * + * @param x Tensor. + * @param alpha Scaling factor for the sigmoid function. + * @returns a Tensor of the same shape as x + */ + apply(x, alpha = 1) { + return tidy(() => mul(sigmoid(mul(x, alpha)), x)); + } +}; +Swish.className = "swish"; +serialization_exports.registerClass(Swish); +var Mish = class extends Activation { + /** + * Calculate the activation function. + * + * @param x Tensor. + * @returns a Tensor of the same shape as x + */ + apply(x) { + return tidy(() => mul(x, tanh2(softplus(x)))); + } +}; +Mish.className = "mish"; +serialization_exports.registerClass(Mish); +function serializeActivation(activation2) { + return activation2.getClassName(); +} +function deserializeActivation(config, customObjects = {}) { + return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "activation"); +} +function getActivation(identifier) { + if (identifier == null) { + const config = {}; + config["className"] = "linear"; + config["config"] = {}; + return deserializeActivation(config); + } + if (typeof identifier === "string") { + const config = {}; + config["className"] = identifier; + config["config"] = {}; + return deserializeActivation(config); + } else if (identifier instanceof Activation) { + return identifier; + } else { + return deserializeActivation(identifier); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/regularizers.js +function assertObjectArgs(args) { + if (args != null && typeof args !== "object") { + throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`); + } +} +var Regularizer = class extends serialization_exports.Serializable { +}; +var L1L2 = class extends Regularizer { + constructor(args) { + super(); + assertObjectArgs(args); + this.l1 = args == null || args.l1 == null ? 0.01 : args.l1; + this.l2 = args == null || args.l2 == null ? 0.01 : args.l2; + this.hasL1 = this.l1 !== 0; + this.hasL2 = this.l2 !== 0; + } + /** + * Porting note: Renamed from __call__. + * @param x Variable of which to calculate the regularization score. + */ + apply(x) { + return tidy(() => { + let regularization = zeros([1]); + if (this.hasL1) { + regularization = add2(regularization, sum2(mul(this.l1, abs(x)))); + } + if (this.hasL2) { + regularization = add2(regularization, sum2(mul(this.l2, square2(x)))); + } + return reshape(regularization, []); + }); + } + getConfig() { + return { "l1": this.l1, "l2": this.l2 }; + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls({ l1: config["l1"], l2: config["l2"] }); + } +}; +L1L2.className = "L1L2"; +serialization_exports.registerClass(L1L2); +function l1(args) { + assertObjectArgs(args); + return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 }); +} +function l2(args) { + assertObjectArgs(args); + return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 }); +} +var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = { + "l1l2": "L1L2" +}; +function serializeRegularizer(constraint) { + return serializeKerasObject(constraint); +} +function deserializeRegularizer(config, customObjects = {}) { + return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "regularizer"); +} +function getRegularizer(identifier) { + if (identifier == null) { + return null; + } + if (typeof identifier === "string") { + const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier; + const config = { className, config: {} }; + return deserializeRegularizer(config); + } else if (identifier instanceof Regularizer) { + return identifier; + } else { + return deserializeRegularizer(identifier); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js +var ReLU = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.supportsMasking = true; + if (args != null) { + this.maxValue = args.maxValue; + } + } + call(inputs, kwargs) { + inputs = getExactlyOneTensor(inputs); + let output = relu(inputs); + if (this.maxValue != null) { + output = clipByValue(output, 0, this.maxValue); + } + return output; + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const config = { maxValue: this.maxValue }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +ReLU.className = "ReLU"; +serialization_exports.registerClass(ReLU); +var LeakyReLU = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.DEFAULT_ALPHA = 0.3; + if (args == null) { + args = {}; + } + this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha; + } + call(inputs, kwargs) { + const x = getExactlyOneTensor(inputs); + return leakyRelu(x, this.alpha); + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const config = { alpha: this.alpha }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +LeakyReLU.className = "LeakyReLU"; +serialization_exports.registerClass(LeakyReLU); +var PReLU = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.DEFAULT_ALPHA_INITIALIZER = "zeros"; + if (args == null) { + args = {}; + } + this.supportsMasking = true; + this.alphaInitializer = getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER); + this.alphaRegularizer = getRegularizer(args.alphaRegularizer); + this.alphaConstraint = getConstraint(args.alphaConstraint); + if (args.sharedAxes == null) { + this.sharedAxes = null; + } else if (Array.isArray(args.sharedAxes)) { + this.sharedAxes = args.sharedAxes; + } else if (typeof args.sharedAxes === "number") { + this.sharedAxes = [args.sharedAxes]; + } else { + throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`); + } + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const paramShape = inputShape.slice(1); + if (this.sharedAxes != null) { + for (const i of this.sharedAxes) { + paramShape[i - 1] = 1; + } + } + this.alpha = this.addWeight("alpha", paramShape, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint); + const axes = {}; + if (this.sharedAxes != null) { + for (let i = 1; i < inputShape.length; ++i) { + axes[i] = inputShape[i]; + } + } + this.inputSpec = [new InputSpec({ + ndim: inputShape.length, + axes + })]; + this.built = true; + } + call(inputs, kwargs) { + inputs = getExactlyOneTensor(inputs); + return prelu(inputs, this.alpha.read()); + } + getConfig() { + const config = { + alphaInitializer: serializeInitializer(this.alphaInitializer), + alphaRegularizer: serializeRegularizer(this.alphaRegularizer), + alphaConstraint: serializeConstraint(this.alphaConstraint), + sharedAxes: this.sharedAxes + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +PReLU.className = "PReLU"; +serialization_exports.registerClass(PReLU); +var ELU = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.DEFAULT_ALPHA = 1; + if (args == null) { + args = {}; + } + if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) { + throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`); + } + this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha; + } + call(inputs, kwargs) { + const x = getExactlyOneTensor(inputs); + return elu(x); + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const config = { alpha: this.alpha }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +ELU.className = "ELU"; +serialization_exports.registerClass(ELU); +var ThresholdedReLU = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.DEFAULT_THETA = 1; + if (args == null) { + args = {}; + } + this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta; + } + call(inputs, kwargs) { + const x = getExactlyOneTensor(inputs); + return mul(x, cast(greater(x, this.theta), "float32")); + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const config = { theta: this.theta }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +ThresholdedReLU.className = "ThresholdedReLU"; +serialization_exports.registerClass(ThresholdedReLU); +var Softmax3 = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.DEFAULT_AXIS = 1; + if (args == null) { + args = {}; + } + this.softmax = new Softmax2().apply; + this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis; + } + call(inputs, kwargs) { + return tidy(() => { + let x = getExactlyOneTensor(inputs); + const mask = kwargs["mask"]; + if (mask != null) { + const adder = mul(sub(ones2(x.shape), cast(mask, x.dtype)), scalar(-1e9)); + x = add2(x, adder); + } + if (this.axis instanceof Array) { + if (this.axis.length > 1) { + return exp(sub(x, logSumExp(x, this.axis, true))); + } else { + return this.softmax(x, this.axis[0]); + } + } + return this.softmax(x, this.axis); + }); + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const config = { axis: this.axis }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Softmax3.className = "Softmax"; +serialization_exports.registerClass(Softmax3); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js +function normalizeArray(value, n, name) { + if (typeof value === "number") { + return pyListRepeat(value, n); + } else { + if (value.length !== n) { + throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`); + } + for (let i = 0; i < n; ++i) { + const singleValue = value[i]; + if (!isInteger(singleValue)) { + throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`); + } + } + return value; + } +} +function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) { + if (inputLength == null) { + return inputLength; + } + const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1); + let outputLength; + if (padding === "same") { + outputLength = inputLength; + } else { + outputLength = inputLength - dilatedFilterSize + 1; + } + return Math.floor((outputLength + stride - 1) / stride); +} +function deconvLength(dimSize, strideSize, kernelSize, padding) { + if (dimSize == null) { + return null; + } + if (padding === "valid") { + dimSize = dimSize * strideSize + max2([kernelSize - strideSize, 0]); + } else if (padding === "same") { + dimSize = dimSize * strideSize; + } else { + throw new ValueError(`Unsupport padding mode: ${padding}.`); + } + return dimSize; +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js +function preprocessConv2DInput(x, dataFormat) { + return tidy(() => { + checkDataFormat(dataFormat); + if (dataFormat === "channelsFirst") { + return transpose(x, [0, 2, 3, 1]); + } else { + return x; + } + }); +} +function preprocessConv3DInput(x, dataFormat) { + return tidy(() => { + checkDataFormat(dataFormat); + if (dataFormat === "channelsFirst") { + return transpose(x, [0, 2, 3, 4, 1]); + } else { + return x; + } + }); +} +function conv1dWithBias(x, kernel, bias, strides = 1, padding = "valid", dataFormat, dilationRate = 1) { + return tidy(() => { + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + checkDataFormat(dataFormat); + if (x.shape.length !== 3) { + throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`); + } + if (kernel.shape.length !== 3) { + throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`); + } + if (bias != null && bias.shape.length !== 1) { + throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`); + } + if (dataFormat === "channelsFirst") { + x = transpose(x, [0, 2, 1]); + } + if (padding === "causal") { + throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet."); + } + let y = conv1d(x, kernel, strides, padding === "same" ? "same" : "valid", "NWC", dilationRate); + if (bias != null) { + y = biasAdd(y, bias); + } + return y; + }); +} +function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = "valid", dataFormat, dilationRate, activation2 = null) { + return tidy(() => { + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + checkDataFormat(dataFormat); + if (x.rank !== 3 && x.rank !== 4) { + throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`); + } + if (kernel.rank !== 3 && kernel.rank !== 4) { + throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`); + } + let y = preprocessConv2DInput(x, dataFormat); + if (padding === "causal") { + throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet."); + } + y = fused_ops_exports.conv2d({ + x: y, + filter: kernel, + strides, + pad: padding === "same" ? "same" : "valid", + dilations: dilationRate, + dataFormat: "NHWC", + bias, + activation: activation2 + }); + if (dataFormat === "channelsFirst") { + y = transpose(y, [0, 3, 1, 2]); + } + return y; + }); +} +function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = "valid", dataFormat, dilationRate) { + return tidy(() => { + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + checkDataFormat(dataFormat); + if (x.rank !== 4 && x.rank !== 5) { + throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`); + } + if (kernel.rank !== 4 && kernel.rank !== 5) { + throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`); + } + let y = preprocessConv3DInput(x, dataFormat); + if (padding === "causal") { + throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet."); + } + y = conv3d(y, kernel, strides, padding === "same" ? "same" : "valid", "NDHWC", dilationRate); + if (bias != null) { + y = biasAdd(y, bias); + } + if (dataFormat === "channelsFirst") { + y = transpose(y, [0, 4, 1, 2, 3]); + } + return y; + }); +} +var BaseConv = class _BaseConv extends Layer { + constructor(rank, args) { + super(args); + this.bias = null; + this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal"; + this.DEFAULT_BIAS_INITIALIZER = "zeros"; + _BaseConv.verifyArgs(args); + this.rank = rank; + assertPositiveInteger(this.rank, "rank"); + if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) { + throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`); + } + this.kernelSize = normalizeArray(args.kernelSize, rank, "kernelSize"); + this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, "strides"); + this.padding = args.padding == null ? "valid" : args.padding; + checkPaddingMode(this.padding); + this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat; + checkDataFormat(this.dataFormat); + this.activation = getActivation(args.activation); + this.useBias = args.useBias == null ? true : args.useBias; + this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER); + this.biasConstraint = getConstraint(args.biasConstraint); + this.biasRegularizer = getRegularizer(args.biasRegularizer); + this.activityRegularizer = getRegularizer(args.activityRegularizer); + this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, "dilationRate"); + if (this.rank === 1 && (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) { + throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`); + } else if (this.rank === 2) { + if (typeof this.dilationRate === "number") { + this.dilationRate = [this.dilationRate, this.dilationRate]; + } else if (this.dilationRate.length !== 2) { + throw new ValueError(`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 ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`); + } + } + } + static verifyArgs(args) { + assert2("kernelSize" in args, `required key 'kernelSize' not in config`); + if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 3)) { + throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`); + } + } + getConfig() { + const config = { + kernelSize: this.kernelSize, + strides: this.strides, + padding: this.padding, + dataFormat: this.dataFormat, + dilationRate: this.dilationRate, + activation: serializeActivation(this.activation), + useBias: this.useBias, + biasInitializer: serializeInitializer(this.biasInitializer), + biasRegularizer: serializeRegularizer(this.biasRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + biasConstraint: serializeConstraint(this.biasConstraint) + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +var Conv = class _Conv extends BaseConv { + constructor(rank, args) { + super(rank, args); + this.kernel = null; + _Conv.verifyArgs(args); + this.filters = args.filters; + assertPositiveInteger(this.filters, "filters"); + this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.kernelConstraint = getConstraint(args.kernelConstraint); + this.kernelRegularizer = getRegularizer(args.kernelRegularizer); + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1; + if (inputShape[channelAxis] == null) { + throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`); + } + const inputDim = inputShape[channelAxis]; + const kernelShape = this.kernelSize.concat([inputDim, this.filters]); + this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } + this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }]; + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + let outputs; + const biasValue = this.bias == null ? null : this.bias.read(); + const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName()); + if (fusedActivationName != null && this.rank === 2) { + outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName); + } else { + if (this.rank === 1) { + outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]); + } else if (this.rank === 2) { + outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate); + } else if (this.rank === 3) { + outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate); + } else { + throw new NotImplementedError("convolutions greater than 3D are not implemented yet."); + } + if (this.activation != null) { + outputs = this.activation.apply(outputs); + } + } + return outputs; + }); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const newSpace = []; + const space = this.dataFormat === "channelsLast" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2); + for (let i = 0; i < space.length; ++i) { + const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === "number" ? this.dilationRate : this.dilationRate[i]); + newSpace.push(newDim); + } + let outputShape = [inputShape[0]]; + if (this.dataFormat === "channelsLast") { + outputShape = outputShape.concat(newSpace); + outputShape.push(this.filters); + } else { + outputShape.push(this.filters); + outputShape = outputShape.concat(newSpace); + } + return outputShape; + } + getConfig() { + const config = { + filters: this.filters, + kernelInitializer: serializeInitializer(this.kernelInitializer), + kernelRegularizer: serializeRegularizer(this.kernelRegularizer), + kernelConstraint: serializeConstraint(this.kernelConstraint) + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + static verifyArgs(args) { + if (!("filters" in args) || typeof args.filters !== "number" || args.filters < 1) { + throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`); + } + } +}; +var Conv2D2 = class _Conv2D extends Conv { + constructor(args) { + super(2, args); + _Conv2D.verifyArgs(args); + } + getConfig() { + const config = super.getConfig(); + delete config["rank"]; + return config; + } + static verifyArgs(args) { + if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 2)) { + throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`); + } + } +}; +Conv2D2.className = "Conv2D"; +serialization_exports.registerClass(Conv2D2); +var Conv3D2 = class _Conv3D extends Conv { + constructor(args) { + super(3, args); + _Conv3D.verifyArgs(args); + } + getConfig() { + const config = super.getConfig(); + delete config["rank"]; + return config; + } + static verifyArgs(args) { + if (typeof args.kernelSize !== "number") { + if (!(Array.isArray(args.kernelSize) && (args.kernelSize.length === 1 || args.kernelSize.length === 3))) { + throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`); + } + } + } +}; +Conv3D2.className = "Conv3D"; +serialization_exports.registerClass(Conv3D2); +var Conv2DTranspose = class extends Conv2D2 { + constructor(args) { + super(args); + this.inputSpec = [new InputSpec({ ndim: 4 })]; + if (this.padding !== "same" && this.padding !== "valid") { + throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`); + } + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape.length !== 4) { + throw new ValueError("Input should have rank 4; Received input shape: " + JSON.stringify(inputShape)); + } + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1; + if (inputShape[channelAxis] == null) { + throw new ValueError("The channel dimension of the inputs should be defined. Found `None`."); + } + const inputDim = inputShape[channelAxis]; + const kernelShape = this.kernelSize.concat([this.filters, inputDim]); + this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } + this.inputSpec = [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })]; + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + let input2 = getExactlyOneTensor(inputs); + if (input2.shape.length !== 4) { + throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`); + } + const inputShape = input2.shape; + const batchSize = inputShape[0]; + let hAxis; + let wAxis; + if (this.dataFormat === "channelsFirst") { + hAxis = 2; + wAxis = 3; + } else { + hAxis = 1; + wAxis = 2; + } + const height = inputShape[hAxis]; + const width = inputShape[wAxis]; + const kernelH = this.kernelSize[0]; + const kernelW = this.kernelSize[1]; + const strideH = this.strides[0]; + const strideW = this.strides[1]; + const outHeight = deconvLength(height, strideH, kernelH, this.padding); + const outWidth = deconvLength(width, strideW, kernelW, this.padding); + const outputShape = [batchSize, outHeight, outWidth, this.filters]; + if (this.dataFormat !== "channelsLast") { + input2 = transpose(input2, [0, 2, 3, 1]); + } + let outputs = conv2dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding); + if (this.dataFormat !== "channelsLast") { + outputs = transpose(outputs, [0, 3, 1, 2]); + } + if (this.bias != null) { + outputs = biasAdd(outputs, this.bias.read(), this.dataFormat); + } + if (this.activation != null) { + outputs = this.activation.apply(outputs); + } + return outputs; + }); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const outputShape = inputShape.slice(); + let channelAxis; + let heightAxis; + let widthAxis; + if (this.dataFormat === "channelsFirst") { + channelAxis = 1; + heightAxis = 2; + widthAxis = 3; + } else { + channelAxis = 3; + heightAxis = 1; + widthAxis = 2; + } + const kernelH = this.kernelSize[0]; + const kernelW = this.kernelSize[1]; + const strideH = this.strides[0]; + const strideW = this.strides[1]; + outputShape[channelAxis] = this.filters; + outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding); + outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding); + return outputShape; + } + getConfig() { + const config = super.getConfig(); + delete config["dilationRate"]; + return config; + } +}; +Conv2DTranspose.className = "Conv2DTranspose"; +serialization_exports.registerClass(Conv2DTranspose); +var Conv3DTranspose = class extends Conv3D2 { + constructor(args) { + super(args); + this.inputSpec = [new InputSpec({ ndim: 5 })]; + if (this.padding !== "same" && this.padding !== "valid") { + throw new ValueError(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`); + } + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape.length !== 5) { + throw new ValueError("Input should have rank 5; Received input shape: " + JSON.stringify(inputShape)); + } + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1; + if (inputShape[channelAxis] == null) { + throw new ValueError("The channel dimension of the inputs should be defined. Found `None`."); + } + const inputDim = inputShape[channelAxis]; + const kernelShape = this.kernelSize.concat([this.filters, inputDim]); + this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } + this.inputSpec = [new InputSpec({ ndim: 5, axes: { [channelAxis]: inputDim } })]; + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + let input2 = getExactlyOneTensor(inputs); + if (input2.shape.length !== 5) { + throw new ValueError(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`); + } + const inputShape = input2.shape; + const batchSize = inputShape[0]; + let hAxis; + let wAxis; + let dAxis; + if (this.dataFormat === "channelsFirst") { + dAxis = 2; + hAxis = 3; + wAxis = 4; + } else { + dAxis = 1; + hAxis = 2; + wAxis = 3; + } + const depth = inputShape[dAxis]; + const height = inputShape[hAxis]; + const width = inputShape[wAxis]; + const kernelD = this.kernelSize[0]; + const kernelH = this.kernelSize[1]; + const kernelW = this.kernelSize[2]; + const strideD = this.strides[0]; + const strideH = this.strides[1]; + const strideW = this.strides[2]; + const outDepth = deconvLength(depth, strideD, kernelD, this.padding); + const outHeight = deconvLength(height, strideH, kernelH, this.padding); + const outWidth = deconvLength(width, strideW, kernelW, this.padding); + const outputShape = [batchSize, outDepth, outHeight, outWidth, this.filters]; + if (this.dataFormat !== "channelsLast") { + input2 = transpose(input2, [0, 2, 3, 4, 1]); + } + let outputs = conv3dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding); + if (this.dataFormat !== "channelsLast") { + outputs = transpose(outputs, [0, 4, 1, 2, 3]); + } + if (this.bias !== null) { + outputs = biasAdd(outputs, this.bias.read(), this.dataFormat); + } + if (this.activation !== null) { + outputs = this.activation.apply(outputs); + } + return outputs; + }); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const outputShape = inputShape.slice(); + let channelAxis; + let depthAxis; + let heightAxis; + let widthAxis; + if (this.dataFormat === "channelsFirst") { + channelAxis = 1; + depthAxis = 2; + heightAxis = 3; + widthAxis = 4; + } else { + channelAxis = 4; + depthAxis = 1; + heightAxis = 2; + widthAxis = 3; + } + const kernelD = this.kernelSize[0]; + const kernelH = this.kernelSize[1]; + const kernelW = this.kernelSize[2]; + const strideD = this.strides[0]; + const strideH = this.strides[1]; + const strideW = this.strides[2]; + outputShape[channelAxis] = this.filters; + outputShape[depthAxis] = deconvLength(outputShape[depthAxis], strideD, kernelD, this.padding); + outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding); + outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding); + return outputShape; + } + getConfig() { + const config = super.getConfig(); + delete config["dilationRate"]; + return config; + } +}; +Conv3DTranspose.className = "Conv3DTranspose"; +serialization_exports.registerClass(Conv3DTranspose); +var SeparableConv = class extends Conv { + constructor(rank, config) { + super(rank, config); + this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform"; + this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform"; + this.depthwiseKernel = null; + this.pointwiseKernel = null; + if (config.filters == null) { + throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified."); + } + if (config.kernelInitializer != null || config.kernelRegularizer != null || config.kernelConstraint != null) { + throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead."); + } + if (config.padding != null && config.padding !== "same" && config.padding !== "valid") { + throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`); + } + this.depthMultiplier = config.depthMultiplier == null ? 1 : config.depthMultiplier; + this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER); + this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer); + this.depthwiseConstraint = getConstraint(config.depthwiseConstraint); + this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER); + this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer); + this.pointwiseConstraint = getConstraint(config.pointwiseConstraint); + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape.length < this.rank + 2) { + throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(inputShape)}`); + } + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1; + if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) { + throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`); + } + const inputDim = inputShape[channelAxis]; + const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]); + const pointwiseKernelShape = []; + for (let i = 0; i < this.rank; ++i) { + pointwiseKernelShape.push(1); + } + pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters); + const trainable = true; + this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, "float32", this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint); + this.pointwiseKernel = this.addWeight("pointwise_kernel", pointwiseKernelShape, "float32", this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint); + } else { + this.bias = null; + } + this.inputSpec = [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })]; + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + let output; + if (this.rank === 1) { + throw new NotImplementedError("1D separable convolution is not implemented yet."); + } else if (this.rank === 2) { + if (this.dataFormat === "channelsFirst") { + inputs = transpose(inputs, [0, 2, 3, 1]); + } + output = separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC"); + } + if (this.useBias) { + output = biasAdd(output, this.bias.read(), this.dataFormat); + } + if (this.activation != null) { + output = this.activation.apply(output); + } + if (this.dataFormat === "channelsFirst") { + output = transpose(output, [0, 3, 1, 2]); + } + return output; + }); + } + getConfig() { + const config = super.getConfig(); + delete config["rank"]; + delete config["kernelInitializer"]; + delete config["kernelRegularizer"]; + delete config["kernelConstraint"]; + config["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer); + config["pointwiseInitializer"] = serializeInitializer(this.pointwiseInitializer); + config["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer); + config["pointwiseRegularizer"] = serializeRegularizer(this.pointwiseRegularizer); + config["depthwiseConstraint"] = serializeConstraint(this.depthwiseConstraint); + config["pointwiseConstraint"] = serializeConstraint(this.pointwiseConstraint); + return config; + } +}; +SeparableConv.className = "SeparableConv"; +var SeparableConv2D = class extends SeparableConv { + constructor(args) { + super(2, args); + } +}; +SeparableConv2D.className = "SeparableConv2D"; +serialization_exports.registerClass(SeparableConv2D); +var Conv1D = class _Conv1D extends Conv { + constructor(args) { + super(1, args); + _Conv1D.verifyArgs(args); + this.inputSpec = [{ ndim: 3 }]; + } + getConfig() { + const config = super.getConfig(); + delete config["rank"]; + delete config["dataFormat"]; + return config; + } + static verifyArgs(args) { + if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 1)) { + throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`); + } + } +}; +Conv1D.className = "Conv1D"; +serialization_exports.registerClass(Conv1D); +var Cropping2D = class extends Layer { + constructor(args) { + super(args); + if (typeof args.cropping === "number") { + this.cropping = [[args.cropping, args.cropping], [args.cropping, args.cropping]]; + } else if (typeof args.cropping[0] === "number") { + this.cropping = [ + [args.cropping[0], args.cropping[0]], + [args.cropping[1], args.cropping[1]] + ]; + } else { + this.cropping = args.cropping; + } + this.dataFormat = args.dataFormat === void 0 ? "channelsLast" : args.dataFormat; + this.inputSpec = [{ ndim: 4 }]; + } + computeOutputShape(inputShape) { + if (this.dataFormat === "channelsFirst") { + return [ + inputShape[0], + inputShape[1], + inputShape[2] - this.cropping[0][0] - this.cropping[0][1], + inputShape[3] - this.cropping[1][0] - this.cropping[1][1] + ]; + } else { + return [ + inputShape[0], + inputShape[1] - this.cropping[0][0] - this.cropping[0][1], + inputShape[2] - this.cropping[1][0] - this.cropping[1][1], + inputShape[3] + ]; + } + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + if (this.dataFormat === "channelsLast") { + const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2); + return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3); + } else { + const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3); + return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4); + } + }); + } + getConfig() { + const config = { cropping: this.cropping, dataFormat: this.dataFormat }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Cropping2D.className = "Cropping2D"; +serialization_exports.registerClass(Cropping2D); +var UpSampling2D = class extends Layer { + constructor(args) { + super(args); + this.DEFAULT_SIZE = [2, 2]; + this.inputSpec = [{ ndim: 4 }]; + this.size = args.size == null ? this.DEFAULT_SIZE : args.size; + this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat; + checkDataFormat(this.dataFormat); + this.interpolation = args.interpolation == null ? "nearest" : args.interpolation; + checkInterpolationFormat(this.interpolation); + } + computeOutputShape(inputShape) { + if (this.dataFormat === "channelsFirst") { + const height = inputShape[2] == null ? null : this.size[0] * inputShape[2]; + const width = inputShape[3] == null ? null : this.size[1] * inputShape[3]; + return [inputShape[0], inputShape[1], height, width]; + } else { + const height = inputShape[1] == null ? null : this.size[0] * inputShape[1]; + const width = inputShape[2] == null ? null : this.size[1] * inputShape[2]; + return [inputShape[0], height, width, inputShape[3]]; + } + } + call(inputs, kwargs) { + return tidy(() => { + let input2 = getExactlyOneTensor(inputs); + const inputShape = input2.shape; + if (this.dataFormat === "channelsFirst") { + input2 = transpose(input2, [0, 2, 3, 1]); + const height = this.size[0] * inputShape[2]; + const width = this.size[1] * inputShape[3]; + const resized = this.interpolation === "nearest" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]); + return transpose(resized, [0, 3, 1, 2]); + } else { + const height = this.size[0] * inputShape[1]; + const width = this.size[1] * inputShape[2]; + return this.interpolation === "nearest" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]); + } + }); + } + getConfig() { + const config = { + size: this.size, + dataFormat: this.dataFormat, + interpolation: this.interpolation + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +UpSampling2D.className = "UpSampling2D"; +serialization_exports.registerClass(UpSampling2D); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_depthwise.js +function depthwiseConv2d3(x, depthwiseKernel, strides = [1, 1], padding = "valid", dataFormat, dilationRate) { + return tidy(() => { + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + checkDataFormat(dataFormat); + let y = preprocessConv2DInput(x, dataFormat); + if (x.rank !== 4) { + throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`); + } + if (depthwiseKernel.rank !== 4) { + throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`); + } + y = depthwiseConv2d(y, depthwiseKernel, strides, padding === "same" ? "same" : "valid", "NHWC", dilationRate); + if (dataFormat === "channelsFirst") { + y = transpose(y, [0, 3, 1, 2]); + } + return y; + }); +} +var DepthwiseConv2D = class extends BaseConv { + constructor(args) { + super(2, args); + this.depthwiseKernel = null; + this.depthMultiplier = args.depthMultiplier == null ? 1 : args.depthMultiplier; + this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.depthwiseConstraint = getConstraint(args.depthwiseConstraint); + this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer); + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape.length < 4) { + throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`); + } + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : 3; + if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) { + throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`); + } + const inputDim = inputShape[channelAxis]; + const depthwiseKernelShape = [ + this.kernelSize[0], + this.kernelSize[1], + inputDim, + this.depthMultiplier + ]; + this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } else { + this.bias = null; + } + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + let outputs = depthwiseConv2d3(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null); + if (this.useBias) { + outputs = biasAdd(outputs, this.bias.read(), this.dataFormat); + } + if (this.activation != null) { + outputs = this.activation.apply(outputs); + } + return outputs; + }); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1]; + const cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2]; + const outFilters = this.dataFormat === "channelsFirst" ? inputShape[1] * this.depthMultiplier : inputShape[3] * this.depthMultiplier; + const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]); + const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]); + if (this.dataFormat === "channelsFirst") { + return [inputShape[0], outFilters, outRows, outCols]; + } else { + return [inputShape[0], outRows, outCols, outFilters]; + } + } + getConfig() { + const config = super.getConfig(); + config["depthMultiplier"] = this.depthMultiplier; + config["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer); + config["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer); + config["depthwiseConstraint"] = serializeConstraint(this.depthwiseRegularizer); + return config; + } +}; +DepthwiseConv2D.className = "DepthwiseConv2D"; +serialization_exports.registerClass(DepthwiseConv2D); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/recurrent.js +function standardizeArgs(inputs, initialState, constants, numConstants) { + if (Array.isArray(inputs)) { + if (initialState != null || constants != null) { + throw new ValueError("When inputs is an array, neither initialState or constants should be provided"); + } + if (numConstants != null) { + constants = inputs.slice(inputs.length - numConstants, inputs.length); + inputs = inputs.slice(0, inputs.length - numConstants); + } + if (inputs.length > 1) { + initialState = inputs.slice(1, inputs.length); + } + inputs = inputs[0]; + } + function toListOrNull(x) { + if (x == null || Array.isArray(x)) { + return x; + } else { + return [x]; + } + } + initialState = toListOrNull(initialState); + constants = toListOrNull(constants); + return { inputs, initialState, constants }; +} +function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) { + return tidy(() => { + const ndim = inputs.shape.length; + if (ndim < 3) { + throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`); + } + const axes = [1, 0].concat(range2(2, ndim)); + inputs = transpose(inputs, axes); + if (constants != null) { + throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet."); + } + if (unroll) { + console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."); + } + if (mask != null) { + mask = cast(cast(mask, "bool"), "float32"); + if (mask.rank === ndim - 1) { + mask = expandDims(mask, -1); + } + mask = transpose(mask, axes); + } + if (goBackwards) { + inputs = reverse(inputs, 0); + if (mask != null) { + mask = reverse(mask, 0); + } + } + const perStepOutputs = []; + let lastOutput; + let states = initialStates; + const timeSteps = inputs.shape[0]; + const perStepInputs = unstack(inputs); + let perStepMasks; + if (mask != null) { + perStepMasks = unstack(mask); + } + for (let t = 0; t < timeSteps; ++t) { + const currentInput = perStepInputs[t]; + const stepOutputs = tidy(() => stepFunction(currentInput, states)); + if (mask == null) { + lastOutput = stepOutputs[0]; + states = stepOutputs[1]; + } else { + const maskedOutputs = tidy(() => { + const stepMask = perStepMasks[t]; + const negStepMask = sub(onesLike(stepMask), stepMask); + const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask)); + const newStates = states.map((state, i) => { + return add2(mul(stepOutputs[1][i], stepMask), mul(state, negStepMask)); + }); + return { output, newStates }; + }); + lastOutput = maskedOutputs.output; + states = maskedOutputs.newStates; + } + if (needPerStepOutputs) { + perStepOutputs.push(lastOutput); + } + } + let outputs; + if (needPerStepOutputs) { + const axis = 1; + outputs = stack(perStepOutputs, axis); + } + return [lastOutput, outputs, states]; + }); +} +var RNN = class _RNN extends Layer { + constructor(args) { + super(args); + let cell; + if (args.cell == null) { + throw new ValueError("cell property is missing for the constructor of RNN."); + } else if (Array.isArray(args.cell)) { + cell = new StackedRNNCells({ cells: args.cell }); + } else { + cell = args.cell; + } + if (cell.stateSize == null) { + throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state)."); + } + this.cell = cell; + this.returnSequences = args.returnSequences == null ? false : args.returnSequences; + this.returnState = args.returnState == null ? false : args.returnState; + this.goBackwards = args.goBackwards == null ? false : args.goBackwards; + this._stateful = args.stateful == null ? false : args.stateful; + this.unroll = args.unroll == null ? false : args.unroll; + this.supportsMasking = true; + this.inputSpec = [new InputSpec({ ndim: 3 })]; + this.stateSpec = null; + this.states_ = null; + this.numConstants = null; + this.keptStates = []; + } + // Porting Note: This is the equivalent of `RNN.states` property getter in + // PyKeras. + getStates() { + if (this.states_ == null) { + const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1; + return range2(0, numStates).map((x) => null); + } else { + return this.states_; + } + } + // Porting Note: This is the equivalent of the `RNN.states` property setter in + // PyKeras. + setStates(states) { + this.states_ = states; + } + computeOutputShape(inputShape) { + if (isArrayOfShapes(inputShape)) { + inputShape = inputShape[0]; + } + inputShape = inputShape; + let stateSize = this.cell.stateSize; + if (!Array.isArray(stateSize)) { + stateSize = [stateSize]; + } + const outputDim = stateSize[0]; + let outputShape; + if (this.returnSequences) { + outputShape = [inputShape[0], inputShape[1], outputDim]; + } else { + outputShape = [inputShape[0], outputDim]; + } + if (this.returnState) { + const stateShape = []; + for (const dim of stateSize) { + stateShape.push([inputShape[0], dim]); + } + return [outputShape].concat(stateShape); + } else { + return outputShape; + } + } + computeMask(inputs, mask) { + return tidy(() => { + if (Array.isArray(mask)) { + mask = mask[0]; + } + const outputMask = this.returnSequences ? mask : null; + if (this.returnState) { + const stateMask = this.states.map((s) => null); + return [outputMask].concat(stateMask); + } else { + return outputMask; + } + }); + } + /** + * Get the current state tensors of the RNN. + * + * If the state hasn't been set, return an array of `null`s of the correct + * length. + */ + get states() { + if (this.states_ == null) { + const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1; + const output = []; + for (let i = 0; i < numStates; ++i) { + output.push(null); + } + return output; + } else { + return this.states_; + } + } + set states(s) { + this.states_ = s; + } + build(inputShape) { + const constantShape = null; + if (this.numConstants != null) { + throw new NotImplementedError("Constants support is not implemented in RNN yet."); + } + if (isArrayOfShapes(inputShape)) { + inputShape = inputShape[0]; + } + inputShape = inputShape; + const batchSize = this.stateful ? inputShape[0] : null; + const inputDim = inputShape.slice(2); + this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] }); + const stepInputShape = [inputShape[0]].concat(inputShape.slice(2)); + if (constantShape != null) { + throw new NotImplementedError("Constants support is not implemented in RNN yet."); + } else { + this.cell.build(stepInputShape); + } + let stateSize; + if (Array.isArray(this.cell.stateSize)) { + stateSize = this.cell.stateSize; + } else { + stateSize = [this.cell.stateSize]; + } + if (this.stateSpec != null) { + if (!util_exports.arraysEqual(this.stateSpec.map((spec) => spec.shape[spec.shape.length - 1]), stateSize)) { + throw new ValueError(`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 = stateSize.map((dim) => new InputSpec({ shape: [null, dim] })); + } + if (this.stateful) { + this.resetStates(); + } + } + /** + * Reset the state tensors of the RNN. + * + * If the `states` argument is `undefined` or `null`, will set the + * state tensor(s) of the RNN to all-zero tensors of the appropriate + * shape(s). + * + * If `states` is provided, will set the state tensors of the RNN to its + * value. + * + * @param states Optional externally-provided initial states. + * @param training Whether this call is done during training. For stateful + * RNNs, this affects whether the old states are kept or discarded. In + * particular, if `training` is `true`, the old states will be kept so + * that subsequent backpropgataion through time (BPTT) may work properly. + * Else, the old states will be discarded. + */ + resetStates(states, training = false) { + tidy(() => { + if (!this.stateful) { + throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful."); + } + const batchSize = this.inputSpec[0].shape[0]; + if (batchSize == null) { + throw new ValueError("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) { + if (Array.isArray(this.cell.stateSize)) { + this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim])); + } else { + this.states_ = [zeros([batchSize, this.cell.stateSize])]; + } + } else if (states == null) { + dispose(this.states_); + if (this.keptStates != null) { + dispose(this.keptStates); + this.keptStates = []; + } + if (Array.isArray(this.cell.stateSize)) { + this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim])); + } else { + this.states_[0] = zeros([batchSize, this.cell.stateSize]); + } + } else { + if (!Array.isArray(states)) { + states = [states]; + } + if (states.length !== this.states_.length) { + throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`); + } + if (training === true) { + this.keptStates.push(this.states_.slice()); + } else { + dispose(this.states_); + } + for (let index = 0; index < this.states_.length; ++index) { + const value = states[index]; + const dim = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[index] : this.cell.stateSize; + const expectedShape = [batchSize, dim]; + if (!util_exports.arraysEqual(value.shape, expectedShape)) { + throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`); + } + this.states_[index] = value; + } + } + this.states_ = this.states_.map((state) => keep(state.clone())); + }); + } + apply(inputs, kwargs) { + let initialState = kwargs == null ? null : kwargs["initialState"]; + let constants = kwargs == null ? null : kwargs["constants"]; + if (kwargs == null) { + kwargs = {}; + } + const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants); + inputs = standardized.inputs; + initialState = standardized.initialState; + constants = standardized.constants; + let additionalInputs = []; + let additionalSpecs = []; + if (initialState != null) { + kwargs["initialState"] = initialState; + additionalInputs = additionalInputs.concat(initialState); + this.stateSpec = []; + for (const state of initialState) { + this.stateSpec.push(new InputSpec({ shape: state.shape })); + } + additionalSpecs = additionalSpecs.concat(this.stateSpec); + } + if (constants != null) { + kwargs["constants"] = constants; + additionalInputs = additionalInputs.concat(constants); + this.numConstants = constants.length; + } + const isTensor = additionalInputs[0] instanceof SymbolicTensor; + if (isTensor) { + const fullInput = [inputs].concat(additionalInputs); + const fullInputSpec = this.inputSpec.concat(additionalSpecs); + const originalInputSpec = this.inputSpec; + this.inputSpec = fullInputSpec; + const output = super.apply(fullInput, kwargs); + this.inputSpec = originalInputSpec; + return output; + } else { + return super.apply(inputs, kwargs); + } + } + // tslint:disable-next-line:no-any + call(inputs, kwargs) { + return tidy(() => { + const mask = kwargs == null ? null : kwargs["mask"]; + const training = kwargs == null ? null : kwargs["training"]; + let initialState = kwargs == null ? null : kwargs["initialState"]; + inputs = getExactlyOneTensor(inputs); + if (initialState == null) { + if (this.stateful) { + initialState = this.states_; + } else { + initialState = this.getInitialState(inputs); + } + } + const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1; + if (initialState.length !== numStates) { + throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`); + } + if (this.unroll) { + console.warn("Ignoring unroll = true for RNN layer, due to imperative backend."); + } + const cellCallKwargs = { training }; + const step5 = (inputs2, states2) => { + const outputs2 = this.cell.call([inputs2].concat(states2), cellCallKwargs); + return [outputs2[0], outputs2.slice(1)]; + }; + const rnnOutputs = rnn(step5, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences); + const lastOutput = rnnOutputs[0]; + const outputs = rnnOutputs[1]; + const states = rnnOutputs[2]; + if (this.stateful) { + this.resetStates(states, training); + } + const output = this.returnSequences ? outputs : lastOutput; + if (this.returnState) { + return [output].concat(states); + } else { + return output; + } + }); + } + getInitialState(inputs) { + return tidy(() => { + let initialState = zeros(inputs.shape); + initialState = sum2(initialState, [1, 2]); + initialState = expandDims2(initialState); + if (Array.isArray(this.cell.stateSize)) { + return this.cell.stateSize.map((dim) => dim > 1 ? tile2(initialState, [1, dim]) : initialState); + } else { + return this.cell.stateSize > 1 ? [tile2(initialState, [1, this.cell.stateSize])] : [initialState]; + } + }); + } + get trainableWeights() { + if (!this.trainable) { + return []; + } + return this.cell.trainableWeights; + } + get nonTrainableWeights() { + if (!this.trainable) { + return this.cell.weights; + } + return this.cell.nonTrainableWeights; + } + setFastWeightInitDuringBuild(value) { + super.setFastWeightInitDuringBuild(value); + if (this.cell != null) { + this.cell.setFastWeightInitDuringBuild(value); + } + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { + returnSequences: this.returnSequences, + returnState: this.returnState, + goBackwards: this.goBackwards, + stateful: this.stateful, + unroll: this.unroll + }; + if (this.numConstants != null) { + config["numConstants"] = this.numConstants; + } + const cellConfig = this.cell.getConfig(); + if (this.getClassName() === _RNN.className) { + config["cell"] = { + "className": this.cell.getClassName(), + "config": cellConfig + }; + } + return Object.assign(Object.assign(Object.assign({}, cellConfig), baseConfig), config); + } + /** @nocollapse */ + static fromConfig(cls, config, customObjects = {}) { + const cellConfig = config["cell"]; + const cell = deserialize(cellConfig, customObjects); + return new cls(Object.assign(config, { cell })); + } +}; +RNN.className = "RNN"; +serialization_exports.registerClass(RNN); +var RNNCell = class extends Layer { +}; +var SimpleRNNCell = class extends RNNCell { + constructor(args) { + super(args); + this.DEFAULT_ACTIVATION = "tanh"; + this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal"; + this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal"; + this.DEFAULT_BIAS_INITIALIZER = "zeros"; + this.units = args.units; + assertPositiveInteger(this.units, `units`); + this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation); + this.useBias = args.useBias == null ? true : args.useBias; + this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER); + this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER); + this.kernelRegularizer = getRegularizer(args.kernelRegularizer); + this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer); + this.biasRegularizer = getRegularizer(args.biasRegularizer); + this.kernelConstraint = getConstraint(args.kernelConstraint); + this.recurrentConstraint = getConstraint(args.recurrentConstraint); + this.biasConstraint = getConstraint(args.biasConstraint); + this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]); + this.recurrentDropout = min2([ + 1, + max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout]) + ]); + this.dropoutFunc = args.dropoutFunc; + this.stateSize = this.units; + this.dropoutMask = null; + this.recurrentDropoutMask = null; + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + this.kernel = this.addWeight("kernel", [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } else { + this.bias = null; + } + this.built = true; + } + // Porting Note: PyKeras' equivalent of this method takes two tensor inputs: + // `inputs` and `states`. Here, the two tensors are combined into an + // `Tensor[]` Array as the first input argument. + // Similarly, PyKeras' equivalent of this method returns two values: + // `output` and `[output]`. Here the two are combined into one length-2 + // `Tensor[]`, consisting of `output` repeated. + call(inputs, kwargs) { + return tidy(() => { + inputs = inputs; + if (inputs.length !== 2) { + throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`); + } + let prevOutput = inputs[1]; + inputs = inputs[0]; + const training = kwargs["training"] == null ? false : kwargs["training"]; + if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) { + this.dropoutMask = generateDropoutMask({ + ones: () => onesLike(inputs), + rate: this.dropout, + training, + dropoutFunc: this.dropoutFunc + }); + } + if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) { + this.recurrentDropoutMask = generateDropoutMask({ + ones: () => onesLike(prevOutput), + rate: this.recurrentDropout, + training, + dropoutFunc: this.dropoutFunc + }); + } + let h; + const dpMask = this.dropoutMask; + const recDpMask = this.recurrentDropoutMask; + if (dpMask != null) { + h = dot2(mul(inputs, dpMask), this.kernel.read()); + } else { + h = dot2(inputs, this.kernel.read()); + } + if (this.bias != null) { + h = biasAdd(h, this.bias.read()); + } + if (recDpMask != null) { + prevOutput = mul(prevOutput, recDpMask); + } + let output = add2(h, dot2(prevOutput, this.recurrentKernel.read())); + if (this.activation != null) { + output = this.activation.apply(output); + } + return [output, output]; + }); + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { + units: this.units, + activation: serializeActivation(this.activation), + useBias: this.useBias, + kernelInitializer: serializeInitializer(this.kernelInitializer), + recurrentInitializer: serializeInitializer(this.recurrentInitializer), + biasInitializer: serializeInitializer(this.biasInitializer), + kernelRegularizer: serializeRegularizer(this.kernelRegularizer), + recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer), + biasRegularizer: serializeRegularizer(this.biasRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + kernelConstraint: serializeConstraint(this.kernelConstraint), + recurrentConstraint: serializeConstraint(this.recurrentConstraint), + biasConstraint: serializeConstraint(this.biasConstraint), + dropout: this.dropout, + recurrentDropout: this.recurrentDropout + }; + return Object.assign(Object.assign({}, baseConfig), config); + } +}; +SimpleRNNCell.className = "SimpleRNNCell"; +serialization_exports.registerClass(SimpleRNNCell); +var SimpleRNN = class extends RNN { + constructor(args) { + args.cell = new SimpleRNNCell(args); + super(args); + } + call(inputs, kwargs) { + return tidy(() => { + if (this.cell.dropoutMask != null) { + dispose(this.cell.dropoutMask); + this.cell.dropoutMask = null; + } + if (this.cell.recurrentDropoutMask != null) { + dispose(this.cell.recurrentDropoutMask); + this.cell.recurrentDropoutMask = null; + } + const mask = kwargs == null ? null : kwargs["mask"]; + const training = kwargs == null ? null : kwargs["training"]; + const initialState = kwargs == null ? null : kwargs["initialState"]; + return super.call(inputs, { mask, training, initialState }); + }); + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config); + } +}; +SimpleRNN.className = "SimpleRNN"; +serialization_exports.registerClass(SimpleRNN); +var GRUCell = class extends RNNCell { + constructor(args) { + super(args); + this.DEFAULT_ACTIVATION = "tanh"; + this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid"; + this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal"; + this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal"; + this.DEFAULT_BIAS_INITIALIZER = "zeros"; + if (args.resetAfter) { + throw new ValueError(`GRUCell does not support reset_after parameter set to true.`); + } + this.units = args.units; + assertPositiveInteger(this.units, "units"); + this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation); + this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation); + this.useBias = args.useBias == null ? true : args.useBias; + this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER); + this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER); + this.kernelRegularizer = getRegularizer(args.kernelRegularizer); + this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer); + this.biasRegularizer = getRegularizer(args.biasRegularizer); + this.kernelConstraint = getConstraint(args.kernelConstraint); + this.recurrentConstraint = getConstraint(args.recurrentConstraint); + this.biasConstraint = getConstraint(args.biasConstraint); + this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]); + this.recurrentDropout = min2([ + 1, + max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout]) + ]); + this.dropoutFunc = args.dropoutFunc; + this.implementation = args.implementation; + this.stateSize = this.units; + this.dropoutMask = null; + this.recurrentDropoutMask = null; + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const inputDim = inputShape[inputShape.length - 1]; + this.kernel = this.addWeight("kernel", [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } else { + this.bias = null; + } + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = inputs; + if (inputs.length !== 2) { + throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`); + } + const training = kwargs["training"] == null ? false : kwargs["training"]; + let hTMinus1 = inputs[1]; + inputs = inputs[0]; + if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) { + this.dropoutMask = generateDropoutMask({ + ones: () => onesLike(inputs), + rate: this.dropout, + training, + count: 3, + dropoutFunc: this.dropoutFunc + }); + } + if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) { + this.recurrentDropoutMask = generateDropoutMask({ + ones: () => onesLike(hTMinus1), + rate: this.recurrentDropout, + training, + count: 3, + dropoutFunc: this.dropoutFunc + }); + } + const dpMask = this.dropoutMask; + const recDpMask = this.recurrentDropoutMask; + let z; + let r; + let hh; + if (0 < this.dropout && this.dropout < 1) { + inputs = mul(inputs, dpMask[0]); + } + let matrixX = dot2(inputs, this.kernel.read()); + if (this.useBias) { + matrixX = biasAdd(matrixX, this.bias.read()); + } + if (0 < this.recurrentDropout && this.recurrentDropout < 1) { + hTMinus1 = mul(hTMinus1, recDpMask[0]); + } + const recurrentKernelValue = this.recurrentKernel.read(); + const [rk1, rk2] = split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1); + const matrixInner = dot2(hTMinus1, rk1); + const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1); + const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1); + z = this.recurrentActivation.apply(add2(xZ, recurrentZ)); + r = this.recurrentActivation.apply(add2(xR, recurrentR)); + const recurrentH = dot2(mul(r, hTMinus1), rk2); + hh = this.activation.apply(add2(xH, recurrentH)); + const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh)); + return [h, h]; + }); + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { + units: this.units, + activation: serializeActivation(this.activation), + recurrentActivation: serializeActivation(this.recurrentActivation), + useBias: this.useBias, + kernelInitializer: serializeInitializer(this.kernelInitializer), + recurrentInitializer: serializeInitializer(this.recurrentInitializer), + biasInitializer: serializeInitializer(this.biasInitializer), + kernelRegularizer: serializeRegularizer(this.kernelRegularizer), + recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer), + biasRegularizer: serializeRegularizer(this.biasRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + kernelConstraint: serializeConstraint(this.kernelConstraint), + recurrentConstraint: serializeConstraint(this.recurrentConstraint), + biasConstraint: serializeConstraint(this.biasConstraint), + dropout: this.dropout, + recurrentDropout: this.recurrentDropout, + implementation: this.implementation, + resetAfter: false + }; + return Object.assign(Object.assign({}, baseConfig), config); + } +}; +GRUCell.className = "GRUCell"; +serialization_exports.registerClass(GRUCell); +var GRU = class extends RNN { + constructor(args) { + if (args.implementation === 0) { + console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."); + } + args.cell = new GRUCell(args); + super(args); + } + call(inputs, kwargs) { + return tidy(() => { + if (this.cell.dropoutMask != null) { + dispose(this.cell.dropoutMask); + this.cell.dropoutMask = null; + } + if (this.cell.recurrentDropoutMask != null) { + dispose(this.cell.recurrentDropoutMask); + this.cell.recurrentDropoutMask = null; + } + const mask = kwargs == null ? null : kwargs["mask"]; + const training = kwargs == null ? null : kwargs["training"]; + const initialState = kwargs == null ? null : kwargs["initialState"]; + return super.call(inputs, { mask, training, initialState }); + }); + } + /** @nocollapse */ + static fromConfig(cls, config) { + if (config["implmentation"] === 0) { + config["implementation"] = 1; + } + return new cls(config); + } +}; +GRU.className = "GRU"; +serialization_exports.registerClass(GRU); +var LSTMCell = class extends RNNCell { + constructor(args) { + super(args); + 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 = args.units; + assertPositiveInteger(this.units, "units"); + this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation); + this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation); + this.useBias = args.useBias == null ? true : args.useBias; + this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER); + this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER); + this.unitForgetBias = args.unitForgetBias; + this.kernelRegularizer = getRegularizer(args.kernelRegularizer); + this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer); + this.biasRegularizer = getRegularizer(args.biasRegularizer); + this.kernelConstraint = getConstraint(args.kernelConstraint); + this.recurrentConstraint = getConstraint(args.recurrentConstraint); + this.biasConstraint = getConstraint(args.biasConstraint); + this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]); + this.recurrentDropout = min2([ + 1, + max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout]) + ]); + this.dropoutFunc = args.dropoutFunc; + this.implementation = args.implementation; + this.stateSize = [this.units, this.units]; + this.dropoutMask = null; + this.recurrentDropoutMask = null; + } + build(inputShape) { + var _a; + inputShape = getExactlyOneShape(inputShape); + const inputDim = inputShape[inputShape.length - 1]; + this.kernel = this.addWeight("kernel", [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint); + let biasInitializer; + if (this.useBias) { + if (this.unitForgetBias) { + const capturedBiasInit = this.biasInitializer; + const capturedUnits = this.units; + biasInitializer = new (_a = class CustomInit extends Initializer { + apply(shape, dtype) { + const bI = capturedBiasInit.apply([capturedUnits]); + const bF = new Ones().apply([capturedUnits]); + const bCAndH = capturedBiasInit.apply([capturedUnits * 2]); + return concatAlongFirstAxis(concatAlongFirstAxis(bI, bF), bCAndH); + } + }, /** @nocollapse */ + _a.className = "CustomInit", _a)(); + } else { + biasInitializer = this.biasInitializer; + } + this.bias = this.addWeight("bias", [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } else { + this.bias = null; + } + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + const training = kwargs["training"] == null ? false : kwargs["training"]; + inputs = inputs; + if (inputs.length !== 3) { + throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`); + } + let hTMinus1 = inputs[1]; + const cTMinus1 = inputs[2]; + inputs = inputs[0]; + if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) { + this.dropoutMask = generateDropoutMask({ + ones: () => onesLike(inputs), + rate: this.dropout, + training, + count: 4, + dropoutFunc: this.dropoutFunc + }); + } + if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) { + this.recurrentDropoutMask = generateDropoutMask({ + ones: () => onesLike(hTMinus1), + rate: this.recurrentDropout, + training, + count: 4, + dropoutFunc: this.dropoutFunc + }); + } + const dpMask = this.dropoutMask; + const recDpMask = this.recurrentDropoutMask; + let i; + let f; + let c; + let o; + if (0 < this.dropout && this.dropout < 1) { + inputs = mul(inputs, dpMask[0]); + } + let z = dot2(inputs, this.kernel.read()); + if (0 < this.recurrentDropout && this.recurrentDropout < 1) { + hTMinus1 = mul(hTMinus1, recDpMask[0]); + } + z = add2(z, dot2(hTMinus1, this.recurrentKernel.read())); + if (this.useBias) { + z = biasAdd(z, this.bias.read()); + } + const [z0, z1, z2, z3] = split(z, 4, z.rank - 1); + i = this.recurrentActivation.apply(z0); + f = this.recurrentActivation.apply(z1); + c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(z2))); + o = this.recurrentActivation.apply(z3); + const h = mul(o, this.activation.apply(c)); + return [h, h, c]; + }); + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { + units: this.units, + activation: serializeActivation(this.activation), + recurrentActivation: serializeActivation(this.recurrentActivation), + useBias: this.useBias, + kernelInitializer: serializeInitializer(this.kernelInitializer), + recurrentInitializer: serializeInitializer(this.recurrentInitializer), + biasInitializer: serializeInitializer(this.biasInitializer), + unitForgetBias: this.unitForgetBias, + kernelRegularizer: serializeRegularizer(this.kernelRegularizer), + recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer), + biasRegularizer: serializeRegularizer(this.biasRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + kernelConstraint: serializeConstraint(this.kernelConstraint), + recurrentConstraint: serializeConstraint(this.recurrentConstraint), + biasConstraint: serializeConstraint(this.biasConstraint), + dropout: this.dropout, + recurrentDropout: this.recurrentDropout, + implementation: this.implementation + }; + return Object.assign(Object.assign({}, baseConfig), config); + } +}; +LSTMCell.className = "LSTMCell"; +serialization_exports.registerClass(LSTMCell); +var LSTM = class extends RNN { + constructor(args) { + if (args.implementation === 0) { + console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."); + } + args.cell = new LSTMCell(args); + super(args); + } + call(inputs, kwargs) { + return tidy(() => { + if (this.cell.dropoutMask != null) { + dispose(this.cell.dropoutMask); + this.cell.dropoutMask = null; + } + if (this.cell.recurrentDropoutMask != null) { + dispose(this.cell.recurrentDropoutMask); + this.cell.recurrentDropoutMask = null; + } + const mask = kwargs == null ? null : kwargs["mask"]; + const training = kwargs == null ? null : kwargs["training"]; + const initialState = kwargs == null ? null : kwargs["initialState"]; + return super.call(inputs, { mask, training, initialState }); + }); + } + /** @nocollapse */ + static fromConfig(cls, config) { + if (config["implmentation"] === 0) { + config["implementation"] = 1; + } + return new cls(config); + } +}; +LSTM.className = "LSTM"; +serialization_exports.registerClass(LSTM); +var StackedRNNCells = class extends RNNCell { + constructor(args) { + super(args); + this.cells = args.cells; + } + get stateSize() { + const stateSize = []; + for (const cell of this.cells.slice().reverse()) { + if (Array.isArray(cell.stateSize)) { + stateSize.push(...cell.stateSize); + } else { + stateSize.push(cell.stateSize); + } + } + return stateSize; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = inputs; + let states = inputs.slice(1); + const nestedStates = []; + for (const cell of this.cells.slice().reverse()) { + if (Array.isArray(cell.stateSize)) { + nestedStates.push(states.splice(0, cell.stateSize.length)); + } else { + nestedStates.push(states.splice(0, 1)); + } + } + nestedStates.reverse(); + const newNestedStates = []; + let callInputs; + for (let i = 0; i < this.cells.length; ++i) { + const cell = this.cells[i]; + states = nestedStates[i]; + if (i === 0) { + callInputs = [inputs[0]].concat(states); + } else { + callInputs = [callInputs[0]].concat(states); + } + callInputs = cell.call(callInputs, kwargs); + newNestedStates.push(callInputs.slice(1)); + } + states = []; + for (const cellStates of newNestedStates.slice().reverse()) { + states.push(...cellStates); + } + return [callInputs[0]].concat(states); + }); + } + build(inputShape) { + if (isArrayOfShapes(inputShape)) { + inputShape = inputShape[0]; + } + inputShape = inputShape; + let outputDim; + this.cells.forEach((cell, i) => { + nameScope(`RNNCell_${i}`, () => { + cell.build(inputShape); + if (Array.isArray(cell.stateSize)) { + outputDim = cell.stateSize[0]; + } else { + outputDim = cell.stateSize; + } + inputShape = [inputShape[0], outputDim]; + }); + }); + this.built = true; + } + getConfig() { + const baseConfig = super.getConfig(); + const getCellConfig = (cell) => { + return { + "className": cell.getClassName(), + "config": cell.getConfig() + }; + }; + const cellConfigs = this.cells.map(getCellConfig); + const config = { "cells": cellConfigs }; + return Object.assign(Object.assign({}, baseConfig), config); + } + /** @nocollapse */ + static fromConfig(cls, config, customObjects = {}) { + const cells = []; + for (const cellConfig of config["cells"]) { + cells.push(deserialize(cellConfig, customObjects)); + } + return new cls({ cells }); + } + get trainableWeights() { + if (!this.trainable) { + return []; + } + const weights = []; + for (const cell of this.cells) { + weights.push(...cell.trainableWeights); + } + return weights; + } + get nonTrainableWeights() { + const weights = []; + for (const cell of this.cells) { + weights.push(...cell.nonTrainableWeights); + } + if (!this.trainable) { + const trainableWeights = []; + for (const cell of this.cells) { + trainableWeights.push(...cell.trainableWeights); + } + return trainableWeights.concat(weights); + } + return weights; + } + /** + * Retrieve the weights of a the model. + * + * @returns A flat `Array` of `tf.Tensor`s. + */ + getWeights() { + const weights = []; + for (const cell of this.cells) { + weights.push(...cell.weights); + } + return batchGetValue(weights); + } + /** + * Set the weights of the model. + * + * @param weights An `Array` of `tf.Tensor`s with shapes and types matching + * the output of `getWeights()`. + */ + setWeights(weights) { + const tuples = []; + for (const cell of this.cells) { + const numParams = cell.weights.length; + const inputWeights = weights.splice(numParams); + for (let i = 0; i < cell.weights.length; ++i) { + tuples.push([cell.weights[i], inputWeights[i]]); + } + } + batchSetValue(tuples); + } +}; +StackedRNNCells.className = "StackedRNNCells"; +serialization_exports.registerClass(StackedRNNCells); +function generateDropoutMask(args) { + const { ones: ones4, rate, training = false, count: count2 = 1, dropoutFunc } = args; + const droppedInputs = () => dropoutFunc != null ? dropoutFunc(ones4(), rate) : dropout2(ones4(), rate); + const createMask = () => inTrainPhase(droppedInputs, ones4, training); + if (!count2 || count2 <= 1) { + return keep(createMask().clone()); + } + const masks = Array(count2).fill(void 0).map(createMask); + return masks.map((m) => keep(m.clone())); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js +var __rest = function(s, e) { + var t = {}; + for (var p2 in s) + if (Object.prototype.hasOwnProperty.call(s, p2) && e.indexOf(p2) < 0) + t[p2] = s[p2]; + if (s != null && typeof Object.getOwnPropertySymbols === "function") + for (var i = 0, p2 = Object.getOwnPropertySymbols(s); i < p2.length; i++) { + if (e.indexOf(p2[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p2[i])) + t[p2[i]] = s[p2[i]]; + } + return t; +}; +var ConvRNN2D = class extends RNN { + constructor(args) { + if (args.unroll) { + throw new NotImplementedError("Unrolling is not possible with convolutional RNNs."); + } + if (Array.isArray(args.cell)) { + throw new NotImplementedError("It is not possible at the moment to stack convolutional cells."); + } + super(args); + this.inputSpec = [new InputSpec({ ndim: 5 })]; + } + call(inputs, kwargs) { + return tidy(() => { + if (this.cell.dropoutMask != null) { + dispose(this.cell.dropoutMask); + this.cell.dropoutMask = null; + } + if (this.cell.recurrentDropoutMask != null) { + dispose(this.cell.recurrentDropoutMask); + this.cell.recurrentDropoutMask = null; + } + if (kwargs && kwargs["constants"]) { + throw new ValueError("ConvRNN2D cell does not support constants"); + } + const mask = kwargs == null ? null : kwargs["mask"]; + const training = kwargs == null ? null : kwargs["training"]; + const initialState = kwargs == null ? null : kwargs["initialState"]; + return super.call(inputs, { mask, training, initialState }); + }); + } + computeOutputShape(inputShape) { + let outShape = this.computeSingleOutputShape(inputShape); + if (!this.returnSequences) { + outShape = [outShape[0], ...outShape.slice(2)]; + } + if (this.returnState) { + outShape = [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])]; + } + return outShape; + } + getInitialState(inputs) { + return tidy(() => { + const { stateSize } = this.cell; + const inputShape = inputs.shape; + const outputShape = this.computeSingleOutputShape(inputShape); + const stateShape = [outputShape[0], ...outputShape.slice(2)]; + const initialState = zeros(stateShape); + if (Array.isArray(stateSize)) { + return Array(stateSize.length).fill(initialState); + } + return [initialState]; + }); + } + resetStates(states, training = false) { + tidy(() => { + if (!this.stateful) { + throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful."); + } + const inputShape = this.inputSpec[0].shape; + const outputShape = this.computeSingleOutputShape(inputShape); + const stateShape = [outputShape[0], ...outputShape.slice(2)]; + const batchSize = inputShape[0]; + if (batchSize == null) { + throw new ValueError("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) { + if (Array.isArray(this.cell.stateSize)) { + this.states_ = this.cell.stateSize.map(() => zeros(stateShape)); + } else { + this.states_ = [zeros(stateShape)]; + } + } else if (states == null) { + dispose(this.states_); + if (this.keptStates != null) { + dispose(this.keptStates); + this.keptStates = []; + } + if (Array.isArray(this.cell.stateSize)) { + this.states_ = this.cell.stateSize.map(() => zeros(stateShape)); + } else { + this.states_[0] = zeros(stateShape); + } + } else { + if (!Array.isArray(states)) { + states = [states]; + } + if (states.length !== this.states_.length) { + throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`); + } + if (training) { + this.keptStates.push(this.states_.slice()); + } else { + dispose(this.states_); + } + for (let index = 0; index < this.states_.length; ++index) { + const value = states[index]; + const expectedShape = stateShape; + if (!util_exports.arraysEqual(value.shape, expectedShape)) { + throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`); + } + this.states_[index] = value; + } + } + this.states_ = this.states_.map((state) => keep(state.clone())); + }); + } + computeSingleOutputShape(inputShape) { + const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell; + const isChannelsFirst = dataFormat === "channelsFirst"; + const h = inputShape[isChannelsFirst ? 3 : 2]; + const w = inputShape[isChannelsFirst ? 4 : 3]; + const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]); + const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]); + const outShape = [ + ...inputShape.slice(0, 2), + ...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters] + ]; + return outShape; + } +}; +ConvRNN2D.className = "ConvRNN2D"; +var ConvLSTM2DCell = class extends LSTMCell { + constructor(args) { + const { filters, kernelSize, strides, padding, dataFormat, dilationRate } = args; + super(Object.assign(Object.assign({}, args), { units: filters })); + this.filters = filters; + assertPositiveInteger(this.filters, "filters"); + this.kernelSize = normalizeArray(kernelSize, 2, "kernelSize"); + this.kernelSize.forEach((size) => assertPositiveInteger(size, "kernelSize")); + this.strides = normalizeArray(strides || 1, 2, "strides"); + this.strides.forEach((stride) => assertPositiveInteger(stride, "strides")); + this.padding = padding || "valid"; + checkPaddingMode(this.padding); + this.dataFormat = dataFormat || "channelsLast"; + checkDataFormat(this.dataFormat); + this.dilationRate = normalizeArray(dilationRate || 1, 2, "dilationRate"); + this.dilationRate.forEach((rate) => assertPositiveInteger(rate, "dilationRate")); + } + build(inputShape) { + var _a; + inputShape = getExactlyOneShape(inputShape); + const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1; + if (inputShape[channelAxis] == null) { + throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`); + } + const inputDim = inputShape[channelAxis]; + const numOfKernels = 4; + const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]); + this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]); + this.recurrentKernel = this.addWeight("recurrent_kernel", recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint); + if (this.useBias) { + let biasInitializer; + if (this.unitForgetBias) { + const init2 = this.biasInitializer; + const filters = this.filters; + biasInitializer = new (_a = class CustomInit extends Initializer { + apply(shape, dtype) { + const biasI = init2.apply([filters]); + const biasF = ones2([filters]); + const biasCAndO = init2.apply([filters * 2]); + return concatenate([biasI, biasF, biasCAndO]); + } + }, /** @nocollapse */ + _a.className = "CustomInit", _a)(); + } else { + biasInitializer = this.biasInitializer; + } + this.bias = this.addWeight("bias", [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + if (inputs.length !== 3) { + throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`); + } + const training = kwargs["training"] || false; + const x = inputs[0]; + const hTMinus1 = inputs[1]; + const cTMinus1 = inputs[2]; + const numOfKernels = 4; + if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) { + this.dropoutMask = generateDropoutMask({ + ones: () => onesLike(x), + rate: this.dropout, + training, + count: numOfKernels, + dropoutFunc: this.dropoutFunc + }); + } + const dropoutMask = this.dropoutMask; + const applyDropout = (x2, mask, index) => { + if (!mask || !mask[index]) { + return x2; + } + return mul(mask[index], x2); + }; + let xI = applyDropout(x, dropoutMask, 0); + let xF = applyDropout(x, dropoutMask, 1); + let xC = applyDropout(x, dropoutMask, 2); + let xO = applyDropout(x, dropoutMask, 3); + if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) { + this.recurrentDropoutMask = generateDropoutMask({ + ones: () => onesLike(hTMinus1), + rate: this.recurrentDropout, + training, + count: numOfKernels, + dropoutFunc: this.dropoutFunc + }); + } + const recDropoutMask = this.recurrentDropoutMask; + let hI = applyDropout(hTMinus1, recDropoutMask, 0); + let hF = applyDropout(hTMinus1, recDropoutMask, 1); + let hC = applyDropout(hTMinus1, recDropoutMask, 2); + let hO = applyDropout(hTMinus1, recDropoutMask, 3); + const kernelChannelAxis = 3; + const [kernelI, kernelF, kernelC, kernelO] = split(this.kernel.read(), numOfKernels, kernelChannelAxis); + const [biasI, biasF, biasC, biasO] = this.useBias ? split(this.bias.read(), numOfKernels) : [null, null, null, null]; + xI = this.inputConv(xI, kernelI, biasI, this.padding); + xF = this.inputConv(xF, kernelF, biasF, this.padding); + xC = this.inputConv(xC, kernelC, biasC, this.padding); + xO = this.inputConv(xO, kernelO, biasO, this.padding); + const [recKernelI, recKernelF, recKernelC, recKernelO] = split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis); + hI = this.recurrentConv(hI, recKernelI); + hF = this.recurrentConv(hF, recKernelF); + hC = this.recurrentConv(hC, recKernelC); + hO = this.recurrentConv(hO, recKernelO); + const i = this.recurrentActivation.apply(add2(xI, hI)); + const f = this.recurrentActivation.apply(add2(xF, hF)); + const c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(add2(xC, hC)))); + const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c)); + return [h, h, c]; + }); + } + getConfig() { + const _a = super.getConfig(), { "units": _ } = _a, baseConfig = __rest(_a, ["units"]); + const config = { + filters: this.filters, + kernelSize: this.kernelSize, + padding: this.padding, + dataFormat: this.dataFormat, + dilationRate: this.dilationRate, + strides: this.strides + }; + return Object.assign(Object.assign({}, baseConfig), config); + } + inputConv(x, w, b, padding) { + const out = conv2d(x, w, this.strides, padding || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate); + if (b) { + return biasAdd(out, b, this.dataFormat); + } + return out; + } + recurrentConv(x, w) { + const strides = 1; + return conv2d(x, w, strides, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC"); + } +}; +ConvLSTM2DCell.className = "ConvLSTM2DCell"; +serialization_exports.registerClass(ConvLSTM2DCell); +var ConvLSTM2D = class extends ConvRNN2D { + constructor(args) { + const cell = new ConvLSTM2DCell(args); + super(Object.assign(Object.assign({}, args), { cell })); + } + /** @nocollapse */ + static fromConfig(cls, config) { + return new cls(config); + } +}; +ConvLSTM2D.className = "ConvLSTM2D"; +serialization_exports.registerClass(ConvLSTM2D); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/core.js +var Dropout = class extends Layer { + constructor(args) { + super(args); + this.rate = Math.max(Math.min(args.rate, 1), 0); + this.noiseShape = args.noiseShape; + this.seed = args.seed; + this.supportsMasking = true; + } + getNoiseShape(input2) { + if (this.noiseShape == null) { + return this.noiseShape; + } + const inputShape = input2.shape; + const noiseShape = []; + for (let i = 0; i < this.noiseShape.length; ++i) { + noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]); + } + return noiseShape; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + if (0 < this.rate && this.rate < 1) { + const training = kwargs["training"] == null ? false : kwargs["training"]; + const noiseShape = this.getNoiseShape(input2); + const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training); + return output; + } + return inputs; + }); + } + getConfig() { + const config = { + rate: this.rate, + noiseShape: this.noiseShape, + seed: this.seed + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + dispose() { + return super.dispose(); + } +}; +Dropout.className = "Dropout"; +serialization_exports.registerClass(Dropout); +var SpatialDropout1D = class extends Dropout { + constructor(args) { + super(args); + this.inputSpec = [{ ndim: 3 }]; + } + getNoiseShape(input2) { + const inputShape = input2.shape; + return [inputShape[0], 1, inputShape[2]]; + } +}; +SpatialDropout1D.className = "SpatialDropout1D"; +serialization_exports.registerClass(SpatialDropout1D); +var Dense = class extends Layer { + constructor(args) { + super(args); + this.activation = null; + this.useBias = true; + this.kernel = null; + this.bias = null; + this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal"; + this.DEFAULT_BIAS_INITIALIZER = "zeros"; + if (args.batchInputShape == null && args.inputShape == null && args.inputDim != null) { + let batchSize = null; + if (args.batchSize != null) { + batchSize = args.batchSize; + } + this.batchInputShape = [batchSize, args.inputDim]; + } + this.units = args.units; + assertPositiveInteger(this.units, "units"); + this.activation = getActivation(args.activation); + if (args.useBias != null) { + this.useBias = args.useBias; + } + this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER); + this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER); + this.kernelConstraint = getConstraint(args.kernelConstraint); + this.biasConstraint = getConstraint(args.biasConstraint); + this.kernelRegularizer = getRegularizer(args.kernelRegularizer); + this.biasRegularizer = getRegularizer(args.biasRegularizer); + this.activityRegularizer = getRegularizer(args.activityRegularizer); + this.supportsMasking = true; + this.inputSpec = [{ minNDim: 2 }]; + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const inputLastDim = inputShape[inputShape.length - 1]; + if (this.kernel == null) { + this.kernel = this.addWeight("kernel", [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint); + if (this.useBias) { + this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint); + } + } + this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }]; + this.built = true; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const outputShape = inputShape.slice(); + outputShape[outputShape.length - 1] = this.units; + return outputShape; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName()); + let output; + if (fusedActivationName != null) { + output = dot2(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null); + } else { + output = dot2(input2, this.kernel.read()); + if (this.bias != null) { + output = biasAdd(output, this.bias.read()); + } + if (this.activation != null) { + output = this.activation.apply(output); + } + } + return output; + }); + } + getConfig() { + const config = { + units: this.units, + activation: serializeActivation(this.activation), + useBias: this.useBias, + kernelInitializer: serializeInitializer(this.kernelInitializer), + biasInitializer: serializeInitializer(this.biasInitializer), + kernelRegularizer: serializeRegularizer(this.kernelRegularizer), + biasRegularizer: serializeRegularizer(this.biasRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + kernelConstraint: serializeConstraint(this.kernelConstraint), + biasConstraint: serializeConstraint(this.biasConstraint) + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Dense.className = "Dense"; +serialization_exports.registerClass(Dense); +var Flatten = class extends Layer { + constructor(args) { + args = args || {}; + super(args); + this.inputSpec = [{ minNDim: 3 }]; + this.dataFormat = args.dataFormat; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + for (const dim of inputShape.slice(1)) { + if (dim == null) { + throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`); + } + } + return [inputShape[0], arrayProd(inputShape, 1)]; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + let input2 = getExactlyOneTensor(inputs); + if (this.dataFormat === "channelsFirst" && input2.rank > 1) { + const permutation = [0]; + for (let i = 2; i < input2.rank; ++i) { + permutation.push(i); + } + permutation.push(1); + input2 = transpose(input2, permutation); + } + return batchFlatten(input2); + }); + } + getConfig() { + const config = {}; + if (this.dataFormat != null) { + config["dataFormat"] = this.dataFormat; + } + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Flatten.className = "Flatten"; +serialization_exports.registerClass(Flatten); +var Activation2 = class extends Layer { + constructor(args) { + super(args); + this.supportsMasking = true; + this.activation = getActivation(args.activation); + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + return this.activation.apply(input2); + }); + } + getConfig() { + const config = { activation: serializeActivation(this.activation) }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Activation2.className = "Activation"; +serialization_exports.registerClass(Activation2); +var RepeatVector = class extends Layer { + constructor(args) { + super(args); + this.n = args.n; + this.inputSpec = [{ ndim: 2 }]; + } + computeOutputShape(inputShape) { + return [inputShape[0], this.n, inputShape[1]]; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + return repeat(inputs, this.n); + }); + } + getConfig() { + const config = { + n: this.n + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +RepeatVector.className = "RepeatVector"; +serialization_exports.registerClass(RepeatVector); +var Reshape2 = class extends Layer { + constructor(args) { + super(args); + this.targetShape = args.targetShape; + for (let i = 0; i < this.targetShape.length; ++i) { + if (this.isUnknown(this.targetShape[i])) { + this.targetShape[i] = null; + } + } + } + isUnknown(dim) { + return dim < 0 || dim == null; + } + /** + * Finds and replaces a missing dimension in output shape. + * + * This is a near direct port of the internal Numpy function + * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`. + * + * @param inputShape: Original shape of array begin reshape. + * @param outputShape: Target shape of the array, with at most a single + * `null` or negative number, which indicates an underdetermined dimension + * that should be derived from `inputShape` and the known dimensions of + * `outputShape`. + * @returns: The output shape with `null` replaced with its computed value. + * @throws: ValueError: If `inputShape` and `outputShape` do not match. + */ + fixUnknownDimension(inputShape, outputShape) { + const errorMsg = "Total size of new array must be unchanged."; + const finalShape = outputShape.slice(); + let known = 1; + let unknown = null; + for (let i = 0; i < finalShape.length; ++i) { + const dim = finalShape[i]; + if (this.isUnknown(dim)) { + if (unknown === null) { + unknown = i; + } else { + throw new ValueError("Can only specifiy one unknown dimension."); + } + } else { + known *= dim; + } + } + const originalSize = arrayProd(inputShape); + if (unknown !== null) { + if (known === 0 || originalSize % known !== 0) { + throw new ValueError(errorMsg); + } + finalShape[unknown] = originalSize / known; + } else if (originalSize !== known) { + throw new ValueError(errorMsg); + } + return finalShape; + } + computeOutputShape(inputShape) { + let anyUnknownDims = false; + for (let i = 0; i < inputShape.length; ++i) { + if (this.isUnknown(inputShape[i])) { + anyUnknownDims = true; + break; + } + } + if (anyUnknownDims) { + return inputShape.slice(0, 1).concat(this.targetShape); + } else { + return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape)); + } + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + const inputShape = input2.shape; + const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape)); + return reshape(input2, outputShape); + }); + } + getConfig() { + const config = { + targetShape: this.targetShape + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Reshape2.className = "Reshape"; +serialization_exports.registerClass(Reshape2); +var Permute = class extends Layer { + constructor(args) { + super(args); + if (args.dims == null) { + throw new Error("Required configuration field `dims` is missing during Permute constructor call."); + } + if (!Array.isArray(args.dims)) { + throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`); + } + const expectedSortedIndices = range2(1, args.dims.length + 1); + if (!util_exports.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) { + throw new Error("Invalid permutation `dims`: " + JSON.stringify(args.dims) + " `dims` must contain consecutive integers starting from 1."); + } + this.dims = args.dims; + this.dimsIncludingBatch = [0].concat(this.dims); + this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })]; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const outputShape = inputShape.slice(); + this.dims.forEach((dim, i) => { + outputShape[i + 1] = inputShape[dim]; + }); + return outputShape; + } + call(inputs, kwargs) { + return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch); + } + getConfig() { + const config = { + dims: this.dims + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Permute.className = "Permute"; +serialization_exports.registerClass(Permute); +var Masking = class extends Layer { + constructor(args) { + super(args == null ? {} : args); + this.supportsMasking = true; + if (args != null) { + this.maskValue = args.maskValue == null ? 0 : args.maskValue; + } else { + this.maskValue = 0; + } + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { maskValue: this.maskValue }; + Object.assign(config, baseConfig); + return config; + } + computeMask(inputs, mask) { + const input2 = getExactlyOneTensor(inputs); + const axis = -1; + return any(notEqual(input2, this.maskValue), axis); + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + const axis = -1; + const keepDims = true; + const booleanMask = any(notEqual(input2, this.maskValue), axis, keepDims); + const output = mul(input2, cast(booleanMask, input2.dtype)); + return output; + }); + } +}; +Masking.className = "Masking"; +serialization_exports.registerClass(Masking); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/embeddings.js +var Embedding = class extends Layer { + constructor(args) { + super(args); + this.embeddings = null; + this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform"; + if (args.batchInputShape == null && args.inputShape == null) { + let batchSize = null; + if (args.batchSize != null) { + batchSize = args.batchSize; + } + if (args.inputLength == null) { + this.batchInputShape = [batchSize, null]; + } else { + this.batchInputShape = [batchSize].concat(toList(args.inputLength)); + } + } + this.inputDim = args.inputDim; + assertPositiveInteger(this.inputDim, "inputDim"); + this.outputDim = args.outputDim; + assertPositiveInteger(this.outputDim, "outputDim"); + this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER); + this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer); + this.activityRegularizer = getRegularizer(args.activityRegularizer); + this.embeddingsConstraint = getConstraint(args.embeddingsConstraint); + this.maskZero = args.maskZero; + this.supportsMasking = args.maskZero; + this.inputLength = args.inputLength; + } + build(inputShape) { + this.embeddings = this.addWeight("embeddings", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint); + this.built = true; + } + // Override warnOnIncompatibleInputShape because an embedding layer allows + // the input to have varying ranks. + warnOnIncompatibleInputShape(inputShape) { + } + computeMask(inputs, mask) { + return tidy(() => { + if (!this.maskZero) { + return null; + } else { + inputs = getExactlyOneTensor(inputs); + return notEqual(inputs, zerosLike(inputs)); + } + }); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (this.inputLength == null) { + return [...inputShape, this.outputDim]; + } + const inLens = toList(this.inputLength); + if (inLens.length !== inputShape.length - 1) { + throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`); + } else { + let i = 0; + for (let k = 0; k < inLens.length; ++k) { + const s1 = inLens[k]; + const s2 = inputShape[k + 1]; + if (s1 != null && s2 != null && s1 !== s2) { + throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`); + } else if (s1 == null) { + inLens[i] = s2; + } + i++; + } + } + return [inputShape[0], ...inLens, this.outputDim]; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + let input2 = getExactlyOneTensor(inputs); + if (input2.dtype !== "int32") { + input2 = cast2(input2, "int32"); + } + const output = gather2(this.embeddings.read(), reshape(input2, [input2.size])); + return reshape(output, getExactlyOneShape(this.computeOutputShape(input2.shape))); + }); + } + getConfig() { + const config = { + inputDim: this.inputDim, + outputDim: this.outputDim, + embeddingsInitializer: serializeInitializer(this.embeddingsInitializer), + embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer), + activityRegularizer: serializeRegularizer(this.activityRegularizer), + embeddingsConstraint: serializeConstraint(this.embeddingsConstraint), + maskZero: this.maskZero, + inputLength: this.inputLength + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Embedding.className = "Embedding"; +serialization_exports.registerClass(Embedding); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/merge.js +var Merge = class extends Layer { + constructor(args) { + super(args || {}); + this.supportsMasking = true; + } + /** + * Logic for merging multiple tensors, to be overridden by subclasses. + * @param inputs + */ + mergeFunction(inputs) { + throw new NotImplementedError(); + } + /** + * Computes the shape of the result of an elementwise operation. + * + * @param shape1: Shape of the first tensor. + * @param shape2: Shape of the second tensor. + * @returns Expected output shape when an elementwise operation is carried + * out on 2 tensors with shapes `shape1` and `shape2`. + * @throws ValueError: If `shape1` and `shape2` are not compatible for + * element-wise operations. + */ + computeElementwiseOpOutputShape(shape1, shape2) { + if (shape1 == null || shape2 == null) { + return null; + } else if (shape1.length < shape2.length) { + return this.computeElementwiseOpOutputShape(shape2, shape1); + } else if (shape2.length === 0) { + return shape1; + } + const outputShape = shape1.slice(0, shape1.length - shape2.length); + for (let k = 0; k < shape2.length; ++k) { + const i = shape1[shape1.length - shape2.length + k]; + const j = shape2[k]; + if (i == null || j == null || i < 0 || j < 0) { + outputShape.push(null); + } else if (i === 1) { + outputShape.push(j); + } else if (j === 1) { + outputShape.push(i); + } else { + if (i !== j) { + throw new ValueError("Operands could not be broadcast together with shapes " + JSON.stringify(shape1) + " " + JSON.stringify(shape2)); + } + outputShape.push(i); + } + } + return outputShape; + } + build(inputShape) { + if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) { + inputShape = [getExactlyOneShape(inputShape)]; + } + inputShape = inputShape; + if (inputShape.length < 2) { + throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`); + } + let batchSizes = []; + for (const shape of inputShape) { + if (shape != null && shape[0] !== null) { + batchSizes.push(shape[0]); + } + } + batchSizes = unique2(batchSizes); + if (batchSizes.length > 1) { + throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`); + } + let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1); + for (let i = 1; i < inputShape.length; ++i) { + const shape = inputShape[i] == null ? null : inputShape[i].slice(1); + outputShape = this.computeElementwiseOpOutputShape(outputShape, shape); + } + const allRanks = inputShape.map((shape) => shape.length); + if (inputShape.indexOf(null) === -1 && unique2(allRanks).length === 1) { + this.reshapeRequired = false; + } else { + this.reshapeRequired = true; + } + } + call(inputs, kwargs) { + return tidy(() => { + inputs = inputs; + if (this.reshapeRequired) { + const reshapedInputs = []; + const inputDims = inputs.map((input2) => input2.rank); + if (inputDims.indexOf(null) === -1) { + const maxNDim = max2(inputDims); + for (let x of inputs) { + const xNDim = x.rank; + for (let k = 0; k < maxNDim - xNDim; ++k) { + x = expandDims2(x, 1); + } + reshapedInputs.push(x); + } + return this.mergeFunction(reshapedInputs); + } else { + let transposed = false; + for (const x of inputs) { + const xNDim = x.rank; + if (xNDim == null) { + const xShape = x.shape; + const batchSize = xShape[0]; + const newShape = xShape.slice(1).concat([batchSize]); + let xTransposed = reshape(x, [batchSize].concat(arrayProd(xShape.slice(1)))); + xTransposed = transpose(xTransposed, [1, 0]); + xTransposed = reshape(xTransposed, newShape); + reshapedInputs.push(xTransposed); + transposed = true; + } else if (xNDim > 1) { + const dims = range2(1, xNDim).concat([0]); + reshapedInputs.push(transpose(x, dims)); + transposed = true; + } else { + reshapedInputs.push(x); + } + } + let y = this.mergeFunction(reshapedInputs); + const yNDim = y.rank; + if (transposed) { + if (yNDim == null) { + const yShape = y.shape; + const yNDim2 = yShape.length; + const batchSize = yShape[yNDim2 - 1]; + const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1)); + y = reshape(transpose(reshape(y, [-1, batchSize]), [1, 0]), newShape); + } else if (yNDim > 1) { + const dims = [yNDim - 1].concat(range2(0, yNDim - 1)); + y = transpose(y, dims); + } + } + return y; + } + } else { + return this.mergeFunction(inputs); + } + }); + } + computeOutputShape(inputShape) { + inputShape = inputShape; + let outputShape; + if (inputShape[0] == null) { + outputShape = null; + } else { + outputShape = inputShape[0].slice(1); + } + for (let i = 1; i < inputShape.length; ++i) { + const shape = inputShape[i] == null ? null : inputShape[i].slice(1); + outputShape = this.computeElementwiseOpOutputShape(outputShape, shape); + } + let batchSizes = []; + for (const shape of inputShape) { + if (shape != null && shape[0] !== null) { + batchSizes.push(shape[0]); + } + } + batchSizes = unique2(batchSizes); + if (batchSizes.length === 1) { + outputShape = batchSizes.concat(outputShape); + } else { + outputShape = [null].concat(outputShape); + } + return outputShape; + } + computeMask(inputs, mask) { + return tidy(() => { + if (mask == null) { + return null; + } + if (!Array.isArray(mask)) { + throw new ValueError("`mask` should be an Array"); + } + if (!Array.isArray(inputs)) { + throw new ValueError("`inputs` should be an Array"); + } + if (mask.length !== inputs.length) { + throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`); + } + if (mask.every((m) => m == null)) { + return null; + } + mask = mask.map((m) => m == null ? m : expandDims(m, 0)); + let output = mask[0]; + for (let i = 1; i < mask.length - 1; ++i) { + output = logicalAnd(output, mask[i]); + } + return output; + }); + } +}; +var Add2 = class extends Merge { + constructor(args) { + super(args); + } + mergeFunction(inputs) { + return tidy(() => { + let output = inputs[0].clone(); + for (let i = 1; i < inputs.length; ++i) { + output = add2(output, inputs[i]); + } + return output; + }); + } +}; +Add2.className = "Add"; +serialization_exports.registerClass(Add2); +var Multiply2 = class extends Merge { + constructor(args) { + super(args); + } + mergeFunction(inputs) { + return tidy(() => { + let output = inputs[0].clone(); + for (let i = 1; i < inputs.length; ++i) { + output = mul(output, inputs[i]); + } + return output; + }); + } +}; +Multiply2.className = "Multiply"; +serialization_exports.registerClass(Multiply2); +var Average = class extends Merge { + constructor(args) { + super(args); + } + mergeFunction(inputs) { + return tidy(() => { + let output = inputs[0].clone(); + for (let i = 1; i < inputs.length; ++i) { + output = add2(output, inputs[i]); + } + return mul(1 / inputs.length, output); + }); + } +}; +Average.className = "Average"; +serialization_exports.registerClass(Average); +var Maximum2 = class extends Merge { + constructor(args) { + super(args); + } + mergeFunction(inputs) { + return tidy(() => { + let output = inputs[0]; + for (let i = 1; i < inputs.length; ++i) { + output = maximum(output, inputs[i]); + } + return output; + }); + } +}; +Maximum2.className = "Maximum"; +serialization_exports.registerClass(Maximum2); +var Minimum2 = class extends Merge { + constructor(args) { + super(args); + } + mergeFunction(inputs) { + return tidy(() => { + let output = inputs[0]; + for (let i = 1; i < inputs.length; ++i) { + output = minimum(output, inputs[i]); + } + return output; + }); + } +}; +Minimum2.className = "Minimum"; +serialization_exports.registerClass(Minimum2); +var Concatenate = class extends Merge { + constructor(args) { + super(args); + this.DEFAULT_AXIS = -1; + if (args == null) { + args = {}; + } + this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis; + this.supportsMasking = true; + this.reshapeRequired = false; + } + build(inputShape) { + if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) || inputShape.length === 1) { + throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs"); + } + inputShape = inputShape; + let allNoneShape = true; + for (const shape of inputShape) { + if (shape != null) { + allNoneShape = false; + break; + } + } + if (allNoneShape) { + return; + } + const shapeSet = []; + for (let i = 0; i < inputShape.length; ++i) { + const shapeWithoutConcatAxis = inputShape[i].slice(); + shapeWithoutConcatAxis.splice(this.axis, 1); + let exists = false; + for (const shape of shapeSet) { + if (util_exports.arraysEqual(shape, shapeWithoutConcatAxis)) { + exists = true; + break; + } + } + if (!exists) { + shapeSet.push(shapeWithoutConcatAxis); + } + } + if (shapeSet.length > 1) { + throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(inputShape)); + } + } + mergeFunction(inputs) { + return tidy(() => { + return concatenate(inputs, this.axis); + }); + } + computeOutputShape(inputShape) { + if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) { + throw new ValueError("A `Concatenate` layer should be called on a list of inputs."); + } + const inputShapes = inputShape; + const outputShape = inputShapes[0].slice(); + const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis; + for (const shape of inputShapes.slice(1)) { + if (outputShape[axis] == null || shape[axis] == null) { + outputShape[axis] = null; + break; + } + outputShape[axis] += shape[axis]; + } + return outputShape; + } + computeMask(inputs, mask) { + if (mask == null) { + return null; + } + if (!Array.isArray(mask)) { + throw new ValueError("`mask` should be an array for Concatenate"); + } + if (!Array.isArray(inputs)) { + throw new ValueError("`inputs` should be an array for Concatenate"); + } + if (mask.length !== inputs.length) { + throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`); + } + return tidy(() => { + let allNullMasks = true; + mask.forEach((m) => { + if (m != null) { + allNullMasks = false; + return; + } + }); + if (allNullMasks) { + return null; + } + const outputMasks = []; + for (let i = 0; i < inputs.length; ++i) { + if (mask[i] == null) { + outputMasks.push(cast(onesLike(inputs[i]), "bool")); + } else if (mask[i].rank < inputs[i].rank) { + outputMasks.push(expandDims(mask[i], -1)); + } else { + outputMasks.push(mask[i]); + } + } + const concatenatedMasks = concat(outputMasks, this.axis); + return all(concatenatedMasks, -1, false); + }); + } + getConfig() { + const config = { + "axis": this.axis + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Concatenate.className = "Concatenate"; +serialization_exports.registerClass(Concatenate); +function interpretAxis(axis, dim) { + while (axis < 0) { + axis += dim; + } + return axis; +} +function batchDot(x, y, axes) { + if (x.shape.length > 3 || y.shape.length > 3) { + throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet"); + } + util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`); + util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`); + if (typeof axes === "number") { + axes = [axes, axes]; + } + if (x.dtype === "complex64" || y.dtype === "complex64") { + throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet."); + } + const xNDim = x.shape.length; + const yNDim = y.shape.length; + if (axes == null) { + axes = [xNDim - 1, yNDim - 2]; + } + const axesArray = axes; + return tidy(() => { + let diff; + if (xNDim > yNDim) { + diff = xNDim - yNDim; + const diffShape = []; + for (let i = 0; i < diff; ++i) { + diffShape.push(1); + } + y = reshape(y, y.shape.concat(diffShape)); + } else if (yNDim > xNDim) { + diff = yNDim - xNDim; + const diffShape = []; + for (let i = 0; i < diff; ++i) { + diffShape.push(1); + } + x = reshape(x, x.shape.concat(diffShape)); + } else { + diff = 0; + } + let out; + if (x.shape.length === 2 && y.shape.length === 2) { + if (axesArray[0] === axesArray[1]) { + out = sum2(mul(x, y), axesArray[0]); + } else { + out = sum2(mul(transpose(x, [1, 0]), y), axesArray[1]); + } + } else { + const adjX = axesArray[0] !== x.shape.length - 1; + const adjY = axesArray[1] === y.shape.length - 1; + out = matMul(x, y, adjX, adjY); + } + if (diff > 0) { + let idx; + if (xNDim > yNDim) { + idx = xNDim + yNDim - 3; + } else { + idx = xNDim - 1; + } + const squeezeAxes = []; + for (let i = idx; i < idx + diff; ++i) { + squeezeAxes.push(i); + } + out = squeeze(out, squeezeAxes); + } + if (out.shape.length === 1) { + out = expandDims(out, 1); + } + return out; + }); +} +var Dot = class extends Merge { + constructor(args) { + super(args); + this.axes = args.axes; + this.normalize = args.normalize == null ? false : args.normalize; + this.supportsMasking = true; + this.reshapeRequired = false; + } + build(inputShape) { + util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs."); + const shape1 = inputShape[0]; + const shape2 = inputShape[1]; + if (shape1.length > 3 || shape2.length > 3) { + throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet."); + } + const axes = this.interpretAxes(shape1, shape2); + if (shape1[axes[0]] !== shape2[axes[1]]) { + throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`); + } + } + mergeFunction(inputs) { + if (inputs.length !== 2) { + throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`); + } + let x1 = inputs[0]; + let x2 = inputs[1]; + let axes; + if (!Array.isArray(this.axes)) { + axes = [ + interpretAxis(this.axes, x1.shape.length), + interpretAxis(this.axes, x2.shape.length) + ]; + } else { + axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length)); + } + if (this.normalize) { + x1 = l2Normalize(x1, axes[0]); + x2 = l2Normalize(x2, axes[1]); + } + return batchDot(x1, x2, axes); + } + interpretAxes(shape1, shape2) { + let axes; + if (!Array.isArray(this.axes)) { + axes = [ + interpretAxis(this.axes, shape1.length), + interpretAxis(this.axes, shape2.length) + ]; + } else { + axes = this.axes; + } + return axes; + } + computeOutputShape(inputShape) { + util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs."); + const shape1 = inputShape[0].slice(); + const shape2 = inputShape[1].slice(); + if (shape1.length > 3 || shape2.length > 3) { + throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet."); + } + const axes = this.interpretAxes(shape1, shape2); + shape1.splice(axes[0], 1); + shape2.splice(axes[1], 1); + shape2.splice(0, 1); + const outputShape = shape1.concat(shape2); + if (outputShape.length === 1) { + outputShape.push(1); + } + return outputShape; + } + computeMask(inputs, mask) { + return null; + } + getConfig() { + const config = { + "axes": this.axes, + "normalize": this.normalize + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +Dot.className = "Dot"; +serialization_exports.registerClass(Dot); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/noise.js +var GaussianNoise = class extends Layer { + constructor(args) { + super(args); + this.supportsMasking = true; + this.stddev = args.stddev; + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { stddev: this.stddev }; + Object.assign(config, baseConfig); + return config; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + const noised = () => add2(randomNormal2(input2.shape, 0, this.stddev), input2); + const output = inTrainPhase(noised, () => input2, kwargs["training"] || false); + return output; + }); + } +}; +GaussianNoise.className = "GaussianNoise"; +serialization_exports.registerClass(GaussianNoise); +var GaussianDropout = class extends Layer { + constructor(args) { + super(args); + this.supportsMasking = true; + this.rate = args.rate; + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { rate: this.rate }; + Object.assign(config, baseConfig); + return config; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + const input2 = getExactlyOneTensor(inputs); + if (this.rate > 0 && this.rate < 1) { + const noised = () => { + const stddev = Math.sqrt(this.rate / (1 - this.rate)); + return mul(input2, randomNormal2(input2.shape, 1, stddev)); + }; + return inTrainPhase(noised, () => input2, kwargs["training"] || false); + } + return input2; + }); + } +}; +GaussianDropout.className = "GaussianDropout"; +serialization_exports.registerClass(GaussianDropout); +var AlphaDropout = class extends Layer { + constructor(args) { + super(args); + this.supportsMasking = true; + this.rate = args.rate; + this.noiseShape = args.noiseShape; + } + _getNoiseShape(inputs) { + return this.noiseShape || getExactlyOneTensor(inputs).shape; + } + computeOutputShape(inputShape) { + return inputShape; + } + getConfig() { + const baseConfig = super.getConfig(); + const config = { rate: this.rate }; + Object.assign(config, baseConfig); + return config; + } + call(inputs, kwargs) { + return tidy(() => { + if (this.rate < 1 && this.rate > 0) { + const noiseShape = this._getNoiseShape(inputs); + const droppedInputs = () => { + const input2 = getExactlyOneTensor(inputs); + const alpha = 1.6732632423543772; + const scale2 = 1.0507009873554805; + const alphaP = -alpha * scale2; + let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate); + keptIdx = cast2(keptIdx, "float32"); + const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5; + const b = -a * alphaP * this.rate; + const x = add2(mul(input2, keptIdx), mul(add2(keptIdx, -1), alphaP)); + return add2(mul(x, a), b); + }; + return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs["training"] || false); + } + return inputs; + }); + } +}; +AlphaDropout.className = "AlphaDropout"; +serialization_exports.registerClass(AlphaDropout); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/normalization.js +function batchNormalization(x, mean4, variance, beta, gamma, epsilon3 = 1e-3) { + let out; + if (x.rank === 2) { + out = batchNorm2d(x, mean4, variance, beta, gamma, epsilon3); + } else if (x.rank === 3) { + out = batchNorm3d(x, mean4, variance, beta, gamma, epsilon3); + } else if (x.rank === 4) { + out = batchNorm4d(x, mean4, variance, beta, gamma, epsilon3); + } else { + throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`); + } + return out; +} +function regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) { + return tidy(() => { + const meanAndVariance = moments(x, reductionAxes); + const mean4 = meanAndVariance.mean; + const variance = meanAndVariance.variance; + const normed = batchNormalization(x, mean4, variance, beta, gamma, epsilon3); + return [normed, mean4, variance]; + }); +} +function broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) { + return tidy(() => { + const meanAndVariance = moments(x, reductionAxes); + const mean4 = meanAndVariance.mean; + const variance = meanAndVariance.variance; + const targetShape = []; + for (const axis of range2(0, x.rank)) { + if (reductionAxes.indexOf(axis) !== -1) { + targetShape.push(1); + } else { + targetShape.push(x.shape[axis]); + } + } + const broadcastMean = reshape(mean4, targetShape); + const broadcastVariance = reshape(variance, targetShape); + const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape); + const broadcastBeta = beta == null ? null : reshape(beta, targetShape); + const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon3); + return [normed, mean4, variance]; + }); +} +function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) { + if (util_exports.arraysEqual(reductionAxes.slice().sort(), range2(0, x.rank - 1))) { + return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3); + } else { + return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3); + } +} +var BatchNormalization = class extends Layer { + constructor(args) { + if (args == null) { + args = {}; + } + super(args); + this.supportsMasking = true; + this.axis = args.axis == null ? -1 : args.axis; + this.momentum = args.momentum == null ? 0.99 : args.momentum; + this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon; + this.center = args.center == null ? true : args.center; + this.scale = args.scale == null ? true : args.scale; + this.betaInitializer = getInitializer(args.betaInitializer || "zeros"); + this.gammaInitializer = getInitializer(args.gammaInitializer || "ones"); + this.movingMeanInitializer = getInitializer(args.movingMeanInitializer || "zeros"); + this.movingVarianceInitializer = getInitializer(args.movingVarianceInitializer || "ones"); + this.betaConstraint = getConstraint(args.betaConstraint); + this.gammaConstraint = getConstraint(args.gammaConstraint); + this.betaRegularizer = getRegularizer(args.betaRegularizer); + this.gammaRegularizer = getRegularizer(args.gammaRegularizer); + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const axis = this.axis >= 0 ? this.axis : this.axis + inputShape.length; + const dim = inputShape[axis]; + if (dim == null) { + throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`); + } + this.inputSpec = [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })]; + const shape = [dim]; + if (this.scale) { + this.gamma = this.addWeight("gamma", shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint); + } + if (this.center) { + this.beta = this.addWeight("beta", shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint); + } + this.movingMean = this.addWeight("moving_mean", shape, null, this.movingMeanInitializer, null, false); + this.movingVariance = this.addWeight("moving_variance", shape, null, this.movingVarianceInitializer, null, false); + this.built = true; + } + call(inputs, kwargs) { + return tidy(() => { + const training = kwargs["training"] == null ? false : kwargs["training"]; + const input2 = getExactlyOneTensor(inputs); + const inputShape = input2.shape; + const ndim = inputShape.length; + const reductionAxes = range2(0, ndim); + const axis = this.axis >= 0 ? this.axis : this.axis + ndim; + reductionAxes.splice(axis, 1); + const broadcastShape = pyListRepeat(1, ndim); + broadcastShape[axis] = inputShape[axis]; + const sortedReductionAxes = reductionAxes.slice(); + sortedReductionAxes.sort(); + const needsBroadcasting = !util_exports.arraysEqual(sortedReductionAxes, range2(0, ndim).slice(0, ndim - 1)); + const normalizeInference = () => { + if (needsBroadcasting) { + const broadcastMovingMean = reshape(this.movingMean.read(), broadcastShape); + const broadcastMovingVariance = reshape(this.movingVariance.read(), broadcastShape); + const broadcastBeta = this.center ? reshape(this.beta.read(), broadcastShape) : null; + const broadcastGamma = this.scale ? reshape(this.gamma.read(), broadcastShape) : null; + return batchNormalization(input2, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon); + } else { + return batchNormalization(input2, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon); + } + }; + if (!training) { + return normalizeInference(); + } + const [normedTraining, mean4, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon); + const doMovingAverage = (variable2, value, momentum) => { + tidy(() => { + const decay = 1 - momentum; + const origValue = variable2.read(); + const updateDelta = mul(sub(origValue, value), decay); + variable2.write(sub(origValue, updateDelta)); + }); + }; + const updateMovingMeanAndVariance = () => { + doMovingAverage(this.movingMean, mean4, this.momentum); + doMovingAverage(this.movingVariance, variance, this.momentum); + }; + updateMovingMeanAndVariance(); + return normedTraining; + }); + } + getConfig() { + const config = { + axis: this.axis, + momentum: this.momentum, + epsilon: this.epsilon, + center: this.center, + scale: this.scale, + betaInitializer: serializeInitializer(this.betaInitializer), + gammaInitializer: serializeInitializer(this.gammaInitializer), + movingMeanInitializer: serializeInitializer(this.movingMeanInitializer), + movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer), + betaRegularizer: serializeRegularizer(this.betaRegularizer), + gammaRegularizer: serializeRegularizer(this.gammaRegularizer), + betaConstraint: serializeConstraint(this.betaConstraint), + gammaConstraint: serializeConstraint(this.gammaConstraint) + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +BatchNormalization.className = "BatchNormalization"; +serialization_exports.registerClass(BatchNormalization); +var LayerNormalization = class extends Layer { + constructor(args) { + if (args == null) { + args = {}; + } + super(args); + this.axis = args.axis == null ? -1 : args.axis; + if (typeof this.axis === "number") { + if (!Number.isInteger(this.axis)) { + throw new Error(`Expected axis to be an integer, but received ${this.axis}`); + } + } else if (Array.isArray(this.axis)) { + for (const axis of this.axis) { + if (!Number.isInteger(axis)) { + 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 = args.epsilon == null ? 1e-3 : args.epsilon; + this.center = args.center == null ? true : args.center; + this.scale = args.scale == null ? true : args.scale; + this.betaInitializer = getInitializer(args.betaInitializer || "zeros"); + this.gammaInitializer = getInitializer(args.gammaInitializer || "ones"); + this.betaRegularizer = getRegularizer(args.betaRegularizer); + this.gammaRegularizer = getRegularizer(args.gammaRegularizer); + this.supportsMasking = true; + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const nDims = inputShape.length; + if (typeof this.axis === "number") { + this.axis = [this.axis]; + } + for (let i = 0; i < this.axis.length; ++i) { + if (this.axis[i] < 0) { + this.axis[i] += nDims; + } + } + for (const axis of this.axis) { + if (axis < 0 || axis >= nDims) { + throw new Error(`Invalid axis: ${axis}`); + } + } + if (this.axis.length !== unique2(this.axis).length) { + throw new Error(`Found duplicate axes in: ${this.axis}`); + } + const paramShape = this.axis.map((axis) => inputShape[axis]); + const trainable = true; + if (this.scale) { + this.gamma = this.addWeight("gamma", paramShape, "float32", this.gammaInitializer, this.gammaRegularizer, trainable); + } else { + this.gamma = null; + } + if (this.center) { + this.beta = this.addWeight("beta", paramShape, "float32", this.betaInitializer, this.betaRegularizer, trainable); + } else { + this.beta = null; + } + this.built = true; + } + call(inputs, kwargs) { + const input2 = getExactlyOneTensor(inputs); + const inputShape = input2.shape; + const nDims = inputShape.length; + return tidy(() => { + const keepDims = true; + let { mean: mean4, variance } = moments(input2, this.axis, keepDims); + const broadcastShape = pyListRepeat(1, nDims); + for (const dim of this.axis) { + broadcastShape[dim] = inputShape[dim]; + } + const broadcast = (v) => { + if (v != null && v.shape.length !== nDims) { + return reshape(v, broadcastShape); + } else { + return v; + } + }; + let scale2 = this.scale ? broadcast(this.gamma.read()) : null; + let offset = this.center ? broadcast(this.beta.read()) : null; + const momentsTiling = []; + const scaleOffsetTiling = []; + for (let i = 0; i < nDims; ++i) { + if (this.axis.indexOf(i) !== -1) { + momentsTiling.push(inputShape[i]); + scaleOffsetTiling.push(1); + } else { + momentsTiling.push(1); + scaleOffsetTiling.push(inputShape[i]); + } + } + mean4 = tile(mean4, momentsTiling); + variance = tile(variance, momentsTiling); + if (scale2 != null) { + scale2 = tile(scale2, scaleOffsetTiling); + } + if (offset != null) { + offset = tile(offset, scaleOffsetTiling); + } + return batchNormalization(input2, mean4, variance, offset, scale2, this.epsilon); + }); + } + getConfig() { + const config = { + axis: this.axis, + epsilon: this.epsilon, + center: this.center, + scale: this.scale, + betaInitializer: serializeInitializer(this.betaInitializer), + gammaInitializer: serializeInitializer(this.gammaInitializer), + betaRegularizer: serializeRegularizer(this.betaRegularizer), + gammaRegularizer: serializeRegularizer(this.gammaRegularizer) + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +LayerNormalization.className = "LayerNormalization"; +serialization_exports.registerClass(LayerNormalization); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/padding.js +function spatial2dPadding(x, padding, dataFormat) { + return tidy(() => { + if (x.rank !== 4) { + throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`); + } + if (padding == null) { + padding = [[1, 1], [1, 1]]; + } + if (padding.length !== 2 || padding[0].length !== 2 || padding[1].length !== 2) { + throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers."); + } + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + if (dataFormat !== "channelsLast" && dataFormat !== "channelsFirst") { + throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`); + } + let pattern; + if (dataFormat === "channelsFirst") { + pattern = [[0, 0], [0, 0], padding[0], padding[1]]; + } else { + pattern = [[0, 0], padding[0], padding[1], [0, 0]]; + } + return pad(x, pattern); + }); +} +var ZeroPadding2D = class extends Layer { + constructor(args) { + if (args == null) { + args = {}; + } + super(args); + this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat; + if (args.padding == null) { + this.padding = [[1, 1], [1, 1]]; + } else if (typeof args.padding === "number") { + this.padding = [[args.padding, args.padding], [args.padding, args.padding]]; + } else { + args.padding = args.padding; + if (args.padding.length !== 2) { + throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`); + } + let heightPadding; + let widthPadding; + if (typeof args.padding[0] === "number") { + heightPadding = [args.padding[0], args.padding[0]]; + widthPadding = [args.padding[1], args.padding[1]]; + } else { + args.padding = args.padding; + if (args.padding[0].length !== 2) { + throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`); + } + heightPadding = args.padding[0]; + if (args.padding[1].length !== 2) { + throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`); + } + widthPadding = args.padding[1]; + } + this.padding = [heightPadding, widthPadding]; + } + this.inputSpec = [new InputSpec({ ndim: 4 })]; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + let rows; + let cols; + if (this.dataFormat === "channelsFirst") { + if (inputShape[2] != null && inputShape[2] >= 0) { + rows = inputShape[2] + this.padding[0][0] + this.padding[0][1]; + } else { + rows = null; + } + if (inputShape[3] != null && inputShape[3] >= 0) { + cols = inputShape[3] + this.padding[1][0] + this.padding[1][1]; + } else { + cols = null; + } + return [inputShape[0], inputShape[1], rows, cols]; + } else { + if (inputShape[1] != null && inputShape[1] >= 0) { + rows = inputShape[1] + this.padding[0][0] + this.padding[0][1]; + } else { + rows = null; + } + if (inputShape[2] != null && inputShape[2] >= 0) { + cols = inputShape[2] + this.padding[1][0] + this.padding[1][1]; + } else { + cols = null; + } + return [inputShape[0], rows, cols, inputShape[3]]; + } + } + call(inputs, kwargs) { + return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat)); + } + getConfig() { + const config = { + padding: this.padding, + dataFormat: this.dataFormat + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +ZeroPadding2D.className = "ZeroPadding2D"; +serialization_exports.registerClass(ZeroPadding2D); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/pooling.js +function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) { + return tidy(() => { + checkDataFormat(dataFormat); + checkPoolMode(poolMode); + checkPaddingMode(padding); + if (strides == null) { + strides = [1, 1]; + } + if (padding == null) { + padding = "valid"; + } + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + if (poolMode == null) { + poolMode = "max"; + } + x = preprocessConv2DInput(x, dataFormat); + let y; + const paddingString = padding === "same" ? "same" : "valid"; + if (poolMode === "max") { + y = maxPool(x, poolSize, strides, paddingString); + } else { + y = avgPool( + // TODO(cais): Rank check? + x, + poolSize, + strides, + paddingString + ); + } + if (dataFormat === "channelsFirst") { + y = transpose(y, [0, 3, 1, 2]); + } + return y; + }); +} +function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) { + return tidy(() => { + checkDataFormat(dataFormat); + checkPoolMode(poolMode); + checkPaddingMode(padding); + if (strides == null) { + strides = [1, 1, 1]; + } + if (padding == null) { + padding = "valid"; + } + if (dataFormat == null) { + dataFormat = imageDataFormat(); + } + if (poolMode == null) { + poolMode = "max"; + } + x = preprocessConv3DInput(x, dataFormat); + let y; + const paddingString = padding === "same" ? "same" : "valid"; + if (poolMode === "max") { + y = maxPool3d(x, poolSize, strides, paddingString); + } else { + y = avgPool3d(x, poolSize, strides, paddingString); + } + if (dataFormat === "channelsFirst") { + y = transpose(y, [0, 4, 1, 2, 3]); + } + return y; + }); +} +var Pooling1D = class extends Layer { + /** + * + * @param args Parameters for the Pooling layer. + * + * config.poolSize defaults to 2. + */ + constructor(args) { + if (args.poolSize == null) { + args.poolSize = 2; + } + super(args); + if (typeof args.poolSize === "number") { + this.poolSize = [args.poolSize]; + } else if (Array.isArray(args.poolSize) && args.poolSize.length === 1 && typeof args.poolSize[0] === "number") { + this.poolSize = args.poolSize; + } else { + throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`); + } + assertPositiveInteger(this.poolSize, "poolSize"); + if (args.strides == null) { + this.strides = this.poolSize; + } else { + if (typeof args.strides === "number") { + this.strides = [args.strides]; + } else if (Array.isArray(args.strides) && args.strides.length === 1 && typeof args.strides[0] === "number") { + this.strides = args.strides; + } else { + throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`); + } + } + assertPositiveInteger(this.strides, "strides"); + this.padding = args.padding == null ? "valid" : args.padding; + checkPaddingMode(this.padding); + this.inputSpec = [new InputSpec({ ndim: 3 })]; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]); + return [inputShape[0], length, inputShape[2]]; + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + inputs = expandDims2(getExactlyOneTensor(inputs), 2); + const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast"); + return squeeze(output, [2]); + }); + } + getConfig() { + const config = { + poolSize: this.poolSize, + padding: this.padding, + strides: this.strides + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +var MaxPooling1D = class extends Pooling1D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool2d(inputs, poolSize, strides, padding, dataFormat, "max"); + } +}; +MaxPooling1D.className = "MaxPooling1D"; +serialization_exports.registerClass(MaxPooling1D); +var AveragePooling1D = class extends Pooling1D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool2d(inputs, poolSize, strides, padding, dataFormat, "avg"); + } +}; +AveragePooling1D.className = "AveragePooling1D"; +serialization_exports.registerClass(AveragePooling1D); +var Pooling2D = class extends Layer { + constructor(args) { + if (args.poolSize == null) { + args.poolSize = [2, 2]; + } + super(args); + this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize]; + if (args.strides == null) { + this.strides = this.poolSize; + } else if (Array.isArray(args.strides)) { + if (args.strides.length !== 2) { + throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`); + } + this.strides = args.strides; + } else { + this.strides = [args.strides, args.strides]; + } + assertPositiveInteger(this.poolSize, "poolSize"); + assertPositiveInteger(this.strides, "strides"); + this.padding = args.padding == null ? "valid" : args.padding; + this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat; + checkDataFormat(this.dataFormat); + checkPaddingMode(this.padding); + this.inputSpec = [new InputSpec({ ndim: 4 })]; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + let rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1]; + let cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2]; + rows = convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]); + cols = convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]); + if (this.dataFormat === "channelsFirst") { + return [inputShape[0], inputShape[1], rows, cols]; + } else { + return [inputShape[0], rows, cols, inputShape[3]]; + } + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat); + }); + } + getConfig() { + const config = { + poolSize: this.poolSize, + padding: this.padding, + strides: this.strides, + dataFormat: this.dataFormat + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +var MaxPooling2D = class extends Pooling2D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool2d(inputs, poolSize, strides, padding, dataFormat, "max"); + } +}; +MaxPooling2D.className = "MaxPooling2D"; +serialization_exports.registerClass(MaxPooling2D); +var AveragePooling2D = class extends Pooling2D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool2d(inputs, poolSize, strides, padding, dataFormat, "avg"); + } +}; +AveragePooling2D.className = "AveragePooling2D"; +serialization_exports.registerClass(AveragePooling2D); +var Pooling3D = class extends Layer { + constructor(args) { + if (args.poolSize == null) { + args.poolSize = [2, 2, 2]; + } + super(args); + this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize, args.poolSize]; + if (args.strides == null) { + this.strides = this.poolSize; + } else if (Array.isArray(args.strides)) { + if (args.strides.length !== 3) { + throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`); + } + this.strides = args.strides; + } else { + this.strides = [args.strides, args.strides, args.strides]; + } + assertPositiveInteger(this.poolSize, "poolSize"); + assertPositiveInteger(this.strides, "strides"); + this.padding = args.padding == null ? "valid" : args.padding; + this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat; + checkDataFormat(this.dataFormat); + checkPaddingMode(this.padding); + this.inputSpec = [new InputSpec({ ndim: 5 })]; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + let depths = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1]; + let rows = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2]; + let cols = this.dataFormat === "channelsFirst" ? inputShape[4] : inputShape[3]; + depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]); + rows = convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]); + cols = convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]); + if (this.dataFormat === "channelsFirst") { + return [inputShape[0], inputShape[1], depths, rows, cols]; + } else { + return [inputShape[0], depths, rows, cols, inputShape[4]]; + } + } + call(inputs, kwargs) { + return tidy(() => { + this.invokeCallHook(inputs, kwargs); + return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat); + }); + } + getConfig() { + const config = { + poolSize: this.poolSize, + padding: this.padding, + strides: this.strides, + dataFormat: this.dataFormat + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +var MaxPooling3D = class extends Pooling3D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool3d(inputs, poolSize, strides, padding, dataFormat, "max"); + } +}; +MaxPooling3D.className = "MaxPooling3D"; +serialization_exports.registerClass(MaxPooling3D); +var AveragePooling3D = class extends Pooling3D { + constructor(args) { + super(args); + } + poolingFunction(inputs, poolSize, strides, padding, dataFormat) { + checkDataFormat(dataFormat); + checkPaddingMode(padding); + return pool3d(inputs, poolSize, strides, padding, dataFormat, "avg"); + } +}; +AveragePooling3D.className = "AveragePooling3D"; +serialization_exports.registerClass(AveragePooling3D); +var GlobalPooling1D = class extends Layer { + constructor(args) { + super(args); + this.inputSpec = [new InputSpec({ ndim: 3 })]; + } + computeOutputShape(inputShape) { + return [inputShape[0], inputShape[2]]; + } + call(inputs, kwargs) { + throw new NotImplementedError(); + } +}; +var GlobalAveragePooling1D = class extends GlobalPooling1D { + constructor(args) { + super(args || {}); + } + call(inputs, kwargs) { + return tidy(() => { + const input2 = getExactlyOneTensor(inputs); + return mean(input2, 1); + }); + } +}; +GlobalAveragePooling1D.className = "GlobalAveragePooling1D"; +serialization_exports.registerClass(GlobalAveragePooling1D); +var GlobalMaxPooling1D = class extends GlobalPooling1D { + constructor(args) { + super(args || {}); + } + call(inputs, kwargs) { + return tidy(() => { + const input2 = getExactlyOneTensor(inputs); + return max(input2, 1); + }); + } +}; +GlobalMaxPooling1D.className = "GlobalMaxPooling1D"; +serialization_exports.registerClass(GlobalMaxPooling1D); +var GlobalPooling2D = class extends Layer { + constructor(args) { + super(args); + this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat; + checkDataFormat(this.dataFormat); + this.inputSpec = [new InputSpec({ ndim: 4 })]; + } + computeOutputShape(inputShape) { + inputShape = inputShape; + if (this.dataFormat === "channelsLast") { + return [inputShape[0], inputShape[3]]; + } else { + return [inputShape[0], inputShape[1]]; + } + } + call(inputs, kwargs) { + throw new NotImplementedError(); + } + getConfig() { + const config = { dataFormat: this.dataFormat }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +var GlobalAveragePooling2D = class extends GlobalPooling2D { + call(inputs, kwargs) { + return tidy(() => { + const input2 = getExactlyOneTensor(inputs); + if (this.dataFormat === "channelsLast") { + return mean(input2, [1, 2]); + } else { + return mean(input2, [2, 3]); + } + }); + } +}; +GlobalAveragePooling2D.className = "GlobalAveragePooling2D"; +serialization_exports.registerClass(GlobalAveragePooling2D); +var GlobalMaxPooling2D = class extends GlobalPooling2D { + call(inputs, kwargs) { + return tidy(() => { + const input2 = getExactlyOneTensor(inputs); + if (this.dataFormat === "channelsLast") { + return max(input2, [1, 2]); + } else { + return max(input2, [2, 3]); + } + }); + } +}; +GlobalMaxPooling2D.className = "GlobalMaxPooling2D"; +serialization_exports.registerClass(GlobalMaxPooling2D); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/wrappers.js +var Wrapper = class extends Layer { + constructor(args) { + super(args); + this.layer = args.layer; + } + build(inputShape) { + this.built = true; + } + // TODO(cais): Implement activityRegularizer getter. + get trainable() { + if (this.layer != null) { + return this.layer.trainable; + } else { + return false; + } + } + set trainable(value) { + if (this.layer != null) { + this.layer.trainable = value; + } + } + get trainableWeights() { + return this.layer.trainableWeights; + } + // TODO(cais): Implement setter for trainableWeights. + get nonTrainableWeights() { + return this.layer.nonTrainableWeights; + } + // TODO(cais): Implement setter for nonTrainableWeights. + get updates() { + return this.layer._updates; + } + // TODO(cais): Implement getUpdatesFor(). + get losses() { + return this.layer.losses; + } + // TODO(cais): Implement getLossesFor(). + getWeights() { + return this.layer.getWeights(); + } + setWeights(weights) { + this.layer.setWeights(weights); + } + getConfig() { + const config = { + "layer": { + "className": this.layer.getClassName(), + "config": this.layer.getConfig() + } + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + setFastWeightInitDuringBuild(value) { + super.setFastWeightInitDuringBuild(value); + if (this.layer != null) { + this.layer.setFastWeightInitDuringBuild(value); + } + } + /** @nocollapse */ + static fromConfig(cls, config, customObjects = {}) { + const layerConfig = config["layer"]; + const layer = deserialize(layerConfig, customObjects); + delete config["layer"]; + const newConfig = { layer }; + Object.assign(newConfig, config); + return new cls(newConfig); + } +}; +var TimeDistributed = class extends Wrapper { + constructor(args) { + super(args); + this.supportsMasking = true; + } + build(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape.length < 3) { + throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`); + } + this.inputSpec = [{ shape: inputShape }]; + const childInputShape = [inputShape[0]].concat(inputShape.slice(2)); + if (!this.layer.built) { + this.layer.build(childInputShape); + this.layer.built = true; + } + super.build(inputShape); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const childInputShape = [inputShape[0]].concat(inputShape.slice(2)); + const childOutputShape = this.layer.computeOutputShape(childInputShape); + const timesteps = inputShape[1]; + return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1)); + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + const step5 = (inputs2, states) => { + const output = getExactlyOneTensor(this.layer.call(inputs2, kwargs)); + return [output, []]; + }; + const rnnOutputs = rnn( + step5, + inputs, + [], + false, + null, + null, + false, + true + /* needPerStepOutputs */ + ); + const y = rnnOutputs[1]; + return y; + }); + } +}; +TimeDistributed.className = "TimeDistributed"; +serialization_exports.registerClass(TimeDistributed); +function checkBidirectionalMergeMode(value) { + checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, "BidirectionalMergeMode", value); +} +var DEFAULT_BIDIRECTIONAL_MERGE_MODE = "concat"; +var Bidirectional = class extends Wrapper { + constructor(args) { + super(args); + const layerConfig = args.layer.getConfig(); + const forwDict = {}; + forwDict["className"] = args.layer.getClassName(); + forwDict["config"] = layerConfig; + this.forwardLayer = deserialize(forwDict); + layerConfig["goBackwards"] = layerConfig["goBackwards"] === true ? false : true; + const backDict = {}; + backDict["className"] = args.layer.getClassName(); + backDict["config"] = layerConfig; + this.backwardLayer = deserialize(backDict); + this.forwardLayer.name = "forward_" + this.forwardLayer.name; + this.backwardLayer.name = "backward_" + this.backwardLayer.name; + this.mergeMode = args.mergeMode === void 0 ? DEFAULT_BIDIRECTIONAL_MERGE_MODE : args.mergeMode; + checkBidirectionalMergeMode(this.mergeMode); + if (args.weights) { + throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet."); + } + this._stateful = args.layer.stateful; + this.returnSequences = args.layer.returnSequences; + this.returnState = args.layer.returnState; + this.supportsMasking = true; + this._trainable = true; + this.inputSpec = args.layer.inputSpec; + this.numConstants = null; + } + get trainable() { + return this._trainable; + } + set trainable(value) { + this._trainable = value; + if (this.forwardLayer != null) { + this.forwardLayer.trainable = value; + } + if (this.backwardLayer != null) { + this.backwardLayer.trainable = value; + } + } + getWeights() { + return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights()); + } + setWeights(weights) { + const numWeights = weights.length; + const numeightsOver2 = Math.floor(numWeights / 2); + this.forwardLayer.setWeights(weights.slice(0, numeightsOver2)); + this.backwardLayer.setWeights(weights.slice(numeightsOver2)); + } + computeOutputShape(inputShape) { + let layerShapes = this.forwardLayer.computeOutputShape(inputShape); + if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) { + layerShapes = [layerShapes]; + } + layerShapes = layerShapes; + let outputShape; + let outputShapes; + let stateShape; + if (this.returnState) { + stateShape = layerShapes.slice(1); + outputShape = layerShapes[0]; + } else { + outputShape = layerShapes[0]; + } + outputShape = outputShape; + if (this.mergeMode === "concat") { + outputShape[outputShape.length - 1] *= 2; + outputShapes = [outputShape]; + } else if (this.mergeMode == null) { + outputShapes = [outputShape, outputShape.slice()]; + } else { + outputShapes = [outputShape]; + } + if (this.returnState) { + if (this.mergeMode == null) { + return outputShapes.concat(stateShape).concat(stateShape.slice()); + } + return [outputShape].concat(stateShape).concat(stateShape.slice()); + } + return singletonOrArray(outputShapes); + } + apply(inputs, kwargs) { + let initialState = kwargs == null ? null : kwargs["initialState"]; + let constants = kwargs == null ? null : kwargs["constants"]; + if (kwargs == null) { + kwargs = {}; + } + const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants); + inputs = standardized.inputs; + initialState = standardized.initialState; + constants = standardized.constants; + if (Array.isArray(inputs)) { + initialState = inputs.slice(1); + inputs = inputs[0]; + } + if ((initialState == null || initialState.length === 0) && constants == null) { + return super.apply(inputs, kwargs); + } + const additionalInputs = []; + const additionalSpecs = []; + if (initialState != null) { + const numStates = initialState.length; + if (numStates % 2 > 0) { + throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs."); + } + kwargs["initialState"] = initialState; + additionalInputs.push(...initialState); + const stateSpecs = initialState.map((state) => new InputSpec({ shape: state.shape })); + this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2); + this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2); + additionalSpecs.push(...stateSpecs); + } + if (constants != null) { + throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet."); + } + const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor; + for (const tensor2 of additionalInputs) { + if (tensor2 instanceof SymbolicTensor !== isSymbolicTensor) { + throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors"); + } + } + if (isSymbolicTensor) { + const fullInput = [inputs].concat(additionalInputs); + const fullInputSpec = this.inputSpec.concat(additionalSpecs); + const originalInputSpec = this.inputSpec; + this.inputSpec = fullInputSpec; + const output = super.apply(fullInput, kwargs); + this.inputSpec = originalInputSpec; + return output; + } else { + return super.apply(inputs, kwargs); + } + } + call(inputs, kwargs) { + return tidy(() => { + const initialState = kwargs["initialState"]; + let y; + let yRev; + if (initialState == null) { + y = this.forwardLayer.call(inputs, kwargs); + yRev = this.backwardLayer.call(inputs, kwargs); + } else { + const forwardState = initialState.slice(0, initialState.length / 2); + const backwardState = initialState.slice(initialState.length / 2); + y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState })); + yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState })); + } + let states; + if (this.returnState) { + if (Array.isArray(y)) { + states = y.slice(1).concat(yRev.slice(1)); + } else { + } + y = y[0]; + yRev = yRev[0]; + } + if (this.returnSequences) { + yRev = reverse(yRev, 1); + } + let output; + if (this.mergeMode === "concat") { + output = concatenate([y, yRev]); + } else if (this.mergeMode === "sum") { + output = add2(y, yRev); + } else if (this.mergeMode === "ave") { + output = mul(0.5, add2(y, yRev)); + } else if (this.mergeMode === "mul") { + output = mul(y, yRev); + } else if (this.mergeMode == null) { + output = [y, yRev]; + } + if (this.returnState) { + if (this.mergeMode == null) { + return output.concat(states); + } + return [output].concat(states); + } + return output; + }); + } + resetStates(states) { + this.forwardLayer.resetStates(); + this.backwardLayer.resetStates(); + } + build(inputShape) { + nameScope(this.forwardLayer.name, () => { + this.forwardLayer.build(inputShape); + }); + nameScope(this.backwardLayer.name, () => { + this.backwardLayer.build(inputShape); + }); + this.built = true; + } + computeMask(inputs, mask) { + if (Array.isArray(mask)) { + mask = mask[0]; + } + let outputMask; + if (this.returnSequences) { + if (this.mergeMode == null) { + outputMask = [mask, mask]; + } else { + outputMask = mask; + } + } else { + if (this.mergeMode == null) { + outputMask = [null, null]; + } else { + outputMask = null; + } + } + if (this.returnState) { + const states = this.forwardLayer.states; + const stateMask = states.map((state) => null); + if (Array.isArray(outputMask)) { + return outputMask.concat(stateMask).concat(stateMask); + } else { + return [outputMask].concat(stateMask).concat(stateMask); + } + } else { + return outputMask; + } + } + get trainableWeights() { + return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights); + } + get nonTrainableWeights() { + return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights); + } + // TODO(cais): Implement constraints(). + setFastWeightInitDuringBuild(value) { + super.setFastWeightInitDuringBuild(value); + if (this.forwardLayer != null) { + this.forwardLayer.setFastWeightInitDuringBuild(value); + } + if (this.backwardLayer != null) { + this.backwardLayer.setFastWeightInitDuringBuild(value); + } + } + getConfig() { + const config = { + "mergeMode": this.mergeMode + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + /** @nocollapse */ + static fromConfig(cls, config) { + const rnnLayer = deserialize(config["layer"]); + delete config["layer"]; + if (config["numConstants"] != null) { + throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`); + } + const newConfig = config; + newConfig["layer"] = rnnLayer; + return new cls(newConfig); + } +}; +Bidirectional.className = "Bidirectional"; +serialization_exports.registerClass(Bidirectional); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/image_preprocessing.js +var Rescaling = class extends Layer { + constructor(args) { + super(args); + this.scale = args.scale; + if (args.offset) { + this.offset = args.offset; + } else { + this.offset = 0; + } + } + getConfig() { + const config = { + "scale": this.scale, + "offset": this.offset + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + if (inputs.dtype !== "float32") { + inputs = cast2(inputs, "float32"); + } + return add2(mul(inputs, this.scale), this.offset); + }); + } +}; +Rescaling.className = "Rescaling"; +serialization_exports.registerClass(Rescaling); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/center_crop.js +var { resizeBilinear: resizeBilinear2, cropAndResize: cropAndResize2 } = image; +var CenterCrop = class extends Layer { + constructor(args) { + super(args); + this.height = args.height; + this.width = args.width; + } + centerCrop(inputs, hBuffer, wBuffer, height, width, inputHeight, inputWidth, dtype) { + return tidy(() => { + let input2; + let isRank3 = false; + const top = hBuffer / inputHeight; + const left = wBuffer / inputWidth; + const bottom = (height + hBuffer) / inputHeight; + const right = (width + wBuffer) / inputWidth; + const bound = [top, left, bottom, right]; + const boxesArr = []; + if (inputs.rank === 3) { + isRank3 = true; + input2 = stack([inputs]); + } else { + input2 = inputs; + } + for (let i = 0; i < input2.shape[0]; i++) { + boxesArr.push(bound); + } + const boxes = tensor(boxesArr, [boxesArr.length, 4]); + const boxInd = range(0, boxesArr.length, 1, "int32"); + const cropSize = [height, width]; + const cropped = cropAndResize2(input2, boxes, boxInd, cropSize, "nearest"); + if (isRank3) { + return cast2(getExactlyOneTensor(unstack(cropped)), dtype); + } + return cast2(cropped, dtype); + }); + } + upsize(inputs, height, width, dtype) { + return tidy(() => { + const outputs = resizeBilinear2(inputs, [height, width]); + return cast2(outputs, dtype); + }); + } + call(inputs, kwargs) { + return tidy(() => { + const rankedInputs = getExactlyOneTensor(inputs); + const dtype = rankedInputs.dtype; + const inputShape = rankedInputs.shape; + const inputHeight = inputShape[inputShape.length - 3]; + const inputWidth = inputShape[inputShape.length - 2]; + let hBuffer = 0; + if (inputHeight !== this.height) { + hBuffer = Math.floor((inputHeight - this.height) / 2); + } + let wBuffer = 0; + if (inputWidth !== this.width) { + wBuffer = Math.floor((inputWidth - this.width) / 2); + if (wBuffer === 0) { + wBuffer = 1; + } + } + if (hBuffer >= 0 && wBuffer >= 0) { + return this.centerCrop(rankedInputs, hBuffer, wBuffer, this.height, this.width, inputHeight, inputWidth, dtype); + } else { + return this.upsize(inputs, this.height, this.width, dtype); + } + }); + } + getConfig() { + const config = { + "height": this.height, + "width": this.width + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const hAxis = inputShape.length - 3; + const wAxis = inputShape.length - 2; + inputShape[hAxis] = this.height; + inputShape[wAxis] = this.width; + return inputShape; + } +}; +CenterCrop.className = "CenterCrop"; +serialization_exports.registerClass(CenterCrop); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/preprocessing_utils.js +function encodeCategoricalInputs(inputs, outputMode, depth, weights) { + let input2 = getExactlyOneTensor(inputs); + if (input2.dtype !== "int32") { + input2 = cast2(input2, "int32"); + } + if (outputMode === "int") { + return input2; + } + const originalShape = input2.shape; + if (input2.rank === 0) { + input2 = expandDims(input2, -1); + } + if (outputMode === "oneHot") { + if (input2.shape[input2.shape.length - 1] !== 1) { + input2 = expandDims(input2, -1); + } + } + if (input2.rank > 2) { + throw new ValueError(`When outputMode is not int, maximum output rank is 2 Received outputMode ${outputMode} and input shape ${originalShape} which would result in output rank ${input2.rank}.`); + } + const binaryOutput = ["multiHot", "oneHot"].includes(outputMode); + const denseBincountInput = input2; + let binCounts; + if (typeof weights !== "undefined" && outputMode === "count") { + binCounts = denseBincount(denseBincountInput, weights, depth, binaryOutput); + } else { + binCounts = denseBincount(denseBincountInput, [], depth, binaryOutput); + } + if (outputMode !== "tfIdf") { + return binCounts; + } + if (weights) { + return mul(binCounts, weights); + } else { + throw new ValueError(`When outputMode is 'tfIdf', weights must be provided.`); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/category_encoding.js +var CategoryEncoding = class extends Layer { + constructor(args) { + super(args); + this.numTokens = args.numTokens; + if (args.outputMode) { + this.outputMode = args.outputMode; + } else { + this.outputMode = "multiHot"; + } + } + getConfig() { + const config = { + "numTokens": this.numTokens, + "outputMode": this.outputMode + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + if (inputShape == null) { + return [this.numTokens]; + } + if (this.outputMode === "oneHot" && inputShape[inputShape.length - 1] !== 1) { + inputShape.push(this.numTokens); + return inputShape; + } + inputShape[inputShape.length - 1] = this.numTokens; + return inputShape; + } + call(inputs, kwargs) { + return tidy(() => { + inputs = getExactlyOneTensor(inputs); + if (inputs.dtype !== "int32") { + inputs = cast2(inputs, "int32"); + } + let countWeights; + if (typeof kwargs["countWeights"] !== "undefined") { + if (this.outputMode !== "count") { + throw new ValueError(`countWeights is not used when outputMode !== count. + Received countWeights=${kwargs["countWeights"]}`); + } + countWeights = getExactlyOneTensor(kwargs["countWeights"]); + } + const maxValue = max(inputs); + const minValue = min(inputs); + const greaterEqualMax = greater(this.numTokens, maxValue).bufferSync().get(0); + const greaterMin = greaterEqual(minValue, 0).bufferSync().get(0); + if (!(greaterEqualMax && greaterMin)) { + throw new ValueError(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`); + } + return encodeCategoricalInputs(inputs, this.outputMode, this.numTokens, countWeights); + }); + } +}; +CategoryEncoding.className = "CategoryEncoding"; +serialization_exports.registerClass(CategoryEncoding); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/image_resizing.js +var INTERPOLATION_KEYS = ["bilinear", "nearest"]; +var INTERPOLATION_METHODS = new Set(INTERPOLATION_KEYS); +var Resizing = class extends Layer { + constructor(args) { + super(args); + this.height = args.height; + this.width = args.width; + if (args.interpolation) { + if (INTERPOLATION_METHODS.has(args.interpolation)) { + this.interpolation = args.interpolation; + } else { + throw new ValueError(`Invalid interpolation parameter: ${args.interpolation} is not implemented`); + } + } else { + this.interpolation = "bilinear"; + } + this.cropToAspectRatio = Boolean(args.cropToAspectRatio); + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const numChannels = inputShape[2]; + return [this.height, this.width, numChannels]; + } + getConfig() { + const config = { + "height": this.height, + "width": this.width, + "interpolation": this.interpolation, + "cropToAspectRatio": this.cropToAspectRatio + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + call(inputs, kwargs) { + return tidy(() => { + const size = [this.height, this.width]; + if (this.interpolation === "bilinear") { + return image.resizeBilinear(inputs, size, !this.cropToAspectRatio); + } else if (this.interpolation === "nearest") { + return image.resizeNearestNeighbor(inputs, size, !this.cropToAspectRatio); + } else { + throw new Error(`Interpolation is ${this.interpolation} but only ${[...INTERPOLATION_METHODS]} are supported`); + } + }); + } +}; +Resizing.className = "Resizing"; +serialization_exports.registerClass(Resizing); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/backend/random_seed.js +var RandomSeed = class { + constructor(seed) { + this.seed = seed; + } + next() { + if (this.seed === void 0) { + return void 0; + } + return this.seed++; + } +}; +RandomSeed.className = "RandomSeed"; + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/engine/base_random_layer.js +var BaseRandomLayer = class extends Layer { + constructor(args) { + super(args); + this.randomGenerator = new RandomSeed(args.seed); + } + getConfig() { + const config = { + "seed": this.randomGenerator.seed + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } +}; +BaseRandomLayer.className = "BaseRandomLayer"; + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/layers/preprocessing/random_width.js +var INTERPOLATION_KEYS2 = ["bilinear", "nearest"]; +var INTERPOLATION_METHODS2 = new Set(INTERPOLATION_KEYS2); +var RandomWidth = class extends BaseRandomLayer { + constructor(args) { + super(args); + const { factor, interpolation = "bilinear" } = args; + this.factor = factor; + if (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 ValueError(`Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`); + } + if (this.widthLower < -1 || this.widthUpper < -1) { + throw new ValueError(`factor must have values larger than -1. Got: ${this.factor}`); + } + if (this.widthUpper < this.widthLower) { + throw new ValueError(`factor cannot have upper bound less than lower bound. Got upper bound: ${this.widthUpper}. Got lower bound: ${this.widthLower} - `);if(n)if(WR.has(n))this.interpolation=n;else throw new z(`Invalid interpolation parameter: ${n} is not implemented`)}getConfig(){let t={factor:this.factor,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.imgHeight,-1,e]}call(t,e){return B(()=>{let n=St(t);this.imgHeight=n.shape[n.shape.length-3];let o=n.shape[n.shape.length-2];this.widthFactor=Hn([1],1+this.widthLower,1+this.widthUpper,"float32",this.randomGenerator.next());let s=this.widthFactor.dataSync()[0]*o;s=Math.round(s);let i=[this.imgHeight,s];switch(this.interpolation){case"bilinear":return hn.resizeBilinear(t,i);case"nearest":return hn.resizeNearestNeighbor(t,i);default:throw new Error(`Interpolation is ${this.interpolation} - but only ${[...WR]} are supported`)}})}};sd.className="RandomWidth";J.registerClass(sd);function yJ(r){return new xi(r)}function bJ(r){return new lf(r)}function wJ(r){return new of(r)}function IJ(r){return new sf(r)}function CJ(r){return new af(r)}function vJ(r){return new cf(r)}function SJ(r){return new uf(r)}function NJ(r){return new qu(r)}function kJ(r){return new Dl(r)}function TJ(r){return new pf(r)}function _J(r){return new $l(r)}function EJ(r){return new mf(r)}function AJ(r){return new ff(r)}function DJ(r){return new df(r)}function $J(r){return new hf(r)}function RJ(r){return new gf(r)}function FJ(r){return new Sf(r)}function OJ(r){return new Cf(r)}function PJ(r){return new np(r)}function MJ(r){return new If(r)}function LJ(r){return new vf(r)}function zJ(r){return new Nf(r)}function BJ(r){return new kf(r)}function VJ(r){return new Tf(r)}function GJ(r){return new Ef(r)}function WJ(r){return new Af(r)}function UJ(r){return new $f(r)}function HJ(r){return new Of(r)}function qJ(r){return new Rf(r)}function KJ(r){return new Ff(r)}function jJ(r){return new Df(r)}function XJ(r){return new Pf(r)}function YJ(r){return new Bf(r)}function ZJ(r){return new Vf(r)}function JJ(r){return new Gf(r)}function QN(r){return new Uf(r)}function QJ(r){return QN(r)}function t9(r){return QN(r)}function tk(r){return new qf(r)}function e9(r){return tk(r)}function r9(r){return tk(r)}function ek(r){return new jf(r)}function n9(r){return ek(r)}function o9(r){return ek(r)}function s9(r){return new Xf(r)}function i9(r){return new Zf(r)}function UR(r){return new Yf(r)}function HR(r){return new Jf(r)}function qR(r){return new Wf(r)}function KR(r){return new Hf(r)}function a9(r){return new Kf(r)}function l9(r){return new yf(r)}function u9(r){return new tp(r)}function c9(r){return new bf(r)}function p9(r){return new Fl(r)}function m9(r){return new xf(r)}function f9(r){return new Qc(r)}function d9(r){return new wf(r)}function h9(r){return new rp(r)}function g9(r){return new Dn(r)}function x9(r){return new ep(r)}function y9(r){return new td(r)}function b9(r){return new Qf(r)}var w9=UR,I9=HR,C9=qR,v9=KR;function S9(r){return new Mf(r)}function N9(r){return new Lf(r)}function k9(r){return new zf(r)}function T9(r){return new _f(r)}function _9(r){return new ed(r)}function E9(r){return new rd(r)}function A9(r){return new od(r)}function D9(r){return new nd(r)}function $9(r){return new sd(r)}var XR={};Kt(XR,{MAPE:()=>W9,MSE:()=>q9,binaryAccuracy:()=>R9,binaryCrossentropy:()=>F9,categoricalAccuracy:()=>P9,categoricalCrossentropy:()=>M9,cosineProximity:()=>B9,mape:()=>U9,meanAbsoluteError:()=>V9,meanAbsolutePercentageError:()=>G9,meanSquaredError:()=>H9,mse:()=>K9,precision:()=>L9,recall:()=>z9,sparseCategoricalAccuracy:()=>O9});function R9(r,t){return Ph(r,t)}function F9(r,t){return nb(r,t)}function O9(r,t){return ob(r,t)}function P9(r,t){return Mh(r,t)}function M9(r,t){return Lh(r,t)}function L9(r,t){return VN(r,t)}function z9(r,t){return yR(r,t)}function B9(r,t){return Oh(r,t)}function V9(r,t){return Jm(r,t)}function G9(r,t){return Gu(r,t)}function W9(r,t){return Gu(r,t)}function U9(r,t){return Gu(r,t)}function H9(r,t){return wa(r,t)}function q9(r,t){return wa(r,t)}function K9(r,t){return wa(r,t)}var YR={};Kt(YR,{modelFromJSON:()=>RR});var ZR={};Kt(ZR,{l1:()=>X9,l1l2:()=>j9,l2:()=>Y9});function j9(r){return new Wu(r)}function X9(r){return MR(r)}function Y9(r){return LR(r)}var Mb=class extends Al{constructor(){super(...arguments),this.model=null}setModel(t){if(!(t instanceof jn))throw new Error("model must be a LayersModel, not some other Container");this.model=t}};function Pb(r,t){return rt}var Lb=class extends Mb{constructor(t){if(super(),t==null&&(t={}),t.restoreBestWeights)throw new kt("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=t.monitor||"val_loss",this.minDelta=Math.abs(t.minDelta||0),this.patience=t.patience||0,this.verbose=t.verbose||0,this.mode=t.mode||"auto",this.baseline=t.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. 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TypeError(`Node type ${r.op} is not implemented`)}};function Xn(r,t,e=""){if(!(typeof r=="number"||typeof t=="number")){y.assert(r.length===t.length,()=>e+` Shapes ${r} and ${t} must match`);for(let n=0;ne+` Shapes ${r} and ${t} must match`)}}}function sF(r){return!(typeof r=="number"||r.some(t=>t<0))}function id(r,t,e){let n=Jb(r,e),o=!sF(n);if(o&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${n}`);if(o&&t.forEach(s=>{n=Jb(s.shape,n)}),!sF(n))throw new Error(`Non-fully-defined elementShape: ${n}`);return n}function Jb(r,t){if(typeof r=="number")return t;if(typeof t=="number")return r;if(r.length!==t.length)throw new Error(`Incompatible ranks during merge: ${r} vs. ${t}`);let e=[];for(let n=0;n=0&&s>=0&&o!==s)throw new Error(`Incompatible shape during merge: ${r} vs. ${t}`);e[n]=o>=0?o:s}return e}var Qb=class{constructor(t,e,n,o,s,i,a){this.name=t,this.dtype=e,this.maxSize=n,this.elementShape=o,this.identicalElementShapes=s,this.dynamicSize=i,this.clearAfterRead=a,this.tensors=[],this.closed_=!1,this.idTensor=ft(0),$e(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.tensor.id))&&e.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||t>=this.size())throw new Error(`Tried to read from index ${t}, but array size is: ${this.size()}`);let e=this.tensors[t];if(e.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${t} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(e.cleared=!0),e.read=!0,e.tensor}readMany(t){return t.map(e=>this.read(e))}write(t,e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||!this.dynamicSize&&t>=this.maxSize)throw new Error(`Tried to write to index ${t}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[t]||{};if(e.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, - because the value dtype is ${e.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=e.shape),Xn(this.elementShape,e.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${t}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, because it has already been written.`);n.tensor=e,$e(e),n.written=!0,this.tensors[t]=n}writeMany(t,e){if(t.length!==e.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${t.length} is not the same as tensors size: ${e.length}.`);t.forEach((n,o)=>this.write(n,e[o]))}gather(t,e){if(e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${e}`);if(t)t=t.slice(0,this.size());else{t=[];for(let o=0;o=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(t,xr(e,0))}split(t,e){if(e.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${e.dtype}`);let n=0,o=t.map(u=>(n+=u,n));if(n!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to + `); + } + if (interpolation) { + if (INTERPOLATION_METHODS2.has(interpolation)) { + this.interpolation = interpolation; + } else { + throw new ValueError(`Invalid interpolation parameter: ${interpolation} is not implemented`); + } + } + } + getConfig() { + const config = { + "factor": this.factor, + "interpolation": this.interpolation + }; + const baseConfig = super.getConfig(); + Object.assign(config, baseConfig); + return config; + } + computeOutputShape(inputShape) { + inputShape = getExactlyOneShape(inputShape); + const numChannels = inputShape[2]; + return [this.imgHeight, -1, numChannels]; + } + call(inputs, kwargs) { + return tidy(() => { + const input2 = getExactlyOneTensor(inputs); + this.imgHeight = input2.shape[input2.shape.length - 3]; + const imgWidth = input2.shape[input2.shape.length - 2]; + this.widthFactor = randomUniform([1], 1 + this.widthLower, 1 + this.widthUpper, "float32", this.randomGenerator.next()); + let adjustedWidth = this.widthFactor.dataSync()[0] * imgWidth; + adjustedWidth = Math.round(adjustedWidth); + const size = [this.imgHeight, adjustedWidth]; + switch (this.interpolation) { + case "bilinear": + return image.resizeBilinear(inputs, size); + case "nearest": + return image.resizeNearestNeighbor(inputs, size); + default: + throw new Error(`Interpolation is ${this.interpolation} + but only ${[...INTERPOLATION_METHODS2]} are supported`); + } + }); + } +}; +RandomWidth.className = "RandomWidth"; +serialization_exports.registerClass(RandomWidth); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js +function inputLayer(args) { + return new InputLayer(args); +} +function elu3(args) { + return new ELU(args); +} +function reLU(args) { + return new ReLU(args); +} +function leakyReLU(args) { + return new LeakyReLU(args); +} +function prelu2(args) { + return new PReLU(args); +} +function softmax2(args) { + return new Softmax3(args); +} +function thresholdedReLU(args) { + return new ThresholdedReLU(args); +} +function conv1d2(args) { + return new Conv1D(args); +} +function conv2d3(args) { + return new Conv2D2(args); +} +function conv2dTranspose2(args) { + return new Conv2DTranspose(args); +} +function conv3d2(args) { + return new Conv3D2(args); +} +function conv3dTranspose2(args) { + return new Conv3DTranspose(args); +} +function separableConv2d2(args) { + return new SeparableConv2D(args); +} +function cropping2D(args) { + return new Cropping2D(args); +} +function upSampling2d(args) { + return new UpSampling2D(args); +} +function depthwiseConv2d4(args) { + return new DepthwiseConv2D(args); +} +function activation(args) { + return new Activation2(args); +} +function dense(args) { + return new Dense(args); +} +function dropout3(args) { + return new Dropout(args); +} +function spatialDropout1d(args) { + return new SpatialDropout1D(args); +} +function flatten3(args) { + return new Flatten(args); +} +function repeatVector(args) { + return new RepeatVector(args); +} +function reshape2(args) { + return new Reshape2(args); +} +function permute(args) { + return new Permute(args); +} +function embedding(args) { + return new Embedding(args); +} +function add3(args) { + return new Add2(args); +} +function average(args) { + return new Average(args); +} +function concatenate2(args) { + return new Concatenate(args); +} +function maximum2(args) { + return new Maximum2(args); +} +function minimum2(args) { + return new Minimum2(args); +} +function multiply(args) { + return new Multiply2(args); +} +function dot3(args) { + return new Dot(args); +} +function batchNormalization2(args) { + return new BatchNormalization(args); +} +function layerNormalization(args) { + return new LayerNormalization(args); +} +function zeroPadding2d(args) { + return new ZeroPadding2D(args); +} +function averagePooling1d(args) { + return new AveragePooling1D(args); +} +function avgPool1d(args) { + return averagePooling1d(args); +} +function avgPooling1d(args) { + return averagePooling1d(args); +} +function averagePooling2d(args) { + return new AveragePooling2D(args); +} +function avgPool2d(args) { + return averagePooling2d(args); +} +function avgPooling2d(args) { + return averagePooling2d(args); +} +function averagePooling3d(args) { + return new AveragePooling3D(args); +} +function avgPool3d2(args) { + return averagePooling3d(args); +} +function avgPooling3d(args) { + return averagePooling3d(args); +} +function globalAveragePooling1d(args) { + return new GlobalAveragePooling1D(args); +} +function globalAveragePooling2d(args) { + return new GlobalAveragePooling2D(args); +} +function globalMaxPooling1d(args) { + return new GlobalMaxPooling1D(args); +} +function globalMaxPooling2d(args) { + return new GlobalMaxPooling2D(args); +} +function maxPooling1d(args) { + return new MaxPooling1D(args); +} +function maxPooling2d(args) { + return new MaxPooling2D(args); +} +function maxPooling3d(args) { + return new MaxPooling3D(args); +} +function gru(args) { + return new GRU(args); +} +function gruCell(args) { + return new GRUCell(args); +} +function lstm(args) { + return new LSTM(args); +} +function lstmCell(args) { + return new LSTMCell(args); +} +function simpleRNN(args) { + return new SimpleRNN(args); +} +function simpleRNNCell(args) { + return new SimpleRNNCell(args); +} +function convLstm2d(args) { + return new ConvLSTM2D(args); +} +function convLstm2dCell(args) { + return new ConvLSTM2DCell(args); +} +function rnn2(args) { + return new RNN(args); +} +function stackedRNNCells(args) { + return new StackedRNNCells(args); +} +function bidirectional(args) { + return new Bidirectional(args); +} +function timeDistributed(args) { + return new TimeDistributed(args); +} +var globalMaxPool1d = globalMaxPooling1d; +var globalMaxPool2d = globalMaxPooling2d; +var maxPool1d = maxPooling1d; +var maxPool2d = maxPooling2d; +function gaussianNoise(args) { + return new GaussianNoise(args); +} +function gaussianDropout(args) { + return new GaussianDropout(args); +} +function alphaDropout(args) { + return new AlphaDropout(args); +} +function masking(args) { + return new Masking(args); +} +function rescaling(args) { + return new Rescaling(args); +} +function centerCrop(args) { + return new CenterCrop(args); +} +function resizing(args) { + return new Resizing(args); +} +function categoryEncoding(args) { + return new CategoryEncoding(args); +} +function randomWidth(args) { + return new RandomWidth(args); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_metrics.js +var exports_metrics_exports = {}; +__export(exports_metrics_exports, { + MAPE: () => MAPE2, + MSE: () => MSE2, + binaryAccuracy: () => binaryAccuracy2, + binaryCrossentropy: () => binaryCrossentropy3, + categoricalAccuracy: () => categoricalAccuracy2, + categoricalCrossentropy: () => categoricalCrossentropy3, + cosineProximity: () => cosineProximity2, + mape: () => mape2, + meanAbsoluteError: () => meanAbsoluteError2, + meanAbsolutePercentageError: () => meanAbsolutePercentageError2, + meanSquaredError: () => meanSquaredError3, + mse: () => mse2, + precision: () => precision2, + recall: () => recall2, + sparseCategoricalAccuracy: () => sparseCategoricalAccuracy2 +}); +function binaryAccuracy2(yTrue, yPred) { + return binaryAccuracy(yTrue, yPred); +} +function binaryCrossentropy3(yTrue, yPred) { + return binaryCrossentropy2(yTrue, yPred); +} +function sparseCategoricalAccuracy2(yTrue, yPred) { + return sparseCategoricalAccuracy(yTrue, yPred); +} +function categoricalAccuracy2(yTrue, yPred) { + return categoricalAccuracy(yTrue, yPred); +} +function categoricalCrossentropy3(yTrue, yPred) { + return categoricalCrossentropy2(yTrue, yPred); +} +function precision2(yTrue, yPred) { + return precision(yTrue, yPred); +} +function recall2(yTrue, yPred) { + return recall(yTrue, yPred); +} +function cosineProximity2(yTrue, yPred) { + return cosineProximity(yTrue, yPred); +} +function meanAbsoluteError2(yTrue, yPred) { + return meanAbsoluteError(yTrue, yPred); +} +function meanAbsolutePercentageError2(yTrue, yPred) { + return meanAbsolutePercentageError(yTrue, yPred); +} +function MAPE2(yTrue, yPred) { + return meanAbsolutePercentageError(yTrue, yPred); +} +function mape2(yTrue, yPred) { + return meanAbsolutePercentageError(yTrue, yPred); +} +function meanSquaredError3(yTrue, yPred) { + return meanSquaredError2(yTrue, yPred); +} +function MSE2(yTrue, yPred) { + return meanSquaredError2(yTrue, yPred); +} +function mse2(yTrue, yPred) { + return meanSquaredError2(yTrue, yPred); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_models.js +var exports_models_exports = {}; +__export(exports_models_exports, { + modelFromJSON: () => modelFromJSON +}); + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/exports_regularizers.js +var exports_regularizers_exports = {}; +__export(exports_regularizers_exports, { + l1: () => l12, + l1l2: () => l1l2, + l2: () => l22 +}); +function l1l2(config) { + return new L1L2(config); +} +function l12(config) { + return l1(config); +} +function l22(config) { + return l2(config); +} + +// node_modules/.pnpm/@tensorflow+tfjs-layers@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-layers/dist/callbacks.js +var Callback = class extends BaseCallback { + constructor() { + super(...arguments); + this.model = null; + } + setModel(model2) { + if (!(model2 instanceof LayersModel)) { + throw new Error("model must be a LayersModel, not some other Container"); + } + this.model = model2; + } +}; +function less2(currVal, prevVal) { + return currVal < prevVal; +} +function greater2(currVal, prevVal) { + return currVal > prevVal; +} +var EarlyStopping = class extends Callback { + constructor(args) { + super(); + if (args == null) { + args = {}; + } + if (args.restoreBestWeights) { + throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet."); + } + this.monitor = args.monitor || "val_loss"; + this.minDelta = Math.abs(args.minDelta || 0); + this.patience = args.patience || 0; + this.verbose = args.verbose || 0; + this.mode = args.mode || "auto"; + this.baseline = args.baseline; + if (["auto", "min", "max"].indexOf(this.mode) === -1) { + console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`); + this.mode = "auto"; + } + if (this.mode === "min") { + this.monitorFunc = less2; + } else if (this.mode === "max") { + this.monitorFunc = greater2; + } else { + if (this.monitor.indexOf("acc") !== -1) { + this.monitorFunc = greater2; + } else { + this.monitorFunc = less2; + } + } + if (this.monitorFunc === less2) { + this.minDelta *= -1; + } + } + async onTrainBegin(logs) { + this.wait = 0; + this.stoppedEpoch = 0; + if (this.baseline != null) { + this.best = this.baseline; + } else { + this.best = this.monitorFunc === less2 ? Infinity : -Infinity; + } + } + async onEpochEnd(epoch, logs) { + await resolveScalarsInLogs(logs); + const current = this.getMonitorValue(logs); + if (current == null) { + return; + } + if (this.monitorFunc(current - this.minDelta, this.best)) { + this.best = current; + this.wait = 0; + } else { + this.wait++; + if (this.wait >= this.patience) { + this.stoppedEpoch = epoch; + this.model.stopTraining = true; + } + } + } + async onTrainEnd(logs) { + if (this.stoppedEpoch > 0 && this.verbose) { + console.log(`Epoch ${this.stoppedEpoch}: early stopping.`); + } + } + getMonitorValue(logs) { + if (logs == null) { + logs = {}; + } + const monitorValue = logs[this.monitor]; + if (monitorValue == null) { + console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`); + } + return monitorValue; + } +}; +function earlyStopping(args) { + return new EarlyStopping(args); +} +var callbacks = { earlyStopping }; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/flags.js +var ENV4 = env(); +ENV4.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (debugValue) => { + if (debugValue) { + console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance."); + } +}); + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/data/compiled_api.js +var DataType; +(function(DataType2) { + DataType2[DataType2["DT_INVALID"] = 0] = "DT_INVALID"; + DataType2[DataType2["DT_FLOAT"] = 1] = "DT_FLOAT"; + DataType2[DataType2["DT_DOUBLE"] = 2] = "DT_DOUBLE"; + DataType2[DataType2["DT_INT32"] = 3] = "DT_INT32"; + DataType2[DataType2["DT_UINT8"] = 4] = "DT_UINT8"; + DataType2[DataType2["DT_INT16"] = 5] = "DT_INT16"; + DataType2[DataType2["DT_INT8"] = 6] = "DT_INT8"; + DataType2[DataType2["DT_STRING"] = 7] = "DT_STRING"; + DataType2[DataType2["DT_COMPLEX64"] = 8] = "DT_COMPLEX64"; + DataType2[DataType2["DT_INT64"] = 9] = "DT_INT64"; + DataType2[DataType2["DT_BOOL"] = 10] = "DT_BOOL"; + DataType2[DataType2["DT_QINT8"] = 11] = "DT_QINT8"; + DataType2[DataType2["DT_QUINT8"] = 12] = "DT_QUINT8"; + DataType2[DataType2["DT_QINT32"] = 13] = "DT_QINT32"; + DataType2[DataType2["DT_BFLOAT16"] = 14] = "DT_BFLOAT16"; + DataType2[DataType2["DT_QINT16"] = 15] = "DT_QINT16"; + DataType2[DataType2["DT_QUINT16"] = 16] = "DT_QUINT16"; + DataType2[DataType2["DT_UINT16"] = 17] = "DT_UINT16"; + DataType2[DataType2["DT_COMPLEX128"] = 18] = "DT_COMPLEX128"; + DataType2[DataType2["DT_HALF"] = 19] = "DT_HALF"; + DataType2[DataType2["DT_RESOURCE"] = 20] = "DT_RESOURCE"; + DataType2[DataType2["DT_VARIANT"] = 21] = "DT_VARIANT"; + DataType2[DataType2["DT_UINT32"] = 22] = "DT_UINT32"; + DataType2[DataType2["DT_UINT64"] = 23] = "DT_UINT64"; + DataType2[DataType2["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF"; + DataType2[DataType2["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF"; + DataType2[DataType2["DT_INT32_REF"] = 103] = "DT_INT32_REF"; + DataType2[DataType2["DT_UINT8_REF"] = 104] = "DT_UINT8_REF"; + DataType2[DataType2["DT_INT16_REF"] = 105] = "DT_INT16_REF"; + DataType2[DataType2["DT_INT8_REF"] = 106] = "DT_INT8_REF"; + DataType2[DataType2["DT_STRING_REF"] = 107] = "DT_STRING_REF"; + DataType2[DataType2["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF"; + DataType2[DataType2["DT_INT64_REF"] = 109] = "DT_INT64_REF"; + DataType2[DataType2["DT_BOOL_REF"] = 110] = "DT_BOOL_REF"; + DataType2[DataType2["DT_QINT8_REF"] = 111] = "DT_QINT8_REF"; + DataType2[DataType2["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF"; + DataType2[DataType2["DT_QINT32_REF"] = 113] = "DT_QINT32_REF"; + DataType2[DataType2["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF"; + DataType2[DataType2["DT_QINT16_REF"] = 115] = "DT_QINT16_REF"; + DataType2[DataType2["DT_QUINT16_REF"] = 116] = "DT_QUINT16_REF"; + DataType2[DataType2["DT_UINT16_REF"] = 117] = "DT_UINT16_REF"; + DataType2[DataType2["DT_COMPLEX128_REF"] = 118] = "DT_COMPLEX128_REF"; + DataType2[DataType2["DT_HALF_REF"] = 119] = "DT_HALF_REF"; + DataType2[DataType2["DT_RESOURCE_REF"] = 120] = "DT_RESOURCE_REF"; + DataType2[DataType2["DT_VARIANT_REF"] = 121] = "DT_VARIANT_REF"; + DataType2[DataType2["DT_UINT32_REF"] = 122] = "DT_UINT32_REF"; + DataType2[DataType2["DT_UINT64_REF"] = 123] = "DT_UINT64_REF"; +})(DataType || (DataType = {})); +var SaverDef; +(function(SaverDef2) { + let CheckpointFormatVersion; + (function(CheckpointFormatVersion2) { + CheckpointFormatVersion2[CheckpointFormatVersion2["LEGACY"] = 0] = "LEGACY"; + CheckpointFormatVersion2[CheckpointFormatVersion2["V1"] = 1] = "V1"; + CheckpointFormatVersion2[CheckpointFormatVersion2["V2"] = 2] = "V2"; + })(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {})); +})(SaverDef || (SaverDef = {})); + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/register.js +var CUSTOM_OPS = {}; +function registerOp(name, opFunc) { + const opMapper = { + tfOpName: name, + category: "custom", + inputs: [], + attrs: [], + customExecutor: opFunc + }; + CUSTOM_OPS[name] = opMapper; +} +function getRegisteredOp(name) { + return CUSTOM_OPS[name]; +} +function deregisterOp(name) { + delete CUSTOM_OPS[name]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/utils.js +function getParamValue(paramName, node, tensorMap, context, resourceManager) { + const inputParam = node.inputParams[paramName]; + if (inputParam && inputParam.inputIndexStart !== void 0) { + const start = inputParam.inputIndexStart; + const end = inputParam.inputIndexEnd === 0 ? void 0 : inputParam.inputIndexEnd === void 0 ? start + 1 : inputParam.inputIndexEnd; + const shiftedStart = start < 0 ? node.inputNames.length + start : start; + if (inputParam.type === "tensor") { + return getTensor(node.inputNames[shiftedStart], tensorMap, context, resourceManager); + } + if (inputParam.type === "tensors") { + const inputs = node.inputs.slice(start, end); + const inputNames = node.inputNames.slice(start, end).filter((_name, index) => { + var _a; + return ((_a = inputs[index]) === null || _a === void 0 ? void 0 : _a.op) !== "NoOp"; + }); + return inputNames.map((name) => getTensor(name, tensorMap, context, resourceManager)); + } + const tensor2 = getTensor(node.inputNames[shiftedStart], tensorMap, context, resourceManager); + const data = tensor2.dataSync(); + return inputParam.type === "number" ? data[0] : util_exports.toNestedArray(tensor2.shape, data); + } + const attrParam = node.attrParams[paramName]; + return attrParam && attrParam.value; +} +function getTensor(name, tensorsMap, context, resourceManager) { + const [nodeName, index] = parseNodeName(name, context); + if (resourceManager != null) { + const tensor2 = resourceManager.getHashTableHandleByName(nodeName); + if (tensor2 != null) { + return tensor2; + } + } + const contextId = context.currentContextIds.find((contextId2) => { + return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId2)]; + }); + return contextId !== void 0 ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : void 0; +} +function getTensorsForCurrentContext(name, tensorsMap, context) { + return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)]; +} +function getNodeNameAndIndex(inputName, context) { + const [nodeName, index, outputName] = parseNodeName(inputName, context); + return [ + getNodeNameWithContextId(nodeName, context && context.currentContextId), + index, + outputName + ]; +} +function getNodeNameWithContextId(name, contextId) { + return !!contextId ? `${name}-${contextId}` : name; +} +function parseNodeName(name, context) { + if (name === "") { + return ["", 0, void 0]; + } + const isCacheEnabled = context != null && context.parseNodeNameCache != null; + if (isCacheEnabled) { + const cachedResult = context.parseNodeNameCache.get(name); + if (cachedResult != null) { + return cachedResult; + } + } + const parts = name.split(":"); + let result; + if (parts.length === 1) { + result = [name, 0, void 0]; + } else { + const nodeName = parts[0]; + const outputName = parts.length === 3 ? parts[1] : void 0; + const index = Number(parts[parts.length - 1]); + result = [nodeName, index, outputName]; + } + if (isCacheEnabled) { + context.parseNodeNameCache.set(name, result); + } + return result; +} +function getPadding(node, tensorMap, context) { + let pad3 = getParamValue("pad", node, tensorMap, context); + if (pad3 === "explicit") { + pad3 = getParamValue("explicitPaddings", node, tensorMap, context); + const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]]; + for (let i = 0; i < 4; i++) { + explicitPadding[i][0] = pad3[i * 2]; + explicitPadding[i][1] = pad3[i * 2 + 1]; + } + return explicitPadding; + } + return pad3; +} +function cloneTensor(tensor2) { + return tensor2.kept ? tensor2 : clone(tensor2); +} + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/arithmetic.js +var arithmetic_exports = {}; +__export(arithmetic_exports, { + json: () => json +}); +var json = [ + { + "tfOpName": "Add", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "AddV2", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "AddN", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "end": 0, + "name": "tensors", + "type": "tensors" + } + ] + }, + { + "tfOpName": "BiasAdd", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "notSupported": true + } + ] + }, + { + "tfOpName": "Sub", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "RealDiv", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Div", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "DivNoNan", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "FloorDiv", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Mul", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Maximum", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Minimum", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Pow", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "SquaredDifference", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Mod", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "FloorMod", + "category": "arithmetic", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/basic_math.js +var basic_math_exports = {}; +__export(basic_math_exports, { + json: () => json2 +}); +var json2 = [ + { + "tfOpName": "Abs", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Acos", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Asin", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Atan", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Atan2", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "y", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Ceil", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "ClipByValue", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "clipValueMin", + "type": "number" + }, + { + "start": 2, + "name": "clipValueMax", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Complex", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "real", + "type": "tensor" + }, + { + "start": 1, + "name": "imag", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "ComplexAbs", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Cos", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Cosh", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Elu", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Exp", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Floor", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Log", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Imag", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "Tout", + "name": "outputType", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Neg", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Real", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "Tout", + "name": "outputType", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Prelu", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "alpha", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Relu", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Relu6", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Selu", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Sigmoid", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Sin", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Sinh", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Sqrt", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Rsqrt", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Square", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Tan", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Tanh", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Sign", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Round", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Expm1", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Log1p", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Reciprocal", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Softplus", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Asinh", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Acosh", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Atanh", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Erf", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "LeakyRelu", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "alpha", + "name": "alpha", + "type": "number", + "defaultValue": 0.2 + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "IsNan", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "IsFinite", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "IsInf", + "category": "basic_math", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/control.js +var control_exports = {}; +__export(control_exports, { + json: () => json3 +}); +var json3 = [ + { + "tfOpName": "EmptyTensorList", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "elementShape", + "type": "shape" + }, + { + "start": 1, + "name": "maxNumElements", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "LoopCond", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "pred", + "type": "tensor" + } + ] + }, + { + "tfOpName": "Switch", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "data", + "type": "tensor" + }, + { + "start": 1, + "name": "pred", + "type": "tensor" + } + ] + }, + { + "tfOpName": "Merge", + "category": "control", + "inputs": [ + { + "start": 0, + "end": 0, + "name": "tensors", + "type": "tensors" + } + ] + }, + { + "tfOpName": "Enter", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "frame_name", + "name": "frameName", + "type": "string" + }, + { + "tfName": "is_constant", + "name": "isConstant", + "type": "bool" + } + ] + }, + { + "tfOpName": "Exit", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "NextIteration", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "TensorArrayV3", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "size", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "dtype", + "name": "dtype", + "type": "dtype" + }, + { + "tfName": "element_shape", + "name": "elementShape", + "type": "shape" + }, + { + "tfName": "dynamic_size", + "name": "dynamicSize", + "type": "bool" + }, + { + "tfName": "clear_after_read", + "name": "clearAfterRead", + "type": "bool" + }, + { + "tfName": "identical_element_shapes", + "name": "identicalElementShapes", + "type": "bool" + }, + { + "tfName": "tensor_array_name", + "name": "name", + "type": "string" + } + ] + }, + { + "tfOpName": "TensorArrayWriteV3", + "category": 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"start": 2, + "name": "flowIn", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "dtype", + "name": "dtype", + "type": "dtype" + }, + { + "tfName": "element_shape", + "name": "elementShape", + "type": "shape" + } + ] + }, + { + "tfOpName": "TensorArrayScatterV3", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorArrayId", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "number[]" + }, + { + "start": 2, + "name": "tensor", + "type": "tensor" + }, + { + "start": 3, + "name": "flowIn", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorArrayConcatV3", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorArrayId", + "type": "tensor" + }, + { + "start": 1, + "name": "flowIn", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "dtype", + "name": "dtype", + "type": "dtype" + }, + { + "tfName": "element_shape_except0", + "name": "elementShapeExcept0", + "type": "shape", + "notSupported": true + } + ] + }, + { + "tfOpName": "TensorArraySplitV3", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorArrayId", + "type": "tensor" + }, + { + "start": 1, + "name": "tensor", + "type": "tensor" + }, + { + "start": 2, + "name": "lengths", + "type": "number[]" + }, + { + "start": 3, + "name": "flowIn", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorArraySizeV3", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorArrayId", + "type": "tensor" + }, + { + "start": 1, + "name": "flowIn", + "type": "number" + } + ] + }, + { + "tfOpName": "TensorArrayCloseV3", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorArrayId", + "type": "tensor" + } + ] + }, + { + "tfOpName": "StatelessIf", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "cond", + "type": "tensor" + }, + { + "start": 1, + "end": 0, + "name": "args", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "then_branch", + "name": "thenBranch", + "type": "func" + }, + { + "tfName": "else_branch", + "name": "elseBranch", + "type": "func" + } + ] + }, + { + "tfOpName": "If", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "cond", + "type": "tensor" + }, + { + "start": 1, + "end": 0, + "name": "args", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "then_branch", + "name": "thenBranch", + "type": "func" + }, + { + "tfName": "else_branch", + "name": "elseBranch", + "type": "func" + } + ] + }, + { + "tfOpName": "StatelessWhile", + "category": "control", + "inputs": [ + { + "start": 0, + "end": 0, + "name": "args", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "cond", + "name": "cond", + "type": "func" + }, + { + "tfName": "body", + "name": "body", + "type": "func" + } + ] + }, + { + "tfOpName": "While", + "category": "control", + "inputs": [ + { + "start": 0, + "end": 0, + "name": "args", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "cond", + "name": "cond", + "type": "func" + }, + { + "tfName": "body", + "name": "body", + "type": "func" + } + ] + }, + { + "tfOpName": "TensorListScatter", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "number[]" + }, + { + "start": 2, + "name": "elementShape", + "type": "shape" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListScatterV2", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "number[]" + }, + { + "start": 2, + "name": "elementShape", + "type": "shape" + }, + { + "start": 3, + "name": "numElements", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListGather", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorListId", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "number[]" + }, + { + "start": 2, + "name": "elementShape", + "type": "shape" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListGetItem", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorListId", + "type": "tensor" + }, + { + "start": 1, + "name": "index", + "type": "number" + }, + { + "start": 2, + "name": "elementShape", + "type": "shape" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListSetItem", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorListId", + "type": "tensor" + }, + { + "start": 1, + "name": "index", + "type": "number" + }, + { + "start": 2, + "name": "tensor", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListReserve", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "elementShape", + "type": "shape" + }, + { + "start": 1, + "name": "numElements", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListFromTensor", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + }, + { + "start": 1, + "name": "elementShape", + "type": "shape" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListStack", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorListId", + "type": "tensor" + }, + { + "start": 1, + "name": "elementShape", + "type": "shape" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + }, + { + "tfName": "num_elements", + "name": "numElements", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListSplit", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + }, + { + "start": 1, + "name": "elementShape", + "type": "shape" + }, + { + "start": 2, + "name": "lengths", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListConcat", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorListId", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "element_shape", + "name": "elementShape", + "type": "shape" + }, + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListConcatV2", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorListId", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "element_shape", + "name": "elementShape", + "type": "shape" + }, + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListPopBack", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorListId", + "type": "tensor" + }, + { + "start": 1, + "name": "elementShape", + "type": "shape" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListPushBack", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorListId", + "type": "tensor" + }, + { + "start": 1, + "name": "tensor", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "element_dtype", + "name": "elementDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TensorListLength", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorListId", + "type": "tensor" + } + ] + }, + { + "tfOpName": "TensorListResize", + "category": "control", + "inputs": [ + { + "start": 0, + "name": "tensorListId", + "type": "tensor" + }, + { + "start": 1, + "name": "size", + "type": "number" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/convolution.js +var convolution_exports = {}; +__export(convolution_exports, { + json: () => json4 +}); +var json4 = [ + { + "tfOpName": "AvgPool", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "notSupported": true + }, + { + "tfName": "ksize", + "name": "kernelSize", + "type": "number[]" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "MaxPool", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "notSupported": true + }, + { + "tfName": "ksize", + "name": "kernelSize", + "type": "number[]" + }, + { + "tfName": "explicit_paddings", + "name": "explicitPaddings", + "type": "number[]", + "defaultValue": [], + "notSupported": true + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "MaxPoolWithArgmax", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "ksize", + "name": "kernelSize", + "type": "number[]" + }, + { + "tfName": "include_batch_in_index", + "name": "includeBatchInIndex", + "type": "bool" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "AvgPool3D", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "notSupported": true + }, + { + "tfName": "ksize", + "name": "kernelSize", + "type": "number[]" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "MaxPool3D", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "notSupported": true + }, + { + "tfName": "ksize", + "name": "kernelSize", + "type": "number[]" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Conv1D", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "filter", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "stride", + "name": "stride", + "type": "number" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "defaultValue": "NWC" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "dilation", + "name": "dilation", + "type": "number", + "defaultValue": 1 + } + ] + }, + { + "tfOpName": "Conv2D", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "filter", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "useCudnnOnGpu", + "name": "useCudnnOnGpu", + "type": "bool" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "defaultValue": "NHWC" + }, + { + "tfName": "explicit_paddings", + "name": "explicitPaddings", + "type": "number[]", + "defaultValue": [] + }, + { + "tfName": "dilations", + "name": "dilations", + "type": "number[]" + } + ] + }, + { + "tfOpName": "_FusedConv2D", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "filter", + "type": "tensor" + }, + { + "start": 2, + "end": 0, + "name": "args", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "num_args", + "name": "numArgs", + "type": "number" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "explicit_paddings", + "name": "explicitPaddings", + "type": "number[]", + "defaultValue": [] + }, + { + "tfName": "use_cudnn_on_gpu", + "name": "useCudnnOnGpu", + "type": "bool", + "defaultValue": true + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "defaultValue": "NHWC" + }, + { + "tfName": "dilations", + "name": "dilations", + "type": "number[]", + "defaultValue": [ + 1, + 1, + 1, + 1 + ] + }, + { + "tfName": "fused_ops", + "name": "fusedOps", + "type": "string[]", + "defaultValue": [] + }, + { + "tfName": "epsilon", + "name": "epsilon", + "type": "number", + "defaultValue": 1e-4 + }, + { + "tfName": "leakyrelu_alpha", + "name": "leakyreluAlpha", + "type": "number", + "defaultValue": 0.2 + } + ] + }, + { + "tfOpName": "Conv2DBackpropInput", + "category": "convolution", + "inputs": [ + { + "start": 2, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "filter", + "type": "tensor" + }, + { + "start": 0, + "name": "outputShape", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "notSupported": true + }, + { + "tfName": "explicit_paddings", + "name": "explicitPaddings", + "type": "number[]", + "defaultValue": [] + }, + { + "tfName": "dilations", + "name": "dilations", + "type": "number[]", + "notSupported": true + } + ] + }, + { + "tfOpName": "DepthwiseConv2d", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "input", + "type": "tensor" + }, + { + "start": 1, + "name": "filter", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "defaultValue": "NHWC" + }, + { + "tfName": "explicit_paddings", + "name": "explicitPaddings", + "type": "number[]", + "defaultValue": [] + }, + { + "tfName": "dilations", + "name": "dilations", + "type": "number[]" + } + ] + }, + { + "tfOpName": "DepthwiseConv2dNative", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "input", + "type": "tensor" + }, + { + "start": 1, + "name": "filter", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "defaultValue": "NHWC" + }, + { + "tfName": "explicit_paddings", + "name": "explicitPaddings", + "type": "number[]", + "defaultValue": [] + }, + { + "tfName": "dilations", + "name": "dilations", + "type": "number[]" + } + ] + }, + { + "tfOpName": "FusedDepthwiseConv2dNative", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "filter", + "type": "tensor" + }, + { + "start": 2, + "end": 0, + "name": "args", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "num_args", + "name": "numArgs", + "type": "number" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "defaultValue": "NHWC" + }, + { + "tfName": "dilations", + "name": "dilations", + "type": "number[]", + "defaultValue": [ + 1, + 1, + 1, + 1 + ] + }, + { + "tfName": "fused_ops", + "name": "fusedOps", + "type": "string[]", + "defaultValue": [] + }, + { + "tfName": "explicit_paddings", + "name": "explicitPaddings", + "type": "number[]", + "defaultValue": [] + } + ] + }, + { + "tfOpName": "Conv3D", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "filter", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "defaultValue": "NHWC" + }, + { + "tfName": "dilations", + "name": "dilations", + "type": "number[]" + } + ] + }, + { + "tfOpName": "Dilation2D", + "category": "convolution", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "filter", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "strides", + "name": "strides", + "type": "number[]" + }, + { + "tfName": "rates", + "name": "dilations", + "type": "number[]" + }, + { + "tfName": "padding", + "name": "pad", + "type": "string" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/creation.js +var creation_exports = {}; +__export(creation_exports, { + json: () => json5 +}); +var json5 = [ + { + "tfOpName": "Fill", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "shape", + "type": "number[]" + }, + { + "start": 1, + "name": "value", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "LinSpace", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "start", + "type": "number" + }, + { + "start": 1, + "name": "stop", + "type": "number" + }, + { + "start": 2, + "name": "num", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "OneHot", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "indices", + "type": "tensor" + }, + { + "start": 1, + "name": "depth", + "type": "number" + }, + { + "start": 2, + "name": "onValue", + "type": "number", + "defaultValue": 1 + }, + { + "start": 3, + "name": "offValue", + "type": "number", + "defaultValue": 0 + } + ], + "attrs": [ + { + "tfName": "axis", + "name": "axis", + "type": "number", + "notSupported": true + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "Ones", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "shape", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "OnesLike", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "dtype", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "RandomStandardNormal", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "shape", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "seed", + "name": "seed", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "seed2", + "name": "seed2", + "type": "number", + "defaultValue": 0, + "notSupported": true + }, + { + "tfName": "dtype", + "name": "dtype", + "type": "dtype" + }, + { + "tfName": "T", + "name": "T", + "type": "number", + "notSupported": true + } + ] + }, + { + "tfOpName": "RandomUniform", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "shape", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "minval", + "name": "minval", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "maxval", + "name": "maxval", + "type": "number", + "defaultValue": 1 + }, + { + "tfName": "dtype", + "name": "dtype", + "type": "dtype" + }, + { + "tfName": "seed", + "name": "seed", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "seed2", + "name": "seed2", + "type": "number", + "defaultValue": 0, + "notSupported": true + }, + { + "tfName": "T", + "name": "T", + "type": "number", + "notSupported": true + } + ] + }, + { + "tfOpName": "RandomUniformInt", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "shape", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "minval", + "name": "minval", + "type": "number" + }, + { + "tfName": "maxval", + "name": "maxval", + "type": "number" + }, + { + "tfName": "seed", + "name": "seed", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "seed2", + "name": "seed2", + "type": "number", + "defaultValue": 0, + "notSupported": true + } + ] + }, + { + "tfOpName": "Range", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "start", + "type": "number" + }, + { + "start": 1, + "name": "stop", + "type": "number" + }, + { + "start": 2, + "name": "step", + "type": "number", + "defaultValue": 0 + } + ], + "attrs": [ + { + "tfName": "Tidx", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "TruncatedNormal", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "shape", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "means", + "name": "mean", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "stddev", + "name": "stdDev", + "type": "number", + "defaultValue": 1 + }, + { + "tfName": "seed", + "name": "seed", + "type": "number" + }, + { + "tfName": "seed2", + "name": "seed2", + "type": "number", + "defaultValue": 0, + "notSupported": true + }, + { + "tfName": "dtype", + "name": "dtype", + "type": "dtype" + }, + { + "tfName": "T", + "name": "T", + "type": "number", + "notSupported": true + } + ] + }, + { + "tfOpName": "Zeros", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "shape", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "ZerosLike", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "Multinomial", + "category": "creation", + "inputs": [ + { + "start": 0, + "name": "logits", + "type": "tensor" + }, + { + "start": 1, + "name": "numSamples", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "seed", + "name": "seed", + "type": "number" + }, + { + "tfName": "seed2", + "name": "seed2", + "type": "number" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype" + }, + { + "tfName": "output_dtype", + "name": "output_dtype", + "type": "dtype" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/dynamic.js +var dynamic_exports = {}; +__export(dynamic_exports, { + json: () => json6 +}); +var json6 = [ + { + "tfOpName": "NonMaxSuppressionV2", + "category": "dynamic", + "inputs": [ + { + "start": 0, + "name": "boxes", + "type": "tensor" + }, + { + "start": 1, + "name": "scores", + "type": "tensor" + }, + { + "start": 2, + "name": "maxOutputSize", + "type": "number" + }, + { + "start": 3, + "name": "iouThreshold", + "type": "number" + } + ] + }, + { + "tfOpName": "NonMaxSuppressionV3", + "category": "dynamic", + "inputs": [ + { + "start": 0, + "name": "boxes", + "type": "tensor" + }, + { + "start": 1, + "name": "scores", + "type": "tensor" + }, + { + "start": 2, + "name": "maxOutputSize", + "type": "number" + }, + { + "start": 3, + "name": "iouThreshold", + "type": "number" + }, + { + "start": 4, + "name": "scoreThreshold", + "type": "number" + } + ] + }, + { + "tfOpName": "NonMaxSuppressionV4", + "category": "dynamic", + "inputs": [ + { + "start": 0, + "name": "boxes", + "type": "tensor" + }, + { + "start": 1, + "name": "scores", + "type": "tensor" + }, + { + "start": 2, + "name": "maxOutputSize", + "type": "number" + }, + { + "start": 3, + "name": "iouThreshold", + "type": "number" + }, + { + "start": 4, + "name": "scoreThreshold", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "T_threshold", + "name": "threshold", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "pad_to_max_output_size", + "name": "padToMaxOutputSize", + "type": "bool" + } + ] + }, + { + "tfOpName": "NonMaxSuppressionV5", + "category": "dynamic", + "inputs": [ + { + "start": 0, + "name": "boxes", + "type": "tensor" + }, + { + "start": 1, + "name": "scores", + "type": "tensor" + }, + { + "start": 2, + "name": "maxOutputSize", + "type": "number" + }, + { + "start": 3, + "name": "iouThreshold", + "type": "number" + }, + { + "start": 4, + "name": "scoreThreshold", + "type": "number" + }, + { + "start": 5, + "name": "softNmsSigma", + "type": "number" + } + ] + }, + { + "tfOpName": "Where", + "category": "dynamic", + "inputs": [ + { + "start": 0, + "name": "condition", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "ListDiff", + "category": "dynamic", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "y", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/evaluation.js +var evaluation_exports = {}; +__export(evaluation_exports, { + json: () => json7 +}); +var json7 = [ + { + "tfOpName": "LowerBound", + "category": "evaluation", + "inputs": [ + { + "start": 0, + "name": "sortedSequence", + "type": "tensor" + }, + { + "start": 1, + "name": "values", + "type": "tensor" + } + ] + }, + { + "tfOpName": "TopKV2", + "category": "evaluation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "k", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "sorted", + "name": "sorted", + "type": "bool" + } + ] + }, + { + "tfOpName": "UpperBound", + "category": "evaluation", + "inputs": [ + { + "start": 0, + "name": "sortedSequence", + "type": "tensor" + }, + { + "start": 1, + "name": "values", + "type": "tensor" + } + ] + }, + { + "tfOpName": "Unique", + "category": "evaluation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "UniqueV2", + "category": "evaluation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/graph.js +var graph_exports = {}; +__export(graph_exports, { + json: () => json8 +}); +var json8 = [ + { + "tfOpName": "PlaceholderWithDefault", + "category": "graph", + "inputs": [ + { + "start": 0, + "name": "default", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "shape", + "name": "shape", + "type": "shape" + }, + { + "tfName": "dtype", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "Placeholder", + "category": "graph", + "attrs": [ + { + "tfName": "shape", + "name": "shape", + "type": "shape" + }, + { + "tfName": "dtype", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "Const", + "category": "graph" + }, + { + "tfOpName": "Identity", + "category": "graph", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "IdentityN", + "category": "graph", + "inputs": [ + { + "start": 0, + "end": 0, + "name": "x", + "type": "tensors" + } + ] + }, + { + "tfOpName": "Snapshot", + "category": "graph", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "Rank", + "category": "graph", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "Size", + "category": "graph", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "Shape", + "category": "graph", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "ShapeN", + "category": "graph", + "inputs": [ + { + "start": 0, + "end": 0, + "name": "x", + "type": "tensors" + } + ] + }, + { + "tfOpName": "Print", + "category": "graph", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "data", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "message", + "name": "message", + "type": "string" + }, + { + "tfName": "first_n", + "name": "firstN", + "type": "number", + "notSupported": true + }, + { + "tfName": "summarize", + "name": "summarize", + "type": "number", + "defaultValue": 3 + } + ] + }, + { + "tfOpName": "NoOp", + "category": "graph", + "inputs": [] + }, + { + "tfOpName": "StopGradient", + "category": "graph", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "FakeQuantWithMinMaxVars", + "category": "graph", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "min", + "name": "min", + "type": "number" + }, + { + "tfName": "max", + "name": "max", + "type": "number" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/hash_table.js +var hash_table_exports = {}; +__export(hash_table_exports, { + json: () => json9 +}); +var json9 = [ + { + "tfOpName": "HashTable", + "category": "hash_table", + "inputs": [], + "attrs": [ + { + "tfName": "shared_name", + "name": "sharedName", + "type": "string" + }, + { + "tfName": "use_node_name_sharing", + "name": "useNodeNameSharing", + "type": "bool" + }, + { + "tfName": "key_dtype", + "name": "keyDType", + "type": "dtype" + }, + { + "tfName": "value_dtype", + "name": "valueDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "HashTableV2", + "category": "hash_table", + "inputs": [], + "attrs": [ + { + "tfName": "shared_name", + "name": "sharedName", + "type": "string" + }, + { + "tfName": "use_node_name_sharing", + "name": "useNodeNameSharing", + "type": "bool" + }, + { + "tfName": "key_dtype", + "name": "keyDType", + "type": "dtype" + }, + { + "tfName": "value_dtype", + "name": "valueDType", + "type": "dtype" + } + ] + }, + { + "tfOpName": "LookupTableImport", + "category": "hash_table", + "inputs": [ + { + "start": 0, + "name": "tableHandle", + "type": "tensor" + }, + { + "start": 1, + "name": "keys", + "type": "tensor" + }, + { + "start": 2, + "name": "values", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "Tin", + "name": "tIn", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "Tout", + "name": "tOut", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "LookupTableImportV2", + "category": "hash_table", + "inputs": [ + { + "start": 0, + "name": "tableHandle", + "type": "tensor" + }, + { + "start": 1, + "name": "keys", + "type": "tensor" + }, + { + "start": 2, + "name": "values", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "Tin", + "name": "tIn", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "Tout", + "name": "tOut", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "LookupTableFind", + "category": "hash_table", + "inputs": [ + { + "start": 0, + "name": "tableHandle", + "type": "tensor" + }, + { + "start": 1, + "name": "keys", + "type": "tensor" + }, + { + "start": 2, + "name": "defaultValue", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "Tin", + "name": "tIn", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "Tout", + "name": "tOut", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "LookupTableFindV2", + "category": "hash_table", + "inputs": [ + { + "start": 0, + "name": "tableHandle", + "type": "tensor" + }, + { + "start": 1, + "name": "keys", + "type": "tensor" + }, + { + "start": 2, + "name": "defaultValue", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "Tin", + "name": "tIn", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "Tout", + "name": "tOut", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "LookupTableSize", + "category": "hash_table", + "inputs": [ + { + "start": 0, + "name": "tableHandle", + "type": "tensor" + } + ] + }, + { + "tfOpName": "LookupTableSizeV2", + "category": "hash_table", + "inputs": [ + { + "start": 0, + "name": "tableHandle", + "type": "tensor" + } + ] + }, + { + "tfOpName": "InitializeTable", + "category": "hash_table", + "inputs": [ + { + "start": 0, + "name": "tableHandle", + "type": "tensor" + }, + { + "start": 1, + "name": "keys", + "type": "tensor" + }, + { + "start": 2, + "name": "values", + "type": "tensor" + } + ] + }, + { + "tfOpName": "InitializeTableV2", + "category": "hash_table", + "inputs": [ + { + "start": 0, + "name": "tableHandle", + "type": "tensor" + }, + { + "start": 1, + "name": "keys", + "type": "tensor" + }, + { + "start": 2, + "name": "values", + "type": "tensor" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/image.js +var image_exports = {}; +__export(image_exports, { + json: () => json10 +}); +var json10 = [ + { + "tfOpName": "ResizeBilinear", + "category": "image", + "inputs": [ + { + "start": 0, + "name": "images", + "type": "tensor" + }, + { + "start": 1, + "name": "size", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "align_corners", + "name": "alignCorners", + "type": "bool" + }, + { + "tfName": "half_pixel_centers", + "name": "halfPixelCenters", + "type": "bool" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "ResizeNearestNeighbor", + "category": "image", + "inputs": [ + { + "start": 0, + "name": "images", + "type": "tensor" + }, + { + "start": 1, + "name": "size", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "align_corners", + "name": "alignCorners", + "type": "bool" + }, + { + "tfName": "half_pixel_centers", + "name": "halfPixelCenters", + "type": "bool" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "CropAndResize", + "category": "image", + "inputs": [ + { + "start": 0, + "name": "image", + "type": "tensor" + }, + { + "start": 1, + "name": "boxes", + "type": "tensor" + }, + { + "start": 2, + "name": "boxInd", + "type": "tensor" + }, + { + "start": 3, + "name": "cropSize", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "method", + "name": "method", + "type": "string" + }, + { + "tfName": "extrapolation_value", + "name": "extrapolationValue", + "type": "number" + } + ] + }, + { + "tfOpName": "ImageProjectiveTransformV3", + "category": "image", + "inputs": [ + { + "start": 0, + "name": "images", + "type": "tensor" + }, + { + "start": 1, + "name": "transforms", + "type": "tensor" + }, + { + "start": 2, + "name": "outputShape", + "type": "number[]" + }, + { + "start": 3, + "name": "fillValue", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "interpolation", + "name": "interpolation", + "type": "string" + }, + { + "tfName": "fill_mode", + "name": "fillMode", + "type": "string" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/logical.js +var logical_exports = {}; +__export(logical_exports, { + json: () => json11 +}); +var json11 = [ + { + "tfOpName": "Equal", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "NotEqual", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Greater", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "GreaterEqual", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Less", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "LessEqual", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "LogicalAnd", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "LogicalNot", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "LogicalOr", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Select", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "condition", + "type": "tensor" + }, + { + "start": 1, + "name": "a", + "type": "tensor" + }, + { + "start": 2, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "SelectV2", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "condition", + "type": "tensor" + }, + { + "start": 1, + "name": "a", + "type": "tensor" + }, + { + "start": 2, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "BitwiseAnd", + "category": "logical", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "y", + "type": "tensor" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/matrices.js +var matrices_exports = {}; +__export(matrices_exports, { + json: () => json12 +}); +var json12 = [ + { + "tfOpName": "_FusedMatMul", + "category": "matrices", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + }, + { + "start": 2, + "end": 0, + "name": "args", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "num_args", + "name": "numArgs", + "type": "number" + }, + { + "tfName": "fused_ops", + "name": "fusedOps", + "type": "string[]", + "defaultValue": [] + }, + { + "tfName": "epsilon", + "name": "epsilon", + "type": "number", + "defaultValue": 1e-4 + }, + { + "tfName": "transpose_a", + "name": "transposeA", + "type": "bool", + "defaultValue": false + }, + { + "tfName": "transpose_b", + "name": "transposeB", + "type": "bool", + "defaultValue": false + }, + { + "tfName": "leakyrelu_alpha", + "name": "leakyreluAlpha", + "type": "number", + "defaultValue": 0.2 + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "MatMul", + "category": "matrices", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "transpose_a", + "name": "transposeA", + "type": "bool", + "defaultValue": false + }, + { + "tfName": "transpose_b", + "name": "transposeB", + "type": "bool", + "defaultValue": false + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "BatchMatMul", + "category": "matrices", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "adj_x", + "name": "transposeA", + "type": "bool", + "defaultValue": false + }, + { + "tfName": "adj_y", + "name": "transposeB", + "type": "bool", + "defaultValue": false + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "BatchMatMulV2", + "category": "matrices", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "b", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "adj_x", + "name": "transposeA", + "type": "bool", + "defaultValue": false + }, + { + "tfName": "adj_y", + "name": "transposeB", + "type": "bool", + "defaultValue": false + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Transpose", + "category": "matrices", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "perm", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Einsum", + "category": "matrices", + "inputs": [ + { + "start": 0, + "end": 0, + "name": "tensors", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "equation", + "name": "equation", + "type": "string" + }, + { + "tfName": "N", + "name": "n", + "type": "number", + "defaultValue": 2 + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "MatrixBandPart", + "category": "matrices", + "inputs": [ + { + "start": 0, + "name": "a", + "type": "tensor" + }, + { + "start": 1, + "name": "numLower", + "type": "tensor" + }, + { + "start": 1, + "name": "numUpper", + "type": "tensor" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/normalization.js +var normalization_exports = {}; +__export(normalization_exports, { + json: () => json13 +}); +var json13 = [ + { + "tfOpName": "EuclideanNorm", + "category": "normalization", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "keep_dims", + "name": "keepDims", + "type": "bool", + "defaultValue": false + } + ] + }, + { + "tfOpName": "FusedBatchNorm", + "category": "normalization", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "scale", + "type": "tensor" + }, + { + "start": 2, + "name": "offset", + "type": "tensor" + }, + { + "start": 3, + "name": "mean", + "type": "tensor" + }, + { + "start": 4, + "name": "variance", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "epsilon", + "name": "epsilon", + "type": "number", + "defaultValue": 1e-3 + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "notSupported": true + } + ] + }, + { + "tfOpName": "FusedBatchNormV2", + "category": "normalization", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "scale", + "type": "tensor" + }, + { + "start": 2, + "name": "offset", + "type": "tensor" + }, + { + "start": 3, + "name": "mean", + "type": "tensor" + }, + { + "start": 4, + "name": "variance", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "epsilon", + "name": "epsilon", + "type": "number", + "defaultValue": 1e-3 + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "notSupported": true + } + ] + }, + { + "tfOpName": "FusedBatchNormV3", + "category": "normalization", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "scale", + "type": "tensor" + }, + { + "start": 2, + "name": "offset", + "type": "tensor" + }, + { + "start": 3, + "name": "mean", + "type": "tensor" + }, + { + "start": 4, + "name": "variance", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "epsilon", + "name": "epsilon", + "type": "number", + "defaultValue": 1e-3 + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string", + "notSupported": true + } + ] + }, + { + "tfOpName": "LRN", + "category": "normalization", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "depth_radius", + "name": "radius", + "type": "number", + "defaultValue": 5 + }, + { + "tfName": "bias", + "name": "bias", + "type": "number", + "defaultValue": 1 + }, + { + "tfName": "alpha", + "name": "alpha", + "type": "number", + "defaultValue": 1 + }, + { + "tfName": "beta", + "name": "beta", + "type": "number", + "defaultValue": 0.5 + } + ] + }, + { + "tfOpName": "Softmax", + "category": "normalization", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "LogSoftmax", + "category": "normalization", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/reduction.js +var reduction_exports = {}; +__export(reduction_exports, { + json: () => json14 +}); +var json14 = [ + { + "tfOpName": "Bincount", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "size", + "type": "number" + }, + { + "start": 2, + "name": "weights", + "type": "tensor" + } + ] + }, + { + "tfOpName": "DenseBincount", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "size", + "type": "number" + }, + { + "start": 2, + "name": "weights", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "binary_output", + "name": "binaryOutput", + "type": "bool" + } + ] + }, + { + "tfOpName": "Max", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "keep_dims", + "name": "keepDims", + "type": "bool" + } + ] + }, + { + "tfOpName": "Mean", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "keep_dims", + "name": "keepDims", + "type": "bool" + } + ] + }, + { + "tfOpName": "Min", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "keep_dims", + "name": "keepDims", + "type": "bool" + } + ] + }, + { + "tfOpName": "Sum", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "keep_dims", + "name": "keepDims", + "type": "bool" + } + ] + }, + { + "tfOpName": "All", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "keep_dims", + "name": "keepDims", + "type": "bool" + } + ] + }, + { + "tfOpName": "Any", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "keep_dims", + "name": "keepDims", + "type": "bool" + } + ] + }, + { + "tfOpName": "ArgMax", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number" + } + ] + }, + { + "tfOpName": "ArgMin", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number" + } + ] + }, + { + "tfOpName": "Prod", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "keep_dims", + "name": "keepDims", + "type": "bool" + }, + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "Cumprod", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "exclusive", + "name": "exclusive", + "type": "bool" + }, + { + "tfName": "reverse", + "name": "reverse", + "type": "bool" + } + ] + }, + { + "tfOpName": "Cumsum", + "category": "reduction", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "exclusive", + "name": "exclusive", + "type": "bool" + }, + { + "tfName": "reverse", + "name": "reverse", + "type": "bool" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/slice_join.js +var slice_join_exports = {}; +__export(slice_join_exports, { + json: () => json15 +}); +var json15 = [ + { + "tfOpName": "ConcatV2", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "end": -1, + "name": "tensors", + "type": "tensors" + }, + { + "start": -1, + "name": "axis", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "N", + "name": "n", + "type": "number", + "defaultValue": 2 + } + ] + }, + { + "tfOpName": "Concat", + "category": "slice_join", + "inputs": [ + { + "start": 1, + "end": 0, + "name": "tensors", + "type": "tensors" + }, + { + "start": 0, + "name": "axis", + "type": "number" + } + ], + "attrs": [ + { + "tfName": "N", + "name": "n", + "type": "number", + "defaultValue": 2 + } + ] + }, + { + "tfOpName": "GatherV2", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "tensor" + }, + { + "start": 2, + "name": "axis", + "type": "number", + "defaultValue": 0 + } + ], + "attrs": [ + { + "tfName": "batch_dims", + "name": "batchDims", + "type": "number", + "defaultValue": 0 + } + ] + }, + { + "tfOpName": "Gather", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "validate_indices", + "name": "validateIndices", + "type": "bool", + "notSupported": true + } + ] + }, + { + "tfOpName": "Reverse", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "dims", + "type": "bool[]" + } + ] + }, + { + "tfOpName": "ReverseV2", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number[]" + } + ] + }, + { + "tfOpName": "Slice", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "begin", + "type": "number[]" + }, + { + "start": 2, + "name": "size", + "type": "number[]" + } + ] + }, + { + "tfOpName": "StridedSlice", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "begin", + "type": "number[]" + }, + { + "start": 2, + "name": "end", + "type": "number[]" + }, + { + "start": 3, + "name": "strides", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "begin_mask", + "name": "beginMask", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "end_mask", + "name": "endMask", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "new_axis_mask", + "name": "newAxisMask", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "ellipsis_mask", + "name": "ellipsisMask", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "shrink_axis_mask", + "name": "shrinkAxisMask", + "type": "number", + "defaultValue": 0 + } + ] + }, + { + "tfOpName": "Pack", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "end": 0, + "name": "tensors", + "type": "tensors" + } + ], + "attrs": [ + { + "tfName": "axis", + "name": "axis", + "type": "number", + "defaultValue": 0 + } + ] + }, + { + "tfOpName": "Unpack", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "axis", + "name": "axis", + "type": "number", + "defaultValue": 0 + }, + { + "tfName": "num", + "name": "num", + "type": "number", + "defaultValue": 0, + "notSupported": true + } + ] + }, + { + "tfOpName": "Tile", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "reps", + "type": "number[]" + } + ] + }, + { + "tfOpName": "Split", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "axis", + "type": "number", + "defaultValue": 0 + }, + { + "start": 1, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "num_split", + "name": "numOrSizeSplits", + "type": "number", + "defaultValue": 1 + } + ] + }, + { + "tfOpName": "SplitV", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "numOrSizeSplits", + "type": "number[]" + }, + { + "start": 2, + "name": "axis", + "type": "number", + "defaultValue": 0 + } + ] + }, + { + "tfOpName": "ScatterNd", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "indices", + "type": "tensor" + }, + { + "start": 1, + "name": "values", + "type": "tensor" + }, + { + "start": 2, + "name": "shape", + "type": "number[]" + } + ] + }, + { + "tfOpName": "GatherNd", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "tensor" + } + ] + }, + { + "tfOpName": "SparseToDense", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "sparseIndices", + "type": "tensor" + }, + { + "start": 1, + "name": "outputShape", + "type": "number[]" + }, + { + "start": 2, + "name": "sparseValues", + "type": "tensor" + }, + { + "start": 3, + "name": "defaultValue", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "validate_indices", + "name": "validateIndices", + "type": "bool", + "defaultValue": false, + "notSupported": true + } + ] + }, + { + "tfOpName": "TensorScatterUpdate", + "category": "slice_join", + "inputs": [ + { + "start": 0, + "name": "tensor", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "tensor" + }, + { + "start": 2, + "name": "values", + "type": "tensor" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/sparse.js +var sparse_exports = {}; +__export(sparse_exports, { + json: () => json16 +}); +var json16 = [ + { + "tfOpName": "SparseFillEmptyRows", + "category": "sparse", + "inputs": [ + { + "start": 0, + "name": "indices", + "type": "tensor" + }, + { + "start": 1, + "name": "values", + "type": "tensor" + }, + { + "start": 2, + "name": "denseShape", + "type": "tensor" + }, + { + "start": 3, + "name": "defaultValue", + "type": "tensor" + } + ] + }, + { + "tfOpName": "SparseReshape", + "category": "sparse", + "inputs": [ + { + "start": 0, + "name": "inputIndices", + "type": "tensor" + }, + { + "start": 1, + "name": "inputShape", + "type": "tensor" + }, + { + "start": 2, + "name": "newShape", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "T", + "name": "dtype", + "type": "dtype", + "notSupported": true + } + ] + }, + { + "tfOpName": "SparseSegmentMean", + "category": "sparse", + "inputs": [ + { + "start": 0, + "name": "data", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "tensor" + }, + { + "start": 2, + "name": "segmentIds", + "type": "tensor" + } + ] + }, + { + "tfOpName": "SparseSegmentSum", + "category": "sparse", + "inputs": [ + { + "start": 0, + "name": "data", + "type": "tensor" + }, + { + "start": 1, + "name": "indices", + "type": "tensor" + }, + { + "start": 2, + "name": "segmentIds", + "type": "tensor" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/spectral.js +var spectral_exports = {}; +__export(spectral_exports, { + json: () => json17 +}); +var json17 = [ + { + "tfOpName": "FFT", + "category": "spectral", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "IFFT", + "category": "spectral", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ] + }, + { + "tfOpName": "RFFT", + "category": "spectral", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "fft_length", + "type": "number", + "notSupported": true + } + ] + }, + { + "tfOpName": "IRFFT", + "category": "spectral", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "fft_length", + "type": "number", + "notSupported": true + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/string.js +var string_exports = {}; +__export(string_exports, { + json: () => json18 +}); +var json18 = [ + { + "tfOpName": "StaticRegexReplace", + "category": "string", + "inputs": [ + { + "start": 0, + "name": "input", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "pattern", + "name": "pattern", + "type": "string" + }, + { + "tfName": "rewrite", + "name": "rewrite", + "type": "string" + }, + { + "tfName": "replace_global", + "name": "replaceGlobal", + "type": "bool" + } + ] + }, + { + "tfOpName": "StringNGrams", + "category": "string", + "inputs": [ + { + "start": 0, + "name": "data", + "type": "tensor" + }, + { + "start": 1, + "name": "dataSplits", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "separator", + "name": "separator", + "type": "string" + }, + { + "tfName": "ngram_widths", + "name": "nGramWidths", + "type": "number[]" + }, + { + "tfName": "left_pad", + "name": "leftPad", + "type": "string" + }, + { + "tfName": "right_pad", + "name": "rightPad", + "type": "string" + }, + { + "tfName": "pad_width", + "name": "padWidth", + "type": "number" + }, + { + "tfName": "preserve_short_sequences", + "name": "preserveShortSequences", + "type": "bool" + } + ], + "outputs": [ + "ngrams", + "ngrams_splits" + ] + }, + { + "tfOpName": "StringSplit", + "category": "string", + "inputs": [ + { + "start": 0, + "name": "input", + "type": "tensor" + }, + { + "start": 1, + "name": "delimiter", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "skip_empty", + "name": "skipEmpty", + "type": "bool" + } + ], + "outputs": [ + "indices", + "values", + "shape" + ] + }, + { + "tfOpName": "StringToHashBucketFast", + "category": "string", + "inputs": [ + { + "start": 0, + "name": "input", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "num_buckets", + "name": "numBuckets", + "type": "number" + } + ] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/transformation.js +var transformation_exports = {}; +__export(transformation_exports, { + json: () => json19 +}); +var json19 = [ + { + "tfOpName": "Cast", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "SrcT", + "name": "sdtype", + "type": "dtype", + "notSupported": true + }, + { + "tfName": "DstT", + "name": "dtype", + "type": "dtype" + } + ] + }, + { + "tfOpName": "ExpandDims", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "axis", + "type": "number" + } + ] + }, + { + "tfOpName": "MirrorPad", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "padding", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "mode", + "name": "mode", + "type": "string" + } + ] + }, + { + "tfOpName": "Pad", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "padding", + "type": "number[]" + } + ], + "attrs": [ + { + "tfName": "constant_value", + "name": "constantValue", + "type": "number", + "defaultValue": 0 + } + ] + }, + { + "tfOpName": "PadV2", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "padding", + "type": "number[]" + }, + { + "start": 2, + "name": "constantValue", + "type": "number", + "defaultValue": 0 + } + ] + }, + { + "tfOpName": "Reshape", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "shape", + "type": "number[]" + } + ] + }, + { + "tfOpName": "EnsureShape", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "shape", + "type": "number[]" + } + ] + }, + { + "tfOpName": "Squeeze", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "axis", + "tfDeprecatedName": "squeeze_dims", + "name": "axis", + "type": "number[]" + } + ] + }, + { + "tfOpName": "SpaceToBatchND", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "blockShape", + "type": "number[]" + }, + { + "start": 2, + "name": "paddings", + "type": "number[]" + } + ] + }, + { + "tfOpName": "BatchToSpaceND", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "blockShape", + "type": "number[]" + }, + { + "start": 2, + "name": "crops", + "type": "number[]" + } + ] + }, + { + "tfOpName": "DepthToSpace", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + } + ], + "attrs": [ + { + "tfName": "block_size", + "name": "blockSize", + "type": "number" + }, + { + "tfName": "data_format", + "name": "dataFormat", + "type": "string" + } + ] + }, + { + "tfOpName": "BroadcastTo", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "x", + "type": "tensor" + }, + { + "start": 1, + "name": "shape", + "type": "number[]" + } + ], + "attrs": [] + }, + { + "tfOpName": "BroadcastArgs", + "category": "transformation", + "inputs": [ + { + "start": 0, + "name": "s0", + "type": "tensor" + }, + { + "start": 1, + "name": "s1", + "type": "tensor" + } + ], + "attrs": [] + } +]; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_mapper.js +var OperationMapper = class { + // Singleton instance for the mapper + static get Instance() { + return this._instance || (this._instance = new this()); + } + // Loads the op mapping from the JSON file. + constructor() { + const ops = [ + arithmetic_exports, + basic_math_exports, + control_exports, + convolution_exports, + creation_exports, + dynamic_exports, + evaluation_exports, + graph_exports, + hash_table_exports, + image_exports, + logical_exports, + matrices_exports, + normalization_exports, + reduction_exports, + slice_join_exports, + sparse_exports, + spectral_exports, + string_exports, + transformation_exports + ]; + const mappersJson = [].concat(...ops.map((op2) => op2.json)); + this.opMappers = mappersJson.reduce((map, mapper) => { + map[mapper.tfOpName] = mapper; + return map; + }, {}); + } + // Converts the model inference graph from Tensorflow GraphDef to local + // representation for TensorFlow.js API + transformGraph(graph, signature = {}) { + const tfNodes = graph.node; + const placeholders = []; + const weights = []; + const initNodes = []; + const nodes = tfNodes.reduce((map, node) => { + map[node.name] = this.mapNode(node); + if (node.op.startsWith("Placeholder")) { + placeholders.push(map[node.name]); + } else if (node.op === "Const") { + weights.push(map[node.name]); + } else if (node.input == null || node.input.length === 0) { + initNodes.push(map[node.name]); + } + return map; + }, {}); + let inputs = []; + const outputs = []; + let inputNodeNameToKey = {}; + let outputNodeNameToKey = {}; + if (signature != null) { + inputNodeNameToKey = this.mapSignatureEntries(signature.inputs); + outputNodeNameToKey = this.mapSignatureEntries(signature.outputs); + } + const allNodes = Object.keys(nodes); + allNodes.forEach((key) => { + const node = nodes[key]; + node.inputNames.forEach((name, index) => { + const [nodeName, , outputName] = getNodeNameAndIndex(name); + const inputNode = nodes[nodeName]; + if (inputNode.outputs != null) { + const outputIndex = inputNode.outputs.indexOf(outputName); + if (outputIndex !== -1) { + const inputName = `${nodeName}:${outputIndex}`; + node.inputNames[index] = inputName; + } + } + node.inputs.push(inputNode); + inputNode.children.push(node); + }); + }); + if (Object.keys(outputNodeNameToKey).length === 0) { + allNodes.forEach((key) => { + const node = nodes[key]; + if (node.children.length === 0) { + outputs.push(node); + } + }); + } else { + Object.keys(outputNodeNameToKey).forEach((name) => { + const [nodeName] = getNodeNameAndIndex(name); + const node = nodes[nodeName]; + if (node != null) { + node.signatureKey = outputNodeNameToKey[name]; + outputs.push(node); + } + }); + } + if (Object.keys(inputNodeNameToKey).length > 0) { + Object.keys(inputNodeNameToKey).forEach((name) => { + const [nodeName] = getNodeNameAndIndex(name); + const node = nodes[nodeName]; + if (node) { + node.signatureKey = inputNodeNameToKey[name]; + inputs.push(node); + } + }); + } else { + inputs = placeholders; + } + let functions = {}; + if (graph.library != null && graph.library.function != null) { + functions = graph.library.function.reduce((functions2, func2) => { + functions2[func2.signature.name] = this.mapFunction(func2); + return functions2; + }, {}); + } + const result = { nodes, inputs, outputs, weights, placeholders, signature, functions }; + if (initNodes.length > 0) { + result.initNodes = initNodes; + } + return result; + } + mapSignatureEntries(entries) { + return Object.keys(entries || {}).reduce((prev, curr) => { + prev[entries[curr].name] = curr; + return prev; + }, {}); + } + mapNode(node) { + const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {}; + if (node.attr == null) { + node.attr = {}; + } + const newNode = { + name: node.name, + op: node.op, + category: mapper.category, + inputNames: (node.input || []).map((input2) => input2.startsWith("^") ? input2.slice(1) : input2), + inputs: [], + children: [], + inputParams: {}, + attrParams: {}, + rawAttrs: node.attr, + outputs: mapper.outputs + }; + if (mapper.inputs != null) { + newNode.inputParams = mapper.inputs.reduce((map, param) => { + map[param.name] = { + type: param.type, + inputIndexStart: param.start, + inputIndexEnd: param.end + }; + return map; + }, {}); + } + if (mapper.attrs != null) { + newNode.attrParams = mapper.attrs.reduce((map, param) => { + const type = param.type; + let value = void 0; + switch (param.type) { + case "string": + value = getStringParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "string[]": + value = getStringArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "number": + value = getNumberParam(node.attr, param.tfName, param.defaultValue || 0); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "number[]": + value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "bool": + value = getBoolParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "bool[]": + value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "shape": + value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "shape[]": + value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "dtype": + value = getDtypeParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "dtype[]": + value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "func": + value = getFuncParam(node.attr, param.tfName, param.defaultValue); + if (value === void 0 && !!param.tfDeprecatedName) { + value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case "tensor": + case "tensors": + break; + default: + throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`); + } + map[param.name] = { value, type }; + return map; + }, {}); + } + return newNode; + } + // map the TFunctionDef to TFJS graph object + mapFunction(functionDef) { + const tfNodes = functionDef.nodeDef; + const placeholders = []; + const weights = []; + let nodes = {}; + if (tfNodes != null) { + nodes = tfNodes.reduce((map, node) => { + map[node.name] = this.mapNode(node); + if (node.op === "Const") { + weights.push(map[node.name]); + } + return map; + }, {}); + } + const inputs = []; + const outputs = []; + functionDef.signature.inputArg.forEach((arg) => { + const [nodeName] = getNodeNameAndIndex(arg.name); + const node = { + name: nodeName, + op: "Placeholder", + inputs: [], + inputNames: [], + category: "graph", + inputParams: {}, + attrParams: { dtype: { value: parseDtypeParam(arg.type), type: "dtype" } }, + children: [] + }; + node.signatureKey = arg.name; + inputs.push(node); + nodes[nodeName] = node; + }); + const allNodes = Object.keys(nodes); + allNodes.forEach((key) => { + const node = nodes[key]; + node.inputNames.forEach((name, index) => { + const [nodeName, , outputName] = getNodeNameAndIndex(name); + const inputNode = nodes[nodeName]; + if (inputNode.outputs != null) { + const outputIndex = inputNode.outputs.indexOf(outputName); + if (outputIndex !== -1) { + const inputName = `${nodeName}:${outputIndex}`; + node.inputNames[index] = inputName; + } + } + node.inputs.push(inputNode); + inputNode.children.push(node); + }); + }); + const returnNodeMap = functionDef.ret; + functionDef.signature.outputArg.forEach((output) => { + const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]); + const node = nodes[nodeName]; + if (node != null) { + node.defaultOutput = index; + outputs.push(node); + } + }); + const signature = this.mapArgsToSignature(functionDef); + return { nodes, inputs, outputs, weights, placeholders, signature }; + } + mapArgsToSignature(functionDef) { + return { + methodName: functionDef.signature.name, + inputs: functionDef.signature.inputArg.reduce((map, arg) => { + map[arg.name] = this.mapArgToTensorInfo(arg); + return map; + }, {}), + outputs: functionDef.signature.outputArg.reduce((map, arg) => { + map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret); + return map; + }, {}) + }; + } + mapArgToTensorInfo(arg, nameMap2) { + let name = arg.name; + if (nameMap2 != null) { + name = nameMap2[name]; + } + return { name, dtype: arg.type }; + } +}; +function decodeBase64(text) { + const global2 = env().global; + if (typeof global2.atob !== "undefined") { + return global2.atob(text); + } else if (typeof Buffer !== "undefined") { + return new Buffer(text, "base64").toString(); + } else { + throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()"); + } +} +function parseStringParam(s, keepCase) { + const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s); + return keepCase ? value : value.toLowerCase(); +} +function getStringParam(attrs, name, def, keepCase = false) { + const param = attrs[name]; + if (param != null) { + return parseStringParam(param.s, keepCase); + } + return def; +} +function getBoolParam(attrs, name, def) { + const param = attrs[name]; + return param ? param.b : def; +} +function getNumberParam(attrs, name, def) { + const param = attrs[name] || {}; + const value = param["i"] != null ? param["i"] : param["f"] != null ? param["f"] : def; + return typeof value === "number" ? value : parseInt(value, 10); +} +function parseDtypeParam(value) { + if (typeof value === "string") { + value = DataType[value]; + } + switch (value) { + case DataType.DT_FLOAT: + case DataType.DT_HALF: + return "float32"; + case DataType.DT_INT32: + case DataType.DT_INT64: + case DataType.DT_INT8: + case DataType.DT_UINT8: + return "int32"; + case DataType.DT_BOOL: + return "bool"; + case DataType.DT_DOUBLE: + return "float32"; + case DataType.DT_STRING: + return "string"; + case DataType.DT_COMPLEX64: + case DataType.DT_COMPLEX128: + return "complex64"; + default: + return null; + } +} +function getFuncParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.func) { + return param.func.name; + } + return def; +} +function getDtypeParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.type) { + return parseDtypeParam(param.type); + } + return def; +} +function getDtypeArrayParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.list && param.list.type) { + return param.list.type.map((v) => parseDtypeParam(v)); + } + return def; +} +function parseTensorShapeParam(shape) { + if (shape.unknownRank) { + return void 0; + } + if (shape.dim != null) { + return shape.dim.map((dim) => typeof dim.size === "number" ? dim.size : parseInt(dim.size, 10)); + } + return []; +} +function getTensorShapeParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.shape) { + return parseTensorShapeParam(param.shape); + } + return def; +} +function getNumericArrayParam(attrs, name, def) { + const param = attrs[name]; + if (param) { + return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v) => typeof v === "number" ? v : parseInt(v, 10)); + } + return def; +} +function getStringArrayParam(attrs, name, def, keepCase = false) { + const param = attrs[name]; + if (param && param.list && param.list.s) { + return param.list.s.map((v) => { + return parseStringParam(v, keepCase); + }); + } + return def; +} +function getTensorShapeArrayParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.list && param.list.shape) { + return param.list.shape.map((v) => { + return parseTensorShapeParam(v); + }); + } + return def; +} +function getBoolArrayParam(attrs, name, def) { + const param = attrs[name]; + if (param && param.list && param.list.b) { + return param.list.b; + } + return def; +} + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/node_value_impl.js +var NodeValueImpl = class { + constructor(node, tensorMap, context) { + this.node = node; + this.tensorMap = tensorMap; + this.context = context; + this.inputs = []; + this.attrs = {}; + this.inputs = node.inputNames.map((name) => this.getInput(name)); + if (node.rawAttrs != null) { + this.attrs = Object.keys(node.rawAttrs).reduce((attrs, key) => { + attrs[key] = this.getAttr(key); + return attrs; + }, {}); + } + } + /** + * Return the value of the attribute or input param. + * @param name String: name of attribute or input param. + */ + getInput(name) { + return getTensor(name, this.tensorMap, this.context); + } + /** + * Return the value of the attribute or input param. + * @param name String: name of attribute or input param. + */ + getAttr(name, defaultValue) { + const value = this.node.rawAttrs[name]; + if (value.tensor != null) { + return getTensor(name, this.tensorMap, this.context); + } + if (value.i != null || value.f != null) { + return getNumberParam(this.node.rawAttrs, name, defaultValue); + } + if (value.s != null) { + return getStringParam(this.node.rawAttrs, name, defaultValue); + } + if (value.b != null) { + return getBoolParam(this.node.rawAttrs, name, defaultValue); + } + if (value.shape != null) { + return getTensorShapeParam(this.node.rawAttrs, name, defaultValue); + } + if (value.type != null) { + return getDtypeParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list != null) { + if (value.list.i != null || value.list.f != null) { + return getNumericArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.s != null) { + return getStringArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.shape != null) { + return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.b != null) { + return getBoolArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.type != null) { + return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue); + } + } + return defaultValue; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops_for_converter.js +var ops_for_converter_exports = {}; +__export(ops_for_converter_exports, { + OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX, + abs: () => abs, + acos: () => acos, + acosh: () => acosh, + add: () => add2, + addN: () => addN, + all: () => all, + any: () => any, + argMax: () => argMax, + argMin: () => argMin, + asin: () => asin, + asinh: () => asinh, + atan: () => atan, + atan2: () => atan2, + atanh: () => atanh, + avgPool: () => avgPool, + avgPool3d: () => avgPool3d, + basicLSTMCell: () => basicLSTMCell, + batchNorm: () => batchNorm, + batchNorm2d: () => batchNorm2d, + batchNorm3d: () => batchNorm3d, + batchNorm4d: () => batchNorm4d, + batchToSpaceND: () => batchToSpaceND, + bincount: () => bincount, + bitwiseAnd: () => bitwiseAnd, + booleanMaskAsync: () => booleanMaskAsync, + broadcastArgs: () => broadcastArgs, + broadcastTo: () => broadcastTo, + buffer: () => buffer, + cast: () => cast, + ceil: () => ceil, + clipByValue: () => clipByValue, + clone: () => clone, + complex: () => complex, + concat: () => concat, + concat1d: () => concat1d, + concat2d: () => concat2d, + concat3d: () => concat3d, + concat4d: () => concat4d, + conv1d: () => conv1d, + conv2d: () => conv2d, + conv2dTranspose: () => conv2dTranspose, + conv3d: () => conv3d, + conv3dTranspose: () => conv3dTranspose, + cos: () => cos, + cosh: () => cosh, + cosineWindow: () => cosineWindow, + cumprod: () => cumprod, + cumsum: () => cumsum, + denseBincount: () => denseBincount, + depthToSpace: () => depthToSpace, + depthwiseConv2d: () => depthwiseConv2d, + diag: () => diag, + dilation2d: () => dilation2d, + div: () => div, + divNoNan: () => divNoNan, + dot: () => dot, + dropout: () => dropout, + einsum: () => einsum, + elu: () => elu, + enclosingPowerOfTwo: () => enclosingPowerOfTwo, + ensureShape: () => ensureShape, + equal: () => equal, + erf: () => erf, + euclideanNorm: () => euclideanNorm, + exp: () => exp, + expandDims: () => expandDims, + expm1: () => expm1, + eye: () => eye, + fft: () => fft, + fill: () => fill, + floor: () => floor, + floorDiv: () => floorDiv, + fused: () => fused_ops_exports, + gather: () => gather, + gatherND: () => gatherND, + greater: () => greater, + greaterEqual: () => greaterEqual, + ifft: () => ifft, + imag: () => imag, + image: () => image, + inTopKAsync: () => inTopKAsync, + irfft: () => irfft, + isFinite: () => isFinite2, + isInf: () => isInf, + isNaN: () => isNaN2, + leakyRelu: () => leakyRelu, + less: () => less, + lessEqual: () => lessEqual, + linalg: () => linalg, + linspace: () => linspace, + localResponseNormalization: () => localResponseNormalization, + log: () => log2, + log1p: () => log1p, + logSigmoid: () => logSigmoid, + logSoftmax: () => logSoftmax, + logSumExp: () => logSumExp, + logicalAnd: () => logicalAnd, + logicalNot: () => logicalNot, + logicalOr: () => logicalOr, + logicalXor: () => logicalXor, + losses: () => losses, + lowerBound: () => lowerBound, + matMul: () => matMul, + max: () => max, + maxPool: () => maxPool, + maxPool3d: () => maxPool3d, + maxPoolWithArgmax: () => maxPoolWithArgmax, + maximum: () => maximum, + mean: () => mean, + meshgrid: () => meshgrid, + min: () => min, + minimum: () => minimum, + mirrorPad: () => mirrorPad, + mod: () => mod, + moments: () => moments, + movingAverage: () => movingAverage, + mul: () => mul, + multiRNNCell: () => multiRNNCell, + multinomial: () => multinomial, + neg: () => neg, + norm: () => norm, + notEqual: () => notEqual, + oneHot: () => oneHot, + ones: () => ones2, + onesLike: () => onesLike, + op: () => op, + outerProduct: () => outerProduct, + pad: () => pad, + pad1d: () => pad1d, + pad2d: () => pad2d, + pad3d: () => pad3d, + pad4d: () => pad4d, + pool: () => pool, + pow: () => pow, + prelu: () => prelu, + print: () => print, + prod: () => prod, + raggedGather: () => raggedGather, + raggedRange: () => raggedRange, + raggedTensorToTensor: () => raggedTensorToTensor, + rand: () => rand, + randomGamma: () => randomGamma, + randomNormal: () => randomNormal, + randomStandardNormal: () => randomStandardNormal, + randomUniform: () => randomUniform, + randomUniformInt: () => randomUniformInt, + range: () => range, + real: () => real, + reciprocal: () => reciprocal, + relu: () => relu, + relu6: () => relu6, + reshape: () => reshape, + reverse: () => reverse, + reverse1d: () => reverse1d, + reverse2d: () => reverse2d, + reverse3d: () => reverse3d, + reverse4d: () => reverse4d, + rfft: () => rfft, + round: () => round2, + rsqrt: () => rsqrt, + scalar: () => scalar, + scatterND: () => scatterND, + searchSorted: () => searchSorted, + selu: () => selu, + separableConv2d: () => separableConv2d, + setdiff1dAsync: () => setdiff1dAsync, + sigmoid: () => sigmoid, + sign: () => sign, + signal: () => signal, + sin: () => sin, + sinh: () => sinh, + slice: () => slice, + slice1d: () => slice1d, + slice2d: () => slice2d, + slice3d: () => slice3d, + slice4d: () => slice4d, + softmax: () => softmax, + softplus: () => softplus, + spaceToBatchND: () => spaceToBatchND, + sparse: () => sparse, + sparseToDense: () => sparseToDense, + spectral: () => spectral, + split: () => split, + sqrt: () => sqrt, + square: () => square, + squaredDifference: () => squaredDifference, + squeeze: () => squeeze, + stack: () => stack, + step: () => step, + stridedSlice: () => stridedSlice, + string: () => string, + sub: () => sub, + sum: () => sum2, + tan: () => tan, + tanh: () => tanh2, + tensor: () => tensor, + tensor1d: () => tensor1d, + tensor2d: () => tensor2d, + tensor3d: () => tensor3d, + tensor4d: () => tensor4d, + tensor5d: () => tensor5d, + tensor6d: () => tensor6d, + tensorScatterUpdate: () => tensorScatterUpdate, + tile: () => tile, + topk: () => topk, + transpose: () => transpose, + truncatedNormal: () => truncatedNormal, + unique: () => unique, + unsortedSegmentSum: () => unsortedSegmentSum, + unstack: () => unstack, + upperBound: () => upperBound, + variable: () => variable, + where: () => where, + whereAsync: () => whereAsync, + zeros: () => zeros, + zerosLike: () => zerosLike +}); + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/arithmetic_executor.js +var executeOp = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "BiasAdd": + case "AddV2": + case "Add": { + return [ops.add(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "AddN": { + return [ops.addN(getParamValue("tensors", node, tensorMap, context))]; + } + case "FloorMod": + case "Mod": + return [ops.mod(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + case "Mul": + return [ops.mul(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + case "RealDiv": + case "Div": { + return [ops.div(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "DivNoNan": { + return [ops.divNoNan(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "FloorDiv": { + return [ops.floorDiv(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Sub": { + return [ops.sub(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Minimum": { + return [ops.minimum(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Maximum": { + return [ops.maximum(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Pow": { + return [ops.pow(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "SquaredDifference": { + return [ops.squaredDifference(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/basic_math_executor.js +var executeOp2 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Abs": + case "ComplexAbs": + return [ops.abs(getParamValue("x", node, tensorMap, context))]; + case "Acos": + return [ops.acos(getParamValue("x", node, tensorMap, context))]; + case "Acosh": + return [ops.acosh(getParamValue("x", node, tensorMap, context))]; + case "Asin": + return [ops.asin(getParamValue("x", node, tensorMap, context))]; + case "Asinh": + return [ops.asinh(getParamValue("x", node, tensorMap, context))]; + case "Atan": + return [ops.atan(getParamValue("x", node, tensorMap, context))]; + case "Atan2": + return [ops.atan2(getParamValue("x", node, tensorMap, context), getParamValue("y", node, tensorMap, context))]; + case "Atanh": + return [ops.atanh(getParamValue("x", node, tensorMap, context))]; + case "Ceil": + return [ops.ceil(getParamValue("x", node, tensorMap, context))]; + case "Complex": + return [ops.complex(getParamValue("real", node, tensorMap, context), getParamValue("imag", node, tensorMap, context))]; + case "Cos": + return [ops.cos(getParamValue("x", node, tensorMap, context))]; + case "Cosh": + return [ops.cosh(getParamValue("x", node, tensorMap, context))]; + case "Elu": + return [ops.elu(getParamValue("x", node, tensorMap, context))]; + case "Erf": + return [ops.erf(getParamValue("x", node, tensorMap, context))]; + case "Exp": + return [ops.exp(getParamValue("x", node, tensorMap, context))]; + case "Expm1": { + return [ops.expm1(getParamValue("x", node, tensorMap, context))]; + } + case "Floor": + return [ops.floor(getParamValue("x", node, tensorMap, context))]; + case "Log": + return [ops.log(getParamValue("x", node, tensorMap, context))]; + case "Log1p": { + return [ops.log1p(getParamValue("x", node, tensorMap, context))]; + } + case "Imag": + return [ops.imag(getParamValue("x", node, tensorMap, context))]; + case "Neg": + return [ops.neg(getParamValue("x", node, tensorMap, context))]; + case "Reciprocal": { + return [ops.reciprocal(getParamValue("x", node, tensorMap, context))]; + } + case "Real": + return [ops.real(getParamValue("x", node, tensorMap, context))]; + case "Relu": + return [ops.relu(getParamValue("x", node, tensorMap, context))]; + case "Round": { + return [ops.round(getParamValue("x", node, tensorMap, context))]; + } + case "Selu": + return [ops.selu(getParamValue("x", node, tensorMap, context))]; + case "Sigmoid": + return [ops.sigmoid(getParamValue("x", node, tensorMap, context))]; + case "Sin": + return [ops.sin(getParamValue("x", node, tensorMap, context))]; + case "Sign": { + return [ops.sign(getParamValue("x", node, tensorMap, context))]; + } + case "Sinh": { + return [ops.sinh(getParamValue("x", node, tensorMap, context))]; + } + case "Softplus": { + return [ops.softplus(getParamValue("x", node, tensorMap, context))]; + } + case "Sqrt": { + return [ops.sqrt(getParamValue("x", node, tensorMap, context))]; + } + case "Square": { + return [ops.square(getParamValue("x", node, tensorMap, context))]; + } + case "Tanh": { + return [ops.tanh(getParamValue("x", node, tensorMap, context))]; + } + case "Tan": + return [ops.tan(getParamValue("x", node, tensorMap, context))]; + case "ClipByValue": + return [ops.clipByValue(getParamValue("x", node, tensorMap, context), getParamValue("clipValueMin", node, tensorMap, context), getParamValue("clipValueMax", node, tensorMap, context))]; + case "Relu6": + return [ops.relu6(getParamValue("x", node, tensorMap, context))]; + case "Rsqrt": + return [ops.rsqrt(getTensor(node.inputNames[0], tensorMap, context))]; + case "LeakyRelu": + return [ops.leakyRelu(getParamValue("x", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context))]; + case "Prelu": + return [ops.prelu(getParamValue("x", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context))]; + case "IsNan": + return [ops.isNaN(getTensor(node.inputNames[0], tensorMap, context))]; + case "IsInf": + return [ops.isInf(getTensor(node.inputNames[0], tensorMap, context))]; + case "IsFinite": + return [ops.isFinite(getTensor(node.inputNames[0], tensorMap, context))]; + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_utils.js +function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = "") { + if (typeof shapeA === "number" || typeof shapeB === "number") { + return; + } + util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); + for (let i = 0; i < shapeA.length; i++) { + const dim0 = shapeA[i]; + const dim1 = shapeB[i]; + util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); + } +} +function fullDefinedShape(elementShape) { + if (typeof elementShape === "number" || elementShape.some((dim) => dim < 0)) { + return false; + } + return true; +} +function inferElementShape(listElementShape, tensors, elementShape) { + let partialShape = mergeElementShape(listElementShape, elementShape); + const notfullDefinedShape = !fullDefinedShape(partialShape); + if (notfullDefinedShape && tensors.length === 0) { + throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${partialShape}`); + } + if (notfullDefinedShape) { + tensors.forEach((tensor2) => { + partialShape = mergeElementShape(tensor2.shape, partialShape); + }); + } + if (!fullDefinedShape(partialShape)) { + throw new Error(`Non-fully-defined elementShape: ${partialShape}`); + } + return partialShape; +} +function mergeElementShape(elementShapeA, elementShapeB) { + if (typeof elementShapeA === "number") { + return elementShapeB; + } + if (typeof elementShapeB === "number") { + return elementShapeA; + } + if (elementShapeA.length !== elementShapeB.length) { + throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`); + } + const result = []; + for (let i = 0; i < elementShapeA.length; ++i) { + const dim0 = elementShapeA[i]; + const dim1 = elementShapeB[i]; + if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) { + throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`); + } + result[i] = dim0 >= 0 ? dim0 : dim1; + } + return result; +} + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_array.js +var TensorArray = class { + constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) { + this.name = name; + this.dtype = dtype; + this.maxSize = maxSize; + this.elementShape = elementShape; + this.identicalElementShapes = identicalElementShapes; + this.dynamicSize = dynamicSize; + this.clearAfterRead = clearAfterRead; + this.tensors = []; + this.closed_ = false; + this.idTensor = scalar(0); + keep(this.idTensor); + } + get id() { + return this.idTensor.id; + } + get closed() { + return this.closed_; + } + /** + * Dispose the tensors and idTensor and mark the TensoryArray as closed. + */ + clearAndClose(keepIds) { + this.tensors.forEach((tensor2) => { + if (keepIds == null || !keepIds.has(tensor2.tensor.id)) { + tensor2.tensor.dispose(); + } + }); + this.tensors = []; + this.closed_ = true; + this.idTensor.dispose(); + } + size() { + return this.tensors.length; + } + /** + * Read the value at location index in the TensorArray. + * @param index Number the index to read from. + */ + read(index) { + if (this.closed_) { + throw new Error(`TensorArray ${this.name} has already been closed.`); + } + if (index < 0 || index >= this.size()) { + throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`); + } + const tensorWithState = this.tensors[index]; + if (tensorWithState.cleared) { + throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`); + } + if (this.clearAfterRead) { + tensorWithState.cleared = true; + } + tensorWithState.read = true; + return tensorWithState.tensor; + } + /** + * Helper method to read multiple tensors from the specified indices. + */ + readMany(indices) { + return indices.map((index) => this.read(index)); + } + /** + * Write value into the index of the TensorArray. + * @param index number the index to write to. + * @param tensor + */ + write(index, tensor2) { + if (this.closed_) { + throw new Error(`TensorArray ${this.name} has already been closed.`); + } + if (index < 0 || !this.dynamicSize && index >= this.maxSize) { + throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`); + } + const t = this.tensors[index] || {}; + if (tensor2.dtype !== this.dtype) { + throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, + because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`); + } + if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) { + this.elementShape = tensor2.shape; + } + assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`); + if (t.read) { + throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`); + } + if (t.written) { + throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`); + } + t.tensor = tensor2; + keep(tensor2); + t.written = true; + this.tensors[index] = t; + } + /** + * Helper method to write multiple tensors to the specified indices. + */ + writeMany(indices, tensors) { + if (indices.length !== tensors.length) { + throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`); + } + indices.forEach((i, index) => this.write(i, tensors[index])); + } + /** + * Return selected values in the TensorArray as a packed Tensor. All of + * selected values must have been written and their shapes must all match. + * @param [indices] number[] Optional. Taking values in [0, max_value). If the + * TensorArray is not dynamic, max_value=size(). If not specified returns + * all tensors in the original order. + * @param [dtype] + */ + gather(indices, dtype) { + if (!!dtype && dtype !== this.dtype) { + throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`); + } + if (!indices) { + indices = []; + for (let i = 0; i < this.size(); i++) { + indices.push(i); + } + } else { + indices = indices.slice(0, this.size()); + } + if (indices.length === 0) { + return tensor([], [0].concat(this.elementShape)); + } + const tensors = this.readMany(indices); + assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, "TensorArray shape mismatch: "); + return stack(tensors, 0); + } + /** + * Return the values in the TensorArray as a concatenated Tensor. + */ + concat(dtype) { + if (!!dtype && dtype !== this.dtype) { + throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`); + } + if (this.size() === 0) { + return tensor([], [0].concat(this.elementShape)); + } + const indices = []; + for (let i = 0; i < this.size(); i++) { + indices.push(i); + } + const tensors = this.readMany(indices); + assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`); + return concat(tensors, 0); + } + /** + * Scatter the values of a Tensor in specific indices of a TensorArray. + * @param indices nummber[] values in [0, max_value). If the + * TensorArray is not dynamic, max_value=size(). + * @param tensor Tensor input tensor. + */ + scatter(indices, tensor2) { + if (tensor2.dtype !== this.dtype) { + throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`); + } + if (indices.length !== tensor2.shape[0]) { + throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`); + } + const maxIndex = Math.max(...indices); + if (!this.dynamicSize && maxIndex >= this.maxSize) { + throw new Error(`Max index must be < array size (${maxIndex} vs. ${this.maxSize})`); + } + this.writeMany(indices, unstack(tensor2, 0)); + } + /** + * Split the values of a Tensor into the TensorArray. + * @param length number[] with the lengths to use when splitting value along + * its first dimension. + * @param tensor Tensor, the tensor to split. + */ + split(length, tensor2) { + if (tensor2.dtype !== this.dtype) { + throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`); + } + let totalLength = 0; + const cumulativeLengths = length.map((len) => { + totalLength += len; + return totalLength; + }); + if (totalLength !== tensor2.shape[0]) { + throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${n}, and tensor's shape is: ${e.shape}`);if(!this.dynamicSize&&t.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${t.length}), and the TensorArray is not marked as dynamically resizeable`);let s=n===0?0:e.size/n,i=[];B(()=>{e=R(e,[1,n,s]);for(let u=0;u{if(n!==s.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${s.dtype}`);Xn(e,s.shape,"TensorList shape mismatch: "),$e(s)}),this.idTensor=ft(0),this.maxNumElements=o,$e(this.idTensor)}copy(){return new Ml([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.id))&&e.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(t,e,n=-1){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);Xn(t,this.elementShape,"TensorList shape mismatch: ");let o=id(this.elementShape,this.tensors,t);return B(()=>{let s=this.tensors.map(i=>R(i,o));return qe(s,0)})}popBack(t,e){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=id(this.elementShape,this.tensors,t),o=this.tensors.pop();return o.kept=!1,Xn(o.shape,t,"TensorList shape mismatch: "),R(o,n)}pushBack(t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(Xn(t.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");$e(t),this.tensors.push(t)}resize(t){if(t<0)throw new Error(`TensorListResize expects size to be non-negative. 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Defaults to -1 + * meaning that the size of `tensors` is unbounded. + */ + constructor(tensors, elementShape, elementDtype, maxNumElements = -1) { + this.tensors = tensors; + this.elementShape = elementShape; + this.elementDtype = elementDtype; + if (tensors != null) { + tensors.forEach((tensor2) => { + if (elementDtype !== tensor2.dtype) { + throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`); + } + assertShapesMatchAllowUndefinedSize(elementShape, tensor2.shape, "TensorList shape mismatch: "); + keep(tensor2); + }); + } + this.idTensor = scalar(0); + this.maxNumElements = maxNumElements; + keep(this.idTensor); + } + /** + * Get a new TensorList containing a copy of the underlying tensor container. + */ + copy() { + return new _TensorList([...this.tensors], this.elementShape, this.elementDtype); + } + /** + * Dispose the tensors and idTensor and clear the tensor list. + */ + clearAndClose(keepIds) { + this.tensors.forEach((tensor2) => { + if (keepIds == null || !keepIds.has(tensor2.id)) { + tensor2.dispose(); + } + }); + this.tensors.length = 0; + this.idTensor.dispose(); + } + /** + * The size of the tensors in the tensor list. + */ + size() { + return this.tensors.length; + } + /** + * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1) + * tf.Tensor. + * @param elementShape shape of each tensor + * @param elementDtype data type of each tensor + * @param numElements the number of elements to stack + */ + stack(elementShape, elementDtype, numElements = -1) { + if (elementDtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); + } + if (numElements !== -1 && this.tensors.length !== numElements) { + throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`); + } + assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, "TensorList shape mismatch: "); + const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + return tidy(() => { + const reshapedTensors = this.tensors.map((tensor2) => reshape(tensor2, outputElementShape)); + return stack(reshapedTensors, 0); + }); + } + /** + * Pop a tensor from the end of the list. + * @param elementShape shape of the tensor + * @param elementDtype data type of the tensor + */ + popBack(elementShape, elementDtype) { + if (elementDtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); + } + if (this.size() === 0) { + throw new Error("Trying to pop from an empty list."); + } + const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + const tensor2 = this.tensors.pop(); + tensor2.kept = false; + assertShapesMatchAllowUndefinedSize(tensor2.shape, elementShape, "TensorList shape mismatch: "); + return reshape(tensor2, outputElementShape); + } + /** + * Push a tensor to the end of the list. + * @param tensor Tensor to be pushed. + */ + pushBack(tensor2) { + if (tensor2.dtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`); + } + assertShapesMatchAllowUndefinedSize(tensor2.shape, this.elementShape, "TensorList shape mismatch: "); + if (this.maxNumElements === this.size()) { + throw new Error(`Trying to push element into a full list.`); + } + keep(tensor2); + this.tensors.push(tensor2); + } + /** + * Update the size of the list. + * @param size the new size of the list. + */ + resize(size) { + if (size < 0) { + throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`); + } + if (this.maxNumElements !== -1 && size > this.maxNumElements) { + throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`); + } + const destTensorList = new _TensorList([], this.elementShape, this.elementDtype, this.maxNumElements); + destTensorList.tensors.length = size; + for (let i = 0; i < Math.min(this.tensors.length, size); ++i) { + destTensorList.tensors[i] = this.tensors[i]; + } + return destTensorList; + } + /** + * Retrieve the element at the provided index + * @param elementShape shape of the tensor + * @param elementDtype dtype of the tensor + * @param elementIndex index of the tensor + */ + getItem(elementIndex, elementShape, elementDtype) { + if (elementDtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); + } + if (elementIndex < 0 || elementIndex > this.tensors.length) { + throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`); + } + if (this.tensors[elementIndex] == null) { + throw new Error(`element at index ${elementIndex} is null.`); + } + assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, "TensorList shape mismatch: "); + const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + return reshape(this.tensors[elementIndex], outputElementShape); + } + /** + * Set the tensor at the index + * @param elementIndex index of the tensor + * @param tensor the tensor to be inserted into the list + */ + setItem(elementIndex, tensor2) { + if (tensor2.dtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`); + } + if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) { + throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`); + } + assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, "TensorList shape mismatch: "); + keep(tensor2); + if (this.tensors[elementIndex] != null) { + this.tensors[elementIndex].kept = false; + } + this.tensors[elementIndex] = tensor2; + } + /** + * Return selected values in the TensorList as a stacked Tensor. All of + * selected values must have been written and their shapes must all match. + * @param indices indices of tensors to gather + * @param elementDtype output tensor dtype + * @param elementShape output tensor element shape + */ + gather(indices, elementDtype, elementShape) { + if (elementDtype !== this.elementDtype) { + throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`); + } + assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: "); + indices = indices.slice(0, this.size()); + const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + if (indices.length === 0) { + return tensor([], [0].concat(outputElementShape)); + } + return tidy(() => { + const tensors = indices.map((i) => reshape(this.tensors[i], outputElementShape)); + return stack(tensors, 0); + }); + } + /** + * Return the values in the TensorList as a concatenated Tensor. + * @param elementDtype output tensor dtype + * @param elementShape output tensor element shape + */ + concat(elementDtype, elementShape) { + if (!!elementDtype && elementDtype !== this.elementDtype) { + throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`); + } + assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: "); + const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + if (this.size() === 0) { + return tensor([], [0].concat(outputElementShape)); + } + return tidy(() => { + const tensors = this.tensors.map((t) => reshape(t, outputElementShape)); + return concat(tensors, 0); + }); + } +}; +function fromTensor(tensor2, elementShape, elementDtype) { + const dtype = tensor2.dtype; + if (tensor2.shape.length < 1) { + throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`); + } + if (tensor2.dtype !== elementDtype) { + throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`); + } + const tensorElementShape = tensor2.shape.slice(1); + assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, "TensorList shape mismatch: "); + const tensorList = unstack(tensor2); + return new TensorList(tensorList, elementShape, dtype); +} +function reserve(elementShape, elementDtype, numElements, maxNumElements) { + return new TensorList([], elementShape, elementDtype, maxNumElements); +} +function scatter(tensor2, indices, elementShape, numElements) { + if (indices.length !== tensor2.shape[0]) { + throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`); + } + const maxIndex = Math.max(...indices); + if (numElements != null && numElements !== -1 && maxIndex >= numElements) { + throw new Error(`Max index must be < array size (${maxIndex} vs. ${numElements})`); + } + const list = new TensorList([], elementShape, tensor2.dtype, numElements); + const tensors = unstack(tensor2, 0); + indices.forEach((value, index) => { + list.setItem(value, tensors[index]); + }); + return list; +} +function split2(tensor2, length, elementShape) { + let totalLength = 0; + const cumulativeLengths = length.map((len) => { + totalLength += len; + return totalLength; + }); + if (totalLength !== tensor2.shape[0]) { + throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${n}, and tensor's shape is: ${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=v("thenBranch",r,t,e),o=v("elseBranch",r,t,e),s=v("cond",r,t,e),i=v("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 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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=v("keyDType",r,t,e),i=v("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=v("tableHandle",r,t,e,n),s=v("keys",r,t,e),i=v("values",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let 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o=v("images",r,t,e),s=v("transforms",r,t,e),i=v("outputShape",r,t,e),a=v("fillValue",r,t,e),u=v("interpolation",r,t,e),l=v("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 bF=(r,t,e,n=ae)=>{switch(r.op){case"Equal":return[n.equal(v("a",r,t,e),v("b",r,t,e))];case"NotEqual":return[n.notEqual(v("a",r,t,e),v("b",r,t,e))];case"Greater":return[n.greater(v("a",r,t,e),v("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(v("a",r,t,e),v("b",r,t,e))];case"Less":return[n.less(v("a",r,t,e),v("b",r,t,e))];case"LessEqual":return[n.lessEqual(v("a",r,t,e),v("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(v("a",r,t,e),v("b",r,t,e))];case"LogicalNot":return[n.logicalNot(v("a",r,t,e))];case"LogicalOr":return[n.logicalOr(v("a",r,t,e),v("b",r,t,e))];case"Select":case"SelectV2":return[n.where(v("condition",r,t,e),v("a",r,t,e),v("b",r,t,e))];case"BitwiseAnd":return[n.bitwiseAnd(v("a",r,t,e),v("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var wF=(r,t,e,n=ae)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(v("a",r,t,e),v("b",r,t,e),v("transposeA",r,t,e),v("transposeB",r,t,e))];case"Einsum":return[n.einsum(v("equation",r,t,e),...v("tensors",r,t,e))];case"Transpose":return[n.transpose(v("x",r,t,e),v("perm",r,t,e))];case"_FusedMatMul":let[o,s]=v("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=v("numArgs",r,t,e),l=v("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]=v("args",r,t,e);return[n.fused.matMul({a:v("a",r,t,e),b:v("b",r,t,e),transposeA:v("transposeA",r,t,e),transposeB:v("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case"MatrixBandPart":return[n.linalg.bandPart(v("a",r,t,e),v("numLower",r,t,e),v("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var IF=(r,t,e,n=ae)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(v("x",r,t,e),v("axis",r,t,e),v("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(v("x",r,t,e),v("radius",r,t,e),v("bias",r,t,e),v("alpha",r,t,e),v("beta",r,t,e))];case"Softmax":return[n.softmax(v("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var CF=(r,t,e,n=ae)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v("paramsNestedSplits",r,t,e),v("paramsDenseValues",r,t,e),v("indices",r,t,e),v("outputRaggedRank",r,t,e));return o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v("starts",r,t,e),v("limits",r,t,e),v("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(v("shape",r,t,e),v("values",r,t,e),v("defaultValue",r,t,e),v("rowPartitionTensors",r,t,e),v("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var vF=(r,t,e,n=ae)=>{switch(r.op){case"Max":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.max(v("x",r,t,e),a,u)]}case"Mean":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.mean(v("x",r,t,e),a,u)]}case"Min":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.min(v("x",r,t,e),a,u)]}case"Sum":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.sum(v("x",r,t,e),a,u)]}case"All":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.all(v("x",r,t,e),a,u)]}case"Any":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.any(v("x",r,t,e),a,u)]}case"ArgMax":{let a=v("axis",r,t,e);return[n.argMax(v("x",r,t,e),a)]}case"ArgMin":{let a=v("axis",r,t,e);return[n.argMin(v("x",r,t,e),a)]}case"Prod":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.prod(v("x",r,t,e),a,u)]}case"Cumprod":{let a=v("axis",r,t,e),u=v("exclusive",r,t,e),l=v("reverse",r,t,e);return[n.cumprod(v("x",r,t,e),a,u,l)]}case"Cumsum":{let a=v("axis",r,t,e),u=v("exclusive",r,t,e),l=v("reverse",r,t,e);return[n.cumsum(v("x",r,t,e),a,u,l)]}case"Bincount":let o=v("x",r,t,e),s=v("weights",r,t,e),i=v("size",r,t,e);return[n.bincount(o,s,i)];case"DenseBincount":{let a=v("x",r,t,e),u=v("weights",r,t,e),l=v("size",r,t,e),c=v("binaryOutput",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var SF=(r,t,e,n=ae)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=v("n",r,t,e),s=v("axis",r,t,e),i=v("tensors",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case"Gather":{let o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gather(o,n.cast(s,"int32"),0)]}case"GatherV2":{let o=v("axis",r,t,e),s=v("batchDims",r,t,e),i=v("x",r,t,e),a=v("indices",r,t,e);return[n.gather(i,n.cast(a,"int32"),o,s)]}case"Reverse":{let o=v("dims",r,t,e),s=[];for(let a=0;a{let o=v("axis",r,t,e),s=v("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=v("axis",r,t,e),s=v("tensor",r,t,e);return n.unstack(s,o)}case"Tile":{let o=v("reps",r,t,e);return[n.tile(v("x",r,t,e),o)]}case"Split":case"SplitV":{let o=v("axis",r,t,e),s=v("numOrSizeSplits",r,t,e),i=v("x",r,t,e);return n.split(i,s,o)}case"ScatterNd":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("shape",r,t,e);return[n.scatterND(o,s,i)]}case"GatherNd":{let o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gatherND(o,s)]}case"SparseToDense":{let o=v("sparseIndices",r,t,e),s=v("outputShape",r,t,e),i=v("sparseValues",r,t,e),a=v("defaultValue",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case"TensorScatterUpdate":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("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=ae)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v("indices",r,t,e),v("values",r,t,e),v("denseShape",r,t,e),v("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v("inputIndices",r,t,e),v("inputShape",r,t,e),v("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var kF=(r,t,e,n=ae)=>{switch(r.op){case"FFT":return[n.fft(v("x",r,t,e))];case"IFFT":return[n.ifft(v("x",r,t,e))];case"RFFT":return[n.rfft(v("x",r,t,e))];case"IRFFT":return[n.irfft(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var TF=(r,t,e,n=ae)=>{switch(r.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(v("input",r,t,e),v("pattern",r,t,e),v("rewrite",r,t,e),v("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v("data",r,t,e),v("dataSplits",r,t,e),v("separator",r,t,e),v("nGramWidths",r,t,e),v("leftPad",r,t,e),v("rightPad",r,t,e),v("padWidth",r,t,e),v("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v("input",r,t,e),v("delimiter",r,t,e),v("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(v("input",r,t,e),v("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _F=(r,t,e,n=ae)=>{switch(r.op){case"Cast":return[n.cast(v("x",r,t,e),v("dtype",r,t,e))];case"ExpandDims":{let o=v("axis",r,t,e);return[n.expandDims(v("x",r,t,e),o)]}case"Squeeze":{let o=v("axis",r,t,e);return[n.squeeze(v("x",r,t,e),o)]}case"Reshape":return[n.reshape(v("x",r,t,e),v("shape",r,t,e))];case"EnsureShape":return[n.ensureShape(v("x",r,t,e),v("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(v("x",r,t,e),v("padding",r,t,e),v("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(v("x",r,t,e),v("padding",r,t,e),v("constantValue",r,t,e))];case"SpaceToBatchND":{let o=v("blockShape",r,t,e),s=v("paddings",r,t,e);return[n.spaceToBatchND(v("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=v("blockShape",r,t,e),s=v("crops",r,t,e);return[n.batchToSpaceND(v("x",r,t,e),o,s)]}case"DepthToSpace":{let o=v("blockSize",r,t,e),s=v("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(v("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(v("x",r,t,e),v("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(v("s0",r,t,e),v("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Nk(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>nF(i,a,u));case"basic_math":return o(()=>oF(i,a,u));case"control":return cF(i,a,u);case"convolution":return o(()=>mF(i,a,u));case"creation":return o(()=>fF(i,a,u));case"dynamic":return dF(i,a,u);case"evaluation":return o(()=>hF(i,a,u));case"image":return o(()=>yF(i,a,u));case"graph":return o(()=>gF(i,a,u));case"logical":return o(()=>bF(i,a,u));case"matrices":return o(()=>wF(i,a,u));case"normalization":return o(()=>IF(i,a,u));case"ragged":return o(()=>CF(i,a,u));case"reduction":return o(()=>vF(i,a,u));case"slice_join":return o(()=>SF(i,a,u));case"sparse":return o(()=>NF(i,a,u));case"spectral":return o(()=>kF(i,a,u));case"string":return o(()=>TF(i,a,u));case"transformation":return o(()=>_F(i,a,u));case"hash_table":return xF(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 kk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>vn(m)[0]));n=n||[];let c=new Set(n.map(m=>vn(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Ku(m)||jQ(m)||XQ(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 EF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>vn(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=WQ(d,u);return UQ(h,u),h}function WQ(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 ad=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function UQ(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 ad(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new ad(`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 ad(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new ad(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function AF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Ku(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new op(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=kk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${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=EF(this.graph,n),u=AF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return $e(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[vn(m)[0]]),s=e.map(m=>vn(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Kh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=vn(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=Nk(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. 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c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=Ii(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]=vn(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 ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=vn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=vn(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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ZF={};Kt(ZF,{CSVDataset:()=>cd,Dataset:()=>vi,FileDataSource:()=>hd,TextLineDataset:()=>ud,URLDataSource:()=>gd,array:()=>VF,csv:()=>qF,func:()=>KF,generator:()=>jF,microphone:()=>YF,version_data:()=>Kk,webcam:()=>XF,zip:()=>GF});var BF=Xl(bh());var MF=Xl(bh());function $F(r,t){return rw(r,t)}function rw(r,t,e=new Map,n=new Set){if(r==null)return null;if(typeof Blob=="function"&&r instanceof Blob)return r.slice();if(n.has(r))throw new Error("Circular references are not supported.");if(e.has(r))return e.get(r);let o=t(r);if(o.recurse&&o.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(o.recurse)if(ju(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let a=r[i],u=rw(a,t,e,n);s[i]=u}return n.delete(r),r.__proto__&&(s.__proto__=r.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return e.set(r,o.value),o.value}function RF(r,t=_k){return FF(r,t)}function FF(r,t,e=new Set){let 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tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}},Ok=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Pk=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=So.getTensorsInContainer(t.value),n=this.transform(t.value),o=So.getTensorsInContainer(n);for(let s of e)So.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Mk=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=So.getTensorsInContainer(t.value),n=await this.transform(t.value),o=So.getTensorsInContainer(n);for(let s of e)So.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},ip=class extends tr{constructor(){super(),this.outputQueue=new sp,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}}},Lk=class extends ip{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=So.getTensorsInContainer(t.value),n=this.transform(t.value),o=So.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)So.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}},Ll;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Ll||(Ll={}));var zk=class extends tr{constructor(t,e=Ll.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 Ll.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Ll.SHORTEST:return{value:null,done:!0};case Ll.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},iw=class extends tr{constructor(t,e){super(),this.upstream=t,this.bufferSize=e,this.buffer=new ld(e)}summary(){return`${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()}},Bk=class extends iw{constructor(t,e,n){super(t,e),this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=MF.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 vi=class{constructor(){this.size=null}batch(t,e=!0){let n=this;y.assert(t>0,()=>`batchSize needs to be positive, but it is - ${t}`);let o;return this.size===1/0||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),$n(async()=>(await n.iterator()).columnMajorBatch(t,e,ntt),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,$n(async()=>(await e.iterator()).concatenate(await t.iterator()),n)}filter(t){let e=this,n;return this.size===1/0?n=1/0:n=null,$n(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 $n(async()=>(await e.iterator()).map(n=>B(()=>t(n))),this.size)}mapAsync(t){let e=this;return $n(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 $n(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,n;return this.size!=null&&t>0?n=this.size*t:t===0?n=0:this.size!=null&&(t===void 0||t<0)?n=1/0:n=null,$n(async()=>{let o=Xh(async()=>({value:await e.iterator(),done:!1}));return LF(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=BF.alea(e||y.now().toString());return $n(async()=>{let i=s.int32();return n&&(i+=s.int32()),(await o.iterator()).shuffle(t,i.toString())},this.size)}take(t){let e=this,n;return this.size!=null&&this.size>t?n=t:this.size!=null&&this.size<=t?n=this.size:n=null,$n(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()}};vi.MAX_BUFFER_SIZE=1e4;function $n(r,t=null){return new class extends vi{constructor(){super(...arguments),this.size=t}async iterator(){return r()}}}function VF(r){return $n(async()=>Vk(r),r.length)}function GF(r){if(!ju(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 vi)return{value:n.iterator(),recurse:!1};if(ju(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return zF(e,Ll.SHORTEST)},t)}function ntt(r){if(r===null)return null;let t=r[0];return OF(t)?{value:ott(r),recurse:!1}:{value:null,recurse:!0}}function ott(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ot?qe(r):sr(r)}var ud=class extends vi{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"),WF=Symbol("field"),lw=Symbol("quote"),Gk=Symbol("quoteafterquote"),UF=Symbol("quoteinquote"),cd=class extends vi{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 ud(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 pd(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 md=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=fi([i,s,u,a],[1,4])}else this.cropBox=fi([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 md(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: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ar(Q(t,"float32"),0),n;n=hn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var fd=class{};var Zh=class extends tr{split(t){return new Wk(this,t)}},Wk=class extends Zh{constructor(t,e){super(),this.upstream=t,this.impl=new Uk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Uk=class extends ip{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var uw=class extends tr{decodeUTF8(){return new Hk(this)}},Hk=class extends Zh{constructor(t){super(),this.upstream=t,this.impl=new qk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},qk=class extends ip{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=Tk();this.decoder=new e("utf8")}}summary(){return`${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 dd=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=wt(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(Ct)!==Ct)continue;let Rt=G.get(W,ot,mt,Ct,q);st+=Rt}}}P.set(st*V,W,H,K,X,q)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var aP={kernelName:Jl,backendName:"cpu",kernelFunc:tet};function eet(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=wt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,D=wt(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 lP={kernelName:Zl,backendName:"cpu",kernelFunc:eet};function ret(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 uP={kernelName:ys,backendName:"cpu",kernelFunc:ret};function net(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=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=Bo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var cP={kernelName:Pi,backendName:"cpu",kernelFunc:net};function oet(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=bd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var pP={kernelName:Oa,backendName:"cpu",kernelFunc:oet};function set(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 mP={kernelName:Ql,backendName:"cpu",kernelFunc:set};var iet=At(yo,(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 Zr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(I=>Mo({inputs:{input:I},backend:e})),g=u.map(I=>va({inputs:{input:I},backend:e})),x=Yu({inputs:h,backend:e,attrs:{axis:s}}),b=Yu({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=ap(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 gP={kernelName:Mi,backendName:"cpu",kernelFunc:Yu};function _T(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],Ct=et+mt*D;for(let Rt=0;Rt=m.inWidth)continue;let ge=gt+qt*E[1],re=Ct+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 Ct=0;Ct=l.inWidth)continue;let ce=mt+Ht*F[2],ge=gt+qt*l.inChannels,re=ce;for(let xe=0;xeMath.cos(r)),vP={kernelName:is,backendName:"cpu",kernelFunc:det};var het=At(as,r=>Math.cosh(r)),SP={kernelName:as,backendName:"cpu",kernelFunc:het};function get(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=wt([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,Ct=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 OP={kernelName:ou,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 ${ou}, 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 PP={kernelName:nu,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 ${nu}, 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 Net(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype==="float32"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${A}.`)}else if(o.dtype==="int32"&&(A<0||A>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${A}.`);d===1?(I[0]=A*g,I[1]=A*g,I[2]=A*g):I[E]=A*g}let N=w*4;x[N+0]=Math.round(I[0]),x[N+1]=Math.round(I[1]),x[N+2]=Math.round(I[2]),x[N+3]=Math.round(I[3])}s.width=f,s.height=m;let b=new ImageData(x,f,m);return p.putImageData(b,0,0),o}var MP={kernelName:Zg,backendName:"cpu",kernelFunc:Net};function zl(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"sum");let a;o.dtype==="bool"?a=Lo({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=Zr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=Ge({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,"int32"),g=xd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I=0&&(m=zl({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var zP={kernelName:Wp,backendName:"cpu",kernelFunc:ket};function Tet(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],"eluGrad");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,"float32",s)}var BP={kernelName:Ga,backendName:"cpu",kernelFunc:Tet};var _et=S.ERF_P,Eet=S.ERF_A1,Aet=S.ERF_A2,Det=S.ERF_A3,$et=S.ERF_A4,Ret=S.ERF_A5,Fet=At(fs,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+_et*e);return t*(1-((((Ret*n+$et)*n+Det)*n+Aet)*n+Eet)*n*Math.exp(-e*e))}),VP={kernelName:fs,backendName:"cpu",kernelFunc:Fet};function Sd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Qt({inputs:{x:o},backend:e,attrs:{shape:a}})}var GP={kernelName:Li,backendName:"cpu",kernelFunc:Sd};var Oet=Jt((r,t)=>r/t),eg=oe(ps,Oet),rg={kernelName:ps,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 XP={kernelName:zi,backendName:"cpu",kernelFunc:Uet};function Het(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 YP={kernelName:Hp,backendName:"cpu",kernelFunc:Het};var qet=At(ws,r=>Number.isFinite(r)?1:0,"bool"),ZP={kernelName:ws,backendName:"cpu",kernelFunc:qet};var Ket=At(Is,r=>Math.abs(r)===1/0?1:0,"bool"),JP={kernelName:Is,backendName:"cpu",kernelFunc:Ket};var jet=At(Cs,r=>Number.isNaN(r)?1:0,"bool"),QP={kernelName:Cs,backendName:"cpu",kernelFunc:jet};function Xet(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 tM={kernelName:Xa,backendName:"cpu",kernelFunc:Xet};var Yet=At(Ns,r=>Math.log1p(r)),eM={kernelName:Ns,backendName:"cpu",kernelFunc:Yet};var Zet=Jt((r,t)=>r&&t),Jet=oe(Ya,Zet,null,"bool"),rM={kernelName:Ya,backendName:"cpu",kernelFunc:Jet};var Qet=At(Za,r=>r?0:1,"bool"),nM={kernelName:Za,backendName:"cpu",kernelFunc:Qet};var trt=Jt((r,t)=>r||t),ert=oe(Ja,trt,null,"bool"),oM={kernelName:Ja,backendName:"cpu",kernelFunc:ert};function rrt(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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+ const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape); + const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength; + const tensors = tidy(() => { + const tensors2 = []; + tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]); + for (let i = 0; i < length.length; ++i) { + const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1]; + const indices = [0, previousLength, 0]; + const sizes = [1, length[i], elementPerRow]; + tensors2[i] = reshape(slice(tensor2, indices, sizes), outputElementShape); + } + tensor2.dispose(); + return tensors2; + }); + const list = new TensorList([], elementShape, tensor2.dtype, length.length); + for (let i = 0; i < tensors.length; i++) { + list.setItem(i, tensors[i]); + } + return list; +} + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/control_executor.js +var executeOp3 = async (node, tensorMap, context) => { + switch (node.op) { + case "If": + case "StatelessIf": { + const thenFunc = getParamValue("thenBranch", node, tensorMap, context); + const elseFunc = getParamValue("elseBranch", node, tensorMap, context); + const cond = getParamValue("cond", node, tensorMap, context); + const args = getParamValue("args", node, tensorMap, context); + const condValue = await cond.data(); + if (condValue[0]) { + return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap); + } else { + return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap); + } + } + case "While": + case "StatelessWhile": { + const bodyFunc = getParamValue("body", node, tensorMap, context); + const condFunc = getParamValue("cond", node, tensorMap, context); + const args = getParamValue("args", node, tensorMap, context); + const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap); + const argIds = args.map((tensor2) => tensor2.id); + let condValue = await condResult[0].data(); + condResult.forEach((tensor2) => { + if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1) { + tensor2.dispose(); + } + }); + let result = args; + while (condValue[0]) { + const origResult = result; + result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap); + const resultIds = result.map((tensor2) => tensor2.id); + origResult.forEach((tensor2) => { + if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) { + tensor2.dispose(); + } + }); + const condResult2 = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap); + condValue = await condResult2[0].data(); + condResult2.forEach((tensor2) => { + if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) { + tensor2.dispose(); + } + }); + } + return result; + } + case "LoopCond": { + const pred = getParamValue("pred", node, tensorMap, context); + return [cloneTensor(pred)]; + } + case "Switch": { + const pred = getParamValue("pred", node, tensorMap, context); + let data = getParamValue("data", node, tensorMap, context); + if (!data.kept) { + data = cloneTensor(data); + } + return (await pred.data())[0] ? [void 0, data] : [data, void 0]; + } + case "Merge": { + const inputName = node.inputNames.find((name) => getTensor(name, tensorMap, context) !== void 0); + if (inputName) { + const data = getTensor(inputName, tensorMap, context); + return [cloneTensor(data)]; + } + return void 0; + } + case "Enter": { + const frameId = getParamValue("frameName", node, tensorMap, context); + const data = getParamValue("tensor", node, tensorMap, context); + context.enterFrame(frameId); + return [cloneTensor(data)]; + } + case "Exit": { + const data = getParamValue("tensor", node, tensorMap, context); + context.exitFrame(); + return [cloneTensor(data)]; + } + case "NextIteration": { + const data = getParamValue("tensor", node, tensorMap, context); + context.nextIteration(); + return [cloneTensor(data)]; + } + case "TensorArrayV3": { + const size = getParamValue("size", node, tensorMap, context); + const dtype = getParamValue("dtype", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const dynamicSize = getParamValue("dynamicSize", node, tensorMap, context); + const clearAfterRead = getParamValue("clearAfterRead", node, tensorMap, context); + const identicalElementShapes = getParamValue("identicalElementShapes", node, tensorMap, context); + const name = getParamValue("name", node, tensorMap, context); + const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead); + context.addTensorArray(tensorArray); + return [tensorArray.idTensor, scalar(1)]; + } + case "TensorArrayWriteV3": { + const id = getParamValue("tensorArrayId", node, tensorMap, context); + const index = getParamValue("index", node, tensorMap, context); + const writeTensor = getParamValue("tensor", node, tensorMap, context); + const writeTensorArray = context.getTensorArray(id.id); + writeTensorArray.write(index, writeTensor); + return [writeTensorArray.idTensor]; + } + case "TensorArrayReadV3": { + const readId = getParamValue("tensorArrayId", node, tensorMap, context); + const readIndex = getParamValue("index", node, tensorMap, context); + const readTensorArray = context.getTensorArray(readId.id); + return [readTensorArray.read(readIndex)]; + } + case "TensorArrayGatherV3": { + const gatherId = getParamValue("tensorArrayId", node, tensorMap, context); + const gatherIndices = getParamValue("indices", node, tensorMap, context); + const gatherDtype = getParamValue("dtype", node, tensorMap, context); + const gatherTensorArray = context.getTensorArray(gatherId.id); + return [gatherTensorArray.gather(gatherIndices, gatherDtype)]; + } + case "TensorArrayScatterV3": { + const scatterId = getParamValue("tensorArrayId", node, tensorMap, context); + const scatterIndices = getParamValue("indices", node, tensorMap, context); + const scatterTensor = getParamValue("tensor", node, tensorMap, context); + const scatterTensorArray = context.getTensorArray(scatterId.id); + scatterTensorArray.scatter(scatterIndices, scatterTensor); + return [scatterTensorArray.idTensor]; + } + case "TensorArrayConcatV3": { + const concatId = getParamValue("tensorArrayId", node, tensorMap, context); + const concatTensorArray = context.getTensorArray(concatId.id); + const concatDtype = getParamValue("dtype", node, tensorMap, context); + return [concatTensorArray.concat(concatDtype)]; + } + case "TensorArraySplitV3": { + const splitId = getParamValue("tensorArrayId", node, tensorMap, context); + const splitTensor = getParamValue("tensor", node, tensorMap, context); + const lengths = getParamValue("lengths", node, tensorMap, context); + const splitTensorArray = context.getTensorArray(splitId.id); + splitTensorArray.split(lengths, splitTensor); + return [splitTensorArray.idTensor]; + } + case "TensorArraySizeV3": { + const sizeId = getParamValue("tensorArrayId", node, tensorMap, context); + const sizeTensorArray = context.getTensorArray(sizeId.id); + return [scalar(sizeTensorArray.size(), "int32")]; + } + case "TensorArrayCloseV3": { + const closeId = getParamValue("tensorArrayId", node, tensorMap, context); + const closeTensorArray = context.getTensorArray(closeId.id); + closeTensorArray.clearAndClose(); + return [closeTensorArray.idTensor]; + } + case "TensorListSetItem": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const index = getParamValue("index", node, tensorMap, context); + const writeTensor = getParamValue("tensor", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + tensorList.setItem(index, writeTensor); + return [tensorList.idTensor]; + } + case "TensorListGetItem": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const readIndex = getParamValue("index", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDType = getParamValue("elementDType", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + return [tensorList.getItem(readIndex, elementShape, elementDType)]; + } + case "TensorListScatterV2": + case "TensorListScatter": { + const scatterIndices = getParamValue("indices", node, tensorMap, context); + const scatterTensor = getParamValue("tensor", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const numElements = getParamValue("numElements", node, tensorMap, context); + const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements); + context.addTensorList(tensorList); + return [tensorList.idTensor]; + } + case "TensorListReserve": + case "EmptyTensorList": { + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDtype = getParamValue("elementDType", node, tensorMap, context); + let numElementsParam; + if (node.op === "TensorListReserve") { + numElementsParam = "numElements"; + } else { + numElementsParam = "maxNumElements"; + } + const numElements = getParamValue(numElementsParam, node, tensorMap, context); + const maxNumElements = node.op === "TensorListReserve" ? -1 : numElements; + const tensorList = reserve(elementShape, elementDtype, numElements, maxNumElements); + context.addTensorList(tensorList); + return [tensorList.idTensor]; + } + case "TensorListGather": { + const gatherId = getParamValue("tensorListId", node, tensorMap, context); + const gatherIndices = getParamValue("indices", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDtype = getParamValue("elementDType", node, tensorMap, context); + const tensorList = context.getTensorList(gatherId.id); + return [tensorList.gather(gatherIndices, elementDtype, elementShape)]; + } + case "TensorListStack": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDtype = getParamValue("elementDType", node, tensorMap, context); + const numElements = getParamValue("numElements", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + return [tensorList.stack(elementShape, elementDtype, numElements)]; + } + case "TensorListFromTensor": { + const tensor2 = getParamValue("tensor", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDtype = getParamValue("elementDType", node, tensorMap, context); + const tensorList = fromTensor(tensor2, elementShape, elementDtype); + context.addTensorList(tensorList); + return [tensorList.idTensor]; + } + case "TensorListConcat": + case "TensorListConcatV2": { + const concatId = getParamValue("tensorListId", node, tensorMap, context); + const tensorList = context.getTensorList(concatId.id); + const concatDtype = getParamValue("dtype", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + return [tensorList.concat(concatDtype, elementShape)]; + } + case "TensorListPushBack": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const writeTensor = getParamValue("tensor", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + tensorList.pushBack(writeTensor); + return [tensorList.idTensor]; + } + case "TensorListPopBack": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const elementDType = getParamValue("elementDType", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + return [tensorList.popBack(elementShape, elementDType)]; + } + case "TensorListSplit": { + const splitTensor = getParamValue("tensor", node, tensorMap, context); + const elementShape = getParamValue("elementShape", node, tensorMap, context); + const lengths = getParamValue("lengths", node, tensorMap, context); + const tensorList = split2(splitTensor, lengths, elementShape); + context.addTensorList(tensorList); + return [tensorList.idTensor]; + } + case "TensorListLength": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const tensorList = context.getTensorList(idTensor.id); + return [scalar(tensorList.size(), "int32")]; + } + case "TensorListResize": { + const idTensor = getParamValue("tensorListId", node, tensorMap, context); + const size = getParamValue("size", node, tensorMap, context); + const srcTensorList = context.getTensorList(idTensor.id); + const destTensorList = srcTensorList.resize(size); + context.addTensorList(destTensorList); + return [destTensorList.idTensor]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js +function fusedConvAndDepthWiseParams(node, tensorMap, context) { + const [extraOp, activationFunc] = getParamValue("fusedOps", node, tensorMap, context); + const isBiasAdd = extraOp === "biasadd"; + const noBiasAdd = !isBiasAdd; + const isPrelu = activationFunc === "prelu"; + const isBatchNorm = extraOp === "fusedbatchnorm"; + const numArgs = getParamValue("numArgs", node, tensorMap, context); + if (isBiasAdd) { + if (isPrelu && numArgs !== 2) { + throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha."); + } + if (!isPrelu && isBiasAdd && numArgs !== 1) { + throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias."); + } + } + if (isBatchNorm) { + throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported"); + } + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getPadding(node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + const dilations = getParamValue("dilations", node, tensorMap, context); + let [biasArg, preluArg] = getParamValue("args", node, tensorMap, context); + if (noBiasAdd) { + preluArg = biasArg; + biasArg = void 0; + } + const leakyreluAlpha = getParamValue("leakyreluAlpha", node, tensorMap, context); + return { + stride, + pad: pad3, + dataFormat, + dilations, + biasArg, + preluArg, + activationFunc, + leakyreluAlpha + }; +} +var executeOp4 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Conv1D": { + const stride = getParamValue("stride", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + const dilation = getParamValue("dilation", node, tensorMap, context); + return [ops.conv1d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), stride, pad3, dataFormat, dilation)]; + } + case "Conv2D": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getPadding(node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + const dilations = getParamValue("dilations", node, tensorMap, context); + return [ops.conv2d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])]; + } + case "_FusedConv2D": { + const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context); + return [ops.fused.conv2d({ + x: getParamValue("x", node, tensorMap, context), + filter: getParamValue("filter", node, tensorMap, context), + strides: [stride[1], stride[2]], + pad: pad3, + dataFormat, + dilations: [dilations[1], dilations[2]], + bias: biasArg, + activation: activationFunc, + preluActivationWeights: preluArg, + leakyreluAlpha + })]; + } + case "FusedDepthwiseConv2dNative": { + const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context); + return [ops.fused.depthwiseConv2d({ + x: getParamValue("x", node, tensorMap, context), + filter: getParamValue("filter", node, tensorMap, context), + strides: [stride[1], stride[2]], + pad: pad3, + dataFormat, + dilations: [dilations[1], dilations[2]], + bias: biasArg, + activation: activationFunc, + preluActivationWeights: preluArg, + leakyreluAlpha + })]; + } + case "Conv2DBackpropInput": + case "Conv2dTranspose": { + const shape = getParamValue("outputShape", node, tensorMap, context); + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getPadding(node, tensorMap, context); + return [ops.conv2dTranspose(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), shape, [stride[1], stride[2]], pad3)]; + } + case "DepthwiseConv2dNative": + case "DepthwiseConv2d": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getPadding(node, tensorMap, context); + const dilations = getParamValue("dilations", node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + return [ops.depthwiseConv2d(getParamValue("input", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])]; + } + case "Conv3D": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + const dilations = getParamValue("dilations", node, tensorMap, context); + return [ops.conv3d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2], stride[3]], pad3, dataFormat, [dilations[1], dilations[2], dilations[3]])]; + } + case "AvgPool": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const kernelSize = getParamValue("kernelSize", node, tensorMap, context); + return [ops.avgPool(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)]; + } + case "MaxPool": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const kernelSize = getParamValue("kernelSize", node, tensorMap, context); + return [ops.maxPool(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)]; + } + case "MaxPoolWithArgmax": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const kernelSize = getParamValue("kernelSize", node, tensorMap, context); + const includeBatchInIndex = getParamValue("includeBatchInIndex", node, tensorMap, context); + const { result, indexes } = ops.maxPoolWithArgmax(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3, includeBatchInIndex); + return [result, indexes]; + } + case "AvgPool3D": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const kernelSize = getParamValue("kernelSize", node, tensorMap, context); + return [ops.avgPool3d(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)]; + } + case "MaxPool3D": { + const stride = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const kernelSize = getParamValue("kernelSize", node, tensorMap, context); + return [ops.maxPool3d(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)]; + } + case "Dilation2D": { + const strides = getParamValue("strides", node, tensorMap, context); + const pad3 = getParamValue("pad", node, tensorMap, context); + const dilations = getParamValue("dilations", node, tensorMap, context); + const strideHeight = strides[1]; + const strideWidth = strides[2]; + const dilationHeight = dilations[1]; + const dilationWidth = dilations[2]; + return [ops.dilation2d( + getParamValue("x", node, tensorMap, context), + getParamValue("filter", node, tensorMap, context), + [strideHeight, strideWidth], + pad3, + [dilationHeight, dilationWidth], + "NHWC" + /* dataFormat */ + )]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/creation_executor.js +var executeOp5 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Fill": { + const shape = getParamValue("shape", node, tensorMap, context); + const dtype = getParamValue("dtype", node, tensorMap, context); + const value = getParamValue("value", node, tensorMap, context); + return [ops.fill(shape, value, dtype)]; + } + case "LinSpace": { + const start = getParamValue("start", node, tensorMap, context); + const stop = getParamValue("stop", node, tensorMap, context); + const num = getParamValue("num", node, tensorMap, context); + return [ops.linspace(start, stop, num)]; + } + case "Multinomial": { + const logits = getParamValue("logits", node, tensorMap, context); + const numSamples = getParamValue("numSamples", node, tensorMap, context); + const seed = getParamValue("seed", node, tensorMap, context); + return [ops.multinomial(logits, numSamples, seed)]; + } + case "OneHot": { + const indices = getParamValue("indices", node, tensorMap, context); + const depth = getParamValue("depth", node, tensorMap, context); + const onValue = getParamValue("onValue", node, tensorMap, context); + const offValue = getParamValue("offValue", node, tensorMap, context); + const dtype = getParamValue("dtype", node, tensorMap, context); + return [ops.oneHot(indices, depth, onValue, offValue, dtype)]; + } + case "Ones": { + return [ops.ones(getParamValue("shape", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))]; + } + case "OnesLike": { + return [ops.onesLike(getParamValue("x", node, tensorMap, context))]; + } + case "RandomStandardNormal": { + return [ops.randomStandardNormal(getParamValue("shape", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context), getParamValue("seed", node, tensorMap, context))]; + } + case "RandomUniform": { + return [ops.randomUniform( + // tslint:disable-next-line:no-any + getParamValue("shape", node, tensorMap, context), + getParamValue("minval", node, tensorMap, context), + getParamValue("maxval", node, tensorMap, context), + getParamValue("dtype", node, tensorMap, context) + )]; + } + case "RandomUniformInt": { + return [ops.randomUniformInt(getParamValue("shape", node, tensorMap, context), getParamValue("minval", node, tensorMap, context), getParamValue("maxval", node, tensorMap, context), getParamValue("seed", node, tensorMap, context))]; + } + case "Range": { + const start = getParamValue("start", node, tensorMap, context); + const stop = getParamValue("stop", node, tensorMap, context); + const step5 = getParamValue("step", node, tensorMap, context); + return [ops.range(start, stop, step5, getParamValue("dtype", node, tensorMap, context))]; + } + case "TruncatedNormal": { + const shape = getParamValue("shape", node, tensorMap, context); + const mean4 = getParamValue("mean", node, tensorMap, context); + const stdDev = getParamValue("stdDev", node, tensorMap, context); + const seed = getParamValue("seed", node, tensorMap, context); + return [ops.truncatedNormal(shape, mean4, stdDev, getParamValue("dtype", node, tensorMap, context), seed)]; + } + case "Zeros": { + return [ops.zeros(getParamValue("shape", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))]; + } + case "ZerosLike": { + return [ops.zerosLike(getParamValue("x", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/dynamic_executor.js +function nmsParams(node, tensorMap, context) { + const boxes = getParamValue("boxes", node, tensorMap, context); + const scores = getParamValue("scores", node, tensorMap, context); + const maxOutputSize = getParamValue("maxOutputSize", node, tensorMap, context); + const iouThreshold = getParamValue("iouThreshold", node, tensorMap, context); + const scoreThreshold = getParamValue("scoreThreshold", node, tensorMap, context); + const softNmsSigma = getParamValue("softNmsSigma", node, tensorMap, context); + return { + boxes, + scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + softNmsSigma + }; +} +var executeOp6 = async (node, tensorMap, context, resourceManager, ops = ops_for_converter_exports) => { + switch (node.op) { + case "NonMaxSuppressionV5": { + const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context); + const result = await ops.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + return [result.selectedIndices, result.selectedScores]; + } + case "NonMaxSuppressionV4": { + const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context); + const padToMaxOutputSize = getParamValue("padToMaxOutputSize", node, tensorMap, context); + const result = await ops.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize); + return [result.selectedIndices, result.validOutputs]; + } + case "NonMaxSuppressionV3": + case "NonMaxSuppressionV2": { + const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context); + return [await ops.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)]; + } + case "Where": { + const condition = ops.cast(getParamValue("condition", node, tensorMap, context), "bool"); + const result = [await ops.whereAsync(condition)]; + condition.dispose(); + return result; + } + case "ListDiff": { + return ops.setdiff1dAsync(getParamValue("x", node, tensorMap, context), getParamValue("y", node, tensorMap, context)); + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/evaluation_executor.js +var executeOp7 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "LowerBound": { + const sortedSequence = getParamValue("sortedSequence", node, tensorMap, context); + const values = getParamValue("values", node, tensorMap, context); + return [ops.lowerBound(sortedSequence, values)]; + } + case "TopKV2": { + const x = getParamValue("x", node, tensorMap, context); + const k = getParamValue("k", node, tensorMap, context); + const sorted = getParamValue("sorted", node, tensorMap, context); + const result = ops.topk(x, k, sorted); + return [result.values, result.indices]; + } + case "UpperBound": { + const sortedSequence = getParamValue("sortedSequence", node, tensorMap, context); + const values = getParamValue("values", node, tensorMap, context); + return [ops.upperBound(sortedSequence, values)]; + } + case "Unique": { + const x = getParamValue("x", node, tensorMap, context); + const result = ops.unique(x); + return [result.values, result.indices]; + } + case "UniqueV2": { + const x = getParamValue("x", node, tensorMap, context); + const axis = getParamValue("axis", node, tensorMap, context); + const result = ops.unique(x, axis); + return [result.values, result.indices]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/graph_executor.js +var executeOp8 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Const": { + return tensorMap[node.name]; + } + case "PlaceholderWithDefault": + const def = getParamValue("default", node, tensorMap, context); + return [getTensor(node.name, tensorMap, context) || def]; + case "Placeholder": + return [getTensor(node.name, tensorMap, context)]; + case "Identity": + case "StopGradient": + case "FakeQuantWithMinMaxVars": { + const data2 = getParamValue("x", node, tensorMap, context); + return [cloneTensor(data2)]; + } + case "IdentityN": + return getParamValue("x", node, tensorMap, context).map((t) => cloneTensor(t)); + case "Snapshot": + const snapshot = getParamValue("x", node, tensorMap, context); + return [cloneTensor(snapshot)]; + case "Shape": + return [ops.tensor1d(getParamValue("x", node, tensorMap, context).shape, "int32")]; + case "ShapeN": + return getParamValue("x", node, tensorMap, context).map((t) => ops.tensor1d(t.shape)); + case "Size": + return [ops.scalar(getParamValue("x", node, tensorMap, context).size, "int32")]; + case "Rank": + return [ops.scalar(getParamValue("x", node, tensorMap, context).rank, "int32")]; + case "NoOp": + return [ops.scalar(1)]; + case "Print": + const input2 = getParamValue("x", node, tensorMap, context); + const data = getParamValue("data", node, tensorMap, context); + const message = getParamValue("message", node, tensorMap, context); + const summarize = getParamValue("summarize", node, tensorMap, context); + console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."); + console.log(message); + for (let i = 0; i < data.length; i++) { + console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0, summarize)); + } + return [input2]; + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/hash_table.js +var HashTable = class { + get id() { + return this.handle.id; + } + /** + * Constructor of HashTable. Creates a hash table. + * + * @param keyDType `dtype` of the table keys. + * @param valueDType `dtype` of the table values. + */ + constructor(keyDType, valueDType) { + this.keyDType = keyDType; + this.valueDType = valueDType; + this.handle = scalar(0); + this.tensorMap = /* @__PURE__ */ new Map(); + keep(this.handle); + } + /** + * Dispose the tensors and handle and clear the hashtable. + */ + clearAndClose() { + this.tensorMap.forEach((value) => value.dispose()); + this.tensorMap.clear(); + this.handle.dispose(); + } + /** + * The number of items in the hash table. + */ + size() { + return this.tensorMap.size; + } + /** + * The number of items in the hash table as a rank-0 tensor. + */ + tensorSize() { + return scalar(this.size(), "int32"); + } + /** + * Replaces the contents of the table with the specified keys and values. + * @param keys Keys to store in the hashtable. + * @param values Values to store in the hashtable. + */ + async import(keys, values) { + this.checkKeyAndValueTensor(keys, values); + const $keys = await keys.data(); + this.tensorMap.forEach((value) => value.dispose()); + this.tensorMap.clear(); + return tidy(() => { + const $values = unstack(values); + const keysLength = $keys.length; + const valuesLength = $values.length; + util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`); + for (let i = 0; i < keysLength; i++) { + const key = $keys[i]; + const value = $values[i]; + keep(value); + this.tensorMap.set(key, value); + } + return this.handle; + }); + } + /** + * Looks up keys in a hash table, outputs the corresponding values. + * + * Performs batch lookups, for every element in the key tensor, `find` + * stacks the corresponding value into the return tensor. + * + * If an element is not present in the table, the given `defaultValue` is + * used. + * + * @param keys Keys to look up. Must have the same type as the keys of the + * table. + * @param defaultValue The scalar `defaultValue` is the value output for keys + * not present in the table. It must also be of the same type as the + * table values. + */ + async find(keys, defaultValue) { + this.checkKeyAndValueTensor(keys, defaultValue); + const $keys = await keys.data(); + return tidy(() => { + const result = []; + for (let i = 0; i < $keys.length; i++) { + const key = $keys[i]; + const value = this.findWithDefault(key, defaultValue); + result.push(value); + } + return stack(result); + }); + } + // tslint:disable-next-line: no-any + findWithDefault(key, defaultValue) { + const result = this.tensorMap.get(key); + return result != null ? result : defaultValue; + } + checkKeyAndValueTensor(key, value) { + if (key.dtype !== this.keyDType) { + throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`); + } + if (value.dtype !== this.valueDType) { + throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`); + } + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/hash_table_executor.js +var executeOp9 = async (node, tensorMap, context, resourceManager) => { + switch (node.op) { + case "HashTable": + case "HashTableV2": { + const existingTableHandle = resourceManager.getHashTableHandleByName(node.name); + if (existingTableHandle != null) { + return [existingTableHandle]; + } else { + const keyDType = getParamValue("keyDType", node, tensorMap, context); + const valueDType = getParamValue("valueDType", node, tensorMap, context); + const hashTable = new HashTable(keyDType, valueDType); + resourceManager.addHashTable(node.name, hashTable); + return [hashTable.handle]; + } + } + case "InitializeTable": + case "InitializeTableV2": + case "LookupTableImport": + case "LookupTableImportV2": { + const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager); + const keys = getParamValue("keys", node, tensorMap, context); + const values = getParamValue("values", node, tensorMap, context); + const hashTable = resourceManager.getHashTableById(handle.id); + return [await hashTable.import(keys, values)]; + } + case "LookupTableFind": + case "LookupTableFindV2": { + const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager); + const keys = getParamValue("keys", node, tensorMap, context); + const defaultValue = getParamValue("defaultValue", node, tensorMap, context); + const hashTable = resourceManager.getHashTableById(handle.id); + return [await hashTable.find(keys, defaultValue)]; + } + case "LookupTableSize": + case "LookupTableSizeV2": { + const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager); + const hashTable = resourceManager.getHashTableById(handle.id); + return [hashTable.tensorSize()]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/image_executor.js +var executeOp10 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "ResizeBilinear": { + const images = getParamValue("images", node, tensorMap, context); + const size = getParamValue("size", node, tensorMap, context); + const alignCorners = getParamValue("alignCorners", node, tensorMap, context); + const halfPixelCenters = getParamValue("halfPixelCenters", node, tensorMap, context); + return [ops.image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)]; + } + case "ResizeNearestNeighbor": { + const images = getParamValue("images", node, tensorMap, context); + const size = getParamValue("size", node, tensorMap, context); + const alignCorners = getParamValue("alignCorners", node, tensorMap, context); + const halfPixelCenters = getParamValue("halfPixelCenters", node, tensorMap, context); + return [ops.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)]; + } + case "CropAndResize": { + const image2 = getParamValue("image", node, tensorMap, context); + const boxes = getParamValue("boxes", node, tensorMap, context); + const boxInd = getParamValue("boxInd", node, tensorMap, context); + const cropSize = getParamValue("cropSize", node, tensorMap, context); + const method = getParamValue("method", node, tensorMap, context); + const extrapolationValue = getParamValue("extrapolationValue", node, tensorMap, context); + return [ops.image.cropAndResize(image2, boxes, boxInd, cropSize, method, extrapolationValue)]; + } + case "ImageProjectiveTransformV3": { + const images = getParamValue("images", node, tensorMap, context); + const transforms = getParamValue("transforms", node, tensorMap, context); + const outputShape = getParamValue("outputShape", node, tensorMap, context); + const fillValue = getParamValue("fillValue", node, tensorMap, context); + const interpolation = getParamValue("interpolation", node, tensorMap, context); + const fillMode = getParamValue("fillMode", node, tensorMap, context); + return [ops.image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/logical_executor.js +var executeOp11 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Equal": { + return [ops.equal(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "NotEqual": { + return [ops.notEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Greater": { + return [ops.greater(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "GreaterEqual": { + return [ops.greaterEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Less": { + return [ops.less(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "LessEqual": { + return [ops.lessEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "LogicalAnd": { + return [ops.logicalAnd(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "LogicalNot": { + return [ops.logicalNot(getParamValue("a", node, tensorMap, context))]; + } + case "LogicalOr": { + return [ops.logicalOr(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "Select": + case "SelectV2": { + return [ops.where(getParamValue("condition", node, tensorMap, context), getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + case "BitwiseAnd": { + return [ops.bitwiseAnd(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/matrices_executor.js +var executeOp12 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "BatchMatMul": + case "BatchMatMulV2": + case "MatMul": + return [ops.matMul(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context), getParamValue("transposeA", node, tensorMap, context), getParamValue("transposeB", node, tensorMap, context))]; + case "Einsum": + return [ops.einsum(getParamValue("equation", node, tensorMap, context), ...getParamValue("tensors", node, tensorMap, context))]; + case "Transpose": + return [ops.transpose(getParamValue("x", node, tensorMap, context), getParamValue("perm", node, tensorMap, context))]; + case "_FusedMatMul": + const [extraOp, activationFunc] = getParamValue("fusedOps", node, tensorMap, context); + const isBiasAdd = extraOp === "biasadd"; + const isPrelu = activationFunc === "prelu"; + const numArgs = getParamValue("numArgs", node, tensorMap, context); + const leakyreluAlpha = getParamValue("leakyreluAlpha", node, tensorMap, context); + if (isBiasAdd) { + if (isPrelu && numArgs !== 2) { + throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha."); + } + if (!isPrelu && numArgs !== 1) { + throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias."); + } + } + const [biasArg, preluArg] = getParamValue("args", node, tensorMap, context); + return [ops.fused.matMul({ + a: getParamValue("a", node, tensorMap, context), + b: getParamValue("b", node, tensorMap, context), + transposeA: getParamValue("transposeA", node, tensorMap, context), + transposeB: getParamValue("transposeB", node, tensorMap, context), + bias: biasArg, + activation: activationFunc, + preluActivationWeights: preluArg, + leakyreluAlpha + })]; + case "MatrixBandPart": + return [ops.linalg.bandPart(getParamValue("a", node, tensorMap, context), getParamValue("numLower", node, tensorMap, context), getParamValue("numUpper", node, tensorMap, context))]; + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/normalization_executor.js +var executeOp13 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "EuclideanNorm": + return [ops.euclideanNorm(getParamValue("x", node, tensorMap, context), getParamValue("axis", node, tensorMap, context), getParamValue("keepDims", node, tensorMap, context))]; + case "FusedBatchNorm": + case "FusedBatchNormV2": { + return [ops.batchNorm(getParamValue("x", node, tensorMap, context), getParamValue("mean", node, tensorMap, context), getParamValue("variance", node, tensorMap, context), getParamValue("offset", node, tensorMap, context), getParamValue("scale", node, tensorMap, context), getParamValue("epsilon", node, tensorMap, context))]; + } + case "FusedBatchNormV3": { + return [ops.batchNorm(getParamValue("x", node, tensorMap, context), getParamValue("mean", node, tensorMap, context), getParamValue("variance", node, tensorMap, context), getParamValue("offset", node, tensorMap, context), getParamValue("scale", node, tensorMap, context), getParamValue("epsilon", node, tensorMap, context))]; + } + case "LRN": { + return [ops.localResponseNormalization(getParamValue("x", node, tensorMap, context), getParamValue("radius", node, tensorMap, context), getParamValue("bias", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context), getParamValue("beta", node, tensorMap, context))]; + } + case "Softmax": { + return [ops.softmax(getParamValue("x", node, tensorMap, context))]; + } + case "LogSoftmax": { + return [ops.logSoftmax(getParamValue("x", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/ragged_executor.js +var executeOp14 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "RaggedGather": { + const { outputNestedSplits, outputDenseValues } = ops.raggedGather(getParamValue("paramsNestedSplits", node, tensorMap, context), getParamValue("paramsDenseValues", node, tensorMap, context), getParamValue("indices", node, tensorMap, context), getParamValue("outputRaggedRank", node, tensorMap, context)); + return outputNestedSplits.concat(outputDenseValues); + } + case "RaggedRange": { + const { rtNestedSplits, rtDenseValues } = ops.raggedRange(getParamValue("starts", node, tensorMap, context), getParamValue("limits", node, tensorMap, context), getParamValue("splits", node, tensorMap, context)); + return [rtNestedSplits, rtDenseValues]; + } + case "RaggedTensorToTensor": { + return [ops.raggedTensorToTensor(getParamValue("shape", node, tensorMap, context), getParamValue("values", node, tensorMap, context), getParamValue("defaultValue", node, tensorMap, context), getParamValue("rowPartitionTensors", node, tensorMap, context), getParamValue("rowPartitionTypes", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/reduction_executor.js +var executeOp15 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Max": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.max(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "Mean": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.mean(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "Min": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.min(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "Sum": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.sum(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "All": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.all(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "Any": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.any(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "ArgMax": { + const axis = getParamValue("axis", node, tensorMap, context); + return [ops.argMax(getParamValue("x", node, tensorMap, context), axis)]; + } + case "ArgMin": { + const axis = getParamValue("axis", node, tensorMap, context); + return [ops.argMin(getParamValue("x", node, tensorMap, context), axis)]; + } + case "Prod": { + const axis = getParamValue("axis", node, tensorMap, context); + const keepDims = getParamValue("keepDims", node, tensorMap, context); + return [ops.prod(getParamValue("x", node, tensorMap, context), axis, keepDims)]; + } + case "Cumprod": { + const axis = getParamValue("axis", node, tensorMap, context); + const exclusive = getParamValue("exclusive", node, tensorMap, context); + const reverse5 = getParamValue("reverse", node, tensorMap, context); + return [ops.cumprod(getParamValue("x", node, tensorMap, context), axis, exclusive, reverse5)]; + } + case "Cumsum": { + const axis = getParamValue("axis", node, tensorMap, context); + const exclusive = getParamValue("exclusive", node, tensorMap, context); + const reverse5 = getParamValue("reverse", node, tensorMap, context); + return [ops.cumsum(getParamValue("x", node, tensorMap, context), axis, exclusive, reverse5)]; + } + case "Bincount": + const x = getParamValue("x", node, tensorMap, context); + const weights = getParamValue("weights", node, tensorMap, context); + const size = getParamValue("size", node, tensorMap, context); + return [ops.bincount(x, weights, size)]; + case "DenseBincount": { + const x2 = getParamValue("x", node, tensorMap, context); + const weights2 = getParamValue("weights", node, tensorMap, context); + const size2 = getParamValue("size", node, tensorMap, context); + const binaryOutput = getParamValue("binaryOutput", node, tensorMap, context); + return [ops.denseBincount(x2, weights2, size2, binaryOutput)]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/slice_join_executor.js +var executeOp16 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "ConcatV2": + case "Concat": { + const n = getParamValue("n", node, tensorMap, context); + const axis = getParamValue("axis", node, tensorMap, context); + let inputs = getParamValue("tensors", node, tensorMap, context); + inputs = inputs.slice(0, n); + return [ops.concat(inputs, axis)]; + } + case "Gather": { + const input2 = getParamValue("x", node, tensorMap, context); + const indices = getParamValue("indices", node, tensorMap, context); + return [ops.gather(input2, ops.cast(indices, "int32"), 0)]; + } + case "GatherV2": { + const axis = getParamValue("axis", node, tensorMap, context); + const batchDims = getParamValue("batchDims", node, tensorMap, context); + const input2 = getParamValue("x", node, tensorMap, context); + const indices = getParamValue("indices", node, tensorMap, context); + return [ops.gather(input2, ops.cast(indices, "int32"), axis, batchDims)]; + } + case "Reverse": { + const dims = getParamValue("dims", node, tensorMap, context); + const axis = []; + for (let i = 0; i < dims.length; i++) { + if (dims[i]) { + axis.push(i); + } + } + const input2 = getParamValue("x", node, tensorMap, context); + return [ops.reverse(input2, axis)]; + } + case "ReverseV2": { + const axis = getParamValue("axis", node, tensorMap, context); + const input2 = getParamValue("x", node, tensorMap, context); + return [ops.reverse(input2, axis)]; + } + case "Slice": { + const begin = getParamValue("begin", node, tensorMap, context); + const size = getParamValue("size", node, tensorMap, context); + return [ops.slice(getParamValue("x", node, tensorMap, context), begin, size)]; + } + case "StridedSlice": { + const begin = getParamValue("begin", node, tensorMap, context); + const end = getParamValue("end", node, tensorMap, context); + const strides = getParamValue("strides", node, tensorMap, context); + const beginMask = getParamValue("beginMask", node, tensorMap, context); + const endMask = getParamValue("endMask", node, tensorMap, context); + const ellipsisMask = getParamValue("ellipsisMask", node, tensorMap, context); + const newAxisMask = getParamValue("newAxisMask", node, tensorMap, context); + const shrinkAxisMask = getParamValue("shrinkAxisMask", node, tensorMap, context); + const tensor2 = getParamValue("x", node, tensorMap, context); + return [ops.stridedSlice(tensor2, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)]; + } + case "Pack": { + return tidy(() => { + const axis = getParamValue("axis", node, tensorMap, context); + const tensors = getParamValue("tensors", node, tensorMap, context); + const shape = tensors[0].shape; + const squeezedShape = ops.squeeze(tensors[0]).shape; + const mapped = tensors.map((tensor2) => { + const sameShape = util_exports.arraysEqual(tensor2.shape, shape); + if (!sameShape && !util_exports.arraysEqual(ops.squeeze(tensor2).shape, squeezedShape)) { + throw new Error("the input tensors shape does not match"); + } + return sameShape ? tensor2 : ops.reshape(tensor2, shape); + }); + return [ops.stack(mapped, axis)]; + }); + } + case "Unpack": { + const axis = getParamValue("axis", node, tensorMap, context); + const tensor2 = getParamValue("tensor", node, tensorMap, context); + return ops.unstack(tensor2, axis); + } + case "Tile": { + const reps = getParamValue("reps", node, tensorMap, context); + return [ops.tile(getParamValue("x", node, tensorMap, context), reps)]; + } + case "Split": + case "SplitV": { + const axis = getParamValue("axis", node, tensorMap, context); + const numOrSizeSplits = getParamValue("numOrSizeSplits", node, tensorMap, context); + const tensor2 = getParamValue("x", node, tensorMap, context); + return ops.split(tensor2, numOrSizeSplits, axis); + } + case "ScatterNd": { + const indices = getParamValue("indices", node, tensorMap, context); + const values = getParamValue("values", node, tensorMap, context); + const shape = getParamValue("shape", node, tensorMap, context); + return [ops.scatterND(indices, values, shape)]; + } + case "GatherNd": { + const x = getParamValue("x", node, tensorMap, context); + const indices = getParamValue("indices", node, tensorMap, context); + return [ops.gatherND(x, indices)]; + } + case "SparseToDense": { + const indices = getParamValue("sparseIndices", node, tensorMap, context); + const shape = getParamValue("outputShape", node, tensorMap, context); + const sparseValues = getParamValue("sparseValues", node, tensorMap, context); + const defaultValue = getParamValue("defaultValue", node, tensorMap, context); + return [ops.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : ops.cast(defaultValue, sparseValues.dtype))]; + } + case "TensorScatterUpdate": { + const indices = getParamValue("indices", node, tensorMap, context); + const values = getParamValue("values", node, tensorMap, context); + const tensor2 = getParamValue("tensor", node, tensorMap, context); + return [ops.tensorScatterUpdate(tensor2, indices, values)]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/sparse_executor.js +var executeOp17 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "SparseFillEmptyRows": { + const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = ops.sparse.sparseFillEmptyRows(getParamValue("indices", node, tensorMap, context), getParamValue("values", node, tensorMap, context), getParamValue("denseShape", node, tensorMap, context), getParamValue("defaultValue", node, tensorMap, context)); + return [ + outputIndices, + outputValues, + emptyRowIndicator, + reverseIndexMap + ]; + } + case "SparseReshape": { + const { outputIndices, outputShape } = ops.sparse.sparseReshape(getParamValue("inputIndices", node, tensorMap, context), getParamValue("inputShape", node, tensorMap, context), getParamValue("newShape", node, tensorMap, context)); + return [outputIndices, outputShape]; + } + case "SparseSegmentMean": { + const outputData = ops.sparse.sparseSegmentMean(getParamValue("data", node, tensorMap, context), getParamValue("indices", node, tensorMap, context), getParamValue("segmentIds", node, tensorMap, context)); + return [outputData]; + } + case "SparseSegmentSum": { + const outputData = ops.sparse.sparseSegmentSum(getParamValue("data", node, tensorMap, context), getParamValue("indices", node, tensorMap, context), getParamValue("segmentIds", node, tensorMap, context)); + return [outputData]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/spectral_executor.js +var executeOp18 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "FFT": { + return [ops.fft(getParamValue("x", node, tensorMap, context))]; + } + case "IFFT": { + return [ops.ifft(getParamValue("x", node, tensorMap, context))]; + } + case "RFFT": { + return [ops.rfft(getParamValue("x", node, tensorMap, context))]; + } + case "IRFFT": { + return [ops.irfft(getParamValue("x", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/string_executor.js +var executeOp19 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "StaticRegexReplace": { + return [ops.string.staticRegexReplace(getParamValue("input", node, tensorMap, context), getParamValue("pattern", node, tensorMap, context), getParamValue("rewrite", node, tensorMap, context), getParamValue("replaceGlobal", node, tensorMap, context))]; + } + case "StringNGrams": { + const { nGrams, nGramsSplits } = ops.string.stringNGrams(getParamValue("data", node, tensorMap, context), getParamValue("dataSplits", node, tensorMap, context), getParamValue("separator", node, tensorMap, context), getParamValue("nGramWidths", node, tensorMap, context), getParamValue("leftPad", node, tensorMap, context), getParamValue("rightPad", node, tensorMap, context), getParamValue("padWidth", node, tensorMap, context), getParamValue("preserveShortSequences", node, tensorMap, context)); + return [nGrams, nGramsSplits]; + } + case "StringSplit": { + const { indices, values, shape } = ops.string.stringSplit(getParamValue("input", node, tensorMap, context), getParamValue("delimiter", node, tensorMap, context), getParamValue("skipEmpty", node, tensorMap, context)); + return [indices, values, shape]; + } + case "StringToHashBucketFast": { + const output = ops.string.stringToHashBucketFast(getParamValue("input", node, tensorMap, context), getParamValue("numBuckets", node, tensorMap, context)); + return [output]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/transformation_executor.js +var executeOp20 = (node, tensorMap, context, ops = ops_for_converter_exports) => { + switch (node.op) { + case "Cast": { + return [ops.cast(getParamValue("x", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))]; + } + case "ExpandDims": { + const axis = getParamValue("axis", node, tensorMap, context); + return [ops.expandDims(getParamValue("x", node, tensorMap, context), axis)]; + } + case "Squeeze": { + const axis = getParamValue("axis", node, tensorMap, context); + return [ops.squeeze(getParamValue("x", node, tensorMap, context), axis)]; + } + case "Reshape": { + return [ops.reshape(getParamValue("x", node, tensorMap, context), getParamValue("shape", node, tensorMap, context))]; + } + case "EnsureShape": { + return [ops.ensureShape(getParamValue("x", node, tensorMap, context), getParamValue("shape", node, tensorMap, context))]; + } + case "MirrorPad": { + return [ops.mirrorPad(getParamValue("x", node, tensorMap, context), getParamValue("padding", node, tensorMap, context), getParamValue("mode", node, tensorMap, context))]; + } + case "PadV2": + case "Pad": { + return [ops.pad(getParamValue("x", node, tensorMap, context), getParamValue("padding", node, tensorMap, context), getParamValue("constantValue", node, tensorMap, context))]; + } + case "SpaceToBatchND": { + const blockShape = getParamValue("blockShape", node, tensorMap, context); + const paddings = getParamValue("paddings", node, tensorMap, context); + return [ops.spaceToBatchND(getParamValue("x", node, tensorMap, context), blockShape, paddings)]; + } + case "BatchToSpaceND": { + const blockShape = getParamValue("blockShape", node, tensorMap, context); + const crops = getParamValue("crops", node, tensorMap, context); + return [ops.batchToSpaceND(getParamValue("x", node, tensorMap, context), blockShape, crops)]; + } + case "DepthToSpace": { + const blockSize = getParamValue("blockSize", node, tensorMap, context); + const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase(); + return [ops.depthToSpace(getParamValue("x", node, tensorMap, context), blockSize, dataFormat)]; + } + case "BroadcastTo": { + return [ops.broadcastTo(getParamValue("x", node, tensorMap, context), getParamValue("shape", node, tensorMap, context))]; + } + case "BroadcastArgs": { + return [ops.broadcastArgs(getParamValue("s0", node, tensorMap, context), getParamValue("s1", node, tensorMap, context))]; + } + default: + throw TypeError(`Node type ${node.op} is not implemented`); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_executor.js +function executeOp21(node, tensorMap, context, resourceManager, tidy2 = tidy) { + const value = ((node2, tensorMap2, context2) => { + switch (node2.category) { + case "arithmetic": + return tidy2(() => executeOp(node2, tensorMap2, context2)); + case "basic_math": + return tidy2(() => executeOp2(node2, tensorMap2, context2)); + case "control": + return executeOp3(node2, tensorMap2, context2); + case "convolution": + return tidy2(() => executeOp4(node2, tensorMap2, context2)); + case "creation": + return tidy2(() => executeOp5(node2, tensorMap2, context2)); + case "dynamic": + return executeOp6(node2, tensorMap2, context2); + case "evaluation": + return tidy2(() => executeOp7(node2, tensorMap2, context2)); + case "image": + return tidy2(() => executeOp10(node2, tensorMap2, context2)); + case "graph": + return tidy2(() => executeOp8(node2, tensorMap2, context2)); + case "logical": + return tidy2(() => executeOp11(node2, tensorMap2, context2)); + case "matrices": + return tidy2(() => executeOp12(node2, tensorMap2, context2)); + case "normalization": + return tidy2(() => executeOp13(node2, tensorMap2, context2)); + case "ragged": + return tidy2(() => executeOp14(node2, tensorMap2, context2)); + case "reduction": + return tidy2(() => executeOp15(node2, tensorMap2, context2)); + case "slice_join": + return tidy2(() => executeOp16(node2, tensorMap2, context2)); + case "sparse": + return tidy2(() => executeOp17(node2, tensorMap2, context2)); + case "spectral": + return tidy2(() => executeOp18(node2, tensorMap2, context2)); + case "string": + return tidy2(() => executeOp19(node2, tensorMap2, context2)); + case "transformation": + return tidy2(() => executeOp20(node2, tensorMap2, context2)); + case "hash_table": + return executeOp9(node2, tensorMap2, context2, resourceManager); + case "custom": + const opMapper = getRegisteredOp(node2.op); + if (opMapper && opMapper.customExecutor) { + return opMapper.customExecutor(new NodeValueImpl(node2, tensorMap2, context2)); + } else { + throw TypeError(`Custom op ${node2.op} is not registered.`); + } + default: + throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`); + } + })(node, tensorMap, context); + if (util_exports.isPromise(value)) { + return value.then((data) => [].concat(data)); + } + return [].concat(value); +} + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/execution_context.js +var ExecutionContext = class { + constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}, parseNodeNameCache) { + this.weightMap = weightMap; + this.tensorArrayMap = tensorArrayMap; + this.tensorListMap = tensorListMap; + this.functionMap = functionMap; + this.parseNodeNameCache = parseNodeNameCache; + this.rootContext = { id: 0, frameName: "", iterationId: 0 }; + this.contexts = [this.rootContext]; + this.lastId = 0; + this.generateCurrentContextIds(); + } + newFrame(id, frameName) { + return { id, frameName, iterationId: 0 }; + } + /** + * Set the current context + * @param contexts: ExecutionContextInfo[] the current path of execution + * frames + */ + set currentContext(contexts2) { + if (this.contexts !== contexts2) { + this.contexts = contexts2; + this.generateCurrentContextIds(); + } + } + get currentContext() { + return this.contexts; + } + /** + * Returns the current context in string format. + */ + get currentContextId() { + return this._currentContextIds[0]; + } + /** + * Returns the current context and all parent contexts in string format. + * This allow access to the nodes in the current and parent frames. + */ + get currentContextIds() { + return this._currentContextIds; + } + generateCurrentContextIds() { + const names = []; + for (let i = 0; i < this.contexts.length - 1; i++) { + const contexts2 = this.contexts.slice(0, this.contexts.length - i); + names.push(this.contextIdforContexts(contexts2)); + } + names.push(""); + this._currentContextIds = names; + } + contextIdforContexts(contexts2) { + return contexts2 ? contexts2.map((context) => context.id === 0 && context.iterationId === 0 ? "" : `${context.frameName}-${context.iterationId}`).join("/") : ""; + } + /** + * Enter a new frame, a new context is pushed on the current context list. + * @param frameId new frame id + */ + enterFrame(frameId) { + if (this.contexts) { + this.lastId++; + this.contexts = this.contexts.slice(); + this.contexts.push(this.newFrame(this.lastId, frameId)); + this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)); + } + } + /** + * Exit the current frame, the last context is removed from the current + * context list. + */ + 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"); + } + } + /** + * Enter the next iteration of a loop, the iteration id of last context is + * increased. + */ + nextIteration() { + if (this.contexts && this.contexts.length > 0) { + this.contexts = this.contexts.slice(); + this.lastId++; + const context = Object.assign({}, this.contexts[this.contexts.length - 1]); + context.iterationId += 1; + context.id = this.lastId; + this.contexts.splice(-1, 1, context); + this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts)); + } else { + throw new Error("Cannot increase frame iteration, the context is empty"); + } + } + getWeight(name) { + return this.weightMap[name]; + } + addTensorArray(tensorArray) { + this.tensorArrayMap[tensorArray.id] = tensorArray; + } + getTensorArray(id) { + return this.tensorArrayMap[id]; + } + addTensorList(tensorList) { + this.tensorListMap[tensorList.id] = tensorList; + } + getTensorList(id) { + return this.tensorListMap[id]; + } + dispose(keepIds) { + for (const key in this.tensorArrayMap) { + this.tensorArrayMap[key].clearAndClose(keepIds); + } + for (const key in this.tensorListMap) { + this.tensorListMap[key].clearAndClose(keepIds); + } + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/model_analysis.js +function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) { + const usedNodes = /* @__PURE__ */ new Set(); + const missingInputs = []; + let dynamicNode = null; + let syncInputs = null; + const seen = /* @__PURE__ */ new Set(); + const inputNodeNames = new Set(Object.keys(inputs).map((name) => parseNodeName(name)[0])); + initNodes = initNodes || []; + const initNodeNames = new Set(initNodes.map((node) => parseNodeName(node.name)[0])); + const frontier = [...outputs]; + while (frontier.length > 0) { + const node = frontier.pop(); + if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) { + if (dynamicNode == null) { + dynamicNode = node; + syncInputs = dynamicNode.children.map((child) => child.name).filter((name) => usedNodes.has(name)); + } + } + usedNodes.add(node.name); + if (weightMap[node.name] != null) { + continue; + } + if (inputNodeNames.has(node.name)) { + continue; + } + if (initNodeNames.has(node.name)) { + continue; + } + if (node.inputs.length === 0) { + missingInputs.push(node.name); + continue; + } + node.inputs.forEach((input2) => { + if (seen.has(input2.name)) { + return; + } + seen.add(input2.name); + frontier.push(input2); + }); + } + return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs }; +} +function getNodesInTopologicalOrder(graph, executionInfo) { + const { usedNodes, inputs } = executionInfo; + const inputNodes = Object.keys(inputs).map((name) => parseNodeName(name)[0]).map((name) => graph.nodes[name]); + const initNodes = graph.initNodes || []; + const isUsed = (node) => usedNodes.has(typeof node === "string" ? node : node.name); + function unique6(nodes) { + return [...new Map(nodes.map((node) => [node.name, node])).values()]; + } + const predefinedNodes = unique6([ + ...inputNodes, + ...graph.weights, + ...initNodes + ]).filter(isUsed); + const allNodes = unique6([ + ...predefinedNodes, + ...Object.values(graph.nodes) + ]).filter(isUsed); + const nameToNode = new Map(allNodes.map((node) => [node.name, node])); + const inCounts = {}; + for (const node of allNodes) { + inCounts[node.name] = inCounts[node.name] || 0; + for (const child of node.children) { + if (!isUsed(child)) { + inCounts[child.name] = Number.POSITIVE_INFINITY; + } + inCounts[child.name] = (inCounts[child.name] || 0) + 1; + } + } + const frontier = Object.entries(inCounts).filter(([, inCount]) => inCount === 0).map(([name]) => name); + const orderedNodeNames = [...frontier]; + while (frontier.length > 0) { + const nodeName = frontier.pop(); + const node = nameToNode.get(nodeName); + for (const child of node.children.filter(isUsed)) { + if (--inCounts[child.name] === 0) { + orderedNodeNames.push(child.name); + frontier.push(child.name); + } + } + } + const orderedNodes = orderedNodeNames.map((name) => nameToNode.get(name)); + const filteredOrderedNodes = filterPredefinedReachableNodes(orderedNodes, predefinedNodes); + validateNodesExecutionOrder(filteredOrderedNodes, predefinedNodes); + return filteredOrderedNodes; +} +function filterPredefinedReachableNodes(orderedNodes, predefinedNodes) { + const nameToNode = new Map(orderedNodes.map((node) => [node.name, node])); + const stack2 = predefinedNodes.map((node) => node.name); + const predefinedReachableNodeNames = new Set(stack2); + while (stack2.length > 0) { + const nodeName = stack2.pop(); + const node = nameToNode.get(nodeName); + for (const child of node.children) { + if (!nameToNode.has(child.name) || predefinedReachableNodeNames.has(child.name)) { + continue; + } + predefinedReachableNodeNames.add(child.name); + stack2.push(child.name); + } + } + const filteredOrderedNodes = orderedNodes.filter((node) => predefinedReachableNodeNames.has(node.name)); + return filteredOrderedNodes; +} +var NodesExecutionOrderError = class extends Error { + constructor(message) { + super(`NodesExecutionOrderError: ${message}`); + } +}; +function validateNodesExecutionOrder(orderedNodes, predefinedNodes) { + const nodeNameToOrder = new Map(orderedNodes.map((node, order) => [node.name, order])); + const predefinedNodeNames = new Set(predefinedNodes.map((node) => node.name)); + const isPredefined = (node) => predefinedNodeNames.has(typeof node === "string" ? node : node.name); + const willBeExecutedNodeNames = new Set(orderedNodes.map((node) => node.name)); + const willBeExecuted = (node) => willBeExecutedNodeNames.has(typeof node === "string" ? node : node.name); + for (const node of orderedNodes) { + for (const child of node.children.filter(willBeExecuted)) { + if (!nodeNameToOrder.has(child.name)) { + throw new NodesExecutionOrderError(`Child ${child.name} of node ${node.name} is unreachable.`); + } + if (nodeNameToOrder.get(node.name) > nodeNameToOrder.get(child.name)) { + throw new NodesExecutionOrderError(`Node ${node.name} is scheduled to run after its child ${child.name}.`); + } + } + if (!isPredefined(node)) { + for (const input2 of node.inputs) { + if (!nodeNameToOrder.has(input2.name)) { + throw new NodesExecutionOrderError(`Input ${input2.name} of node ${node.name} is unreachable.`); + } + if (nodeNameToOrder.get(input2.name) > nodeNameToOrder.get(node.name)) { + throw new NodesExecutionOrderError(`Node ${node.name} is scheduled to run before its input ${input2.name}.`); + } + } + } + } +} +function getNodeLiveUntilMap(orderedNodes) { + const nodeNameToOrder = new Map(orderedNodes.map((node, order) => [node.name, order])); + const INF_LIFE = Number.MAX_SAFE_INTEGER; + const selfLifespans = orderedNodes.map((node, nodeOrder) => isControlFlow(node) ? INF_LIFE : nodeOrder); + const getSelfLifeSpan = (node) => { + const selfLife = selfLifespans[nodeNameToOrder.get(node.name)]; + if (selfLife == null) { + return -1; + } + return selfLife; + }; + const liveUntilOrders = orderedNodes.map((node, nodeOrder) => { + return node.children.map(getSelfLifeSpan).reduce((a, b) => Math.max(a, b), selfLifespans[nodeOrder]); + }); + const liveUntilMap = /* @__PURE__ */ new Map(); + for (let nodeOrder = 0; nodeOrder < orderedNodes.length; ++nodeOrder) { + const liveUntilOrder = liveUntilOrders[nodeOrder]; + if (liveUntilOrder === INF_LIFE) { + continue; + } + const node = orderedNodes[nodeOrder]; + const liveUntilNode = orderedNodes[liveUntilOrder]; + if (!liveUntilMap.has(liveUntilNode.name)) { + liveUntilMap.set(liveUntilNode.name, []); + } + liveUntilMap.get(liveUntilNode.name).push(node); + } + return liveUntilMap; +} +var CONTROL_FLOW_OPS = /* @__PURE__ */ new Set([ + "Switch", + "Merge", + "Enter", + "Exit", + "NextIteration", + "StatelessIf", + "StatelessWhile", + "if", + "While" +]); +var DYNAMIC_SHAPE_OPS = /* @__PURE__ */ new Set([ + "NonMaxSuppressionV2", + "NonMaxSuppressionV3", + "NonMaxSuppressionV5", + "Where" +]); +var HASH_TABLE_OPS = /* @__PURE__ */ new Set([ + "HashTable", + "HashTableV2", + "LookupTableImport", + "LookupTableImportV2", + "LookupTableFind", + "LookupTableFindV2", + "LookupTableSize", + "LookupTableSizeV2" +]); +function isControlFlow(node) { + return CONTROL_FLOW_OPS.has(node.op); +} +function isDynamicShape(node) { + return DYNAMIC_SHAPE_OPS.has(node.op); +} +function isHashTable(node) { + return HASH_TABLE_OPS.has(node.op); +} + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_executor.js +var GraphExecutor = class _GraphExecutor { + get weightIds() { + return this.parent ? this.parent.weightIds : this._weightIds; + } + get functionExecutorMap() { + return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap; + } + get weightMap() { + return this.parent ? this.parent.weightMap : this._weightMap; + } + set weightMap(weightMap) { + const weightIds = Object.keys(weightMap).map((key) => weightMap[key].map((tensor2) => tensor2.id)); + this._weightIds = [].concat(...weightIds); + this._weightMap = weightMap; + } + /** + * Set `ResourceManager` shared by executors of a model. + * @param resourceManager: `ResourceManager` of the `GraphModel`. + */ + set resourceManager(resourceManager) { + this._resourceManager = resourceManager; + } + get inputs() { + return this._inputs.map((node) => { + return { + name: node.name, + shape: node.attrParams["shape"] ? node.attrParams["shape"].value : void 0, + dtype: node.attrParams["dtype"] ? node.attrParams["dtype"].value : void 0 + }; + }); + } + get outputs() { + return this._outputs.map((node) => { + return { + name: node.name, + shape: node.attrParams["shape"] ? node.attrParams["shape"].value : void 0, + dtype: node.attrParams["dtype"] ? node.attrParams["dtype"].value : void 0 + }; + }); + } + get inputNodes() { + return this._inputs.map((node) => node.signatureKey || node.name); + } + get outputNodes() { + return this._outputs.map((node) => { + const name = node.signatureKey || node.name; + return node.defaultOutput ? `${name}:${node.defaultOutput}` : name; + }); + } + get functions() { + return Object.keys(this._functions).reduce((map, key) => { + map[key] = this._functions[key].signature; + return map; + }, {}); + } + /** + * + * @param graph Graph the model or function graph to be executed. + * @param parent When building function exector you need to set the parent + * executor. Since the weights and function executor maps are set at parant + * level, that function executor can access the function maps and weight maps + * through the parent. + */ + constructor(graph, parent) { + this.graph = graph; + this.parent = parent; + this.compiledMap = /* @__PURE__ */ new Map(); + this.parseNodeNameCache = /* @__PURE__ */ new Map(); + this._weightMap = {}; + this.SEPARATOR = ","; + this._functions = {}; + this._functionExecutorMap = {}; + this.keepIntermediateTensors = false; + this._outputs = graph.outputs; + this._inputs = graph.inputs; + this._initNodes = graph.initNodes; + this._signature = graph.signature; + this._functions = graph.functions; + if (graph.functions != null) { + Object.keys(graph.functions).forEach((name) => { + this._functionExecutorMap[name] = new _GraphExecutor(graph.functions[name], this); + }); + } + } + getCompilationKey(inputs, outputs) { + const sortedInputs = inputs.map((node) => node.name).sort(); + const sortedOutputs = outputs.map((node) => node.name).sort(); + return sortedInputs.join(this.SEPARATOR) + "--" + sortedOutputs.join(this.SEPARATOR); + } + /** + * Compiles the inference graph and returns the minimal set of nodes that are + * required for execution, in the correct execution order. + * @returns {Object} compilation The compile result. + * @returns {Node[]} compilation.orderedNodes Nodes in the correct execution + * order. + * @returns {Map} compilation.nodeLiveUntilMap A map from node + * to disposable nodes after its execution. That is, for a node `x`, + * `nodeLiveUntilMap[x]` indicates all nodes whose intermediate + * tensors should be disposed after `x` is executed. + */ + compile(inputs, outputs) { + const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes); + const { missingInputs, dynamicNode, syncInputs } = executionInfo; + if (dynamicNode != null) { + throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`); + } + if (missingInputs.length > 0) { + const outNames = outputs.map((n) => n.name); + const inNames = Object.keys(inputs); + throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`); + } + const orderedNodes = getNodesInTopologicalOrder(this.graph, executionInfo); + const nodeLiveUntilMap = getNodeLiveUntilMap(orderedNodes); + return { orderedNodes, nodeLiveUntilMap }; + } + cloneAndKeepTensor(tensor2) { + if (tensor2 == null) { + return null; + } + const clone2 = tensor2.clone(); + keep(clone2); + return clone2; + } + cloneTensorList(tensors) { + if (!tensors) { + return null; + } + const clonedTensor = tensors.map((tensor2) => { + return this.cloneAndKeepTensor(tensor2); + }); + return clonedTensor; + } + cloneTensorMap(tensorsMap) { + return Object.fromEntries(Object.entries(tensorsMap).map(([name, tensorsList]) => { + return [name, this.cloneTensorList(tensorsList)]; + })); + } + /** + * Executes the inference for given input tensors. + * @param inputs Tensor map for the model inputs, keyed by the input node + * names. + * @param outputs Optional. output node name from the Tensorflow model, if + * no outputs are specified, the default outputs of the model would be used. + * You can inspect intermediate nodes of the model by adding them to the + * outputs array. + */ + execute(inputs, outputs) { + this.disposeIntermediateTensors(); + inputs = this.mapInputs(inputs); + const names = Object.keys(inputs).sort(); + this.checkInputs(inputs); + this.checkInputShapeAndType(inputs); + outputs = this.mapOutputs(outputs); + this.checkOutputs(outputs); + const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]); + const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]); + const outputNodeNameSet = new Set(outputNodeNames); + let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]); + if (outputNodes.length === 0) { + outputNodes = this._outputs; + } + const compilationKey = this.getCompilationKey(inputNodes, outputNodes); + let compilation = this.compiledMap.get(compilationKey); + if (compilation == null) { + compilation = this.compile(inputs, outputNodes); + this.compiledMap.set(compilationKey, compilation); + } + try { + this.keepIntermediateTensors = env().getBool("KEEP_INTERMEDIATE_TENSORS"); + } catch (e) { + this.keepIntermediateTensors = false; + console.warn(e.message); + } + const tensorArrayMap = {}; + const tensorListMap = {}; + return tidy(() => { + const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache); + const tensorsMap = Object.assign({}, this.weightMap); + if (this.keepIntermediateTensors) { + this.clonedTensorsMap = this.cloneTensorMap(this.weightMap); + } + Object.keys(inputs).forEach((name) => { + const [nodeName, index] = parseNodeName(name, context); + const tensors = []; + tensors[index] = inputs[name]; + tensorsMap[nodeName] = tensors; + if (this.keepIntermediateTensors) { + this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors); + } + }); + const tensorsToKeep = this.getFrozenTensorIds(tensorsMap); + const { orderedNodes, nodeLiveUntilMap } = compilation; + for (const node of orderedNodes) { + if (tensorsMap[node.name]) { + continue; + } + const tensors = executeOp21(node, tensorsMap, context, this._resourceManager); + if (util_exports.isPromise(tensors)) { + throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`); + } + tensorsMap[node.name] = tensors; + if (this.keepIntermediateTensors) { + this.clonedTensorsMap[node.name] = this.cloneTensorList(tensors); + } + this.checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorsMap, context, tensorsToKeep, outputNodeNameSet, nodeLiveUntilMap.get(node.name)); + } + if (this.parent == null) { + context.dispose(tensorsToKeep); + } + return outputs.map((name) => getTensor(name, tensorsMap, context)); + }); + } + getFrozenTensorIds(tensorMap) { + const ids = [].concat.apply([], Object.keys(tensorMap).map((key) => tensorMap[key]).map((tensors) => tensors.map((tensor2) => tensor2.id))); + return new Set(ids); + } + checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount) { + if (isControlFlow(node) || outputNodeNameSet.has(nodeName)) { + return; + } + for (const tensor2 of tensorMap[nodeName]) { + if (tensor2 == null) { + continue; + } + intermediateTensorConsumerCount[tensor2.id] = (intermediateTensorConsumerCount[tensor2.id] || 0) + node.children.length; + } + for (const input2 of node.inputs) { + if (isControlFlow(input2)) { + continue; + } + const tensors = getTensorsForCurrentContext(input2.name, tensorMap, context); + if (tensors == null) { + continue; + } + for (const tensor2 of tensors) { + if (!tensor2 || tensor2.kept || tensorsToKeep.has(tensor2.id)) { + continue; + } + const count2 = intermediateTensorConsumerCount[tensor2.id]; + if (count2 === 1) { + tensor2.dispose(); + delete intermediateTensorConsumerCount[tensor2.id]; + } else if (count2 != null) { + intermediateTensorConsumerCount[tensor2.id]--; + } + } + } + } + checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorMap, context, tensorsToKeep, outputNodeNameSet, liveUntilNodes) { + function isNonDisposableNode(node2) { + return isControlFlow(node2) || outputNodeNameSet.has(node2.name); + } + if (isControlFlow(node) || liveUntilNodes == null) { + return; + } + for (const nodeToDispose of liveUntilNodes) { + if (isNonDisposableNode(nodeToDispose)) { + continue; + } + const tensors = getTensorsForCurrentContext(nodeToDispose.name, tensorMap, context); + for (const tensor2 of tensors) { + if (!tensor2 || tensor2.kept || tensorsToKeep.has(tensor2.id)) { + continue; + } + tensor2.dispose(); + } + } + } + /** + * Executes the inference for given input tensors in Async fashion. + * @param inputs Tensor map for the model inputs, keyed by the input node + * names. + * @param outputs output node name from the Tensorflow model, if no outputs + * are specified, the default outputs of the model would be used. You can + * inspect intermediate nodes of the model by adding them to the outputs + * array. + */ + async executeAsync(inputs, outputs) { + return this._executeAsync(inputs, outputs); + } + disposeIntermediateTensors() { + if (!this.clonedTensorsMap) { + return; + } + Object.values(this.clonedTensorsMap).forEach((tensorsList) => { + for (const tensor2 of tensorsList) { + if (tensor2 && !tensor2.isDisposed) { + tensor2.dispose(); + } + } + }); + this.clonedTensorsMap = null; + } + getIntermediateTensors() { + return this.clonedTensorsMap; + } + /** + * Executes the inference for given input tensors in Async fashion. + * @param inputs Tensor map for the model inputs, keyed by the input node + * names. + * @param outputs Optional. output node name from the Tensorflow model, + * if no outputs are specified, the default outputs of the model would be + * used. You can inspect intermediate nodes of the model by adding them to + * the outputs array. + * @param isFunctionExecution Optional. Flag for executing a function. + * @param tensorArrayMap Optional, global TensorArray map by id. Used for + * function execution. + * @param tensorArrayMap Optinal global TensorList map by id. Used for + * function execution. + */ + async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) { + this.disposeIntermediateTensors(); + if (!isFunctionExecution) { + inputs = this.mapInputs(inputs); + this.checkInputs(inputs); + this.checkInputShapeAndType(inputs); + outputs = this.mapOutputs(outputs); + this.checkOutputs(outputs); + } + try { + this.keepIntermediateTensors = env().getBool("KEEP_INTERMEDIATE_TENSORS"); + } catch (e) { + this.keepIntermediateTensors = false; + console.warn(e.message); + } + const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache); + if (this.keepIntermediateTensors) { + this.clonedTensorsMap = this.cloneTensorMap(this.weightMap); + } + const tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution); + const results = outputs.map((name) => getTensor(name, tensorsMap, context)); + const outputIds = results.map((t) => t.id); + const inputIds = Object.keys(inputs).map((name) => inputs[name].id); + const keepIds = /* @__PURE__ */ new Set([...outputIds, ...inputIds, ...this.weightIds]); + Object.values(tensorsMap).forEach((tensorsList) => { + tensorsList.forEach((tensor2) => { + if (tensor2 && !tensor2.isDisposed && !keepIds.has(tensor2.id)) { + tensor2.dispose(); + } + }); + }); + if (this.parent == null) { + context.dispose(keepIds); + } + return results; + } + async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) { + const mappedInputs = inputs.reduce((map, tensor2, index) => { + map[this.inputs[index].name] = tensor2; + return map; + }, {}); + return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap); + } + /** + * When there are control flow nodes in the graph, the graph execution use + * ExecutionContext to keep track of the frames and loop iterators. + * @param inputs placeholder tensors for the graph. + * @param context the execution context object for current execution. + * @param outputNames Optional. output node name from the Tensorflow model, + * if no outputs are specified, the default outputs of the model would be + * used. You can inspect intermediate nodes of the model by adding them to + * the outputs array. + * @param isFunctionExecution Flag for executing a function. + */ + async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) { + const names = Object.keys(inputs); + const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]); + const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]); + const outputNodeNameSet = new Set(outputNodeNames); + let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]); + if (outputNodes.length === 0) { + outputNodes = this._outputs; + } + const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes); + const stack2 = [ + ...inputNodes, + ...this.graph.weights, + ...this._initNodes || [] + ].map((node) => { + return { node, contexts: context.currentContext }; + }); + const tensorsMap = Object.assign({}, this.weightMap); + Object.keys(inputs).forEach((name) => { + const [nodeName, index] = parseNodeName(name); + const tensors = []; + tensors[index] = inputs[name]; + tensorsMap[nodeName] = tensors; + }); + const intermediateTensorConsumerCount = {}; + const tensorsToKeep = this.getFrozenTensorIds(tensorsMap); + const added = {}; + while (stack2.length > 0) { + const promises = this.processStack(inputNodes, stack2, context, tensorsMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes); + await Promise.all(promises); + } + if (dynamicNode == null && !isFunctionExecution) { + console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`); + } + const missingOutputs = outputNodes.filter((node) => !isControlFlow(node) && !getTensor(node.name, tensorsMap, context)).map((node) => node.name); + if (missingOutputs.length > 0) { + let alternativeMsg = ""; + if (dynamicNode != null) { + alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`; + } + throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`); + } + return tensorsMap; + } + processStack(inputNodes, stack2, context, tensorMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes) { + const promises = []; + while (stack2.length > 0) { + const item = stack2.pop(); + context.currentContext = item.contexts; + let nodeName = ""; + if (item.node.op === "Enter" && getParamValue("isConstant", item.node, tensorMap, context)) { + [nodeName] = getNodeNameAndIndex(item.node.name, context); + } + if (tensorMap[item.node.name] == null) { + const tensors = executeOp21(item.node, tensorMap, context, this._resourceManager); + if (!nodeName) { + [nodeName] = getNodeNameAndIndex(item.node.name, context); + } + const currentContext = context.currentContext; + if (util_exports.isPromise(tensors)) { + promises.push(tensors.then((t) => { + tensorMap[nodeName] = t; + if (this.keepIntermediateTensors) { + this.clonedTensorsMap[nodeName] = this.cloneTensorList(t); + } + context.currentContext = currentContext; + this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount); + this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes); + return t; + })); + } else { + tensorMap[nodeName] = tensors; + if (this.keepIntermediateTensors) { + this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors); + } + this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount); + this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes); + } + } else { + this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes); + } + } + return promises; + } + processChildNodes(node, stack2, context, tensorMap, added, usedNodes) { + node.children.forEach((childNode) => { + const [nodeName] = getNodeNameAndIndex(childNode.name, context); + if (added[nodeName] || !usedNodes.has(childNode.name)) { + return; + } + if (childNode.op === "Merge") { + if (childNode.inputNames.some((name) => { + return !!getTensor(name, tensorMap, context); + })) { + added[nodeName] = true; + stack2.push({ contexts: context.currentContext, node: childNode }); + } + } else if (childNode.inputNames.every((name) => { + return !!getTensor(name, tensorMap, context); + })) { + added[nodeName] = true; + stack2.push({ contexts: context.currentContext, node: childNode }); + } + }); + } + /** + * Releases the memory used by the weight tensors. + */ + dispose() { + Object.keys(this.weightMap).forEach((key) => this.weightMap[key].forEach((tensor2) => tensor2.dispose())); + } + checkInputShapeAndType(inputs) { + Object.keys(inputs).forEach((name) => { + const input2 = inputs[name]; + const [nodeName] = parseNodeName(name); + const node = this.graph.nodes[nodeName]; + if (node.attrParams["shape"] && node.attrParams["shape"].value) { + const shape = node.attrParams["shape"].value; + const match = shape.length === input2.shape.length && input2.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim); + util_exports.assert(match, () => `The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`); + } + if (node.attrParams["dtype"] && node.attrParams["dtype"].value) { + util_exports.assert(input2.dtype === node.attrParams["dtype"].value, () => `The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams["dtype"].value}, but was ${input2.dtype}`); + } + }); + } + mapInputs(inputs) { + var _a, _b; + const result = {}; + for (const inputName in inputs) { + const tensor2 = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.inputs) === null || _b === void 0 ? void 0 : _b[inputName]; + if (tensor2 != null) { + result[tensor2.name] = inputs[inputName]; + } else { + result[inputName] = inputs[inputName]; + } + } + return result; + } + checkInputs(inputs) { + const notInGraph = Object.keys(inputs).filter((name) => { + const [nodeName] = parseNodeName(name); + return this.graph.nodes[nodeName] == null; + }); + if (notInGraph.length > 0) { + throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`); + } + } + mapOutputs(outputs) { + return outputs.map((name) => { + var _a, _b; + const tensor2 = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.outputs) === null || _b === void 0 ? void 0 : _b[name]; + if (tensor2 != null) { + return tensor2.name; + } + return name; + }, {}); + } + checkOutputs(outputs) { + outputs.forEach((name) => { + const [normalizedName] = parseNodeName(name); + if (!this.graph.nodes[normalizedName]) { + throw new Error(`The output '${name}' is not found in the graph`); + } + }); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/resource_manager.js +var ResourceManager = class { + constructor(hashTableNameToHandle = {}, hashTableMap = {}) { + this.hashTableNameToHandle = hashTableNameToHandle; + this.hashTableMap = hashTableMap; + } + /** + * Register a `HashTable` in the resource manager. + * + * The `HashTable` can be retrieved by `resourceManager.getHashTableById`, + * where id is the table handle tensor's id. + * + * @param name Op node name that creates the `HashTable`. + * @param hashTable The `HashTable` to be added to resource manager. + */ + addHashTable(name, hashTable) { + this.hashTableNameToHandle[name] = hashTable.handle; + this.hashTableMap[hashTable.id] = hashTable; + } + /** + * Get the table handle by node name. + * @param name Op node name that creates the `HashTable`. This name is also + * used in the inputs list of lookup and import `HashTable` ops. + */ + getHashTableHandleByName(name) { + return this.hashTableNameToHandle[name]; + } + /** + * Get the actual `HashTable` by its handle tensor's id. + * @param id The id of the handle tensor. + */ + getHashTableById(id) { + return this.hashTableMap[id]; + } + /** + * Dispose `ResourceManager`, including its hashTables and tensors in them. + */ + dispose() { + for (const key in this.hashTableMap) { + this.hashTableMap[key].clearAndClose(); + delete this.hashTableMap[key]; + } + for (const name in this.hashTableNameToHandle) { + this.hashTableNameToHandle[name].dispose(); + delete this.hashTableNameToHandle[name]; + } + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.js +var TFHUB_SEARCH_PARAM = "?tfjs-format=file"; +var DEFAULT_MODEL_NAME = "model.json"; +var GraphModel = class { + // Returns the version information for the tensorflow model GraphDef. + 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; + } + /** + * @param modelUrl url for the model, or an `io.IOHandler`. + * @param weightManifestUrl url for the weight file generated by + * scripts/convert.py script. + * @param requestOption options for Request, which allows to send credentials + * and custom headers. + * @param onProgress Optional, progress callback function, fired periodically + * before the load is completed. + */ + constructor(modelUrl, loadOptions = {}, tfio = io_exports) { + this.modelUrl = modelUrl; + this.loadOptions = loadOptions; + this.version = "n/a"; + this.io = tfio; + if (loadOptions == null) { + this.loadOptions = {}; + } + this.resourceManager = new ResourceManager(); + } + findIOHandler() { + const path = this.modelUrl; + if (path.load != null) { + this.handler = path; + } else if (this.loadOptions.requestInit != null) { + this.handler = this.io.browserHTTPRequest(path, this.loadOptions); + } else { + const handlers = this.io.getLoadHandlers(path, this.loadOptions); + if (handlers.length === 0) { + handlers.push(this.io.browserHTTPRequest(path, this.loadOptions)); + } else if (handlers.length > 1) { + throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`); + } + this.handler = handlers[0]; + } + } + /** + * Loads the model and weight files, construct the in memory weight map and + * compile the inference graph. + */ + load() { + this.findIOHandler(); + if (this.handler.load == null) { + throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented."); + } + const loadResult = this.handler.load(); + if (util_exports.isPromise(loadResult)) { + return loadResult.then((artifacts) => { + if (artifacts.getWeightStream == null) { + return this.loadSync(artifacts); + } + return this.loadStreaming(artifacts); + }); + } + return this.loadSync(loadResult); + } + /** + * Synchronously construct the in memory weight map and + * compile the inference graph. + * + * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} + */ + loadSync(artifacts) { + const weightMap = this.io.decodeWeights(artifacts.weightData, artifacts.weightSpecs); + return this.loadWithWeightMap(artifacts, weightMap); + } + async loadStreaming(artifacts) { + if (artifacts.getWeightStream == null) { + throw new Error("Model artifacts missing streamWeights function"); + } + const weightMap = await decodeWeightsStream(artifacts.getWeightStream(), artifacts.weightSpecs); + return this.loadWithWeightMap(artifacts, weightMap); + } + loadWithWeightMap(artifacts, weightMap) { + this.artifacts = artifacts; + const graph = this.artifacts.modelTopology; + let signature = this.artifacts.signature; + if (this.artifacts.userDefinedMetadata != null) { + const metadata = this.artifacts.userDefinedMetadata; + if (metadata.signature != null) { + signature = metadata.signature; + } + if (metadata.structuredOutputKeys != null) { + this.structuredOutputKeys = metadata.structuredOutputKeys; + } + } + this.signature = signature; + this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`; + this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature)); + this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap); + this.executor.resourceManager = this.resourceManager; + if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) { + const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer); + this.initializer = new GraphExecutor(initializer); + this.initializer.weightMap = this.executor.weightMap; + this.initializer.resourceManager = this.resourceManager; + this.initializerSignature = artifacts.initializerSignature; + } + return true; + } + /** + * Save the configuration and/or weights of the GraphModel. + * + * An `IOHandler` is an object that has a `save` method of the proper + * signature defined. The `save` method manages the storing or + * transmission of serialized data ("artifacts") that represent the + * model's topology and weights onto or via a specific medium, such as + * file downloads, local storage, IndexedDB in the web browser and HTTP + * requests to a server. TensorFlow.js provides `IOHandler` + * implementations for a number of frequently used saving mediums, such as + * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io` + * for more details. + * + * This method also allows you to refer to certain types of `IOHandler`s + * as URL-like string shortcuts, such as 'localstorage://' and + * 'indexeddb://'. + * + * Example 1: Save `model`'s topology and weights to browser [local + * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage); + * then load it back. + * + * ```js + * const modelUrl = + * 'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json'; + * const model = await tf.loadGraphModel(modelUrl); + * const zeros = tf.zeros([1, 224, 224, 3]); + * model.predict(zeros).print(); + * + * const saveResults = await model.save('localstorage://my-model-1'); + * + * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1'); + * console.log('Prediction from loaded model:'); + * model.predict(zeros).print(); + * ``` + * + * @param handlerOrURL An instance of `IOHandler` or a URL-like, + * scheme-based string shortcut for `IOHandler`. + * @param config Options for saving the model. + * @returns A `Promise` of `SaveResult`, which summarizes the result of + * the saving, such as byte sizes of the saved artifacts for the model's + * topology and weight values. + * + * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} + */ + async save(handlerOrURL, config) { + if (typeof handlerOrURL === "string") { + const handlers = this.io.getSaveHandlers(handlerOrURL); + if (handlers.length === 0) { + throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`); + } else if (handlers.length > 1) { + throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`); + } + handlerOrURL = handlers[0]; + } + if (handlerOrURL.save == null) { + throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined."); + } + return handlerOrURL.save(this.artifacts); + } + addStructuredOutputNames(outputTensors) { + if (this.structuredOutputKeys) { + const outputTensorsArray = outputTensors instanceof Tensor ? [outputTensors] : outputTensors; + const outputTensorMap = {}; + outputTensorsArray.forEach((outputTensor, i) => outputTensorMap[this.structuredOutputKeys[i]] = outputTensor); + return outputTensorMap; + } + return outputTensors; + } + /** + * Execute the inference for the input tensors. + * + * @param input The input tensors, when there is single input for the model, + * inputs param should be a `tf.Tensor`. For models with mutliple inputs, + * inputs params should be in either `tf.Tensor`[] if the input order is + * fixed, or otherwise NamedTensorMap format. + * + * For model with multiple inputs, we recommend you use NamedTensorMap as the + * input type, if you use `tf.Tensor`[], the order of the array needs to + * follow the + * order of inputNodes array. @see {@link GraphModel.inputNodes} + * + * You can also feed any intermediate nodes using the NamedTensorMap as the + * input type. For example, given the graph + * InputNode => Intermediate => OutputNode, + * you can execute the subgraph Intermediate => OutputNode by calling + * model.execute('IntermediateNode' : tf.tensor(...)); + * + * This is useful for models that uses tf.dynamic_rnn, where the intermediate + * state needs to be fed manually. + * + * For batch inference execution, the tensors for each input need to be + * concatenated together. For example with mobilenet, the required input shape + * is [1, 244, 244, 3], which represents the [batch, height, width, channel]. + * If we are provide a batched data of 100 images, the input tensor should be + * in the shape of [100, 244, 244, 3]. + * + * @param config Prediction configuration for specifying the batch size. + * Currently the batch size option is ignored for graph model. + * + * @returns Inference result tensors. If the model is converted and it + * originally had structured_outputs in tensorflow, then a NamedTensorMap + * will be returned matching the structured_outputs. If no structured_outputs + * are present, the output will be single `tf.Tensor` if the model has single + * output node, otherwise Tensor[]. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + predict(inputs, config) { + const outputTensors = this.execute(inputs, this.outputNodes); + return this.addStructuredOutputNames(outputTensors); + } + /** + * Execute the inference for the input tensors in async fashion, use this + * method when your model contains control flow ops. + * + * @param input The input tensors, when there is single input for the model, + * inputs param should be a `tf.Tensor`. For models with mutliple inputs, + * inputs params should be in either `tf.Tensor`[] if the input order is + * fixed, or otherwise NamedTensorMap format. + * + * For model with multiple inputs, we recommend you use NamedTensorMap as the + * input type, if you use `tf.Tensor`[], the order of the array needs to + * follow the + * order of inputNodes array. @see {@link GraphModel.inputNodes} + * + * You can also feed any intermediate nodes using the NamedTensorMap as the + * input type. For example, given the graph + * InputNode => Intermediate => OutputNode, + * you can execute the subgraph Intermediate => OutputNode by calling + * model.execute('IntermediateNode' : tf.tensor(...)); + * + * This is useful for models that uses tf.dynamic_rnn, where the intermediate + * state needs to be fed manually. + * + * For batch inference execution, the tensors for each input need to be + * concatenated together. For example with mobilenet, the required input shape + * is [1, 244, 244, 3], which represents the [batch, height, width, channel]. + * If we are provide a batched data of 100 images, the input tensor should be + * in the shape of [100, 244, 244, 3]. + * + * @param config Prediction configuration for specifying the batch size. + * Currently the batch size option is ignored for graph model. + * + * @returns A Promise of inference result tensors. If the model is converted + * and it originally had structured_outputs in tensorflow, then a + * NamedTensorMap will be returned matching the structured_outputs. If no + * structured_outputs are present, the output will be single `tf.Tensor` if + * the model has single output node, otherwise Tensor[]. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async predictAsync(inputs, config) { + const outputTensors = await this.executeAsync(inputs, this.outputNodes); + return this.addStructuredOutputNames(outputTensors); + } + normalizeInputs(inputs) { + var _a; + if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) { + const signatureInputs = (_a = this.signature) === null || _a === void 0 ? void 0 : _a.inputs; + if (signatureInputs != null) { + for (const input2 in signatureInputs) { + const tensor2 = signatureInputs[input2]; + if (tensor2.resourceId != null) { + inputs[input2] = this.resourceIdToCapturedInput[tensor2.resourceId]; + } + } + } + return inputs; + } + inputs = Array.isArray(inputs) ? inputs : [inputs]; + const numCapturedInputs = Object.keys(this.resourceIdToCapturedInput).length; + if (inputs.length + numCapturedInputs !== this.inputNodes.length) { + throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length - numCapturedInputs} non-resource placeholders, while there are ${inputs.length} input tensors provided.`); + } + let inputIndex = 0; + return this.inputNodes.reduce((map, inputName) => { + var _a2, _b, _c; + const resourceId = (_c = (_b = (_a2 = this.signature) === null || _a2 === void 0 ? void 0 : _a2.inputs) === null || _b === void 0 ? void 0 : _b[inputName]) === null || _c === void 0 ? void 0 : _c.resourceId; + if (resourceId != null) { + map[inputName] = this.resourceIdToCapturedInput[resourceId]; + } else { + map[inputName] = inputs[inputIndex++]; + } + return map; + }, {}); + } + normalizeOutputs(outputs) { + outputs = outputs || this.outputNodes; + return !Array.isArray(outputs) ? [outputs] : outputs; + } + executeInitializerGraph() { + if (this.initializer == null) { + return []; + } + if (this.initializerSignature == null) { + return this.initializer.execute({}, []); + } else { + return this.initializer.execute({}, Object.keys(this.initializerSignature.outputs)); + } + } + async executeInitializerGraphAsync() { + if (this.initializer == null) { + return []; + } + if (this.initializerSignature == null) { + return this.initializer.executeAsync({}, []); + } else { + return this.initializer.executeAsync({}, Object.keys(this.initializerSignature.outputs)); + } + } + setResourceIdToCapturedInput(outputs) { + this.resourceIdToCapturedInput = {}; + if (this.initializerSignature) { + const signatureOutputs = this.initializerSignature.outputs; + const outputNames = Object.keys(signatureOutputs); + for (let i = 0; i < outputNames.length; i++) { + const outputName = outputNames[i]; + const tensorInfo = signatureOutputs[outputName]; + this.resourceIdToCapturedInput[tensorInfo.resourceId] = outputs[i]; + } + } + } + /** + * Executes inference for the model for given input tensors. + * @param inputs tensor, tensor array or tensor map of the inputs for the + * model, keyed by the input node names. + * @param outputs output node name from the TensorFlow model, if no + * outputs are specified, the default outputs of the model would be used. + * You can inspect intermediate nodes of the model by adding them to the + * outputs array. + * + * @returns A single tensor if provided with a single output or no outputs + * are provided and there is only one default output, otherwise return a + * tensor array. The order of the tensor array is the same as the outputs + * if provided, otherwise the order of outputNodes attribute of the model. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + execute(inputs, outputs) { + if (this.resourceIdToCapturedInput == null) { + this.setResourceIdToCapturedInput(this.executeInitializerGraph()); + } + inputs = this.normalizeInputs(inputs); + outputs = this.normalizeOutputs(outputs); + const result = this.executor.execute(inputs, outputs); + return result.length > 1 ? result : result[0]; + } + /** + * Executes inference for the model for given input tensors in async + * fashion, use this method when your model contains control flow ops. + * @param inputs tensor, tensor array or tensor map of the inputs for the + * model, keyed by the input node names. + * @param outputs output node name from the TensorFlow model, if no outputs + * are specified, the default outputs of the model would be used. You can + * inspect intermediate nodes of the model by adding them to the outputs + * array. + * + * @returns A Promise of single tensor if provided with a single output or + * no outputs are provided and there is only one default output, otherwise + * return a tensor map. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + async executeAsync(inputs, outputs) { + if (this.resourceIdToCapturedInput == null) { + this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()); + } + inputs = this.normalizeInputs(inputs); + outputs = this.normalizeOutputs(outputs); + const result = await this.executor.executeAsync(inputs, outputs); + return result.length > 1 ? result : result[0]; + } + /** + * Get intermediate tensors for model debugging mode (flag + * KEEP_INTERMEDIATE_TENSORS is true). + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + getIntermediateTensors() { + return this.executor.getIntermediateTensors(); + } + /** + * Dispose intermediate tensors for model debugging mode (flag + * KEEP_INTERMEDIATE_TENSORS is true). + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + disposeIntermediateTensors() { + this.executor.disposeIntermediateTensors(); + } + convertTensorMapToTensorsMap(map) { + return Object.keys(map).reduce((newMap, key) => { + newMap[key] = [map[key]]; + return newMap; + }, {}); + } + /** + * Releases the memory used by the weight tensors and resourceManager. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + dispose() { + this.executor.dispose(); + if (this.initializer) { + this.initializer.dispose(); + if (this.resourceIdToCapturedInput) { + dispose(this.resourceIdToCapturedInput); + } + } + this.resourceManager.dispose(); + } +}; +async function loadGraphModel(modelUrl, options = {}, tfio = io_exports) { + if (modelUrl == null) { + throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model"); + } + if (options == null) { + options = {}; + } + if (options.fromTFHub && typeof modelUrl === "string") { + modelUrl = getTFHubUrl(modelUrl); + } + const model2 = new GraphModel(modelUrl, options, tfio); + await model2.load(); + return model2; +} +function loadGraphModelSync(modelSource) { + if (modelSource == null) { + throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model"); + } + let ioHandler; + if (modelSource instanceof Array) { + const [modelJSON, weights] = modelSource; + if (!modelJSON) { + throw new Error("modelJSON must be the first element of the array"); + } + if (!weights || !(weights instanceof ArrayBuffer)) { + throw new Error("An ArrayBuffer of weights must be the second element of the array"); + } + if (!("modelTopology" in modelJSON)) { + throw new Error("Model JSON is missing 'modelTopology'"); + } + if (!("weightsManifest" in modelJSON)) { + throw new Error("Model JSON is missing 'weightsManifest'"); + } + const weightSpecs = io_exports.getWeightSpecs(modelJSON.weightsManifest); + const modelArtifacts = io_exports.getModelArtifactsForJSONSync(modelJSON, weightSpecs, weights); + ioHandler = io_exports.fromMemorySync(modelArtifacts); + } else if ("load" in modelSource) { + ioHandler = modelSource; + } else if ("modelTopology" in modelSource && "weightSpecs" in modelSource && "weightData" in modelSource) { + ioHandler = io_exports.fromMemorySync(modelSource); + } else { + throw new Error("Unknown model format"); + } + const model2 = new GraphModel(ioHandler); + model2.load(); + return model2; +} +function getTFHubUrl(modelUrl) { + if (!modelUrl.endsWith("/")) { + modelUrl = modelUrl + "/"; + } + return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`; +} + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/dist/version.js +var version3 = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/index.js +var dist_exports2 = {}; +__export(dist_exports2, { + CSVDataset: () => CSVDataset, + Dataset: () => Dataset, + FileDataSource: () => FileDataSource, + TextLineDataset: () => TextLineDataset, + URLDataSource: () => URLDataSource, + array: () => array, + csv: () => csv, + func: () => func, + generator: () => generator, + microphone: () => microphone, + version_data: () => version4, + webcam: () => webcam, + zip: () => zip +}); + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/dataset.js +var seedrandom3 = __toESM(require_seedrandom2()); + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js +var seedrandom2 = __toESM(require_seedrandom2()); + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/util/deep_map.js +function deepMap(input2, mapFn) { + return deepMapInternal(input2, mapFn); +} +function deepMapInternal(input2, mapFn, seen = /* @__PURE__ */ new Map(), containedIn = /* @__PURE__ */ new Set()) { + if (input2 == null) { + return null; + } + if (typeof Blob === "function" && input2 instanceof Blob) { + return input2.slice(); + } + if (containedIn.has(input2)) { + throw new Error("Circular references are not supported."); + } + if (seen.has(input2)) { + return seen.get(input2); + } + const result = mapFn(input2); + if (result.recurse && result.value !== null) { + throw new Error("A deep map function may not return both a value and recurse=true."); + } + if (!result.recurse) { + seen.set(input2, result.value); + return result.value; + } else if (isIterable2(input2)) { + const mappedIterable = Array.isArray(input2) ? [] : {}; + containedIn.add(input2); + for (const k in input2) { + const child = input2[k]; + const childResult = deepMapInternal(child, mapFn, seen, containedIn); + mappedIterable[k] = childResult; + } + containedIn.delete(input2); + if (input2.__proto__) { + mappedIterable.__proto__ = input2.__proto__; + } + return mappedIterable; + } else { + throw new Error(`Can't recurse into non-iterable type: ${input2}`); + } +} +function deepZip(inputs, zipFn = zipToList) { + return deepZipInternal(inputs, zipFn); +} +function deepZipInternal(inputs, zipFn, containedIn = /* @__PURE__ */ new Set()) { + const input2 = inputs[0]; + if (containedIn.has(input2)) { + throw new Error("Circular references are not supported."); + } + const result = zipFn(inputs); + if (result.recurse && result.value !== null) { + throw new Error("A deep zip function may not return both a value and recurse=true."); + } + if (!result.recurse) { + return result.value; + } else if (isIterable2(input2)) { + const mappedIterable = Array.isArray(input2) ? [] : {}; + containedIn.add(input2); + for (const k in input2) { + const children = inputs.map((x) => x[k]); + const childResult = deepZipInternal(children, zipFn, containedIn); + mappedIterable[k] = childResult; + } + containedIn.delete(input2); + return mappedIterable; + } else { + throw new Error(`Can't recurse into non-iterable type: ${input2}`); + } +} +function zipToList(x) { + if (x === null) { + return null; + } + if (isIterable2(x[0])) { + return { value: null, recurse: true }; + } else { + return { value: x, recurse: false }; + } +} +async function deepMapAndAwaitAll(input2, mapFn) { + const seen = /* @__PURE__ */ new Map(); + deepMapInternal(input2, mapFn, seen); + for (const key of Array.from(seen.keys())) { + const value = seen.get(key); + if (util_exports.isPromise(value)) { + const mappedValue = await value; + seen.set(key, mappedValue); + } + } + const result = deepMapInternal(input2, mapFn, seen); + return result; +} +function isIterable2(obj) { + let isTextDecoder = false; + if (env().get("IS_BROWSER")) { + isTextDecoder = obj instanceof TextDecoder; + } else { + const { StringDecoder } = require_string_decoder(); + isTextDecoder = obj instanceof StringDecoder; + } + return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === "object" && !(obj instanceof Tensor) && !(obj instanceof Promise) && !isTextDecoder); +} +function canTensorify(obj) { + return obj == null || isPrimitive(obj) || Array.isArray(obj) || typeof obj === "object" && obj instanceof Tensor || util_exports.isTypedArray(obj); +} +function isPrimitive(value) { + return value === null || typeof value !== "object" && typeof value !== "function"; +} + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js +function deepClone(container) { + return deepMap(container, cloneIfTensor); +} +function cloneIfTensor(item) { + if (item instanceof Tensor) { + return { value: item.clone(), recurse: false }; + } else if (isIterable2(item)) { + return { value: null, recurse: true }; + } else { + return { value: item, recurse: false }; + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/util/ring_buffer.js +var RingBuffer = class { + /** + * Constructs a `RingBuffer`. + * @param capacity The number of items that the buffer can accomodate. + */ + constructor(capacity) { + this.capacity = capacity; + this.begin = 0; + this.end = 0; + if (capacity == null) { + throw new RangeError("Can't create a ring buffer of unknown capacity."); + } + if (capacity < 1) { + throw new RangeError("Can't create ring buffer of capacity < 1."); + } + this.data = new Array(capacity); + this.doubledCapacity = 2 * capacity; + } + /** + * Map any index into the range 0 <= index < 2*capacity. + */ + wrap(index) { + while (index < 0) { + index += this.doubledCapacity; + } + return index % this.doubledCapacity; + } + get(index) { + if (index < 0) { + throw new RangeError("Can't get item at a negative index."); + } + return this.data[index % this.capacity]; + } + set(index, value) { + if (index < 0) { + throw new RangeError("Can't set item at a negative index."); + } + this.data[index % this.capacity] = value; + } + /** + * Returns the current number of items in the buffer. + */ + length() { + let length = this.end - this.begin; + if (length < 0) { + length = this.doubledCapacity + length; + } + return length; + } + /** + * Reports whether the buffer is full. + * @returns true if the number of items in the buffer equals its capacity, and + * false otherwise. + */ + isFull() { + return this.length() === this.capacity; + } + /** + * Reports whether the buffer is empty. + * @returns true if the number of items in the buffer equals zero, and + * false otherwise. + */ + isEmpty() { + return this.length() === 0; + } + /** + * Adds an item to the end of the buffer. + */ + push(value) { + if (this.isFull()) { + throw new RangeError("Ring buffer is full."); + } + this.set(this.end, value); + this.end = this.wrap(this.end + 1); + } + /** + * Adds many items to the end of the buffer, in order. + */ + pushAll(values) { + for (const value of values) { + this.push(value); + } + } + /** + * Removes and returns the last item in the buffer. + */ + pop() { + if (this.isEmpty()) { + throw new RangeError("Ring buffer is empty."); + } + this.end = this.wrap(this.end - 1); + const result = this.get(this.end); + this.set(this.end, void 0); + return result; + } + /** + * Adds an item to the beginning of the buffer. + */ + unshift(value) { + if (this.isFull()) { + throw new RangeError("Ring buffer is full."); + } + this.begin = this.wrap(this.begin - 1); + this.set(this.begin, value); + } + /** + * Removes and returns the first item in the buffer. + */ + shift() { + if (this.isEmpty()) { + throw new RangeError("Ring buffer is empty."); + } + const result = this.get(this.begin); + this.set(this.begin, void 0); + this.begin = this.wrap(this.begin + 1); + return result; + } + /** + * Removes and returns a specific item in the buffer, and moves the last item + * to the vacated slot. This is useful for implementing a shuffling stream. + * Note that this operation necessarily scrambles the original order. + * + * @param relativeIndex: the index of the item to remove, relative to the + * first item in the buffer (e.g., hiding the ring nature of the underlying + * storage). + */ + shuffleExcise(relativeIndex) { + if (this.isEmpty()) { + throw new RangeError("Ring buffer is empty."); + } + const index = this.wrap(this.begin + relativeIndex); + const result = this.get(index); + this.set(index, this.pop()); + return result; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js +var GrowingRingBuffer = class _GrowingRingBuffer extends RingBuffer { + /** + * Constructs a `GrowingRingBuffer`. + */ + constructor() { + super(_GrowingRingBuffer.INITIAL_CAPACITY); + } + isFull() { + return false; + } + push(value) { + if (super.isFull()) { + this.expand(); + } + super.push(value); + } + unshift(value) { + if (super.isFull()) { + this.expand(); + } + super.unshift(value); + } + /** + * Doubles the capacity of the buffer. + */ + expand() { + const newCapacity = this.capacity * 2; + const newData = new Array(newCapacity); + const len = this.length(); + for (let i = 0; i < len; i++) { + newData[i] = this.get(this.wrap(this.begin + i)); + } + this.data = newData; + this.capacity = newCapacity; + this.doubledCapacity = 2 * this.capacity; + this.begin = 0; + this.end = len; + } +}; +GrowingRingBuffer.INITIAL_CAPACITY = 32; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js +function iteratorFromItems(items) { + return new ArrayIterator(items); +} +function iteratorFromFunction(func2) { + return new FunctionCallIterator(func2); +} +function iteratorFromConcatenated(baseIterators, baseErrorHandler) { + return new ChainedIterator(baseIterators, baseErrorHandler); +} +function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) { + return new ZipIterator(iterators, mismatchMode); +} +var LazyIterator = class { + /** + * Collect all remaining elements of a bounded stream into an array. + * Obviously this will succeed only for small streams that fit in memory. + * Useful for testing. + * + * @returns A Promise for an array of stream elements, which will resolve + * when the stream is exhausted. + */ + async toArray() { + const result = []; + let x = await this.next(); + while (!x.done) { + result.push(x.value); + x = await this.next(); + } + return result; + } + /** + * Collect all elements of this dataset into an array with prefetching 100 + * elements. This is useful for testing, because the prefetch changes the + * order in which the Promises are resolved along the processing pipeline. + * This may help expose bugs where results are dependent on the order of + * Promise resolution rather than on the logical order of the stream (i.e., + * due to hidden mutable state). + * + * @returns A Promise for an array of stream elements, which will resolve + * when the stream is exhausted. + */ + async toArrayForTest() { + const stream = this.prefetch(100); + const result = []; + let x = await stream.next(); + while (!x.done) { + result.push(x.value); + x = await stream.next(); + } + return result; + } + /** + * Draw items from the stream until it is exhausted. + * + * This can be useful when the stream has side effects but no output. In + * that case, calling this function guarantees that the stream will be + * fully processed. + */ + async resolveFully() { + let x = await this.next(); + while (!x.done) { + x = await this.next(); + } + } + /** + * Draw items from the stream until it is exhausted, or a predicate fails. + * + * This can be useful when the stream has side effects but no output. In + * that case, calling this function guarantees that the stream will be + * fully processed. + */ + async resolveWhile(predicate) { + let x = await this.next(); + let shouldContinue = predicate(x.value); + while (!x.done && shouldContinue) { + x = await this.next(); + shouldContinue = predicate(x.value); + } + } + /** + * Handles errors thrown on this stream using a provided handler function. + * + * @param handler A function that handles any `Error` thrown during a `next()` + * call and returns true if the stream should continue (dropping the failed + * call) or false if the stream should quietly terminate. If the handler + * itself throws (or rethrows) an `Error`, that will be propagated. + * + * @returns A `LazyIterator` of elements passed through from upstream, + * possibly filtering or terminating on upstream `next()` calls that + * throw an `Error`. + */ + handleErrors(handler) { + return new ErrorHandlingLazyIterator(this, handler); + } + // TODO(soergel): Implement reduce() etc. + /** + * Filters this stream according to `predicate`. + * + * @param predicate A function mapping a stream element to a boolean or a + * `Promise` for one. + * + * @returns A `LazyIterator` of elements for which the predicate was true. + */ + filter(predicate) { + return new FilterIterator(this, predicate); + } + /** + * Maps this stream through a 1-to-1 transform. + * + * @param transform A function mapping a stream element to a transformed + * element. + * + * @returns A `LazyIterator` of transformed elements. + */ + map(transform5) { + return new MapIterator(this, transform5); + } + /** + * Maps this stream through an async 1-to-1 transform. + * + * @param transform A function mapping a stream element to a `Promise` for a + * transformed stream element. + * + * @returns A `LazyIterator` of transformed elements. + */ + mapAsync(transform5) { + return new AsyncMapIterator(this, transform5); + } + /** + * Maps this stream through a 1-to-1 transform, forcing serial execution. + * + * @param transform A function mapping a stream element to a transformed + * element. + * + * @returns A `LazyIterator` of transformed elements. + */ + serialMapAsync(transform5) { + return new AsyncMapIterator(this, transform5).serial(); + } + /** + * Maps this stream through a 1-to-many transform. + * + * @param transform A function mapping a stream element to an array of + * transformed elements. + * + * @returns A `DataStream` of transformed elements. + */ + flatmap(transform5) { + return new FlatmapIterator(this, transform5); + } + /** + * Apply a function to every element of the stream. + * + * @param f A function to apply to each stream element. + */ + async forEachAsync(f) { + return this.map(f).resolveFully(); + } + /** + * Apply a function to every element of the stream, forcing serial execution. + * + * @param f A function to apply to each stream element. Should return 'true' + * to indicate that the stream should continue, or 'false' to cause it to + * terminate. + */ + async serialForEach(f) { + return this.serialMapAsync(f).resolveWhile((x) => x === true); + } + /** + * Groups elements into batches, represented as arrays of elements. + * + * We can think of the elements of this iterator as 'rows' (even if they are + * nested structures). By the same token, consecutive values for a given + * key within the elements form a 'column'. This matches the usual sense of + * 'row' and 'column' when processing tabular data (e.g., parsing a CSV). + * + * Thus, "Row-major" means that the resulting batch is simply a collection of + * rows: `[row1, row2, row3, ...]`. This is contrast to the column-major + * form, which is needed for vectorized computation. + * + * @param batchSize The number of elements desired per batch. + * @param smallLastBatch Whether to emit the final batch when it has fewer + * than batchSize elements. Default true. + * @returns A `LazyIterator` of batches of elements, represented as arrays + * of the original element type. + */ + rowMajorBatch(batchSize, smallLastBatch = true) { + return new RowMajorBatchIterator(this, batchSize, smallLastBatch); + } + /** + * Groups elements into batches, represented in column-major form. + * + * We can think of the elements of this iterator as 'rows' (even if they are + * nested structures). By the same token, consecutive values for a given + * key within the elements form a 'column'. This matches the usual sense of + * 'row' and 'column' when processing tabular data (e.g., parsing a CSV). + * + * Thus, "column-major" means that the resulting batch is a (potentially + * nested) structure representing the columns. Each column entry, then, + * contains a collection of the values found in that column for a range of + * input elements. This representation allows for vectorized computation, in + * contrast to the row-major form. + * + * The inputs should all have the same nested structure (i.e., of arrays and + * dicts). The result is a single object with the same nested structure, + * where the leaves are arrays collecting the values of the inputs at that + * location (or, optionally, the result of a custom function applied to those + * arrays). + * + * @param batchSize The number of elements desired per batch. + * @param smallLastBatch Whether to emit the final batch when it has fewer + * than batchSize elements. Default true. + * @param zipFn: (optional) A function that expects an array of elements at a + * single node of the object tree, and returns a `DeepMapResult`. The + * `DeepMapResult` either provides a result value for that node (i.e., + * representing the subtree), or indicates that the node should be processed + * recursively. The default zipFn recurses as far as possible and places + * arrays at the leaves. + * @returns A `LazyIterator` of batches of elements, represented as an object + * with collections at the leaves. + */ + columnMajorBatch(batchSize, smallLastBatch = true, zipFn = zipToList) { + const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch); + return rowBatches.map((x) => deepZip(x, zipFn)); + } + /** + * Concatenate this `LazyIterator` with another. + * + * @param iterator A `LazyIterator` to be concatenated onto this one. + * @param baseErrorHandler An optional function that can intercept `Error`s + * raised during a `next()` call on the base stream. This function can + * decide whether the error should be propagated, whether the error should + * be ignored, or whether the base stream should be terminated. + * @returns A `LazyIterator`. + */ + concatenate(iterator, baseErrorHandler) { + return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler); + } + /** + * Limits this stream to return at most `count` items. + * + * @param count The maximum number of items to provide from the stream. If + * a negative or undefined value is given, the entire stream is returned + * unaltered. + */ + take(count2) { + if (count2 < 0 || count2 == null) { + return this; + } + return new TakeIterator(this, count2); + } + /** + * Skips the first `count` items in this stream. + * + * @param count The number of items to skip. If a negative or undefined + * value is given, the entire stream is returned unaltered. + */ + skip(count2) { + if (count2 < 0 || count2 == null) { + return this; + } + return new SkipIterator(this, count2); + } + /** + * Prefetch the first `bufferSize` items in this stream. + * + * Note this prefetches Promises, but makes no guarantees about when those + * Promises resolve. + * + * @param bufferSize: An integer specifying the number of elements to be + * prefetched. + */ + prefetch(bufferSize) { + return new PrefetchIterator(this, bufferSize); + } + // TODO(soergel): deep sharded shuffle, where supported + /** + * Randomly shuffles the elements of this stream. + * + * @param bufferSize: An integer specifying the number of elements from + * this stream from which the new stream will sample. + * @param seed: (Optional.) An integer specifying the random seed that + * will be used to create the distribution. + */ + shuffle(windowSize, seed) { + return new ShuffleIterator(this, windowSize, seed); + } + /** + * Force an iterator to execute serially: each next() call will await the + * prior one, so that they cannot execute concurrently. + */ + serial() { + return new SerialIterator(this); + } +}; +var ArrayIterator = class extends LazyIterator { + constructor(items) { + super(); + this.items = items; + this.trav = 0; + } + summary() { + return `Array of ${this.items.length} items`; + } + async next() { + if (this.trav >= this.items.length) { + return { value: null, done: true }; + } + const item = this.items[this.trav]; + this.trav++; + return { value: deepClone(item), done: false }; + } +}; +var FunctionCallIterator = class extends LazyIterator { + constructor(nextFn) { + super(); + this.nextFn = nextFn; + } + summary() { + return `Function call`; + } + async next() { + try { + return this.nextFn(); + } catch (e) { + e.message = `Error thrown while iterating through a dataset: ${e.message}`; + throw e; + } + } +}; +var SerialIterator = class extends LazyIterator { + constructor(upstream) { + super(); + this.upstream = upstream; + this.lastRead = Promise.resolve({ value: null, done: false }); + } + summary() { + return `${this.upstream.summary()} -> Serial`; + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + return this.upstream.next(); + } +}; +var SkipIterator = class extends LazyIterator { + constructor(upstream, maxCount) { + super(); + this.upstream = upstream; + this.maxCount = maxCount; + this.count = 0; + this.lastRead = Promise.resolve({ value: null, done: false }); + } + summary() { + return `${this.upstream.summary()} -> Skip`; + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + while (this.count++ < this.maxCount) { + const skipped = await this.upstream.next(); + if (skipped.done) { + return skipped; + } + dispose(skipped.value); + } + return this.upstream.next(); + } +}; +var TakeIterator = class extends LazyIterator { + constructor(upstream, maxCount) { + super(); + this.upstream = upstream; + this.maxCount = maxCount; + this.count = 0; + } + summary() { + return `${this.upstream.summary()} -> Take`; + } + async next() { + if (this.count++ >= this.maxCount) { + return { value: null, done: true }; + } + return this.upstream.next(); + } +}; +var RowMajorBatchIterator = class extends LazyIterator { + constructor(upstream, batchSize, enableSmallLastBatch = true) { + super(); + this.upstream = upstream; + this.batchSize = batchSize; + this.enableSmallLastBatch = enableSmallLastBatch; + this.lastRead = Promise.resolve({ value: null, done: false }); + } + summary() { + return `${this.upstream.summary()} -> RowMajorBatch`; + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + const batch = []; + while (batch.length < this.batchSize) { + const item = await this.upstream.next(); + if (item.done) { + if (this.enableSmallLastBatch && batch.length > 0) { + return { value: batch, done: false }; + } + return { value: null, done: true }; + } + batch.push(item.value); + } + return { value: batch, done: false }; + } +}; +var FilterIterator = class extends LazyIterator { + constructor(upstream, predicate) { + super(); + this.upstream = upstream; + this.predicate = predicate; + this.lastRead = Promise.resolve({ value: null, done: false }); + } + summary() { + return `${this.upstream.summary()} -> Filter`; + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + while (true) { + const item = await this.upstream.next(); + if (item.done || this.predicate(item.value)) { + return item; + } + dispose(item.value); + } + } +}; +var MapIterator = class extends LazyIterator { + constructor(upstream, transform5) { + super(); + this.upstream = upstream; + this.transform = transform5; + } + summary() { + return `${this.upstream.summary()} -> Map`; + } + async next() { + const item = await this.upstream.next(); + if (item.done) { + return { value: null, done: true }; + } + const inputTensors = tensor_util_exports.getTensorsInContainer(item.value); + const mapped = this.transform(item.value); + const outputTensors = tensor_util_exports.getTensorsInContainer(mapped); + for (const t of inputTensors) { + if (!tensor_util_exports.isTensorInList(t, outputTensors)) { + t.dispose(); + } + } + return { value: mapped, done: false }; + } +}; +var ErrorHandlingLazyIterator = class extends LazyIterator { + constructor(upstream, handler) { + super(); + this.upstream = upstream; + this.handler = handler; + this.count = 0; + this.lastRead = Promise.resolve({ value: null, done: false }); + } + summary() { + return `${this.upstream.summary()} -> handleErrors`; + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + while (true) { + try { + return await this.upstream.next(); + } catch (e) { + if (!this.handler(e)) { + return { value: null, done: true }; + } + } + } + } +}; +var AsyncMapIterator = class extends LazyIterator { + constructor(upstream, transform5) { + super(); + this.upstream = upstream; + this.transform = transform5; + } + summary() { + return `${this.upstream.summary()} -> AsyncMap`; + } + async next() { + const item = await this.upstream.next(); + if (item.done) { + return { value: null, done: true }; + } + const inputTensors = tensor_util_exports.getTensorsInContainer(item.value); + const mapped = await this.transform(item.value); + const outputTensors = tensor_util_exports.getTensorsInContainer(mapped); + for (const t of inputTensors) { + if (!tensor_util_exports.isTensorInList(t, outputTensors)) { + t.dispose(); + } + } + return { value: mapped, done: false }; + } +}; +var OneToManyIterator = class extends LazyIterator { + constructor() { + super(); + this.outputQueue = new GrowingRingBuffer(); + this.lastRead = Promise.resolve({ value: null, done: false }); + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + async serialNext() { + while (this.outputQueue.length() === 0) { + if (!await this.pump()) { + return { value: null, done: true }; + } + } + return { value: this.outputQueue.shift(), done: false }; + } +}; +var FlatmapIterator = class extends OneToManyIterator { + constructor(upstream, transform5) { + super(); + this.upstream = upstream; + this.transform = transform5; + } + summary() { + return `${this.upstream.summary()} -> Flatmap`; + } + async pump() { + const item = await this.upstream.next(); + if (item.done) { + return false; + } + const inputTensors = tensor_util_exports.getTensorsInContainer(item.value); + const mappedArray = this.transform(item.value); + const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray); + this.outputQueue.pushAll(mappedArray); + for (const t of inputTensors) { + if (!tensor_util_exports.isTensorInList(t, outputTensors)) { + t.dispose(); + } + } + return true; + } +}; +var ChainedIterator = class extends LazyIterator { + constructor(iterators, baseErrorHandler) { + super(); + this.baseErrorHandler = baseErrorHandler; + this.lastRead = null; + this.iterator = null; + this.moreIterators = iterators; + } + summary() { + const upstreamSummaries = "TODO: fill in upstream of chained summaries"; + return `${upstreamSummaries} -> Chained`; + } + async next() { + this.lastRead = this.readFromChain(this.lastRead); + return this.lastRead; + } + async readFromChain(lastRead) { + await lastRead; + if (this.iterator == null) { + const iteratorResult = await this.moreIterators.next(); + if (iteratorResult.done) { + return { value: null, done: true }; + } + this.iterator = iteratorResult.value; + if (this.baseErrorHandler != null) { + this.iterator = this.iterator.handleErrors(this.baseErrorHandler); + } + } + const itemResult = await this.iterator.next(); + if (itemResult.done) { + this.iterator = null; + return this.readFromChain(lastRead); + } + return itemResult; + } +}; +var ZipMismatchMode; +(function(ZipMismatchMode2) { + ZipMismatchMode2[ZipMismatchMode2["FAIL"] = 0] = "FAIL"; + ZipMismatchMode2[ZipMismatchMode2["SHORTEST"] = 1] = "SHORTEST"; + ZipMismatchMode2[ZipMismatchMode2["LONGEST"] = 2] = "LONGEST"; +})(ZipMismatchMode || (ZipMismatchMode = {})); +var ZipIterator = class extends LazyIterator { + constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) { + super(); + this.iterators = iterators; + this.mismatchMode = mismatchMode; + this.count = 0; + this.currentPromise = null; + } + summary() { + const upstreamSummaries = "TODO: fill in upstream of zip summaries"; + return `{${upstreamSummaries}} -> Zip`; + } + async nextState(afterState) { + await afterState; + let numIterators = 0; + let iteratorsDone = 0; + function getNext(container) { + if (container instanceof LazyIterator) { + const result = container.next(); + return { + value: result.then((x) => { + numIterators++; + if (x.done) { + iteratorsDone++; + } + return x.value; + }), + recurse: false + }; + } else { + return { value: null, recurse: true }; + } + } + const mapped = await deepMapAndAwaitAll(this.iterators, getNext); + if (numIterators === iteratorsDone) { + return { value: null, done: true }; + } + if (iteratorsDone > 0) { + switch (this.mismatchMode) { + case ZipMismatchMode.FAIL: + throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`); + case ZipMismatchMode.SHORTEST: + return { value: null, done: true }; + case ZipMismatchMode.LONGEST: + default: + } + } + this.count++; + return { value: mapped, done: false }; + } + async next() { + this.currentPromise = this.nextState(this.currentPromise); + return this.currentPromise; + } +}; +var PrefetchIterator = class extends LazyIterator { + constructor(upstream, bufferSize) { + super(); + this.upstream = upstream; + this.bufferSize = bufferSize; + this.buffer = new RingBuffer(bufferSize); + } + summary() { + return `${this.upstream.summary()} -> Prefetch`; + } + /** + * Refill the prefetch buffer. Returns only after the buffer is full, or + * the upstream source is exhausted. + */ + refill() { + while (!this.buffer.isFull()) { + const v = this.upstream.next(); + this.buffer.push(v); + } + } + next() { + this.refill(); + return this.buffer.shift(); + } +}; +var ShuffleIterator = class extends PrefetchIterator { + constructor(upstream, windowSize, seed) { + super(upstream, windowSize); + this.upstream = upstream; + this.windowSize = windowSize; + this.upstreamExhausted = false; + this.random = seedrandom2.alea(seed || util_exports.now().toString()); + this.lastRead = Promise.resolve({ value: null, done: false }); + } + async next() { + this.lastRead = this.lastRead.then(() => this.serialNext()); + return this.lastRead; + } + randomInt(max6) { + return Math.floor(this.random() * max6); + } + chooseIndex() { + return this.randomInt(this.buffer.length()); + } + async serialNext() { + if (!this.upstreamExhausted) { + this.refill(); + } + while (!this.buffer.isEmpty()) { + const chosenIndex = this.chooseIndex(); + const result = await this.buffer.shuffleExcise(chosenIndex); + if (result.done) { + this.upstreamExhausted = true; + } else { + this.refill(); + return result; + } + } + return { value: null, done: true }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/dataset.js +var Dataset = class { + constructor() { + this.size = null; + } + // TODO(soergel): Make Datasets report whether repeated iterator() calls + // produce the same result (e.g., reading from a file) or different results + // (e.g., from the webcam). Currently we don't make this distinction but it + // could be important for the user to know. + // abstract isDeterministic(): boolean; + /** + * Groups elements into batches. + * + * It is assumed that each of the incoming dataset elements has the same + * structure -- i.e. the same set of keys at each location in an object + * hierarchy. For each key, the resulting `Dataset` provides a batched + * element collecting all of the incoming values for that key. + * + * * Incoming primitives are grouped into a 1-D Tensor. + * * Incoming Tensors are grouped into a new Tensor where the 0th axis is + * the batch dimension. + * * Incoming arrays are converted to Tensor and then batched. + * * A nested array is interpreted as an n-D Tensor, so the batched result + * has n+1 dimensions. + * * An array that cannot be converted to Tensor produces an error. + * + * If an array should not be batched as a unit, it should first be converted + * to an object with integer keys. + * + * Here are a few examples: + * + * Batch a dataset of numbers: + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4); + * await a.forEachAsync(e => e.print()); + * ``` + * + * Batch a dataset of arrays: + * ```js + * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4); + * await b.forEachAsync(e => e.print()); + * ``` + * + * Batch a dataset of objects: + * ```js + * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13}, + * {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17}, + * {a: 8, b: 18}]).batch(4); + * await c.forEachAsync(e => { + * console.log('{'); + * for(var key in e) { + * console.log(key+':'); + * e[key].print(); + * } + * console.log('}'); + * }) + * ``` + * + * @param batchSize The number of elements desired per batch. + * @param smallLastBatch Whether to emit the final batch when it has fewer + * than batchSize elements. Default true. + * @returns A `Dataset`, from which a stream of batches can be obtained. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + batch(batchSize, smallLastBatch = true) { + const base = this; + util_exports.assert(batchSize > 0, () => `batchSize needs to be positive, but it is + ${batchSize}`); + let size; + if (this.size === Infinity || this.size == null) { + size = this.size; + } else if (smallLastBatch) { + size = Math.ceil(this.size / batchSize); + } else { + size = Math.floor(this.size / batchSize); + } + return datasetFromIteratorFn(async () => { + return (await base.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat); + }, size); + } + /** + * Concatenates this `Dataset` with another. + * + * ```js + * const a = tf.data.array([1, 2, 3]); + * const b = tf.data.array([4, 5, 6]); + * const c = a.concatenate(b); + * await c.forEachAsync(e => console.log(e)); + * ``` + * + * @param dataset A `Dataset` to be concatenated onto this one. + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + concatenate(dataset) { + const base = this; + let size; + if (this.size === Infinity || dataset.size === Infinity) { + size = Infinity; + } else if (this.size != null && dataset.size != null) { + size = this.size + dataset.size; + } else { + size = null; + } + return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size); + } + /** + * Filters this dataset according to `predicate`. + * + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) + * .filter(x => x%2 === 0); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param predicate A function mapping a dataset element to a boolean or a + * `Promise` for one. + * + * @returns A `Dataset` of elements for which the predicate was true. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + filter(predicate) { + const base = this; + let size; + if (this.size === Infinity) { + size = Infinity; + } else { + size = null; + } + return datasetFromIteratorFn(async () => { + return (await base.iterator()).filter((x) => tidy(() => predicate(x))); + }, size); + } + /** + * Apply a function to every element of the dataset. + * + * After the function is applied to a dataset element, any Tensors contained + * within that element are disposed. + * + * ```js + * const a = tf.data.array([1, 2, 3]); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param f A function to apply to each dataset element. + * @returns A `Promise` that resolves after all elements have been processed. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + async forEachAsync(f) { + return (await this.iterator()).forEachAsync(f); + } + /** + * Maps this dataset through a 1-to-1 transform. + * + * ```js + * const a = tf.data.array([1, 2, 3]).map(x => x*x); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param transform A function mapping a dataset element to a transformed + * dataset element. + * + * @returns A `Dataset` of transformed elements. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + map(transform5) { + const base = this; + return datasetFromIteratorFn(async () => { + return (await base.iterator()).map((x) => tidy(() => transform5(x))); + }, this.size); + } + /** + * Maps this dataset through an async 1-to-1 transform. + * + * ```js + * const a = + * tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){ + * setTimeout(() => { + * resolve(x * x); + * }, Math.random()*1000 + 500); + * })); + * console.log(await a.toArray()); + * ``` + * + * @param transform A function mapping a dataset element to a `Promise` for a + * transformed dataset element. This transform is responsible for disposing + * any intermediate `Tensor`s, i.e. by wrapping its computation in + * `tf.tidy()`; that cannot be automated here (as it is in the synchronous + * `map()` case). + * + * @returns A `Dataset` of transformed elements. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + mapAsync(transform5) { + const base = this; + return datasetFromIteratorFn(async () => { + return (await base.iterator()).mapAsync(transform5); + }, this.size); + } + /** + * Creates a `Dataset` that prefetches elements from this dataset. + * + * @param bufferSize: An integer specifying the number of elements to be + * prefetched. + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + prefetch(bufferSize) { + if (bufferSize == null) { + throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified."); + } + const base = this; + return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size); + } + /** + * Repeats this dataset `count` times. + * + * NOTE: If this dataset is a function of global state (e.g. a random number + * generator), then different repetitions may produce different elements. + * + * ```js + * const a = tf.data.array([1, 2, 3]).repeat(3); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param count: (Optional) An integer, representing the number of times + * the dataset should be repeated. The default behavior (if `count` is + * `undefined` or negative) is for the dataset be repeated indefinitely. + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + repeat(count2) { + const base = this; + let size; + if (this.size != null && count2 > 0) { + size = this.size * count2; + } else if (count2 === 0) { + size = 0; + } else if (this.size != null && (count2 === void 0 || count2 < 0)) { + size = Infinity; + } else { + size = null; + } + return datasetFromIteratorFn(async () => { + const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false })); + return iteratorFromConcatenated(iteratorIterator.take(count2)); + }, size); + } + /** + * Creates a `Dataset` that skips `count` initial elements from this dataset. + * + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param count: The number of elements of this dataset that should be skipped + * to form the new dataset. If `count` is greater than the size of this + * dataset, the new dataset will contain no elements. If `count` + * is `undefined` or negative, skips the entire dataset. + * + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + skip(count2) { + const base = this; + let size; + if (this.size != null && count2 >= 0 && this.size >= count2) { + size = this.size - count2; + } else if (this.size != null && (this.size < count2 || count2 === void 0 || count2 < 0)) { + size = 0; + } else { + size = null; + } + return datasetFromIteratorFn(async () => (await base.iterator()).skip(count2), size); + } + /** + * Pseudorandomly shuffles the elements of this dataset. This is done in a + * streaming manner, by sampling from a given number of prefetched elements. + * + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param bufferSize: An integer specifying the number of elements from this + * dataset from which the new dataset will sample. + * @param seed: (Optional) An integer specifying the random seed that will + * be used to create the distribution. + * @param reshuffleEachIteration: (Optional) A boolean, which if true + * indicates that the dataset should be pseudorandomly reshuffled each time + * it is iterated over. If false, elements will be returned in the same + * shuffled order on each iteration. (Defaults to `true`.) + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + shuffle(bufferSize, seed, reshuffleEachIteration = true) { + if (bufferSize == null || bufferSize < 0) { + if (this.size == null) { + throw new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."); + } else { + throw new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`); + } + } + const base = this; + const random = seedrandom3.alea(seed || util_exports.now().toString()); + return datasetFromIteratorFn(async () => { + let seed2 = random.int32(); + if (reshuffleEachIteration) { + seed2 += random.int32(); + } + return (await base.iterator()).shuffle(bufferSize, seed2.toString()); + }, this.size); + } + /** + * Creates a `Dataset` with at most `count` initial elements from this + * dataset. + * + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3); + * await a.forEachAsync(e => console.log(e)); + * ``` + * + * @param count: The number of elements of this dataset that should be taken + * to form the new dataset. If `count` is `undefined` or negative, or if + * `count` is greater than the size of this dataset, the new dataset will + * contain all elements of this dataset. + * @returns A `Dataset`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + take(count2) { + const base = this; + let size; + if (this.size != null && this.size > count2) { + size = count2; + } else if (this.size != null && this.size <= count2) { + size = this.size; + } else { + size = null; + } + return datasetFromIteratorFn(async () => (await base.iterator()).take(count2), size); + } + /** + * Collect all elements of this dataset into an array. + * + * Obviously this will succeed only for small datasets that fit in memory. + * Useful for testing and generally should be avoided if possible. + * + * ```js + * const a = tf.data.array([1, 2, 3, 4, 5, 6]); + * console.log(await a.toArray()); + * ``` + * + * @returns A Promise for an array of elements, which will resolve + * when a new stream has been obtained and fully consumed. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + async toArray() { + if (this.size === Infinity) { + throw new Error("Can not convert infinite data stream to array."); + } + return (await this.iterator()).toArray(); + } + /** + * Collect all elements of this dataset into an array with prefetching 100 + * elements. This is useful for testing, because the prefetch changes the + * order in which the Promises are resolved along the processing pipeline. + * This may help expose bugs where results are dependent on the order of + * Promise resolution rather than on the logical order of the stream (i.e., + * due to hidden mutable state). + * + * @returns A Promise for an array of elements, which will resolve + * when a new stream has been obtained and fully consumed. + */ + async toArrayForTest() { + if (this.size === Infinity) { + throw new Error("Can not convert infinite data stream to array."); + } + return (await this.iterator()).toArrayForTest(); + } +}; +Dataset.MAX_BUFFER_SIZE = 1e4; +function datasetFromIteratorFn(iteratorFn, size = null) { + return new class extends Dataset { + constructor() { + super(...arguments); + this.size = size; + } + /* + * Provide a new stream of elements. Note this will also start new streams + * from any underlying `Dataset`s. + */ + async iterator() { + return iteratorFn(); + } + }(); +} +function array(items) { + return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length); +} +function zip(datasets) { + if (!isIterable2(datasets)) { + throw new Error("The argument to zip() must be an object or array."); + } + let size; + if (Array.isArray(datasets)) { + for (let i = 0; i < datasets.length; i++) { + size = size == null ? datasets[i].size : Math.min(size, datasets[i].size); + } + } else if (datasets instanceof Object) { + for (const ds in datasets) { + size = size == null ? datasets[ds].size : Math.min(size, datasets[ds].size); + } + } + return datasetFromIteratorFn(async () => { + const streams = await deepMapAndAwaitAll(datasets, (d) => { + if (d instanceof Dataset) { + return { value: d.iterator(), recurse: false }; + } else if (isIterable2(d)) { + return { value: null, recurse: true }; + } else { + throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives."); + } + }); + return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST); + }, size); +} +function deepBatchConcat(rows) { + if (rows === null) { + return null; + } + const exampleRow = rows[0]; + if (canTensorify(exampleRow)) { + const value = batchConcat(rows); + return { value, recurse: false }; + } + return { value: null, recurse: true }; +} +function batchConcat(arrays) { + if (arrays.length === 0) { + throw new Error("Can't make a batch of zero elements."); + } + if (arrays[0] instanceof Tensor) { + return stack(arrays); + } else { + return tensor(arrays); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/datasets/text_line_dataset.js +var TextLineDataset = class extends Dataset { + /** + * Create a `TextLineDataset`. + * + * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream. + */ + constructor(input2) { + super(); + this.input = input2; + } + async iterator() { + const inputIterator = await this.input.iterator(); + const utf8Iterator = inputIterator.decodeUTF8(); + const lineIterator = utf8Iterator.split("\n").map((line) => { + if (line.endsWith("\r")) { + line = line.slice(0, -1); + } + return line; + }); + return lineIterator; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js +var CODE_QUOTE = '"'; +var STATE_OUT = Symbol("out"); +var STATE_FIELD = Symbol("field"); +var STATE_QUOTE = Symbol("quote"); +var STATE_QUOTE_AFTER_QUOTE = Symbol("quoteafterquote"); +var STATE_WITHIN_QUOTE_IN_QUOTE = Symbol("quoteinquote"); +var CSVDataset = class extends Dataset { + /** + * Returns column names of the csv dataset. If `configuredColumnsOnly` is + * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is + * false and `columnNames` is provided, `columnNames`. If + * `configuredColumnsOnly` is false and `columnNames` is not provided, return + * all column names parsed from the csv file. For example usage please go to + * `tf.data.csv`. + * + * @doc {heading: 'Data', subheading: 'Classes'} + */ + async columnNames() { + if (!this.columnNamesValidated) { + await this.setColumnNames(); + } + return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames; + } + /* 1) If `columnNames` is provided as string[], use this string[] as output + * keys in corresponding order. The length must match the number of inferred + * columns if `hasHeader` is true . + * 2) If `columnNames` is not provided, parse header line as `columnNames` if + * hasHeader is true. If `hasHeader` is false, throw an error. + * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must + * exist in parsed `columnNames`. + */ + async setColumnNames() { + const columnNamesFromFile = await this.maybeReadHeaderLine(); + if (!this.fullColumnNames && !columnNamesFromFile) { + throw new Error("Column names must be provided if there is no header line."); + } else if (this.fullColumnNames && columnNamesFromFile) { + util_exports.assert(columnNamesFromFile.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 (" + columnNamesFromFile.length.toString() + ")."); + } + if (!this.fullColumnNames) { + this.fullColumnNames = columnNamesFromFile; + } + const counts = this.fullColumnNames.reduce((countAcc, name) => { + countAcc[name] = countAcc[name] + 1 || 1; + return countAcc; + }, {}); + const duplicateNames = Object.keys(counts).filter((name) => counts[name] > 1); + util_exports.assert(duplicateNames.length === 0, () => "Duplicate column names found: " + duplicateNames.toString()); + if (this.columnConfigs) { + for (const key of Object.keys(this.columnConfigs)) { + const index = this.fullColumnNames.indexOf(key); + if (index === -1) { + throw new Error('The key "' + key + '" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + ")."); + } + } + } + this.columnNamesValidated = true; + } + async maybeReadHeaderLine() { + if (this.hasHeader) { + const iter = await this.base.iterator(); + const firstElement = await iter.next(); + if (firstElement.done) { + throw new Error("No data was found for CSV parsing."); + } + const firstLine = firstElement.value; + const headers = this.parseRow(firstLine, false); + return headers; + } else { + return null; + } + } + /** + * Create a `CSVDataset`. + * + * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream. + * @param csvConfig (Optional) A CSVConfig object that contains configurations + * of reading and decoding from CSV file(s). + * + * hasHeader: (Optional) A boolean value that indicates whether the first + * row of provided CSV file is a header line with column names, and should + * not be included in the data. Defaults to `true`. + * + * columnNames: (Optional) A list of strings that corresponds to + * the CSV column names, in order. If provided, it ignores the column + * names inferred from the header row. If not provided, infers the column + * names from the first row of the records. If hasHeader is false and + * columnNames is not provided, this method throws an error. + * + * columnConfigs: (Optional) A dictionary whose key is column names, value + * is an object stating if this column is required, column's data type, + * default value, and if this column is label. If provided, keys must + * correspond to names provided in columnNames or inferred from the file + * header lines. If isLabel is true any column, returns an array of two + * items: the first item is a dict of features key/value pairs, the second + * item is a dict of labels key/value pairs. If no feature is marked as + * label, returns a dict of features only. + * + * configuredColumnsOnly (Optional) If true, only columns provided in + * columnConfigs will be parsed and provided during iteration. + * + * delimiter (Optional) The string used to parse each line of the input + * file. Defaults to `,`. + */ + constructor(input2, csvConfig) { + super(); + this.input = input2; + this.hasHeader = true; + this.fullColumnNames = null; + this.columnNamesValidated = false; + this.columnConfigs = null; + this.configuredColumnsOnly = false; + this.delimiter = ","; + this.delimWhitespace = false; + this.base = new TextLineDataset(input2); + if (!csvConfig) { + csvConfig = {}; + } + this.hasHeader = csvConfig.hasHeader === false ? false : true; + this.fullColumnNames = csvConfig.columnNames; + this.columnConfigs = csvConfig.columnConfigs; + this.configuredColumnsOnly = csvConfig.configuredColumnsOnly; + if (csvConfig.delimWhitespace) { + util_exports.assert(csvConfig.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true."); + this.delimWhitespace = true; + this.delimiter = " "; + } else { + this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ","; + } + } + async iterator() { + if (!this.columnNamesValidated) { + await this.setColumnNames(); + } + let lines = await this.base.iterator(); + if (this.hasHeader) { + lines = lines.skip(1); + } + return lines.map((x) => this.makeDataElement(x)); + } + makeDataElement(line) { + const values = this.parseRow(line); + const features = {}; + const labels = {}; + for (let i = 0; i < this.fullColumnNames.length; i++) { + const key = this.fullColumnNames[i]; + const config = this.columnConfigs ? this.columnConfigs[key] : null; + if (this.configuredColumnsOnly && !config) { + continue; + } else { + const value = values[i]; + let parsedValue = null; + if (value === "") { + if (config && config.default !== void 0) { + parsedValue = config.default; + } else if (config && (config.required || config.isLabel)) { + throw new Error(`Required column ${key} is empty in this line: ${line}`); + } else { + parsedValue = void 0; + } + } else { + const valueAsNum = Number(value); + if (isNaN(valueAsNum)) { + if (config && config.dtype === "bool") { + parsedValue = this.getBoolean(value); + } else { + parsedValue = value; + } + } else if (!config || !config.dtype) { + parsedValue = valueAsNum; + } else { + switch (config.dtype) { + case "float32": + parsedValue = valueAsNum; + break; + case "int32": + parsedValue = Math.floor(valueAsNum); + break; + case "bool": + parsedValue = this.getBoolean(value); + break; + default: + parsedValue = valueAsNum; + } + } + } + config && config.isLabel ? labels[key] = parsedValue : features[key] = parsedValue; + } + } + if (Object.keys(labels).length === 0) { + return features; + } else { + return { xs: features, ys: labels }; + } + } + getBoolean(value) { + if (value === "1" || value.toLowerCase() === "true") { + return 1; + } else { + return 0; + } + } + // adapted from https://beta.observablehq.com/@mbostock/streaming-csv + parseRow(line, validateElementCount = true) { + const result = []; + let readOffset = 0; + const readLength = line.length; + let currentState = STATE_OUT; + for (let i = 0; i < readLength; i++) { + switch (currentState) { + case STATE_OUT: + switch (line.charAt(i)) { + case CODE_QUOTE: + readOffset = i + 1; + currentState = STATE_QUOTE; + break; + case this.delimiter: + readOffset = i + 1; + if (this.delimiter === " " && this.delimWhitespace) { + break; + } + result.push(""); + currentState = STATE_OUT; + break; + default: + currentState = STATE_FIELD; + readOffset = i; + break; + } + break; + case STATE_FIELD: + switch (line.charAt(i)) { + case this.delimiter: + result.push(line.substring(readOffset, i)); + currentState = STATE_OUT; + readOffset = i + 1; + break; + default: + } + break; + case STATE_QUOTE: + switch (line.charAt(i)) { + case CODE_QUOTE: + currentState = STATE_QUOTE_AFTER_QUOTE; + break; + default: + } + break; + case STATE_QUOTE_AFTER_QUOTE: + switch (line.charAt(i)) { + case this.delimiter: + result.push(line.substring(readOffset, i - 1)); + currentState = STATE_OUT; + readOffset = i + 1; + break; + case CODE_QUOTE: + currentState = STATE_QUOTE; + break; + default: + currentState = STATE_WITHIN_QUOTE_IN_QUOTE; + break; + } + break; + case STATE_WITHIN_QUOTE_IN_QUOTE: + switch (line.charAt(i)) { + case CODE_QUOTE: + currentState = STATE_QUOTE; + break; + default: + } + break; + default: + } + } + if (currentState === STATE_QUOTE_AFTER_QUOTE) { + result.push(line.substring(readOffset, readLength - 1)); + } else { + result.push(line.substring(readOffset)); + } + if (validateElementCount && result.length !== this.fullColumnNames.length) { + throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`); + } + return result; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/microphone_iterator.js +var MicrophoneIterator = class _MicrophoneIterator extends LazyIterator { + constructor(microphoneConfig) { + super(); + this.microphoneConfig = microphoneConfig; + this.isClosed = false; + this.fftSize = microphoneConfig.fftSize || 1024; + const fftSizeLog2 = Math.log2(this.fftSize); + if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 || !Number.isInteger(fftSizeLog2)) { + throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`); + } + this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43; + this.sampleRateHz = microphoneConfig.sampleRateHz; + this.columnTruncateLength = microphoneConfig.columnTruncateLength || this.fftSize; + this.audioTrackConstraints = microphoneConfig.audioTrackConstraints; + this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0; + this.includeSpectrogram = microphoneConfig.includeSpectrogram === false ? false : true; + this.includeWaveform = microphoneConfig.includeWaveform === true ? true : false; + if (!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`; + } + // Construct a MicrophoneIterator and start the audio stream. + static async create(microphoneConfig = {}) { + if (!env().get("IS_BROWSER")) { + throw new Error("microphone API is only supported in browser environment."); + } + const microphoneIterator = new _MicrophoneIterator(microphoneConfig); + await microphoneIterator.start(); + return microphoneIterator; + } + // Start the audio stream and FFT. + async start() { + try { + this.stream = await navigator.mediaDevices.getUserMedia({ + audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints, + video: false + }); + } catch (e) { + throw new Error(`Error thrown while initializing video stream: ${e.message}`); + } + if (!this.stream) { + throw new Error("Could not obtain audio from microphone."); + } + const ctxConstructor = ( + // tslint:disable-next-line:no-any + window.AudioContext || window.webkitAudioContext + ); + this.audioContext = new ctxConstructor(); + if (!this.sampleRateHz) { + this.sampleRateHz = this.audioContext.sampleRate; + } else if (this.audioContext.sampleRate !== this.sampleRateHz) { + throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`); + } + const streamSource = this.audioContext.createMediaStreamSource(this.stream); + this.analyser = this.audioContext.createAnalyser(); + this.analyser.fftSize = this.fftSize * 2; + this.analyser.smoothingTimeConstant = this.smoothingTimeConstant; + streamSource.connect(this.analyser); + this.freqData = new Float32Array(this.fftSize); + this.timeData = new Float32Array(this.fftSize); + return; + } + async next() { + if (this.isClosed) { + return { value: null, done: true }; + } + let spectrogramTensor; + let waveformTensor; + const audioDataQueue = await this.getAudioData(); + if (this.includeSpectrogram) { + const freqData = this.flattenQueue(audioDataQueue.freqDataQueue); + spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]); + } + if (this.includeWaveform) { + const timeData = this.flattenQueue(audioDataQueue.timeDataQueue); + waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]); + } + return { + value: { "spectrogram": spectrogramTensor, "waveform": waveformTensor }, + done: false + }; + } + // Capture one result from the audio stream, and extract the value from + // iterator.next() result. + async capture() { + return (await this.next()).value; + } + async getAudioData() { + const freqDataQueue = []; + const timeDataQueue = []; + let currentFrames = 0; + return new Promise((resolve) => { + const intervalID = setInterval(() => { + if (this.includeSpectrogram) { + this.analyser.getFloatFrequencyData(this.freqData); + if (this.freqData[0] === -Infinity) { + resolve({ freqDataQueue, timeDataQueue }); + } + freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength)); + } + if (this.includeWaveform) { + this.analyser.getFloatTimeDomainData(this.timeData); + timeDataQueue.push(this.timeData.slice()); + } + if (++currentFrames === this.numFrames) { + clearInterval(intervalID); + resolve({ freqDataQueue, timeDataQueue }); + } + }, this.fftSize / this.sampleRateHz * 1e3); + }); + } + // Stop the audio stream and pause the iterator. + stop() { + if (!this.isClosed) { + this.isClosed = true; + this.analyser.disconnect(); + this.audioContext.close(); + if (this.stream != null && this.stream.getTracks().length > 0) { + this.stream.getTracks()[0].stop(); + } + } + } + // Override toArray() function to prevent collecting. + toArray() { + throw new Error("Can not convert infinite audio stream to array."); + } + // Return audio sampling rate in Hz + getSampleRate() { + return this.sampleRateHz; + } + flattenQueue(queue) { + const frameSize = queue[0].length; + const freqData = new Float32Array(queue.length * frameSize); + queue.forEach((data, i) => freqData.set(data, i * frameSize)); + return freqData; + } + getTensorFromAudioDataArray(freqData, shape) { + const vals = new Float32Array(util_exports.sizeFromShape(shape)); + vals.set(freqData, vals.length - freqData.length); + return tensor(vals, shape); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/webcam_iterator.js +var WebcamIterator = class _WebcamIterator extends LazyIterator { + constructor(webcamVideoElement, webcamConfig) { + super(); + this.webcamVideoElement = webcamVideoElement; + this.webcamConfig = webcamConfig; + this.isClosed = true; + this.resize = false; + if (this.needToResize()) { + this.resize = true; + this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth]; + this.cropBoxInd = tensor1d([0], "int32"); + if (this.webcamConfig.centerCrop) { + const widthCroppingRatio = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width; + const heightCroppingRatio = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height; + const widthCropStart = (1 - widthCroppingRatio) / 2; + const heightCropStart = (1 - heightCroppingRatio) / 2; + const widthCropEnd = widthCropStart + widthCroppingRatio; + const heightCropEnd = heightCroppingRatio + heightCropStart; + this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]); + } else { + this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]); + } + } + } + summary() { + return `webcam`; + } + // Construct a WebcamIterator and start it's video stream. + static async create(webcamVideoElement, webcamConfig = {}) { + if (!env().get("IS_BROWSER")) { + throw new Error("tf.data.webcam is only supported in browser environment."); + } + if (!webcamVideoElement) { + webcamVideoElement = document.createElement("video"); + if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) { + throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element."); + } + webcamVideoElement.width = webcamConfig.resizeWidth; + webcamVideoElement.height = webcamConfig.resizeHeight; + } + const webcamIterator = new _WebcamIterator(webcamVideoElement, webcamConfig); + await webcamIterator.start(); + return webcamIterator; + } + // Async function to start video stream. + async start() { + if (this.webcamConfig.facingMode) { + util_exports.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) { + e.message = `Error thrown while initializing video stream: ${e.message}`; + throw e; + } + if (!this.stream) { + throw new Error("Could not obtain video from webcam."); + } + try { + this.webcamVideoElement.srcObject = this.stream; + } catch (error) { + console.log(error); + this.webcamVideoElement.src = window.URL.createObjectURL(this.stream); + } + this.webcamVideoElement.play(); + this.isClosed = false; + return new Promise((resolve) => { + this.webcamVideoElement.onloadedmetadata = () => { + resolve(); + }; + }); + } + async next() { + if (this.isClosed) { + return { value: null, done: true }; + } + let img; + try { + img = browser_exports.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(img), done: false }; + } catch (e) { + throw new Error(`Error thrown cropping the video: ${e.message}`); + } finally { + img.dispose(); + } + } else { + return { value: img, done: false }; + } + } + needToResize() { + if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) { + return true; + } + return false; + } + // Cropping and resizing each frame based on config + cropAndResizeFrame(img) { + return tidy(() => { + const expandedImage = expandDims(cast(img, "float32"), 0); + let resizedImage; + resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear"); + const shape = resizedImage.shape; + return reshape(resizedImage, shape.slice(1)); + }); + } + // Capture one frame from the video stream, and extract the value from + // iterator.next() result. + async capture() { + return (await this.next()).value; + } + // Stop the video stream and pause webcam iterator. + stop() { + const tracks = this.stream.getTracks(); + tracks.forEach((track) => track.stop()); + try { + this.webcamVideoElement.srcObject = null; + } catch (error) { + console.log(error); + this.webcamVideoElement.src = null; + } + this.isClosed = true; + } + // Override toArray() function to prevent collecting. + toArray() { + throw new Error("Can not convert infinite video stream to array."); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/datasource.js +var DataSource = class { +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/string_iterator.js +var StringIterator = class extends LazyIterator { + /** + * Splits a string stream on a given separator. + * + * It is assumed that the incoming chunk boundaries have no semantic meaning, + * so conceptually the incoming stream is treated simply as the concatenation + * of its elements. + * + * The outgoing stream provides chunks corresponding to the results of the + * standard string split() operation (even if such a chunk spanned incoming + * chunks). The separators are not included. + * + * A typical usage is to split a text file (represented as a stream with + * arbitrary chunk boundaries) into lines. + * + * @param upstream A readable stream of strings that can be treated as + * concatenated. + * @param separator A character to split on. + */ + split(separator) { + return new SplitIterator(this, separator); + } +}; +var SplitIterator = class extends StringIterator { + constructor(upstream, separator) { + super(); + this.upstream = upstream; + this.impl = new SplitIteratorImpl(upstream, separator); + } + summary() { + return this.impl.summary(); + } + async next() { + return this.impl.next(); + } +}; +var SplitIteratorImpl = class extends OneToManyIterator { + constructor(upstream, separator) { + super(); + this.upstream = upstream; + this.separator = separator; + this.carryover = ""; + } + summary() { + return `${this.upstream.summary()} -> Split('${this.separator}')`; + } + async pump() { + const chunkResult = await this.upstream.next(); + if (chunkResult.done) { + if (this.carryover === "") { + return false; + } + this.outputQueue.push(this.carryover); + this.carryover = ""; + return true; + } + const lines = chunkResult.value.split(this.separator); + lines[0] = this.carryover + lines[0]; + for (const line of lines.slice(0, -1)) { + this.outputQueue.push(line); + } + this.carryover = lines[lines.length - 1]; + return true; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/byte_chunk_iterator.js +var ByteChunkIterator = class extends LazyIterator { + /** + * Decode a stream of UTF8-encoded byte arrays to a stream of strings. + * + * The byte arrays producetd from the ByteChunkIterator on which this is + * called will be interpreted as concatenated. No assumptions are made about + * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a + * character may span the boundary between chunks. This naturally happens, + * for instance, when reading fixed-size byte arrays from a file. + */ + decodeUTF8() { + return new Utf8Iterator(this); + } +}; +var Utf8Iterator = class extends StringIterator { + constructor(upstream) { + super(); + this.upstream = upstream; + this.impl = new Utf8IteratorImpl(upstream); + } + summary() { + return this.impl.summary(); + } + async next() { + return this.impl.next(); + } +}; +var Utf8IteratorImpl = class extends OneToManyIterator { + constructor(upstream) { + super(); + this.upstream = upstream; + if (env().get("IS_BROWSER")) { + this.decoder = new TextDecoder("utf-8"); + } else { + const { StringDecoder } = require_string_decoder(); + this.decoder = new StringDecoder("utf8"); + } + } + summary() { + return `${this.upstream.summary()} -> Utf8`; + } + async pump() { + const chunkResult = await this.upstream.next(); + let chunk; + if (chunkResult.done) { + return false; + } else { + chunk = chunkResult.value; + } + let text; + if (env().get("IS_BROWSER")) { + text = this.decoder.decode(chunk, { stream: true }); + } else { + text = this.decoder.write(Buffer.from(chunk.buffer)); + } + this.outputQueue.push(text); + return true; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/file_chunk_iterator.js +var FileChunkIterator = class extends ByteChunkIterator { + constructor(file, options = {}) { + super(); + this.file = file; + this.options = options; + util_exports.assert(file instanceof Uint8Array || (env().get("IS_BROWSER") ? file instanceof File || file instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now."); + this.offset = options.offset || 0; + this.chunkSize = options.chunkSize || 1024 * 1024; + } + summary() { + return `FileChunks ${this.file}`; + } + async next() { + if (this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size)) { + return { value: null, done: true }; + } + const chunk = new Promise((resolve, reject) => { + const end = this.offset + this.chunkSize; + if (this.file instanceof Uint8Array) { + resolve(new Uint8Array(this.file.slice(this.offset, end))); + } else { + const fileReader = new FileReader(); + fileReader.onload = (event) => { + let data = fileReader.result; + if (data instanceof ArrayBuffer) { + data = new Uint8Array(data); + } + if (!(data instanceof Uint8Array)) { + return reject(new TypeError("FileReader returned unknown type.")); + } + resolve(data); + }; + fileReader.onabort = (event) => { + return reject(new Error("Aborted")); + }; + fileReader.onerror = (event) => { + return reject(new Error(event.type)); + }; + const slice5 = this.file.slice(this.offset, end); + fileReader.readAsArrayBuffer(slice5); + } + this.offset = end; + }); + return { value: await chunk, done: false }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/iterators/url_chunk_iterator.js +async function urlChunkIterator(url, options = {}, fetchFunc) { + let urlString; + let requestInit; + if (typeof url === "string") { + urlString = url; + } else { + urlString = url.url; + requestInit = getRequestInitFromRequest(url); + } + const response = await (fetchFunc || util_exports.fetch)(urlString, requestInit); + if (response.ok) { + const uint8Array = new Uint8Array(await response.arrayBuffer()); + return new FileChunkIterator(uint8Array, options); + } else { + throw new Error(response.statusText); + } +} +var getRequestInitFromRequest = (request) => { + const init2 = { + method: request.method, + headers: request.headers, + body: request.body, + mode: request.mode, + credentials: request.credentials, + cache: request.cache, + redirect: request.redirect, + referrer: request.referrer, + integrity: request.integrity + }; + return init2; +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/util/source_util.js +function isLocalPath(source) { + return typeof source === "string" && source.slice(0, 7) === "file://"; +} + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/sources/file_data_source.js +var FileDataSource = class extends DataSource { + /** + * Create a `FileDataSource`. + * + * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to + * read. Local file only works in node environment. + * @param options Options passed to the underlying `FileChunkIterator`s, + * such as {chunksize: 1024}. + */ + constructor(input2, options = {}) { + super(); + this.input = input2; + this.options = options; + } + async iterator() { + if (isLocalPath(this.input) && env().get("IS_NODE")) { + const fs = require_fs(); + this.input = fs.readFileSync(this.input.slice(7)); + } + return new FileChunkIterator(this.input, this.options); + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/sources/url_data_source.js +var URLDataSource = class extends DataSource { + /** + * Create a `URLDataSource`. + * + * @param url A source URL string, or a `Request` object. + * @param options Options passed to the underlying `FileChunkIterator`s, + * such as {chunksize: 1024}. + */ + constructor(url, fileOptions = {}) { + super(); + this.url = url; + this.fileOptions = fileOptions; + } + // TODO(soergel): provide appropriate caching options. Currently this + // will download the URL anew for each call to iterator(). Since we have + // to treat the downloaded file as a blob/buffer anyway, we may as well retain + // it-- but that raises GC issues. Also we may want a persistent disk cache. + async iterator() { + if (isLocalPath(this.url)) { + return new FileDataSource(this.url, this.fileOptions).iterator(); + } else { + return urlChunkIterator(this.url, this.fileOptions); + } + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/readers.js +function csv(source, csvConfig = {}) { + return new CSVDataset(new URLDataSource(source), csvConfig); +} +function func(f) { + const iter = iteratorFromFunction(f); + return datasetFromIteratorFn(async () => iter); +} +function generator(generator2) { + return datasetFromIteratorFn(async () => { + const gen = await generator2(); + return iteratorFromFunction(() => gen.next()); + }); +} +async function webcam(webcamVideoElement, webcamConfig) { + return WebcamIterator.create(webcamVideoElement, webcamConfig); +} +async function microphone(microphoneConfig) { + return MicrophoneIterator.create(microphoneConfig); +} + +// node_modules/.pnpm/@tensorflow+tfjs-data@4.16.0_@tensorflow+tfjs-core@4.16.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-data/dist/version.js +var version4 = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/cpu_util.js +function assertNotComplex(tensor2, opName) { + if (!Array.isArray(tensor2)) { + tensor2 = [tensor2]; + } + tensor2.forEach((t) => { + if (t != null) { + util_exports.assert(t.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the CPU backend.`); + } + }); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js +var whereImpl2 = kernel_impls_exports.whereImpl; +var MathBackendCPU = class _MathBackendCPU extends KernelBackend { + nextDataId() { + return _MathBackendCPU.nextDataId++; + } + constructor() { + super(); + this.blockSize = 48; + this.firstUse = true; + this.data = new DataStorage(this, engine()); + } + write(values, shape, dtype) { + if (this.firstUse) { + this.firstUse = false; + if (env().get("IS_NODE")) { + backend_util_exports.warn("\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================"); + } + } + const dataId = { id: this.nextDataId() }; + this.data.set(dataId, { values, dtype, refCount: 1 }); + return dataId; + } + /** + * Create a data bucket in cpu backend. + * @param shape Shape of the `TensorInfo`. + * @param dtype DType of the `TensorInfo`. + * @param values The value of the `TensorInfo` stored as a flattened array. + */ + makeTensorInfo(shape, dtype, values) { + let outId; + if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) { + const encodedValues = values.map((d) => util_exports.encodeString(d)); + outId = this.write(encodedValues, shape, dtype); + } else { + outId = this.write(values, shape, dtype); + } + return { dataId: outId, shape, dtype }; + } + /** Return refCount of a `TensorData`. */ + refCount(dataId) { + if (this.data.has(dataId)) { + const tensorData = this.data.get(dataId); + return tensorData.refCount; + } + return 0; + } + /** Increase refCount of a `TensorData`. */ + incRef(dataId) { + const tensorData = this.data.get(dataId); + tensorData.refCount++; + } + /** Decrease refCount of a `TensorData`. */ + decRef(dataId) { + if (this.data.has(dataId)) { + const tensorData = this.data.get(dataId); + tensorData.refCount--; + } + } + move(dataId, values, shape, dtype, refCount) { + this.data.set(dataId, { values, dtype, refCount }); + } + numDataIds() { + return this.data.numDataIds(); + } + async read(dataId) { + return this.readSync(dataId); + } + readSync(dataId) { + const { dtype, complexTensorInfos } = this.data.get(dataId); + if (dtype === "complex64") { + const realValues = this.readSync(complexTensorInfos.real.dataId); + const imagValues = this.readSync(complexTensorInfos.imag.dataId); + return backend_util_exports.mergeRealAndImagArrays(realValues, imagValues); + } + return util_exports.convertBackendValuesAndArrayBuffer(this.data.get(dataId).values, dtype); + } + bufferSync(t) { + const data = this.readSync(t.dataId); + if (t.dtype === "string") { + try { + const strings = data.map((d) => util_exports.decodeString(d)); + return buffer(t.shape, t.dtype, strings); + } catch (_a) { + throw new Error("Failed to decode encoded string bytes into utf-8"); + } + } + return buffer(t.shape, t.dtype, data); + } + makeOutput(values, shape, dtype) { + return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this); + } + /** + * Dispose the memory if the dataId has 0 refCount. Return true if the memory + * is released or memory is not managed in this backend, false if memory is + * not cleared. + * @param dataId + * @oaram force Optional, remove the data regardless of refCount + */ + disposeData(dataId, force = false) { + if (this.data.has(dataId)) { + this.data.get(dataId).refCount--; + if (!force && this.data.get(dataId).refCount > 0) { + return false; + } + const { complexTensorInfos } = this.data.get(dataId); + if (complexTensorInfos != null) { + this.disposeData(complexTensorInfos.real.dataId, true); + this.disposeData(complexTensorInfos.imag.dataId, true); + } + this.data.delete(dataId); + } + return true; + } + disposeIntermediateTensorInfo(tensorInfo) { + this.disposeData(tensorInfo.dataId); + } + async time(f) { + const start = util_exports.now(); + f(); + const kernelMs = util_exports.now() - start; + return { kernelMs }; + } + memory() { + return { + // Unreliable due to automatic gc. The numbers above are cumulative. + unreliable: true, + reasons: ["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."] + }; + } + where(condition) { + assertNotComplex([condition], "where"); + const condVals = this.readSync(condition.dataId); + return whereImpl2(condition.shape, condVals); + } + dispose() { + } + floatPrecision() { + return 32; + } + /** Returns the smallest representable number. */ + epsilon() { + return super.epsilon(); + } +}; +MathBackendCPU.nextDataId = 0; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js +var shared_exports = {}; +__export(shared_exports, { + addImpl: () => addImpl, + bincountImpl: () => bincountImpl, + bincountReduceImpl: () => bincountReduceImpl, + bitwiseAndImpl: () => bitwiseAndImpl, + castImpl: () => castImpl, + ceilImpl: () => ceilImpl, + concatImpl: () => concatImpl, + equalImpl: () => equalImpl, + expImpl: () => expImpl, + expm1Impl: () => expm1Impl, + floorDivImpl: () => floorDivImpl, + floorImpl: () => floorImpl, + gatherNdImpl: () => gatherNdImpl, + gatherV2Impl: () => gatherV2Impl, + greaterEqualImpl: () => greaterEqualImpl, + greaterImpl: () => greaterImpl, + lessEqualImpl: () => lessEqualImpl, + lessImpl: () => lessImpl, + linSpaceImpl: () => linSpaceImpl, + logImpl: () => logImpl, + maxImpl: () => maxImpl, + maximumImpl: () => maximumImpl, + minimumImpl: () => minimumImpl, + multiplyImpl: () => multiplyImpl, + negImpl: () => negImpl, + notEqualImpl: () => notEqualImpl, + prodImpl: () => prodImpl, + raggedGatherImpl: () => raggedGatherImpl, + raggedRangeImpl: () => raggedRangeImpl, + raggedTensorToTensorImpl: () => raggedTensorToTensorImpl, + rangeImpl: () => rangeImpl, + rsqrtImpl: () => rsqrtImpl, + scatterImpl: () => scatterImpl, + sigmoidImpl: () => sigmoidImpl, + simpleAbsImpl: () => simpleAbsImpl, + sliceImpl: () => sliceImpl, + sparseFillEmptyRowsImpl: () => sparseFillEmptyRowsImpl, + sparseReshapeImpl: () => sparseReshapeImpl, + sparseSegmentReductionImpl: () => sparseSegmentReductionImpl, + sqrtImpl: () => sqrtImpl, + squaredDifferenceImpl: () => squaredDifferenceImpl, + staticRegexReplaceImpl: () => staticRegexReplaceImpl, + stridedSliceImpl: () => stridedSliceImpl, + stringNGramsImpl: () => stringNGramsImpl, + stringSplitImpl: () => stringSplitImpl, + stringToHashBucketFastImpl: () => stringToHashBucketFastImpl, + subImpl: () => subImpl, + tileImpl: () => tileImpl, + topKImpl: () => topKImpl, + transposeImpl: () => transposeImpl, + uniqueImpl: () => uniqueImpl +}); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js +function simpleAbsImpl(vals) { + const resultValues = new Float32Array(vals.length); + for (let i = 0; i < vals.length; ++i) { + resultValues[i] = Math.abs(vals[i]); + } + return resultValues; +} +var abs2 = (args) => { + const { x } = args.inputs; + const cpuBackend = args.backend; + assertNotComplex(x, "abs"); + let resultValues = new Float32Array(util_exports.sizeFromShape(x.shape)); + const values = cpuBackend.data.get(x.dataId).values; + resultValues = simpleAbsImpl(values); + return cpuBackend.makeOutput(resultValues, x.shape, x.dtype); +}; +var absConfig = { + kernelName: Abs, + backendName: "cpu", + kernelFunc: abs2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_impl.js +function createSimpleBinaryKernelImpl(op2) { + return (aShape, bShape, aVals, bVals, dtype) => { + const newShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + const resultRank = newShape.length; + const resultStrides = util_exports.computeStrides(newShape); + const resultSize = util_exports.sizeFromShape(newShape); + const result = util_exports.getTypedArrayFromDType(dtype, resultSize); + const aRank = aShape.length; + const bRank = bShape.length; + const aStrides = util_exports.computeStrides(aShape); + const bStrides = util_exports.computeStrides(bShape); + const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape); + const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape); + if (aBroadcastDims.length + bBroadcastDims.length === 0) { + for (let i = 0; i < result.length; ++i) { + result[i] = op2(aVals[i % aVals.length], bVals[i % bVals.length]); + } + } else { + for (let i = 0; i < result.length; ++i) { + const loc = util_exports.indexToLoc(i, resultRank, resultStrides); + const aLoc = loc.slice(-aRank); + aBroadcastDims.forEach((d) => aLoc[d] = 0); + const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides); + const bLoc = loc.slice(-bRank); + bBroadcastDims.forEach((d) => bLoc[d] = 0); + const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides); + result[i] = op2(aVals[aIndex], bVals[bIndex]); + } + } + return [result, newShape]; + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Complex.js +function complex2(args) { + const { inputs, backend: backend2 } = args; + const { real: real4, imag: imag4 } = inputs; + const realVals = backend2.data.get(real4.dataId).values; + const imagVals = backend2.data.get(imag4.dataId).values; + const complexInfo = backend2.makeTensorInfo(real4.shape, "complex64"); + const complex4 = backend2.data.get(complexInfo.dataId); + complex4.complexTensorInfos = { + real: backend2.makeTensorInfo(real4.shape, "float32", realVals), + imag: backend2.makeTensorInfo(imag4.shape, "float32", imagVals) + }; + return complexInfo; +} +var complexConfig = { + kernelName: Complex, + backendName: "cpu", + kernelFunc: complex2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/zeros_impl.js +function zeros3(backend2, shape, dtype = "float32") { + if (dtype === "complex64") { + const real4 = zeros3(backend2, shape, "float32"); + const imag4 = zeros3(backend2, shape, "float32"); + return complex2({ inputs: { real: real4, imag: imag4 }, backend: backend2 }); + } + const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype); + return backend2.makeTensorInfo(shape, dtype, values); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Identity.js +function identity2(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + backend2.incRef(x.dataId); + return { dataId: x.dataId, shape: x.shape, dtype: x.dtype }; +} +var identityConfig = { + kernelName: Identity, + backendName: "cpu", + kernelFunc: identity2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Real.js +function real2(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const real4 = backend2.data.get(input2.dataId).complexTensorInfos.real; + const realVal = backend2.data.get(real4.dataId).values; + return backend2.makeTensorInfo(real4.shape, real4.dtype, realVal); +} +var realConfig = { + kernelName: Real, + backendName: "cpu", + kernelFunc: real2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cast.js +function castImpl(values, shape, inputType, dtype) { + if (dtype === "int32") { + const resultValues = Int32Array.from(values); + return [shape, "int32", resultValues]; + } + if (dtype === "bool") { + const zero = util_exports.toTypedArray([0], inputType); + const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0)(shape, [], values, zero, "bool"); + return [resultShape, "bool", resultData]; + } + throw new Error(`Error in Cast: failed to cast ${inputType} to ${dtype}`); +} +function cast3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dtype } = attrs; + if (dtype === "complex64") { + if (x.dtype === "complex64") { + return identity2({ inputs: { x }, backend: backend2 }); + } + const zerosTensorInfo = zeros3(backend2, x.shape, x.dtype); + const floatX = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } }); + const result = complex2({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(zerosTensorInfo); + backend2.disposeIntermediateTensorInfo(floatX); + return result; + } + if (x.dtype === "complex64") { + const realPart = real2({ inputs: { input: x }, backend: backend2 }); + const result = cast3({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } }); + backend2.disposeIntermediateTensorInfo(realPart); + return result; + } + if (!util_exports.hasEncodingLoss(x.dtype, dtype)) { + const result = identity2({ inputs: { x }, backend: backend2 }); + return { dataId: result.dataId, shape: result.shape, dtype }; + } + const values = backend2.data.get(x.dataId).values; + const [resultShape, resultType, resultData] = castImpl(values, x.shape, x.dtype, dtype); + return backend2.makeTensorInfo(resultShape, resultType, resultData); +} +var castConfig = { + kernelName: Cast, + backendName: "cpu", + kernelFunc: cast3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_utils.js +function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) { + if (complexImpl == null) { + return ({ inputs, backend: backend2 }) => { + const { a, b } = inputs; + const cpuBackend = backend2; + assertNotComplex([a, b], name); + const aVals = cpuBackend.data.get(a.dataId).values; + const bVals = cpuBackend.data.get(b.dataId).values; + const decodedAVals = a.dtype === "string" ? ( + // tslint:disable-next-line: no-any + backend_util_exports.fromUint8ToStringArray(aVals) + ) : aVals; + const decodedBVals = a.dtype === "string" ? ( + // tslint:disable-next-line: no-any + backend_util_exports.fromUint8ToStringArray(bVals) + ) : bVals; + const $dtype = dtype || a.dtype; + const [resultData, resultShape] = simpleImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype); + return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData); + }; + } + return ({ inputs, backend: backend2 }) => { + const { a, b } = inputs; + const cpuBackend = backend2; + if (a.dtype === "complex64" || b.dtype === "complex64") { + const $aComplex = cast3({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: "complex64" } }); + const $aComplexVals = cpuBackend.data.get($aComplex.dataId); + const aReal = $aComplexVals.complexTensorInfos.real; + const aImag = $aComplexVals.complexTensorInfos.imag; + const aRealVals = cpuBackend.data.get(aReal.dataId).values; + const aImagVals = cpuBackend.data.get(aImag.dataId).values; + const $bComplex = cast3({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: "complex64" } }); + const $bComplexVals = cpuBackend.data.get($bComplex.dataId); + const bReal = $bComplexVals.complexTensorInfos.real; + const bImag = $bComplexVals.complexTensorInfos.imag; + const bRealVals = cpuBackend.data.get(bReal.dataId).values; + const bImagVals = cpuBackend.data.get(bImag.dataId).values; + const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals); + const resultReal = cpuBackend.makeTensorInfo(resultShape, "float32", resultRealData); + const resultImag = cpuBackend.makeTensorInfo(resultShape, "float32", resultImagData); + const result = complex2({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend }); + cpuBackend.disposeIntermediateTensorInfo($aComplex); + cpuBackend.disposeIntermediateTensorInfo($bComplex); + cpuBackend.disposeIntermediateTensorInfo(resultReal); + cpuBackend.disposeIntermediateTensorInfo(resultImag); + return result; + } else { + const aVals = cpuBackend.data.get(a.dataId).values; + const bVals = cpuBackend.data.get(b.dataId).values; + const $dtype = dtype || a.dtype; + const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype); + return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData); + } + }; +} +function createComplexBinaryKernelImpl(op2) { + return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => { + const resultShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + const resultSize = util_exports.sizeFromShape(resultShape); + const resultRank = resultShape.length; + const resultStrides = util_exports.computeStrides(resultShape); + const resultRealVals = util_exports.getTypedArrayFromDType("float32", resultSize); + const resultImagVals = util_exports.getTypedArrayFromDType("float32", resultSize); + const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, resultShape); + const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, resultShape); + const aVals = backend_util_exports.mergeRealAndImagArrays(aRealVals, aImagVals); + const bVals = backend_util_exports.mergeRealAndImagArrays(bRealVals, bImagVals); + const aRank = aShape.length; + const aStrides = util_exports.computeStrides(aShape); + const bRank = bShape.length; + const bStrides = util_exports.computeStrides(bShape); + if (aBroadcastDims.length + bBroadcastDims.length === 0) { + for (let i = 0; i < resultRealVals.length; i++) { + const aIdx = i % aVals.length; + const bIdx = i % bVals.length; + const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]); + resultRealVals[i] = result.real; + resultImagVals[i] = result.imag; + } + } else { + for (let i = 0; i < resultRealVals.length; i++) { + const loc = util_exports.indexToLoc(i, resultRank, resultStrides); + const aLoc = loc.slice(-aRank); + aBroadcastDims.forEach((d) => aLoc[d] = 0); + const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides); + const bLoc = loc.slice(-bRank); + bBroadcastDims.forEach((d) => bLoc[d] = 0); + const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides); + const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]); + resultRealVals[i] = opResult.real; + resultImagVals[i] = opResult.imag; + } + } + return [resultRealVals, resultImagVals, resultShape]; + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js +var addImpl = createSimpleBinaryKernelImpl((a, b) => a + b); +var addComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => { + return { real: aReal + bReal, imag: aImag + bImag }; +}); +var add4 = binaryKernelFunc(Add, addImpl, addComplexImpl); +var addConfig = { + kernelName: Add, + backendName: "cpu", + kernelFunc: add4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount_impl.js +function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) { + const weightsSize = util_exports.sizeFromShape(weightsShape); + const outVals = util_exports.makeZerosTypedArray(size, weightsDtype); + for (let i = 0; i < xVals.length; i++) { + const value = xVals[i]; + if (value < 0) { + throw new Error("Input x must be non-negative!"); + } + if (value >= size) { + continue; + } + if (weightsSize > 0) { + outVals[value] += weightsVals[i]; + } else { + outVals[value] += 1; + } + } + return outVals; +} +function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) { + const numRows = xBuf.shape[0]; + const numCols = xBuf.shape[1]; + const outBuf = buffer([numRows, size], weightsBuf.dtype); + for (let i = 0; i < numRows; i++) { + for (let j = 0; j < numCols; j++) { + const value = xBuf.get(i, j); + if (value < 0) { + throw new Error("Input x must be non-negative!"); + } + if (value >= size) { + continue; + } + if (binaryOutput) { + outBuf.set(1, i, value); + } else { + if (weightsBuf.size > 0) { + outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value); + } else { + outBuf.set(outBuf.get(i, value) + 1, i, value); + } + } + } + } + return outBuf; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BitwiseAnd.js +var bitwiseAndImpl = createSimpleBinaryKernelImpl((a, b) => a & b); +var bitwiseAnd2 = binaryKernelFunc(BitwiseAnd, bitwiseAndImpl); +var bitwiseAndConfig = { + kernelName: BitwiseAnd, + backendName: "cpu", + kernelFunc: bitwiseAnd2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_impl.js +function createSimpleUnaryImpl(op2) { + return (values, dtype, attrs) => { + const newValues = util_exports.getArrayFromDType(dtype, values.length); + for (let i = 0; i < values.length; ++i) { + newValues[i] = op2(values[i], attrs); + } + return newValues; + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_utils.js +function unaryKernelFunc(name, op2, dtype) { + const impl = createSimpleUnaryImpl(op2); + return unaryKernelFuncFromImpl(name, impl, dtype); +} +function unaryKernelFuncFromImpl(name, unaryImpl, dtype) { + return ({ inputs, attrs, backend: backend2 }) => { + const { x } = inputs; + assertNotComplex(x, name); + const cpuBackend = backend2; + const values = cpuBackend.data.get(x.dataId).values; + let decoded; + if (x.dtype === "string") { + if (!Array.isArray(values)) { + throw new Error("String tensor's value was not an instance of Array"); + } + decoded = backend_util_exports.fromUint8ToStringArray(values); + } else { + decoded = values; + } + const $dtype = dtype || x.dtype; + const newValues = unaryImpl(decoded, $dtype, attrs); + return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues); + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js +var ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi)); +var ceil2 = unaryKernelFuncFromImpl(Ceil, ceilImpl); +var ceilConfig = { + kernelName: Ceil, + backendName: "cpu", + kernelFunc: ceil2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js +function concatImpl(inputs, outShape, dtype, simplyConcat) { + const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape)); + if (simplyConcat && dtype !== "string") { + let offset = 0; + inputs.forEach((input2) => { + const size = util_exports.sizeFromShape(input2.shape); + outVals.set(input2.vals, offset); + offset += size; + }); + } else { + let colOffset = 0; + inputs.forEach((input2) => { + const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals; + let tIdx = 0; + for (let row = 0; row < input2.shape[0]; ++row) { + const resIdx = row * outShape[1] + colOffset; + for (let col = 0; col < input2.shape[1]; ++col) { + outVals[resIdx + col] = decodedData[tIdx++]; + } + } + colOffset += input2.shape[1]; + }); + } + return outVals; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Equal.js +var equalImpl = createSimpleBinaryKernelImpl((a, b) => a === b ? 1 : 0); +var equal2 = binaryKernelFunc(Equal, equalImpl, null, "bool"); +var equalConfig = { + kernelName: Equal, + backendName: "cpu", + kernelFunc: equal2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Exp.js +var expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi)); +var exp2 = unaryKernelFuncFromImpl(Exp, expImpl, "float32"); +var expConfig = { + kernelName: Exp, + backendName: "cpu", + kernelFunc: exp2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Expm1.js +var expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi)); +var expm12 = unaryKernelFuncFromImpl(Expm1, expm1Impl); +var expm1Config = { + kernelName: Expm1, + backendName: "cpu", + kernelFunc: expm12 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Floor.js +var floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi)); +var floor2 = unaryKernelFuncFromImpl(Floor, floorImpl); +var floorConfig = { + kernelName: Floor, + backendName: "cpu", + kernelFunc: floor2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FloorDiv.js +var floorDivImpl = createSimpleBinaryKernelImpl((a, b) => Math.floor(a / b)); +var floorDiv2 = binaryKernelFunc(FloorDiv, floorDivImpl, null, "int32"); +var floorDivConfig = { + kernelName: FloorDiv, + backendName: "cpu", + kernelFunc: floorDiv2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd_Impl.js +function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) { + const outBuf = buffer([numSlices, sliceSize], dtype); + for (let i = 0; i < numSlices; i++) { + const index = []; + let flattenIndex = 0; + for (let j = 0; j < sliceRank; j++) { + const dim = indicesData[i * sliceRank + j]; + flattenIndex += dim * strides[j]; + index.push(dim); + } + if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) { + throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`); + } + for (let k = 0; k < sliceSize; k++) { + outBuf.values[i * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k)); + } + } + return outBuf; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js +function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) { + const outBuf = buffer(flattenOutputShape, xBuf.dtype); + for (let i = 0; i < outBuf.size; ++i) { + const newLoc = outBuf.indexToLoc(i); + const originalLoc = newLoc.slice(); + const batchIdx = originalLoc[0]; + const indicesIdx = originalLoc[2]; + const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]); + originalLoc[2] = indicesBuf.values[indicesIndex]; + const originalIndex = xBuf.locToIndex(originalLoc); + if (0 <= originalIndex && originalIndex < xBuf.values.length) { + outBuf.values[i] = xBuf.values[originalIndex]; + } + } + return outBuf; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Greater.js +var greaterImpl = createSimpleBinaryKernelImpl((a, b) => a > b ? 1 : 0); +var greater3 = binaryKernelFunc(Greater, greaterImpl, null, "bool"); +var greaterConfig = { + kernelName: Greater, + backendName: "cpu", + kernelFunc: greater3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GreaterEqual.js +var greaterEqualImpl = createSimpleBinaryKernelImpl((a, b) => a >= b ? 1 : 0); +var greaterEqual2 = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null, "bool"); +var greaterEqualConfig = { + kernelName: GreaterEqual, + backendName: "cpu", + kernelFunc: greaterEqual2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Less.js +var lessImpl = createSimpleBinaryKernelImpl((a, b) => a < b ? 1 : 0); +var less3 = binaryKernelFunc(Less, lessImpl, null, "bool"); +var lessConfig = { + kernelName: Less, + backendName: "cpu", + kernelFunc: less3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LessEqual.js +var lessEqualImpl = createSimpleBinaryKernelImpl((a, b) => a <= b ? 1 : 0); +var lessEqual2 = binaryKernelFunc(LessEqual, lessEqualImpl, null, "bool"); +var lessEqualConfig = { + kernelName: LessEqual, + backendName: "cpu", + kernelFunc: lessEqual2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace_impl.js +function linSpaceImpl(start, stop, num) { + const step5 = (stop - start) / (num - 1); + const values = util_exports.makeZerosTypedArray(num, "float32"); + values[0] = start; + for (let i = 1; i < values.length; i++) { + values[i] = values[i - 1] + step5; + } + return values; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log.js +var logImpl = createSimpleUnaryImpl((xi) => Math.log(xi)); +var log3 = unaryKernelFuncFromImpl(Log, logImpl); +var logConfig = { + kernelName: Log, + backendName: "cpu", + kernelFunc: log3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js +function maxImpl(aVals, reduceSize, outShape, dtype) { + const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape)); + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let max6 = aVals[offset]; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + if (Number.isNaN(value) || value > max6) { + max6 = value; + } + } + vals[i] = max6; + } + return vals; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Maximum.js +var maximumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.max(aValue, bValue)); +var maximum3 = binaryKernelFunc(Maximum, maximumImpl); +var maximumConfig = { + kernelName: Maximum, + backendName: "cpu", + kernelFunc: maximum3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Minimum.js +var minimumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.min(aValue, bValue)); +var minimum3 = binaryKernelFunc(Minimum, minimumImpl); +var minimumConfig = { + kernelName: Minimum, + backendName: "cpu", + kernelFunc: minimum3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multiply.js +var multiplyImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue * bValue); +var multiplyComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => { + return { + real: aReal * bReal - aImag * bImag, + imag: aReal * bImag + aImag * bReal + }; +}); +var multiply2 = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl); +var multiplyConfig = { + kernelName: Multiply, + backendName: "cpu", + kernelFunc: multiply2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Neg.js +function negImpl(xVals, xShape, xDtype) { + const minusOne = util_exports.createScalarValue(-1, xDtype); + return multiplyImpl([], xShape, minusOne, xVals, xDtype); +} +function neg2(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + assertNotComplex(x, "neg"); + const xVals = backend2.data.get(x.dataId).values; + const [res, newShape] = negImpl(xVals, x.shape, x.dtype); + return backend2.makeTensorInfo(newShape, x.dtype, res); +} +var negConfig = { + kernelName: Neg, + backendName: "cpu", + kernelFunc: neg2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NotEqual.js +var notEqualImpl = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0); +var notEqual2 = binaryKernelFunc(NotEqual, notEqualImpl, null, "bool"); +var notEqualConfig = { + kernelName: NotEqual, + backendName: "cpu", + kernelFunc: notEqual2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose_impl.js +function transposeImpl(xVals, xShape, dtype, perm, newShape) { + const xRank = xShape.length; + const xSize = util_exports.sizeFromShape(xShape); + const xStrides = util_exports.computeStrides(xShape); + const newStrides = util_exports.computeStrides(newShape); + const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape)); + for (let i = 0; i < xSize; ++i) { + const loc = util_exports.indexToLoc(i, xRank, xStrides); + const newLoc = new Array(loc.length); + for (let i2 = 0; i2 < newLoc.length; i2++) { + newLoc[i2] = loc[perm[i2]]; + } + const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides); + result[newIndex] = xVals[i]; + } + return result; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose.js +function transpose2(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x } = inputs; + const { perm } = attrs; + assertNotComplex(x, "transpose"); + const xRank = x.shape.length; + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[perm[i]]; + } + const values = backend2.data.get(x.dataId).values; + const result = transposeImpl(values, x.shape, x.dtype, perm, newShape); + const dataId = backend2.write(result, newShape, x.dtype); + return { dataId, shape: newShape, dtype: x.dtype }; +} +var transposeConfig = { + kernelName: Transpose, + backendName: "cpu", + kernelFunc: transpose2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js +function prodImpl(xShape, xDtype, xVals, reductionAxes) { + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, reductionAxes); + const outDtype = upcastType(xDtype, "int32"); + const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype); + const reduceSize = util_exports.sizeFromShape(reduceShape); + for (let i = 0; i < outVals.length; ++i) { + const offset = i * reduceSize; + let prod5 = 1; + for (let j = 0; j < reduceSize; ++j) { + prod5 *= xVals[offset + j]; + } + outVals[i] = prod5; + } + return { outVals, outShape, outDtype }; +} +function prod2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + assertNotComplex(x, "prod"); + const xRank = x.shape.length; + const axes = util_exports.parseAxisParam(axis, x.shape); + const permutation = backend_util_exports.getAxesPermutation(axes, xRank); + let reductionAxes = axes; + let permutedX = x; + const intermediateTensorInfos = []; + if (permutation != null) { + permutedX = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } }); + intermediateTensorInfos.push(permutedX); + reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank); + } + const xVals = backend2.data.get(permutedX.dataId).values; + const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes); + let resultShape = outShape; + if (keepDims) { + resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes); + } + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return backend2.makeTensorInfo(resultShape, outDtype, outVals); +} +var prodConfig = { + kernelName: Prod, + backendName: "cpu", + kernelFunc: prod2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedGather_impl.js +function validateIndices(indices, indicesShape, numParams) { + indices.forEach((index, i) => { + if (index < 0 || index >= numParams) { + const locString = util_exports.indexToLoc(i, indicesShape.length, util_exports.computeStrides(indicesShape)).join(","); + throw new Error(`indices[${locString}] = ${index} is not in [0, ${numParams})`); + } + }); +} +function validateSplits(paramsNestedSplits, numParamsDenseValues) { + for (let dim = 0; dim < paramsNestedSplits.length; ++dim) { + const splits = paramsNestedSplits[dim]; + const lastSplit = dim === paramsNestedSplits.length - 1 ? numParamsDenseValues : paramsNestedSplits[dim + 1].length; + if (splits.length === 0) { + throw new Error("Ragged splits may not be empty"); + } + if (splits[0] < 0) { + throw new Error("Ragged splits must be non-negative"); + } + if (splits[splits.length - 1] > lastSplit) { + throw new Error("Ragged splits must not point past values"); + } + for (let i = 1; i < splits.length; ++i) { + if (splits[i - 1] > splits[i]) { + throw new Error("Ragged splits must be sorted in ascending order"); + } + } + } +} +function makeSplits(indices, indicesShape, paramsNestedSplits, numParamsDenseValues) { + const valueSlices = []; + let numValues = 0; + const numSplits = indicesShape.length - 1 + paramsNestedSplits.length; + const outSplits = new Array(numSplits).fill(null).map(() => [0]); + validateSplits(paramsNestedSplits, numParamsDenseValues); + let nrows = 1; + for (let dim = 0; dim < indicesShape.length - 1; ++dim) { + nrows *= indicesShape[dim]; + const rowLength = indicesShape[dim + 1]; + for (let i = 1; i < nrows + 1; ++i) { + outSplits[dim].push(i * rowLength); + } + } + for (let i = 0; i < indices.length; ++i) { + let start = indices[i]; + let limit = indices[i] + 1; + for (let dim = 0; dim < paramsNestedSplits.length; ++dim) { + const splits = paramsNestedSplits[dim]; + const outDim = dim + indicesShape.length - 1; + if (outDim >= 0) { + const outSplitsOutDim = outSplits[outDim]; + const delta = outSplitsOutDim[outSplitsOutDim.length - 1] - splits[start]; + for (let j = start; j < limit; ++j) { + outSplits[outDim].push(splits[j + 1] + delta); + } + } + start = splits[start]; + limit = splits[limit]; + } + if (limit !== start) { + valueSlices.push([start, limit]); + numValues += limit - start; + } + } + return { outSplits, valueSlices, numValues }; +} +function getSplits(outSplits) { + const splitsOut = []; + for (let i = 0; i < outSplits.length; ++i) { + const numSplits = outSplits[i].length; + const splits = util_exports.getArrayFromDType("int32", numSplits); + splitsOut.push(splits); + outSplits[i].forEach((value, j) => splits[j] = value); + } + return splitsOut; +} +function computeFlatOuterDims(orig, numOutDims) { + const outDims = orig.slice(0, numOutDims); + while (outDims.length < numOutDims) { + outDims.push(1); + } + for (let inDim = numOutDims; inDim < orig.length; inDim++) { + outDims[numOutDims - 1] *= orig[inDim]; + } + return outDims; +} +function writeValueSlices(paramsDenseValues, paramsDenseValuesShape, valueSlices, valueSize, values, valuesShape) { + const denseM = computeFlatOuterDims(paramsDenseValuesShape, 2)[1]; + const valuesM = computeFlatOuterDims(valuesShape, 2)[1]; + let outPos = 0; + for (const slice5 of valueSlices) { + for (let i = slice5[0]; i < slice5[1]; ++i) { + for (let j = 0; j < valueSize; ++j) { + values[outPos * valuesM + j] = paramsDenseValues[i * denseM + j]; + } + ++outPos; + } + } +} +function getValues(paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, valueSlices, numValues) { + const valuesShape = paramsDenseValuesShape.slice(); + valuesShape[0] = numValues; + const valuesOut = util_exports.getArrayFromDType(paramsDenseValuesDType, util_exports.sizeFromShape(valuesShape)); + const numElements = paramsDenseValues.length; + const valueSize = numElements === 0 ? 0 : numElements / paramsDenseValuesShape[0]; + writeValueSlices(paramsDenseValues, paramsDenseValuesShape, valueSlices, valueSize, valuesOut, valuesShape); + return [valuesOut, valuesShape]; +} +function raggedGatherImpl(paramsNestedSplits, paramsNestedSplitsShapes, paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, indices, indicesShape, outputRaggedRank) { + if (paramsNestedSplits.length === 0) { + throw new Error("paramsNestedSplits must be non empty"); + } + if (paramsNestedSplitsShapes[0].length === 0) { + throw new Error("Split tensors must not be scalars"); + } + const numParams = paramsNestedSplitsShapes[0][0] - 1; + validateIndices(indices, indicesShape, numParams); + if (paramsDenseValuesShape.length === 0) { + throw new Error("params.rank must be nonzero"); + } + const numParamsDenseValues = paramsDenseValuesShape[0]; + const { outSplits, valueSlices, numValues } = makeSplits(indices, indicesShape, paramsNestedSplits, numParamsDenseValues); + const outputNestedSplits = getSplits(outSplits); + const outputDenseValues = getValues(paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, valueSlices, numValues); + return [outputNestedSplits, outputDenseValues[0], outputDenseValues[1]]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedRange_impl.js +var INT32_MAX2 = 2147483647; +function raggedRangeImpl(starts, startsShape, startsDType, limits, limitsShape, deltas, deltasShape) { + if (startsShape.length > 1) { + throw new Error("starts must be a scalar or vector"); + } + if (limitsShape.length > 1) { + throw new Error("limits must be a scalar or vector"); + } + if (deltasShape.length > 1) { + throw new Error("deltas must be a scalar or vector"); + } + const broadcastStarts = startsShape.length === 0; + const broadcastLimits = limitsShape.length === 0; + const broadcastDeltas = deltasShape.length === 0; + const inSizes = []; + if (!broadcastStarts) { + inSizes.push(startsShape[0]); + } + if (!broadcastLimits) { + inSizes.push(limitsShape[0]); + } + if (!broadcastDeltas) { + inSizes.push(deltasShape[0]); + } + for (let i = 1; i < inSizes.length; ++i) { + if (inSizes[i] !== inSizes[i - 1]) { + throw new Error("starts, limits, and deltas must have the same shape"); + } + } + const nRows = inSizes.length === 0 ? 1 : inSizes[0]; + const rtNestedSplits = util_exports.getArrayFromDType("int32", nRows + 1); + rtNestedSplits[0] = 0; + for (let row = 0; row < nRows; ++row) { + const start = broadcastStarts ? starts[0] : starts[row]; + const limit = broadcastLimits ? limits[0] : limits[row]; + const delta = broadcastDeltas ? deltas[0] : deltas[row]; + if (delta === 0) { + throw new Error("Requires delta != 0"); + } + let size; + if (delta > 0 && limit < start || delta < 0 && limit > start) { + size = 0; + } else { + size = Math.ceil(Math.abs((limit - start) / delta)); + if (size > INT32_MAX2) { + throw new Error(`Requires ((limit - start) / delta) <= ${INT32_MAX2}`); + } + } + rtNestedSplits[row + 1] = rtNestedSplits[row] + size; + } + const nVals = rtNestedSplits[nRows]; + const rtDenseValues = util_exports.getArrayFromDType(startsDType, nVals); + let valueIndex = 0; + for (let row = 0; row < nRows; ++row) { + const rowSize = rtNestedSplits[row + 1] - rtNestedSplits[row]; + let value = broadcastStarts ? starts[0] : starts[row]; + const delta = broadcastDeltas ? deltas[0] : deltas[row]; + for (let i = 0; i < rowSize; ++i) { + rtDenseValues[valueIndex++] = value; + value += delta; + } + } + return [rtNestedSplits, rtDenseValues]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedTensorToTensor_impl.js +var RowPartitionType2 = backend_util_exports.RowPartitionType; +var RaggedTensorToTensorOp = class _RaggedTensorToTensorOp { + constructor(shape, shapeShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypeStrings) { + this.shape = shape; + this.shapeShape = shapeShape; + this.values = values; + this.valuesShape = valuesShape; + this.valuesDType = valuesDType; + this.defaultValue = defaultValue; + this.defaultValueShape = defaultValueShape; + this.rowPartitionValues = rowPartitionValues; + this.rowPartitionValuesShapes = rowPartitionValuesShapes; + this.rowPartitionTypes = backend_util_exports.getRowPartitionTypesHelper(rowPartitionTypeStrings); + this.raggedRank = backend_util_exports.getRaggedRank(this.rowPartitionTypes); + } + getRowPartitionTypeByDimension(dimension) { + if (this.rowPartitionTypes[0] === RowPartitionType2.FIRST_DIM_SIZE) { + return this.rowPartitionTypes[dimension + 1]; + } else { + return this.rowPartitionTypes[dimension]; + } + } + // Returns the relationship between dimension and dimension + 1. + getRowPartitionTensor(dimension) { + if (this.rowPartitionTypes[0] === RowPartitionType2.FIRST_DIM_SIZE) { + return this.rowPartitionValues[dimension + 1]; + } else { + return this.rowPartitionValues[dimension]; + } + } + getMaxWidth(dimension) { + const rowPartitionTensor = this.getRowPartitionTensor(dimension - 1); + switch (this.getRowPartitionTypeByDimension(dimension - 1)) { + case RowPartitionType2.VALUE_ROWIDS: + return _RaggedTensorToTensorOp.getMaxWidthValueRowID(rowPartitionTensor); + case RowPartitionType2.ROW_SPLITS: + return _RaggedTensorToTensorOp.getMaxWidthRowSplit(rowPartitionTensor); + default: + throw new Error(`Cannot handle partition type ${RowPartitionType2[this.getRowPartitionTypeByDimension(dimension - 1)]}`); + } + } + static getMaxWidthRowSplit(rowSplit) { + const tensorLength = rowSplit.length; + if (tensorLength === 0 || tensorLength === 1) { + return 0; + } + let maxWidth = 0; + for (let i = 0; i < tensorLength - 1; ++i) { + const currentWidth = rowSplit[i + 1] - rowSplit[i]; + if (currentWidth > maxWidth) { + maxWidth = currentWidth; + } + } + return maxWidth; + } + static getMaxWidthValueRowID(valueRowIds) { + const indexLength = valueRowIds.length; + if (indexLength === 0) { + return 0; + } + let firstEqualIndex = 0; + let firstEqualIndexValue = valueRowIds[0]; + let maxWidth = 0; + for (let i = 1; i < indexLength; ++i) { + const value = valueRowIds[i]; + if (value !== firstEqualIndexValue) { + firstEqualIndexValue = value; + maxWidth = Math.max(i - firstEqualIndex, maxWidth); + firstEqualIndex = i; + } + } + return Math.max(indexLength - firstEqualIndex, maxWidth); + } + tensorShapeFromTensor(t, tShape, isPartial = true) { + if (tShape.length === 0) { + if (t[0] === -1) { + return []; + } + throw new Error(`The only valid scalar shape tensor is the fully unknown shape specified as -1.`); + } + return makeShape(t, isPartial); + } + calculateOutputSize(firstDim) { + const valueShape = this.valuesShape; + const defaultValueShape = this.defaultValueShape; + backend_util_exports.validateDefaultValueShape(defaultValueShape, valueShape); + const shape = this.tensorShapeFromTensor(this.shape, this.shapeShape); + const outputShape = backend_util_exports.combineRaggedTensorToTensorShapes(this.raggedRank, shape, valueShape); + const result = outputShape; + if (result[0] < 0) { + result[0] = firstDim; + } + for (let i = 1; i <= this.raggedRank; ++i) { + if (result[i] < 0) { + result[i] = this.getMaxWidth(i); + } + } + return result; + } + /** + * The outputIndex represents the index in the output tensor + * where the first element of a particular dimension would be written. + * If it is -1, it indicates that the index is out of scope. + * Example, given firstDimension = 10, firstDimensionOutput = 6, + * and outputIndexMultiplier = 100: + * result = [0 100 200 300 400 500 -1 -1 -1 -1] + * If firstDimensionOutput = 11 instead, then: + * result = [0 100 200 300 400 500 600 700 800 900] + */ + calculateFirstParentOutputIndex(firstDimension, outputIndexMultiplier, firstDimensionOutput) { + const minDimension = Math.min(firstDimension, firstDimensionOutput); + const result = []; + let currentOutputIndex = 0; + for (let i = 0; i < minDimension; ++i, currentOutputIndex += outputIndexMultiplier) { + result.push(currentOutputIndex); + } + for (let i = minDimension; i < firstDimension; ++i) { + result.push(-1); + } + util_exports.assert(result.length === firstDimension, () => "Final length of result must be equal to firstDimension."); + return result; + } + calculateOutputIndexRowSplit(rowSplit, parentOutputIndex, outputIndexMultiplier, outputSize) { + const rowSplitSize = rowSplit.length; + const result = []; + for (let i = 0; i < rowSplitSize - 1; ++i) { + const rowLength = rowSplit[i + 1] - rowSplit[i]; + let realLength = Math.min(outputSize, rowLength); + let parentOutputIndexCurrent = parentOutputIndex[i]; + if (parentOutputIndexCurrent === -1) { + realLength = 0; + } + for (let j = 0; j < realLength; ++j) { + result.push(parentOutputIndexCurrent); + parentOutputIndexCurrent += outputIndexMultiplier; + } + for (let j = 0; j < rowLength - realLength; ++j) { + result.push(-1); + } + } + if (rowSplitSize > 0 && result.length !== rowSplit[rowSplitSize - 1]) { + throw new Error("Invalid row split size."); + } + return result; + } + // Calculate the output index of the first element of a list. + // The parentOutputIndex is the same computation for the previous list. + // -1 indicates an element or list that is out of range. + // The outputIndexMultiplier is the number of output indices one moves + // forward for each column. + // E.g., given: + // valueRowIds:[0 1 2 2 2 3 5 5 6] + // parentOutputIndex:[1000 1100 2000 2100 -1 3000 4000] + // outputIndexMultiplier: 10 + // outputSize: 2 + // You get: + // result = [1000 1100 2000 2010 -1 2100 -1 -1 3000] + // result[0] = parentOutputIndex[valueRowIds[0]] + // result[1] = parentOutputIndex[valueRowIds[1]] + // result[2] = parentOutputIndex[valueRowIds[2]] + // result[3] = parentOutputIndex[valueRowIds[2] + 10] + // result[4] = -1 because it is the third element the size is 2. + // result[5] = parentOutputIndex[valueRowIds[3]] + // result[6] = -1 because parentOutputIndex[valueRowIds[6]] == -1 + // result[7] = -1 because parentOutputIndex[valueRowIds[6]] == -1 + // result[8] = parentOutputIndex[valueRowIds[7]] + calculateOutputIndexValueRowID(valueRowIds, parentOutputIndex, outputIndexMultiplier, outputSize) { + const indexSize = valueRowIds.length; + const result = []; + if (indexSize === 0) { + return []; + } + let currentOutputColumn = 0; + let currentValueRowId = valueRowIds[0]; + if (currentValueRowId >= parentOutputIndex.length) { + throw new Error(`Got currentValueRowId=${currentValueRowId}, which is not less than ${parentOutputIndex.length}`); + } + let currentOutputIndex = parentOutputIndex[currentValueRowId]; + result.push(currentOutputIndex); + for (let i = 1; i < indexSize; ++i) { + const nextValueRowId = valueRowIds[i]; + if (nextValueRowId === currentValueRowId) { + if (currentOutputIndex >= 0) { + ++currentOutputColumn; + if (currentOutputColumn < outputSize) { + currentOutputIndex += outputIndexMultiplier; + } else { + currentOutputIndex = -1; + } + } + } else { + currentOutputColumn = 0; + currentValueRowId = nextValueRowId; + if (nextValueRowId >= parentOutputIndex.length) { + throw new Error(`Got nextValueRowId=${nextValueRowId} which is not less than ${parentOutputIndex.length}`); + } + currentOutputIndex = parentOutputIndex[nextValueRowId]; + } + result.push(currentOutputIndex); + } + if (result.length !== valueRowIds.length) { + throw new Error("Invalid row ids."); + } + return result; + } + calculateOutputIndex(dimension, parentOutputIndex, outputIndexMultiplier, outputSize) { + const rowPartitionTensor = this.getRowPartitionTensor(dimension); + const partitionType = this.getRowPartitionTypeByDimension(dimension); + switch (partitionType) { + case RowPartitionType2.VALUE_ROWIDS: + return this.calculateOutputIndexValueRowID(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize); + case RowPartitionType2.ROW_SPLITS: + if (rowPartitionTensor.length - 1 > parentOutputIndex.length) { + throw new Error(`Row partition size is greater than output size: ${rowPartitionTensor.length - 1} > ${parentOutputIndex.length}`); + } + return this.calculateOutputIndexRowSplit(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize); + default: + throw new Error(`Unsupported partition type: ${RowPartitionType2[partitionType]}`); + } + } + getFirstDimensionSize() { + const firstPartitionTensor = this.rowPartitionValues[0]; + if (this.rowPartitionTypes.length === 0) { + throw new Error("No row_partition_types given."); + } + const firstPartitionType = this.rowPartitionTypes[0]; + switch (firstPartitionType) { + case RowPartitionType2.FIRST_DIM_SIZE: + return firstPartitionTensor[0]; + case RowPartitionType2.VALUE_ROWIDS: + throw new Error("Cannot handle VALUE_ROWIDS in first dimension."); + case RowPartitionType2.ROW_SPLITS: + return this.rowPartitionValuesShapes[0][0] - 1; + default: + throw new Error(`Cannot handle type ${RowPartitionType2[firstPartitionType]}`); + } + } + compute() { + const firstPartitionTensor = this.rowPartitionValues[0]; + if (firstPartitionTensor.length <= 0) { + throw new Error("Invalid first partition input. Tensor requires at least one element."); + } + const firstDimension = this.getFirstDimensionSize(); + const outputSize = this.calculateOutputSize(firstDimension); + const multiplier = new Array(this.raggedRank + 1); + multiplier[multiplier.length - 1] = 1; + for (let i = multiplier.length - 2; i >= 0; --i) { + multiplier[i] = multiplier[i + 1] * outputSize[i + 1]; + } + const outputShape = makeShape(outputSize, false); + const outputTensor = util_exports.getArrayFromDType(this.valuesDType, util_exports.sizeFromShape(outputShape)); + const fullSize = multiplier[0] * outputSize[0]; + if (fullSize > 0) { + let outputIndex = this.calculateFirstParentOutputIndex(firstDimension, multiplier[0], outputSize[0]); + for (let i = 1; i <= this.raggedRank; ++i) { + const newOutputIndex = this.calculateOutputIndex(i - 1, outputIndex, multiplier[i], outputSize[i]); + outputIndex = newOutputIndex; + } + this.setOutput(this.raggedRank, outputIndex, outputTensor, outputShape); + } + return [outputShape, outputTensor]; + } + setOutput(raggedRank, outputIndex, outputTensor, outputShape) { + if (outputTensor.length === 0) { + return; + } + const valuesBase = this.values; + const outputBase = outputTensor; + let elementShape = outputShape.slice(); + elementShape = elementShape.slice(raggedRank + 1); + const valueElementSize = util_exports.sizeFromShape(elementShape); + const outputIndexSize = outputIndex.length; + let defaultValue = this.defaultValue; + if (defaultValue.length !== valueElementSize && defaultValue.length !== 1) { + const srcShape = this.defaultValueShape; + tidy(() => { + const defaultValueTensor = reshape(defaultValue, srcShape); + const bCastDefault = broadcastTo(defaultValueTensor, elementShape); + defaultValue = bCastDefault.dataSync(); + }); + } + let srcStart = 0; + let dstStart = 0; + let dstEnd = 0; + for (let srcI = 0; srcI <= outputIndexSize; ++srcI) { + let dstI = srcI < outputIndexSize ? outputIndex[srcI] : -1; + if (dstI === dstEnd) { + ++dstEnd; + continue; + } + if (dstStart < dstEnd) { + const src = valuesBase.subarray(srcStart * valueElementSize); + const dst = outputBase.subarray(dstStart * valueElementSize); + const nVals = (dstEnd - dstStart) * valueElementSize; + copyArray(dst, src, nVals); + } + if (srcI >= outputIndexSize) { + const outputSize = outputTensor.length; + dstI = Math.floor(outputSize / valueElementSize); + } + if (dstI > dstEnd) { + if (this.defaultValue.length === 1) { + outputBase.subarray(dstEnd * valueElementSize, dstI * valueElementSize).fill(this.defaultValue[0]); + dstEnd = dstI; + } else { + while (dstI > dstEnd) { + const dst = outputBase.slice(dstEnd * valueElementSize); + copyArray(dst, defaultValue, valueElementSize); + ++dstEnd; + } + } + } + if (dstI < 0) { + srcStart = srcI + 1; + dstStart = dstEnd; + } else { + srcStart = srcI; + dstStart = dstEnd; + dstEnd = dstStart + 1; + } + } + } +}; +function copyArray(dst, src, size) { + for (let i = 0; i < size; i++) { + dst[i] = src[i]; + } +} +function makeShape(shape, isPartial) { + const out = []; + for (let dim of shape) { + if (dim < 0) { + if (!isPartial) { + throw new Error(`Dimension ${dim} must be >= 0`); + } + if (dim < -1) { + throw new Error(`Dimension ${dim} must be >= -1`); + } + dim = -1; + } + out.push(dim); + } + return out; +} +function raggedTensorToTensorImpl(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes) { + return new RaggedTensorToTensorOp(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes).compute(); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js +function rangeImpl(start, stop, step5, dtype) { + const sameStartStop = start === stop; + const increasingRangeNegativeStep = start < stop && step5 < 0; + const decreasingRangePositiveStep = stop < start && step5 > 1; + if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) { + return util_exports.makeZerosTypedArray(0, dtype); + } + const numElements = Math.abs(Math.ceil((stop - start) / step5)); + const values = util_exports.makeZerosTypedArray(numElements, dtype); + if (stop < start && step5 === 1) { + step5 = -1; + } + values[0] = start; + for (let i = 1; i < values.length; i++) { + values[i] = values[i - 1] + step5; + } + return values; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Rsqrt.js +var rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi)); +var rsqrt2 = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl); +var rsqrtConfig = { + kernelName: Rsqrt, + backendName: "cpu", + kernelFunc: rsqrt2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Scatter_impl.js +function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) { + const flattenShape = [outputSize / sliceSize, sliceSize]; + const indicesData = indices.values; + const updatesData = updates.values; + if (outputSize === 0) { + return buffer(shape, updates.dtype); + } + const outBuf = defaultValue instanceof TensorBuffer ? defaultValue : buffer(flattenShape, updates.dtype); + if (typeof defaultValue === "string") { + outBuf.values.fill(defaultValue); + } else if (typeof defaultValue === "number") { + outBuf.values.fill(defaultValue); + } else if (typeof defaultValue === "boolean") { + outBuf.values.fill(+defaultValue); + } + for (let i = 0; i < numUpdates; i++) { + const index = []; + let flattenIndex = 0; + for (let j = 0; j < sliceRank; j++) { + const dim = indicesData[i * sliceRank + j]; + index.push(dim); + flattenIndex += dim * strides[j]; + } + if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) { + throw new Error(`Invalid indices: ${index} does not index into ${shape}`); + } + for (let k = 0; k < sliceSize; k++) { + if (sumDupeIndices) { + outBuf.values[flattenIndex * sliceSize + k] += updatesData[i * sliceSize + k]; + } else { + outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i * sliceSize + k]; + } + } + } + return outBuf; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sigmoid.js +var sigmoidImpl = createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi))); +var sigmoid2 = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi))); +var sigmoidConfig = { + kernelName: Sigmoid, + backendName: "cpu", + kernelFunc: sigmoid2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js +function sliceImpl(vals, begin, size, shape, dtype) { + const isContinous = slice_util_exports.isSliceContinous(shape, begin, size); + const length = util_exports.sizeFromShape(size); + const xStrides = util_exports.computeStrides(shape); + if (isContinous) { + const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides); + if (dtype === "string") { + return vals.slice(flatOffset, flatOffset + length); + } + return vals.subarray(flatOffset, flatOffset + length); + } + const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(vals) : vals; + const inBuf = buffer(shape, dtype, decodedData); + const outBuf = buffer(size, dtype); + for (let i = 0; i < outBuf.size; ++i) { + const outLoc = outBuf.indexToLoc(i); + const inLoc = outLoc.map((idx, j) => idx + begin[j]); + outBuf.set(inBuf.get(...inLoc), ...outLoc); + } + if (dtype === "string") { + return backend_util_exports.fromStringArrayToUint8(outBuf.values); + } + return outBuf.values; +} +function slice2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { begin, size } = attrs; + assertNotComplex(x, "slice"); + const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size); + slice_util_exports.assertParamsValid(x, $begin, $size); + const vals = backend2.data.get(x.dataId).values; + const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype); + return backend2.makeTensorInfo($size, x.dtype, outVals); +} +var sliceConfig = { + kernelName: Slice, + backendName: "cpu", + kernelFunc: slice2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows_impl.js +function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) { + const indicesCount = indicesShape[0]; + const denseRows = denseShape[0]; + const emptyRowIndicator = new Array(denseRows); + const reverseIndexMap = new Array(indicesCount); + const rank = indicesShape[1]; + if (denseRows === 0) { + if (indicesCount !== 0) { + throw new Error(backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount)); + } + const outputIndices = util_exports.getArrayFromDType(indicesDType, 0); + const outputValues = util_exports.getArrayFromDType(valuesDType, 0); + return [ + outputIndices, + [0, rank], + outputValues, + emptyRowIndicator, + reverseIndexMap + ]; + } + let rowsAreOrdered = true; + let lastIndicesRow = 0; + const csrOffset = new Array(denseRows).fill(0); + for (let i = 0; i < indicesCount; ++i) { + const row = indices[i * rank]; + if (row < 0) { + throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row)); + } + if (row >= denseRows) { + throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows)); + } + ++csrOffset[row]; + rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow; + lastIndicesRow = row; + } + let allRowsFull = true; + for (let row = 0; row < denseRows; ++row) { + const rowEmpty = csrOffset[row] === 0; + emptyRowIndicator[row] = rowEmpty; + allRowsFull = allRowsFull && !rowEmpty; + csrOffset[row] = Math.max(csrOffset[row], 1); + if (row > 0) { + csrOffset[row] += csrOffset[row - 1]; + } + } + if (allRowsFull && rowsAreOrdered) { + const outputIndices = indices; + const outputValues = values; + for (let i = 0; i < indicesCount; ++i) { + reverseIndexMap[i] = i; + } + return [ + outputIndices, + [indicesCount, rank], + outputValues, + emptyRowIndicator, + reverseIndexMap + ]; + } else { + const fullIndicesCount = csrOffset[denseRows - 1]; + const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank); + const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount); + const filledCount = new Array(denseRows).fill(0); + for (let i = 0; i < indicesCount; ++i) { + const row = indices[i * rank]; + const offset = filledCount[row]; + const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset; + filledCount[row]++; + for (let j = 0; j < rank; ++j) { + outputIndices[outputI * rank + j] = indices[i * rank + j]; + } + outputValues[outputI] = values[i]; + reverseIndexMap[i] = outputI; + } + for (let row = 0; row < denseRows; ++row) { + const rowCount = filledCount[row]; + if (rowCount === 0) { + const startingIndex = row === 0 ? 0 : csrOffset[row - 1]; + outputIndices[startingIndex * rank + 0] = row; + for (let col = 1; col < rank; ++col) { + outputIndices[startingIndex * rank + col] = 0; + } + outputValues[startingIndex] = defaultValue; + } + } + return [ + outputIndices, + [fullIndicesCount, rank], + outputValues, + emptyRowIndicator, + reverseIndexMap + ]; + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape_impl.js +function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) { + const denseSize = util_exports.sizeFromShape(inputShape); + const nnz = inputIndicesShape[0]; + const outputRank = targetShape.length; + const outputShape = []; + let product = 1; + let unknownIndex = -1; + for (let d = 0; d < outputRank; ++d) { + const size = targetShape[d]; + if (size === -1) { + if (unknownIndex !== -1) { + throw new Error(backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d)); + } + unknownIndex = d; + outputShape.push(1); + } else { + if (size < 0) { + throw new Error(backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(d, size)); + } + product *= size; + outputShape.push(size); + } + } + if (unknownIndex !== -1) { + if (product <= 0) { + throw new Error(backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage()); + } + const missing = Math.trunc(denseSize / product); + if (product * missing !== denseSize) { + throw new Error(backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape)); + } + outputShape[unknownIndex] = missing; + } + const outputSize = util_exports.sizeFromShape(outputShape); + if (outputSize !== denseSize) { + throw new Error(backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape)); + } + const inputRank = inputShape.length; + const inputStrides = []; + if (inputRank > 0) { + inputStrides[inputRank - 1] = 1; + for (let d = inputRank - 2; d >= 0; --d) { + inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1]; + } + } + const outputStrides = []; + if (outputRank > 0) { + outputStrides[outputRank - 1] = 1; + for (let d = outputRank - 2; d >= 0; --d) { + outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1]; + } + } + const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank); + for (let i = 0; i < nnz; ++i) { + let id = 0; + for (let j = 0; j < inputRank; ++j) { + id += inputIndices[i * inputRank + j] * inputStrides[j]; + } + for (let j = 0; j < outputRank; ++j) { + newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]); + id %= outputStrides[j]; + } + } + return [newIndices, [nnz, outputRank], outputShape]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentReduction_impl.js +function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) { + const numIndices = indices.length; + const inputFlat = [inputShape[0], input2.length / inputShape[0]]; + const numCol = inputFlat[1]; + const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0; + const outputRows = lastSegmentIdPlusOne; + if (outputRows < 0) { + throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + } + const outputShape = inputShape.slice(); + outputShape[0] = outputRows; + const outputLength = outputShape.reduce((product, value) => product * value, 1); + const output = util_exports.getArrayFromDType(inputDType, outputLength); + if (numIndices === 0) { + if (outputRows > 0) { + output.fill(defaultValue); + } + return [output, outputShape]; + } + if (outputRows <= 0) { + throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + } + let start = 0, end = 1; + let uninitializedIndex = 0; + let outIndex = segmentIds[start]; + while (true) { + let nextIndex = 0; + if (end < numIndices) { + nextIndex = segmentIds[end]; + if (outIndex === nextIndex) { + ++end; + continue; + } + if (outIndex >= nextIndex) { + throw new Error(backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage()); + } + } + if (outIndex < 0 || outIndex >= outputRows) { + throw new Error(backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows)); + } + if (outIndex > uninitializedIndex) { + output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol); + } + for (let i = start; i < end; ++i) { + const index = indices[i]; + if (index < 0 || index >= inputFlat[0]) { + throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0])); + } + for (let j = 0; j < numCol; j++) { + output[outIndex * numCol + j] += input2[index * numCol + j]; + } + } + if (isMean) { + for (let j = 0; j < numCol; j++) { + output[outIndex * numCol + j] /= end - start; + } + } + start = end; + ++end; + uninitializedIndex = outIndex + 1; + outIndex = nextIndex; + if (end > numIndices) { + break; + } + } + if (uninitializedIndex < outputRows) { + output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol); + } + return [output, outputShape]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sqrt.js +var sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi)); +var sqrt2 = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi)); +var sqrtConfig = { + kernelName: Sqrt, + backendName: "cpu", + kernelFunc: sqrt2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SquaredDifference.js +var squaredDifferenceImpl = createSimpleBinaryKernelImpl((a, b) => { + const diff = a - b; + return diff * diff; +}); +var squaredDifference2 = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl); +var squaredDifferenceConfig = { + kernelName: SquaredDifference, + backendName: "cpu", + kernelFunc: squaredDifference2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StaticRegexReplace.js +var staticRegexReplaceImpl = createSimpleUnaryImpl((x, attrs) => { + const { pattern, replaceGlobal, rewrite } = attrs; + return x.replace(new RegExp(pattern, replaceGlobal ? "g" : ""), rewrite); +}); +var staticRegexReplace2 = unaryKernelFuncFromImpl(StaticRegexReplace, staticRegexReplaceImpl); +var staticRegexReplaceConfig = { + kernelName: StaticRegexReplace, + backendName: "cpu", + kernelFunc: staticRegexReplace2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js +function stridedSliceImpl(outShape, xBuf, strides, begin) { + const outBuf = buffer(outShape, xBuf.dtype); + for (let i = 0; i < outBuf.size; i++) { + const loc = outBuf.indexToLoc(i); + const newLoc = new Array(loc.length); + for (let j = 0; j < newLoc.length; j++) { + newLoc[j] = loc[j] * strides[j] + begin[j]; + } + outBuf.set(xBuf.get(...newLoc), ...loc); + } + return outBuf; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams_impl.js +var StringNGramsOp = class { + constructor(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) { + this.separator = util_exports.encodeString(separator); + this.nGramWidths = nGramWidths; + this.leftPad = util_exports.encodeString(leftPad); + this.rightPad = util_exports.encodeString(rightPad2); + this.padWidth = padWidth; + this.preserveShort = preserveShortSequences; + } + getPadWidth(nGramWidth) { + return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1); + } + getNumNGrams(length, nGramWidth) { + const padWidth = this.getPadWidth(nGramWidth); + return Math.max(0, length + 2 * padWidth - nGramWidth + 1); + } + createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) { + for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) { + const padWidth = this.getPadWidth(nGramWidth); + const leftPadding = Math.max(0, padWidth - nGramIndex); + const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1))); + const numTokens = nGramWidth - (leftPadding + rightPadding); + const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth); + let nGramSize = 0; + nGramSize += leftPadding * this.leftPad.length; + for (let n = 0; n < numTokens; ++n) { + nGramSize += data[dataStartIndex + n].length; + } + nGramSize += rightPadding * this.rightPad.length; + const numSeparators = leftPadding + rightPadding + numTokens - 1; + nGramSize += numSeparators * this.separator.length; + output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize); + const nGram = output[outputStartIndex + nGramIndex]; + let nextNGramIndex = 0; + const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value); + for (let n = 0; n < leftPadding; ++n) { + appendToNGram(this.leftPad); + appendToNGram(this.separator); + } + for (let n = 0; n < numTokens - 1; ++n) { + appendToNGram(data[dataStartIndex + n]); + appendToNGram(this.separator); + } + if (numTokens > 0) { + appendToNGram(data[dataStartIndex + numTokens - 1]); + for (let n = 0; n < rightPadding; ++n) { + appendToNGram(this.separator); + appendToNGram(this.rightPad); + } + } else { + for (let n = 0; n < rightPadding - 1; ++n) { + appendToNGram(this.rightPad); + appendToNGram(this.separator); + } + appendToNGram(this.rightPad); + } + } + } + // Data and splits together form the definition of the ragged tensor, + // where data is 1 dimensional and contains the values of the tensor + // and splits denotes the indices at which each row starts. + compute(data, splits) { + const inputDataSize = data.length; + const splitsSize = splits.length; + if (splitsSize > 0) { + let prevSplit = splits[0]; + if (prevSplit !== 0) { + throw new Error(`First split value must be 0, got ${prevSplit}`); + } + for (let i = 1; i < splitsSize; ++i) { + let validSplits = splits[i] >= prevSplit; + validSplits = validSplits && splits[i] <= inputDataSize; + if (!validSplits) { + throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`); + } + prevSplit = splits[i]; + } + if (prevSplit !== inputDataSize) { + throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`); + } + } + const numBatchItems = splitsSize - 1; + const nGramsSplits = util_exports.getArrayFromDType("int32", splitsSize); + if (inputDataSize === 0 || splitsSize === 0) { + const empty = new Array(inputDataSize); + for (let i = 0; i <= numBatchItems; ++i) { + nGramsSplits[i] = 0; + } + return [empty, nGramsSplits]; + } + nGramsSplits[0] = 0; + for (let i = 1; i <= numBatchItems; ++i) { + const length = splits[i] - splits[i - 1]; + let numNGrams = 0; + this.nGramWidths.forEach((nGramWidth) => { + numNGrams += this.getNumNGrams(length, nGramWidth); + }); + if (this.preserveShort && length > 0 && numNGrams === 0) { + numNGrams = 1; + } + nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams; + } + const nGrams = new Array(nGramsSplits[numBatchItems]); + for (let i = 0; i < numBatchItems; ++i) { + const splitIndex = splits[i]; + let outputStartIdx = nGramsSplits[i]; + this.nGramWidths.forEach((nGramWidth) => { + const length = splits[i + 1] - splits[i]; + const numNGrams = this.getNumNGrams(length, nGramWidth); + this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth); + outputStartIdx += numNGrams; + }); + if (this.preserveShort && outputStartIdx === nGramsSplits[i]) { + const dataLength = splits[i + 1] - splits[i]; + if (dataLength === 0) { + continue; + } + const nGramWidth = dataLength + 2 * this.padWidth; + const numNGrams = 1; + this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth); + } + } + return [nGrams, nGramsSplits]; + } +}; +function stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) { + return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences).compute(data, dataSplits); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit_impl.js +function split3(str, delimiters, skipEmpty, result) { + if (!str.length) { + return; + } + if (delimiters.length === 0) { + for (let i = 0; i < str.length; ++i) { + result.push(str.subarray(i, i + 1)); + } + return; + } + if (delimiters.length === 1) { + const delimiter = delimiters[0]; + let f = str.indexOf(delimiter); + while (f !== -1) { + const token = str.subarray(0, f); + if (!skipEmpty || token.length !== 0) { + result.push(token); + } + str = str.subarray(f + 1); + f = str.indexOf(delimiter); + } + if (!skipEmpty || str.length !== 0) { + result.push(str); + } + return; + } + let tokenStart = 0; + for (let i = 0; i < str.length + 1; i++) { + if (i === str.length || delimiters.indexOf(str[i]) !== -1) { + const token = str.subarray(tokenStart, i); + if (!skipEmpty || token.length !== 0) { + result.push(token); + } + tokenStart = i + 1; + } + } +} +function stringSplitImpl(input2, delimiter, skipEmpty) { + const batchSize = input2.length; + const tokens = []; + let outputSize = 0; + let maxNumEntries = 0; + const numIndices = new Array(batchSize); + for (let i = 0; i < batchSize; ++i) { + const prevTokensLength = tokens.length; + split3(input2[i], delimiter, skipEmpty, tokens); + const nEntries = tokens.length - prevTokensLength; + numIndices[i] = nEntries; + outputSize += nEntries; + maxNumEntries = Math.max(maxNumEntries, nEntries); + } + const indices = util_exports.getArrayFromDType("int32", outputSize * 2); + const values = new Array(outputSize); + const shape = [batchSize, maxNumEntries]; + let c = 0; + for (let i = 0; i < batchSize; ++i) { + for (let j = 0; j < numIndices[i]; ++j) { + indices[c * 2] = i; + indices[c * 2 + 1] = j; + values[c] = tokens[c]; + ++c; + } + } + return [indices, values, shape]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast_impl.js +function stringToHashBucketFastImpl(input2, numBuckets) { + const output = util_exports.getArrayFromDType("int32", input2.length); + for (let i = 0; i < input2.length; ++i) { + output[i] = util_exports.fingerPrint64(input2[i]).modulo(numBuckets).getLowBitsUnsigned(); + } + return output; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sub.js +var subImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue - bValue); +var subComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => { + return { real: aReal - bReal, imag: aImag - bImag }; +}); +var sub2 = binaryKernelFunc(Sub, subImpl, subComplexImpl); +var subConfig = { + kernelName: Sub, + backendName: "cpu", + kernelFunc: sub2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js +function tileImpl(xBuf, reps) { + const newShape = new Array(xBuf.rank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = xBuf.shape[i] * reps[i]; + } + const result = buffer(newShape, xBuf.dtype); + for (let i = 0; i < result.values.length; ++i) { + const newLoc = result.indexToLoc(i); + const originalLoc = new Array(xBuf.rank); + for (let j = 0; j < originalLoc.length; j++) { + originalLoc[j] = newLoc[j] % xBuf.shape[j]; + } + const originalIndex = xBuf.locToIndex(originalLoc); + result.values[i] = xBuf.values[originalIndex]; + } + return result; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK_impl.js +var comparePair = (a, b) => { + const valueDiff = b.value - a.value; + return valueDiff === 0 ? a.index - b.index : valueDiff; +}; +function select(array2, k, left = 0, right = array2.length - 1) { + while (right > left) { + if (right - left > 600) { + const n = right - left + 1; + const i2 = k - left + 1; + const z = Math.log(n); + const s = 0.5 * Math.exp(2 * z / 3); + const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i2 - n / 2); + const newLeft = Math.max(left, Math.floor(k - i2 * s / n + sd)); + const newRight = Math.min(right, Math.floor(k + (n - i2) * s / n + sd)); + select(array2, k, newLeft, newRight); + } + const t = array2[k]; + let i = left; + let j = right; + util_exports.swap(array2, left, k); + if (comparePair(array2[right], t) > 0) { + util_exports.swap(array2, left, right); + } + while (i < j) { + util_exports.swap(array2, i, j); + i++; + j--; + while (comparePair(array2[i], t) < 0) { + i = i + 1; + } + while (comparePair(array2[j], t) > 0) { + j = j - 1; + } + } + if (comparePair(array2[left], t) === 0) { + util_exports.swap(array2, left, j); + } else { + j = j + 1; + util_exports.swap(array2, j, right); + } + if (j <= k) { + left = j + 1; + } + if (k <= j) { + right = j - 1; + } + } +} +function topKImpl(x, xShape, xDtype, k, sorted) { + const lastDim = xShape[xShape.length - 1]; + const [batch, size] = [x.length / lastDim, lastDim]; + const allTopKVals = util_exports.getTypedArrayFromDType(xDtype, batch * k); + const allTopKIndices = util_exports.getTypedArrayFromDType("int32", batch * k); + for (let b = 0; b < batch; b++) { + const offset = b * size; + const vals = x.subarray(offset, offset + size); + let valAndInd = new Array(vals.length); + vals.forEach((value, index) => valAndInd[index] = { value, index }); + if (k < valAndInd.length) { + select(valAndInd, k); + valAndInd = valAndInd.slice(0, k); + } + if (sorted) { + valAndInd.sort(comparePair); + } + const outOffset = b * k; + const topKVals = allTopKVals.subarray(outOffset, outOffset + k); + const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k); + for (let i = 0; i < k; i++) { + topKVals[i] = valAndInd[i].value; + topKIndices[i] = valAndInd[i].index; + } + } + const outputShape = xShape.slice(); + outputShape[outputShape.length - 1] = k; + return [ + buffer(outputShape, xDtype, allTopKVals), + buffer(outputShape, "int32", allTopKIndices) + ]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique_impl.js +function uniqueImpl(values, axis, shape, dtype) { + const $axis = util_exports.parseAxisParam(axis, shape)[0]; + const newShape = [1, shape[0], 1]; + for (let i = 0; i < $axis; i++) { + newShape[0] *= shape[i]; + } + newShape[1] = shape[$axis]; + for (let i = $axis + 1; i < shape.length; i++) { + newShape[2] *= shape[i]; + } + const uniqueElements = /* @__PURE__ */ new Map(); + const indices = new Int32Array(shape[$axis]); + const inputBuffer = new TensorBuffer(newShape, dtype, values); + const uniqueIndices = []; + const is1DTensor = newShape[0] === 1 && newShape[2] === 1; + for (let i = 0; i < shape[$axis]; i++) { + let element; + if (is1DTensor) { + element = values[i].toString(); + } else { + const axisValues = []; + for (let m = 0; m < newShape[0]; m++) { + for (let n = 0; n < newShape[2]; n++) { + axisValues.push(inputBuffer.get(m, i, n)); + } + } + element = axisValues.join(","); + } + const existingIndex = uniqueElements.get(element); + if (existingIndex != null) { + indices[i] = existingIndex; + } else { + const uniqueIndex = uniqueElements.size; + uniqueElements.set(element, uniqueIndex); + indices[i] = uniqueIndex; + uniqueIndices.push(i); + } + } + const outputTmpShape = newShape.slice(); + outputTmpShape[1] = uniqueElements.size; + const outputBuffer = new TensorBuffer(outputTmpShape, dtype); + uniqueIndices.forEach((uniqueElementIndex, i) => { + for (let m = 0; m < newShape[0]; m++) { + for (let n = 0; n < newShape[2]; n++) { + outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n); + } + } + }); + const outputShape = shape.slice(); + outputShape[$axis] = outputTmpShape[1]; + return { + outputValues: outputBuffer.values, + outputShape, + indices + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/version.js +var version5 = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/base.js +registerBackend( + "cpu", + () => new MathBackendCPU(), + 1 + /* priority */ +); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Elu.js +var elu4 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1); +var eluConfig = { + kernelName: Elu, + backendName: "cpu", + kernelFunc: elu4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LeakyRelu.js +function leakyRelu2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { alpha } = attrs; + assertNotComplex([x], "leakyRelu"); + const xSize = util_exports.sizeFromShape(x.shape); + const xVals = backend2.data.get(x.dataId).values; + const outVals = util_exports.getTypedArrayFromDType("float32", xSize); + for (let i = 0; i < xVals.length; i++) { + outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i]; + } + return backend2.makeTensorInfo(x.shape, "float32", outVals); +} +var leakyReluConfig = { + kernelName: LeakyRelu, + backendName: "cpu", + kernelFunc: leakyRelu2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prelu.js +var preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue); +function prelu3(args) { + const { inputs, backend: backend2 } = args; + const { x, alpha } = inputs; + assertNotComplex([x, alpha], "prelu"); + const aVals = backend2.data.get(x.dataId).values; + const bVals = backend2.data.get(alpha.dataId).values; + const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, "float32"); + return backend2.makeTensorInfo(resultShape, "float32", resultData); +} +var preluConfig = { + kernelName: Prelu, + backendName: "cpu", + kernelFunc: prelu3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu.js +var relu2 = unaryKernelFunc(Relu, (xi) => Math.max(0, xi)); +var reluConfig = { + kernelName: Relu, + backendName: "cpu", + kernelFunc: relu2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu6.js +var relu62 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6)); +var relu6Config = { + kernelName: Relu6, + backendName: "cpu", + kernelFunc: relu62 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fused_utils.js +function applyActivation2(backend2, x, activation2, preluActivationWeights, leakyreluAlpha) { + if (activation2 === "linear") { + return identity2({ inputs: { x }, backend: backend2 }); + } else if (activation2 === "relu") { + return relu2({ inputs: { x }, backend: backend2 }); + } else if (activation2 === "elu") { + return elu4({ inputs: { x }, backend: backend2 }); + } else if (activation2 === "relu6") { + return relu62({ inputs: { x }, backend: backend2 }); + } else if (activation2 === "prelu") { + return prelu3({ inputs: { x, alpha: preluActivationWeights }, backend: backend2 }); + } else if (activation2 === "leakyrelu") { + return leakyRelu2({ inputs: { x }, backend: backend2, attrs: { alpha: leakyreluAlpha } }); + } else if (activation2 === "sigmoid") { + return sigmoid2({ inputs: { x }, backend: backend2 }); + } + throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reshape.js +function reshape3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { shape } = attrs; + const xSize = util_exports.sizeFromShape(x.shape); + const $shape = util_exports.inferFromImplicitShape(shape, xSize); + const $xSize = util_exports.sizeFromShape($shape); + util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`); + backend2.incRef(x.dataId); + const xData = backend2.data.get(x.dataId); + if (xData.complexTensorInfos != null) { + const real4 = xData.complexTensorInfos.real; + const imag4 = xData.complexTensorInfos.imag; + real4.shape = $shape; + imag4.shape = $shape; + } + return { dataId: x.dataId, shape: $shape, dtype: x.dtype }; +} +var reshapeConfig = { + kernelName: Reshape, + backendName: "cpu", + kernelFunc: reshape3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchMatMul.js +function batchMatMul(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b } = inputs; + const { transposeA, transposeB } = attrs; + assertNotComplex([a, b], "matMul"); + const aRank = a.shape.length; + const bRank = b.shape.length; + const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1]; + const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2]; + const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2]; + const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1]; + const outerDimsA = a.shape.slice(0, -2); + const outerDimsB = b.shape.slice(0, -2); + const batchDimA = util_exports.sizeFromShape(outerDimsA); + const batchDimB = util_exports.sizeFromShape(outerDimsB); + const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); + const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA]; + const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB]; + const a3d = reshape3({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } }); + const b3d = reshape3({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } }); + const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2]; + const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1]; + const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2]; + const batchDim = Math.max(batchDimA, batchDimB); + const a3dValues = backend2.data.get(a3d.dataId).values; + const b3dValues = backend2.data.get(b3d.dataId).values; + const a3dStrides = util_exports.computeStrides(a3d.shape); + const b3dStrides = util_exports.computeStrides(b3d.shape); + const [aBatch, aOuterStep, aInnerStep] = transposeA ? [a3dStrides[0], 1, a3dStrides[1]] : [a3dStrides[0], a3dStrides[1], 1]; + const [bInnerStep, bOuterStep, bBatch] = transposeB ? [1, b3dStrides[1], b3dStrides[0]] : [b3dStrides[1], 1, b3dStrides[0]]; + const size = leftDim * rightDim; + const result = buffer([batchDim, leftDim, rightDim], a3d.dtype); + const resVals = result.values; + const blockSize = backend2.blockSize; + for (let bi = 0; bi < batchDim; bi++) { + const batchIndexA = bi % batchDimA; + const batchIndexB = bi % batchDimB; + for (let i0 = 0; i0 < leftDim; i0 += blockSize) { + const iBlock = Math.min(i0 + blockSize, leftDim); + for (let j0 = 0; j0 < rightDim; j0 += blockSize) { + const jBlock = Math.min(j0 + blockSize, rightDim); + for (let k02 = 0; k02 < sharedDim; k02 += blockSize) { + const kBlock = Math.min(k02 + blockSize, sharedDim); + for (let i = i0; i < iBlock; i++) { + for (let j = j0; j < jBlock; j++) { + let sum6 = 0; + for (let k = k02; k < kBlock; k++) { + const aVal = ( + // tslint:disable-next-line: max-line-length + a3dValues[batchIndexA * aBatch + i * aOuterStep + k * aInnerStep] + ); + const bVal = ( + // tslint:disable-next-line: max-line-length + b3dValues[k * bInnerStep + j * bOuterStep + batchIndexB * bBatch] + ); + sum6 += aVal * bVal; + } + resVals[bi * size + (i * rightDim + j)] += sum6; + } + } + } + } + } + } + backend2.disposeIntermediateTensorInfo(a3d); + backend2.disposeIntermediateTensorInfo(b3d); + return backend2.makeTensorInfo(outShape, result.dtype, result.values); +} +var batchMatMulConfig = { + kernelName: BatchMatMul, + backendName: "cpu", + kernelFunc: batchMatMul +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/_FusedMatMul.js +function _fusedMatMul(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b, bias, preluActivationWeights } = inputs; + const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs; + let current; + let addRes; + let activationRes; + const intermediates = []; + const matMulRes = batchMatMul({ inputs: { a, b }, attrs: { transposeA, transposeB }, backend: backend2 }); + current = matMulRes; + if (bias) { + addRes = add4({ inputs: { a: current, b: bias }, backend: backend2 }); + intermediates.push(current); + current = addRes; + } + if (activation2) { + activationRes = applyActivation2(backend2, current, activation2, preluActivationWeights, leakyreluAlpha); + intermediates.push(current); + current = activationRes; + } + for (const i of intermediates) { + backend2.disposeIntermediateTensorInfo(i); + } + return current; +} +var _fusedMatMulConfig = { + kernelName: _FusedMatMul, + backendName: "cpu", + kernelFunc: _fusedMatMul +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acos.js +var acos2 = unaryKernelFunc(Acos, (xi) => Math.acos(xi)); +var acosConfig = { + kernelName: Acos, + backendName: "cpu", + kernelFunc: acos2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acosh.js +var acosh2 = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi)); +var acoshConfig = { + kernelName: Acosh, + backendName: "cpu", + kernelFunc: acosh2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AddN.js +function addN2(args) { + const { inputs, backend: backend2 } = args; + const tensors = inputs; + assertNotComplex(inputs, "addN"); + const vals = tensors.map((t) => backend2.data.get(t.dataId).values); + const outBuf = buffer(tensors[0].shape, tensors[0].dtype); + const outVals = outBuf.values; + for (let i = 0; i < tensors.length; i++) { + const currVals = vals[i]; + for (let j = 0; j < outVals.length; j++) { + outVals[j] += currVals[j]; + } + } + return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); +} +var addNConfig = { + kernelName: AddN, + backendName: "cpu", + kernelFunc: addN2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/All.js +function all2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + assertNotComplex(x, "all"); + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + if (permutedAxes != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("all", axes, $x.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype); + const aVals = backend2.data.get($x.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let all5 = aVals[offset]; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + all5 = all5 && value; + } + vals[i] = all5; + } + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo($x); + } + const result = backend2.makeTensorInfo(outShape, $x.dtype, vals); + if (keepDims) { + const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } }); + backend2.disposeIntermediateTensorInfo(result); + return reshapedResult; + } + return result; +} +var allConfig = { + kernelName: All, + backendName: "cpu", + kernelFunc: all2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Any.js +function any2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + assertNotComplex(x, "any"); + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + if (permutedAxes != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("any", axes, $x.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype); + const aVals = backend2.data.get($x.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let anyVal = aVals[offset]; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + anyVal = anyVal || value; + } + vals[i] = anyVal; + } + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo($x); + } + const result = backend2.makeTensorInfo(outShape, $x.dtype, vals); + if (keepDims) { + const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } }); + backend2.disposeIntermediateTensorInfo(result); + return reshapedResult; + } + return result; +} +var anyConfig = { + kernelName: Any, + backendName: "cpu", + kernelFunc: any2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMax.js +function argMax2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis } = attrs; + assertNotComplex(x, "argMax"); + let axes = util_exports.parseAxisParam(axis, x.shape); + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + const intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length); + } + axes = [axes[0]]; + backend_util_exports.assertAxesAreInnerMostDims("argMax", axes, $x.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes); + const outSize = util_exports.sizeFromShape(outShape); + const vals = util_exports.makeZerosTypedArray(outSize, "int32"); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const aVals = backend2.data.get($x.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let max6 = aVals[offset]; + let maxIndex = 0; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + if (value > max6) { + max6 = value; + maxIndex = j; + } + } + vals[i] = maxIndex; + } + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return backend2.makeTensorInfo(outShape, "int32", vals); +} +var argMaxConfig = { + kernelName: ArgMax, + backendName: "cpu", + kernelFunc: argMax2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMin.js +function argMin2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis } = attrs; + assertNotComplex(x, "argMin"); + let axes = util_exports.parseAxisParam(axis, x.shape); + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + const intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length); + } + axes = [axes[0]]; + backend_util_exports.assertAxesAreInnerMostDims("argMin", axes, $x.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes); + const outSize = util_exports.sizeFromShape(outShape); + const vals = util_exports.makeZerosTypedArray(outSize, "int32"); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const aVals = backend2.data.get($x.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let min6 = aVals[offset]; + let minIndex = 0; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + if (value < min6) { + min6 = value; + minIndex = j; + } + } + vals[i] = minIndex; + } + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return backend2.makeTensorInfo(outShape, "int32", vals); +} +var argMinConfig = { + kernelName: ArgMin, + backendName: "cpu", + kernelFunc: argMin2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asin.js +var asin2 = unaryKernelFunc(Asin, (xi) => Math.asin(xi)); +var asinConfig = { + kernelName: Asin, + backendName: "cpu", + kernelFunc: asin2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asinh.js +var asinh2 = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi)); +var asinhConfig = { + kernelName: Asinh, + backendName: "cpu", + kernelFunc: asinh2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan.js +var atan3 = unaryKernelFunc(Atan, (xi) => Math.atan(xi)); +var atanConfig = { + kernelName: Atan, + backendName: "cpu", + kernelFunc: atan3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan2.js +var atan2Impl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.atan2(aValue, bValue)); +var atan22 = binaryKernelFunc(Atan2, atan2Impl); +var atan2Config = { + kernelName: Atan2, + backendName: "cpu", + kernelFunc: atan22 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atanh.js +var atanh2 = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi)); +var atanhConfig = { + kernelName: Atanh, + backendName: "cpu", + kernelFunc: atanh2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/pool_utils.js +function pool2(xValues, xShape, dtype, strides, convInfo, poolType) { + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY; + const output = buffer(convInfo.outShape, dtype); + const outputVals = output.values; + const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3]; + const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3]; + const outputColStrides = convInfo.outShape[3]; + for (let b = 0; b < convInfo.batchSize; ++b) { + const outputBatchOffset = b * outputBatchStrides; + const inputBatchOffset = b * strides[0]; + for (let d = 0; d < convInfo.inChannels; ++d) { + for (let yR = 0; yR < convInfo.outHeight; ++yR) { + const xRCorner = yR * strideHeight - padTop; + const xRMin = Math.max(0, xRCorner); + const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner); + const outputRowOffset = outputBatchOffset + yR * outputRowStrides; + for (let yC = 0; yC < convInfo.outWidth; ++yC) { + const xCCorner = yC * strideWidth - padLeft; + const xCMin = Math.max(0, xCCorner); + const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner); + let minMaxValue = initialValue; + let avgValue = 0; + let count2 = 0; + for (let xR = xRMin; xR < xRMax; xR += dilationHeight) { + const xROffset = inputBatchOffset + xR * strides[1]; + for (let xC = xCMin; xC < xCMax; xC += dilationWidth) { + const xCOffset = xROffset + xC * strides[2]; + const pixel = xValues[xCOffset + d]; + if (poolType === "max" && pixel > minMaxValue) { + minMaxValue = pixel; + } else if (poolType === "avg") { + avgValue += pixel; + count2++; + } + } + if (isNaN(minMaxValue)) { + break; + } + } + const outputOffset = outputRowOffset + yC * outputColStrides + d; + outputVals[outputOffset] = poolType === "avg" ? avgValue / count2 : minMaxValue; + } + } + } + } + return output; +} +function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) { + const maxPositions = buffer(convInfo.outShape, "int32"); + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const xBuf = buffer(xShape, dtype, xValues); + for (let b = 0; b < convInfo.batchSize; ++b) { + for (let d = 0; d < convInfo.inChannels; ++d) { + for (let yR = 0; yR < convInfo.outHeight; ++yR) { + const xRCorner = yR * strideHeight - padTop; + let xRMin = xRCorner; + while (xRMin < 0) { + xRMin += dilationHeight; + } + const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner); + for (let yC = 0; yC < convInfo.outWidth; ++yC) { + const xCCorner = yC * strideWidth - padLeft; + let xCMin = xCCorner; + while (xCMin < 0) { + xCMin += dilationWidth; + } + const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner); + let maxValue = Number.NEGATIVE_INFINITY; + let maxPosition = -1; + for (let xR = xRMin; xR < xRMax; xR += dilationHeight) { + const wR = xR - xRCorner; + for (let xC = xCMin; xC < xCMax; xC += dilationWidth) { + const wC = xC - xCCorner; + const pixel = xBuf.get(b, xR, xC, d); + if (pixel > maxValue) { + maxValue = pixel; + if (flattenPositions) { + maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d; + } else { + maxPosition = wR * effectiveFilterWidth + wC; + } + } + } + } + maxPositions.set(maxPosition, b, yR, yC, d); + } + } + } + } + return maxPositions; +} +function pool3d2(xValues, xShape, dtype, strides, convInfo, poolType) { + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = convInfo.padInfo.front; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY; + const output = buffer(convInfo.outShape, dtype); + const outputVals = output.values; + const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4]; + const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4]; + const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4]; + const outputColStrides = convInfo.outShape[4]; + for (let batch = 0; batch < convInfo.batchSize; ++batch) { + const outputBatchOffset = batch * outputBatchStrides; + const inputBatchOffset = batch * strides[0]; + for (let channel = 0; channel < convInfo.inChannels; ++channel) { + for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) { + const xDepthCorner = yDepth * strideDepth - padFront; + let xDepthMin = xDepthCorner; + while (xDepthMin < 0) { + xDepthMin += dilationDepth; + } + const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner); + const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides; + for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) { + const xRowCorner = yRow * strideHeight - padTop; + let xRowMin = xRowCorner; + while (xRowMin < 0) { + xRowMin += dilationHeight; + } + const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner); + const outputRowOffset = outputDepthOffset + yRow * outputRowStrides; + for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) { + const xColCorner = yCol * strideWidth - padLeft; + let xColMin = xColCorner; + while (xColMin < 0) { + xColMin += dilationWidth; + } + const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner); + const outputColOffset = outputRowOffset + yCol * outputColStrides; + let minMaxValue = initialValue; + let avgValue = 0; + let count2 = 0; + for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) { + const xDepthOffset = inputBatchOffset + xDepth * strides[1]; + for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) { + const xRowOffset = xDepthOffset + xRow * strides[2]; + for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) { + const xColOffset = xRowOffset + xCol * strides[3]; + const pixel = xValues[xColOffset + channel]; + if (poolType === "max" && pixel > minMaxValue) { + minMaxValue = pixel; + } else if (poolType === "avg") { + avgValue += pixel; + count2++; + } + if (isNaN(minMaxValue)) { + break; + } + } + if (isNaN(minMaxValue)) { + break; + } + } + if (isNaN(minMaxValue)) { + break; + } + } + const outputOffset = outputColOffset + channel; + outputVals[outputOffset] = poolType === "avg" ? avgValue / Math.max(count2, 1) : minMaxValue; + } + } + } + } + } + return output; +} +function maxPool3dPositions(xBuf, convInfo) { + const maxPositions = buffer(convInfo.outShape, "int32"); + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = convInfo.padInfo.front; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + for (let batch = 0; batch < convInfo.batchSize; ++batch) { + for (let channel = 0; channel < convInfo.inChannels; ++channel) { + for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) { + const xDepthCorner = yDepth * strideDepth - padFront; + let xDepthMin = xDepthCorner; + while (xDepthMin < 0) { + xDepthMin += dilationDepth; + } + const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner); + for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) { + const xRowCorner = yRow * strideHeight - padTop; + let xRowMin = xRowCorner; + while (xRowMin < 0) { + xRowMin += dilationHeight; + } + const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner); + for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) { + const xColCorner = yCol * strideWidth - padLeft; + let xColMin = xColCorner; + while (xColMin < 0) { + xColMin += dilationWidth; + } + const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner); + let maxValue = Number.NEGATIVE_INFINITY; + let maxPosition = -1; + for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) { + const wDepth = xDepth - xDepthCorner; + for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) { + const wRow = xRow - xRowCorner; + for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) { + const wCol = xCol - xColCorner; + const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel); + if (pixel >= maxValue) { + maxValue = pixel; + maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol; + } + } + } + } + maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel); + } + } + } + } + } + return maxPositions; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool.js +function avgPool2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + assertNotComplex(x, "avgPool"); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = 1; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + let res; + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) { + res = identity2({ inputs: { x }, backend: backend2 }); + } else { + const xValues = backend2.data.get(x.dataId).values; + const strides2 = util_exports.computeStrides(x.shape); + const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, "avg"); + res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values); + } + return res; +} +var avgPoolConfig = { + kernelName: AvgPool, + backendName: "cpu", + kernelFunc: avgPool2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3D.js +function avgPool3D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs; + assertNotComplex(x, "avgPool3d"); + const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat); + const xValues = backend2.data.get(x.dataId).values; + const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "avg"); + return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values); +} +var avgPool3DConfig = { + kernelName: AvgPool3D, + backendName: "cpu", + kernelFunc: avgPool3D +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3DGrad.js +function avgPool3DGrad(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + assertNotComplex([dy, input2], "avgPool3DGrad"); + const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode); + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const filterDepth = convInfo.filterDepth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const dx = buffer(input2.shape, "float32"); + const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth); + const dyBuf = backend2.bufferSync(dy); + for (let batch = 0; batch < convInfo.batchSize; ++batch) { + for (let channel = 0; channel < convInfo.inChannels; ++channel) { + for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) { + for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) { + for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) { + const dyDepthCorner = dxDepth - padFront; + const dyRowCorner = dxRow - padTop; + const dyColCorner = dxCol - padLeft; + let dotProd = 0; + for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) { + const dyDepth = (dyDepthCorner + wDepth) / strideDepth; + if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) { + continue; + } + for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) { + const dyRow = (dyRowCorner + wRow) / strideHeight; + if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) { + continue; + } + for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) { + const dyCol = (dyColCorner + wCol) / strideWidth; + if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) { + continue; + } + const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel); + dotProd += pixel; + } + } + } + dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel); + } + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var avgPool3DGradConfig2 = { + kernelName: AvgPool3DGrad, + backendName: "cpu", + kernelFunc: avgPool3DGrad +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPoolGrad.js +function avgPoolGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const x = input2; + assertNotComplex([dy, input2], "avgPoolGrad"); + const { filterSize, strides, pad: pad3 } = attrs; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3); + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const dx = buffer(x.shape, "float32"); + const avgMultiplier = 1 / (filterHeight * filterWidth); + const dyData = backend2.data.get(dy.dataId).values; + const dyBuf = buffer(dy.shape, "float32", dyData); + for (let b = 0; b < convInfo.batchSize; ++b) { + for (let d = 0; d < convInfo.inChannels; ++d) { + for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) { + for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) { + const dyRCorner = dxR - padTop; + const dyCCorner = dxC - padLeft; + let dotProd = 0; + for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) { + const dyR = (dyRCorner + wR) / strideHeight; + if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) { + continue; + } + for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) { + const dyC = (dyCCorner + wC) / strideWidth; + if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) { + continue; + } + const pixel = dyBuf.get(b, dyR, dyC, d); + dotProd += pixel; + } + } + dx.set(dotProd * avgMultiplier, b, dxR, dxC, d); + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var avgPoolGradConfig2 = { + kernelName: AvgPoolGrad, + backendName: "cpu", + kernelFunc: avgPoolGrad2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchNorm.js +function batchNorm2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, scale: scale2, offset, mean: mean4, variance } = inputs; + util_exports.assert(mean4.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."); + util_exports.assert(offset == null || mean4.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."); + util_exports.assert(scale2 == null || mean4.shape.length === scale2.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); + assertNotComplex([x, mean4, variance, scale2, offset], "batchNorm"); + let { varianceEpsilon } = attrs; + if (varianceEpsilon == null) { + varianceEpsilon = 1e-3; + } + const xVals = backend2.data.get(x.dataId).values; + const mVals = backend2.data.get(mean4.dataId).values; + const varVals = backend2.data.get(variance.dataId).values; + const sVals = scale2 ? backend2.data.get(scale2.dataId).values : new Float32Array([1]); + const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]); + const outVals = new Float32Array(xVals.length); + const offValsLength = offVals.length; + const sValsLength = sVals.length; + const varValsLength = varVals.length; + const mValsLength = mVals.length; + let offi = 0; + let mi = 0; + let si = 0; + let vi = 0; + for (let i = 0; i < xVals.length; ++i) { + outVals[i] = offVals[offi++] + (xVals[i] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon); + if (offi >= offValsLength) { + offi = 0; + } + if (mi >= mValsLength) { + mi = 0; + } + if (si >= sValsLength) { + si = 0; + } + if (vi >= varValsLength) { + vi = 0; + } + } + return backend2.makeTensorInfo(x.shape, x.dtype, outVals); +} +var batchNormConfig = { + kernelName: FusedBatchNorm, + backendName: "cpu", + kernelFunc: batchNorm2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchToSpaceND.js +function batchToSpaceND2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, crops } = attrs; + assertNotComplex([x], "batchToSpaceND"); + const prod5 = blockShape.reduce((a, b) => a * b); + const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5); + const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length); + const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5); + const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length); + const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length); + const xReshaped = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } }); + const xTransposed = transpose2({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } }); + const xTransposedReshaped = reshape3({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } }); + const result = slice2({ + inputs: { x: xTransposedReshaped }, + backend: backend2, + attrs: { begin: sliceBeginCoords, size: sliceSize } + }); + backend2.disposeIntermediateTensorInfo(xReshaped); + backend2.disposeIntermediateTensorInfo(xTransposed); + backend2.disposeIntermediateTensorInfo(xTransposedReshaped); + return result; +} +var batchToSpaceNDConfig = { + kernelName: BatchToSpaceND, + backendName: "cpu", + kernelFunc: batchToSpaceND2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount.js +function bincount2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, weights } = inputs; + const { size } = attrs; + const xVals = backend2.data.get(x.dataId).values; + const weightsVals = backend2.data.get(weights.dataId).values; + const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size); + return backend2.makeTensorInfo([size], weights.dtype, outVals); +} +var bincountConfig = { + kernelName: Bincount, + backendName: "cpu", + kernelFunc: bincount2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BroadcastArgs.js +function broadcastArgs2(args) { + const { inputs, backend: backend2 } = args; + const { s0, s1 } = inputs; + const s0Vals = backend2.data.get(s0.dataId).values; + const s1Vals = backend2.data.get(s1.dataId).values; + const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals)); + return backend2.makeTensorInfo([broadcastShape.length], "int32", Int32Array.from(broadcastShape)); +} +var broadcastArgsConfig = { + kernelName: BroadcastArgs, + backendName: "cpu", + kernelFunc: broadcastArgs2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ClipByValue.js +var clipByValue2 = unaryKernelFunc(ClipByValue, (xi, attrs) => { + const clipAttrs = attrs; + if (xi > clipAttrs.clipValueMax) { + return clipAttrs.clipValueMax; + } + return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi; +}); +var clipByValueConfig = { + kernelName: ClipByValue, + backendName: "cpu", + kernelFunc: clipByValue2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ComplexAbs.js +var complexAbs = (args) => { + const { x } = args.inputs; + const cpuBackend = args.backend; + const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape)); + const complexVals = cpuBackend.data.get(x.dataId); + const real4 = complexVals.complexTensorInfos.real; + const imag4 = complexVals.complexTensorInfos.imag; + const realVals = cpuBackend.data.get(real4.dataId).values; + const imagVals = cpuBackend.data.get(imag4.dataId).values; + for (let i = 0; i < realVals.length; i++) { + const real5 = realVals[i]; + const imag5 = imagVals[i]; + resultValues[i] = Math.hypot(real5, imag5); + } + return cpuBackend.makeOutput(resultValues, x.shape, "float32"); +}; +var complexAbsConfig = { + kernelName: ComplexAbs, + backendName: "cpu", + kernelFunc: complexAbs +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Imag.js +function imag2(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const imag4 = backend2.data.get(input2.dataId).complexTensorInfos.imag; + const imagVal = backend2.data.get(imag4.dataId).values; + return backend2.makeTensorInfo(imag4.shape, imag4.dtype, imagVal); +} +var imagConfig = { + kernelName: Imag, + backendName: "cpu", + kernelFunc: imag2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat.js +function concat2(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0]; + const shapes = inputs.map((t) => t.shape); + backend_util_exports.assertParamsConsistent(shapes, $axis); + let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis); + if (util_exports.sizeFromShape(outShape) === 0) { + return backend2.makeTensorInfo(outShape, inputs[0].dtype, []); + } + const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0); + if ($inputs.length === 1) { + return identity2({ inputs: { x: $inputs[0] }, backend: backend2 }); + } + if ($inputs[0].dtype === "complex64") { + const reals = $inputs.map((t) => real2({ inputs: { input: t }, backend: backend2 })); + const imags = $inputs.map((t) => imag2({ inputs: { input: t }, backend: backend2 })); + const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } }); + const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } }); + const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 }); + reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r)); + imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i)); + backend2.disposeIntermediateTensorInfo(realConcated); + backend2.disposeIntermediateTensorInfo(imagConcated); + return result; + } + const inputs2D = $inputs.map((t) => { + const innerSize = util_exports.sizeFromShape(t.shape.slice($axis)); + const shape = [-1, innerSize]; + return reshape3({ inputs: { x: t }, backend: backend2, attrs: { shape } }); + }); + const inputsValShapes = inputs2D.map((t) => { + return { vals: backend2.data.get(t.dataId).values, shape: t.shape }; + }); + outShape = backend_util_exports.computeOutShape( + inputs2D.map((t) => t.shape), + 1 + /* axis */ + ); + const simplyConcat = inputs2D[0].shape[0] === 1; + const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat); + const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), $axis); + const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals); + inputs2D.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return outInfo; +} +var concatConfig = { + kernelName: Concat, + backendName: "cpu", + kernelFunc: concat2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2D.js +function conv2D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs; + assertNotComplex([x, filter], "conv2d"); + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const padLeft = convInfo.padInfo.left; + const padTop = convInfo.padInfo.top; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const y = new TensorBuffer(convInfo.outShape, x.dtype); + const xStrides = util_exports.computeStrides(x.shape); + const filterStrides = util_exports.computeStrides(filter.shape); + const xBatchStride = xStrides[0]; + const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2]; + const xColStride = isChannelsLast ? xStrides[2] : 1; + const xChannelStride = isChannelsLast ? 1 : xStrides[1]; + const yBatchStride = y.strides[0]; + const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2]; + const yColStride = isChannelsLast ? y.strides[2] : 1; + const yChannelStride = isChannelsLast ? 1 : y.strides[1]; + const xVals = backend2.data.get(x.dataId).values; + const wVals = backend2.data.get(filter.dataId).values; + const yVals = y.values; + for (let b = 0; b < convInfo.batchSize; ++b) { + const xOffset1 = b * xBatchStride; + const yOffset1 = b * yBatchStride; + for (let yR = 0; yR < convInfo.outHeight; ++yR) { + const yOffset2 = yOffset1 + yR * yRowStride; + const xRCorner = yR * convInfo.strideHeight - padTop; + for (let wR = 0; wR < filterHeight; ++wR) { + const xR = xRCorner + wR * dilationHeight; + if (xR < 0 || xR >= convInfo.inHeight) { + continue; + } + const wOffset1 = wR * filterStrides[0]; + const xOffset2 = xOffset1 + xR * xRowStride; + for (let yC = 0; yC < convInfo.outWidth; ++yC) { + const yOffset3 = yOffset2 + yC * yColStride; + const xCCorner = yC * convInfo.strideWidth - padLeft; + for (let wC = 0; wC < filterWidth; ++wC) { + const xC = xCCorner + wC * dilationWidth; + if (xC < 0 || xC >= convInfo.inWidth) { + continue; + } + const wOffset2 = wOffset1 + wC * filterStrides[1]; + const xOffset3 = xOffset2 + xC * xColStride; + let wOffset3 = wOffset2; + for (let d1 = 0; d1 < convInfo.inChannels; ++d1) { + const xVal = xVals[xOffset3 + d1 * xChannelStride]; + for (let d2 = 0; d2 < convInfo.outChannels; ++d2) { + yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2]; + } + wOffset3 += convInfo.outChannels; + } + } + } + } + } + } + return backend2.makeTensorInfo(y.shape, y.dtype, yVals); +} +var conv2DConfig = { + kernelName: Conv2D, + backendName: "cpu", + kernelFunc: conv2D +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropFilter.js +function conv2DBackpropFilter2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs; + assertNotComplex([x, dy], "conv2dBackpropFilter"); + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat); + const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const dW = new TensorBuffer(convInfo.filterShape, "float32"); + const leftPad = convInfo.padInfo.left; + const topPad = convInfo.padInfo.top; + const xVals = backend2.data.get(x.dataId).values; + const dyVals = backend2.data.get(dy.dataId).values; + const xBuf = new TensorBuffer(x.shape, x.dtype, xVals); + const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals); + for (let wR = 0; wR < filterHeight; ++wR) { + const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight)); + const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight); + for (let wC = 0; wC < filterWidth; ++wC) { + const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth)); + const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth); + for (let d1 = 0; d1 < convInfo.inChannels; ++d1) { + for (let d2 = 0; d2 < convInfo.outChannels; ++d2) { + let dotProd = 0; + for (let b = 0; b < convInfo.batchSize; ++b) { + for (let yR = yRMin; yR < yRMax; ++yR) { + const xR = wR + yR * strideHeight - topPad; + for (let yC = yCMin; yC < yCMax; ++yC) { + const xC = wC + yC * strideWidth - leftPad; + if (isChannelsLast) { + dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2); + } else { + dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC); + } + } + } + } + dW.set(dotProd, wR, wC, d1, d2); + } + } + } + } + return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values); +} +var conv2DBackpropFilterConfig = { + kernelName: Conv2DBackpropFilter, + backendName: "cpu", + kernelFunc: conv2DBackpropFilter2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropInput.js +function conv2DBackpropInput2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs; + assertNotComplex([dy, filter], "conv2dBackpropInput"); + const filterStrides = util_exports.computeStrides(filter.shape); + const dyStrides = util_exports.computeStrides(dy.shape); + let $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat); + const dx = new TensorBuffer(convInfo.inShape, "float32"); + const dxValues = dx.values; + const dyValues = backend2.data.get(dy.dataId).values; + const fltValues = backend2.data.get(filter.dataId).values; + const [fltS0, fltS1, fltS2] = filterStrides; + const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo; + $dataFormat = convInfo.dataFormat; + const topPad = filterHeight - 1 - convInfo.padInfo.top; + const leftPad = filterWidth - 1 - convInfo.padInfo.left; + const isChannelsLast = $dataFormat === "channelsLast"; + const xBatchStride = dx.strides[0]; + const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2]; + const xColStride = isChannelsLast ? dx.strides[2] : 1; + const xChannelStride = isChannelsLast ? 1 : dx.strides[1]; + const yBatchStride = dyStrides[0]; + const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2]; + const yColStride = isChannelsLast ? dyStrides[2] : 1; + const yChannelStride = isChannelsLast ? 1 : dyStrides[1]; + for (let b = 0; b < batchSize; ++b) { + for (let d1 = 0; d1 < inChannels; ++d1) { + for (let xR = 0; xR < inHeight; ++xR) { + const xRCorner = xR - topPad; + const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight)); + const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight); + for (let xC = 0; xC < inWidth; ++xC) { + const xCCorner = xC - leftPad; + const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth)); + const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth); + let dotProd = 0; + for (let yR = xRMin; yR < yRMax; ++yR) { + const wR = yR * strideHeight - xRCorner; + for (let yC = xCMin; yC < yCMax; ++yC) { + const wC = yC * strideWidth - xCCorner; + const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC; + const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1; + for (let d2 = 0; d2 < outChannels; ++d2) { + const pixel = dyValues[dyOffset + yChannelStride * d2]; + const weight = fltValues[fltOffset + d2]; + dotProd += pixel * weight; + } + } + } + const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1; + dxValues[dxOffset] = dotProd; + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var conv2DBackpropInputConfig = { + kernelName: Conv2DBackpropInput, + backendName: "cpu", + kernelFunc: conv2DBackpropInput2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3D.js +function conv3D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + assertNotComplex([x, filter], "conv3d"); + const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3); + const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo; + const padFront = padInfo.front; + const padLeft = padInfo.left; + const padTop = padInfo.top; + const y = new TensorBuffer(convInfo.outShape, x.dtype); + const xVals = backend2.data.get(x.dataId).values; + const wVals = backend2.data.get(filter.dataId).values; + const yVals = y.values; + const xStrides = util_exports.computeStrides(x.shape); + const filterStrides = util_exports.computeStrides(filter.shape); + for (let b = 0; b < convInfo.batchSize; ++b) { + const xOffset1 = b * xStrides[0]; + const yOffset1 = b * y.strides[0]; + for (let yF = 0; yF < convInfo.outDepth; ++yF) { + const yOffset2 = yOffset1 + yF * y.strides[1]; + const xFCorner = yF * convInfo.strideDepth - padFront; + for (let wF = 0; wF < filterDepth; ++wF) { + const xF = xFCorner + wF * dilationDepth; + if (xF < 0 || xF >= convInfo.inDepth) { + continue; + } + const wOffset1 = wF * filterStrides[0]; + const xOffset2 = xOffset1 + xF * xStrides[1]; + for (let yR = 0; yR < convInfo.outHeight; ++yR) { + const yOffset3 = yOffset2 + yR * y.strides[2]; + const xRCorner = yR * convInfo.strideHeight - padTop; + for (let wR = 0; wR < filterHeight; ++wR) { + const xR = xRCorner + wR * dilationHeight; + if (xR < 0 || xR >= convInfo.inHeight) { + continue; + } + const wOffset2 = wOffset1 + wR * filterStrides[1]; + const xOffset3 = xOffset2 + xR * xStrides[2]; + for (let yC = 0; yC < convInfo.outWidth; ++yC) { + const yOffset4 = yOffset3 + yC * convInfo.outChannels; + const xCCorner = yC * convInfo.strideWidth - padLeft; + for (let wC = 0; wC < filterWidth; ++wC) { + const xC = xCCorner + wC * dilationWidth; + if (xC < 0 || xC >= convInfo.inWidth) { + continue; + } + const wOffset3 = wOffset2 + wC * filterStrides[2]; + const xOffset4 = xOffset3 + xC * convInfo.inChannels; + let wOffset4 = wOffset3; + for (let d1 = 0; d1 < convInfo.inChannels; ++d1) { + const xVal = xVals[xOffset4 + d1]; + for (let d2 = 0; d2 < convInfo.outChannels; ++d2) { + yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2]; + } + wOffset4 += convInfo.outChannels; + } + } + } + } + } + } + } + } + return backend2.makeTensorInfo(y.shape, y.dtype, y.values); +} +var conv3DConfig = { + kernelName: Conv3D, + backendName: "cpu", + kernelFunc: conv3D +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropFilterV2.js +function conv3DBackpropFilterV2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, pad: pad3, filterShape } = attrs; + assertNotComplex([x, dy], "conv3dBackpropFilterV2"); + const xStrides = util_exports.computeStrides(x.shape); + const dyStrides = util_exports.computeStrides(dy.shape); + const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3); + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const filterDepth = convInfo.filterDepth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const dw = new TensorBuffer(convInfo.filterShape, "float32"); + const dwValues = dw.values; + const [dwS0, dwS1, dwS2, dwS3] = dw.strides; + const dyValues = backend2.data.get(dy.dataId).values; + const [dyS0, dyS1, dyS2, dyS3] = dyStrides; + const xValues = backend2.data.get(x.dataId).values; + const [xS0, xS1, xS2, xS3] = xStrides; + const frontPad = convInfo.padInfo.front; + const leftPad = convInfo.padInfo.left; + const topPad = convInfo.padInfo.top; + for (let wF = 0; wF < filterDepth; ++wF) { + const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth)); + const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth); + const wOffset1 = wF * dwS0; + for (let wR = 0; wR < filterHeight; ++wR) { + const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight)); + const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight); + const wOffset2 = wR * dwS1 + wOffset1; + for (let wC = 0; wC < filterWidth; ++wC) { + const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth)); + const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth); + const wOffset3 = wC * dwS2 + wOffset2; + for (let d1 = 0; d1 < convInfo.inChannels; ++d1) { + const wOffset4 = d1 * dwS3 + wOffset3; + for (let d2 = 0; d2 < convInfo.outChannels; ++d2) { + let dotProd = 0; + for (let b = 0; b < convInfo.batchSize; ++b) { + const xOffset1 = b * xS0; + const yOffset1 = b * dyS0; + for (let yF = yFMin; yF < yFMax; ++yF) { + const xF = wF + yF * strideDepth - frontPad; + const xOffset2 = xF * xS1 + xOffset1; + const yOffset2 = yF * dyS1 + yOffset1; + for (let yR = yRMin; yR < yRMax; ++yR) { + const xR = wR + yR * strideHeight - topPad; + const xOffset3 = xR * xS2 + xOffset2; + const yOffset3 = yR * dyS2 + yOffset2; + for (let yC = yCMin; yC < yCMax; ++yC) { + const xC = wC + yC * strideWidth - leftPad; + const xOffset4 = xC * xS3 + xOffset3; + const yOffset4 = yC * dyS3 + yOffset3; + dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2]; + } + } + } + } + dwValues[wOffset4 + d2] = dotProd; + } + } + } + } + } + return backend2.makeTensorInfo(dw.shape, dw.dtype, dw.values); +} +var conv3DBackpropFilterV2Config = { + kernelName: Conv3DBackpropFilterV2, + backendName: "cpu", + kernelFunc: conv3DBackpropFilterV2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropInputV2.js +function conv3DBackpropInputV2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { pad: pad3, strides, inputShape } = attrs; + assertNotComplex([dy], "conv3dBackpropInputV2"); + const dyStrides = util_exports.computeStrides(dy.shape); + const filterStrides = util_exports.computeStrides(filter.shape); + const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3); + const dx = new TensorBuffer(convInfo.inShape, "float32"); + const dxValues = dx.values; + const [dxS0, dxS1, dxS2, dxS3] = dx.strides; + const dyValues = backend2.data.get(dy.dataId).values; + const [dyS0, dyS1, dyS2, dyS3] = dyStrides; + const fltValues = backend2.data.get(filter.dataId).values; + const [fltS0, fltS1, fltS2, fltS3] = filterStrides; + const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo; + const frontPad = filterDepth - 1 - convInfo.padInfo.front; + const topPad = filterHeight - 1 - convInfo.padInfo.top; + const leftPad = filterWidth - 1 - convInfo.padInfo.left; + for (let b = 0; b < batchSize; ++b) { + for (let d1 = 0; d1 < inChannels; ++d1) { + for (let xF = 0; xF < inDepth; ++xF) { + const xFCorner = xF - frontPad; + const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth)); + const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth); + for (let xR = 0; xR < inHeight; ++xR) { + const xRCorner = xR - topPad; + const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight)); + const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight); + for (let xC = 0; xC < inWidth; ++xC) { + const xCCorner = xC - leftPad; + const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth)); + const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth); + let dotProd = 0; + for (let yF = xFMin; yF < yFMax; ++yF) { + const wF = yF * strideDepth - xFCorner; + for (let yR = xRMin; yR < yRMax; ++yR) { + const wR = yR * strideHeight - xRCorner; + for (let yC = xCMin; yC < yCMax; ++yC) { + const wC = yC * strideWidth - xCCorner; + const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC; + const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1; + for (let d2 = 0; d2 < outChannels; ++d2) { + const pixel = dyValues[dyOffset + d2]; + const weight = fltValues[fltOffset + d2]; + dotProd += pixel * weight; + } + } + } + } + dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd; + } + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var conv3DBackpropInputV2Config = { + kernelName: Conv3DBackpropInputV2, + backendName: "cpu", + kernelFunc: conv3DBackpropInputV2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cos.js +var cos2 = unaryKernelFunc(Cos, (xi) => Math.cos(xi)); +var cosConfig = { + kernelName: Cos, + backendName: "cpu", + kernelFunc: cos2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cosh.js +var cosh2 = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi)); +var coshConfig = { + kernelName: Cosh, + backendName: "cpu", + kernelFunc: cosh2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/CropAndResize.js +function cropAndResize3(args) { + const { inputs, backend: backend2, attrs } = args; + const { image: image2, boxes, boxInd } = inputs; + const { cropSize, method, extrapolationValue } = attrs; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const numBoxes = boxes.shape[0]; + const [cropHeight, cropWidth] = cropSize; + const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], "float32"); + const boxVals = backend2.data.get(boxes.dataId).values; + const boxIndVals = backend2.data.get(boxInd.dataId).values; + const imageVals = backend2.data.get(image2.dataId).values; + const inStride = util_exports.computeStrides(image2.shape); + const outStride = util_exports.computeStrides(output.shape); + for (let b = 0; b < numBoxes; b++) { + const startInd = b * 4; + const y1 = boxVals[startInd]; + const x1 = boxVals[startInd + 1]; + const y2 = boxVals[startInd + 2]; + const x2 = boxVals[startInd + 3]; + const bInd = boxIndVals[b]; + if (bInd >= batch) { + continue; + } + const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0; + const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0; + for (let y = 0; y < cropHeight; y++) { + const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1); + if (yInd < 0 || yInd > imageHeight - 1) { + for (let x = 0; x < cropWidth; x++) { + for (let c = 0; c < numChannels; c++) { + const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = extrapolationValue; + } + } + continue; + } + if (method === "bilinear") { + const topInd = Math.floor(yInd); + const bottomInd = Math.ceil(yInd); + const yLerp = yInd - topInd; + for (let x = 0; x < cropWidth; x++) { + const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1); + if (xInd < 0 || xInd > imageWidth - 1) { + for (let c = 0; c < numChannels; c++) { + const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = extrapolationValue; + } + continue; + } + const leftInd = Math.floor(xInd); + const rightInd = Math.ceil(xInd); + const xLerp = xInd - leftInd; + for (let c = 0; c < numChannels; c++) { + let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0]; + const topLeft = imageVals[ind]; + ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0]; + const topRight = imageVals[ind]; + ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0]; + const bottomLeft = imageVals[ind]; + ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0]; + const bottomRight = imageVals[ind]; + const top = topLeft + (topRight - topLeft) * xLerp; + const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp; + ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = top + (bottom - top) * yLerp; + } + } + } else { + for (let x = 0; x < cropWidth; ++x) { + const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1); + if (xInd < 0 || xInd > imageWidth - 1) { + for (let c = 0; c < numChannels; c++) { + const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = extrapolationValue; + } + continue; + } + const closestX = Math.round(xInd); + const closestY = Math.round(yInd); + for (let c = 0; c < numChannels; c++) { + const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0]; + const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[outInd] = imageVals[inInd]; + } + } + } + } + } + return backend2.makeTensorInfo(output.shape, output.dtype, output.values); +} +var cropAndResizeConfig = { + kernelName: CropAndResize, + backendName: "cpu", + kernelFunc: cropAndResize3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumprod.js +function cumprod2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + assertNotComplex(x, "cumprod"); + const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length); + let $x = x; + if (permutation != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } }); + } + const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0]; + if (permutedAxis !== $x.shape.length - 1) { + throw new Error(`backend.cumprod in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`); + } + const resultDtype = upcastType($x.dtype, "int32"); + const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype); + const aVals = backend2.data.get($x.dataId).values; + const finalDim = $x.shape[$x.shape.length - 1]; + const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j; + for (let i = 0; i < aVals.length; i += finalDim) { + for (let j = 0; j < finalDim; j++) { + const idx = indexAdjuster(i, j); + if (j === 0) { + vals[idx] = exclusive ? 1 : aVals[idx]; + } else { + const prevIdx = indexAdjuster(i, j - 1); + vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx]; + } + } + } + const result = backend2.makeTensorInfo($x.shape, resultDtype, vals); + if (permutation != null) { + const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation); + const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } }); + backend2.disposeIntermediateTensorInfo(result); + backend2.disposeIntermediateTensorInfo($x); + return reverseTransposedResult; + } + return result; +} +var cumprodConfig = { + kernelName: Cumprod, + backendName: "cpu", + kernelFunc: cumprod2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumsum.js +function cumsum2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + assertNotComplex(x, "cumsum"); + const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length); + let $x = x; + if (permutation != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } }); + } + const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0]; + if (permutedAxis !== $x.shape.length - 1) { + throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`); + } + const resultDtype = upcastType($x.dtype, "int32"); + const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype); + const aVals = backend2.data.get($x.dataId).values; + const finalDim = $x.shape[$x.shape.length - 1]; + const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j; + for (let i = 0; i < aVals.length; i += finalDim) { + for (let j = 0; j < finalDim; j++) { + const idx = indexAdjuster(i, j); + if (j === 0) { + vals[idx] = exclusive ? 0 : aVals[idx]; + } else { + const prevIdx = indexAdjuster(i, j - 1); + vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx]; + } + } + } + const result = backend2.makeTensorInfo($x.shape, resultDtype, vals); + if (permutation != null) { + const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation); + const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } }); + backend2.disposeIntermediateTensorInfo(result); + backend2.disposeIntermediateTensorInfo($x); + return reverseTransposedResult; + } + return result; +} +var cumsumConfig = { + kernelName: Cumsum, + backendName: "cpu", + kernelFunc: cumsum2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DenseBincount.js +function denseBincount2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, weights } = inputs; + const { size, binaryOutput } = attrs; + if (x.shape.length === 1) { + const xVals = backend2.data.get(x.dataId).values; + const weightsVals = backend2.data.get(weights.dataId).values; + const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size); + return backend2.makeTensorInfo([size], weights.dtype, outVals); + } else if (x.shape.length === 2) { + const xBuf = backend2.bufferSync(x); + const weightsBuf = backend2.bufferSync(weights); + const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput); + return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values); + } + throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`); +} +var denseBincountConfig = { + kernelName: DenseBincount, + backendName: "cpu", + kernelFunc: denseBincount2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthToSpace.js +function depthToSpace2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockSize, dataFormat } = attrs; + util_exports.assert(dataFormat === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`); + const batchSize = x.shape[0]; + const inputHeight = x.shape[1]; + const inputWidth = x.shape[2]; + const inputDepth = x.shape[3]; + const outputHeight = inputHeight * blockSize; + const outputWidth = inputWidth * blockSize; + const outputDepth = inputDepth / (blockSize * blockSize); + const xValues = backend2.data.get(x.dataId).values; + const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth); + let outputIdx = 0; + for (let b = 0; b < batchSize; ++b) { + for (let h = 0; h < outputHeight; ++h) { + const inH = Math.floor(h / blockSize); + const offsetH = h % blockSize; + for (let w = 0; w < outputWidth; ++w) { + const inW = Math.floor(w / blockSize); + const offsetW = w % blockSize; + const offsetD = (offsetH * blockSize + offsetW) * outputDepth; + for (let d = 0; d < outputDepth; ++d) { + const inD = d + offsetD; + const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b)); + result[outputIdx++] = xValues[inputIdx]; + } + } + } + } + return backend2.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result); +} +var depthToSpaceConfig = { + kernelName: DepthToSpace, + backendName: "cpu", + kernelFunc: depthToSpace2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNative.js +function depthwiseConv2dNative(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations, dimRoundingMode } = attrs; + assertNotComplex([x, filter], "depthwiseConv2DNative"); + const xStrides = util_exports.computeStrides(x.shape); + const filterStrides = util_exports.computeStrides(filter.shape); + let $dilations = dilations; + if ($dilations == null) { + $dilations = [1, 1]; + } + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`); + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filter.shape, + strides, + $dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo; + const padLeft = padInfo.left; + const padTop = padInfo.top; + const chMul = convInfo.outChannels / convInfo.inChannels; + const y = new TensorBuffer(convInfo.outShape, x.dtype); + const xVals = backend2.data.get(x.dataId).values; + const wVals = backend2.data.get(filter.dataId).values; + const yVals = y.values; + for (let b = 0; b < convInfo.batchSize; ++b) { + const xOffset1 = b * xStrides[0]; + const yOffset1 = b * y.strides[0]; + for (let yR = 0; yR < convInfo.outHeight; ++yR) { + const yOffset2 = yOffset1 + yR * y.strides[1]; + const xRCorner = yR * convInfo.strideHeight - padTop; + for (let wR = 0; wR < filterHeight; ++wR) { + const xR = xRCorner + wR * dilationHeight; + if (xR < 0 || xR >= convInfo.inHeight) { + continue; + } + const wOffset1 = wR * filterStrides[0]; + const xOffset2 = xOffset1 + xR * xStrides[1]; + for (let yC = 0; yC < convInfo.outWidth; ++yC) { + const yOffset3 = yOffset2 + yC * y.strides[2]; + const xCCorner = yC * convInfo.strideWidth - padLeft; + for (let wC = 0; wC < filterWidth; ++wC) { + const xC = xCCorner + wC * dilationWidth; + if (xC < 0 || xC >= convInfo.inWidth) { + continue; + } + const wOffset2 = wOffset1 + wC * filterStrides[1]; + const xOffset3 = xOffset2 + xC * convInfo.inChannels; + let yOffset4 = yOffset3; + let wOffset3 = wOffset2; + for (let d1 = 0; d1 < convInfo.inChannels; ++d1) { + const xVal = xVals[xOffset3 + d1]; + for (let q = 0; q < chMul; ++q) { + yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q]; + } + yOffset4 += chMul; + wOffset3 += chMul; + } + } + } + } + } + } + return backend2.makeTensorInfo(y.shape, y.dtype, y.values); +} +var depthwiseConv2dNativeConfig = { + kernelName: DepthwiseConv2dNative, + backendName: "cpu", + kernelFunc: depthwiseConv2dNative +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js +function depthwiseConv2dNativeBackpropFilter2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs; + assertNotComplex([x, dy], "depthwiseConv2dNativeBackpropFilter"); + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filterShape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo; + const dW = new TensorBuffer(convInfo.filterShape, "float32"); + const leftPad = convInfo.padInfo.left; + const topPad = convInfo.padInfo.top; + const chMul = convInfo.outChannels / convInfo.inChannels; + const xVals = backend2.data.get(x.dataId).values; + const xBuf = new TensorBuffer(x.shape, x.dtype, xVals); + const dyVals = backend2.data.get(dy.dataId).values; + const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals); + for (let wR = 0; wR < filterHeight; ++wR) { + const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight)); + const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight); + for (let wC = 0; wC < filterWidth; ++wC) { + const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth)); + const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth); + for (let d2 = 0; d2 < convInfo.outChannels; ++d2) { + const d1 = Math.trunc(d2 / chMul); + const dm = d2 % chMul; + let dotProd = 0; + for (let b = 0; b < convInfo.batchSize; ++b) { + for (let yR = yRMin; yR < yRMax; ++yR) { + const xR = wR + yR * strideHeight - topPad; + for (let yC = yCMin; yC < yCMax; ++yC) { + const xC = wC + yC * strideWidth - leftPad; + dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2); + } + } + } + dW.set(dotProd, wR, wC, d1, dm); + } + } + } + return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values); +} +var depthwiseConv2dNativeBackpropFilterConfig = { + kernelName: DepthwiseConv2dNativeBackpropFilter, + backendName: "cpu", + kernelFunc: depthwiseConv2dNativeBackpropFilter2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropInput.js +function depthwiseConv2dNativeBackpropInput2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs; + assertNotComplex([dy, filter], "depthwiseConv2DNativeBackpropInput"); + const dyStrides = util_exports.computeStrides(dy.shape); + const filterStrides = util_exports.computeStrides(filter.shape); + const convInfo = backend_util_exports.computeConv2DInfo( + inputShape, + filter.shape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const dx = new TensorBuffer(convInfo.inShape, "float32"); + const dxValues = dx.values; + const [dxS0, dxS1, dxS2] = dx.strides; + const dyValues = backend2.data.get(dy.dataId).values; + const [dyS0, dyS1, dyS2] = dyStrides; + const fltValues = backend2.data.get(filter.dataId).values; + const [fltS0, fltS1, fltS2] = filterStrides; + const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo; + const topPad = filterHeight - 1 - convInfo.padInfo.top; + const leftPad = filterWidth - 1 - convInfo.padInfo.left; + const chMul = outChannels / inChannels; + for (let b = 0; b < batchSize; ++b) { + for (let d1 = 0; d1 < inChannels; ++d1) { + for (let xR = 0; xR < inHeight; ++xR) { + const xRCorner = xR - topPad; + const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight)); + const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight); + for (let xC = 0; xC < inWidth; ++xC) { + const xCCorner = xC - leftPad; + const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth)); + const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth); + let dotProd = 0; + for (let yR = xRMin; yR < yRMax; ++yR) { + const wR = yR * strideHeight - xRCorner; + for (let yC = xCMin; yC < yCMax; ++yC) { + const wC = yC * strideWidth - xCCorner; + const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC; + const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1; + for (let dm = 0; dm < chMul; ++dm) { + const d2 = d1 * chMul + dm; + const pixel = dyValues[dyOffset + d2]; + const weight = fltValues[fltOffset + dm]; + dotProd += pixel * weight; + } + } + } + dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd; + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var depthwiseConv2dNativeBackpropInputConfig = { + kernelName: DepthwiseConv2dNativeBackpropInput, + backendName: "cpu", + kernelFunc: depthwiseConv2dNativeBackpropInput2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Diag.js +function diag2(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + const xSize = util_exports.sizeFromShape(x.shape); + const xVals = backend2.data.get(x.dataId).values; + const outBuf = buffer([xSize, xSize], x.dtype); + const vals = outBuf.values; + for (let i = 0; i < xVals.length; i++) { + vals[i * xSize + i] = xVals[i]; + } + const outShape = [...x.shape, ...x.shape]; + return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values); +} +var diagConfig = { + kernelName: Diag, + backendName: "cpu", + kernelFunc: diag2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2D.js +var dilation2DConfig = { + kernelName: Dilation2D, + backendName: "cpu", + kernelFunc: ({ inputs, backend: backend2, attrs }) => { + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + const cpuBackend = backend2; + const xVals = cpuBackend.data.get(x.dataId).values; + const xRank = x.shape.length; + const filterVals = cpuBackend.data.get(filter.dataId).values; + const filterRank = filter.shape.length; + const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + const outSize = util_exports.sizeFromShape(outShape); + const outRank = outShape.length; + const outputVals = util_exports.getArrayFromDType(x.dtype, outSize); + for (let b = 0; b < batchSize; ++b) { + for (let hOut = 0; hOut < outHeight; ++hOut) { + const hBeg = hOut * strideHeight - padInfo.top; + for (let wOut = 0; wOut < outWidth; ++wOut) { + const wBeg = wOut * strideWidth - padInfo.left; + for (let d = 0; d < inChannels; ++d) { + let curVal = Number.MIN_SAFE_INTEGER; + for (let h = 0; h < filterHeight; ++h) { + const hIn = hBeg + h * dilationHeight; + if (hIn >= 0 && hIn < inHeight) { + for (let w = 0; w < filterWidth; ++w) { + const wIn = wBeg + w * dilationWidth; + if (wIn >= 0 && wIn < inWidth) { + const xIndex = util_exports.locToIndex([b, hIn, wIn, d], xRank, util_exports.computeStrides(x.shape)); + const filterIndex = util_exports.locToIndex([h, w, d], filterRank, util_exports.computeStrides(filter.shape)); + const val = xVals[xIndex] + filterVals[filterIndex]; + if (val > curVal) { + curVal = val; + } + } + } + } + } + const outputIndex = util_exports.locToIndex([b, hOut, wOut, d], outRank, util_exports.computeStrides(outShape)); + outputVals[outputIndex] = curVal; + } + } + } + } + const dataId = cpuBackend.write(util_exports.toTypedArray(outputVals, x.dtype), outShape, x.dtype); + return { dataId, shape: outShape, dtype: x.dtype }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropFilter.js +var dilation2DBackpropFilterConfig = { + kernelName: Dilation2DBackpropFilter, + backendName: "cpu", + kernelFunc: ({ inputs, backend: backend2, attrs }) => { + const { x, filter, dy } = inputs; + const { strides, pad: pad3, dilations } = attrs; + const cpuBackend = backend2; + const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values); + const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values); + const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`); + const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values); + const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype); + for (let b = 0; b < batchSize; ++b) { + for (let hOut = 0; hOut < outHeight; ++hOut) { + const hBeg = hOut * strideHeight - padInfo.top; + for (let wOut = 0; wOut < outWidth; ++wOut) { + const wBeg = wOut * strideWidth - padInfo.left; + for (let d = 0; d < inChannels; ++d) { + let curVal = Number.MIN_SAFE_INTEGER; + let hMax = 0; + let wMax = 0; + for (let h = 0; h < filterHeight; ++h) { + const hIn = hBeg + h * dilationHeight; + if (hIn >= 0 && hIn < inHeight) { + for (let w = 0; w < filterWidth; ++w) { + const wIn = wBeg + w * dilationWidth; + if (wIn >= 0 && wIn < inWidth) { + const val = $x[b][hIn][wIn][d] + $filter[h][w][d]; + if (val > curVal) { + curVal = val; + hMax = h; + wMax = w; + } + } + } + } + } + gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d]; + } + } + } + } + const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype); + return { dataId, shape: filter.shape, dtype: filter.dtype }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropInput.js +var dilation2DBackpropInputConfig = { + kernelName: Dilation2DBackpropInput, + backendName: "cpu", + kernelFunc: ({ inputs, backend: backend2, attrs }) => { + const { x, filter, dy } = inputs; + const { strides, pad: pad3, dilations } = attrs; + const cpuBackend = backend2; + const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values); + const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values); + const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`); + const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values); + const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype); + for (let b = 0; b < batchSize; ++b) { + for (let hOut = 0; hOut < outHeight; ++hOut) { + const hBeg = hOut * strideHeight - padInfo.top; + for (let wOut = 0; wOut < outWidth; ++wOut) { + const wBeg = wOut * strideWidth - padInfo.left; + for (let d = 0; d < inChannels; ++d) { + let curVal = Number.MIN_SAFE_INTEGER; + let hInMax = hBeg < 0 ? 0 : hBeg; + let wInMax = wBeg < 0 ? 0 : wBeg; + for (let h = 0; h < filterHeight; ++h) { + const hIn = hBeg + h * dilationHeight; + if (hIn >= 0 && hIn < inHeight) { + for (let w = 0; w < filterWidth; ++w) { + const wIn = wBeg + w * dilationWidth; + if (wIn >= 0 && wIn < inWidth) { + const val = $x[b][hIn][wIn][d] + $filter[h][w][d]; + if (val > curVal) { + curVal = val; + hInMax = hIn; + wInMax = wIn; + } + } + } + } + } + gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d]; + } + } + } + } + const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), x.shape, x.dtype); + return { dataId, shape: x.shape, dtype: x.dtype }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Draw.js +function draw2(args) { + const { inputs, backend: backend2, attrs } = args; + const { image: image2 } = inputs; + const { canvas, options } = attrs; + const { contextOptions, imageOptions } = options || {}; + const alpha = (imageOptions === null || imageOptions === void 0 ? void 0 : imageOptions.alpha) || 1; + const contextType = (contextOptions === null || contextOptions === void 0 ? void 0 : contextOptions.contextType) || "2d"; + if (contextType !== "2d") { + throw new Error(`Context type ${contextOptions.contextType} is not supported by the CPU backend.`); + } + const ctx = canvas.getContext(contextType, (contextOptions === null || contextOptions === void 0 ? void 0 : contextOptions.contextAttributes) || {}); + if (ctx == null) { + throw new Error(`Could not get the context with ${contextType} type.`); + } + const [height, width] = image2.shape.slice(0, 2); + const depth = image2.shape.length === 2 ? 1 : image2.shape[2]; + const data = backend2.data.get(image2.dataId).values; + const multiplier = image2.dtype === "float32" ? 255 : 1; + const bytes = new Uint8ClampedArray(width * height * 4); + for (let i = 0; i < height * width; ++i) { + const rgba = [0, 0, 0, 255 * alpha]; + for (let d = 0; d < depth; d++) { + const value = data[i * depth + d]; + if (image2.dtype === "float32") { + if (value < 0 || value > 1) { + throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`); + } + } else if (image2.dtype === "int32") { + if (value < 0 || value > 255) { + throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`); + } + } + if (depth === 1) { + rgba[0] = value * multiplier; + rgba[1] = value * multiplier; + rgba[2] = value * multiplier; + } else { + rgba[d] = value * multiplier; + } + } + const j = i * 4; + bytes[j + 0] = Math.round(rgba[0]); + bytes[j + 1] = Math.round(rgba[1]); + bytes[j + 2] = Math.round(rgba[2]); + bytes[j + 3] = Math.round(rgba[3]); + } + canvas.width = width; + canvas.height = height; + const imageData = new ImageData(bytes, width, height); + ctx.putImageData(imageData, 0, 0); + return image2; +} +var drawConfig = { + kernelName: Draw, + backendName: "cpu", + kernelFunc: draw2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sum.js +function sum3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + assertNotComplex(x, "sum"); + let $x; + if (x.dtype === "bool") { + $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "int32" } }); + } else { + $x = identity2({ inputs: { x }, backend: backend2 }); + } + const xRank = $x.shape.length; + const axes = util_exports.parseAxisParam(axis, $x.shape); + const permutation = backend_util_exports.getAxesPermutation(axes, xRank); + let reductionAxes = axes; + let permutedX = $x; + if (permutation != null) { + permutedX = transpose2({ inputs: { x: $x }, backend: backend2, attrs: { perm: permutation } }); + reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, permutedX.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, reductionAxes); + const resultDtype = backend_util_exports.upcastType(permutedX.dtype, "int32"); + let result = zeros3(backend2, outShape, resultDtype); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const vals = backend2.data.get(result.dataId).values; + const aVals = backend2.data.get(permutedX.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let sum6 = 0; + for (let j = 0; j < reduceSize; ++j) { + sum6 += aVals[offset + j]; + } + vals[i] = sum6; + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes); + const oldResult = result; + result = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: newShape } }); + backend2.disposeIntermediateTensorInfo(oldResult); + } + backend2.disposeIntermediateTensorInfo($x); + if (permutation != null) { + backend2.disposeIntermediateTensorInfo(permutedX); + } + return result; +} +var sumConfig = { + kernelName: Sum, + backendName: "cpu", + kernelFunc: sum3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Einsum.js +function einsum2(args) { + const { inputs, backend: backend2, attrs } = args; + const { equation } = attrs; + const tensors = inputs; + const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length); + backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors); + const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims); + const nSteps = steps.length; + let out = null; + let numDimsRemaining = allDims.length; + const tensorsToDispose = []; + for (let i = 0; i < nSteps; ++i) { + for (const idTerm of steps[i]) { + const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]); + let x; + if (backend_util_exports.isIdentityPermutation(perm)) { + x = tensors[idTerm]; + } else { + x = transpose2({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } }); + tensorsToDispose.push(x); + } + const targetShape = x.shape.slice(); + for (let k = 0; k < dimsToExpand.length; ++k) { + targetShape.splice(dimsToExpand[k], 0, 1); + } + if (!util_exports.arraysEqual(x.shape, targetShape)) { + x = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } }); + tensorsToDispose.push(x); + } + if (out === null) { + out = x; + } else { + out = multiply2({ inputs: { a: x, b: out }, backend: backend2 }); + tensorsToDispose.push(out); + } + } + if (i < nSteps - 1) { + if (path[i] >= 0) { + out = sum3({ + inputs: { x: out }, + backend: backend2, + attrs: { + axis: path[i] - (allDims.length - numDimsRemaining), + keepDims: false + } + }); + tensorsToDispose.push(out); + } + numDimsRemaining--; + } + } + for (const tensorInfo of tensorsToDispose) { + if (tensorInfo === out) { + continue; + } + backend2.disposeIntermediateTensorInfo(tensorInfo); + } + return out; +} +var einsumConfig = { + kernelName: Einsum, + backendName: "cpu", + kernelFunc: einsum2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/EluGrad.js +function eluGrad(args) { + const { inputs, backend: backend2 } = args; + const { dy, y } = inputs; + assertNotComplex([dy, y], "eluGrad"); + const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape)); + const values = backend2.data.get(y.dataId).values; + const dyValues = backend2.data.get(dy.dataId).values; + for (let i = 0; i < values.length; ++i) { + const v = values[i]; + if (v >= 0) { + resultValues[i] = dyValues[i]; + } else { + resultValues[i] = dyValues[i] * (v + 1); + } + } + return backend2.makeTensorInfo(y.shape, "float32", resultValues); +} +var eluGradConfig2 = { + kernelName: EluGrad, + backendName: "cpu", + kernelFunc: eluGrad +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Erf.js +var p = backend_util_exports.ERF_P; +var a1 = backend_util_exports.ERF_A1; +var a2 = backend_util_exports.ERF_A2; +var a3 = backend_util_exports.ERF_A3; +var a4 = backend_util_exports.ERF_A4; +var a5 = backend_util_exports.ERF_A5; +var erf2 = unaryKernelFunc(Erf, (xi) => { + const sign4 = Math.sign(xi); + const v = Math.abs(xi); + const t = 1 / (1 + p * v); + return sign4 * (1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-v * v)); +}); +var erfConfig = { + kernelName: Erf, + backendName: "cpu", + kernelFunc: erf2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ExpandDims.js +function expandDims3(args) { + const { inputs, backend: backend2, attrs } = args; + const { input: input2 } = inputs; + const { dim } = attrs; + const inputRank = input2.shape.length; + const newShape = input2.shape.slice(); + let $dim = dim; + if (dim < 0) { + util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`); + $dim = inputRank + dim + 1; + } + newShape.splice($dim, 0, 1); + return reshape3({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } }); +} +var expandDimsConfig = { + kernelName: ExpandDims, + backendName: "cpu", + kernelFunc: expandDims3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RealDiv.js +var realDivImpl = createSimpleBinaryKernelImpl((a, b) => a / b); +var div2 = binaryKernelFunc(RealDiv, realDivImpl); +var realDivConfig = { + kernelName: RealDiv, + backendName: "cpu", + kernelFunc: div2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fft_utils.js +function fftBatch(input2, inverse, cpuBackend) { + const inputShape = input2.shape; + const batch = inputShape[0]; + const innerDim = inputShape[1]; + const inputVals = cpuBackend.data.get(input2.dataId); + const real2D = inputVals.complexTensorInfos.real; + const imag2D = inputVals.complexTensorInfos.imag; + const resultShape = [batch, innerDim]; + const resultSize = util_exports.sizeFromShape(resultShape); + const resultReal = util_exports.getTypedArrayFromDType("float32", resultSize); + const resultImag = util_exports.getTypedArrayFromDType("float32", resultSize); + for (let b = 0; b < batch; b++) { + const r = slice2({ + inputs: { x: real2D }, + backend: cpuBackend, + attrs: { begin: [b, 0], size: [1, innerDim] } + }); + const i = slice2({ + inputs: { x: imag2D }, + backend: cpuBackend, + attrs: { begin: [b, 0], size: [1, innerDim] } + }); + const input3 = complex2({ inputs: { real: r, imag: i }, backend: cpuBackend }); + const { real: real4, imag: imag4 } = fftImpl(input3, inverse, cpuBackend); + const res = backend_util_exports.mergeRealAndImagArrays(real4, imag4); + for (let d = 0; d < innerDim; d++) { + const c = backend_util_exports.getComplexWithIndex(res, d); + resultReal[b * innerDim + d] = c.real; + resultImag[b * innerDim + d] = c.imag; + } + cpuBackend.disposeIntermediateTensorInfo(r); + cpuBackend.disposeIntermediateTensorInfo(i); + cpuBackend.disposeIntermediateTensorInfo(input3); + } + const $realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultReal); + const $imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultImag); + const result = complex2({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend }); + cpuBackend.disposeIntermediateTensorInfo($realInfo); + cpuBackend.disposeIntermediateTensorInfo($imagInfo); + return result; +} +function fftImpl(input2, inverse, cpuBackend) { + const inputSize = util_exports.sizeFromShape(input2.shape); + const inputVals = cpuBackend.data.get(input2.dataId); + const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values; + const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values; + if (isExponentOf2(inputSize)) { + const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend); + const resultShape = [input2.shape[0], input2.shape[1]]; + if (inverse) { + const realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.real); + const imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.imag); + const sizeInfo = cpuBackend.makeTensorInfo([], "float32", util_exports.createScalarValue(inputSize, "float32")); + const sizeInfoCopy = identity2({ inputs: { x: sizeInfo }, backend: cpuBackend }); + const divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend }); + const divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend }); + const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values; + const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values; + cpuBackend.disposeIntermediateTensorInfo(realInfo); + cpuBackend.disposeIntermediateTensorInfo(imagInfo); + cpuBackend.disposeIntermediateTensorInfo(sizeInfo); + cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy); + cpuBackend.disposeIntermediateTensorInfo(divRealInfo); + cpuBackend.disposeIntermediateTensorInfo(divImagInfo); + return { real: divRealVals, imag: divImagVals }; + } + return result; + } else { + const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals); + const rawOutput = fourierTransformByMatmul(data, inputSize, inverse); + return backend_util_exports.splitRealAndImagArrays(rawOutput); + } +} +function isExponentOf2(size) { + return (size & size - 1) === 0; +} +function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) { + if (size === 1) { + return { real: realVals, imag: imagVals }; + } + const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals); + const half = size / 2; + const evenComplex = backend_util_exports.complexWithEvenIndex(data); + const evenRealVals = evenComplex.real; + const evenImagVals = evenComplex.imag; + const evenShape = [evenRealVals.length]; + const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenRealVals); + const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenImagVals); + const evenTensorInfo = complex2({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend }); + const oddComplex = backend_util_exports.complexWithOddIndex(data); + const oddRealVals = oddComplex.real; + const oddImagVals = oddComplex.imag; + const oddShape = [oddRealVals.length]; + const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddRealVals); + const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddImagVals); + const oddTensorInfo = complex2({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend }); + const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend); + const $evenRealVals = $evenComplex.real; + const $evenImagVals = $evenComplex.imag; + const $evenShape = [$evenRealVals.length]; + const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenRealVals); + const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenImagVals); + const $evenTensorInfo = complex2({ + inputs: { real: $evenRealInfo, imag: $evenImagInfo }, + backend: cpuBackend + }); + const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend); + const $oddRealVals = $oddComplex.real; + const $oddImagVals = $oddComplex.imag; + const $oddShape = [$oddRealVals.length]; + const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddRealVals); + const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddImagVals); + const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend }); + const e = backend_util_exports.exponents(size, inverse); + const eShape = [e.real.length]; + const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.real); + const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.imag); + const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend }); + const exponentInfo = multiply2({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend }); + const addPart = add4({ + inputs: { a: $evenTensorInfo, b: exponentInfo }, + backend: cpuBackend + }); + const subPart = sub2({ + inputs: { a: $evenTensorInfo, b: exponentInfo }, + backend: cpuBackend + }); + const addPartReal = real2({ inputs: { input: addPart }, backend: cpuBackend }); + const subPartReal = real2({ inputs: { input: subPart }, backend: cpuBackend }); + const addPartImag = imag2({ inputs: { input: addPart }, backend: cpuBackend }); + const subPartImag = imag2({ inputs: { input: subPart }, backend: cpuBackend }); + const $real = concat2({ + inputs: [addPartReal, subPartReal], + backend: cpuBackend, + attrs: { axis: 0 } + }); + const $imag = concat2({ + inputs: [addPartImag, subPartImag], + backend: cpuBackend, + attrs: { axis: 0 } + }); + const $realVals = cpuBackend.data.get($real.dataId).values; + const $imagVals = cpuBackend.data.get($imag.dataId).values; + cpuBackend.disposeIntermediateTensorInfo(evenRealInfo); + cpuBackend.disposeIntermediateTensorInfo(evenImagInfo); + cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo); + cpuBackend.disposeIntermediateTensorInfo(oddRealInfo); + cpuBackend.disposeIntermediateTensorInfo(oddImagInfo); + cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo); + cpuBackend.disposeIntermediateTensorInfo($evenRealInfo); + cpuBackend.disposeIntermediateTensorInfo($evenImagInfo); + cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo); + cpuBackend.disposeIntermediateTensorInfo($oddRealInfo); + cpuBackend.disposeIntermediateTensorInfo($oddImagInfo); + cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo); + cpuBackend.disposeIntermediateTensorInfo(eRealInfo); + cpuBackend.disposeIntermediateTensorInfo(eImagInfo); + cpuBackend.disposeIntermediateTensorInfo(complexInfo); + cpuBackend.disposeIntermediateTensorInfo(exponentInfo); + cpuBackend.disposeIntermediateTensorInfo(addPart); + cpuBackend.disposeIntermediateTensorInfo(subPart); + cpuBackend.disposeIntermediateTensorInfo(addPartReal); + cpuBackend.disposeIntermediateTensorInfo(addPartImag); + cpuBackend.disposeIntermediateTensorInfo(subPartReal); + cpuBackend.disposeIntermediateTensorInfo(subPartImag); + cpuBackend.disposeIntermediateTensorInfo($real); + cpuBackend.disposeIntermediateTensorInfo($imag); + return { real: $realVals, imag: $imagVals }; +} +function fourierTransformByMatmul(data, size, inverse) { + const ret = new Float32Array(size * 2); + for (let r = 0; r < size; r++) { + let real4 = 0; + let imag4 = 0; + for (let c = 0; c < size; c++) { + const e = backend_util_exports.exponent(r * c, size, inverse); + const term = backend_util_exports.getComplexWithIndex(data, c); + real4 += term.real * e.real - term.imag * e.imag; + imag4 += term.real * e.imag + term.imag * e.real; + } + if (inverse) { + real4 /= size; + imag4 /= size; + } + backend_util_exports.assignToTypedArray(ret, real4, imag4, r); + } + return ret; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FFT.js +function fft2(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const inputSize = util_exports.sizeFromShape(input2.shape); + const innerDimensionSize = input2.shape[input2.shape.length - 1]; + const batch = inputSize / innerDimensionSize; + const input2D = reshape3({ + inputs: { x: input2 }, + backend: backend2, + attrs: { shape: [batch, innerDimensionSize] } + }); + const result = fftBatch(input2D, false, backend2); + const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } }); + backend2.disposeIntermediateTensorInfo(input2D); + backend2.disposeIntermediateTensorInfo(result); + return resultReshaped; +} +var fftConfig = { + kernelName: FFT, + backendName: "cpu", + kernelFunc: fft2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Fill.js +function fill2(args) { + const { backend: backend2, attrs } = args; + const { shape, value, dtype } = attrs; + const $dtype = dtype || util_exports.inferDtype(value); + const values = util_exports.getArrayFromDType($dtype, util_exports.sizeFromShape(shape)); + fillValues(values, value, $dtype); + return backend2.makeTensorInfo(shape, $dtype, values); +} +var fillConfig = { + kernelName: Fill, + backendName: "cpu", + kernelFunc: fill2 +}; +function fillValues(values, value, dtype) { + if (dtype === "string") { + values.fill(value); + } else { + values.fill(value); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FlipLeftRight.js +var flipLeftRightConfig = { + kernelName: FlipLeftRight, + backendName: "cpu", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { image: image2 } = inputs; + const cpuBackend = backend2; + const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape)); + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const imageVals = cpuBackend.data.get(image2.dataId).values; + for (let batchIdx = 0; batchIdx < batch; batchIdx++) { + const batchOffset = batchIdx * imageWidth * imageHeight * numChannels; + for (let row = 0; row < imageHeight; row++) { + const rowOffset = row * (imageWidth * numChannels); + for (let col = 0; col < imageWidth; col++) { + const colOffset = col * numChannels; + for (let channel = 0; channel < numChannels; channel++) { + const coordX = Math.round(imageWidth - col - 1); + const outIdx = batchOffset + rowOffset + colOffset + channel; + let outputValue = imageVals[outIdx]; + if (coordX >= 0 && coordX < imageWidth) { + const rotatedColOffset = coordX * numChannels; + const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel; + outputValue = imageVals[imageIdx]; + } + output[outIdx] = outputValue; + } + } + } + } + const dataId = cpuBackend.write(output, image2.shape, image2.dtype); + return { dataId, shape: image2.shape, dtype: image2.dtype }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedConv2D.js +function fusedConv2D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + let result = conv2D({ + inputs: { x, filter }, + backend: backend2, + attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } + }); + if (bias) { + const resultOld = result; + if (dataFormat === "NCHW" && bias.shape.length === 1 && bias.shape[0] !== 1) { + const reshapedBias = reshape3({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } }); + result = add4({ inputs: { a: result, b: reshapedBias }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(reshapedBias); + } else { + result = add4({ inputs: { a: result, b: bias }, backend: backend2 }); + } + backend2.disposeIntermediateTensorInfo(resultOld); + } + if (activation2) { + const resultOld = result; + if (dataFormat === "NCHW" && activation2 === "prelu" && preluActivationWeights.shape.length === 1 && preluActivationWeights.shape[0] !== 1) { + const reshapedAlpha = reshape3({ + inputs: { x: preluActivationWeights }, + backend: backend2, + attrs: { shape: [preluActivationWeights.shape[0], 1, 1] } + }); + result = applyActivation2(backend2, result, activation2, reshapedAlpha, leakyreluAlpha); + backend2.disposeIntermediateTensorInfo(reshapedAlpha); + } else { + result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha); + } + backend2.disposeIntermediateTensorInfo(resultOld); + } + return result; +} +var fusedConv2DConfig = { + kernelName: FusedConv2D, + backendName: "cpu", + kernelFunc: fusedConv2D +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedDepthwiseConv2D.js +function fusedDepthwiseConv2D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + let result = depthwiseConv2dNative({ + inputs: { x, filter }, + backend: backend2, + attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } + }); + if (bias) { + const oldResult = result; + result = add4({ inputs: { a: result, b: bias }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(oldResult); + } + if (activation2) { + const oldResult = result; + result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha); + backend2.disposeIntermediateTensorInfo(oldResult); + } + return result; +} +var fusedDepthwiseConv2DConfig = { + kernelName: FusedDepthwiseConv2D, + backendName: "cpu", + kernelFunc: fusedDepthwiseConv2D +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd.js +function gatherNd(args) { + const { inputs, backend: backend2 } = args; + const { params, indices } = inputs; + const paramsSize = util_exports.sizeFromShape(params.shape); + const indicesShape = indices.shape; + const sliceRank = indicesShape[indicesShape.length - 1]; + const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices); + if (numSlices === 0) { + return backend2.makeTensorInfo(resultShape, params.dtype, []); + } + const indicesData = backend2.data.get(indices.dataId).values; + const paramsBuf = backend2.bufferSync(params); + const outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize); + return backend2.makeTensorInfo(resultShape, params.dtype, outBuf.values); +} +var gatherNdConfig = { + kernelName: GatherNd, + backendName: "cpu", + kernelFunc: gatherNd +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2.js +function gatherV2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, indices } = inputs; + const { axis, batchDims } = attrs; + assertNotComplex([x, indices], "gatherV2"); + const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0]; + const indicesVals = backend2.data.get(indices.dataId).values; + const axisDim = x.shape[parsedAxis]; + for (let i = 0; i < indicesVals.length; ++i) { + const index = indicesVals[i]; + util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`); + } + let $batchDims = batchDims; + if (batchDims == null) { + $batchDims = 0; + } + const indicesSize = util_exports.sizeFromShape(indices.shape); + const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims); + const flattenX = reshape3({ + inputs: { x }, + backend: backend2, + attrs: { + shape: [ + shapeInfo.batchSize, + shapeInfo.outerSize, + shapeInfo.dimSize, + shapeInfo.sliceSize + ] + } + }); + const flattenIndex = reshape3({ + inputs: { x: indices }, + backend: backend2, + attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] } + }); + const flattenOutputShape = [ + shapeInfo.batchSize, + shapeInfo.outerSize, + indicesSize / shapeInfo.batchSize, + shapeInfo.sliceSize + ]; + const indicesBuf = backend2.bufferSync(flattenIndex); + const xBuf = backend2.bufferSync(flattenX); + const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape); + backend2.disposeIntermediateTensorInfo(flattenX); + backend2.disposeIntermediateTensorInfo(flattenIndex); + return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values); +} +var gatherV2Config = { + kernelName: GatherV2, + backendName: "cpu", + kernelFunc: gatherV2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IFFT.js +function ifft2(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const inputSize = util_exports.sizeFromShape(input2.shape); + const innerDimensionSize = input2.shape[input2.shape.length - 1]; + const batch = inputSize / innerDimensionSize; + const input2D = reshape3({ + inputs: { x: input2 }, + backend: backend2, + attrs: { shape: [batch, innerDimensionSize] } + }); + const result = fftBatch(input2D, true, backend2); + const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } }); + backend2.disposeIntermediateTensorInfo(input2D); + backend2.disposeIntermediateTensorInfo(result); + return resultReshaped; +} +var ifftConfig = { + kernelName: IFFT, + backendName: "cpu", + kernelFunc: ifft2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsFinite.js +var isFinite3 = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, "bool"); +var isFiniteConfig = { + kernelName: IsFinite, + backendName: "cpu", + kernelFunc: isFinite3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsInf.js +var isInf2 = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, "bool"); +var isInfConfig = { + kernelName: IsInf, + backendName: "cpu", + kernelFunc: isInf2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsNaN.js +var isNaN3 = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, "bool"); +var isNaNConfig = { + kernelName: IsNan, + backendName: "cpu", + kernelFunc: isNaN3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace.js +function linSpace(args) { + const { backend: backend2, attrs } = args; + const { start, stop, num } = attrs; + const outVals = linSpaceImpl(start, stop, num); + return backend2.makeTensorInfo([outVals.length], "float32", outVals); +} +var linSpaceConfig = { + kernelName: LinSpace, + backendName: "cpu", + kernelFunc: linSpace +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log1p.js +var log1p2 = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi)); +var log1pConfig = { + kernelName: Log1p, + backendName: "cpu", + kernelFunc: log1p2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalAnd.js +var logicalAndImpl = createSimpleBinaryKernelImpl((a, b) => a && b); +var logicalAnd2 = binaryKernelFunc(LogicalAnd, logicalAndImpl, null, "bool"); +var logicalAndConfig = { + kernelName: LogicalAnd, + backendName: "cpu", + kernelFunc: logicalAnd2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalNot.js +var logicalNot2 = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, "bool"); +var logicalNotConfig = { + kernelName: LogicalNot, + backendName: "cpu", + kernelFunc: logicalNot2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalOr.js +var logicalOrImpl = createSimpleBinaryKernelImpl((a, b) => a || b); +var logicalOr2 = binaryKernelFunc(LogicalOr, logicalOrImpl, null, "bool"); +var logicalOrConfig = { + kernelName: LogicalOr, + backendName: "cpu", + kernelFunc: logicalOr2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRN.js +function lRN(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + assertNotComplex(x, "LRN"); + const channels = x.shape[3]; + const maxD = channels - 1; + const xValues = backend2.data.get(x.dataId).values; + const size = util_exports.sizeFromShape(x.shape); + const result = new Float32Array(size); + function sumAcrossChannels(offset) { + const currentChannel = offset % channels; + let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius); + const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD); + let sum6 = 0; + for (; beginSumOffset <= endSumOffset; beginSumOffset++) { + const z = xValues[beginSumOffset]; + sum6 += z * z; + } + return sum6; + } + for (let offset = 0; offset < size; offset++) { + const sum6 = sumAcrossChannels(offset); + const val = xValues[offset] * Math.pow(bias + alpha * sum6, -beta); + result[offset] = val; + } + return backend2.makeTensorInfo(x.shape, x.dtype, result); +} +var LRNConfig = { + kernelName: LRN, + backendName: "cpu", + kernelFunc: lRN +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRNGrad.js +function lRNGrad(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, y, dy } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + assertNotComplex(dy, "LRNGrad"); + const dySize = util_exports.sizeFromShape(dy.shape); + const channels = dy.shape[3]; + const dyValues = backend2.data.get(dy.dataId).values; + const xValues = backend2.data.get(x.dataId).values; + const yValues = backend2.data.get(y.dataId).values; + const result = new Float32Array(dySize); + const size = dySize; + for (let offset = 0; offset < size; offset++) { + const currentChannel = offset % channels; + const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius); + const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1); + let norm2 = 0; + for (let k = depthBegin; k < depthEnd; k++) { + norm2 += Math.pow(xValues[k], 2); + } + norm2 = alpha * norm2 + bias; + for (let k = depthBegin; k < depthEnd; k++) { + let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm2; + if (offset === k) { + dyi += Math.pow(norm2, -beta); + } + dyi *= dyValues[offset]; + result[k] += dyi; + } + } + return backend2.makeTensorInfo(dy.shape, x.dtype, result); +} +var LRNGradConfig = { + kernelName: LRNGrad, + backendName: "cpu", + kernelFunc: lRNGrad +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max.js +function max3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { reductionIndices, keepDims } = attrs; + const cpuBackend = backend2; + let xShape = x.shape; + const xRank = xShape.length; + const origAxes = util_exports.parseAxisParam(reductionIndices, xShape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let xVals = cpuBackend.data.get(x.dataId).values; + if (permutedAxes != null) { + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = xShape[permutedAxes[i]]; + } + xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + xShape = newShape; + } + assertNotComplex(x, "max"); + backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank); + const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype); + const dataId = cpuBackend.write(result, maxOutShape, x.dtype); + let outShape = maxOutShape; + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes); + outShape = newShape; + } + return { dataId, shape: outShape, dtype: x.dtype }; +} +var maxConfig = { + kernelName: Max, + backendName: "cpu", + kernelFunc: max3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool.js +function maxPool2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + assertNotComplex(x, "maxPool"); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = 1; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + let res; + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) { + res = identity2({ inputs: { x }, backend: backend2 }); + } else { + const xValues = backend2.data.get(x.dataId).values; + const strides2 = util_exports.computeStrides(x.shape); + const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, "max"); + res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values); + } + return res; +} +var maxPoolConfig = { + kernelName: MaxPool, + backendName: "cpu", + kernelFunc: maxPool2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3D.js +function maxPool3D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs; + assertNotComplex(x, "maxPool3d"); + const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat); + const xValues = backend2.data.get(x.dataId).values; + const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "max"); + return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values); +} +var maxPool3DConfig = { + kernelName: MaxPool3D, + backendName: "cpu", + kernelFunc: maxPool3D +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3DGrad.js +function maxPool3DGrad(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + assertNotComplex([dy, input2], "maxPool3DGrad"); + const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode); + const inputBuf = backend2.bufferSync(input2); + const maxPosBuf = maxPool3dPositions(inputBuf, convInfo); + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const dx = buffer(input2.shape, "float32"); + const dyBuf = backend2.bufferSync(dy); + for (let batch = 0; batch < convInfo.batchSize; ++batch) { + for (let channel = 0; channel < convInfo.inChannels; ++channel) { + for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) { + for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) { + for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) { + const dyDepthCorner = dxDepth - padFront; + const dyRowCorner = dxRow - padTop; + const dyColCorner = dxCol - padLeft; + let dotProd = 0; + for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) { + const dyDepth = (dyDepthCorner + wDepth) / strideDepth; + if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) { + continue; + } + for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) { + const dyRow = (dyRowCorner + wRow) / strideHeight; + if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) { + continue; + } + for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) { + const dyCol = (dyColCorner + wCol) / strideWidth; + if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) { + continue; + } + const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel); + const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol; + const mask = maxPos === curPos ? 1 : 0; + if (mask === 0) { + continue; + } + const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel); + dotProd += pixel * mask; + } + } + } + dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel); + } + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var maxPool3DGradConfig2 = { + kernelName: MaxPool3DGrad, + backendName: "cpu", + kernelFunc: maxPool3DGrad +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolGrad.js +function maxPoolGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2, output } = inputs; + const x = input2; + assertNotComplex([input2, output], "maxPoolGrad"); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode); + const xValues = backend2.data.get(x.dataId).values; + const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values); + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const dx = buffer(x.shape, "float32"); + const dyData = backend2.data.get(dy.dataId).values; + const dyBuf = buffer(dy.shape, "float32", dyData); + for (let b = 0; b < convInfo.batchSize; ++b) { + for (let d = 0; d < convInfo.inChannels; ++d) { + for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) { + for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) { + const dyRCorner = dxR - padTop; + const dyCCorner = dxC - padLeft; + let dotProd = 0; + for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) { + const dyR = (dyRCorner + wR) / strideHeight; + if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) { + continue; + } + for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) { + const dyC = (dyCCorner + wC) / strideWidth; + if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) { + continue; + } + const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d); + const curPos = wR * effectiveFilterWidth + wC; + const mask = maxPos === curPos ? 1 : 0; + if (mask === 0) { + continue; + } + const pixel = dyBuf.get(b, dyR, dyC, d); + dotProd += pixel * mask; + } + } + dx.set(dotProd, b, dxR, dxC, d); + } + } + } + } + return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values); +} +var maxPoolGradConfig2 = { + kernelName: MaxPoolGrad, + backendName: "cpu", + kernelFunc: maxPoolGrad2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax_impl.js +function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) { + const strides = util_exports.computeStrides(xShape); + const maxPools = pool2(xValues, xShape, dtype, strides, convInfo, "max"); + const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex); + return [maxPools.values, maxPositions.values]; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax.js +var maxPoolWithArgmaxConfig = { + kernelName: MaxPoolWithArgmax, + backendName: "cpu", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { x } = inputs; + const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs; + const cpuBackend = backend2; + assertNotComplex(x, "MaxPoolWithArgmax"); + const values = cpuBackend.data.get(x.dataId).values; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3); + const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo); + const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype); + const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype); + return [ + { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype }, + { dataId: indexesDataId, shape: convInfo.outShape, dtype: "int32" } + ]; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mean.js +function mean2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + const axes = util_exports.parseAxisParam(axis, x.shape); + const shapes = backend_util_exports.computeOutAndReduceShapes(x.shape, axes); + const reduceShape = shapes[1]; + const reduceSize = util_exports.sizeFromShape(reduceShape); + const toDispose = []; + const reduceSizeScalar = backend2.makeTensorInfo([], "float32", new Float32Array([reduceSize])); + toDispose.push(reduceSizeScalar); + const $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } }); + toDispose.push($x); + const res = div2({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend2 }); + toDispose.push(res); + const result = sum3({ inputs: { x: res }, backend: backend2, attrs: { axis, keepDims } }); + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var meanConfig = { + kernelName: Mean, + backendName: "cpu", + kernelFunc: mean2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Min.js +function min3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + assertNotComplex(x, "min"); + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + if (permutedAxes != null) { + $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("min", axes, $x.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype); + const aVals = backend2.data.get($x.dataId).values; + for (let i = 0; i < vals.length; ++i) { + const offset = i * reduceSize; + let min6 = aVals[offset]; + for (let j = 0; j < reduceSize; ++j) { + const value = aVals[offset + j]; + if (Number.isNaN(value) || value < min6) { + min6 = value; + } + } + vals[i] = min6; + } + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo($x); + } + const result = backend2.makeTensorInfo(outShape, $x.dtype, vals); + if (keepDims) { + const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } }); + backend2.disposeIntermediateTensorInfo(result); + return reshapedResult; + } + return result; +} +var minConfig = { + kernelName: Min, + backendName: "cpu", + kernelFunc: min3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MirrorPad.js +function mirrorPad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { paddings, mode } = attrs; + assertNotComplex(x, "mirrorPad"); + const outShape = paddings.map( + (p2, i) => p2[0] + x.shape[i] + p2[1] + /* afterPad */ + ); + const start = paddings.map((p2) => p2[0]); + const end = paddings.map((p2, i) => p2[0] + x.shape[i]); + const offset = mode === "reflect" ? 0 : 1; + const xVals = backend2.data.get(x.dataId).values; + const xRank = x.shape.length; + const xStrides = util_exports.computeStrides(x.shape); + const resultSize = util_exports.sizeFromShape(outShape); + const resultRank = outShape.length; + const resultStrides = util_exports.computeStrides(outShape); + const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize); + for (let i = 0; i < resultSize; i++) { + let coords2 = util_exports.indexToLoc(i, resultRank, resultStrides); + for (let i2 = 0; i2 < resultRank; i2++) { + if (coords2[i2] < start[i2]) { + coords2[i2] = start[i2] * 2 - coords2[i2] - offset; + } else if (coords2[i2] >= end[i2]) { + coords2[i2] = (end[i2] - 1) * 2 - coords2[i2] + offset; + } + } + coords2 = coords2.map((c, i2) => c - start[i2]); + const inIndex = util_exports.locToIndex(coords2, xRank, xStrides); + resVals[i] = xVals[inIndex]; + } + const outId = backend2.write(resVals, outShape, x.dtype); + return { dataId: outId, shape: outShape, dtype: x.dtype }; +} +var mirrorPadConfig = { + kernelName: MirrorPad, + backendName: "cpu", + kernelFunc: mirrorPad2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mod.js +var modImpl = createSimpleBinaryKernelImpl((aValue, bValue) => { + const rem = aValue % bValue; + if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) { + return rem; + } else { + return (rem + bValue) % bValue; + } +}); +var mod2 = binaryKernelFunc(Mod, modImpl); +var modConfig = { + kernelName: Mod, + backendName: "cpu", + kernelFunc: mod2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js +var seedrandom4 = __toESM(require_seedrandom2()); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softmax.js +function softmax3(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { dim } = attrs; + const logitsRank = logits.shape.length; + let $dim = dim; + if ($dim === -1) { + $dim = logitsRank - 1; + } + if ($dim !== logitsRank - 1) { + throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`); + } + const axes = util_exports.parseAxisParam([$dim], logits.shape); + const maxLogit = max3({ + inputs: { x: logits }, + backend: backend2, + attrs: { reductionIndices: axes, keepDims: false } + }); + const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes); + const maxLogitReshaped = reshape3({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } }); + const a = sub2({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend2 }); + const b = exp2({ inputs: { x: a }, backend: backend2 }); + const sumExp = sum3({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } }); + const sumReshaped = reshape3({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } }); + const result = div2({ inputs: { a: b, b: sumReshaped }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(maxLogit); + backend2.disposeIntermediateTensorInfo(maxLogitReshaped); + backend2.disposeIntermediateTensorInfo(a); + backend2.disposeIntermediateTensorInfo(b); + backend2.disposeIntermediateTensorInfo(sumExp); + backend2.disposeIntermediateTensorInfo(sumReshaped); + return result; +} +var softmaxConfig = { + kernelName: Softmax, + backendName: "cpu", + kernelFunc: softmax3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js +function multinomial2(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { numSamples, seed, normalized } = attrs; + assertNotComplex(logits, "multinomial"); + const probabilities = normalized ? logits : softmax3({ inputs: { logits }, backend: backend2, attrs: { dim: -1 } }); + const batchSize = probabilities.shape[0]; + const numEvents = probabilities.shape[1]; + const probVals = backend2.data.get(probabilities.dataId).values; + const resShape = [batchSize, numSamples]; + const resVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape), "int32"); + for (let b = 0; b < batchSize; ++b) { + const offset = b * numEvents; + const cdf = new Float32Array(numEvents - 1); + cdf[0] = probVals[offset]; + for (let event = 1; event < cdf.length; ++event) { + cdf[event] = cdf[event - 1] + probVals[offset + event]; + } + const random = seedrandom4.alea(seed.toString()); + const outOffset = b * numSamples; + for (let sampleId = 0; sampleId < numSamples; ++sampleId) { + const r = random(); + resVals[outOffset + sampleId] = cdf.length; + for (let event = 0; event < cdf.length; event++) { + if (r < cdf[event]) { + resVals[outOffset + sampleId] = event; + break; + } + } + } + } + if (!normalized) { + backend2.disposeIntermediateTensorInfo(probabilities); + } + return backend2.makeTensorInfo(resShape, "int32", resVals); +} +var multinomialConfig = { + kernelName: Multinomial, + backendName: "cpu", + kernelFunc: multinomial2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV3.js +var nonMaxSuppressionV3Impl2 = kernel_impls_exports.nonMaxSuppressionV3Impl; +function nonMaxSuppressionV3(args) { + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold } = attrs; + assertNotComplex(boxes, "NonMaxSuppression"); + const boxesVals = backend2.data.get(boxes.dataId).values; + const scoresVals = backend2.data.get(scores.dataId).values; + const { selectedIndices } = nonMaxSuppressionV3Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold); + return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)); +} +var nonMaxSuppressionV3Config = { + kernelName: NonMaxSuppressionV3, + backendName: "cpu", + kernelFunc: nonMaxSuppressionV3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV4.js +var nonMaxSuppressionV4Impl2 = kernel_impls_exports.nonMaxSuppressionV4Impl; +function nonMaxSuppressionV4(args) { + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs; + assertNotComplex(boxes, "NonMaxSuppressionPadded"); + const boxesVals = backend2.data.get(boxes.dataId).values; + const scoresVals = backend2.data.get(scores.dataId).values; + const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize); + return [ + backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)), + backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs])) + ]; +} +var nonMaxSuppressionV4Config = { + kernelName: NonMaxSuppressionV4, + backendName: "cpu", + kernelFunc: nonMaxSuppressionV4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV5.js +var nonMaxSuppressionV5Impl2 = kernel_impls_exports.nonMaxSuppressionV5Impl; +function nonMaxSuppressionV5(args) { + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs; + assertNotComplex(boxes, "NonMaxSuppressionWithScore"); + const boxesVals = backend2.data.get(boxes.dataId).values; + const scoresVals = backend2.data.get(scores.dataId).values; + const maxOutputSizeVal = maxOutputSize; + const iouThresholdVal = iouThreshold; + const scoreThresholdVal = scoreThreshold; + const softNmsSigmaVal = softNmsSigma; + const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl2(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal); + return [ + backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)), + backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores)) + ]; +} +var nonMaxSuppressionV5Config = { + kernelName: NonMaxSuppressionV5, + backendName: "cpu", + kernelFunc: nonMaxSuppressionV5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OneHot.js +function oneHot2(args) { + const { inputs, backend: backend2, attrs } = args; + const { indices } = inputs; + const { dtype, depth, onValue, offValue } = attrs; + assertNotComplex(indices, "oneHot"); + const indicesSize = util_exports.sizeFromShape(indices.shape); + const res = new Float32Array(indicesSize * depth); + res.fill(offValue); + const indicesVal = backend2.data.get(indices.dataId).values; + for (let event = 0; event < indicesSize; ++event) { + if (indicesVal[event] >= 0 && indicesVal[event] < depth) { + res[event * depth + indicesVal[event]] = onValue; + } + } + return backend2.makeTensorInfo([...indices.shape, depth], dtype, res); +} +var oneHotConfig = { + kernelName: OneHot, + backendName: "cpu", + kernelFunc: oneHot2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ZerosLike.js +function zerosLike2(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (x.dtype === "string") { + throw new Error("zerosLike is not supported for string tensors"); + } else if (x.dtype === "complex64") { + const realPart = real2({ inputs: { input: x }, backend: backend2 }); + const r = zerosLike2({ inputs: { x: realPart }, backend: backend2 }); + const imagPart = imag2({ inputs: { input: x }, backend: backend2 }); + const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 }); + const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(r); + backend2.disposeIntermediateTensorInfo(imagPart); + backend2.disposeIntermediateTensorInfo(i); + return result; + } else { + return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } }); + } +} +var zerosLikeConfig = { + kernelName: ZerosLike, + backendName: "cpu", + kernelFunc: zerosLike2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OnesLike.js +function onesLike2(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (x.dtype === "string") { + throw new Error("onesLike is not supported for string tensors"); + } else if (x.dtype === "complex64") { + const realPart = real2({ inputs: { input: x }, backend: backend2 }); + const r = onesLike2({ inputs: { x: realPart }, backend: backend2 }); + const imagPart = imag2({ inputs: { input: x }, backend: backend2 }); + const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 }); + const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(r); + backend2.disposeIntermediateTensorInfo(imagPart); + backend2.disposeIntermediateTensorInfo(i); + return result; + } else { + return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } }); + } +} +var onesLikeConfig = { + kernelName: OnesLike, + backendName: "cpu", + kernelFunc: onesLike2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pack.js +function pack(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + if (inputs.length === 1) { + return expandDims3({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } }); + } + const shape = inputs[0].shape; + const dtype = inputs[0].dtype; + inputs.forEach((t) => { + util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes"); + util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes"); + }); + const intermediateTensorInfos = []; + const expandedTensors = inputs.map((t) => { + const expandedT = expandDims3({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } }); + intermediateTensorInfos.push(expandedT); + return expandedT; + }); + const result = concat2({ inputs: expandedTensors, backend: backend2, attrs: { axis } }); + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var packConfig = { + kernelName: Pack, + backendName: "cpu", + kernelFunc: pack +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/PadV2.js +function padV2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { paddings, constantValue } = attrs; + assertNotComplex(x, "pad"); + const outShape = paddings.map( + (p2, i) => p2[0] + x.shape[i] + p2[1] + /* afterPad */ + ); + const start = paddings.map((p2) => p2[0]); + const xVals = backend2.data.get(x.dataId).values; + const xSize = util_exports.sizeFromShape(x.shape); + const xRank = x.shape.length; + const xStrides = util_exports.computeStrides(x.shape); + const resultSize = util_exports.sizeFromShape(outShape); + const resultRank = outShape.length; + const resultStrides = util_exports.computeStrides(outShape); + const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize); + if (constantValue !== 0) { + resVals.fill(constantValue); + } + for (let i = 0; i < xSize; i++) { + const coords2 = util_exports.indexToLoc(i, xRank, xStrides); + const outCoords = coords2.map((c, i2) => c + start[i2]); + const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides); + resVals[outIndex] = xVals[i]; + } + const outId = backend2.write(resVals, outShape, x.dtype); + return { dataId: outId, shape: outShape, dtype: x.dtype }; +} +var padV2Config = { + kernelName: PadV2, + backendName: "cpu", + kernelFunc: padV2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pow.js +var powImpl = createSimpleBinaryKernelImpl((a, b) => Math.pow(a, b)); +var pow2 = binaryKernelFunc(Pow, powImpl); +var powConfig = { + kernelName: Pow, + backendName: "cpu", + kernelFunc: pow2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedGather.js +function raggedGather2(args) { + const { inputs, backend: backend2, attrs } = args; + const { paramsNestedSplits, paramsDenseValues, indices } = inputs; + const { outputRaggedRank } = attrs; + const $paramsNestedSplits = paramsNestedSplits.map((t) => backend2.data.get(t.dataId).values); + const $paramsNestedSplitsShapes = paramsNestedSplits.map((t) => t.shape); + const $paramsDenseValues = backend2.data.get(paramsDenseValues.dataId).values; + const $indices = backend2.data.get(indices.dataId).values; + const [outputNestedSplits, outputDenseValues, outputDenseValuesShape] = raggedGatherImpl($paramsNestedSplits, $paramsNestedSplitsShapes, $paramsDenseValues, paramsDenseValues.shape, paramsDenseValues.dtype, $indices, indices.shape, outputRaggedRank); + const outputNestedSplitsTensors = outputNestedSplits.map((splits) => backend2.makeTensorInfo([splits.length], "int32", splits)); + const outputDenseValuesTensor = backend2.makeTensorInfo(outputDenseValuesShape, paramsDenseValues.dtype, outputDenseValues); + return outputNestedSplitsTensors.concat([outputDenseValuesTensor]); +} +var raggedGatherConfig = { + kernelName: RaggedGather, + backendName: "cpu", + kernelFunc: raggedGather2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedRange.js +function raggedRange2(args) { + const { inputs, backend: backend2 } = args; + const { starts, limits, deltas } = inputs; + const $starts = backend2.data.get(starts.dataId).values; + const $limits = backend2.data.get(limits.dataId).values; + const $deltas = backend2.data.get(deltas.dataId).values; + const [rtNestedSplitsData, rtDenseValuesData] = raggedRangeImpl($starts, starts.shape, starts.dtype, $limits, limits.shape, $deltas, deltas.shape); + const rtNestedSplits = backend2.makeTensorInfo([rtNestedSplitsData.length], "int32", rtNestedSplitsData); + const rtDenseValues = backend2.makeTensorInfo([rtDenseValuesData.length], starts.dtype, rtDenseValuesData); + return [rtNestedSplits, rtDenseValues]; +} +var raggedRangeConfig = { + kernelName: RaggedRange, + backendName: "cpu", + kernelFunc: raggedRange2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedTensorToTensor.js +function raggedTensorToTensor2(args) { + const { inputs, backend: backend2, attrs } = args; + const { shape, values, defaultValue, rowPartitionTensors } = inputs; + const { rowPartitionTypes } = attrs; + const $shape = backend2.data.get(shape.dataId).values; + const $values = backend2.data.get(values.dataId).values; + const $defaultValue = backend2.data.get(defaultValue.dataId).values; + const $rowPartitionValues = rowPartitionTensors.map((t) => backend2.data.get(t.dataId).values); + const rowPartitionValuesShapes = rowPartitionTensors.map((t) => t.shape); + const [outputShape, output] = raggedTensorToTensorImpl($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes); + return backend2.makeTensorInfo(outputShape, values.dtype, output); +} +var raggedTensorToTensorConfig = { + kernelName: RaggedTensorToTensor, + backendName: "cpu", + kernelFunc: raggedTensorToTensor2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range.js +function range3(args) { + const { backend: backend2, attrs } = args; + const { start, stop, dtype, step: step5 } = attrs; + const values = rangeImpl(start, stop, step5, dtype); + return backend2.makeTensorInfo([values.length], dtype, values); +} +var rangeConfig = { + kernelName: Range, + backendName: "cpu", + kernelFunc: range3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reciprocal.js +var reciprocal2 = unaryKernelFunc(Reciprocal, (xi) => 1 / xi); +var reciprocalConfig = { + kernelName: Reciprocal, + backendName: "cpu", + kernelFunc: reciprocal2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinear.js +function resizeBilinear3(args) { + const { inputs, backend: backend2, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + assertNotComplex(images, "resizeBilinear"); + const imagesStrides = util_exports.computeStrides(images.shape); + const [newHeight, newWidth] = size; + const [batch, oldHeight, oldWidth, numChannels] = images.shape; + const xValues = backend2.data.get(images.dataId).values; + const result = new Float32Array(util_exports.sizeFromShape([batch, newHeight, newWidth, numChannels])); + const effectiveInputSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutputSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + let outputIdx = 0; + const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0]; + const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1]; + for (let b = 0; b < batch; b++) { + for (let r = 0; r < newHeight; r++) { + let sourceFracRow; + if (halfPixelCenters) { + sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5; + } else { + sourceFracRow = effectiveRowSizeRatio * r; + } + const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow)); + const rowFrac = sourceFracRow - sourceRowFloor; + const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow)); + const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1]; + const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1]; + for (let c = 0; c < newWidth; c++) { + let sourceFracCol; + if (halfPixelCenters) { + sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5; + } else { + sourceFracCol = effectiveColSizeRatio * c; + } + const sourceColFloor = Math.max(0, Math.floor(sourceFracCol)); + const colFrac = sourceFracCol - sourceColFloor; + const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol)); + const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2]; + const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2]; + const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2]; + const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2]; + for (let d = 0; d < numChannels; d++) { + const topLeft = xValues[topLeftOffest + d]; + const bottomLeft = xValues[botLeftOffset + d]; + const topRight = xValues[topRightOffset + d]; + const bottomRight = xValues[botRightOffest + d]; + const top = topLeft + (topRight - topLeft) * colFrac; + const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac; + const newValue = top + (bottom - top) * rowFrac; + result[outputIdx++] = newValue; + } + } + } + } + return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], "float32", result); +} +var resizeBilinearConfig = { + kernelName: ResizeBilinear, + backendName: "cpu", + kernelFunc: resizeBilinear3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinearGrad.js +function resizeBilinearGrad(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + assertNotComplex([dy, images], "resizeBilinearGrad"); + const imagesStrides = util_exports.computeStrides(images.shape); + const [batch, xHeight, xWidth, depth] = images.shape; + const [, yHeight, yWidth] = dy.shape; + const output = new Float32Array(batch * xHeight * xWidth * depth); + const effectiveXSize = [ + alignCorners && yHeight > 1 ? xHeight - 1 : xHeight, + alignCorners && yWidth > 1 ? xWidth - 1 : xWidth + ]; + const effectiveYSize = [ + alignCorners && yHeight > 1 ? yHeight - 1 : yHeight, + alignCorners && yWidth > 1 ? yWidth - 1 : yWidth + ]; + const heightScale = effectiveXSize[0] / effectiveYSize[0]; + const widthScale = effectiveXSize[1] / effectiveYSize[1]; + const dyValues = backend2.data.get(dy.dataId).values; + let offset = 0; + for (let b = 0; b < batch; b++) { + const bOffset = b * imagesStrides[0]; + for (let r = 0; r < yHeight; r++) { + const dxR = r * heightScale; + const topDxRIndex = Math.floor(dxR); + const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1); + const topDxROffset = bOffset + topDxRIndex * imagesStrides[1]; + const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1]; + const dxRLerp = dxR - topDxRIndex; + const inverseDxRLerp = 1 - dxRLerp; + for (let c = 0; c < yWidth; c++) { + const dxC = c * widthScale; + const leftDxCIndex = Math.floor(dxC); + const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1); + const dxCLerp = dxC - leftDxCIndex; + const inverseDxCLerp = 1 - dxCLerp; + const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2]; + const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2]; + const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2]; + const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2]; + const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp; + const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp; + const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp; + const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp; + for (let d = 0; d < depth; d++) { + const dyVal = dyValues[offset++]; + output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp; + output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp; + output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp; + output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp; + } + } + } + } + return backend2.makeTensorInfo([batch, xWidth, xHeight, depth], "float32", output); +} +var resizeBilinearGradConfig2 = { + kernelName: ResizeBilinearGrad, + backendName: "cpu", + kernelFunc: resizeBilinearGrad +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighbor.js +function resizeNearestNeighbor2(args) { + const { inputs, backend: backend2, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + assertNotComplex(images, "resizeNearestNeighbor"); + const imagesStrides = util_exports.computeStrides(images.shape); + const [newHeight, newWidth] = size; + const [batch, oldHeight, oldWidth, numChannels] = images.shape; + const xValues = backend2.data.get(images.dataId).values; + const output = new Float32Array(batch * newHeight * newWidth * numChannels); + const effectiveInputSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutputSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0]; + const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1]; + let outputOffset = 0; + for (let b = 0; b < batch; b++) { + const batchOffset = b * imagesStrides[0]; + for (let r = 0; r < newHeight; r++) { + const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r + 0.5) : effectiveRowSizeRatio * r; + let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow)); + if (halfPixelCenters) { + sourceNearestRow = Math.max(0, sourceNearestRow); + } + const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1]; + for (let c = 0; c < newWidth; c++) { + const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c; + let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol)); + if (halfPixelCenters) { + sourceNearestCol = Math.max(0, sourceNearestCol); + } + const colOffset = rowOffset + sourceNearestCol * imagesStrides[2]; + for (let d = 0; d < numChannels; d++) { + const newVal = xValues[colOffset + d]; + output[outputOffset++] = newVal; + } + } + } + } + return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output); +} +var resizeNearestNeighborConfig = { + kernelName: ResizeNearestNeighbor, + backendName: "cpu", + kernelFunc: resizeNearestNeighbor2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighborGrad.js +function resizeNearestNeighborGrad(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + assertNotComplex([dy, images], "resizeNearestNeighborGrad"); + const imagesStrides = util_exports.computeStrides(images.shape); + const dyStrides = util_exports.computeStrides(dy.shape); + const [batch, xHeight, xWidth, depth] = images.shape; + const [, yHeight, yWidth] = dy.shape; + const output = new Float32Array(batch * xHeight * xWidth * depth); + const dyValues = backend2.data.get(dy.dataId).values; + const effectiveXSize = [ + alignCorners && yHeight > 1 ? xHeight - 1 : xHeight, + alignCorners && yWidth > 1 ? xWidth - 1 : xWidth + ]; + const effectiveYSize = [ + alignCorners && yHeight > 1 ? yHeight - 1 : yHeight, + alignCorners && yWidth > 1 ? yWidth - 1 : yWidth + ]; + const heightScale = effectiveXSize[0] / effectiveYSize[0]; + const widthScale = effectiveXSize[1] / effectiveYSize[1]; + const invHeightScale = 1 / heightScale; + const invWidthScale = 1 / widthScale; + const winHeight = Math.ceil(invHeightScale) * 2 + 2; + const winWidth = Math.ceil(invWidthScale) * 2 + 2; + for (let b = 0; b < batch; b++) { + const batchOffset = b * imagesStrides[0]; + for (let r = 0; r < xHeight; r++) { + const rowOffset = batchOffset + r * imagesStrides[1]; + const startRLerp = Math.floor(r * invHeightScale); + const startDyR = Math.floor(startRLerp - winHeight / 2); + for (let c = 0; c < xWidth; c++) { + const colOffset = rowOffset + c * imagesStrides[2]; + const startCLerp = Math.floor(c * invWidthScale); + const startDyC = Math.floor(startCLerp - winWidth / 2); + for (let d = 0; d < depth; d++) { + let accum = 0; + for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) { + const dyR = dyRIndex + startDyR; + if (dyR < 0 || dyR >= yHeight) { + continue; + } + const dyROffset = batchOffset + dyR * dyStrides[1]; + const sourceFracRow = dyR * heightScale; + const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow)); + if (r !== sourceNearestRow) { + continue; + } + for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) { + const dyC = dyCIndex + startDyC; + if (dyC < 0 || dyC >= yWidth) { + continue; + } + const dyCOffset = dyROffset + dyC * dyStrides[2]; + const sourceFracCol = dyC * widthScale; + const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol)); + if (c === sourceNearestCol) { + accum += dyValues[dyCOffset + d]; + } + } + } + output[colOffset + d] = accum; + } + } + } + } + return backend2.makeTensorInfo(images.shape, images.dtype, output); +} +var resizeNearestNeighborGradConfig2 = { + kernelName: ResizeNearestNeighborGrad, + backendName: "cpu", + kernelFunc: resizeNearestNeighborGrad +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reverse.js +function reverse2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dims } = attrs; + assertNotComplex(x, "reverse"); + const xRank = x.shape.length; + const $dims = util_exports.parseAxisParam(dims, x.shape); + if (xRank === 0) { + return identity2({ inputs: { x }, backend: backend2 }); + } + const outBuf = new TensorBuffer(x.shape, x.dtype); + const xBuf = backend2.bufferSync(x); + for (let i = 0; i < outBuf.size; i++) { + const outLoc = outBuf.indexToLoc(i); + const inLoc = outLoc.slice(); + $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]); + outBuf.set(xBuf.get(...inLoc), ...outLoc); + } + return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); +} +var reverseConfig = { + kernelName: Reverse, + backendName: "cpu", + kernelFunc: reverse2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RotateWithOffset.js +var rotateWithOffsetConfig = { + kernelName: RotateWithOffset, + backendName: "cpu", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { image: image2 } = inputs; + const { radians, fillValue, center } = attrs; + const cpuBackend = backend2; + const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape)); + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth); + const fullOpacityValue = 255; + const sinFactor = Math.sin(radians); + const cosFactor = Math.cos(radians); + const imageVals = cpuBackend.data.get(image2.dataId).values; + for (let batchIdx = 0; batchIdx < batch; batchIdx++) { + const batchOffset = batchIdx * imageWidth * imageHeight * numChannels; + for (let row = 0; row < imageHeight; row++) { + const rowOffset = row * (imageWidth * numChannels); + for (let col = 0; col < imageWidth; col++) { + const colOffset = col * numChannels; + for (let channel = 0; channel < numChannels; channel++) { + const coords2 = [batch, row, col, channel]; + const x = coords2[2]; + const y = coords2[1]; + let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor; + let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor; + coordX = Math.round(coordX + centerX); + coordY = Math.round(coordY + centerY); + let outputValue = fillValue; + if (typeof fillValue !== "number") { + if (channel === 3) { + outputValue = fullOpacityValue; + } else { + outputValue = fillValue[channel]; + } + } + if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) { + const rotatedRowOffset = coordY * (imageWidth * numChannels); + const rotatedColOffset = coordX * numChannels; + const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel; + outputValue = imageVals[imageIdx]; + } + const outIdx = batchOffset + rowOffset + colOffset + channel; + output[outIdx] = outputValue; + } + } + } + } + const dataId = cpuBackend.write(output, image2.shape, image2.dtype); + return { dataId, shape: image2.shape, dtype: image2.dtype }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Round.js +var round3 = unaryKernelFunc(Round, (xi) => { + const base = Math.floor(xi); + if (xi - base < 0.5) { + return Math.floor(xi); + } else if (xi - base > 0.5) { + return Math.ceil(xi); + } else { + if (base % 2 === 0) { + return base; + } else { + return base + 1; + } + } +}); +var roundConfig = { + kernelName: Round, + backendName: "cpu", + kernelFunc: round3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ScatterNd.js +function scatterNd(args) { + const { inputs, backend: backend2, attrs } = args; + const { indices, updates } = inputs; + const { shape } = attrs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape); + const sumDupeIndices = true; + const indicesBuf = backend2.bufferSync(indices); + const updatesBuf = backend2.bufferSync(updates); + const outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0, sumDupeIndices); + return backend2.makeTensorInfo(shape, outBuf.dtype, outBuf.values); +} +var scatterNdConfig = { + kernelName: ScatterNd, + backendName: "cpu", + kernelFunc: scatterNd +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted_impl.js +function lowerBound2(array2, value) { + let left = 0; + let right = array2.length; + let mid = 0; + while (left < right) { + mid = Math.floor((left + right) / 2); + if (array2[mid] < value) { + left = mid + 1; + } else { + right = mid; + } + } + return right; +} +function upperBound2(array2, value) { + let left = 0; + let right = array2.length; + let mid = 0; + while (left < right) { + mid = Math.floor((left + right) / 2); + if (array2[mid] <= value) { + left = mid + 1; + } else { + right = mid; + } + } + return right; +} +function searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues, side) { + const output = util_exports.getArrayFromDType("int32", batchSize * numValues); + for (let b = 0; b < batchSize; ++b) { + const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs); + const outputOffset = b * numValues; + for (let i = 0; i < numValues; ++i) { + output[outputOffset + i] = side === "left" ? lowerBound2(sortedInputsSlice, values[i + outputOffset]) : upperBound2(sortedInputsSlice, values[i + outputOffset]); + } + } + return output; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted.js +function searchSorted2(args) { + const { inputs, backend: backend2, attrs } = args; + const { sortedSequence, values } = inputs; + const { side } = attrs; + const $sortedSequence = backend2.data.get(sortedSequence.dataId).values; + const $values = backend2.data.get(values.dataId).values; + const output = searchSortedImpl($sortedSequence, $values, sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side); + return backend2.makeTensorInfo(values.shape, "int32", output); +} +var searchSortedConfig = { + kernelName: SearchSorted, + backendName: "cpu", + kernelFunc: searchSorted2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Select.js +function select2(args) { + const { inputs, backend: backend2 } = args; + const { condition, t, e } = inputs; + assertNotComplex([condition, t, e], "select"); + const conditionRank = condition.shape.length; + const values = backend2.data.get(condition.dataId).values; + const tValues = backend2.data.get(t.dataId).values; + const eValues = backend2.data.get(e.dataId).values; + const resultDtype = upcastType(t.dtype, e.dtype); + const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape), resultDtype); + let index = 0; + const offset = conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1)); + for (let i = 0; i < values.length; i++) { + for (let j = 0; j < offset; j++) { + if (values[i] === 1) { + newValues[index++] = tValues[i]; + } else { + newValues[index++] = eValues[i]; + } + } + } + return backend2.makeTensorInfo(t.shape, resultDtype, newValues); +} +var selectConfig = { + kernelName: Select, + backendName: "cpu", + kernelFunc: select2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Selu.js +var scaleAlpha = backend_util_exports.SELU_SCALEALPHA; +var scale = backend_util_exports.SELU_SCALE; +var selu2 = unaryKernelFunc(Selu, (xi) => { + if (xi >= 0) { + return scale * xi; + } else { + return scaleAlpha * (Math.exp(xi) - 1); + } +}); +var seluConfig = { + kernelName: Selu, + backendName: "cpu", + kernelFunc: selu2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sign.js +var sign2 = unaryKernelFunc(Sign, (xi) => { + if (xi < 0) { + return -1; + } else if (xi > 0) { + return 1; + } else { + return 0; + } +}); +var signConfig = { + kernelName: Sign, + backendName: "cpu", + kernelFunc: sign2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sin.js +var sin2 = unaryKernelFunc(Sin, (xi) => Math.sin(xi)); +var sinConfig = { + kernelName: Sin, + backendName: "cpu", + kernelFunc: sin2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sinh.js +var sinh2 = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi)); +var sinhConfig = { + kernelName: Sinh, + backendName: "cpu", + kernelFunc: sinh2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softplus.js +var epsilon2 = 11920928955078125e-23; +var threshold2 = Math.log(epsilon2) + 2; +var softplus2 = unaryKernelFunc(Softplus, (xi) => { + const tooLarge = xi > -threshold2; + const tooSmall = xi < threshold2; + const expX = Math.exp(xi); + let result; + if (tooSmall) { + result = expX; + } else if (tooLarge) { + result = xi; + } else { + result = Math.log(1 + expX); + } + return result; +}); +var softplusConfig = { + kernelName: Softplus, + backendName: "cpu", + kernelFunc: softplus2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SpaceToBatchND.js +function spaceToBatchND2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, paddings } = attrs; + assertNotComplex([x], "spaceToBatchND"); + const prod5 = util_exports.sizeFromShape(blockShape); + const completePaddings = [[0, 0]]; + completePaddings.push(...paddings); + for (let i = 1 + blockShape.length; i < x.shape.length; ++i) { + completePaddings.push([0, 0]); + } + const paddedX = padV2Config.kernelFunc({ + inputs: { x }, + backend: backend2, + attrs: { paddings: completePaddings, constantValue: 0 } + }); + const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false); + const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false); + const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false); + const reshapeInputs = { x: paddedX }; + const reshapeAttrs = { shape: reshapedPaddedShape }; + const paddedXReshaped = reshape3({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs }); + const transposeInputs = { x: paddedXReshaped }; + const transposeAttrs = { perm: permutedReshapedPaddedPermutation }; + const paddedXT = transpose2({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs }); + const resultReshapeInputs = { x: paddedXT }; + const resultReshapeAttrs = { shape: flattenShape }; + const result = reshape3({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs }); + backend2.disposeIntermediateTensorInfo(paddedX); + backend2.disposeIntermediateTensorInfo(paddedXReshaped); + backend2.disposeIntermediateTensorInfo(paddedXT); + return result; +} +var spaceToBatchNDConfig = { + kernelName: SpaceToBatchND, + backendName: "cpu", + kernelFunc: spaceToBatchND2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows.js +function sparseFillEmptyRows2(args) { + const { inputs, backend: backend2 } = args; + const { indices, values, denseShape, defaultValue } = inputs; + if (denseShape.shape.length !== 1) { + throw new Error(`Dense shape must be a vector, saw: + ${denseShape.shape}`); + } + if (indices.shape.length !== 2) { + throw new Error(`Indices must be a matrix, saw: + ${indices.shape}`); + } + if (values.shape.length !== 1) { + throw new Error(`Values must be a vector, saw: + ${values.shape}`); + } + if (defaultValue.shape.length !== 0) { + throw new Error(`Default value must be a scalar, saw: + ${defaultValue.shape}`); + } + const $indices = backend2.data.get(indices.dataId).values; + const $values = backend2.data.get(values.dataId).values; + const $denseShape = backend2.data.get(denseShape.dataId).values; + const $defaultValue = backend2.data.get(defaultValue.dataId).values[0]; + const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue); + return [ + backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices), + backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues), + backend2.makeTensorInfo([emptyRowIndicator.length], "bool", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))), + backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap)) + ]; +} +var sparseFillEmptyRowsConfig = { + kernelName: SparseFillEmptyRows, + backendName: "cpu", + kernelFunc: sparseFillEmptyRows2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape.js +function sparseReshape2(args) { + const { inputs, backend: backend2 } = args; + const { inputIndices, inputShape, newShape } = inputs; + if (inputIndices.shape.length !== 2) { + throw new Error(`Input indices should be a matrix but received shape + ${inputIndices.shape}`); + } + if (inputShape.shape.length !== 1) { + throw new Error(`Input shape should be a vector but received shape + ${inputShape.shape}`); + } + if (newShape.shape.length !== 1) { + throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`); + } + const $inputShape = Array.from(backend2.data.get(inputShape.dataId).values); + const $inputIndices = backend2.data.get(inputIndices.dataId).values; + const targetShape = Array.from(backend2.data.get(newShape.dataId).values); + const [newIndices, indicesShape, outputShape] = sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape); + return [ + backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices), + backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape)) + ]; +} +var sparseReshapeConfig = { + kernelName: SparseReshape, + backendName: "cpu", + kernelFunc: sparseReshape2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentMean.js +function sparseSegmentMean2(args) { + const { inputs, backend: backend2 } = args; + const { data, indices, segmentIds } = inputs; + if (data.shape.length < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if (indices.shape.length !== 1) { + throw new Error(`Indices should be a vector but received shape + ${indices.shape}`); + } + if (segmentIds.shape.length !== 1) { + throw new Error(`Segment ids should be a vector but received shape + ${segmentIds.shape}`); + } + if (indices.shape[0] !== segmentIds.shape[0]) { + throw new Error(`segmentIds and indices should have same size.`); + } + const $data = backend2.data.get(data.dataId).values; + const $indices = backend2.data.get(indices.dataId).values; + const $segmentIds = backend2.data.get(segmentIds.dataId).values; + const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true); + return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData); +} +var sparseSegmentMeanConfig = { + kernelName: SparseSegmentMean, + backendName: "cpu", + kernelFunc: sparseSegmentMean2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentSum.js +function sparseSegmentSum2(args) { + const { inputs, backend: backend2 } = args; + const { data, indices, segmentIds } = inputs; + if (data.shape.length < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if (indices.shape.length !== 1) { + throw new Error(`Indices should be a vector but received shape + ${indices.shape}`); + } + if (segmentIds.shape.length !== 1) { + throw new Error(`Segment ids should be a vector but received shape + ${segmentIds.shape}`); + } + if (indices.shape[0] !== segmentIds.shape[0]) { + throw new Error(`segmentIds and indices should have same size.`); + } + const $data = backend2.data.get(data.dataId).values; + const $indices = backend2.data.get(indices.dataId).values; + const $segmentIds = backend2.data.get(segmentIds.dataId).values; + const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds); + return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData); +} +var sparseSegmentSumConfig = { + kernelName: SparseSegmentSum, + backendName: "cpu", + kernelFunc: sparseSegmentSum2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseToDense.js +function sparseToDense2(args) { + const { inputs, backend: backend2, attrs } = args; + const { sparseIndices, sparseValues, defaultValue } = inputs; + const { outputShape } = attrs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape); + const sumDupeIndices = false; + const indicesBuf = backend2.bufferSync(sparseIndices); + let outBuf; + switch (sparseValues.dtype) { + case "bool": { + const updatesBuf = backend2.bufferSync(sparseValues); + const $defaultValue = Boolean(backend2.data.get(defaultValue.dataId).values[0]); + outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + break; + } + case "float32": { + const updatesBuf = backend2.bufferSync(sparseValues); + const $defaultValue = backend2.data.get(defaultValue.dataId).values[0]; + outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + break; + } + case "int32": { + const updatesBuf = backend2.bufferSync(sparseValues); + const $defaultValue = backend2.data.get(defaultValue.dataId).values[0]; + outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + break; + } + case "string": { + const updatesBuf = backend2.bufferSync(sparseValues); + const $defaultValue = util_exports.decodeString(backend2.data.get(defaultValue.dataId).values[0]); + outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + break; + } + default: + throw new Error(`Unsupported type ${sparseValues.dtype}`); + } + return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values); +} +var sparseToDenseConfig = { + kernelName: SparseToDense, + backendName: "cpu", + kernelFunc: sparseToDense2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SplitV.js +function splitV(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { numOrSizeSplits, axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, x.shape)[0]; + const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis); + const begin = new Array(x.shape.length).fill(0); + const size = x.shape.slice(); + return splitSizes.map((s) => { + const sliceSize = [...size]; + sliceSize[$axis] = s; + const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } }); + begin[$axis] += s; + return sliceT; + }); +} +var splitVConfig = { + kernelName: SplitV, + backendName: "cpu", + kernelFunc: splitV +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Square.js +var squareConfig = { + kernelName: Square, + backendName: "cpu", + kernelFunc: ({ inputs, backend: backend2 }) => { + const { x } = inputs; + const cpuBackend = backend2; + assertNotComplex(x, "square"); + const values = cpuBackend.data.get(x.dataId).values; + const newValues = new Float32Array(values.length); + for (let i = 0; i < values.length; ++i) { + const value = values[i]; + newValues[i] = value * value; + } + const dataId = cpuBackend.write(newValues, x.shape, x.dtype); + return { dataId, shape: x.shape, dtype: x.dtype }; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Step.js +var step2 = unaryKernelFunc(Step, (xi, attrs) => { + const stepAttrs = attrs; + if (isNaN(xi)) { + return NaN; + } else { + return xi > 0 ? 1 : stepAttrs.alpha; + } +}); +var stepConfig = { + kernelName: Step, + backendName: "cpu", + kernelFunc: step2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice.js +function stridedSlice2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs; + assertNotComplex(x, "stridedSlice"); + const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); + let result; + if (isIdentity) { + result = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); + } else if (sliceDim0 || isSimpleSlice) { + util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`); + const size = slice_util_exports.computeOutShape($begin, $end, $strides); + const sliced = slice2({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } }); + result = reshape3({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeIntermediateTensorInfo(sliced); + } else { + const xBuf = backend2.bufferSync(x); + const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin); + result = backend2.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values); + } + return result; +} +var stridedSliceConfig = { + kernelName: StridedSlice, + backendName: "cpu", + kernelFunc: stridedSlice2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams.js +function stringNGrams2(args) { + const { inputs, backend: backend2, attrs } = args; + const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs; + const { data, dataSplits } = inputs; + const $data = backend2.data.get(data.dataId).values; + const $dataSplits = backend2.data.get(dataSplits.dataId).values; + const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences); + return [ + backend2.makeTensorInfo([nGrams.length], "string", nGrams), + backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits) + ]; +} +var stringNGramsConfig = { + kernelName: StringNGrams, + backendName: "cpu", + kernelFunc: stringNGrams2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit.js +function stringSplit2(args) { + const { inputs, backend: backend2, attrs } = args; + const { skipEmpty } = attrs; + const { input: input2, delimiter } = inputs; + if (input2.dtype !== "string") { + throw new Error("Input must be of datatype string"); + } + if (input2.shape.length !== 1) { + throw new Error(`Input must be a vector, got shape: ${input2.shape}`); + } + if (delimiter.shape.length !== 0) { + throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`); + } + const $input = backend2.data.get(input2.dataId).values; + const $delimiter = backend2.data.get(delimiter.dataId).values[0]; + const [indices, values, shape] = stringSplitImpl($input, $delimiter, skipEmpty); + const outputSize = values.length; + return [ + backend2.makeTensorInfo([outputSize, 2], "int32", indices), + backend2.makeTensorInfo([outputSize], "string", values), + backend2.makeTensorInfo([2], "int32", new Int32Array(shape)) + ]; +} +var stringSplitConfig = { + kernelName: StringSplit, + backendName: "cpu", + kernelFunc: stringSplit2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast.js +function stringToHashBucketFast2(args) { + const { inputs, backend: backend2, attrs } = args; + const { numBuckets } = attrs; + const { input: input2 } = inputs; + if (input2.dtype !== "string") { + throw new Error("Input must be of datatype string"); + } + if (numBuckets <= 0) { + throw new Error(`Number of buckets must be at least 1`); + } + const $input = backend2.data.get(input2.dataId).values; + const output = stringToHashBucketFastImpl($input, numBuckets); + return backend2.makeTensorInfo(input2.shape, "int32", output); +} +var stringToHashBucketFastConfig = { + kernelName: StringToHashBucketFast, + backendName: "cpu", + kernelFunc: stringToHashBucketFast2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tan.js +var tan2 = unaryKernelFunc(Tan, (xi) => Math.tan(xi)); +var tanConfig = { + kernelName: Tan, + backendName: "cpu", + kernelFunc: tan2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tanh.js +var tanh3 = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi)); +var tanhConfig = { + kernelName: Tanh, + backendName: "cpu", + kernelFunc: tanh3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TensorScatterUpdate.js +function tensorScatterUpdate2(args) { + const { inputs, backend: backend2 } = args; + const { tensor: tensor2, indices, updates } = inputs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, tensor2.shape); + const sumDupeIndices = false; + const indicesBuf = backend2.bufferSync(indices); + const updatesBuf = backend2.bufferSync(updates); + const tensorBuf = backend2.bufferSync(tensor2); + const outBuf = scatterImpl(indicesBuf, updatesBuf, tensor2.shape, outputSize, sliceSize, numUpdates, sliceRank, strides, tensorBuf, sumDupeIndices); + return backend2.makeTensorInfo(tensor2.shape, outBuf.dtype, outBuf.values); +} +var tensorScatterUpdateConfig = { + kernelName: TensorScatterUpdate, + backendName: "cpu", + kernelFunc: tensorScatterUpdate2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile.js +function tile3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { reps } = attrs; + assertNotComplex(x, "tile"); + const outBuf = tileImpl(backend2.bufferSync(x), reps); + return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); +} +var tileConfig = { + kernelName: Tile, + backendName: "cpu", + kernelFunc: tile3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK.js +function topK(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { k, sorted } = attrs; + assertNotComplex(x, "topk"); + const xVals = backend2.data.get(x.dataId).values; + const [allTopKVals, allTopKIndices] = topKImpl(xVals, x.shape, x.dtype, k, sorted); + return [ + backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values), + backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values) + ]; +} +var topKConfig = { + kernelName: TopK, + backendName: "cpu", + kernelFunc: topK +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transform.js +function transform2(args) { + const { inputs, attrs, backend: backend2 } = args; + const { image: image2, transforms } = inputs; + const { interpolation, fillMode, fillValue, outputShape } = attrs; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth]; + const outShape = [batch, outHeight, outWidth, numChannels]; + const inStrides = util_exports.computeStrides(image2.shape); + const batchInStride = inStrides[0]; + const rowInStride = inStrides[1]; + const colInStride = inStrides[2]; + const outStrides = util_exports.computeStrides(outShape); + const batchOutStride = outStrides[0]; + const rowOutStride = outStrides[1]; + const colOutStride = outStrides[2]; + const outVals = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(outShape)); + outVals.fill(fillValue); + const imageVals = backend2.data.get(image2.dataId).values; + const transformVals = backend2.data.get(transforms.dataId).values; + for (let b = 0; b < batch; ++b) { + const transform5 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8); + for (let outY = 0; outY < outHeight; ++outY) { + for (let outX = 0; outX < outWidth; ++outX) { + for (let channel = 0; channel < numChannels; ++channel) { + let val; + const projection = transform5[6] * outX + transform5[7] * outY + 1; + if (projection === 0) { + continue; + } + const inX = (transform5[0] * outX + transform5[1] * outY + transform5[2]) / projection; + const inY = (transform5[3] * outX + transform5[4] * outY + transform5[5]) / projection; + const x = mapCoord(inX, imageWidth, fillMode); + const y = mapCoord(inY, imageHeight, fillMode); + switch (interpolation) { + case "nearest": + val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue); + break; + case "bilinear": + val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue); + break; + default: + throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${interpolation}`); + } + const ind = b * batchOutStride + outY * rowOutStride + outX * colOutStride + channel; + outVals[ind] = val; + } + } + } + return backend2.makeTensorInfo(outShape, image2.dtype, outVals); + } + const dataId = backend2.write(outVals, outShape, image2.dtype); + return { dataId, shape: image2.shape, dtype: image2.dtype }; +} +var transformConfig = { + kernelName: Transform, + backendName: "cpu", + kernelFunc: transform2 +}; +function mapCoord(outCoord, len, mode) { + switch (mode) { + case "reflect": + return mapCoordReflect(outCoord, len); + case "wrap": + return mapCoordWrap(outCoord, len); + case "nearest": + return mapCoordNearest(outCoord, len); + case "constant": + default: + return mapCoordConstant(outCoord, len); + } +} +function mapCoordReflect(outCoord, len) { + let inCoord = outCoord; + if (inCoord < 0) { + if (len <= 1) { + inCoord = 0; + } else { + const sz2 = 2 * len; + if (inCoord < sz2) { + inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord; + } + inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1; + } + } else if (inCoord > len - 1) { + if (len <= 1) { + inCoord = 0; + } else { + const sz2 = 2 * len; + inCoord -= sz2 * Math.trunc(inCoord / sz2); + if (inCoord >= len) { + inCoord = sz2 - inCoord - 1; + } + } + } + return util_exports.clamp(0, inCoord, len - 1); +} +function mapCoordWrap(outCoord, len) { + let inCoord = outCoord; + if (inCoord < 0) { + if (len <= 1) { + inCoord = 0; + } else { + const sz = len - 1; + inCoord += len * (Math.trunc(-inCoord / sz) + 1); + } + } else if (inCoord > len - 1) { + if (len <= 1) { + inCoord = 0; + } else { + const sz = len - 1; + inCoord -= len * Math.trunc(inCoord / sz); + } + } + return util_exports.clamp(0, inCoord, len - 1); +} +function mapCoordConstant(outCoord, len) { + return outCoord; +} +function mapCoordNearest(outCoord, len) { + return util_exports.clamp(0, outCoord, len - 1); +} +function readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) { + const ind = batch * batchStride + y * rowStride + x * colStride + channel; + if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) { + return imageVals[ind]; + } else { + return fillValue; + } +} +function nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) { + const $y = Math.round(y); + const $x = Math.round(x); + return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue); +} +function bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) { + const yFloor = Math.floor(y); + const xFloor = Math.floor(x); + const yCeil = yFloor + 1; + const xCeil = xFloor + 1; + const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue); + const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue); + return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique.js +function unique3(args) { + const { inputs, attrs, backend: backend2 } = args; + const { axis } = attrs; + const { x } = inputs; + assertNotComplex(x, "unique"); + const values = backend2.data.get(x.dataId).values; + const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype); + return [ + backend2.makeTensorInfo(outputShape, x.dtype, outputValues), + backend2.makeTensorInfo([indices.length], "int32", indices) + ]; +} +var uniqueConfig = { + kernelName: Unique, + backendName: "cpu", + kernelFunc: unique3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unpack.js +function unpack(args) { + const { inputs, backend: backend2, attrs } = args; + const { value } = inputs; + let { axis } = attrs; + if (axis < 0) { + axis += value.shape.length; + } + const valueRank = value.shape.length; + const num = value.shape[axis]; + const outShape = new Array(valueRank - 1); + let outIndex = 0; + for (let i = 0; i < valueRank; i++) { + if (i !== axis) { + outShape[outIndex++] = value.shape[i]; + } + } + const begin = new Array(valueRank).fill(0); + const size = value.shape.slice(); + size[axis] = 1; + const res = new Array(num); + for (let i = 0; i < res.length; i++) { + begin[axis] = i; + const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size } }); + res[i] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } }); + backend2.disposeIntermediateTensorInfo(tempRes); + } + return res; +} +var unpackConfig = { + kernelName: Unpack, + backendName: "cpu", + kernelFunc: unpack +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/UnsortedSegmentSum.js +function unsortedSegmentSum2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, segmentIds } = inputs; + const { numSegments } = attrs; + assertNotComplex(x, "unsortedSegmentSum"); + const xRank = x.shape.length; + const segmentIdsRank = segmentIds.shape.length; + const res = []; + const intermediates = []; + const numIters = xRank - segmentIdsRank; + let $segmentIds = segmentIds; + for (let i = 0; i < numIters; ++i) { + const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i + 1 } }); + $segmentIds = expanded; + intermediates.push(expanded); + } + for (let i = 0; i < numSegments; ++i) { + const scalarValue = util_exports.createScalarValue(i, "int32"); + const segmentId = backend2.makeTensorInfo([], "int32", scalarValue); + const mask = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 }); + const maskCasted = cast3({ inputs: { x: mask }, backend: backend2, attrs: { dtype: "float32" } }); + const mul2 = multiply2({ inputs: { a: maskCasted, b: x }, backend: backend2 }); + const sumTensorInfo = sum3({ inputs: { x: mul2 }, backend: backend2, attrs: { axis: 0, keepDims: false } }); + res.push(sumTensorInfo); + intermediates.push(segmentId); + intermediates.push(mask); + intermediates.push(maskCasted); + intermediates.push(mul2); + intermediates.push(sumTensorInfo); + } + const result = pack({ inputs: res, backend: backend2, attrs: { axis: 0 } }); + intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var unsortedSegmentSumConfig = { + kernelName: UnsortedSegmentSum, + backendName: "cpu", + kernelFunc: unsortedSegmentSum2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/register_all_kernels.js +var kernelConfigs = [ + _fusedMatMulConfig, + absConfig, + acosConfig, + acoshConfig, + addConfig, + addNConfig, + allConfig, + anyConfig, + argMaxConfig, + argMinConfig, + asinConfig, + asinhConfig, + atanConfig, + atan2Config, + atanhConfig, + avgPoolConfig, + avgPool3DConfig, + avgPool3DGradConfig2, + avgPoolGradConfig2, + batchMatMulConfig, + batchNormConfig, + batchToSpaceNDConfig, + bincountConfig, + bitwiseAndConfig, + broadcastArgsConfig, + castConfig, + ceilConfig, + clipByValueConfig, + complexConfig, + complexAbsConfig, + concatConfig, + conv2DConfig, + conv2DBackpropFilterConfig, + conv2DBackpropInputConfig, + conv3DConfig, + conv3DBackpropFilterV2Config, + conv3DBackpropInputV2Config, + cosConfig, + coshConfig, + cropAndResizeConfig, + cumprodConfig, + cumsumConfig, + denseBincountConfig, + depthToSpaceConfig, + depthwiseConv2dNativeConfig, + depthwiseConv2dNativeBackpropFilterConfig, + depthwiseConv2dNativeBackpropInputConfig, + diagConfig, + dilation2DConfig, + dilation2DBackpropFilterConfig, + dilation2DBackpropInputConfig, + drawConfig, + einsumConfig, + eluConfig, + eluGradConfig2, + equalConfig, + erfConfig, + expConfig, + expandDimsConfig, + expm1Config, + fftConfig, + fillConfig, + flipLeftRightConfig, + floorConfig, + floorDivConfig, + fusedConv2DConfig, + fusedDepthwiseConv2DConfig, + gatherNdConfig, + gatherV2Config, + greaterConfig, + greaterEqualConfig, + identityConfig, + ifftConfig, + imagConfig, + isFiniteConfig, + isInfConfig, + isNaNConfig, + leakyReluConfig, + lessConfig, + lessEqualConfig, + linSpaceConfig, + logConfig, + log1pConfig, + logicalAndConfig, + logicalNotConfig, + logicalOrConfig, + LRNConfig, + LRNGradConfig, + maxConfig, + maximumConfig, + maxPoolConfig, + maxPool3DConfig, + maxPool3DGradConfig2, + maxPoolGradConfig2, + maxPoolWithArgmaxConfig, + meanConfig, + minConfig, + minimumConfig, + mirrorPadConfig, + modConfig, + multinomialConfig, + multiplyConfig, + negConfig, + nonMaxSuppressionV3Config, + nonMaxSuppressionV4Config, + nonMaxSuppressionV5Config, + notEqualConfig, + oneHotConfig, + onesLikeConfig, + packConfig, + padV2Config, + powConfig, + preluConfig, + prodConfig, + raggedGatherConfig, + raggedRangeConfig, + raggedTensorToTensorConfig, + rangeConfig, + realConfig, + realDivConfig, + reciprocalConfig, + reluConfig, + relu6Config, + reshapeConfig, + resizeBilinearConfig, + resizeBilinearGradConfig2, + resizeNearestNeighborConfig, + resizeNearestNeighborGradConfig2, + reverseConfig, + rotateWithOffsetConfig, + roundConfig, + rsqrtConfig, + scatterNdConfig, + searchSortedConfig, + selectConfig, + seluConfig, + sigmoidConfig, + signConfig, + sinConfig, + sinhConfig, + sliceConfig, + softmaxConfig, + softplusConfig, + spaceToBatchNDConfig, + sparseFillEmptyRowsConfig, + sparseReshapeConfig, + sparseSegmentMeanConfig, + sparseSegmentSumConfig, + sparseToDenseConfig, + splitVConfig, + sqrtConfig, + squareConfig, + squaredDifferenceConfig, + staticRegexReplaceConfig, + stepConfig, + stridedSliceConfig, + stringNGramsConfig, + stringSplitConfig, + stringToHashBucketFastConfig, + subConfig, + sumConfig, + tanConfig, + tanhConfig, + tensorScatterUpdateConfig, + tileConfig, + topKConfig, + transformConfig, + transposeConfig, + uniqueConfig, + unpackConfig, + unsortedSegmentSumConfig, + zerosLikeConfig +]; +for (const kernelConfig of kernelConfigs) { + registerKernel(kernelConfig); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js +var webgl_util_exports = {}; +__export(webgl_util_exports, { + assertNotComplex: () => assertNotComplex2, + bindCanvasToFramebuffer: () => bindCanvasToFramebuffer, + bindColorTextureToFramebuffer: () => bindColorTextureToFramebuffer, + bindTextureToProgramUniformSampler: () => bindTextureToProgramUniformSampler, + bindTextureUnit: () => bindTextureUnit, + bindVertexBufferToProgramAttribute: () => bindVertexBufferToProgramAttribute, + callAndCheck: () => callAndCheck, + canBeRepresented: () => canBeRepresented, + createFragmentShader: () => createFragmentShader, + createFramebuffer: () => createFramebuffer, + createProgram: () => createProgram, + createStaticIndexBuffer: () => createStaticIndexBuffer, + createStaticVertexBuffer: () => createStaticVertexBuffer, + createTexture: () => createTexture, + createVertexShader: () => createVertexShader, + getBatchDim: () => getBatchDim, + getExtensionOrThrow: () => getExtensionOrThrow, + getFramebufferErrorMessage: () => getFramebufferErrorMessage, + getMaxTexturesInShader: () => getMaxTexturesInShader, + getNumChannels: () => getNumChannels, + getProgramUniformLocation: () => getProgramUniformLocation, + getProgramUniformLocationOrThrow: () => getProgramUniformLocationOrThrow, + getRowsCols: () => getRowsCols, + getShapeAs3D: () => getShapeAs3D, + getTextureShapeFromLogicalShape: () => getTextureShapeFromLogicalShape, + getWebGLDisjointQueryTimerVersion: () => getWebGLDisjointQueryTimerVersion, + getWebGLErrorMessage: () => getWebGLErrorMessage, + getWebGLMaxTextureSize: () => getWebGLMaxTextureSize, + hasExtension: () => hasExtension, + isCapableOfRenderingToFloatTexture: () => isCapableOfRenderingToFloatTexture, + isDownloadFloatTextureEnabled: () => isDownloadFloatTextureEnabled, + isReshapeFree: () => isReshapeFree, + isWebGLFenceEnabled: () => isWebGLFenceEnabled, + isWebGLVersionEnabled: () => isWebGLVersionEnabled, + linkProgram: () => linkProgram, + logShaderSourceAndInfoLog: () => logShaderSourceAndInfoLog, + resetMaxTextureSize: () => resetMaxTextureSize, + resetMaxTexturesInShader: () => resetMaxTexturesInShader, + unbindColorTextureFromFramebuffer: () => unbindColorTextureFromFramebuffer, + unbindTextureUnit: () => unbindTextureUnit, + validateFramebuffer: () => validateFramebuffer, + validateProgram: () => validateProgram, + validateTextureSize: () => validateTextureSize +}); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/canvas_util.js +var contexts = {}; +var WEBGL_ATTRIBUTES = { + alpha: false, + antialias: false, + premultipliedAlpha: false, + preserveDrawingBuffer: false, + depth: false, + stencil: false, + failIfMajorPerformanceCaveat: true +}; +function setWebGLContext(webGLVersion, gl) { + contexts[webGLVersion] = gl; +} +function getWebGLContext(webGLVersion, customCanvas) { + if (!(webGLVersion in contexts) || customCanvas != null) { + const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas); + if (newCtx !== null) { + contexts[webGLVersion] = newCtx; + } else { + console.log("Could not get context for WebGL version", webGLVersion); + return null; + } + } + const gl = contexts[webGLVersion]; + if (gl == null || gl.isContextLost()) { + delete contexts[webGLVersion]; + return getWebGLContext(webGLVersion); + } + gl.disable(gl.DEPTH_TEST); + gl.disable(gl.STENCIL_TEST); + gl.disable(gl.BLEND); + gl.disable(gl.DITHER); + gl.disable(gl.POLYGON_OFFSET_FILL); + gl.disable(gl.SAMPLE_COVERAGE); + gl.enable(gl.SCISSOR_TEST); + gl.enable(gl.CULL_FACE); + gl.cullFace(gl.BACK); + return contexts[webGLVersion]; +} +function createCanvas(webGLVersion) { + if (!env().getBool("IS_SAFARI") && typeof OffscreenCanvas !== "undefined" && webGLVersion === 2) { + return new OffscreenCanvas(300, 150); + } else if (typeof document !== "undefined") { + return document.createElement("canvas"); + } else { + throw new Error("Cannot create a canvas in this context"); + } +} +function getWebGLRenderingContext(webGLVersion, customCanvas) { + if (webGLVersion !== 1 && webGLVersion !== 2) { + throw new Error("Cannot get WebGL rendering context, WebGL is disabled."); + } + const canvas = customCanvas == null ? createCanvas(webGLVersion) : customCanvas; + canvas.addEventListener("webglcontextlost", (ev) => { + ev.preventDefault(); + delete contexts[webGLVersion]; + }, false); + if (env().getBool("SOFTWARE_WEBGL_ENABLED")) { + WEBGL_ATTRIBUTES.failIfMajorPerformanceCaveat = false; + } + if (webGLVersion === 1) { + return ( + // tslint:disable-next-line + canvas.getContext("webgl", WEBGL_ATTRIBUTES) || canvas.getContext("experimental-webgl", WEBGL_ATTRIBUTES) + ); + } + return canvas.getContext("webgl2", WEBGL_ATTRIBUTES); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/tex_util.js +var PackingScheme; +(function(PackingScheme2) { + PackingScheme2[PackingScheme2["DENSE"] = 0] = "DENSE"; + PackingScheme2[PackingScheme2["SHARED_BATCH"] = 1] = "SHARED_BATCH"; +})(PackingScheme || (PackingScheme = {})); +var TextureUsage; +(function(TextureUsage2) { + TextureUsage2[TextureUsage2["RENDER"] = 0] = "RENDER"; + TextureUsage2[TextureUsage2["UPLOAD"] = 1] = "UPLOAD"; + TextureUsage2[TextureUsage2["PIXELS"] = 2] = "PIXELS"; + TextureUsage2[TextureUsage2["DOWNLOAD"] = 3] = "DOWNLOAD"; +})(TextureUsage || (TextureUsage = {})); +var PhysicalTextureType; +(function(PhysicalTextureType2) { + PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16"; + PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32"; + PhysicalTextureType2[PhysicalTextureType2["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE"; + PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32"; + PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16"; +})(PhysicalTextureType || (PhysicalTextureType = {})); +function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) { + return [columns, rows]; +} +function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) { + return matrixSize * channelsPerTexture; +} +function getDenseTexShape(shape) { + const size = util_exports.sizeFromShape(shape); + const texelsNeeded = Math.ceil(size / 4); + return util_exports.sizeToSquarishShape(texelsNeeded); +} +function getPackedMatrixTextureShapeWidthHeight(rows, columns) { + return [ + Math.max(1, Math.ceil(columns / 2)), + Math.max(1, Math.ceil(rows / 2)) + ]; +} +function getPackedRGBAArraySizeFromMatrixShape(rows, columns) { + const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns); + return w * h * 4; +} +function getTextureConfig(gl, textureHalfFloatExtension) { + const glany = gl; + let internalFormatFloat; + let internalFormatHalfFloat; + let internalFormatPackedHalfFloat; + let internalFormatPackedFloat; + let textureFormatFloat; + let downloadTextureFormat; + let downloadUnpackNumChannels; + let defaultNumChannels; + let textureTypeHalfFloat; + let textureTypeFloat; + if (env().getNumber("WEBGL_VERSION") === 2) { + internalFormatFloat = glany.R32F; + internalFormatHalfFloat = glany.R16F; + internalFormatPackedHalfFloat = glany.RGBA16F; + internalFormatPackedFloat = glany.RGBA32F; + textureFormatFloat = glany.RED; + downloadUnpackNumChannels = 4; + defaultNumChannels = 1; + textureTypeHalfFloat = glany.HALF_FLOAT; + textureTypeFloat = glany.FLOAT; + downloadTextureFormat = glany.RGBA8; + } else { + internalFormatFloat = gl.RGBA; + internalFormatHalfFloat = gl.RGBA; + internalFormatPackedHalfFloat = gl.RGBA; + internalFormatPackedFloat = glany.RGBA; + textureFormatFloat = gl.RGBA; + downloadUnpackNumChannels = 4; + defaultNumChannels = 4; + textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null; + textureTypeFloat = gl.FLOAT; + downloadTextureFormat = gl.RGBA; + } + return { + internalFormatFloat, + internalFormatHalfFloat, + internalFormatPackedHalfFloat, + internalFormatPackedFloat, + textureFormatFloat, + downloadTextureFormat, + downloadUnpackNumChannels, + defaultNumChannels, + textureTypeHalfFloat, + textureTypeFloat + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js +function callAndCheck(gl, func2) { + const returnValue = func2(); + if (env().getBool("DEBUG")) { + checkWebGLError(gl); + } + return returnValue; +} +function checkWebGLError(gl) { + const error = gl.getError(); + if (error !== gl.NO_ERROR) { + throw new Error("WebGL Error: " + getWebGLErrorMessage(gl, error)); + } +} +var MIN_FLOAT16 = 596e-10; +var MAX_FLOAT16 = 65504; +function canBeRepresented(num) { + if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) { + return true; + } + return false; +} +function getWebGLErrorMessage(gl, status) { + switch (status) { + case gl.NO_ERROR: + return "NO_ERROR"; + case gl.INVALID_ENUM: + return "INVALID_ENUM"; + case gl.INVALID_VALUE: + return "INVALID_VALUE"; + case gl.INVALID_OPERATION: + return "INVALID_OPERATION"; + case gl.INVALID_FRAMEBUFFER_OPERATION: + return "INVALID_FRAMEBUFFER_OPERATION"; + case gl.OUT_OF_MEMORY: + return "OUT_OF_MEMORY"; + case gl.CONTEXT_LOST_WEBGL: + return "CONTEXT_LOST_WEBGL"; + default: + return `Unknown error code ${status}`; + } +} +function getExtensionOrThrow(gl, extensionName) { + return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension "' + extensionName + '" not supported on this browser.'); +} +function createVertexShader(gl, vertexShaderSource) { + const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), "Unable to create vertex WebGLShader."); + callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource)); + callAndCheck(gl, () => gl.compileShader(vertexShader)); + if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) { + console.log(gl.getShaderInfoLog(vertexShader)); + throw new Error("Failed to compile vertex shader."); + } + return vertexShader; +} +function createFragmentShader(gl, fragmentShaderSource) { + const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), "Unable to create fragment WebGLShader."); + callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource)); + callAndCheck(gl, () => gl.compileShader(fragmentShader)); + if (env().get("ENGINE_COMPILE_ONLY")) { + return fragmentShader; + } + if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) { + logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader)); + throw new Error("Failed to compile fragment shader."); + } + return fragmentShader; +} +var lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g; +function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) { + const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog); + if (lineNumberRegexResult == null) { + console.log(`Couldn't parse line number in error: ${shaderInfoLog}`); + console.log(shaderSource); + return; + } + const lineNumber = +lineNumberRegexResult[1]; + const shaderLines = shaderSource.split("\n"); + const pad3 = shaderLines.length.toString().length + 2; + const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line); + let maxLineLength = 0; + for (let i = 0; i < linesWithLineNumbers.length; i++) { + maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength); + } + const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1); + const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber); + const afterErrorLines = linesWithLineNumbers.slice(lineNumber); + console.log(beforeErrorLines.join("\n")); + console.log(shaderInfoLog.split("\n")[0]); + console.log(`%c ${util_exports.rightPad(errorLine[0], maxLineLength)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"); + console.log(afterErrorLines.join("\n")); +} +function createProgram(gl) { + return throwIfNull(gl, () => gl.createProgram(), "Unable to create WebGLProgram."); +} +function linkProgram(gl, program) { + callAndCheck(gl, () => gl.linkProgram(program)); + if (env().get("ENGINE_COMPILE_ONLY")) { + return; + } + if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) { + console.log(gl.getProgramInfoLog(program)); + throw new Error("Failed to link vertex and fragment shaders."); + } +} +function validateProgram(gl, program) { + callAndCheck(gl, () => gl.validateProgram(program)); + if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) { + console.log(gl.getProgramInfoLog(program)); + throw new Error("Shader program validation failed."); + } +} +function createStaticVertexBuffer(gl, data) { + const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer"); + callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2)); + callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW)); + return buffer2; +} +function createStaticIndexBuffer(gl, data) { + const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer"); + callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer2)); + callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW)); + return buffer2; +} +function getNumChannels() { + if (env().getNumber("WEBGL_VERSION") === 2) { + return 1; + } + return 4; +} +function createTexture(gl) { + return throwIfNull(gl, () => gl.createTexture(), "Unable to create WebGLTexture."); +} +function validateTextureSize(width, height) { + const maxTextureSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE"); + if (width <= 0 || height <= 0) { + const requested = `[${width}x${height}]`; + throw new Error("Requested texture size " + requested + " is invalid."); + } + if (width > maxTextureSize || height > maxTextureSize) { + const requested = `[${width}x${height}]`; + const max6 = `[${maxTextureSize}x${maxTextureSize}]`; + throw new Error("Requested texture size " + requested + " greater than WebGL maximum on this browser / GPU " + max6 + "."); + } +} +function createFramebuffer(gl) { + return throwIfNull(gl, () => gl.createFramebuffer(), "Unable to create WebGLFramebuffer."); +} +function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer2, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) { + const loc = gl.getAttribLocation(program, attribute); + if (loc === -1) { + return false; + } + callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2)); + callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes)); + callAndCheck(gl, () => gl.enableVertexAttribArray(loc)); + return true; +} +function bindTextureUnit(gl, texture, textureUnit) { + validateTextureUnit(gl, textureUnit); + callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit)); + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture)); +} +function unbindTextureUnit(gl, textureUnit) { + validateTextureUnit(gl, textureUnit); + callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit)); + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); +} +function getProgramUniformLocationOrThrow(gl, program, uniformName) { + return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform "' + uniformName + '" not present in program.'); +} +function getProgramUniformLocation(gl, program, uniformName) { + return gl.getUniformLocation(program, uniformName); +} +function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) { + callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit)); + callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit)); +} +function bindCanvasToFramebuffer(gl) { + callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null)); + callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height)); + callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height)); +} +function bindColorTextureToFramebuffer(gl, texture, framebuffer) { + callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer)); + callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0)); +} +function unbindColorTextureFromFramebuffer(gl, framebuffer) { + callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer)); + callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0)); +} +function validateFramebuffer(gl) { + const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER); + if (status !== gl.FRAMEBUFFER_COMPLETE) { + throw new Error("Error binding framebuffer: " + getFramebufferErrorMessage(gl, status)); + } +} +function getFramebufferErrorMessage(gl, status) { + switch (status) { + case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT: + return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT"; + case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT: + return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT"; + case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS: + return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS"; + case gl.FRAMEBUFFER_UNSUPPORTED: + return "FRAMEBUFFER_UNSUPPORTED"; + default: + return `unknown error ${status}`; + } +} +function throwIfNull(gl, returnTOrNull, failureMessage) { + const tOrNull = callAndCheck(gl, () => returnTOrNull()); + if (tOrNull == null) { + throw new Error(failureMessage); + } + return tOrNull; +} +function validateTextureUnit(gl, textureUnit) { + const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1; + const glTextureUnit = textureUnit + gl.TEXTURE0; + if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) { + const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`; + throw new Error(`textureUnit must be in ${textureUnitRange}.`); + } +} +function getBatchDim(shape, dimsToSkip = 2) { + return util_exports.sizeFromShape(shape.slice(0, shape.length - dimsToSkip)); +} +function getRowsCols(shape) { + if (shape.length === 0) { + throw Error("Cannot get rows and columns of an empty shape array."); + } + return [ + shape.length > 1 ? shape[shape.length - 2] : 1, + shape[shape.length - 1] + ]; +} +function getShapeAs3D(shape) { + let shapeAs3D = [1, 1, 1]; + const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1; + if (!isScalar) { + shapeAs3D = [getBatchDim(shape), ...getRowsCols(shape)]; + } + return shapeAs3D; +} +function getTextureShapeFromLogicalShape(logShape, isPacked = false) { + let maxTexSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE"); + let maxSizeForNarrowTex = env().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE"); + if (maxSizeForNarrowTex === Infinity && env().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE")) { + maxSizeForNarrowTex = maxTexSize / 2; + } + if (isPacked) { + maxTexSize = maxTexSize * 2; + maxSizeForNarrowTex = maxSizeForNarrowTex * 2; + logShape = logShape.map((d, i) => i >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i]) : logShape[i]); + if (logShape.length === 1) { + logShape = [2, logShape[0]]; + } + } + if (logShape.length !== 2) { + const squeezeResult = util_exports.squeezeShape(logShape); + logShape = squeezeResult.newShape; + } + let size = util_exports.sizeFromShape(logShape); + let textureShape = null; + if (logShape.length <= 1 && size <= maxTexSize) { + textureShape = [1, size]; + } else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) { + textureShape = logShape; + } else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) { + textureShape = [logShape[0] * logShape[1], logShape[2]]; + } else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) { + textureShape = [logShape[0], logShape[1] * logShape[2]]; + } else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) { + textureShape = [logShape[0] * logShape[1] * logShape[2], logShape[3]]; + } else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) { + textureShape = [logShape[0], logShape[1] * logShape[2] * logShape[3]]; + } + const isLongNarrowTex = textureShape != null && Math.max(...textureShape) > maxSizeForNarrowTex && Math.min(...textureShape) <= (isPacked ? 2 : 1) && Math.min(...textureShape) > 0; + if (textureShape == null || isLongNarrowTex) { + if (isPacked) { + const batchDim = getBatchDim(logShape); + let rows = 2, cols = 2; + if (logShape.length) { + [rows, cols] = getRowsCols(logShape); + } + size = batchDim * (rows / 2) * (cols / 2); + textureShape = util_exports.sizeToSquarishShape(size).map((d) => d * 2); + } else { + textureShape = util_exports.sizeToSquarishShape(size); + } + } + return textureShape; +} +function isEven(n) { + return n % 2 === 0; +} +function isReshapeFree(shape1, shape2) { + shape1 = shape1.slice(-2); + shape2 = shape2.slice(-2); + if (util_exports.arraysEqual(shape1, shape2)) { + return true; + } + if (!shape1.length || !shape2.length) { + return true; + } + if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) { + return true; + } + if (shape1.length !== shape2.length) { + const shape1Cols = shape1[shape1.length - 1]; + const shape2Cols = shape2[shape2.length - 1]; + if (shape1Cols === shape2Cols) { + return true; + } + if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) { + return true; + } + } + return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]); +} +var MAX_TEXTURE_SIZE; +var MAX_TEXTURES_IN_SHADER; +function getWebGLMaxTextureSize(webGLVersion) { + if (MAX_TEXTURE_SIZE == null) { + const gl = getWebGLContext(webGLVersion); + MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE); + } + return MAX_TEXTURE_SIZE; +} +function resetMaxTextureSize() { + MAX_TEXTURE_SIZE = null; +} +function resetMaxTexturesInShader() { + MAX_TEXTURES_IN_SHADER = null; +} +function getMaxTexturesInShader(webGLVersion) { + if (MAX_TEXTURES_IN_SHADER == null) { + const gl = getWebGLContext(webGLVersion); + MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS); + } + return Math.min(16, MAX_TEXTURES_IN_SHADER); +} +function getWebGLDisjointQueryTimerVersion(webGLVersion) { + if (webGLVersion === 0) { + return 0; + } + let queryTimerVersion; + const gl = getWebGLContext(webGLVersion); + if (hasExtension(gl, "EXT_disjoint_timer_query_webgl2") && webGLVersion === 2) { + queryTimerVersion = 2; + } else if (hasExtension(gl, "EXT_disjoint_timer_query")) { + queryTimerVersion = 1; + } else { + queryTimerVersion = 0; + } + return queryTimerVersion; +} +function hasExtension(gl, extensionName) { + const ext = gl.getExtension(extensionName); + return ext != null; +} +function isWebGLVersionEnabled(webGLVersion) { + try { + const gl = getWebGLContext(webGLVersion); + if (gl != null) { + return true; + } + } catch (e) { + console.log("Error when getting WebGL context: ", e); + return false; + } + return false; +} +function isCapableOfRenderingToFloatTexture(webGLVersion) { + if (webGLVersion === 0) { + return false; + } + const gl = getWebGLContext(webGLVersion); + if (webGLVersion === 1) { + if (!hasExtension(gl, "OES_texture_float")) { + return false; + } + } else { + if (!hasExtension(gl, "EXT_color_buffer_float")) { + return false; + } + } + const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl); + return isFrameBufferComplete; +} +function isDownloadFloatTextureEnabled(webGLVersion) { + if (webGLVersion === 0) { + return false; + } + const gl = getWebGLContext(webGLVersion); + if (webGLVersion === 1) { + if (!hasExtension(gl, "OES_texture_float")) { + return false; + } + if (!hasExtension(gl, "WEBGL_color_buffer_float")) { + return false; + } + } else { + if (hasExtension(gl, "EXT_color_buffer_float")) { + return createFloatTextureAndBindToFramebuffer(gl); + } + const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float"; + if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) { + const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT); + return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension); + } + return false; + } + const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl); + return isFrameBufferComplete; +} +function createFloatTextureAndBindToFramebuffer(gl) { + const texConfig = getTextureConfig(gl); + const texture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, texture); + const width = 1; + const height = 1; + gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null); + const frameBuffer = gl.createFramebuffer(); + gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer); + gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); + const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE; + gl.bindTexture(gl.TEXTURE_2D, null); + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + gl.deleteTexture(texture); + gl.deleteFramebuffer(frameBuffer); + return isFrameBufferComplete; +} +function createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension) { + const texConfig = getTextureConfig(gl, textureHalfFloatExtension); + const texture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, texture); + const width = 1; + const height = 1; + gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null); + const frameBuffer = gl.createFramebuffer(); + gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer); + gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); + const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE; + gl.bindTexture(gl.TEXTURE_2D, null); + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + gl.deleteTexture(texture); + gl.deleteFramebuffer(frameBuffer); + return isFrameBufferComplete; +} +function isWebGLFenceEnabled(webGLVersion) { + if (webGLVersion !== 2) { + return false; + } + const gl = getWebGLContext(webGLVersion); + const isEnabled = gl.fenceSync != null; + return isEnabled; +} +function assertNotComplex2(tensor2, opName) { + if (!Array.isArray(tensor2)) { + tensor2 = [tensor2]; + } + tensor2.forEach((t) => { + if (t != null) { + util_exports.assert(t.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the WebGL backend.`); + } + }); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/flags_webgl.js +var ENV5 = env(); +ENV5.registerFlag("HAS_WEBGL", () => ENV5.getNumber("WEBGL_VERSION") > 0); +ENV5.registerFlag("WEBGL_VERSION", () => { + if (isWebGLVersionEnabled(2)) { + return 2; + } else if (isWebGLVersionEnabled(1)) { + return 1; + } + return 0; +}); +ENV5.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false); +ENV5.registerFlag("WEBGL_BUFFER_SUPPORTED", () => ENV5.get("WEBGL_VERSION") === 2); +ENV5.registerFlag("WEBGL_CPU_FORWARD", () => true); +ENV5.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false); +ENV5.registerFlag("WEBGL_PACK", () => ENV5.getBool("HAS_WEBGL")); +ENV5.registerFlag("WEBGL_PACK_NORMALIZATION", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_CLIP", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_REDUCE", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_LAZILY_UNPACK", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_CONV_IM2COL", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE", () => ENV5.getBool("WEBGL_PACK")); +ENV5.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => getWebGLMaxTextureSize(ENV5.getNumber("WEBGL_VERSION"))); +ENV5.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => getMaxTexturesInShader(ENV5.getNumber("WEBGL_VERSION"))); +ENV5.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => { + const webGLVersion = ENV5.getNumber("WEBGL_VERSION"); + if (webGLVersion === 0) { + return 0; + } + return getWebGLDisjointQueryTimerVersion(webGLVersion); +}); +ENV5.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => ENV5.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !device_util_exports.isMobile()); +ENV5.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => isCapableOfRenderingToFloatTexture(ENV5.getNumber("WEBGL_VERSION"))); +ENV5.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => { + return ENV5.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : ENV5.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"); +}); +ENV5.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => isDownloadFloatTextureEnabled(ENV5.getNumber("WEBGL_VERSION"))); +ENV5.registerFlag("WEBGL_FENCE_API_ENABLED", () => isWebGLFenceEnabled(ENV5.getNumber("WEBGL_VERSION"))); +ENV5.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => { + const useUniforms = ENV5.getBool("WEBGL_RENDER_FLOAT32_ENABLED"); + return useUniforms ? 4 : 0; +}); +ENV5.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => { + return -1; +}, (threshold3) => { + if (!(typeof threshold3 === "number")) { + throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${threshold3}.`); + } + if (threshold3 < 0 && threshold3 !== -1) { + throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold3}.`); + } +}); +ENV5.registerFlag("WEBGL_FLUSH_THRESHOLD", () => { + return device_util_exports.isMobile() ? 1 : -1; +}, (threshold3) => { + if (!(typeof threshold3 === "number")) { + throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${threshold3}.`); + } + if (threshold3 < 0 && threshold3 !== -1) { + throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold3}.`); + } +}); +ENV5.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128); +ENV5.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false); +ENV5.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5); +ENV5.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128); +ENV5.registerFlag("WEBGL_EXP_CONV", () => false); +ENV5.registerFlag("SOFTWARE_WEBGL_ENABLED", () => ENV5.getBool("IS_TEST")); +ENV5.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE", () => Infinity); +ENV5.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE", () => false); +ENV5.registerFlag("WEBGL2_ISNAN_CUSTOM", () => false); +ENV5.registerFlag("ENGINE_COMPILE_ONLY", () => false); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/glsl_version.js +function getGlslDifferences() { + let version10; + let attribute; + let varyingVs; + let varyingFs; + let texture2D; + let output; + let defineOutput; + let defineSpecialNaN; + let defineSpecialInf; + let defineRound; + if (env().getNumber("WEBGL_VERSION") === 2) { + version10 = "#version 300 es"; + attribute = "in"; + varyingVs = "out"; + varyingFs = "in"; + texture2D = "texture"; + output = "outputColor"; + defineOutput = "out vec4 outputColor;"; + defineSpecialNaN = env().getBool("WEBGL2_ISNAN_CUSTOM") ? ` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -78,7 +54575,9 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram } #define isnan(value) isnan_custom(value) - `:"",u="",l=` + ` : ""; + defineSpecialInf = ``; + defineRound = ` #define round(value) newRound(value) int newRound(float value) { return int(floor(value + 0.5)); @@ -87,7 +54586,16 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram ivec4 newRound(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `):(r="",t="attribute",e="varying",n="varying",o="texture2D",s="gl_FragColor",i="",a=` + `; + } else { + version10 = ""; + attribute = "attribute"; + varyingVs = "varying"; + varyingFs = "varying"; + texture2D = "texture2D"; + output = "gl_FragColor"; + defineOutput = ""; + defineSpecialNaN = ` #define isnan(value) isnan_custom(value) bool isnan_custom(float val) { return (val > 0. || val < 1. || val == 0.) ? false : true; @@ -95,7 +54603,8 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram bvec4 isnan_custom(vec4 val) { return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w)); } - `,u=` + `; + defineSpecialInf = ` uniform float INFINITY; bool isinf(float val) { @@ -104,7 +54613,8 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram bvec4 isinf(vec4 val) { return equal(abs(val), vec4(INFINITY)); } - `,l=` + `; + defineRound = ` int round(float value) { return int(floor(value + 0.5)); } @@ -112,15 +54622,74 @@ Hi, looks like you are running TensorFlow.js in Node.js. 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 ki(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 yp(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}; ${a};`}).join("")}function Fnt(r,t){let e=r.length,n=r.map(s=>`${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 TL(r,t,e="index"){let n=r.map((s,i)=>i),o=Fnt(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 Ed(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 Ad(){return` + return { + version: version10, + attribute, + varyingVs, + varyingFs, + texture2D, + output, + defineOutput, + defineSpecialNaN, + defineSpecialInf, + defineRound + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler_util.js +function getLogicalCoordinatesFromFlatIndex(coords2, shape, index = "index") { + const strides = util_exports.computeStrides(shape); + return strides.map((stride, i) => { + const line1 = `int ${coords2[i]} = ${index} / ${stride}`; + const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${stride}` : `index -= ${coords2[i]} * ${stride}`; + return `${line1}; ${line2};`; + }).join(""); +} +function getOutputLogicalCoordinatesFromFlatIndexByUniform(coords2, shape, index = "index") { + const strides = util_exports.computeStrides(shape); + return strides.map((_, i) => { + const line1 = `int ${coords2[i]} = ${index} / outShapeStrides[${i}]`; + const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * outShapeStrides[${i}]` : `index -= ${coords2[i]} * outShapeStrides[${i}]`; + return `${line1}; ${line2};`; + }).join(""); +} +function symbolicallyComputeStrides(indicesArr, variableName) { + const numCoords = indicesArr.length; + const shape = indicesArr.map((d) => `${variableName}[${d}]`); + const strides = new Array(numCoords - 1); + strides[numCoords - 2] = shape[numCoords - 1]; + for (let i = numCoords - 3; i >= 0; --i) { + strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`; + } + return strides; +} +function getLogicalCoordinatesFromFlatIndexByUniform(coords2, variableName, index = "index") { + const indicesArray = coords2.map((_, i) => i); + const strides = symbolicallyComputeStrides(indicesArray, variableName); + return strides.map((_, i) => { + const line1 = `int ${coords2[i]} = ${index} / ${strides[i]}`; + const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${strides[i]}` : `index -= ${coords2[i]} * ${strides[i]}`; + return `${line1}; ${line2};`; + }).join(""); +} +function getFlatIndexFrom3D(shape) { + const strides = util_exports.computeStrides(shape).map((d) => d.toString()); + return ` + int getFlatIndex(ivec3 coords) { + return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z; + } +`; +} +function getFlatIndexFrom3DOutput() { + return ` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var Lw=` +`; +} +var ENCODE_FLOAT_SNIPPET = ` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -159,27 +54728,213 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`;var{getBroadcastDims:_L}=S;function EL(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=>Ont(f,t,e.packedInputs,e.enableShapeUniforms)).join(` -`),i=t.texShape,a=We(),u=Lnt(a),l,c,p=Vnt(a);return t.isPacked?(l=Pnt(t.logicalShape,i,e.enableShapeUniforms),c=Bnt(a)):(l=Mnt(t.logicalShape,i,e.enableShapeUniforms),c=znt(a)),e.packedInputs&&(p+=Hnt),[p,u,c,o,l,s,e.userCode].join(` -`)}function $d(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return not(r,t);case 1:return sot(r,t);case 2:return aot(r,t);case 3:return uot(r,t);case 4:return pot(r,t);case 5:return mot(r);case 6:return fot(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function AL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return rot(r);case 1:return oot(r,t);case 2:return iot(r,t);case 3:return lot(r,t);default:return cot(r,t)}}function Ont(r,t,e=!1,n){let o="";e?o+=AL(r,n):o+=$d(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=dot(r,t):o+=hot(r,t)),o}function Pnt(r,t,e){switch(r.length){case 0:return DL();case 1:return qnt(r,t,e);case 2:return tot(r,t,e);case 3:return jnt(r,t,e);default:return Ynt(r,t,e)}}function Mnt(r,t,e){switch(r.length){case 0:return DL();case 1:return Knt(r,t,e);case 2:return eot(r,t,e);case 3:return Xnt(r,t,e);case 4:return Znt(r,t,e);case 5:return Jnt(r,t);case 6:return Qnt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function Lnt(r){return` +`; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler.js +var { getBroadcastDims: getBroadcastDims2 } = backend_util_exports; +function makeShader(inputsInfo, outputShape, program) { + const prefixSnippets = []; + inputsInfo.forEach((x) => { + const size = util_exports.sizeFromShape(x.shapeInfo.logicalShape); + if (x.shapeInfo.isUniform) { + prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ""};`); + } else { + prefixSnippets.push(`uniform sampler2D ${x.name};`); + prefixSnippets.push(`uniform int offset${x.name};`); + } + if (program.enableShapeUniforms) { + const { uniformShape } = getUniformInfoFromShape(program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape); + switch (uniformShape.length) { + case 1: + prefixSnippets.push(`uniform int ${x.name}Shape;`); + break; + case 2: + prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`); + break; + case 3: + prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`); + break; + case 4: + prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`); + break; + default: + break; + } + prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`); + } + }); + if (program.enableShapeUniforms) { + switch (outputShape.logicalShape.length) { + case 1: + prefixSnippets.push(`uniform int outShape;`); + break; + case 2: + prefixSnippets.push(`uniform ivec2 outShape;`); + prefixSnippets.push(`uniform int outShapeStrides;`); + break; + case 3: + prefixSnippets.push(`uniform ivec3 outShape;`); + prefixSnippets.push(`uniform ivec2 outShapeStrides;`); + break; + case 4: + prefixSnippets.push(`uniform ivec4 outShape;`); + prefixSnippets.push(`uniform ivec3 outShapeStrides;`); + break; + default: + break; + } + prefixSnippets.push(`uniform ivec2 outTexShape;`); + } + if (program.customUniforms) { + program.customUniforms.forEach((d) => { + prefixSnippets.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`); + }); + } + const inputPrefixSnippet = prefixSnippets.join("\n"); + const inputSamplingSnippet = inputsInfo.map((x) => getInputSamplingSnippet(x, outputShape, program.packedInputs, program.enableShapeUniforms)).join("\n"); + const outTexShape = outputShape.texShape; + const glsl = getGlslDifferences(); + const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl); + let outputSamplingSnippet; + let floatTextureSetOutputSnippet; + let shaderPrefix = getShaderPrefix(glsl); + if (outputShape.isPacked) { + outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms); + floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl); + } else { + outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms); + floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl); + } + if (program.packedInputs) { + shaderPrefix += SHADER_PACKED_PREFIX; + } + const source = [ + shaderPrefix, + floatTextureSampleSnippet, + floatTextureSetOutputSnippet, + inputPrefixSnippet, + outputSamplingSnippet, + inputSamplingSnippet, + program.userCode + ].join("\n"); + return source; +} +function getSamplerFromInInfo(inInfo, enableShapeUniforms = false) { + const shape = inInfo.shapeInfo.logicalShape; + switch (shape.length) { + case 0: + return getSamplerScalar(inInfo, enableShapeUniforms); + case 1: + return getSampler1D(inInfo, enableShapeUniforms); + case 2: + return getSampler2D(inInfo, enableShapeUniforms); + case 3: + return getSampler3D(inInfo, enableShapeUniforms); + case 4: + return getSampler4D(inInfo, enableShapeUniforms); + case 5: + return getSampler5D(inInfo); + case 6: + return getSampler6D(inInfo); + default: + throw new Error(`${shape.length}-D input sampling is not yet supported`); + } +} +function getPackedSamplerFromInInfo(inInfo, enableShapeUniforms) { + const shape = inInfo.shapeInfo.logicalShape; + switch (shape.length) { + case 0: + return getPackedSamplerScalar(inInfo); + case 1: + return getPackedSampler1D(inInfo, enableShapeUniforms); + case 2: + return getPackedSampler2D(inInfo, enableShapeUniforms); + case 3: + return getPackedSampler3D(inInfo, enableShapeUniforms); + default: + return getPackedSamplerND(inInfo, enableShapeUniforms); + } +} +function getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false, enableShapeUniforms) { + let res = ""; + if (usesPackedTextures) { + res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms); + } else { + res += getSamplerFromInInfo(inInfo, enableShapeUniforms); + } + const inShape = inInfo.shapeInfo.logicalShape; + const outShape = outShapeInfo.logicalShape; + if (inShape.length <= outShape.length) { + if (usesPackedTextures) { + res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo); + } else { + res += getSamplerAtOutputCoords(inInfo, outShapeInfo); + } + } + return res; +} +function getPackedOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) { + switch (outShape.length) { + case 0: + return getOutputScalarCoords(); + case 1: + return getOutputPacked1DCoords(outShape, outTexShape, enableShapeUniforms); + case 2: + return getOutputPacked2DCoords(outShape, outTexShape, enableShapeUniforms); + case 3: + return getOutputPacked3DCoords(outShape, outTexShape, enableShapeUniforms); + default: + return getOutputPackedNDCoords(outShape, outTexShape, enableShapeUniforms); + } +} +function getOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) { + switch (outShape.length) { + case 0: + return getOutputScalarCoords(); + case 1: + return getOutput1DCoords(outShape, outTexShape, enableShapeUniforms); + case 2: + return getOutput2DCoords(outShape, outTexShape, enableShapeUniforms); + case 3: + return getOutput3DCoords(outShape, outTexShape, enableShapeUniforms); + case 4: + return getOutput4DCoords(outShape, outTexShape, enableShapeUniforms); + case 5: + return getOutput5DCoords(outShape, outTexShape); + case 6: + return getOutput6DCoords(outShape, outTexShape); + default: + throw new Error(`${outShape.length}-D output sampling is not yet supported`); + } +} +function getFloatTextureSampleSnippet(glsl) { + return ` float sampleTexture(sampler2D textureSampler, vec2 uv) { - return ${r.texture2D}(textureSampler, uv).r; + return ${glsl.texture2D}(textureSampler, uv).r; } - `}function znt(r){return` + `; +} +function getFloatTextureSetRSnippet(glsl) { + return ` void setOutput(float val) { - ${r.output} = vec4(val, 0, 0, 0); + ${glsl.output} = vec4(val, 0, 0, 0); } - `}function Bnt(r){return` + `; +} +function getFloatTextureSetRGBASnippet(glsl) { + return ` void setOutput(vec4 val) { - ${r.output} = val; + ${glsl.output} = val; } - `}function Vnt(r){return`${r.version} + `; +} +function getShaderPrefix(glsl) { + const SHADER_PREFIX = `${glsl.version} precision highp float; precision highp int; precision highp sampler2D; - ${r.varyingFs} vec2 resultUV; - ${r.defineOutput} + ${glsl.varyingFs} vec2 resultUV; + ${glsl.defineOutput} const vec2 halfCR = vec2(0.5, 0.5); struct ivec5 @@ -202,9 +54957,9 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram }; uniform float NAN; - ${r.defineSpecialNaN} - ${r.defineSpecialInf} - ${r.defineRound} + ${glsl.defineSpecialNaN} + ${glsl.defineSpecialInf} + ${glsl.defineRound} int imod(int x, int y) { return x - y * (x / y); @@ -229,10 +54984,13 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return fract((p3.x + p3.y) * p3.z); } - ${Gnt} - ${Wnt} - ${Unt} - `}var Gnt=` + ${SAMPLE_1D_SNIPPET} + ${SAMPLE_2D_SNIPPET} + ${SAMPLE_3D_SNIPPET} + `; + return SHADER_PREFIX; +} +var SAMPLE_1D_SNIPPET = ` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; @@ -244,7 +55002,8 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,Wnt=` +`; +var SAMPLE_2D_SNIPPET = ` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); @@ -252,7 +55011,8 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,Unt=` +`; +var SAMPLE_3D_SNIPPET = ` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { @@ -261,7 +55021,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } -`,Hnt=` +`; +var SHADER_PACKED_PREFIX = ` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? @@ -272,68 +55033,111 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function DL(){return` +`; +function getOutputScalarCoords() { + return ` int getOutputCoords() { return 0; } - `}function qnt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?` + `; +} +function getOutputPacked1DCoords(shape, texShape, enableShapeUniforms) { + const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)]; + if (packedTexShape[0] === 1) { + if (enableShapeUniforms) { + return ` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } - `:` + `; + } + return ` int getOutputCoords() { - return 2 * int(resultUV.x * ${n[1]}.0); + return 2 * int(resultUV.x * ${packedTexShape[1]}.0); } - `:n[1]===1?e?` + `; + } + if (packedTexShape[1] === 1) { + if (enableShapeUniforms) { + return ` int getOutputCoords() { return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0)); } - `:` + `; + } + return ` int getOutputCoords() { - return 2 * int(resultUV.y * ${n[0]}.0); + return 2 * int(resultUV.y * ${packedTexShape[0]}.0); } - `:e?` + `; + } + if (enableShapeUniforms) { + return ` int getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y); } - `:` + `; + } + return ` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * - vec2(${n[0]}, ${n[1]})); - return 2 * (resTexRC.x * ${n[1]} + resTexRC.y); + vec2(${packedTexShape[0]}, ${packedTexShape[1]})); + return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y); } - `}function Knt(r,t,e){return t[0]===1?e?` + `; +} +function getOutput1DCoords(shape, texShape, enableShapeUniforms) { + if (texShape[0] === 1) { + if (enableShapeUniforms) { + return ` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } - `:` + `; + } + return ` int getOutputCoords() { - return int(resultUV.x * ${t[1]}.0); + return int(resultUV.x * ${texShape[1]}.0); } - `:t[1]===1?e?` + `; + } + if (texShape[1] === 1) { + if (enableShapeUniforms) { + return ` int getOutputCoords() { return int(resultUV.y * float(outTexShape[0])); } - `:` + `; + } + return ` int getOutputCoords() { - return int(resultUV.y * ${t[0]}.0); + return int(resultUV.y * ${texShape[0]}.0); } - `:e?` + `; + } + if (enableShapeUniforms) { + return ` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); return resTexRC.x * outTexShape[1] + resTexRC.y; } - `:` + `; + } + return ` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * - vec2(${t[0]}, ${t[1]})); - return resTexRC.x * ${t[1]} + resTexRC.y; + vec2(${texShape[0]}, ${texShape[1]})); + return resTexRC.x * ${texShape[1]} + resTexRC.y; } - `}function jnt(r,t,e){if(e)return` + `; +} +function getOutputPacked3DCoords(shape, texShape, enableShapeUniforms) { + if (enableShapeUniforms) { + return ` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); @@ -350,37 +55154,54 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return` + `; + } + const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)]; + const texelsInLogicalRow = Math.ceil(shape[2] / 2); + const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2); + return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * - vec2(${n[0]}, ${n[1]})); - int index = resTexRC.x * ${n[1]} + resTexRC.y; + vec2(${packedTexShape[0]}, ${packedTexShape[1]})); + int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y; - int b = index / ${s}; - index -= b * ${s}; + int b = index / ${texelsInBatch}; + index -= b * ${texelsInBatch}; - int r = 2 * (index / ${o}); - int c = imod(index, ${o}) * 2; + int r = 2 * (index / ${texelsInLogicalRow}); + int c = imod(index, ${texelsInLogicalRow}) * 2; return ivec3(b, r, c); } - `}function Xnt(r,t,e){if(e)return` + `; +} +function getOutput3DCoords(shape, texShape, enableShapeUniforms) { + if (enableShapeUniforms) { + const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], shape); + return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${yp(["r","c","d"],r)} + ${coordsFromIndexSnippet2} return ivec3(r, c, d); } -`;let n=ki(["r","c","d"],r);return` +`; + } + const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape); + return ` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * - vec2(${t[0]}, ${t[1]})); - int index = resTexRC.x * ${t[1]} + resTexRC.y; - ${n} + vec2(${texShape[0]}, ${texShape[1]})); + int index = resTexRC.x * ${texShape[1]} + resTexRC.y; + ${coordsFromIndexSnippet} return ivec3(r, c, d); } - `}function Ynt(r,t,e){if(e)return` + `; +} +function getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) { + if (enableShapeUniforms) { + return ` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -402,74 +55223,115 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec4(b2, b, r, c); } - `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a="",u="b, r, c";for(let l=2;l=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(` -`);let m="";i<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=` + `; +} +function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) { + const texName = inputInfo.name; + const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1); + const funcName = "get" + texFuncSnippet + "AtOutCoords"; + const inRank = inputInfo.shapeInfo.logicalShape.length; + const outRank = outShapeInfo.logicalShape.length; + const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape); + const type = getCoordsDataType(outRank); + const rankDiff = outRank - inRank; + let coordsSnippet; + const fields = ["x", "y", "z", "w", "u", "v"]; + if (inRank === 0) { + coordsSnippet = ""; + } else if (outRank < 2 && broadcastDims.length >= 1) { + coordsSnippet = "coords = 0;"; + } else { + coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n"); + } + let unpackedCoordsSnippet = ""; + if (outRank < 2 && inRank > 0) { + unpackedCoordsSnippet = "coords"; + } else { + unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", "); + } + let output = `return outputValue;`; + const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape); + const isInputScalar = inSize === 1; + const outSize = util_exports.sizeFromShape(outShapeInfo.logicalShape); + const isOutputScalar = outSize === 1; + if (inRank === 1 && !isInputScalar && !isOutputScalar) { + output = ` return vec4(outputValue.xy, outputValue.xy); - `;else if(h&&!x)i===1?f=` + `; + } else if (isInputScalar && !isOutputScalar) { + if (outRank === 1) { + output = ` return vec4(outputValue.x, outputValue.x, 0., 0.); - `:f=` + `; + } else { + output = ` return vec4(outputValue.x); - `;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` - vec4 ${o}() { - ${u} coords = getOutputCoords(); - ${c} - vec4 outputValue = get${n}(${m}); - ${f} + `; } - `}function hot(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); + } else if (broadcastDims.length) { + const rows = inRank - 2; + const cols = inRank - 1; + if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) { + output = `return vec4(outputValue.x);`; + } else if (broadcastDims.indexOf(rows) > -1) { + output = `return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);`; + } else if (broadcastDims.indexOf(cols) > -1) { + output = `return vec4(outputValue.xx, outputValue.zz);`; + } + } + return ` + vec4 ${funcName}() { + ${type} coords = getOutputCoords(); + ${coordsSnippet} + vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet}); + ${output} + } + `; +} +function getSamplerAtOutputCoords(inputInfo, outShapeInfo) { + const texName = inputInfo.name; + const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1); + const funcName = "get" + texFuncSnippet + "AtOutCoords"; + const outTexShape = outShapeInfo.texShape; + const inTexShape = inputInfo.shapeInfo.texShape; + const inRank = inputInfo.shapeInfo.logicalShape.length; + const outRank = outShapeInfo.logicalShape.length; + if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && util_exports.arraysEqual(inTexShape, outTexShape)) { + return ` + float ${funcName}() { + return sampleTexture(${texName}, resultUV); } - `;let l=zt(u),c=_L(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(); - ${m} - return get${n}(${d}); + `; + } + const type = getCoordsDataType(outRank); + const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape); + const rankDiff = outRank - inRank; + let coordsSnippet; + const fields = ["x", "y", "z", "w", "u", "v"]; + if (inRank === 0) { + coordsSnippet = ""; + } else if (outRank < 2 && broadcastDims.length >= 1) { + coordsSnippet = "coords = 0;"; + } else { + coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n"); + } + let unpackedCoordsSnippet = ""; + if (outRank < 2 && inRank > 0) { + unpackedCoordsSnippet = "coords"; + } else { + unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", "); + } + return ` + float ${funcName}() { + ${type} coords = getOutputCoords(); + ${coordsSnippet} + return get${texFuncSnippet}(${unpackedCoordsSnippet}); } - `}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 ${r} is not yet supported`)}function zw(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.lengthr[e]).join(", ")}function RL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=EL(o,i,t),u=zT(r.gl,a),l=r.createProgram(u);return L().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},n1(r,t,l)))}function n1(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,"NAN",!1),L().getNumber("WEBGL_VERSION")===1&&(u=r.getUniformLocation(e,"INFINITY",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${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 $L(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. Shapes ${o} and ${i} must match`);if(e.isUniform&&s.isUniform)return;let a=e.texShape,u=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(a,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${u} must match`)})}function FL(r,t,e,n,o){t.program.enableShapeUniforms||($L(t.inShapeInfos,e),$L([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=zw(r.packedInputs,i.shape,u),m="",f="",d="";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${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=Zu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` + `; +} +function getCoordsDataType(rank) { + if (rank <= 1) { + return "int"; + } else if (rank === 2) { + return "ivec2"; + } else if (rank === 3) { + return "ivec3"; + } else if (rank === 4) { + return "ivec4"; + } else if (rank === 5) { + return "ivec5"; + } else if (rank === 6) { + return "ivec6"; + } else { + throw Error(`GPU for rank ${rank} is not yet supported`); + } +} +function getUniformInfoFromShape(isPacked, shape, texShape) { + const { newShape, keptDims } = util_exports.squeezeShape(shape); + const rank = shape.length; + const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1; + const squeezeShape2 = useSqueezePackedShape ? shape.slice(1) : newShape; + const useSqueezeShape = !isPacked && rank > 1 && !util_exports.arraysEqual(shape, texShape) && newShape.length < rank || useSqueezePackedShape; + const uniformShape = useSqueezeShape ? squeezeShape2 : shape; + return { useSqueezeShape, uniformShape, keptDims }; +} +function squeezeInputInfo(inInfo, squeezedShape) { + const newInputInfo = JSON.parse(JSON.stringify(inInfo)); + newInputInfo.shapeInfo.logicalShape = squeezedShape; + return newInputInfo; +} +function getSqueezedParams(params, keptDims) { + return keptDims.map((d) => params[d]).join(", "); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js +function compileProgram(gpgpu, program, inputs, output) { + const inputInfos = inputs.map((input2, i) => { + const shapeInfo = { + logicalShape: input2.shape, + texShape: input2.isUniform ? null : input2.texData.texShape, + isUniform: input2.isUniform, + isPacked: input2.isUniform ? false : input2.texData.isPacked, + flatOffset: null + }; + if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) { + shapeInfo.flatOffset = input2.texData.slice.flatOffset; + } + return { name: program.variableNames[i], shapeInfo }; + }); + const inShapeInfos = inputInfos.map((x) => x.shapeInfo); + const outShapeInfo = { + logicalShape: output.shape, + texShape: output.texData.texShape, + isUniform: false, + isPacked: output.texData.isPacked, + flatOffset: null + }; + const source = makeShader(inputInfos, outShapeInfo, program); + const fragmentShader = createFragmentShader(gpgpu.gl, source); + const webGLProgram = gpgpu.createProgram(fragmentShader); + if (!env().get("ENGINE_COMPILE_ONLY")) { + gpgpu.buildVao(webGLProgram); + return Object.assign({ + program, + fragmentShader, + source, + webGLProgram, + inShapeInfos, + outShapeInfo + }, getUniformLocations(gpgpu, program, webGLProgram)); + } else { + return { + program, + fragmentShader, + source, + webGLProgram, + inShapeInfos, + outShapeInfo, + variablesLocations: null, + customUniformLocations: null, + infLoc: null, + nanLoc: null, + outShapeLocation: null, + outShapeStridesLocation: null, + outTexShapeLocation: null + }; + } +} +function getUniformLocations(gpgpu, program, webGLProgram) { + const variablesLocations = []; + const customUniformLocations = []; + let outShapeLocation; + let outTexShapeLocation; + let outShapeStridesLocation; + let infLoc = null; + let nanLoc = null; + nanLoc = gpgpu.getUniformLocation(webGLProgram, "NAN", false); + if (env().getNumber("WEBGL_VERSION") === 1) { + infLoc = gpgpu.getUniformLocation(webGLProgram, "INFINITY", false); + } + const shouldThrow = false; + for (const varName of program.variableNames) { + const varLocs = { + name: varName, + uniform: gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow), + offset: gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow) + }; + if (program.enableShapeUniforms) { + varLocs.shape = gpgpu.getUniformLocation(webGLProgram, `${varName}Shape`, shouldThrow); + varLocs.texShape = gpgpu.getUniformLocation(webGLProgram, `${varName}TexShape`, shouldThrow); + } + variablesLocations.push(varLocs); + } + if (program.enableShapeUniforms) { + outShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outShape", shouldThrow); + outShapeStridesLocation = gpgpu.getUniformLocation(webGLProgram, "outShapeStrides", shouldThrow); + outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outTexShape", shouldThrow); + } + if (program.customUniforms) { + for (const d of program.customUniforms) { + customUniformLocations.push(gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow)); + } + } + return { + variablesLocations, + customUniformLocations, + infLoc, + nanLoc, + outShapeLocation, + outShapeStridesLocation, + outTexShapeLocation + }; +} +function validateBinaryAndProgram(shapeInfos, inputs) { + if (shapeInfos.length !== inputs.length) { + throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`); + } + shapeInfos.forEach((s, i) => { + const shapeA = s.logicalShape; + const input2 = inputs[i]; + const shapeB = input2.shape; + if (!util_exports.arraysEqual(shapeA, shapeB)) { + throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`); + } + if (s.isUniform && input2.isUniform) { + return; + } + const texShapeA = s.texShape; + const texShapeB = input2.isUniform ? null : input2.texData.texShape; + if (!util_exports.arraysEqual(texShapeA, texShapeB)) { + throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`); + } + }); +} +function runProgram(gpgpu, binary, inputs, output, customUniformValues) { + if (!binary.program.enableShapeUniforms) { + validateBinaryAndProgram(binary.inShapeInfos, inputs); + validateBinaryAndProgram([binary.outShapeInfo], [output]); + } + const outTex = output.texData.texture; + const outTexShape = output.texData.texShape; + if (output.texData.isPacked) { + gpgpu.setOutputPackedMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]); + } else { + gpgpu.setOutputMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]); + } + gpgpu.setProgram(binary.webGLProgram); + gpgpu.bindVertexArray(binary.webGLProgram.vao); + if (env().getNumber("WEBGL_VERSION") === 1) { + if (binary.infLoc !== null) { + gpgpu.gl.uniform1f(binary.infLoc, Infinity); + } + } + if (binary.nanLoc !== null) { + gpgpu.gl.uniform1f(binary.nanLoc, NaN); + } + for (let i = 0; i < inputs.length; ++i) { + const input2 = inputs[i]; + const { uniform: varLoc, offset: varOffsetLoc, shape: varShapeLoc, texShape: varTexShapeLoc } = binary.variablesLocations[i]; + if (varShapeLoc) { + const { uniformShape } = getUniformInfoFromShape(binary.program.packedInputs, input2.shape, input2.texData.texShape); + switch (uniformShape.length) { + case 1: + gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape)); + break; + case 2: + gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape)); + break; + case 3: + gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape)); + break; + case 4: + gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape)); + break; + default: + break; + } + } + if (varTexShapeLoc) { + gpgpu.gl.uniform2i(varTexShapeLoc, input2.texData.texShape[0], input2.texData.texShape[1]); + } + if (varLoc == null) { + continue; + } + if (input2.isUniform) { + if (util_exports.sizeFromShape(input2.shape) < 2) { + gpgpu.gl.uniform1f(varLoc, input2.uniformValues[0]); + } else { + let vals = input2.uniformValues; + if (!(vals instanceof Float32Array)) { + vals = new Float32Array(vals); + } + gpgpu.gl.uniform1fv(varLoc, vals); + } + continue; + } + if (input2.texData.slice != null && varOffsetLoc != null) { + gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset); + } + gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i); + } + const outShapeLoc = binary.outShapeLocation; + if (outShapeLoc) { + switch (output.shape.length) { + case 1: + gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape)); + break; + case 2: + gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape)); + break; + case 3: + gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape)); + break; + case 4: + gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape)); + break; + default: + break; + } + } + if (binary.outShapeStridesLocation) { + const strides = util_exports.computeStrides(output.shape); + switch (output.shape.length) { + case 2: + gpgpu.gl.uniform1iv(binary.outShapeStridesLocation, new Int32Array(strides)); + break; + case 3: + gpgpu.gl.uniform2iv(binary.outShapeStridesLocation, new Int32Array(strides)); + break; + case 4: + gpgpu.gl.uniform3iv(binary.outShapeStridesLocation, new Int32Array(strides)); + break; + default: + break; + } + } + if (binary.outTexShapeLocation) { + gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]); + } + if (binary.program.customUniforms && customUniformValues) { + for (let i = 0; i < binary.program.customUniforms.length; ++i) { + const d = binary.program.customUniforms[i]; + const customLoc = binary.customUniformLocations[i]; + const customValue = customUniformValues[i]; + if (d.type === "float") { + gpgpu.gl.uniform1fv(customLoc, customValue); + } else if (d.type === "vec2") { + gpgpu.gl.uniform2fv(customLoc, customValue); + } else if (d.type === "vec3") { + gpgpu.gl.uniform3fv(customLoc, customValue); + } else if (d.type === "vec4") { + gpgpu.gl.uniform4fv(customLoc, customValue); + } else if (d.type === "int") { + gpgpu.gl.uniform1iv(customLoc, customValue); + } else if (d.type === "ivec2") { + gpgpu.gl.uniform2iv(customLoc, customValue); + } else if (d.type === "ivec3") { + gpgpu.gl.uniform3iv(customLoc, customValue); + } else if (d.type === "ivec4") { + gpgpu.gl.uniform4iv(customLoc, customValue); + } else { + throw Error(`uniform type ${d.type} is not supported yet.`); + } + } + } + gpgpu.executeProgram(); +} +function makeShaderKey(program, inputs, output) { + let keyInputs = ""; + inputs.concat(output).forEach((x) => { + const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0; + if (program.enableShapeUniforms && !x.isUniform) { + const xTexShape = x.texData.texShape; + const { useSqueezeShape, uniformShape, keptDims } = getUniformInfoFromShape(program.packedInputs, x.shape, xTexShape); + let rank1 = "", rank2 = "", rank34 = ""; + if (uniformShape.length === 1 && program.packedInputs) { + const packedTexShape = [Math.ceil(xTexShape[0] / 2), Math.ceil(xTexShape[1] / 2)]; + rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`; + } else if (uniformShape.length === 2 && !program.packedInputs) { + rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`; + } else if (uniformShape.length > 2 && !program.packedInputs) { + const strides = util_exports.computeStrides(uniformShape); + rank34 = `${strides[0] === xTexShape[1]}_${strides[strides.length - 1] === xTexShape[1]}`; + } + const xRank = x.shape.length; + const isLogicalShapTexShapeEqual = uniformShape.length === 2 && util_exports.arraysEqual(x.shape, xTexShape); + const isScalar = util_exports.sizeFromShape(x.shape) === 1; + const broadcastDims = backend_util_exports.getBroadcastDims(x.shape, output.shape); + const isInOutTexShapeEqual = !program.packedInputs && xRank === output.shape.length && util_exports.arraysEqual(xTexShape, output.texData.texShape); + const isTexShapeGreaterThanOne = program.packedInputs || uniformShape.length > 2 ? "" : `${xTexShape[0] > 1}_${xTexShape[1] > 1}`; + keyInputs += `${xRank}_${isInOutTexShapeEqual}_${useSqueezeShape ? keptDims : ""}_${uniformShape.length}_${isScalar}_${broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`; + } else { + const texShape = x.isUniform ? "uniform" : x.texData.texShape; + keyInputs += `${x.shape}_${texShape}_${hasOffset}`; + } + }); + const keyUserCode = program.userCode; + let key = program.constructor.name; + key += "_" + keyInputs + "_" + keyUserCode + `${env().getNumber("WEBGL_VERSION")}`; + return key; +} +function useShapeUniforms(rank) { + return env().getBool("WEBGL_USE_SHAPES_UNIFORMS") && rank <= 4; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_gpu.js +var DecodeMatrixProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = false; + this.packedOutput = true; + this.outPackingScheme = PackingScheme.DENSE; + this.customUniforms = [{ name: "texShape", type: "ivec2" }]; + const glsl = getGlslDifferences(); + this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + this.userCode = ` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?yp(["r","c","d"],t):ki(["r","c","d"],t)} + ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], outputShape) : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)} return ivec3(r, c, d); } @@ -1006,11 +56638,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result[i] = getA(rc.x, rc.y, rc.z); } - ${e.output} = result; + ${glsl.output} = result; } - `}};var Vw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Zu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_packed_gpu.js +var DecodeMatrixPackedProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.outPackingScheme = PackingScheme.DENSE; + this.customUniforms = [{ name: "texShape", type: "ivec2" }]; + const glsl = getGlslDifferences(); + this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + this.userCode = ` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?yp(["r","c","d"],t):ki(["r","c","d"],t)} + ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], outputShape) : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)} return ivec3(r, c, d); } @@ -1026,52 +56673,125 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z)); } - ${e.output} = result; + ${glsl.output} = result; } - `}};var Gw=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Jr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=` - ${Lw} + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_gpu.js +var EncodeFloatProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.outTexUsage = TextureUsage.DOWNLOAD; + const glsl = getGlslDifferences(); + this.outputShape = outputShape; + this.userCode = ` + ${ENCODE_FLOAT_SNIPPET} void main() { float x = getAAtOutCoords(); - ${e.output} = encode_float(x); + ${glsl.output} = encode_float(x); } - `}};var Ww=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Jr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=` - ${Lw} + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_packed_gpu.js +var EncodeFloatPackedProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = false; + this.outTexUsage = TextureUsage.DOWNLOAD; + const glsl = getGlslDifferences(); + this.outputShape = outputShape; + this.userCode = ` + ${ENCODE_FLOAT_SNIPPET} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); - ${e.output} = encode_float(x); + ${glsl.output} = encode_float(x); } - `}};var yot={R:0,G:1,B:2,A:3},cg=class{constructor(t,e=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let o=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let s="result";e&&(s="floor(result * 255. + 0.5)");let i="";for(let a=0;am1,createBufferFromOutputTexture:()=>h1,createFloat16MatrixTexture:()=>l1,createFloat16PackedMatrixTexture:()=>p1,createFloat32MatrixTexture:()=>a1,createIndexBuffer:()=>i1,createPackedMatrixTexture:()=>c1,createUnsignedBytesMatrixTexture:()=>u1,createVertexBuffer:()=>s1,createVertexShader:()=>o1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>x1,downloadFloat32MatrixFromBuffer:()=>g1,downloadMatrixFromPackedOutputTexture:()=>b1,downloadPackedMatrixFromBuffer:()=>y1,getInternalFormatForFloat16MatrixTexture:()=>qw,getInternalFormatForFloat16PackedMatrixTexture:()=>Xw,getInternalFormatForFloat32MatrixTexture:()=>Hw,getInternalFormatForPackedMatrixTexture:()=>jw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Kw,uploadDenseMatrixToTexture:()=>f1,uploadPixelDataToTexture:()=>d1});function o1(r){let t=We(),e=`${t.version} + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_util.js +var gpgpu_util_exports = {}; +__export(gpgpu_util_exports, { + bindVertexProgramAttributeStreams: () => bindVertexProgramAttributeStreams, + createBufferFromOutputTexture: () => createBufferFromOutputTexture, + createFloat16MatrixTexture: () => createFloat16MatrixTexture, + createFloat16PackedMatrixTexture: () => createFloat16PackedMatrixTexture, + createFloat32MatrixTexture: () => createFloat32MatrixTexture, + createIndexBuffer: () => createIndexBuffer, + createPackedMatrixTexture: () => createPackedMatrixTexture, + createUnsignedBytesMatrixTexture: () => createUnsignedBytesMatrixTexture, + createVertexBuffer: () => createVertexBuffer, + createVertexShader: () => createVertexShader2, + downloadByteEncodedFloatMatrixFromOutputTexture: () => downloadByteEncodedFloatMatrixFromOutputTexture, + downloadFloat32MatrixFromBuffer: () => downloadFloat32MatrixFromBuffer, + downloadMatrixFromPackedOutputTexture: () => downloadMatrixFromPackedOutputTexture, + downloadPackedMatrixFromBuffer: () => downloadPackedMatrixFromBuffer, + getInternalFormatForFloat16MatrixTexture: () => getInternalFormatForFloat16MatrixTexture, + getInternalFormatForFloat16PackedMatrixTexture: () => getInternalFormatForFloat16PackedMatrixTexture, + getInternalFormatForFloat32MatrixTexture: () => getInternalFormatForFloat32MatrixTexture, + getInternalFormatForPackedMatrixTexture: () => getInternalFormatForPackedMatrixTexture, + getInternalFormatForUnsignedBytesMatrixTexture: () => getInternalFormatForUnsignedBytesMatrixTexture, + uploadDenseMatrixToTexture: () => uploadDenseMatrixToTexture, + uploadPixelDataToTexture: () => uploadPixelDataToTexture +}); +function createVertexShader2(gl) { + const glsl = getGlslDifferences(); + const vertexShaderSource = `${glsl.version} precision highp float; - ${t.attribute} vec3 clipSpacePos; - ${t.attribute} vec2 uv; - ${t.varyingVs} vec2 resultUV; + ${glsl.attribute} vec3 clipSpacePos; + ${glsl.attribute} vec2 uv; + ${glsl.varyingVs} vec2 resultUV; void main() { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; - }`;return LT(r,e)}function s1(r){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return GT(r,t)}function i1(r){let t=new Uint16Array([0,1,2,2,1,3]);return WT(r,t)}function pg(r,t,e,n,o,s){HT(t,e);let i=UT(r),a=r.TEXTURE_2D;return ht(r,()=>r.bindTexture(a,i)),ht(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),ht(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),ht(r,()=>r.texParameteri(a,r.TEXTURE_MIN_FILTER,r.NEAREST)),ht(r,()=>r.texParameteri(a,r.TEXTURE_MAG_FILTER,r.NEAREST)),L().getNumber("WEBGL_VERSION")===1?ht(r,()=>r.texImage2D(a,0,n,t,e,0,o,s,null)):ht(r,()=>r.texStorage2D(a,1,n,t,e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:i,texShape:[e,t]}}function Hw(r){return r.internalFormatFloat}function a1(r,t,e,n){let[o,s]=xp(t,e);return pg(r,o,s,Hw(n),n.textureFormatFloat,r.FLOAT)}function qw(r){return r.internalFormatHalfFloat}function l1(r,t,e,n){let[o,s]=xp(t,e);return pg(r,o,s,qw(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Kw(r){return r.downloadTextureFormat}function u1(r,t,e,n){let[o,s]=xp(t,e);return pg(r,o,s,Kw(n),r.RGBA,r.UNSIGNED_BYTE)}function jw(r){return r.internalFormatPackedFloat}function c1(r,t,e,n){let[o,s]=Sa(t,e);return pg(r,o,s,jw(n),r.RGBA,r.FLOAT)}function Xw(r){return r.internalFormatPackedHalfFloat}function p1(r,t,e,n){let[o,s]=Sa(t,e);return pg(r,o,s,Xw(n),r.RGBA,n.textureTypeHalfFloat)}function m1(r,t,e){return ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,e)),Ow(r,t,"clipSpacePos",e,3,20,0)&&Ow(r,t,"uv",e,2,20,12)}function f1(r,t,e,n,o,s){ht(r,()=>r.bindTexture(r.TEXTURE_2D,t));let i,a,u;o instanceof Uint8Array?(i=new Uint8Array(e*n*4),a=r.UNSIGNED_BYTE,u=r.RGBA):(i=new Float32Array(e*n*4),a=r.FLOAT,u=s.internalFormatPackedFloat),i.set(o),L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,e,n,r.RGBA,a,i)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,u,e,n,0,r.RGBA,a,i)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function d1(r,t,e){ht(r,()=>r.bindTexture(r.TEXTURE_2D,t)),e.data instanceof Uint8Array?L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,e.width,e.height,r.RGBA,r.UNSIGNED_BYTE,e.data)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,e.width,e.height,0,r.RGBA,r.UNSIGNED_BYTE,e.data)):L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,e)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function h1(r,t,e,n){let o=r.createBuffer();ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));let a=4*4*t*e;return ht(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,a,r.STREAM_READ)),ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,0)),ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function g1(r,t,e){let n=r,o=new Float32Array(e);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function x1(r,t,e,n){let[o,s]=xp(t,e),i=4,a=new Uint8Array(IL(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function y1(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array(CL(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function b1(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var wp=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=L().getNumber("WEBGL_VERSION");if(t!=null?(this.gl=t,FT(e,t)):this.gl=Yn(e),t=this.gl,L().getNumber("WEBGL_VERSION")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension("OES_vertex_array_object");if(s==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let 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=Nd(this.gl,s),Zn(this.gl,i))this.textureHalfFloatExtension=Nd(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),Zn(this.gl,o))this.colorBufferHalfFloatExtension=Nd(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",Zn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Zn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=s1(this.gl),this.indexBuffer=i1(this.gl),this.framebuffer=qT(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(),a1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),l1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),u1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),d1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),f1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),p1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),c1(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,()=>x1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return y1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return g1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=h1(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,()=>b1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=o1(e));let n=BT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),VT(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)),m1(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?KT(this.gl,t,e):jT(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(),XT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=Sa(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),kd(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=Nd(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=bot(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&&kd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ug(this.gl,this.outputTexture,this.framebuffer),this.debug&&kd(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&&kd(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 bot(r){let t=0;for(;t`${r}.${e}`)}function er(r,t){return t===1?[r]:I1(r,t)}function Tz(r,t){if(r===1)return"rc";let e="";for(let n=0;n gl.bindTexture(tex2d, texture)); + callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE)); + callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE)); + callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST)); + callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST)); + if (env().getNumber("WEBGL_VERSION") === 1) { + callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null)); + } else { + callAndCheck(gl, () => gl.texStorage2D(tex2d, 1, internalFormat, width, height)); + } + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); + return { texture, texShape: [height, width] }; +} +function getInternalFormatForFloat32MatrixTexture(textureConfig) { + return textureConfig.internalFormatFloat; +} +function createFloat32MatrixTexture(gl, rows, columns, textureConfig) { + const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns); + return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT); +} +function getInternalFormatForFloat16MatrixTexture(textureConfig) { + return textureConfig.internalFormatHalfFloat; +} +function createFloat16MatrixTexture(gl, rows, columns, textureConfig) { + const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns); + return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat); +} +function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) { + return textureConfig.downloadTextureFormat; +} +function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) { + const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns); + return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE); +} +function getInternalFormatForPackedMatrixTexture(textureConfig) { + return textureConfig.internalFormatPackedFloat; +} +function createPackedMatrixTexture(gl, rows, columns, textureConfig) { + const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns); + return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT); +} +function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) { + return textureConfig.internalFormatPackedHalfFloat; +} +function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) { + const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns); + return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat); +} +function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) { + const posOffset = 0; + const uvOffset = 3 * 4; + const stride = 3 * 4 + 2 * 4; + callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer)); + const success = bindVertexBufferToProgramAttribute(gl, program, "clipSpacePos", vertexBuffer, 3, stride, posOffset); + return success && bindVertexBufferToProgramAttribute(gl, program, "uv", vertexBuffer, 2, stride, uvOffset); +} +function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) { + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture)); + let dataForUpload, texelDataType, internalFormat; + if (data instanceof Uint8Array) { + dataForUpload = new Uint8Array(width * height * 4); + texelDataType = gl.UNSIGNED_BYTE; + internalFormat = gl.RGBA; + } else { + dataForUpload = new Float32Array(width * height * 4); + texelDataType = gl.FLOAT; + internalFormat = textureConfig.internalFormatPackedFloat; + } + dataForUpload.set(data); + if (env().getNumber("WEBGL_VERSION") === 2) { + callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload)); + } else { + callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload)); + } + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); +} +function uploadPixelDataToTexture(gl, texture, pixels) { + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture)); + if (pixels.data instanceof Uint8Array) { + if (env().getNumber("WEBGL_VERSION") === 2) { + callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data)); + } else { + callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data)); + } + } else { + if (env().getNumber("WEBGL_VERSION") === 2) { + callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels)); + } else { + callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels)); + } + } + callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); +} +function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) { + const buffer2 = gl2.createBuffer(); + callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2)); + const bytesPerFloat = 4; + const valuesPerTexel = 4; + const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns; + callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ)); + callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0)); + callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null)); + return buffer2; +} +function downloadFloat32MatrixFromBuffer(gl, buffer2, size) { + const gl2 = gl; + const downloadTarget = new Float32Array(size); + gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2); + gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget); + gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null); + return downloadTarget; +} +function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) { + const [w, h] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns); + const numChannels = 4; + const downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels)); + callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget)); + return new Float32Array(downloadTarget.buffer); +} +function downloadPackedMatrixFromBuffer(gl, buffer2, batch, rows, cols, physicalRows, physicalCols, textureConfig) { + const gl2 = gl; + const downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols)); + gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2); + gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget); + gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null); + return downloadTarget; +} +function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) { + const packedRGBA = new Float32Array(physicalRows * physicalCols * 4); + callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA)); + return packedRGBA; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_context.js +var GPGPUContext = class { + constructor(gl) { + this.outputTexture = null; + this.program = null; + this.disposed = false; + this.itemsToPoll = []; + const glVersion = env().getNumber("WEBGL_VERSION"); + if (gl != null) { + this.gl = gl; + setWebGLContext(glVersion, gl); + } else { + this.gl = getWebGLContext(glVersion); + } + gl = this.gl; + if (env().getNumber("WEBGL_VERSION") === 2) { + const gl2 = gl; + this.createVertexArray = () => { + return callAndCheck(gl2, () => gl2.createVertexArray()); + }; + this.bindVertexArray = (vao) => { + return callAndCheck(gl2, () => gl2.bindVertexArray(vao)); + }; + this.deleteVertexArray = (vao) => { + return callAndCheck(gl2, () => gl2.deleteVertexArray(vao)); + }; + this.getVertexArray = () => { + return callAndCheck(gl2, () => gl2.getParameter(gl2.VERTEX_ARRAY_BINDING)); + }; + } else if (gl != null) { + const ext = gl.getExtension("OES_vertex_array_object"); + if (ext == null) { + throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object."); + } + this.createVertexArray = () => { + return callAndCheck(gl, () => ext.createVertexArrayOES()); + }; + this.bindVertexArray = (vao) => { + return callAndCheck(gl, () => ext.bindVertexArrayOES(vao)); + }; + this.deleteVertexArray = (vao) => { + return callAndCheck(gl, () => ext.deleteVertexArrayOES(vao)); + }; + this.getVertexArray = () => { + return callAndCheck(gl, () => gl.getParameter(ext.VERTEX_ARRAY_BINDING_OES)); + }; + } + let COLOR_BUFFER_FLOAT = "WEBGL_color_buffer_float"; + const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float"; + this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"); + if (env().getNumber("WEBGL_VERSION") === 1) { + const TEXTURE_FLOAT = "OES_texture_float"; + const TEXTURE_HALF_FLOAT = "OES_texture_half_float"; + this.textureFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_FLOAT); + if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) { + this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT); + } else if (env().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."); + } + this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT); + if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) { + this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT); + } else if (env().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 { + COLOR_BUFFER_FLOAT = "EXT_color_buffer_float"; + if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) { + this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT); + } else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) { + this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT); + } else { + throw new Error("GL context does not support color renderable floats"); + } + } + this.vertexBuffer = createVertexBuffer(this.gl); + this.indexBuffer = createIndexBuffer(this.gl); + this.framebuffer = createFramebuffer(this.gl); + this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension); + } + get debug() { + return env().getBool("DEBUG"); + } + dispose() { + if (this.disposed) { + return; + } + if (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."); + } + if (this.outputTexture != null) { + console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing."); + } + const gl = this.gl; + callAndCheck(gl, () => gl.finish()); + callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null)); + callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer)); + callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null)); + callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null)); + callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer)); + this.disposed = true; + } + createFloat32MatrixTexture(rows, columns) { + this.throwIfDisposed(); + return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig); + } + createFloat16MatrixTexture(rows, columns) { + this.throwIfDisposed(); + return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig); + } + createUnsignedBytesMatrixTexture(rows, columns) { + this.throwIfDisposed(); + return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig); + } + uploadPixelDataToTexture(texture, pixels) { + this.throwIfDisposed(); + uploadPixelDataToTexture(this.gl, texture, pixels); + } + uploadDenseMatrixToTexture(texture, width, height, data) { + this.throwIfDisposed(); + uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig); + } + createFloat16PackedMatrixTexture(rows, columns) { + this.throwIfDisposed(); + return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig); + } + createPackedMatrixTexture(rows, columns) { + this.throwIfDisposed(); + return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig); + } + deleteMatrixTexture(texture) { + this.throwIfDisposed(); + if (this.outputTexture === texture) { + unbindColorTextureFromFramebuffer(this.gl, this.framebuffer); + this.outputTexture = null; + } + callAndCheck(this.gl, () => this.gl.deleteTexture(texture)); + } + downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) { + return this.downloadMatrixDriver(texture, () => downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig)); + } + downloadPackedMatrixFromBuffer(buffer2, batch, rows, columns, physicalRows, physicalCols) { + return downloadPackedMatrixFromBuffer(this.gl, buffer2, batch, rows, columns, physicalRows, physicalCols, this.textureConfig); + } + downloadFloat32MatrixFromBuffer(buffer2, size) { + return downloadFloat32MatrixFromBuffer(this.gl, buffer2, size); + } + createBufferFromTexture(texture, rows, columns) { + this.bindTextureToFrameBuffer(texture); + const result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig); + this.unbindTextureToFrameBuffer(); + return result; + } + createAndWaitForFence() { + const fenceContext = this.createFence(this.gl); + return this.pollFence(fenceContext); + } + createFence(gl) { + let query; + let isFencePassed; + if (env().getBool("WEBGL_FENCE_API_ENABLED")) { + const gl2 = gl; + const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0); + gl.flush(); + isFencePassed = () => { + const status = gl2.clientWaitSync(sync, 0, 0); + return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED; + }; + query = sync; + } else if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0) { + query = this.beginQuery(); + this.endQuery(); + isFencePassed = () => this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")); + } else { + isFencePassed = () => true; + } + return { query, isFencePassed }; + } + downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) { + return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols)); + } + createProgram(fragmentShader) { + this.throwIfDisposed(); + const gl = this.gl; + if (this.vertexShader == null) { + this.vertexShader = createVertexShader2(gl); + } + const program = createProgram(gl); + callAndCheck(gl, () => gl.attachShader(program, this.vertexShader)); + callAndCheck(gl, () => gl.attachShader(program, fragmentShader)); + linkProgram(gl, program); + const program2 = Object.assign(program, { vao: this.createVertexArray() }); + if (this.debug) { + validateProgram(gl, program2); + } + return program2; + } + buildVao(program) { + this.setProgram(program); + this.bindVertexArray(program.vao); + const gl = this.gl; + callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, this.indexBuffer)); + bindVertexProgramAttributeStreams(gl, program, this.vertexBuffer); + } + deleteProgram(program) { + this.throwIfDisposed(); + if (program === this.program) { + this.program = null; + } + if (program != null) { + callAndCheck(this.gl, () => this.gl.deleteProgram(program)); + this.deleteVertexArray(program.vao); + } + } + setProgram(program) { + this.throwIfDisposed(); + this.program = program; + if (this.program != null) { + if (this.debug) { + validateProgram(this.gl, this.program); + } + } + callAndCheck(this.gl, () => this.gl.useProgram(program)); + } + getUniformLocation(program, uniformName, shouldThrow = true) { + this.throwIfDisposed(); + if (shouldThrow) { + return getProgramUniformLocationOrThrow(this.gl, program, uniformName); + } else { + return getProgramUniformLocation(this.gl, program, uniformName); + } + } + getAttributeLocation(program, attribute) { + this.throwIfDisposed(); + return callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute)); + } + getUniformLocationNoThrow(program, uniformName) { + this.throwIfDisposed(); + return this.gl.getUniformLocation(program, uniformName); + } + setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) { + this.throwIfDisposed(); + this.throwIfNoProgram(); + bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit); + } + setOutputMatrixTexture(outputMatrixTexture, rows, columns) { + this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows); + } + setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) { + this.throwIfDisposed(); + const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns); + this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height); + } + setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) { + this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows); + } + setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) { + throw new Error("setOutputPackedMatrixWriteRegion not implemented."); + } + debugValidate() { + if (this.program != null) { + validateProgram(this.gl, this.program); + } + validateFramebuffer(this.gl); + } + executeProgram() { + this.throwIfDisposed(); + this.throwIfNoProgram(); + const gl = this.gl; + if (this.debug) { + const boundVao = this.getVertexArray(); + console.assert(boundVao === this.program.vao, "VAO changed between setProgram and executeProgram!"); + this.debugValidate(); + } + callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0)); + } + blockUntilAllProgramsCompleted() { + this.throwIfDisposed(); + callAndCheck(this.gl, () => this.gl.finish()); + } + getQueryTimerExtension() { + if (this.disjointQueryTimerExtension == null) { + this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query"); + } + return this.disjointQueryTimerExtension; + } + getQueryTimerExtensionWebGL2() { + return this.getQueryTimerExtension(); + } + getQueryTimerExtensionWebGL1() { + return this.getQueryTimerExtension(); + } + beginQuery() { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) { + const gl2 = this.gl; + const ext2 = this.getQueryTimerExtensionWebGL2(); + const query2 = gl2.createQuery(); + gl2.beginQuery(ext2.TIME_ELAPSED_EXT, query2); + return query2; + } + const ext = this.getQueryTimerExtensionWebGL1(); + const query = ext.createQueryEXT(); + ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query); + return query; + } + endQuery() { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) { + const gl2 = this.gl; + const ext2 = this.getQueryTimerExtensionWebGL2(); + gl2.endQuery(ext2.TIME_ELAPSED_EXT); + return; + } + const ext = this.getQueryTimerExtensionWebGL1(); + ext.endQueryEXT(ext.TIME_ELAPSED_EXT); + } + async waitForQueryAndGetTime(query) { + await util_exports.repeatedTry(() => this.disposed || // while testing contexts are created / disposed + // in rapid succession, so without this check we + // may poll for the query timer indefinitely + this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))); + return this.getQueryTime(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")); + } + getQueryTime(query, queryTimerVersion) { + if (queryTimerVersion === 0) { + return null; + } + if (queryTimerVersion === 2) { + const gl2 = this.gl; + const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT); + return timeElapsedNanos / 1e6; + } else { + const ext = this.getQueryTimerExtensionWebGL1(); + const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT); + return timeElapsedNanos / 1e6; + } + } + isQueryAvailable(query, queryTimerVersion) { + if (queryTimerVersion === 0) { + return true; + } + if (queryTimerVersion === 2) { + const gl2 = this.gl; + const ext = this.getQueryTimerExtensionWebGL2(); + const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE); + if (this.disjoint == null) { + this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT); + } + return available && !this.disjoint; + } else { + const ext = this.getQueryTimerExtensionWebGL1(); + const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT); + if (this.disjoint == null) { + this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT); + } + return available && !this.disjoint; + } + } + pollFence(fenceContext) { + return new Promise((resolve) => { + this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve()); + }); + } + pollItems() { + const index = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn)); + for (let i = 0; i <= index; ++i) { + const { resolveFn } = this.itemsToPoll[i]; + resolveFn(); + } + this.itemsToPoll = this.itemsToPoll.slice(index + 1); + } + addItemToPoll(isDoneFn, resolveFn) { + this.itemsToPoll.push({ isDoneFn, resolveFn }); + if (this.itemsToPoll.length > 1) { + return; + } + let scheduleFn = void 0; + if ("setTimeoutCustom" in env().platform) { + scheduleFn = env().platform.setTimeoutCustom.bind(env().platform); + } + util_exports.repeatedTry(() => { + this.pollItems(); + return this.itemsToPoll.length === 0; + }, () => 0, null, scheduleFn); + } + bindTextureToFrameBuffer(texture) { + this.throwIfDisposed(); + bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer); + if (this.debug) { + validateFramebuffer(this.gl); + } + } + unbindTextureToFrameBuffer() { + if (this.outputTexture != null) { + bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer); + if (this.debug) { + validateFramebuffer(this.gl); + } + } else { + unbindColorTextureFromFramebuffer(this.gl, this.framebuffer); + } + } + downloadMatrixDriver(texture, downloadAndDecode) { + this.bindTextureToFrameBuffer(texture); + const result = downloadAndDecode(); + this.unbindTextureToFrameBuffer(); + return result; + } + setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) { + this.throwIfDisposed(); + const gl = this.gl; + bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer); + if (this.debug) { + validateFramebuffer(gl); + } + this.outputTexture = outputMatrixTextureMaybePacked; + callAndCheck(gl, () => gl.viewport(0, 0, width, height)); + callAndCheck(gl, () => gl.scissor(0, 0, width, height)); + } + setOutputMatrixWriteRegionDriver(x, y, width, height) { + this.throwIfDisposed(); + callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height)); + } + 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 linearSearchLastTrue(arr) { + let i = 0; + for (; i < arr.length; ++i) { + const isDone = arr[i](); + if (!isDone) { + break; + } + } + return i - 1; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/shared.js +var { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, bitwiseAndImpl: bitwiseAndImplCPU, castImpl: castImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, raggedGatherImpl: raggedGatherImplCPU, raggedRangeImpl: raggedRangeImplCPU, raggedTensorToTensorImpl: raggedTensorToTensorImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, staticRegexReplaceImpl: staticRegexReplaceImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/packing_util.js +function getVecChannels(name, rank) { + return ["x", "y", "z", "w", "u", "v"].slice(0, rank).map((d) => `${name}.${d}`); +} +function getChannels(name, rank) { + if (rank === 1) { + return [name]; + } + return getVecChannels(name, rank); +} +function getSourceCoords(rank, dims) { + if (rank === 1) { + return "rc"; + } + let coords2 = ""; + for (let i = 0; i < rank; i++) { + coords2 += dims[i]; + if (i < rank - 1) { + coords2 += ","; + } + } + return coords2; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pack_gpu.js +var PackProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = false; + this.packedOutput = true; + this.outputShape = outputShape; + this.rank = outputShape.length; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + if (this.rank === 0) { + this.userCode = ` void main() { setOutput(vec4(getA(), 0., 0., 0.)); } - `;else{let e=er("rc",this.rank),n=zt(this.rank),o=this.getOutOfBoundsCondition(e),s=this.getSetup(e),i=this.getOutput(e);this.userCode=` + `; + } else { + const channels = getChannels("rc", this.rank); + const dtype = getCoordsDataType(this.rank); + const outOfBoundsCondition = this.getOutOfBoundsCondition(channels); + const setup76 = this.getSetup(channels); + const output = this.getOutput(channels); + this.userCode = ` void main() { - ${n} rc = getOutputCoords(); + ${dtype} rc = getOutputCoords(); - if(${o}) { + if(${outOfBoundsCondition}) { setOutput(vec4(0)); } else { - ${s} + ${setup76} - setOutput(vec4(${i})); + setOutput(vec4(${output})); } } - `}}getSourceCoordsArr(t){let e=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let i=2;i ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let e="";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`; + } + let cond = ""; + for (let i = this.rank - 2; i < this.rank; i++) { + cond += `${dims[i]} >= ${this.enableShapeUniforms ? `outShape[${i}]` : this.outputShape[i]}`; + if (i < this.rank - 1) { + cond += "||"; + } + } + return cond; + } + getSetup(dims) { + if (this.rank === 1) { + return ""; + } + const innerDims = dims.slice(-2); + const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1]; + const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2]; + return ` + int r = ${innerDims[0]}; + int c = ${innerDims[1]}; int rp1 = r + 1; int cp1 = c + 1; - bool cEdge = cp1 >= ${n}; - bool rEdge = rp1 >= ${o}; - `}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]}), - cEdge ? 0. : getA(${e[1]}), - rEdge ? 0. : getA(${e[2]}), - rEdge || cEdge ? 0. : getA(${e[3]})`}};var Pd=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){":""} + bool cEdge = cp1 >= ${col}; + bool rEdge = rp1 >= ${row}; + `; + } + getOutput(dims) { + const sourceCoords = this.getSourceCoordsArr(dims); + if (this.rank === 1) { + const outShape = this.enableShapeUniforms ? "outShape" : this.outputShape[0]; + return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`; + } + return `getA(${sourceCoords[0]}), + cEdge ? 0. : getA(${sourceCoords[1]}), + rEdge ? 0. : getA(${sourceCoords[2]}), + rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reshape_packed_gpu.js +var ReshapePackedProgram = class { + constructor(outputShape, inputShape) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [{ name: "inputShape", type: "ivec3" }]; + this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + let mainLoop = ``; + for (let i = 0; i < 4; i++) { + let thisRC = `thisRC = rc;`; + if (i % 2 === 1) { + thisRC += `thisRC.z += 1;`; + } + if (i > 1) { + thisRC += `thisRC.y += 1;`; + } + mainLoop += ` + ${thisRC} + ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""} int flatIndex = getFlatIndex(thisRC); ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex); vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z)); - result[${o}] = + result[${i}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); - ${o>0?"}":""} - `}this.userCode=` - ${wot(e,this.enableShapeUniforms)} - ${this.enableShapeUniforms?Ad():Ed(t)} + ${i > 0 ? "}" : ""} + `; + } + this.userCode = ` + ${getReshapedInputCoords(inputShape, this.enableShapeUniforms)} + ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)} void main() { ivec3 rc = getOutputCoords(); @@ -1167,21 +57625,229 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 result = vec4(0.); ivec3 thisRC; - int rows = ${this.enableShapeUniforms?"outShape[1]":t[1]}; - int cols = ${this.enableShapeUniforms?"outShape[2]":t[2]}; + int rows = ${this.enableShapeUniforms ? "outShape[1]" : outputShape[1]}; + int cols = ${this.enableShapeUniforms ? "outShape[2]" : outputShape[2]}; - ${n} + ${mainLoop} setOutput(result); } - `}};function wot(r,t){return` + `; + } +}; +function getReshapedInputCoords(shape, enableShapeUniforms) { + const coordsFromIndexSnippet = enableShapeUniforms ? getLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], "inputShape") : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape); + return ` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?TL(["r","c","d"],"inputShape"):ki(["r","c","d"],r)} + ${coordsFromIndexSnippet} return ivec3(r, c, d); } - `}var tI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=Ez(e,n),s=Az(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=_z(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===zr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===zr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===zr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===zr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===zr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=Ez(n,o),i=Az(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=_z(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 Iot(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 _z(r,t,e,n,o){let s=Cot(t,n),i;if(o){let[u,l]=Sa(r[0],r[1]);i=u*l}else{let[u,l]=xp(r[0],r[1]);i=u*l}let a=Iot(e,s);return i*a}function Cot(r,t){switch(r){case zr.PACKED_2X2_FLOAT32:return jw(t);case zr.PACKED_2X2_FLOAT16:return Xw(t);case zr.UNPACKED_FLOAT32:return Hw(t);case zr.UNPACKED_FLOAT16:return qw(t);case zr.PACKED_4X1_UNSIGNED_BYTE:return Kw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function vot(r){return L().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?zr.PACKED_2X2_FLOAT32:zr.UNPACKED_FLOAT32:r?zr.PACKED_2X2_FLOAT16:zr.UNPACKED_FLOAT16}function Ez(r,t){if(r===Jr.UPLOAD)return zr.PACKED_2X2_FLOAT32;if(r===Jr.RENDER||r==null)return vot(t);if(r===Jr.DOWNLOAD||r===Jr.PIXELS)return zr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function Az(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var Br=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` + `; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/texture_manager.js +var TextureManager = class { + constructor(gpgpu) { + this.gpgpu = gpgpu; + this.numUsedTextures = 0; + this.numFreeTextures = 0; + this._numBytesAllocated = 0; + this._numBytesFree = 0; + this.freeTextures = {}; + this.usedTextures = {}; + this.logEnabled = false; + } + acquireTexture(shapeRC, usage, isPacked) { + const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked); + const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked); + if (!(shapeKey in this.freeTextures)) { + this.freeTextures[shapeKey] = []; + } + if (!(shapeKey in this.usedTextures)) { + this.usedTextures[shapeKey] = []; + } + const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked); + if (this.freeTextures[shapeKey].length > 0) { + this.numFreeTextures--; + this.numUsedTextures++; + this._numBytesFree -= texBytes; + this.log(); + const newTexture2 = this.freeTextures[shapeKey].pop(); + this.usedTextures[shapeKey].push(newTexture2); + return newTexture2; + } + let newTexture; + if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) { + newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]); + } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) { + newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]); + } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) { + newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]); + } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) { + newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]); + } else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) { + newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]); + } + this.usedTextures[shapeKey].push(newTexture); + this.numUsedTextures++; + this._numBytesAllocated += texBytes; + this.log(); + return newTexture; + } + releaseTexture(texture, shape, logicalTexType, isPacked) { + if (this.freeTextures == null) { + return; + } + const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked); + const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked); + if (!(shapeKey in this.freeTextures)) { + this.freeTextures[shapeKey] = []; + } + const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked); + const deleteTexThreshold = env().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD"); + if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) { + this.gpgpu.deleteMatrixTexture(texture.texture); + this._numBytesAllocated -= texBytes; + } else { + this.freeTextures[shapeKey].push(texture); + this.numFreeTextures++; + this._numBytesFree += texBytes; + } + this.numUsedTextures--; + const texList = this.usedTextures[shapeKey]; + const texIndex = texList && texList.indexOf(texture); + if (texIndex == null || texIndex < 0) { + throw new Error("Cannot release a texture that was never provided by this texture manager"); + } + texList[texIndex] = texList[texList.length - 1]; + texList.pop(); + this.log(); + } + log() { + if (!this.logEnabled) { + return; + } + const total = this.numFreeTextures + this.numUsedTextures; + console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`); + const freeRatio = this._numBytesFree / this._numBytesAllocated; + console.log(`Bytes allocated: ${this._numBytesAllocated}`); + console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`); + } + get numBytesAllocated() { + return this._numBytesAllocated; + } + get numBytesFree() { + return this._numBytesFree; + } + getNumUsedTextures() { + return this.numUsedTextures; + } + getNumFreeTextures() { + return this.numFreeTextures; + } + dispose() { + if (this.freeTextures == null) { + return; + } + for (const texShape in this.freeTextures) { + this.freeTextures[texShape].forEach((tex) => { + this.gpgpu.deleteMatrixTexture(tex.texture); + }); + } + for (const texShape in this.usedTextures) { + this.usedTextures[texShape].forEach((tex) => { + this.gpgpu.deleteMatrixTexture(tex.texture); + }); + } + this.freeTextures = null; + this.usedTextures = null; + this.numUsedTextures = 0; + this.numFreeTextures = 0; + this._numBytesAllocated = 0; + this._numBytesFree = 0; + } +}; +function numBytesForInternalFormat(gl, internalFormat) { + const glany = gl; + if (internalFormat === glany.R32F) { + return 4; + } else if (internalFormat === glany.R16F) { + return 2; + } else if (internalFormat === glany.RGBA32F) { + return 16; + } else if (internalFormat === gl.RGBA) { + return 16; + } else if (internalFormat === glany.RGBA16F) { + return 8; + } else if (internalFormat === glany.RGBA8) { + return 4; + } + throw new Error(`Unknown internal format ${internalFormat}`); +} +function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) { + const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig); + let numElements; + if (isPacked) { + const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]); + numElements = packedWidth * packedHeight; + } else { + const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]); + numElements = width * height; + } + const bytesPerElement2 = numBytesForInternalFormat(gl, internalFormat); + return numElements * bytesPerElement2; +} +function internalFormatForPhysicalTexType(physicalTexType, textureConfig) { + switch (physicalTexType) { + case PhysicalTextureType.PACKED_2X2_FLOAT32: + return getInternalFormatForPackedMatrixTexture(textureConfig); + case PhysicalTextureType.PACKED_2X2_FLOAT16: + return getInternalFormatForFloat16PackedMatrixTexture(textureConfig); + case PhysicalTextureType.UNPACKED_FLOAT32: + return getInternalFormatForFloat32MatrixTexture(textureConfig); + case PhysicalTextureType.UNPACKED_FLOAT16: + return getInternalFormatForFloat16MatrixTexture(textureConfig); + case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE: + return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig); + default: + throw new Error(`Unknown physical texture type ${physicalTexType}`); + } +} +function getPhysicalTextureForRendering(isPacked) { + if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")) { + if (isPacked) { + return PhysicalTextureType.PACKED_2X2_FLOAT32; + } + return PhysicalTextureType.UNPACKED_FLOAT32; + } + if (isPacked) { + return PhysicalTextureType.PACKED_2X2_FLOAT16; + } + return PhysicalTextureType.UNPACKED_FLOAT16; +} +function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) { + if (logicalTexType === TextureUsage.UPLOAD) { + return PhysicalTextureType.PACKED_2X2_FLOAT32; + } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) { + return getPhysicalTextureForRendering(isPacked); + } else if (logicalTexType === TextureUsage.DOWNLOAD || logicalTexType === TextureUsage.PIXELS) { + return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE; + } + throw new Error(`Unknown logical texture type ${logicalTexType}`); +} +function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) { + return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_gpu.js +var UnaryOpProgram = class { + constructor(aShape, opSnippet) { + this.variableNames = ["A"]; + this.outputShape = aShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + this.userCode = ` float unaryOperation(float x) { - ${e} + ${opSnippet} } void main() { @@ -1190,11 +57856,25 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},yr="if (isnan(x)) return x;",Dz="return x;",C1="return abs(x);";var $z="return (x >= 0.0) ? x : (exp(x) - 1.0);",Rz=yr+` + `; + } +}; +var CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`; +var LINEAR = `return x;`; +var ABS = `return abs(x);`; +var ELU2 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`; +var RELU = CHECK_NAN_SNIPPET + ` return (x < 0.0) ? 0.0 : x; -`,Fz=yr+` +`; +var RELU6 = CHECK_NAN_SNIPPET + ` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Na="return x;",Oz="return 1.0 / (1.0 + exp(-1.0 * x));";var Mz="return x;",Lz=` +`; +var CLONE = "return x;"; +var SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_packed_gpu.js +var LINEAR2 = `return x;`; +var ELU3 = ` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1203,7 +57883,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,zz=` +`; +var RELU2 = ` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1213,7 +57894,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,Bz=` +`; +var RELU62 = ` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1223,9 +57905,18 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,Vz="return 1.0 / (1.0 + exp(-1.0 * x));",Fn=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` +`; +var SIGMOID2 = `return 1.0 / (1.0 + exp(-1.0 * x));`; +var UnaryOpPackedProgram = class { + constructor(aShape, opSnippet) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = aShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + this.userCode = ` vec4 unaryOperation(vec4 x) { - ${e} + ${opSnippet} } void main() { @@ -1234,19 +57925,942 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}};var eI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let e=t.length,n=er("rc",e),o=zt(e),s=Tz(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=` - void main() { - ${o} rc = getOutputCoords(); - vec4 packedInput = getA(${s}); + `; + } +}; - setOutput(getChannel(packedInput, ${a})); +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unpack_gpu.js +var UnpackProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = false; + this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const rank = outputShape.length; + const channels = getChannels("rc", rank); + const dtype = getCoordsDataType(rank); + const sourceCoords = getSourceCoords(rank, channels); + const innerDims = channels.slice(-2); + const coords2 = rank <= 1 ? "rc" : `vec2(${innerDims.join(",")})`; + this.userCode = ` + void main() { + ${dtype} rc = getOutputCoords(); + vec4 packedInput = getA(${sourceCoords}); + + setOutput(getChannel(packedInput, ${coords2})); } - `}};var Not=jr.whereImpl,kot=1e-7,Tot=1e-4,rI={};function _ot(r){return r in rI||(rI[r]={}),rI[r]}var Eot=L().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Aot=600;function Dot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Aot/1024/1024}var Qu=class extends Uo{nextDataId(){return Qu.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 wp)e=t;else{let n=Yn(L().getNumber("WEBGL_VERSION"),t);e=new wp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Yn(L().getNumber("WEBGL_VERSION"));e=new wp(n),this.binaryCache=_ot(L().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new tI(this.gpgpu),this.numMBBeforeWarning=Dot(),this.texData=new Da(this,Wn())}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=Td(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:Jr.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:Jr.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 Fn(a,Na):m=new Br(a,Na);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 Fn(o,Na):d=new Br(o,Na);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)&&Wn().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 Fn(s,Na):f=new Br(s,Na);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=Wn().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 wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(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=Eot){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 Wn().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=[Vl(t.shape),...Gl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Vl(e),...Gl(e)],i=new Pd(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=Td(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===Zu.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&&!Ju(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=OL(t,c,p),f=this.getAndSaveBinary(m,()=>RL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||FL(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?kot:Tot}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=YT(n,u),e.texShape=p),s!=null){let m=Td(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=Sa(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=Jr.PIXELS:w.usage=Jr.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=$ot(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}=n1(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=Wn().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 Wn().makeTensorFromDataId(l,e,n,u)}};Qu.nextDataId=0;function $ot(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 Qu,2);var JDe={forceHalfFloat:Wz};var Md=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js +var whereImpl3 = kernel_impls_exports.whereImpl; +var EPSILON_FLOAT322 = 1e-7; +var EPSILON_FLOAT162 = 1e-4; +var binaryCaches = {}; +function getBinaryCache(webGLVersion) { + if (webGLVersion in binaryCaches) { + return binaryCaches[webGLVersion]; + } + binaryCaches[webGLVersion] = {}; + return binaryCaches[webGLVersion]; +} +var CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"); +var BEFORE_PAGING_CONSTANT = 600; +function numMBBeforeWarning() { + if (env().global.screen == null) { + return 1024; + } + return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024; +} +var MathBackendWebGL = class _MathBackendWebGL extends KernelBackend { + nextDataId() { + return _MathBackendWebGL.nextDataId++; + } + constructor(gpuResource) { + super(); + this.pendingRead = /* @__PURE__ */ new WeakMap(); + this.pendingDisposal = /* @__PURE__ */ new WeakSet(); + this.dataRefCount = /* @__PURE__ */ new WeakMap(); + this.numBytesInGPU = 0; + this.uploadWaitMs = 0; + this.downloadWaitMs = 0; + this.lastGlFlushTime = 0; + this.warnedAboutMemory = false; + this.pendingDeletes = 0; + this.disposed = false; + if (!env().getBool("HAS_WEBGL")) { + throw new Error("WebGL is not supported on this device"); + } + let newGPGPU; + if (gpuResource != null) { + if (gpuResource instanceof GPGPUContext) { + newGPGPU = gpuResource; + } else { + const gl = getWebGLContext(env().getNumber("WEBGL_VERSION"), gpuResource); + newGPGPU = new GPGPUContext(gl); + } + this.binaryCache = {}; + this.gpgpuCreatedLocally = false; + } else { + const gl = getWebGLContext(env().getNumber("WEBGL_VERSION")); + newGPGPU = new GPGPUContext(gl); + this.binaryCache = getBinaryCache(env().getNumber("WEBGL_VERSION")); + this.gpgpuCreatedLocally = true; + } + this.gpgpu = newGPGPU; + this.canvas = this.gpgpu.gl.canvas; + this.textureManager = new TextureManager(this.gpgpu); + this.numMBBeforeWarning = numMBBeforeWarning(); + this.texData = new DataStorage(this, engine()); + } + numDataIds() { + return this.texData.numDataIds() - this.pendingDeletes; + } + // Writes a new entry to the data store with a WebGL texture, and registers it + // to the texture manager. + writeTexture(texture, shape, dtype, texHeight, texWidth, channels) { + const input2 = this.makeTensorInfo(shape, dtype); + const inData = this.texData.get(input2.dataId); + inData.isPacked = false; + inData.texture = { texture, texShape: [texHeight, texWidth] }; + inData.texShape = [texHeight, texWidth]; + const shapeAs3D = getShapeAs3D(shape); + const program = new EncodeMatrixProgram(shapeAs3D, false, channels); + const output = this.runWebGLProgram(program, [input2], dtype, [[texHeight, texWidth]]); + output.shape = shape; + inData.texture = null; + this.disposeIntermediateTensorInfo(input2); + return output.dataId; + } + write(values, shape, dtype) { + if (env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || env().getBool("DEBUG")) { + this.checkNumericalProblems(values); + } + if (dtype === "complex64" && values != null) { + throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`); + } + const dataId = { id: this.nextDataId() }; + this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 }); + return dataId; + } + /** Return refCount of a `TensorData`. */ + refCount(dataId) { + if (this.texData.has(dataId)) { + const tensorData = this.texData.get(dataId); + return tensorData.refCount; + } + return 0; + } + /** Increase refCount of a `TextureData`. */ + incRef(dataId) { + const texData = this.texData.get(dataId); + texData.refCount++; + } + /** Decrease refCount of a `TextureData`. */ + decRef(dataId) { + if (this.texData.has(dataId)) { + const texData = this.texData.get(dataId); + texData.refCount--; + } + } + move(dataId, values, shape, dtype, refCount) { + if (env().getBool("DEBUG")) { + this.checkNumericalProblems(values); + } + if (dtype === "complex64") { + throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`); + } + this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount }); + } + disposeIntermediateTensorInfo(tensorInfo) { + this.disposeData(tensorInfo.dataId); + } + readSync(dataId) { + const texData = this.texData.get(dataId); + const { values, dtype, complexTensorInfos, slice: slice5, shape, isPacked } = texData; + if (slice5 != null) { + let program; + if (isPacked) { + program = new UnaryOpPackedProgram(shape, CLONE); + } else { + program = new UnaryOpProgram(shape, CLONE); + } + const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype); + const data = this.readSync(res.dataId); + this.disposeIntermediateTensorInfo(res); + return data; + } + if (values != null) { + return this.convertAndCacheOnCPU(dataId); + } + if (dtype === "string") { + return values; + } + const shouldTimeProgram = this.activeTimers != null; + let start; + if (shouldTimeProgram) { + start = util_exports.now(); + } + let result; + if (dtype === "complex64") { + const realValues = this.readSync(complexTensorInfos.real.dataId); + const imagValues = this.readSync(complexTensorInfos.imag.dataId); + result = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues); + } else { + result = this.getValuesFromTexture(dataId); + } + if (shouldTimeProgram) { + this.downloadWaitMs += util_exports.now() - start; + } + return this.convertAndCacheOnCPU(dataId, result); + } + async read(dataId) { + if (this.pendingRead.has(dataId)) { + const subscribers2 = this.pendingRead.get(dataId); + return new Promise((resolve) => subscribers2.push(resolve)); + } + const texData = this.texData.get(dataId); + const { values, shape, slice: slice5, dtype, complexTensorInfos, isPacked } = texData; + if (slice5 != null) { + let program; + if (isPacked) { + program = new UnaryOpPackedProgram(shape, CLONE); + } else { + program = new UnaryOpProgram(shape, CLONE); + } + const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype); + const data = this.read(res.dataId); + this.disposeIntermediateTensorInfo(res); + return data; + } + if (values != null) { + return this.convertAndCacheOnCPU(dataId); + } + if (env().getBool("DEBUG")) { + if (!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && env().getNumber("WEBGL_VERSION") === 2) { + throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`); + } + } + let buffer2 = null; + let tmpDownloadTarget; + if (dtype !== "complex64" && env().get("WEBGL_BUFFER_SUPPORTED")) { + tmpDownloadTarget = this.decode(dataId); + const tmpData = this.texData.get(tmpDownloadTarget.dataId); + buffer2 = this.gpgpu.createBufferFromTexture(tmpData.texture.texture, ...getDenseTexShape(shape)); + } + this.pendingRead.set(dataId, []); + if (dtype !== "complex64") { + await this.gpgpu.createAndWaitForFence(); + } + let vals; + if (dtype === "complex64") { + const ps = await Promise.all([ + this.read(complexTensorInfos.real.dataId), + this.read(complexTensorInfos.imag.dataId) + ]); + const realValues = ps[0]; + const imagValues = ps[1]; + vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues); + } else if (buffer2 == null) { + vals = this.getValuesFromTexture(dataId); + } else { + const size = util_exports.sizeFromShape(shape); + vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2, size); + } + if (tmpDownloadTarget != null) { + this.disposeIntermediateTensorInfo(tmpDownloadTarget); + } + if (buffer2 != null) { + const gl = this.gpgpu.gl; + callAndCheck(gl, () => gl.deleteBuffer(buffer2)); + } + const dTypeVals = this.convertAndCacheOnCPU(dataId, vals); + const subscribers = this.pendingRead.get(dataId); + this.pendingRead.delete(dataId); + subscribers.forEach((resolve) => resolve(dTypeVals)); + if (this.pendingDisposal.has(dataId)) { + this.pendingDisposal.delete(dataId); + if (this.disposeData(dataId)) { + engine().removeDataId(dataId, this); + } + this.pendingDeletes--; + } + return dTypeVals; + } + /** + * Read tensor to a new texture that is densely packed for ease of use. + * @param dataId The source tensor. + * @param options + * customTexShape: Optional. If set, will use the user defined texture + * shape to create the texture. + */ + readToGPU(dataId, options = {}) { + const texData = this.texData.get(dataId); + const { values, shape, slice: slice5, dtype, isPacked, texture } = texData; + if (dtype === "complex64") { + throw new Error("Does not support reading texture for complex64 dtype."); + } + if (slice5 != null) { + let program; + if (isPacked) { + program = new UnaryOpPackedProgram(shape, CLONE); + } else { + program = new UnaryOpProgram(shape, CLONE); + } + const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype); + const gpuResouorce = this.readToGPU(res, options); + this.disposeIntermediateTensorInfo(res); + return gpuResouorce; + } + if (texture == null) { + if (values != null) { + throw new Error("Data is not on GPU but on CPU."); + } else { + throw new Error("There is no data on GPU or CPU."); + } + } + const tmpTarget = this.decode(dataId, options.customTexShape); + const tensorRef = engine().makeTensorFromTensorInfo(tmpTarget); + const tmpData = this.texData.get(tmpTarget.dataId); + return Object.assign({ tensorRef }, tmpData.texture); + } + bufferSync(t) { + const data = this.readSync(t.dataId); + if (t.dtype === "string") { + try { + const strings = data.map((d) => util_exports.decodeString(d)); + return buffer(t.shape, t.dtype, strings); + } catch (_a) { + throw new Error("Failed to decode encoded string bytes into utf-8"); + } + } + return buffer(t.shape, t.dtype, data); + } + checkNumericalProblems(values) { + if (values == null) { + return; + } + for (let i = 0; i < values.length; i++) { + const num = values[i]; + if (!canBeRepresented(num)) { + if (env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")) { + throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`); + } + throw Error(`The value ${num} cannot be represented on this device.`); + } + } + } + getValuesFromTexture(dataId) { + const { shape, dtype, isPacked } = this.texData.get(dataId); + const size = util_exports.sizeFromShape(shape); + if (env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) { + const tmpTarget = this.decode(dataId); + const tmpData2 = this.texData.get(tmpTarget.dataId); + const vals2 = this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture.texture, ...getDenseTexShape(shape)).subarray(0, size); + this.disposeIntermediateTensorInfo(tmpTarget); + return vals2; + } + const shouldUsePackedProgram = env().getBool("WEBGL_PACK") && isPacked === true; + const outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape; + const program = shouldUsePackedProgram ? new EncodeFloatPackedProgram(outputShape) : new EncodeFloatProgram(outputShape); + const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], "float32"); + const tmpData = this.texData.get(output.dataId); + const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size); + this.disposeIntermediateTensorInfo(output); + return vals; + } + timerAvailable() { + return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0; + } + time(f) { + const oldActiveTimers = this.activeTimers; + const newActiveTimers = []; + let outerMostTime = false; + if (this.programTimersStack == null) { + this.programTimersStack = newActiveTimers; + outerMostTime = true; + } else { + this.activeTimers.push(newActiveTimers); + } + this.activeTimers = newActiveTimers; + f(); + const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null); + const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null); + this.activeTimers = oldActiveTimers; + if (outerMostTime) { + this.programTimersStack = null; + } + const res = { + uploadWaitMs: this.uploadWaitMs, + downloadWaitMs: this.downloadWaitMs, + kernelMs: null, + wallMs: null + // will be filled by the engine + }; + return (async () => { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { + const kernelMs = await Promise.all(flattenedActiveTimerQueries); + res["kernelMs"] = util_exports.sum(kernelMs); + res["getExtraProfileInfo"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", "); + } else { + res["kernelMs"] = { + error: "WebGL query timers are not supported in this environment." + }; + } + this.uploadWaitMs = 0; + this.downloadWaitMs = 0; + return res; + })(); + } + memory() { + return { + unreliable: false, + numBytesInGPU: this.numBytesInGPU, + numBytesInGPUAllocated: this.textureManager.numBytesAllocated, + numBytesInGPUFree: this.textureManager.numBytesFree + }; + } + startTimer() { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { + return this.gpgpu.beginQuery(); + } + return { startMs: util_exports.now(), endMs: null }; + } + endTimer(query) { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { + this.gpgpu.endQuery(); + return query; + } + query.endMs = util_exports.now(); + return query; + } + async getQueryTime(query) { + if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) { + return this.gpgpu.waitForQueryAndGetTime(query); + } + const timerQuery = query; + return timerQuery.endMs - timerQuery.startMs; + } + /** + * Decrease the RefCount on the dataId and dispose the memory if the dataId + * has 0 refCount. If there are pending read on the data, the disposal would + * added to the pending delete queue. Return true if the dataId is removed + * from backend or the backend does not contain the dataId, false if the + * dataId is not removed. Memory may or may not be released even when dataId + * is removed, which also depends on dataRefCount, see `releaseGPU`. + * @param dataId + * @oaram force Optional, remove the data regardless of refCount + */ + disposeData(dataId, force = false) { + if (this.pendingDisposal.has(dataId)) { + return false; + } + if (!this.texData.has(dataId)) { + return true; + } + if (force) { + this.texData.get(dataId).refCount = 0; + } else { + this.texData.get(dataId).refCount--; + } + if (!force && this.texData.get(dataId).refCount > 0) { + return false; + } + if (this.pendingRead.has(dataId)) { + this.pendingDisposal.add(dataId); + this.pendingDeletes++; + return false; + } + this.releaseGPUData(dataId); + const { complexTensorInfos } = this.texData.get(dataId); + if (complexTensorInfos != null) { + this.disposeData(complexTensorInfos.real.dataId, force); + this.disposeData(complexTensorInfos.imag.dataId, force); + } + this.texData.delete(dataId); + return true; + } + releaseGPUData(dataId) { + const { texture, dtype, texShape, usage, isPacked, slice: slice5 } = this.texData.get(dataId); + const key = slice5 && slice5.origDataId || dataId; + const refCount = this.dataRefCount.get(key); + if (refCount > 1) { + this.dataRefCount.set(key, refCount - 1); + } else { + this.dataRefCount.delete(key); + if (texture != null) { + this.numBytesInGPU -= this.computeBytes(texShape, dtype); + this.textureManager.releaseTexture(texture, texShape, usage, isPacked); + } + } + const texData = this.texData.get(dataId); + texData.texture = null; + texData.texShape = null; + texData.isPacked = false; + texData.slice = null; + } + getTexture(dataId) { + this.uploadToGPU(dataId); + return this.texData.get(dataId).texture.texture; + } + /** + * Returns internal information for the specific data bucket. Used in unit + * tests. + */ + getDataInfo(dataId) { + return this.texData.get(dataId); + } + /* + Tests whether all the inputs to an op are small and on the CPU. This heuristic + determines when it would be faster to execute a kernel on the CPU. WebGL + kernels opt into running this check and forwarding when appropriate. + TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more + sustainable strategy for optimizing backend execution of ops. + */ + shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) { + return env().getBool("WEBGL_CPU_FORWARD") && inputs.every((input2) => this.texData.get(input2.dataId).texture == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold); + } + getGPGPUContext() { + return this.gpgpu; + } + where(condition) { + backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead"); + const condVals = condition.dataSync(); + return whereImpl3(condition.shape, condVals); + } + packedUnaryOp(x, op2, dtype) { + const program = new UnaryOpPackedProgram(x.shape, op2); + const outInfo = this.compileAndRun(program, [x], dtype); + return engine().makeTensorFromTensorInfo(outInfo); + } + // TODO(msoulanille) remove this once the backend has been modularized + // a copy is needed here to break a circular dependency. + // Also remove the op from unary_op. + abs(x) { + if (this.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") { + const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values); + return this.makeOutput(x.shape, x.dtype, outValues); + } + if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) { + return this.packedUnaryOp(x, ABS, x.dtype); + } + const program = new UnaryOpProgram(x.shape, ABS); + const outInfo = this.compileAndRun(program, [x]); + return engine().makeTensorFromTensorInfo(outInfo); + } + makeTensorInfo(shape, dtype, values) { + let dataId; + if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) { + const encodedValues = values.map((d) => util_exports.encodeString(d)); + dataId = this.write(encodedValues, shape, dtype); + } else { + dataId = this.write(values, shape, dtype); + } + this.texData.get(dataId).usage = null; + return { dataId, shape, dtype }; + } + makeOutput(shape, dtype, values) { + return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this); + } + unpackTensor(input2) { + const program = new UnpackProgram(input2.shape); + return this.runWebGLProgram(program, [input2], input2.dtype); + } + packTensor(input2) { + const program = new PackProgram(input2.shape); + const preventEagerUnpackingOutput = true; + return this.runWebGLProgram(program, [input2], input2.dtype, null, preventEagerUnpackingOutput); + } + packedReshape(input2, afterShape) { + const input3DShape = [ + getBatchDim(input2.shape), + ...getRowsCols(input2.shape) + ]; + const input3D = { + dtype: input2.dtype, + shape: input3DShape, + dataId: input2.dataId + }; + const afterShapeAs3D = [ + getBatchDim(afterShape), + ...getRowsCols(afterShape) + ]; + const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape); + const preventEagerUnpackingOfOutput = true; + const customValues = [input3DShape]; + const output = this.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput); + return { dataId: output.dataId, shape: afterShape, dtype: output.dtype }; + } + decode(dataId, customTexShape) { + const texData = this.texData.get(dataId); + const { isPacked, shape, dtype } = texData; + if (customTexShape != null) { + const size = util_exports.sizeFromShape(shape); + const texSize = customTexShape[0] * customTexShape[1] * 4; + util_exports.assert(size <= texSize, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."); + } + const shapeAs3D = getShapeAs3D(shape); + let program; + if (isPacked) { + program = new DecodeMatrixPackedProgram(shapeAs3D); + } else { + program = new DecodeMatrixProgram(shapeAs3D); + } + const preventEagerUnpackingOfOutput = true; + const customValues = [customTexShape != null ? customTexShape : getDenseTexShape(shapeAs3D)]; + const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, customValues, preventEagerUnpackingOfOutput, customTexShape); + return { dtype, shape, dataId: out.dataId }; + } + runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false, customTexShape) { + const output = this.makeTensorInfo(program.outputShape, outputDtype); + const outData = this.texData.get(output.dataId); + if (program.packedOutput) { + outData.isPacked = true; + } + if (program.outPackingScheme === PackingScheme.DENSE) { + const texelShape = customTexShape != null ? customTexShape : getDenseTexShape(program.outputShape); + outData.texShape = texelShape.map((d) => d * 2); + } + if (program.outTexUsage != null) { + outData.usage = program.outTexUsage; + } + if (util_exports.sizeFromShape(output.shape) === 0) { + outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0); + return output; + } + const dataToDispose = []; + const inputsData = inputs.map((input2) => { + if (input2.dtype === "complex64") { + throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`); + } + let texData = this.texData.get(input2.dataId); + if (texData.texture == null) { + if (!program.packedInputs && util_exports.sizeFromShape(input2.shape) <= env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM")) { + return { + shape: input2.shape, + texData: null, + isUniform: true, + uniformValues: texData.values + }; + } + if (program.packedInputs) { + texData.isPacked = true; + texData.shape = input2.shape; + } + } + this.uploadToGPU(input2.dataId); + if (!!texData.isPacked !== !!program.packedInputs) { + input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2); + dataToDispose.push(input2); + texData = this.texData.get(input2.dataId); + } else if (texData.isPacked && !isReshapeFree(texData.shape, input2.shape)) { + const savedInput = input2; + const targetShape = input2.shape; + input2.shape = texData.shape; + input2 = this.packedReshape(input2, targetShape); + dataToDispose.push(input2); + texData = this.texData.get(input2.dataId); + savedInput.shape = targetShape; + } + return { shape: input2.shape, texData, isUniform: false }; + }); + this.uploadToGPU(output.dataId); + const outputData = { shape: output.shape, texData: outData, isUniform: false }; + const key = makeShaderKey(program, inputsData, outputData); + const binary = this.getAndSaveBinary(key, () => { + return compileProgram(this.gpgpu, program, inputsData, outputData); + }); + const shouldTimeProgram = this.activeTimers != null; + let query; + if (shouldTimeProgram) { + query = this.startTimer(); + } + if (!env().get("ENGINE_COMPILE_ONLY")) { + runProgram(this.gpgpu, binary, inputsData, outputData, customUniformValues); + } + dataToDispose.forEach((info) => this.disposeIntermediateTensorInfo(info)); + if (shouldTimeProgram) { + query = this.endTimer(query); + this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) }); + } + const glFlushThreshold = env().getNumber("WEBGL_FLUSH_THRESHOLD"); + if (glFlushThreshold > 0) { + const time2 = util_exports.now(); + if (time2 - this.lastGlFlushTime > glFlushThreshold) { + this.gpgpu.gl.flush(); + this.lastGlFlushTime = time2; + } + } + if (!env().getBool("WEBGL_LAZILY_UNPACK") && outData.isPacked && preventEagerUnpackingOfOutput === false) { + const unpacked = this.unpackTensor(output); + this.disposeIntermediateTensorInfo(output); + return unpacked; + } + return output; + } + compileAndRun(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false) { + outputDtype = outputDtype || inputs[0].dtype; + const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput); + return outInfo; + } + getAndSaveBinary(key, getBinary) { + if (!(key in this.binaryCache)) { + this.binaryCache[key] = getBinary(); + } + return this.binaryCache[key]; + } + getTextureManager() { + return this.textureManager; + } + dispose() { + if (this.disposed) { + return; + } + if (!env().getBool("IS_TEST")) { + const allKeys = Object.keys(this.binaryCache); + allKeys.forEach((key) => { + this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram); + delete this.binaryCache[key]; + }); + } + this.textureManager.dispose(); + if (this.canvas != null && (typeof HTMLCanvasElement !== "undefined" && this.canvas instanceof HTMLCanvasElement)) { + this.canvas.remove(); + } else { + this.canvas = null; + } + if (this.gpgpuCreatedLocally) { + this.gpgpu.program = null; + this.gpgpu.dispose(); + } + this.disposed = true; + } + floatPrecision() { + if (this.floatPrecisionValue == null) { + this.floatPrecisionValue = tidy(() => { + if (!env().get("WEBGL_RENDER_FLOAT32_ENABLED")) { + const debugFlag = env().getBool("DEBUG"); + env().set("DEBUG", false); + const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0]; + env().set("DEBUG", debugFlag); + if (underflowCheckValue > 0) { + return 32; + } + } + return 16; + }); + } + return this.floatPrecisionValue; + } + /** Returns the smallest representable number. */ + epsilon() { + return this.floatPrecision() === 32 ? EPSILON_FLOAT322 : EPSILON_FLOAT162; + } + uploadToGPU(dataId) { + const texData = this.texData.get(dataId); + const { shape, dtype, values, texture, usage, isPacked } = texData; + if (texture != null) { + return; + } + const shouldTimeProgram = this.activeTimers != null; + let start; + if (shouldTimeProgram) { + start = util_exports.now(); + } + let texShape = texData.texShape; + if (texShape == null) { + texShape = getTextureShapeFromLogicalShape(shape, isPacked); + texData.texShape = texShape; + } + if (values != null) { + const shapeAs3D = getShapeAs3D(shape); + let program; + let width = texShape[1], height = texShape[0]; + const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray; + if (isPacked || !isByteArray) { + [width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]); + } + if (isPacked) { + program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray); + } else { + program = new EncodeMatrixProgram(shapeAs3D, isByteArray); + } + const tempDenseInputTexShape = isByteArray ? [height, width] : texShape; + const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype); + const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId); + if (isByteArray) { + tempDenseInputTexData.usage = TextureUsage.PIXELS; + } else { + tempDenseInputTexData.usage = TextureUsage.UPLOAD; + } + tempDenseInputTexData.texShape = tempDenseInputTexShape; + this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values); + const customValues = [[height, width]]; + const preventEagerUnpacking = true; + const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, customValues, preventEagerUnpacking); + const outputTexData = this.texData.get(encodedOutputTarget.dataId); + texData.texShape = outputTexData.texShape; + texData.isPacked = outputTexData.isPacked; + texData.usage = outputTexData.usage; + if (!env().get("ENGINE_COMPILE_ONLY")) { + texData.texture = outputTexData.texture; + texData.values = null; + this.texData.delete(encodedOutputTarget.dataId); + } else { + this.disposeData(encodedOutputTarget.dataId); + } + this.disposeIntermediateTensorInfo(tempDenseInputHandle); + if (shouldTimeProgram) { + this.uploadWaitMs += util_exports.now() - start; + } + } else { + const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked); + texData.texture = newTexture; + } + } + convertAndCacheOnCPU(dataId, float32Values) { + const texData = this.texData.get(dataId); + const { dtype } = texData; + if (float32Values != null) { + texData.values = float32ToTypedArray(float32Values, dtype); + } + return texData.values; + } + acquireTexture(texShape, texType, dtype, isPacked) { + this.numBytesInGPU += this.computeBytes(texShape, dtype); + if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) { + const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2); + this.warnedAboutMemory = true; + console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`); + } + return this.textureManager.acquireTexture(texShape, texType, isPacked); + } + computeBytes(shape, dtype) { + return shape[0] * shape[1] * util_exports.bytesPerElement(dtype); + } + checkCompileCompletion() { + for (const [, binary] of Object.entries(this.binaryCache)) { + this.checkCompletion_(binary); + } + } + async checkCompileCompletionAsync() { + const ps = []; + if (this.gpgpu.parallelCompilationExtension) { + for (const [, binary] of Object.entries(this.binaryCache)) { + ps.push(this.checkCompletionAsync_(binary)); + } + return Promise.all(ps); + } else { + for (const [, binary] of Object.entries(this.binaryCache)) { + const p2 = new Promise((resolve) => { + try { + this.checkCompletion_(binary); + resolve(true); + } catch (error) { + throw error; + } + }); + ps.push(p2); + } + return Promise.all(ps); + } + } + async checkCompletionAsync_(binary) { + if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) { + return this.checkCompletion_(binary); + } else { + await nextFrame(); + return this.checkCompletionAsync_(binary); + } + } + checkCompletion_(binary) { + if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) { + console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram)); + if (this.gpgpu.gl.getShaderParameter(binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) { + logShaderSourceAndInfoLog(binary.source, this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader)); + throw new Error("Failed to compile fragment shader."); + } + throw new Error("Failed to link vertex and fragment shaders."); + } + return true; + } + getUniformLocations() { + for (const binary of Object.values(this.binaryCache)) { + this.gpgpu.buildVao(binary.webGLProgram); + const { variablesLocations, customUniformLocations, infLoc, nanLoc, outShapeLocation, outShapeStridesLocation, outTexShapeLocation } = getUniformLocations(this.gpgpu, binary.program, binary.webGLProgram); + binary.variablesLocations = variablesLocations; + binary.customUniformLocations = customUniformLocations; + binary.infLoc = infLoc; + binary.nanLoc = nanLoc; + binary.outShapeLocation = outShapeLocation; + binary.outShapeStridesLocation = outShapeStridesLocation; + binary.outTexShapeLocation = outTexShapeLocation; + } + } + /** + * Create a TF.js tensor out of an existing WebGL texture. A new texture will + * be created. + */ + createTensorFromGPUData(values, shape, dtype) { + values.channels = values.channels || "RGBA"; + const { texture, height, width, channels } = values; + const backend2 = engine().backend; + if (!backend2.gpgpu.gl.isTexture(texture)) { + 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)'.`); + } + const dataId = backend2.writeTexture(texture, shape, dtype, height, width, channels); + return engine().makeTensorFromDataId(dataId, shape, dtype, backend2); + } +}; +MathBackendWebGL.nextDataId = 0; +function float32ToTypedArray(a, dtype) { + if (dtype === "float32" || dtype === "complex64") { + return a; + } else if (dtype === "int32" || dtype === "bool") { + const result = dtype === "int32" ? new Int32Array(a.length) : new Uint8Array(a.length); + for (let i = 0; i < result.length; ++i) { + result[i] = Math.round(a[i]); + } + return result; + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/version.js +var version6 = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl.js +function forceHalfFloat() { + env().set("WEBGL_FORCE_F16_TEXTURES", true); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/base.js +if (device_util_exports.isBrowser()) { + registerBackend( + "webgl", + () => new MathBackendWebGL(), + 2 + /* priority */ + ); +} +var webgl = { forceHalfFloat }; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_gpu.js +var CHECK_NAN_SNIPPET2 = ` if (isnan(a)) return a; if (isnan(b)) return b; -`;var On=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=de(this.outputShape.length),this.userCode=` +`; +var BinaryOpProgram = class { + constructor(op2, aShape, bShape) { + this.variableNames = ["A", "B"]; + this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + this.userCode = ` float binaryOperation(float a, float b) { - ${t} + ${op2} } void main() { @@ -1254,44 +58868,82 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}};var Qn=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_packed_gpu.js +var CHECK_NAN_SNIPPET_PACKED = ` 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 Jn=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 BinaryOpPackedProgram = class { + constructor(op2, aShape, bShape, checkOutOfBounds = false) { + this.variableNames = ["A", "B"]; + this.supportsBroadcasting = true; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + const rank = this.outputShape.length; + this.enableShapeUniforms = useShapeUniforms(rank); + let checkOutOfBoundsString = ""; + if (checkOutOfBounds) { + if (rank === 0 || util_exports.sizeFromShape(this.outputShape) === 1) { + checkOutOfBoundsString = ` result.y = 0.; result.z = 0.; result.w = 0.; - `;else if(i=` - ${zt(s)} coords = getOutputCoords(); - `,s===1)this.enableShapeUniforms?i+=` + `; + } else { + const dtype = getCoordsDataType(rank); + checkOutOfBoundsString = ` + ${dtype} coords = getOutputCoords(); + `; + if (rank === 1) { + if (this.enableShapeUniforms) { + checkOutOfBoundsString += ` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; result.w = 0.; - `:i+=` + `; + } else { + checkOutOfBoundsString += ` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; - `;else{let u=er("coords",s);this.enableShapeUniforms?i+=` + `; + } + } else { + const channels = getChannels("coords", rank); + if (this.enableShapeUniforms) { + checkOutOfBoundsString += ` bool nextRowOutOfBounds = - (${u[s-2]} + 1) >= outShape[${s} - 2]; + (${channels[rank - 2]} + 1) >= outShape[${rank} - 2]; bool nextColOutOfBounds = - (${u[s-1]} + 1) >= outShape[${s} - 1]; + (${channels[rank - 1]} + 1) >= outShape[${rank} - 1]; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; - `:i+=` + `; + } else { + checkOutOfBoundsString += ` bool nextRowOutOfBounds = - (${u[s-2]} + 1) >= ${this.outputShape[s-2]}; + (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]}; bool nextColOutOfBounds = - (${u[s-1]} + 1) >= ${this.outputShape[s-1]}; + (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]}; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; - `}this.userCode=` + `; + } + } + } + } + this.userCode = ` vec4 binaryOperation(vec4 a, vec4 b) { - ${t} + ${op2} } void main() { @@ -1299,41 +58951,271 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec4 b = getBAtOutCoords(); vec4 result = binaryOperation(a, b); - ${i} + ${checkOutOfBoundsString} 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 Uz={kernelName:bo,backendName:"webgl",kernelFunc:rr};function Pn(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 Hz={kernelName:zp,backendName:"webgl",kernelFunc:Pn};var v1="return (a < 0.) ? b * a : a;",S1=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Identity.js +function identity3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + backend2.incRef(x.dataId); + return { dataId: x.dataId, shape: x.shape, dtype: x.dtype }; +} +var identityConfig2 = { + kernelName: Identity, + backendName: "webgl", + kernelFunc: identity3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Complex.js +function complex3(args) { + const { inputs, backend: backend2 } = args; + const { real: real4, imag: imag4 } = inputs; + const complexInfo = backend2.makeTensorInfo(real4.shape, "complex64"); + const complex4 = backend2.texData.get(complexInfo.dataId); + const realTensorInfo = identity3({ inputs: { x: real4 }, backend: backend2 }); + const imagTensorInfo = identity3({ inputs: { x: imag4 }, backend: backend2 }); + complex4.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo }; + return complexInfo; +} +var complexConfig2 = { + kernelName: Complex, + backendName: "webgl", + kernelFunc: complex3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LeakyRelu.js +var LEAKYRELU = `return (a < 0.) ? b * a : a;`; +var LEAKYRELU_PACKED = ` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function Rot(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 Jn(S1,o.shape,i.shape):new On(v1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var qz={kernelName:vs,backendName:"webgl",kernelFunc:Rot};var N1="return (a < 0.) ? b * a : a;",k1=` +`; +function leakyRelu3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { alpha } = attrs; + const $alpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(alpha, "float32")); + const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape); + const result = backend2.runWebGLProgram(program, [x, $alpha], "float32"); + backend2.disposeIntermediateTensorInfo($alpha); + return result; +} +var leakyReluConfig2 = { + kernelName: LeakyRelu, + backendName: "webgl", + kernelFunc: leakyRelu3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prelu.js +var PRELU = `return (a < 0.) ? b * a : a;`; +var PRELU_PACKED = ` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function Fot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jn(k1,n.shape,o.shape):new On(N1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var Kz={kernelName:zs,backendName:"webgl",kernelFunc:Fot};var Vo="if (isnan(x)) return x;";function It({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 Fn(i.shape,t):c=new Br(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 On(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Pn({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 Jn(t,u.shape,l.shape,e):f=new On(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Wl(r,t=!1){if(r==="linear")return t?Mz:Dz;if(r==="relu")return t?zz:Rz;if(r==="elu")return t?Lz:$z;if(r==="relu6")return t?Bz:Fz;if(r==="prelu")return t?k1:N1;if(r==="leakyrelu")return t?S1:v1;if(r==="sigmoid")return t?Vz:Oz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Ld=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 prelu4(args) { + const { inputs, backend: backend2 } = args; + const { x, alpha } = inputs; + const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) : new BinaryOpProgram(PRELU, x.shape, alpha.shape); + return backend2.runWebGLProgram(program, [x, alpha], "float32"); +} +var preluConfig2 = { + kernelName: Prelu, + backendName: "webgl", + kernelFunc: prelu4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/kernel_funcs_utils.js +var CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`; +function unaryKernelFunc2({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) { + return ({ inputs, backend: backend2 }) => { + const { x } = inputs; + const webglBackend = backend2; + const $dtype = dtype || x.dtype; + if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) { + const xData = webglBackend.texData.get(x.dataId); + const outValues = cpuKernelImpl(xData.values, $dtype); + return webglBackend.makeTensorInfo(x.shape, $dtype, outValues); + } + const shouldUsePackedProgram = env().getBool("WEBGL_PACK_UNARY_OPERATIONS") && packedOpSnippet != null; + let program; + if (shouldUsePackedProgram) { + program = new UnaryOpPackedProgram(x.shape, packedOpSnippet); + } else { + program = new UnaryOpProgram(x.shape, opSnippet); + } + return webglBackend.runWebGLProgram(program, [x], $dtype); + }; +} +function binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) { + return ({ inputs, backend: backend2 }) => { + const { a, b } = inputs; + const webglBackend = backend2; + if (supportsComplex && a.dtype === "complex64") { + const aData = webglBackend.texData.get(a.dataId); + const bData = webglBackend.texData.get(b.dataId); + const [real4, imag4] = [ + [aData.complexTensorInfos.real, bData.complexTensorInfos.real], + [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag] + ].map((complexParts) => { + const [aPart, bPart] = complexParts; + const aHandle = { + dataId: aPart.dataId, + dtype: aPart.dtype, + shape: a.shape + }; + const bHandle = { + dataId: bPart.dataId, + dtype: bPart.dtype, + shape: b.shape + }; + const program2 = new BinaryOpProgram(opSnippet, a.shape, b.shape); + return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype)); + }); + const complexOutput = complex3({ inputs: { real: real4, imag: imag4 }, backend: webglBackend }); + webglBackend.disposeIntermediateTensorInfo(real4); + webglBackend.disposeIntermediateTensorInfo(imag4); + return complexOutput; + } + const $dtype = dtype || upcastType(a.dtype, b.dtype); + if ((a.dtype === "string" || b.dtype === "string" || webglBackend.shouldExecuteOnCPU([a, b])) && cpuKernelImpl != null) { + const aVals = webglBackend.texData.get(a.dataId).values; + const bVals = webglBackend.texData.get(b.dataId).values; + const decodedAVals = a.dtype === "string" ? ( + // tslint:disable-next-line: no-any + backend_util_exports.fromUint8ToStringArray(aVals) + ) : aVals; + const decodedBVals = a.dtype === "string" ? ( + // tslint:disable-next-line: no-any + backend_util_exports.fromUint8ToStringArray(bVals) + ) : bVals; + const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype); + const out = webglBackend.makeTensorInfo(outShape, $dtype); + const outData = webglBackend.texData.get(out.dataId); + outData.values = outValues; + return out; + } + const shouldUsePackedProgram = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") && packedOpSnippet != null; + let program; + if (shouldUsePackedProgram) { + program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds); + } else { + program = new BinaryOpProgram(opSnippet, a.shape, b.shape); + } + return webglBackend.runWebGLProgram(program, [a, b], $dtype); + }; +} +function mapActivationToShaderProgram(activation2, packed = false) { + if (activation2 === "linear") { + if (packed) { + return LINEAR2; + } + return LINEAR; + } else if (activation2 === "relu") { + if (packed) { + return RELU2; + } + return RELU; + } else if (activation2 === "elu") { + if (packed) { + return ELU3; + } + return ELU2; + } else if (activation2 === "relu6") { + if (packed) { + return RELU62; + } + return RELU6; + } else if (activation2 === "prelu") { + if (packed) { + return PRELU_PACKED; + } + return PRELU; + } else if (activation2 === "leakyrelu") { + if (packed) { + return LEAKYRELU_PACKED; + } + return LEAKYRELU; + } else if (activation2 === "sigmoid") { + if (packed) { + return SIGMOID2; + } + return SIGMOID; + } + throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mulmat_packed_gpu.js +var MatMulPackedProgram = class { + constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyreluActivation = false) { + this.variableNames = ["matrixA", "matrixB"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const sharedDim = transposeA ? aShape[1] : aShape[2]; + const sharedDimensionPacked = Math.ceil(sharedDim / 2); + const aSample = transposeA ? "i * 2, rc.y" : "rc.y, i * 2"; + const bSample = transposeB ? "rc.z, i * 2" : "i * 2, rc.z"; + const aSwizzle = transposeA ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"]; + const bSwizzle = transposeB ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"]; + let activationSnippet = "", applyActivationSnippet = ""; + if (activation2) { + if (hasPreluActivation) { + activationSnippet = `vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); - ${a} - }`:l?g=`vec4 activation(vec4 a) { + ${activation2} + }`; + } else if (hasLeakyreluActivation) { + activationSnippet = `vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); - ${a} - }`:g=`vec4 activation(vec4 x) { - ${a} - }`,x="result = activation(result);");let b=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let w="rc.x",I="rc.x";t[0]`The new shape (${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&&!Ju(o.shape,u)&&!(c.texture!==null&&Ju(c.shape,u))?Yz(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var Zz={kernelName:Ui,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}) { + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multiply.js +var MUL = "return a * b;"; +function multiply3(args) { + const { inputs, backend: backend2 } = args; + const { a, b } = inputs; + const dtype = backend_util_exports.upcastType(a.dtype, b.dtype); + if (a.dtype === "complex64") { + const aData = backend2.texData.get(a.dataId); + const bData = backend2.texData.get(b.dataId); + const realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a.shape, b.shape); + const imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a.shape, b.shape); + const inputs2 = [ + { + dataId: aData.complexTensorInfos.real.dataId, + dtype: aData.complexTensorInfos.real.dtype, + shape: a.shape + }, + { + dataId: aData.complexTensorInfos.imag.dataId, + dtype: aData.complexTensorInfos.imag.dtype, + shape: a.shape + }, + { + dataId: bData.complexTensorInfos.real.dataId, + dtype: bData.complexTensorInfos.real.dtype, + shape: b.shape + }, + { + dataId: bData.complexTensorInfos.imag.dataId, + dtype: bData.complexTensorInfos.imag.dtype, + shape: b.shape + } + ]; + const realPart = backend2.runWebGLProgram(realProgram, inputs2, "float32"); + const imagPart = backend2.runWebGLProgram(imagProgram, inputs2, "float32"); + const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(imagPart); + return complexOutput; + } + if (backend2.shouldExecuteOnCPU([a, b])) { + const aData = backend2.texData.get(a.dataId); + const bData = backend2.texData.get(b.dataId); + const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype); + const out = backend2.makeTensorInfo(outShape, dtype); + const outData = backend2.texData.get(out.dataId); + outData.values = outValues; + return out; + } + let program; + if (env().getBool("WEBGL_PACK_BINARY_OPERATIONS")) { + program = new BinaryOpPackedProgram(MUL, a.shape, b.shape); + } else { + program = new BinaryOpProgram(MUL, a.shape, b.shape); + } + return backend2.runWebGLProgram(program, [a, b], dtype); +} +var multiplyConfig2 = { + kernelName: Multiply, + backendName: "webgl", + kernelFunc: multiply3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reshape.js +function packedReshape(input2, afterShape, backend2) { + const input3DShape = [ + getBatchDim(input2.shape), + ...getRowsCols(input2.shape) + ]; + const input3D = { + dtype: input2.dtype, + shape: input3DShape, + dataId: input2.dataId + }; + const afterShapeAs3D = [ + getBatchDim(afterShape), + ...getRowsCols(afterShape) + ]; + const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape); + const preventEagerUnpackingOfOutput = true; + const customValues = [input3DShape]; + const output = backend2.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput); + return { dataId: output.dataId, shape: afterShape, dtype: output.dtype }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reshape.js +function reshape4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { shape } = attrs; + const webglBackend = backend2; + const xSize = util_exports.sizeFromShape(x.shape); + const $shape = util_exports.inferFromImplicitShape(shape, xSize); + const $xSize = util_exports.sizeFromShape($shape); + util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`); + const xTexData = webglBackend.texData.get(x.dataId); + if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) && !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) { + return packedReshape(x, $shape, webglBackend); + } + webglBackend.incRef(x.dataId); + return { dataId: x.dataId, shape: $shape, dtype: x.dtype }; +} +var reshapeConfig2 = { + kernelName: Reshape, + backendName: "webgl", + kernelFunc: reshape4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mean_gpu.js +var MeanProgram = class { + constructor(reduceInfo, divisor) { + this.variableNames = ["x"]; + const { windowSize, batchSize, inSize, outSize } = reduceInfo; + this.outputShape = [batchSize, outSize]; + const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4; + const windowSizeVec4Remainder = windowSize % 4; + let updateSnippet = `sumValue += dot(values, ones);`; + if (divisor != null) { + const denominator = 1 / divisor; + updateSnippet = `sumValue += dot(values * ${util_exports.isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`; + } + let checkOutOfBounds = ""; + if (inSize % windowSize > 0) { + checkOutOfBounds = ` + if (inIdx < 0 || inIdx >= ${inSize}) { return 0.0; } - `),this.userCode=` + `; + } + this.userCode = ` const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float getValue(int batch, int inIdx) { - ${c} + ${checkOutOfBounds} return getX(batch, inIdx); } @@ -1377,11 +59401,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; - int inOffset = outIdx * ${n}; + int inOffset = outIdx * ${windowSize}; float sumValue = 0.0; - for (int i = 0; i < ${a}; i += 4) { + for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), @@ -1390,64 +59414,112 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, getValue(batch, inIdx + 3) ); - ${l} + ${updateSnippet} } - int inIdx = inOffset + ${a}; - if (${u===1}) { + int inIdx = inOffset + ${windowSizeNearestVec4}; + if (${windowSizeVec4Remainder === 1}) { vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0); - ${l} - } else if (${u===2}) { + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), 0.0, 0.0); - ${l} - } else if (${u===3}) { + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), 0.0); - ${l} + ${updateSnippet} } setOutput(sumValue); } - `}};var nI=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${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 (${e==="sum"}) { + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reduce_gpu.js +var ReduceProgram = class { + constructor(reduceInfo, reduceType) { + this.variableNames = ["x"]; + const { windowSize, batchSize, inSize, outSize } = reduceInfo; + this.outputShape = [batchSize, outSize]; + let initializationValue = "0.0"; + let compareOp = ``; + if (reduceType === "prod") { + initializationValue = "1.0"; + } else if (reduceType === "min") { + initializationValue = "1.0 / 1e-20"; + compareOp = `min`; + } else if (reduceType === "max") { + initializationValue = "-1.0 / 1e-20"; + compareOp = `max`; + } + let returnValue = `${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + if (reduceType === "sum") { + returnValue = `sumValue`; + } else if (reduceType === "prod") { + returnValue = `prodValue`; + } else if (reduceType === "all") { + returnValue = `allValue`; + } else if (reduceType === "any") { + returnValue = `anyValue`; + } + const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4; + const windowSizeVec4Remainder = windowSize % 4; + let updateSnippet = ` + if (${reduceType === "sum"}) { sumValue += dot(values, ones); - } else if (${e==="prod"}) { + } else if (${reduceType === "prod"}) { vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]); prodValue *= tmp[0] * tmp[1]; } else { - minMaxValue = ${u}(values, minMaxValue); - if (${e==="min"} || ${e==="max"}) { - minMaxValue = ${u}(values, minMaxValue); + minMaxValue = ${compareOp}(values, minMaxValue); + if (${reduceType === "min"} || ${reduceType === "max"}) { + minMaxValue = ${compareOp}(values, minMaxValue); bvec4 isNaN = isnan(values); if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) { minMaxValue = vec4(NAN); } } } - `,f="vec4";e==="all"?(a="1.0",m=` + `; + let vecType = `vec4`; + if (reduceType === "all") { + initializationValue = "1.0"; + updateSnippet = ` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); - `,f="bvec4"):e==="any"&&(a="0.0",m=` + `; + vecType = `bvec4`; + } else if (reduceType === "any") { + initializationValue = "0.0"; + updateSnippet = ` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); - `,f="bvec4");let d="";s%n>0&&(d=` - if (inIdx < 0 || inIdx >= ${s}) { + `; + vecType = `bvec4`; + } + let checkOutOfBounds = ""; + if (inSize % windowSize > 0) { + checkOutOfBounds = ` + if (inIdx < 0 || inIdx >= ${inSize}) { return initializationValue; } - `),this.userCode=` - const float initializationValue = ${a}; + `; + } + this.userCode = ` + const float initializationValue = ${initializationValue}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float getValue(int batch, int inIdx) { - ${d} + ${checkOutOfBounds} return getX(batch, inIdx); } @@ -1455,174 +59527,713 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; - int inOffset = outIdx * ${n}; + int inOffset = outIdx * ${windowSize}; - vec4 minMaxValue = vec4(${a}); + vec4 minMaxValue = vec4(${initializationValue}); float prodValue = 1.0; float sumValue = 0.0; float allValue = 1.0; float anyValue = 0.0; - for (int i = 0; i < ${c}; i += 4) { + for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) { int inIdx = inOffset + i; - ${f} values = ${f}( + ${vecType} values = ${vecType}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); - ${m} + ${updateSnippet} } - int inIdx = inOffset + ${c}; - if (${p===1}) { - ${f} values = ${f}( + int inIdx = inOffset + ${windowSizeNearestVec4}; + if (${windowSizeVec4Remainder === 1}) { + ${vecType} values = ${vecType}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); - ${m} - } else if (${p===2}) { - ${f} values = ${f}( + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 2}) { + ${vecType} values = ${vecType}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); - ${m} - } else if (${p===3}) { - ${f} values = ${f}( + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 3}) { + ${vecType} values = ${vecType}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); - ${m} + ${updateSnippet} } - setOutput(${l}); + setOutput(${returnValue}); } - `}};function Pot(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 to(r,t,e,n){let o=Pot(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 6) { + throw Error(`Transpose for rank ${rank} is not yet supported`); + } + const originalOrder = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"]; + const switchedCoords = new Array(rank); + for (let i = 0; i < newDim.length; i++) { + switchedCoords[newDim[i]] = originalOrder[i]; + } + return switchedCoords.join(); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_packed_gpu.js +var TransposePackedProgram = class { + constructor(aShape, newDim) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + const outputShape = new Array(aShape.length); + for (let i = 0; i < outputShape.length; i++) { + outputShape[i] = aShape[newDim[i]]; + } + this.outputShape = outputShape; + this.rank = outputShape.length; + if (this.rank > 6) { + throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`); + } + const dtype = getCoordsDataType(this.rank); + const outputOrder = getVecChannels("rc", this.rank); + const switchedOrder = new Array(this.rank); + for (let i = 0; i < newDim.length; i++) { + switchedOrder[newDim[i]] = outputOrder[i]; + } + const innerDims = `vec2(${switchedOrder.slice(-2).join()})`; + const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`; + const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`; + this.userCode = ` + void main() { + ${dtype} rc = getOutputCoords(); vec4 result = vec4(0.); - result[0] = ${l}; - if(${u}) { - result[1] = ${l}; + result[0] = ${getc}; + if(${nextColumn}) { + result[1] = ${getc}; } - --${s[this.rank-1]}; - if(++${s[this.rank-2]} < ${n[this.rank-2]}) { - result[2] = ${l}; - if(${u}) { - result[3] = ${l}; + --${outputOrder[this.rank - 1]}; + if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) { + result[2] = ${getc}; + if(${nextColumn}) { + result[3] = ${getc}; } } setOutput(result); } - `}};function tc(r,t,e){let n=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sI(r.shape,t):new oI(r.shape,t);return e.runWebGLProgram(n,[r],r.dtype)}function Jz(r,t,e,n){let o=t,s=r.shape.length,i=y.parseAxisParam(o,r.shape),a=i,u=S.getAxesPermutation(a,s),l=u!=null,c=r;l&&(c=tc(r,u,n),a=S.getInnerMostAxes(a.length,s)),S.assertAxesAreInnerMostDims("sum",a,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,a),f=p;e&&(f=S.expandShapeToKeepDim(p,i));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=rt({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=xc(r.dtype),w=to(x,b,"sum",n),I=rt({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),l&&n.disposeIntermediateTensorInfo(c),I}function Cp(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;return Jz(o,s,i,e)}var Qz={kernelName:ri,backendName:"webgl",kernelFunc:Cp};function Pe(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{perm:s}=n,i=e,a=o.shape.length,u=new Array(a);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?Wl(u,!0):null,K=G||W||q||H!=null,X;if((f===1||d===1)&&V>_1&&K===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,at=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(ot));let it=d===1?2:1,mt=nt;at&&(mt=rt({inputs:{x:nt},backend:o,attrs:{shape:[P,1,V]}}),F.push(mt));let gt=fg({inputs:{a:ot,b:mt},backend:o});X=Cp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new Ld(N,E,[P,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let at=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));st.push(at),F.push(at)}X=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:X},backend:o,attrs:{shape:I}});F.push(X);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function Lot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return vp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var e3={kernelName:Zi,backendName:"webgl",kernelFunc:Lot};var r3="return abs(x);";function zot(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=Zw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Fn(n.shape,r3):o=new Br(n.shape,r3),e.runWebGLProgram(o,[n],n.dtype)}var n3={kernelName:$i,backendName:"webgl",kernelFunc:zot};var Bot=yr+` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose_impl.js +function transposeImpl2(x, perm, backend2) { + const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new TransposePackedProgram(x.shape, perm) : new TransposeProgram(x.shape, perm); + return backend2.runWebGLProgram(program, [x], x.dtype); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum_impl.js +function sumImpl(x, axis, keepDims, backend2) { + const reductionIndices = axis; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + const sumInputIsTransposed = permutedAxes != null; + let sumInput = x; + if (sumInputIsTransposed) { + sumInput = transposeImpl2(x, permutedAxes, backend2); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank); + const [sumOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(sumInput.shape, axes); + let outShape = sumOutShape; + if (keepDims) { + outShape = backend_util_exports.expandShapeToKeepDim(sumOutShape, origAxes); + } + const inSize = util_exports.sizeFromShape(reduceShape); + const xSize = util_exports.sizeFromShape(x.shape); + const batchSize = xSize / inSize; + const reshapedInput = reshape4({ inputs: { x: sumInput }, attrs: { shape: [batchSize, inSize] }, backend: backend2 }); + const outType = sumOutType(x.dtype); + const reduced = reduce(reshapedInput, outType, "sum", backend2); + const out = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(reshapedInput); + backend2.disposeIntermediateTensorInfo(reduced); + if (sumInputIsTransposed) { + backend2.disposeIntermediateTensorInfo(sumInput); + } + return out; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum.js +function sum4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + return sumImpl(x, axis, keepDims, backend2); +} +var sumConfig2 = { + kernelName: Sum, + backendName: "webgl", + kernelFunc: sum4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose.js +function transpose3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { perm } = attrs; + const webglBackend = backend2; + const xRank = x.shape.length; + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[perm[i]]; + } + let out; + if (webglBackend.shouldExecuteOnCPU([x])) { + const xTexData = webglBackend.texData.get(x.dataId); + const values = xTexData.values; + const outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape); + out = webglBackend.makeTensorInfo(newShape, x.dtype); + const outData = webglBackend.texData.get(out.dataId); + outData.values = outValues; + } else { + out = transposeImpl2(x, perm, webglBackend); + } + return out; +} +var transposeConfig2 = { + kernelName: Transpose, + backendName: "webgl", + kernelFunc: transpose3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul_impl.js +var MATMUL_SHARED_DIM_THRESHOLD = 1e3; +function batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) { + const aRank = a.shape.length; + const bRank = b.shape.length; + const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1]; + const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2]; + const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2]; + const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1]; + const outerDimsA = a.shape.slice(0, -2); + const outerDimsB = b.shape.slice(0, -2); + const batchDimA = util_exports.sizeFromShape(outerDimsA); + const batchDimB = util_exports.sizeFromShape(outerDimsB); + const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); + const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA]; + const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB]; + const a3d = reshape4({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } }); + const b3d = reshape4({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } }); + const intermediates = [a3d, b3d]; + const batchDim = Math.max(batchDimA, batchDimB); + const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2]; + const hasBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + const hasLeakyreluAlpha = activation2 === "leakyrelu"; + const fusedActivation = activation2 != null ? mapActivationToShaderProgram(activation2, true) : null; + const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null; + let out; + if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) { + let aVec = a3d; + let bVec = b3d; + if (transposeA) { + aVec = transpose3({ inputs: { x: a3d }, backend: backend2, attrs: { perm: [0, 2, 1] } }); + intermediates.push(aVec); + } + if (transposeB) { + bVec = transpose3({ inputs: { x: b3d }, backend: backend2, attrs: { perm: [0, 2, 1] } }); + intermediates.push(bVec); + } + const shouldReshapeA = outerShapeB !== 1; + const shouldReshapeB = outerShapeB === 1; + let aVec3d = aVec; + if (shouldReshapeA) { + aVec3d = reshape4({ + inputs: { x: aVec }, + backend: backend2, + attrs: { shape: [batchDim, sharedDim, 1] } + }); + intermediates.push(aVec3d); + } + const axis = outerShapeB === 1 ? 2 : 1; + let bVec3d = bVec; + if (shouldReshapeB) { + bVec3d = reshape4({ + inputs: { x: bVec }, + backend: backend2, + attrs: { shape: [batchDim, 1, sharedDim] } + }); + intermediates.push(bVec3d); + } + const product = multiply3({ inputs: { a: aVec3d, b: bVec3d }, backend: backend2 }); + out = sum4({ inputs: { x: product }, backend: backend2, attrs: { axis, keepDims: true } }); + intermediates.push(product); + } else { + const dtype = upcastType(a.dtype, b.dtype); + const program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + const inputs = [a3d, b3d]; + if (bias != null) { + inputs.push(bias); + } + if (hasPreluActivationWeights) { + inputs.push(preluActivationWeights); + } + if (hasLeakyreluAlpha) { + const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32")); + inputs.push($leakyreluAlpha); + intermediates.push($leakyreluAlpha); + } + out = backend2.runWebGLProgram(program, inputs, dtype); + } + const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } }); + intermediates.push(out); + for (const i of intermediates) { + backend2.disposeIntermediateTensorInfo(i); + } + return outReshaped; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/_FusedMatMul.js +function _fusedMatMul2(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b, bias, preluActivationWeights } = inputs; + const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs; + return batchMatMulImpl({ + a, + b, + transposeA, + transposeB, + backend: backend2, + bias, + preluActivationWeights, + leakyreluAlpha, + activation: activation2 + }); +} +var _fusedMatMulConfig2 = { + kernelName: _FusedMatMul, + backendName: "webgl", + kernelFunc: _fusedMatMul2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Abs.js +var ABS2 = `return abs(x);`; +function abs3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (backend2.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") { + const xData = backend2.texData.get(x.dataId); + const outValues = simpleAbsImplCPU(xData.values); + return backend2.makeTensorInfo(x.shape, x.dtype, outValues); + } + let program; + if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) { + program = new UnaryOpPackedProgram(x.shape, ABS2); + } else { + program = new UnaryOpProgram(x.shape, ABS2); + } + return backend2.runWebGLProgram(program, [x], x.dtype); +} +var absConfig2 = { + kernelName: Abs, + backendName: "webgl", + kernelFunc: abs3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acos.js +var ACOS = CHECK_NAN_SNIPPET + ` if (abs(x) > 1.) { return NAN; } return acos(x); -`,Vot=It({opSnippet:Bot}),o3={kernelName:qo,backendName:"webgl",kernelFunc:Vot};var Got=yr+` +`; +var acos3 = unaryKernelFunc2({ opSnippet: ACOS }); +var acosConfig2 = { + kernelName: Acos, + backendName: "webgl", + kernelFunc: acos3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acosh.js +var ACOSH = CHECK_NAN_SNIPPET + ` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,Wot=It({opSnippet:Got}),s3={kernelName:Ko,backendName:"webgl",kernelFunc:Wot};var i3="return a + b;",Uot=ue({opSnippet:i3,packedOpSnippet:i3,supportsComplex:!0,cpuKernelImpl:PL}),a3={kernelName:ao,backendName:"webgl",kernelFunc:Uot};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(` - `)} +return log(x + sqrt(x * x - 1.0));`; +var acosh3 = unaryKernelFunc2({ opSnippet: ACOSH }); +var acoshConfig2 = { + kernelName: Acosh, + backendName: "webgl", + kernelFunc: acosh3 +}; - float result = ${o}; +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Add.js +var ADD = "return a + b;"; +var addKernelFunc = binaryKernelFunc2({ + opSnippet: ADD, + packedOpSnippet: ADD, + supportsComplex: true, + cpuKernelImpl: addImplCPU +}); +var addConfig2 = { + kernelName: Add, + backendName: "webgl", + kernelFunc: addKernelFunc +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_gpu.js +var AddNProgram = class { + constructor(outputShape, shapes) { + this.outputShape = []; + this.outputShape = outputShape; + this.variableNames = shapes.map((_, i) => `T${i}`); + const snippets = []; + this.variableNames.forEach((variable2) => { + snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`); + }); + const operation = this.variableNames.map((variable2) => { + return `v${variable2}`; + }).join(" + "); + this.userCode = ` + void main() { + ${snippets.join("\n ")} + + float result = ${operation}; setOutput(result); } - `}};var aI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${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(` - `)} + `; + } +}; - vec4 result = ${o}; +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_packed_gpu.js +var AddNPackedProgram = class { + constructor(outputShape, shapes) { + this.outputShape = []; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = outputShape; + this.variableNames = shapes.map((_, i) => `T${i}`); + const snippets = []; + this.variableNames.forEach((variable2) => { + snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`); + }); + const operation = this.variableNames.map((variable2) => { + return `v${variable2}`; + }).join(" + "); + this.userCode = ` + void main() { + ${snippets.join("\n ")} + + vec4 result = ${operation}; setOutput(result); } - `}};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 l3={kernelName:jo,backendName:"webgl",kernelFunc:lI};function Hot(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=to(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 u3={kernelName:Ra,backendName:"webgl",kernelFunc:Hot};function qot(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=to(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 c3={kernelName:Fa,backendName:"webgl",kernelFunc:qot};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AddN.js +function addN3(args) { + const { inputs, backend: backend2 } = args; + const tensors = inputs; + if (tensors.length === 1) { + return identity3({ inputs: { x: tensors[0] }, backend: backend2 }); + } + if (tensors.length > env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) { + const midIndex = Math.floor(tensors.length / 2); + const leftSide = addN3({ inputs: tensors.slice(0, midIndex), backend: backend2 }); + const rightSide = addN3({ inputs: tensors.slice(midIndex), backend: backend2 }); + return addN3({ inputs: [leftSide, rightSide], backend: backend2 }); + } + const dtype = tensors.map((t) => t.dtype).reduce((d1, d2) => upcastType(d1, d2)); + const shapes = tensors.map((t) => t.shape); + const usePackedOp = env().getBool("WEBGL_PACK"); + const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes); + return backend2.runWebGLProgram(program, tensors, dtype); +} +var addNConfig2 = { + kernelName: AddN, + backendName: "webgl", + kernelFunc: addN3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/All.js +function all3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let permutedX = x; + if (permutedAxes != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("all", axes, xRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes); + const inSize = util_exports.sizeFromShape(reduceShape); + const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } }); + const reduced = reduce(a2D, a2D.dtype, "all", backend2); + let res; + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } }); + } else { + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } }); + } + backend2.disposeIntermediateTensorInfo(a2D); + backend2.disposeIntermediateTensorInfo(reduced); + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo(permutedX); + } + return res; +} +var allConfig2 = { + kernelName: All, + backendName: "webgl", + kernelFunc: all3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Any.js +function any3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let permutedX = x; + if (permutedAxes != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("any", axes, xRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes); + const inSize = util_exports.sizeFromShape(reduceShape); + const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } }); + const reduced = reduce(a2D, a2D.dtype, "any", backend2); + let res; + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } }); + } else { + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } }); + } + backend2.disposeIntermediateTensorInfo(a2D); + backend2.disposeIntermediateTensorInfo(reduced); + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo(permutedX); + } + return res; +} +var anyConfig2 = { + kernelName: Any, + backendName: "webgl", + kernelFunc: any3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_gpu.js +var ArgMinMaxProgram = class { + constructor(reduceInfo, op2, firstPass) { + this.variableNames = ["A"]; + const { windowSize, batchSize, outSize } = reduceInfo; + if (!firstPass) { + this.variableNames.push("bestIndicesA"); + } + this.outputShape = [batchSize, outSize]; + const compOp = op2 === "max" ? ">" : "<"; + const indexSnippet = firstPass ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));"; + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; - int inOffset = outIdx * ${o}; + int inOffset = outIdx * ${windowSize}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); - for (int i = 0; i < ${o}; i++) { - int inIdx = ${u}; + for (int i = 0; i < ${windowSize}; i++) { + int inIdx = ${indexSnippet}; float candidate = getA(batch, inIdx); - if (candidate ${a} bestValue) { + if (candidate ${compOp} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } - `}};var cI=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${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); - ++${c[u-2]}; - ${D} sourceLocA = ${D}(${c.join()}, 0); - --${c[u-1]}; - ${D} sourceLocB = ${D}(${c.join()}, 0); - --${c[u-2]};`}else m=u,p=` - ${l} sourceLocR = coords; - ++${c[u-1]}; - ${l} sourceLocG = coords; - ++${c[u-2]}; - ${l} sourceLocA = coords; - --${c[u-1]}; - ${l} sourceLocB = coords; - --${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?"":` - inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), - getBestIndicesAChannel(${x.join()}), - getBestIndicesAChannel(${b.join()}), - getBestIndicesAChannel(${w.join()})));`,E=`vec4( - getAChannel(${g.join()}), - hasNextCol ? getAChannel(${x.join()}) : 0., - hasNextRow ? getAChannel(${b.join()}) : 0., - hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?"":` - float getBestIndicesAChannel(${h.join()}) { - return getChannel(getBestIndicesA(${f.join()}), - vec2(${f.slice(-2).join()})); - }`;this.userCode=` - float getAChannel(${h.join()}) { - return getChannel(getA(${f.join()}), - vec2(${f.slice(-2).join()})); + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_packed_gpu.js +var ArgMinMaxPackedProgram = class { + constructor(shape, windowSize, op2, firstPass) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + util_exports.assert(shape.length > 2, () => `Packed arg${op2.charAt(0).toUpperCase() + op2.slice(1)} supports only inputs with rank above 2.`); + const inSize = shape[shape.length - 1]; + const outSize = Math.ceil(inSize / windowSize); + this.outputShape = shape.slice(0, -1); + if (outSize > 1) { + this.outputShape.push(outSize); + } + if (!firstPass) { + this.variableNames.push("bestIndicesA"); + } + const outShape = this.outputShape; + const rank = outShape.length; + const dtype = getCoordsDataType(rank); + const coords2 = getChannels("coords", rank); + let sourceLocSetup; + let sourceRank; + if (outSize === 1) { + sourceRank = rank + 1; + const sourceLocDType = getCoordsDataType(sourceRank); + sourceLocSetup = ` + ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords2.join()}, 0); + ++${coords2[rank - 1]}; + ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords2.join()}, 0); + ++${coords2[rank - 2]}; + ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords2.join()}, 0); + --${coords2[rank - 1]}; + ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords2.join()}, 0); + --${coords2[rank - 2]};`; + } else { + sourceRank = rank; + sourceLocSetup = ` + ${dtype} sourceLocR = coords; + ++${coords2[rank - 1]}; + ${dtype} sourceLocG = coords; + ++${coords2[rank - 2]}; + ${dtype} sourceLocA = coords; + --${coords2[rank - 1]}; + ${dtype} sourceLocB = coords; + --${coords2[rank - 2]};`; + } + const channels = ["x", "y", "z", "w", "u", "v"].slice(0, sourceRank); + const inChannel = "." + channels[sourceRank - 1]; + const intChannels = channels.map((x) => "int " + x); + const srcRCoords = getChannels("sourceLocR", sourceRank - 1).concat("inIdx.r"); + const srcGCoords = getChannels("sourceLocG", sourceRank - 1).concat("inIdx.g"); + const srcBCoords = getChannels("sourceLocB", sourceRank - 1).concat("inIdx.b"); + const srcACoords = getChannels("sourceLocA", sourceRank - 1).concat("inIdx.a"); + const compOp = op2 === "max" ? "greaterThan" : "lessThan"; + const fetchCandidateIdx = firstPass ? "" : ` + inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}), + getBestIndicesAChannel(${srcGCoords.join()}), + getBestIndicesAChannel(${srcBCoords.join()}), + getBestIndicesAChannel(${srcACoords.join()})));`; + const fetchValue = `vec4( + getAChannel(${srcRCoords.join()}), + hasNextCol ? getAChannel(${srcGCoords.join()}) : 0., + hasNextRow ? getAChannel(${srcBCoords.join()}) : 0., + hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`; + const getBestIndicesAChannelSnippet = firstPass ? "" : ` + float getBestIndicesAChannel(${intChannels.join()}) { + return getChannel(getBestIndicesA(${channels.join()}), + vec2(${channels.slice(-2).join()})); + }`; + this.userCode = ` + float getAChannel(${intChannels.join()}) { + return getChannel(getA(${channels.join()}), + vec2(${channels.slice(-2).join()})); } - ${A} + ${getBestIndicesAChannelSnippet} void main() { - ${l} coords = getOutputCoords(); - bool hasNextCol = ${c[u-1]} < ${a[u-1]-1}; - bool hasNextRow = ${c[u-2]} < ${a[u-2]-1}; - ${p} - ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d}, - sourceLocB${d}, sourceLocA${d}) * ${e}; + ${dtype} coords = getOutputCoords(); + bool hasNextCol = ${coords2[rank - 1]} < ${outShape[rank - 1] - 1}; + bool hasNextRow = ${coords2[rank - 2]} < ${outShape[rank - 2] - 1}; + ${sourceLocSetup} + ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel}, + sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); - vec4 bestValue = ${E}; + vec4 bestValue = ${fetchValue}; - for (int i = 0; i < ${e}; i++) { + for (int i = 0; i < ${windowSize}; i++) { inIdx = srcIdx; - ${N} - vec4 candidate = ${E}; + ${fetchCandidateIdx} + vec4 candidate = ${fetchValue}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( - vec4(${I}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); + vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); bestValue = vec4(replace.x ? candidate.x : bestValue.x, replace.y ? candidate.y : bestValue.y, @@ -1633,27 +60244,215 @@ return log(x + sqrt(x * x - 1.0));`,Wot=It({opSnippet:Got}),s3={kernelName:Ko,ba } setOutput(bestIndex); } - `}};function p3(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=p3(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function m3(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=m3(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=p3(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 m3(r,t,n)}function Kot(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 f3={kernelName:Ri,backendName:"webgl",kernelFunc:Kot};function jot(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 d3={kernelName:Fi,backendName:"webgl",kernelFunc:jot};var Xot=yr+` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/arg_min_max.js +function argReduce(backend2, x, reduceType, bestIndicesA = null) { + let batchSize = x.shape[0]; + let inSize = x.shape[1]; + if (bestIndicesA != null) { + batchSize = bestIndicesA.shape[0]; + inSize = bestIndicesA.shape[1]; + } + const windowSize = backend_util_exports.computeOptimalWindowSize(inSize); + const reduceInfo = { windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize) }; + const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null); + const inputs = [x]; + if (bestIndicesA != null) { + inputs.push(bestIndicesA); + } + const output = backend2.runWebGLProgram(program, inputs, "int32"); + if (output.shape[1] === 1) { + return output; + } + const result = argReduce(backend2, x, reduceType, output); + backend2.disposeIntermediateTensorInfo(output); + return result; +} +function argReducePacked(backend2, x, reduceType, bestIndicesA = null) { + const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape; + const inSize = inShape[inShape.length - 1]; + const windowSize = backend_util_exports.computeOptimalWindowSize(inSize); + const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null); + const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA]; + const output = backend2.runWebGLProgram(program, inputs, "int32"); + if (output.shape.length === x.shape.length) { + const result = argReducePacked(backend2, x, reduceType, output); + backend2.disposeIntermediateTensorInfo(output); + return result; + } + return output; +} +function argMinMaxReduce(backend2, x, axis, reduceType) { + const axes = [axis]; + backend_util_exports.assertAxesAreInnerMostDims("arg" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length); + if (!env().getBool("WEBGL_PACK_REDUCE") || x.shape.length <= 2) { + const intermediateTensorInfos = []; + const xtexData = backend2.texData.get(x.dataId); + const xIsPacked = xtexData !== null && xtexData.isPacked; + let xUnPacked = x; + if (xIsPacked) { + xUnPacked = backend2.unpackTensor(x); + intermediateTensorInfos.push(xUnPacked); + } + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xUnPacked.shape, axes); + const inSize = util_exports.sizeFromShape(reduceShape); + const a2D = reshape4({ inputs: { x: xUnPacked }, backend: backend2, attrs: { shape: [-1, inSize] } }); + intermediateTensorInfos.push(a2D); + const reduced = argReduce(backend2, a2D, reduceType); + intermediateTensorInfos.push(reduced); + const reshaped = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } }); + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return reshaped; + } + return argReducePacked(backend2, x, reduceType); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMax.js +function argMax3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis } = attrs; + let axes = util_exports.parseAxisParam(axis, x.shape); + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + const intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("argMax", [axes[0]], $x.shape.length); + const out = argMinMaxReduce(backend2, $x, axes[0], "max"); + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return out; +} +var argMaxConfig2 = { + kernelName: ArgMax, + backendName: "webgl", + kernelFunc: argMax3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMin.js +function argMin3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis } = attrs; + let axes = util_exports.parseAxisParam(axis, x.shape); + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + const intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("argMin", [axes[0]], $x.shape.length); + const out = argMinMaxReduce(backend2, $x, axes[0], "min"); + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return out; +} +var argMinConfig2 = { + kernelName: ArgMin, + backendName: "webgl", + kernelFunc: argMin3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asin.js +var ASIN = CHECK_NAN_SNIPPET + ` if (abs(x) > 1.) { return NAN; } return asin(x); -`,Yot=It({opSnippet:Xot}),h3={kernelName:Xo,backendName:"webgl",kernelFunc:Yot};var Zot=yr+"return log(x + sqrt(x * x + 1.0));",Jot=It({opSnippet:Zot}),g3={kernelName:Yo,backendName:"webgl",kernelFunc:Jot};var Qot=yr+` +`; +var asin3 = unaryKernelFunc2({ opSnippet: ASIN }); +var asinConfig2 = { + kernelName: Asin, + backendName: "webgl", + kernelFunc: asin3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asinh.js +var ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`; +var asinh3 = unaryKernelFunc2({ opSnippet: ASINH }); +var asinhConfig2 = { + kernelName: Asinh, + backendName: "webgl", + kernelFunc: asinh3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan.js +var ATAN = CHECK_NAN_SNIPPET + ` return atan(x); -`,tst=It({opSnippet:Qot}),x3={kernelName:Zo,backendName:"webgl",kernelFunc:tst};var est=Md+` +`; +var atan4 = unaryKernelFunc2({ opSnippet: ATAN }); +var atanConfig2 = { + kernelName: Atan, + backendName: "webgl", + kernelFunc: atan4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan2.js +var ATAN2 = CHECK_NAN_SNIPPET2 + ` return atan(a, b); -`,rst=` +`; +var ATAN2_PACKED = ` 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); - `+Qn+` + ` + CHECK_NAN_SNIPPET_PACKED + ` return result; -`,nst=ue({opSnippet:est,packedOpSnippet:rst}),y3={kernelName:Qo,backendName:"webgl",kernelFunc:nst};var ost=yr+` +`; +var atan23 = binaryKernelFunc2({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED }); +var atan2Config2 = { + kernelName: Atan2, + backendName: "webgl", + kernelFunc: atan23 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atanh.js +var ATANH = CHECK_NAN_SNIPPET + ` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelName:Jo,backendName:"webgl",kernelFunc:sst};var Ti=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}); +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`; +var atanh3 = unaryKernelFunc2({ opSnippet: ATANH }); +var atanhConfig2 = { + kernelName: Atanh, + backendName: "webgl", + kernelFunc: atanh3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pool_gpu.js +var Pool2DProgram = class { + constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) { + this.variableNames = ["x"]; + if (poolType === "avg" && computePositions) { + throw new Error("Cannot compute positions for average pool."); + } + const filterWidth = convInfo.filterWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + this.outputShape = convInfo.outShape; + const isAvgPool = poolType === "avg"; + const batchFlattenPositionStr = `((batch * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`; + const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`; + let initializationValue = "0.0"; + if (!isAvgPool) { + initializationValue = "-1.0 / 1e-20"; + } + if (computePositions) { + const compareOp2 = ">="; + this.userCode = ` + const ivec2 strides = ivec2(${strideHeight}, ${strideWidth}); + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); @@ -1671,19 +60470,19 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN int minMaxPosition = 0; float avgValue = 0.0; - for (int wR = 0; wR < ${p}; - wR += ${l}) { + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { int xR = xRCorner + wR; - if (xR < 0 || xR >= ${t.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${m}; - wC += ${c}) { + for (int wC = 0; wC < ${effectiveFilterWidth}; + wC += ${dilationWidth}) { int xC = xCCorner + wC; - if (xC < 0 || xC >= ${t.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } @@ -1693,31 +60492,42 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${D} currMinMaxValue) { + if (value ${compareOp2} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; - minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`}; + minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`}; } } } setOutput(float(minMaxPosition)); } - `;return}let w="max",I=`${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=` - if (${h}) { + `; + return; + } + const compareOp = "max"; + let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + if (poolType === "avg") { + returnValue = `avgValue / max(count, 1.0)`; + } + const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4; + const filterWidthVec4Remainder = filterWidth % 4; + const updateSnippet = ` + if (${isAvgPool}) { avgValue += dot(values, ones); } else { - minMaxValue = ${w}(values, minMaxValue); + minMaxValue = ${compareOp}(values, minMaxValue); } - `;this.userCode=` - const ivec2 strides = ivec2(${a}, ${u}); - const ivec2 pads = ivec2(${f}, ${d}); - const float initializationValue = ${b}; + `; + this.userCode = ` + const ivec2 strides = ivec2(${strideHeight}, ${strideWidth}); + const ivec2 pads = ivec2(${padTop}, ${padLeft}); + const float initializationValue = ${initializationValue}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xR, int xC, int d) { - if (xC < 0 || xC >= ${t.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { return initializationValue; } count += 1.0; @@ -1735,33 +60545,33 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined - vec4 minMaxValue = vec4(${b}); + vec4 minMaxValue = vec4(${initializationValue}); float avgValue = 0.0; count = 0.0; - for (int wR = 0; wR < ${p}; - wR += ${l}) { + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { int xR = xRCorner + wR; - if (xR < 0 || xR >= ${t.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${N}; wC += 4) { - int xC = xCCorner + wC * ${c}; + for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) { + int xC = xCCorner + wC * ${dilationWidth}; vec4 values = vec4( getValue(batch, xR, xC, d), - getValue(batch, xR, xC + ${c}, d), - getValue(batch, xR, xC + 2 * ${c}, d), - getValue(batch, xR, xC + 3 * ${c}, d) + getValue(batch, xR, xC + ${dilationWidth}, d), + getValue(batch, xR, xC + 2 * ${dilationWidth}, d), + getValue(batch, xR, xC + 3 * ${dilationWidth}, d) ); - ${A} + ${updateSnippet} } - int xC = xCCorner + ${N}; - if (${E===1}) { + int xC = xCCorner + ${filterWidthNearestVec4}; + if (${filterWidthVec4Remainder === 1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, @@ -1769,33 +60579,63 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN initializationValue ); - ${A} - } else if (${E===2}) { + ${updateSnippet} + } else if (${filterWidthVec4Remainder === 2}) { vec4 values = vec4( getValue(batch, xR, xC, d), - getValue(batch, xR, xC + ${c}, d), + getValue(batch, xR, xC + ${dilationWidth}, d), initializationValue, initializationValue ); - ${A} - } else if (${E===3}) { + ${updateSnippet} + } else if (${filterWidthVec4Remainder === 3}) { vec4 values = vec4( getValue(batch, xR, xC, d), - getValue(batch, xR, xC + ${c}, d), - getValue(batch, xR, xC + 2 * ${c}, d), + getValue(batch, xR, xC + ${dilationWidth}, d), + getValue(batch, xR, xC + 2 * ${dilationWidth}, d), initializationValue ); - ${A} + ${updateSnippet} } } - setOutput(${I}); + setOutput(${returnValue}); } - `}},ec=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",I="0.0";if(w||(I="-1.0 / 1e-20"),n){let P=">=";this.userCode=` + `; + } +}; +var Pool3DProgram = class { + constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) { + this.variableNames = ["x"]; + if (poolType === "avg" && computePositions) { + throw new Error("Cannot compute positions for average pool."); + } + const filterWidth = convInfo.filterWidth; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = convInfo.padInfo.front; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + this.outputShape = convInfo.outShape; + const isAvgPool = poolType === "avg"; + let initializationValue = "0.0"; + if (!isAvgPool) { + initializationValue = "-1.0 / 1e-20"; + } + if (computePositions) { + const compareOp2 = ">="; + this.userCode = ` const ivec3 strides = - ivec3(${a}, ${u}, ${l}); - const ivec3 pads = ivec3(${g}, ${x}, ${b}); + ivec3(${strideDepth}, ${strideHeight}, ${strideWidth}); + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); void main() { ivec5 coords = getOutputCoords(); @@ -1813,27 +60653,27 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN float minMaxValueFound = 0.0; int minMaxPosition = 0; - for (int wD = 0; wD < ${f}; - wD += ${c}) { + for (int wD = 0; wD < ${effectiveFilterDepth}; + wD += ${dilationDepth}) { int xD = xDCorner + wD; - if (xD < 0 || xD >= ${t.inDepth}) { + if (xD < 0 || xD >= ${convInfo.inDepth}) { continue; } - for (int wR = 0; wR < ${d}; - wR += ${p}) { + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { int xR = xRCorner + wR; - if (xR < 0 || xR >= ${t.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${h}; - wC += ${m}) { + for (int wC = 0; wC < ${effectiveFilterWidth}; + wC += ${dilationWidth}) { int xC = xCCorner + wC; - if (xC < 0 || xC >= ${t.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } @@ -1843,34 +60683,45 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${P} currMinMaxValue) { + if (value ${compareOp2} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; - 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} + - wR * ${h} + wC`}; + minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} + + wR * ${effectiveFilterWidth} + wC`}; } } } } setOutput(float(minMaxPosition)); } - `;return}let N="max",E=`${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=` - if (${w}) { + `; + return; + } + const compareOp = "max"; + let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`; + if (poolType === "avg") { + returnValue = `avgValue / max(count, 1.0)`; + } + const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4; + const filterWidthVec4Remainder = filterWidth % 4; + const updateSnippet = ` + if (${isAvgPool}) { avgValue += dot(values, ones); } else { - minMaxValue = ${N}(values, minMaxValue); + minMaxValue = ${compareOp}(values, minMaxValue); } - `;this.userCode=` + `; + this.userCode = ` const ivec3 strides = - ivec3(${a}, ${u}, ${l}); - const ivec3 pads = ivec3(${g}, ${x}, ${b}); - const float initializationValue = ${I}; + ivec3(${strideDepth}, ${strideHeight}, ${strideWidth}); + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); + const float initializationValue = ${initializationValue}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xD, int xR, int xC, int ch) { - if (xC < 0 || xC >= ${t.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { return initializationValue; } count += 1.0; @@ -1889,41 +60740,41 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined - vec4 minMaxValue = vec4(${I}); + vec4 minMaxValue = vec4(${initializationValue}); float avgValue = 0.0; count = 0.0; - for (int wD = 0; wD < ${f}; - wD += ${c}) { + for (int wD = 0; wD < ${effectiveFilterDepth}; + wD += ${dilationDepth}) { int xD = xDCorner + wD; - if (xD < 0 || xD >= ${t.inDepth}) { + if (xD < 0 || xD >= ${convInfo.inDepth}) { continue; } - for (int wR = 0; wR < ${d}; - wR += ${p}) { + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { int xR = xRCorner + wR; - if (xR < 0 || xR >= ${t.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${A}; wC += 4) { - int xC = xCCorner + wC * ${m}; + for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) { + int xC = xCCorner + wC * ${dilationWidth}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), - getValue(batch, xD, xR, xC + ${m}, ch), - getValue(batch, xD, xR, xC + 2 * ${m}, ch), - getValue(batch, xD, xR, xC + 3 * ${m}, ch) + getValue(batch, xD, xR, xC + ${dilationWidth}, ch), + getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch), + getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch) ); - ${F} + ${updateSnippet} } - int xC = xCCorner + ${A}; - if (${D===1}) { + int xC = xCCorner + ${filterWidthNearestVec4}; + if (${filterWidthVec4Remainder === 1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, @@ -1931,33 +60782,90 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN initializationValue ); - ${F} - } else if (${D===2}) { + ${updateSnippet} + } else if (${filterWidthVec4Remainder === 2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), - getValue(batch, xD, xR, xC + ${m}, ch), + getValue(batch, xD, xR, xC + ${dilationWidth}, ch), initializationValue, initializationValue ); - ${F} - } else if (${D===3}) { + ${updateSnippet} + } else if (${filterWidthVec4Remainder === 3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), - getValue(batch, xD, xR, xC + ${m}, ch), - getValue(batch, xD, xR, xC + 2 * ${m}, ch), + getValue(batch, xD, xR, xC + ${dilationWidth}, ch), + getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch), initializationValue ); - ${F} + ${updateSnippet} } } } - setOutput(${E}); + setOutput(${returnValue}); } - `}};function ist(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ni(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 Ti(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var w3={kernelName:ts,backendName:"webgl",kernelFunc:ist};function ast(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 ec(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var I3={kernelName:Oi,backendName:"webgl",kernelFunc:ast};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}); + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool.js +function avgPool3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + assertNotComplex2(x, "avgPool"); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = 1; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) { + return identity3({ inputs: { x }, backend: backend2 }); + } + const avgPoolProgram = new Pool2DProgram(convInfo, "avg", false); + return backend2.runWebGLProgram(avgPoolProgram, [x], "float32"); +} +var avgPoolConfig2 = { + kernelName: AvgPool, + backendName: "webgl", + kernelFunc: avgPool3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3D.js +function avgPool3D2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs; + const dilations = [1, 1, 1]; + const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat); + const avgPoolProgram = new Pool3DProgram(convInfo, "avg", false); + return backend2.runWebGLProgram(avgPoolProgram, [x], "float32"); +} +var avgPool3DConfig2 = { + kernelName: AvgPool3D, + backendName: "webgl", + kernelFunc: avgPool3D2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/avg_pool_backprop_gpu.js +var AvgPool2DBackpropProgram = class { + constructor(convInfo) { + this.variableNames = ["dy"]; + this.outputShape = convInfo.inShape; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const avgMultiplier = 1 / (filterHeight * filterWidth); + this.userCode = ` + const ivec2 pads = ivec2(${padTop}, ${padLeft}); + const float avgMultiplier = float(${avgMultiplier}); void main() { ivec4 coords = getOutputCoords(); @@ -1971,20 +60879,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${u}; - wR += ${i}) { - float dyR = float(dyRCorner + wR) / ${o}.0; + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - for (int wC = 0; wC < ${l}; - wC+= ${a}) { - float dyC = float(dyCCorner + wC) / ${s}.0; + for (int wC = 0; wC < ${effectiveFilterWidth}; + wC+= ${dilationWidth}) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } @@ -1997,9 +60905,32 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}},fI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=` - const ivec3 pads = ivec3(${d}, ${h}, ${g}); - const float avgMultiplier = float(${x}); + `; + } +}; +var AvgPool3DBackpropProgram = class { + constructor(convInfo) { + this.variableNames = ["dy"]; + this.outputShape = convInfo.inShape; + const filterDepth = convInfo.filterDepth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth); + this.userCode = ` + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); + const float avgMultiplier = float(${avgMultiplier}); void main() { ivec5 coords = getOutputCoords(); @@ -2016,30 +60947,30 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wD = 0; wD < ${p}; - wD += ${u}) { - float dyD = float(dyDCorner + wD) / ${s}.0; + for (int wD = 0; wD < ${effectiveFilterDepth}; + wD += ${dilationDepth}) { + float dyD = float(dyDCorner + wD) / ${strideDepth}.0; - if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) { + if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); - for (int wR = 0; wR < ${m}; - wR += ${l}) { - float dyR = float(dyRCorner + wR) / ${i}.0; + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - for (int wC = 0; wC < ${f}; - wC += ${c}) { - float dyC = float(dyCCorner + wC) / ${a}.0; + for (int wC = 0; wC < ${effectiveFilterWidth}; + wC += ${dilationWidth}) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } @@ -2053,77 +60984,503 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}};function lst(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:Jl,backendName:"webgl",kernelFunc:lst};function ust(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;Ni([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 v3={kernelName:Zl,backendName:"webgl",kernelFunc:ust};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return vp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var S3={kernelName:es,backendName:"webgl",kernelFunc:cst};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3DGrad.js +function avgPool3DGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const x = input2; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = [1, 1, 1]; + const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo); + return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype); +} +var avgPool3DGradConfig3 = { + kernelName: AvgPool3DGrad, + backendName: "webgl", + kernelFunc: avgPool3DGrad2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPoolGrad.js +function avgPoolGrad3(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const x = input2; + assertNotComplex2([dy, input2], "avgPoolGrad"); + const { filterSize, strides, pad: pad3 } = attrs; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3); + const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo); + return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype); +} +var avgPoolGradConfig3 = { + kernelName: AvgPoolGrad, + backendName: "webgl", + kernelFunc: avgPoolGrad3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul.js +function batchMatMul2(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b } = inputs; + const { transposeA, transposeB } = attrs; + return batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2 }); +} +var batchMatMulConfig2 = { + kernelName: BatchMatMul, + backendName: "webgl", + kernelFunc: batchMatMul2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_gpu.js +var BatchNormProgram = class { + constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) { + this.outputShape = []; + this.variableNames = ["x", "mean", "variance"]; + backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape); + backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape); + let offsetSnippet = "0.0"; + if (offsetShape != null) { + backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape); + this.variableNames.push("offset"); + offsetSnippet = "getOffsetAtOutCoords()"; + } + let scaleSnippet = "1.0"; + if (scaleShape != null) { + backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape); + this.variableNames.push("scale"); + scaleSnippet = "getScaleAtOutCoords()"; + } + this.outputShape = xShape; + this.userCode = ` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); - float offset = ${a}; - float scale = ${u}; - float inv = scale * inversesqrt(variance + float(${i})); + float offset = ${offsetSnippet}; + float scale = ${scaleSnippet}; + float inv = scale * inversesqrt(variance + float(${varianceEpsilon})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_packed_gpu.js +var BatchNormPackedProgram = class { + constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) { + this.packedInputs = true; + this.packedOutput = true; + this.variableNames = ["x", "mean", "variance"]; + backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape); + backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape); + let offsetSnippet = "vec4(0.0)"; + if (offsetShape != null) { + backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape); + this.variableNames.push("offset"); + offsetSnippet = "getOffsetAtOutCoords()"; + } + let scaleSnippet = "vec4(1.0)"; + if (scaleShape != null) { + backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape); + this.variableNames.push("scale"); + scaleSnippet = "getScaleAtOutCoords()"; + } + this.outputShape = xShape; + this.userCode = ` void main() { - vec4 offset = ${a}; - vec4 scale = ${u}; + vec4 offset = ${offsetSnippet}; + vec4 scale = ${scaleSnippet}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); - vec4 inv = scale * inversesqrt(variance + vec4(${i})); + vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon})); setOutput((x - mean) * inv + offset); } - `}};var pst=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool("WEBGL_PACK_NORMALIZATION")?new hI(n.shape,o.shape,s.shape,c,p,u):new dI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},N3={kernelName:ys,backendName:"webgl",kernelFunc:pst};var gI=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=mst(this.rank),o,s=t.map((i,a)=>`sourceLoc.${E1[a]} = start[${a}] + coords.${E1[a]};`);o=` - ${e} sourceLoc; - ${e} coords = getOutputCoords(); - ${s.join(` -`)} - `,this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchNorm.js +var batchNorm3 = ({ inputs, backend: backend2, attrs }) => { + const { x, mean: mean4, variance, offset, scale: scale2 } = inputs; + util_exports.assert(mean4.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."); + util_exports.assert(offset == null || mean4.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."); + util_exports.assert(scale2 == null || mean4.shape.length === scale2.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); + let { varianceEpsilon } = attrs; + if (varianceEpsilon == null) { + varianceEpsilon = 1e-3; + } + const finalInputs = [x, mean4, variance]; + let offsetShape = null; + if (offset != null) { + offsetShape = offset.shape; + finalInputs.push(offset); + } + let scaleShape = null; + if (scale2 != null) { + scaleShape = scale2.shape; + finalInputs.push(scale2); + } + const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new BatchNormPackedProgram(x.shape, mean4.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean4.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon); + const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype); + return output; +}; +var batchNormConfig2 = { + kernelName: FusedBatchNorm, + backendName: "webgl", + kernelFunc: batchNorm3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_gpu.js +var SliceProgram = class { + constructor(destSize) { + this.variableNames = ["source"]; + this.outputShape = destSize; + this.rank = destSize.length; + const dtype = getCoordsDataType(this.rank); + this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; + const sourceCoords = getCoords(this.rank); + let body; + const coordSum = destSize.map((_, i) => { + return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`; + }); + body = ` + ${dtype} sourceLoc; + ${dtype} coords = getOutputCoords(); + ${coordSum.join("\n")} + `; + this.userCode = ` void main() { - ${o} - setOutput(getSource(${n})); + ${body} + setOutput(getSource(${sourceCoords})); } - `}},E1=["x","y","z","w","u","v"];function mst(r){if(r===1)return"sourceLoc";if(r<=6)return E1.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]}; - result.y = ${i}; - --${o[this.rank-1]}; + `; + } +}; +var coords = ["x", "y", "z", "w", "u", "v"]; +function getCoords(rank) { + if (rank === 1) { + return "sourceLoc"; + } else if (rank <= 6) { + return coords.slice(0, rank).map((x) => "sourceLoc." + x).join(","); + } else { + throw Error(`Slicing for rank ${rank} is not yet supported`); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_packed_gpu.js +var SlicePackedProgram = class { + constructor(destSize) { + this.variableNames = ["source"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = destSize; + this.rank = destSize.length; + this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; + const dtype = getCoordsDataType(this.rank); + const coords2 = getChannels("coords", this.rank); + const sourceLoc = getChannels("sourceLoc", this.rank); + const innerDims = this.rank === 1 ? "sourceLoc" : `vec2(${sourceLoc.slice(-2).join()})`; + const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`; + const upperRow = ` + result.x = ${getChannel}; + if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) { + ++${sourceLoc[this.rank - 1]}; + result.y = ${getChannel}; + --${sourceLoc[this.rank - 1]}; } - `,u=this.rank===1?"":` - --${n[this.rank-1]}; - if (++${n[this.rank-2]} < ${t[this.rank-2]}) { - ++${o[this.rank-2]}; - result.z = ${i}; - if (++${n[this.rank-1]} < ${t[this.rank-1]}) { - ++${o[this.rank-1]}; - result.w = ${i}; + `; + const lowerRow = this.rank === 1 ? "" : ` + --${coords2[this.rank - 1]}; + if (++${coords2[this.rank - 2]} < ${destSize[this.rank - 2]}) { + ++${sourceLoc[this.rank - 2]}; + result.z = ${getChannel}; + if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) { + ++${sourceLoc[this.rank - 1]}; + result.w = ${getChannel}; } } - `,l=this.rank<=4?`sourceLoc = coords + - ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(` -`);this.userCode=` + `; + const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords + + ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` : destSize.map((_, i) => `${sourceLoc[i]} = ${coords2[i]} + start[${i}];`).join("\n"); + this.userCode = ` void main() { - ${e} coords = getOutputCoords(); - ${e} sourceLoc; - ${l} + ${dtype} coords = getOutputCoords(); + ${dtype} sourceLoc; + ${sourceLocSetup} vec4 result = vec4(0.); - ${a} - ${u} + ${upperRow} + ${lowerRow} setOutput(result); } - `}};function fst(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 _i(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=dz(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),fst(o,a,u,e)}var k3={kernelName:qi,backendName:"webgl",kernelFunc:_i};var dst=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=_i({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},T3={kernelName:Pi,backendName:"webgl",kernelFunc:dst};function hst(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 _3={kernelName:Oa,backendName:"webgl",kernelFunc:hst};var gst=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js +function shallowSlice(x, begin, size, backend2) { + const xTexData = backend2.texData.get(x.dataId); + const t = backend2.makeTensorInfo(size, x.dtype); + const newTexData = backend2.texData.get(t.dataId); + Object.assign(newTexData, xTexData); + newTexData.refCount = 1; + newTexData.shape = size; + newTexData.dtype = x.dtype; + let flatOffset = slice_util_exports.computeFlatOffset(begin, util_exports.computeStrides(x.shape)); + if (xTexData.slice) { + flatOffset += xTexData.slice.flatOffset; + } + newTexData.slice = { + flatOffset, + // Point to the original dataId, which is used to do ref counting. + origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId + }; + const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1; + backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1); + return t; +} +function slice3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { begin, size } = attrs; + const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size); + slice_util_exports.assertParamsValid(x, $begin, $size); + if (util_exports.sizeFromShape($size) === 0) { + return backend2.makeTensorInfo($size, x.dtype, []); + } + if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string") { + const xTexData = backend2.texData.get(x.dataId); + const outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype); + return backend2.makeTensorInfo($size, x.dtype, outValues); + } + const { isPacked } = backend2.texData.get(x.dataId); + const isContinous = slice_util_exports.isSliceContinous(x.shape, $begin, $size); + if (isPacked || !isContinous) { + const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new SlicePackedProgram($size) : new SliceProgram($size); + const customValues = [$begin]; + return backend2.runWebGLProgram(program, [x], x.dtype, customValues); + } + backend2.uploadToGPU(x.dataId); + return shallowSlice(x, $begin, $size, backend2); +} +var sliceConfig2 = { + kernelName: Slice, + backendName: "webgl", + kernelFunc: slice3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchToSpaceND.js +var batchToSpaceND3 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, crops } = attrs; + util_exports.assert(x.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"); + const prod5 = blockShape.reduce((a, b) => a * b); + const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5); + const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length); + const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5); + const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length); + const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length); + const toDispose = []; + const reshapedIntermediate = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } }); + const transposedIntermediate = transpose3({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } }); + const reshapedIntermediate2 = reshape4({ + inputs: { x: transposedIntermediate }, + backend: backend2, + attrs: { shape: reshapedPermuted } + }); + const sliced = slice3({ + inputs: { x: reshapedIntermediate2 }, + backend: backend2, + attrs: { begin: sliceBeginCoords, size: sliceSize } + }); + toDispose.push(reshapedIntermediate); + toDispose.push(transposedIntermediate); + toDispose.push(reshapedIntermediate2); + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return sliced; +}; +var batchToSpaceNDConfig2 = { + kernelName: BatchToSpaceND, + backendName: "webgl", + kernelFunc: batchToSpaceND3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Bincount.js +function bincount3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, weights } = inputs; + const { size } = attrs; + const xVals = backend2.readSync(x.dataId); + const weightsVals = backend2.readSync(weights.dataId); + const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size); + return backend2.makeTensorInfo([size], weights.dtype, outVals); +} +var bincountConfig2 = { + kernelName: Bincount, + backendName: "webgl", + kernelFunc: bincount3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BitwiseAnd.js +var BITWISEAND = ` int r = int(a.r) & int(b.r); int g = int(a.g) & int(b.g); int rb = int(a.b) & int(b.b); int ra = int(a.a) & int(b.a); return vec4(r, g, rb, ra); -`,xst=` +`; +var BITWISEAND_UNPACKED = ` return float(int(a.r) & int(b.r)); -`;function yst(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=L().getNumber("WEBGL_VERSION");if(e.shouldExecuteOnCPU([n,o])||i===1){let u=e.texData.get(n.dataId).values,l=e.texData.get(o.dataId).values,[c,p]=LL(n.shape,o.shape,u,l,n.dtype),m=e.makeTensorInfo(p,n.dtype),f=e.texData.get(m.dataId);return f.values=c,m}let a;return s?a=new Jn(gst,n.shape,o.shape,!1):a=new On(xst,n.shape,o.shape),e.runWebGLProgram(a,[n,o],n.dtype)}var E3={kernelName:Pa,backendName:"webgl",kernelFunc:yst};function bst(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 A3={kernelName:Ql,backendName:"webgl",kernelFunc:bst};var wst="return float(a != b);",A1=ue({opSnippet:wst,cpuKernelImpl:sz,dtype:"bool"}),D3={kernelName:el,backendName:"webgl",kernelFunc:A1};function Ul(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 $3={kernelName:Yp,backendName:"webgl",kernelFunc:Ul};var Ist="return float(int(x));";function R3(r,t){let e=new Br(r.shape,Ist),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function D1(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=D1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=Pn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=Ul({inputs:{input:o},backend:e}),a=D1({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]=zL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return R3(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=A1({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 F3={kernelName:xo,backendName:"webgl",kernelFunc:D1};var O3="return ceil(x);",Cst=It({opSnippet:O3,packedOpSnippet:O3,cpuKernelImpl:BL}),P3={kernelName:rs,backendName:"webgl",kernelFunc:Cst};var yI=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` +`; +function bitwiseAnd3(args) { + const { inputs, backend: backend2 } = args; + const { a, b } = inputs; + const shouldUsePackedProgram = env().getBool("WEBGL_PACK_BINARY_OPERATIONS"); + const versionNumber = env().getNumber("WEBGL_VERSION"); + if (backend2.shouldExecuteOnCPU([a, b]) || versionNumber === 1) { + const aVals = backend2.texData.get(a.dataId).values; + const bVals = backend2.texData.get(b.dataId).values; + const [outValues, outShape] = bitwiseAndImplCPU(a.shape, b.shape, aVals, bVals, a.dtype); + const out = backend2.makeTensorInfo(outShape, a.dtype); + const outData = backend2.texData.get(out.dataId); + outData.values = outValues; + return out; + } + let program; + if (shouldUsePackedProgram) { + program = new BinaryOpPackedProgram(BITWISEAND, a.shape, b.shape, false); + } else { + program = new BinaryOpProgram(BITWISEAND_UNPACKED, a.shape, b.shape); + } + return backend2.runWebGLProgram(program, [a, b], a.dtype); +} +var bitwiseAndConfig2 = { + kernelName: BitwiseAnd, + backendName: "webgl", + kernelFunc: bitwiseAnd3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BroadcastArgs.js +function broadcastArgs3(args) { + const { inputs, backend: backend2 } = args; + const { s0, s1 } = inputs; + const s0Vals = backend2.readSync(s0.dataId); + const s1Vals = backend2.readSync(s1.dataId); + const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals)); + return backend2.makeTensorInfo([broadcastShape.length], "int32", Int32Array.from(broadcastShape)); +} +var broadcastArgsConfig2 = { + kernelName: BroadcastArgs, + backendName: "webgl", + kernelFunc: broadcastArgs3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NotEqual.js +var NOT_EQUAL = `return float(a != b);`; +var notEqual3 = binaryKernelFunc2({ opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: "bool" }); +var notEqualConfig2 = { + kernelName: NotEqual, + backendName: "webgl", + kernelFunc: notEqual3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Real.js +function real3(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const inputData = backend2.texData.get(input2.dataId); + return identity3({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 }); +} +var realConfig2 = { + kernelName: Real, + backendName: "webgl", + kernelFunc: real3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/int.js +var TO_INT = `return float(int(x));`; +function int(input2, backend2) { + const program = new UnaryOpProgram(input2.shape, TO_INT); + const output = backend2.runWebGLProgram(program, [input2], "int32"); + return { dataId: output.dataId, shape: output.shape, dtype: output.dtype }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cast.js +function cast4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dtype } = attrs; + if (dtype === "complex64") { + if (x.dtype === "complex64") { + return identity3({ inputs: { x }, backend: backend2 }); + } + const zerosTensor = zeros(x.shape); + const floatX = cast4({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } }); + const result = complex3({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 }); + zerosTensor.dispose(); + backend2.disposeIntermediateTensorInfo(floatX); + return result; + } + if (x.dtype === "complex64") { + const realPart = real3({ inputs: { input: x }, backend: backend2 }); + const result = cast4({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } }); + backend2.disposeIntermediateTensorInfo(realPart); + return result; + } + if (!util_exports.hasEncodingLoss(x.dtype, dtype)) { + const result = identity3({ inputs: { x }, backend: backend2 }); + return { dataId: result.dataId, shape: result.shape, dtype }; + } + if (backend2.shouldExecuteOnCPU([x])) { + const values = backend2.texData.get(x.dataId).values; + const [resultShape, resultType, resultData] = castImplCPU(values, x.shape, x.dtype, dtype); + return backend2.makeTensorInfo(resultShape, resultType, resultData); + } + if (dtype === "int32") { + return int(x, backend2); + } + if (dtype === "bool") { + const zerosTensorInfo = backend2.makeTensorInfo([], "bool", util_exports.getTypedArrayFromDType("bool", 1)); + const binaryInputs = { a: x, b: zerosTensorInfo }; + const result = notEqual3({ inputs: binaryInputs, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(zerosTensorInfo); + return result; + } + throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`); +} +var castConfig2 = { + kernelName: Cast, + backendName: "webgl", + kernelFunc: cast4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Ceil.js +var CEIL = `return ceil(x);`; +var ceil3 = unaryKernelFunc2({ opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU }); +var ceilConfig2 = { + kernelName: Ceil, + backendName: "webgl", + kernelFunc: ceil3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_gpu.js +var ClipProgram = class { + constructor(aShape) { + this.variableNames = ["A"]; + this.customUniforms = [ + { name: "minVal", type: "float" }, + { name: "maxVal", type: "float" } + ]; + this.outputShape = aShape; + this.userCode = ` void main() { float value = getAAtOutCoords(); @@ -2134,7 +61491,22 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN setOutput(clamp(value, minVal, maxVal)); } - `}};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_packed_gpu.js +var ClipPackedProgram = class { + constructor(aShape) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [ + { name: "minVal", type: "float" }, + { name: "maxVal", type: "float" } + ]; + this.outputShape = aShape; + this.userCode = ` void main() { vec4 value = getAAtOutCoords(); @@ -2145,7 +61517,36 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function vst(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 M3={kernelName:yo,backendName:"webgl",kernelFunc:vst};var wI=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ClipByValue.js +function clipByValue3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { clipValueMin, clipValueMax } = attrs; + let program; + if (env().getBool("WEBGL_PACK_CLIP")) { + program = new ClipPackedProgram(x.shape); + } else { + program = new ClipProgram(x.shape); + } + const customValues = [[clipValueMin], [clipValueMax]]; + return backend2.runWebGLProgram(program, [x], x.dtype, customValues); +} +var clipByValueConfig2 = { + kernelName: ClipByValue, + backendName: "webgl", + kernelFunc: clipByValue3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/complex_abs_gpu.js +var ComplexAbsProgram = class { + constructor(shape) { + this.variableNames = ["real", "imag"]; + this.outputShape = shape; + this.userCode = ` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2158,96 +61559,353 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function L3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Sst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new wI(n.shape),i=[L3(n,o.complexTensorInfos.real),L3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var z3={kernelName:tu,backendName:"webgl",kernelFunc:Sst};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${i}`); + const offsets = new Array(shapes.length - 1); + offsets[0] = shapes[0][1]; + for (let i = 1; i < offsets.length; i++) { + offsets[i] = offsets[i - 1] + shapes[i][1]; + } + const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`]; + for (let i = 1; i < offsets.length; i++) { + const shift = offsets[i - 1]; + snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`); + } + const lastIndex = offsets.length; + const lastShift = offsets[offsets.length - 1]; + snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`); + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int yR = coords.x; int yC = coords.y; - ${n.join(` - `)} + ${snippets.join("\n ")} } - `}};var vI=class{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(t,e);let n=this.outputShape,o=n.length,s=zt(o),i=er("coords",o),a=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((h,g)=>`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h `T${i}`); + const offsets = new Array(shapes.length - 1); + offsets[0] = shapes[0][axis]; + for (let i = 1; i < offsets.length; i++) { + offsets[i] = offsets[i - 1] + shapes[i][axis]; + } + const channel = channels[axis]; + const lastChannels = channels.slice(-2); + const allChannels = channels.join(); + let getValueSnippet = `if (${channel} < ${offsets[0]}) { return getChannel( - getT0(${p}), vec2(${c.join()})); - }`;for(let h=1;h= ${u[h-1]}) { + getT0(${allChannels}), vec2(${lastChannels.join()})); + }`; + for (let i = 1; i < offsets.length; i++) { + const shift2 = offsets[i - 1]; + getValueSnippet += ` + if (${channel} < ${offsets[i]} && ${channel} >= ${offsets[i - 1]}) { return getChannel( - getT${h}(${CI(a,l,g)}), - vec2(${CI(c,l,g)})); - }`}let f=u.length,d=u[u.length-1];m+=` + getT${i}(${shiftedChannels(channels, channel, shift2)}), + vec2(${shiftedChannels(lastChannels, channel, shift2)})); + }`; + } + const lastIndex = offsets.length; + const shift = offsets[offsets.length - 1]; + getValueSnippet += ` return getChannel( - getT${f}(${CI(a,l,d)}), - vec2(${CI(c,l,d)}));`,this.userCode=` - float getValue(${a.map(h=>"int "+h)}) { - ${m} + getT${lastIndex}(${shiftedChannels(channels, channel, shift)}), + vec2(${shiftedChannels(lastChannels, channel, shift)}));`; + this.userCode = ` + float getValue(${channels.map((x) => "int " + x)}) { + ${getValueSnippet} } void main() { - ${s} coords = getOutputCoords(); - vec4 result = vec4(getValue(${i}), 0., 0., 0.); + ${dtype} coords = getOutputCoords(); + vec4 result = vec4(getValue(${coords2}), 0., 0., 0.); - ${i[o-1]} = ${i[o-1]} + 1; - if (${i[o-1]} < ${n[o-1]}) { - result.g = getValue(${i}); + ${coords2[rank - 1]} = ${coords2[rank - 1]} + 1; + if (${coords2[rank - 1]} < ${shape[rank - 1]}) { + result.g = getValue(${coords2}); } - ${i[o-2]} = ${i[o-2]} + 1; - if (${i[o-2]} < ${n[o-2]}) { - result.a = getValue(${i}); + ${coords2[rank - 2]} = ${coords2[rank - 2]} + 1; + if (${coords2[rank - 2]} < ${shape[rank - 2]}) { + result.a = getValue(${coords2}); } - ${i[o-1]} = ${i[o-1]} - 1; - if (${i[o-2]} < ${n[o-2]} && - ${i[o-1]} < ${n[o-1]}) { - result.b = getValue(${i}); + ${coords2[rank - 1]} = ${coords2[rank - 1]} - 1; + if (${coords2[rank - 2]} < ${shape[rank - 2]} && + ${coords2[rank - 1]} < ${shape[rank - 1]}) { + result.b = getValue(${coords2}); } setOutput(result); } - `}};function CI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function Sp(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 B3={kernelName:qp,backendName:"webgl",kernelFunc:Sp};function zd(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>Ul({inputs:{input:b},backend:e})),d=r.map(b=>Sp({inputs:{input:b},backend:e})),h=zd(f,t,e),g=zd(d,t,e),x=Pn({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=VL(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 Br(r[0].shape,Na):new Fn(r[0].shape,Na);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}=Nst(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 Nst(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 $1(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}):zd(u,s,e)}var V3={kernelName:Mi,backendName:"webgl",kernelFunc:$1};var Bd=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) { - float b = getLeakyreluAlphaAtOutCoords(); - ${n} - }`:I=` - float activation(float x) { - ${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=` - ${I} + `; + } +}; +function shiftedChannels(channels, channel, shift) { + const channelIdx = channels.indexOf(channel); + const res = channels.map((c, idx) => { + if (idx === channelIdx) { + return `${c} - ${shift}`; + } else { + return c; + } + }); + return res.join(); +} - const ivec2 strides = ivec2(${u}, ${l}); - const ivec2 pads = ivec2(${i}, ${a}); +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Imag.js +function imag3(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const inputData = backend2.texData.get(input2.dataId); + return identity3({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 }); +} +var imagConfig2 = { + kernelName: Imag, + backendName: "webgl", + kernelFunc: imag3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js +function concatImpl2(inputs, axis, backend2) { + const dtype = inputs[0].dtype; + if (dtype === "complex64") { + const reals = inputs.map((t) => real3({ inputs: { input: t }, backend: backend2 })); + const imags = inputs.map((t) => imag3({ inputs: { input: t }, backend: backend2 })); + const realConcated = concatImpl2(reals, axis, backend2); + const imagConcated = concatImpl2(imags, axis, backend2); + const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 }); + reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r)); + imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i)); + backend2.disposeIntermediateTensorInfo(realConcated); + backend2.disposeIntermediateTensorInfo(imagConcated); + return result2; + } + let runOnCpu = backend2.shouldExecuteOnCPU(inputs); + if (dtype === "string") { + runOnCpu = true; + } + if (runOnCpu) { + const tensors2D2 = inputs.map((t) => { + const innerSize = util_exports.sizeFromShape(t.shape.slice(axis)); + const shape = [-1, innerSize]; + return reshape4({ inputs: { x: t }, backend: backend2, attrs: { shape } }); + }); + const inputsValShapes = tensors2D2.map((t) => { + return { vals: backend2.readSync(t.dataId), shape: t.shape }; + }); + const outShape2 = backend_util_exports.computeOutShape( + tensors2D2.map((t) => t.shape), + 1 + /* axis */ + ); + const simplyConcat = tensors2D2[0].shape[0] === 1; + const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat); + const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis); + const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals); + tensors2D2.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return outInfo; + } + const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0); + const shouldPack = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && $inputs[0].shape.length > 1; + if ($inputs.length === 1) { + const program2 = shouldPack ? new UnaryOpProgram(inputs[0].shape, CLONE) : new UnaryOpPackedProgram(inputs[0].shape, CLONE); + return backend2.runWebGLProgram(program2, inputs, dtype); + } + const maxTexturesInShader = env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER"); + if ($inputs.length > maxTexturesInShader) { + const reducedInputs = []; + for (let i = 0; i < $inputs.length; i += maxTexturesInShader) { + const subArray = $inputs.slice(i, i + maxTexturesInShader); + reducedInputs.push(concatImpl2(subArray, axis, backend2)); + } + const result2 = concatImpl2(reducedInputs, axis, backend2); + for (const i of reducedInputs) { + backend2.disposeIntermediateTensorInfo(i); + } + return result2; + } + if (shouldPack) { + const program2 = new ConcatPackedProgram($inputs.map((t) => t.shape), axis); + return backend2.runWebGLProgram(program2, $inputs, dtype); + } + const { tensors2D, outShape } = computeTensors2D($inputs, axis, backend2); + const program = new ConcatProgram(tensors2D.map((t) => t.shape)); + const result = backend2.runWebGLProgram(program, tensors2D, dtype); + tensors2D.forEach((r) => backend2.disposeIntermediateTensorInfo(r)); + const reshapedResult = reshape4({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(result); + return reshapedResult; +} +function computeTensors2D(inputs, axis, backend2) { + const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis); + const tensors2D = inputs.map((x) => reshape4({ + inputs: { x }, + attrs: { shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))] }, + backend: backend2 + })); + return { tensors2D, outShape }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js +function concat3(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0]; + const shapes = inputs.map((t) => t.shape); + backend_util_exports.assertParamsConsistent(shapes, $axis); + const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis); + if (util_exports.sizeFromShape(outShape) === 0) { + return backend2.makeTensorInfo(outShape, inputs[0].dtype, []); + } + const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0); + if ($inputs.length === 1) { + return identity3({ inputs: { x: $inputs[0] }, backend: backend2 }); + } + return concatImpl2($inputs, $axis, backend2); +} +var concatConfig2 = { + kernelName: Concat, + backendName: "webgl", + kernelFunc: concat3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu.js +var Conv2DProgram = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) { + this.variableNames = ["x", "W"]; + this.outputShape = convInfo.outShape; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4; + const inputDepthVec4Remainder = convInfo.inChannels % 4; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const rowDim = isChannelsLast ? 1 : 2; + const colDim = isChannelsLast ? 2 : 3; + const channelDim = isChannelsLast ? 3 : 1; + let activationSnippet = "", applyActivationSnippet = ""; + if (activation2) { + if (hasPreluActivationWeights) { + activationSnippet = `float activation(float a) { + float b = getPreluActivationWeightsAtOutCoords(); + ${activation2} + }`; + } else if (hasLeakyreluAlpha) { + activationSnippet = `float activation(float a) { + float b = getLeakyreluAlphaAtOutCoords(); + ${activation2} + }`; + } else { + activationSnippet = ` + float activation(float x) { + ${activation2} + } + `; + } + applyActivationSnippet = `result = activation(result);`; + } + const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : ""; + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivationWeights) { + this.variableNames.push("preluActivationWeights"); + } + if (hasLeakyreluAlpha) { + this.variableNames.push("leakyreluAlpha"); + } + this.userCode = ` + ${activationSnippet} + + const ivec2 strides = ivec2(${strideHeight}, ${strideWidth}); + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; - int d2 = coords[${w}]; + int d2 = coords[${channelDim}]; ivec2 xRCCorner = - ivec2(coords[${x}], coords[${b}]) * strides - pads; + ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${m}; wR++) { - int xR = xRCorner + wR * ${c}; + for (int wR = 0; wR < ${filterHeight}; wR++) { + int xR = xRCorner + wR * ${dilationHeight}; - if (xR < 0 || xR >= ${t.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${f}; wC++) { - int xC = xCCorner + wC * ${p}; + for (int wC = 0; wC < ${filterWidth}; wC++) { + int xC = xCCorner + wC * ${dilationWidth}; - if (xC < 0 || xC >= ${t.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } - for (int d1 = 0; d1 < ${d}; d1 += 4) { + for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), @@ -2255,7 +61913,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN getW(wR, wC, d1 + 3, d2) ); - if (${g}) { + if (${isChannelsLast}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), @@ -2274,57 +61932,57 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } } - if (${h===1}) { + if (${inputDepthVec4Remainder === 1}) { - if (${g}) { + if (${isChannelsLast}) { dotProd += - getX(batch, xR, xC, ${d}) * - getW(wR, wC, ${d}, d2); + getX(batch, xR, xC, ${inputDepthNearestVec4}) * + getW(wR, wC, ${inputDepthNearestVec4}, d2); } else { dotProd += - getX(batch, ${d}, xR, xC) * - getW(wR, wC, ${d}, d2); + getX(batch, ${inputDepthNearestVec4}, xR, xC) * + getW(wR, wC, ${inputDepthNearestVec4}, d2); } - } else if (${h===2}) { + } else if (${inputDepthVec4Remainder === 2}) { vec2 wValues = vec2( - getW(wR, wC, ${d}, d2), - getW(wR, wC, ${d} + 1, d2) + getW(wR, wC, ${inputDepthNearestVec4}, d2), + getW(wR, wC, ${inputDepthNearestVec4} + 1, d2) ); - if (${g}) { + if (${isChannelsLast}) { vec2 xValues = vec2( - getX(batch, xR, xC, ${d}), - getX(batch, xR, xC, ${d} + 1) + getX(batch, xR, xC, ${inputDepthNearestVec4}), + getX(batch, xR, xC, ${inputDepthNearestVec4} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( - getX(batch, ${d}, xR, xC), - getX(batch, ${d} + 1, xR, xC) + getX(batch, ${inputDepthNearestVec4}, xR, xC), + getX(batch, ${inputDepthNearestVec4} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } - } else if (${h===3}) { + } else if (${inputDepthVec4Remainder === 3}) { vec3 wValues = vec3( - getW(wR, wC, ${d}, d2), - getW(wR, wC, ${d} + 1, d2), - getW(wR, wC, ${d} + 2, d2) + getW(wR, wC, ${inputDepthNearestVec4}, d2), + getW(wR, wC, ${inputDepthNearestVec4} + 1, d2), + getW(wR, wC, ${inputDepthNearestVec4} + 2, d2) ); - if (${g}) { + if (${isChannelsLast}) { vec3 xValues = vec3( - getX(batch, xR, xC, ${d}), - getX(batch, xR, xC, ${d} + 1), - getX(batch, xR, xC, ${d} + 2) + getX(batch, xR, xC, ${inputDepthNearestVec4}), + getX(batch, xR, xC, ${inputDepthNearestVec4} + 1), + getX(batch, xR, xC, ${inputDepthNearestVec4} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( - getX(batch, ${d}, xR, xC), - getX(batch, ${d} + 1, xR, xC), - getX(batch, ${d} + 2, xR, xC) + getX(batch, ${inputDepthNearestVec4}, xR, xC), + getX(batch, ${inputDepthNearestVec4} + 1, xR, xC), + getX(batch, ${inputDepthNearestVec4} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } @@ -2334,13 +61992,34 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } float result = dotProd; - ${E} - ${N} + ${addBiasSnippet} + ${applyActivationSnippet} setOutput(result); } - `}},SI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` - const ivec3 strides = ivec3(${s}, ${i}, ${a}); - const ivec3 pads = ivec3(${e}, ${n}, ${o}); + `; + } +}; +var Conv3DProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "W"]; + this.outputShape = convInfo.outShape; + const padFront = convInfo.padInfo.front; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const filterDepth = convInfo.filterDepth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4; + const inputDepthVec4Remainder = convInfo.inChannels % 4; + this.userCode = ` + const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth}); + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); void main() { ivec5 coords = getOutputCoords(); @@ -2356,28 +62035,28 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; - for (int wF = 0; wF < ${p}; wF++) { - int xF = xFCorner + wF * ${u}; + for (int wF = 0; wF < ${filterDepth}; wF++) { + int xF = xFCorner + wF * ${dilationDepth}; - if (xF < 0 || xF >= ${t.inDepth}) { + if (xF < 0 || xF >= ${convInfo.inDepth}) { continue; } - for (int wR = 0; wR < ${m}; wR++) { - int xR = xRCorner + wR * ${l}; + for (int wR = 0; wR < ${filterHeight}; wR++) { + int xR = xRCorner + wR * ${dilationHeight}; - if (xR < 0 || xR >= ${t.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int wC = 0; wC < ${f}; wC++) { - int xC = xCCorner + wC * ${c}; + for (int wC = 0; wC < ${filterWidth}; wC++) { + int xC = xCCorner + wC * ${dilationWidth}; - if (xC < 0 || xC >= ${t.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } - for (int d1 = 0; d1 < ${d}; d1 += 4) { + for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), @@ -2394,30 +62073,30 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN dotProd += dot(xValues, wValues); } - if (${h===1}) { + if (${inputDepthVec4Remainder === 1}) { dotProd += - getX(batch, xF, xR, xC, ${d}) * - getW(wF, wR, wC, ${d}, d2); - } else if (${h===2}) { + getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) * + getW(wF, wR, wC, ${inputDepthNearestVec4}, d2); + } else if (${inputDepthVec4Remainder === 2}) { vec2 xValues = vec2( - getX(batch, xF, xR, xC, ${d}), - getX(batch, xF, xR, xC, ${d} + 1) + getX(batch, xF, xR, xC, ${inputDepthNearestVec4}), + getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1) ); vec2 wValues = vec2( - getW(wF, wR, wC, ${d}, d2), - getW(wF, wR, wC, ${d} + 1, d2) + getW(wF, wR, wC, ${inputDepthNearestVec4}, d2), + getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2) ); dotProd += dot(xValues, wValues); - } else if (${h===3}) { + } else if (${inputDepthVec4Remainder === 3}) { vec3 xValues = vec3( - getX(batch, xF, xR, xC, ${d}), - getX(batch, xF, xR, xC, ${d} + 1), - getX(batch, xF, xR, xC, ${d} + 2) + getX(batch, xF, xR, xC, ${inputDepthNearestVec4}), + getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1), + getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2) ); vec3 wValues = vec3( - getW(wF, wR, wC, ${d}, d2), - getW(wF, wR, wC, ${d} + 1, d2), - getW(wF, wR, wC, ${d} + 2, d2) + getW(wF, wR, wC, ${inputDepthNearestVec4}, d2), + getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2), + getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2) ); dotProd += dot(xValues, wValues); } @@ -2426,41 +62105,84 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}};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.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu.js +var Conv2DPackedProgram = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) { + this.variableNames = ["x", "W"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [ + { name: "pads", type: "ivec2" }, + { name: "strides", type: "ivec2" }, + { name: "dilations", type: "ivec2" }, + { name: "inDims", type: "ivec2" } + ]; + this.outputShape = convInfo.outShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const padLeft = convInfo.padInfo.left; + const strideWidth = convInfo.strideWidth; + const dilationWidth = convInfo.dilationWidth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const texelsAcross = filterWidth; + let mainLoop = ` int xR; int xC; int xCOffset; - vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { - `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=` - xC = xCCorner + ${x*u}; - `,a===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) { - xTexelC${x} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${x}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${x}Ready = 1; + xTexelC${colIndex}Ready = 1; } - `,u===1&&x>0?m+=` - xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy); - `:m+=` + `; + if (dilationWidth === 1 && colIndex > 0) { + mainLoop += ` + xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy); + `; + } else { + mainLoop += ` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { @@ -2472,137 +62194,206 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN previous.zw = vec2(0.0); } - xC${x} = vec4(previous.zw, xTexelC${x}.xy); + xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy); } else { - xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy); + xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy); } - `):m+=` - if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) { - xTexelC${x} = getX(batch, xR, xC, d1); + `; + } + } else { + mainLoop += ` + if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { - xTexelC${x}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${x}Ready = 1; + xTexelC${colIndex}Ready = 1; } - xC${x} = xTexelC${x}; - `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { - xTexelC${x+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${x+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${x+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - `,u>1?m+=` + `; + if (dilationWidth > 1) { + mainLoop += ` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); - xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy); + xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy); } else { - xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy); + xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy); } - `:m+=` - xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy); - `):b===1?m+=` - xC${x+1} = xTexelC${x}; - `:m+=` - xCOffset = xC + ${b}; + `; + } else { + mainLoop += ` + xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy); + `; + } + } else { + if (nextTexelOffset === 1) { + mainLoop += ` + xC${colIndex + 1} = xTexelC${colIndex}; + `; + } else { + mainLoop += ` + xCOffset = xC + ${nextTexelOffset}; - if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { - xTexelC${x+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { - xTexelC${x+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${x+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${x+1} = xTexelC${x+1}; - `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) { - xTexelC${x} = getX(batch, xR, xCOffset, d1); + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${x}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${x}Ready = 1; + xTexelC${colIndex}Ready = 1; } - if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) { - xTexelC${x+1} = getX(batch, xR, xC + 1, d1); + if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { - xTexelC${x+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${x+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw); - `,x+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } - xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy); - `)):(m+=` - if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) { - xTexelC${x} = getX(batch, xR, xC, d1); + xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy); + `; + } + } else { + mainLoop += ` + if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { - xTexelC${x}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${x}Ready = 1; + xTexelC${colIndex}Ready = 1; } xCOffset = xC + strides[1]; - if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { - xTexelC${x+1} = getX(batch, xR, xCOffset, d1); + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { - xTexelC${x+1}.zw = vec2(0.); + xTexelC${colIndex + 1}.zw = vec2(0.); } - xTexelC${x+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${x} = vec4( - xTexelC${x}.xy, xTexelC${x+1}.xy); - `,x+1= 0) { + if(d0 < inputShape[${rowDim}] && d0 >= 0) { // Use custom imod instead mod. On Intel GPU, mod may generate // unexpected value. // https://github.com/tensorflow/tfjs/issues/5447 @@ -2638,25 +62459,28 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / inChannels); - if(d1 < inputShape[${a}] && d1 >= 0) { + if(d1 < inputShape[${colDim}] && d1 >= 0) { ch = imod(pos, inChannels); - if (${s}) { + if (${isChannelsLast}) { innerDims = vec2(d1, ch); - result[${c*2+p}] = getChannel( + result[${row * 2 + col}] = getChannel( getA(rc.x, d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); - result[${c*2+p}] = getChannel( + result[${row * 2 + col}] = getChannel( getA(rc.x, ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } - `;this.userCode=` + `; + } + } + this.userCode = ` void main() { ivec3 rc = getOutputCoords(); @@ -2665,11 +62489,255 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; - ${l} + ${unrolled} - ${o.output} = result; + ${glsl.output} = result; } - `}};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>_1)&&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(Ju(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=vp({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=vp({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?Wl(a,!0):null,q=new Ld(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 kst(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 Vd(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 Bd(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:ns,backendName:"webgl",kernelFunc:kst};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js +function getShapeForBatchMatMul(shape, isChannelsLast) { + const length = shape.length; + if (length >= 3) { + return isChannelsLast ? [ + ...shape.slice(0, -3), + shape[length - 3] * shape[length - 2], + shape[length - 1] + /* channel */ + ] : [ + ...shape.slice(0, -3), + shape[length - 3], + shape[length - 2] * shape[length - 1] + /* height * width */ + ]; + } else if (!isChannelsLast && length === 1 && shape[0] > 1) { + return [shape[0], 1]; + } else { + return null; + } +} +function conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) { + const xShape = x.shape; + const xTexData = backend2.texData.get(x.dataId); + const sharedMatMulDim = convInfo.inChannels; + const outerShapeX = xShape[0] * xShape[1] * xShape[2]; + const outerShapeFilter = convInfo.outChannels; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const transposeA = false; + const transposeB = false; + let out; + const intermediates = []; + if (preluActivationWeights != null) { + const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast); + if (targetShape != null) { + preluActivationWeights = reshape4({ + inputs: { x: preluActivationWeights }, + backend: backend2, + attrs: { shape: targetShape } + }); + intermediates.push(preluActivationWeights); + } + } + if (bias != null) { + const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast); + if (targetShape != null) { + bias = reshape4({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } }); + intermediates.push(bias); + } + } + const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD; + const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked && isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 && util_exports.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3)); + if (canOptimize) { + const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1); + const xReshaped = { + dataId: x.dataId, + shape: [1, targetShape, convInfo.inChannels], + dtype: x.dtype + }; + const originalXTexDataShape = xTexData.shape; + xTexData.shape = xTexData.shape.slice(); + xTexData.shape[xTexData.shape.length - 2]++; + util_exports.assert(isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`); + const filterReshaped = reshape4({ + inputs: { x: filter }, + backend: backend2, + attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] } + }); + intermediates.push(filterReshaped); + const pointwiseConv = batchMatMulImpl({ + a: xReshaped, + b: filterReshaped, + backend: backend2, + transposeA, + transposeB, + bias, + activation: activation2, + preluActivationWeights, + leakyreluAlpha + }); + const pointwiseConvTexData = backend2.texData.get(pointwiseConv.dataId); + util_exports.assert(pointwiseConvTexData.isPacked, () => "batchMatMul result is expected to be packed"); + xTexData.shape = originalXTexDataShape; + pointwiseConvTexData.shape = convInfo.outShape; + out = identity3({ inputs: { x: pointwiseConv }, backend: backend2 }); + out.shape = convInfo.outShape; + intermediates.push(pointwiseConv); + } else { + const numCols = convInfo.outHeight * convInfo.outWidth; + const xReshaped = reshape4({ + inputs: { x }, + backend: backend2, + attrs: { + shape: isChannelsLast ? [convInfo.batchSize, numCols, convInfo.inChannels] : [convInfo.batchSize, convInfo.inChannels, numCols] + } + }); + const filterReshaped = reshape4({ + inputs: { x: filter }, + backend: backend2, + attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] } + }); + const result = batchMatMulImpl({ + a: isChannelsLast ? xReshaped : filterReshaped, + b: isChannelsLast ? filterReshaped : xReshaped, + transposeA: !isChannelsLast, + transposeB, + backend: backend2, + bias, + activation: activation2, + preluActivationWeights, + leakyreluAlpha + }); + out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } }); + intermediates.push(xReshaped); + intermediates.push(filterReshaped); + intermediates.push(result); + } + for (const i of intermediates) { + backend2.disposeIntermediateTensorInfo(i); + } + return out; +} +function conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) { + const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo; + const isChannelsLast = dataFormat === "channelsLast"; + const sharedDim = filterWidth * filterHeight * inChannels; + const numCols = outHeight * outWidth; + const x2ColShape = [convInfo.batchSize, sharedDim, numCols]; + const transposeA = true; + const transposeB = false; + const intermediates = []; + if (preluActivationWeights != null) { + const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast); + if (targetShape != null) { + preluActivationWeights = reshape4({ + inputs: { x: preluActivationWeights }, + backend: backend2, + attrs: { shape: targetShape } + }); + intermediates.push(preluActivationWeights); + } + } + if (bias != null) { + const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast); + if (targetShape != null) { + bias = reshape4({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } }); + intermediates.push(bias); + } + } + const w2Row = reshape4({ + inputs: { x: filter }, + backend: backend2, + attrs: { shape: [1, sharedDim, util_exports.sizeFromShape(filter.shape) / sharedDim] } + }); + intermediates.push(w2Row); + const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo); + const customValues = [ + x.shape, + [convInfo.padInfo.top, convInfo.padInfo.left], + [convInfo.strideHeight, convInfo.strideWidth], + [convInfo.dilationHeight, convInfo.dilationWidth], + [convInfo.inChannels], + [convInfo.filterWidth * convInfo.inChannels], + [convInfo.outWidth] + ]; + const im2Col = backend2.runWebGLProgram(im2ColProgram, [x], "float32", customValues); + const im2ColReshaped = reshape4({ inputs: { x: im2Col }, backend: backend2, attrs: { shape: x2ColShape } }); + intermediates.push(im2Col); + intermediates.push(im2ColReshaped); + const hasBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + const hasLeakyreluAlpha = activation2 === "leakyrelu"; + const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null; + const matmulProgram = new MatMulPackedProgram(isChannelsLast ? im2ColReshaped.shape : w2Row.shape, isChannelsLast ? w2Row.shape : im2ColReshaped.shape, isChannelsLast ? [convInfo.batchSize, numCols, convInfo.outChannels] : [convInfo.batchSize, convInfo.outChannels, numCols], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + const inputs = isChannelsLast ? [im2ColReshaped, w2Row] : [w2Row, im2ColReshaped]; + if (bias) { + inputs.push(bias); + } + if (hasPreluActivationWeights) { + inputs.push(preluActivationWeights); + } + if (hasLeakyreluAlpha) { + const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32")); + inputs.push($leakyreluAlpha); + intermediates.push($leakyreluAlpha); + } + const product = backend2.runWebGLProgram(matmulProgram, inputs, "float32"); + const out = reshape4({ inputs: { x: product }, backend: backend2, attrs: { shape: convInfo.outShape } }); + intermediates.push(product); + for (const i of intermediates) { + backend2.disposeIntermediateTensorInfo(i); + } + return out; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js +function conv2d4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); + let out; + if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) { + out = conv2dByMatMul({ x, filter, convInfo, backend: backend2 }); + } else if (convInfo.strideWidth <= 2 && $dataFormat === "channelsLast" && env().getBool("WEBGL_EXP_CONV")) { + const program = new Conv2DPackedProgram(convInfo); + const customValues = [ + [convInfo.padInfo.top, convInfo.padInfo.left], + [convInfo.strideHeight, convInfo.strideWidth], + [convInfo.dilationHeight, convInfo.dilationWidth], + [convInfo.inHeight, convInfo.inWidth] + ]; + out = backend2.runWebGLProgram(program, [x, filter], "float32", customValues); + } else if (env().getBool("WEBGL_CONV_IM2COL")) { + out = conv2dWithIm2Row({ x, filter, convInfo, backend: backend2 }); + } else { + const program = new Conv2DProgram(convInfo); + out = backend2.runWebGLProgram(program, [x, filter], "float32"); + } + const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } }); + backend2.disposeIntermediateTensorInfo(out); + return outReshaped; +} +var conv2DConfig2 = { + kernelName: Conv2D, + backendName: "webgl", + kernelFunc: conv2d4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu.js +var Conv2DDerFilterProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "dy"]; + this.outputShape = convInfo.filterShape; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2681,24 +62749,24 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int b = 0; b < ${t.batchSize}; b++) { - for (int yR = 0; yR < ${t.outHeight}; yR++) { - int xR = wR + yR * ${e} - ${o}; + for (int b = 0; b < ${convInfo.batchSize}; b++) { + for (int yR = 0; yR < ${convInfo.outHeight}; yR++) { + int xR = wR + yR * ${strideHeight} - ${padTop}; - if (xR < 0 || xR >= ${t.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int yC = 0; yC < ${t.outWidth}; yC++) { - int xC = wC + yC * ${n} - ${s}; + for (int yC = 0; yC < ${convInfo.outWidth}; yC++) { + int xC = wC + yC * ${strideWidth} - ${padLeft}; - if (xC < 0 || xC >= ${t.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } - ${i?`float dyValue = getDy(b, yR, yC, d2); + ${isChannelsLast ? `float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); - dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC); + dotProd += (xValue * dyValue);` : `float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue);`} } @@ -2706,45 +62774,62 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}},AI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=` - const ivec2 pads = ivec2(${a}, ${u}); + `; + } +}; +var Conv2DDerInputProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "W"]; + this.outputShape = convInfo.inShape; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const padTop = filterHeight - 1 - convInfo.padInfo.top; + const padLeft = filterWidth - 1 - convInfo.padInfo.left; + const rowDim = isChannelsLast ? 1 : 2; + const colDim = isChannelsLast ? 2 : 3; + const channelDim = isChannelsLast ? 3 : 1; + this.userCode = ` + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; - int d1 = coords[${p}]; + int d1 = coords[${channelDim}]; - ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads; + ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${e}; wR++) { - float dyR = float(dyRCorner + wR) / ${o}.0; + for (int wR = 0; wR < ${filterHeight}; wR++) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - int wRPerm = ${e} - 1 - wR; + int wRPerm = ${filterHeight} - 1 - wR; - for (int wC = 0; wC < ${n}; wC++) { - float dyC = float(dyCCorner + wC) / ${s}.0; + for (int wC = 0; wC < ${filterWidth}; wC++) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); - int wCPerm = ${n} - 1 - wC; + int wCPerm = ${filterWidth} - 1 - wC; - for (int d2 = 0; d2 < ${t.outChannels}; d2++) { + for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) { - if (${i}) { + if (${isChannelsLast}) { float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; @@ -2759,7 +62844,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}},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=` + `; + } +}; +var Conv3DDerFilterProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "dy"]; + this.outputShape = convInfo.filterShape; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const padFront = convInfo.padInfo.front; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + this.userCode = ` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2770,25 +62868,25 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN float dotProd = 0.0; - for (int b = 0; b < ${t.batchSize}; b++) { - for (int yF = 0; yF < ${t.outDepth}; yF++) { - int xF = wF + yF * ${e} - ${s}; + for (int b = 0; b < ${convInfo.batchSize}; b++) { + for (int yF = 0; yF < ${convInfo.outDepth}; yF++) { + int xF = wF + yF * ${strideDepth} - ${padFront}; - if (xF < 0 || xF >= ${t.inDepth}) { + if (xF < 0 || xF >= ${convInfo.inDepth}) { continue; } - for (int yR = 0; yR < ${t.outHeight}; yR++) { - int xR = wR + yR * ${n} - ${i}; + for (int yR = 0; yR < ${convInfo.outHeight}; yR++) { + int xR = wR + yR * ${strideHeight} - ${padTop}; - if (xR < 0 || xR >= ${t.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int yC = 0; yC < ${t.outWidth}; yC++) { - int xC = wC + yC * ${o} - ${a}; + for (int yC = 0; yC < ${convInfo.outWidth}; yC++) { + int xC = wC + yC * ${strideWidth} - ${padLeft}; - if (xC < 0 || xC >= ${t.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } @@ -2801,8 +62899,24 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}},$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}, ${c}); + `; + } +}; +var Conv3DDerInputProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "W"]; + this.outputShape = convInfo.inShape; + const filterDepth = convInfo.filterDepth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const padFront = filterDepth - 1 - convInfo.padInfo.front; + const padTop = filterHeight - 1 - convInfo.padInfo.top; + const padLeft = filterWidth - 1 - convInfo.padInfo.left; + this.userCode = ` + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); void main() { ivec5 coords = getOutputCoords(); @@ -2816,39 +62930,39 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN int dyCCorner = dyCorner.z; float dotProd = 0.0; - for (int wF = 0; wF < ${e}; wF++) { - float dyF = float(dyFCorner + wF) / ${s}.0; + for (int wF = 0; wF < ${filterDepth}; wF++) { + float dyF = float(dyFCorner + wF) / ${strideDepth}.0; - if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) { + if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); - int wFPerm = ${e} - 1 - wF; + int wFPerm = ${filterDepth} - 1 - wF; - for (int wR = 0; wR < ${n}; wR++) { - float dyR = float(dyRCorner + wR) / ${i}.0; + for (int wR = 0; wR < ${filterHeight}; wR++) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - int wRPerm = ${n} - 1 - wR; + int wRPerm = ${filterHeight} - 1 - wR; - for (int wC = 0; wC < ${o}; wC++) { - float dyC = float(dyCCorner + wC) / ${a}.0; + for (int wC = 0; wC < ${filterWidth}; wC++) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); - int wCPerm = ${o} - 1 - wC; + int wCPerm = ${filterWidth} - 1 - wC; - for (int d2 = 0; d2 < ${t.outChannels}; d2++) { + for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; @@ -2858,8 +62972,43 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}};function Tst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new EI(m);return e.runWebGLProgram(f,[o,s],"float32")}var W3={kernelName:Bp,backendName:"webgl",kernelFunc:Tst};var RI=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=de(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=` - const ivec2 pads = ivec2(${o}, ${s}); + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropFilter.js +function conv2DBackpropFilter3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat); + const program = new Conv2DDerFilterProgram(convInfo); + return backend2.runWebGLProgram(program, [x, dy], "float32"); +} +var conv2DBackpropFilterConfig2 = { + kernelName: Conv2DBackpropFilter, + backendName: "webgl", + kernelFunc: conv2DBackpropFilter3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_packed_gpu.js +var Conv2DDerInputPackedProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "W"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [ + { name: "strides", type: "vec2" } + ]; + this.outputShape = convInfo.inShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = filterHeight - 1 - convInfo.padInfo.top; + const padLeft = filterWidth - 1 - convInfo.padInfo.left; + this.userCode = ` + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); @@ -2871,29 +63020,29 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN int dyCCorner = dyCorner.y; vec4 result = vec4(0.); - for (int wR = 0; wR < ${e}; wR++) { + for (int wR = 0; wR < ${filterHeight}; wR++) { float dyR = float(dyRCorner + wR) / strides[0]; - if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - int wRPerm = ${e} - 1 - wR; + int wRPerm = ${filterHeight} - 1 - wR; - for (int wC = 0; wC < ${n}; wC++) { - int wCPerm = ${n} - 1 - wC; + for (int wC = 0; wC < ${filterWidth}; wC++) { + int wCPerm = ${filterWidth} - 1 - wC; float dyC = float(dyCCorner + wC) / strides[1]; - bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0) + bool idyCVal = (dyC >= 0.0) && (dyC < ${convInfo.outWidth}.0) && (fract(dyC) == 0.0); int idyC = int(dyC); float dyC2 = float(dyCCorner + wC + 1) / strides[1]; - bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0) + bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${convInfo.outWidth}.0) && (fract(dyC2) == 0.0); int idyC2 = int(dyC2); if (idyCVal && idyCVal2) { - for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) { + for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC, d2); vec4 dySample2 = (idyC / 2 == idyC2 / 2) ? @@ -2910,7 +63059,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN dot(dyValue, wValue.zw)); } } else if (idyCVal) { - for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) { + for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC, d2); vec2 dyValue = mod(float(idyC), 2.) == 0. ? @@ -2919,7 +63068,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN dot(dyValue, wValue.zw)); } } else if (idyCVal2) { - for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) { + for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC2, d2); vec2 dyValue = mod(float(idyC2), 2.) == 0. ? @@ -2932,19 +63081,140 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(result); } - `}};function _st(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 U3={kernelName:os,backendName:"webgl",kernelFunc:_st};function Est(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 H3={kernelName:ss,backendName:"webgl",kernelFunc:Est};function Ast(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 q3={kernelName:Ma,backendName:"webgl",kernelFunc:Ast};function Dst(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 K3={kernelName:La,backendName:"webgl",kernelFunc:Dst};var $st=Vo+` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropInput.js +function conv2DBackpropInput3(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat); + if (env().getBool("WEBGL_PACK_CONV2DTRANSPOSE") && $dataFormat === "channelsLast") { + const customValues = [ + [convInfo.strideHeight, convInfo.strideWidth] + ]; + const program = new Conv2DDerInputPackedProgram(convInfo); + return backend2.runWebGLProgram(program, [dy, filter], "float32", customValues); + } else { + const program = new Conv2DDerInputProgram(convInfo); + return backend2.runWebGLProgram(program, [dy, filter], "float32"); + } +} +var conv2DBackpropInputConfig2 = { + kernelName: Conv2DBackpropInput, + backendName: "webgl", + kernelFunc: conv2DBackpropInput3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3D.js +function conv3D2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3); + const program = new Conv3DProgram(convInfo); + return backend2.runWebGLProgram(program, [x, filter], "float32"); +} +var conv3DConfig2 = { + kernelName: Conv3D, + backendName: "webgl", + kernelFunc: conv3D2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropFilterV2.js +function conv3DBackpropFilterV22(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, pad: pad3, filterShape } = attrs; + const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3); + const program = new Conv3DDerFilterProgram(convInfo); + return backend2.runWebGLProgram(program, [x, dy], "float32"); +} +var conv3DBackpropFilterV2Config2 = { + kernelName: Conv3DBackpropFilterV2, + backendName: "webgl", + kernelFunc: conv3DBackpropFilterV22 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropInputV2.js +function conv3DBackpropInput2(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { pad: pad3, strides, inputShape } = attrs; + const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3); + const program = new Conv3DDerInputProgram(convInfo); + return backend2.runWebGLProgram(program, [dy, filter], "float32"); +} +var conv3DBackpropInputConfig = { + kernelName: Conv3DBackpropInputV2, + backendName: "webgl", + kernelFunc: conv3DBackpropInput2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cos.js +var COS = CHECK_NAN_SNIPPET_UNARY + ` return cos(x); -`,Rst=` +`; +var COS_PACKED = ` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${Qn} + ${CHECK_NAN_SNIPPET_PACKED} return result; -`,Fst=It({opSnippet:$st,packedOpSnippet:Rst}),j3={kernelName:is,backendName:"webgl",kernelFunc:Fst};var Ost=` +`; +var cos3 = unaryKernelFunc2({ opSnippet: COS, packedOpSnippet: COS_PACKED }); +var cosConfig2 = { + kernelName: Cos, + backendName: "webgl", + kernelFunc: cos3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cosh.js +var COSH = ` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,Pst=It({opSnippet:Ost}),X3={kernelName:as,backendName:"webgl",kernelFunc:Pst};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(${w}); +`; +var cosh3 = unaryKernelFunc2({ opSnippet: COSH }); +var coshConfig2 = { + kernelName: Cosh, + backendName: "webgl", + kernelFunc: cosh3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/crop_and_resize_gpu.js +var CropAndResizeProgram = class { + constructor(imageShape, boxShape, cropSize, method, extrapolationValue) { + this.variableNames = ["Image", "Boxes", "BoxInd"]; + this.outputShape = []; + const [batch, imageHeight, imageWidth, depth] = imageShape; + const [numBoxes] = boxShape; + const [cropHeight, cropWidth] = cropSize; + this.outputShape = [numBoxes, cropHeight, cropWidth, depth]; + const methodId = method === "bilinear" ? 1 : 0; + const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`]; + const [heightRatio, heightScale, inY] = cropHeight > 1 ? [ + `${(imageHeight - 1) / (cropHeight - 1)}`, + "(y2-y1) * height_ratio", + `y1*${inputHeightFloat} + float(y)*(height_scale)` + ] : [ + "0.0", + "0.0", + `0.5 * (y1+y2) * ${inputHeightFloat}` + ]; + const [widthRatio, widthScale, inX] = cropWidth > 1 ? [ + `${(imageWidth - 1) / (cropWidth - 1)}`, + "(x2-x1) * width_ratio", + `x1*${inputWidthFloat} + float(x)*(width_scale)` + ] : [ + "0.0", + "0.0", + `0.5 * (x1+x2) * ${inputWidthFloat}` + ]; + this.userCode = ` + const float height_ratio = float(${heightRatio}); + const float width_ratio = float(${widthRatio}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2960,26 +63230,26 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // get image in batch index int bInd = round(getBoxInd(b)); - if(bInd < 0 || bInd >= ${i}) { + if(bInd < 0 || bInd >= ${batch}) { return; } - float height_scale = ${x}; - float width_scale = ${I}; + float height_scale = ${heightScale}; + float width_scale = ${widthScale}; - float in_y = ${b}; - if( in_y < 0.0 || in_y > ${d} ) { - setOutput(float(${s})); + float in_y = ${inY}; + if( in_y < 0.0 || in_y > ${inputHeightFloat} ) { + setOutput(float(${extrapolationValue})); return; } - float in_x = ${N}; - if( in_x < 0.0 || in_x > ${h} ) { - setOutput(float(${s})); + float in_x = ${inX}; + if( in_x < 0.0 || in_x > ${inputWidthFloat} ) { + setOutput(float(${extrapolationValue})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); - if(${f} == 1) { + if(${methodId} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); @@ -3003,20 +63273,188 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN setOutput(newValue); } } - `}};var Mst=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")},Y3={kernelName:Ba,backendName:"webgl",kernelFunc:Mst};var Np;(function(r){r.Prod="*",r.Sum="+"})(Np||(Np={}));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===Np.Prod?"1.0":"0.0",a=n?i:`getX(${Z3(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 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js +var cropAndResize4 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { image: image2, boxes, boxInd } = inputs; + const { cropSize, method, extrapolationValue } = attrs; + const program = new CropAndResizeProgram(image2.shape, boxes.shape, cropSize, method, extrapolationValue); + return backend2.runWebGLProgram(program, [image2, boxes, boxInd], "float32"); +}; +var cropAndResizeConfig2 = { + kernelName: CropAndResize, + backendName: "webgl", + kernelFunc: cropAndResize4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/cum_gpu.js +var CumOpType; +(function(CumOpType2) { + CumOpType2["Prod"] = "*"; + CumOpType2["Sum"] = "+"; +})(CumOpType || (CumOpType = {})); +var CumProgram = class { + constructor(op2, outputShape, exclusive, reverse5) { + this.op = op2; + this.outputShape = outputShape; + this.variableNames = ["x"]; + this.customUniforms = [{ name: "index", type: "float" }]; + const rank = this.outputShape.length; + const initVal = this.op === CumOpType.Prod ? "1.0" : "0.0"; + const val = exclusive ? initVal : `getX(${getCoords2(rank, "coords", this.op)})`; + const length = this.outputShape[this.outputShape.length - 1]; + let condition = ""; + let idxString = ""; + if (exclusive) { + condition = reverse5 ? `end != ${length - 1}` : "end != 0"; + idxString = reverse5 ? "end + 1" : "end - 1"; + } else { + condition = reverse5 ? `end + pow2 < ${length}` : "end >= pow2"; + idxString = reverse5 ? "end + pow2" : "end - pow2"; + } + this.userCode = ` void main() { - ${zt(s)} coords = getOutputCoords(); - int end = ${J3(s,"coords",this.op)}; - float val = ${a}; + ${getCoordsDataType(rank)} coords = getOutputCoords(); + int end = ${getFinalCoord(rank, "coords", this.op)}; + float val = ${val}; int pow2 = int(pow(2.0, index)); - if (${l}) { - int idx = ${c}; - ${J3(s,"coords",this.op)} = idx; - val ${this.op}= getX(${Z3(s,"coords",this.op)}); + if (${condition}) { + int idx = ${idxString}; + ${getFinalCoord(rank, "coords", this.op)} = idx; + val ${this.op}= getX(${getCoords2(rank, "coords", this.op)}); } setOutput(val); } - `}};function Z3(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 ${r} is not yet supported`)}function J3(r,t,e){if(r===1)return`${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=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new hg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new hg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Lst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(Np.Prod,o,e,s,i,a)}var Q3={kernelName:za,backendName:"webgl",kernelFunc:Lst};function zst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(Np.Sum,o,e,s,i,a)}var tB={kernelName:ls,backendName:"webgl",kernelFunc:zst};function Bst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Yw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=ML(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var eB={kernelName:eu,backendName:"webgl",kernelFunc:Bst};var PI=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=` + `; + } +}; +function getCoords2(rank, name, op2) { + if (rank === 1) { + return `${name}`; + } else if (rank === 2) { + return `${name}.x, ${name}.y`; + } else if (rank === 3) { + return `${name}.x, ${name}.y, ${name}.z`; + } else if (rank === 4) { + return `${name}.x, ${name}.y, ${name}.z, ${name}.w`; + } else { + throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`); + } +} +function getFinalCoord(rank, name, op2) { + if (rank === 1) { + return `${name}`; + } else if (rank === 2) { + return `${name}.y`; + } else if (rank === 3) { + return `${name}.z`; + } else if (rank === 4) { + return `${name}.w`; + } else { + throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`); + } +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cum_impl.js +function cumImpl(op2, x, backend2, axis, exclusive, reverse5) { + const xRank = x.shape.length; + const permutation = backend_util_exports.getAxesPermutation([axis], xRank); + let permutedX = x; + if (permutation != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } }); + } + const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0]; + if (permutedAxis !== xRank - 1) { + throw new Error(`WebGL cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`); + } + const size = permutedX.shape[permutedAxis]; + let result = identity3({ inputs: { x: permutedX }, backend: backend2 }); + for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) { + const program = new CumProgram(op2, permutedX.shape, false, reverse5); + const customValues = [[i]]; + const prevResult = result; + result = backend2.runWebGLProgram(program, [result], result.dtype, customValues); + backend2.disposeIntermediateTensorInfo(prevResult); + } + if (exclusive) { + const program = new CumProgram(op2, permutedX.shape, exclusive, reverse5); + const prevResult = result; + result = backend2.runWebGLProgram(program, [result], result.dtype); + backend2.disposeIntermediateTensorInfo(prevResult); + } + if (permutation != null) { + const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation); + const reverseTransposedResult = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } }); + backend2.disposeIntermediateTensorInfo(result); + backend2.disposeIntermediateTensorInfo(permutedX); + return reverseTransposedResult; + } + return result; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumprod.js +function cumprod3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + return cumImpl(CumOpType.Prod, x, backend2, axis, exclusive, reverse5); +} +var cumprodConfig2 = { + kernelName: Cumprod, + backendName: "webgl", + kernelFunc: cumprod3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumsum.js +function cumsum3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + return cumImpl(CumOpType.Sum, x, backend2, axis, exclusive, reverse5); +} +var cumsumConfig2 = { + kernelName: Cumsum, + backendName: "webgl", + kernelFunc: cumsum3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DenseBincount.js +function denseBincount3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, weights } = inputs; + const { size, binaryOutput } = attrs; + if (x.shape.length === 1) { + const xVals = backend2.readSync(x.dataId); + const weightsVals = backend2.readSync(weights.dataId); + const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size); + return backend2.makeTensorInfo([size], weights.dtype, outVals); + } else if (x.shape.length === 2) { + const xBuf = backend2.bufferSync(x); + const weightsBuf = backend2.bufferSync(weights); + const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput); + return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values); + } + throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`); +} +var denseBincountConfig2 = { + kernelName: DenseBincount, + backendName: "webgl", + kernelFunc: denseBincount3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/depth_to_space_gpu.js +var DepthToSpaceProgram = class { + constructor(outputShape, blockSize, dataFormat) { + this.variableNames = ["x"]; + this.outputShape = []; + this.outputShape = outputShape; + this.blockSize = blockSize; + this.dataFormat = dataFormat; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3024,37 +63462,134 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN int w = ${this.getWidthCoordString()}; int d = ${this.getDepthCoordString()}; - int in_h = h / ${e}; - int offset_h = imod(h, ${e}); - int in_w = w / ${e}; - int offset_w = imod(w, ${e}); - int offset_d = (offset_h * ${e} + offset_w) * + int in_h = h / ${blockSize}; + int offset_h = imod(h, ${blockSize}); + int in_w = w / ${blockSize}; + int offset_w = imod(w, ${blockSize}); + int offset_d = (offset_h * ${blockSize} + offset_w) * ${this.getOutputDepthSize()}; int in_d = d + offset_d; float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Vst(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 rB={kernelName:Va,backendName:"webgl",kernelFunc:Vst};var Gd=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() { + if (this.dataFormat === "NHWC") { + return `coords[1]`; + } else { + return `coords[2]`; + } + } + getWidthCoordString() { + if (this.dataFormat === "NHWC") { + return `coords[2]`; + } else { + return `coords[3]`; + } + } + getDepthCoordString() { + if (this.dataFormat === "NHWC") { + return `coords[3]`; + } else { + return `coords[1]`; + } + } + getOutputDepthSize() { + if (this.dataFormat === "NHWC") { + return this.outputShape[3]; + } else { + return this.outputShape[1]; + } + } + getInputSamplingString() { + if (this.dataFormat === "NHWC") { + return `getX(b, in_h, in_w, in_d)`; + } else { + return `getX(b, in_d, in_h, in_w)`; + } + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthToSpace.js +function depthToSpace3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockSize, dataFormat } = attrs; + const batchSize = x.shape[0]; + const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2]; + const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3]; + const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1]; + const outputHeight = inputHeight * blockSize; + const outputWidth = inputWidth * blockSize; + const outputDepth = inputDepth / (blockSize * blockSize); + const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth]; + const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat); + return backend2.runWebGLProgram(program, [x], x.dtype); +} +var depthToSpaceConfig2 = { + kernelName: DepthToSpace, + backendName: "webgl", + kernelFunc: depthToSpace3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu_depthwise.js +var DepthwiseConv2DProgram = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) { + this.variableNames = ["x", "W"]; + this.customUniforms = [ + { name: "pads", type: "ivec2" }, + { name: "strides", type: "ivec2" }, + { name: "dilations", type: "ivec2" }, + { name: "inDims", type: "ivec2" } + ]; + this.outputShape = convInfo.outShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const channelMul = convInfo.outChannels / convInfo.inChannels; + let activationSnippet = "", applyActivationSnippet = ""; + if (activation2) { + if (hasPreluActivation) { + activationSnippet = `float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); - ${n} - }`:s?l=`float activation(float a) { + ${activation2} + }`; + } else if (hasLeakyReluAlpha) { + activationSnippet = `float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); - ${n} - }`:l=` + ${activation2} + }`; + } else { + activationSnippet = ` float activation(float x) { - ${n} + ${activation2} } - `,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=` - ${l} + `; + } + applyActivationSnippet = `result = activation(result);`; + } + const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : ""; + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivation) { + this.variableNames.push("preluActivationWeights"); + } + if (hasLeakyReluAlpha) { + this.variableNames.push("leakyreluAlpha"); + } + this.userCode = ` + ${activationSnippet} void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; - int d1 = d2 / ${u}; - int q = d2 - d1 * ${u}; + int d1 = d2 / ${channelMul}; + int q = d2 - d1 * ${channelMul}; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; @@ -3063,14 +63598,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations. - for (int wR = 0; wR < ${i}; wR++) { + for (int wR = 0; wR < ${filterHeight}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } - for (int wC = 0; wC < ${a}; wC++) { + for (int wC = 0; wC < ${filterWidth}; wC++) { int xC = xCCorner + wC * dilations[1]; if (xC < 0 || xC >= inDims[1]) { @@ -3084,44 +63619,88 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } float result = dotProd; - ${p} - ${c} + ${addBiasSnippet} + ${applyActivationSnippet} setOutput(result); } - `}};var Wd=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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu_depthwise.js +var DepthwiseConvPacked2DProgram = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) { + this.variableNames = ["x", "W"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [ + { name: "pads", type: "ivec2" }, + { name: "strides", type: "ivec2" }, + { name: "dilations", type: "ivec2" }, + { name: "inDims", type: "ivec2" } + ]; + this.outputShape = convInfo.outShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + const channelMul = convInfo.outChannels / convInfo.inChannels; + const padLeft = convInfo.padInfo.left; + const strideWidth = convInfo.strideWidth; + const dilationWidth = convInfo.dilationWidth; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const texelsAcross = filterWidth; + let mainLoop = ` int xR; int xC; int xCOffset; - vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) { - `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=` - xC = xCCorner + ${b*l}; - `,u===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { - xTexelC${b} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${b}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${b}Ready = 1; + xTexelC${colIndex}Ready = 1; } - `,l===1&&b>0?f+=` - xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy); - `:f+=` + `; + if (dilationWidth === 1 && colIndex > 0) { + mainLoop += ` + xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy); + `; + } else { + mainLoop += ` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { @@ -3133,174 +63712,296 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN previous.zw = vec2(0.0); } - xC${b} = vec4(previous.zw, xTexelC${b}.xy); + xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy); } else { - xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy); + xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy); } - `):f+=` - if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { - xTexelC${b} = getX(batch, xR, xC, d1); + `; + } + } else { + mainLoop += ` + if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { - xTexelC${b}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${b}Ready = 1; + xTexelC${colIndex}Ready = 1; } - xC${b} = xTexelC${b}; - `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { - xTexelC${b+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${b+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${b+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - `,l>1?f+=` + `; + if (dilationWidth > 1) { + mainLoop += ` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); - xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy); + xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy); } else { - xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy); + xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy); } - `:f+=` - xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy); - `):w===1?f+=` - xC${b+1} = xTexelC${b}; - `:f+=` - xCOffset = xC + ${w}; + `; + } else { + mainLoop += ` + xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy); + `; + } + } else { + if (nextTexelOffset === 1) { + mainLoop += ` + xC${colIndex + 1} = xTexelC${colIndex}; + `; + } else { + mainLoop += ` + xCOffset = xC + ${nextTexelOffset}; - if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { - xTexelC${b+1} = getX(batch, xR, xCOffset, d1); + if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { - xTexelC${b+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${b+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${b+1} = xTexelC${b+1}; - `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { - xTexelC${b} = getX(batch, xR, xCOffset, d1); + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { - xTexelC${b}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${b}Ready = 1; + xTexelC${colIndex}Ready = 1; } - if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) { - xTexelC${b+1} = getX(batch, xR, xC + 1, d1); + if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { - xTexelC${b+1}.zw = vec2(0.0); + xTexelC${colIndex + 1}.zw = vec2(0.0); } - xTexelC${b+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw); - `,b+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } - xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy); - `)):(f+=` - if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { - xTexelC${b} = getX(batch, xR, xC, d1); + xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy); + `; + } + } else { + mainLoop += ` + if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) { + xTexelC${colIndex} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { - xTexelC${b}.zw = vec2(0.0); + xTexelC${colIndex}.zw = vec2(0.0); } - xTexelC${b}Ready = 1; + xTexelC${colIndex}Ready = 1; } xCOffset = xC + strides[1]; - if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { - xTexelC${b+1} = getX(batch, xR, xCOffset, d1); + if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) { + xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { - xTexelC${b+1}.zw = vec2(0.); + xTexelC${colIndex + 1}.zw = vec2(0.); } - xTexelC${b+1}Ready = 1; + xTexelC${colIndex + 1}Ready = 1; } - xC${b} = vec4( - xTexelC${b}.xy, xTexelC${b+1}.xy); - `,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 Wd(p):m=new Gd(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 nB={kernelName:us,backendName:"webgl",kernelFunc:Gst};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js +function depthwiseConv2dNative2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations, dimRoundingMode } = attrs; + let $dilations = dilations; + if ($dilations == null) { + $dilations = [1, 1]; + } + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`); + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filter.shape, + strides, + $dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + let program; + if (env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) { + program = new DepthwiseConvPacked2DProgram(convInfo); + } else { + program = new DepthwiseConv2DProgram(convInfo); + } + const customValues = [ + [convInfo.padInfo.top, convInfo.padInfo.left], + [convInfo.strideHeight, convInfo.strideWidth], + [convInfo.dilationHeight, convInfo.dilationWidth], + [convInfo.inHeight, convInfo.inWidth] + ]; + return backend2.runWebGLProgram(program, [x, filter], "float32", customValues); +} +var depthwiseConv2dNativeConfig2 = { + kernelName: DepthwiseConv2dNative, + backendName: "webgl", + kernelFunc: depthwiseConv2dNative2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu_depthwise.js +var DepthwiseConv2DDerFilterProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "dy"]; + this.outputShape = convInfo.filterShape; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const padTop = convInfo.padInfo.top; + const padLeft = convInfo.padInfo.left; + const channelMul = convInfo.outChannels / convInfo.inChannels; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int dm = coords.w; - int d2 = d1 * ${i} + dm; + int d2 = d1 * ${channelMul} + dm; float dotProd = 0.0; // TO DO: Vec4 over the batch size - for (int b = 0; b < ${t.batchSize}; b++) { - for (int yR = 0; yR < ${t.outHeight}; yR++) { - int xR = wR + yR * ${e} - ${o}; + for (int b = 0; b < ${convInfo.batchSize}; b++) { + for (int yR = 0; yR < ${convInfo.outHeight}; yR++) { + int xR = wR + yR * ${strideHeight} - ${padTop}; - if (xR < 0 || xR >= ${t.inHeight}) { + if (xR < 0 || xR >= ${convInfo.inHeight}) { continue; } - for (int yC = 0; yC < ${t.outWidth}; yC++) { - int xC = wC + yC * ${n} - ${s}; + for (int yC = 0; yC < ${convInfo.outWidth}; yC++) { + int xC = wC + yC * ${strideWidth} - ${padLeft}; - if (xC < 0 || xC >= ${t.inWidth}) { + if (xC < 0 || xC >= ${convInfo.inWidth}) { continue; } @@ -3312,8 +64013,22 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}},LI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=` - const ivec2 pads = ivec2(${i}, ${a}); + `; + } +}; +var DepthwiseConv2DDerInputProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "W"]; + this.outputShape = convInfo.inShape; + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const padTop = filterHeight - 1 - convInfo.padInfo.top; + const padLeft = filterWidth - 1 - convInfo.padInfo.left; + const channelMul = convInfo.outChannels / convInfo.inChannels; + this.userCode = ` + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); @@ -3325,30 +64040,30 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN float dotProd = 0.0; - for (int wR = 0; wR < ${e}; wR++) { - float dyR = float(dyRCorner + wR) / ${o}.0; + for (int wR = 0; wR < ${filterHeight}; wR++) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - int wRPerm = ${e} - 1 - wR; + int wRPerm = ${filterHeight} - 1 - wR; - for (int wC = 0; wC < ${n}; wC++) { - float dyC = float(dyCCorner + wC) / ${s}.0; + for (int wC = 0; wC < ${filterWidth}; wC++) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); - int wCPerm = ${n} - 1 - wC; + int wCPerm = ${filterWidth} - 1 - wC; // TO DO: Vec4 over the channelMul - for (int dm = 0; dm < ${u}; dm++) { - int d2 = d1 * ${u} + dm; + for (int dm = 0; dm < ${channelMul}; dm++) { + int d2 = d1 * ${channelMul} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; @@ -3357,15 +64072,103 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}};function Wst(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 oB={kernelName:Vp,backendName:"webgl",kernelFunc:Wst};function Ust(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 sB={kernelName:Gp,backendName:"webgl",kernelFunc:Ust};var zI=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js +function depthwiseConv2dNativeBackpropFilter3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs; + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filterShape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const program = new DepthwiseConv2DDerFilterProgram(convInfo); + return backend2.runWebGLProgram(program, [x, dy], "float32"); +} +var depthwiseConv2dNativeBackpropFilterConfig2 = { + kernelName: DepthwiseConv2dNativeBackpropFilter, + backendName: "webgl", + kernelFunc: depthwiseConv2dNativeBackpropFilter3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropInput.js +function depthwiseConv2dNativeBackpropInput3(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs; + const convInfo = backend_util_exports.computeConv2DInfo( + inputShape, + filter.shape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const program = new DepthwiseConv2DDerInputProgram(convInfo); + return backend2.runWebGLProgram(program, [dy, filter], "float32"); +} +var depthwiseConv2dNativeBackpropInputConfig2 = { + kernelName: DepthwiseConv2dNativeBackpropInput, + backendName: "webgl", + kernelFunc: depthwiseConv2dNativeBackpropInput3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/diag_gpu.js +var DiagProgram = class { + constructor(size) { + this.variableNames = ["X"]; + this.outputShape = [size, size]; + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function Hst(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 iB={kernelName:ru,backendName:"webgl",kernelFunc:Hst};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}, ${m}); + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Diag.js +function diag3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + const outShape = [...x.shape, ...x.shape]; + const xSize = util_exports.sizeFromShape(x.shape); + const flat = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [xSize] } }); + const program = new DiagProgram(xSize); + const res = backend2.runWebGLProgram(program, [flat], flat.dtype); + const out = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } }); + backend2.disposeIntermediateTensorInfo(flat); + backend2.disposeIntermediateTensorInfo(res); + return out; +} +var diagConfig2 = { + kernelName: Diag, + backendName: "webgl", + kernelFunc: diag3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/dilation_gpu.js +var Dilation2DProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "W"]; + this.outputShape = convInfo.outShape; + const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo; + const { top: padTop, left: padLeft } = padInfo; + this.userCode = ` + const ivec2 strides = ivec2(${strideHeight}, ${strideWidth}); + const ivec2 pads = ivec2(${padTop}, ${padLeft}); const float neg_infinity = -3.4e38; void main() { @@ -3378,14 +64181,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; - for (int h = 0; h < ${a}; h++) { - int hIn = hBeg + h * ${l}; + for (int h = 0; h < ${filterHeight}; h++) { + int hIn = hBeg + h * ${dilationHeight}; - if (hIn >= 0 && hIn < ${e}) { - for (int w = 0; w < ${u}; w++) { - int wIn = wBeg + w * ${c}; + if (hIn >= 0 && hIn < ${inHeight}) { + for (int w = 0; w < ${filterWidth}; w++) { + int wIn = wBeg + w * ${dilationWidth}; - if (wIn >= 0 && wIn < ${n}) { + if (wIn >= 0 && wIn < ${inWidth}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); @@ -3401,7 +64204,98 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN float result = curVal; setOutput(result); } - `}};function qst(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 aB={kernelName:cs,backendName:"webgl",kernelFunc:qst};function Kst(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=Cp({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 lB={kernelName:Wp,backendName:"webgl",kernelFunc:Kst};var jst="return (x >= 0.0) ? x : (exp(x) - 1.0);",Xst=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Dilation2D.js +function dilation2D(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + const convInfo = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + let out; + const program = new Dilation2DProgram(convInfo); + out = backend2.runWebGLProgram(program, [x, filter], "float32"); + const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } }); + backend2.disposeIntermediateTensorInfo(out); + return outReshaped; +} +var dilation2DConfig2 = { + kernelName: Dilation2D, + backendName: "webgl", + kernelFunc: dilation2D +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Einsum.js +function einsum3(args) { + const { inputs, backend: backend2, attrs } = args; + const { equation } = attrs; + const tensors = inputs; + const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length); + backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors); + const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims); + const nSteps = steps.length; + let out = null; + let numDimsRemaining = allDims.length; + const tensorsToDispose = []; + for (let i = 0; i < nSteps; ++i) { + for (const idTerm of steps[i]) { + const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]); + let x; + if (backend_util_exports.isIdentityPermutation(perm)) { + x = tensors[idTerm]; + } else { + x = transpose3({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } }); + tensorsToDispose.push(x); + } + const targetShape = x.shape.slice(); + for (let k = 0; k < dimsToExpand.length; ++k) { + targetShape.splice(dimsToExpand[k], 0, 1); + } + if (!util_exports.arraysEqual(x.shape, targetShape)) { + x = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } }); + tensorsToDispose.push(x); + } + if (out === null) { + out = x; + } else { + out = multiply3({ inputs: { a: x, b: out }, backend: backend2 }); + tensorsToDispose.push(out); + } + } + if (i < nSteps - 1) { + if (path[i] >= 0) { + out = sum4({ + inputs: { x: out }, + backend: backend2, + attrs: { + axis: path[i] - (allDims.length - numDimsRemaining), + keepDims: false + } + }); + tensorsToDispose.push(out); + } + numDimsRemaining--; + } + } + for (const tensorInfo of tensorsToDispose) { + if (tensorInfo === out) { + continue; + } + backend2.disposeIntermediateTensorInfo(tensorInfo); + } + return out; +} +var einsumConfig2 = { + kernelName: Einsum, + backendName: "webgl", + kernelFunc: einsum3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Elu.js +var ELU4 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`; +var ELU_PACKED = ` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3410,29 +64304,78 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,Yst=It({opSnippet:jst,packedOpSnippet:Xst}),uB={kernelName:ms,backendName:"webgl",kernelFunc:Yst};var Zst="return (b >= 0.0) ? a : a * (b + 1.0);",Jst=` +`; +var elu5 = unaryKernelFunc2({ opSnippet: ELU4, packedOpSnippet: ELU_PACKED }); +var eluConfig2 = { + kernelName: Elu, + backendName: "webgl", + kernelFunc: elu5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/EluGrad.js +var ELU_DER = `return (b >= 0.0) ? a : a * (b + 1.0);`; +var ELU_DER_PACKED = ` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,Qst=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jn(Jst,n.shape,o.shape):new On(Zst,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},cB={kernelName:Ga,backendName:"webgl",kernelFunc:Qst};var tit=` +`; +var eluGrad2 = (args) => { + const { inputs, backend: backend2 } = args; + const { dy, y } = inputs; + const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) : new BinaryOpProgram(ELU_DER, dy.shape, y.shape); + return backend2.runWebGLProgram(program, [dy, y], dy.dtype); +}; +var eluGradConfig3 = { + kernelName: EluGrad, + backendName: "webgl", + kernelFunc: eluGrad2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Equal.js +var PACKED_EQUAL = ` return vec4(equal(a, b)); -`,eit="return float(a == b);",rit=ue({opSnippet:eit,packedOpSnippet:tit,dtype:"bool",cpuKernelImpl:GL}),pB={kernelName:Wa,backendName:"webgl",kernelFunc:rit};var nit=` +`; +var EQUAL = `return float(a == b);`; +var equal3 = binaryKernelFunc2({ + opSnippet: EQUAL, + packedOpSnippet: PACKED_EQUAL, + dtype: "bool", + cpuKernelImpl: equalImplCPU +}); +var equalConfig2 = { + kernelName: Equal, + backendName: "webgl", + kernelFunc: equal3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Erf.js +var ERF = ` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. - float p = ${S.ERF_P}; - float a1 = ${S.ERF_A1}; - float a2 = ${S.ERF_A2}; - float a3 = ${S.ERF_A3}; - float a4 = ${S.ERF_A4}; - float a5 = ${S.ERF_A5}; + float p = ${backend_util_exports.ERF_P}; + float a1 = ${backend_util_exports.ERF_A1}; + float a2 = ${backend_util_exports.ERF_A2}; + float a3 = ${backend_util_exports.ERF_A3}; + float a4 = ${backend_util_exports.ERF_A4}; + float a5 = ${backend_util_exports.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,oit=It({opSnippet:nit}),mB={kernelName:fs,backendName:"webgl",kernelFunc:oit};var sit=Vo+` +`; +var erf3 = unaryKernelFunc2({ opSnippet: ERF }); +var erfConfig2 = { + kernelName: Erf, + backendName: "webgl", + kernelFunc: erf3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Exp.js +var EXP = CHECK_NAN_SNIPPET_UNARY + ` return exp(x); -`,iit=` +`; +var EXP_PACKED = ` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3441,21 +64384,80 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN result.a = isNaN.a ? x.a : result.a; return result; -`,R1=It({opSnippet:sit,packedOpSnippet:iit,cpuKernelImpl:WL,dtype:"float32"}),fB={kernelName:ds,backendName:"webgl",kernelFunc:R1};function VI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var dB={kernelName:Li,backendName:"webgl",kernelFunc:VI};var hB="return exp(x) - 1.0;",ait=It({opSnippet:hB,packedOpSnippet:hB,cpuKernelImpl:UL}),gB={kernelName:hs,backendName:"webgl",kernelFunc:ait};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}; +`; +var exp3 = unaryKernelFunc2({ + opSnippet: EXP, + packedOpSnippet: EXP_PACKED, + cpuKernelImpl: expImplCPU, + dtype: "float32" +}); +var expConfig2 = { + kernelName: Exp, + backendName: "webgl", + kernelFunc: exp3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ExpandDims.js +function expandDims4(args) { + const { inputs, attrs, backend: backend2 } = args; + const { dim } = attrs; + const { input: input2 } = inputs; + const inputRank = input2.shape.length; + const newShape = input2.shape.slice(); + let $dim = dim; + if (dim < 0) { + util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`); + $dim = inputRank + dim + 1; + } + newShape.splice($dim, 0, 1); + return reshape4({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } }); +} +var expandDimsConfig2 = { + kernelName: ExpandDims, + backendName: "webgl", + kernelFunc: expandDims4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Expm1.js +var EXPM1 = `return exp(x) - 1.0;`; +var expm13 = unaryKernelFunc2({ opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU }); +var expm1Config2 = { + kernelName: Expm1, + backendName: "webgl", + kernelFunc: expm13 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/fft_gpu.js +var FFTProgram = class { + constructor(component, inputShape, inverse) { + this.variableNames = ["real", "imag"]; + const innerDim = inputShape[1]; + this.outputShape = inputShape; + const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`; + const resultDenominator = inverse ? `${innerDim}.0` : "1.0"; + let opString; + if (component === "real") { + opString = "return real * expR - imag * expI;"; + } else if (component === "imag") { + opString = "return real * expI + imag * expR;"; + } else { + throw new Error(`FFT component must be either "real" or "imag", got ${component}.`); + } + this.userCode = ` + const float exponentMultiplier = ${exponentMultiplierSnippet}; float unaryOpComplex(float real, float expR, float imag, float expI) { - ${a} + ${opString} } float mulMatDFT(int batch, int index) { - float indexRatio = float(index) / float(${o}); + float indexRatio = float(index) / float(${innerDim}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; - for (int i = 0; i < ${o}; i++) { + for (int i = 0; i < ${innerDim}; i++) { // x = (-2|2 * PI / N) * index * i; float x = exponentMultiplierTimesIndexRatio * float(i); float expR = cos(x); @@ -3464,7 +64466,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN float imag = getImag(batch, i); result += - unaryOpComplex(real, expR, imag, expI) / ${i}; + unaryOpComplex(real, expR, imag, expI) / ${resultDenominator}; } return result; @@ -3474,26 +64476,142 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};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=Pn({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 lit(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!1,e)}var xB={kernelName:Up,backendName:"webgl",kernelFunc:lit};var WI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT_impl.js +function fftImpl2(x, inverse, backend2) { + const xData = backend2.texData.get(x.dataId); + const inputSize = util_exports.sizeFromShape(x.shape); + const innerDimensionSize = x.shape[x.shape.length - 1]; + const batch = inputSize / innerDimensionSize; + const input2D = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [batch, innerDimensionSize] } }); + const xShape = input2D.shape; + const realProgram = new FFTProgram("real", xShape, inverse); + const imagProgram = new FFTProgram("imag", xShape, inverse); + const inputs = [ + { + dataId: xData.complexTensorInfos.real.dataId, + dtype: xData.complexTensorInfos.real.dtype, + shape: xShape + }, + { + dataId: xData.complexTensorInfos.imag.dataId, + dtype: xData.complexTensorInfos.imag.dtype, + shape: xShape + } + ]; + const realPart = backend2.runWebGLProgram(realProgram, inputs, "float32"); + const imagPart = backend2.runWebGLProgram(imagProgram, inputs, "float32"); + const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(imagPart); + const complexOutputReshaped = reshape4({ inputs: { x: complexOutput }, backend: backend2, attrs: { shape: x.shape } }); + backend2.disposeIntermediateTensorInfo(input2D); + backend2.disposeIntermediateTensorInfo(complexOutput); + return complexOutputReshaped; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT.js +function fft3(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + return fftImpl2(input2, false, backend2); +} +var fftConfig2 = { + kernelName: FFT, + backendName: "webgl", + kernelFunc: fft3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/fill_gpu.js +var FillProgram = class { + constructor(shape, value) { + this.outputShape = []; + this.customUniforms = [{ name: "value", type: "float" }]; + this.variableNames = ["x"]; + this.outputShape = shape; + this.userCode = ` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Hl(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 yB={kernelName:su,backendName:"webgl",kernelFunc:Hl};var UI=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Fill.js +function fill3(args) { + const { backend: backend2, attrs } = args; + const { shape, value } = attrs; + let { dtype } = attrs; + dtype = dtype || util_exports.inferDtype(value); + if (dtype === "string") { + const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape)); + values.fill(value); + return backend2.makeTensorInfo(shape, dtype, values); + } else { + const program = new FillProgram(shape, value); + const customValues = [[value]]; + return backend2.runWebGLProgram(program, [], dtype, customValues); + } +} +var fillConfig2 = { + kernelName: Fill, + backendName: "webgl", + kernelFunc: fill3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/flip_left_right_gpu.js +var FlipLeftRightProgram = class { + constructor(imageShape) { + this.variableNames = ["Image"]; + this.outputShape = []; + const imageWidth = imageShape[2]; + this.outputShape = imageShape; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; - int coordX = ${e} - x - 1; + int coordX = ${imageWidth} - x - 1; float outputValue; - if(coordX >= 0 && coordX < ${e}) { + if(coordX >= 0 && coordX < ${imageWidth}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } - `}};var bB={kernelName:Ua,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 wB="return floor(x);",uit=It({opSnippet:wB,packedOpSnippet:wB,cpuKernelImpl:HL}),IB={kernelName:gs,backendName:"webgl",kernelFunc:uit};var cit=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FlipLeftRight.js +var flipLeftRightConfig2 = { + kernelName: FlipLeftRight, + backendName: "webgl", + kernelFunc: ({ inputs, backend: backend2 }) => { + const { image: image2 } = inputs; + const webglBackend = backend2; + const program = new FlipLeftRightProgram(image2.shape); + const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype); + return output; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Floor.js +var FLOOR = `return floor(x);`; +var floor3 = unaryKernelFunc2({ opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU }); +var floorConfig2 = { + kernelName: Floor, + backendName: "webgl", + kernelFunc: floor3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FloorDiv.js +var INT_DIV = ` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3503,7 +64621,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } else { return NAN; } -`,pit=` +`; +var INT_DIV_PACKED = ` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3524,15 +64643,30 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,mit=ue({opSnippet:cit,packedOpSnippet:pit,dtype:"int32"}),CB={kernelName:xs,backendName:"webgl",kernelFunc:mit};var HI=class{constructor(t){this.variableNames=["A"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=` +`; +var floorDiv3 = binaryKernelFunc2({ opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: "int32" }); +var floorDivConfig2 = { + kernelName: FloorDiv, + backendName: "webgl", + kernelFunc: floorDiv3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_gpu.js +var FromPixelsProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + const glsl = getGlslDifferences(); + const [height, width] = outputShape; + this.outputShape = outputShape; + this.userCode = ` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; - vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0); + vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0); - vec4 values = ${e.texture2D}(A, uv); + vec4 values = ${glsl.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; @@ -3546,7 +64680,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN setOutput(floor(value * 255.0 + 0.5)); } - `}};var qI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=We(),[n,o]=t;this.outputShape=t,this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_packed_gpu.js +var FromPixelsPackedProgram = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.packedInputs = false; + this.packedOutput = true; + const glsl = getGlslDifferences(); + const [height, width] = outputShape; + this.outputShape = outputShape; + this.userCode = ` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3561,8 +64708,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / - vec2(${o}.0, ${n}.0); - vec4 values = ${e.texture2D}(A, uv); + vec2(${width}.0, ${height}.0); + vec4 values = ${glsl.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; @@ -3578,41 +64725,485 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } } - ${e.output} = result; + ${glsl.output} = result; } - `}};var vB={kernelName:oh,backendName:"webgl",kernelFunc:fit},Ud,F1=L().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function fit(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");(Ud==null||h!==F1)&&(F1=h,Ud=document.createElement("canvas").getContext("2d",{willReadFrequently:F1})),Ud.canvas.width=u,Ud.canvas.height=l,Ud.drawImage(o,0,0,u,l),o=Ud.canvas}let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=Jr.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 dit(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?Wl(f,!0):null,F=new Vd(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?Wl(f,!1):null,F=new Bd(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 SB={kernelName:Ji,backendName:"webgl",kernelFunc:dit};function hit(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?Wl(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 Wd(g,I,b,N,E):A=new Gd(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 NB={kernelName:Qi,backendName:"webgl",kernelFunc:hit};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=` - int index;`;for(let a=0;a { + const inputs2 = [x, filter]; + const alignInputWithDataFormat = (input2, dataFormat2) => { + if (dataFormat2 === "NCHW" && input2.shape.length === 1 && input2.shape[0] !== 1) { + const alignedInput = reshape4({ + inputs: { x: input2 }, + backend: backend2, + attrs: { shape: [input2.shape[0], 1, 1] } + }); + intermediates.push(alignedInput); + return alignedInput; + } + return input2; + }; + if (hasBias) { + inputs2.push(alignInputWithDataFormat(bias, dataFormat)); + } + if (hasPreluActivationWeights) { + inputs2.push(alignInputWithDataFormat(preluActivationWeights, dataFormat)); + } + if (hasLeakyreluAlpha) { + const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32")); + inputs2.push($leakyreluAlpha); + intermediates.push($leakyreluAlpha); + } + return inputs2; + }; + if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) { + out = conv2dByMatMul({ + x, + filter, + convInfo, + backend: backend2, + bias, + activation: activation2, + preluActivationWeights, + leakyreluAlpha + }); + } else if (convInfo.strideWidth <= 2 && $dataFormat === "channelsLast" && env().getBool("WEBGL_EXP_CONV")) { + const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null; + const program = new Conv2DPackedProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + const customValues = [ + [convInfo.padInfo.top, convInfo.padInfo.left], + [convInfo.strideHeight, convInfo.strideWidth], + [convInfo.dilationHeight, convInfo.dilationWidth], + [convInfo.inHeight, convInfo.inWidth] + ]; + const inputs2 = prepareInputs(); + out = backend2.runWebGLProgram(program, inputs2, "float32", customValues); + } else if (env().getBool("WEBGL_CONV_IM2COL")) { + out = conv2dWithIm2Row({ + x, + filter, + convInfo, + backend: backend2, + bias, + activation: activation2, + preluActivationWeights, + leakyreluAlpha + }); + } else { + const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, false) : null; + const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + const inputs2 = prepareInputs(); + out = backend2.runWebGLProgram(program, inputs2, "float32"); + } + const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } }); + intermediates.push(out); + intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return outReshaped; +} +var fusedConv2DConfig2 = { + kernelName: FusedConv2D, + backendName: "webgl", + kernelFunc: fusedConv2d +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedDepthwiseConv2D.js +function fusedDepthwiseConv2D2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const intermediates = []; + let $dilations = dilations; + if ($dilations == null) { + $dilations = [1, 1]; + } + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`); + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filter.shape, + strides, + $dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const shouldPackDepthwiseConv = env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1; + const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, shouldPackDepthwiseConv) : null; + const programInputs = [x, filter]; + const hasBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + const hasLeakyreluAlpha = activation2 === "leakyrelu"; + if (hasBias) { + programInputs.push(bias); + } + if (hasPreluActivationWeights) { + programInputs.push(preluActivationWeights); + } + if (hasLeakyreluAlpha) { + const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32")); + programInputs.push($leakyreluAlpha); + intermediates.push($leakyreluAlpha); + } + let program; + if (shouldPackDepthwiseConv) { + program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + } else { + program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha); + } + const customValues = [ + [convInfo.padInfo.top, convInfo.padInfo.left], + [convInfo.strideHeight, convInfo.strideWidth], + [convInfo.dilationHeight, convInfo.dilationWidth], + [convInfo.inHeight, convInfo.inWidth] + ]; + const result = backend2.runWebGLProgram(program, programInputs, "float32", customValues); + intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var fusedDepthwiseConv2DConfig2 = { + kernelName: FusedDepthwiseConv2D, + backendName: "webgl", + kernelFunc: fusedDepthwiseConv2D2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_nd_gpu.js +var GatherNDProgram = class { + constructor(sliceDim, strides, shape, paramsShape) { + this.sliceDim = sliceDim; + this.strides = strides; + this.paramsShape = paramsShape; + this.variableNames = ["x", "indices"]; + this.outputShape = shape; + const dtype = getCoordsDataType(shape.length); + let mainLoop = ` + int index;`; + for (let j = 0; j < this.sliceDim; j++) { + mainLoop += ` + index = round(getIndices(coords[0], ${j})); out_of_bounds = out_of_bounds || index < 0; - out_of_bounds = out_of_bounds || index >= ${this.paramsShape[a]}; - flattenIndex += index * ${this.strides[a]};`;this.userCode=` + out_of_bounds = out_of_bounds || index >= ${this.paramsShape[j]}; + flattenIndex += index * ${this.strides[j]};`; + } + this.userCode = ` void main() { - ${s} coords = getOutputCoords(); + ${dtype} coords = getOutputCoords(); int flattenIndex = 0; bool out_of_bounds = false; - ${i} + ${mainLoop} setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1])); } - `}};function git(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=qL(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 kB={kernelName:Ha,backendName:"webgl",kernelFunc:git};var jI=class{constructor(t,e){this.variableNames=["A","indices"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=xit(t,2);this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js +function gatherNd2(args) { + const { inputs, backend: backend2 } = args; + const { params, indices } = inputs; + const indicesShape = indices.shape; + const sliceRank = indicesShape[indicesShape.length - 1]; + const paramsSize = util_exports.sizeFromShape(params.shape); + const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices); + const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } }); + const flattenX = reshape4({ + inputs: { x: params }, + backend: backend2, + attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] } + }); + if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === "string") { + const indicesData = backend2.readSync(indices.dataId); + const paramsBuf = backend2.bufferSync(params); + const outValue = gatherNdImplCPU(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize); + return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values); + } + const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize], params.shape); + const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype); + const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } }); + backend2.disposeIntermediateTensorInfo(flattenIndices); + backend2.disposeIntermediateTensorInfo(flattenX); + backend2.disposeIntermediateTensorInfo(res); + return reshaped; +} +var gatherNdConfig2 = { + kernelName: GatherNd, + backendName: "webgl", + kernelFunc: gatherNd2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_gpu.js +var GatherProgram = class { + constructor(aShape, outputShape) { + this.variableNames = ["A", "indices"]; + this.outputShape = outputShape; + this.rank = outputShape.length; + const dtype = getCoordsDataType(this.rank); + const sourceCoords = getSourceCoords2(aShape, 2); + this.userCode = ` void main() { - ${n} resRC = getOutputCoords(); + ${dtype} resRC = getOutputCoords(); int index = int(getIndices(resRC.x, resRC.z)); - float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0; - setOutput(inBounds * getA(${o})); + float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0; + setOutput(inBounds * getA(${sourceCoords})); } - `}};function xit(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, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=e.bufferSync(f),w=e.bufferSync(m),I=KL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new jI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var TB={kernelName:zi,backendName:"webgl",kernelFunc:O1};var yit="return float(a > b);",bit=` + `; + } +}; +function getSourceCoords2(aShape, axis) { + const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"]; + const sourceCoords = []; + for (let i = 0; i < aShape.length; i++) { + if (i === 2) { + sourceCoords.push("index"); + } else { + sourceCoords.push(`${currentCoords[i]}`); + } + } + return sourceCoords.join(); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherV2.js +function gatherV22(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, indices } = inputs; + const { axis, batchDims } = attrs; + const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0]; + if (env().get("DEBUG")) { + const indicesVals = backend2.readSync(indices.dataId); + const axisDim = x.shape[parsedAxis]; + for (let i = 0; i < indicesVals.length; ++i) { + const index = indicesVals[i]; + util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`); + } + } + const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims); + const indicesSize = util_exports.sizeFromShape(indices.shape); + const toDispose = []; + const flattenX = reshape4({ + inputs: { x }, + backend: backend2, + attrs: { + shape: [ + shapeInfo.batchSize, + shapeInfo.outerSize, + shapeInfo.dimSize, + shapeInfo.sliceSize + ] + } + }); + const flattenIndex = reshape4({ + inputs: { x: indices }, + backend: backend2, + attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] } + }); + toDispose.push(flattenX); + toDispose.push(flattenIndex); + const flattenOutputShape = [ + shapeInfo.batchSize, + shapeInfo.outerSize, + indicesSize / shapeInfo.batchSize, + shapeInfo.sliceSize + ]; + if (backend2.shouldExecuteOnCPU([x, indices]) || x.dtype === "string") { + const indicesBuf = backend2.bufferSync(flattenIndex); + const xBuf = backend2.bufferSync(flattenX); + const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape); + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values); + } + const program = new GatherProgram(flattenX.shape, flattenOutputShape); + const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype); + toDispose.push(res); + const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } }); + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return reshaped; +} +var gatherV2Config2 = { + kernelName: GatherV2, + backendName: "webgl", + kernelFunc: gatherV22 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Greater.js +var GREATER = `return float(a > b);`; +var GREATER_PACKED = ` return vec4(greaterThan(a, b)); -`,wit=ue({opSnippet:yit,packedOpSnippet:bit,cpuKernelImpl:jL,dtype:"bool"}),_B={kernelName:qa,backendName:"webgl",kernelFunc:wit};var Iit="return float(a >= b);",Cit=` +`; +var greater4 = binaryKernelFunc2({ + opSnippet: GREATER, + packedOpSnippet: GREATER_PACKED, + cpuKernelImpl: greaterImplCPU, + dtype: "bool" +}); +var greaterConfig2 = { + kernelName: Greater, + backendName: "webgl", + kernelFunc: greater4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GreaterEqual.js +var GREATER_EQUAL = `return float(a >= b);`; +var GREATER_EQUAL_PACKED = ` return vec4(greaterThanEqual(a, b)); -`,vit=ue({opSnippet:Iit,packedOpSnippet:Cit,dtype:"bool",cpuKernelImpl:XL}),EB={kernelName:bs,backendName:"webgl",kernelFunc:vit};function Sit(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!0,e)}var AB={kernelName:Hp,backendName:"webgl",kernelFunc:Sit};var Nit="return float(!isnan(x) && !isinf(x));",kit=It({opSnippet:Nit,dtype:"bool"}),DB={kernelName:ws,backendName:"webgl",kernelFunc:kit};var Tit="return float(isinf(x));",_it=It({opSnippet:Tit,dtype:"bool"}),$B={kernelName:Is,backendName:"webgl",kernelFunc:_it};var Eit="return float(isnan(x));",Ait=It({opSnippet:Eit,dtype:"bool"}),RB={kernelName:Cs,backendName:"webgl",kernelFunc:Ait};var Dit="return float(a < b);",$it=` +`; +var greaterEqual3 = binaryKernelFunc2({ + opSnippet: GREATER_EQUAL, + packedOpSnippet: GREATER_EQUAL_PACKED, + dtype: "bool", + cpuKernelImpl: greaterEqualImplCPU +}); +var greaterEqualConfig2 = { + kernelName: GreaterEqual, + backendName: "webgl", + kernelFunc: greaterEqual3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IFFT.js +function ifft3(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + return fftImpl2(input2, true, backend2); +} +var ifftConfig2 = { + kernelName: IFFT, + backendName: "webgl", + kernelFunc: ifft3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsFinite.js +var IS_FINITE = `return float(!isnan(x) && !isinf(x));`; +var isFinite4 = unaryKernelFunc2({ opSnippet: IS_FINITE, dtype: "bool" }); +var isFiniteConfig2 = { + kernelName: IsFinite, + backendName: "webgl", + kernelFunc: isFinite4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsInf.js +var IS_INF = `return float(isinf(x));`; +var isInf3 = unaryKernelFunc2({ opSnippet: IS_INF, dtype: "bool" }); +var isInfConfig2 = { + kernelName: IsInf, + backendName: "webgl", + kernelFunc: isInf3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsNaN.js +var IS_NAN = `return float(isnan(x));`; +var isNaN4 = unaryKernelFunc2({ opSnippet: IS_NAN, dtype: "bool" }); +var isNaNConfig2 = { + kernelName: IsNan, + backendName: "webgl", + kernelFunc: isNaN4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Less.js +var LESS = `return float(a < b);`; +var LESS_PACKED = ` return vec4(lessThan(a, b)); -`,Rit=ue({opSnippet:Dit,packedOpSnippet:$it,cpuKernelImpl:YL,dtype:"bool"}),FB={kernelName:Ka,backendName:"webgl",kernelFunc:Rit};var Fit="return float(a <= b);",Oit=` +`; +var less4 = binaryKernelFunc2({ + opSnippet: LESS, + packedOpSnippet: LESS_PACKED, + cpuKernelImpl: lessImplCPU, + dtype: "bool" +}); +var lessConfig2 = { + kernelName: Less, + backendName: "webgl", + kernelFunc: less4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LessEqual.js +var LESS_EQUAL = `return float(a <= b);`; +var LESS_EQUAL_PACKED = ` return vec4(lessThanEqual(a, b)); -`,Pit=ue({opSnippet:Fit,packedOpSnippet:Oit,cpuKernelImpl:ZL,dtype:"bool"}),OB={kernelName:ja,backendName:"webgl",kernelFunc:Pit};function Mit(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=JL(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var PB={kernelName:Xa,backendName:"webgl",kernelFunc:Mit};var Lit=Vo+` +`; +var lessEqual3 = binaryKernelFunc2({ + opSnippet: LESS_EQUAL, + packedOpSnippet: LESS_EQUAL_PACKED, + cpuKernelImpl: lessEqualImplCPU, + dtype: "bool" +}); +var lessEqualConfig2 = { + kernelName: LessEqual, + backendName: "webgl", + kernelFunc: lessEqual3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LinSpace.js +function linSpace2(args) { + const { backend: backend2, attrs } = args; + const { start, stop, num } = attrs; + const outVals = linSpaceImplCPU(start, stop, num); + return backend2.makeTensorInfo([outVals.length], "float32", outVals); +} +var linSpaceConfig2 = { + kernelName: LinSpace, + backendName: "webgl", + kernelFunc: linSpace2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log.js +var LOG = CHECK_NAN_SNIPPET_UNARY + ` return x < 0.0 ? 0./0. : log(x); -`,zit=` +`; +var LOG_PACKED = ` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); @@ -3620,18 +65211,85 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={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; -`,Bit=It({opSnippet:Lit,packedOpSnippet:zit,cpuKernelImpl:QL}),MB={kernelName:Ss,backendName:"webgl",kernelFunc:Bit};var Vit=Vo+` +`; +var log4 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU }); +var logConfig2 = { + kernelName: Log, + backendName: "webgl", + kernelFunc: log4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log1p.js +var LOG1P = CHECK_NAN_SNIPPET_UNARY + ` return log(1.0 + x); -`,Git=It({opSnippet:Vit}),LB={kernelName:Ns,backendName:"webgl",kernelFunc:Git};var Wit="return float(a >= 1.0 && b >= 1.0);",Uit=` +`; +var log1p3 = unaryKernelFunc2({ opSnippet: LOG1P }); +var log1pConfig2 = { + kernelName: Log1p, + backendName: "webgl", + kernelFunc: log1p3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalAnd.js +var LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`; +var LOGICAL_AND_PACKED = ` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,Hit=ue({opSnippet:Wit,packedOpSnippet:Uit,dtype:"bool"}),zB={kernelName:Ya,backendName:"webgl",kernelFunc:Hit};var qit="return float(!(x >= 1.0));",Kit=It({opSnippet:qit}),BB={kernelName:Za,backendName:"webgl",kernelFunc:Kit};var jit="return float(a >= 1.0 || b >= 1.0);",Xit=` +`; +var logicalAnd3 = binaryKernelFunc2({ + opSnippet: LOGICAL_AND, + packedOpSnippet: LOGICAL_AND_PACKED, + dtype: "bool" +}); +var logicalAndConfig2 = { + kernelName: LogicalAnd, + backendName: "webgl", + kernelFunc: logicalAnd3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalNot.js +var LOGICAL_NOT = `return float(!(x >= 1.0));`; +var logicalNot3 = unaryKernelFunc2({ opSnippet: LOGICAL_NOT }); +var logicalNotConfig2 = { + kernelName: LogicalNot, + backendName: "webgl", + kernelFunc: logicalNot3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalOr.js +var LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`; +var LOGICAL_OR_PACKED = ` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,Yit=ue({opSnippet:jit,packedOpSnippet:Xit,dtype:"bool"}),VB={kernelName:Ja,backendName:"webgl",kernelFunc:Yit};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=` +`; +var logicalOr3 = binaryKernelFunc2({ opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: "bool" }); +var logicalOrConfig2 = { + kernelName: LogicalOr, + backendName: "webgl", + kernelFunc: logicalOr3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_gpu.js +var LRNProgram = class { + constructor(xShape, radius, bias, alpha, beta) { + this.variableNames = ["x"]; + this.outputShape = []; + const rad = radius; + const maxD = xShape[3] - 1; + this.outputShape = xShape; + let powOperator; + const basis = `float(${bias}) + float(${alpha}) * sum`; + if (beta === 0.5) { + powOperator = `inversesqrt(${basis})`; + } else if (beta === 1) { + powOperator = `1.0/(${basis})`; + } else { + powOperator = `exp(log(${basis}) * float(-${beta}));`; + } + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3640,17 +65298,40 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; - for (int j = -${i}; j <= ${i}; j++) { + for (int j = -${rad}; j <= ${rad}; j++) { int idx = d + j; - if (idx >= 0 && idx <= ${a}) { + if (idx >= 0 && idx <= ${maxD}) { float z = getX(b, r, c, idx); sum += z * z; } } - float val = x * ${u}; + float val = x * ${powOperator}; setOutput(val); } - `}};var YI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_packed_gpu.js +var LRNPackedProgram = class { + constructor(xShape, radius, bias, alpha, beta) { + this.variableNames = ["x"]; + this.outputShape = []; + this.packedInputs = true; + this.packedOutput = true; + const rad = radius; + const maxD = xShape[3] - 1; + this.outputShape = xShape; + let powOperator; + const basis = `float(${bias}) + float(${alpha}) * sum`; + if (beta === 0.5) { + powOperator = `inversesqrt(${basis})`; + } else if (beta === 1) { + powOperator = `1.0/(${basis})`; + } else { + powOperator = `exp(log(${basis}) * float(-${beta}));`; + } + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3674,7 +65355,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); - int firstChannel = d - ${i}; + int firstChannel = d - ${rad}; vec2 cache = vec2(0.); if(firstChannel >= 0){ vec4 firstChannelFrag = getX(b, r, c, firstChannel); @@ -3685,10 +65366,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } ivec2 depth = ivec2(d, d + 1); - for (int j = - ${i}; j <= ${i}; j++) { + for (int j = - ${rad}; j <= ${rad}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); - bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a})); + bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD})); bool depthInRange = aboveLowerBound.x && belowUpperBound.x; bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; @@ -3709,10 +65390,39 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN sum += z * z; } } - vec4 result = xAtOutputCoords * ${u}; + vec4 result = xAtOutputCoords * ${powOperator}; setOutput(result); } - `}};var Zit=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)},GB={kernelName:ks,backendName:"webgl",kernelFunc:Zit};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRN.js +var lrn = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) : new LRNProgram(x.shape, depthRadius, bias, alpha, beta); + return backend2.runWebGLProgram(program, [x], x.dtype); +}; +var LRNConfig2 = { + kernelName: LRN, + backendName: "webgl", + kernelFunc: lrn +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_grad_gpu.js +var LRNGradProgram = class { + constructor(inputShape, depthRadius, bias, alpha, beta) { + this.variableNames = ["inputImage", "outputImage", "dy"]; + this.outputShape = []; + this.outputShape = inputShape; + this.depth = inputShape[3]; + this.depthRadius = depthRadius; + this.bias = bias; + this.alpha = alpha; + this.beta = beta; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3721,9 +65431,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { - int depthBegin = int(max(0.0, float(d - ${e}))); + int depthBegin = int(max(0.0, float(d - ${depthRadius}))); int depthEnd = int(min(float(${this.depth}), - float(d + ${e} + 1))); + float(d + ${depthRadius} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; @@ -3741,19 +65451,19 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } } - norm = float(${o}) * norm + float(${n}); + norm = float(${alpha}) * norm + float(${bias}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ - float dyi = -2.0 * float(${o}) - * float(${s}) + float dyi = -2.0 * float(${alpha}) + * float(${beta}) * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { - dyi += pow(norm, -1.0 * ${s}); + dyi += pow(norm, -1.0 * ${beta}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); @@ -3767,17 +65477,169 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(result); } - `}};var Jit=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)},WB={kernelName:Qa,backendName:"webgl",kernelFunc:Jit};function UB(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=to(a,r.dtype,"max",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function P1(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 { + const { inputs, backend: backend2, attrs } = args; + const { x, y, dy } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta); + return backend2.runWebGLProgram(program, [x, y, dy], x.dtype); +}; +var LRNGradConfig2 = { + kernelName: LRNGrad, + backendName: "webgl", + kernelFunc: lrnGrad +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max_impl.js +function maxImpl2(x, reduceShape, outShape, backend2) { + const inSize = util_exports.sizeFromShape(reduceShape); + const xSize = util_exports.sizeFromShape(x.shape); + const batchSize = xSize / inSize; + const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 }); + const reduced = reduce(reshapedInput, x.dtype, "max", backend2); + const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(reshapedInput); + backend2.disposeIntermediateTensorInfo(reduced); + return reshapedOutput; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max.js +function max4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { reductionIndices, keepDims } = attrs; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + const maxInputIsTransposed = permutedAxes != null; + const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]); + let maxInput = x; + if (maxInputIsTransposed) { + if (shouldExecuteOnCPU) { + const xTexData = backend2.texData.get(maxInput.dataId); + const values = xTexData.values; + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[permutedAxes[i]]; + } + const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape); + maxInput = backend2.makeTensorInfo(newShape, x.dtype); + const maxInputData = backend2.texData.get(maxInput.dataId); + maxInputData.values = maxInputValues; + } else { + maxInput = transposeImpl2(x, permutedAxes, backend2); + } + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank); + const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(maxInput.shape, axes); + let outShape = maxOutShape; + if (keepDims) { + outShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes); + } + let out; + if (shouldExecuteOnCPU) { + const xTexData = backend2.texData.get(maxInput.dataId); + const values = xTexData.values; + const outValues = maxImplCPU(values, util_exports.sizeFromShape(reduceShape), outShape, x.dtype); + out = backend2.makeTensorInfo(outShape, x.dtype); + const outData = backend2.texData.get(out.dataId); + outData.values = outValues; + } else { + out = maxImpl2(maxInput, reduceShape, outShape, backend2); + } + if (maxInputIsTransposed) { + backend2.disposeIntermediateTensorInfo(maxInput); + } + return out; +} +var maxConfig2 = { + kernelName: Max, + backendName: "webgl", + kernelFunc: max4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Maximum.js +var MAXIMUM = CHECK_NAN_SNIPPET2 + ` return max(a, b); -`,tat=` +`; +var MAXIMUM_PACKED = ` vec4 result = vec4(max(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); - `+Qn+` + ` + CHECK_NAN_SNIPPET_PACKED + ` return result; -`,eat=ue({opSnippet:Qit,packedOpSnippet:tat,cpuKernelImpl:ez}),qB={kernelName:_s,backendName:"webgl",kernelFunc:eat};function rat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ni(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 Ti(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var KB={kernelName:Es,backendName:"webgl",kernelFunc:rat};function nat(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 ec(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var jB={kernelName:Bi,backendName:"webgl",kernelFunc:nat};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}); +`; +var maximum4 = binaryKernelFunc2({ + opSnippet: MAXIMUM, + packedOpSnippet: MAXIMUM_PACKED, + cpuKernelImpl: maximumImplCPU +}); +var maximumConfig2 = { + kernelName: Maximum, + backendName: "webgl", + kernelFunc: maximum4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool.js +function maxPool3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + assertNotComplex2(x, "maxPool"); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = 1; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) { + return identity3({ inputs: { x }, backend: backend2 }); + } + const maxPoolProgram = new Pool2DProgram(convInfo, "max", false); + return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype); +} +var maxPoolConfig2 = { + kernelName: MaxPool, + backendName: "webgl", + kernelFunc: maxPool3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3D.js +function maxPool3d2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs; + const dilations = [1, 1, 1]; + const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat); + const maxPoolProgram = new Pool3DProgram(convInfo, "max", false); + return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype); +} +var maxPool3DConfig2 = { + kernelName: MaxPool3D, + backendName: "webgl", + kernelFunc: maxPool3d2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/max_pool_backprop_gpu.js +var MaxPool2DBackpropProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "maxPos"]; + this.outputShape = convInfo.inShape; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationHeight = convInfo.dilationHeight; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1; + this.userCode = ` + const ivec2 pads = ivec2(${padTop}, ${padLeft}); void main() { ivec4 coords = getOutputCoords(); @@ -3791,30 +65653,30 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wR = 0; wR < ${s}; - wR += ${o}) { - float dyR = float(dyRCorner + wR) / ${e}.0; + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - for (int wC = 0; wC < ${i}; wC++) { - float dyC = float(dyCCorner + wC) / ${n}.0; + for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); - int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d)); + int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. - int curPosValue = wR * ${i} + wC; + int curPosValue = wR * ${effectiveFilterWidth} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; @@ -3822,8 +65684,28 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}},QI=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=` - const ivec3 pads = ivec3(${p}, ${m}, ${f}); + `; + } +}; +var MaxPool3DBackpropProgram = class { + constructor(convInfo) { + this.variableNames = ["dy", "maxPos"]; + this.outputShape = convInfo.inShape; + const strideDepth = convInfo.strideDepth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const dilationDepth = convInfo.dilationDepth; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const effectiveFilterDepth = convInfo.effectiveFilterDepth; + const effectiveFilterHeight = convInfo.effectiveFilterHeight; + const effectiveFilterWidth = convInfo.effectiveFilterWidth; + const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front; + const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1; + this.userCode = ` + const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft}); void main() { ivec5 coords = getOutputCoords(); @@ -3840,44 +65722,44 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; - for (int wD = 0; wD < ${u}; - wD += ${s}) { - float dyD = float(dyDCorner + wD) / ${e}.0; + for (int wD = 0; wD < ${effectiveFilterDepth}; + wD += ${dilationDepth}) { + float dyD = float(dyDCorner + wD) / ${strideDepth}.0; - if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) { + if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); - for (int wR = 0; wR < ${l}; - wR += ${i}) { - float dyR = float(dyRCorner + wR) / ${n}.0; + for (int wR = 0; wR < ${effectiveFilterHeight}; + wR += ${dilationHeight}) { + float dyR = float(dyRCorner + wR) / ${strideHeight}.0; - if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || + if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); - for (int wC = 0; wC < ${c}; - wC += ${a}) { - float dyC = float(dyCCorner + wC) / ${o}.0; + for (int wC = 0; wC < ${effectiveFilterWidth}; + wC += ${dilationWidth}) { + float dyC = float(dyCCorner + wC) / ${strideWidth}.0; - if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || + if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); - int maxPosValue = ${d} - + int maxPosValue = ${lastIndex} - int(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. int curPosValue = - wD * ${l} * ${c} + - wR * ${c} + wC; + wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} + + wR * ${effectiveFilterWidth} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; @@ -3886,107 +65768,385 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } setOutput(dotProd); } - `}};function oat(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 ec(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 XB={kernelName:au,backendName:"webgl",kernelFunc:oat};function sat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;Ni([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 Ti(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 YB={kernelName:iu,backendName:"webgl",kernelFunc:sat};function ZB(r,t,e,n){let o=new Ti(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new Ti(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var JB={kernelName:lu,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]=ZB(n,a,c,u);return[p,m]}};function QB(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=to(a,"float32","mean",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var tV={kernelName:As,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;D { + const { x } = inputs; + const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs; + const webglBackend = backend2; + util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`); + const dilations = [1, 1]; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3); + const [result, indexes] = maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, webglBackend); + return [result, indexes]; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean_impl.js +function meanImpl(x, reduceShape, outShape, backend2) { + const inSize = util_exports.sizeFromShape(reduceShape); + const xSize = util_exports.sizeFromShape(x.shape); + const batchSize = xSize / inSize; + const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 }); + const reduced = reduce(reshapedInput, "float32", "mean", backend2); + const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(reshapedInput); + backend2.disposeIntermediateTensorInfo(reduced); + return reshapedOutput; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean.js +var meanConfig2 = { + kernelName: Mean, + backendName: "webgl", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { x } = inputs; + const { keepDims, axis } = attrs; + const webglBackend = backend2; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + const meanInputIsTransposed = permutedAxes != null; + const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]); + const intermediates = []; + let meanInput = x; + if (meanInputIsTransposed) { + if (shouldExecuteOnCPU) { + const xTexData = webglBackend.texData.get(meanInput.dataId); + const values = xTexData.values; + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[permutedAxes[i]]; + } + const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape); + meanInput = webglBackend.makeTensorInfo(newShape, x.dtype); + const meanInputData = webglBackend.texData.get(meanInput.dataId); + meanInputData.values = meanInputValues; + } else { + meanInput = transposeImpl2(x, permutedAxes, webglBackend); + } + intermediates.push(meanInput); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + } + backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank); + const [meanOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(meanInput.shape, axes); + let outShape = meanOutShape; + if (keepDims) { + outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes); + } + const out = meanImpl(meanInput, reduceShape, outShape, webglBackend); + for (const i of intermediates) { + webglBackend.disposeIntermediateTensorInfo(i); + } + return out; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Min.js +function min4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + const xRank = x.shape.length; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let permutedX = x; + if (permutedAxes != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("min", axes, xRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes); + const inSize = util_exports.sizeFromShape(reduceShape); + const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } }); + const reduced = reduce(a2D, a2D.dtype, "min", backend2); + let res; + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes); + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } }); + } else { + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } }); + } + backend2.disposeIntermediateTensorInfo(a2D); + backend2.disposeIntermediateTensorInfo(reduced); + if (permutedAxes != null) { + backend2.disposeIntermediateTensorInfo(permutedX); + } + return res; +} +var minConfig2 = { + kernelName: Min, + backendName: "webgl", + kernelFunc: min4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Minimum.js +var MINIMUM = CHECK_NAN_SNIPPET2 + ` return min(a, b); -`,lat=` +`; +var MINIMUM_PACKED = ` vec4 result = vec4(min(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); - `+Qn+` + ` + CHECK_NAN_SNIPPET_PACKED + ` return result; -`,uat=ue({opSnippet:aat,packedOpSnippet:lat,cpuKernelImpl:rz}),rV={kernelName:$s,backendName:"webgl",kernelFunc:uat};var tC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=e.map((c,p)=>c[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=` - int start = ${i}; - int end = ${a}; +`; +var minimum4 = binaryKernelFunc2({ + opSnippet: MINIMUM, + packedOpSnippet: MINIMUM_PACKED, + cpuKernelImpl: minimumImplCPU +}); +var minimumConfig2 = { + kernelName: Minimum, + backendName: "webgl", + kernelFunc: minimum4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_gpu.js +var MirrorPadProgram = class { + constructor(xShape, paddings, mode) { + this.variableNames = ["x"]; + this.outputShape = paddings.map( + (p2, i) => p2[0] + xShape[i] + p2[1] + /* afterPad */ + ); + const rank = xShape.length; + const dtype = getCoordsDataType(rank); + const start = paddings.map((p2) => p2[0]).join(","); + const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(","); + const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank); + const offset = mode === "reflect" ? 0 : 1; + if (rank === 1) { + this.userCode = ` + int start = ${start}; + int end = ${end}; void main() { int outC = getOutputCoords(); if (outC < start) { - outC = start * 2 - outC - ${l}; + outC = start * 2 - outC - ${offset}; } else if(outC >= end) { - outC = (end - 1) * 2 - outC + ${l}; + outC = (end - 1) * 2 - outC + ${offset}; } setOutput(getX(outC - start)); } - `;return}this.userCode=` - ${s} start = ${s}(${i}); - ${s} end = ${s}(${a}); + `; + return; + } + this.userCode = ` + ${dtype} start = ${dtype}(${start}); + ${dtype} end = ${dtype}(${end}); void main() { - ${s} outC = getOutputCoords(); - for (int i = 0; i < ${o}; i++) { + ${dtype} outC = getOutputCoords(); + for (int i = 0; i < ${rank}; i++) { if (outC[i] < start[i]) { - outC[i] = start[i] * 2 - outC[i] - ${l}; + outC[i] = start[i] * 2 - outC[i] - ${offset}; } else if(outC[i] >= end[i]) { - outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; + outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset}; } } - ${s} coords = outC - start; - setOutput(getX(${u})); + ${dtype} coords = outC - start; + setOutput(getX(${unpackedCoords})); } - `}};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=` - ${s} source = rc; + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_packed_gpu.js +var MirrorPadPackedProgram = class { + constructor(xShape, paddings, mode) { + this.variableNames = ["x"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = paddings.map( + (p2, i) => p2[0] + xShape[i] + p2[1] + /* afterPad */ + ); + const rank = xShape.length; + const dtype = getCoordsDataType(rank); + const start = paddings.map((p2) => p2[0]).join(","); + const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(","); + const coords2 = getChannels("rc", rank); + const source = getChannels("source", rank); + const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`; + const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`; + const offset = mode === "reflect" ? 0 : 1; + let mainLoop = ""; + if (rank === 1) { + const padSetup = ` + ${dtype} source = rc; if (source < start) { - source = start * 2 - source - ${m}; + source = start * 2 - source - ${offset}; } else if (source >= end) { - source = (end - 1) * 2 - source + ${m}; + source = (end - 1) * 2 - source + ${offset}; } source -= start; - `;f=` - ${s} rc = outputLoc; - ${d} - result[0] = getChannel(getX(${l.join()}), ${p}); - ${u[o-1]} += 1; - if(${c}) { - ${d} - result[1] = getChannel(getX(${l.join()}), ${p}); + `; + mainLoop = ` + ${dtype} rc = outputLoc; + ${padSetup} + result[0] = getChannel(getX(${source.join()}), ${innerDims}); + ${coords2[rank - 1]} += 1; + if(${cLimit}) { + ${padSetup} + result[1] = getChannel(getX(${source.join()}), ${innerDims}); } - `}else{let d=` - ${s} source = rc; - ${s} lt = ${s}(lessThan(source, start)); - ${s} gte = ${s}(greaterThanEqual(source, end)); - ${s} orig = 1 - (lt + gte); + `; + } else { + const padSetup = ` + ${dtype} source = rc; + ${dtype} lt = ${dtype}(lessThan(source, start)); + ${dtype} gte = ${dtype}(greaterThanEqual(source, end)); + ${dtype} orig = 1 - (lt + gte); source = orig * source + - lt * (start * 2 - source - ${m}) + - gte * ((end - 1) * 2 - source + ${m}); + lt * (start * 2 - source - ${offset}) + + gte * ((end - 1) * 2 - source + ${offset}); source -= start; - `;f=` - ${s} rc = outputLoc; - ${d} - result[0] = getChannel(getX(${l.join()}), ${p}); - ${u[o-1]} += 1; - if(${c}) { - ${d} - result[1] = getChannel(getX(${l.join()}), ${p}); + `; + mainLoop = ` + ${dtype} rc = outputLoc; + ${padSetup} + result[0] = getChannel(getX(${source.join()}), ${innerDims}); + ${coords2[rank - 1]} += 1; + if(${cLimit}) { + ${padSetup} + result[1] = getChannel(getX(${source.join()}), ${innerDims}); } rc = outputLoc; - ${u[o-2]} += 1; - if(${u[o-2]} < ${this.outputShape[o-2]}) { - ${d} - result[2] = getChannel(getX(${l.join()}), ${p}); - ${u[o-1]} += 1; - if(${c}) { - ${d} - result[3] = getChannel(getX(${l.join()}), ${p}); + ${coords2[rank - 2]} += 1; + if(${coords2[rank - 2]} < ${this.outputShape[rank - 2]}) { + ${padSetup} + result[2] = getChannel(getX(${source.join()}), ${innerDims}); + ${coords2[rank - 1]} += 1; + if(${cLimit}) { + ${padSetup} + result[3] = getChannel(getX(${source.join()}), ${innerDims}); } } - `}this.userCode=` - const ${s} start = ${s}(${i}); - const ${s} end = ${s}(${a}); + `; + } + this.userCode = ` + const ${dtype} start = ${dtype}(${start}); + const ${dtype} end = ${dtype}(${end}); void main() { - ${s} outputLoc = getOutputCoords(); + ${dtype} outputLoc = getOutputCoords(); vec4 result = vec4(0.); - ${f} + ${mainLoop} setOutput(result); } - `}};var cat=({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)},nV={kernelName:Rs,backendName:"webgl",kernelFunc:cat};var pat=`if (b == 0.0) return NAN; - return mod(a, b);`,mat=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MirrorPad.js +var mirrorPadKernelFunc = ({ inputs, backend: backend2, attrs }) => { + const { x } = inputs; + const { paddings, mode } = attrs; + const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new MirrorPadPackedProgram(x.shape, paddings, mode) : new MirrorPadProgram(x.shape, paddings, mode); + const output = backend2.runWebGLProgram(program, [x], x.dtype); + return output; +}; +var mirrorPadConfig2 = { + kernelName: MirrorPad, + backendName: "webgl", + kernelFunc: mirrorPadKernelFunc +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mod.js +var MOD = `if (b == 0.0) return NAN; + return mod(a, b);`; +var MOD_PACKED = ` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+Qn+` + ` + CHECK_NAN_SNIPPET_PACKED + ` return result; -`,fat=ue({opSnippet:pat,packedOpSnippet:mat}),oV={kernelName:Fs,backendName:"webgl",kernelFunc:fat};var rC=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=` +`; +var mod3 = binaryKernelFunc2({ + opSnippet: MOD, + packedOpSnippet: MOD_PACKED +}); +var modConfig2 = { + kernelName: Mod, + backendName: "webgl", + kernelFunc: mod3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/multinomial_gpu.js +var MultinomialProgram = class { + constructor(batchSize, numOutcomes, numSamples) { + this.variableNames = ["probs"]; + this.customUniforms = [{ name: "seed", type: "float" }]; + this.outputShape = [batchSize, numSamples]; + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3994,7 +66154,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN float r = random(seed); float cdf = 0.0; - for (int i = 0; i < ${e-1}; i++) { + for (int i = 0; i < ${numOutcomes - 1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { @@ -4004,13 +66164,19 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sst=It({opSnippet:ost}),b3={kernelN } // If no other event happened, last event happened. - setOutput(float(${e-1})); + setOutput(float(${numOutcomes - 1})); } - `}};var dat=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RealDiv.js +var DIV = ` if (a == b) { return 1.0; }; -return a / b;`,hat=` +return a / b;`; +var DIV_PACKED = ` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -4028,9 +66194,87 @@ return a / b;`,hat=` } return result; -`,M1=ue({opSnippet:dat,packedOpSnippet:hat,checkOutOfBounds:!0}),sV={kernelName:ps,backendName:"webgl",kernelFunc:M1};var iV="return a - b;",L1=ue({opSnippet:iV,packedOpSnippet:iV,supportsComplex:!0,cpuKernelImpl:vz}),aV={kernelName:si,backendName:"webgl",kernelFunc:L1};function z1(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=P1({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=L1({inputs:{a:o,b:l},backend:e}),p=R1({inputs:{x:c},backend:e}),m=Cp({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=rt({inputs:{x:m},backend:e,attrs:{shape:u}}),d=M1({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 lV={kernelName:ni,backendName:"webgl",kernelFunc:z1};function gat(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:z1({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 uV={kernelName:tl,backendName:"webgl",kernelFunc:gat};var xat=yr+` +`; +var realDiv = binaryKernelFunc2({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true }); +var realDivConfig2 = { + kernelName: RealDiv, + backendName: "webgl", + kernelFunc: realDiv +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sub.js +var SUB = "return a - b;"; +var sub3 = binaryKernelFunc2({ + opSnippet: SUB, + packedOpSnippet: SUB, + supportsComplex: true, + cpuKernelImpl: subImplCPU +}); +var subConfig2 = { + kernelName: Sub, + backendName: "webgl", + kernelFunc: sub3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softmax.js +function softmax4(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { dim } = attrs; + const axes = util_exports.parseAxisParam([dim], logits.shape); + const maxLogit = max4({ + inputs: { x: logits }, + backend: backend2, + attrs: { reductionIndices: axes, keepDims: false } + }); + const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes); + const maxLogitsReshaped = reshape4({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } }); + const a = sub3({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 }); + const b = exp3({ inputs: { x: a }, backend: backend2 }); + const sumExp = sum4({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } }); + const sumExpReshaped = reshape4({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } }); + const res = realDiv({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(maxLogit); + backend2.disposeIntermediateTensorInfo(maxLogitsReshaped); + backend2.disposeIntermediateTensorInfo(a); + backend2.disposeIntermediateTensorInfo(b); + backend2.disposeIntermediateTensorInfo(sumExp); + backend2.disposeIntermediateTensorInfo(sumExpReshaped); + return res; +} +var softmaxConfig2 = { + kernelName: Softmax, + backendName: "webgl", + kernelFunc: softmax4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multinomial.js +function multinomial3(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { numSamples, seed, normalized } = attrs; + const probs = normalized ? logits : softmax4({ inputs: { logits }, backend: backend2, attrs: { dim: logits.shape.length - 1 } }); + const batchSize = probs.shape[0]; + const numOutcomes = probs.shape[1]; + const program = new MultinomialProgram(batchSize, numOutcomes, numSamples); + const customValues = [[seed]]; + const res = backend2.runWebGLProgram(program, [probs], "int32", customValues); + if (!normalized) { + backend2.disposeIntermediateTensorInfo(probs); + } + return res; +} +var multinomialConfig2 = { + kernelName: Multinomial, + backendName: "webgl", + kernelFunc: multinomial3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Neg.js +var NEG = CHECK_NAN_SNIPPET + ` return -x; -`,yat=` +`; +var NEG_PACKED = ` vec4 result = -x; bvec4 isNaN = isnan(x); @@ -4040,16 +66284,236 @@ return a / b;`,hat=` result.a = isNaN.a ? x.a : result.a; return result; -`;function bat(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=oz(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Fn(n.shape,yat):o=new Br(n.shape,xat),e.runWebGLProgram(o,[n],n.dtype)}var cV={kernelName:Vi,backendName:"webgl",kernelFunc:bat};var wat=jr.nonMaxSuppressionV3Impl;function Iat(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}=wat(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var pV={kernelName:rl,backendName:"webgl",kernelFunc:Iat};var Cat=jr.nonMaxSuppressionV4Impl;function vat(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}=Cat(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var mV={kernelName:nl,backendName:"webgl",kernelFunc:vat};var Sat=jr.nonMaxSuppressionV5Impl;function Nat(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}=Sat(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var fV={kernelName:ol,backendName:"webgl",kernelFunc:Nat};var nC=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` +`; +function neg3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (backend2.shouldExecuteOnCPU([x])) { + const xData = backend2.texData.get(x.dataId); + const [outValues, newShape] = negImplCPU(xData.values, x.shape, x.dtype); + return backend2.makeTensorInfo(newShape, x.dtype, outValues); + } + let program; + if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) { + program = new UnaryOpPackedProgram(x.shape, NEG_PACKED); + } else { + program = new UnaryOpProgram(x.shape, NEG); + } + return backend2.runWebGLProgram(program, [x], x.dtype); +} +var negConfig2 = { + kernelName: Neg, + backendName: "webgl", + kernelFunc: neg3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV3.js +var nonMaxSuppressionV3Impl3 = kernel_impls_exports.nonMaxSuppressionV3Impl; +function nonMaxSuppressionV32(args) { + backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold } = attrs; + const boxesVals = backend2.readSync(boxes.dataId); + const scoresVals = backend2.readSync(scores.dataId); + const { selectedIndices } = nonMaxSuppressionV3Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold); + return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)); +} +var nonMaxSuppressionV3Config2 = { + kernelName: NonMaxSuppressionV3, + backendName: "webgl", + kernelFunc: nonMaxSuppressionV32 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV4.js +var nonMaxSuppressionV4Impl3 = kernel_impls_exports.nonMaxSuppressionV4Impl; +function nonMaxSuppressionV42(args) { + backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs; + const boxesVals = backend2.readSync(boxes.dataId); + const scoresVals = backend2.readSync(scores.dataId); + const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize); + return [ + backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)), + backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs])) + ]; +} +var nonMaxSuppressionV4Config2 = { + kernelName: NonMaxSuppressionV4, + backendName: "webgl", + kernelFunc: nonMaxSuppressionV42 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV5.js +var nonMaxSuppressionV5Impl3 = kernel_impls_exports.nonMaxSuppressionV5Impl; +function nonMaxSuppressionV52(args) { + backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs; + const boxesVals = backend2.readSync(boxes.dataId); + const scoresVals = backend2.readSync(scores.dataId); + const maxOutputSizeVal = maxOutputSize; + const iouThresholdVal = iouThreshold; + const scoreThresholdVal = scoreThreshold; + const softNmsSigmaVal = softNmsSigma; + const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl3(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal); + return [ + backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)), + backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores)) + ]; +} +var nonMaxSuppressionV5Config2 = { + kernelName: NonMaxSuppressionV5, + backendName: "webgl", + kernelFunc: nonMaxSuppressionV52 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/onehot_gpu.js +var OneHotProgram = class { + constructor(numIndices, depth, onValue, offValue) { + this.variableNames = ["indices"]; + this.outputShape = [numIndices, depth]; + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); - setOutput(mix(float(${o}), float(${n}), + setOutput(mix(float(${offValue}), float(${onValue}), float(index == coords.y))); } - `}};var kat=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},dV={kernelName:Ps,backendName:"webgl",kernelFunc:kat};function xg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=Ul({inputs:{input:n},backend:e}),s=xg({inputs:{x:o},backend:e}),i=Sp({inputs:{input:n},backend:e}),a=xg({inputs:{x:i},backend:e}),u=Pn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Hl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var hV={kernelName:Yi,backendName:"webgl",kernelFunc:xg};function gV(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=Ul({inputs:{input:n},backend:e}),s=gV({inputs:{x:o},backend:e}),i=Sp({inputs:{input:n},backend:e}),a=xg({inputs:{x:i},backend:e}),u=Pn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Hl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var xV={kernelName:Gi,backendName:"webgl",kernelFunc:gV};function Tat(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=$1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var yV={kernelName:Wi,backendName:"webgl",kernelFunc:Tat};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}; + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OneHot.js +var oneHot3 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { indices } = inputs; + const { dtype, depth, onValue, offValue } = attrs; + const indicesSize = util_exports.sizeFromShape(indices.shape); + const program = new OneHotProgram(indicesSize, depth, onValue, offValue); + const reshaped = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [indicesSize] } }); + const result = backend2.runWebGLProgram(program, [reshaped], dtype); + backend2.disposeIntermediateTensorInfo(reshaped); + const outShape = [...indices.shape, depth]; + const out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outShape } }); + backend2.disposeIntermediateTensorInfo(result); + return out; +}; +var oneHotConfig2 = { + kernelName: OneHot, + backendName: "webgl", + kernelFunc: oneHot3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ZerosLike.js +function zerosLike3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (x.dtype === "complex64") { + const realPart = real3({ inputs: { input: x }, backend: backend2 }); + const r = zerosLike3({ inputs: { x: realPart }, backend: backend2 }); + const imagPart = imag3({ inputs: { input: x }, backend: backend2 }); + const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 }); + const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(r); + backend2.disposeIntermediateTensorInfo(imagPart); + backend2.disposeIntermediateTensorInfo(i); + return result; + } else { + return fill3({ + attrs: { + shape: x.shape, + dtype: x.dtype, + value: x.dtype === "string" ? "" : 0 + }, + backend: backend2 + }); + } +} +var zerosLikeConfig2 = { + kernelName: ZerosLike, + backendName: "webgl", + kernelFunc: zerosLike3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OnesLike.js +function onesLike3(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (x.dtype === "string") { + throw new Error("onesLike is not supported under string dtype"); + } else if (x.dtype === "complex64") { + const realPart = real3({ inputs: { input: x }, backend: backend2 }); + const r = onesLike3({ inputs: { x: realPart }, backend: backend2 }); + const imagPart = imag3({ inputs: { input: x }, backend: backend2 }); + const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 }); + const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 }); + backend2.disposeIntermediateTensorInfo(realPart); + backend2.disposeIntermediateTensorInfo(r); + backend2.disposeIntermediateTensorInfo(imagPart); + backend2.disposeIntermediateTensorInfo(i); + return result; + } else { + return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 }); + } +} +var onesLikeConfig2 = { + kernelName: OnesLike, + backendName: "webgl", + kernelFunc: onesLike3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pack.js +function pack2(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + if (inputs.length === 1) { + return expandDims4({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } }); + } + const shape = inputs[0].shape; + const dtype = inputs[0].dtype; + inputs.forEach((t) => { + util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes"); + util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes"); + }); + const intermediateTensorInfos = []; + const expandedTensors = inputs.map((t) => { + const expandedT = expandDims4({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } }); + intermediateTensorInfos.push(expandedT); + return expandedT; + }); + const result = concat3({ inputs: expandedTensors, backend: backend2, attrs: { axis } }); + intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var packConfig2 = { + kernelName: Pack, + backendName: "webgl", + kernelFunc: pack2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_gpu.js +var PadProgram = class { + constructor(xShape, paddings, constantValue) { + this.variableNames = ["x"]; + this.customUniforms = [{ name: "value", type: "float" }]; + this.outputShape = paddings.map( + (p2, i) => p2[0] + xShape[i] + p2[1] + /* afterPad */ + ); + const rank = xShape.length; + const type = getCoordsDataType(rank); + const start = paddings.map((p2) => p2[0]).join(","); + const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(","); + const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank); + if (rank === 1) { + this.userCode = ` + int start = ${start}; + int end = ${end}; void main() { int outC = getOutputCoords(); @@ -4059,44 +66523,112 @@ return a / b;`,hat=` setOutput(getX(outC - start)); } } - `;return}this.userCode=` - ${s} start = ${s}(${i}); - ${s} end = ${s}(${a}); + `; + return; + } + this.userCode = ` + ${type} start = ${type}(${start}); + ${type} end = ${type}(${end}); void main() { - ${s} outC = getOutputCoords(); + ${type} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { - ${s} coords = outC - start; - setOutput(getX(${u})); + ${type} coords = outC - start; + setOutput(getX(${unpackedCoords})); } } - `}};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(${c}) { - `,o===1?"":`} + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_packed_gpu.js +var PadPackedProgram = class { + constructor(xShape, paddings, constantValue) { + this.variableNames = ["x"]; + this.packedInputs = true; + this.packedOutput = true; + this.customUniforms = [{ name: "value", type: "float" }]; + this.outputShape = paddings.map( + (p2, i) => p2[0] + xShape[i] + p2[1] + /* afterPad */ + ); + const rank = xShape.length; + const dtype = getCoordsDataType(rank); + const start = paddings.map((p2) => p2[0]).join(","); + const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(","); + const coords2 = getChannels("rc", rank); + const source = getChannels("source", rank); + const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`; + const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`; + const componentSetup = [ + `${dtype} rc = outputLoc;`, + `${coords2[rank - 1]} += 1; + if(${cLimit}) { + `, + rank === 1 ? "" : `} rc = outputLoc; - ${u[o-2]} += 1; - if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${u[o-1]} += 1; - if(${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= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))"; + let mainLoop = ""; + for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) { + mainLoop += ` + ${componentSetup[i]} + if (${paddingArea}) { + result[${i}] = float(value); } else { - ${s} source = rc - start; - result[${h}] = getChannel(getX(${l.join()}), ${p}); + ${dtype} source = rc - start; + result[${i}] = getChannel(getX(${source.join()}), ${innerDims}); } - `;d+=o===1?"} ":"}}",this.userCode=` - const ${s} start = ${s}(${i}); - const ${s} end = ${s}(${a}); + `; + } + mainLoop += rank === 1 ? `} ` : `}}`; + this.userCode = ` + const ${dtype} start = ${dtype}(${start}); + const ${dtype} end = ${dtype}(${end}); void main() { - ${s} outputLoc = getOutputCoords(); + ${dtype} outputLoc = getOutputCoords(); vec4 result = vec4(0.); - ${d} + ${mainLoop} setOutput(result); } - `}};var B1=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 Hl({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)},bV={kernelName:Ms,backendName:"webgl",kernelFunc:B1};var _at=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/PadV2.js +var padV22 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { paddings, constantValue } = attrs; + if (util_exports.sizeFromShape(x.shape) === 0) { + const outputShape = paddings.map( + (p2, i) => p2[0] + x.shape[i] + p2[1] + /* afterPad */ + ); + return fill3({ + backend: backend2, + attrs: { shape: outputShape, value: constantValue, dtype: x.dtype } + }); + } + const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new PadPackedProgram(x.shape, paddings, constantValue) : new PadProgram(x.shape, paddings, constantValue); + const customValues = [[constantValue]]; + return backend2.runWebGLProgram(program, [x], x.dtype, customValues); +}; +var padV2Config2 = { + kernelName: PadV2, + backendName: "webgl", + kernelFunc: padV22 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pow.js +var POW = ` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4105,7 +66637,8 @@ return a / b;`,hat=` } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); -`,Eat=` +`; +var POW_PACKED = ` // 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); @@ -4121,11 +66654,146 @@ return a / b;`,hat=` 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); - `+Qn+` + ` + CHECK_NAN_SNIPPET_PACKED + ` return result; -`,Aat=ue({opSnippet:_at,packedOpSnippet:Eat}),wV={kernelName:Ls,backendName:"webgl",kernelFunc:Aat};function Dat(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}=iz(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=xc(o.dtype),w=to(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 IV={kernelName:Bs,backendName:"webgl",kernelFunc:Dat};function $at(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=az(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var CV={kernelName:Kp,backendName:"webgl",kernelFunc:$at};function Rat(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]=lz(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 vV={kernelName:jp,backendName:"webgl",kernelFunc:Rat};function Fat(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]=uz(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var SV={kernelName:Xp,backendName:"webgl",kernelFunc:Fat};var V1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=cz(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},NV={kernelName:uu,backendName:"webgl",kernelFunc:V1};var Oat="return 1.0 / x;",Pat=It({opSnippet:Oat}),kV={kernelName:Vs,backendName:"webgl",kernelFunc:Pat};var Mat=yr+` +`; +var pow3 = binaryKernelFunc2({ opSnippet: POW, packedOpSnippet: POW_PACKED }); +var powConfig2 = { + kernelName: Pow, + backendName: "webgl", + kernelFunc: pow3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prod.js +function prod3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + const xRank = x.shape.length; + const toDispose = []; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let permutedX = x; + if (permutedAxes != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + toDispose.push(permutedX); + } + backend_util_exports.assertAxesAreInnerMostDims("prod", axes, xRank); + let res; + if (backend2.shouldExecuteOnCPU([permutedX])) { + const xVals = backend2.texData.get(permutedX.dataId).values; + const { outVals, outShape, outDtype } = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes); + res = backend2.makeTensorInfo(outShape, outDtype, outVals); + } else { + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes); + const inSize = util_exports.sizeFromShape(reduceShape); + const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } }); + const outputDType = sumOutType(x.dtype); + const reduced = reduce(a2D, outputDType, "prod", backend2); + res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } }); + toDispose.push(a2D); + toDispose.push(reduced); + } + if (keepDims) { + toDispose.push(res); + const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes); + res = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: newShape } }); + } + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return res; +} +var prodConfig2 = { + kernelName: Prod, + backendName: "webgl", + kernelFunc: prod3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RaggedGather.js +function raggedGather3(args) { + const { inputs, backend: backend2, attrs } = args; + const { paramsNestedSplits, paramsDenseValues, indices } = inputs; + const { outputRaggedRank } = attrs; + const $paramsNestedSplits = paramsNestedSplits.map((t) => backend2.readSync(t.dataId)); + const $paramsNestedSplitsShapes = paramsNestedSplits.map((t) => t.shape); + const $paramsDenseValues = backend2.readSync(paramsDenseValues.dataId); + const $indices = backend2.readSync(indices.dataId); + const [outputNestedSplits, outputDenseValues, outputDenseValuesShape] = raggedGatherImplCPU($paramsNestedSplits, $paramsNestedSplitsShapes, $paramsDenseValues, paramsDenseValues.shape, paramsDenseValues.dtype, $indices, indices.shape, outputRaggedRank); + const outputNestedSplitsTensors = outputNestedSplits.map((splits) => backend2.makeTensorInfo([splits.length], "int32", splits)); + const outputDenseValuesTensor = backend2.makeTensorInfo(outputDenseValuesShape, paramsDenseValues.dtype, outputDenseValues); + return outputNestedSplitsTensors.concat([outputDenseValuesTensor]); +} +var raggedGatherConfig2 = { + kernelName: RaggedGather, + backendName: "webgl", + kernelFunc: raggedGather3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RaggedRange.js +function raggedRange3(args) { + const { inputs, backend: backend2 } = args; + const { starts, limits, deltas } = inputs; + const $starts = backend2.readSync(starts.dataId); + const $limits = backend2.readSync(limits.dataId); + const $deltas = backend2.readSync(deltas.dataId); + const [rtNestedSplitsData, rtDenseValuesData] = raggedRangeImplCPU($starts, starts.shape, starts.dtype, $limits, limits.shape, $deltas, deltas.shape); + const rtNestedSplits = backend2.makeTensorInfo([rtNestedSplitsData.length], "int32", rtNestedSplitsData); + const rtDenseValues = backend2.makeTensorInfo([rtDenseValuesData.length], starts.dtype, rtDenseValuesData); + return [rtNestedSplits, rtDenseValues]; +} +var raggedRangeConfig2 = { + kernelName: RaggedRange, + backendName: "webgl", + kernelFunc: raggedRange3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RaggedTensorToTensor.js +function raggedTensorToTensor3(args) { + const { inputs, backend: backend2, attrs } = args; + const { shape, values, defaultValue, rowPartitionTensors } = inputs; + const { rowPartitionTypes } = attrs; + const $shape = backend2.readSync(shape.dataId); + const $values = backend2.readSync(values.dataId); + const $defaultValue = backend2.readSync(defaultValue.dataId); + const $rowPartitionValues = rowPartitionTensors.map((t) => backend2.readSync(t.dataId)); + const rowPartitionValuesShapes = rowPartitionTensors.map((t) => t.shape); + const [outputShape, output] = raggedTensorToTensorImplCPU($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes); + return backend2.makeTensorInfo(outputShape, values.dtype, output); +} +var raggedTensorToTensorConfig2 = { + kernelName: RaggedTensorToTensor, + backendName: "webgl", + kernelFunc: raggedTensorToTensor3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Range.js +var range4 = (args) => { + const { backend: backend2, attrs } = args; + const { start, stop, step: step5, dtype } = attrs; + const values = rangeImplCPU(start, stop, step5, dtype); + return backend2.makeTensorInfo([values.length], dtype, values); +}; +var rangeConfig2 = { + kernelName: Range, + backendName: "webgl", + kernelFunc: range4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reciprocal.js +var RECIPROCAL = `return 1.0 / x;`; +var reciprocal3 = unaryKernelFunc2({ opSnippet: RECIPROCAL }); +var reciprocalConfig2 = { + kernelName: Reciprocal, + backendName: "webgl", + kernelFunc: reciprocal3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu.js +var RELU3 = CHECK_NAN_SNIPPET + ` return (x < 0.0) ? 0.0 : x; -`,Lat=` +`; +var RELU_PACKED = ` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4135,9 +66803,19 @@ return a / b;`,hat=` result.a = isNaN.a ? x.a : result.a; return result; -`,zat=It({opSnippet:Mat,packedOpSnippet:Lat}),TV={kernelName:Gs,backendName:"webgl",kernelFunc:zat};var Bat=yr+` +`; +var relu3 = unaryKernelFunc2({ opSnippet: RELU3, packedOpSnippet: RELU_PACKED }); +var reluConfig2 = { + kernelName: Relu, + backendName: "webgl", + kernelFunc: relu3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu6.js +var RELU63 = CHECK_NAN_SNIPPET + ` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Vat=` +`; +var RELU6_PACKED = ` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -4147,11 +66825,40 @@ return a / b;`,hat=` result.a = isNaN.a ? x.a : result.a; return result; -`,Gat=It({opSnippet:Bat,packedOpSnippet:Vat}),_V={kernelName:Hs,backendName:"webgl",kernelFunc:Gat};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=` +`; +var relu63 = unaryKernelFunc2({ opSnippet: RELU63, packedOpSnippet: RELU6_PACKED }); +var relu6Config2 = { + kernelName: Relu6, + backendName: "webgl", + kernelFunc: relu63 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_gpu.js +var ResizeBilinearProgram = class { + constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) { + this.variableNames = ["A"]; + this.outputShape = []; + const [batch, oldHeight, oldWidth, depth] = inputShape; + this.outputShape = [batch, newHeight, newWidth, depth]; + const effectiveInSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + let sourceFracIndexRC; + if (halfPixelCenters) { + sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)`; + } else { + sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`; + } + this.userCode = ` const vec2 effectiveInputOverOutputRatioRC = vec2( - ${c[0]/p[0]}, - ${c[1]/p[1]}); - const vec2 inputShapeRC = vec2(${a}.0, ${u}.0); + ${effectiveInSize[0] / effectiveOutSize[0]}, + ${effectiveInSize[1] / effectiveOutSize[1]}); + const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0); void main() { ivec4 coords = getOutputCoords(); @@ -4160,7 +66867,7 @@ return a / b;`,hat=` ivec2 yRC = coords.yz; // Fractional source index. - vec2 sourceFracIndexRC = ${m}; + vec2 sourceFracIndexRC = ${sourceFracIndexRC}; // Compute the four integer indices. ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0))); @@ -4180,13 +66887,40 @@ return a / b;`,hat=` setOutput(newValue); } - `}};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_packed_gpu.js +var ResizeBilinearPackedProgram = class { + constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = []; + const [batch, oldHeight, oldWidth, depth] = inputShape; + this.outputShape = [batch, newHeight, newWidth, depth]; + const effectiveInSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + let sourceFracIndexRC; + if (halfPixelCenters) { + sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)`; + } else { + sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`; + } + this.userCode = ` const vec3 effectiveInputOverOutputRatioRC = vec3( - ${c[0]/p[0]}, - ${c[1]/p[1]}, - ${c[1]/p[1]}); - const vec3 inputShapeRC = vec3(${a}.0, ${u}.0, - ${u}.0); + ${effectiveInSize[0] / effectiveOutSize[0]}, + ${effectiveInSize[1] / effectiveOutSize[1]}, + ${effectiveInSize[1] / effectiveOutSize[1]}); + const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0, + ${oldWidth}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); @@ -4200,7 +66934,7 @@ return a / b;`,hat=` ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. - vec3 sourceFracIndexRC = ${m}; + vec3 sourceFracIndexRC = ${sourceFracIndexRC}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); @@ -4208,8 +66942,8 @@ return a / b;`,hat=` min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); // Should we calculate next column and row elements in 2x2 packed cell. - bool hasNextCol = d < ${l-1}; - bool hasNextRow = coords.z < ${n-1}; + bool hasNextCol = d < ${depth - 1}; + bool hasNextRow = coords.z < ${newWidth - 1}; // In parallel, construct four corners for all four components in // packed 2x2 cell. @@ -4257,7 +66991,48 @@ return a / b;`,hat=` setOutput(newValue); } - `}};function Wat(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 EV={kernelName:Us,backendName:"webgl",kernelFunc:Wat};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinear.js +function resizeBilinear4(args) { + const { inputs, backend: backend2, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + const [newHeight, newWidth] = size; + const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters); + return backend2.runWebGLProgram(program, [images], "float32"); +} +var resizeBilinearConfig2 = { + kernelName: ResizeBilinear, + backendName: "webgl", + kernelFunc: resizeBilinear4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_backprop_gpu.js +var ResizeBilinearBackpropProgram = class { + constructor(dyShape, inputShape, alignCorners) { + this.variableNames = ["dy"]; + this.outputShape = []; + this.outputShape = inputShape; + const [, xHeight, xWidth] = inputShape; + const [, yHeight, yWidth] = dyShape; + const effectiveXSize = [ + alignCorners && yHeight > 1 ? xHeight - 1 : xHeight, + alignCorners && yWidth > 1 ? xWidth - 1 : xWidth + ]; + const effectiveYSize = [ + alignCorners && yHeight > 1 ? yHeight - 1 : yHeight, + alignCorners && yWidth > 1 ? yWidth - 1 : yWidth + ]; + const heightScale = effectiveXSize[0] / effectiveYSize[0]; + const widthScale = effectiveXSize[1] / effectiveYSize[1]; + const invHeightScale = 1 / heightScale; + const invWidthScale = 1 / widthScale; + const winHeight = Math.ceil(invHeightScale) * 2 + 2; + const winWidth = Math.ceil(invWidthScale) * 2 + 2; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4267,14 +67042,14 @@ return a / b;`,hat=` float accumulator = 0.0; - const float heightScale = float(${c}); - const float widthScale = float(${p}); + const float heightScale = float(${heightScale}); + const float widthScale = float(${widthScale}); - const float invHeightScale = float(${m}); - const float invWidthScale = float(${f}); + const float invHeightScale = float(${invHeightScale}); + const float invWidthScale = float(${invWidthScale}); - const int winHeight = int(${d}); - const int winWidth = int(${h}); + const int winHeight = int(${winHeight}); + const int winWidth = int(${winWidth}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); @@ -4288,7 +67063,7 @@ return a / b;`,hat=` int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy - if (dyR < 0 || dyR >= ${i}) { + if (dyR < 0 || dyR >= ${yHeight}) { continue; } @@ -4296,19 +67071,19 @@ return a / b;`,hat=` int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy - if (dyC < 0 || dyC >= ${a}) { + if (dyC < 0 || dyC >= ${yWidth}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); - int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0)); + int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0)); float dxRLerp = dxR - float(topDxRIndex); float inverseDxRLerp = 1.0 - dxRLerp; float dxC = float(dyC) * widthScale; int leftDxCIndex = int(floor(dxC)); - int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0)); + int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; @@ -4338,11 +67113,51 @@ return a / b;`,hat=` setOutput(accumulator); } - `}};function Uat(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 AV={kernelName:il,backendName:"webgl",kernelFunc:Uat};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinearGrad.js +function resizeBilinearGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + const program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners); + return backend2.runWebGLProgram(program, [dy], dy.dtype); +} +var resizeBilinearGradConfig3 = { + kernelName: ResizeBilinearGrad, + backendName: "webgl", + kernelFunc: resizeBilinearGrad2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_gpu.js +var ResizeNearestNeighborProgram = class { + constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) { + this.variableNames = ["A"]; + this.outputShape = []; + const [batch, oldHeight, oldWidth, depth] = inputShape; + this.outputShape = [batch, newHeight, newWidth, depth]; + const effectiveInSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + const roundBase = alignCorners ? "0.5" : "0.0"; + let sourceFracIndexRC; + if (halfPixelCenters) { + sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`; + } else { + sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`; + } + this.userCode = ` const vec2 effectiveInputOverOutputRatioRC = vec2( - ${c[0]/p[0]}, - ${c[1]/p[1]}); - const vec2 inputShapeRC = vec2(${a}.0, ${u}.0); + ${effectiveInSize[0] / effectiveOutSize[0]}, + ${effectiveInSize[1] / effectiveOutSize[1]}); + const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0); void main() { ivec4 coords = getOutputCoords(); @@ -4351,22 +67166,50 @@ return a / b;`,hat=` ivec2 yRC = coords.yz; // Fractional source index. - vec2 sourceFracIndexRC = ${f}; + vec2 sourceFracIndexRC = ${sourceFracIndexRC}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( - min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m}))); + min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } - `}};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_packed_gpu.js +var ResizeNearestNeighborPackedProgram = class { + constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) { + this.variableNames = ["A"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = []; + const [batch, oldHeight, oldWidth, depth] = inputShape; + this.outputShape = [batch, newHeight, newWidth, depth]; + const effectiveInSize = [ + alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight, + alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth + ]; + const effectiveOutSize = [ + alignCorners && newHeight > 1 ? newHeight - 1 : newHeight, + alignCorners && newWidth > 1 ? newWidth - 1 : newWidth + ]; + const roundBase = alignCorners ? "0.5" : "0.0"; + let sourceFracIndexRC; + if (halfPixelCenters) { + sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))`; + } else { + sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`; + } + this.userCode = ` const vec3 effectiveInputOverOutputRatioRC = vec3( - ${c[0]/p[0]}, - ${c[1]/p[1]}, - ${c[1]/p[1]}); - const vec3 inputShapeRC = vec3(${a}.0, ${u}.0, - ${u}.0); + ${effectiveInSize[0] / effectiveOutSize[0]}, + ${effectiveInSize[1] / effectiveOutSize[1]}, + ${effectiveInSize[1] / effectiveOutSize[1]}); + const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0, + ${oldWidth}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); @@ -4380,15 +67223,15 @@ return a / b;`,hat=` ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. - vec3 sourceFracIndexRC = ${f}; + vec3 sourceFracIndexRC = ${sourceFracIndexRC}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( - min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m}))); + min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase}))); // Should we calculate next column and row elements in 2x2 packed cell. - bool hasNextCol = d < ${l-1}; - bool hasNextRow = coords.z < ${n-1}; + bool hasNextCol = d < ${depth - 1}; + bool hasNextRow = coords.z < ${newWidth - 1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), @@ -4401,7 +67244,48 @@ return a / b;`,hat=` setOutput(newValue); } - `}};function Hat(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 DV={kernelName:Ws,backendName:"webgl",kernelFunc:Hat};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighbor.js +function resizeNearestNeighbor3(args) { + const { inputs, backend: backend2, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + const [newHeight, newWidth] = size; + const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeNearestNeighborPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters); + return backend2.runWebGLProgram(program, [images], images.dtype); +} +var resizeNearestNeighborConfig2 = { + kernelName: ResizeNearestNeighbor, + backendName: "webgl", + kernelFunc: resizeNearestNeighbor3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_backprop_gpu.js +var ResizeNearestNeigborBackpropProgram = class { + constructor(dyShape, inputShape, alignCorners) { + this.variableNames = ["dy"]; + this.outputShape = []; + this.outputShape = inputShape; + const [, xHeight, xWidth] = inputShape; + const [, yHeight, yWidth] = dyShape; + const effectiveXSize = [ + alignCorners && yHeight > 1 ? xHeight - 1 : xHeight, + alignCorners && yWidth > 1 ? xWidth - 1 : xWidth + ]; + const effectiveYSize = [ + alignCorners && yHeight > 1 ? yHeight - 1 : yHeight, + alignCorners && yWidth > 1 ? yWidth - 1 : yWidth + ]; + const heightScale = effectiveXSize[0] / effectiveYSize[0]; + const widthScale = effectiveXSize[1] / effectiveYSize[1]; + const invHeightScale = 1 / heightScale; + const invWidthScale = 1 / widthScale; + const winHeight = Math.ceil(invHeightScale) * 2 + 2; + const winWidth = Math.ceil(invWidthScale) * 2 + 2; + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4411,14 +67295,14 @@ return a / b;`,hat=` float accumulator = 0.0; - const float heightScale = float(${c}); - const float widthScale = float(${p}); + const float heightScale = float(${heightScale}); + const float widthScale = float(${widthScale}); - const float invHeightScale = float(${m}); - const float invWidthScale = float(${f}); + const float invHeightScale = float(${invHeightScale}); + const float invWidthScale = float(${invWidthScale}); - const int winHeight = int(${d}); - const int winWidth = int(${h}); + const int winHeight = int(${winHeight}); + const int winWidth = int(${winWidth}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); @@ -4432,7 +67316,7 @@ return a / b;`,hat=` int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy - if (dyR < 0 || dyR >= ${i}) { + if (dyR < 0 || dyR >= ${yHeight}) { continue; } @@ -4440,26 +67324,26 @@ return a / b;`,hat=` int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy - if (dyC < 0 || dyC >= ${a}) { + if (dyC < 0 || dyC >= ${yWidth}) { continue; } float sourceFracRow = - float(${u[0]}) * - (float(dyR) / float(${l[0]})); + float(${effectiveXSize[0]}) * + (float(dyR) / float(${effectiveYSize[0]})); float sourceFracCol = - float(${u[1]}) * - (float(dyC) / float(${l[1]})); + float(${effectiveXSize[1]}) * + (float(dyC) / float(${effectiveYSize[1]})); int sourceNearestRow = int(min( - float(int(${o}) - 1), - ${n} ? float(round(sourceFracRow)) : + float(int(${xHeight}) - 1), + ${alignCorners} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( - float(int(${s}) - 1), - ${n} ? float(round(sourceFracCol)) : + float(int(${xWidth}) - 1), + ${alignCorners} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { @@ -4471,47 +67355,176 @@ return a / b;`,hat=` setOutput(accumulator); } - `}};function qat(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 $V={kernelName:sl,backendName:"webgl",kernelFunc:qat};var mC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighborGrad.js +function resizeNearestNeighborGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + const program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners); + return backend2.runWebGLProgram(program, [dy], dy.dtype); +} +var resizeNearestNeighborGradConfig3 = { + kernelName: ResizeNearestNeighborGrad, + backendName: "webgl", + kernelFunc: resizeNearestNeighborGrad2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_gpu.js +var ReverseProgram = class { + constructor(xShape, axis) { + this.variableNames = ["x"]; + const rank = xShape.length; + if (rank > 4) { + throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`); + } + this.outputShape = xShape; + if (rank === 1) { + this.userCode = ` void main() { int coord = getOutputCoords(); - setOutput(getX(${t[0]} - coord - 1)); + setOutput(getX(${xShape[0]} - coord - 1)); } - `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(","),i=zt(n);this.userCode=` - void main() { - ${i} coords = getOutputCoords(); - setOutput(getX(${s})); + `; + return; + } + const getInCoord = (i) => { + if (axis.indexOf(i) !== -1 && xShape[i] !== 1) { + return `${xShape[i]} - coords[${i}] - 1`; } - `}};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 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=` + return `coords[${i}]`; + }; + const inCoords = xShape.map((_, i) => getInCoord(i)).join(","); + const type = getCoordsDataType(rank); + this.userCode = ` + void main() { + ${type} coords = getOutputCoords(); + setOutput(getX(${inCoords})); + } + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_packed_gpu.js +var ReversePackedProgram = class { + constructor(xShape, axis) { + this.variableNames = ["x"]; + this.packedInputs = true; + this.packedOutput = true; + const rank = xShape.length; + if (rank > 4) { + throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`); + } + this.outputShape = xShape; + const channels = getChannels("rc", rank); + const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`; + const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`; + const type = getCoordsDataType(rank); + if (rank === 1) { + this.userCode = ` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); - result.r = getChannel(getX(${t[0]} - rc - 1), - ${t[0]} - rc - 1); - if(${s}){ - result.g = getChannel(getX(${t[0]} - (rc + 1) - 1), - ${t[0]} - (rc + 1) - 1); + result.r = getChannel(getX(${xShape[0]} - rc - 1), + ${xShape[0]} - rc - 1); + if(${nextColumn}){ + result.g = getChannel(getX(${xShape[0]} - (rc + 1) - 1), + ${xShape[0]} - (rc + 1) - 1); } setOutput(result); } - `:this.userCode=` + `; + } else { + this.userCode = ` void main() { - ${a} rc = getOutputCoords(); + ${type} rc = getOutputCoords(); vec4 result = vec4(0.); - result.r = ${u(o.slice())}; - if(${s}){ - result.g = ${l(o.slice())}; + result.r = ${getR(channels.slice())}; + if(${nextColumn}){ + result.g = ${getG(channels.slice())}; } - if(${i}) { - result.b = ${c(o.slice())}; - if(${s}) { - result.a = ${p(o.slice())}; + if(${nextRow}) { + result.b = ${getB(channels.slice())}; + if(${nextColumn}) { + result.a = ${getA(channels.slice())}; } } setOutput(result); } - `;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function Kat(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 RV={kernelName:qs,backendName:"webgl",kernelFunc:Kat};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=` + `; + } + function getR(channels2) { + return getChannel(channels2); + } + function getG(channels2) { + channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`; + return getChannel(channels2); + } + function getB(channels2) { + channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`; + return getChannel(channels2); + } + function getA(channels2) { + channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`; + channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`; + return getChannel(channels2); + } + function getChannel(channels2) { + const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels2)); + const inCoords = inCoordsArray.join(","); + const innerDims = inCoordsArray.slice(-2).join(","); + return `getChannel(getX(${inCoords}), vec2(${innerDims}))`; + } + function getInCoord(i, channels1) { + if (axis.indexOf(i) !== -1 && xShape[i] !== 1) { + return `${xShape[i]} - ${channels1[i]} - 1`; + } else { + return `${channels1[i]}`; + } + } + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reverse.js +function reverse3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dims } = attrs; + const xRank = x.shape.length; + const $dims = util_exports.parseAxisParam(dims, x.shape); + if (xRank === 0) { + return identity3({ inputs: { x }, backend: backend2 }); + } + const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new ReversePackedProgram(x.shape, $dims) : new ReverseProgram(x.shape, $dims); + return backend2.runWebGLProgram(program, [x], x.dtype); +} +var reverseConfig2 = { + kernelName: Reverse, + backendName: "webgl", + kernelFunc: reverse3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/rotate_gpu.js +var RotateProgram = class { + constructor(imageShape, fillValue) { + this.variableNames = ["Image"]; + this.outputShape = []; + this.customUniforms = [{ name: "params", type: "vec4" }]; + const imageHeight = imageShape[1]; + const imageWidth = imageShape[2]; + this.outputShape = imageShape; + let fillSnippet = ""; + if (typeof fillValue === "number") { + fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`; + } else { + fillSnippet = ` + vec3 fill = vec3(${fillValue.join(",")}); + float outputValue = fill[coords[3]];`; + } + this.userCode = ` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -4522,13 +67535,34 @@ return a / b;`,hat=` (float(y) - params[1]) * params[3]; int coordX = int(round(coordXFloat + params[0])); int coordY = int(round(coordYFloat + params[1])); - ${s} - if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) { + ${fillSnippet} + if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } - `}};var FV={kernelName:hl,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 jat=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RotateWithOffset.js +var rotateWithOffsetConfig2 = { + kernelName: RotateWithOffset, + backendName: "webgl", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { image: image2 } = inputs; + const { radians, fillValue, center } = attrs; + const webglBackend = backend2; + const program = new RotateProgram(image2.shape, fillValue); + const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]); + const customValues = [[centerX, centerY, Math.sin(radians), Math.cos(radians)]]; + const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype, customValues); + return output; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Round.js +var ROUND = ` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4543,45 +67577,123 @@ return a / b;`,hat=` return base + 1.0; } } -`,Xat=It({opSnippet:jat}),OV={kernelName:Ks,backendName:"webgl",kernelFunc:Xat};var Yat="return inversesqrt(x);",Zat=It({opSnippet:Yat,cpuKernelImpl:pz}),PV={kernelName:js,backendName:"webgl",kernelFunc:Zat};var rc=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}); +`; +var round4 = unaryKernelFunc2({ opSnippet: ROUND }); +var roundConfig2 = { + kernelName: Round, + backendName: "webgl", + kernelFunc: round4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Rsqrt.js +var RSQRT = `return inversesqrt(x);`; +var rsqrt3 = unaryKernelFunc2({ opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU }); +var rsqrtConfig2 = { + kernelName: Rsqrt, + backendName: "webgl", + kernelFunc: rsqrt3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_gpu.js +var ScatterProgram = class { + constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true, defaultIsTensor = false) { + this.variableNames = ["updates", "indices", "defaultValue"]; + this.outputShape = shape; + const stridesType = getCoordsDataType(strides.length); + const dtype = getCoordsDataType(shape.length); + let indicesString = ""; + if (indicesRank === 1) { + indicesString = "i"; + } else if (indicesRank === 2) { + indicesString = "i, j"; + } + const indicesSnippet = `getIndices(${indicesString})`; + let updatesString = ""; + if (updatesRank === 1) { + updatesString = "i"; + } else if (updatesRank === 2) { + updatesString = "i, coords[1]"; + } + const updatesSnippet = `getUpdates(${updatesString})`; + let defaultValuesString = ""; + if (defaultIsTensor) { + defaultValuesString = "coords[0], coords[1]"; + } + const defaultValueSnippet = `getDefaultValue(${defaultValuesString})`; + const strideString = sliceDim > 1 ? "strides[j]" : "strides"; + this.userCode = ` + ${stridesType} strides = ${stridesType}(${strides}); void main() { - ${c} coords = getOutputCoords(); + ${dtype} coords = getOutputCoords(); float sum = 0.0; bool found = false; - for (int i = 0; i < ${t}; i++) { + for (int i = 0; i < ${updateSize}; i++) { int flattenedIndex = 0; - for (int j = 0; j < ${e}; j++) { - int index = round(${m}); - flattenedIndex += index * ${x}; + for (int j = 0; j < ${sliceDim}; j++) { + int index = round(${indicesSnippet}); + flattenedIndex += index * ${strideString}; } if (flattenedIndex == coords[0]) { - sum += ${d}; + sum += ${updatesSnippet}; found = true; } } - setOutput(mix(${g}, sum, float(found))); + setOutput(mix(${defaultValueSnippet}, sum, float(found))); } - `}};var hC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${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}); + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_packed_gpu.js +var ScatterPackedProgram = class { + constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true, defaultIsTensor = false) { + this.variableNames = ["updates", "indices", "defaultValue"]; + this.packedInputs = true; + this.packedOutput = true; + this.outputShape = shape; + const stridesType = getCoordsDataType(strides.length); + const dtype = getCoordsDataType(shape.length); + let indicesString = ""; + if (indicesRank === 1) { + indicesString = "i"; + } else if (indicesRank === 2) { + indicesString = "i, j"; + } + const indicesSnippet = `getIndices(${indicesString})`; + let updatesString = ""; + if (updatesRank === 1) { + updatesString = "i"; + } else if (updatesRank === 2) { + updatesString = "i, coords[1]"; + } + const updatesSnippet = `getUpdates(${updatesString})`; + let defaultValuesString = ""; + if (defaultIsTensor) { + defaultValuesString = "coords[0], coords[1]"; + } + const defaultValueSnippet = `getDefaultValue(${defaultValuesString})`; + const strideString = sliceDim > 1 ? "strides[j]" : "strides"; + const strideString2 = sliceDim > 1 ? "strides[j + 1]" : "strides"; + this.userCode = ` + ${stridesType} strides = ${stridesType}(${strides}); void main() { - ${c} coords = getOutputCoords(); + ${dtype} coords = getOutputCoords(); vec4 sum = vec4(0.); vec4 found = vec4(0.); - for (int i = 0; i < ${t}; i+=2) { + for (int i = 0; i < ${updateSize}; i+=2) { ivec2 flattenedIndex = ivec2(0); - for (int j = 0; j < ${e}; j+=2) { - ivec4 index = round(${m}); - flattenedIndex += index.xz * ${x}; - if (j + 1 < ${e}) { - flattenedIndex += index.yw * ${b}; + for (int j = 0; j < ${sliceDim}; j+=2) { + ivec4 index = round(${indicesSnippet}); + flattenedIndex += index.xz * ${strideString}; + if (j + 1 < ${sliceDim}) { + flattenedIndex += index.yw * ${strideString2}; } } if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] || flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) { - vec4 updVals = ${d}; + vec4 updVals = ${updatesSnippet}; if (flattenedIndex[0] == coords[0]) { sum.xy += updVals.xy; found.xy = vec2(1.); @@ -4598,16 +67710,63 @@ return a / b;`,hat=` } } } - setOutput(mix(${g}, sum, found)); + setOutput(mix(${defaultValueSnippet}, sum, found)); } - `}};function Jat(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 rc(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 MV={kernelName:al,backendName:"webgl",kernelFunc:Jat};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ScatterNd.js +function scatterNd2(args) { + const { inputs, backend: backend2, attrs } = args; + const { indices, updates } = inputs; + const { shape } = attrs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape); + const flattenShape = [outputSize / sliceSize, sliceSize]; + if (outputSize === 0) { + return backend2.makeTensorInfo(shape, indices.dtype); + } + const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } }); + const flattenX = reshape4({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } }); + const defaultValue = backend2.makeTensorInfo([], "float32", new Float32Array([0])); + let program; + if (env().getBool("WEBGL_PACK")) { + program = new ScatterPackedProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape); + } else { + program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape); + } + const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype); + const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape } }); + backend2.disposeIntermediateTensorInfo(flattenIndices); + backend2.disposeIntermediateTensorInfo(flattenX); + backend2.disposeIntermediateTensorInfo(res); + backend2.disposeIntermediateTensorInfo(defaultValue); + return reshaped; +} +var scatterNdConfig2 = { + kernelName: ScatterNd, + backendName: "webgl", + kernelFunc: scatterNd2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/search_sorted_gpu.js +var SearchSortedProgram = class { + constructor(batchSize, numInputs, numValues, side) { + this.variableNames = ["sortedSequence", "values"]; + this.customUniforms = [{ name: "numInputs", type: "int" }]; + this.outputShape = [batchSize, numValues]; + const webGL2LoopHead = "while (left < right) {"; + const webGL1LoopHead = `for (int i = 0; i < ${Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`; + const loopHead = env().getNumber("WEBGL_VERSION") === 2 ? webGL2LoopHead : webGL1LoopHead; + const boundComparator = side === "left" ? "<" : "<="; + this.userCode = ` int findBound(int batch, float value) { int left = 0; int right = numInputs; int mid; - ${a} + ${loopHead} mid = (left + right) / 2; - if (getSortedSequence(batch, mid) ${u} value) { + if (getSortedSequence(batch, mid) ${boundComparator} value) { left = mid + 1; } else { right = mid; @@ -4625,25 +67784,99 @@ return a / b;`,hat=` setOutput(float(findBound(batch, value))); } - `}};function Qat(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new gC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],"int32",u)}var LV={kernelName:ul,backendName:"webgl",kernelFunc:Qat};var xC=class{constructor(t,e,n){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c= 1.0) { - setOutput(getA(${s})); - } else { - setOutput(getB(${s})); + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SearchSorted.js +function searchSorted3(args) { + const { inputs, backend: backend2, attrs } = args; + const { sortedSequence, values } = inputs; + const { side } = attrs; + const program = new SearchSortedProgram(sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side); + const customValues = [[sortedSequence.shape[1]]]; + return backend2.runWebGLProgram(program, [sortedSequence, values], "int32", customValues); +} +var searchSortedConfig2 = { + kernelName: SearchSorted, + backendName: "webgl", + kernelFunc: searchSorted3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/select_gpu.js +var SelectProgram = class { + constructor(cRank, shape, rank) { + this.variableNames = ["c", "a", "b"]; + this.outputShape = shape; + let cCoords; + let abCoords; + if (rank > 4) { + throw Error(`Where for rank ${rank} is not yet supported`); + } + if (rank === 1) { + abCoords = `resRC`; + cCoords = `resRC`; + } else { + const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"]; + const cCoordVars = []; + const abCoordVars = []; + for (let i = 0; i < shape.length; i++) { + abCoordVars.push(`${currentCoords[i]}`); + if (i < cRank) { + cCoordVars.push(`${currentCoords[i]}`); } } - `}};function tlt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new xC(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ur(o.dtype,s.dtype))}var zV={kernelName:Hi,backendName:"webgl",kernelFunc:tlt};var elt=` + cCoords = cCoordVars.join(); + abCoords = abCoordVars.join(); + } + const dtype = getCoordsDataType(rank); + this.userCode = ` + void main() { + ${dtype} resRC = getOutputCoords(); + float cVal = getC(${cCoords}); + if (cVal >= 1.0) { + setOutput(getA(${abCoords})); + } else { + setOutput(getB(${abCoords})); + } + } + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js +function select3(args) { + const { inputs, backend: backend2 } = args; + const { condition, t, e } = inputs; + const program = new SelectProgram(condition.shape.length, t.shape, t.shape.length); + return backend2.runWebGLProgram(program, [condition, t, e], upcastType(t.dtype, e.dtype)); +} +var selectConfig2 = { + kernelName: Select, + backendName: "webgl", + kernelFunc: select3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Selu.js +var SELU = ` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 - float scaleAlpha = ${S.SELU_SCALEALPHA}; - float scale = ${S.SELU_SCALE}; + float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA}; + float scale = ${backend_util_exports.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,rlt=It({opSnippet:elt}),BV={kernelName:Xs,backendName:"webgl",kernelFunc:rlt};var nlt=Vo+` +`; +var selu3 = unaryKernelFunc2({ opSnippet: SELU }); +var seluConfig2 = { + kernelName: Selu, + backendName: "webgl", + kernelFunc: selu3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sigmoid.js +var SIGMOID3 = CHECK_NAN_SNIPPET_UNARY + ` return 1.0 / (1.0 + exp(-1.0 * x)); -`,olt=` +`; +var SIGMOID_PACKED = ` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4653,20 +67886,61 @@ return a / b;`,hat=` result.a = isNaN.a ? x.a : result.a; return result; -`,slt=It({opSnippet:nlt,packedOpSnippet:olt,cpuKernelImpl:fz}),VV={kernelName:Qs,backendName:"webgl",kernelFunc:slt};var ilt=` +`; +var sigmoid3 = unaryKernelFunc2({ + opSnippet: SIGMOID3, + packedOpSnippet: SIGMOID_PACKED, + cpuKernelImpl: sigmoidImplCPU +}); +var sigmoidConfig2 = { + kernelName: Sigmoid, + backendName: "webgl", + kernelFunc: sigmoid3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sign.js +var SIGN = ` if (isnan(x)) { return 0.0; } return sign(x); -`,alt=It({opSnippet:ilt}),GV={kernelName:Js,backendName:"webgl",kernelFunc:alt};var llt=Vo+` +`; +var sign3 = unaryKernelFunc2({ opSnippet: SIGN }); +var signConfig2 = { + kernelName: Sign, + backendName: "webgl", + kernelFunc: sign3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sin.js +var SIN = CHECK_NAN_SNIPPET_UNARY + ` return sin(x); -`,ult=` +`; +var SIN_PACKED = ` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${Qn} + ${CHECK_NAN_SNIPPET_PACKED} return result; -`,clt=It({opSnippet:llt,packedOpSnippet:ult}),WV={kernelName:Ys,backendName:"webgl",kernelFunc:clt};var plt=` +`; +var sin3 = unaryKernelFunc2({ opSnippet: SIN, packedOpSnippet: SIN_PACKED }); +var sinConfig2 = { + kernelName: Sin, + backendName: "webgl", + kernelFunc: sin3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sinh.js +var SINH = ` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,mlt=It({opSnippet:plt}),UV={kernelName:Zs,backendName:"webgl",kernelFunc:mlt};var flt=` +`; +var sinh3 = unaryKernelFunc2({ opSnippet: SINH }); +var sinhConfig2 = { + kernelName: Sinh, + backendName: "webgl", + kernelFunc: sinh3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softplus.js +var SOFTPLUS = ` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4686,33 +67960,545 @@ return a / b;`,hat=` result = log(exp_x + 1.0); } return result; -`,dlt=It({opSnippet:flt}),HV={kernelName:ti,backendName:"webgl",kernelFunc:dlt};var hlt=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},qV={kernelName:Ki,backendName:"webgl",kernelFunc:hlt};function glt(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: - ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw: - ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw: - ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw: - ${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]=hz(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 KV={kernelName:cu,backendName:"webgl",kernelFunc:glt};function xlt(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]=gz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var jV={kernelName:cl,backendName:"webgl",kernelFunc:xlt};function ylt(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 - ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${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 XV={kernelName:pu,backendName:"webgl",kernelFunc:ylt};function blt(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 - ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape - ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=Jw(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var YV={kernelName:mu,backendName:"webgl",kernelFunc:blt};function wlt(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype==="string"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),I=mz(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new rc(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var ZV={kernelName:pl,backendName:"webgl",kernelFunc:wlt};function Ilt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=_i({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var JV={kernelName:ji,backendName:"webgl",kernelFunc:Ilt};var QV="return sqrt(x);",Clt=It({opSnippet:QV,packedOpSnippet:QV,cpuKernelImpl:xz}),tG={kernelName:ei,backendName:"webgl",kernelFunc:Clt};var vlt="return x * x;",Slt=It({opSnippet:vlt}),eG={kernelName:fu,backendName:"webgl",kernelFunc:Slt};var rG="return (a - b) * (a - b);",Nlt=ue({opSnippet:rG,packedOpSnippet:rG}),nG={kernelName:oi,backendName:"webgl",kernelFunc:Nlt};function klt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!=="string")throw new Error("Input must be of datatype string");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=yz(i,"string",n);return e.makeTensorInfo(o.shape,"string",a)}var oG={kernelName:cc,backendName:"webgl",kernelFunc:klt};function Tlt({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=yr+` - return x > 0.0 ? 1.0 : float(${t.alpha}); - `,s=new Br(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var sG={kernelName:wo,backendName:"webgl",kernelFunc:Tlt};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}); +`; +var softplus3 = unaryKernelFunc2({ opSnippet: SOFTPLUS }); +var softplusConfig2 = { + kernelName: Softplus, + backendName: "webgl", + kernelFunc: softplus3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SpaceToBatchND.js +var spaceToBatchND3 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, paddings } = attrs; + util_exports.assert(x.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"); + const prod5 = blockShape.reduce((a, b) => a * b); + const completePaddings = [[0, 0]]; + completePaddings.push(...paddings); + for (let i = 1 + blockShape.length; i < x.shape.length; ++i) { + completePaddings.push([0, 0]); + } + const toDispose = []; + const paddedX = padV22({ + inputs: { x }, + backend: backend2, + attrs: { paddings: completePaddings, constantValue: 0 } + }); + const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false); + const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false); + const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false); + const reshapedPaddedX = reshape4({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } }); + const paddedXT = transpose3({ + inputs: { x: reshapedPaddedX }, + backend: backend2, + attrs: { perm: permutedReshapedPaddedPermutation } + }); + const result = reshape4({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } }); + toDispose.push(paddedX); + toDispose.push(reshapedPaddedX); + toDispose.push(paddedXT); + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +}; +var spaceToBatchNDConfig2 = { + kernelName: SpaceToBatchND, + backendName: "webgl", + kernelFunc: spaceToBatchND3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseFillEmptyRows.js +function sparseFillEmptyRows3(args) { + const { inputs, backend: backend2 } = args; + const { indices, values, denseShape, defaultValue } = inputs; + if (denseShape.shape.length !== 1) { + throw new Error(`Dense shape must be a vector, saw: + ${denseShape.shape}`); + } + if (indices.shape.length !== 2) { + throw new Error(`Indices must be a matrix, saw: + ${indices.shape}`); + } + if (values.shape.length !== 1) { + throw new Error(`Values must be a vector, saw: + ${values.shape}`); + } + if (defaultValue.shape.length !== 0) { + throw new Error(`Default value must be a scalar, saw: + ${defaultValue.shape}`); + } + const $indices = backend2.readSync(indices.dataId); + const $values = backend2.readSync(values.dataId); + const $denseShape = backend2.readSync(denseShape.dataId); + const $defaultValue = backend2.readSync(defaultValue.dataId)[0]; + const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue); + return [ + backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices), + backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues), + backend2.makeTensorInfo([emptyRowIndicator.length], "bool", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))), + backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap)) + ]; +} +var sparseFillEmptyRowsConfig2 = { + kernelName: SparseFillEmptyRows, + backendName: "webgl", + kernelFunc: sparseFillEmptyRows3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseReshape.js +function sparseReshape3(args) { + const { inputs, backend: backend2 } = args; + const { inputIndices, inputShape, newShape } = inputs; + if (inputIndices.shape.length !== 2) { + throw new Error(`Input indices should be a matrix but received shape ${inputIndices.shape}`); + } + if (inputShape.shape.length !== 1) { + throw new Error(`Input shape should be a vector but received shape ${inputShape.shape}`); + } + if (newShape.shape.length !== 1) { + throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`); + } + const $inputShape = Array.from(backend2.readSync(inputShape.dataId)); + const $inputIndices = backend2.readSync(inputIndices.dataId); + const targetShape = Array.from(backend2.readSync(newShape.dataId)); + const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape); + return [ + backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices), + backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape)) + ]; +} +var sparseReshapeConfig2 = { + kernelName: SparseReshape, + backendName: "webgl", + kernelFunc: sparseReshape3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentMean.js +function sparseSegmentMean3(args) { + const { inputs, backend: backend2 } = args; + const { data, indices, segmentIds } = inputs; + if (data.shape.length < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if (indices.shape.length !== 1) { + throw new Error(`Indices should be a vector but received shape + ${indices.shape}`); + } + if (segmentIds.shape.length !== 1) { + throw new Error(`Segment ids should be a vector but received shape + ${segmentIds.shape}`); + } + const $data = backend2.readSync(data.dataId); + const $indices = backend2.readSync(indices.dataId); + const $segmentIds = backend2.readSync(segmentIds.dataId); + const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds, true); + return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData); +} +var sparseSegmentMeanConfig2 = { + kernelName: SparseSegmentMean, + backendName: "webgl", + kernelFunc: sparseSegmentMean3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentSum.js +function sparseSegmentSum3(args) { + const { inputs, backend: backend2 } = args; + const { data, indices, segmentIds } = inputs; + if (data.shape.length < 1) { + throw new Error(`Data should be at least 1 dimensional but received scalar`); + } + if (indices.shape.length !== 1) { + throw new Error(`Indices should be a vector but received shape + ${indices.shape}`); + } + if (segmentIds.shape.length !== 1) { + throw new Error(`Segment ids should be a vector but received shape + ${segmentIds.shape}`); + } + const $data = backend2.readSync(data.dataId); + const $indices = backend2.readSync(indices.dataId); + const $segmentIds = backend2.readSync(segmentIds.dataId); + const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds); + return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData); +} +var sparseSegmentSumConfig2 = { + kernelName: SparseSegmentSum, + backendName: "webgl", + kernelFunc: sparseSegmentSum3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseToDense.js +function sparseToDense3(args) { + const { inputs, backend: backend2, attrs } = args; + const { sparseIndices, sparseValues, defaultValue } = inputs; + const { outputShape } = attrs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape); + const sumDupeIndices = false; + if (sparseValues.dtype === "string") { + const indicesBuf = backend2.bufferSync(sparseIndices); + const updatesBuf = backend2.bufferSync(sparseValues); + const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]); + const outBuf = scatterImplCPU(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values); + } + const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices); + const res = backend2.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype); + const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } }); + backend2.disposeIntermediateTensorInfo(res); + return reshaped; +} +var sparseToDenseConfig2 = { + kernelName: SparseToDense, + backendName: "webgl", + kernelFunc: sparseToDense3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SplitV.js +function splitV2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { numOrSizeSplits, axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, x.shape)[0]; + const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis); + const xRank = x.shape.length; + const begin = new Array(xRank).fill(0); + const size = x.shape.slice(); + return splitSizes.map((s) => { + const sliceSize = [...size]; + sliceSize[$axis] = s; + const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } }); + begin[$axis] += s; + return sliceT; + }); +} +var splitVConfig2 = { + kernelName: SplitV, + backendName: "webgl", + kernelFunc: splitV2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sqrt.js +var SQRT = `return sqrt(x);`; +var sqrt3 = unaryKernelFunc2({ opSnippet: SQRT, packedOpSnippet: SQRT, cpuKernelImpl: sqrtImplCPU }); +var sqrtConfig2 = { + kernelName: Sqrt, + backendName: "webgl", + kernelFunc: sqrt3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Square.js +var SQUARE = `return x * x;`; +var square3 = unaryKernelFunc2({ opSnippet: SQUARE }); +var squareConfig2 = { + kernelName: Square, + backendName: "webgl", + kernelFunc: square3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SquaredDifference.js +var SQUARED_DIFFERENCE = "return (a - b) * (a - b);"; +var squaredDifference3 = binaryKernelFunc2({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE }); +var squaredDifferenceConfig2 = { + kernelName: SquaredDifference, + backendName: "webgl", + kernelFunc: squaredDifference3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StaticRegexReplace.js +function staticRegexReplace3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + if (x.dtype !== "string") { + throw new Error("Input must be of datatype string"); + } + const $x = backend2.readSync(x.dataId); + const stringInput = backend_util_exports.fromUint8ToStringArray($x); + const output = staticRegexReplaceImplCPU(stringInput, "string", attrs); + return backend2.makeTensorInfo(x.shape, "string", output); +} +var staticRegexReplaceConfig2 = { + kernelName: StaticRegexReplace, + backendName: "webgl", + kernelFunc: staticRegexReplace3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Step.js +function step3({ inputs, attrs, backend: backend2 }) { + const { x } = inputs; + const opSnippet = CHECK_NAN_SNIPPET + ` + return x > 0.0 ? 1.0 : float(${attrs.alpha}); + `; + const program = new UnaryOpProgram(x.shape, opSnippet); + return backend2.runWebGLProgram(program, [x], x.dtype); +} +var stepConfig2 = { + kernelName: Step, + backendName: "webgl", + kernelFunc: step3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/strided_slice_gpu.js +var StridedSliceProgram = class { + constructor(begin, strides, size) { + this.variableNames = ["x"]; + this.outputShape = size; + const rank = size.length; + const inputDtype = getCoordsDataType(size.length); + const dtype = getCoordsDataType(size.length); + let newCoords = ""; + if (rank === 1) { + newCoords = "coords * strides + begin"; + } else { + let outputAxis = 0; + newCoords = size.map((_, i) => { + outputAxis++; + return size.length === 1 ? `coords * strides[${i}] + begin[${i}]` : `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`; + }).join(","); + } + this.userCode = ` + ${inputDtype} begin = ${inputDtype}(${begin}); + ${inputDtype} strides = ${inputDtype}(${strides}); void main() { - ${i} coords = getOutputCoords(); - setOutput(getX(${a})); + ${dtype} coords = getOutputCoords(); + setOutput(getX(${newCoords})); } - `}};function _lt(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=_i({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=wt(o.shape,o.dtype,D),P=bz(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,P.values)}else{let D=new yC(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 iG={kernelName:ml,backendName:"webgl",kernelFunc:_lt};function Elt(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]=wz(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var aG={kernelName:du,backendName:"webgl",kernelFunc:Elt};function Alt(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]=Iz(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 lG={kernelName:hu,backendName:"webgl",kernelFunc:Alt};function Dlt(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=Cz(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var uG={kernelName:gu,backendName:"webgl",kernelFunc:Dlt};var $lt="return tan(x);",Rlt=It({opSnippet:$lt}),cG={kernelName:ii,backendName:"webgl",kernelFunc:Rlt};var Flt=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StridedSlice.js +function stridedSlice3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs; + const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); + let result; + if (isIdentity) { + result = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); + } else if (sliceDim0 || isSimpleSlice) { + util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`); + const size = slice_util_exports.computeOutShape($begin, $end, $strides); + const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } }); + result = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeIntermediateTensorInfo(sliced); + } else { + const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]); + if (shouldExecuteOnCPU) { + const values = backend2.readSync(x.dataId); + const xBuf = buffer(x.shape, x.dtype, values); + const resultValues = stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin); + result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values); + } else { + const program = new StridedSliceProgram($begin, $strides, finalShapeSparse); + result = backend2.runWebGLProgram(program, [x], x.dtype); + } + } + const resultReshaped = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeIntermediateTensorInfo(result); + return resultReshaped; +} +var stridedSliceConfig2 = { + kernelName: StridedSlice, + backendName: "webgl", + kernelFunc: stridedSlice3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringNGrams.js +function stringNGrams3(args) { + const { inputs, backend: backend2, attrs } = args; + const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs; + const { data, dataSplits } = inputs; + const $data = backend2.readSync(data.dataId); + const $dataSplits = backend2.readSync(dataSplits.dataId); + const [nGrams, nGramsSplits] = stringNGramsImplCPU($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences); + return [ + backend2.makeTensorInfo([nGrams.length], "string", nGrams), + backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits) + ]; +} +var stringNGramsConfig2 = { + kernelName: StringNGrams, + backendName: "webgl", + kernelFunc: stringNGrams3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringSplit.js +function stringSplit3(args) { + const { inputs, backend: backend2, attrs } = args; + const { skipEmpty } = attrs; + const { input: input2, delimiter } = inputs; + if (input2.dtype !== "string") { + throw new Error("Input must be of datatype string"); + } + if (input2.shape.length !== 1) { + throw new Error(`Input must be a vector, got shape: ${input2.shape}`); + } + if (delimiter.shape.length !== 0) { + throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`); + } + const $input = backend2.readSync(input2.dataId); + const $delimiter = backend2.readSync(delimiter.dataId)[0]; + const [indices, values, shape] = stringSplitImplCPU($input, $delimiter, skipEmpty); + const outputSize = values.length; + return [ + backend2.makeTensorInfo([outputSize, 2], "int32", indices), + backend2.makeTensorInfo([outputSize], "string", values), + backend2.makeTensorInfo([2], "int32", new Int32Array(shape)) + ]; +} +var stringSplitConfig2 = { + kernelName: StringSplit, + backendName: "webgl", + kernelFunc: stringSplit3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringToHashBucketFast.js +function stringToHashBucketFast3(args) { + const { inputs, backend: backend2, attrs } = args; + const { numBuckets } = attrs; + const { input: input2 } = inputs; + if (input2.dtype !== "string") { + throw new Error("Input must be of datatype string"); + } + if (numBuckets <= 0) { + throw new Error(`Number of buckets must be at least 1`); + } + const $input = backend2.readSync(input2.dataId); + const output = stringToHashBucketFastImplCPU($input, numBuckets); + return backend2.makeTensorInfo(input2.shape, "int32", output); +} +var stringToHashBucketFastConfig2 = { + kernelName: StringToHashBucketFast, + backendName: "webgl", + kernelFunc: stringToHashBucketFast3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tan.js +var TAN = `return tan(x);`; +var tan3 = unaryKernelFunc2({ opSnippet: TAN }); +var tanConfig2 = { + kernelName: Tan, + backendName: "webgl", + kernelFunc: tan3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tanh.js +var TANH = ` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); -`,Olt=It({opSnippet:Flt}),pG={kernelName:ai,backendName:"webgl",kernelFunc:Olt};function Plt(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 rc(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 mG={kernelName:ll,backendName:"webgl",kernelFunc:Plt};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=wt(o.shape,o.dtype,l),p=Sz(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 fG={kernelName:lo,backendName:"webgl",kernelFunc:G1};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=` + `; + } +}; +function getSourceCoords3(aShape) { + const rank = aShape.length; + if (rank > 5) { + throw Error(`Tile for rank ${rank} is not yet supported`); + } + if (rank === 1) { + return `imod(resRC, ${aShape[0]})`; + } + const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"]; + const sourceCoords = []; + for (let i = 0; i < aShape.length; i++) { + sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`); + } + return sourceCoords.join(); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js +function tile4(params) { + const { inputs, backend: backend2, attrs } = params; + const { x } = inputs; + const { reps } = attrs; + if (x.dtype === "string" || x.shape.length > 5) { + const data = backend2.readSync(x.dataId); + const value = x.dtype === "string" ? data.map((d) => util_exports.decodeString(d)) : data; + const buf = buffer(x.shape, x.dtype, value); + const outBuf = tileImplCPU(buf, reps); + return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); + } + const program = new TileProgram(x.shape, reps); + const output = backend2.runWebGLProgram(program, [x], x.dtype); + return output; +} +var tileConfig2 = { + kernelName: Tile, + backendName: "webgl", + kernelFunc: tile4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/top_k_gpu.js +var SwapProgram = class { + /** + * @param shape desired output shape (can be larger than input shape, output + * will be padded with -Infinity) + */ + constructor(shape) { + 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 = shape; + this.userCode = ` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4752,7 +68538,22 @@ return a / b;`,hat=` setOutput(float(i1)); } } - `}},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=` + `; + } +}; +var MergeProgram = class { + /** + * @param shape desired output shape (must be half of the input size) + */ + constructor(shape) { + this.variableNames = ["x", "indices"]; + this.customUniforms = [ + { name: "n", type: "int" }, + { name: "firstPass", type: "int" }, + { name: "k", type: "int" } + ]; + this.outputShape = shape; + this.userCode = ` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); @@ -4786,10 +68587,143 @@ return a / b;`,hat=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function kp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function dG(r){let t=1;for(;tu){let P=e.readSync(o.dataId),[V,G]=Nz(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,Hl({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&&kp(e,f);let x=dG(s),b=dG(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),kp(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),kp(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=_i({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),kp(e,E);let A=O1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});kp(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),kp(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),kp(e,F),[A,w]}var hG={kernelName:fl,backendName:"webgl",kernelFunc:Llt};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js +function disposeIntermediateTensorInfoOrNull(backend2, tensorInfo) { + if (tensorInfo !== null) { + backend2.disposeIntermediateTensorInfo(tensorInfo); + } +} +function roundUpToPow2(num) { + let pow22 = 1; + while (pow22 < num) { + pow22 *= 2; + } + return pow22; +} +function topK2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { k, sorted } = attrs; + const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"); + const TOPK_K_CPU_HANDOFF_THRESHOLD = env().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"); + const xShape = x.shape; + const lastDim = xShape[xShape.length - 1]; + if (backend2.shouldExecuteOnCPU([x]) || lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD || k > TOPK_K_CPU_HANDOFF_THRESHOLD) { + const xVals = backend2.readSync(x.dataId); + const [allTopKVals, allTopKIndices] = topKImplCPU(xVals, xShape, x.dtype, k, sorted); + return [ + backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values), + backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values) + ]; + } + if (k === 0) { + xShape[xShape.length - 1] = 0; + return [ + backend2.makeTensorInfo(xShape, x.dtype, []), + backend2.makeTensorInfo(xShape, "int32", []) + ]; + } + if (lastDim === 1) { + return [ + x, + fill3({ attrs: { shape: xShape, dtype: "int32", value: 0 }, backend: backend2 }) + ]; + } + const xtexData = backend2.texData.get(x.dataId); + const xIsPacked = xtexData !== null && xtexData.isPacked; + const xUnPacked = xIsPacked ? backend2.unpackTensor(x) : x; + const xSize = util_exports.sizeFromShape(xShape); + const batch = xSize / lastDim; + const x2D = reshape4({ inputs: { x: xUnPacked }, attrs: { shape: [batch, lastDim] }, backend: backend2 }); + if (xIsPacked) { + disposeIntermediateTensorInfoOrNull(backend2, xUnPacked); + } + const kPow2 = roundUpToPow2(k); + const lastDimPow2 = roundUpToPow2(lastDim); + let indices = null; + const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices]; + const runSwap = (dir, inc, shape) => { + const inputs2 = getInputs(); + const program = new SwapProgram(shape); + const fistPass = indices === null ? 1 : 0; + const customValues = [[lastDim], [fistPass], [Number.NEGATIVE_INFINITY], [dir], [inc]]; + const prevIndices2 = indices; + indices = backend2.runWebGLProgram(program, inputs2, "int32", customValues); + disposeIntermediateTensorInfoOrNull(backend2, prevIndices2); + }; + for (let len = 1; len < kPow2; len *= 2) { + const dir = len * 2; + for (let inc = len; inc >= 1; inc /= 2) { + runSwap(dir, inc, [batch, lastDimPow2]); + } + } + for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) { + const inputs2 = getInputs(); + const mergeProgram = new MergeProgram([batch, indicesSize / 2]); + const firstPass = indices === null ? 1 : 0; + const customValues = [[lastDim], [firstPass], [kPow2]]; + const prevIndices2 = indices; + indices = backend2.runWebGLProgram(mergeProgram, inputs2, "int32", customValues); + disposeIntermediateTensorInfoOrNull(backend2, prevIndices2); + const len = kPow2 / 2; + const dir = len * 2; + for (let inc = len; inc >= 1; inc /= 2) { + runSwap(dir, inc, indices.shape); + } + } + let prevIndices = indices; + indices = slice3({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } }); + disposeIntermediateTensorInfoOrNull(backend2, prevIndices); + let values = gatherV22({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } }); + disposeIntermediateTensorInfoOrNull(backend2, x2D); + const newShape = xShape.slice(0, -1); + newShape.push(k); + prevIndices = indices; + indices = reshape4({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 }); + disposeIntermediateTensorInfoOrNull(backend2, prevIndices); + const prevValues = values; + values = reshape4({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 }); + disposeIntermediateTensorInfoOrNull(backend2, prevValues); + return [values, indices]; +} +var topKConfig2 = { + kernelName: TopK, + backendName: "webgl", + kernelFunc: topK2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transform_gpu.js +var TransformProgram = class { + constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) { + this.variableNames = ["Image", "Transforms"]; + this.outputShape = outShape; + const interpolationModeId = interpolation === "nearest" ? 1 : 2; + let fillModeId; + switch (fillMode) { + case "constant": + fillModeId = 1; + break; + case "reflect": + fillModeId = 2; + break; + case "wrap": + fillModeId = 3; + break; + case "nearest": + fillModeId = 4; + break; + default: + fillModeId = 1; + break; + } + this.userCode = ` float mapCoord(float outCoord, float len) { float inCoord = outCoord; - if(${u} == 2) { + if(${fillModeId} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; @@ -4813,7 +68747,7 @@ return a / b;`,hat=` } } return clamp(inCoord, 0.0, len - 1.0); - } else if (${u} == 3) { + } else if (${fillModeId} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; @@ -4830,7 +68764,7 @@ return a / b;`,hat=` } } return clamp(inCoord, 0.0, len - 1.0); - } else if (${u} == 4) { + } else if (${fillModeId} == 4) { return clamp(outCoord, 0.0, len - 1.0); } else { return outCoord; @@ -4840,10 +68774,10 @@ return a / b;`,hat=` float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; - if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) { + if (0 <= coordY && coordY < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) { outputValue = getImage(batch, coordY, coordX, channel); } else { - outputValue = float(${s}); + outputValue = float(${fillValue}); } return outputValue; } @@ -4867,14 +68801,14 @@ return a / b;`,hat=` float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { - outputValue = float(${s}); + outputValue = float(${fillValue}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; - float mapX = mapCoord(inX, float(${e})); - float mapY = mapCoord(inY, float(${t})); + float mapX = mapCoord(inX, float(${imageWidth})); + float mapY = mapCoord(inY, float(${imageHeight})); - if (${a} == 1) { + if (${interpolationModeId} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, @@ -4898,26 +68832,134 @@ return a / b;`,hat=` } setOutput(outputValue); } - `}};function zlt(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 gG={kernelName:dl,backendName:"webgl",kernelFunc:zlt};function Blt(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;Ni(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}=kz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],"int32",l)]}var xG={kernelName:xu,backendName:"webgl",kernelFunc:Blt};function Vlt(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 yG={kernelName:Xi,backendName:"webgl",kernelFunc:Vlt};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=` + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transform.js +function transform3(args) { + const { inputs, backend: backend2, attrs } = args; + const { image: image2, transforms } = inputs; + const { interpolation, fillMode, fillValue, outputShape } = attrs; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth]; + const outShape = [ + batch, + outHeight, + outWidth, + numChannels + ]; + const program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape); + return backend2.runWebGLProgram(program, [image2, transforms], "float32"); +} +var transformConfig2 = { + kernelName: Transform, + backendName: "webgl", + kernelFunc: transform3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unique.js +function unique4(args) { + const { inputs, attrs, backend: backend2 } = args; + const { axis } = attrs; + const { x } = inputs; + assertNotComplex2(x, "unique"); + console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded"); + const values = backend2.readSync(x.dataId); + const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype); + return [ + backend2.makeTensorInfo(outputShape, x.dtype, outputValues), + backend2.makeTensorInfo([indices.length], "int32", indices) + ]; +} +var uniqueConfig2 = { + kernelName: Unique, + backendName: "webgl", + kernelFunc: unique4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unpack.js +function unpack2(args) { + const { inputs, backend: backend2, attrs } = args; + const { value } = inputs; + let { axis } = attrs; + if (axis < 0) { + axis += value.shape.length; + } + const x = value; + const xRank = x.shape.length; + const num = value.shape[axis]; + const outShape = new Array(xRank - 1); + let outIndex = 0; + for (let i = 0; i < xRank; i++) { + if (i !== axis) { + outShape[outIndex++] = x.shape[i]; + } + } + const toDispose = []; + const begin = new Array(xRank).fill(0); + const size = x.shape.slice(); + size[axis] = 1; + const res = new Array(num); + for (let i = 0; i < res.length; i++) { + begin[axis] = i; + const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size } }); + const reshaped = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } }); + res[i] = reshaped; + toDispose.push(sliced); + } + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return res; +} +var unpackConfig2 = { + kernelName: Unpack, + backendName: "webgl", + kernelFunc: unpack2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/segment_gpu.js +var SegmentOpProgram = class { + constructor(segOpInfo, segOpType) { + this.variableNames = ["x", "segmentIds"]; + const windowSize = segOpInfo.windowSize; + const batchSize = segOpInfo.batchSize; + const inSize = segOpInfo.inSize; + const numSegments = segOpInfo.numSegments; + const outSize = numSegments * Math.ceil(inSize / windowSize); + this.outputShape = [batchSize, outSize]; + const initializationValue = "0.0"; + const returnValue = `sumValue`; + const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4; + const windowSizeVec4Remainder = windowSize % 4; + const updateSnippet = ` sumValue += dot(values, segFilter); - `,f="";s%n>0&&(f=` - if (inIdx < 0 || inIdx >= ${s}) { + `; + let checkValueOutOfBounds = ""; + if (inSize % windowSize > 0) { + checkValueOutOfBounds = ` + if (inIdx < 0 || inIdx >= ${inSize}) { return initializationValue; } - `);let d="";s%n>0&&(d=` - if (inIdx < 0 || inIdx >= ${s}) { + `; + } + let checkSegmentIdOutOfBounds = ""; + if (inSize % windowSize > 0) { + checkSegmentIdOutOfBounds = ` + if (inIdx < 0 || inIdx >= ${inSize}) { return -1.0; } - `),this.userCode=` - const float initializationValue = ${u}; + `; + } + this.userCode = ` + const float initializationValue = ${initializationValue}; float getValue(int batch, int inIdx) { - ${f} + ${checkValueOutOfBounds} return getX(batch, inIdx); } float getSegmentIdAtIndex(int inIdx) { - ${d} + ${checkSegmentIdOutOfBounds} return getSegmentIds(inIdx); } @@ -4926,12 +68968,12 @@ return a / b;`,hat=` int batch = coords[0]; int outIdx = coords[1]; int inOffset = int(floor(float(outIdx) / float( - ${i})) * float(${n})); - int currentSeg = int(mod(float(outIdx), float(${i}))); + ${numSegments})) * float(${windowSize})); + int currentSeg = int(mod(float(outIdx), float(${numSegments}))); float sumValue = 0.0; - for (int i = 0; i < ${c}; i += 4) { + for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), @@ -4947,11 +68989,11 @@ return a / b;`,hat=` int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 ); - ${m} + ${updateSnippet} } - int inIdx = inOffset + ${c}; - if (${p===1}) { + int inIdx = inOffset + ${windowSizeNearestVec4}; + if (${windowSizeVec4Remainder === 1}) { vec4 values = vec4( getValue(batch, inIdx), initializationValue, @@ -4968,8 +69010,8 @@ return a / b;`,hat=` 0 ); - ${m} - } else if (${p===2}) { + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), @@ -4984,8 +69026,8 @@ return a / b;`,hat=` 0 ); - ${m} - } else if (${p===3}) { + ${updateSnippet} + } else if (${windowSizeVec4Remainder === 3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), @@ -5000,10 +69042,6224 @@ return a / b;`,hat=` 0 ); - ${m} + ${updateSnippet} } - setOutput(${l}); + setOutput(${returnValue}); } - `}};function Glt(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=xc(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=V1({backend:e,attrs:{start:0,stop:D,step:1,dtype:"float32"}}),K=G1({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 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nc;(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"})(nc||(nc={}));var wG;function Ult(r){wG=r.wasm.cwrap(Zi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Hlt(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 ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=nc[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the 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wut(r){aW=r.wasm.cwrap(yo,null,["number","number","number","number"])}function Iut(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 aW(a,s,i,l),u}var lW={kernelName:yo,backendName:"wasm",setupFunc:wut,kernelFunc:Iut};function W1(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 Tp({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 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NW={kernelName:za,backendName:"wasm",setupFunc:Fut,kernelFunc:Out};var kW;function Put(r){kW=r.wasm.cwrap(ls,null,["number","number","number","number","number","number"])}function Mut(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=go({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;kW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=go({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var TW={kernelName:ls,backendName:"wasm",setupFunc:Put,kernelFunc:Mut};var _W;function Lut(r){_W=r.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function zut(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 _W(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),Nt[s.dtype],a,p(c)),c}var EW={kernelName:eu,backendName:"wasm",setupFunc:Lut,kernelFunc:zut};var AW;function But(r){AW=r.wasm.cwrap(Va,null,["number","number","number","array","number","array","array","number","number"])}function Vut(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 AW(x,s,i==="NHWC"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var DW={kernelName:Va,backendName:"wasm",setupFunc:But,kernelFunc:Vut};var $W;function Gut(r){$W=r.wasm.cwrap(us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Wut(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}, ${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 LW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[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 zW={kernelName:ou,backendName:"wasm",setupFunc:jut,kernelFunc:Xut};var BW;function Yut(r){BW=r.wasm.cwrap(nu,null,["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,{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. 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cU={kernelName:zi,backendName:"wasm",setupFunc:mct,kernelFunc:fct};var dct=!1,pU=ee(qa,dct,"bool");var hct=!1,mU=ee(bs,hct,"bool");var fU=yt(ws,"bool");var dU=yt(Is,"bool");var hU=yt(Cs,"bool");var gU;function gct(r){gU=r.wasm.cwrap(vs,null,["number","number","number","number"])}function xct(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;gU(o,Nt[t.dtype],e,i)}return s}var xU={kernelName:vs,backendName:"wasm",setupFunc:gct,kernelFunc:xct};var yct=!1,yU=ee(Ka,yct,"bool");var bct=!1,bU=ee(ja,bct,"bool");var wU;function wct(r){wU=r.wasm.cwrap(Xa,null,["number","number","number","number"])}function Ict(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],"float32");return wU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var IU={kernelName:Xa,backendName:"wasm",setupFunc:wct,kernelFunc:Ict};var CU=yt(Ss);var vU=yt(Ns);var Cct=!1,SU=ee(Ya,Cct,"bool");var NU=yt(Za);var vct=!1,kU=ee(Ja,vct,"bool");var Sct=!1,TU=ee(v_,Sct,"bool");var _U;function Nct(r){_U=r.wasm.cwrap(ks,null,["number","number","number","number","number","number","number"])}function kct(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 _U(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var EU={kernelName:ks,backendName:"wasm",setupFunc:Nct,kernelFunc:kct};var AU;function Tct(r){AU=r.wasm.cwrap(Qa,null,["number","number","number","number","number","number","number","number","number"])}function _ct(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 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OH={kernelName:Xi,backendName:"wasm",kernelFunc:hmt};function gmt(r){let{inputs:{x:t},backend:e}=r,n=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(n).fill(0),n}var PH={kernelName:Yi,backendName:"wasm",kernelFunc:gmt};var xmt=[IG,CG,vG,SG,NG,TG,$G,FG,OG,PG,MG,LG,zG,BG,VG,WG,XG,HG,KG,JG,tW,rW,nW,oW,sW,iW,lW,uW,pW,fW,hW,xW,bW,wW,IW,vW,NW,TW,EW,DW,RW,OW,MW,zW,VW,GW,UW,HW,qW,KW,jW,XW,YW,JW,QW,tU,rU,oU,iU,lU,cU,pU,mU,_G,fU,dU,hU,xU,yU,bU,IU,vU,CU,SU,NU,kU,TU,EU,DU,RU,FU,PU,LU,BU,GU,UU,qU,jU,XU,ZU,e4,r4,n4,o4,i4,l4,c4,p4,f4,d4,h4,kC,x4,b4,I4,C4,v4,S4,N4,k4,YG,_4,A4,$4,F4,P4,L4,z4,B4,G4,U4,q4,K4,X4,Y4,Z4,J4,QG,QU,Q4,tH,rH,oH,iH,aH,uH,cH,pH,mH,fH,hH,xH,yH,bH,wH,IH,vH,SH,NH,TH,EH,DH,RH,AG,FH,OH,PH];for(let r of xmt)pc(r);var j1=L();j1.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>{try{return WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11]))}catch(r){return!1}});j1.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(j1.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(r){return!1}});var e_=Xl(BH()),qH=Xl(GH()),r_=Xl(WH());var UH=e_.default||e_,ymt=r_.default||r_,Ig=class extends Uo{constructor(t){super(),this.wasm=t,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(jH),t_=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Da(this,Wn())}write(t,e,n){let o={id:this.dataIdNextNumber++};return this.move(o,t,e,n,1),o}numDataIds(){return this.dataIdMap.numDataIds()}async time(t){let e=y.now();return t(),{kernelMs:y.now()-e}}move(t,e,n,o,s){let i=this.dataIdNextNumber++;if(o==="string"){let c=e;this.dataIdMap.set(t,{id:i,stringBytes:c,shape:n,dtype:o,memoryOffset:null,refCount:s});return}let a=y.sizeFromShape(n),u=a*y.bytesPerElement(o),l=this.wasm._malloc(u)>>>0;this.dataIdMap.set(t,{id:i,memoryOffset:l,shape:n,dtype:o,refCount:s}),this.wasm.tfjs.registerTensor(i,a,l),e!=null&&this.wasm.HEAPU8.set(new Uint8Array(e.buffer,e.byteOffset,u),l)}async read(t){return 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 wmt(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 ${e}`)}}};function bmt(r){return(t,e)=>(y.fetch(r,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function HH(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 KH(){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=qH.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([l],{type:"application/javascript"});return URL.createObjectURL(c)}return a.endsWith(".wasm")?HH(r,t,yg!=null?yg:u):u+a},n_&&(o.instantiateWasm=bmt(HH(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 = "+UH.toString()],{type:"text/javascript"}),i=UH(o)):i=ymt(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 wmt(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 Imt=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],DC=null,yg=null,bg={},wg=!1,n_=!1;function Cmt(r,t=!1){if(K0("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,n_=t}function vmt(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=Imt.filter(n=>bg[n]==null);if(e.length>0)throw new Error(`There were no entries found for the following binaries: ${e.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}n_=t}var jH=-1,t_=-1;function Smt(r){jH=r}function Nmt(){if(t_===-1)throw new Error("WASM backend not initialized.");return t_}var kmt="4.7.0";var Tmt=2;im("wasm",async()=>{let{wasm:r}=await KH();return new Ig(r)},Tmt);var XH="4.7.0",_mt="4.7.0",Emt="4.7.0",Amt="4.7.0",Dmt="4.7.0",$mt={tfjs:XH,"tfjs-core":XH,"tfjs-converter":_mt,"tfjs-backend-cpu":Emt,"tfjs-backend-webgl":Amt,"tfjs-backend-wasm":Dmt};export{$i as Abs,qo as Acos,Ko as Acosh,$c as AdadeltaOptimizer,Rc as AdagradOptimizer,Fc as AdamOptimizer,Oc as AdamaxOptimizer,ao as Add,jo as AddN,Ra as All,Fa as Any,Ri as ArgMax,Fi as ArgMin,Xo as Asin,Yo as Asinh,Zo as Atan,Qo as Atan2,Jo as Atanh,ts as AvgPool,Oi as AvgPool3D,Jl as AvgPool3DGrad,Zl as AvgPoolGrad,Ig as BackendWasm,es as BatchMatMul,Pi as BatchToSpaceND,Oa as Bincount,Pa as BitwiseAnd,Ql as BroadcastArgs,C_ as BroadcastTo,Mb as Callback,Yy as CallbackList,xo as Cast,rs as Ceil,yo as ClipByValue,zp as Complex,tu as ComplexAbs,Mi as Concat,ns as Conv2D,Bp as Conv2DBackpropFilter,os as Conv2DBackpropInput,ss as Conv3D,Ma as Conv3DBackpropFilterV2,La as Conv3DBackpropInputV2,is as Cos,as as Cosh,Ba as CropAndResize,za as Cumprod,ls as Cumsum,Jy as CustomCallback,Da as DataStorage,eu as DenseBincount,Va as DepthToSpace,us as DepthwiseConv2dNative,Vp as DepthwiseConv2dNativeBackpropFilter,Gp as DepthwiseConv2dNativeBackpropInput,ru as Diag,cs as Dilation2D,ou as Dilation2DBackpropFilter,nu as Dilation2DBackpropInput,Zg as Draw,g0 as ENV,Lb as EarlyStopping,Wp as Einsum,ms as Elu,Ga as EluGrad,rh as Environment,Wa as Equal,fs as Erf,ds as Exp,Li as ExpandDims,hs as Expm1,Up as FFT,su as Fill,Ua as FlipLeftRight,gs as Floor,xs as FloorDiv,oh as FromPixels,ys as FusedBatchNorm,Ji as FusedConv2D,Qi as FusedDepthwiseConv2D,wp as GPGPUContext,Ha as GatherNd,zi as GatherV2,jh as GraphModel,qa as Greater,bs as GreaterEqual,Zy as History,Hp as IFFT,bo as Identity,qp as Imag,Ie as InputSpec,ws as IsFinite,Is as IsInf,Cs as IsNan,Uo as KernelBackend,ks as LRN,Qa as LRNGrad,Dh as LayerVariable,jn as LayersModel,vs as LeakyRelu,Ka as Less,ja as LessEqual,Xa as LinSpace,Ss as Log,Ns as Log1p,S_ as LogSoftmax,Ya as LogicalAnd,Za as LogicalNot,Ja as LogicalOr,v_ as LogicalXor,Lmt as LowerBound,Xu as MathBackendCPU,Qu as MathBackendWebGL,zmt as MatrixBandPart,Ts as Max,Es as MaxPool,Bi as MaxPool3D,au as MaxPool3DGrad,iu as MaxPoolGrad,lu as MaxPoolWithArgmax,_s as Maximum,As as Mean,Ds as Min,$s as Minimum,Rs as MirrorPad,Fs as Mod,Pc as MomentumOptimizer,tl as Multinomial,Os as Multiply,Vi as Neg,rl as NonMaxSuppressionV3,nl as NonMaxSuppressionV4,ol as NonMaxSuppressionV5,el as NotEqual,M0 as OP_SCOPE_SUFFIX,Ps as OneHot,Gi as OnesLike,Kr as Optimizer,Nh as OptimizerConstructors,Wi as Pack,Ms as PadV2,Bmt as Pool,Ls as Pow,zs as Prelu,Bs as Prod,Mc as RMSPropOptimizer,Dn as RNN,Kp as RaggedGather,jp as RaggedRange,Xp as RaggedTensorToTensor,uu as Range,T0 as Rank,Yp as Real,ps as RealDiv,Vs as Reciprocal,Ze as Reduction,Gs as Relu,Hs as Relu6,Ui as Reshape,Us as ResizeBilinear,il as ResizeBilinearGrad,Ws as ResizeNearestNeighbor,sl as ResizeNearestNeighborGrad,qs as Reverse,hl as RotateWithOffset,Ks as Round,js as Rsqrt,Sl as SGDOptimizer,al as ScatterNd,ul as SearchSorted,Hi as Select,Xs as Selu,Ia as Sequential,Qs as Sigmoid,Js as Sign,Ys as Sin,Zs as Sinh,qi as Slice,ni as Softmax,ti as Softplus,Ki as SpaceToBatchND,cu as SparseFillEmptyRows,cl as SparseReshape,pu as SparseSegmentMean,mu as SparseSegmentSum,pl as SparseToDense,ji as SplitV,ei as Sqrt,fu as Square,oi as SquaredDifference,cc as StaticRegexReplace,wo as Step,ml as StridedSlice,du as StringNGrams,hu as StringSplit,gu as StringToHashBucketFast,si as Sub,ri as Sum,nn as SymbolicTensor,ii as Tan,ai as Tanh,Ot as Tensor,le as TensorBuffer,ll as TensorScatterUpdate,lo as Tile,fl as TopK,dl as Transform,uo as Transpose,xu as Unique,Xi as Unpack,yu as UnsortedSegmentSum,Vmt as UpperBound,gl as Variable,Yi as ZerosLike,Zi as _FusedMatMul,Ee as abs,hx as acos,gx as acosh,Y as add,IE as addN,lm as all,bc as any,oa as argMax,xx as argMin,yx as asin,bx as asinh,wx as atan,Ix as atan2,Cx as atanh,Su as avgPool,vx as avgPool3d,wE as backend,S as backend_util,SE as basicLSTMCell,aa as batchNorm,Sx as batchNorm2d,Nx as batchNorm3d,kx as batchNorm4d,Nu as batchToSpaceND,Tx as bincount,kE as bitwiseAnd,F5 as booleanMaskAsync,TE as broadcastArgs,la as broadcastTo,Hr as broadcast_util,Ay as browser,wt as buffer,J9 as callbacks,Q as cast,_x as ceil,Sr as clipByValue,cn as clone,kn as complex,ie as concat,Ex as concat1d,Ax as concat2d,Dx as concat3d,$x as concat4d,fR as constraints,cm as conv1d,Tn as conv2d,mm as conv2dTranspose,Rx as conv3d,Ox as conv3dTranspose,jmt as copyRegisteredKernels,ku as cos,fm as cosh,Ih as cosineWindow,Ic as cumprod,dm as cumsum,fn as customGrad,ZF as data,gh as denseBincount,K0 as deprecationWarn,Px as depthToSpace,ua as depthwiseConv2d,rQ as deregisterOp,Cu as device_util,_E as diag,Mx as dilation2d,uht as disableDeprecationWarnings,Tt as dispose,cht as disposeVariables,ct as div,Lx as divNoNan,zx as dot,cN as dropout,AE as einsum,ca as elu,lht as enableDebugMode,aht as enableProdMode,pN as enclosingPowerOfTwo,Wn as engine,DE as ensureShape,L as env,Fr as equal,Bx as erf,Vx as euclideanNorm,ir as exp,ar as expandDims,Gx as expm1,Cc as eye,Ou as fft,No as fill,ght as findBackend,xht as findBackendFactory,pa as floor,am as floorDiv,Wz as forceHalfFloat,Lu as fused,ma as gather,U5 as gatherND,Dy as gather_util,dht as getBackend,b0 as getGradient,ih as getKernel,Jg as getKernelsForBackend,Nmt as getThreadsCount,w1 as gpgpu_util,M6 as grad,L6 as grads,Fe as greater,mn as greaterEqual,vl as ifft,Tu as imag,hn as image,K5 as inTopKAsync,dR as initializers,KN as input,Lr as io,Tm as irfft,Wx as isFinite,Ux as isInf,Hx as isNaN,$e as keep,jr as kernel_impls,jR as layers,_u as leakyRelu,Il as less,Un as lessEqual,fN as linalg,FE as linspace,JQ as loadGraphModel,QQ as loadGraphModelSync,FR as loadLayersModel,qx as localResponseNormalization,kr as log,Eu as log1p,Xx as logSigmoid,hm as logSoftmax,gm as logSumExp,Pr as logicalAnd,Au as logicalNot,xm as logicalOr,Yx as logicalXor,j8 as losses,OE as lowerBound,Bt as matMul,k2 as math,Nr as max,Du as maxPool,Jx as maxPool3d,PE as maxPoolWithArgmax,_n as maximum,ke as mean,fh as memory,ME as meshgrid,XR as metrics,bl as min,mo as minimum,Qx as mirrorPad,ty as mod,JZ as model,YR as models,vc as moments,M5 as movingAverage,$ as mul,LE as multiRNNCell,zE as multinomial,Ut as neg,kh as nextFrame,wl as norm,mi as notEqual,fa as oneHot,dr as ones,Ir as onesLike,k as op,BE as outerProduct,dn as pad,VE as pad1d,GE as pad2d,WE as pad3d,UE as pad4d,ey as pool,pn as pow,Ru as prelu,dx as print,ry as prod,pht as profile,HE as raggedGather,qE as raggedRange,KE as raggedTensorToTensor,jE as rand,hA as randomGamma,kc as randomNormal,gA as randomStandardNormal,Hn as randomUniform,xA as randomUniformInt,da as range,fht as ready,Cl as real,ly as reciprocal,im as registerBackend,tJ as registerCallbackConstructor,k_ as registerGradient,pc as registerKernel,eQ as registerOp,ZR as regularizers,Mr as relu,ym as relu6,hht as removeBackend,R as reshape,hr as reverse,yA as reverse1d,bA as reverse2d,wA as reverse3d,IA as reverse4d,Pu as rfft,bm as round,wm as rsqrt,ft as scalar,z5 as scatterND,Mu as scatter_util,yh as searchSorted,Im as selu,Cm as separableConv2d,QZ as sequential,J as serialization,WK as setBackend,yht as setPlatform,Smt as setThreadsCount,Cmt as setWasmPath,vmt as setWasmPaths,FT as setWebGLContext,CA as setdiff1dAsync,Nw as shared,en as sigmoid,uy as sign,K8 as signal,vm as sin,Sm as sinh,Pt as slice,Nm as slice1d,wh as slice2d,km as slice3d,Tc as slice4d,ze as slice_util,Fu as softmax,pi as softplus,$u as spaceToBatchND,X8 as sparse,G5 as sparseToDense,q8 as spectral,gr as split,Ne as sqrt,Wt as square,_m as squaredDifference,qn as squeeze,qe as stack,To as step,cy as stridedSlice,Y8 as string,lt as sub,pt as sum,xc as sumOutType,py as tan,ia as tanh,sr as tensor,Ke as tensor1d,fi as tensor2d,my as tensor3d,vA as tensor4d,SA as tensor5d,NA as tensor6d,TA as tensorScatterUpdate,So as tensor_util,dA as test_util,B as tidy,Or as tile,mht as time,fy as topk,zc as train,Vt as transpose,Am as truncatedNormal,dy as unique,Kmt as unregisterGradient,qmt as unregisterKernel,Dm as unsortedSegmentSum,xr as unstack,ur as upcastType,_A as upperBound,y as util,z6 as valueAndGrad,B6 as valueAndGrads,hy as variable,Kx as variableGrads,$mt as version,DF as version_converter,B2 as version_core,MO as version_cpu,ef as version_layers,kmt as version_wasm,Gz as version_webgl,JDe as webgl,_d as webgl_util,be as where,xy as whereAsync,Te as zeros,vt as zerosLike}; + `; + } +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/UnsortedSegmentSum.js +function unsortedSegmentSum3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, segmentIds } = inputs; + const { numSegments } = attrs; + const xRank = x.shape.length; + const toDispose = []; + let axis = 0; + const permutation = backend_util_exports.getAxesPermutation([axis], xRank); + let permutedX = x; + if (permutation != null) { + permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } }); + toDispose.push(permutedX); + axis = backend_util_exports.getInnerMostAxes(1, xRank)[0]; + } + const outShape = backend_util_exports.segment_util.computeOutShape(permutedX.shape, axis, numSegments); + const inSize = util_exports.sizeFromShape([permutedX.shape[axis]]); + const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } }); + toDispose.push(a2D); + const outputDType = sumOutType(x.dtype); + const segOpCompute = (x2, segOpType, segmentIds2, dtype, numSegments2) => { + const batchSize = x2.shape[0]; + const inSize2 = x2.shape[1]; + const windowSize = backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2, numSegments2); + const segOpInfo = { windowSize, inSize: inSize2, batchSize, numSegments: numSegments2 }; + const program = new SegmentOpProgram(segOpInfo, segOpType); + const output = backend2.compileAndRun(program, [x2, segmentIds2], dtype); + toDispose.push(output); + if (output.shape[1] === numSegments2) { + return output; + } + const rangeInfo = range4({ + backend: backend2, + attrs: { start: 0, stop: numSegments2, step: 1, dtype: "float32" } + }); + const tileInfo = tile4({ + inputs: { x: rangeInfo }, + backend: backend2, + attrs: { reps: [inSize2 / windowSize] } + }); + toDispose.push(rangeInfo); + toDispose.push(tileInfo); + const result2 = segOpCompute(output, segOpType, tileInfo, dtype, numSegments2); + return result2; + }; + const segOpResult = segOpCompute(a2D, "unsortedSegmentSum", segmentIds, outputDType, numSegments); + const reshaped = reshape4({ inputs: { x: segOpResult }, backend: backend2, attrs: { shape: outShape } }); + let result = reshaped; + if (permutation != null) { + toDispose.push(reshaped); + const perm = backend_util_exports.getUndoAxesPermutation(permutation); + result = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm } }); + } + toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t)); + return result; +} +var unsortedSegmentSumConfig2 = { + kernelName: UnsortedSegmentSum, + backendName: "webgl", + kernelFunc: unsortedSegmentSum3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js +var kernelConfigs2 = [ + _fusedMatMulConfig2, + absConfig2, + acosConfig2, + acoshConfig2, + addConfig2, + addNConfig2, + allConfig2, + anyConfig2, + argMaxConfig2, + argMinConfig2, + asinConfig2, + asinhConfig2, + atanConfig2, + atan2Config2, + atanhConfig2, + avgPoolConfig2, + avgPool3DConfig2, + avgPool3DGradConfig3, + avgPoolGradConfig3, + batchMatMulConfig2, + batchNormConfig2, + batchToSpaceNDConfig2, + bincountConfig2, + bitwiseAndConfig2, + broadcastArgsConfig2, + castConfig2, + ceilConfig2, + clipByValueConfig2, + complexConfig2, + complexAbsConfig2, + concatConfig2, + conv2DConfig2, + conv2DBackpropFilterConfig2, + conv2DBackpropInputConfig2, + conv3DConfig2, + conv3DBackpropFilterV2Config2, + conv3DBackpropInputConfig, + cosConfig2, + coshConfig2, + cropAndResizeConfig2, + cumprodConfig2, + cumsumConfig2, + denseBincountConfig2, + depthToSpaceConfig2, + depthwiseConv2dNativeConfig2, + depthwiseConv2dNativeBackpropFilterConfig2, + depthwiseConv2dNativeBackpropInputConfig2, + diagConfig2, + dilation2DConfig2, + einsumConfig2, + eluConfig2, + eluGradConfig3, + equalConfig2, + erfConfig2, + expConfig2, + expandDimsConfig2, + expm1Config2, + fftConfig2, + fillConfig2, + flipLeftRightConfig2, + floorConfig2, + floorDivConfig2, + fromPixelsConfig, + fusedConv2DConfig2, + fusedDepthwiseConv2DConfig2, + gatherNdConfig2, + gatherV2Config2, + greaterConfig2, + greaterEqualConfig2, + identityConfig2, + ifftConfig2, + imagConfig2, + isFiniteConfig2, + isInfConfig2, + isNaNConfig2, + leakyReluConfig2, + lessConfig2, + lessEqualConfig2, + linSpaceConfig2, + logConfig2, + log1pConfig2, + logicalAndConfig2, + logicalNotConfig2, + logicalOrConfig2, + LRNConfig2, + LRNGradConfig2, + maxConfig2, + maximumConfig2, + maxPoolConfig2, + maxPool3DConfig2, + maxPool3DGradConfig3, + maxPoolGradConfig3, + maxPoolWithArgmaxConfig2, + meanConfig2, + minConfig2, + minimumConfig2, + mirrorPadConfig2, + modConfig2, + multinomialConfig2, + multiplyConfig2, + negConfig2, + nonMaxSuppressionV3Config2, + nonMaxSuppressionV4Config2, + nonMaxSuppressionV5Config2, + notEqualConfig2, + oneHotConfig2, + onesLikeConfig2, + packConfig2, + padV2Config2, + powConfig2, + preluConfig2, + prodConfig2, + raggedGatherConfig2, + raggedRangeConfig2, + raggedTensorToTensorConfig2, + rangeConfig2, + realConfig2, + realDivConfig2, + reciprocalConfig2, + reluConfig2, + relu6Config2, + reshapeConfig2, + resizeBilinearConfig2, + resizeBilinearGradConfig3, + resizeNearestNeighborConfig2, + resizeNearestNeighborGradConfig3, + reverseConfig2, + rotateWithOffsetConfig2, + roundConfig2, + rsqrtConfig2, + scatterNdConfig2, + searchSortedConfig2, + selectConfig2, + seluConfig2, + sigmoidConfig2, + signConfig2, + sinConfig2, + sinhConfig2, + sliceConfig2, + softmaxConfig2, + softplusConfig2, + spaceToBatchNDConfig2, + sparseFillEmptyRowsConfig2, + sparseReshapeConfig2, + sparseSegmentMeanConfig2, + sparseSegmentSumConfig2, + sparseToDenseConfig2, + splitVConfig2, + sqrtConfig2, + squareConfig2, + squaredDifferenceConfig2, + staticRegexReplaceConfig2, + stepConfig2, + stridedSliceConfig2, + stringNGramsConfig2, + stringSplitConfig2, + stringToHashBucketFastConfig2, + subConfig2, + sumConfig2, + tanConfig2, + tanhConfig2, + tensorScatterUpdateConfig2, + tileConfig2, + topKConfig2, + transformConfig2, + transposeConfig2, + uniqueConfig2, + unpackConfig2, + unsortedSegmentSumConfig2, + zerosLikeConfig2 +]; +for (const kernelConfig of kernelConfigs2) { + registerKernel(kernelConfig); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/types.js +var CppDType; +(function(CppDType2) { + CppDType2[CppDType2["float32"] = 0] = "float32"; + CppDType2[CppDType2["int32"] = 1] = "int32"; + CppDType2[CppDType2["bool"] = 2] = "bool"; + CppDType2[CppDType2["string"] = 3] = "string"; + CppDType2[CppDType2["complex64"] = 4] = "complex64"; +})(CppDType || (CppDType = {})); +var FusableActivation; +(function(FusableActivation2) { + FusableActivation2[FusableActivation2["linear"] = 0] = "linear"; + FusableActivation2[FusableActivation2["relu"] = 1] = "relu"; + FusableActivation2[FusableActivation2["relu6"] = 2] = "relu6"; + FusableActivation2[FusableActivation2["prelu"] = 3] = "prelu"; + FusableActivation2[FusableActivation2["leakyrelu"] = 4] = "leakyrelu"; + FusableActivation2[FusableActivation2["sigmoid"] = 5] = "sigmoid"; + FusableActivation2[FusableActivation2["elu"] = 6] = "elu"; +})(FusableActivation || (FusableActivation = {})); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/_FusedMatMul.js +var wasmFusedMatMul; +function setup(backend2) { + wasmFusedMatMul = backend2.wasm.cwrap(_FusedMatMul, null, [ + "number", + "array", + "number", + "number", + "array", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // out_id + ]); +} +function fusedBatchMatMul(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b, bias, preluActivationWeights } = inputs; + if (a.dtype !== "float32" || b.dtype !== "float32") { + throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`); + } + const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs; + const aId = backend2.dataIdMap.get(a.dataId).id; + const bId = backend2.dataIdMap.get(b.dataId).id; + let biasId = 0; + if (bias != null) { + const biasData = backend2.dataIdMap.get(bias.dataId); + if (biasData.shape.length !== 1) { + throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`); + } + biasId = biasData.id; + } + const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id; + const fusedActivation = FusableActivation[activation2]; + if (fusedActivation == null) { + throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`); + } + const leftDim = transposeA ? a.shape[2] : a.shape[1]; + const rightDim = transposeB ? b.shape[1] : b.shape[2]; + const batchDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + const out = backend2.makeOutput([...batchDims, leftDim, rightDim], a.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer); + const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer); + wasmFusedMatMul(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, leakyreluAlpha || 0, outId); + return out; +} +var _fusedMatMulConfig3 = { + kernelName: _FusedMatMul, + backendName: "wasm", + setupFunc: setup, + kernelFunc: fusedBatchMatMul +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/unary_kernel.js +function createUnaryKernelConfig(kernelName, outType) { + let wasmFunc8; + function setupFunc3(backend2) { + wasmFunc8 = backend2.wasm.cwrap(kernelName, null, [ + "number", + "number", + "number" + // out_id + ]); + } + function kernelFunc3(args) { + const { backend: backend2, inputs: { x } } = args; + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(x.shape, outType || x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + if (util_exports.sizeFromShape(out.shape) === 0) { + return out; + } + wasmFunc8(xId, CppDType[x.dtype], outId); + return out; + } + return { kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3 }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Abs.js +var absConfig3 = createUnaryKernelConfig(Abs); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Acos.js +var acosConfig3 = createUnaryKernelConfig(Acos); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Acosh.js +var acoshConfig3 = createUnaryKernelConfig(Acosh); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/binary_kernel.js +function createBinaryKernelConfig(kernelName, supportsFullBroadcast20, dtype) { + let wasmFunc8; + function setupFunc3(backend2) { + wasmFunc8 = backend2.wasm.cwrap(kernelName, null, [ + "number", + "array", + "number", + "number", + "array", + "number", + "number", + "number" + // out_id + ]); + } + function kernelFunc3(args) { + const { backend: backend2, inputs } = args; + const { a, b } = inputs; + const aId = backend2.dataIdMap.get(a.dataId).id; + const bId = backend2.dataIdMap.get(b.dataId).id; + const outputType = dtype != null ? dtype : a.dtype; + const newShape = backend_util_exports.assertAndGetBroadcastShape(a.shape, b.shape); + const out = backend2.makeOutput(newShape, outputType); + if (util_exports.sizeFromShape(newShape) === 0) { + return out; + } + const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer); + const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + const kernelFunc4 = () => wasmFunc8(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a.dtype], outId); + kernelFunc4(); + return out; + } + return { kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3 }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Add.js +var supportsFullBroadcast = true; +var addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AddN.js +var wasmFunc; +function setupFunc(backend2) { + wasmFunc = backend2.wasm.cwrap(AddN, null, [ + "array", + "number", + "number", + "number" + // out_id + ]); +} +function addn(args) { + const { inputs, backend: backend2 } = args; + const out = backend2.makeOutput(inputs[0].shape, inputs[0].dtype); + if (util_exports.sizeFromShape(out.shape) === 0) { + return out; + } + const inputIds = inputs.map((x) => backend2.dataIdMap.get(x.dataId).id); + const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId); + return out; +} +var addNConfig3 = { + kernelName: AddN, + backendName: "wasm", + setupFunc, + kernelFunc: addn +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Identity.js +function identity4(args) { + const { inputs: { x }, backend: backend2 } = args; + if (x.dtype === "string") { + return tensor(backend2.readSync(x.dataId), x.shape, x.dtype); + } + const out = backend2.makeOutput(x.shape, x.dtype); + const inVals = backend2.typedArrayFromHeap(x); + const outVals = backend2.typedArrayFromHeap(out); + outVals.set(inVals); + return out; +} +var identityConfig3 = { + kernelName: Identity, + backendName: "wasm", + kernelFunc: identity4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transpose.js +var wasmTranspose; +function setup2(backend2) { + wasmTranspose = backend2.wasm.cwrap(Transpose, null, [ + "number", + "array", + "number", + "number", + "number", + "array", + "number" + // perm.length + ]); +} +function transpose4(args) { + const { inputs, backend: backend2, attrs } = args; + const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm); + let permIsNoOp = true; + for (let i = 0; i < perm.length; i++) { + if (perm[i] !== i) { + permIsNoOp = false; + } + } + const outShape = computeOutShape4(inputs.x.shape, attrs.perm); + const x = { + dataId: inputs.x.dataId, + shape: reducedShape, + dtype: inputs.x.dtype + }; + if (permIsNoOp) { + const cloned = identity4({ inputs, backend: backend2 }); + cloned.shape = outShape; + return cloned; + } + const out = backend2.makeOutput(outShape, x.dtype); + const xId = backend2.dataIdMap.get(x.dataId).id; + const outId = backend2.dataIdMap.get(out.dataId).id; + const permBytes = new Uint8Array(new Int32Array(perm).buffer); + const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length); + return out; +} +function computeOutShape4(inShape, perm) { + const outShape = new Array(inShape.length); + for (let i = 0; i < outShape.length; i++) { + outShape[i] = inShape[perm[i]]; + } + return outShape; +} +function removeOneSizeDims(shape, perm) { + const newShape = []; + const newPerm = []; + for (let i = 0; i < shape.length; ++i) { + if (shape[i] !== 1) { + newShape.push(shape[i]); + } + if (shape[perm[i]] !== 1) { + newPerm.push(perm[i]); + } + } + for (let i = 0; i < newPerm.length; ++i) { + let minValIdx = -1; + for (let j = 0; j < newPerm.length; ++j) { + if (newPerm[j] >= i && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) { + minValIdx = j; + } + } + newPerm[minValIdx] = i; + } + return [newShape, newPerm]; +} +var transposeConfig3 = { + kernelName: Transpose, + backendName: "wasm", + kernelFunc: transpose4, + setupFunc: setup2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/kernel_utils.js +function permuteAxesAndTranspose(x, axis, backend2) { + const xShape = x.shape; + const xRank = x.shape.length; + const originalAxes = util_exports.parseAxisParam(axis, xShape); + let axes = originalAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let xTransposed = null; + let inputWasTransposed = false; + if (permutedAxes != null) { + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = xShape[permutedAxes[i]]; + } + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 }); + const xId = backend2.dataIdMap.get(x.dataId).id; + const transposedId = backend2.dataIdMap.get(xTransposed.dataId).id; + if (transposedId !== xId) { + inputWasTransposed = true; + } + } + return { transposed: xTransposed, originalAxes, axes, inputWasTransposed }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/All.js +var wasmAll; +function setup3(backend2) { + wasmAll = backend2.wasm.cwrap(All, null, ["number, number, number"]); +} +function all4(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + input2 = transposed; + inputId = transposedId; + } + const inputRank = input2.shape.length; + backend_util_exports.assertAxesAreInnerMostDims("all", axes, inputRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, x.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmAll(inputId, reduceSize, outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var allConfig3 = { + kernelName: All, + backendName: "wasm", + setupFunc: setup3, + kernelFunc: all4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Any.js +var wasmAny; +function setup4(backend2) { + wasmAny = backend2.wasm.cwrap(Any, null, ["number, number, number"]); +} +function any4(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + input2 = transposed; + inputId = transposedId; + } + const inputRank = input2.shape.length; + backend_util_exports.assertAxesAreInnerMostDims("any", axes, inputRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, x.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmAny(inputId, reduceSize, outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var anyConfig3 = { + kernelName: Any, + backendName: "wasm", + setupFunc: setup4, + kernelFunc: any4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/argminmax_kernel.js +function createArgMinMaxKernelConfig(kernelName) { + let wasmFunc8; + function setupFunc3(backend2) { + wasmFunc8 = backend2.wasm.cwrap(kernelName, null, [ + "number", + "number", + "number", + "number", + "number" + // out_id + ]); + } + function kernelFunc3(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + if (transposedId !== xId) { + input2 = transposed; + inputId = transposedId; + } + } + const outShape = input2.shape.slice(0, -1); + const out = backend2.makeOutput(outShape, "int32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + const outerSize = util_exports.sizeFromShape(out.shape); + const innerSize = input2.shape[axes[0]]; + wasmFunc8(inputId, CppDType[input2.dtype], outerSize, innerSize, outId); + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + return out; + } + return { + kernelName, + backendName: "wasm", + setupFunc: setupFunc3, + kernelFunc: kernelFunc3 + }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMax.js +var argMaxConfig3 = createArgMinMaxKernelConfig(ArgMax); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMin.js +var argMinConfig3 = createArgMinMaxKernelConfig(ArgMin); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Asin.js +var asinConfig3 = createUnaryKernelConfig(Asin); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Asinh.js +var asinhConfig3 = createUnaryKernelConfig(Asinh); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Atan.js +var atanConfig3 = createUnaryKernelConfig(Atan); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Atan2.js +var atan2Config3 = createBinaryKernelConfig( + Atan2, + /*supportsFullBroadcast=*/ + false +); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Atanh.js +var atanhConfig3 = createUnaryKernelConfig(Atanh); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool.js +var wasmAvgPool; +function setup5(backend2) { + wasmAvgPool = backend2.wasm.cwrap(AvgPool, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function avgPool4(args) { + const { inputs, attrs, backend: backend2 } = args; + const x = inputs.x; + const xId = backend2.dataIdMap.get(x.dataId).id; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const channels = convInfo.inChannels; + if (convInfo.dataFormat !== "channelsLast") { + throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`); + } + if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) { + throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId); + return out; +} +var avgPoolConfig3 = { + kernelName: AvgPool, + backendName: "wasm", + setupFunc: setup5, + kernelFunc: avgPool4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool3D.js +var wasmAvgPool3D; +function setup6(backend2) { + wasmAvgPool3D = backend2.wasm.cwrap("AvgPool3D", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function avgPool3D3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs; + const convInfo = backend_util_exports.computePool3DInfo( + x.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3, + dimRoundingMode, + dataFormat + ); + const out = backend2.makeOutput(convInfo.outShape, x.dtype); + wasmAvgPool3D( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(out.dataId).id, + convInfo.batchSize, + // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inDepth, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outDepth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideDepth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationDepth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterDepth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.front, + convInfo.padInfo.top, + convInfo.padInfo.left + ); + return out; +} +var avgPool3DConfig3 = { + kernelName: AvgPool3D, + backendName: "wasm", + setupFunc: setup6, + kernelFunc: avgPool3D3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool3DGrad.js +var wasmAvgPool3DGrad; +function setup7(backend2) { + wasmAvgPool3DGrad = backend2.wasm.cwrap("AvgPool3DGrad", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // filterWidth + ]); +} +function avgPool3DGrad3(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool3DInfo( + input2.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3, + dimRoundingMode + ); + const dx = backend2.makeOutput(input2.shape, input2.dtype); + wasmAvgPool3DGrad( + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(dx.dataId).id, + convInfo.batchSize, + // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inDepth, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outDepth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideDepth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationDepth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterDepth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.front, + convInfo.padInfo.top, + convInfo.padInfo.left, + convInfo.filterDepth, + convInfo.filterHeight, + convInfo.filterWidth + ); + return dx; +} +var avgPool3DGradConfig4 = { + kernelName: AvgPool3DGrad, + backendName: "wasm", + setupFunc: setup7, + kernelFunc: avgPool3DGrad3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPoolGrad.js +var wasmAvgPoolGrad; +function setup8(backend2) { + wasmAvgPoolGrad = backend2.wasm.cwrap("AvgPoolGrad", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // filterWidth + ]); +} +function avgPoolGrad4(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3 } = attrs; + const convInfo = backend_util_exports.computePool2DInfo( + input2.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3 + ); + const dx = backend2.makeOutput(input2.shape, input2.dtype); + wasmAvgPoolGrad( + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(dx.dataId).id, + convInfo.batchSize, + // Since Pool ops (AvgPool and MaxPool) support 2D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.top, + convInfo.padInfo.left, + convInfo.filterHeight, + convInfo.filterWidth + ); + return dx; +} +var avgPoolGradConfig4 = { + kernelName: AvgPoolGrad, + backendName: "wasm", + setupFunc: setup8, + kernelFunc: avgPoolGrad4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reshape.js +function reshape5(args) { + const { inputs, attrs } = args; + const { x } = inputs; + const { shape } = attrs; + const xSize = util_exports.sizeFromShape(x.shape); + const $shape = util_exports.inferFromImplicitShape(shape, xSize); + util_exports.assert(xSize === util_exports.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`); + args.backend.incRef(x.dataId); + return { dataId: x.dataId, shape: $shape, dtype: x.dtype }; +} +var reshapeConfig3 = { + kernelName: Reshape, + backendName: "wasm", + kernelFunc: reshape5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchMatMul.js +var wasmBatchMatMul; +function setup9(backend2) { + wasmBatchMatMul = backend2.wasm.cwrap(BatchMatMul, null, [ + "number", + "array", + "number", + "number", + "array", + "number", + "number", + "number", + "number" + // out_id + ]); +} +function batchMatMul3(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b } = inputs; + const { transposeA, transposeB } = attrs; + if (a.dtype !== "float32" || b.dtype !== "float32") { + throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`); + } + const aRank = a.shape.length; + const bRank = b.shape.length; + const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1]; + const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2]; + const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2]; + const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1]; + const outerDimsA = a.shape.slice(0, -2); + const outerDimsB = b.shape.slice(0, -2); + const batchDimA = util_exports.sizeFromShape(outerDimsA); + const batchDimB = util_exports.sizeFromShape(outerDimsB); + const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); + const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA]; + const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB]; + const a3d = reshape5({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } }); + const b3d = reshape5({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } }); + const a3dId = backend2.dataIdMap.get(a3d.dataId).id; + const b3dId = backend2.dataIdMap.get(b3d.dataId).id; + const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1]; + const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2]; + const batchDim = Math.max(batchDimA, batchDimB); + const out = backend2.makeOutput([batchDim, leftDim, rightDim], a3d.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer); + const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer); + wasmBatchMatMul(a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes, b3d.shape.length, transposeA, transposeB, outId); + backend2.disposeData(a3d.dataId); + backend2.disposeData(b3d.dataId); + out.shape = outShape; + return out; +} +var batchMatMulConfig3 = { + kernelName: BatchMatMul, + backendName: "wasm", + setupFunc: setup9, + kernelFunc: batchMatMul3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Slice.js +function slice4(args) { + const { inputs: { x }, attrs: { begin, size }, backend: backend2 } = args; + const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size); + const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_); + const xVals = backend2.readSync(x.dataId); + const out = backend2.makeOutput(size_, x.dtype); + const xStrides = util_exports.computeStrides(x.shape); + const outData = backend2.dataIdMap.get(out.dataId); + if (isContinous) { + const flatOffset = slice_util_exports.computeFlatOffset(begin_, xStrides); + if (x.dtype === "string") { + outData.stringBytes = xVals.slice(flatOffset, flatOffset + util_exports.sizeFromShape(size_)); + } else { + const outVals2 = backend2.typedArrayFromHeap(out); + outVals2.set(xVals.subarray(flatOffset, flatOffset + util_exports.sizeFromShape(size_))); + } + return out; + } + if (x.dtype === "string") { + const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype); + outData.stringBytes = res; + return out; + } + const outVals = backend2.typedArrayFromHeap(out); + const rank = x.shape.length; + if (rank === 2) { + slice2d2(xVals, xStrides[0], outVals, begin_, size_); + } else if (rank === 3) { + slice3d2(xVals, xStrides[0], xStrides[1], outVals, begin_, size_); + } else if (rank === 4) { + slice4d2(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_); + } else { + const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype); + outVals.set(res); + } + return out; +} +function slice2d2(xVals, xStride, outVals, begin, size) { + let outOffset = 0; + const beginI = begin[0]; + const beginJ = begin[1]; + const endI = beginI + size[0]; + for (let i = beginI; i < endI; i++) { + const xOffset = i * xStride + beginJ; + outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset); + outOffset += size[1]; + } +} +function slice3d2(xVals, xStride1, xStride2, outVals, begin, size) { + let outOffset = 0; + const beginI = begin[0]; + const beginJ = begin[1]; + const beginK = begin[2]; + const endI = beginI + size[0]; + const endJ = beginJ + size[1]; + for (let i = beginI; i < endI; i++) { + for (let j = beginJ; j < endJ; j++) { + const xOffset = i * xStride1 + j * xStride2 + beginK; + outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset); + outOffset += size[2]; + } + } +} +function slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size) { + let outOffset = 0; + const beginI = begin[0]; + const beginJ = begin[1]; + const beginK = begin[2]; + const endI = beginI + size[0]; + const endJ = beginJ + size[1]; + const endK = beginK + size[2]; + const beginL = begin[3]; + for (let i = beginI; i < endI; i++) { + for (let j = beginJ; j < endJ; j++) { + for (let k = beginK; k < endK; k++) { + const xOffset = i * xStride1 + j * xStride2 + k * xStride3 + beginL; + outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset); + outOffset += size[3]; + } + } + } +} +var sliceConfig3 = { + kernelName: Slice, + backendName: "wasm", + kernelFunc: slice4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchToSpaceND.js +function batchToSpaceND4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, crops } = attrs; + const prod5 = blockShape.reduce((a, b) => a * b); + const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5); + const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length); + const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5); + const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length); + const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length); + const xReshaped = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } }); + const xTransposed = transpose4({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } }); + const xTransposedReshaped = reshape5({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } }); + const result = slice4({ + inputs: { x: xTransposedReshaped }, + backend: backend2, + attrs: { begin: sliceBeginCoords, size: sliceSize } + }); + backend2.disposeData(xReshaped.dataId); + backend2.disposeData(xTransposed.dataId); + backend2.disposeData(xTransposedReshaped.dataId); + return result; +} +var batchToSpaceNDConfig3 = { + kernelName: BatchToSpaceND, + backendName: "wasm", + kernelFunc: batchToSpaceND4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Bincount.js +var wasmBincount; +function setup10(backend2) { + wasmBincount = backend2.wasm.cwrap(Bincount, null, [ + "number", + "number", + "boolean", + "number", + "number", + "number" + // outId + ]); +} +function bincount4(args) { + const { backend: backend2, inputs, attrs } = args; + const { x, weights } = inputs; + const { size } = attrs; + const hasWeights = weights.shape.reduce((p2, v) => p2 * v, 1) !== 0; + const outShape = x.shape.length === 1 ? [size] : [x.shape[0], size]; + const out = backend2.makeOutput(outShape, weights.dtype); + function tensorId(x2) { + return backend2.dataIdMap.get(x2.dataId).id; + } + wasmBincount(tensorId(x), size, hasWeights, tensorId(weights), CppDType[weights.dtype], tensorId(out)); + return out; +} +var bincountConfig3 = { + kernelName: Bincount, + backendName: "wasm", + setupFunc: setup10, + kernelFunc: bincount4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BitwiseAnd.js +var supportsFullBroadcast2 = true; +var bitwiseAndConfig3 = createBinaryKernelConfig(BitwiseAnd, supportsFullBroadcast2); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BroadcastArgs.js +function broadcastArgs4(args) { + const { inputs, backend: backend2 } = args; + const { s0, s1 } = inputs; + const s0Vals = backend2.typedArrayFromHeap(s0); + const s1Vals = backend2.typedArrayFromHeap(s1); + const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals)); + return backend2.makeOutput( + [broadcastShape.length], + "int32", + /*memoryOffset=*/ + void 0, + /*values=*/ + new Int32Array(broadcastShape) + ); +} +var broadcastArgsConfig3 = { + kernelName: BroadcastArgs, + backendName: "wasm", + kernelFunc: broadcastArgs4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cast.js +function cast5(args) { + const { inputs: { x }, attrs: { dtype }, backend: backend2 } = args; + const out = backend2.makeOutput(x.shape, dtype); + const inVals = backend2.typedArrayFromHeap(x); + const outVals = backend2.typedArrayFromHeap(out); + outVals.set(inVals); + return out; +} +var castConfig3 = { + kernelName: Cast, + backendName: "wasm", + kernelFunc: cast5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Ceil.js +var ceilConfig3 = createUnaryKernelConfig(Ceil); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ClipByValue.js +var wasmClip; +function setup11(backend2) { + wasmClip = backend2.wasm.cwrap(ClipByValue, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function clip(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { clipValueMin, clipValueMax } = attrs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(x.shape, x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmClip(xId, clipValueMin, clipValueMax, outId); + return out; +} +var clipByValueConfig3 = { + kernelName: ClipByValue, + backendName: "wasm", + setupFunc: setup11, + kernelFunc: clip +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Concat.js +function concat4(args) { + const { inputs, backend: backend2 } = args; + const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0]; + const shapes = inputs.map((t) => t.shape); + backend_util_exports.assertParamsConsistent(shapes, axis); + let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis); + const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0); + if ($inputs.length === 1) { + return identity4({ inputs: { x: $inputs[0] }, backend: backend2 }); + } + const out = backend2.makeOutput(outShape, inputs[0].dtype); + if (util_exports.sizeFromShape(outShape) === 0) { + return out; + } + if ($inputs[0].dtype === "string") { + const inputs2D = $inputs.map((t) => { + const innerSize = util_exports.sizeFromShape(t.shape.slice(axis)); + const shape = [-1, innerSize]; + return reshape5({ inputs: { x: t }, backend: backend2, attrs: { shape } }); + }); + const inputsValShapes = inputs2D.map((t) => { + return { vals: backend2.readSync(t.dataId), shape: t.shape }; + }); + outShape = backend_util_exports.computeOutShape( + inputs2D.map((t) => t.shape), + 1 + /* axis */ + ); + const simplyConcat = inputs2D[0].shape[0] === 1; + const outVals2 = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat); + const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), axis); + out.shape = finalOutShape; + const outData = backend2.dataIdMap.get(out.dataId); + outData.stringBytes = backend_util_exports.fromStringArrayToUint8(outVals2); + inputs2D.forEach((t) => backend2.disposeData(t.dataId)); + return out; + } + const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis)); + let sumInnerDims = 0; + const innerDims = $inputs.map((input2) => { + const innerDim = util_exports.sizeFromShape(input2.shape.slice(axis)); + sumInnerDims += innerDim; + return innerDim; + }); + const inVals = $inputs.map((input2) => backend2.typedArrayFromHeap(input2)); + const outVals = backend2.typedArrayFromHeap(out); + for (let b = 0; b < batchDim; b++) { + let outOffset = b * sumInnerDims; + for (let i = 0; i < inVals.length; i++) { + const innerDim = innerDims[i]; + const inOffset = b * innerDim; + const vals = inVals[i].subarray(inOffset, inOffset + innerDim); + outVals.set(vals, outOffset); + outOffset += innerDim; + } + } + return out; +} +var concatConfig3 = { + kernelName: Concat, + backendName: "wasm", + kernelFunc: concat4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2D.js +var wasmConv2d; +function setup12(backend2) { + wasmConv2d = backend2.wasm.cwrap(Conv2D, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function conv2d5(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x, filter } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const filterId = backend2.dataIdMap.get(filter.dataId).id; + const { strides, dilations, pad: pad3, dimRoundingMode, dataFormat } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const inputChannels = convInfo.inChannels; + const outputChannels = convInfo.outChannels; + const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0; + if (convInfo.dataFormat !== "channelsLast") { + throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId); + return out; +} +var conv2DConfig3 = { + kernelName: Conv2D, + backendName: "wasm", + setupFunc: setup12, + kernelFunc: conv2d5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2DBackpropInput.js +var wasmConv2DBackpropInput; +function setup13(backend2) { + wasmConv2DBackpropInput = backend2.wasm.cwrap(Conv2DBackpropInput, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function conv2DBackpropInput4(args) { + const { backend: backend2, inputs, attrs } = args; + const { dy, filter } = inputs; + const { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape } = attrs; + const dilations = 1; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); + const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo; + const topPad = filterHeight - 1 - convInfo.padInfo.top; + const leftPad = filterWidth - 1 - convInfo.padInfo.left; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const dxStrides = util_exports.computeStrides(convInfo.inShape); + const dyStrides = util_exports.computeStrides(dy.shape); + const [fltS0, fltS1, fltS2] = util_exports.computeStrides(filter.shape); + const xBatchStride = dxStrides[0]; + const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2]; + const xColStride = isChannelsLast ? dxStrides[2] : 1; + const xChannelStride = isChannelsLast ? 1 : dxStrides[1]; + const yBatchStride = dyStrides[0]; + const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2]; + const yColStride = isChannelsLast ? dyStrides[2] : 1; + const yChannelStride = isChannelsLast ? 1 : dyStrides[1]; + const out = backend2.makeOutput(convInfo.inShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + const dyId = backend2.dataIdMap.get(dy.dataId).id; + const filterId = backend2.dataIdMap.get(filter.dataId).id; + wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId); + return out; +} +var conv2DBackpropInputConfig3 = { + kernelName: Conv2DBackpropInput, + backendName: "wasm", + setupFunc: setup13, + kernelFunc: conv2DBackpropInput4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv3D.js +var wasmConv3D; +function setup14(backend2) { + wasmConv3D = backend2.wasm.cwrap(Conv3D, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function conv3D3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + if (x.dtype !== "float32") { + throw new Error(`Tensor x must have dtype float32, got ${x.dtype}`); + } + if (filter.dtype !== "float32") { + throw new Error(`Tensor filter must have dtype float32, got ${filter.dtype}`); + } + const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3); + const out = backend2.makeOutput(convInfo.outShape, x.dtype); + wasmConv3D(backend2.dataIdMap.get(x.dataId).id, backend2.dataIdMap.get(filter.dataId).id, backend2.dataIdMap.get(out.dataId).id, convInfo.batchSize, convInfo.inDepth, convInfo.inHeight, convInfo.inWidth, convInfo.inChannels, convInfo.outDepth, convInfo.outHeight, convInfo.outWidth, convInfo.outChannels, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationDepth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left); + return out; +} +var conv3DConfig3 = { + kernelName: Conv3D, + backendName: "wasm", + setupFunc: setup14, + kernelFunc: conv3D3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv3DBackpropFilterV2.js +var wasmConv3DBackpropFilterV2; +function setup15(backend2) { + wasmConv3DBackpropFilterV2 = backend2.wasm.cwrap(Conv3DBackpropFilterV2, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function conv3DBackpropFilterV23(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, dy } = inputs; + const { strides, pad: pad3, filterShape } = attrs; + if (x.dtype !== "float32") { + throw new Error(`Tensor dy must have dtype float32, got ${x.dtype}`); + } + if (dy.dtype !== "float32") { + throw new Error(`Tensor filter must have dtype float32, got ${dy.dtype}`); + } + const convInfo = backend_util_exports.computeConv3DInfo( + x.shape, + filterShape, + strides, + /*dilations=*/ + 1, + pad3 + ); + const dw = backend2.makeOutput(convInfo.filterShape, dy.dtype); + wasmConv3DBackpropFilterV2(backend2.dataIdMap.get(x.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dw.dataId).id, convInfo.batchSize, convInfo.inDepth, convInfo.inHeight, convInfo.inWidth, convInfo.inChannels, convInfo.outDepth, convInfo.outHeight, convInfo.outWidth, convInfo.outChannels, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationDepth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left); + return dw; +} +var conv3DBackpropFilterV2Config3 = { + kernelName: Conv3DBackpropFilterV2, + backendName: "wasm", + setupFunc: setup15, + kernelFunc: conv3DBackpropFilterV23 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv3DBackpropInputV2.js +var wasmConv3DBackpropInputV2; +function setup16(backend2) { + wasmConv3DBackpropInputV2 = backend2.wasm.cwrap(Conv3DBackpropInputV2, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function conv3DBackpropInputV22(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { pad: pad3, strides, inputShape } = attrs; + if (dy.dtype !== "float32") { + throw new Error(`Tensor dy must have dtype float32, got ${dy.dtype}`); + } + if (filter.dtype !== "float32") { + throw new Error(`Tensor filter must have dtype float32, got ${filter.dtype}`); + } + const convInfo = backend_util_exports.computeConv3DInfo( + inputShape, + filter.shape, + strides, + /*dilations=*/ + 1, + pad3 + ); + const dx = backend2.makeOutput(convInfo.inShape, dy.dtype); + wasmConv3DBackpropInputV2(backend2.dataIdMap.get(filter.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dx.dataId).id, convInfo.batchSize, convInfo.inDepth, convInfo.inHeight, convInfo.inWidth, convInfo.inChannels, convInfo.outDepth, convInfo.outHeight, convInfo.outWidth, convInfo.outChannels, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationDepth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, convInfo.padInfo.front, convInfo.padInfo.top, convInfo.padInfo.left); + return dx; +} +var conv3DBackpropInputV2Config2 = { + kernelName: Conv3DBackpropInputV2, + backendName: "wasm", + setupFunc: setup16, + kernelFunc: conv3DBackpropInputV22 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cos.js +var cosConfig3 = createUnaryKernelConfig(Cos); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cosh.js +var coshConfig3 = createUnaryKernelConfig(Cosh); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/CropAndResize.js +var InterpolationMethod; +(function(InterpolationMethod2) { + InterpolationMethod2[InterpolationMethod2["bilinear"] = 0] = "bilinear"; + InterpolationMethod2[InterpolationMethod2["nearest"] = 1] = "nearest"; +})(InterpolationMethod || (InterpolationMethod = {})); +var wasmCropAndResize; +function setup17(backend2) { + wasmCropAndResize = backend2.wasm.cwrap(CropAndResize, null, [ + "number", + "number", + "number", + "number", + "array", + "number", + "number", + "number", + "number", + "number" + // out id + ]); +} +function cropAndResize5(args) { + const { backend: backend2, inputs, attrs } = args; + const { method, extrapolationValue, cropSize } = attrs; + const { image: image2, boxes, boxInd } = inputs; + const numBoxes = boxes.shape[0]; + const [cropHeight, cropWidth] = cropSize; + const outShape = [numBoxes, cropHeight, cropWidth, image2.shape[3]]; + let imagesData = backend2.dataIdMap.get(image2.dataId); + let castedData; + if (image2.dtype !== "float32") { + castedData = cast5({ backend: backend2, inputs: { x: image2 }, attrs: { dtype: "float32" } }); + imagesData = backend2.dataIdMap.get(castedData.dataId); + } + const imagesId = imagesData.id; + const boxesId = backend2.dataIdMap.get(boxes.dataId).id; + const boxIndId = backend2.dataIdMap.get(boxInd.dataId).id; + const out = backend2.makeOutput(outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + const imagesShapeBytes = new Uint8Array(new Int32Array(image2.shape).buffer); + wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId); + if (castedData != null) { + backend2.disposeData(castedData.dataId); + } + return out; +} +var cropAndResizeConfig3 = { + kernelName: CropAndResize, + backendName: "wasm", + setupFunc: setup17, + kernelFunc: cropAndResize5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumprod.js +var wasmCumprod; +function setup18(backend2) { + wasmCumprod = backend2.wasm.cwrap(Cumprod, null, [ + "number", + "number", + "number", + "number", + "number", + "number" + // dtype + ]); +} +function cumprod4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + const xRank = x.shape.length; + util_exports.assert(x.dtype === "float32" || x.dtype === "int32", () => `cumprod does not support ${x.dtype} tensors in the WASM backend`); + const permutation = backend_util_exports.getAxesPermutation([axis], xRank); + let permutedX = x; + if (permutation !== null) { + permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 }); + } + const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0]; + backend_util_exports.assertAxesAreInnerMostDims("cumprod", [permutedAxis], xRank); + const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype); + const finalDim = permutedX.shape[permutedAxis]; + const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id; + const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id; + wasmCumprod(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]); + let out = permutedOut; + if (permutation !== null) { + const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation); + out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 }); + backend2.disposeData(permutedX.dataId); + backend2.disposeData(permutedOut.dataId); + } + return out; +} +var cumprodConfig3 = { + kernelName: Cumprod, + backendName: "wasm", + setupFunc: setup18, + kernelFunc: cumprod4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumsum.js +var wasmCumsum; +function setup19(backend2) { + wasmCumsum = backend2.wasm.cwrap(Cumsum, null, [ + "number", + "number", + "number", + "number", + "number", + "number" + // dtype + ]); +} +function cumsum4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, exclusive, reverse: reverse5 } = attrs; + const xRank = x.shape.length; + util_exports.assert(x.dtype === "float32" || x.dtype === "int32", () => `cumsum does not support ${x.dtype} tensors in the WASM backend`); + const permutation = backend_util_exports.getAxesPermutation([axis], xRank); + let permutedX = x; + if (permutation !== null) { + permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 }); + } + const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0]; + backend_util_exports.assertAxesAreInnerMostDims("cumsum", [permutedAxis], xRank); + const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype); + const finalDim = permutedX.shape[permutedAxis]; + const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id; + const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id; + wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]); + let out = permutedOut; + if (permutation !== null) { + const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation); + out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 }); + backend2.disposeData(permutedX.dataId); + backend2.disposeData(permutedOut.dataId); + } + return out; +} +var cumsumConfig3 = { + kernelName: Cumsum, + backendName: "wasm", + setupFunc: setup19, + kernelFunc: cumsum4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DenseBincount.js +var wasmDenseBincount; +function setup20(backend2) { + wasmDenseBincount = backend2.wasm.cwrap("DenseBincount", null, [ + "number", + "array", + "number", + "number", + "boolean", + "number", + "number", + "boolean", + "number" + // outId + ]); +} +function denseBincount4(args) { + const { backend: backend2, inputs, attrs } = args; + const { x, weights } = inputs; + const { size, binaryOutput } = attrs; + const hasWeights = weights.shape.reduce((p2, v) => p2 * v, 1) !== 0; + const outShape = x.shape.length === 1 ? [size] : [x.shape[0], size]; + const out = backend2.makeOutput(outShape, weights.dtype); + function tensorId(x2) { + return backend2.dataIdMap.get(x2.dataId).id; + } + wasmDenseBincount(tensorId(x), new Uint8Array(new Int32Array(x.shape).buffer), x.shape.length, size, hasWeights, tensorId(weights), CppDType[weights.dtype], binaryOutput, tensorId(out)); + return out; +} +var denseBincountConfig3 = { + kernelName: DenseBincount, + backendName: "wasm", + setupFunc: setup20, + kernelFunc: denseBincount4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthToSpace.js +var wasmDepthToSpace; +function setup21(backend2) { + wasmDepthToSpace = backend2.wasm.cwrap(DepthToSpace, null, [ + "number", + "number", + "number", + "array", + "number", + "array", + "array", + "number", + "number" + // outId + ]); +} +function depthToSpace4(args) { + const { backend: backend2, inputs, attrs } = args; + const { x } = inputs; + const { blockSize, dataFormat } = attrs; + const batchSize = x.shape[0]; + const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2]; + const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3]; + const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1]; + const outputHeight = inputHeight * blockSize; + const outputWidth = inputWidth * blockSize; + const outputDepth = inputDepth / (blockSize * blockSize); + const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth]; + const out = backend2.makeOutput(outputShape, "float32"); + const xData = backend2.dataIdMap.get(x.dataId); + const xId = xData.id; + const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer); + const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer); + const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + const channelsLast = dataFormat === "NHWC" ? 1 : 0; + wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId); + return out; +} +var depthToSpaceConfig3 = { + kernelName: DepthToSpace, + backendName: "wasm", + setupFunc: setup21, + kernelFunc: depthToSpace4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthwiseConv2dNative.js +var wasmDepthwiseConv2d; +function setup22(backend2) { + wasmDepthwiseConv2d = backend2.wasm.cwrap(DepthwiseConv2dNative, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function depthwiseConv2d5(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x, filter } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const filterId = backend2.dataIdMap.get(filter.dataId).id; + const { strides, dilations, pad: pad3, dimRoundingMode } = attrs; + const $dilations = dilations == null ? [1, 1] : dilations; + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filter.shape, + strides, + $dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const inputChannels = convInfo.inChannels; + const outputChannels = convInfo.outChannels; + const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0; + if (convInfo.dataFormat !== "channelsLast") { + throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId); + return out; +} +var depthwiseConv2dNativeConfig3 = { + kernelName: DepthwiseConv2dNative, + backendName: "wasm", + setupFunc: setup22, + kernelFunc: depthwiseConv2d5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Diag.js +var wasmDiag; +function setup23(backend2) { + wasmDiag = backend2.wasm.cwrap("Diag", null, [ + "number", + "number", + "number", + "number" + // outId + ]); +} +function diag4(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + const xSize = util_exports.sizeFromShape(x.shape); + const out = backend2.makeOutput([...x.shape, ...x.shape], x.dtype); + wasmDiag(backend2.dataIdMap.get(x.dataId).id, CppDType[x.dtype], xSize, backend2.dataIdMap.get(out.dataId).id); + return out; +} +var diagConfig3 = { + kernelName: Diag, + backendName: "wasm", + setupFunc: setup23, + kernelFunc: diag4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Dilation2D.js +var wasmDilation2D; +function setup24(backend2) { + wasmDilation2D = backend2.wasm.cwrap(Dilation2D, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function dilation2D2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations } = attrs; + if (x.dtype !== filter.dtype) { + throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${x.dtype} and ${filter.dtype}`); + } + const dilationInfo = backend_util_exports.computeDilation2DInfo( + x.shape, + filter.shape, + strides, + pad3, + /*dataFormat=*/ + "NHWC", + dilations + ); + const out = backend2.makeOutput(dilationInfo.outShape, x.dtype); + wasmDilation2D( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(filter.dataId).id, + backend2.dataIdMap.get(out.dataId).id, + CppDType[x.dtype], + dilationInfo.batchSize, + /*depth=*/ + dilationInfo.inChannels, + dilationInfo.inHeight, + dilationInfo.inWidth, + dilationInfo.outHeight, + dilationInfo.outWidth, + dilationInfo.strideHeight, + dilationInfo.strideWidth, + dilationInfo.dilationHeight, + dilationInfo.dilationWidth, + dilationInfo.filterHeight, + dilationInfo.filterWidth, + dilationInfo.padInfo.top, + dilationInfo.padInfo.left + ); + return out; +} +var dilation2DConfig3 = { + kernelName: Dilation2D, + backendName: "wasm", + setupFunc: setup24, + kernelFunc: dilation2D2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Dilation2DBackpropFilter.js +var wasmDilation2DBackpropFilter; +function setup25(backend2) { + wasmDilation2DBackpropFilter = backend2.wasm.cwrap(Dilation2DBackpropFilter, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function dilation2DBackpropFilter(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, dy } = inputs; + const { strides, pad: pad3, dilations } = attrs; + if (x.dtype !== filter.dtype || x.dtype !== dy.dtype) { + throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${x.dtype}, ${filter.dtype}, and ${dy.dtype}`); + } + const dilationInfo = backend_util_exports.computeDilation2DInfo( + x.shape, + filter.shape, + strides, + pad3, + /*dataFormat=*/ + "NHWC", + dilations + ); + const gradients = backend2.makeOutput(filter.shape, filter.dtype); + wasmDilation2DBackpropFilter( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(filter.dataId).id, + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(gradients.dataId).id, + CppDType[x.dtype], + dilationInfo.batchSize, + /*depth=*/ + dilationInfo.inChannels, + dilationInfo.inHeight, + dilationInfo.inWidth, + dilationInfo.outHeight, + dilationInfo.outWidth, + dilationInfo.strideHeight, + dilationInfo.strideWidth, + dilationInfo.dilationHeight, + dilationInfo.dilationWidth, + dilationInfo.filterHeight, + dilationInfo.filterWidth, + dilationInfo.padInfo.top, + dilationInfo.padInfo.left + ); + return gradients; +} +var dilation2DBackpropFilterConfig2 = { + kernelName: Dilation2DBackpropFilter, + backendName: "wasm", + setupFunc: setup25, + kernelFunc: dilation2DBackpropFilter +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Dilation2DBackpropInput.js +var wasmDilation2DBackpropInput; +function setup26(backend2) { + wasmDilation2DBackpropInput = backend2.wasm.cwrap(Dilation2DBackpropInput, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function dilation2DBackpropInput(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, dy } = inputs; + const { strides, pad: pad3, dilations } = attrs; + if (x.dtype !== filter.dtype || x.dtype !== dy.dtype) { + throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${x.dtype}, ${filter.dtype}, and ${dy.dtype}`); + } + const dilationInfo = backend_util_exports.computeDilation2DInfo( + x.shape, + filter.shape, + strides, + pad3, + /*dataFormat=*/ + "NHWC", + dilations + ); + const gradients = backend2.makeOutput(x.shape, x.dtype); + wasmDilation2DBackpropInput( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(filter.dataId).id, + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(gradients.dataId).id, + CppDType[x.dtype], + dilationInfo.batchSize, + /*depth=*/ + dilationInfo.inChannels, + dilationInfo.inHeight, + dilationInfo.inWidth, + dilationInfo.outHeight, + dilationInfo.outWidth, + dilationInfo.strideHeight, + dilationInfo.strideWidth, + dilationInfo.dilationHeight, + dilationInfo.dilationWidth, + dilationInfo.filterHeight, + dilationInfo.filterWidth, + dilationInfo.padInfo.top, + dilationInfo.padInfo.left + ); + return gradients; +} +var dilation2DBackpropInputConfig2 = { + kernelName: Dilation2DBackpropInput, + backendName: "wasm", + setupFunc: setup26, + kernelFunc: dilation2DBackpropInput +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Elu.js +var eluConfig3 = createUnaryKernelConfig(Elu); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/EluGrad.js +var wasmEluGrad; +function setup27(backend2) { + wasmEluGrad = backend2.wasm.cwrap(EluGrad, null, [ + "number", + "number", + "number" + // outId + ]); +} +function eluGrad3(args) { + const { inputs, backend: backend2 } = args; + const { dy, y } = inputs; + const out = backend2.makeOutput(y.shape, "float32"); + const tensorId = (x) => { + return backend2.dataIdMap.get(x.dataId).id; + }; + wasmEluGrad(tensorId(y), tensorId(dy), tensorId(out)); + return out; +} +var eluGradConfig4 = { + kernelName: EluGrad, + backendName: "wasm", + setupFunc: setup27, + kernelFunc: eluGrad3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Equal.js +var supportsFullBroadcast3 = false; +var equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast3, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Erf.js +var erfConfig3 = createUnaryKernelConfig(Erf); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Exp.js +var expConfig3 = createUnaryKernelConfig(Exp, "float32"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ExpandDims.js +function expandDims5(args) { + const { inputs, attrs, backend: backend2 } = args; + const { input: input2 } = inputs; + const { dim } = attrs; + const inputRank = input2.shape.length; + const newShape = input2.shape.slice(); + let $dim = dim; + if (dim < 0) { + util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`); + $dim = inputRank + dim + 1; + } + newShape.splice($dim, 0, 1); + return reshape5({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } }); +} +var expandDimsConfig3 = { + kernelName: ExpandDims, + backendName: "wasm", + kernelFunc: expandDims5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Expm1.js +var expm1Config3 = createUnaryKernelConfig(Expm1, "float32"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Fill.js +function fill4(args) { + const { attrs: { shape, value }, backend: backend2 } = args; + let { attrs: { dtype } } = args; + dtype = dtype || util_exports.inferDtype(value); + const out = backend2.makeOutput(shape, dtype); + const outVals = backend2.typedArrayFromHeap(out); + outVals.fill(value); + return out; +} +var fillConfig3 = { + kernelName: Fill, + backendName: "wasm", + kernelFunc: fill4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FlipLeftRight.js +var wasmFlipLeftRight; +function setup28(backend2) { + wasmFlipLeftRight = backend2.wasm.cwrap(FlipLeftRight, null, [ + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function flipLeftRight2(args) { + const { inputs, backend: backend2 } = args; + const { image: image2 } = inputs; + const out = backend2.makeOutput(image2.shape, image2.dtype); + const imageId = backend2.dataIdMap.get(image2.dataId).id; + const outId = backend2.dataIdMap.get(out.dataId).id; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId); + return out; +} +var flipLeftRightConfig3 = { + kernelName: FlipLeftRight, + backendName: "wasm", + kernelFunc: flipLeftRight2, + setupFunc: setup28 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Floor.js +var floorConfig3 = createUnaryKernelConfig(Floor); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FloorDiv.js +var supportsFullBroadcast4 = false; +var floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast4); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedBatchNorm.js +var wasmBatchNorm; +function setup29(backend2) { + wasmBatchNorm = backend2.wasm.cwrap(FusedBatchNorm, null, ["number", "number", "number", "number", "number", "number", "number"]); +} +function fusedBatchNorm(args) { + const { backend: backend2, inputs, attrs } = args; + const { varianceEpsilon } = attrs; + const { x, mean: mean4, variance, offset, scale: scale2 } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const meanId = backend2.dataIdMap.get(mean4.dataId).id; + const varianceId = backend2.dataIdMap.get(variance.dataId).id; + const offsetId = offset != null ? backend2.dataIdMap.get(offset.dataId).id : 0; + const scaleId = scale2 != null ? backend2.dataIdMap.get(scale2.dataId).id : 0; + const out = backend2.makeOutput(x.shape, x.dtype); + if (util_exports.sizeFromShape(x.shape) === 0) { + return out; + } + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId); + return out; +} +var fusedBatchNormConfig = { + kernelName: FusedBatchNorm, + backendName: "wasm", + setupFunc: setup29, + kernelFunc: fusedBatchNorm +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedConv2D.js +var wasmFusedConv2d; +function setup30(backend2) { + wasmFusedConv2d = backend2.wasm.cwrap(FusedConv2D, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function fusedConv2d2(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode); + const fusedActivation = FusableActivation[activation2]; + if (fusedActivation == null) { + throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`); + } + const xId = backend2.dataIdMap.get(x.dataId).id; + const filterId = backend2.dataIdMap.get(filter.dataId).id; + const outputChannels = convInfo.outChannels; + let biasId = 0; + if (bias != null) { + const biasData = backend2.dataIdMap.get(bias.dataId); + if (biasData.shape.length !== 1) { + throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`); + } + if (biasData.shape[0] !== outputChannels) { + throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`); + } + biasId = biasData.id; + } + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const inputChannels = convInfo.inChannels; + const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0; + const batchSize = convInfo.batchSize; + const inHeight = convInfo.inHeight; + const inWidth = convInfo.inWidth; + if (dataFormat !== "NHWC") { + throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id; + wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId); + return out; +} +var fusedConv2DConfig3 = { + kernelName: FusedConv2D, + backendName: "wasm", + setupFunc: setup30, + kernelFunc: fusedConv2d2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedDepthwiseConv2D.js +var wasmFusedDepthwiseConv2d; +function setup31(backend2) { + wasmFusedDepthwiseConv2d = backend2.wasm.cwrap(FusedDepthwiseConv2D, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function fusedDepthwiseConv2d(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const convInfo = backend_util_exports.computeConv2DInfo( + x.shape, + filter.shape, + strides, + dilations, + pad3, + dimRoundingMode, + true + /* depthwise */ + ); + const fusedActivation = FusableActivation[activation2]; + if (fusedActivation == null) { + throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`); + } + const xId = backend2.dataIdMap.get(x.dataId).id; + const filterId = backend2.dataIdMap.get(filter.dataId).id; + const outputChannels = convInfo.outChannels; + let biasId = 0; + if (bias != null) { + const biasData = backend2.dataIdMap.get(bias.dataId); + if (biasData.shape.length !== 1) { + throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`); + } + if (biasData.shape[0] !== outputChannels) { + throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`); + } + biasId = biasData.id; + } + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const inputChannels = convInfo.inChannels; + const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0; + const batchSize = convInfo.batchSize; + const inHeight = convInfo.inHeight; + const inWidth = convInfo.inWidth; + if (dataFormat !== "NHWC") { + throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id; + wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId); + return out; +} +var fusedDepthwiseConv2DConfig3 = { + kernelName: FusedDepthwiseConv2D, + backendName: "wasm", + setupFunc: setup31, + kernelFunc: fusedDepthwiseConv2d +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherNd.js +var wasmGatherNd; +function setup32(backend2) { + wasmGatherNd = backend2.wasm.cwrap(GatherNd, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number" + // outId + ]); +} +function gatherNd3(args) { + const { backend: backend2, inputs } = args; + const { params, indices } = inputs; + const [resultShape, numSlices, sliceSize, strides] = gather_nd_util_exports.prepareAndValidate(params, indices); + const out = backend2.makeOutput(resultShape, params.dtype); + if (numSlices === 0) { + return out; + } + const indicesShape = indices.shape; + const sliceRank = indicesShape[indicesShape.length - 1]; + const xData = backend2.dataIdMap.get(params.dataId); + const xId = xData.id; + const indicesData = backend2.dataIdMap.get(indices.dataId); + const indicesId = indicesData.id; + const stridesBytes = new Uint8Array(new Int32Array(strides).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId); + return out; +} +var gatherNdConfig3 = { + kernelName: GatherNd, + backendName: "wasm", + setupFunc: setup32, + kernelFunc: gatherNd3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherV2.js +var wasmGather; +function setup33(backend2) { + wasmGather = backend2.wasm.cwrap("Gather", null, [ + "number", + "number", + "array", + "number", + "number", + "number", + "array", + "number" + // outId + ]); +} +function gatherV23(args) { + const { backend: backend2, inputs, attrs } = args; + const { x, indices } = inputs; + const { axis, batchDims } = attrs; + const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0]; + const indicesVals = backend2.readSync(indices.dataId); + const axisDim = x.shape[parsedAxis]; + for (let i = 0; i < indicesVals.length; ++i) { + const index = indicesVals[i]; + util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`); + } + const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims); + const flattenX = reshape5({ + inputs: { x }, + attrs: { + shape: [ + shapeInfo.batchSize, + shapeInfo.outerSize, + shapeInfo.dimSize, + shapeInfo.sliceSize + ] + }, + backend: backend2 + }); + const indicesSize = util_exports.sizeFromShape(indices.shape); + const flattenIndex = reshape5({ + inputs: { x: indices }, + attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }, + backend: backend2 + }); + const flattenOutputShape = [ + shapeInfo.batchSize, + shapeInfo.outerSize, + indicesSize / shapeInfo.batchSize, + shapeInfo.sliceSize + ]; + const out = backend2.makeOutput(flattenOutputShape, x.dtype); + if (util_exports.sizeFromShape(x.shape) === 0) { + return out; + } + const stridesSize = flattenX.shape.length - 1; + const xData = backend2.dataIdMap.get(flattenX.dataId); + const xId = xData.id; + const indicesData = backend2.dataIdMap.get(flattenIndex.dataId); + const indicesId = indicesData.id; + const outId = backend2.dataIdMap.get(out.dataId).id; + const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer); + const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer); + wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, shapeInfo.batchSize, outStridesBytes, outId); + backend2.disposeData(flattenX.dataId); + backend2.disposeData(flattenIndex.dataId); + out.shape = shapeInfo.outputShape; + return out; +} +var gatherV2Config3 = { + kernelName: GatherV2, + backendName: "wasm", + setupFunc: setup33, + kernelFunc: gatherV23 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Greater.js +var supportsFullBroadcast5 = false; +var greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast5, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GreaterEqual.js +var supportsFullBroadcast6 = false; +var greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast6, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/IsFinite.js +var isFiniteConfig3 = createUnaryKernelConfig(IsFinite, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/IsInf.js +var isInfConfig3 = createUnaryKernelConfig(IsInf, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/IsNan.js +var isNaNConfig3 = createUnaryKernelConfig(IsNan, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LeakyRelu.js +var wasmFunc2; +function setupFunc2(backend2) { + wasmFunc2 = backend2.wasm.cwrap(LeakyRelu, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function leakyRelu4(args) { + const { inputs: { x }, attrs: { alpha }, backend: backend2 } = args; + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(x.shape, "float32"); + if (util_exports.sizeFromShape(x.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmFunc2(xId, CppDType[x.dtype], alpha, outId); + } + return out; +} +var leakyReluConfig3 = { + kernelName: LeakyRelu, + backendName: "wasm", + setupFunc: setupFunc2, + kernelFunc: leakyRelu4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Less.js +var supportsFullBroadcast7 = false; +var lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast7, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LessEqual.js +var supportsFullBroadcast8 = false; +var lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast8, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LinSpace.js +var wasmLinSpace; +function setup34(backend2) { + wasmLinSpace = backend2.wasm.cwrap(LinSpace, null, [ + "number", + "number", + "number", + "number" + // num + ]); +} +function linSpace3(args) { + const { attrs, backend: backend2 } = args; + const { start, stop, num } = attrs; + const numInt = Math.floor(num); + const out = backend2.makeOutput([numInt], "float32"); + wasmLinSpace(backend2.dataIdMap.get(out.dataId).id, start, stop, numInt); + return out; +} +var linSpaceConfig3 = { + kernelName: LinSpace, + backendName: "wasm", + setupFunc: setup34, + kernelFunc: linSpace3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log.js +var logConfig3 = createUnaryKernelConfig(Log); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log1p.js +var log1pConfig3 = createUnaryKernelConfig(Log1p); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalAnd.js +var supportsFullBroadcast9 = false; +var logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast9, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalNot.js +var logicalNotConfig3 = createUnaryKernelConfig(LogicalNot); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalOr.js +var supportsFullBroadcast10 = false; +var logicalOrConfig3 = createBinaryKernelConfig(LogicalOr, supportsFullBroadcast10, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalXor.js +var supportsFullBroadcast11 = false; +var logicalXorConfig = createBinaryKernelConfig(LogicalXor, supportsFullBroadcast11, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LRN.js +var wasmLRN; +function setup35(backend2) { + wasmLRN = backend2.wasm.cwrap(LRN, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // beta + ]); +} +function lrn2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + if (x.dtype !== "float32") { + throw new Error("LRN error: x must have dtype float32"); + } + const out = backend2.makeOutput(x.shape, x.dtype); + wasmLRN( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(out.dataId).id, + /*channels=*/ + x.shape[3], + depthRadius, + bias, + alpha, + beta + ); + return out; +} +var lrnConfig = { + kernelName: LRN, + backendName: "wasm", + setupFunc: setup35, + kernelFunc: lrn2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LRNGrad.js +var wasmLRNGrad; +function setup36(backend2) { + wasmLRNGrad = backend2.wasm.cwrap(LRNGrad, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // beta + ]); +} +function lrnGrad2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, y, dy } = inputs; + const { depthRadius, bias, alpha, beta } = attrs; + if (x.dtype !== "float32" || y.dtype !== "float32" || dy.dtype !== "float32") { + throw new Error("LRNGrad error: x, y, and dy must have dtype float32"); + } + const dx = backend2.makeOutput(x.shape, x.dtype); + wasmLRNGrad( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(y.dataId).id, + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(dx.dataId).id, + /*channels=*/ + dy.shape[3], + depthRadius, + bias, + alpha, + beta + ); + return dx; +} +var lrnGradConfig2 = { + kernelName: LRNGrad, + backendName: "wasm", + setupFunc: setup36, + kernelFunc: lrnGrad2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Max.js +var wasmMax; +function setup37(backend2) { + wasmMax = backend2.wasm.cwrap(Max, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function max5(args) { + const { backend: backend2, inputs, attrs } = args; + const { reductionIndices: axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + input2 = transposed; + inputId = transposedId; + } + const inputRank = input2.shape.length; + backend_util_exports.assertAxesAreInnerMostDims("max", axes, inputRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, x.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmMax(inputId, CppDType[x.dtype], reduceSize, outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var maxConfig3 = { + kernelName: Max, + backendName: "wasm", + setupFunc: setup37, + kernelFunc: max5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Maximum.js +var supportsFullBroadcast12 = false; +var maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast12); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool.js +var wasmMaxPool; +function setup38(backend2) { + wasmMaxPool = backend2.wasm.cwrap(MaxPool, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function maxPool4(args) { + const { inputs, attrs, backend: backend2 } = args; + const x = inputs.x; + const xId = backend2.dataIdMap.get(x.dataId).id; + util_exports.assert(x.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${x.dtype}.`); + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode); + const filterHeight = convInfo.filterHeight; + const filterWidth = convInfo.filterWidth; + const padTop = convInfo.padInfo.top; + const padRight = convInfo.padInfo.right; + const padBottom = convInfo.padInfo.bottom; + const padLeft = convInfo.padInfo.left; + const dilationHeight = convInfo.dilationHeight; + const dilationWidth = convInfo.dilationWidth; + const strideHeight = convInfo.strideHeight; + const strideWidth = convInfo.strideWidth; + const inputChannels = convInfo.inChannels; + const outputChannels = convInfo.outChannels; + if (convInfo.dataFormat !== "channelsLast") { + throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`); + } + const out = backend2.makeOutput(convInfo.outShape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId); + return out; +} +var maxPoolConfig3 = { + kernelName: MaxPool, + backendName: "wasm", + setupFunc: setup38, + kernelFunc: maxPool4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool3D.js +var wasmMaxPool3D; +function setup39(backend2) { + wasmMaxPool3D = backend2.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" + // padLeft + ]); +} +function maxPool3D2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs; + const convInfo = backend_util_exports.computePool3DInfo( + x.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3, + dimRoundingMode, + dataFormat + ); + const out = backend2.makeOutput(convInfo.outShape, x.dtype); + wasmMaxPool3D( + backend2.dataIdMap.get(x.dataId).id, + backend2.dataIdMap.get(out.dataId).id, + convInfo.batchSize, + // Since Pool3D ops (AvgPool3D and MaxPool3D) support 3D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inDepth, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outDepth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideDepth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationDepth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterDepth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.front, + convInfo.padInfo.top, + convInfo.padInfo.left + ); + return out; +} +var maxPool3DConfig3 = { + kernelName: MaxPool3D, + backendName: "wasm", + setupFunc: setup39, + kernelFunc: maxPool3D2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool3DGrad.js +var wasmMaxPool3DGrad; +function setup40(backend2) { + wasmMaxPool3DGrad = backend2.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" + // padLeft + ]); +} +function maxPool3DGrad3(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool3DInfo( + input2.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3, + dimRoundingMode + ); + const dx = backend2.makeOutput(input2.shape, input2.dtype); + wasmMaxPool3DGrad( + backend2.dataIdMap.get(input2.dataId).id, + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(dx.dataId).id, + convInfo.batchSize, + // Since Pool3D ops (MaxPool3D and MaxPool3D) support 3D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inDepth, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outDepth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideDepth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationDepth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterDepth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.front, + convInfo.padInfo.top, + convInfo.padInfo.left + ); + return dx; +} +var maxPool3DGradConfig4 = { + kernelName: MaxPool3DGrad, + backendName: "wasm", + setupFunc: setup40, + kernelFunc: maxPool3DGrad3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPoolGrad.js +var wasmMaxPoolGrad; +function setup41(backend2) { + wasmMaxPoolGrad = backend2.wasm.cwrap("MaxPoolGrad", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function maxPoolGrad4(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, input: input2 } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const convInfo = backend_util_exports.computePool2DInfo( + input2.shape, + filterSize, + strides, + /*dilations=*/ + 1, + pad3, + dimRoundingMode + ); + const dx = backend2.makeOutput(input2.shape, input2.dtype); + wasmMaxPoolGrad( + backend2.dataIdMap.get(input2.dataId).id, + backend2.dataIdMap.get(dy.dataId).id, + backend2.dataIdMap.get(dx.dataId).id, + convInfo.batchSize, + // Since Pool ops (MaxPool and MaxPool) support 2D filter only, in + // channels should always equal to out channels. + /*channelSize=*/ + convInfo.inChannels, + convInfo.inHeight, + convInfo.inWidth, + convInfo.outHeight, + convInfo.outWidth, + convInfo.strideHeight, + convInfo.strideWidth, + convInfo.dilationHeight, + convInfo.dilationWidth, + convInfo.effectiveFilterHeight, + convInfo.effectiveFilterWidth, + convInfo.padInfo.top, + convInfo.padInfo.left + ); + return dx; +} +var maxPoolGradConfig4 = { + kernelName: MaxPoolGrad, + backendName: "wasm", + setupFunc: setup41, + kernelFunc: maxPoolGrad4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPoolWithArgmax.js +var wasmMaxPoolWithArgmax; +function setup42(backend2) { + wasmMaxPoolWithArgmax = backend2.wasm.cwrap("MaxPoolWithArgmax", null, [ + "number", + "number", + "number", + "number", + "boolean", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // padLeft + ]); +} +function maxPoolWithArgmax2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs; + util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`); + const dilations = [1, 1]; + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3); + const pooled = backend2.makeOutput(convInfo.outShape, x.dtype); + const indexes = backend2.makeOutput(convInfo.outShape, "int32"); + wasmMaxPoolWithArgmax(backend2.dataIdMap.get(x.dataId).id, backend2.dataIdMap.get(pooled.dataId).id, backend2.dataIdMap.get(indexes.dataId).id, CppDType[x.dtype], includeBatchInIndex, convInfo.batchSize, convInfo.inChannels, convInfo.inHeight, convInfo.inWidth, convInfo.outHeight, convInfo.outWidth, convInfo.strideHeight, convInfo.strideWidth, convInfo.dilationHeight, convInfo.dilationWidth, convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth, convInfo.padInfo.top, convInfo.padInfo.left); + return [pooled, indexes]; +} +var maxPoolWithArgmaxConfig3 = { + kernelName: MaxPoolWithArgmax, + backendName: "wasm", + setupFunc: setup42, + kernelFunc: maxPoolWithArgmax2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Mean.js +var wasmMean; +function setup43(backend2) { + wasmMean = backend2.wasm.cwrap(Mean, null, ["number, number, number"]); +} +function mean3(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + let reductionAxes = axes; + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + if (transposedId !== xId) { + input2 = transposed; + inputId = transposedId; + reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length); + } + } + backend_util_exports.assertAxesAreInnerMostDims("mean", reductionAxes, input2.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + let castedInput = input2; + if (input2.dtype !== "float32") { + castedInput = cast5({ backend: backend2, inputs: { x: input2 }, attrs: { dtype: "float32" } }); + inputId = backend2.dataIdMap.get(castedInput.dataId).id; + } + const out = backend2.makeOutput(outShape, "float32"); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmMean(inputId, reduceSize, outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + if (input2.dtype !== "float32") { + backend2.disposeData(castedInput.dataId); + } + return out; +} +var meanConfig3 = { + kernelName: Mean, + backendName: "wasm", + setupFunc: setup43, + kernelFunc: mean3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Min.js +var wasmMin; +function setup44(backend2) { + wasmMin = backend2.wasm.cwrap(Min, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function min5(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + if (transposedId !== xId) { + input2 = transposed; + inputId = transposedId; + } + } + const inputRank = input2.shape.length; + backend_util_exports.assertAxesAreInnerMostDims("min", axes, inputRank); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, input2.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmMin(inputId, CppDType[x.dtype], reduceSize, outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var minConfig3 = { + kernelName: Min, + backendName: "wasm", + setupFunc: setup44, + kernelFunc: min5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Minimum.js +var supportsFullBroadcast13 = false; +var minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast13); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MirrorPad.js +var MirrorPaddingMode; +(function(MirrorPaddingMode2) { + MirrorPaddingMode2[MirrorPaddingMode2["reflect"] = 0] = "reflect"; + MirrorPaddingMode2[MirrorPaddingMode2["symmetric"] = 1] = "symmetric"; +})(MirrorPaddingMode || (MirrorPaddingMode = {})); +var wasmMirrorPad; +function setup45(backend2) { + wasmMirrorPad = backend2.wasm.cwrap(MirrorPad, null, [ + "number", + "array", + "number", + "number", + "array", + "array", + "number", + "number" + // outId + ]); +} +function mirrorPad3(args) { + const { inputs: { x }, backend: backend2, attrs: { paddings, mode } } = args; + const outShape = paddings.map( + (p2, i) => p2[0] + x.shape[i] + p2[1] + /* afterPad */ + ); + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(outShape, x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]); + const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]); + const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer); + const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer); + wasmMirrorPad(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, MirrorPaddingMode[mode], outId); + return out; +} +var mirrorPadConfig3 = { + kernelName: MirrorPad, + backendName: "wasm", + kernelFunc: mirrorPad3, + setupFunc: setup45 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softmax.js +var wasmFunc3; +function setup46(backend2) { + wasmFunc3 = backend2.wasm.cwrap(Softmax, null, [ + "number", + "number", + "number", + "number" + // batch + ]); +} +function softmax5(args) { + const { backend: backend2, inputs: { logits }, attrs: { dim } } = args; + const xId = backend2.dataIdMap.get(logits.dataId).id; + const out = backend2.makeOutput(logits.shape, logits.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const channels = logits.shape[dim]; + const batch = util_exports.sizeFromShape(logits.shape) / channels; + if (util_exports.sizeFromShape(out.shape) === 0) { + return out; + } + wasmFunc3(xId, outId, channels, batch); + return out; +} +var softmaxConfig3 = { + kernelName: Softmax, + backendName: "wasm", + setupFunc: setup46, + kernelFunc: softmax5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multinomial.js +var wasmMultinomial; +function setup47(backend2) { + wasmMultinomial = backend2.wasm.cwrap(Multinomial, null, [ + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function multinomial4(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { numSamples, seed, normalized } = attrs; + if (logits.dtype !== "float32") { + throw new Error(`Tensor logits must have dtype float32, got ${logits.dtype}`); + } + const probabilities = normalized ? logits : softmax5({ + inputs: { logits }, + backend: backend2, + attrs: { dim: logits.shape.length - 1 } + }); + const [batchSize, numEvents] = probabilities.shape; + const out = backend2.makeOutput([batchSize, numSamples], "int32"); + wasmMultinomial(backend2.dataIdMap.get(probabilities.dataId).id, batchSize, numEvents, numSamples, seed, backend2.dataIdMap.get(out.dataId).id); + if (!normalized) { + backend2.disposeData(probabilities.dataId); + } + return out; +} +var multinomialConfig3 = { + kernelName: Multinomial, + backendName: "wasm", + setupFunc: setup47, + kernelFunc: multinomial4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Mod.js +var modConfig3 = createBinaryKernelConfig( + Mod, + /*supportsFullBroadcast=*/ + true +); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multiply.js +var supportsFullBroadcast14 = true; +var multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast14); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Neg.js +var negConfig3 = createUnaryKernelConfig(Neg); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppression_util.js +function parseResultStruct(backend2, resOffset) { + const result = new Int32Array(backend2.wasm.HEAPU8.buffer, resOffset, 4); + const pSelectedIndices = result[0]; + const selectedSize = result[1]; + const pSelectedScores = result[2]; + const pValidOutputs = result[3]; + backend2.wasm._free(resOffset); + return { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs }; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV3.js +var wasmFunc4; +function setup48(backend2) { + wasmFunc4 = backend2.wasm.cwrap( + NonMaxSuppressionV3, + "number", + // Result* + [ + "number", + "number", + "number", + "number", + "number" + // scoreThreshold + ] + ); +} +function kernelFunc(args) { + const { backend: backend2, inputs, attrs } = args; + const { iouThreshold, maxOutputSize, scoreThreshold } = attrs; + const { boxes, scores } = inputs; + const boxesId = backend2.dataIdMap.get(boxes.dataId).id; + const scoresId = backend2.dataIdMap.get(scores.dataId).id; + const resOffset = wasmFunc4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold); + const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset); + backend2.wasm._free(pSelectedScores); + backend2.wasm._free(pValidOutputs); + const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices); + return selectedIndicesTensor; +} +var nonMaxSuppressionV3Config3 = { + kernelName: NonMaxSuppressionV3, + backendName: "wasm", + setupFunc: setup48, + kernelFunc +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV4.js +var wasmFunc5; +function setup49(backend2) { + wasmFunc5 = backend2.wasm.cwrap( + NonMaxSuppressionV4, + "number", + // Result* + [ + "number", + "number", + "number", + "number", + "number", + "bool" + // padToMaxOutputSize + ] + ); +} +function nonMaxSuppressionV43(args) { + const { backend: backend2, inputs, attrs } = args; + const { iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize } = attrs; + const { boxes, scores } = inputs; + const boxesId = backend2.dataIdMap.get(boxes.dataId).id; + const scoresId = backend2.dataIdMap.get(scores.dataId).id; + const resOffset = wasmFunc5(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize); + const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset); + backend2.wasm._free(pSelectedScores); + const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices); + const validOutputsTensor = backend2.makeOutput([], "int32", pValidOutputs); + return [selectedIndicesTensor, validOutputsTensor]; +} +var nonMaxSuppressionV4Config3 = { + kernelName: NonMaxSuppressionV4, + backendName: "wasm", + setupFunc: setup49, + kernelFunc: nonMaxSuppressionV43 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV5.js +var wasmFunc6; +function setup50(backend2) { + wasmFunc6 = backend2.wasm.cwrap( + NonMaxSuppressionV5, + "number", + // Result* + [ + "number", + "number", + "number", + "number", + "number", + "number" + // softNmsSigma + ] + ); +} +function kernelFunc2(args) { + const { backend: backend2, inputs, attrs } = args; + const { iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma } = attrs; + const { boxes, scores } = inputs; + const boxesId = backend2.dataIdMap.get(boxes.dataId).id; + const scoresId = backend2.dataIdMap.get(scores.dataId).id; + const resOffset = wasmFunc6(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset); + backend2.wasm._free(pValidOutputs); + const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices); + const selectedScoresTensor = backend2.makeOutput([selectedSize], "float32", pSelectedScores); + return [selectedIndicesTensor, selectedScoresTensor]; +} +var nonMaxSuppressionV5Config3 = { + kernelName: NonMaxSuppressionV5, + backendName: "wasm", + setupFunc: setup50, + kernelFunc: kernelFunc2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NotEqual.js +var supportsFullBroadcast15 = false; +var notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast15, "bool"); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OneHot.js +var wasmOneHot; +function setup51(backend2) { + wasmOneHot = backend2.wasm.cwrap(OneHot, null, [ + "number", + "number", + "number", + "number", + "number" + // out_id + ]); +} +function oneHot4(args) { + const { inputs, backend: backend2, attrs } = args; + const { indices } = inputs; + const { dtype, depth, onValue, offValue } = attrs; + const out = backend2.makeOutput([...indices.shape, depth], dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const indicesData = backend2.dataIdMap.get(indices.dataId); + const indicesId = indicesData.id; + wasmOneHot(indicesId, depth, onValue, offValue, outId); + return out; +} +var oneHotConfig3 = { + kernelName: OneHot, + backendName: "wasm", + setupFunc: setup51, + kernelFunc: oneHot4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OnesLike.js +function onesLike4(args) { + const { inputs: { x }, backend: backend2 } = args; + const out = backend2.makeOutput(x.shape, x.dtype); + const outVals = backend2.typedArrayFromHeap(out); + outVals.fill(1); + return out; +} +var onesLikeConfig3 = { + kernelName: OnesLike, + backendName: "wasm", + kernelFunc: onesLike4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pack.js +function pack3(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + if (inputs.length === 1) { + return expandDims5({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } }); + } + const shape = inputs[0].shape; + const dtype = inputs[0].dtype; + inputs.forEach((t) => { + util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes"); + util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes"); + }); + const intermediateTensorInfos = []; + const expandedTensors = inputs.map((t) => { + const expandedT = expandDims5({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } }); + intermediateTensorInfos.push(expandedT); + return expandedT; + }); + const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } }); + intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId)); + return result; +} +var packConfig3 = { + kernelName: Pack, + backendName: "wasm", + kernelFunc: pack3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/PadV2.js +var wasmPadV2; +function setup52(backend2) { + wasmPadV2 = backend2.wasm.cwrap(PadV2, null, [ + "number", + "array", + "number", + "number", + "array", + "array", + "number", + "number" + // outId + ]); +} +function pad2(args) { + const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args; + const outShape = paddings.map( + (p2, i) => p2[0] + x.shape[i] + p2[1] + /* afterPad */ + ); + if (util_exports.sizeFromShape(x.shape) === 0) { + return fill4({ + backend: backend2, + attrs: { shape: outShape, value: constantValue, dtype: x.dtype } + }); + } + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(outShape, x.dtype); + const outTensorData = backend2.dataIdMap.get(out.dataId); + const outId = outTensorData.id; + const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]); + const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]); + const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer); + const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer); + wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId); + return out; +} +var padV2Config3 = { + kernelName: PadV2, + backendName: "wasm", + kernelFunc: pad2, + setupFunc: setup52 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pow.js +var supportsFullBroadcast16 = false; +var powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast16); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prelu.js +var wasmPrelu; +function setup53(backend2) { + wasmPrelu = backend2.wasm.cwrap(Prelu, null, [ + "number", + "number", + "number" + // out_id + ]); +} +function prelu5(args) { + const { inputs, backend: backend2 } = args; + const { x, alpha } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const weightsId = backend2.dataIdMap.get(alpha.dataId).id; + let inputId = xId; + const input2 = x; + let castedInput = input2; + if (input2.dtype !== "float32") { + castedInput = cast5({ backend: backend2, inputs: { x }, attrs: { dtype: "float32" } }); + inputId = backend2.dataIdMap.get(castedInput.dataId).id; + } + const out = backend2.makeOutput(x.shape, "float32"); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmPrelu(inputId, weightsId, outId); + if (input2.dtype !== "float32") { + backend2.disposeData(castedInput.dataId); + } + return out; +} +var preluConfig3 = { + kernelName: Prelu, + backendName: "wasm", + setupFunc: setup53, + kernelFunc: prelu5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prod.js +var wasmProd; +function setup54(backend2) { + wasmProd = backend2.wasm.cwrap(Prod, null, [ + "number", + "number", + "number", + "number" + ]); +} +function prod4(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + let reductionAxes = axes; + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + if (transposedId !== xId) { + input2 = transposed; + inputId = transposedId; + reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length); + } + } + backend_util_exports.assertAxesAreInnerMostDims("prod", reductionAxes, input2.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, input2.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmProd(inputId, reduceSize, CppDType[out.dtype], outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var prodConfig3 = { + kernelName: Prod, + backendName: "wasm", + setupFunc: setup54, + kernelFunc: prod4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Range.js +var range5 = (args) => { + const { backend: backend2, attrs } = args; + const { start, stop, step: step5, dtype } = attrs; + const values = rangeImpl(start, stop, step5, dtype); + const out = backend2.makeOutput([values.length], dtype); + const outVals = backend2.typedArrayFromHeap(out); + outVals.set(values); + return out; +}; +var rangeConfig3 = { + kernelName: Range, + backendName: "wasm", + kernelFunc: range5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RealDiv.js +var supportsFullBroadcast17 = true; +var realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast17); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reciprocal.js +var reciprocalConfig3 = createUnaryKernelConfig(Reciprocal); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu.js +var reluConfig3 = createUnaryKernelConfig(Relu); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu6.js +var relu6Config3 = createUnaryKernelConfig(Relu6); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinear.js +var wasmResizeBilinear; +function setup55(backend2) { + wasmResizeBilinear = backend2.wasm.cwrap(ResizeBilinear, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function resizeBilinear5(args) { + const { backend: backend2, inputs, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + const [newHeight, newWidth] = size; + const [batch, oldHeight, oldWidth, numChannels] = images.shape; + const outShape = [batch, newHeight, newWidth, numChannels]; + let xData = backend2.dataIdMap.get(images.dataId); + let castedData; + if (xData.dtype !== "float32") { + castedData = cast5({ backend: backend2, inputs: { x: images }, attrs: { dtype: "float32" } }); + xData = backend2.dataIdMap.get(castedData.dataId); + } + const xId = xData.id; + const out = backend2.makeOutput(outShape, "float32"); + if (util_exports.sizeFromShape(images.shape) === 0) { + return out; + } + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId); + if (castedData != null) { + backend2.disposeData(castedData.dataId); + } + return out; +} +var resizeBilinearConfig3 = { + kernelName: ResizeBilinear, + backendName: "wasm", + setupFunc: setup55, + kernelFunc: resizeBilinear5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinearGrad.js +var wasmResizeBilinearGrad; +function setup56(backend2) { + wasmResizeBilinearGrad = backend2.wasm.cwrap(ResizeBilinearGrad, null, [ + "number", + "number", + "number", + "array", + "array", + "boolean" + // alignCorners + ]); +} +function resizeBilinearGrad3(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + const dx = backend2.makeOutput(images.shape, "float32"); + let xData = backend2.dataIdMap.get(images.dataId); + let castedData; + if (xData.dtype !== "float32") { + castedData = cast5({ + backend: backend2, + inputs: { x: images }, + attrs: { dtype: "float32" } + }); + xData = backend2.dataIdMap.get(castedData.dataId); + } + wasmResizeBilinearGrad(backend2.dataIdMap.get(images.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dx.dataId).id, new Uint8Array(new Int32Array(images.shape).buffer), new Uint8Array(new Int32Array(dy.shape).buffer), alignCorners); + if (castedData != null) { + backend2.disposeData(castedData.dataId); + } + return dx; +} +var resizeBilinearGradConfig4 = { + kernelName: ResizeBilinearGrad, + backendName: "wasm", + setupFunc: setup56, + kernelFunc: resizeBilinearGrad3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeNearestNeighbor.js +var wasmResizeNearestNeighbor; +function setup57(backend2) { + wasmResizeNearestNeighbor = backend2.wasm.cwrap(ResizeNearestNeighbor, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function resizeNearestNeighbor4(args) { + const { backend: backend2, inputs, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + const [newHeight, newWidth] = size; + const [batch, oldHeight, oldWidth, numChannels] = images.shape; + const outShape = [batch, newHeight, newWidth, numChannels]; + const out = backend2.makeOutput(outShape, "float32"); + if (util_exports.sizeFromShape(images.shape) === 0) { + return out; + } + let xData = backend2.dataIdMap.get(images.dataId); + let castedData; + if (xData.dtype !== "float32") { + castedData = cast5({ + backend: backend2, + inputs: { x: images }, + attrs: { dtype: "float32" } + }); + xData = backend2.dataIdMap.get(castedData.dataId); + } + const xId = xData.id; + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmResizeNearestNeighbor(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId); + if (castedData != null) { + backend2.disposeData(castedData.dataId); + } + return out; +} +var resizeNearestNeighborConfig3 = { + kernelName: ResizeNearestNeighbor, + backendName: "wasm", + setupFunc: setup57, + kernelFunc: resizeNearestNeighbor4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeNearestNeighborGrad.js +var wasmResizeNearestNeighborGrad; +function setup58(backend2) { + wasmResizeNearestNeighborGrad = backend2.wasm.cwrap(ResizeNearestNeighborGrad, null, [ + "number", + "number", + "number", + "array", + "array", + "boolean" + // alignCorners + ]); +} +function resizeNearestNeighborGrad3(args) { + const { inputs, backend: backend2, attrs } = args; + const { images, dy } = inputs; + const { alignCorners } = attrs; + const dx = backend2.makeOutput(images.shape, "float32"); + let xData = backend2.dataIdMap.get(images.dataId); + let castedData; + if (xData.dtype !== "float32") { + castedData = cast5({ + backend: backend2, + inputs: { x: images }, + attrs: { dtype: "float32" } + }); + xData = backend2.dataIdMap.get(castedData.dataId); + } + wasmResizeNearestNeighborGrad(backend2.dataIdMap.get(images.dataId).id, backend2.dataIdMap.get(dy.dataId).id, backend2.dataIdMap.get(dx.dataId).id, new Uint8Array(new Int32Array(images.shape).buffer), new Uint8Array(new Int32Array(dy.shape).buffer), alignCorners); + if (castedData != null) { + backend2.disposeData(castedData.dataId); + } + return dx; +} +var resizeNearestNeighborGradConfig4 = { + kernelName: ResizeNearestNeighborGrad, + backendName: "wasm", + setupFunc: setup58, + kernelFunc: resizeNearestNeighborGrad3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reverse.js +var wasmReverse; +function setup59(backend2) { + wasmReverse = backend2.wasm.cwrap(Reverse, null, [ + "number", + "array", + "number", + "array", + "number", + "number" + // out_id + ]); +} +function reverse4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dims } = attrs; + const axes = util_exports.parseAxisParam(dims, x.shape); + if (x.shape.length === 0) { + return identity4({ inputs: { x }, backend: backend2 }); + } + const out = backend2.makeOutput(x.shape, x.dtype); + const xId = backend2.dataIdMap.get(x.dataId).id; + const outId = backend2.dataIdMap.get(out.dataId).id; + const axesBytes = new Uint8Array(new Int32Array(axes).buffer); + const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId); + const reshaped = reshape5({ inputs: { x: out }, attrs: { shape: x.shape }, backend: backend2 }); + backend2.disposeData(out.dataId); + return reshaped; +} +var reverseConfig3 = { + kernelName: Reverse, + backendName: "wasm", + kernelFunc: reverse4, + setupFunc: setup59 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RotateWithOffset.js +var wasmRotate; +function setup60(backend2) { + wasmRotate = backend2.wasm.cwrap(RotateWithOffset, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number", + "number" + // outId + ]); +} +function rotateWithOffset2(args) { + const { inputs, backend: backend2, attrs } = args; + const { image: image2 } = inputs; + const { radians, fillValue, center } = attrs; + const out = backend2.makeOutput(image2.shape, image2.dtype); + const imageId = backend2.dataIdMap.get(image2.dataId).id; + const outId = backend2.dataIdMap.get(out.dataId).id; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth); + const fillIsBlack = fillValue === 0; + const fullOpacityValue = 255; + const fillValues2 = typeof fillValue === "number" ? [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] : [...fillValue, fullOpacityValue]; + const fillBytes = new Uint8Array(new Int32Array(fillValues2).buffer); + wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues2.length, outId); + return out; +} +var rotateWithOffsetConfig3 = { + kernelName: RotateWithOffset, + backendName: "wasm", + kernelFunc: rotateWithOffset2, + setupFunc: setup60 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Round.js +var roundConfig3 = createUnaryKernelConfig(Round); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Rsqrt.js +var rsqrtConfig3 = createUnaryKernelConfig(Rsqrt); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ScatterNd.js +var wasmScatterNd; +function setup61(backend2) { + wasmScatterNd = backend2.wasm.cwrap(ScatterNd, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number", + "number" + // outId + ]); +} +function scatterNd3(args) { + const { backend: backend2, inputs, attrs } = args; + const { indices, updates } = inputs; + const { shape } = attrs; + const out = backend2.makeOutput(shape, updates.dtype); + if (util_exports.sizeFromShape(shape) === 0) { + return out; + } + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = scatter_nd_util_exports.calculateShapes(updates, indices, shape); + const indicesData = backend2.dataIdMap.get(indices.dataId); + const indicesId = indicesData.id; + const updatesData = backend2.dataIdMap.get(updates.dataId); + const updatesId = updatesData.id; + const stridesBytes = new Uint8Array(new Int32Array(strides).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId); + return out; +} +var scatterNdConfig3 = { + kernelName: ScatterNd, + backendName: "wasm", + setupFunc: setup61, + kernelFunc: scatterNd3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SearchSorted.js +var wasmSearchSorted; +function setup62(backend2) { + wasmSearchSorted = backend2.wasm.cwrap(SearchSorted, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "bool", + "number" + // outId + ]); +} +function searchSorted4(args) { + const { inputs, backend: backend2, attrs } = args; + const { sortedSequence, values } = inputs; + const { side } = attrs; + if (sortedSequence.dtype !== values.dtype) { + throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${sortedSequence.dtype} and ${values.dtype}`); + } + const out = backend2.makeOutput(values.shape, "int32"); + function tensorId(x) { + return backend2.dataIdMap.get(x.dataId).id; + } + wasmSearchSorted( + tensorId(sortedSequence), + tensorId(values), + /*batchSize=*/ + sortedSequence.shape[0], + /*sequenceSize=*/ + sortedSequence.shape[1], + /*valuesSize=*/ + values.shape[1], + /*dtype=*/ + CppDType[sortedSequence.dtype], + /*isSideLeft=*/ + side === "left", + tensorId(out) + ); + return out; +} +var searchSortedConfig3 = { + kernelName: SearchSorted, + backendName: "wasm", + setupFunc: setup62, + kernelFunc: searchSorted4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Select.js +var wasmSelect; +function setup63(backend2) { + wasmSelect = backend2.wasm.cwrap("SelectV2", null, [ + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function select4(args) { + const { inputs, backend: backend2 } = args; + const { condition, t, e } = inputs; + const conditionId = backend2.dataIdMap.get(condition.dataId).id; + const tId = backend2.dataIdMap.get(t.dataId).id; + const eId = backend2.dataIdMap.get(e.dataId).id; + const out = backend2.makeOutput(t.shape, t.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const cRank = condition.shape.length; + const tRank = t.shape.length; + const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1)); + wasmSelect(conditionId, tId, eId, offset, outId); + return out; +} +var selectConfig3 = { + kernelName: Select, + backendName: "wasm", + kernelFunc: select4, + setupFunc: setup63 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Selu.js +var seluConfig3 = createUnaryKernelConfig(Selu); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sigmoid.js +var wasmFunc7; +function setup64(backend2) { + wasmFunc7 = backend2.wasm.cwrap(Sigmoid, null, ["number", "number"]); +} +function sigmoid4(args) { + const { backend: backend2, inputs: { x } } = args; + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(x.shape, x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + if (util_exports.sizeFromShape(out.shape) === 0) { + return out; + } + wasmFunc7(xId, outId); + return out; +} +var sigmoidConfig3 = { + kernelName: "Sigmoid", + backendName: "wasm", + setupFunc: setup64, + kernelFunc: sigmoid4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sign.js +var signConfig3 = createUnaryKernelConfig(Sign); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sin.js +var sinConfig3 = createUnaryKernelConfig(Sin); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sinh.js +var sinhConfig3 = createUnaryKernelConfig(Sinh); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softplus.js +var softplusConfig3 = createUnaryKernelConfig(Softplus); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SpaceToBatchND.js +function spaceToBatchND4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, paddings } = attrs; + const prod5 = util_exports.sizeFromShape(blockShape); + const completePaddings = [[0, 0]]; + completePaddings.push(...paddings); + for (let i = 1 + blockShape.length; i < x.shape.length; ++i) { + completePaddings.push([0, 0]); + } + const paddedX = padV2Config3.kernelFunc({ + inputs: { x }, + backend: backend2, + attrs: { paddings: completePaddings, constantValue: 0 } + }); + const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false); + const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false); + const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false); + const reshapeInputs = { x: paddedX }; + const reshapeAttrs = { shape: reshapedPaddedShape }; + const paddedXReshaped = reshape5({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs }); + const transposeInputs = { x: paddedXReshaped }; + const transposeAttrs = { perm: permutedReshapedPaddedPermutation }; + const paddedXT = transpose4({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs }); + const resultReshapeInputs = { x: paddedXT }; + const resultReshapeAttrs = { shape: flattenShape }; + const result = reshape5({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs }); + backend2.disposeData(paddedX.dataId); + backend2.disposeData(paddedXReshaped.dataId); + backend2.disposeData(paddedXT.dataId); + return result; +} +var spaceToBatchNDConfig3 = { + kernelName: SpaceToBatchND, + backendName: "wasm", + kernelFunc: spaceToBatchND4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseFillEmptyRows.js +var wasmSparseFillEmptyRows; +function setup65(backend2) { + wasmSparseFillEmptyRows = backend2.wasm.cwrap("SparseFillEmptyRows", "number", [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // exceptionValuesId + ]); +} +function sparseFillEmptyRows4(args) { + const { backend: backend2, inputs } = args; + const { indices, values, denseShape, defaultValue } = inputs; + const indicesCount = indices.shape[0]; + const rank = indices.shape[1]; + const denseRows = backend2.readSync(denseShape.dataId)[0]; + const maxOutputIndicesShape = [indicesCount + denseRows, rank]; + const indicesId = backend2.dataIdMap.get(indices.dataId).id; + const valuesId = backend2.dataIdMap.get(values.dataId).id; + const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id; + const outputIndices = backend2.makeOutput(maxOutputIndicesShape, indices.dtype); + const outputIndicesId = backend2.dataIdMap.get(outputIndices.dataId).id; + const outputValues = backend2.makeOutput(maxOutputIndicesShape.slice(0, 1), values.dtype); + const outputValuesId = backend2.dataIdMap.get(outputValues.dataId).id; + const emptyRowIndicator = backend2.makeOutput([denseRows], "bool"); + const emptyRowIndicatorId = backend2.dataIdMap.get(emptyRowIndicator.dataId).id; + const reverseIndexMap = backend2.makeOutput([indicesCount], indices.dtype); + const reverseIndexMapId = backend2.dataIdMap.get(reverseIndexMap.dataId).id; + const exceptionValues = backend2.makeOutput([4], "int32"); + const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id; + const outputRows = wasmSparseFillEmptyRows(indicesId, valuesId, CppDType[values.dtype], indicesCount, denseRows, rank, defaultValueId, outputIndicesId, outputValuesId, emptyRowIndicatorId, reverseIndexMapId, exceptionValuesId); + const exceptionValuesArray = backend2.readSync(exceptionValues.dataId); + let exceptionMessage; + switch (exceptionValuesArray[0]) { + case 1: { + exceptionMessage = backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(exceptionValuesArray[1]); + break; + } + case 2: { + exceptionMessage = backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + } + case 3: + exceptionMessage = backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]); + break; + default: + exceptionMessage = ""; + } + backend2.disposeData(exceptionValues.dataId); + if (exceptionMessage) { + backend2.disposeData(outputIndices.dataId); + backend2.disposeData(outputValues.dataId); + backend2.disposeData(emptyRowIndicator.dataId); + backend2.disposeData(reverseIndexMap.dataId); + throw new Error(exceptionMessage); + } + let resizedIndices = outputIndices; + let resizedValues = outputValues; + if (outputRows !== maxOutputIndicesShape[0]) { + resizedIndices = slice4({ + inputs: { x: outputIndices }, + attrs: { begin: 0, size: [outputRows, rank] }, + backend: backend2 + }); + resizedValues = slice4({ + inputs: { x: outputValues }, + attrs: { begin: 0, size: outputRows }, + backend: backend2 + }); + backend2.disposeData(outputIndices.dataId); + backend2.disposeData(outputValues.dataId); + } + return [resizedIndices, resizedValues, emptyRowIndicator, reverseIndexMap]; +} +var sparseFillEmptyRowsConfig3 = { + kernelName: SparseFillEmptyRows, + backendName: "wasm", + setupFunc: setup65, + kernelFunc: sparseFillEmptyRows4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseReshape.js +var wasmSparseReshape; +function setup66(backend2) { + wasmSparseReshape = backend2.wasm.cwrap(SparseReshape, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // exceptionValuesId + ]); +} +function sparseReshape4(args) { + const { backend: backend2, inputs } = args; + const { inputIndices, inputShape, newShape } = inputs; + if (inputIndices.shape.length !== 2) { + throw new Error(`Input indices should be a matrix but received shape + ${inputIndices.shape}`); + } + if (inputShape.shape.length !== 1) { + throw new Error(`Input shape should be a vector but received shape + ${inputShape.shape}`); + } + if (newShape.shape.length !== 1) { + throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`); + } + const inputIndicesId = backend2.dataIdMap.get(inputIndices.dataId).id; + const inputShapeId = backend2.dataIdMap.get(inputShape.dataId).id; + const newShapeId = backend2.dataIdMap.get(newShape.dataId).id; + const nnz = inputIndices.shape[0]; + const outputRank = util_exports.sizeFromShape(newShape.shape); + const newIndices = backend2.makeOutput([nnz, outputRank], inputIndices.dtype); + const newIndicesId = backend2.dataIdMap.get(newIndices.dataId).id; + const outputShape = backend2.makeOutput([outputRank], newShape.dtype); + const outputShapeId = backend2.dataIdMap.get(outputShape.dataId).id; + const exceptionValues = backend2.makeOutput([3], "int32"); + const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id; + wasmSparseReshape(inputIndicesId, inputShapeId, newShapeId, nnz, newIndicesId, outputShapeId, exceptionValuesId); + const exceptionValuesArray = backend2.readSync(exceptionValues.dataId); + let exceptionMessage; + switch (exceptionValuesArray[0]) { + case 0: { + exceptionMessage = backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + } + case 1: { + exceptionMessage = backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + } + case 2: + exceptionMessage = backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage(); + break; + case 3: { + const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId)); + exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShapeValues, outputShapeValues); + break; + } + case 4: { + const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId)); + exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShapeValues, outputShapeValues); + break; + } + default: + exceptionMessage = ""; + } + backend2.disposeData(exceptionValues.dataId); + if (exceptionMessage) { + backend2.disposeData(newIndices.dataId); + backend2.disposeData(outputShape.dataId); + throw new Error(exceptionMessage); + } + return [newIndices, outputShape]; +} +var sparseReshapeConfig3 = { + kernelName: SparseReshape, + backendName: "wasm", + setupFunc: setup66, + kernelFunc: sparseReshape4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentReduction.js +var wasmSparseSegmentReduction; +function setup67(backend2) { + wasmSparseSegmentReduction = backend2.wasm.cwrap("SparseSegmentReduction", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + // defaultValue + ]); +} +function sparseSegmentReduction(args, isMean) { + const { backend: backend2, inputs } = args; + const { data, indices, segmentIds } = inputs; + const numIndices = indices.shape[0]; + const segmentIdsBack = backend2.readSync(segmentIds.dataId, numIndices - 1, numIndices)[0]; + const lastSegmentIdPlusOne = numIndices > 0 ? segmentIdsBack + 1 : 0; + const outputRows = lastSegmentIdPlusOne; + if (outputRows < 0) { + throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + } + const outputShape = data.shape.slice(); + outputShape[0] = outputRows; + const dataId = backend2.dataIdMap.get(data.dataId).id; + const indicesId = backend2.dataIdMap.get(indices.dataId).id; + const segmentIdsId = backend2.dataIdMap.get(segmentIds.dataId).id; + const output = backend2.makeOutput(outputShape, data.dtype); + const outputId = backend2.dataIdMap.get(output.dataId).id; + const exceptionValues = backend2.makeOutput([4], "int32"); + const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id; + wasmSparseSegmentReduction(dataId, CppDType[data.dtype], data.shape[0], indicesId, segmentIdsId, outputId, exceptionValuesId, isMean, 0); + const exceptionValuesArray = backend2.readSync(exceptionValues.dataId); + let exceptionMessage; + switch (exceptionValuesArray[0]) { + case 0: { + exceptionMessage = backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage(); + break; + } + case 1: { + exceptionMessage = backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage(); + break; + } + case 2: + exceptionMessage = backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + case 3: + exceptionMessage = backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]); + break; + default: + exceptionMessage = ""; + } + backend2.disposeData(exceptionValues.dataId); + if (exceptionMessage) { + backend2.disposeData(output.dataId); + throw new Error(exceptionMessage); + } + return output; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentMean.js +function sparseSegmentMean4(args) { + return sparseSegmentReduction(args, true); +} +var sparseSegmentMeanConfig3 = { + kernelName: SparseSegmentMean, + backendName: "wasm", + setupFunc: setup67, + kernelFunc: sparseSegmentMean4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentSum.js +function sparseSegmentSum4(args) { + return sparseSegmentReduction(args, false); +} +var sparseSegmentSumConfig3 = { + kernelName: SparseSegmentSum, + backendName: "wasm", + setupFunc: setup67, + kernelFunc: sparseSegmentSum4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseToDense.js +var wasmSparseToDense; +function setup68(backend2) { + wasmSparseToDense = backend2.wasm.cwrap(SparseToDense, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number", + "number" + // outId + ]); +} +function sparseToDense4(args) { + const { backend: backend2, inputs, attrs } = args; + const { sparseIndices, sparseValues, defaultValue } = inputs; + const { outputShape } = attrs; + const out = backend2.makeOutput(outputShape, defaultValue.dtype); + if (util_exports.sizeFromShape(outputShape) === 0) { + return out; + } + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape); + const sparseIndicesId = backend2.dataIdMap.get(sparseIndices.dataId).id; + const sparseValuesId = backend2.dataIdMap.get(sparseValues.dataId).id; + const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id; + const stridesBytes = new Uint8Array(new Int32Array(strides).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmSparseToDense(sparseIndicesId, sparseValuesId, sparseValues.shape.length, defaultValueId, CppDType[defaultValue.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId); + return out; +} +var sparseToDenseConfig3 = { + kernelName: SparseToDense, + backendName: "wasm", + setupFunc: setup68, + kernelFunc: sparseToDense4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SplitV.js +function splitV3(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x } = inputs; + const { numOrSizeSplits, axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, x.shape)[0]; + const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis); + const begin = new Array(x.shape.length).fill(0); + const size = x.shape.slice(); + return splitSizes.map((s) => { + const xSliceSize = [...size]; + xSliceSize[$axis] = s; + const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 }); + begin[$axis] += s; + return xSlice; + }); +} +var splitVConfig3 = { + kernelName: SplitV, + backendName: "wasm", + kernelFunc: splitV3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sqrt.js +var sqrtConfig3 = createUnaryKernelConfig(Sqrt); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Square.js +var squareConfig3 = createUnaryKernelConfig(Square); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SquaredDifference.js +var supportsFullBroadcast18 = true; +var squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast18); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Step.js +var wasmStep; +function setup69(backend2) { + wasmStep = backend2.wasm.cwrap(Step, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function step4(args) { + const { backend: backend2, inputs, attrs } = args; + const { alpha } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const out = backend2.makeOutput(x.shape, x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmStep(xId, alpha, CppDType[x.dtype], outId); + return out; +} +var stepConfig3 = { + kernelName: Step, + backendName: "wasm", + setupFunc: setup69, + kernelFunc: step4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StridedSlice.js +var wasmStridedSlice; +function setup70(backend2) { + wasmStridedSlice = backend2.wasm.cwrap(StridedSlice, null, [ + "number", + "array", + "number", + "array", + "array", + "array", + "array", + "array", + "number", + "number" + // outId + ]); +} +function stridedSlice4(args) { + const { backend: backend2, inputs, attrs } = args; + const { x } = inputs; + const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs; + const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); + let result; + if (isIdentity) { + result = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); + } else if (sliceDim0 || isSimpleSlice) { + util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`); + const size = slice_util_exports.computeOutShape($begin, $end, $strides); + const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } }); + result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeData(sliced.dataId); + } else { + const out = backend2.makeOutput(finalShapeSparse, "float32"); + const xId = backend2.dataIdMap.get(x.dataId).id; + const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer); + const beginBytes = new Uint8Array(new Int32Array($begin).buffer); + const endBytes = new Uint8Array(new Int32Array($end).buffer); + const stridesBytes = new Uint8Array(new Int32Array($strides).buffer); + const outputShapeBytes = new Uint8Array(new Int32Array(finalShapeSparse).buffer); + const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(finalShapeSparse)).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmStridedSlice(xId, xStridesBytes, x.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, finalShapeSparse.length, outId); + result = reshape5({ inputs: { x: out }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeData(out.dataId); + } + return result; +} +var stridedSliceConfig3 = { + kernelName: StridedSlice, + backendName: "wasm", + setupFunc: setup70, + kernelFunc: stridedSlice4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringNGrams.js +function stringNGrams4(args) { + const { backend: backend2, inputs, attrs } = args; + const { data, dataSplits } = inputs; + const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs; + const $data = backend2.readSync(data.dataId); + const $dataSplits = backend2.readSync(dataSplits.dataId); + const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences); + const nGramsOut = backend2.makeOutput([nGrams.length], "string"); + const nGramsOutData = backend2.dataIdMap.get(nGramsOut.dataId); + nGramsOutData.stringBytes = nGrams; + const nGramsSplitsOut = backend2.makeOutput(dataSplits.shape, "int32"); + const nGramsSplitsOutVals = backend2.typedArrayFromHeap(nGramsSplitsOut); + nGramsSplitsOutVals.set(nGramsSplits); + return [nGramsOut, nGramsSplitsOut]; +} +var stringNGramsConfig3 = { + kernelName: StringNGrams, + backendName: "wasm", + kernelFunc: stringNGrams4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringSplit.js +function stringSplit4(args) { + const { backend: backend2, inputs, attrs } = args; + const { input: input2, delimiter } = inputs; + const { skipEmpty } = attrs; + const inputVals = backend2.readSync(input2.dataId); + const delimiterVals = backend2.readSync(delimiter.dataId); + const [indices, values, shape] = stringSplitImpl(inputVals, delimiterVals[0], skipEmpty); + const outputSize = values.length; + const indicesOut = backend2.makeOutput([outputSize, 2], "int32"); + const indicesOutVals = backend2.typedArrayFromHeap(indicesOut); + indicesOutVals.set(indices); + const valuesOut = backend2.makeOutput([outputSize], "string"); + const valuesOutData = backend2.dataIdMap.get(valuesOut.dataId); + valuesOutData.stringBytes = values; + const shapeOut = backend2.makeOutput([2], "int32"); + const shapeOutVals = backend2.typedArrayFromHeap(shapeOut); + shapeOutVals.set(shape); + return [indicesOut, valuesOut, shapeOut]; +} +var stringSplitConfig3 = { + kernelName: StringSplit, + backendName: "wasm", + kernelFunc: stringSplit4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringToHashBucketFast.js +function stringToHashBucketFast4(args) { + const { backend: backend2, inputs, attrs } = args; + const { input: input2 } = inputs; + const { numBuckets } = attrs; + const inputVals = backend2.readSync(input2.dataId); + const values = stringToHashBucketFastImpl(inputVals, numBuckets); + const out = backend2.makeOutput(input2.shape, "int32"); + const outVals = backend2.typedArrayFromHeap(out); + outVals.set(values); + return out; +} +var stringToHashBucketFastConfig3 = { + kernelName: StringToHashBucketFast, + backendName: "wasm", + kernelFunc: stringToHashBucketFast4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sub.js +var supportsFullBroadcast19 = true; +var subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast19); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sum.js +var wasmSum; +function setup71(backend2) { + wasmSum = backend2.wasm.cwrap(Sum, null, [ + "number", + "number", + "number", + "number" + // out_id + ]); +} +function sum5(args) { + const { backend: backend2, inputs, attrs } = args; + const { axis, keepDims } = attrs; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + let inputId = xId; + let input2 = x; + const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2); + let reductionAxes = axes; + if (inputWasTransposed) { + const transposedId = backend2.dataIdMap.get(transposed.dataId).id; + if (transposedId !== xId) { + input2 = transposed; + inputId = transposedId; + reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length); + } + } + backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, input2.shape.length); + const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes); + const reduceSize = util_exports.sizeFromShape(reduceShape); + const out = backend2.makeOutput(outShape, input2.dtype); + if (util_exports.sizeFromShape(input2.shape) !== 0) { + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmSum(inputId, reduceSize, CppDType[out.dtype], outId); + } + if (inputWasTransposed) { + backend2.disposeData(transposed.dataId); + } + if (keepDims) { + const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes); + out.shape = newShape; + } + return out; +} +var sumConfig3 = { + kernelName: Sum, + backendName: "wasm", + setupFunc: setup71, + kernelFunc: sum5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tan.js +var tanConfig3 = createUnaryKernelConfig(Tan); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tanh.js +var tanhConfig3 = createUnaryKernelConfig(Tanh); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/TensorScatterUpdate.js +var wasmTensorScatterUpdate; +function setup72(backend2) { + wasmTensorScatterUpdate = backend2.wasm.cwrap(TensorScatterUpdate, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number", + "number", + "number" + // tensorId + ]); +} +function tensorScatterUpdate4(args) { + const { backend: backend2, inputs, attrs } = args; + const { tensor: tensor2, indices, updates } = inputs; + const {} = attrs; + const out = backend2.makeOutput(tensor2.shape, tensor2.dtype); + if (util_exports.sizeFromShape(tensor2.shape) === 0) { + return out; + } + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = scatter_nd_util_exports.calculateShapes(updates, indices, tensor2.shape); + const indicesData = backend2.dataIdMap.get(indices.dataId); + const indicesId = indicesData.id; + const updatesData = backend2.dataIdMap.get(updates.dataId); + const updatesId = updatesData.id; + const tensorData = backend2.dataIdMap.get(tensor2.dataId); + const tensorId = tensorData.id; + const stridesBytes = new Uint8Array(new Int32Array(strides).buffer); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmTensorScatterUpdate(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId, tensorId); + return out; +} +var tensorScatterUpdateConfig3 = { + kernelName: TensorScatterUpdate, + backendName: "wasm", + setupFunc: setup72, + kernelFunc: tensorScatterUpdate4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tile.js +var wasmTile; +function setup73(backend2) { + wasmTile = backend2.wasm.cwrap(Tile, null, [ + "number", + "array", + "number", + "array", + "number", + "number" + // out_id + ]); +} +function tile5(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const { reps } = attrs; + const newShape = new Array(x.shape.length); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[i] * reps[i]; + } + const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer); + const out = backend2.makeOutput(newShape, x.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId); + return out; +} +var tileConfig3 = { + kernelName: Tile, + backendName: "wasm", + setupFunc: setup73, + kernelFunc: tile5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/TopK.js +var wasmTopK; +function setup74(backend2) { + wasmTopK = backend2.wasm.cwrap(TopK, null, [ + "number", + "array", + "number", + "number", + "number", + "bool", + "number", + "number" + // outIndicesId + ]); +} +var topk2 = ({ inputs, backend: backend2, attrs }) => { + const { x } = inputs; + const { k, sorted } = attrs; + const xId = backend2.dataIdMap.get(x.dataId).id; + const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); + const outputShape = x.shape.slice(); + outputShape[outputShape.length - 1] = k; + const outValues = backend2.makeOutput(outputShape, x.dtype); + const outValuesId = backend2.dataIdMap.get(outValues.dataId).id; + const outIndices = backend2.makeOutput(outputShape, "int32"); + const outIndicesId = backend2.dataIdMap.get(outIndices.dataId).id; + wasmTopK(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted, outValuesId, outIndicesId); + return [outValues, outIndices]; +}; +var topKConfig3 = { + kernelName: TopK, + backendName: "wasm", + setupFunc: setup74, + kernelFunc: topk2 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transform.js +var wasmTransform; +function setup75(backend2) { + wasmTransform = backend2.wasm.cwrap(Transform, null, [ + "number", + "number", + "bool", + "number", + "number", + "number", + "number", + "number", + "number", + "array", + "number", + "array", + "number", + "number", + "number", + "number", + "number" + // outId + ]); +} +function transform4(args) { + const { backend: backend2, inputs, attrs } = args; + const { image: image2, transforms } = inputs; + const { interpolation, fillMode, fillValue, outputShape } = attrs; + const [batch, imageHeight, imageWidth, numChannels] = image2.shape; + const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth]; + const outShape = [ + batch, + outHeight, + outWidth, + numChannels + ]; + const inputStrides = new Uint8Array(new Int32Array(util_exports.computeStrides(image2.shape)).buffer); + const outputStrides = new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer); + const out = backend2.makeOutput(outShape, image2.dtype); + const outId = backend2.dataIdMap.get(out.dataId).id; + const imageData = backend2.dataIdMap.get(image2.dataId); + const imageId = imageData.id; + const transformsData = backend2.dataIdMap.get(transforms.dataId); + const transformsId = transformsData.id; + const interpolationModeId = interpolation === "nearest" ? 1 : 2; + let fillModeId; + switch (fillMode) { + case "constant": + fillModeId = 1; + break; + case "reflect": + fillModeId = 2; + break; + case "wrap": + fillModeId = 3; + break; + case "nearest": + fillModeId = 4; + break; + default: + fillModeId = 1; + break; + } + wasmTransform(imageId, transformsId, transforms.shape[0] > 1, batch, outHeight, outWidth, numChannels, imageWidth, imageHeight, inputStrides, image2.shape.length - 1, outputStrides, outShape.length - 1, interpolationModeId, fillModeId, fillValue, outId); + return out; +} +var transformConfig3 = { + kernelName: Transform, + backendName: "wasm", + setupFunc: setup75, + kernelFunc: transform4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unique.js +function unique5(args) { + const { inputs, attrs, backend: backend2 } = args; + const { axis } = attrs; + const { x } = inputs; + const { outputValues, outputShape, indices } = uniqueImpl(backend2.readSync(x.dataId), axis, x.shape, x.dtype); + return [ + backend2.makeOutput( + outputShape, + x.dtype, + /*memoryOffset=*/ + void 0, + outputValues + ), + backend2.makeOutput( + [indices.length], + "int32", + /*memoryOffset=*/ + void 0, + indices + ) + ]; +} +var uniqueConfig3 = { + kernelName: Unique, + backendName: "wasm", + kernelFunc: unique5 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unpack.js +function unpack3(args) { + const { inputs, backend: backend2, attrs } = args; + const { value } = inputs; + let { axis } = attrs; + if (axis < 0) { + axis += value.shape.length; + } + const numOutputs = value.shape[axis]; + const rank = value.shape.length; + const outShape = new Array(rank - 1); + let outIndex = 0; + for (let i = 0; i < rank; i++) { + if (i !== axis) { + outShape[outIndex++] = value.shape[i]; + } + } + const outs = new Array(numOutputs); + const begin = new Array(rank).fill(0); + const size = value.shape.slice(); + size[axis] = 1; + for (let i = 0; i < outs.length; i++) { + begin[axis] = i; + outs[i] = slice4({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 }); + } + return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape })); +} +var unpackConfig3 = { + kernelName: Unpack, + backendName: "wasm", + kernelFunc: unpack3 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ZerosLike.js +function zerosLike4(args) { + const { inputs: { x }, backend: backend2 } = args; + const out = backend2.makeOutput(x.shape, x.dtype); + const outVals = backend2.typedArrayFromHeap(out); + outVals.fill(0); + return out; +} +var zerosLikeConfig3 = { + kernelName: ZerosLike, + backendName: "wasm", + kernelFunc: zerosLike4 +}; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js +var kernelConfigs3 = [ + _fusedMatMulConfig3, + absConfig3, + acosConfig3, + acoshConfig3, + addConfig3, + addNConfig3, + allConfig3, + anyConfig3, + argMaxConfig3, + argMinConfig3, + asinConfig3, + asinhConfig3, + atanConfig3, + atan2Config3, + atanhConfig3, + avgPoolConfig3, + avgPoolGradConfig4, + avgPool3DConfig3, + avgPool3DGradConfig4, + batchMatMulConfig3, + batchToSpaceNDConfig3, + bincountConfig3, + bitwiseAndConfig3, + broadcastArgsConfig3, + castConfig3, + ceilConfig3, + clipByValueConfig3, + concatConfig3, + conv2DConfig3, + conv2DBackpropInputConfig3, + conv3DConfig3, + conv3DBackpropFilterV2Config3, + conv3DBackpropInputV2Config2, + cosConfig3, + coshConfig3, + cropAndResizeConfig3, + cumprodConfig3, + cumsumConfig3, + denseBincountConfig3, + depthToSpaceConfig3, + depthwiseConv2dNativeConfig3, + diagConfig3, + dilation2DConfig3, + dilation2DBackpropFilterConfig2, + dilation2DBackpropInputConfig2, + eluConfig3, + eluGradConfig4, + equalConfig3, + erfConfig3, + expConfig3, + expandDimsConfig3, + expm1Config3, + fillConfig3, + flipLeftRightConfig3, + floorConfig3, + floorDivConfig3, + fusedBatchNormConfig, + fusedConv2DConfig3, + fusedDepthwiseConv2DConfig3, + gatherNdConfig3, + gatherV2Config3, + greaterConfig3, + greaterEqualConfig3, + identityConfig3, + isFiniteConfig3, + isInfConfig3, + isNaNConfig3, + leakyReluConfig3, + lessConfig3, + lessEqualConfig3, + linSpaceConfig3, + log1pConfig3, + logConfig3, + logicalAndConfig3, + logicalNotConfig3, + logicalOrConfig3, + logicalXorConfig, + lrnConfig, + lrnGradConfig2, + maxConfig3, + maximumConfig3, + maxPoolConfig3, + maxPool3DConfig3, + maxPool3DGradConfig4, + maxPoolGradConfig4, + maxPoolWithArgmaxConfig3, + meanConfig3, + minConfig3, + minimumConfig3, + mirrorPadConfig3, + multinomialConfig3, + modConfig3, + multiplyConfig3, + negConfig3, + nonMaxSuppressionV3Config3, + nonMaxSuppressionV4Config3, + nonMaxSuppressionV5Config3, + notEqualConfig3, + oneHotConfig3, + onesLikeConfig3, + packConfig3, + padV2Config3, + powConfig3, + preluConfig3, + prodConfig3, + rangeConfig3, + realDivConfig3, + reciprocalConfig3, + reluConfig3, + relu6Config3, + reshapeConfig3, + resizeBilinearConfig3, + resizeBilinearGradConfig4, + resizeNearestNeighborConfig3, + resizeNearestNeighborGradConfig4, + reverseConfig3, + rotateWithOffsetConfig3, + roundConfig3, + rsqrtConfig3, + scatterNdConfig3, + searchSortedConfig3, + selectConfig3, + seluConfig3, + sigmoidConfig3, + signConfig3, + sinConfig3, + sinhConfig3, + sliceConfig3, + softmaxConfig3, + softplusConfig3, + spaceToBatchNDConfig3, + sparseFillEmptyRowsConfig3, + sparseReshapeConfig3, + sparseSegmentMeanConfig3, + sparseSegmentSumConfig3, + sparseToDenseConfig3, + splitVConfig3, + sqrtConfig3, + squareConfig3, + squaredDifferenceConfig3, + stepConfig3, + stridedSliceConfig3, + stringNGramsConfig3, + stringSplitConfig3, + stringToHashBucketFastConfig3, + subConfig3, + sumConfig3, + tanConfig3, + tanhConfig3, + tensorScatterUpdateConfig3, + tileConfig3, + topKConfig3, + transformConfig3, + transposeConfig3, + uniqueConfig3, + unpackConfig3, + zerosLikeConfig3 +]; +for (const kernelConfig of kernelConfigs3) { + registerKernel(kernelConfig); +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js +var ENV6 = env(); +ENV6.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => { + try { + return WebAssembly.validate(new Uint8Array([ + 0, + 97, + 115, + 109, + 1, + 0, + 0, + 0, + 1, + 4, + 1, + 96, + 0, + 0, + 3, + 2, + 1, + 0, + 10, + 9, + 1, + 7, + 0, + 65, + 0, + 253, + 15, + 26, + 11 + ])); + } catch (e) { + return false; + } +}); +ENV6.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { + if (ENV6.get("IS_NODE")) { + return false; + } + try { + new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)); + return WebAssembly.validate(new Uint8Array([ + 0, + 97, + 115, + 109, + 1, + 0, + 0, + 0, + 1, + 4, + 1, + 96, + 0, + 0, + 3, + 2, + 1, + 0, + 5, + 4, + 1, + 3, + 1, + 1, + 10, + 11, + 1, + 9, + 0, + 65, + 0, + 254, + 16, + 2, + 0, + 26, + 11 + ])); + } catch (e) { + return false; + } +}); + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js +var wasmFactoryThreadedSimd_import = __toESM(require_tfjs_backend_wasm_threaded_simd()); +var import_tfjs_backend_wasm_threaded_simd_worker = __toESM(require_tfjs_backend_wasm_threaded_simd_worker()); +var wasmFactory_import = __toESM(require_tfjs_backend_wasm()); +var wasmFactoryThreadedSimd = wasmFactoryThreadedSimd_import.default || wasmFactoryThreadedSimd_import; +var wasmFactory = wasmFactory_import.default || wasmFactory_import; +var BackendWasm = class extends KernelBackend { + constructor(wasm) { + super(); + this.wasm = wasm; + this.dataIdNextNumber = 1; + this.wasm.tfjs.initWithThreadsCount(threadsCount); + actualThreadsCount = this.wasm.tfjs.getThreadsCount(); + this.dataIdMap = new DataStorage(this, engine()); + } + write(values, shape, dtype) { + const dataId = { id: this.dataIdNextNumber++ }; + this.move(dataId, values, shape, dtype, 1); + return dataId; + } + numDataIds() { + return this.dataIdMap.numDataIds(); + } + async time(f) { + const start = util_exports.now(); + f(); + const kernelMs = util_exports.now() - start; + return { kernelMs }; + } + move(dataId, values, shape, dtype, refCount) { + const id = this.dataIdNextNumber++; + if (dtype === "string") { + const stringBytes = values; + this.dataIdMap.set(dataId, { id, stringBytes, shape, dtype, memoryOffset: null, refCount }); + return; + } + const size = util_exports.sizeFromShape(shape); + const numBytes = size * util_exports.bytesPerElement(dtype); + const memoryOffset = this.wasm._malloc(numBytes) >>> 0; + this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount }); + this.wasm.tfjs.registerTensor(id, size, memoryOffset); + if (values != null) { + this.wasm.HEAPU8.set(new Uint8Array(values.buffer, values.byteOffset, numBytes), memoryOffset); + } + } + async read(dataId) { + return this.readSync(dataId); + } + readSync(dataId, start, end) { + const { memoryOffset, dtype, shape, stringBytes } = this.dataIdMap.get(dataId); + if (dtype === "string") { + if ((start == null || start === 0) && (end == null || end >= stringBytes.length)) { + return stringBytes; + } + return stringBytes.slice(start, end); + } + start = start || 0; + end = end || util_exports.sizeFromShape(shape); + const bytesPerElement2 = util_exports.bytesPerElement(dtype); + const bytes = this.wasm.HEAPU8.slice(memoryOffset + start * bytesPerElement2, memoryOffset + end * bytesPerElement2); + return typedArrayFromBuffer(bytes.buffer, dtype); + } + /** + * Dispose the memory if the dataId has 0 refCount. Return true if the memory + * is released, false otherwise. + * @param dataId + * @oaram force Optional, remove the data regardless of refCount + */ + disposeData(dataId, force = false) { + if (this.dataIdMap.has(dataId)) { + const data = this.dataIdMap.get(dataId); + data.refCount--; + if (!force && data.refCount > 0) { + return false; + } + this.wasm._free(data.memoryOffset); + this.wasm.tfjs.disposeData(data.id); + this.dataIdMap.delete(dataId); + } + return true; + } + /** Return refCount of a `TensorData`. */ + refCount(dataId) { + if (this.dataIdMap.has(dataId)) { + const tensorData = this.dataIdMap.get(dataId); + return tensorData.refCount; + } + return 0; + } + incRef(dataId) { + const data = this.dataIdMap.get(dataId); + if (data != null) { + data.refCount++; + } + } + floatPrecision() { + return 32; + } + // Returns the memory offset of a tensor. Useful for debugging and unit + // testing. + getMemoryOffset(dataId) { + return this.dataIdMap.get(dataId).memoryOffset; + } + dispose() { + this.wasm.tfjs.dispose(); + if ("PThread" in this.wasm) { + this.wasm.PThread.terminateAllThreads(); + } + this.wasm = null; + } + memory() { + return { unreliable: false }; + } + /** + * Make a tensor info for the output of an op. If `memoryOffset` is not + * present, this method allocates memory on the WASM heap. If `memoryOffset` + * is present, the memory was allocated elsewhere (in c++) and we just record + * the pointer where that memory lives. + */ + makeOutput(shape, dtype, memoryOffset, values) { + let dataId; + if (memoryOffset == null) { + dataId = this.write(values !== null && values !== void 0 ? values : null, shape, dtype); + } else { + const id = this.dataIdNextNumber++; + dataId = { id }; + this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount: 1 }); + const size = util_exports.sizeFromShape(shape); + this.wasm.tfjs.registerTensor(id, size, memoryOffset); + } + return { dataId, shape, dtype }; + } + typedArrayFromHeap({ shape, dtype, dataId }) { + const buffer2 = this.wasm.HEAPU8.buffer; + const { memoryOffset } = this.dataIdMap.get(dataId); + const size = util_exports.sizeFromShape(shape); + switch (dtype) { + case "float32": + return new Float32Array(buffer2, memoryOffset, size); + case "int32": + return new Int32Array(buffer2, memoryOffset, size); + case "bool": + return new Uint8Array(buffer2, memoryOffset, size); + default: + throw new Error(`Unknown dtype ${dtype}`); + } + } +}; +function createInstantiateWasmFunc(path) { + return (imports, callback) => { + util_exports.fetch(path, { credentials: "same-origin" }).then((response) => { + if (!response["ok"]) { + imports.env.a(`failed to load wasm binary file at '${path}'`); + } + response.arrayBuffer().then((binary) => { + WebAssembly.instantiate(binary, imports).then((output) => { + callback(output.instance, output.module); + }); + }); + }); + return {}; + }; +} +function getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) { + if (wasmPath != null) { + return wasmPath; + } + let path = "tfjs-backend-wasm.wasm"; + if (simdSupported && threadsSupported) { + path = "tfjs-backend-wasm-threaded-simd.wasm"; + } else if (simdSupported) { + path = "tfjs-backend-wasm-simd.wasm"; + } + if (wasmFileMap != null) { + if (wasmFileMap[path] != null) { + return wasmFileMap[path]; + } + } + return wasmModuleFolder + path; +} +async function init() { + const [simdSupported, threadsSupported] = await Promise.all([ + env().getAsync("WASM_HAS_SIMD_SUPPORT"), + env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT") + ]); + return new Promise((resolve, reject) => { + const factoryConfig = {}; + factoryConfig.locateFile = (path, prefix) => { + if (path.endsWith(".worker.js")) { + const response = import_tfjs_backend_wasm_threaded_simd_worker.wasmWorkerContents.replace(/\n/g, "\\n"); + const blob = new Blob([response], { type: "application/javascript" }); + return URL.createObjectURL(blob); + } + if (path.endsWith(".wasm")) { + return getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : prefix); + } + return prefix + path; + }; + if (customFetch) { + factoryConfig.instantiateWasm = createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : "")); + } + let initialized = false; + factoryConfig.onAbort = () => { + if (initialized) { + return; + } + if (initAborted) { + return; + } + initAborted = true; + const rejectMsg = "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"; + reject({ message: rejectMsg }); + }; + let wasm; + if (threadsSupported && simdSupported && wasmPath == null) { + factoryConfig.mainScriptUrlOrBlob = new Blob([`var WasmBackendModuleThreadedSimd = ` + wasmFactoryThreadedSimd.toString()], { type: "text/javascript" }); + wasm = wasmFactoryThreadedSimd(factoryConfig); + } else { + wasm = wasmFactory(factoryConfig); + } + wasm.then((module) => { + initialized = true; + initAborted = false; + const voidReturnType = null; + module.tfjs = { + init: module.cwrap("init", null, []), + initWithThreadsCount: module.cwrap("init_with_threads_count", null, ["number"]), + getThreadsCount: module.cwrap("get_threads_count", "number", []), + registerTensor: module.cwrap("register_tensor", null, [ + "number", + "number", + "number" + // memoryOffset + ]), + disposeData: module.cwrap("dispose_data", voidReturnType, ["number"]), + dispose: module.cwrap("dispose", voidReturnType, []) + }; + resolve({ wasm: module }); + }).catch(reject); + }); +} +function typedArrayFromBuffer(buffer2, dtype) { + switch (dtype) { + case "float32": + return new Float32Array(buffer2); + case "int32": + return new Int32Array(buffer2); + case "bool": + return new Uint8Array(buffer2); + default: + throw new Error(`Unknown dtype ${dtype}`); + } +} +var wasmBinaryNames = [ + "tfjs-backend-wasm.wasm", + "tfjs-backend-wasm-simd.wasm", + "tfjs-backend-wasm-threaded-simd.wasm" +]; +var wasmPath = null; +var wasmPathPrefix = null; +var wasmFileMap = {}; +var initAborted = false; +var customFetch = false; +function setWasmPath(path, usePlatformFetch = false) { + deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."); + if (initAborted) { + throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`"); + } + wasmPath = path; + customFetch = usePlatformFetch; +} +function setWasmPaths(prefixOrFileMap, usePlatformFetch = false) { + if (initAborted) { + throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`"); + } + if (typeof prefixOrFileMap === "string") { + wasmPathPrefix = prefixOrFileMap; + } else { + wasmFileMap = prefixOrFileMap; + const missingPaths = wasmBinaryNames.filter((name) => wasmFileMap[name] == null); + if (missingPaths.length > 0) { + throw new Error(`There were no entries found for the following binaries: ${missingPaths.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.`); + } + } + customFetch = usePlatformFetch; +} +var threadsCount = -1; +var actualThreadsCount = -1; +function setThreadsCount(numThreads) { + threadsCount = numThreads; +} +function getThreadsCount() { + if (actualThreadsCount === -1) { + throw new Error(`WASM backend not initialized.`); + } + return actualThreadsCount; +} + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/version.js +var version8 = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/base.js +var WASM_PRIORITY = 2; +registerBackend("wasm", async () => { + const { wasm } = await init(); + return new BackendWasm(wasm); +}, WASM_PRIORITY); + +// dist/tfjs.version.js +var version9 = "4.16.0"; +var version22 = "4.16.0"; +var version32 = "4.16.0"; +var version42 = "4.16.0"; +var version52 = "4.16.0"; +var version62 = { + // tfjs: tfjsVersion, + tfjs: version9, + "tfjs-core": version9, + // 'tfjs-data': tfjsDataVersion, + // 'tfjs-layers': tfjsLayersVersion, + "tfjs-converter": version22, + "tfjs-backend-cpu": version32, + "tfjs-backend-webgl": version42, + "tfjs-backend-wasm": version52 +}; +export { + Abs, + Acos, + Acosh, + AdadeltaOptimizer, + AdagradOptimizer, + AdamOptimizer, + AdamaxOptimizer, + Add, + AddN, + All, + Any, + ArgMax, + ArgMin, + Asin, + Asinh, + Atan, + Atan2, + Atanh, + AvgPool, + AvgPool3D, + AvgPool3DGrad, + AvgPoolGrad, + BackendWasm, + BatchMatMul, + BatchToSpaceND, + Bincount, + BitwiseAnd, + BroadcastArgs, + BroadcastTo, + Callback, + CallbackList, + Cast, + Ceil, + ClipByValue, + Complex, + ComplexAbs, + Concat, + Conv2D, + Conv2DBackpropFilter, + Conv2DBackpropInput, + Conv3D, + Conv3DBackpropFilterV2, + Conv3DBackpropInputV2, + Cos, + Cosh, + CropAndResize, + Cumprod, + Cumsum, + CustomCallback, + DataStorage, + DenseBincount, + DepthToSpace, + DepthwiseConv2dNative, + DepthwiseConv2dNativeBackpropFilter, + DepthwiseConv2dNativeBackpropInput, + Diag, + Dilation2D, + Dilation2DBackpropFilter, + Dilation2DBackpropInput, + Draw, + ENV, + EarlyStopping, + Einsum, + Elu, + EluGrad, + Environment, + Equal, + Erf, + Exp, + ExpandDims, + Expm1, + FFT, + Fill, + FlipLeftRight, + Floor, + FloorDiv, + FromPixels, + FusedBatchNorm, + FusedConv2D, + FusedDepthwiseConv2D, + GPGPUContext, + GatherNd, + GatherV2, + GraphModel, + Greater, + GreaterEqual, + History, + IFFT, + Identity, + Imag, + InputSpec, + IsFinite, + IsInf, + IsNan, + KernelBackend, + LRN, + LRNGrad, + LayerVariable, + LayersModel, + LeakyRelu, + Less, + LessEqual, + LinSpace, + Log, + Log1p, + LogSoftmax, + LogicalAnd, + LogicalNot, + LogicalOr, + LogicalXor, + LowerBound, + MathBackendCPU, + MathBackendWebGL, + MatrixBandPart, + Max, + MaxPool, + MaxPool3D, + MaxPool3DGrad, + MaxPoolGrad, + MaxPoolWithArgmax, + Maximum, + Mean, + Min, + Minimum, + MirrorPad, + Mod, + MomentumOptimizer, + Multinomial, + Multiply, + Neg, + NonMaxSuppressionV3, + NonMaxSuppressionV4, + NonMaxSuppressionV5, + NotEqual, + OP_SCOPE_SUFFIX, + OneHot, + OnesLike, + Optimizer, + OptimizerConstructors, + Pack, + PadV2, + Pool, + Pow, + Prelu, + Prod, + RMSPropOptimizer, + RNN, + RaggedGather, + RaggedRange, + RaggedTensorToTensor, + Range, + Rank, + Real, + RealDiv, + Reciprocal, + Reduction, + Relu, + Relu6, + Reshape, + ResizeBilinear, + ResizeBilinearGrad, + ResizeNearestNeighbor, + ResizeNearestNeighborGrad, + Reverse, + RotateWithOffset, + Round, + Rsqrt, + SGDOptimizer, + ScatterNd, + SearchSorted, + Select, + Selu, + Sequential, + Sigmoid, + Sign, + Sin, + Sinh, + Slice, + Softmax, + Softplus, + SpaceToBatchND, + SparseFillEmptyRows, + SparseReshape, + SparseSegmentMean, + SparseSegmentSum, + SparseToDense, + SplitV, + Sqrt, + Square, + SquaredDifference, + StaticRegexReplace, + Step, + StridedSlice, + StringNGrams, + StringSplit, + StringToHashBucketFast, + Sub, + Sum, + SymbolicTensor, + Tan, + Tanh, + Tensor, + TensorBuffer, + TensorScatterUpdate, + Tile, + TopK, + Transform, + Transpose, + Unique, + Unpack, + UnsortedSegmentSum, + UpperBound, + Variable, + ZerosLike, + _FusedMatMul, + abs, + acos, + acosh, + add2 as add, + addN, + all, + any, + argMax, + argMin, + asin, + asinh, + atan, + atan2, + atanh, + avgPool, + avgPool3d, + backend, + backend_util_exports as backend_util, + basicLSTMCell, + batchNorm, + batchNorm2d, + batchNorm3d, + batchNorm4d, + batchToSpaceND, + bincount, + bitwiseAnd, + booleanMaskAsync, + broadcastArgs, + broadcastTo, + broadcast_util_exports as broadcast_util, + browser_exports as browser, + buffer, + callbacks, + cast, + ceil, + clipByValue, + clone, + complex, + concat, + concat1d, + concat2d, + concat3d, + concat4d, + exports_constraints_exports as constraints, + conv1d, + conv2d, + conv2dTranspose, + conv3d, + conv3dTranspose, + copyRegisteredKernels, + cos, + cosh, + cosineWindow, + cumprod, + cumsum, + customGrad, + dist_exports2 as data, + denseBincount, + deprecationWarn, + depthToSpace, + depthwiseConv2d, + deregisterOp, + device_util_exports as device_util, + diag, + dilation2d, + disableDeprecationWarnings, + dispose, + disposeVariables, + div, + divNoNan, + dot, + dropout, + einsum, + elu, + enableDebugMode, + enableProdMode, + enclosingPowerOfTwo, + engine, + ensureShape, + env, + equal, + erf, + euclideanNorm, + exp, + expandDims, + expm1, + eye, + fft, + fill, + findBackend, + findBackendFactory, + floor, + floorDiv, + forceHalfFloat, + fused_ops_exports as fused, + gather, + gatherND, + gather_nd_util_exports as gather_util, + getBackend, + getGradient, + getKernel, + getKernelsForBackend, + getThreadsCount, + gpgpu_util_exports as gpgpu_util, + grad, + grads, + greater, + greaterEqual, + ifft, + imag, + image, + inTopKAsync, + exports_initializers_exports as initializers, + input, + io_exports as io, + irfft, + isFinite2 as isFinite, + isInf, + isNaN2 as isNaN, + keep, + kernel_impls_exports as kernel_impls, + exports_layers_exports as layers, + leakyRelu, + less, + lessEqual, + linalg, + linspace, + loadGraphModel, + loadGraphModelSync, + loadLayersModel, + localResponseNormalization, + log2 as log, + log1p, + logSigmoid, + logSoftmax, + logSumExp, + logicalAnd, + logicalNot, + logicalOr, + logicalXor, + losses, + lowerBound, + matMul, + math_exports as math, + max, + maxPool, + maxPool3d, + maxPoolWithArgmax, + maximum, + mean, + memory, + meshgrid, + exports_metrics_exports as metrics, + min, + minimum, + mirrorPad, + mod, + model, + exports_models_exports as models, + moments, + movingAverage, + mul, + multiRNNCell, + multinomial, + neg, + nextFrame, + norm, + notEqual, + oneHot, + ones2 as ones, + onesLike, + op, + outerProduct, + pad, + pad1d, + pad2d, + pad3d, + pad4d, + pool, + pow, + prelu, + print, + prod, + profile, + raggedGather, + raggedRange, + raggedTensorToTensor, + rand, + randomGamma, + randomNormal, + randomStandardNormal, + randomUniform, + randomUniformInt, + range, + ready, + real, + reciprocal, + registerBackend, + registerCallbackConstructor, + registerGradient, + registerKernel, + registerOp, + exports_regularizers_exports as regularizers, + relu, + relu6, + removeBackend, + reshape, + reverse, + reverse1d, + reverse2d, + reverse3d, + reverse4d, + rfft, + round2 as round, + rsqrt, + scalar, + scatterND, + scatter_nd_util_exports as scatter_util, + searchSorted, + selu, + separableConv2d, + sequential, + serialization_exports as serialization, + setBackend, + setPlatform, + setThreadsCount, + setWasmPath, + setWasmPaths, + setWebGLContext, + setdiff1dAsync, + shared_exports as shared, + sigmoid, + sign, + signal, + sin, + sinh, + slice, + slice1d, + slice2d, + slice3d, + slice4d, + slice_util_exports as slice_util, + softmax, + softplus, + spaceToBatchND, + sparse, + sparseToDense, + spectral, + split, + sqrt, + square, + squaredDifference, + squeeze, + stack, + step, + stridedSlice, + string, + sub, + sum2 as sum, + sumOutType, + tan, + tanh2 as tanh, + tensor, + tensor1d, + tensor2d, + tensor3d, + tensor4d, + tensor5d, + tensor6d, + tensorScatterUpdate, + tensor_util_exports as tensor_util, + test_util_exports as test_util, + tidy, + tile, + time, + topk, + train, + transpose, + truncatedNormal, + unique, + unregisterGradient, + unregisterKernel, + unsortedSegmentSum, + unstack, + upcastType, + upperBound, + util_exports as util, + valueAndGrad, + valueAndGrads, + variable, + variableGrads, + version62 as version, + version3 as version_converter, + version as version_core, + version5 as version_cpu, + version2 as version_layers, + version8 as version_wasm, + version6 as version_webgl, + webgl, + webgl_util_exports as webgl_util, + where, + whereAsync, + zeros, + zerosLike +}; diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js index 4f836f1..d92dc88 100644 --- a/dist/tfjs.version.js +++ b/dist/tfjs.version.js @@ -4,4 +4,34 @@ author: ' */ -var e="4.7.0";var s="4.7.0";var t="4.7.0";var n="4.7.0";var i="4.7.0";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}; + +// node_modules/.pnpm/@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-core/package.json +var version = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-converter@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-converter/package.json +var version2 = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-cpu/package.json +var version3 = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-webgl/package.json +var version4 = "4.16.0"; + +// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.16.0_@tensorflow+tfjs-core@4.16.0/node_modules/@tensorflow/tfjs-backend-wasm/package.json +var version5 = "4.16.0"; + +// src/tfjs/tf-version.ts +var version6 = { + // tfjs: tfjsVersion, + tfjs: version, + "tfjs-core": version, + // 'tfjs-data': tfjsDataVersion, + // 'tfjs-layers': tfjsLayersVersion, + "tfjs-converter": version2, + "tfjs-backend-cpu": version3, + "tfjs-backend-webgl": version4, + "tfjs-backend-wasm": version5 +}; +export { + version6 as version +}; diff --git a/package-lock.json b/package-lock.json index 64bfdf3..99cfa8e 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1289,18 +1289,18 @@ "dev": true }, "node_modules/@types/node": { - "version": "20.11.4", - "resolved": "https://registry.npmjs.org/@types/node/-/node-20.11.4.tgz", - "integrity": "sha512-6I0fMH8Aoy2lOejL3s4LhyIYX34DPwY8bl5xlNjBvUEk8OHrcuzsFt+Ied4LvJihbtXPM+8zUqdydfIti86v9g==", + "version": "20.11.5", + "resolved": "https://registry.npmjs.org/@types/node/-/node-20.11.5.tgz", + "integrity": "sha512-g557vgQjUUfN76MZAN/dt1z3dzcUsimuysco0KeluHgrPdJXkP/XdAURgyO2W9fZWHRtRBiVKzKn8vyOAwlG+w==", "dev": true, "dependencies": { "undici-types": "~5.26.4" } }, "node_modules/@types/node-fetch": { - "version": "2.6.10", - "resolved": "https://registry.npmjs.org/@types/node-fetch/-/node-fetch-2.6.10.tgz", - "integrity": "sha512-PPpPK6F9ALFTn59Ka3BaL+qGuipRfxNE8qVgkp0bVixeiR2c2/L+IVOiBdu9JhhT22sWnQEp6YyHGI2b2+CMcA==", + "version": "2.6.11", + "resolved": "https://registry.npmjs.org/@types/node-fetch/-/node-fetch-2.6.11.tgz", + "integrity": "sha512-24xFj9R5+rfQJLRyM56qh+wnVSYhyXC2tkoBndtY0U+vubqNsYXGjufB2nn8Q6gt0LrARwL6UBtMCSVCwl4B1g==", "dev": true, "dependencies": { "@types/node": "*", diff --git a/package.json b/package.json index 2f961da..126f332 100644 --- a/package.json +++ b/package.json @@ -42,36 +42,37 @@ "tfjs" ], "devDependencies": { - "@canvas/image": "^1.0.1", - "@microsoft/api-extractor": "^7.35.2", - "@tensorflow/tfjs": "^4.7.0", - "@tensorflow/tfjs-backend-cpu": "^4.7.0", - "@tensorflow/tfjs-backend-wasm": "^4.7.0", - "@tensorflow/tfjs-backend-webgl": "^4.7.0", - "@tensorflow/tfjs-backend-webgpu": "4.7.0", - "@tensorflow/tfjs-converter": "^4.7.0", - "@tensorflow/tfjs-core": "^4.7.0", - "@tensorflow/tfjs-data": "^4.7.0", - "@tensorflow/tfjs-layers": "^4.7.0", - "@tensorflow/tfjs-node": "^4.7.0", - "@tensorflow/tfjs-node-gpu": "^4.7.0", - "@types/node": "^20.3.0", - "@types/offscreencanvas": "^2019.7.0", - "@typescript-eslint/eslint-plugin": "^5.59.9", - "@typescript-eslint/parser": "^5.59.9", - "@vladmandic/build": "^0.9.2", - "@vladmandic/pilogger": "^0.4.8", - "esbuild": "^0.18.1", - "eslint": "^8.42.0", + "@canvas/image": "^2.0.0", + "@microsoft/api-extractor": "^7.39.1", + "@tensorflow/tfjs": "^4.16.0", + "@tensorflow/tfjs-backend-cpu": "^4.16.0", + "@tensorflow/tfjs-backend-wasm": "^4.16.0", + "@tensorflow/tfjs-backend-webgl": "^4.16.0", + "@tensorflow/tfjs-backend-webgpu": "4.16.0", + "@tensorflow/tfjs-converter": "^4.16.0", + "@tensorflow/tfjs-core": "^4.16.0", + "@tensorflow/tfjs-data": "^4.16.0", + "@tensorflow/tfjs-layers": "^4.16.0", + "@tensorflow/tfjs-node": "^4.16.0", + "@tensorflow/tfjs-node-gpu": "^4.16.0", + "@types/node": "^20.11.5", + "@types/offscreencanvas": "^2019.7.3", + "@typescript-eslint/eslint-plugin": "^6.19.0", + "@typescript-eslint/parser": "^6.19.0", + "@vladmandic/build": "^0.9.3", + "@vladmandic/pilogger": "^0.4.9", + "esbuild": "^0.19.11", + "eslint": "^8.56.0", "eslint-config-airbnb-base": "^15.0.0", - "eslint-plugin-import": "^2.27.5", + "eslint-plugin-import": "^2.29.1", "eslint-plugin-json": "^3.1.0", "eslint-plugin-node": "^11.1.0", "eslint-plugin-promise": "^6.1.1", - "rimraf": "^5.0.1", + "node-fetch": "^3.3.2", + "rimraf": "^5.0.5", "seedrandom": "^3.0.5", - "tslib": "^2.5.3", - "typedoc": "^0.24.8", - "typescript": "5.1.3" + "tslib": "^2.6.2", + "typedoc": "^0.25.7", + "typescript": "5.3.3" } }